From fc216f29647acdd1b20ab6a673a4565d3e1aa667 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Tue, 2 May 2023 17:42:20 -0300 Subject: [PATCH 001/102] Rename weight_map to node_weights This way, we have now `node_weights` and `terminal_weights`, instead of `node_map` and `terminal_weights`. Since they both will be used in a similar way, I decided to make the names be more similar. --- src/program/dispatch_table.h | 2 +- src/search_space.cpp | 2 +- src/search_space.h | 20 ++++++++++---------- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/src/program/dispatch_table.h b/src/program/dispatch_table.h index cd7e9cd0..8a6f3470 100644 --- a/src/program/dispatch_table.h +++ b/src/program/dispatch_table.h @@ -216,7 +216,7 @@ extern DispatchTable dtable_predict; // ArgsName[args_type], // node_type, // node, -// SS.weight_map.at(ret_type).at(args_type).at(node_type) +// SS.node_weights.at(ret_type).at(args_type).at(node_type) // ); // } // } diff --git a/src/search_space.cpp b/src/search_space.cpp index cb4a448a..39da28cc 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -46,7 +46,7 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user { // fmt::print("constructing search space...\n"); this->node_map.clear(); - this->weight_map.clear(); + this->node_weights.clear(); this->terminal_map.clear(); this->terminal_types.clear(); this->terminal_weights.clear(); diff --git a/src/search_space.h b/src/search_space.h index d99b03de..cf78f134 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -93,7 +93,7 @@ struct SearchSpace Map node_map; /// @brief A map of weights corresponding to elements in @ref node_map, used to weight probabilities of each node being sampled from the map. - Map weight_map; + Map node_weights; /** * @brief Maps return types to terminals. @@ -106,7 +106,7 @@ struct SearchSpace */ unordered_map> terminal_map; - /// @brief A map of weights corresponding to elements in @ref terminal_map, used to weight probabilities of each node being sampled from the map. + /// @brief A map of weights corresponding to elements in @ref terminal_map, used to weight probabilities of each terminal being sampled from the map. unordered_map> terminal_weights; /// @brief A vector storing the available return types of terminals. @@ -117,7 +117,7 @@ struct SearchSpace NLOHMANN_DEFINE_TYPE_INTRUSIVE(SearchSpace, node_map, - weight_map, + node_weights, terminal_map, terminal_weights, terminal_types @@ -261,7 +261,7 @@ struct SearchSpace if (node_type_map.find(type) != node_type_map.end()) { matches.push_back(node_type_map.at(type)); - weights.push_back(weight_map.at(R).at(arg_hash).at(type)); + weights.push_back(node_weights.at(R).at(arg_hash).at(type)); } } @@ -303,7 +303,7 @@ struct SearchSpace vector get_weights() const { vector v; - for (auto& [ret, arg_w_map]: weight_map) + for (auto& [ret, arg_w_map]: node_weights) { v.push_back(0); for (const auto& [arg, name_map] : arg_w_map) @@ -324,7 +324,7 @@ struct SearchSpace vector get_weights(DataType ret) const { vector v; - for (const auto& [arg, name_map] : weight_map.at(ret)) + for (const auto& [arg, name_map] : node_weights.at(ret)) { v.push_back(0); for (const auto& [name, w]: name_map) @@ -343,7 +343,7 @@ struct SearchSpace vector get_weights(DataType ret, ArgsHash sig_hash) const { vector v; - for (const auto& [name, w]: weight_map.at(ret).at(sig_hash)) + for (const auto& [name, w]: node_weights.at(ret).at(sig_hash)) v.push_back(w); return v; @@ -420,7 +420,7 @@ struct SearchSpace } // if we made it this far, include the node as a match! matches.push_back(node); - weights.push_back(weight_map.at(ret).at(args_type).at(name)); + weights.push_back(node_weights.at(ret).at(args_type).at(name)); } } } @@ -490,7 +490,7 @@ struct SearchSpace node_map[n.ret_type][n.args_type()][n.node_type] = n; // sampling probability map float w = use_all? 1.0 : user_ops.at(name); - weight_map[n.ret_type][n.args_type()][n.node_type] = w; + node_weights[n.ret_type][n.args_type()][n.node_type] = w; } } @@ -692,7 +692,7 @@ template <> struct fmt::formatter: formatter { ArgsName[args_type], node_type, node, - SS.weight_map.at(ret_type).at(args_type).at(node_type) + SS.node_weights.at(ret_type).at(args_type).at(node_type) ); } } From 97c1f40bdfef36857403c1ae78284d0b3a691a27 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 4 May 2023 15:21:30 -0300 Subject: [PATCH 002/102] Fix typo when ngsa2 mutate the soluttions --- src/brush/deap_api/nsga2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 29a275d3..21e4da86 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -50,7 +50,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): off1 = toolbox.mutate(ind1) off2 = toolbox.mutate(ind2) # del ind1.fitness.values, ind2.fitness.values - offspring.extend([off2, off2]) + offspring.extend([off1, off2]) # archive.update(offspring) # Evaluate the individuals with an invalid fitness From 78d08adefd68a47ff578079c0e0868258121c5fb Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 4 May 2023 15:28:38 -0300 Subject: [PATCH 003/102] Fix typo in doc string --- src/program/program.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/program/program.h b/src/program/program.h index 296b7aad..ff858bd6 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -421,7 +421,7 @@ template struct Program * @brief convenience wrapper for :cpp:func:`variation:cross` in variation.h * * @param other another program to cross with this one. - * @return a mutated version of this and the other program + * @return a new version of this and the other program */ Program cross(Program other) const; From 7dbca19cf99e0b8fa4ba4468c7d4a10a2635c1b4 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 4 May 2023 15:29:01 -0300 Subject: [PATCH 004/102] Draft on MAB to optimize mutation probabilities This commit creates a notebook inside the python folder that implements a multi armed bandit (MAB) class to keep track of rewards for choosing different mutation types. This class is designed to wrap some function that makes choices with probabilities. The idea is that, by wrapping the function, we can optimize the probabilities of each choice based on evidence we get from previous tries. I plan to tweak this a little bit more, double-check if the code has bugs, do some experiments to make sure this implementation improves the evolution, and then start to implement it in the C++ version. --- src/brush/MAB_experiments.ipynb | 885 ++++++++++++++++++++++++++++++++ 1 file changed, 885 insertions(+) create mode 100644 src/brush/MAB_experiments.ipynb diff --git a/src/brush/MAB_experiments.ipynb b/src/brush/MAB_experiments.ipynb new file mode 100644 index 00000000..e66fdce6 --- /dev/null +++ b/src/brush/MAB_experiments.ipynb @@ -0,0 +1,885 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Implementing D-MAB, as described in DaCosta et al. - 2008 - Adaptive operator selection with dynamic multi-arm**\n", + "\n", + "> (hybrid between UCB1 and Page-Hinkley (PH) test)\n", + "\n", + "D-MAB maintains four indicators for each arm $i$:\n", + "1. number $n_{i, t}$ of times $i$-th arm has been played up to time $t$;\n", + "2. the average empirical reward $\\widehat{p}_{j, t}$ at time $t$;\n", + "3. the average and maximum deviation $m_i$ and $M_i$ involved in the PH test, initialized to $0$ and updated as detailed below. At each time step $t$:\n", + "\n", + "D-MAB selects the arm $i$ that maximizes equation 1:\n", + "\n", + "$$\\widehat{p}_{i, t} + \\sqrt{\\frac{2 \\log \\sum_{k}n_{k, t}}{n_{i, t}}}$$\n", + "\n", + "> Notice that the sum of the number of times each arm was pulled is equal to the time $\\sum_{k}n_{k, t} = t$, but since their algorithm resets the number of picks, we need to go with the summation. \n", + "\n", + "and receives some reward $r_t$, drawn after reward distribution $p_{i, t}$.\n", + "\n", + "> I think there is a typo in the eq. 1 on the paper. I replaced $j$ with $i$ in the lower indexes.\n", + "\n", + "The four indicators are updated accordingly:\n", + "\n", + "- $\\widehat{p}_{i, t} :=\\frac{1}{n_{i, t} + 1}(n_{i, t}\\widehat{p}_{i, t} + r_t)$\n", + "- $n_{i, t} := n_{i, t}+1$\n", + "- $m_i := m_i + (\\widehat{p}_{i, t} - r_t + \\delta)$\n", + "- $M_i:= \\text{max}(M_i, m_i)$\n", + "\n", + "And if the PH test is triggered ($M_i - m_i > \\lambda$), the bandit is restarted, i.e., for all arms, all indicators are set to zero (the authors argue that, empirically, resetting the values is more robust than decreasing them with some mechanism such as probability matching).\n", + "\n", + "> I will reset to 1 instead of 0 (as the original paper does) to avoid divide by zero when calculating UCB1.\n", + "\n", + "The PH test is a standard test for the change hypothesis. It works by monitoring the difference between $M_i$ and $m_i$, and when the difference is greater than some uuser-specified threshold $\\lambda$, the PH test is triggered, i.e., it is considered that the Change hypothesis holds.\n", + "\n", + "Parameter $\\lambda$ controls the trade-off between false alarms and un-noticed changes. Parameter $\\delta$ enforces the robustness of the test when dealing with slowly varying environments.\n", + "\n", + "We also need a scaling mechanism to control the Exploration _versus_ Exploitation balance. They proposed two, from which I will focus on the first: Multiplicative Scaling (cUCB). **It consists on multiplying all rewards by a fixed user-defined parameter $C_{M-\\text{scale}}$.\n", + "\n", + "This way, we need to give to our D-MAB 3 parameters: $\\lambda$, $\\delta$, and $C_{M - \\text{scale}}$. In the paper they did a sensitivity analysis of the parameters, but I think they should be fine tuned for each specific data set." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "class D_MAB:\n", + " def __init__(self, num_bandits, verbose=False, *, delta, lmbda, scaling, pull_f, reward_f):\n", + " self.num_bandits = num_bandits\n", + " self.verbose = verbose\n", + " self.delta = delta\n", + " self.lmbda = lmbda\n", + " self.scaling = scaling\n", + " self.pull_f = pull_f\n", + " self.reward_f = reward_f\n", + "\n", + " # History of choices and time instant t (just to track the behavior)\n", + " self.history = {i:[] for i in range(self.num_bandits)}\n", + "\n", + " self._reset_indicators()\n", + "\n", + " def _reset_indicators(self):\n", + " self.avg_reward = np.zeros(self.num_bandits)\n", + " self.num_played = np.zeros(self.num_bandits)\n", + " self.avg_deviation = np.zeros(self.num_bandits)\n", + " self.max_deviation = np.zeros(self.num_bandits)\n", + "\n", + " def _calc_UCB1s(self):\n", + " # log1p and +1 on denominator fixes some numeric problems in the original eq.\n", + " scores = np.array([self.avg_reward[i] + np.sqrt(2*np.log1p(sum(self.num_played))/(self.num_played[i]+1))\n", + " for i in range(self.num_bandits)])\n", + " \n", + " return np.nan_to_num(scores, nan=0)\n", + "\n", + " def _scale_reward(self, reward):\n", + " return reward*self.scaling\n", + " \n", + " def playAndOptimize(self, *pull_args):\n", + " # It will pick the bandit that maximizes eq.1. \n", + " UCB1s = self._calc_UCB1s()\n", + "\n", + " # We need to know which arm we picked, what it returned, and how to calculate the reward given what the arm returned\n", + " picked = np.nanargmax(np.nan_to_num(UCB1s, nan=-np.inf))\n", + " pulled = self.pull_f(picked, *pull_args)\n", + " reward = self.reward_f(pulled)\n", + " \n", + " self.history[picked].append(reward)\n", + "\n", + " if self.verbose:\n", + " print(f\"Avg. Rewards: {self.avg_reward}\\nUCB1 scores : {UCB1s}\\nPicked : {picked}\\nReward : {reward}\")\n", + "\n", + " # After choosing, it will implicitly update the parameters based on the return\n", + " if np.isfinite(reward):\n", + " self.avg_reward[picked] = (self.num_played[picked]*self.avg_reward[picked] + self._scale_reward(reward))/(self.num_played[picked]+1)\n", + " self.avg_deviation[picked] = self.avg_deviation[picked] + (self.avg_reward[picked] - self._scale_reward(reward) + self.delta)\n", + " \n", + " self.num_played[picked] = self.num_played[picked] +1\n", + " self.max_deviation[picked] = np.maximum(self.max_deviation[picked], self.avg_deviation[picked])\n", + "\n", + " if (self.max_deviation[picked] - self.avg_deviation[picked] > self.lmbda):\n", + " self._reset_indicators()\n", + " if self.verbose:\n", + " print(\"Reseted indicators ----------------------------------------\")\n", + "\n", + " return picked, pulled, reward" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below I'll create a simple bandit configuration so we can do a sanity check of our `D_MAB` implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "==============================================================\n", + "All bandits with same probs\n", + "------------- Uniformly Distributed Random pulls -------------\n", + "Probabilities for each arm: [1. 1. 1. 1.] (the smaller the better)\n", + "cum. reward for each arm : [-1707, -1628, -1716, -1671]\n", + "pulls for each arm : [2527, 2452, 2508, 2513]\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm: {0: -1847, 1: -1734, 2: -1670, 3: -1559}\n", + "pulls for each arm : {0: 2837, 1: 2500, 2: 2402, 3: 2261}\n", + "(it was expected: similar amount of pulls for each arm)\n", + "\n", + "==============================================================\n", + "One bandit with higher prob\n", + "------------- Uniformly Distributed Random pulls -------------\n", + "Probabilities for each arm: [-1. 0.2 0. 1. ] (the smaller the better)\n", + "cum. reward for each arm : [1633, -483, 80, -1686]\n", + "pulls for each arm : [2435, 2547, 2518, 2500]\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm: {0: 5127, 1: -135, 2: -21, 3: -383}\n", + "pulls for each arm : {0: 7425, 1: 983, 2: 1065, 3: 527}\n", + "(it was expected: more pulls for first arm, less pulls for last)\n", + "\n", + "==============================================================\n", + "Two bandits with higher probs\n", + "------------- Uniformly Distributed Random pulls -------------\n", + "Probabilities for each arm: [-0.2 -1. 0. -1. ] (the smaller the better)\n", + "cum. reward for each arm : [386, 1727, -76, 1651]\n", + "pulls for each arm : [2548, 2529, 2494, 2429]\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm: {0: 110, 1: 2769, 2: 38, 3: 2737}\n", + "pulls for each arm : {0: 976, 1: 4123, 2: 864, 3: 4037}\n", + "(it was expected: 2nd and 4th have similar number of pulls, higher than 1st and 3rd)\n" + ] + } + ], + "source": [ + "# Sanity checks\n", + "import numpy as np\n", + "\n", + "for bandits, descr, expec in [\n", + " (np.array([1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs', 'similar amount of pulls for each arm'),\n", + " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob', 'more pulls for first arm, less pulls for last'),\n", + " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd and 4th have similar number of pulls, higher than 1st and 3rd'),\n", + "]:\n", + " # Implementing simple bandits\n", + " def pullBandit(bandit):\n", + "\n", + " #Get a random number based on a normal dist with mean 0 and var 1\n", + " result = np.random.randn()\n", + " \n", + " # bandits: This is the true reward probabilities, which we shoudn't have access (in the optimizer)\n", + " # return a positive or negative reward based on bandit prob.\n", + " return 1 if result > bandits[bandit] else -1\n", + "\n", + " \n", + " print(\"\\n==============================================================\")\n", + " print(descr)\n", + "\n", + " print(\"------------- Uniformly Distributed Random pulls -------------\")\n", + " picks = [0, 0, 0, 0]\n", + " rewards = [0, 0, 0, 0]\n", + "\n", + " for _ in range(10000):\n", + " index = np.random.randint(len(bandits))\n", + " reward = pullBandit(index)\n", + "\n", + " picks[index] = picks[index]+1\n", + " rewards[index] = rewards[index]+reward\n", + "\n", + " print(\"Probabilities for each arm: \", bandits, \"(the smaller the better)\")\n", + " print(\"cum. reward for each arm : \", rewards)\n", + " print(\"pulls for each arm : \", picks)\n", + "\n", + " print(\"------------------------ optimizing ------------------------\")\n", + "\n", + " # We have the problem that we need to determine delta and lambda values previously.\n", + " # This needs domain knowledge (in SR context, I think we need to know if data is homogenic or\n", + " # if it changes a lot through time).\n", + " optimizer = D_MAB(4, verbose=False, \n", + " delta=0.25, lmbda=1, scaling=2,\n", + " pull_f=pullBandit, reward_f=lambda r:r)\n", + "\n", + " # Let's optimize\n", + " for i in range(10000):\n", + " optimizer.playAndOptimize()\n", + "\n", + " total_rewards = {k : sum(v) for (k, v) in optimizer.history.items()}\n", + " total_played = {k : len(v) for (k, v) in optimizer.history.items()}\n", + "\n", + " print(\"cum. reward for each arm: \", total_rewards)\n", + " print(\"pulls for each arm : \", total_played)\n", + " print(f\"(it was expected: {expec})\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, so the D-MAB seems to work. Now let's add this MAB inside mutation to update PARAMS option and control dinamically the mutaiton probabilities during evolution.\n", + "\n", + "We can import the brush estimator and replace the `_mutation` by a custom function. Ideally, to use this python MAB optimizer, we need to have an object created to keep track of the variables, and the object needs to wrap the _pull_ action, as well as evaluating the reward based on the result.\n", + "\n", + "> we'll need to do a _gambiarra_ to know which mutation is used so we can correctly update `D_MAB`. All MAB logic is implemented in python, and we chose the mutation in python as well. To make sure a specific mutation was used, we force it to happen by setting others' weights to zero. this way we know exactly what happened in the C++ code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from brush import BrushRegressor\n", + "from deap import creator\n", + "import _brush\n", + "from deap_api import nsga2, DeapIndividual \n", + "\n", + "#prg.mutate is a convenient interface that uses the current search space to sample mutations\n", + "\n", + "class BrushRegressorMod(BrushRegressor):\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + "\n", + " def _mutate(self, ind1):\n", + " # Overriding the mutation so it is wrapped with D_MAB\n", + " \n", + " mutation, offspring, reward = self.D_MAB_.playAndOptimize(ind1)\n", + " \n", + " #print(mutation, ind1.prg.get_model(), offspring.prg.get_model(), reward)\n", + " return offspring\n", + " \n", + " def fit(self, X, y):\n", + "\n", + " _brush.set_params(self.get_params())\n", + "\n", + " self.data_ = self._make_data(X,y)\n", + "\n", + " # Creating a wrapper for mutation to be able to control what is happening in the C++\n", + " # code (this should be prettier in a future implementation)\n", + " def _pull_mutation(mutation_idx, ind1):\n", + " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", + " params = self.get_params()\n", + "\n", + " for i, m in enumerate(mutations):\n", + " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", + "\n", + " _brush.set_params(params)\n", + " \n", + " offspring = creator.Individual(ind1.prg.mutate())\n", + "\n", + " return offspring\n", + " \n", + " # Given the result of a pull (the mutated offspring), how do I evaluate it?\n", + " # (here I am manually writing the multi-optimization problem nsga2 is\n", + " # designed to solve)\n", + " def _evaluate_reward(ind):\n", + " if not ind.fitness.valid:\n", + " ind.prg.fit(self.data_)\n", + " fit = (\n", + " np.sum((self.data_.y- ind.prg.predict(self.data_))**2),\n", + " ind.prg.size()\n", + " )\n", + " \n", + " ind.fitness.values = fit\n", + " \n", + " error, size = ind.fitness.values\n", + " return -1.0*error + -1.0*size\n", + " \n", + " # We have 4 different mutations\n", + " self.D_MAB_ = D_MAB(4, verbose=False, \n", + " delta=0.05, lmbda=5, scaling=1e-5, # How to determine these values???\n", + " pull_f=_pull_mutation, reward_f=_evaluate_reward)\n", + "\n", + " if isinstance(self.functions, list):\n", + " self.functions_ = {k:1.0 for k in self.functions}\n", + " else:\n", + " self.functions_ = self.functions\n", + "\n", + " self.search_space_ = _brush.SearchSpace(self.data_, self.functions_)\n", + " self.toolbox_ = self._setup_toolbox(data=self.data_)\n", + "\n", + " archive, logbook = nsga2(self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", + "\n", + " self.archive_ = archive\n", + " self.best_estimator_ = self.archive_[0].prg\n", + " total_played = {k : len(v) for (k, v) in self.D_MAB_.history.items()}\n", + "\n", + " print(total_played)\n", + " print(self.D_MAB_.avg_reward)\n", + " print('best model:',self.best_estimator_.get_model())\n", + " return self\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, lets use this new mutation into an ES algorithm (because this is only based on mutation) and see if it improves the performance" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-------------------------------------- Run 0 --------------------------------------\n", + "{0: 2504, 1: 2440, 2: 2505, 3: 2451}\n", + "[-0.00036238 -0.00147657 -0.00033448 -0.00126673]\n", + "best model: 3.60*Sin(2.72*x1)\n", + "-------------------------------------- Run 1 --------------------------------------\n", + "{0: 2491, 1: 2480, 2: 2491, 3: 2438}\n", + "[-0.00034092 -0.00051414 -0.0003384 -0.00126214]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 2 --------------------------------------\n", + "{0: 63, 1: 3366, 2: 3383, 3: 3088}\n", + "[-1.35675027e+14 -5.23720451e-04 -3.38402328e-04 -3.78375709e-03]\n", + "best model: 2.79*Tanh(36.11*x1)\n", + "-------------------------------------- Run 3 --------------------------------------\n", + "{0: 3316, 1: 33, 2: 3316, 3: 3235}\n", + "[-3.42976253e-04 -9.02122534e+06 -3.33060718e-04 -1.26440401e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 4 --------------------------------------\n", + "{0: 2491, 1: 2479, 2: 2491, 3: 2439}\n", + "[-0.00035025 -0.00055322 -0.00033663 -0.00125379]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 5 --------------------------------------\n", + "{0: 2532, 1: 2353, 2: 2532, 3: 2483}\n", + "[-0.00033139 -0.00052145 -0.00033167 -0.00128344]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 6 --------------------------------------\n", + "{0: 1006, 1: 69, 2: 4474, 3: 4351}\n", + "[-1.19270264e+02 -7.58510521e+08 -3.38995081e-04 -1.23290815e-03]\n", + "best model: 3.68*Sin(2.74*x1)\n", + "-------------------------------------- Run 7 --------------------------------------\n", + "{0: 2404, 1: 2508, 2: 2521, 3: 2467}\n", + "[-0.00238984 -0.00054975 -0.00033945 -0.00125824]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 8 --------------------------------------\n", + "{0: 2492, 1: 2482, 2: 2493, 3: 2433}\n", + "[-0.00035269 -0.00053543 -0.00034014 -0.00138042]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 9 --------------------------------------\n", + "{0: 3342, 1: 67, 2: 3272, 3: 3219}\n", + "[-3.64264747e-04 -3.96631982e+04 -1.15092726e-03 -1.76428450e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 10 --------------------------------------\n", + "{0: 50, 1: 3287, 2: 3320, 3: 3243}\n", + "[-3.75404346e+05 -7.05396891e-04 -3.33864937e-04 -1.20385839e-03]\n", + "best model: 1.77*Atan(44935.85*x1)\n", + "-------------------------------------- Run 11 --------------------------------------\n", + "{0: 2490, 1: 2480, 2: 2491, 3: 2439}\n", + "[-0.00036069 -0.00054273 -0.00034047 -0.00124747]\n", + "best model: -3.68*Sin(-2.74*x1)\n", + "-------------------------------------- Run 12 --------------------------------------\n", + "{0: 37, 1: 3302, 2: 3321, 3: 3240}\n", + "[-1.05164302e+01 -5.52813190e-04 -3.40930649e-04 -1.26843586e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 13 --------------------------------------\n", + "{0: 834, 1: 3028, 2: 3054, 3: 2984}\n", + "[-2.75502973e+02 -6.63639409e-04 -3.38363262e-04 -1.24861669e-03]\n", + "best model: 1.77*Atan(61934.75*x1)\n", + "-------------------------------------- Run 14 --------------------------------------\n", + "{0: 41, 1: 4907, 2: 4938, 3: 14}\n", + "[-5.20817814e+01 -5.29582311e-04 -3.35390689e-04 -2.85428712e+00]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 15 --------------------------------------\n", + "{0: 3, 1: 53, 2: 9725, 3: 119}\n", + "[-4.83653914e+22 -7.22927132e+11 -3.25257909e-04 -3.57751055e+10]\n", + "best model: Sum(-3.00*x2,1.00*x1,1.00*x1)\n", + "-------------------------------------- Run 16 --------------------------------------\n", + "{0: 2491, 1: 2477, 2: 2492, 3: 2440}\n", + "[-0.0003516 -0.00059567 -0.00033944 -0.00124227]\n", + "best model: 15.12*Log1p(-0.25*x2)\n", + "-------------------------------------- Run 17 --------------------------------------\n", + "{0: 2296, 1: 2544, 2: 2557, 3: 2503}\n", + "[-0.00502172 -0.00055057 -0.00033935 -0.00125745]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 18 --------------------------------------\n", + "{0: 2269, 1: 2990, 2: 2309, 3: 2332}\n", + "[-0.00037545 -0.00054674 -0.00035153 -0.00117992]\n", + "best model: Sum(-3.00*x2,2.00*x1)\n", + "-------------------------------------- Run 19 --------------------------------------\n", + "{0: 10, 1: 2, 2: 9827, 3: 61}\n", + "[-7.06470496e+00 -4.92061890e+02 -3.33184955e-04 -8.38450786e+19]\n", + "best model: Sub(Sub(-3.00*x2,-2.00*x1),-0.00)\n", + "-------------------------------------- Run 20 --------------------------------------\n", + "{0: 82, 1: 3287, 2: 3306, 3: 3225}\n", + "[-9.27254828e+08 -5.59666960e-04 -3.36175699e-04 -1.26693352e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 21 --------------------------------------\n", + "{0: 13, 1: 129, 2: 4953, 3: 4805}\n", + "[-3.01053856e+00 -4.48513422e+01 -3.35925360e-04 -1.27177044e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 22 --------------------------------------\n", + "{0: 4864, 1: 130, 2: 4856, 3: 50}\n", + "[-3.37258326e-04 -7.82234841e-01 -3.86614190e-04 -7.52158401e+00]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 23 --------------------------------------\n", + "{0: 20, 1: 4913, 2: 4960, 3: 7}\n", + "[-4.45937630e+08 -6.21522756e-04 -3.37090806e-04 -3.78629295e+00]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 24 --------------------------------------\n", + "{0: 2491, 1: 2479, 2: 2491, 3: 2439}\n", + "[-0.00033773 -0.00054754 -0.00033348 -0.00125877]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 25 --------------------------------------\n", + "{0: 511, 1: 63, 2: 4730, 3: 4596}\n", + "[-2.34806575e+02 -1.14142934e+03 -3.31660989e-04 -1.23667294e-03]\n", + "best model: 3.60*Sin(2.72*x1)\n", + "-------------------------------------- Run 26 --------------------------------------\n", + "{0: 261, 1: 3202, 2: 3256, 3: 3181}\n", + "[-1.39243889e+03 -5.36742485e-04 -3.30236506e-04 -1.21460595e-03]\n", + "best model: 2.79*Tanh(469.33*x1)\n", + "-------------------------------------- Run 27 --------------------------------------\n", + "{0: 3208, 1: 97, 2: 3338, 3: 3257}\n", + "[-1.83823120e-03 -8.56630083e+01 -3.46224261e-04 -1.26335784e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 28 --------------------------------------\n", + "{0: 3250, 1: 107, 2: 3217, 3: 3326}\n", + "[-4.41842511e-04 -9.93119541e-01 -3.29223757e-04 -1.27331961e-03]\n", + "best model: -4.24*x2\n", + "-------------------------------------- Run 29 --------------------------------------\n", + "{0: 2491, 1: 2480, 2: 2491, 3: 2438}\n", + "[-0.00034242 -0.00051649 -0.00033197 -0.00126607]\n", + "best model: -4.24*x2\n", + "Score (30 runs): 0.7299758445645649\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t100 \t[ nan 20.98]\t[ nan 1.05811153]\t[nan 20.]\n", + "1 \t0 \t[ nan 16.1] \t[ nan 5.9084685] \t[nan 1.]\n", + "2 \t0 \t[ nan 8.84] \t[ nan 5.75972222]\t[nan 1.]\n", + "3 \t0 \t[ nan 2.99] \t[ nan 2.31730447]\t[nan 1.]\n", + "4 \t0 \t[ nan 1.28] \t[ nan 0.56709788]\t[nan 1.]\n", + "5 \t0 \t[26.92582146 1.02 ]\t[10.60409505 0.14 ]\t[17.82939148 1. ]\n", + "6 \t0 \t[22.88155502 1.01 ]\t[4.47787129 0.09949874] \t[17.82939148 1. ]\n", + "7 \t0 \t[21.88920412 1.01 ]\t[4.48789957 0.09949874] \t[17.82939148 1. ]\n", + "8 \t0 \t[20.44578463 1.01 ]\t[4.09343184 0.09949874] \t[17.82939148 1. ]\n", + "9 \t0 \t[19.18279257 1.01 ]\t[3.2211926 0.09949874] \t[17.82939148 1. ]\n", + "10 \t0 \t[18.00981892 1. ]\t[1.26299206 0. ] \t[17.82939148 1. ]\n", + "11 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "12 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "13 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "14 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "15 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "16 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "17 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "18 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "19 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "20 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "21 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "22 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "23 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "24 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "25 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "26 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "27 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "28 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "29 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "30 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "31 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "32 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "33 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "34 \t0 \t[17.65109756 1.02 ]\t[1.77400205 0.19899749] \t[4.01456646e-13 1.00000000e+00]\n", + "35 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "36 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "37 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "38 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "39 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "40 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "41 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "42 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "43 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "44 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "45 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "46 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "47 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "48 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "49 \t0 \t[17.65109756 1.02 ]\t[1.77400205 0.19899749] \t[4.01456646e-13 1.00000000e+00]\n", + "50 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "51 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "52 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "53 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "54 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "55 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "56 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "57 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "58 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "59 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "60 \t0 \t[17.65109757 1.02 ]\t[1.77400204 0.19899749] \t[1.35152462e-07 1.00000000e+00]\n", + "61 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "62 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "63 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "64 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "65 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "66 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "67 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "68 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "69 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "70 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "71 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "72 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "73 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "74 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "75 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "76 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "77 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "78 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "79 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "80 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "81 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "82 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "83 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "84 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "85 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "86 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "87 \t0 \t[17.47280365 1.04 ]\t[2.49611479 0.28 ] \t[1.35152462e-07 1.00000000e+00]\n", + "88 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "89 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "90 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "91 \t0 \t[17.65109756 1.02 ]\t[1.77400205 0.19899749] \t[1.59872116e-13 1.00000000e+00]\n", + "92 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "93 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "94 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "95 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "96 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "97 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "98 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "99 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "Final population hypervolume is 48126.359818\n", + "{0: 2502, 1: 2444, 2: 2503, 3: 2451}\n", + "[-0.00033761 -0.00053674 -0.00033033 -0.00127082]\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# I am getting tons of unharmful warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "#df = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", + "#X = df.drop(columns='label')\n", + "#y = df['label']\n", + "\n", + "df = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", + "X = df.drop(columns='target')\n", + "y = df['target']\n", + "\n", + "kwargs = {\n", + " 'pop_size' : 100,\n", + " 'max_gen' : 100,\n", + " 'verbosity' : 0,\n", + " 'max_depth' : 10,\n", + " 'max_size' : 20,\n", + " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", + "}\n", + "\n", + "# 30 executions just to compare avg score\n", + "scores = []\n", + "for i in range(30):\n", + " print(f\"-------------------------------------- Run {i} --------------------------------------\")\n", + " est_mab = BrushRegressorMod(**kwargs)\n", + "\n", + " # use like you would a sklearn regressor\n", + " est_mab.fit(X,y)\n", + " y_pred = est_mab.predict(X)\n", + "\n", + " scores.append(est_mab.score(X,y))\n", + "print(f\"Score (30 runs): {np.mean(scores)}\")\n", + "\n", + "# Single run with verbosity\n", + "kwargs['verbosity'] = 1\n", + "est_mab = BrushRegressorMod(**kwargs)\n", + "\n", + "# use like you would a sklearn regressor\n", + "est_mab.fit(X,y)\n", + "y_pred = est_mab.predict(X)\n", + "\n", + "print('score:', est_mab.score(X,y))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comparing with the original implementation" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: 3366, 1: 16, 2: 3370, 3: 3148}\n", + "[-4.05962183e-04 -1.12526941e+11 -3.62192692e-04 -2.91524196e-03]\n", + "best model: -4.24*x2\n", + "{0: 2490, 1: 2479, 2: 2491, 3: 2440}\n", + "[-0.00036652 -0.00054139 -0.00033253 -0.00123083]\n", + "best model: 3.60*Sin(2.72*x1)\n", + "{0: 3305, 1: 63, 2: 3305, 3: 3227}\n", + "[-3.47628793e-04 -4.52236312e+00 -3.38324719e-04 -1.24529618e-03]\n", + "best model: 3.60*Sin(2.72*x1)\n", + "{0: 84, 1: 3286, 2: 3305, 3: 3225}\n", + "[-3.19134285e+08 -5.59272536e-04 -3.41379892e-04 -1.25437165e-03]\n", + "best model: 3.60*Sin(2.72*x1)\n", + "{0: 19, 1: 107, 2: 110, 3: 9664}\n", + "[-6.07635009e+01 -5.12827890e+17 -8.54437202e+09 -9.15424529e-04]\n", + "best model: 3.68*Sin(2.74*x1)\n", + "{0: 25, 1: 76, 2: 4974, 3: 4825}\n", + "[-2.77204121e+00 -9.65579745e-01 -3.84279937e-04 -1.32060016e-03]\n", + "best model: -4.24*x2\n", + "{0: 129, 1: 4868, 2: 4897, 3: 6}\n", + "[-3.16229731e-01 -5.37142383e-04 -3.59548331e-04 -3.51616980e+17]\n", + "best model: -4.24*x2\n", + "{0: 1537, 1: 2799, 2: 2812, 3: 2752}\n", + "[-0.03085949 -0.00054441 -0.00033255 -0.0012708 ]\n", + "best model: 2.79*Tanh(469.33*x1)\n", + "{0: 45, 1: 3300, 2: 3318, 3: 3237}\n", + "[-5.85096838e+00 -5.39978020e-04 -3.38116597e-04 -1.25995611e-03]\n", + "best model: -4.24*x2\n", + "{0: 4885, 1: 59, 2: 4886, 3: 70}\n", + "[-3.34650998e-04 -7.58935695e+19 -3.33819759e-04 -3.53269682e+00]\n", + "best model: -4.24*x2\n", + "{0: 2403, 1: 2383, 2: 2585, 3: 2529}\n", + "[-0.003463 -0.00383799 -0.00033757 -0.0012668 ]\n", + "best model: 3.60*Sin(2.72*x1)\n", + "{0: 119, 1: 3098, 2: 3385, 3: 3298}\n", + "[-8.54277606e-01 -3.70314404e-03 -3.63485341e-04 -1.33930528e-03]\n", + "best model: 1.26*Floor(-3.00*x2)\n", + "{0: 242, 1: 3191, 2: 3293, 3: 3174}\n", + "[-5.10587726e+02 -5.11324171e-04 -3.27858040e-04 -1.23800979e-03]\n", + "best model: 1.77*Atan(44757.84*x1)\n", + "{0: 2485, 1: 2482, 2: 2493, 3: 2440}\n", + "[-0.00047499 -0.0005332 -0.000337 -0.00126153]\n", + "best model: -4.24*x2\n", + "{0: 64, 1: 3238, 2: 3339, 3: 3259}\n", + "[-8.88350612e+02 -1.47782443e-03 -3.37196647e-04 -1.24099308e-03]\n", + "best model: 1.77*Atan(94709.32*x1)\n", + "{0: 2491, 1: 2479, 2: 2491, 3: 2439}\n", + "[-0.00034939 -0.0005673 -0.00034729 -0.00125779]\n", + "best model: -4.24*x2\n", + "{0: 2490, 1: 2479, 2: 2491, 3: 2440}\n", + "[-0.00036089 -0.00055806 -0.00034671 -0.0012465 ]\n", + "best model: 16.53*Log1p(-0.23*x2)\n", + "{0: 85, 1: 54, 2: 4955, 3: 4806}\n", + "[-4.33770528e+02 -2.90762476e+08 -3.36727177e-04 -1.27426962e-03]\n", + "best model: -4.24*x2\n", + "{0: 2498, 1: 2438, 2: 2509, 3: 2455}\n", + "[-0.00053392 -0.0015817 -0.00033776 -0.0012685 ]\n", + "best model: -4.24*x2\n", + "{0: 328, 1: 3205, 2: 3222, 3: 3145}\n", + "[-6.22900813e+02 -5.37273429e-04 -3.34606208e-04 -1.25118665e-03]\n", + "best model: 1.77*Atan(58403.29*x1)\n", + "{0: 32, 1: 3304, 2: 3323, 3: 3241}\n", + "[-1.11523542e+01 -5.45819514e-04 -3.28464819e-04 -1.27437150e-03]\n", + "best model: -4.24*x2\n", + "{0: 2, 1: 115, 2: 9775, 3: 8}\n", + "[-2.76785925e+01 -2.27590186e+05 -3.35505547e-04 -2.24268908e+02]\n", + "best model: -4.24*x2\n", + "{0: 70, 1: 3331, 2: 3269, 3: 3230}\n", + "[-1.43217558e+03 -5.27008308e-04 -3.26690376e-04 -1.24314573e-03]\n", + "best model: 2.79*Tanh(42.92*x1)\n", + "{0: 239, 1: 1750, 2: 93, 3: 7818}\n", + "[-5.02028184e+02 -8.83763100e+08 -5.62765928e+08 -1.10354608e-03]\n", + "best model: 1.77*Atan(118371.02*x1)\n", + "{0: 924, 1: 3009, 2: 3009, 3: 2958}\n", + "[-1.29857556e+02 -5.76118924e-04 -5.77231624e-04 -1.25136432e-03]\n", + "best model: 1.77*Atan(44757.84*x1)\n", + "{0: 190, 1: 47, 2: 52, 3: 9611}\n", + "[-7.10204387e+01 -1.64681556e+01 -1.00648539e+08 -1.03053944e-03]\n", + "best model: 1.77*Atan(215807.25*x1)\n", + "{0: 2049, 1: 2, 2: 3977, 3: 3872}\n", + "[-2.70667978e-02 -6.33814783e+08 -3.33925210e-04 -1.25067235e-03]\n", + "best model: 2.79*Tanh(469.33*x1)\n", + "{0: 25, 1: 4926, 2: 59, 3: 4890}\n", + "[-1.14996572e+01 -8.30932822e-04 -5.01314766e+06 -1.04938353e-03]\n", + "best model: 1.26*Floor(-3.00*x2)\n", + "{0: 4, 1: 3313, 2: 3332, 3: 3251}\n", + "[-2.76152882e+01 -5.53825483e-04 -3.39124161e-04 -1.25374732e-03]\n", + "best model: -4.24*x2\n", + "{0: 125, 1: 4868, 2: 4895, 3: 12}\n", + "[-2.95068578e+00 -5.26443489e-04 -3.62352455e-04 -1.35562820e+14]\n", + "best model: -4.24*x2\n", + "Score (30 runs): 0.7256975662463311\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t100 \t[ nan 20.83]\t[ nan 1.00054985]\t[nan 20.]\n", + "1 \t100 \t[ nan 12.2] \t[ nan 7.4939976] \t[nan 1.]\n", + "2 \t100 \t[ nan 3.42] \t[ nan 3.09896757]\t[nan 1.]\n", + "3 \t100 \t[28.58473202 1. ]\t[12.62085635 0. ]\t[17.82939148 1. ]\n", + "4 \t100 \t[19.81409328 1. ]\t[3.73706994 0. ] \t[17.82939148 1. ]\n", + "5 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "6 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "7 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "8 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "9 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "10 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "11 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "12 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "13 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "14 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "15 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "16 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "17 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "18 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "19 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "20 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "21 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "22 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "23 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "24 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "25 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "26 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "27 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "28 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "29 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "30 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "31 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "32 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "33 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "34 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "35 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "36 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "37 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "38 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "39 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "40 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "41 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "42 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "43 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "44 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "45 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "46 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "47 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "48 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "49 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "50 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "51 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "52 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "53 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "54 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "55 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "56 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "57 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "58 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "59 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "60 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "61 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "62 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "63 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "64 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "65 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "66 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "67 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "68 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "69 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "70 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "71 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "72 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "73 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "74 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "75 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "76 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "77 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "78 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "79 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "80 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "81 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "82 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "83 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "84 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "85 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "86 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "87 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "88 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "89 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "90 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "91 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "92 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "93 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "94 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "95 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "96 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "97 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "98 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "99 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "Final population hypervolume is 48126.359818\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n" + ] + } + ], + "source": [ + "# 30 executions just to compare avg score\n", + "scores = []\n", + "for _ in range(30):\n", + " kwargs['verbosity'] = 0\n", + "\n", + " est_mab = BrushRegressorMod(**kwargs)\n", + "\n", + " # use like you would a sklearn regressor\n", + " est_mab.fit(X,y)\n", + " y_pred = est_mab.predict(X)\n", + "\n", + " scores.append(est_mab.score(X,y))\n", + "print(f\"Score (30 runs): {np.mean(scores)}\")\n", + "\n", + "# Single run with verbosity\n", + "\n", + "kwargs['verbosity'] = 1\n", + "est = BrushRegressor(**kwargs)\n", + "\n", + "# use like you would a sklearn regressor\n", + "est.fit(X,y)\n", + "y_pred = est.predict(X)\n", + "\n", + "print('score:', est.score(X,y))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "brush", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 418f249657f9ce1f1d0b2f6272daaffe55e56bc6 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Fri, 5 May 2023 09:42:37 -0300 Subject: [PATCH 005/102] Improve mutation and add new comments in the code One thing that was bothering me in the previous mutation that I implemented was that the insert mutation would not be applied if the tree already has the maximum depth --- which is too strict, because the selected spot does not necessarily is the one with maximum depth. Now the mutation checks the allowed depth based the spot, not on the tree. This modification passed all tests. --- src/variation.h | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/src/variation.h b/src/variation.h index 8048f21d..353f15da 100644 --- a/src/variation.h +++ b/src/variation.h @@ -31,6 +31,8 @@ typedef tree::pre_order_iterator Iter; inline void point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "point mutation\n"; + + // get_node_like will sample a similar node based on node_weights or terminal_weights auto newNode = SS.get_node_like(spot.node->data); Tree.replace(spot, newNode); } @@ -41,16 +43,14 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) // cout << "insert mutation\n"; auto spot_type = spot.node->data.ret_type; - // pick a compatible random node to insert. We subtract one to count - // the op node that is already being inserted. + // pick a random compatible node to insert (with probabilities given by + // anode_weights). The -1 represents the node being inserted. auto n = SS.get_op_with_arg(spot_type, spot_type, true, PARAMS["max_size"].get()-Tree.size()-1); // make node n wrap the subtree at the chosen spot auto parent_node = Tree.wrap(spot, n); - // GUI TODO: spot_filled should be any random spot with same type - // now fill the arguments of n appropriately bool spot_filled = false; for (auto a: n.arg_types) @@ -73,6 +73,8 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) inline void delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "delete mutation\n"; + + // get_terminal will sample based on terminal_weights auto terminal = SS.get_terminal(spot.node->data.ret_type); Tree.erase_children(spot); Tree.replace(spot, terminal); @@ -95,7 +97,16 @@ inline void toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpac * - point mutation changes a single node. * - insertion mutation inserts a node as the parent of an existing node, and fills in the other arguments. * - deletion mutation deletes a node - * - toggle_weight mutation turns a node's weight on or off. + * - toggle_weight mutation turns a node's weight on or off. + * + * Every mutation has a probability of occur based on global parameters. The + * place where the mutation will take place is sampled based on attribute + * `get_prob_change` of each node in the tree. Inside each type of mutation, + * when a new node is inserted, it is sampled based on `terminal_weights`. + * + * By default, all probability distributions are uniform, but they can be + * dynamically optimized based on a Multi-Armed Bandit. + * * @tparam T program type * @param parent the program to be mutated * @param SS a search space @@ -120,8 +131,8 @@ Program mutate(const Program& parent, const SearchSpace& SS) // Setting to zero the weight of variations that increase the expression // if the expression is already at the maximum size or depth - if (child.Tree.size()+1 >= PARAMS["max_size"].get() - || child.Tree.max_depth()+1 >= PARAMS["max_depth"].get()) + if (child.Tree.size()+1 >= PARAMS["max_size"].get() + || child.Tree.depth(spot)+child.Tree.max_depth(spot)+1 >= PARAMS["max_depth"].get()) { // avoid using mutations that increase size/depth options["insert"] = 0.0; @@ -166,7 +177,6 @@ Program cross(const Program& root, const Program& other) [](const auto& n){ return n.get_prob_change(); } ); - // GUI TODO: Keep doing random attempts, or test all possilities? bool matching_spots_found = false; for (int tries = 0; tries < 3; ++tries) { From fc2946cb5500e7b87d4cde47414154073111dbb3 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Fri, 5 May 2023 13:58:38 -0300 Subject: [PATCH 006/102] Fix wrong behavior in crossover --- src/variation.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/variation.h b/src/variation.h index 353f15da..cf7344b3 100644 --- a/src/variation.h +++ b/src/variation.h @@ -191,7 +191,7 @@ Program cross(const Program& root, const Program& other) auto allowed_size = PARAMS["max_size"].get() - ( child.Tree.size() - child.Tree.size(child_spot) ); auto allowed_depth = PARAMS["max_depth"].get() - - ( child.Tree.max_depth() - child.Tree.depth(child_spot) ); + ( child.Tree.depth(child_spot) ); // pick a subtree to insert. Selection is based on other_weights vector other_weights(other.Tree.size()); @@ -202,7 +202,7 @@ Program cross(const Program& root, const Program& other) // lambda function to check feasibility of solution and increment the iterator const auto check_and_incrm = [other, &other_iter, allowed_size, allowed_depth]() -> bool { int s = other.Tree.size(other_iter); - int d = other.Tree.depth(other_iter); + int d = other.Tree.max_depth(other_iter); std::advance(other_iter, 1); return (s <= allowed_size) && (d <= allowed_depth); From d20ab106e724948cd813903dd468d7531418cc35 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 11 May 2023 15:42:14 -0300 Subject: [PATCH 007/102] Improved the MAB learner --- ..._square_x1_plus_2_x1_x2_plus_square_x2.csv | 11 + src/brush/MAB_experiments.ipynb | 1565 ++++++++++++----- 2 files changed, 1124 insertions(+), 452 deletions(-) create mode 100644 docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv diff --git a/docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv b/docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv new file mode 100644 index 00000000..0d41128c --- /dev/null +++ b/docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv @@ -0,0 +1,11 @@ +x1,x2,target +-0.44485052,0.89560483,0.20317944798357612 +-0.49109715,0.87110481,0.14440582165867555 +0.88231917,-0.47065155,0.1694702293564644 +0.94669031,0.32214509,1.6099432722931601 +-0.80300709,0.59596947,0.042864576095264506 +-0.581858,0.81329039,0.053560951141112145 +-0.91693663,0.39903285,0.2682243253382885 +-0.98437617,0.17607827,0.65334549514441 +-0.52860637,0.84886707,0.10256691596449008 +-0.89671113,-0.44261626,1.7937978576042122 diff --git a/src/brush/MAB_experiments.ipynb b/src/brush/MAB_experiments.ipynb index e66fdce6..c6f231f6 100644 --- a/src/brush/MAB_experiments.ipynb +++ b/src/brush/MAB_experiments.ipynb @@ -41,7 +41,11 @@ "\n", "We also need a scaling mechanism to control the Exploration _versus_ Exploitation balance. They proposed two, from which I will focus on the first: Multiplicative Scaling (cUCB). **It consists on multiplying all rewards by a fixed user-defined parameter $C_{M-\\text{scale}}$.\n", "\n", - "This way, we need to give to our D-MAB 3 parameters: $\\lambda$, $\\delta$, and $C_{M - \\text{scale}}$. In the paper they did a sensitivity analysis of the parameters, but I think they should be fine tuned for each specific data set." + "This way, we need to give to our D-MAB 3 parameters: $\\lambda$, $\\delta$, and $C_{M - \\text{scale}}$. In the paper they did a sensitivity analysis of the parameters, but I think they should be fine tuned for each specific data set.\n", + "\n", + "> Besides the problem of having to adjust the parameters of the `D_MAB`, I think this is not suited for Symbolic Regression as it is! In symbolic regression we normally have a population with high diversity to explore the search space --- so the mutation that maximizes the reward can be different depending on the expression being mutated. In opposition, if the mutations could be applied regardless of the expression format, then the population would be made of a few set of expressions.\n", + ">\n", + "> We should use something like Contextual Bandits to address this problem. An improvement to the algorithm would be including `context_f` that returns an proper context to use when determining which arm to pull." ] }, { @@ -51,9 +55,10 @@ "outputs": [], "source": [ "class D_MAB:\n", - " def __init__(self, num_bandits, verbose=False, *, delta, lmbda, scaling, pull_f, reward_f):\n", + " def __init__(self, num_bandits, verbose=False, *, policy='max_ucb', delta, lmbda, scaling, pull_f, reward_f):\n", " self.num_bandits = num_bandits\n", " self.verbose = verbose\n", + " self.policy = policy #['max_ucb', 'prob_ucb']\n", " self.delta = delta\n", " self.lmbda = lmbda\n", " self.scaling = scaling\n", @@ -72,23 +77,43 @@ " self.max_deviation = np.zeros(self.num_bandits)\n", "\n", " def _calc_UCB1s(self):\n", + " # We need that avg_reward \\in [0, 1] else we must scale it\n", + " rewards = self._normalize(self.avg_reward)\n", + "\n", " # log1p and +1 on denominator fixes some numeric problems in the original eq.\n", - " scores = np.array([self.avg_reward[i] + np.sqrt(2*np.log1p(sum(self.num_played))/(self.num_played[i]+1))\n", - " for i in range(self.num_bandits)])\n", + " #scores = np.array([rewards[i] + np.sqrt(2*np.log1p(sum(self.num_played))/(self.num_played[i]+1))\n", + " # for i in range(self.num_bandits)])\n", + "\n", + " scores = rewards + np.sqrt(2*np.log1p(sum(self.num_played))/(self.num_played+1))\n", " \n", - " return np.nan_to_num(scores, nan=0)\n", + " return np.nan_to_num(scores, nan=0.0)\n", "\n", " def _scale_reward(self, reward):\n", + " # We need to scale if the reward is not in [0, 1].\n", " return reward*self.scaling\n", " \n", - " def playAndOptimize(self, *pull_args):\n", + " def _normalize(self, p):\n", + " values = np.nan_to_num(p, nan=0)\n", + "\n", + " if np.sum(p)==0.0:\n", + " return np.ones(len(p))/len(p)\n", + " \n", + " return values / (values.sum())\n", + " \n", + " def playAndOptimize(self, **kwargs):\n", + " # For convenience, this takes kwargs that are passed to both pull and reward functions\n", + "\n", " # It will pick the bandit that maximizes eq.1. \n", " UCB1s = self._calc_UCB1s()\n", "\n", " # We need to know which arm we picked, what it returned, and how to calculate the reward given what the arm returned\n", - " picked = np.nanargmax(np.nan_to_num(UCB1s, nan=-np.inf))\n", - " pulled = self.pull_f(picked, *pull_args)\n", - " reward = self.reward_f(pulled)\n", + " picked = (\n", + " np.nanargmax(UCB1s) if self.policy=='max_ucb' else\n", + " np.random.choice(self.num_bandits, p=self._normalize(UCB1s))\n", + " )\n", + " \n", + " pulled = self.pull_f(picked, **kwargs)\n", + " reward = self.reward_f(pulled, **kwargs)\n", " \n", " self.history[picked].append(reward)\n", "\n", @@ -133,34 +158,34 @@ "All bandits with same probs\n", "------------- Uniformly Distributed Random pulls -------------\n", "Probabilities for each arm: [1. 1. 1. 1.] (the smaller the better)\n", - "cum. reward for each arm : [-1707, -1628, -1716, -1671]\n", - "pulls for each arm : [2527, 2452, 2508, 2513]\n", + "cum. reward for each arm : [395.0, 395.0, 396.0, 368.0]\n", + "pulls for each arm : [2466, 2514, 2500, 2520]\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm: {0: -1847, 1: -1734, 2: -1670, 3: -1559}\n", - "pulls for each arm : {0: 2837, 1: 2500, 2: 2402, 3: 2261}\n", + "cum. reward for each arm: {0: 414.0, 1: 412.0, 2: 384.0, 3: 398.0}\n", + "pulls for each arm : {0: 2625, 1: 2557, 2: 2406, 3: 2412}\n", "(it was expected: similar amount of pulls for each arm)\n", "\n", "==============================================================\n", "One bandit with higher prob\n", "------------- Uniformly Distributed Random pulls -------------\n", "Probabilities for each arm: [-1. 0.2 0. 1. ] (the smaller the better)\n", - "cum. reward for each arm : [1633, -483, 80, -1686]\n", - "pulls for each arm : [2435, 2547, 2518, 2500]\n", + "cum. reward for each arm : [2157.0, 1097.0, 1217.0, 384.0]\n", + "pulls for each arm : [2525, 2558, 2483, 2434]\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm: {0: 5127, 1: -135, 2: -21, 3: -383}\n", - "pulls for each arm : {0: 7425, 1: 983, 2: 1065, 3: 527}\n", + "cum. reward for each arm: {0: 3968.0, 1: 781.0, 2: 1031.0, 3: 202.0}\n", + "pulls for each arm : {0: 4760, 1: 1873, 2: 2076, 3: 1291}\n", "(it was expected: more pulls for first arm, less pulls for last)\n", "\n", "==============================================================\n", "Two bandits with higher probs\n", "------------- Uniformly Distributed Random pulls -------------\n", "Probabilities for each arm: [-0.2 -1. 0. -1. ] (the smaller the better)\n", - "cum. reward for each arm : [386, 1727, -76, 1651]\n", - "pulls for each arm : [2548, 2529, 2494, 2429]\n", + "cum. reward for each arm : [1444.0, 2083.0, 1278.0, 2093.0]\n", + "pulls for each arm : [2470, 2480, 2571, 2479]\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm: {0: 110, 1: 2769, 2: 38, 3: 2737}\n", - "pulls for each arm : {0: 976, 1: 4123, 2: 864, 3: 4037}\n", - "(it was expected: 2nd and 4th have similar number of pulls, higher than 1st and 3rd)\n" + "cum. reward for each arm: {0: 1358.0, 1: 2552.0, 2: 967.0, 3: 2275.0}\n", + "pulls for each arm : {0: 2352, 1: 2981, 2: 1922, 3: 2745}\n", + "(it was expected: 2nd approx 4th > 1st > 3rd)\n" ] } ], @@ -171,17 +196,18 @@ "for bandits, descr, expec in [\n", " (np.array([1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs', 'similar amount of pulls for each arm'),\n", " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob', 'more pulls for first arm, less pulls for last'),\n", - " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd and 4th have similar number of pulls, higher than 1st and 3rd'),\n", + " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd approx 4th > 1st > 3rd'),\n", "]:\n", - " # Implementing simple bandits\n", - " def pullBandit(bandit):\n", + " # Implementing simple bandits.\n", + " def pullBandit(bandit, **kwargs):\n", + " # Needs to have kwargs to work with my D_MAB implementation\n", "\n", " #Get a random number based on a normal dist with mean 0 and var 1\n", " result = np.random.randn()\n", " \n", " # bandits: This is the true reward probabilities, which we shoudn't have access (in the optimizer)\n", " # return a positive or negative reward based on bandit prob.\n", - " return 1 if result > bandits[bandit] else -1\n", + " return 1.0 if result > bandits[bandit] else 0.0\n", "\n", " \n", " print(\"\\n==============================================================\")\n", @@ -207,9 +233,9 @@ " # We have the problem that we need to determine delta and lambda values previously.\n", " # This needs domain knowledge (in SR context, I think we need to know if data is homogenic or\n", " # if it changes a lot through time).\n", - " optimizer = D_MAB(4, verbose=False, \n", - " delta=0.25, lmbda=1, scaling=2,\n", - " pull_f=pullBandit, reward_f=lambda r:r)\n", + " optimizer = D_MAB(4, verbose=False, policy='max_ucb',\n", + " delta=0.15, lmbda=0.5, scaling=1, # Lambda seems to control how strong will be the exploitation\n", + " pull_f=pullBandit, reward_f=lambda r, **kwargs:r)\n", "\n", " # Let's optimize\n", " for i in range(10000):\n", @@ -223,6 +249,445 @@ " print(f\"(it was expected: {expec})\")" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Avg. Rewards: [0. 0. 0. 0.]\n", + "UCB1 scores : [0.25 0.25 0.25 0.25]\n", + "Picked : 0\n", + "Reward : 1.0\n", + "Avg. Rewards: [1. 0. 0. 0.]\n", + "UCB1 scores : [1.83255461 1.17741002 1.17741002 1.17741002]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.5 0. 0. 0. ]\n", + "UCB1 scores : [1.8558085 1.48230381 1.48230381 1.48230381]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.33333333 0. 0. 0. ]\n", + "UCB1 scores : [1.83255461 1.66510922 1.66510922 1.66510922]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.25 0. 0. 0. ]\n", + "UCB1 scores : [1.80235601 1.79412258 1.79412258 1.79412258]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0. ]\n", + "UCB1 scores : [1.77282156 1.89301847 1.89301847 1.89301847]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0. ]\n", + "UCB1 scores : [1.80537986 1.39495883 1.9727697 1.9727697 ]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0. ]\n", + "UCB1 scores : [1.83255461 1.44202689 1.44202689 2.03933398]\n", + "Picked : 3\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.2 0. 0. 1. ]\n", + "UCB1 scores : [1.02247517 1.48230381 1.48230381 2.31563714]\n", + "Picked : 3\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.2 0. 0. 1. ]\n", + "UCB1 scores : [1.04275363 1.51742713 1.51742713 2.0723074 ]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.66666667]\n", + "UCB1 scores : [1.12480414 1.54851389 1.54851389 1.86419544]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.5]\n", + "UCB1 scores : [1.19582539 1.57635867 1.57635867 1.71126247]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.4]\n", + "UCB1 scores : [1.25798631 1.60154593 1.60154593 1.59131964]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.4]\n", + "UCB1 scores : [1.27124899 1.32641304 1.62451757 1.60458232]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.4]\n", + "UCB1 scores : [1.28342985 1.34363939 1.34363939 1.61676319]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.33333333]\n", + "UCB1 scores : [1.33635126 1.35955599 1.35955599 1.51503832]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.28571429]\n", + "UCB1 scores : [1.38356944 1.37433944 1.37433944 1.42984288]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0. 0. 0.25]\n", + "UCB1 scores : [1.42600303 1.38813346 1.38813346 1.35699478]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0. 0. 0.25 ]\n", + "UCB1 scores : [1.31720678 1.4010565 1.4010565 1.40890035]\n", + "Picked : 3\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.16666667 0. 0. 0.33333333]\n", + "UCB1 scores : [1.25849467 1.41320729 1.41320729 1.44071218]\n", + "Picked : 3\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.16666667 0. 0. 0.4 ]\n", + "UCB1 scores : [1.22678241 1.42466895 1.42466895 1.44989145]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0. 0. 0.36363636]\n", + "UCB1 scores : [1.25404898 1.43551209 1.43551209 1.40347033]\n", + "Picked : 1\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.16666667 0.33333333 0. 0.36363636]\n", + "UCB1 scores : [1.13947889 1.638062 1.44579718 1.14395122]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0.25 0. 0.36363636]\n", + "UCB1 scores : [1.16649064 1.44787295 1.45557636 1.1938076 ]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0.25 0. 0.36363636]\n", + "UCB1 scores : [1.17259109 1.4550911 1.26863624 1.19846689]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0.2 0. 0.36363636]\n", + "UCB1 scores : [1.19303944 1.3159876 1.27634175 1.23482157]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0.16666667 0. 0.36363636]\n", + "UCB1 scores : [1.20952606 1.20952606 1.28371275 1.26289103]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0.16666667 0. 0.36363636]\n", + "UCB1 scores : [1.21486525 1.21486525 1.154505 1.26696891]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.16666667 0.16666667 0. 0.33333333]\n", + "UCB1 scores : [1.23085907 1.23085907 1.16056811 1.21975379]\n", + "Picked : 0\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.28571429 0.16666667 0. 0.33333333]\n", + "UCB1 scores : [1.28575314 1.19790551 1.16639571 1.14761034]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.25 0.16666667 0. 0.33333333]\n", + "UCB1 scores : [1.20689402 1.21274693 1.17200464 1.17129087]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.25 0.14285714 0. 0.33333333]\n", + "UCB1 scores : [1.22185191 1.12754566 1.17741002 1.18921509]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.22222222 0.14285714 0. 0.33333333]\n", + "UCB1 scores : [1.15442431 1.13949299 1.18262548 1.21070591]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.22222222 0.14285714 0. 0.30769231]\n", + "UCB1 scores : [1.17011333 1.15127151 1.18766334 1.16711487]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.22222222 0.14285714 0. 0.30769231]\n", + "UCB1 scores : [1.17355797 1.15512273 1.08863034 1.17002612]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0.14285714 0. 0.30769231]\n", + "UCB1 scores : [1.11461822 1.16610383 1.09293472 1.1884667 ]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.2 0.14285714 0. 0.28571429]\n", + "UCB1 scores : [1.12844753 1.1773935 1.09710496 1.14841556]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. 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Rewards: [0.15384615 0.16666667 0. 0.22222222]\n", + "UCB1 scores : [1.03103425 1.08287634 1.05722318 1.05115895]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.15384615 0.15384615 0. 0.22222222]\n", + "UCB1 scores : [1.03978197 1.03978197 1.05989563 1.06268709]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.15384615 0.15384615 0. 0.21052632]\n", + "UCB1 scores : [1.04818279 1.04818279 1.06250966 1.03483919]\n", + "Picked : 2\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.15384615 0.15384615 0.14285714 0.21052632]\n", + "UCB1 scores : [0.98583747 0.98583747 1.21237756 0.94856272]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.15384615 0.15384615 0.125 0.21052632]\n", + "UCB1 scores : [0.99406907 0.99406907 1.13584507 0.95888538]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. 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Rewards: [0.14814815 0.10526316 0.06666667 0.15384615]\n", + "UCB1 scores : [0.87811613 0.89123877 0.88877851 0.9005165 ]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.14814815 0.10526316 0.06666667 0.14814815]\n", + "UCB1 scores : [0.88263341 0.89478552 0.89143378 0.88263341]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.14814815 0.1 0.06666667 0.14814815]\n", + "UCB1 scores : [0.88693472 0.87063983 0.89398414 0.88693472]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.14814815 0.1 0.0625 0.14814815]\n", + "UCB1 scores : [0.89053654 0.87340476 0.86471032 0.89053654]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.14285714 0.1 0.0625 0.14814815]\n", + "UCB1 scores : [0.87343963 0.87674124 0.86718162 0.89499108]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.14285714 0.1 0.0625 0.14285714]\n", + "UCB1 scores : [0.87782533 0.88012823 0.86967986 0.87782533]\n", + "Picked : 1\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.14285714 0.0952381 0.0625 0.14285714]\n", + "UCB1 scores : [0.88190713 0.85743671 0.87203827 0.88190713]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.13793103 0.0952381 0.0625 0.14285714]\n", + "UCB1 scores : [0.86552476 0.86059725 0.87447242 0.88617743]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.13793103 0.0952381 0.0625 0.13793103]\n", + "UCB1 scores : [0.86973127 0.86380391 0.87693266 0.86973127]\n", + "Picked : 2\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.13793103 0.0952381 0.05882353 0.13793103]\n", + "UCB1 scores : [0.87307739 0.86641302 0.84977565 0.87307739]\n", + "Picked : 0\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.13333333 0.0952381 0.05882353 0.13793103]\n", + "UCB1 scores : [0.85736402 0.86953017 0.85205346 0.8771642 ]\n", + "Picked : 3\n", + "Reward : 1.0\n", + "Avg. Rewards: [0.13333333 0.0952381 0.05882353 0.16666667]\n", + "UCB1 scores : [0.8381267 0.85607374 0.84409068 0.91153818]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "Avg. Rewards: [0.13333333 0.0952381 0.05882353 0.16129032]\n", + "UCB1 scores : [0.84224041 0.85929345 0.84642399 0.89596468]\n", + "Picked : 3\n", + "Reward : 0.0\n", + "cum. reward for each arm: {0: 4.0, 1: 2.0, 2: 1.0, 3: 5.0}\n", + "pulls for each arm : {0: 30, 1: 21, 2: 17, 3: 32}\n", + "(it was expected: similar amount of pulls for each arm)\n" + ] + } + ], + "source": [ + "# Simple test with verbose\n", + "bandits, descr, expec = (np.array([1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs', 'similar amount of pulls for each arm')\n", + "\n", + "def pullBandit(bandit, **kwargs):\n", + " result = np.random.randn()\n", + " return 1.0 if result > bandits[bandit] else 0.0\n", + "\n", + "optimizer = D_MAB(4, verbose=True, policy='max_ucb',\n", + " delta=0.25, lmbda=10, scaling=1,\n", + " pull_f=pullBandit, reward_f=lambda r, **kwargs:r)\n", + "\n", + "# Let's optimize\n", + "for i in range(100):\n", + " optimizer.playAndOptimize()\n", + "\n", + "total_rewards = {k : sum(v) for (k, v) in optimizer.history.items()}\n", + "total_played = {k : len(v) for (k, v) in optimizer.history.items()}\n", + "\n", + "print(\"cum. reward for each arm: \", total_rewards)\n", + "print(\"pulls for each arm : \", total_played)\n", + "print(f\"(it was expected: {expec})\")" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -237,7 +702,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -255,7 +720,7 @@ " def _mutate(self, ind1):\n", " # Overriding the mutation so it is wrapped with D_MAB\n", " \n", - " mutation, offspring, reward = self.D_MAB_.playAndOptimize(ind1)\n", + " mutation, offspring, reward = self.D_MAB_.playAndOptimize(ind1=ind1)\n", " \n", " #print(mutation, ind1.prg.get_model(), offspring.prg.get_model(), reward)\n", " return offspring\n", @@ -268,7 +733,7 @@ "\n", " # Creating a wrapper for mutation to be able to control what is happening in the C++\n", " # code (this should be prettier in a future implementation)\n", - " def _pull_mutation(mutation_idx, ind1):\n", + " def _pull_mutation(mutation_idx, ind1, **kwargs):\n", " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", " params = self.get_params()\n", "\n", @@ -282,24 +747,50 @@ " return offspring\n", " \n", " # Given the result of a pull (the mutated offspring), how do I evaluate it?\n", + " # I need to return if the reward was positive or negative. We make use of\n", + " # kwargs here.\n", " # (here I am manually writing the multi-optimization problem nsga2 is\n", " # designed to solve)\n", - " def _evaluate_reward(ind):\n", - " if not ind.fitness.valid:\n", - " ind.prg.fit(self.data_)\n", + " def _evaluate_reward(pulled, ind1, **kwargs):\n", + " if True: #not ind1.fitness.valid:\n", + " ind1.prg.fit(self.data_)\n", + " fit = (\n", + " np.sum((self.data_.y- ind1.prg.predict(self.data_))**2),\n", + " ind1.prg.size()\n", + " )\n", + " \n", + " ind1.fitness.setValues(fit)\n", + " # ind1.fitness = fit\n", + "\n", + " # in deap, a negative weight means a minimization problem, while a \n", + " # positive weight is a maximization problem.\n", + "\n", + " # ind1_error, ind1_size = ind1.fitness.values\n", + " # ind1_fitness = -1.0*ind1_error + -1.0*ind1_size\n", + "\n", + " if True: #not pulled.fitness.valid:\n", + " pulled.prg.fit(self.data_)\n", " fit = (\n", - " np.sum((self.data_.y- ind.prg.predict(self.data_))**2),\n", - " ind.prg.size()\n", + " np.sum((self.data_.y- pulled.prg.predict(self.data_))**2),\n", + " pulled.prg.size()\n", " )\n", " \n", - " ind.fitness.values = fit\n", + " pulled.fitness.setValues(fit)\n", + " # pulled.fitness = fit\n", " \n", - " error, size = ind.fitness.values\n", - " return -1.0*error + -1.0*size\n", + " # pulled_error, pulled_size = pulled.fitness.values\n", + " # pulled_fitness = -1.0*pulled_error + -1.0*pulled_size\n", + "\n", + " # We compare fitnesses using the deap overloaded operators\n", + " # from the docs: When comparing fitness values that are **minimized**, ``a > b`` will\n", + " # return :data:`True` if *a* is **smaller** than *b*.\n", + " return 1.0 if pulled.fitness.dominates(ind1.fitness) else 0.0\n", + "\n", + " # return 0.0 if pulled.fitness.values <= ind1.fitness.values else 1.0\n", " \n", " # We have 4 different mutations\n", - " self.D_MAB_ = D_MAB(4, verbose=False, \n", - " delta=0.05, lmbda=5, scaling=1e-5, # How to determine these values???\n", + " self.D_MAB_ = D_MAB(4, verbose=False, policy='max_ucb', \n", + " delta=0.15, lmbda=5, scaling=1,\n", " pull_f=_pull_mutation, reward_f=_evaluate_reward)\n", "\n", " if isinstance(self.functions, list):\n", @@ -332,7 +823,41 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# I am getting tons of unharmful warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "# df = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", + "# X = df.drop(columns='label')\n", + "# y = df['label']\n", + "\n", + "df = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", + "X = df.drop(columns='target')\n", + "y = df['target']\n", + "\n", + "# df = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", + "# X = df.drop(columns='target')\n", + "# y = df['target']\n", + "\n", + "kwargs = {\n", + " 'pop_size' : 160,\n", + " 'max_gen' : 160,\n", + " 'verbosity' : 0,\n", + " 'max_depth' : 10,\n", + " 'max_size' : 20,\n", + " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -340,258 +865,327 @@ "output_type": "stream", "text": [ "-------------------------------------- Run 0 --------------------------------------\n", - "{0: 2504, 1: 2440, 2: 2505, 3: 2451}\n", - "[-0.00036238 -0.00147657 -0.00033448 -0.00126673]\n", - "best model: 3.60*Sin(2.72*x1)\n", - "-------------------------------------- Run 1 --------------------------------------\n", - "{0: 2491, 1: 2480, 2: 2491, 3: 2438}\n", - "[-0.00034092 -0.00051414 -0.0003384 -0.00126214]\n", + "{0: 2171, 1: 478, 2: 21518, 3: 1273}\n", + "[0.0105942 0.00627615 0.01319825 0.00942655]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 1 --------------------------------------\n", + "{0: 2849, 1: 1088, 2: 19959, 3: 1544}\n", + "[0.00807301 0.00643382 0.00971993 0.00712435]\n", + "best model: 3.68*Sin(2.74*x1)\n", + "score: 0.8675268611694\n", "-------------------------------------- Run 2 --------------------------------------\n", - "{0: 63, 1: 3366, 2: 3383, 3: 3088}\n", - "[-1.35675027e+14 -5.23720451e-04 -3.38402328e-04 -3.78375709e-03]\n", - "best model: 2.79*Tanh(36.11*x1)\n", + "{0: 1899, 1: 1038, 2: 18860, 3: 3643}\n", + "[0.00684571 0.00578035 0.00890774 0.00768597]\n", + "best model: Sub(-3.00*x2,-2.00*x1)\n", + "score: 0.999999999999994\n", "-------------------------------------- Run 3 --------------------------------------\n", - "{0: 3316, 1: 33, 2: 3316, 3: 3235}\n", - "[-3.42976253e-04 -9.02122534e+06 -3.33060718e-04 -1.26440401e-03]\n", - "best model: -4.24*x2\n", + "{0: 22499, 1: 198, 2: 2073, 3: 670}\n", + "[0.00634495 0. 0.00455322 0.00350263]\n", + "best model: If(x2>-0.46,-4.09*x2,3.18)\n", + "score: 0.6802750800991533\n", "-------------------------------------- Run 4 --------------------------------------\n", - "{0: 2491, 1: 2479, 2: 2491, 3: 2439}\n", - "[-0.00035025 -0.00055322 -0.00033663 -0.00125379]\n", - "best model: -4.24*x2\n", + "{0: 2204, 1: 1989, 2: 17573, 3: 3674}\n", + "[0.00771325 0.00754148 0.00978774 0.00843767]\n", + "best model: Mean(-7.49*x2,-2.26*x2,-2.25*x2,8.00*x1)\n", + "score: 0.9999999963496158\n", "-------------------------------------- Run 5 --------------------------------------\n", - "{0: 2532, 1: 2353, 2: 2532, 3: 2483}\n", - "[-0.00033139 -0.00052145 -0.00033167 -0.00128344]\n", - "best model: -4.24*x2\n", + "{0: 3365, 1: 987, 2: 18741, 3: 2347}\n", + "[0.00950966 0.0070922 0.01115202 0.00894759]\n", + "best model: Sum(-3.00*x2,1.00*x1,1.00*x1)\n", + "score: 0.9999999999999972\n", "-------------------------------------- Run 6 --------------------------------------\n", - "{0: 1006, 1: 69, 2: 4474, 3: 4351}\n", - "[-1.19270264e+02 -7.58510521e+08 -3.38995081e-04 -1.23290815e-03]\n", - "best model: 3.68*Sin(2.74*x1)\n", + "{0: 2970, 1: 1621, 2: 16380, 3: 4469}\n", + "[0.00841751 0.00740284 0.01007326 0.00895055]\n", + "best model: Sum(-3.00*x2,-0.00,2.00*x1)\n", + "score: 0.999999999999994\n", "-------------------------------------- Run 7 --------------------------------------\n", - "{0: 2404, 1: 2508, 2: 2521, 3: 2467}\n", - "[-0.00238984 -0.00054975 -0.00033945 -0.00125824]\n", - "best model: -4.24*x2\n", + "{0: 2134, 1: 1638, 2: 18855, 3: 2813}\n", + "[0.00843486 0.00793651 0.01076637 0.00888731]\n", + "best model: Sum(-3.00*x2,2.00*x1,-0.00)\n", + "score: 0.999999999999994\n", "-------------------------------------- Run 8 --------------------------------------\n", - "{0: 2492, 1: 2482, 2: 2493, 3: 2433}\n", - "[-0.00035269 -0.00053543 -0.00034014 -0.00138042]\n", - "best model: -4.24*x2\n", + "{0: 2168, 1: 1352, 2: 18865, 3: 3055}\n", + "[0.00830258 0.00739645 0.01054864 0.00883797]\n", + "best model: 3.68*Sin(2.74*x1)\n", + "score: 0.8675268611694\n", "-------------------------------------- Run 9 --------------------------------------\n", - "{0: 3342, 1: 67, 2: 3272, 3: 3219}\n", - "[-3.64264747e-04 -3.96631982e+04 -1.15092726e-03 -1.76428450e-03]\n", - "best model: -4.24*x2\n", + "{0: 1893, 1: 1446, 2: 19446, 3: 2655}\n", + "[0.00739567 0.00691563 0.0096678 0.0079096 ]\n", + "best model: 1.77*Atan(44784.27*x1)\n", + "score: 0.7629047704797927\n", "-------------------------------------- Run 10 --------------------------------------\n", - "{0: 50, 1: 3287, 2: 3320, 3: 3243}\n", - "[-3.75404346e+05 -7.05396891e-04 -3.33864937e-04 -1.20385839e-03]\n", - "best model: 1.77*Atan(44935.85*x1)\n", + "{0: 1994, 1: 1249, 2: 19042, 3: 3155}\n", + "[0.00902708 0.00800641 0.01165844 0.00982567]\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 11 --------------------------------------\n", - "{0: 2490, 1: 2480, 2: 2491, 3: 2439}\n", - "[-0.00036069 -0.00054273 -0.00034047 -0.00124747]\n", - "best model: -3.68*Sin(-2.74*x1)\n", + "{0: 2065, 1: 977, 2: 21216, 3: 1182}\n", + "[0.00871671 0.00716479 0.01107655 0.00761421]\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 12 --------------------------------------\n", - "{0: 37, 1: 3302, 2: 3321, 3: 3240}\n", - "[-1.05164302e+01 -5.52813190e-04 -3.40930649e-04 -1.26843586e-03]\n", + "{0: 3056, 1: 2241, 2: 17696, 3: 2447}\n", + "[0.00850785 0.00803213 0.01017179 0.00817327]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 13 --------------------------------------\n", - "{0: 834, 1: 3028, 2: 3054, 3: 2984}\n", - "[-2.75502973e+02 -6.63639409e-04 -3.38363262e-04 -1.24861669e-03]\n", - "best model: 1.77*Atan(61934.75*x1)\n", + "{0: 5531, 1: 1556, 2: 15699, 3: 2654}\n", + "[0.01048635 0.00835476 0.0114657 0.00941974]\n", + "best model: Min(-4.21*x2,-3.65*x2,2.50*x1,2.20*x1)\n", + "score: 0.7673079213877327\n", "-------------------------------------- Run 14 --------------------------------------\n", - "{0: 41, 1: 4907, 2: 4938, 3: 14}\n", - "[-5.20817814e+01 -5.29582311e-04 -3.35390689e-04 -2.85428712e+00]\n", - "best model: -4.24*x2\n", + "{0: 2756, 1: 1309, 2: 18339, 3: 3036}\n", + "[0.00907112 0.00763942 0.01101478 0.00922266]\n", + "best model: 1.77*Atan(27369.91*x1)\n", + "score: 0.7629018280064254\n", "-------------------------------------- Run 15 --------------------------------------\n", - "{0: 3, 1: 53, 2: 9725, 3: 119}\n", - "[-4.83653914e+22 -7.22927132e+11 -3.25257909e-04 -3.57751055e+10]\n", - "best model: Sum(-3.00*x2,1.00*x1,1.00*x1)\n", + "{0: 4296, 1: 2202, 2: 17734, 3: 1208}\n", + "[0.00861266 0.00772025 0.00975527 0.00662252]\n", + "best model: Sum(-3.00*x2,2.00*x1)\n", + "score: 0.9999999999999978\n", "-------------------------------------- Run 16 --------------------------------------\n", - "{0: 2491, 1: 2477, 2: 2492, 3: 2440}\n", - "[-0.0003516 -0.00059567 -0.00033944 -0.00124227]\n", - "best model: 15.12*Log1p(-0.25*x2)\n", - "-------------------------------------- Run 17 --------------------------------------\n", - "{0: 2296, 1: 2544, 2: 2557, 3: 2503}\n", - "[-0.00502172 -0.00055057 -0.00033935 -0.00125745]\n", + "{0: 2827, 1: 1358, 2: 18698, 3: 2557}\n", + "[0.00955076 0.00810015 0.01155204 0.009386 ]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 17 --------------------------------------\n", + "{0: 1108, 1: 513, 2: 20827, 3: 2992}\n", + "[0.00812274 0.00584795 0.01185961 0.01002674]\n", + "best model: 2.79*Tanh(469.33*x1)\n", + "score: 0.7629093888079747\n", "-------------------------------------- Run 18 --------------------------------------\n", - "{0: 2269, 1: 2990, 2: 2309, 3: 2332}\n", - "[-0.00037545 -0.00054674 -0.00035153 -0.00117992]\n", - "best model: Sum(-3.00*x2,2.00*x1)\n", + "{0: 2647, 1: 1243, 2: 16900, 3: 4650}\n", + "[0.00868908 0.00724055 0.01059172 0.00946237]\n", + "best model: Mean(0.00,6.00*x1,-9.00*x2)\n", + "score: 0.999999999999994\n", "-------------------------------------- Run 19 --------------------------------------\n", - "{0: 10, 1: 2, 2: 9827, 3: 61}\n", - "[-7.06470496e+00 -4.92061890e+02 -3.33184955e-04 -8.38450786e+19]\n", - "best model: Sub(Sub(-3.00*x2,-2.00*x1),-0.00)\n", + "{0: 1370, 1: 2894, 2: 17912, 3: 3264}\n", + "[0.00583942 0.00691085 0.00831845 0.00704657]\n", + "best model: 1.77*Atan(44784.27*x1)\n", + "score: 0.7629047704797927\n", "-------------------------------------- Run 20 --------------------------------------\n", - "{0: 82, 1: 3287, 2: 3306, 3: 3225}\n", - "[-9.27254828e+08 -5.59666960e-04 -3.36175699e-04 -1.26693352e-03]\n", + "{0: 2724, 1: 2819, 2: 15853, 3: 4044}\n", + "[0.00881057 0.00886839 0.01072352 0.00939664]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 21 --------------------------------------\n", - "{0: 13, 1: 129, 2: 4953, 3: 4805}\n", - "[-3.01053856e+00 -4.48513422e+01 -3.35925360e-04 -1.27177044e-03]\n", - "best model: -4.24*x2\n", + "{0: 3662, 1: 2350, 2: 16771, 3: 2657}\n", + "[0.00873839 0.00808511 0.01013655 0.00828002]\n", + "best model: 2.79*Tanh(469.33*x1)\n", + "score: 0.7629093888079747\n", "-------------------------------------- Run 22 --------------------------------------\n", - "{0: 4864, 1: 130, 2: 4856, 3: 50}\n", - "[-3.37258326e-04 -7.82234841e-01 -3.86614190e-04 -7.52158401e+00]\n", + "{0: 2518, 1: 1610, 2: 18130, 3: 3182}\n", + "[0.00754567 0.0068323 0.00932157 0.00785669]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 23 --------------------------------------\n", - "{0: 20, 1: 4913, 2: 4960, 3: 7}\n", - "[-4.45937630e+08 -6.21522756e-04 -3.37090806e-04 -3.78629295e+00]\n", - "best model: -4.24*x2\n", + "{0: 1732, 1: 2975, 2: 18429, 3: 2304}\n", + "[0.00692841 0.00773109 0.00927885 0.00737847]\n", + "best model: Sum(-3.00*x2,2.00*x1)\n", + "score: 0.9999999999999978\n", "-------------------------------------- Run 24 --------------------------------------\n", - "{0: 2491, 1: 2479, 2: 2491, 3: 2439}\n", - "[-0.00033773 -0.00054754 -0.00033348 -0.00125877]\n", - "best model: -4.24*x2\n", + "{0: 4058, 1: 991, 2: 17180, 3: 3211}\n", + "[0.00837851 0.00605449 0.00954598 0.00809717]\n", + "best model: 2.79*Tanh(26112.86*x1)\n", + "score: 0.7629093888079747\n", "-------------------------------------- Run 25 --------------------------------------\n", - "{0: 511, 1: 63, 2: 4730, 3: 4596}\n", - "[-2.34806575e+02 -1.14142934e+03 -3.31660989e-04 -1.23667294e-03]\n", - "best model: 3.60*Sin(2.72*x1)\n", + "{0: 2044, 1: 1596, 2: 18542, 3: 3258}\n", + "[0.0092955 0.00877193 0.01197282 0.01012891]\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 26 --------------------------------------\n", - "{0: 261, 1: 3202, 2: 3256, 3: 3181}\n", - "[-1.39243889e+03 -5.36742485e-04 -3.30236506e-04 -1.21460595e-03]\n", - "best model: 2.79*Tanh(469.33*x1)\n", + "{0: 3997, 1: 2511, 2: 14823, 3: 4109}\n", + "[0.00775582 0.00716846 0.00883762 0.00778778]\n", + "best model: Min(-4.17*x2,3.18,2.22*x1)\n", + "score: 0.7859226715696217\n", "-------------------------------------- Run 27 --------------------------------------\n", - "{0: 3208, 1: 97, 2: 3338, 3: 3257}\n", - "[-1.83823120e-03 -8.56630083e+01 -3.46224261e-04 -1.26335784e-03]\n", + "{0: 1594, 1: 1594, 2: 19724, 3: 2528}\n", + "[0.00752823 0.00752823 0.01024133 0.00830696]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 28 --------------------------------------\n", - "{0: 3250, 1: 107, 2: 3217, 3: 3326}\n", - "[-4.41842511e-04 -9.93119541e-01 -3.29223757e-04 -1.27331961e-03]\n", + "{0: 4727, 1: 2388, 2: 15729, 3: 2596}\n", + "[0.00888513 0.00795645 0.00991799 0.00808937]\n", "best model: -4.24*x2\n", + "score: 0.651326594086648\n", "-------------------------------------- Run 29 --------------------------------------\n", - "{0: 2491, 1: 2480, 2: 2491, 3: 2438}\n", - "[-0.00034242 -0.00051649 -0.00033197 -0.00126607]\n", - "best model: -4.24*x2\n", - "Score (30 runs): 0.7299758445645649\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t100 \t[ nan 20.98]\t[ nan 1.05811153]\t[nan 20.]\n", - "1 \t0 \t[ nan 16.1] \t[ nan 5.9084685] \t[nan 1.]\n", - "2 \t0 \t[ nan 8.84] \t[ nan 5.75972222]\t[nan 1.]\n", - "3 \t0 \t[ nan 2.99] \t[ nan 2.31730447]\t[nan 1.]\n", - "4 \t0 \t[ nan 1.28] \t[ nan 0.56709788]\t[nan 1.]\n", - "5 \t0 \t[26.92582146 1.02 ]\t[10.60409505 0.14 ]\t[17.82939148 1. ]\n", - "6 \t0 \t[22.88155502 1.01 ]\t[4.47787129 0.09949874] \t[17.82939148 1. ]\n", - "7 \t0 \t[21.88920412 1.01 ]\t[4.48789957 0.09949874] \t[17.82939148 1. ]\n", - "8 \t0 \t[20.44578463 1.01 ]\t[4.09343184 0.09949874] \t[17.82939148 1. ]\n", - "9 \t0 \t[19.18279257 1.01 ]\t[3.2211926 0.09949874] \t[17.82939148 1. ]\n", - "10 \t0 \t[18.00981892 1. ]\t[1.26299206 0. ] \t[17.82939148 1. ]\n", - "11 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "12 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "13 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "14 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "15 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "16 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "17 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "18 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "19 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "20 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "21 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "22 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "23 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "24 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "25 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "26 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "27 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "28 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "29 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "30 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "31 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "32 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "33 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "34 \t0 \t[17.65109756 1.02 ]\t[1.77400205 0.19899749] \t[4.01456646e-13 1.00000000e+00]\n", - "35 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "36 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "37 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "38 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "39 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "40 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "41 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "42 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "43 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "44 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "45 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "46 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "47 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "48 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "49 \t0 \t[17.65109756 1.02 ]\t[1.77400205 0.19899749] \t[4.01456646e-13 1.00000000e+00]\n", - "50 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "51 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "52 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "53 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "54 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "55 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "56 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "57 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "58 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "59 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "60 \t0 \t[17.65109757 1.02 ]\t[1.77400204 0.19899749] \t[1.35152462e-07 1.00000000e+00]\n", - "61 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "62 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "63 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "64 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "65 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "66 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "67 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "68 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "69 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "70 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "71 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "72 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "73 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "74 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "75 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "76 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "77 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "78 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "79 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "80 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "81 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "82 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "83 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "84 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "85 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "86 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "87 \t0 \t[17.47280365 1.04 ]\t[2.49611479 0.28 ] \t[1.35152462e-07 1.00000000e+00]\n", - "88 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "89 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "90 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "91 \t0 \t[17.65109756 1.02 ]\t[1.77400205 0.19899749] \t[1.59872116e-13 1.00000000e+00]\n", - "92 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "93 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "94 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "95 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "96 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "97 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "98 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "99 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "Final population hypervolume is 48126.359818\n", - "{0: 2502, 1: 2444, 2: 2503, 3: 2451}\n", - "[-0.00033761 -0.00053674 -0.00033033 -0.00127082]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n" + "{0: 4119, 1: 2164, 2: 17104, 3: 2053}\n", + "[0.00825443 0.00739372 0.009413 0.00730638]\n", + "best model: Median(-22.36*x2,-6.00*x2,11.75*x2,4.00*x1)\n", + "score: 0.9999999999995968\n", + "Score mean (30 runs): 0.8019754956000301\n", + "Score std (30 runs) : 0.14304976030314429\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t160 \t[ nan 20.7375]\t[ nan 0.89782724]\t[nan 20.]\n", + "1 \t0 \t[ nan 13.75625]\t[ nan 6.53141148]\t[nan 1.]\n", + "2 \t0 \t[ nan 4.43125] \t[ nan 3.67529229]\t[nan 1.]\n", + "3 \t0 \t[ nan 1.05] \t[ nan 0.21794495]\t[nan 1.]\n", + "4 \t0 \t[20.78805373 1.0125 ]\t[4.21350124 0.11110243]\t[17.82939148 1. ]\n", + "5 \t0 \t[18.41994362 1.0125 ]\t[2.18973088 0.11110243]\t[17.82939148 1. ]\n", + "6 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "7 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "8 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[4.01456646e-13 1.00000000e+00]\n", + "9 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "10 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "11 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "12 \t0 \t[17.77717035 1.05625 ]\t[0.54768345 0.4220023 ]\t[10.98111534 1. ] \n", + "13 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[1.59872116e-13 1.00000000e+00]\n", + "14 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "15 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "16 \t0 \t[17.71795778 1.0125 ]\t[1.40512544 0.157619 ]\t[1.35152462e-07 1.00000000e+00]\n", + "17 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "18 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "19 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[1.59872116e-13 1.00000000e+00]\n", + "20 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "21 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "22 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "23 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "24 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "25 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "26 \t0 \t[17.61736346 1.05625 ]\t[1.82697719 0.4220023 ]\t[2.03570494e-11 1.00000000e+00]\n", + "27 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "28 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "29 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "30 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "31 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "32 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "33 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "34 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "35 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "36 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "37 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "38 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "39 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "40 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "41 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "42 \t0 \t[17.82013974 1.0125 ]\t[0.11665997 0.157619 ]\t[16.34911346 1. ] \n", + "43 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "44 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "45 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "46 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "47 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "48 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "49 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "50 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "51 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "52 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[4.01456646e-13 1.00000000e+00]\n", + "53 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "54 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "55 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "56 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "57 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "58 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "59 \t0 \t[17.81441762 1.025 ]\t[0.13309053 0.22220486]\t[16.63148308 1. ] \n", + "60 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "61 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "62 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "63 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "64 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "65 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "66 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "67 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "68 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "69 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "70 \t0 \t[17.71795778 1.0125 ]\t[1.40512544 0.157619 ]\t[1.35152462e-07 1.00000000e+00]\n", + "71 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "72 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "73 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "74 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "75 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "76 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "77 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "78 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "79 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "80 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "81 \t0 \t[17.82013974 1.0125 ]\t[0.11665997 0.157619 ]\t[16.34911346 1. ] \n", + "82 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "83 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "84 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "85 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "86 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "87 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "88 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "89 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "90 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "91 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "92 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "93 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "94 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "95 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "96 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "97 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "98 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "99 \t0 \t[17.71602528 1.05625 ]\t[1.40518347 0.4220023 ]\t[1.8656408e-07 1.0000000e+00] \n", + "100\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "101\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "102\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "103\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "104\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "105\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "106\t0 \t[17.70870605 1.03125 ]\t[1.40922857 0.28332567]\t[8.93509622e-08 1.00000000e+00]\n", + "107\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "108\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "109\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "110\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "111\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "112\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "113\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "114\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "115\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "116\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "117\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "118\t0 \t[17.71795778 1.0125 ]\t[1.40512544 0.157619 ]\t[1.35152462e-07 1.00000000e+00]\n", + "119\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "120\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", + "121\t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[4.01456646e-13 1.00000000e+00]\n", + "122\t0 \t[17.82584144 1.00625 ]\t[0.04476431 0.0788095 ]\t[17.26138496 1. ] \n", + "123\t0 \t[17.71440774 1.01875 ]\t[1.4055569 0.17577951]\t[1.59872116e-13 1.00000000e+00]\n", + "124\t0 \t[17.60297405 1.03125 ]\t[1.9809951 0.23510304]\t[1.59872116e-13 1.00000000e+00]\n", + "125\t0 \t[17.58337359 1.03125 ]\t[1.99970409 0.23510304]\t[1.59872116e-13 1.00000000e+00]\n", + "126\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "127\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "128\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "129\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "130\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "131\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "132\t0 \t[17.76311749 1.01875 ]\t[0.6155492 0.17577951]\t[10.92963123 1. ] \n", + "133\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "134\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "135\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "136\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "137\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "138\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "139\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "140\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "141\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "142\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "143\t0 \t[17.76917413 1.03125 ]\t[0.54950648 0.32445868]\t[11.89869499 1. ] \n", + "144\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "145\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "146\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "147\t0 \t[17.69480729 1.01875 ]\t[1.43332946 0.17577951]\t[1.59872116e-13 1.00000000e+00]\n", + "148\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "149\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "150\t0 \t[17.76311749 1.01875 ]\t[0.6155492 0.17577951]\t[10.92963123 1. ] \n", + "151\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "152\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "153\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "154\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "155\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "156\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "157\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "158\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "159\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", + "Final population hypervolume is 48304.155594\n", + "{0: 2340, 1: 2455, 2: 19495, 3: 1150}\n", + "[0.00726496 0.00733198 0.00907925 0.00608696]\n", + "best model: 1.26*Floor(-3.00*x2)\n", + "score: 0.7237639565420584\n" ] } ], "source": [ - "import pandas as pd\n", - "\n", - "# I am getting tons of unharmful warnings\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "#df = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", - "#X = df.drop(columns='label')\n", - "#y = df['label']\n", - "\n", - "df = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", - "X = df.drop(columns='target')\n", - "y = df['target']\n", - "\n", - "kwargs = {\n", - " 'pop_size' : 100,\n", - " 'max_gen' : 100,\n", - " 'verbosity' : 0,\n", - " 'max_depth' : 10,\n", - " 'max_size' : 20,\n", - " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", - "}\n", "\n", "# 30 executions just to compare avg score\n", "scores = []\n", @@ -604,7 +1198,9 @@ " y_pred = est_mab.predict(X)\n", "\n", " scores.append(est_mab.score(X,y))\n", - "print(f\"Score (30 runs): {np.mean(scores)}\")\n", + " print('score:', scores[-1])\n", + "print(f\"Score mean (30 runs): {np.mean(scores)}\")\n", + "print(f\"Score std (30 runs) : {np.std(scores)}\")\n", "\n", "# Single run with verbosity\n", "kwargs['verbosity'] = 1\n", @@ -627,205 +1223,266 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "{0: 3366, 1: 16, 2: 3370, 3: 3148}\n", - "[-4.05962183e-04 -1.12526941e+11 -3.62192692e-04 -2.91524196e-03]\n", - "best model: -4.24*x2\n", - "{0: 2490, 1: 2479, 2: 2491, 3: 2440}\n", - "[-0.00036652 -0.00054139 -0.00033253 -0.00123083]\n", - "best model: 3.60*Sin(2.72*x1)\n", - "{0: 3305, 1: 63, 2: 3305, 3: 3227}\n", - "[-3.47628793e-04 -4.52236312e+00 -3.38324719e-04 -1.24529618e-03]\n", - "best model: 3.60*Sin(2.72*x1)\n", - "{0: 84, 1: 3286, 2: 3305, 3: 3225}\n", - "[-3.19134285e+08 -5.59272536e-04 -3.41379892e-04 -1.25437165e-03]\n", - "best model: 3.60*Sin(2.72*x1)\n", - "{0: 19, 1: 107, 2: 110, 3: 9664}\n", - "[-6.07635009e+01 -5.12827890e+17 -8.54437202e+09 -9.15424529e-04]\n", - "best model: 3.68*Sin(2.74*x1)\n", - "{0: 25, 1: 76, 2: 4974, 3: 4825}\n", - "[-2.77204121e+00 -9.65579745e-01 -3.84279937e-04 -1.32060016e-03]\n", + "-------------------------------------- Run 0 --------------------------------------\n", + "best model: Floor(-2.98*x2)\n", + "score: 0.6624346086007631\n", + "-------------------------------------- Run 1 --------------------------------------\n", "best model: -4.24*x2\n", - "{0: 129, 1: 4868, 2: 4897, 3: 6}\n", - "[-3.16229731e-01 -5.37142383e-04 -3.59548331e-04 -3.51616980e+17]\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 2 --------------------------------------\n", "best model: -4.24*x2\n", - "{0: 1537, 1: 2799, 2: 2812, 3: 2752}\n", - "[-0.03085949 -0.00054441 -0.00033255 -0.0012708 ]\n", - "best model: 2.79*Tanh(469.33*x1)\n", - "{0: 45, 1: 3300, 2: 3318, 3: 3237}\n", - "[-5.85096838e+00 -5.39978020e-04 -3.38116597e-04 -1.25995611e-03]\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 3 --------------------------------------\n", "best model: -4.24*x2\n", - "{0: 4885, 1: 59, 2: 4886, 3: 70}\n", - "[-3.34650998e-04 -7.58935695e+19 -3.33819759e-04 -3.53269682e+00]\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 4 --------------------------------------\n", "best model: -4.24*x2\n", - "{0: 2403, 1: 2383, 2: 2585, 3: 2529}\n", - "[-0.003463 -0.00383799 -0.00033757 -0.0012668 ]\n", - "best model: 3.60*Sin(2.72*x1)\n", - "{0: 119, 1: 3098, 2: 3385, 3: 3298}\n", - "[-8.54277606e-01 -3.70314404e-03 -3.63485341e-04 -1.33930528e-03]\n", - "best model: 1.26*Floor(-3.00*x2)\n", - "{0: 242, 1: 3191, 2: 3293, 3: 3174}\n", - "[-5.10587726e+02 -5.11324171e-04 -3.27858040e-04 -1.23800979e-03]\n", - "best model: 1.77*Atan(44757.84*x1)\n", - "{0: 2485, 1: 2482, 2: 2493, 3: 2440}\n", - "[-0.00047499 -0.0005332 -0.000337 -0.00126153]\n", - "best model: -4.24*x2\n", - "{0: 64, 1: 3238, 2: 3339, 3: 3259}\n", - "[-8.88350612e+02 -1.47782443e-03 -3.37196647e-04 -1.24099308e-03]\n", - "best model: 1.77*Atan(94709.32*x1)\n", - "{0: 2491, 1: 2479, 2: 2491, 3: 2439}\n", - "[-0.00034939 -0.0005673 -0.00034729 -0.00125779]\n", - "best model: -4.24*x2\n", - "{0: 2490, 1: 2479, 2: 2491, 3: 2440}\n", - "[-0.00036089 -0.00055806 -0.00034671 -0.0012465 ]\n", - "best model: 16.53*Log1p(-0.23*x2)\n", - "{0: 85, 1: 54, 2: 4955, 3: 4806}\n", - "[-4.33770528e+02 -2.90762476e+08 -3.36727177e-04 -1.27426962e-03]\n", - "best model: -4.24*x2\n", - "{0: 2498, 1: 2438, 2: 2509, 3: 2455}\n", - "[-0.00053392 -0.0015817 -0.00033776 -0.0012685 ]\n", - "best model: -4.24*x2\n", - "{0: 328, 1: 3205, 2: 3222, 3: 3145}\n", - "[-6.22900813e+02 -5.37273429e-04 -3.34606208e-04 -1.25118665e-03]\n", - "best model: 1.77*Atan(58403.29*x1)\n", - "{0: 32, 1: 3304, 2: 3323, 3: 3241}\n", - "[-1.11523542e+01 -5.45819514e-04 -3.28464819e-04 -1.27437150e-03]\n", - "best model: -4.24*x2\n", - "{0: 2, 1: 115, 2: 9775, 3: 8}\n", - "[-2.76785925e+01 -2.27590186e+05 -3.35505547e-04 -2.24268908e+02]\n", - "best model: -4.24*x2\n", - "{0: 70, 1: 3331, 2: 3269, 3: 3230}\n", - "[-1.43217558e+03 -5.27008308e-04 -3.26690376e-04 -1.24314573e-03]\n", - "best model: 2.79*Tanh(42.92*x1)\n", - "{0: 239, 1: 1750, 2: 93, 3: 7818}\n", - "[-5.02028184e+02 -8.83763100e+08 -5.62765928e+08 -1.10354608e-03]\n", - "best model: 1.77*Atan(118371.02*x1)\n", - "{0: 924, 1: 3009, 2: 3009, 3: 2958}\n", - "[-1.29857556e+02 -5.76118924e-04 -5.77231624e-04 -1.25136432e-03]\n", - "best model: 1.77*Atan(44757.84*x1)\n", - "{0: 190, 1: 47, 2: 52, 3: 9611}\n", - "[-7.10204387e+01 -1.64681556e+01 -1.00648539e+08 -1.03053944e-03]\n", - "best model: 1.77*Atan(215807.25*x1)\n", - "{0: 2049, 1: 2, 2: 3977, 3: 3872}\n", - "[-2.70667978e-02 -6.33814783e+08 -3.33925210e-04 -1.25067235e-03]\n", - "best model: 2.79*Tanh(469.33*x1)\n", - "{0: 25, 1: 4926, 2: 59, 3: 4890}\n", - "[-1.14996572e+01 -8.30932822e-04 -5.01314766e+06 -1.04938353e-03]\n", - "best model: 1.26*Floor(-3.00*x2)\n", - "{0: 4, 1: 3313, 2: 3332, 3: 3251}\n", - "[-2.76152882e+01 -5.53825483e-04 -3.39124161e-04 -1.25374732e-03]\n", - "best model: -4.24*x2\n", - "{0: 125, 1: 4868, 2: 4895, 3: 12}\n", - "[-2.95068578e+00 -5.26443489e-04 -3.62352455e-04 -1.35562820e+14]\n", - "best model: -4.24*x2\n", - "Score (30 runs): 0.7256975662463311\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t100 \t[ nan 20.83]\t[ nan 1.00054985]\t[nan 20.]\n", - "1 \t100 \t[ nan 12.2] \t[ nan 7.4939976] \t[nan 1.]\n", - "2 \t100 \t[ nan 3.42] \t[ nan 3.09896757]\t[nan 1.]\n", - "3 \t100 \t[28.58473202 1. ]\t[12.62085635 0. ]\t[17.82939148 1. ]\n", - "4 \t100 \t[19.81409328 1. ]\t[3.73706994 0. ] \t[17.82939148 1. ]\n", - "5 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "6 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "7 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "8 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "9 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "10 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "11 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "12 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "13 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "14 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "15 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "16 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "17 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "18 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "19 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "20 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "21 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "22 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "23 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "24 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "25 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "26 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "27 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "28 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "29 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "30 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "31 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "32 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "33 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "34 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "35 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "36 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "37 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "38 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "39 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "40 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "41 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "42 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "43 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "44 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "45 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "46 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "47 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "48 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "49 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "50 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "51 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "52 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "53 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "54 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "55 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "56 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "57 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "58 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "59 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "60 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "61 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "62 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "63 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "64 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "65 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "66 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "67 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "68 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "69 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "70 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "71 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "72 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "73 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "74 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "75 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "76 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "77 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "78 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "79 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "80 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "81 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "82 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "83 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "84 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "85 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "86 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "87 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "88 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "89 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "90 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "91 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "92 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "93 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "94 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "95 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "96 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "97 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "98 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "99 \t100 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 5 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 6 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 7 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 8 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 9 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 10 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 11 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 12 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 13 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 14 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 15 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 16 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 17 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 18 --------------------------------------\n", + "best model: Floor(-4.43*x2)\n", + "score: 0.6828901196699741\n", + "-------------------------------------- Run 19 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 20 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 21 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 22 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 23 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 24 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 25 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 26 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 27 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 28 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "-------------------------------------- Run 29 --------------------------------------\n", + "best model: -4.24*x2\n", + "score: 0.651326594086648\n", + "Score mean (30 runs): 0.6527489787565626\n", + "Score std (30 runs) : 0.005941236653589971\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t160 \t[ nan 20.625]\t[ nan 0.8042854]\t[nan 20.]\n", + "1 \t160 \t[ nan 16.53125]\t[ nan 5.41516606]\t[nan 1.]\n", + "2 \t160 \t[ nan 10.26875]\t[ nan 5.81992469]\t[nan 1.]\n", + "3 \t160 \t[ nan 4.34375] \t[ nan 2.71350068]\t[nan 1.]\n", + "4 \t160 \t[ nan 1.78125] \t[ nan 0.8190839] \t[nan 1.]\n", + "5 \t160 \t[30.36090877 1.0125 ]\t[13.72482068 0.11110243]\t[17.82939148 1. ]\n", + "6 \t160 \t[20.26475508 1.00625 ]\t[3.9950179 0.0788095] \t[17.82939148 1. ]\n", + "7 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "8 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "9 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "10 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "11 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "12 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "13 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "14 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "15 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "16 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "17 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "18 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "19 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "20 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "21 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "22 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "23 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "24 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "25 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "26 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "27 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "28 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "29 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "30 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "31 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "32 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "33 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "34 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "35 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "36 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "37 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "38 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "39 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "40 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. 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\t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "103\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "104\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "105\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "106\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "107\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "108\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "109\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "110\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "111\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "112\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "113\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "114\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "115\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "116\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "117\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "118\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "119\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "120\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "121\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "122\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "123\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "124\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "125\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "126\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "127\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "128\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "129\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "130\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "131\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "132\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "133\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "134\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "135\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "136\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "137\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "138\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "139\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "140\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "141\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "142\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "143\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "144\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "145\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "146\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "147\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "148\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "149\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "150\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "151\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "152\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "153\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "154\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "155\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "156\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "157\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "158\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", + "159\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", "Final population hypervolume is 48126.359818\n", "best model: -4.24*x2\n", "score: 0.651326594086648\n" @@ -834,21 +1491,25 @@ ], "source": [ "# 30 executions just to compare avg score\n", + "\n", + "kwargs['verbosity'] = 0\n", + "\n", "scores = []\n", - "for _ in range(30):\n", - " kwargs['verbosity'] = 0\n", + "for i in range(30):\n", "\n", - " est_mab = BrushRegressorMod(**kwargs)\n", + " print(f\"-------------------------------------- Run {i} --------------------------------------\")\n", + " est_mab = BrushRegressor(**kwargs)\n", "\n", " # use like you would a sklearn regressor\n", " est_mab.fit(X,y)\n", " y_pred = est_mab.predict(X)\n", "\n", " scores.append(est_mab.score(X,y))\n", - "print(f\"Score (30 runs): {np.mean(scores)}\")\n", + " print('score:', scores[-1])\n", + "print(f\"Score mean (30 runs): {np.mean(scores)}\")\n", + "print(f\"Score std (30 runs) : {np.std(scores)}\")\n", "\n", "# Single run with verbosity\n", - "\n", "kwargs['verbosity'] = 1\n", "est = BrushRegressor(**kwargs)\n", "\n", From 5b77e5d1cb8a83431a26d7b344d8139a01bfc061 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Wed, 17 May 2023 22:06:34 -0300 Subject: [PATCH 008/102] Add python bind to get program depth --- src/bindings/bind_programs.h | 1 + src/program/program.h | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/src/bindings/bind_programs.h b/src/bindings/bind_programs.h index 35fd1c0c..8ee9b3ef 100644 --- a/src/bindings/bind_programs.h +++ b/src/bindings/bind_programs.h @@ -46,6 +46,7 @@ void bind_program(py::module& m, string name) .def("get_dot_model", &T::get_dot_model, py::arg("extras")="") .def("get_weights", &T::get_weights) .def("size", &T::size) + .def("depth", &T::depth) .def("cross", &T::cross) .def("mutate", &T::mutate) // static_cast(&T::mutate)) .def("set_search_space", &T::set_search_space) diff --git a/src/program/program.h b/src/program/program.h index ff858bd6..7393956f 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -91,6 +91,10 @@ template struct Program return Tree.size(); } + int depth(){ + return Tree.max_depth(); + } + Program& fit(const Dataset& d) { TreeType out = Tree.begin().node->fit(d); From 516505c8e12cf731f3aac0521c918811b9666573 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 18 May 2023 20:16:54 -0300 Subject: [PATCH 009/102] Bug fix - insert mutation causing segmentation fault previously, the insert mutation would use `get_op_with_arg` to find a matching node to insert in the tree. Inside that function, we ignore nodes that would make the final program having more nodes than the `PARAMS['max_size']`. In rare situations this could lead to an empty collection of matching nodes to sample the operator, generating a segmentation fault. This commit fixes that, by making the insert mutation be less strict with the maximum size (just like PTC2): the program size can exceed the maximum size by the number of the highest arity among the operators. --- src/brush/estimator.py | 13 ++++++++++--- src/search_space.h | 30 ++++++++++++++++++++++-------- src/variation.h | 7 ++++++- 3 files changed, 38 insertions(+), 12 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index f1ff642b..d01e2b61 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -88,8 +88,13 @@ def _setup_toolbox(self, data): """Setup the deap toolbox""" toolbox: base.Toolbox = base.Toolbox() - # minimize MAE, minimize size - creator.create("FitnessMulti", base.Fitness, weights=(-1.0,-1.0)) + # creator.create is used to "create new functions", and takes at least + # 2 arguments: the name of the newly created class and a base class + + # minimize MAE, minimize size. When solving multi-objective problems, + # selection and survival must support this feature. This means that + # these selection operators must accept a tuple of fitnesses as argument) + creator.create("FitnessMulti", base.Fitness, weights=(-1.0,-0.5)) # create Individual class, inheriting from self.Individual with a fitness attribute creator.create("Individual", DeapIndividual, fitness=creator.FitnessMulti) @@ -151,7 +156,9 @@ def fit(self, X, y): self.archive_ = archive self.best_estimator_ = self.archive_[0].prg - print('best model:',self.best_estimator_.get_model()) + if self.verbosity > 0: + print('best model:',self.best_estimator_.get_model()) + return self def _make_data(self, X, y=None): diff --git a/src/search_space.h b/src/search_space.h index cf78f134..10c8ba9c 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -377,11 +377,11 @@ struct SearchSpace /// @param ret return type /// @param arg argument type to match /// @param terminal_compatible if true, the other args the returned operator takes must exist in the terminal types. - /// @param max_arg_count if zero, there is no limit on number of arguments of the operator. If not, the operator can have at most `max_arg_count` arguments. + /// @param max_args if zero, there is no limit on number of arguments of the operator. If not, the operator can have at most `max_args` arguments. /// @return a matching operator. Node get_op_with_arg(DataType ret, DataType arg, bool terminal_compatible=true, - int max_arg_count=0) const + int max_arg=0) const { // thoughts (TODO): // this could be templated by return type and arg. although the lookup in the map should be @@ -391,18 +391,15 @@ struct SearchSpace check(ret); auto args_map = node_map.at(ret); - vector matches; + vector matches; vector weights; + vector invalids; for (const auto& [args_type, name_map]: args_map) { for (const auto& [name, node]: name_map) { auto node_arg_types = node.get_arg_types(); - // has no size limit (max_arg_count==0) or the number of - // arguments woudn't exceed the maximum number of arguments - auto within_size_limit = !(max_arg_count) || (node.get_arg_count() <= max_arg_count); - - if ( in(node_arg_types, arg) && within_size_limit ) { + if ( in(node_arg_types, arg) ) { // if checking terminal compatibility, make sure there's // a compatible terminal for the node's other arguments if (terminal_compatible) { @@ -421,10 +418,27 @@ struct SearchSpace // if we made it this far, include the node as a match! matches.push_back(node); weights.push_back(node_weights.at(ret).at(args_type).at(name)); + + // saving for future checking + // has no size limit (max_arg is 0) or the number of + // arguments woudn't exceed the maximum number of arguments + invalids.push_back(!(max_arg) || (node.get_arg_count() <= max_arg)); } } } + // If one or more nodes are respecting the size limit, we'll focus only + // on them. We do that by setting the probabilities of invalid nodes + // to zero. If all of them are invalid, we relax size restriction ( + // we ignore it) to avoid selecting over an empty collection. + if (std::find(invalids.begin(), invalids.end(), false) != invalids.end()) { + std::transform(weights.begin(), weights.end(), + invalids.begin(), weights.begin(), + [](int weight, bool invalid) { + return invalid ? 0.0 : weight; + }); + } + return (*r.select_randomly(matches.begin(), matches.end(), weights.begin(), weights.end())); }; diff --git a/src/variation.h b/src/variation.h index cf7344b3..5cfc2485 100644 --- a/src/variation.h +++ b/src/variation.h @@ -44,7 +44,12 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) auto spot_type = spot.node->data.ret_type; // pick a random compatible node to insert (with probabilities given by - // anode_weights). The -1 represents the node being inserted. + // node_weights). The `-1` represents the node being inserted. + // Ideally, it should always find at least one match (the same node + // used as a reference when calling the function). However, we have a + // size restriction, which will be relaxed here (just as it is in the PTC2 + // algorithm). This mutation can create a new expression that exceeds the + // maximum size by the highest arity among the operators. auto n = SS.get_op_with_arg(spot_type, spot_type, true, PARAMS["max_size"].get()-Tree.size()-1); From 6e2f3af600cb3aebc0de126912ffcb9026e33e26 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 18 May 2023 20:26:59 -0300 Subject: [PATCH 010/102] Delete MAB_experiments.ipynb Removing this file to create a merge request fixing bugs found in this version --- src/brush/MAB_experiments.ipynb | 1546 ------------------------------- 1 file changed, 1546 deletions(-) delete mode 100644 src/brush/MAB_experiments.ipynb diff --git a/src/brush/MAB_experiments.ipynb b/src/brush/MAB_experiments.ipynb deleted file mode 100644 index c6f231f6..00000000 --- a/src/brush/MAB_experiments.ipynb +++ /dev/null @@ -1,1546 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Implementing D-MAB, as described in DaCosta et al. - 2008 - Adaptive operator selection with dynamic multi-arm**\n", - "\n", - "> (hybrid between UCB1 and Page-Hinkley (PH) test)\n", - "\n", - "D-MAB maintains four indicators for each arm $i$:\n", - "1. number $n_{i, t}$ of times $i$-th arm has been played up to time $t$;\n", - "2. the average empirical reward $\\widehat{p}_{j, t}$ at time $t$;\n", - "3. the average and maximum deviation $m_i$ and $M_i$ involved in the PH test, initialized to $0$ and updated as detailed below. At each time step $t$:\n", - "\n", - "D-MAB selects the arm $i$ that maximizes equation 1:\n", - "\n", - "$$\\widehat{p}_{i, t} + \\sqrt{\\frac{2 \\log \\sum_{k}n_{k, t}}{n_{i, t}}}$$\n", - "\n", - "> Notice that the sum of the number of times each arm was pulled is equal to the time $\\sum_{k}n_{k, t} = t$, but since their algorithm resets the number of picks, we need to go with the summation. \n", - "\n", - "and receives some reward $r_t$, drawn after reward distribution $p_{i, t}$.\n", - "\n", - "> I think there is a typo in the eq. 1 on the paper. I replaced $j$ with $i$ in the lower indexes.\n", - "\n", - "The four indicators are updated accordingly:\n", - "\n", - "- $\\widehat{p}_{i, t} :=\\frac{1}{n_{i, t} + 1}(n_{i, t}\\widehat{p}_{i, t} + r_t)$\n", - "- $n_{i, t} := n_{i, t}+1$\n", - "- $m_i := m_i + (\\widehat{p}_{i, t} - r_t + \\delta)$\n", - "- $M_i:= \\text{max}(M_i, m_i)$\n", - "\n", - "And if the PH test is triggered ($M_i - m_i > \\lambda$), the bandit is restarted, i.e., for all arms, all indicators are set to zero (the authors argue that, empirically, resetting the values is more robust than decreasing them with some mechanism such as probability matching).\n", - "\n", - "> I will reset to 1 instead of 0 (as the original paper does) to avoid divide by zero when calculating UCB1.\n", - "\n", - "The PH test is a standard test for the change hypothesis. It works by monitoring the difference between $M_i$ and $m_i$, and when the difference is greater than some uuser-specified threshold $\\lambda$, the PH test is triggered, i.e., it is considered that the Change hypothesis holds.\n", - "\n", - "Parameter $\\lambda$ controls the trade-off between false alarms and un-noticed changes. Parameter $\\delta$ enforces the robustness of the test when dealing with slowly varying environments.\n", - "\n", - "We also need a scaling mechanism to control the Exploration _versus_ Exploitation balance. They proposed two, from which I will focus on the first: Multiplicative Scaling (cUCB). **It consists on multiplying all rewards by a fixed user-defined parameter $C_{M-\\text{scale}}$.\n", - "\n", - "This way, we need to give to our D-MAB 3 parameters: $\\lambda$, $\\delta$, and $C_{M - \\text{scale}}$. In the paper they did a sensitivity analysis of the parameters, but I think they should be fine tuned for each specific data set.\n", - "\n", - "> Besides the problem of having to adjust the parameters of the `D_MAB`, I think this is not suited for Symbolic Regression as it is! In symbolic regression we normally have a population with high diversity to explore the search space --- so the mutation that maximizes the reward can be different depending on the expression being mutated. In opposition, if the mutations could be applied regardless of the expression format, then the population would be made of a few set of expressions.\n", - ">\n", - "> We should use something like Contextual Bandits to address this problem. An improvement to the algorithm would be including `context_f` that returns an proper context to use when determining which arm to pull." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "class D_MAB:\n", - " def __init__(self, num_bandits, verbose=False, *, policy='max_ucb', delta, lmbda, scaling, pull_f, reward_f):\n", - " self.num_bandits = num_bandits\n", - " self.verbose = verbose\n", - " self.policy = policy #['max_ucb', 'prob_ucb']\n", - " self.delta = delta\n", - " self.lmbda = lmbda\n", - " self.scaling = scaling\n", - " self.pull_f = pull_f\n", - " self.reward_f = reward_f\n", - "\n", - " # History of choices and time instant t (just to track the behavior)\n", - " self.history = {i:[] for i in range(self.num_bandits)}\n", - "\n", - " self._reset_indicators()\n", - "\n", - " def _reset_indicators(self):\n", - " self.avg_reward = np.zeros(self.num_bandits)\n", - " self.num_played = np.zeros(self.num_bandits)\n", - " self.avg_deviation = np.zeros(self.num_bandits)\n", - " self.max_deviation = np.zeros(self.num_bandits)\n", - "\n", - " def _calc_UCB1s(self):\n", - " # We need that avg_reward \\in [0, 1] else we must scale it\n", - " rewards = self._normalize(self.avg_reward)\n", - "\n", - " # log1p and +1 on denominator fixes some numeric problems in the original eq.\n", - " #scores = np.array([rewards[i] + np.sqrt(2*np.log1p(sum(self.num_played))/(self.num_played[i]+1))\n", - " # for i in range(self.num_bandits)])\n", - "\n", - " scores = rewards + np.sqrt(2*np.log1p(sum(self.num_played))/(self.num_played+1))\n", - " \n", - " return np.nan_to_num(scores, nan=0.0)\n", - "\n", - " def _scale_reward(self, reward):\n", - " # We need to scale if the reward is not in [0, 1].\n", - " return reward*self.scaling\n", - " \n", - " def _normalize(self, p):\n", - " values = np.nan_to_num(p, nan=0)\n", - "\n", - " if np.sum(p)==0.0:\n", - " return np.ones(len(p))/len(p)\n", - " \n", - " return values / (values.sum())\n", - " \n", - " def playAndOptimize(self, **kwargs):\n", - " # For convenience, this takes kwargs that are passed to both pull and reward functions\n", - "\n", - " # It will pick the bandit that maximizes eq.1. \n", - " UCB1s = self._calc_UCB1s()\n", - "\n", - " # We need to know which arm we picked, what it returned, and how to calculate the reward given what the arm returned\n", - " picked = (\n", - " np.nanargmax(UCB1s) if self.policy=='max_ucb' else\n", - " np.random.choice(self.num_bandits, p=self._normalize(UCB1s))\n", - " )\n", - " \n", - " pulled = self.pull_f(picked, **kwargs)\n", - " reward = self.reward_f(pulled, **kwargs)\n", - " \n", - " self.history[picked].append(reward)\n", - "\n", - " if self.verbose:\n", - " print(f\"Avg. Rewards: {self.avg_reward}\\nUCB1 scores : {UCB1s}\\nPicked : {picked}\\nReward : {reward}\")\n", - "\n", - " # After choosing, it will implicitly update the parameters based on the return\n", - " if np.isfinite(reward):\n", - " self.avg_reward[picked] = (self.num_played[picked]*self.avg_reward[picked] + self._scale_reward(reward))/(self.num_played[picked]+1)\n", - " self.avg_deviation[picked] = self.avg_deviation[picked] + (self.avg_reward[picked] - self._scale_reward(reward) + self.delta)\n", - " \n", - " self.num_played[picked] = self.num_played[picked] +1\n", - " self.max_deviation[picked] = np.maximum(self.max_deviation[picked], self.avg_deviation[picked])\n", - "\n", - " if (self.max_deviation[picked] - self.avg_deviation[picked] > self.lmbda):\n", - " self._reset_indicators()\n", - " if self.verbose:\n", - " print(\"Reseted indicators ----------------------------------------\")\n", - "\n", - " return picked, pulled, reward" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below I'll create a simple bandit configuration so we can do a sanity check of our `D_MAB` implementation." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "==============================================================\n", - "All bandits with same probs\n", - "------------- Uniformly Distributed Random pulls -------------\n", - "Probabilities for each arm: [1. 1. 1. 1.] (the smaller the better)\n", - "cum. reward for each arm : [395.0, 395.0, 396.0, 368.0]\n", - "pulls for each arm : [2466, 2514, 2500, 2520]\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm: {0: 414.0, 1: 412.0, 2: 384.0, 3: 398.0}\n", - "pulls for each arm : {0: 2625, 1: 2557, 2: 2406, 3: 2412}\n", - "(it was expected: similar amount of pulls for each arm)\n", - "\n", - "==============================================================\n", - "One bandit with higher prob\n", - "------------- Uniformly Distributed Random pulls -------------\n", - "Probabilities for each arm: [-1. 0.2 0. 1. ] (the smaller the better)\n", - "cum. reward for each arm : [2157.0, 1097.0, 1217.0, 384.0]\n", - "pulls for each arm : [2525, 2558, 2483, 2434]\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm: {0: 3968.0, 1: 781.0, 2: 1031.0, 3: 202.0}\n", - "pulls for each arm : {0: 4760, 1: 1873, 2: 2076, 3: 1291}\n", - "(it was expected: more pulls for first arm, less pulls for last)\n", - "\n", - "==============================================================\n", - "Two bandits with higher probs\n", - "------------- Uniformly Distributed Random pulls -------------\n", - "Probabilities for each arm: [-0.2 -1. 0. -1. ] (the smaller the better)\n", - "cum. reward for each arm : [1444.0, 2083.0, 1278.0, 2093.0]\n", - "pulls for each arm : [2470, 2480, 2571, 2479]\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm: {0: 1358.0, 1: 2552.0, 2: 967.0, 3: 2275.0}\n", - "pulls for each arm : {0: 2352, 1: 2981, 2: 1922, 3: 2745}\n", - "(it was expected: 2nd approx 4th > 1st > 3rd)\n" - ] - } - ], - "source": [ - "# Sanity checks\n", - "import numpy as np\n", - "\n", - "for bandits, descr, expec in [\n", - " (np.array([1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs', 'similar amount of pulls for each arm'),\n", - " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob', 'more pulls for first arm, less pulls for last'),\n", - " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd approx 4th > 1st > 3rd'),\n", - "]:\n", - " # Implementing simple bandits.\n", - " def pullBandit(bandit, **kwargs):\n", - " # Needs to have kwargs to work with my D_MAB implementation\n", - "\n", - " #Get a random number based on a normal dist with mean 0 and var 1\n", - " result = np.random.randn()\n", - " \n", - " # bandits: This is the true reward probabilities, which we shoudn't have access (in the optimizer)\n", - " # return a positive or negative reward based on bandit prob.\n", - " return 1.0 if result > bandits[bandit] else 0.0\n", - "\n", - " \n", - " print(\"\\n==============================================================\")\n", - " print(descr)\n", - "\n", - " print(\"------------- Uniformly Distributed Random pulls -------------\")\n", - " picks = [0, 0, 0, 0]\n", - " rewards = [0, 0, 0, 0]\n", - "\n", - " for _ in range(10000):\n", - " index = np.random.randint(len(bandits))\n", - " reward = pullBandit(index)\n", - "\n", - " picks[index] = picks[index]+1\n", - " rewards[index] = rewards[index]+reward\n", - "\n", - " print(\"Probabilities for each arm: \", bandits, \"(the smaller the better)\")\n", - " print(\"cum. reward for each arm : \", rewards)\n", - " print(\"pulls for each arm : \", picks)\n", - "\n", - " print(\"------------------------ optimizing ------------------------\")\n", - "\n", - " # We have the problem that we need to determine delta and lambda values previously.\n", - " # This needs domain knowledge (in SR context, I think we need to know if data is homogenic or\n", - " # if it changes a lot through time).\n", - " optimizer = D_MAB(4, verbose=False, policy='max_ucb',\n", - " delta=0.15, lmbda=0.5, scaling=1, # Lambda seems to control how strong will be the exploitation\n", - " pull_f=pullBandit, reward_f=lambda r, **kwargs:r)\n", - "\n", - " # Let's optimize\n", - " for i in range(10000):\n", - " optimizer.playAndOptimize()\n", - "\n", - " total_rewards = {k : sum(v) for (k, v) in optimizer.history.items()}\n", - " total_played = {k : len(v) for (k, v) in optimizer.history.items()}\n", - "\n", - " print(\"cum. reward for each arm: \", total_rewards)\n", - " print(\"pulls for each arm : \", total_played)\n", - " print(f\"(it was expected: {expec})\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Avg. Rewards: [0. 0. 0. 0.]\n", - "UCB1 scores : [0.25 0.25 0.25 0.25]\n", - "Picked : 0\n", - "Reward : 1.0\n", - "Avg. Rewards: [1. 0. 0. 0.]\n", - "UCB1 scores : [1.83255461 1.17741002 1.17741002 1.17741002]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.5 0. 0. 0. ]\n", - "UCB1 scores : [1.8558085 1.48230381 1.48230381 1.48230381]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.33333333 0. 0. 0. ]\n", - "UCB1 scores : [1.83255461 1.66510922 1.66510922 1.66510922]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.25 0. 0. 0. ]\n", - "UCB1 scores : [1.80235601 1.79412258 1.79412258 1.79412258]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0. ]\n", - "UCB1 scores : [1.77282156 1.89301847 1.89301847 1.89301847]\n", - "Picked : 1\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0. ]\n", - "UCB1 scores : [1.80537986 1.39495883 1.9727697 1.9727697 ]\n", - "Picked : 2\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0. ]\n", - "UCB1 scores : [1.83255461 1.44202689 1.44202689 2.03933398]\n", - "Picked : 3\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.2 0. 0. 1. ]\n", - "UCB1 scores : [1.02247517 1.48230381 1.48230381 2.31563714]\n", - "Picked : 3\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.2 0. 0. 1. ]\n", - "UCB1 scores : [1.04275363 1.51742713 1.51742713 2.0723074 ]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.66666667]\n", - "UCB1 scores : [1.12480414 1.54851389 1.54851389 1.86419544]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.5]\n", - "UCB1 scores : [1.19582539 1.57635867 1.57635867 1.71126247]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.4]\n", - "UCB1 scores : [1.25798631 1.60154593 1.60154593 1.59131964]\n", - "Picked : 1\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.4]\n", - "UCB1 scores : [1.27124899 1.32641304 1.62451757 1.60458232]\n", - "Picked : 2\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.4]\n", - "UCB1 scores : [1.28342985 1.34363939 1.34363939 1.61676319]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.33333333]\n", - "UCB1 scores : [1.33635126 1.35955599 1.35955599 1.51503832]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.28571429]\n", - "UCB1 scores : [1.38356944 1.37433944 1.37433944 1.42984288]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.2 0. 0. 0.25]\n", - "UCB1 scores : [1.42600303 1.38813346 1.38813346 1.35699478]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0. 0. 0.25 ]\n", - "UCB1 scores : [1.31720678 1.4010565 1.4010565 1.40890035]\n", - "Picked : 3\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.16666667 0. 0. 0.33333333]\n", - "UCB1 scores : [1.25849467 1.41320729 1.41320729 1.44071218]\n", - "Picked : 3\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.16666667 0. 0. 0.4 ]\n", - "UCB1 scores : [1.22678241 1.42466895 1.42466895 1.44989145]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0. 0. 0.36363636]\n", - "UCB1 scores : [1.25404898 1.43551209 1.43551209 1.40347033]\n", - "Picked : 1\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.16666667 0.33333333 0. 0.36363636]\n", - "UCB1 scores : [1.13947889 1.638062 1.44579718 1.14395122]\n", - "Picked : 1\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0.25 0. 0.36363636]\n", - "UCB1 scores : [1.16649064 1.44787295 1.45557636 1.1938076 ]\n", - "Picked : 2\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0.25 0. 0.36363636]\n", - "UCB1 scores : [1.17259109 1.4550911 1.26863624 1.19846689]\n", - "Picked : 1\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0.2 0. 0.36363636]\n", - "UCB1 scores : [1.19303944 1.3159876 1.27634175 1.23482157]\n", - "Picked : 1\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0.16666667 0. 0.36363636]\n", - "UCB1 scores : [1.20952606 1.20952606 1.28371275 1.26289103]\n", - "Picked : 2\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0.16666667 0. 0.36363636]\n", - "UCB1 scores : [1.21486525 1.21486525 1.154505 1.26696891]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.16666667 0.16666667 0. 0.33333333]\n", - "UCB1 scores : [1.23085907 1.23085907 1.16056811 1.21975379]\n", - "Picked : 0\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.28571429 0.16666667 0. 0.33333333]\n", - "UCB1 scores : [1.28575314 1.19790551 1.16639571 1.14761034]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.25 0.16666667 0. 0.33333333]\n", - "UCB1 scores : [1.20689402 1.21274693 1.17200464 1.17129087]\n", - "Picked : 1\n", - "Reward : 0.0\n", - "Avg. 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Rewards: [0.14285714 0.0952381 0.0625 0.14285714]\n", - "UCB1 scores : [0.88190713 0.85743671 0.87203827 0.88190713]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.13793103 0.0952381 0.0625 0.14285714]\n", - "UCB1 scores : [0.86552476 0.86059725 0.87447242 0.88617743]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.13793103 0.0952381 0.0625 0.13793103]\n", - "UCB1 scores : [0.86973127 0.86380391 0.87693266 0.86973127]\n", - "Picked : 2\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.13793103 0.0952381 0.05882353 0.13793103]\n", - "UCB1 scores : [0.87307739 0.86641302 0.84977565 0.87307739]\n", - "Picked : 0\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.13333333 0.0952381 0.05882353 0.13793103]\n", - "UCB1 scores : [0.85736402 0.86953017 0.85205346 0.8771642 ]\n", - "Picked : 3\n", - "Reward : 1.0\n", - "Avg. Rewards: [0.13333333 0.0952381 0.05882353 0.16666667]\n", - "UCB1 scores : [0.8381267 0.85607374 0.84409068 0.91153818]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "Avg. Rewards: [0.13333333 0.0952381 0.05882353 0.16129032]\n", - "UCB1 scores : [0.84224041 0.85929345 0.84642399 0.89596468]\n", - "Picked : 3\n", - "Reward : 0.0\n", - "cum. reward for each arm: {0: 4.0, 1: 2.0, 2: 1.0, 3: 5.0}\n", - "pulls for each arm : {0: 30, 1: 21, 2: 17, 3: 32}\n", - "(it was expected: similar amount of pulls for each arm)\n" - ] - } - ], - "source": [ - "# Simple test with verbose\n", - "bandits, descr, expec = (np.array([1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs', 'similar amount of pulls for each arm')\n", - "\n", - "def pullBandit(bandit, **kwargs):\n", - " result = np.random.randn()\n", - " return 1.0 if result > bandits[bandit] else 0.0\n", - "\n", - "optimizer = D_MAB(4, verbose=True, policy='max_ucb',\n", - " delta=0.25, lmbda=10, scaling=1,\n", - " pull_f=pullBandit, reward_f=lambda r, **kwargs:r)\n", - "\n", - "# Let's optimize\n", - "for i in range(100):\n", - " optimizer.playAndOptimize()\n", - "\n", - "total_rewards = {k : sum(v) for (k, v) in optimizer.history.items()}\n", - "total_played = {k : len(v) for (k, v) in optimizer.history.items()}\n", - "\n", - "print(\"cum. reward for each arm: \", total_rewards)\n", - "print(\"pulls for each arm : \", total_played)\n", - "print(f\"(it was expected: {expec})\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ok, so the D-MAB seems to work. Now let's add this MAB inside mutation to update PARAMS option and control dinamically the mutaiton probabilities during evolution.\n", - "\n", - "We can import the brush estimator and replace the `_mutation` by a custom function. Ideally, to use this python MAB optimizer, we need to have an object created to keep track of the variables, and the object needs to wrap the _pull_ action, as well as evaluating the reward based on the result.\n", - "\n", - "> we'll need to do a _gambiarra_ to know which mutation is used so we can correctly update `D_MAB`. All MAB logic is implemented in python, and we chose the mutation in python as well. To make sure a specific mutation was used, we force it to happen by setting others' weights to zero. this way we know exactly what happened in the C++ code" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from brush import BrushRegressor\n", - "from deap import creator\n", - "import _brush\n", - "from deap_api import nsga2, DeapIndividual \n", - "\n", - "#prg.mutate is a convenient interface that uses the current search space to sample mutations\n", - "\n", - "class BrushRegressorMod(BrushRegressor):\n", - " def __init__(self, **kwargs):\n", - " super().__init__(**kwargs)\n", - "\n", - " def _mutate(self, ind1):\n", - " # Overriding the mutation so it is wrapped with D_MAB\n", - " \n", - " mutation, offspring, reward = self.D_MAB_.playAndOptimize(ind1=ind1)\n", - " \n", - " #print(mutation, ind1.prg.get_model(), offspring.prg.get_model(), reward)\n", - " return offspring\n", - " \n", - " def fit(self, X, y):\n", - "\n", - " _brush.set_params(self.get_params())\n", - "\n", - " self.data_ = self._make_data(X,y)\n", - "\n", - " # Creating a wrapper for mutation to be able to control what is happening in the C++\n", - " # code (this should be prettier in a future implementation)\n", - " def _pull_mutation(mutation_idx, ind1, **kwargs):\n", - " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", - " params = self.get_params()\n", - "\n", - " for i, m in enumerate(mutations):\n", - " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", - "\n", - " _brush.set_params(params)\n", - " \n", - " offspring = creator.Individual(ind1.prg.mutate())\n", - "\n", - " return offspring\n", - " \n", - " # Given the result of a pull (the mutated offspring), how do I evaluate it?\n", - " # I need to return if the reward was positive or negative. We make use of\n", - " # kwargs here.\n", - " # (here I am manually writing the multi-optimization problem nsga2 is\n", - " # designed to solve)\n", - " def _evaluate_reward(pulled, ind1, **kwargs):\n", - " if True: #not ind1.fitness.valid:\n", - " ind1.prg.fit(self.data_)\n", - " fit = (\n", - " np.sum((self.data_.y- ind1.prg.predict(self.data_))**2),\n", - " ind1.prg.size()\n", - " )\n", - " \n", - " ind1.fitness.setValues(fit)\n", - " # ind1.fitness = fit\n", - "\n", - " # in deap, a negative weight means a minimization problem, while a \n", - " # positive weight is a maximization problem.\n", - "\n", - " # ind1_error, ind1_size = ind1.fitness.values\n", - " # ind1_fitness = -1.0*ind1_error + -1.0*ind1_size\n", - "\n", - " if True: #not pulled.fitness.valid:\n", - " pulled.prg.fit(self.data_)\n", - " fit = (\n", - " np.sum((self.data_.y- pulled.prg.predict(self.data_))**2),\n", - " pulled.prg.size()\n", - " )\n", - " \n", - " pulled.fitness.setValues(fit)\n", - " # pulled.fitness = fit\n", - " \n", - " # pulled_error, pulled_size = pulled.fitness.values\n", - " # pulled_fitness = -1.0*pulled_error + -1.0*pulled_size\n", - "\n", - " # We compare fitnesses using the deap overloaded operators\n", - " # from the docs: When comparing fitness values that are **minimized**, ``a > b`` will\n", - " # return :data:`True` if *a* is **smaller** than *b*.\n", - " return 1.0 if pulled.fitness.dominates(ind1.fitness) else 0.0\n", - "\n", - " # return 0.0 if pulled.fitness.values <= ind1.fitness.values else 1.0\n", - " \n", - " # We have 4 different mutations\n", - " self.D_MAB_ = D_MAB(4, verbose=False, policy='max_ucb', \n", - " delta=0.15, lmbda=5, scaling=1,\n", - " pull_f=_pull_mutation, reward_f=_evaluate_reward)\n", - "\n", - " if isinstance(self.functions, list):\n", - " self.functions_ = {k:1.0 for k in self.functions}\n", - " else:\n", - " self.functions_ = self.functions\n", - "\n", - " self.search_space_ = _brush.SearchSpace(self.data_, self.functions_)\n", - " self.toolbox_ = self._setup_toolbox(data=self.data_)\n", - "\n", - " archive, logbook = nsga2(self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", - "\n", - " self.archive_ = archive\n", - " self.best_estimator_ = self.archive_[0].prg\n", - " total_played = {k : len(v) for (k, v) in self.D_MAB_.history.items()}\n", - "\n", - " print(total_played)\n", - " print(self.D_MAB_.avg_reward)\n", - " print('best model:',self.best_estimator_.get_model())\n", - " return self\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, lets use this new mutation into an ES algorithm (because this is only based on mutation) and see if it improves the performance" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "# I am getting tons of unharmful warnings\n", - "import warnings\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "# df = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", - "# X = df.drop(columns='label')\n", - "# y = df['label']\n", - "\n", - "df = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", - "X = df.drop(columns='target')\n", - "y = df['target']\n", - "\n", - "# df = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", - "# X = df.drop(columns='target')\n", - "# y = df['target']\n", - "\n", - "kwargs = {\n", - " 'pop_size' : 160,\n", - " 'max_gen' : 160,\n", - " 'verbosity' : 0,\n", - " 'max_depth' : 10,\n", - " 'max_size' : 20,\n", - " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------------------------------- Run 0 --------------------------------------\n", - "{0: 2171, 1: 478, 2: 21518, 3: 1273}\n", - "[0.0105942 0.00627615 0.01319825 0.00942655]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 1 --------------------------------------\n", - "{0: 2849, 1: 1088, 2: 19959, 3: 1544}\n", - "[0.00807301 0.00643382 0.00971993 0.00712435]\n", - "best model: 3.68*Sin(2.74*x1)\n", - "score: 0.8675268611694\n", - "-------------------------------------- Run 2 --------------------------------------\n", - "{0: 1899, 1: 1038, 2: 18860, 3: 3643}\n", - "[0.00684571 0.00578035 0.00890774 0.00768597]\n", - "best model: Sub(-3.00*x2,-2.00*x1)\n", - "score: 0.999999999999994\n", - "-------------------------------------- Run 3 --------------------------------------\n", - "{0: 22499, 1: 198, 2: 2073, 3: 670}\n", - "[0.00634495 0. 0.00455322 0.00350263]\n", - "best model: If(x2>-0.46,-4.09*x2,3.18)\n", - "score: 0.6802750800991533\n", - "-------------------------------------- Run 4 --------------------------------------\n", - "{0: 2204, 1: 1989, 2: 17573, 3: 3674}\n", - "[0.00771325 0.00754148 0.00978774 0.00843767]\n", - "best model: Mean(-7.49*x2,-2.26*x2,-2.25*x2,8.00*x1)\n", - "score: 0.9999999963496158\n", - "-------------------------------------- Run 5 --------------------------------------\n", - "{0: 3365, 1: 987, 2: 18741, 3: 2347}\n", - "[0.00950966 0.0070922 0.01115202 0.00894759]\n", - "best model: Sum(-3.00*x2,1.00*x1,1.00*x1)\n", - "score: 0.9999999999999972\n", - "-------------------------------------- Run 6 --------------------------------------\n", - "{0: 2970, 1: 1621, 2: 16380, 3: 4469}\n", - "[0.00841751 0.00740284 0.01007326 0.00895055]\n", - "best model: Sum(-3.00*x2,-0.00,2.00*x1)\n", - "score: 0.999999999999994\n", - "-------------------------------------- Run 7 --------------------------------------\n", - "{0: 2134, 1: 1638, 2: 18855, 3: 2813}\n", - "[0.00843486 0.00793651 0.01076637 0.00888731]\n", - "best model: Sum(-3.00*x2,2.00*x1,-0.00)\n", - "score: 0.999999999999994\n", - "-------------------------------------- Run 8 --------------------------------------\n", - "{0: 2168, 1: 1352, 2: 18865, 3: 3055}\n", - "[0.00830258 0.00739645 0.01054864 0.00883797]\n", - "best model: 3.68*Sin(2.74*x1)\n", - "score: 0.8675268611694\n", - "-------------------------------------- Run 9 --------------------------------------\n", - "{0: 1893, 1: 1446, 2: 19446, 3: 2655}\n", - "[0.00739567 0.00691563 0.0096678 0.0079096 ]\n", - "best model: 1.77*Atan(44784.27*x1)\n", - "score: 0.7629047704797927\n", - "-------------------------------------- Run 10 --------------------------------------\n", - "{0: 1994, 1: 1249, 2: 19042, 3: 3155}\n", - "[0.00902708 0.00800641 0.01165844 0.00982567]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 11 --------------------------------------\n", - "{0: 2065, 1: 977, 2: 21216, 3: 1182}\n", - "[0.00871671 0.00716479 0.01107655 0.00761421]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 12 --------------------------------------\n", - "{0: 3056, 1: 2241, 2: 17696, 3: 2447}\n", - "[0.00850785 0.00803213 0.01017179 0.00817327]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 13 --------------------------------------\n", - "{0: 5531, 1: 1556, 2: 15699, 3: 2654}\n", - "[0.01048635 0.00835476 0.0114657 0.00941974]\n", - "best model: Min(-4.21*x2,-3.65*x2,2.50*x1,2.20*x1)\n", - "score: 0.7673079213877327\n", - "-------------------------------------- Run 14 --------------------------------------\n", - "{0: 2756, 1: 1309, 2: 18339, 3: 3036}\n", - "[0.00907112 0.00763942 0.01101478 0.00922266]\n", - "best model: 1.77*Atan(27369.91*x1)\n", - "score: 0.7629018280064254\n", - "-------------------------------------- Run 15 --------------------------------------\n", - "{0: 4296, 1: 2202, 2: 17734, 3: 1208}\n", - "[0.00861266 0.00772025 0.00975527 0.00662252]\n", - "best model: Sum(-3.00*x2,2.00*x1)\n", - "score: 0.9999999999999978\n", - "-------------------------------------- Run 16 --------------------------------------\n", - "{0: 2827, 1: 1358, 2: 18698, 3: 2557}\n", - "[0.00955076 0.00810015 0.01155204 0.009386 ]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 17 --------------------------------------\n", - "{0: 1108, 1: 513, 2: 20827, 3: 2992}\n", - "[0.00812274 0.00584795 0.01185961 0.01002674]\n", - "best model: 2.79*Tanh(469.33*x1)\n", - "score: 0.7629093888079747\n", - "-------------------------------------- Run 18 --------------------------------------\n", - "{0: 2647, 1: 1243, 2: 16900, 3: 4650}\n", - "[0.00868908 0.00724055 0.01059172 0.00946237]\n", - "best model: Mean(0.00,6.00*x1,-9.00*x2)\n", - "score: 0.999999999999994\n", - "-------------------------------------- Run 19 --------------------------------------\n", - "{0: 1370, 1: 2894, 2: 17912, 3: 3264}\n", - "[0.00583942 0.00691085 0.00831845 0.00704657]\n", - "best model: 1.77*Atan(44784.27*x1)\n", - "score: 0.7629047704797927\n", - "-------------------------------------- Run 20 --------------------------------------\n", - "{0: 2724, 1: 2819, 2: 15853, 3: 4044}\n", - "[0.00881057 0.00886839 0.01072352 0.00939664]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 21 --------------------------------------\n", - "{0: 3662, 1: 2350, 2: 16771, 3: 2657}\n", - "[0.00873839 0.00808511 0.01013655 0.00828002]\n", - "best model: 2.79*Tanh(469.33*x1)\n", - "score: 0.7629093888079747\n", - "-------------------------------------- Run 22 --------------------------------------\n", - "{0: 2518, 1: 1610, 2: 18130, 3: 3182}\n", - "[0.00754567 0.0068323 0.00932157 0.00785669]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 23 --------------------------------------\n", - "{0: 1732, 1: 2975, 2: 18429, 3: 2304}\n", - "[0.00692841 0.00773109 0.00927885 0.00737847]\n", - "best model: Sum(-3.00*x2,2.00*x1)\n", - "score: 0.9999999999999978\n", - "-------------------------------------- Run 24 --------------------------------------\n", - "{0: 4058, 1: 991, 2: 17180, 3: 3211}\n", - "[0.00837851 0.00605449 0.00954598 0.00809717]\n", - "best model: 2.79*Tanh(26112.86*x1)\n", - "score: 0.7629093888079747\n", - "-------------------------------------- Run 25 --------------------------------------\n", - "{0: 2044, 1: 1596, 2: 18542, 3: 3258}\n", - "[0.0092955 0.00877193 0.01197282 0.01012891]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 26 --------------------------------------\n", - "{0: 3997, 1: 2511, 2: 14823, 3: 4109}\n", - "[0.00775582 0.00716846 0.00883762 0.00778778]\n", - "best model: Min(-4.17*x2,3.18,2.22*x1)\n", - "score: 0.7859226715696217\n", - "-------------------------------------- Run 27 --------------------------------------\n", - "{0: 1594, 1: 1594, 2: 19724, 3: 2528}\n", - "[0.00752823 0.00752823 0.01024133 0.00830696]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 28 --------------------------------------\n", - "{0: 4727, 1: 2388, 2: 15729, 3: 2596}\n", - "[0.00888513 0.00795645 0.00991799 0.00808937]\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 29 --------------------------------------\n", - "{0: 4119, 1: 2164, 2: 17104, 3: 2053}\n", - "[0.00825443 0.00739372 0.009413 0.00730638]\n", - "best model: Median(-22.36*x2,-6.00*x2,11.75*x2,4.00*x1)\n", - "score: 0.9999999999995968\n", - "Score mean (30 runs): 0.8019754956000301\n", - "Score std (30 runs) : 0.14304976030314429\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t160 \t[ nan 20.7375]\t[ nan 0.89782724]\t[nan 20.]\n", - "1 \t0 \t[ nan 13.75625]\t[ nan 6.53141148]\t[nan 1.]\n", - "2 \t0 \t[ nan 4.43125] \t[ nan 3.67529229]\t[nan 1.]\n", - "3 \t0 \t[ nan 1.05] \t[ nan 0.21794495]\t[nan 1.]\n", - "4 \t0 \t[20.78805373 1.0125 ]\t[4.21350124 0.11110243]\t[17.82939148 1. ]\n", - "5 \t0 \t[18.41994362 1.0125 ]\t[2.18973088 0.11110243]\t[17.82939148 1. ]\n", - "6 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "7 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "8 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[4.01456646e-13 1.00000000e+00]\n", - "9 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "10 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "11 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "12 \t0 \t[17.77717035 1.05625 ]\t[0.54768345 0.4220023 ]\t[10.98111534 1. ] \n", - "13 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[1.59872116e-13 1.00000000e+00]\n", - "14 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "15 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "16 \t0 \t[17.71795778 1.0125 ]\t[1.40512544 0.157619 ]\t[1.35152462e-07 1.00000000e+00]\n", - "17 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "18 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "19 \t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[1.59872116e-13 1.00000000e+00]\n", - "20 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "21 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "22 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "23 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "24 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "25 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "26 \t0 \t[17.61736346 1.05625 ]\t[1.82697719 0.4220023 ]\t[2.03570494e-11 1.00000000e+00]\n", - "27 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "28 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "29 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "30 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "31 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "32 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "33 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "34 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "35 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "36 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - 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"69 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "70 \t0 \t[17.71795778 1.0125 ]\t[1.40512544 0.157619 ]\t[1.35152462e-07 1.00000000e+00]\n", - "71 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "72 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "73 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "74 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "75 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "76 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "77 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "78 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "79 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "80 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "81 \t0 \t[17.82013974 1.0125 ]\t[0.11665997 0.157619 ]\t[16.34911346 1. ] \n", - "82 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "83 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "84 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "85 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "86 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "87 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "88 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "89 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "90 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "91 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "92 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "93 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "94 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "95 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "96 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "97 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "98 \t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "99 \t0 \t[17.71602528 1.05625 ]\t[1.40518347 0.4220023 ]\t[1.8656408e-07 1.0000000e+00] \n", - "100\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "101\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "102\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "103\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "104\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "105\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "106\t0 \t[17.70870605 1.03125 ]\t[1.40922857 0.28332567]\t[8.93509622e-08 1.00000000e+00]\n", - "107\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "108\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "109\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "110\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "111\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "112\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "113\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "114\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "115\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "116\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "117\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "118\t0 \t[17.71795778 1.0125 ]\t[1.40512544 0.157619 ]\t[1.35152462e-07 1.00000000e+00]\n", - "119\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "120\t0 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ] \n", - "121\t0 \t[17.71795778 1.0125 ]\t[1.40512545 0.157619 ]\t[4.01456646e-13 1.00000000e+00]\n", - "122\t0 \t[17.82584144 1.00625 ]\t[0.04476431 0.0788095 ]\t[17.26138496 1. ] \n", - "123\t0 \t[17.71440774 1.01875 ]\t[1.4055569 0.17577951]\t[1.59872116e-13 1.00000000e+00]\n", - "124\t0 \t[17.60297405 1.03125 ]\t[1.9809951 0.23510304]\t[1.59872116e-13 1.00000000e+00]\n", - "125\t0 \t[17.58337359 1.03125 ]\t[1.99970409 0.23510304]\t[1.59872116e-13 1.00000000e+00]\n", - "126\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "127\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "128\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "129\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "130\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "131\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "132\t0 \t[17.76311749 1.01875 ]\t[0.6155492 0.17577951]\t[10.92963123 1. ] \n", - "133\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "134\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "135\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "136\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "137\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "138\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "139\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "140\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "141\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "142\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "143\t0 \t[17.76917413 1.03125 ]\t[0.54950648 0.32445868]\t[11.89869499 1. ] \n", - "144\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "145\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "146\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "147\t0 \t[17.69480729 1.01875 ]\t[1.43332946 0.17577951]\t[1.59872116e-13 1.00000000e+00]\n", - "148\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "149\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "150\t0 \t[17.76311749 1.01875 ]\t[0.6155492 0.17577951]\t[10.92963123 1. ] \n", - "151\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "152\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "153\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "154\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "155\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "156\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "157\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "158\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "159\t0 \t[17.80624099 1.00625 ]\t[0.29191659 0.0788095 ]\t[14.12531281 1. ] \n", - "Final population hypervolume is 48304.155594\n", - "{0: 2340, 1: 2455, 2: 19495, 3: 1150}\n", - "[0.00726496 0.00733198 0.00907925 0.00608696]\n", - "best model: 1.26*Floor(-3.00*x2)\n", - "score: 0.7237639565420584\n" - ] - } - ], - "source": [ - "\n", - "# 30 executions just to compare avg score\n", - "scores = []\n", - "for i in range(30):\n", - " print(f\"-------------------------------------- Run {i} --------------------------------------\")\n", - " est_mab = BrushRegressorMod(**kwargs)\n", - "\n", - " # use like you would a sklearn regressor\n", - " est_mab.fit(X,y)\n", - " y_pred = est_mab.predict(X)\n", - "\n", - " scores.append(est_mab.score(X,y))\n", - " print('score:', scores[-1])\n", - "print(f\"Score mean (30 runs): {np.mean(scores)}\")\n", - "print(f\"Score std (30 runs) : {np.std(scores)}\")\n", - "\n", - "# Single run with verbosity\n", - "kwargs['verbosity'] = 1\n", - "est_mab = BrushRegressorMod(**kwargs)\n", - "\n", - "# use like you would a sklearn regressor\n", - "est_mab.fit(X,y)\n", - "y_pred = est_mab.predict(X)\n", - "\n", - "print('score:', est_mab.score(X,y))" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Comparing with the original implementation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-------------------------------------- Run 0 --------------------------------------\n", - "best model: Floor(-2.98*x2)\n", - "score: 0.6624346086007631\n", - "-------------------------------------- Run 1 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 2 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 3 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 4 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 5 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 6 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 7 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 8 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 9 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 10 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 11 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 12 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 13 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 14 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 15 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 16 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 17 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 18 --------------------------------------\n", - "best model: Floor(-4.43*x2)\n", - "score: 0.6828901196699741\n", - "-------------------------------------- Run 19 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 20 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 21 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 22 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 23 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 24 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 25 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 26 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 27 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 28 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "-------------------------------------- Run 29 --------------------------------------\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n", - "Score mean (30 runs): 0.6527489787565626\n", - "Score std (30 runs) : 0.005941236653589971\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t160 \t[ nan 20.625]\t[ nan 0.8042854]\t[nan 20.]\n", - "1 \t160 \t[ nan 16.53125]\t[ nan 5.41516606]\t[nan 1.]\n", - "2 \t160 \t[ nan 10.26875]\t[ nan 5.81992469]\t[nan 1.]\n", - "3 \t160 \t[ nan 4.34375] \t[ nan 2.71350068]\t[nan 1.]\n", - "4 \t160 \t[ nan 1.78125] \t[ nan 0.8190839] \t[nan 1.]\n", - "5 \t160 \t[30.36090877 1.0125 ]\t[13.72482068 0.11110243]\t[17.82939148 1. ]\n", - "6 \t160 \t[20.26475508 1.00625 ]\t[3.9950179 0.0788095] \t[17.82939148 1. ]\n", - "7 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "8 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "9 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "10 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "11 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "12 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "13 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "14 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "15 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "16 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "17 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "18 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "19 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "20 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "21 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "22 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "23 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "24 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "25 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "26 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "27 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "28 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "29 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "30 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "31 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "32 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "33 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "34 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "35 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "36 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "37 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "38 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "39 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "40 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "41 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "42 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "43 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "44 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "45 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "46 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "47 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "48 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "49 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "50 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "51 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "52 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "53 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "54 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "55 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "56 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "57 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "58 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "59 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "60 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "61 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "62 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "63 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "64 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "65 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "66 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "67 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "68 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "69 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "70 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "71 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "72 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "73 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "74 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "75 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "76 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "77 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "78 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "79 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "80 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "81 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "82 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "83 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "84 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "85 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "86 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "87 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "88 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "89 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "90 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "91 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "92 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "93 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "94 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "95 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "96 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "97 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "98 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "99 \t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "100\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "101\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "102\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "103\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "104\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "105\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "106\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "107\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "108\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "109\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "110\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "111\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "112\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "113\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "114\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "115\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "116\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "117\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "118\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "119\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "120\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "121\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "122\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "123\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "124\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "125\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "126\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "127\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "128\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "129\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "130\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "131\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "132\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "133\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "134\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "135\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "136\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "137\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "138\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "139\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "140\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "141\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "142\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "143\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "144\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "145\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "146\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "147\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "148\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "149\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "150\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "151\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "152\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "153\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "154\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "155\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "156\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "157\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "158\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "159\t160 \t[17.82939148 1. ]\t[0. 0.] \t[17.82939148 1. ]\n", - "Final population hypervolume is 48126.359818\n", - "best model: -4.24*x2\n", - "score: 0.651326594086648\n" - ] - } - ], - "source": [ - "# 30 executions just to compare avg score\n", - "\n", - "kwargs['verbosity'] = 0\n", - "\n", - "scores = []\n", - "for i in range(30):\n", - "\n", - " print(f\"-------------------------------------- Run {i} --------------------------------------\")\n", - " est_mab = BrushRegressor(**kwargs)\n", - "\n", - " # use like you would a sklearn regressor\n", - " est_mab.fit(X,y)\n", - " y_pred = est_mab.predict(X)\n", - "\n", - " scores.append(est_mab.score(X,y))\n", - " print('score:', scores[-1])\n", - "print(f\"Score mean (30 runs): {np.mean(scores)}\")\n", - "print(f\"Score std (30 runs) : {np.std(scores)}\")\n", - "\n", - "# Single run with verbosity\n", - "kwargs['verbosity'] = 1\n", - "est = BrushRegressor(**kwargs)\n", - "\n", - "# use like you would a sklearn regressor\n", - "est.fit(X,y)\n", - "y_pred = est.predict(X)\n", - "\n", - "print('score:', est.score(X,y))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brush", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 8d5a1b93e9e63164d84d2291fe3e763eef0bbed6 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Fri, 19 May 2023 09:14:43 -0300 Subject: [PATCH 011/102] Add notebooks studying dynamic learning of the mutation weights --- src/brush/D_MAB_experiments.ipynb | 2075 +++++++++++++++++++++++++++++ src/brush/D_TS_experiments.ipynb | 2030 ++++++++++++++++++++++++++++ 2 files changed, 4105 insertions(+) create mode 100644 src/brush/D_MAB_experiments.ipynb create mode 100644 src/brush/D_TS_experiments.ipynb diff --git a/src/brush/D_MAB_experiments.ipynb b/src/brush/D_MAB_experiments.ipynb new file mode 100644 index 00000000..3dd7a6fa --- /dev/null +++ b/src/brush/D_MAB_experiments.ipynb @@ -0,0 +1,2075 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Implementing D-MAB, as described in DaCosta et al. - 2008 - Adaptive operator selection with dynamic multi-arm**\n", + "\n", + "> (hybrid between UCB1 and Page-Hinkley (PH) test)\n", + "\n", + "D-MAB maintains four indicators for each arm $i$:\n", + "1. number $n_{i, t}$ of times $i$-th arm has been played up to time $t$;\n", + "2. the average empirical reward $\\widehat{p}_{j, t}$ at time $t$;\n", + "3. the average and maximum deviation $m_i$ and $M_i$ involved in the PH test, initialized to $0$ and updated as detailed below. At each time step $t$:\n", + "\n", + "D-MAB selects the arm $i$ that maximizes equation 1:\n", + "\n", + "$$\\widehat{p}_{i, t} + \\sqrt{\\frac{2 \\log \\sum_{k}n_{k, t}}{n_{i, t}}}$$\n", + "\n", + "> Notice that the sum of the number of times each arm was pulled is equal to the time $\\sum_{k}n_{k, t} = t$, but since their algorithm resets the number of picks, we need to go with the summation. \n", + "\n", + "and receives some reward $r_t$, drawn after reward distribution $p_{i, t}$.\n", + "\n", + "> I think there is a typo in the eq. 1 on the paper. I replaced $j$ with $i$ in the lower indexes.\n", + "\n", + "The four indicators are updated accordingly:\n", + "\n", + "- $\\widehat{p}_{i, t} :=\\frac{1}{n_{i, t} + 1}(n_{i, t}\\widehat{p}_{i, t} + r_t)$\n", + "- $n_{i, t} := n_{i, t}+1$\n", + "- $m_i := m_i + (\\widehat{p}_{i, t} - r_t + \\delta)$\n", + "- $M_i:= \\text{max}(M_i, m_i)$\n", + "\n", + "And if the PH test is triggered ($M_i - m_i > \\lambda$), the bandit is restarted, i.e., for all arms, all indicators are set to zero (the authors argue that, empirically, resetting the values is more robust than decreasing them with some mechanism such as probability matching).\n", + "\n", + "> I will reset to 1 instead of 0 (as the original paper does) to avoid divide by zero when calculating UCB1.\n", + "\n", + "The PH test is a standard test for the change hypothesis. It works by monitoring the difference between $M_i$ and $m_i$, and when the difference is greater than some uuser-specified threshold $\\lambda$, the PH test is triggered, i.e., it is considered that the Change hypothesis holds.\n", + "\n", + "Parameter $\\lambda$ controls the trade-off between false alarms and un-noticed changes. Parameter $\\delta$ enforces the robustness of the test when dealing with slowly varying environments.\n", + "\n", + "We also need a scaling mechanism to control the Exploration _versus_ Exploitation balance. They proposed two, from which I will focus on the first: Multiplicative Scaling (cUCB). **It consists on multiplying all rewards by a fixed user-defined parameter $C_{M-\\text{scale}}$.\n", + "\n", + "This way, we need to give to our D-MAB 3 parameters: $\\lambda$, $\\delta$, and $C_{M - \\text{scale}}$. In the paper they did a sensitivity analysis of the parameters, but I think they should be fine tuned for each specific data set.\n", + "\n", + "> Brush originally sample the mutations using an uniform distribution. This algorithm chooses the arms using an deterministic approach --- the one that maximizes the UCB1 score. Somehow we need to convert them to have a transparent implementation to the user." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "class D_MAB:\n", + " def __init__(self, num_bandits, delta=0.15, lmbda=0.25):\n", + " self.num_bandits = num_bandits\n", + "\n", + " # Store tuples when update is called. Tuples will have 3 values:\n", + " # (time instant t, arm idx, reward)\n", + " self.pull_history = []\n", + " self.reset_history = []\n", + "\n", + " # This is the probability that should be used to update brush probs\n", + " self._probabilities = np.ones(num_bandits)/num_bandits\n", + "\n", + " self.delta = delta # how to define these values???\n", + " self.lmbda = lmbda\n", + "\n", + " self._reset_indicators() # Creating the indicators \n", + "\n", + " def _reset_indicators(self):\n", + " self._avg_rewards = np.zeros(self.num_bandits)\n", + " self._num_pulls = np.zeros(self.num_bandits)\n", + " self._avg_deviations = np.zeros(self.num_bandits)\n", + " self._max_deviations = np.zeros(self.num_bandits)\n", + "\n", + " @property\n", + " def probabilities(self):\n", + " # How to transform our UCB1 scores into node probabilities?\n", + " return self._probabilities\n", + " \n", + " @probabilities.setter\n", + " def probabilities(self, new_probabilities):\n", + " if len(self._probabilities)==len(new_probabilities):\n", + " self._probabilities = new_probabilities\n", + " else:\n", + " print(f\"New probabilities must have size {self.num_bandits}\")\n", + "\n", + " def choose_arm(self):\n", + " \"\"\"Uses previous recordings of rewards to pick the arm that maximizes\n", + " the UCB1 function. The choice is made in a deterministic way.\n", + " \"\"\"\n", + "\n", + " # We need that the reward is in [0, 1] (not avg_reward, as it seems to\n", + " # render worse results). It looks like normalizing the rewards is a\n", + " # problem: reward should be [0, 1], but not necessarely avg_rewards too\n", + " rs = self._avg_rewards\n", + " ns = self._num_pulls\n", + " \n", + " UCB1s = rs + np.sqrt(2*np.log1p(sum(ns))/(ns+1))\n", + "\n", + " return np.nanargmax(UCB1s)\n", + "\n", + " def update(self, arm_idx, reward):\n", + " # Here we expect that the reward was already scaled to be in the \n", + " # interval [0, 1] (in the original paper, they sugest using a scaling\n", + " # factor as an hyperparameter).\n", + "\n", + " self.pull_history.append( (len(self.pull_history), arm_idx, reward) )\n", + "\n", + " if np.isfinite(reward):\n", + " self._avg_rewards[arm_idx] = \\\n", + " (self._num_pulls[arm_idx]*self._avg_rewards[arm_idx] + reward)/(self._num_pulls[arm_idx]+1)\n", + " self._avg_deviations[arm_idx] = \\\n", + " self._avg_deviations[arm_idx] + (self._avg_rewards[arm_idx] - reward + self.delta)\n", + " \n", + " self._num_pulls[arm_idx] = self._num_pulls[arm_idx] +1\n", + " self._max_deviations[arm_idx] = \\\n", + " np.maximum(self._max_deviations[arm_idx], self._avg_deviations[arm_idx])\n", + "\n", + " if (self._max_deviations[arm_idx] - self._avg_deviations[arm_idx] > self.lmbda):\n", + " self._reset_indicators()\n", + " self.reset_history.append(len(self.pull_history))\n", + "\n", + " return self" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below I'll create a simple bandit configuration so we can do a sanity check of our `D_MAB` implementation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 44.0, 1: 41.0, 2: 28.0, 3: 48.0}\n", + "number of pulls for each arm: {0: 273, 1: 257, 2: 230, 3: 240}\n", + "(it was expected: similar amount of pulls for each arm)\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 462.0, 1: 78.0, 2: 79.0, 3: 19.0}\n", + "number of pulls for each arm: {0: 542, 1: 178, 2: 175, 3: 105}\n", + "(it was expected: more pulls for first arm, less pulls for last)\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 107.0, 1: 235.0, 2: 89.0, 3: 321.0}\n", + "number of pulls for each arm: {0: 177, 1: 292, 2: 163, 3: 368}\n", + "(it was expected: 2nd approx 4th > 1st > 3rd)\n" + ] + } + ], + "source": [ + "# Sanity checks\n", + "\n", + "class Bandits:\n", + " def __init__(self, reward_prob):\n", + " # Implementing simple bandits.\n", + " self.reward_prob = reward_prob # True reward prob., which learner shoudn't know\n", + " self.n_bandits = len(reward_prob) \n", + "\n", + " def pull(self, arm_idx):\n", + " # Sampling over a normal distr. with mu=0 and var=1\n", + " result = np.random.randn()\n", + " \n", + " # return a positive or nullary reward (Bernoulli random variable).\n", + " return 1.0 if result > self.reward_prob[arm_idx] else 0.0\n", + "\n", + "for probs, descr, expec in [\n", + " (np.array([ 1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs' , 'similar amount of pulls for each arm' ),\n", + " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob' , 'more pulls for first arm, less pulls for last'),\n", + " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd approx 4th > 1st > 3rd' ),\n", + "]:\n", + " bandits = Bandits(probs)\n", + "\n", + " print(\"------------------------ optimizing ------------------------\")\n", + "\n", + " learner = D_MAB(4)\n", + " for i in range(1000):\n", + " arm_idx = learner.choose_arm()\n", + " reward = bandits.pull(arm_idx)\n", + "\n", + " learner.update(arm_idx, reward) \n", + "\n", + " total_rewards = {arm_idx : sum([r for (t, i, r) in learner.pull_history if i==arm_idx])\n", + " for arm_idx in range(learner.num_bandits)}\n", + "\n", + " total_pulls = {arm_idx : sum([1 for (t, i, r) in learner.pull_history if i==arm_idx])\n", + " for arm_idx in range(learner.num_bandits)}\n", + "\n", + " print(\"cum. reward for each arm : \", total_rewards)\n", + " print(\"number of pulls for each arm: \", total_pulls)\n", + " print(f\"(it was expected: {expec})\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ok, so the D-MAB seems to work. Now let's add this MAB inside mutation to update PARAMS option and control dinamically the mutaiton probabilities during evolution.\n", + "\n", + "We can import the brush estimator and replace the `_mutation` by a custom function. Ideally, to use this python MAB optimizer, we need to have an object created to keep track of the variables, and the object needs to wrap the _pull_ action, as well as evaluating the reward based on the result.\n", + "\n", + "> we'll need to do a _gambiarra_ to know which mutation is used so we can correctly update `D_MAB`. All MAB logic is implemented in python, and we chose the mutation in python as well. To make sure a specific mutation was used, we force it to happen by setting others' weights to zero. this way we know exactly what happened in the C++ code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from brush.estimator import BrushEstimator\n", + "from sklearn.base import ClassifierMixin, RegressorMixin\n", + "from deap import creator\n", + "import _brush\n", + "from deap_api import nsga2 \n", + "\n", + "class BrushEstimatorMod(BrushEstimator): # Modifying brush estimator\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + "\n", + " def _mutate(self, ind1):\n", + " # Overriding the mutation so it updates our sampling method. Doing the\n", + " # logic on the python-side for now.\n", + "\n", + " # Creating a wrapper for mutation to be able to control what is happening\n", + " # in the C++ code (this should be prettier in a future implementation)\n", + " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", + " params = self.get_params()\n", + " \n", + " ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", + " or ind1.prg.depth()+1>=self.max_depth) else False\n", + "\n", + " # Insert Mutation will not work, even if we force it, when the expression\n", + " # is already at maximum size.\n", + " # In this case, we'll do the mutation without controlling the probabilities.\n", + " if ignore_this_time:\n", + " for i, m in enumerate(mutations):\n", + " params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", + " else:\n", + " mutation_idx = self.learner_.choose_arm()\n", + "\n", + " for i, m in enumerate(mutations):\n", + " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", + "\n", + " _brush.set_params(params)\n", + " \n", + " # ind1.prg.mutate is a convenient interface that uses the current search \n", + " # space to sample mutations\n", + " offspring = creator.Individual(ind1.prg.mutate())\n", + "\n", + " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", + " \n", + " # We compare fitnesses using the deap overloaded operators\n", + " # from the docs: When comparing fitness values that are **minimized**,\n", + " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", + " # (this means that this comparison should work agnostic of min/max problems,\n", + " # or even a single-objective or multi-objective problem)\n", + " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", + " \n", + " if not ignore_this_time:\n", + " self.learner_.update(mutation_idx, reward)\n", + " \n", + " return offspring\n", + " \n", + " def fit(self, X, y):\n", + "\n", + " _brush.set_params(self.get_params())\n", + "\n", + " self.data_ = self._make_data(X,y)\n", + " # self.data_.print()\n", + "\n", + " # set n classes if relevant\n", + " if self.mode==\"classification\":\n", + " self.n_classes_ = len(np.unique(y))\n", + "\n", + " # We have 4 different mutations, and the learner will learn to choose\n", + " # between these options by maximizing the reward when using each one\n", + " self.learner_ = D_MAB(4)\n", + "\n", + " if isinstance(self.functions, list):\n", + " self.functions_ = {k:1.0 for k in self.functions}\n", + " else:\n", + " self.functions_ = self.functions\n", + "\n", + " self.search_space_ = _brush.SearchSpace(self.data_, self.functions_)\n", + "\n", + " self.toolbox_ = self._setup_toolbox(data=self.data_)\n", + "\n", + " archive, logbook = nsga2(\n", + " self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", + "\n", + " self.archive_ = archive\n", + " self.best_estimator_ = self.archive_[0].prg\n", + "\n", + " return self\n", + " \n", + "\n", + "class BrushClassifierMod(BrushEstimatorMod,ClassifierMixin):\n", + " def __init__( self, **kwargs):\n", + " super().__init__(mode='classification',**kwargs)\n", + "\n", + " def _fitness_function(self, ind, data: _brush.Dataset):\n", + " ind.prg.fit(data)\n", + " return (\n", + " np.abs(data.y-ind.prg.predict(data)).sum(), \n", + " ind.prg.size()\n", + " )\n", + " \n", + " def _make_individual(self):\n", + " return creator.Individual(\n", + " self.search_space_.make_classifier(self.max_depth, self.max_size)\n", + " if self.n_classes_ == 2 else\n", + " self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size)\n", + " )\n", + "\n", + " def predict_proba(self, X):\n", + " data = self._make_data(X)\n", + " return self.best_estimator_.predict_proba(data)\n", + "\n", + "\n", + "class BrushRegressorMod(BrushEstimatorMod, RegressorMixin):\n", + " def __init__(self, **kwargs):\n", + " super().__init__(mode='regressor',**kwargs)\n", + "\n", + " def _fitness_function(self, ind, data: _brush.Dataset):\n", + " ind.prg.fit(data)\n", + " return (\n", + " np.sum((data.y- ind.prg.predict(data))**2),\n", + " ind.prg.size()\n", + " )\n", + "\n", + " def _make_individual(self):\n", + " return creator.Individual(\n", + " self.search_space_.make_regressor(self.max_depth, self.max_size)\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression problem" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[ nan 20.925]\t[ nan 0.97435876]\t[nan 20.]\n", + "1 \t200 \t[ nan 16.925]\t[ nan 4.98491474]\t[nan 1.]\n", + "2 \t200 \t[ nan 10.495]\t[ nan 5.4488508] \t[nan 1.]\n", + "3 \t200 \t[ nan 4.73] \t[ nan 2.58400851]\t[nan 1.]\n", + "4 \t200 \t[ nan 2.425] \t[ nan 1.16377618]\t[nan 1.]\n", + "5 \t200 \t[ nan 1.725] \t[ nan 0.69955343]\t[nan 1.]\n", + "6 \t200 \t[ nan 1.43] \t[ nan 0.62056426]\t[nan 1.]\n", + "7 \t200 \t[5.15006834 1.05 ]\t[1.27027425 0.21794495]\t[2.73836088 1. ]\n", + "8 \t200 \t[4.4173494 1.025 ] \t[1.03668311 0.15612495]\t[2.73836088 1. ]\n", + "9 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "10 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "11 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "12 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "13 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "14 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "15 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "16 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "17 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "18 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "19 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "20 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "21 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "22 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "23 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "24 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "25 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "26 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "27 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "28 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "29 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "30 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "31 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "32 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "33 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "34 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "35 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "36 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "37 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "38 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "39 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "Final population hypervolume is 49363.883825\n", + "best model: Square(0.96*x1)\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[ nan 20.695]\t[ nan 1.0686323]\t[nan 13.]\n", + "1 \t0 \t[ nan 15.36] \t[ nan 6.07950656]\t[nan 1.]\n", + "2 \t0 \t[ nan 8.325] \t[ nan 5.0704413] \t[nan 1.]\n", + "3 \t0 \t[ nan 2.915] \t[ nan 1.83242326]\t[nan 1.]\n", + "4 \t0 \t[ nan 1.45] \t[ nan 0.65383484]\t[nan 1.]\n", + "5 \t0 \t[5.69480249 1.035 ]\t[1.72669191 0.18377976]\t[2.61403799 1. ]\n", + "6 \t0 \t[4.74933375 1.01 ]\t[1.15020605 0.09949874]\t[2.61403799 1. ]\n", + "7 \t0 \t[3.86668897 1.005 ]\t[0.08879807 0.07053368]\t[2.61403799 1. ]\n", + "8 \t0 \t[3.86668897 1.005 ]\t[0.08879807 0.07053368]\t[2.61403799 1. ]\n", + "9 \t0 \t[3.85004769 1.035 ]\t[0.18980221 0.32214127]\t[1.90340519 1. ]\n", + "10 \t0 \t[3.8392059 1.06 ] \t[0.24252853 0.47581509]\t[1.80252552 1. ]\n", + "11 \t0 \t[3.84267018 1.045 ]\t[0.21723216 0.35067791]\t[1.79161644 1. ]\n", + "12 \t0 \t[3.86666379 1.005 ]\t[0.08915317 0.07053368]\t[2.60900354 1. ]\n", + "13 \t0 \t[3.86666379 1.005 ]\t[0.08915317 0.07053368]\t[2.60900354 1. ]\n", + "14 \t0 \t[3.86666379 1.005 ]\t[0.08915317 0.07053368]\t[2.60900354 1. ]\n", + "15 \t0 \t[3.85962713 1.015 ]\t[0.13308931 0.15740076]\t[2.46565056 1. ]\n", + "16 \t0 \t[3.84274257 1.045 ]\t[0.21511804 0.35067791]\t[1.90340519 1. ]\n", + "17 \t0 \t[3.84274257 1.045 ]\t[0.21511804 0.35067791]\t[1.90340519 1. ]\n", + "18 \t0 \t[3.83358411 1.045 ]\t[0.31383125 0.35067791]\t[0.07171333 1. ]\n", + "19 \t0 \t[3.80455297 1.095 ]\t[0.4347226 0.63716167]\t[0.00325559 1. ]\n", + "20 \t0 \t[3.82355932 1.06 ]\t[0.34492818 0.40792156]\t[0.00325559 1. ]\n", + "21 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", + "22 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", + "23 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", + "24 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", + "25 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", + "26 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", + "27 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", + "28 \t0 \t[3.83915799 1.045 ]\t[0.21155585 0.30491802]\t[2.45047045 1. ]\n", + "29 \t0 \t[3.8293101 1.065 ] \t[0.25177146 0.41324932]\t[1.90340519 1. ]\n", + "30 \t0 \t[3.83915799 1.045 ]\t[0.21155585 0.30491802]\t[2.45047045 1. ]\n", + "31 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "32 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "33 \t0 \t[3.84672464 1.045 ]\t[0.1840279 0.36465737]\t[2.45543599 1. ]\n", + "34 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "35 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "36 \t0 \t[3.84675711 1.03 ]\t[0.18370814 0.2215852 ]\t[2.51430631 1. ]\n", + "37 \t0 \t[3.84675711 1.03 ]\t[0.18370814 0.2215852 ]\t[2.51430631 1. ]\n", + "38 \t0 \t[3.83317034 1.06 ]\t[0.22652033 0.36932371]\t[2.51430607 1. ]\n", + "39 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "Final population hypervolume is 49370.222358\n" + ] + }, + { + "data": { + "text/html": [ + "
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Brush versionOriginalModified
metricscorebest modelscorebest modelpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
run 00.2929581.00*Square(0.96*x1)0.363372If(x1>0.91,1.61,-0.52*x1)1836224419661595
run 10.363372If(x1>0.91,1.61,-0.52*x1)0.350809If(x1>0.91,1.61,0.38)1142300815631907
run 20.3149720.51*Acos(1.10*x2)0.835548Mean(If(x1>0.91,9.83,1.69),-1.75*x2,-2.39*x1,-...1876306510301661
run 30.363372If(x1>0.91,5.00*x2,-0.52*x1)0.2929580.91*Square(x1)1627251417631731
run 40.292958Square(0.96*x1)1.000000Square(Median(-2.00*x1,-2.00*x2))2001258516021439
run 50.325058Cos(1.72*x2)0.350809If(x1>0.91,1.61,0.38)1930213514372113
run 60.3263581.04*Cos(1.73*x2)0.490733Square(If(x1>0.91,1.27,-0.85*x1))2164208918541552
run 70.325058Cos(1.72*x2)0.508543Median(2.01,-1.94*x2,1.27*x1,1.27)1555245318571768
run 80.198205Abs(0.74*x1)0.2929580.96*Square(-0.98*x1)1386238919221953
run 90.363372If(x1>0.91,1.70*x1,-0.52*x1)0.3263581.04*Cos(1.73*x2)1457222818042160
run 100.397507Square(Sin(-4.25*x2))0.350809If(x1>0.91,1.61,0.38)1591201118142217
run 110.325058Cos(1.72*x2)0.363372If(x1>0.91,1.61,-0.52*x1)1594242617831834
run 120.425247Logistic(50.64*Logabs(-1.15*x1))0.3263581.04*Cos(1.73*x2)1795271215711561
run 130.325058Cos(1.72*x2)0.624433Add(If(x1>0.91,1.82,0.65),-0.67*x2)1686282515871535
run 140.397507Square(Sin(4.25*x2))0.350809If(x1>0.91,1.61,0.38)1602290017901331
run 150.363372If(x1>0.91,5.00*x2,-0.52*x1)0.308425Sum(0.79,0.02*x1,-0.69*x2,0.02*x1)1603238618331809
run 160.3241760.05*Cosh(3.63*x1)1.0000001.22*Square(Mean(1.19*x1,3.62*x2,1.21*x1,1.21*...1965258617351378
run 170.397507Square(Sin(-4.25*x2))0.508543Median(2.01,-1.94*x2,1.27,1.27*x1)1804232619531545
run 180.292958Square(0.96*x1)0.9991292.04*Cos(Sum(0.43*x2,-0.31*x2,-1.08*x1,x2))1158336317661354
run 190.397507Square(Sin(-4.25*x2))0.508543Median(2.01,1.27*x1,-1.94*x2,1.27)2010253916081479
run 200.292958Square(0.96*x1)0.3263581.04*Cos(1.73*x2)1915277513871556
run 210.363372If(x1>0.91,5.00*x2,-0.52*x1)0.350809If(x1>0.91,1.61,0.38)1495257018441733
run 220.363372If(x1>0.91,5.00*x2,-0.52*x1)0.363372If(x1>0.91,1.61,-0.52*x1)1919298211001631
run 230.3046250.06*Cosh(3.34*x1)0.490733If(x1>0.91,1.61,Square(-0.85*x1))1542261217821709
run 240.3263581.04*Cos(1.73*x2)0.649267If(x1>0.91,1.61,Sum(-0.89*x1,-0.34*x2,-0.34*x2))1571271920551300
run 250.275650Logabs(2.31*x1)0.3263581.04*Cos(1.73*x2)1552281014411844
run 260.325058Cos(1.72*x2)0.508543Median(2.01,1.27,1.27*x1,-1.94*x2)1699281516341487
run 270.3263581.04*Cos(1.73*x2)0.350809If(x1>0.91,1.61,0.38)1845269713291763
run 280.397507Square(Sin(4.25*x2))0.573936Median(Median(1.89,2.61*x1,-5.16*x2,4.11),0.22...1667262218991465
run 290.292958Square(0.96*x1)0.3263581.04*Cos(1.73*x2)1821256913591889
\n", + "
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Ob4Rrh0x1DR+ZB25+9z5GyFt+QgghhPiXLTd6pxoNkWTKAtJDJYQQQpQ1igJG/e3br+6HC1tA7QgtxxR9XCWYJFRCCCFEWZJ6DWZ3gtQrd25T73HwqlB0MZUCklAJIYQQpZ2iwJFfTRMXx528ezLl7AVtJhZdbKWEJFRCCCFEaaUocHUf7P4eDv+Ud99TK6Bcw9uPcXQFB22RhFeaSEIlbtO+fXsaNGjA559/fs+28+fPZ/z48SQnJ9s8LiGEEPegz4EN70DMYTAaIeYI5KTc2h9UB5o9A35VoWLZm2/PliShEkVOpVLx22+/0adPH3uHIoQQpcuGd2DHl7dvD6gBIQ3g4a9AI7/6bUH+VIUQQojSIOUq7PzGtFyjF9R9xLQc1hI87j5Rvbh/UoeqjMvIyGDw4MG4u7sTEhLC9OnT8+zPycnhxRdfpHz58ri5udGsWTMiIyPves7ff/+dRo0a4ezsTKVKlZg6dSp6ven13PDwcAD69u2LSqUyr9/rOCGEEHdw4EeYUR9mtQbFAF6h8NgPULuv6UuSqSIhPVQ2oCgKWfosu1zbxcEFlUpV4PYvvfQSmzZt4vfffycwMJDXXnuN/fv306BBAwDGjBnD8ePH+emnnyhXrhy//fYb3bp148iRI1StWvW2823ZsoXBgwczc+ZM2rRpw7lz5xg1ahQAb7/9Nnv27CEwMJB58+bRrVs3NBpNgY4TQgiRj5w0WPMaZN8YJ6XSmKqbq6W/pKipFEVR7B1EcZeamoqXlxcpKSl4enrm2ZednU1UVBQRERE4OzsDkKnLpNniZvYIlV1P7sLVsWBzKaWnp+Pn58ePP/7Io48+CkBiYiIVKlRg1KhRTJw4kUqVKnHp0iXKlStnPq5Tp040bdqUDz744LZB6Z06daJjx468+uqr5vY//vgjkydPJjo6Gsh/DFVBjisL8vt5EkKIO9r+Bax9A/yqQN/vwM0ffCraO6pi426/v62tQD1U/fr1K/AJly9fXuhgRNE6d+4cubm5NGt2K/nz9fWlevXqABw5cgSDwUC1atXyHJeTk4OfX/7TERw6dIht27bx/vvvm7cZDAays7PJzMzE1TX/ZK+wxwkhRJmlz4EdX5mWW42HCo3tGk5ZV6CEysvLy7ysKAq//fYbXl5eNGnSBIB9+/aRnJxsUeJVmrk4uLDryV12u7a1pKeno9Fo2Ldvn/nR3E3u7u53PGbq1Kn5/izcrcelsMcJIUSZdegnSLsGHuVMlc2FXRUooZo3b555+eWXX+axxx5j1qxZ5l+yBoOB559/3ubdaSWFSqUq8GM3e6pcuTKOjo7s2rWLsLAwAJKSkjh9+jTt2rWjYcOGGAwGYmNjadOmTYHO2ahRI06dOkWVKlXu2MbR0RGDwWDxcUIIIW4wGmDbDNNyyzFSiLMYsHhQ+ty5c9m6dWueHguNRsPEiRNp2bIl//vf/6waoLAdd3d3RowYwUsvvYSfnx+BgYG8/vrrqG8MZqxWrRoDBw5k8ODBTJ8+nYYNGxIXF8eGDRuoV68ePXv2vO2cb731Fr169SIsLIxHHnkEtVrNoUOHOHr0KO+99x5getNvw4YNtGrVCicnJ3x8fAp0nBBClHlGI2yfYap+nngOXHyg0RB7RyUoRNkEvV7PyZMnb9t+8uRJjEajVYISRed///sfbdq0oXfv3nTq1InWrVvTuPGt5/Dz5s1j8ODBTJo0ierVq9OnTx/27Nlj7tH6r65du7Jy5UrWrl3LAw88QPPmzfnss8+oWPHWIMnp06ezbt06QkNDadiwYYGPE0KIMu/oMlg/BU78aVpv+gw45T8EQxQti9/ymzhxIgsXLuS1116jadOmAOzatYuPPvqIp556ik8//dQmgdqTpW/5CVFY8vMkhLgjowG+bg7xp6Fmb6jYCpoMBwcne0dWbBW7t/z+7ZNPPiE4OJjp06dz7do1AEJCQnjppZeYNGmS1QMUQgghyqT0WJjbDZIvmtYVxVS409kbHv4anGXccnFicUKlVquZPHkykydPJjU1FUAGowshhBDWtuMr0zip/2o3WZKpYqhQldL1ej2RkZGcO3eOJ598EoDo6Gg8PT3v+Dq9EEIIIQooOwX2zjUt95sN4a1Nyw5O4Oprv7jEHVmcUF28eJFu3bpx6dIlcnJy6Ny5Mx4eHnz88cfk5OQwa9YsW8QphBBClB1750FOKgTUgDr9ZSqZEsDiv6Fx48bRpEkTkpKScHG5VUSyb9++bNiwwarBCSGEEGWOLht2fm1abjVOkqkSwuIeqi1btrB9+3a02rxFxMLDw7l69arVAhNCCCHKpMM/Qfp18KwAdR6xdzSigCxOe41G421VrgGuXLmCh4eHVYISQgghyqR/V0BvMVoqoJcgFidUXbp04fPPPzevq1Qq0tPTefvtt+nRo4c1YxNCCCFKD0W585fRAEkX4MCPkHj+RgX0wfaOWFjA4kd+06dPp2vXrtSqVYvs7GyefPJJzpw5g7+/P0uWLLFFjEIIIUTJdHYDrH4ZctNNdaWU25/w5KvpKKmAXsJY3ENVoUIFDh06xOuvv86ECRNo2LAhH330EQcOHCAwMNAWMYpSSKVSsWLFCnuHka/27dszfvx4i44pzvcjhLCDk6tg5zewcgIknIG0awVLprTuEFgbmj1r+xiFVRWqDpWDgwMDBw5k4MCB1o5HFKH27dvToEGDPI9wBSxfvhxHR0ernjMyMpIOHTqQlJSEt7e3Vc8thChmzq6Hn568te4WCAMWgbMXuPrd+Thnb9AU6teyKAYs7qFasGABq1atMq9PnjwZb29vWrZsycWLF60anBD24OvrKy9YCCEKb/N00/dyDaHuY/DIXAhtCgHVwc3/zl+STJVoFidUH3zwgbn+1I4dO/jyyy+ZNm0a/v7+TJgwweoBCtsYOnQomzZtYsaMGahUKlQqFRcuXGDTpk00bdoUJycnQkJCeOWVV9Dr9ebj0tLSGDhwIG5uboSEhPDZZ5/d9ojs2rVr9OzZExcXFyIiIli8eDHh4eF37Qm7fPkyjz32GN7e3vj6+vLwww9z4cKFe97H0aNHUavVxMXFAZCYmIharWbAgAHmNu+99x6tW7fOc0z37t1xd3cnKCiIp556ivj4ePP+wt5PfHw8ffv2xdXVlapVq/LHH38AcOHCBTp06ACAj48PKpWKoUOH3vPehBAl0MUdcGk7qB1hwGLo/z1EtLF3VKIIWJxQXb58mSpVqgCwYsUKHnnkEUaNGsWHH37Ili1brB5gSaQoCsbMTLt8KYpSoBhnzJhBixYtGDlyJNeuXePatWs4OjrSo0cPHnjgAQ4dOsQ333zDnDlzeO+998zHTZw4kW3btvHHH3+wbt06tmzZwv79+/Oce/DgwURHRxMZGcmyZcv47rvviI2NvWMsOp2Orl274uHhwZYtW9i2bRvu7u5069aN3Nzcu95H7dq18fPzY9OmTYCpTtq/1wE2bdpE+/btAUhOTubBBx+kYcOG7N27l7///pvr16/z2GOP3fEaBb2fqVOn8thjj3H48GF69OjBwIEDSUxMJDQ0lGXLlgFw6tQprl27xowZM+56X0KIEmrrp6bvDZ4Ez3L2jUUUKYv7F93d3UlISCAsLIy1a9cyceJEAJydncnKyrJ6gCWRkpXFqUaN7XLt6vv3oXJ1vWc7Ly8vtFotrq6uBAcHA/D6668TGhrKl19+iUqlokaNGkRHR/Pyyy/z1ltvkZGRwYIFC1i8eDEdO3YEYN68eZQrd+s/jZMnT7J+/Xr27NlDkyZNAJg9ezZVq1a9Yyw///wzRqOR2bNno1KpzOf19vYmMjKSLl263PFYlUpF27ZtiYyM5JFHHiEyMpJhw4Yxe/ZsTp48SeXKldm+fTuTJ08G4Msvv6Rhw4Z88MEH5nPMnTuX0NBQTp8+TbVq1fKc35L7GTp0KE888QRg6smdOXMmu3fvplu3bvj6mubeCgwMlDFUQpRWx1bAmbWgUpsqnIsyxeKEqnPnzjz99NM0bNiQ06dPm2tPHTt2jPDwcGvHJ4rQiRMnaNGihTmpAWjVqhXp6elcuXKFpKQkdDodTZs2Ne/38vKievXq5vVTp07h4OBAo0aNzNuqVKmCj4/PHa976NAhzp49e9u4pezsbM6dy2em9f9o164d3333HWDqjfrggw84ffo0kZGRJCYmotPpaNWqlflaGzduzHcS73Pnzt2WUFlyP/Xq1TMvu7m54enpedeeOSFEKXJkKSwbYVqu3Rf8Kts3HlHkLE6ovvrqK9544w0uX77MsmXL8PMzvbGwb98+86fzsk7l4kL1/fvsdu2SJj09ncaNG7No0aLb9gUEBNzz+Jtjns6cOcPx48dp3bo1J0+eJDIykqSkJJo0aYLrjV679PR0evfuzccff3zbeUJCQu7rPv77ZqBKpcJoNN7XOYUQxVjUZji4BBQjnN9o2qZ2hA6v2zcuYRcWJ1Te3t58+eWXt22fOnWqVQIqDVQqVYEeu9mbVqvNM41QzZo1WbZsGYqimHuptm3bhoeHBxUqVMDHxwdHR0f27NlDWFgYACkpKZw+fZq2bdsCUL16dfR6PQcOHKBxY9Njz7Nnz5KUlHTHOBo1asTPP/9MYGAgnp6eFt9H3bp18fHx4b333qNBgwa4u7vTvn17Pv74Y5KSkszjp25ea9myZYSHh+PgcO8f/8LcT35uzn2Z37RNQogSxKCHcxsg5gj8827efR4hMHY/aIv////C+iwelL558+a7fomSIzw8nF27dnHhwgXi4+N5/vnnuXz5MmPHjuXkyZP8/vvvvP3220ycOBG1Wo2HhwdDhgzhpZdeYuPGjRw7dowRI0agVqvNCViNGjXo1KkTo0aNYvfu3Rw4cIBRo0bh4uKS51Hivw0cOBB/f38efvhhtmzZQlRUFJGRkbzwwgtcuXLlnvdxcxzVokWLzMlTvXr1yMnJYcOGDbRr187cdvTo0SQmJvLEE0+wZ88ezp07x5o1axg2bFi+yU5h7ic/FStWRKVSsXLlSuLi4khPTy/wsUKIYmTrZ7D4sVvJlG8l6PwudHkPnlohyVQZZnFC1b59+9u+OnToYP4SJceLL76IRqOhVq1aBAQEoNPp+Ouvv9i9ezf169fn2WefZcSIEbzxxhvmYz799FNatGhBr1696NSpE61ataJmzZo4Ozub2yxcuJCgoCDatm1L3759GTlyJB4eHnna/JurqyubN28mLCyMfv36UbNmTUaMGEF2dnaBe6zatWuHwWAwJ1RqtZq2bduiUqnM46cAypUrx7Zt2zAYDHTp0oW6desyfvx4vL29Uavz/+dg6f3kp3z58kydOpVXXnmFoKAgxowZU+BjhRDFRE467PzKtOweBA0GwaMLoNUL0HIsBNawb3zCrlRKQd+zvyElJSXPuk6n48CBA7z55pu8//775re/SpPU1FS8vLxISUm57Rd8dnY2UVFRREREWPQLtrTIyMigfPnyTJ8+nREjRuTb5sqVK4SGhrJ+/fpS8fNhy/sp6z9PQhRrO76GNa+aeqXG7AW1xt4RiXu42+9va7O4h8rLyyvPl7+/P507d+bjjz82v5peUB9++CEPPPAAHh4eBAYG0qdPH06dOpWnTXZ2NqNHj8bPzw93d3f69+/P9evX87S5dOkSPXv2xNXVlcDAQF566aU8xSjBNPVHo0aNcHJyokqVKsyfP9/SWxfAgQMHWLJkCefOnWP//v3m6Ycefvhhc5t//vmHP/74g6ioKLZv386AAQMIDw83j7MqaUrb/QghCkGfCztujB9uNU6SKXEbixOqOwkKCrotGbqXTZs2MXr0aHbu3Mm6devQ6XR06dKFjIwMc5sJEybw559/8uuvv7Jp0yaio6Pp16+feb/BYKBnz57k5uayfft2FixYwPz583nrrbfMbaKioujZsycdOnTg4MGDjB8/nqeffpo1a9bc/42XQZ988gn169enU6dOZGRksGXLFvz9/c37dTodr732GrVr16Zv374EBAQQGRlZ6Pnx3N3d7/hVFMVkrX0/QogS6MgvkHoV3IOhvrzRLm5n8SO/w4cP51lXFIVr167x0Ucfodfr2bp1a6GDiYuLIzAwkE2bNtG2bVtSUlIICAhg8eLFPPLII4Cp0GLNmjXZsWMHzZs3Z/Xq1fTq1Yvo6GiCgoIAmDVrFi+//DJxcXFotVpefvllVq1axdGjR83XGjBgAMnJyfz999+3xZGTk0NOTo55PTU1ldDQUHnkZydnz569477y5cubp0IqDeTnSYhiyGiEr5pCwhno/I4U7SxBivKRn8VlExo0aIBKpbptipPmzZszd+7c+wrm5vism1Wl9+3bh06no1OnTuY2NWrUICwszJxQ7dixg7p165qTKYCuXbvy3HPPcezYMRo2bMiOHTvynONmm3/P1/ZvH374oZSBKEZuTnUkhBD3LTcTLu2AtBjIiIPsG+OCc1IhKwn0OZAeC8q/3vrV55iSKScvaDzMPnGLYs/ihCoqKirPulqtJiAg4L4/TRuNRsaPH0+rVq2oU6cOADExMWi12tum6ggKCiImJsbc5t/J1M39N/fdrU1qaipZWVm39XC8+uqr5il14FYP1d1Y2NEnRL7k50gIG1s+Ek6uLNyxzZ8DZ9v2coiSy+KEqmLFiraIg9GjR3P06NH7emRoLU5OTjg5ORWo7c1xNJmZmaXq0ZOwj5uTQWs0MuBVCKu7dvhWMhVYy/S2nps/OLiASgVuAeDoCk7u4OqX91hHF6jYuuhjFiWGxQmVLYwZM4aVK1eyefNmKlSoYN4eHBxMbm4uycnJeXqprl+/bp7QNzg4mN27d+c53823AP/d5r9vBl6/fh1PT8/7ToI0Gg3e3t7mOdtcXV0tKvgoxE1Go5G4uDhcXV0LVMVdCGGhrZ+ZvtfpD4/c3xAVIf7Lrv9rK4rC2LFj+e2334iMjCQiIiLP/saNG+Po6MiGDRvo378/YJqs9tKlS7Ro0QKAFi1a8P777xMbG0tgYCAA69atw9PTk1q1apnb/PXXX3nOvW7dOvM57tfNxE0mwhX3S61WExYWJkm5ENYWfxaO/WZabj3BvrGIUsmuCdXo0aNZvHgxv//+Ox4eHuYxT15eXri4uODl5cWIESOYOHEivr6+eHp6MnbsWFq0aEHz5s0B6NKlC7Vq1eKpp55i2rRpxMTE8MYbbzB69GjzY7tnn32WL7/8ksmTJzN8+HD++ecffvnlF1atWmWV+1CpVISEhBAYGIhOp7PKOUXZpNVq71ixXQhhoYwEuLoPUODAD6bvVbtCcF17RyZKIYvLJlj14nf4FD5v3jyGDh0KmF4jnzRpEkuWLCEnJ4euXbvy9ddfm3uFAC5evMhzzz1HZGQkbm5uDBkyhI8++ijPY5PIyEgmTJjA8ePHqVChAm+++ab5GvdSlK9dCiGEsAKjAb5pBXEn8m4fvgbCmtsnJlHkivL3t8UJ1f79+3F0dKRuXVOG//vvvzNv3jxq1arFlClT0Gq1NgnUniShEkKIEubIUlg2wjTIPKC6aVtEO+gsJXHKkmI99cwzzzzD6dOnATh//jwDBgzA1dWVX3/91eKpZ4QQQgirMxpg7Zum5dYTYVSk6UuSKWFDFidUp0+fpkGDBgD8+uuvtG3blsWLFzN//nyWLVtm7fiEEEIIy6x4HtKiTYU4m42ydzSijLA4oVIUBaPRCMD69evp0aMHAKGhocTHx1s3OiGEEMISSRfh6FLTcpsJ4Oxl33hEmWFxQtWkSRPee+89fvjhBzZt2kTPnj0BUwX1/1YjF0IIIYrUthlg1EOl9lIeQRQpi8smfP755wwcOJAVK1bw+uuvm+dZW7p0KS1btrR6gEIIIQTpcaY5+JKiTHPr5Ucx3iiPALR9qehiEwIrlk3Izs5Go9GYp2IpTeQtPyGEsKMLW2F+L6CAv67CWsCw1abpZESxoygKxozMWxuMBnQxMahUKpyqVrXqtYry97fVCnve7+TIQgghRL4iP8KcTFXuCJ7lQH2HX18aLTR7RpKpIpS5fz/RL76EMTPz3o0BY0YGSj5FsB0CAqi6ZbO1wysyFidUBoOBzz77jF9++YVLly6ZJ3O9KTEx0WrBCSGEKOOu7IMLW0wJ1LhD4FXh3seIIhX76afooqPv6xwqR0c0fn73bliMWZxQTZ06ldmzZzNp0iTeeOMNXn/9dS5cuMCKFSt46623bBGjEEKIsmr7DNP3uo9KMlUMZR06RNbefeDoSMUFC9B4etz7IJUKh6BgVI63UhCVoyOqEj7tlsUJ1aJFi/j+++/p2bMnU6ZM4YknnqBy5crUq1ePnTt38sILL9giTiGEEGVNwjk4/odpuaX8bimOEubMBcCrZ09cGzW0czT2ZXE6GBMTY552xt3dnZSUFAB69epltcmGhRBCCLZ/gXlC46Ba9o5G/EfuxYukrVsHgO/wYXaOxv4sTqgqVKjAtWvXAKhcuTJr164FYM+ePTg5OVk3OiGEEGVTeiwcXGxabjXOvrGIfCXMnw+KglvbNjhXq2bvcOzO4oSqb9++bNiwAYCxY8fy5ptvUrVqVQYPHszw4cOtHqAQQogyaNe3YMiB8k2gotQ4LG70iYmkLP8NAL/hI+wcTfFg8Riqjz76yLz8+OOPU7FiRbZv307VqlXp3bu3VYMTQghRBuWkw57vTcutxkkJhGIoadFilJwcnGvXxrVZU3uHUyzcdx2q5s2b07x5c2vEIoQQQsD+hZCdAn5VoEZPe0cj/sOYlUXSYtPjWL8Rw1FZIeH9cedF2lcPoIKP632fy14sfuQXFhbG4MGDmTNnDufOnbNFTEIIIcoqgw52fGVabjkW1Br7xiNuk7JiBYakJBzLl8ejS5f7Pt+Ja6m8seIoD36yiaSM3HsfUExZ3EP1wQcfsHnzZj7++GNGjhxJ+fLladeuHe3ataN9+/ZUtXLZeCGEEGXIjq8g9Qq4BUK9AfaORvyHYjCQMG8+AL5Dh6JysPxBl6IojFy4l8hTcQAYbsyA17l2ED5uWqvFWtQs/pMYNGgQgwYNAuDatWts2rSJlStX8vzzz2M0GjEYDFYPUgghRBlg0MNeU10jmo0CR5nSrLhJW78B3aVLaLy88O7fr1DnWHf8OutPxObZpnVQ83z7ytYI0W4KNYYqMzOTrVu3EhkZycaNGzlw4AB16tShffv2Vg5PCCFEmXHiD0i+aFpuOsq+sYjbKIpCwtw5AHg/+QRq11vjnX4/eJXNp+MLdJ5dUQkAPN06gpFtKwHg7uSAm5PVphe2C4ujb9myJQcOHKBmzZq0b9+eV155hbZt2+Lj42OL+IQQQpQFigLbbkwz0+4VcPaybzyCbJ2BZfuvkJljevLkeeYo9Q4dxujgyF+VW6PbfB6AXIORT9edxmBUCnxudycHRneoUqIf8f2XxQnVyZMncXNzo0aNGtSoUYOaNWtKMiWEEOL+XNgC1w6Cgws0HWnvaMq0m4nR9LWn+H5LlHn72zsXALCmfCNmbo0BYvIc90C4D51qBhXoGi0q+5WqZAoKkVAlJCRw5MgRIiMjWbNmDa+//jparZZ27drRoUMHRo6UfwhCCCEstG2m6XvDgeDmb99YyrCPVp/k283nUP7V2dStdjDlUmJoHnMcRaUi7aHH6OdfLs9xzloNLzxYlWCvsjvuTaUoSsH76P5DURT27dvHl19+yaJFi0rtoPTU1FS8vLxISUnB09PT3uEIIUTpcv0YfNMSVGoYuw98K9k7ojLpanIW7aZtRP+vR3edagby/eAmXHvzTVKWLsO9Y0dCv/rSjlFapih/f1vcQ7V//34iIyOJjIxk69atpKWlUbduXcaOHUu7du1sEaMQQojSbPsXpu81e0syZUdztkShNyo0i/Bl1qDGqFTg5eKIPi6O1N//AEyFPEX+LE6omjZtSsOGDWnXrh0jR46kbdu2eHnJ4EEhhBCFkHIVjvxqWpZJkO0mKSOXJbsvAfD8fwaLJ/24CEWnw6VBA1wbNbJXiMWexQlVYmKiPPYSQghhHbu+AaMeKraG8o3tHU2ZtXDHRbJ0BmqFeNK26q0xbIb0DJJ++gkAX+mduiuLp56RZEoIIYRVZKfA3vmm5VYv2DWUsiwzV8/87aa3+Z5tXznP3Hwpy5ZiTE1FW7EiHg8+aK8QSwSLEyohhBDCKvbOg9w0CKgBVTrbO5oy6+c9l0nK1BHm60qPOsHm7YpOR8ICU6kE32HDUGlkXsW7KdllSYUQQpRM+lzYNcu03PIFUMvne1syZmaS+OMi0jdtIjMphZQsnXlf1ZQUluVm4eSg5txPt3qnFEVBycpC4+uLV5+H7RF2iSIJlRBCiKJ35FdIuwYeIVD3UXtHUypl7ttHzunT6GJjSfj2OzAaAVAB3vkdoAdjPpv9Ro1E7Vx260sVlCRUQgghipbRCNtvFPJs9iw4lK6K2cVB1pGjXBz0FPyn1GTcg72YmRWEu5szbaoGmDaqVDzQsAo1Kt5eUFXl5IRjUMGqn5d1FidUBoOB+fPns2HDBmJjYzEa8+az//zzj9WCE0IIUQqdXQdxJ0HrAU2G2TuaUil+1ixQFLSVKuFcsyYaX1/cevZkxOp4olOyeatXLYa3jrB3mKWKxQnVuHHjmD9/Pj179qROnTp53gYQQggh7unmNDNNhsokyDaQfeoU6Rs2gEpFhS+/QFMxnJn/nGXZ31eITsnG313LE03D7B1mqWNxQvXTTz/xyy+/0KNHD1vEI4QQojS7sg8ubgW1AzR7zt7RlDqKonB1/AQAPLt3w6lSJX7Zc5mZG86Y24x9sCouWnljz9osTqi0Wi1VqlSxRSxCCCFKE4MeUi5DRjwknDFVRd/4nmlf3UfBq7x94yvB0iIjif/6G/TXrmHMzTVvN6akmJf9n3uOK0mZvPrbEQDqVfBi6kO1aRjmU+TxlgUWJ1STJk1ixowZfPnll/K4TwghyhpdNsQcgcx4SL4MWUmQkwqZiab9mQkQfxqyk0378qWClmOLKuJSR8nNJWbKVPQxMXds4z9mDLqwCLp/sAGDUcHfXcvPo1pIz5QNWZxQbd26lY0bN7J69Wpq166No6Njnv3Lly+3WnBCCCGKgSt7TSUOANZPgYSzlh3v7A1eFcArFNwDoHY/CKpt7SjLjOQVK8zJVOi3s3AsVw40t36dO5Yvh9rJia8jz5KWowfgvT51JJmyMYsTKm9vb/r27WuLWIQQQhQ35zbCD31u3+4dBl5h4B0Kjq7gFgCOLqZ9rn6mhMk9CNwDQeN4+/HiNkajctu2TJ2BhPQcDAf2oVy6CIDuh/kAZI18gZ0BNYhPyuHmoZm5eq4cPUtCRi6bTsUC8Nnj9elWJ6RI7qEsszihmjdvni3iEEIIUdwoCqx/27TsW8mUNKGCBk9A46H2jKxEyNYZeO23Ixy4lEyuPr+Smbdk6QwkZuTmu69K8hVmRM5Aza2EK1nrxtBrQeQs3HvX81byd6N3vXKWBy8sVujCnnFxcZw6dQqA6tWrExAQYLWghBBCFAP75sG1Q+DgAsP+Bg8p8GiJJbsvsXz/1fs+zxOnN6BG4YpnENEegRhVKjZUaoGHtycR7lo8XRzxdrnVC+jnrqWclwvero60rx6Ig0am9SkKFidUGRkZjB07loULF5qLemo0GgYPHswXX3yBq6ur1YMUQghRxAx62DbDtFx/gCRTFsrVG/lu83kAetYN4ek2Eajv8SKXv4cTro55xzkZos4Tt8L0ll7bRd/jVLUqAFIOtfixOKGaOHEimzZt4s8//6RVq1aAaaD6Cy+8wKRJk/jmm2+sHqQQQogidnwFJF0wjYfq+oG9oylxlu+/wrWUbAI9nPj08fo4ORRuQHj0D6ZhNh6dO5mTKVE8WZxQLVu2jKVLl9K+fXvzth49euDi4sJjjz0mCZUQQpR0igJbPzMtN3sWtPLkIT8XEzI4H5eBwaiQozcSk5rN9dRsdAYjfx81vYU3qm0li5KpuK++InHuPJQbc/ApmZkA+I16xvo3IKzK4oQqMzOToHwmSgwMDCTzxl+8EEKIEuzMOrh+FLTu0HSkvaOxO73ByJpj18nI0WNUFGLTcjh0OZkNJ2PvepyPqyNPNivYFC+xn31O5s6dZB09CgZDnn0enTvhUrdOoeMXRcPihKpFixa8/fbbLFy4EGdnZwCysrKYOnUqLVq0sHqAQgghikBGPFzeBfoc2P6FaVuTYeAiVbW/3Xye/605le8+lQrqVfAGwNPZgWBPZ/w9nFCroFPNIFy19/41m3XoEAnffmted+/QgaDXX7txfhUOIVLyoCSwOKGaMWMGXbt2pUKFCtSvXx+AQ4cO4ezszJo1a6weoBBCCBvLSoavmpmqn9+k0ULz0XYLqbhIz9Hz/RbT4PKmEb54ODngqFET4OFEoIcTo9pZ9kgvPwlz5gLg/uCDeD/2KG4PPIDaze2+YxdFy+KEqk6dOpw5c4ZFixZx8uRJAJ544gkGDhyIi4uL1QMUQghhY7u/u5VMhbUEtQbqPgKeZbNnRFEU4tJy0BsVftl7meRMHZX83VgysjkatXWnXMu9cIG0desACJwwXgael2CFqkPl6urKyJHyXF0IIUq8nDTY+bVp+ZG5UKe/feMpBt5deYK526LybHu+QxWrJ1MACfPng6Lg1q6tJFMlXIESqj/++IPu3bvj6OjIH3/8cde2Dz30kFUCE0IIUQR+H2OaxNivCtTqY+9o7O5yYiYLd1wAQOtgKojZKMybhxtYv9q4PiGBlN9WAOA3fITVzy+KVoESqj59+hATE0NgYCB9+vS5YzuVSoXhP28nCCGEKEayUyA3A/YtgKPLIOGMaXvriaZHfWVYbGo2baZtBKBNVX9+GNHMptdLWrQYJScH5zp1cG36gE2vJWyvQAnVzYro/10WQghhZwYdbP0cEs6axkHlpENmwq39+hzIiAWjARQjKPl86K3QFOo/UWQhF1dfbTxrXp7UpbpNr2XMzCRp8WIA/J4egeoeVdRF8WfxGKqFCxfy+OOP4+TklGd7bm4uP/30E4MHD7ZacEIIIe5h33zY+J5lx6jUoHaE8Famwp2VOoC6bM/3FpeWw097LgMw/dH6NAj1tun1kpf/hiE5GcfQUDw6d7bptUTRsDihGjZsGN26dSMwMDDP9rS0NIYNGyYJlRBCFBWD/lbNqICa8MAI0LqBW4Dp+03OXuDsbVrWukptqXzM3nqeHL2RBqHe9GtU3qbXUvR6EufPB8B36BBUmrL9qLW0sDihUhQl367JK1eu4OXlZZWghBBCFMCJ3yH5omm+vZH/yBQxFjoWncKVpCxWHLjK6htTxYzpUMXmj9/S1q1Dd+UKGm9vvPv1s+m1RNEpcB9vw4YNadSoESqVio4dO9KoUSPzV/369WnTpg2dOnWy6OKbN2+md+/elCtXDpVKxYoVK/LsHzp0KCqVKs9Xt27d8rRJTExk4MCBeHp64u3tzYgRI0hPT8/T5vDhw7Rp0wZnZ2dCQ0OZNm2aRXEKIUSxoyimsVMATZ+RZMpCh68k0/uLrTzzwz5zMlWvghcdawbe48j7oyiKuZCnz5NPopb6jaVGgXuobr7dd/DgQbp27Yq7u7t5n1arJTw8nP79LatfkpGRQf369Rk+fDj97pCld+vWjXnz5pnX/zt2a+DAgVy7do1169ah0+kYNmwYo0aNYvGNwX6pqal06dKFTp06MWvWLI4cOcLw4cPx9vZm1KhRFsUrhBDFxvlIiDkMjq4y356FDl1O5uGvtgFQwceFIE9n6lfw5vWeNW3eO5W5ew/ZR4+icnLCZ9BAm15LFK0CJ1Rvv/02AOHh4Tz++OPmefzuR/fu3enevftd2zg5OREcHJzvvhMnTvD333+zZ88emjRpAsAXX3xBjx49+OSTTyhXrhyLFi0iNzeXuXPnotVqqV27NgcPHuTTTz+VhEoIUXJt+9z0vdFgcPW1ayglic5gZMyS/QBo1CoWP92cML+i691LmDMbAK9+fXHwlb+30sTiMVRDhgyxRRx3FBkZSWBgID4+Pjz44IO89957+Pn5AbBjxw68vb3NyRRAp06dUKvV7Nq1i759+7Jjxw7atm2LVqs1t+natSsff/wxSUlJ+PjcPjgzJyeHnJwc83pqaqoN71AIISwUfdDUQ6XSQPPn7R1NsZKcmUtatp6EjFwSM3KIT8vFqCjm/Sdj0ricmAXA7CFNijSZyj59mozNW0Clwm/o0CK7rigaFidUBoOBzz77jF9++YVLly6Rm5ubZ39iYqLVguvWrRv9+vUjIiKCc+fO8dprr9G9e3d27NiBRqMxFxv9NwcHB3x9fYmJMT0Tj4mJISIiIk+boKAg8778EqoPP/yQqVOnWu0+hBDCqrbNMH2v0x98Kto3lmJkz4VEBny3E4NRuWfbN3rWpEN1246X+q/EuabhKx6dO6OtKH9vpY3FCdXUqVOZPXs2kyZN4o033uD111/nwoULrFixgrfeesuqwQ0YMMC8XLduXerVq0flypWJjIykY8eOVr3Wv7366qtMnDjRvJ6amkpoaKjNrieEEAWWGAXHV5iWW71g11CKm8/WncZgVNBq1Dg5qAnwcMLPXYuXi2OeduW8XRjUvGgTGt3166SsWgWA34jhRXptUTQsTqgWLVrE999/T8+ePZkyZQpPPPEElStXpl69euzcuZMXXrDdP/BKlSrh7+/P2bNn6dixI8HBwcTGxuZpo9frSUxMNI+7Cg4O5vr163na3Fy/09gsJyen2wa/CyGEXWXEgy4Ttn5qqnhepRME17V3VMXGgUtJbD+XgINaxcaX2lPeu3i9PZe4cCHodLg2aYJL/fr2DkfYgMUJVUxMDHXrmv4Ru7u7k5KSAkCvXr148803rRvdf1y5coWEhARCQkIAaNGiBcnJyezbt4/GjRsD8M8//2A0GmnWrJm5zeuvv45Op8PR0fQpZd26dVSvXj3fx31CCFHsHPoJfnsm77ZW4+wTSzH1deQ5APo2LG+XZErJzSX36lWM6RkAGFJTMN4Yf6sYjST/9DMAvtI7VWpZnFBVqFCBa9euERYWRuXKlVm7di2NGjViz549FvfqpKenc/bsrbmToqKiOHjwIL6+vvj6+jJ16lT69+9PcHAw586dY/LkyVSpUoWuXbsCULNmTbp168bIkSOZNWsWOp2OMWPGMGDAAMqVM80M/uSTTzJ16lRGjBjByy+/zNGjR5kxYwafffaZpbcuhBBFT58DG94xLWu0pmljqnWD8Db2jasY2X4unnXHr6NSwbPtKxf59XWxsUT1648hPv6u7bSVK+Perl0RRSWKmsUJVd++fdmwYQPNmjVj7NixDBo0iDlz5nDp0iUmTJhg0bn27t1Lhw4dzOs3xy0NGTKEb775hsOHD7NgwQKSk5MpV64cXbp04d13382TuC1atIgxY8bQsWNH1Go1/fv3Z+bMmeb9Xl5erF27ltGjR9O4cWP8/f156623pGSCEKL4Mxpg6XBIvQoeIfDCQXC8/5I1pc2na08D0KNOCJUD3O/R2voS5y8wJ1OaAH9Ujo6oVGo0fn6ob/y+Ujk64v/cs6jK+JyJpZlKUZR7vw5xFzt37mT79u1UrVqV3r17WyuuYiU1NRUvLy9SUlLw9PS0dzhCiLJi2wxYd+Nlnx6fSAHPfOy/lES/r7cDsHpcG2qGFO3/0YaUFM52eBBjZiYVvv4ajwc73PsgUWSK8ve3xT1UmzdvpmXLljg4mA5t3rw5zZs3R6/Xs3nzZtq2bWv1IIUQoszR58COr0zLldpDkxF2Dae4+m7TeQAea1KhyJMpgMRFizBmZuJUvTruHdoX+fVF8WFx32OHDh3yrTWVkpKS5/GdEEKI+3D4Z0i/Dp4VYOBSkEdFtzkfl86a46aag6PaViry6xszM0la+AMAfiNH2nzaGlG8WfwvVFGUfH9oEhIScHNzs0pQQghRphmNsO3GWNDmz4HG8e7ty6jvt0ShKNCpZiBVAj2K9Nr6pCQuj3oGQ3IyjqGheHbrWqTXF8VPgR/53Zy8WKVSMXTo0DwDww0GA4cPH6Zly5bWj1AIIcqa06sh4Qw4eUHjop3uq6SITctm2f4rADzTrmjf7FMUhSvPPU/WwYMA+I0YgcrB4hE0opQp8E+Al5cXYPpB8vDwwMXlVp0PrVZL8+bNGTlSBkwKIcR9uzm1zAMjwKloe15KigXbL5CrN9IozJsmFYu2pmDGtu3mZMpn0CC8+/cr0uuL4qnACdW8eaY5iMLDw3nxxRfl8Z4QQtjCpZ1weZep5lSzZ+0dTbGUnqPnhx0XAVPvVFGPXUqYNQsA3yGDCXr11SK9tii+LO6jfPvtt20RhxBCCLg1dqr+E+ARZN9Yiqmfdl8iNVtPJX83Ote07Z9Ryp8rSd/4j3ndmJtL5t69qBwd8R0uVc/FLRYnVBEREXf9NHD+/Pn7CkgIIcqsuNNwahWggpZj7R1NsaQzGJmzNQowvdmnVtuud0oXE0P0a6+BTnfbPq/+/XAMkoRX3GJxQjV+/Pg86zqdjgMHDvD333/z0ksvWSsuIYQoe7bf6J2q0RP8q9o3lmLqz0PRXEvJJsDDiT4Ny9v0Wonz5oNOh3OtWnj17WvernJ2wqtHD5teW5Q8FidU48blPyHnV199xd69e+87ICGEKJPSYky1p0AmPr4DRVH49kYhz2GtwnF21NjsWvqkJJJ+/RWAgAkTcG/T2mbXEqWD1d7z7N69O6+++qp58LoQQggL7JoFhlwIawGhTe0djV2lZevQGfLOivbd5vPM2xZFjt6Im1bDwGYVbRpD0qLFKJmZONWqiVvrVja9VlliVIx8tu8zTiSeMG/L0eeQmJ2It7M3i3ossmN098dqCdXSpUvx9fW11umEEKLsyE6FPXNNyy1fsG8sNnA8OpWDl5M5Fp3CnguJRCdnc6dpZHVGhVy98a7nG9oqHC8X2xU7NWZkkPSDqQK6fymvgK436vl83+dcSrsEQK4xl8SsRAyKwSbXu5p+lQxdRr77svRZNrlmUbE4oWrYsGGeHy5FUYiJiSEuLo6vv/7aqsEJIUSZsPF9yEkB/2pQrZu9o7GqQ5eT6fv1Noz5508FplZB97ohPNeusk3n7MvYvZurEydhSEnBsWIYHl262OxaxcHqqNUsOL6gyK/bqlwrelfubV730Hrg5+JX5HFYk8UJVZ8+ffKsq9VqAgICaN++PTVq1LBWXEIIUfqlXIHky3DgxmOOFmNK1Zx9Cek5PL1wrzmZ6l4nmAh/N3rUDcHD+c6/frxcHPPtgbJlT1FaZCS5Z8+SMG8+hoQEAPyefhqVxnbjtOzNqBiZe9TUM/pQ5YdoENgAADcHN3ycbVcs1cXBhfoB9Utdz5/UoRJCCHuIPgizO4Hxxiv5nhWg4VN2DcmajEaFp+bsJi4tBwe1in8mtSfMz9XeYeUr9e81XP3PG+wh77+X582+0mjzlc2cTT6Lu6M7rzR9BQ+tVOW/H4UeQxUbG0tsbCxGY95n3fXq1bvvoIQQolRTFPh1iCmZcvYCt0B4+KtS1Tu19ngMx6+lAvBGz5rFNpkypKURfaPauXPdujjXqI73gAG41K5t58hsS1EUZh+ZDcBj1R+TZMoKLE6o9u3bx5AhQzhx4sRtgwpVKhUGg20GsgkhRKmx61tIugBqRxi1CXwj7B2R1WTm6hnw3U4OX0kB4IUHqzC0VfG9v6TFS1CyTIOhQ2d9g4NfyR7HU1D7Y/dzKO4QWrWWQTUH2TucUsHihGr48OFUq1aNOXPmEBQUVOqegQohhE3pc28V8Gw4sMQmU0ajwucbzrD9bDzZ+lsfpNOz9VxIyAQgyNOJ4a2L7/0Zs7JIXGAakF3u44/KRDJlVIwYFaO5d+rhKg8T4Bpg56hKB4sTqvPnz7Ns2TKqVKlii3iEEKJ0O7oMUq+CexB0+9je0VhEURS+jjzHtrPxnIlNJy4t545tp/SuxRPNwnByKL6DupOXLsOQmIhj+fJ4luLK5xm6DBKzE/niwBesv7ge3Y1xe2qVmqG1h9o3uFLE4oSqY8eOHDp0SBIqIYSwlKLAthmm5WbPgqOzfeOx0KbTcfxvzak821pW9mN4qwgcNLeeVvi6aalXwbuIo7NMyh9/cP399wHwe3oEKkfb1bUqSoqikJqbyvXM6yiKwsHYg3y4+8N860o9UvURwjzD7BBl6WRxQjV79myGDBnC0aNHqVOnDo7/+SF86KGHrBacEEKUKmfWQtwJ0HpAk+H2jsZi30SeA6BygBuTulTHzcmBNlX8bTpBcWEpimJKYBUF3bUYMN5KKHRXrxL98isAaAL88erXz15hWtXPJ3/m032fkqnPzHe/n7Mf3SO682z9Z1Gr1DIQ3cosTqh27NjBtm3bWL169W37ZFC6EELcxc3eqSZDwcXbnpFYbN/FJHZFJeKoUfHj080I8XIp8hh0V68S8977GDPyVtpWjAYM8QkoubmmdZQ863ei9vIidNYs1E5ONou5qKTmpvL5/s/NyZSTxglPrakAaoh7CF89+BXezt52jLD0szihGjt2LIMGDeLNN98kKCjIFjEJIUTpc3kPXNxmerOv+fP2jsZiszaZeqf6Nixvl2QK4Pq0/5G+cWOhjlW7ueVZ1/j7ETZnDtoKFawRmt0tObGEdF06vs6+/NHnDzy0HqhVpacMR0lgcUKVkJDAhAkTJJkSQghLbPvc9L3e4+BZzq6hWOrM9TTWHb+OSgWj2la+r3Pp4+LQXb2KMevO87YZs7IxJCbkKc2Ttf8AaWvWgEpF8JQpaDzzPq5SabU4+Puba3mpHB1xCAwElQqNu3upGSOVn0xdJj+e+BGAyQ9MxsvJy84RlU0WJ1T9+vVj48aNVK58f/+ohBCizIg/AydXmZZblbzJj2dtOg9A11rBVAl0L9AxqevWkfrHH+iuRmNISUEfF3fPR3AF4dmzJz6PP3bf5ymJMnQZfHvoW1JzU9Eb9cRnx6M36EnJTSE5J5lQj1C6hne1d5hllsUJVbVq1Xj11VfZunUrdevWvW1Q+gsvlLz/LIQQwqa2fwEoUL0HBFS3dzT3FHkqlqj4DFKydGw6HceBS8kAPNu+YB+kdTExRE+chKLT5btf7eGBg7//XXuNNF5eqD3y9kI5VYrAf/Togt1EKTT7yGzmHZt3x/0j647EQV3oCVDEfVIp/y13fg8REXcu0qZSqTh//vx9B1XcpKam4uXlRUpKCp6etpvlXAhRwmUkQG46JF80TXqsGMGoh9WTwZALw9dCWDN7R3lX+y4m0v+bHbdtb1ctgAXDm97zeEVRuDpxImmr/wYHB0LefRfHcuXQeLjjEBSE2tUVtYt9xmCVZCk5KXRd1pUMXQYDqg8g0DUQrUZLgEsAKpUKT60nLcu1lGLb/1GUv78tTmWjoqJsEYcQQpRsx36DX4feeX9o82KfTAF8vv4MALVCPKkc6I6nswMtKvvRoXpgvu0NyclceOJJci9cMG3412f0sNmzcWte/O+5JFhycgkZugyq+lTl1WavyoDzYkj6BoUQ4n4Z9LB+qmlZowUHF/CvCm7+t7a1m2y/+AogKj6Dl349xN6LSWjUKmYNalygCY0TF/5Abj4ftD0f6o1rs3v3aIlbMnWZfLrvU+Iy427btztmNwCj6o6SZKqYkoRKCCEKIycNDi6GjHhIuQxJUeDqB+MOg1PBBm7bW7bOwA87LpKWrWP9iViOX0sFoH+j8gVKpgzp6ST+aHq7LOTDD3Fv3Qow1XdSa7W2C7yUWnRiET+f+vmO+8M9w+lcsXMRRiQsIQmVEEJYSlHg50FwPjLv9hZjSkwyBfDVxrN88c/ZPNte6lqdoS3DC3R80pIlGFNT0VaqhNfDD6FSS89JYWXqMll4fCEAg2oOopJ3pTz7VahoWa4lGnXxnRuxrJOESgghLBG1BaI230qmavQCjxDT470WJecNtJQsHfO3XQCgV70QfN20dKgeSIca+Y+V+q+0yEjipn8KgN/IkZJM3cOxhGMkZSfdcf/26O3m0geTmkySt/VKIPkbE0KIgrp2GBb0Bm4MvG76DPSYZteQ7kZnMHIxIYPEjFvlCzJy9SSm57LjfAJpOXqqB3kwc0BDi+bjU3Q6Yt55B7gxF16vnlaPvTSJvBzJ2H/GFqitlD4ouSz+W/v7779xd3endevWAHz11Vd8//331KpVi6+++gofHx+rBymEEMXClumAAgE1ILQZdHjV3hHdUbbOQLfPN3MhIf+Jcm8a82AViyc3TvlzJfroawBE/PJLqa5Cfr+MipEvD3wJQAX3CnedkLiyd2V6Ve5VVKEJK7M4oXrppZf4+OOPAThy5AiTJk1i4sSJbNy4kYkTJzJv3p2LjgkhRIkVdxqO/25afmQuBNW2bzz38Ou+K+ZkqpyXM86Ot8be+LlrcdU6UC3InR51Qyw6r2IwkPD99wAEvjgJxxDLji/NrqRdYfmZ5egVPUnZSWToMsjUZXIq6RRujm781OsnmRamFCtUHapatWoBsGzZMnr16sUHH3zA/v376dGjh9UDFEKIYmHVREzVznsW+2RKbzDy3WbTZMZTetdiaKs7F2QG0Ccmouj1oIAhIR5jds6NPQr6hASUf827l3P+PLlRUag9PfEe8IStbqFESc5ORmfU8fKWlzkcdzjfNk/WeFKSqVLO4oRKq9WSmWn61LN+/XoGDx4MgK+vL6mpqdaNTgghioOYo3Bhi2m57ST7xlIAq45c43JiFr5uWh5/IMy8XVEUYt6eQua+fSjZ2ejj41Fycu5ypjvzHTQIjbubtUK2uf3X93Mk/oh5PVOfaR4krjPqiM+Kx2A0mNcTshPM6/nJNeSSkJ1Alj7vJM8uDi48Wu1RNGoNfs5+aDVaXB1c6REhHQ6lncUJVevWrZk4cSKtWrVi9+7d/PyzqWbG6dOnqVChgtUDFEIIu9v0kel7hQegfGP7xnIPiqLwTaSpd2pYy3BSZ3zK5eW/gaJgSE6+84Ea0yNBlaMjDn5+4HBjXeNw27x7Dv5++A4fZrN7sLaYjBieXvs0OmP+cwtag1qlxkHlwNiGY3mq1lM2u44ovixOqL788kuef/55li5dyjfffEP58uUBWL16Nd26dbN6gEIIYVeXd8OJPwEVPPSFvaO5p8jTcZyMScNNq+GJAB1xc+be1sazVy+8H3sUjbc3Gnd3NN7eqF3vXcizpFpwbAE6o44wjzDqBtQ1b/dx8sHN0dTL5uLggq+zr3mfi6MLfs5+dz2vm6MbPk4+eDl54epYev/8RMFYPDlyWSSTIwtRhi0eAKdXQ0RbGPKnvaO5o2ydge82n2f7lkMoUefpWiuITue2kbF5C+4PPkjgxAkAqJyccKxQocxMopuUnUTXZV3J0mfxbedvaVmupb1DEkWoWE+ODHDu3DnmzZvHuXPnmDFjBoGBgaxevZqwsDBq1y7egzWFEKLAYk+akimAnp/ZN5a7iEvLYc7WKH5ac5A56z/CVZ8DuyDjxv6AceNwqlLFrjHay+KTi8nSZ1HTtyYtQlrYOxxRilmcUG3atInu3bvTqlUrNm/ezPvvv09gYCCHDh1izpw5LF261BZxCiFE0dt+4xFfzd7gb/+EJC1bxyPf7ODU9bR89484G4mrPgejtw9uFU2D0d07dsS5erWiDLPYyNBlsPjEYgCervt0memVE/ZhcUL1yiuv8N577zFx4kQ8PG4VKHvwwQf58ssvrRqcEELYTWo0HL4xUW2r8Ta/nJKbi2I03r49O5uso8dQdLms232O6nvPUf0/bTxzM/A0ZtPt4i4AKk77CPe2bW0ec3GlKAr7Y/ez/uJ6UnNTqehZkY5hHe0dlijlLE6ojhw5wuLFi2/bHhgYSHx8vFWCEkIIu9v5DRh1ULEVVGhis8vkXrnKtTffIGvvPhTd3d9Cq3nj626c69TBrU0bq8VXnJxMPEla7u29c3GZcey7vo90XToAxxOOcyH1gnn/sNrDZFJhYXMWJ1Te3t5cu3aNiIi8heIOHDhgfuNPCCFKtOwU2Htj1odW42x6qZh33yFzx867tlF7epLmF8zl5Gyy3TxpXSeUPHMRqzU4+PmhdnPFq0+fYv1oKyYjho2XN5Kpu/uUOEbFSHxWPLnGXABOJpzkaMJRi67l5eRF2/JteajyQ4WOV4iCsjihGjBgAC+//DK//vorKpUKo9HItm3bePHFF81FPoUQokTbOw9y0yCgJlTpXKhTGDMy0F2/nu8+JTub1LVryTl1moxNmwEIeutNvB56mPxyIYOTMw9+uonLiVlM6V2L0HtUPi+usvRZDPprENcz8/9zKQhHtSNhHmG3bfdz8aNJcBPcHExlEAJcA+gW3q1YJ5eidLE4ofrggw8YPXo0oaGhGAwGatWqhcFg4Mknn+SNN96wRYxCCFF09Dmmx31g6p3K0xVUMIb0DM736oU+JqZA7b0e6Y/vk0/mu2/J7kt8tPokKVk6/P5T+bykmRg50ZxM9YjogVajvWt7rVqLn4sfapXp78DPxY9+VfrJ4ztRLBVq6pnvv/+eN998k6NHj5Kenk7Dhg2pWrWqLeITQoiidfhnSI8Bz/JQp7/FhytGI1cnTEAfE4PK0fGOBTPVXl44tuvAplwPdlSoT8bc3be1ScrI5cjVFPP6020q4aItmcnEsfhjbL26FYBpbafRPaK7nSMSwroKVYcKICwsjLCwkvtJSQghbmM0wraZpuXmz4PD3XtQ8pM4fwEZW0zz/pX7+CM87zJp/MRfDrJ8/1U4l3LHNgBVA915s1ctWlXxtzie4uLbw98C0Kp8K0mmRKlkcUKlKApLly5l48aNxMbGYvzPa77Lly+3WnBCCFGkDv4ICWfAyQsaD7H4cGN2Nglz5gDg3qEDHt26EZuazesrjpKSmfcNvti0bC4kmAZm929UgeaVfNGobx/vo3VQ82CNQFy1hf78a3d7Y/ay8fJGVKiY/MBke4cjhE1Y/C90/PjxfPvtt3To0IGgoCAZ8CeEKB0Metgy3bT8wAhw8rh7+3wkL12GISEBx/LlqTBzBiq1ms83nGHd8TsPwu5VL4Tpj9UvbNTF3qnEU0yMnAhAp4qdqORVyc4RCWEbFidUP/zwA8uXL6fHXbqxhRCiRIk/C7NagT7btN5itMWnMObkkDB7NgB+T49A5ehIbGo2S/deAeCNnjUp5+1ibq8CgrycqVve677DL26y9FnEZcZxJf0Kz6x7xrx9bMOxdoxKCNuyOKHy8vKiUiX5hCGEKCUy4mFW61vJVM/p4GbZWKX4b78j7jPTXH+aAH+8+vUDYM62KHINRhqFeTOidUSZ6NF/f+f7/HTqp9u2v9PyHSK8Sma5ByEKwuL3gadMmcLUqVPJysqyRTxCCFG0Nn4A+hv/nz21ApqMsOhwfVIS8bNmmdcDxoxF7eRESpaORTsvAfB8+yplIpmKSoni51M/m9fdHd0J9wxnTf819K3a146RCWF7FvdQPfbYYyxZsoTAwEDCw8NxdHTMs3///v1WC04IIazm4nY4uBhiT0DSBUAxbc9MNH3v+SlU7mDxaZN++AElKwtteDjhS5eicTcVlly4/QLpOXqqBbnzYI1A69xDMbfg2AIUFJoGN2V2l9llIokU4iaLe6iGDBnCvn37GDRoEP379+fhhx/O82WJzZs307t3b8qVK4dKpWLFihV59iuKwltvvUVISAguLi506tSJM2fO5GmTmJjIwIED8fT0xNvbmxEjRpCenp6nzeHDh2nTpg3Ozs6EhoYybdo0S29bCFES6bLhxJ9w6CdY9Cgc+AGu7oXMeMhMMH2hQLVupoHoFoq6FEvcwh8BSBs4gnUX0vj94FV6f7GV6etOA/Bc+8qo83l7r7S5nnGd38/9DpjGSkkyJcoai3uoVq1axZo1a2jduvV9XzwjI4P69eszfPhw+t0Yc/Bv06ZNY+bMmSxYsICIiAjefPNNunbtyvHjx3F2dgZg4MCBXLt2jXXr1qHT6Rg2bBijRo0yT+CcmppKly5d6NSpE7NmzeLIkSMMHz4cb29vRo0add/3IIQoxtZPgV3f3FpXqaHrByhBdcjR+gJgQMU5QwAxx2KITs7ianIWeqOS5zSJGbnEpuaQmGGaVy41W8f11Gz6no7k6fQ0rrgH8MxRJ4zH9uU5rmaIJ73rlbPpLRYXP574Eb1RT6PARjQIbGDvcIQochYnVKGhoXh6elrl4t27d6d79/wLvCmKwueff84bb7xh7vlauHAhQUFBrFixggEDBnDixAn+/vtv9uzZQ5Mmptngv/jiC3r06MEnn3xCuXLlWLRoEbm5ucydOxetVkvt2rU5ePAgn3766R0TqpycHHJycszrqampVrlfIUQRSo+DfTcmOA5rCc6e0PEtsnxq8PBXWzl9/cK/GkdZfHoXXTZPH1sJwIYGXSjv54ajRk2AuxOOGjVdagcxqFnFMtE7lZKTwi+nfgFgRF3Le/qEKA0sTqimT5/O5MmTmTVrFuHh4TYIySQqKoqYmBg6depk3ubl5UWzZs3YsWMHAwYMYMeOHXh7e5uTKYBOnTqhVqvZtWsXffv2ZceOHbRt2xat9lbF465du/Lxxx+TlJSEj4/Pbdf+8MMPmTp1qs3uTQhRBHZ/a3pzr1wjGPYXN2cdfuWnA5y+nndYgFajJtTXBT93Jyr6uhLg4ZRnkmKNSoW/hxMhXi64acDl+5k4rzIVMXYICuLDryej0lpeVb20+PnUz2TqM6niXYU25dvYOxwh7MLihGrQoEFkZmZSuXJlXF1dbxuUnpiYaJXAYm5MKhoUFJRne1BQkHlfTEwMgYF5B3s6ODjg6+ubp01ERMRt57i5L7+E6tVXX2XixInm9dTUVEJDQ+/zjoQQRSYnDXZ/Z1puPcGcTP115Bq/H4wG4KN+dXmogelxnLODpsA9SSkrVxG98taMEAHjx5eaZCrXkMuq86uIyYwhLTfNvD0pO4kMXUaetgbFQEJWAjqjjstplwFT75SMnRJllcUJ1eeff26DMIoXJycnnJyc7B2GEKKw9s2H7BTwqwo1egGmiYafX2R6C7lWiCePNgnNM9WLIT0DQ0I8umsx6GOvo+j0eU5pzEjHkJxMyp+mx3xuLVsS/NabaG3YU1/UZh2axfdHvi/UsRU9K9I1vKuVIxKi5LA4oRoyxPL5rQojODgYgOvXrxMSEmLefv36dRo0aGBuExsbm+c4vV5PYmKi+fjg4GCuX8877cPN9ZtthBAl1+6oROLScsjSGUhIz8Goz2Hgzs/xBJa59Gff78cAOB1j6nHxcHbg52eam5OprCNHufb66+ScPl3ga2q8vCg/cwYad3er309RyzXkcjb5LFn6LH48YXpjsbJXZVqUa4GTxvTBUq1S4+fih1aTtyfOSeOEv4s/KlTU8K2Bo9rxtvMLUVYUKKFKTU01D0S/1wBtaw1Yj4iIIDg4mA0bNpgTqNTUVHbt2sVzzz0HQIsWLUhOTmbfvn00btwYgH/++Qej0UizZs3MbV5//XV0Op358eS6deuoXr16vo/7hBDFk95g5FJiJv9+AW//xSQmLzsMgAYDHzl8TyP1GTzV8VxTfHn1bE1yz17Kc573+9bFw9n0f0H2yZNcePTRWzvVajQ+PjgGBaEJMCUK/6bx80Pt6opn1y4lOplSFIVfT//KxdSL/H7ud1JyUsz7avnV4qeeP8mjOyEsVKCEysfHh2vXrhEYGIi3t3e+/9AURUGlUmEwGAp88fT0dM6ePWtej4qK4uDBg/j6+hIWFsb48eN57733qFq1qrlsQrly5ejTpw8ANWvWpFu3bowcOZJZs2ah0+kYM2YMAwYMoFw509iIJ598kqlTpzJixAhefvlljh49yowZM/jsxjQRQojiRW8wciY2nbRsPTl6A/HpOegNCjP/OcPlxPxnaKgW5M5Txj95NG2zedvJysMZU752nnZVA93pXvdWj3fCd7ceb4V88AHe/cpGNe/Iy5G8u/PdPNsCXAJwc3TjlaavSDIlRCEUKKH6559/8PU11WzZuHGj1S6+d+9eOnS4VZn45kDwIUOGMH/+fCZPnkxGRgajRo0iOTmZ1q1b8/fff5trUAEsWrSIMWPG0LFjR9RqNf3792fmzJnm/V5eXqxdu5bRo0fTuHFj/P39eeutt6QGlRDFUK7eyENfbuVkTNod27g7OeQZ+1Q5wI0l9Q7itP5GctR8NNR6mA6hTelwl8Qg9+JFUv/+G4CIFb/hXKOGdW6imFMUxTxOqmW5llT3rU7rcq1pGtLUzpEJUbKpFEVR7t3slkuXLhEaGnrbJxhFUbh8+TJhYWFWDbA4SE1NxcvLi5SUFKs90hSirFIUhcuJWeiMRuLTckjO0hGfnoNRgZPXUlm0y/SIroKPC1oHNR5ODni7alGpoG/D8jzcoHzeE+akw+d1ICsJPEJg7H7Qut4zjmtvTyH5559xa9eWsG+/tcWtFkvbo7fzzLpncNY483f/v/Fz8bN3SELYTFH+/rZ4UHpERIT58d+/JSYmEhERYdEjPyFE2aIzGHnkm+0cupJy13ZTetdiaKuIu7Yx2zvXlEz5VoYxe0CtuXccsbGkLDeVPvAfObJg1ykF4jLjeGbdMwD0r9ZfkikhrMjihOrmWKn/Sk9Pz/MoTgghADaejOXA5WSSM3OJSck2J1Mezg7myuJ+7lo8bwwUr+DjwpPNKhbs5Ff3wbo3TcttJhYomVIUhYRZs1B0OlwaNsTlxgstZcFn+0xjRzUqDUNrD7VvMEKUMgVOqG6Ob1KpVLz55pu4ut7qUjcYDOzatcv8Np4QouyauzWKXVEJxKXlcD01h6vJtw8kf6NnTZ5uUyn/EygKJF2A1KumyY3Tr4MhF/Q5kBELxhu94LrMW8U7vUKh3uN3jSv3yhWyjx0ndto0dFevAuA3amSZGYAdnR7NX1F/AfBK01cIdpOyMUJYU4ETqgMHDgCmT3dHjhzJM5WLVqulfv36vPjii9aPUAhRYuy7mMQ7K4/ftj3I04ne9crhqtXg5+7EoOZ36IGKPQG/DoW4k5ZduN/3oLlzDaTkFSu49sqreba5Nm+Oe7t2ll2nBFtwbAEGxUDzkOYMqDHA3uEIUeoUOKG6+XbfsGHDmDFjhgzOFkLkcT01m6FzdwPQrloAPeoGE+DhhLODhqYRvjho1Hc/gUEHix+D5Bt1o7Qe4B0Gji7gHggqNTh5gOu/xv2oNdBgEARUu+NpFZ2O+C++NK+7tWmD96OP4NmlS6HvtaRJzE5k+RnTmDGZvFgI27B4DNW8efNsEYcQooTIyjXVhlIUiE3LJkdvBOB/a06RlqPHQa3ivT51CPW995t2eRxddiuZevhraDjQKvGm/vUXuqtX0fj5UWXDetRlcKzn4hOLyTZkU9uvNs2Cm9k7HCFKJYsTKiFE6XQxIYNf914hPt00jUt8eg46vcLV5CxiUrMxGAtWYeXdwiRTRiNsvVFst+NbVkumFKOR+BvFO32HDCmTyVSmLpMlJ5cAMLzO8DIzZkyIoiYJlRACg1Hh6QV7ORObXqD2ahU4O2pMb+l5OOGgVqFWqRjWKpxHm4RaHsCpv0zjppw84YGnC3xY1pEj5Jw+jTEj03Qf6WkYkpPN+w2JSeSeO4fawwOfJ0r3uKHLqZfZHbObbEN2nu3HE46TmptKRc+KdAzraKfohCj9JKESooxbeTiauVujzMnUiNYR+Lpp8XJxxNPFERdHDRH+bvi4mgZ9q1UqfNy0dzulZbKS4I+xpuUHngZnrwIdlr51G5efLljy5TPwSTQeHoWNsNiLz4rn0ZWPkqHLuGObYbWHoSlAWQkhROEUKKFq1KgRGzZswMfHh3feeYcXX3wxT9kEIUTJ8/vBq/y0+zI7zieYt73Wowaj2lYuuiCMBpjdGbISwcEZmj9f4EMTZs0yL3t07oTKUQsqFRo/X9TOLuZ9ag93fJ96yqphFzfzjs4zJ1OdwjrhqM77xmOwezAPVXnIHqEJUWYUKKE6ceIEGRkZ+Pj4MHXqVJ599llJqIQowc7HpTP+54PcnHgqwMOJ0e0rM7hFeNEGcuw3SDhjWu78LrgHFOiwrIMHydy7FxwdqbJuLY7BxaumklExkqHLIC4z7o5tMnQZJGQnEJ8VT64h17xdZ9SRkJWAQTGY1+Oz4jHcqL+VocsgOSc5z7nOp5wHYFanWbQq38rKdyOEKIgCJVQNGjRg2LBhtG7dGkVR+OSTT3B3d8+37VtvvWXVAIUQ1vftpvMoCtSv4MVDDcrzZNMwXLRF9DgoOwVOroKUq3DwR9O2Dm9As4JPWJ4wZw4AXr162SWZSs1NZcPFDcRkxpCpyyQxOxGjYnrbMVufzZ7re0jJufv0OtbWKLARLcu1LNJrCiFuKVBCNX/+fN5++21WrlyJSqVi9erVODjcfqhKpZKESohi7lpKFssPXAHgrd61aFzRt+gu/tdLt6qb3+TsZVEylXM+irT1GwDwe9q6NZUydBlcTrts7h3KNeSSmJWIQTGQpc8iITsBRVGYe3Quqbmp9zyfVq3F1fHOvfm+zr54aj3xd/HPM77J1cEVH2cf87qzgzN+zn7mN/R8nX1xcfjXY02Vmjp+deQNPiHsqEAJVfXq1fnpp58AUKvVbNiw4bbJkYUQJcP3m6PQGRSaRfgWbTIVd+pWMqVxgqqdwSMEaj1U4IHoAInz5oKi4P7ggzhVtt54r1XnV/HKllcsOqZV+VZUcK+Ap9YTL6db9+Dq6Er7Cu3xd/GXJEeIMsLit/yMRqMt4hBCFIG4tBzmbosC4PkOVYr24ttmmr6HtYShKws0kfF/6WJjSVnxO1D43qksfRYxGTEkZieat8VkxORJpoJcg8zLHloPc7Lk7eSNm6MbAB1CO9CpYqdCxSCEKH0KVTbh3LlzfP7555w4cQKAWrVqMW7cOCpb8dOiEML6pq89BUDtcp60repfdBdOuQqHfzYtd36nUMkUQNIPP6DodLg0aoRro0b5tknNTWXluZV5BnobMRKXGcfWq1u5kHrhjucPcg1iVb9VOGmcChWfEKLssjihWrNmDQ899BANGjSgVSvT2yTbtm2jdu3a/Pnnn3Tu3NnqQQoh7l9saja/HbgKwDPtKhfto6idX4NRB+FtIPSBQp3CkJZG0hLT0IO79U59uOtDVp5fec/zBboG4upgGt+kUWkIcgtiRJ0RkkwJIQrF4oTqlVdeYcKECXz00Ue3bX/55ZcloRKiGNodlcgLSw6QozfSKMyb3vVCiu7imYmw98YcoK3GF+oUiqJw5fnRGNPT0VapjHv79vm2u5h6kb+i/gKge3h3HNS3/ovTarQEuwXTPrQ9NXxrFCoOIYS4E4sTqhMnTvDLL7/ctn348OF8/vnn1ohJCGEFiqKQkWsgPi2Hp+bsMk9iPK5TtaLtndozG3QZEFQXqhRu6pOMrVvJ3LMHAL/hI1Cp1be1ydRlMmX7FIyKkTbl2zCt3bT7ClsIISxhcUIVEBDAwYMHqVq1ap7tBw8elDf/hCgmcvVG+n69jWPReV/tn9K7VtGOnYo5ChvfNy23Hg+FTOTiv/0WAOe6dfHq8/Bt+3VGHU+uepJzKecAeKb+M4W6jhBCFJbFCdXIkSMZNWoU58+fp2VLUxG5bdu28fHHHzNx4kSrByiEsNzvB6/mSabctBoWj2xO/VDvog1k7Rum7+7BUKuPRYem5qaSlJ3E/nWLqbF3H0aNml+GhpOxY4q5TbYhm8SsRNJ0aeZk6vHqj1M/oL6VbkAIIQrG4oTqzTffxMPDg+nTp/Pqq68CUK5cOaZMmcILL7xg9QCFEJYxGhVmbTIlF5O7VWd4qwgcNWo06iKuh3R1H5zfaFoesBg0Bf/v5qeTP/H+LlPP1iu/mIps/lNX4cf41RB/5+Neb/Y6A2oMKHTIQghRWBYnVCqVigkTJjBhwgTS0tIA8CjFs7gLUdKsPX6dc3EZeDg78FTzijg7FtGUMv+15VPT9/pPQIXGt+02GA0sP7uca+nXzNtyDDkkZCew+fJm06HnjTQ6p2BUQc7j3RkWWh5PJ8885/HUeuKp9cRD6yFTrwgh7KZQdahukkRKiOJFURS+udE7NbhFRTycHYvqwpARD6lXITsZEs/DyZWAClpPyPeQH0/8yCd7P7njKat6VmbK7lx0ROHdvQcv9p1um9iFEMIK7iuhEkIULzvOJXDocjJODmqGtYoougsvHwVHbn/7l5q9IaD6bZszdZnMPTrX1MS3Jo2DbvVguTm64ePsQ+uzjmRGvQ1AwAtjbRO3EEJYiSRUQpQC0clZjP/5ILujTNOpPP5AKP7uRVSgMuborWRKpQGv8lx39eE3FwdyK1QhfttbJGUnkZSTBECmPpPYzFhSclII9Qhlcc/FeepFgamn7cIbjwLg98wzaMPDi+ZehBCikCShEqKEW3sshrE3inaC6Y2+kW0qFV0AWz8zfa/VBx6dDyoVb617hu3R2+Hs0rseOqbBmNuTKZ2OK+MnkH3sGCoXF3yHDLZN3EIIYUUWJVQ6nY5u3boxa9as2+pQCSGK3tXkLMYsPkCuwZRMfdSvLl1qB+Prpi2aABKj4Nhy03KbiaBScTjuMNujt+OgcuCRao/g7OCMn7Mffi5+5omFfZ198Xfxp4JHBRSDgbjPZ5B7wTRpc+bBgxjiTK/y+Tz2GA6+vkVzL0IIcR8sSqgcHR05fPiwrWIRQtyDwahwPi6dF5ceJiYli8xcA7kGI44aFd8PbkL76kVcXHf7TFCMUKUThNRn/cX1vLTpJQB6Ve7F681fv+vh+rg4kpctI+H772/b596hAwHjpBSLEKJksPiR36BBg5gzZ85tc/kJIWzLaFR44vud5nFSNzmoVfz6bEsaFFXRTl0WnI+EnHQ4sMi0rfVEfjj+A9P2mKZ7UavUjKw78o6nSN+6jevvvkvuxYvmbV6P9MelTl0AHAIDcO/QoWinyBFCiPtgcUKl1+uZO3cu69evp3Hjxri5ueXZ/+mnn1otOCGEybm4dJ6avYvolGwAvFwcmdi5Gk3CffBzcyLYy9m2AeRmQmq0aXnz/+DwTwDEajRkVmjEIX2cOZny0HowpcUUwjzD8j2VotfnTaYcHXFv15bgN95A7Wzj+xBCCBuxOKE6evQojRo1AuD06dN59smnSSGsLzNXz+Pf7iA+PReA13vUZGTbIhx0npsBXzeH5Et5Nq+qWJ9X1ElAPGx7EwB/F3+WPbQMX2dfDKmpGLOyMWakY0gyveGn6HQkLfnJnExV/PEHXBo3lv87hBAlnsUJ1caNG20RhxDihujkLN7/6wQZOXoAEtJzzcnUr8+2oElFH+tfNCsZojZBylXISYOMODDq0F27Stq+ixgSEjHk+IDGAcUIOm0oOftTeEVvQKPWoAL8crSEZuuJfbc9141GMBjuesmASRNxbdLE+vcihBB2UOiyCWfPnuXcuXO0bdsWFxcXFEWRT5lC3Kdj0SkMm7eH2LSc2/bNGNCAB8Jt8MabQQ9zu0HciTybFQWiN/qRGesEeP7noBhqmpf15u9GMvM2U6tBo8HBz8/8OE/l6oJzrVr4Dhpk3fsQQgg7sjihSkhI4LHHHmPjxo2oVCrOnDlDpUqVGDFiBD4+PkyfLtNDCFEYP+68yBsrjgKgVsGUh2rjqjX9E/V319KuWoBtLnzkl1vJVOWO4BZAVqyK1MPXyYw9icpBg2ebRmgq1kLt6oaCwuLzSzmrSaBTxU60D+0AgEqjxsHfH8dy5VA5O6N2c0Mj01MJIcoIixOqCRMm4OjoyKVLl6hZ89Zn1Mcff5yJEydKQiVEIczadI6PVp8EIMzXlefbV2ZA0/wHdVtV0kVY8ZxpudNUaD0eQ3Iylzp1xpieDoD3E08S/PprgKmC+StbXuEvryTcHD14v/87eDl52T5OIYQo5ixOqNauXcuaNWuoUKFCnu1Vq1bl4r9egRZCFMzuqEQ+/tuUTPVtWJ7pj9ZHrS6ix+erJpq+u/jCA0+ji4nh2muvY0xPx7F8eTy6dcX/2WfNzTde3shfUX8B8Hj1xyWZEkKIGyxOqDIyMnB1db1te2JiIk5ORTR3mBClgKIoTF97mi83ngWgTVX/ok2mru6Ds+tNyw/NRJ+Ry/mH+2BMSQEg8JWX8ezcOU+8c47MASDcM5xn6z972ymFEKKssjihatOmDQsXLuTdd98FTKUSjEYj06ZNo0OHDlYPUIjS6vst583JlLero82TKf2ZPWSuWYIh9jpKbi7G2HPo4z3BvzrGhTtI+W2yua33o4/i0alTnuP3Xt/L4fjDaNVa5nWbh4uDi81iFUKIksbihGratGl07NiRvXv3kpuby+TJkzl27BiJiYls27bNFjEKUarEpeXwzsrj/HnIVCjzyWZhTOldG62D2jYXNBpIn/MGVz77DcX434TNHU5fBX4zrapUhM2bi1vz5red5mbvVN+qffF38bdNrEIIUUJZnFDVqVOH06dP8+WXX+Lh4UF6ejr9+vVj9OjRhISE2CJGIUqN2LRseszYSny6qSxCvQpevPdwHZv1TCkZKVx5tB3p53MAFaDgGuGDg48HqFRoQiqiLl8LAMfgIFybNMEpn4nPTyScYFv0NtQqNUNqD7FJrEIIUZIVqg6Vl5cXr79+90lPhRB55eqNDJm7h/j0HJwd1bzRsxa96oXYLpnS67gy4MEbyRS4hntS4Yff0ASUs+g81zOu8+x603ipbuHdCPUItXqsQghR0hUqoUpKSmLOnDmcOGGqXVOrVi2GDRuGr68Nig4KUQrk6A0MnbuHE9dSAZg79AFaVrb+Y7PMZTPJWL8SgPSjl8mOM20v/9pIPAdPtPh8iqIwfuN4ErNNEzIPrzPcarEKIURpYvGgjc2bNxMeHs7MmTNJSkoiKSmJmTNnEhERwebNm20RoxAlWo7ewKDZu9hxPgGAN3rWtH4ypSgkf/wsF1//hviNl4nfeCuZCh71UKGSqcTsRMZvHM/RBFOx0UmNJ1Hdt7o1oxZCiFJDpSiKYskBdevWpUWLFnzzzTdoNBoADAYDzz//PNu3b+fIkSM2CdSeUlNT8fLyIiUlBU/P/07BIcSdZesMDJy9i30XTZMDzx7chE61gqx6DcPFY1wZ/jiZV01z5zl6a3BvbBoX5dauEx6PjSrUecf+M5bIy5EAPF33acY1GmeNcIUQosgU5e9vix/5nT17lqVLl5qTKQCNRsPEiRNZuHChVYMToqR75od95mTqu6ca31cypeTmoouJQR8biz42FmN2Nhk/f07qoThzG/fagVRYvBbVfdaEO5FwwpxM9azUk5F1R97X+YQQorSzOKFq1KgRJ06coHr1vF3/J06coH79+lYLTIiS7PCVZN758zh7/5VMdakdXOjzZR07xqWhwzCmpeW7X62Fcu+8hkefpwp9jZsWHlvI//b+DzAlUx+1+ei+zymEEKVdgRKqw4cPm5dfeOEFxo0bx9mzZ2l+o1bNzp07+eqrr/joI/mPV4iNp2IZNm+PeX14q4gCJ1OZ+/aRdegwOefOYkzPMG1UFNLWrjW30fj74+Dri0PuZVS5SbjVr473O7+itsJExMcTjpuTKY1Kw6i6hXtcKIQQZU2BxlCp1WpUKhX3aqpSqTAYDFYLrriQMVSiILJ1Bj5afZL52y8AEOHvxsvdatCpZiAOmnu//5H44yKuv/feXdtU2fgPjiEhcGkXzO0CakcYdxC8Ktz1uH9Lz00nU58JgFExEp8VT2pOKqeSTrH+4noOxx+mnFs5prefTh3/OgU+rxBCFDfFbgxVVFSUTYMQoiTLyjWw5lgMr/12hMxc0weKphG+LBzeFGdHzT2ONsncf8CcTDmUC8HtgaZoq1RG7eYGmD6suLVsiWNICIpex9E/nyHW1QUi2pOVcJCEKxu4knaF6Ixo8wefDF0GSTmmR45Z+iwSsxIxYkRv1N81FrVKzazOs4jwiijMH4cQQpRJBUqoKlasaOs4hChxFEXhvVUnmLP11gcOrUbNW71rMbBZGCrVvQt25pw/T/KyZSTOmQuAQ1AQVdasQeXomG97XU4a4xe3Z7O7AdwDIPMYbHm1UPFrVJobMWvxd/EnwCWASt6VaBbcTJIpIYSwUKEKe0ZHR7N161ZiY2MxGo159r3wwgtWCUyI4kxRFN78/Sg/7rwEgEat4qH65Zj6cG08nfNPhv7NmJtLblQUUX37wb/+DYX/8vPtyZRBT9KlbRw7v4apF1YQ42BKhKpqfXDzMn3Y8XbyxtvZm8pelfF29jYf6uvsa57E2M/ZD1dHV5w1znnaCCGEuH8WJ1Tz58/nmWeeQavV4ufnl+dTuEqlkoRKlHo3e6ZuJlMvda3OM20rFWicFEDG7t1cee55jBkZ5m2+Qwbj/cgjOAblLauQkxnPhJ+7skWda9pwI5n6X7XBdGvxkhXuRgghhDVYXNgzNDSUZ599lldffRW12uJC6yWSDEoXN52NTWfiLwc5fCUFgEmdqzG24+2TCedHURTSN0Zy5fnnzdscyoVQ4dNPcWnQ4Lb2Bl02Yxa1ZqvKNBefSoEGGjfeaP8p1UJb3v/NCCFEKVeUv78tzogyMzMZMGBAkSRTU6ZMQaVS5fmqUaOGeX92djajR4/Gz88Pd3d3+vfvz/Xr1/Oc49KlS/Ts2RNXV1cCAwN56aWX0OvvPihXiPxk5Rp4as4uczL1avcaBU6mAK6+8II5mVJ7eFBt106q/vNPvsmUYjTy6k+dzMnUF7Wf5/DQIyx8aqckU0IIUQxZnBWNGDGCX3/91Rax5Kt27dpcu3bN/LV161bzvgkTJvDnn3/y66+/smnTJqKjo+nXr595v8FgoGfPnuTm5rJ9+3YWLFjA/Pnzeeutt4osflHyXU3O4oUlB6j51t9cS8lGpYJlz7XkmXaVC3yOmA8+IG3degCcqlcn/Jef0Xh53d5Qlw1zu/HbpxVYbTQlbu+E96V9k+esci9CCCFsw+JHfgaDgV69epGVlUXdunVx/M8A2k8//dRqwU2ZMoUVK1Zw8ODB2/alpKQQEBDA4sWLeeSRRwA4efIkNWvWZMeOHTRv3pzVq1fTq1cvoqOjCboxNmXWrFm8/PLLxMXFodVqCxSHPPIru/ZcSOTRWTvybPtmYCO61w0p8DlS//qLqxMnAeD39AgCX3wxbwNFgdSroBjJPriYIzs/5dUAP647ODDItwEv9/7hvu9DCCHKomJXh+rfPvzwQ9asWWOeeua/g9Kt7cyZM5QrVw5nZ2datGjBhx9+SFhYGPv27UOn09GpUydz2xo1ahAWFmZOqHbs2EHdunXNyRRA165dee655zh27BgNGzbM95o5OTnk5OSY11NTU61+X6L42/uvZCrEy5kpD9WmSUUf/NwLPk9e9vHj5mTKtUkTAiaZlkk8D/N6QFoMb/j78Ke7GwqgqFQQYvp5DXQJYHyP2Va9JyGEELZhcUI1ffp05s6dy9ChQ20QTl7NmjVj/vz5VK9enWvXrjF16lTatGnD0aNHiYmJQavV4u3tneeYoKAgYmJiAIiJicmTTN3cf3PfnXz44YdMnTrVujcjSoyMHD1Ldl/ivVUnAHB2VLNybGuLEqnU1au5/r//oY++BoAmwJ/Q778jM+Es16/s4NSRRRx1zCLTz5vfPdzzHKtVIMK3GmMajsVJc3+THAshhCgaFidUTk5OtGrVyhax3KZ79+7m5Xr16tGsWTMqVqzIL7/8gouLi82u++qrrzJx4kTzempqKqGhoTa7nigeFEVh4Y6LTF97itRs04sLns4OzB7ygEXJVMbu3VydcOvnR6+Brx9O4Pqixpz4d+V0r1vdz/2r9GVMI1PJEV9nX9SqsvEGrRBClBYWJ1Tjxo3jiy++YObMmbaI5668vb2pVq0aZ8+epXPnzuTm5pKcnJynl+r69esEB5smog0ODmb37t15znHzLcCbbfLj5OSEk5P0DJQl6Tl6hs3bzZ4Lpqla3J0caBjmzf8eqU+wl/OdD9TnwvmNoMsC4PCBzag+Wo0DEBUEMx7WkOABOdq8j8N9FRXVXEOoVbkbrg6uDKo1CDdHN1vdnhBCCBuzOKHavXs3//zzDytXrqR27dq3DUpfvny51YL7r/T0dM6dO8dTTz1F48aNcXR0ZMOGDfTv3x+AU6dOcenSJVq0aAFAixYteP/994mNjSUwMBCAdevW4enpSa1atWwWpyg5LiVkMn/7BVYcvEpihql45tCW4bzSvca95+FLuw5zOkHyJRRg5SU/qmw3JeJ6NXzwuAYfFw3v1RuF441q5S5aD5pV74dGXbA5/oQQQpQMFidU3t7eeUoT2NKLL75I7969qVixItHR0bz99ttoNBqeeOIJvLy8GDFiBBMnTsTX1xdPT0/Gjh1LixYtaN68OQBdunShVq1aPPXUU0ybNo2YmBjeeOMNRo8eLT1Qgl3nE3j8u53mdQ9nB754oiHtqwfe+2B9Lix8CJIvka3A30fDqH7M9JgwupyGlOfbMLV8Jdo+MBZHh7v0cAkhhCgVLE6o5s2bZ4s48nXlyhWeeOIJEhISCAgIoHXr1uzcuZOAgAAAPvvsM9RqNf379ycnJ4euXbvy9ddfm4/XaDSsXLmS5557jhYtWuDm5saQIUN45513iuweRPGTqzfy97EYXlhyADANOp/QqRpPNAsr0Dx8ANk/DyUn+jQ5aNi3ryLVL2QDcKlbPTpPX4xaIz1QQghRllhch6oskjpUpUNiRi7vrzrBqiPRZOtMExK7aTX8ObY1lQLc73E0XDy9j6PzpsORI1Q8p0fzn385iZMG0mrkG7YIXQghRCEU6zpUERERd603df78+fsKSAhrWrTrIj/suEhqlo7olOw8+6oGuvPb6Fa4O937n8HRf+aRPXEalbJv35fhosI4+RlaPTHOWmELIYQoYSxOqMaPH59nXafTceDAAf7++29eeukla8UlxH3JyjXw7qrjLN51Kc92dycHXu1Rg34NK+DsqL7rh4P4mEPMipyM//poWm/T46ZArgau1NYR2L479Yd9DIBGq0WjsfifkhBCiFKkUGUT8vPVV1+xd+/e+w5IiPsVm5rNwNm7OBObDkCTij5M7lYDF0cN1YLdcXK4+/gmxaBn6k9dOHA5lsc3G2l43vRs73qgiurDm1G/9SCo0tHm9yGEEKLksNoYqvPnz9OgQYNSOU2LjKEqGXaeT+CHnRdZeywGncH0Y/1su8pM6lINR829C2XqYmK49tNs9m75iYDLBvz/9aOc27I6dWcvR62WgptCCFFSFOsxVHeydOlSfH19rXU6ISzy99EYnv1xn3ndz03L90Oa0CjM567HKYpC7rlzXD++maS3p6PNMlLzX/u1EeH4Dh6M9+OPo5JkSgghxB1YnFA1bNgwz7gTRVGIiYkhLi4uT8kCIYqCoih8tu40M/85C0C1IHdGtI6gT8Pyd320pygK6ZGRXP/oI3QXTeOstDf2RTZQ8UCr7tTp8gxO1araZNJvIYQQpYvFCVWfPn3yrKvVagICAmjfvj01atSwVlxC3FOu3sgryw6z/MBVANpVC+D7wU3QOtzqSTLm5JDyxx8YEpMwZmWStW8/WUePomRl5TlXiivE+0LaI/V48onpeHlVKNJ7EUIIUbJJHaoCkDFUxU+2zsCA73Zy8HIyAP0alud/j9ZHo77Vm5S5dy9XJ0xEHxeX7zn0ajhVAX7soMG3vBvfPrEJR0epai6EEKVFiRxDJURRWX3kGlP/PE5Mqqko1ONNQnmvbx1zMpV96jQJ331H6qpV5mP0LRpwQZNElrOatb6XORWkkKUFfwc1I8O681CbKZJMCSGEKLQCJ1Rq9d1r9gCoVCr0ev19ByXEfxmMClvPxrPzfALfRJ4DQKNWMW/oA7StFmBuF/fVV8R/+RXc6Hh1LFeOS+P6MC75u/+cUcUH4X3p1WaKDDYXQghx3wqcUP3222933Ldjxw5mzpyJ0Wi0SlBC3KQoCpcSM5m89DC7ohLN28N8XZk9pAnVgjxQdDrS1q0j9e81pK1dC4Bz3bp4PfQQJ1uEMG7beACqe1TkoQsHcVIU2lbuSUg7mdNRCCGEdRQ4oXr44Ydv23bq1CleeeUV/vzzTwYOHCiTDgurijwVy3urTnD2RoFOgNohHrRSEhhRGZy2ruPKli2kb9yIkpNjbuPeqwdHR3fixxM/cnjbYQDCPSqy5NwpHDPSwK8K9JY3UoUQQlhPocZQRUdH8/bbb7NgwQK6du3KwYMHqVOnjrVjE2VUrt7I2CX7WXPsunmbr5uWYY1DeOiX6WTu3Enif45RuThzvqo7x1ySWFRrDYbNa837avrW5H/OVXHM2AKo4MlfQKaKEUIIYUUW/VZJSUnhgw8+4IsvvqBBgwZs2LCBNm3a2Co2UQYpisILSw6Yk6n+jSowuVt1/I3ZXBg4iMxzpvFT2iqVcQgIQO3iilurVrzmtY5tCXsB0zg/D60HTYKa8FT1x3kg8RosG2G6wMNfgV9le9yaEEKIUqzACdW0adP4+OOPCQ4OZsmSJfk+AhTifk1eepi/j8UA8MUTDeldvxypa9Zy+l9zSIbOno1761boDDpe3foqm698TlaCqa7U2y3epm2FtvjFn0ez6WPY3OPWyYPqQr3Hi/R+hBBClA0FrkOlVqtxcXGhU6dOaDR3rkC9fPlyqwVXXEgdKtu5nprNnguJnI5JY82x65y6ngbAuw/X5qkW4aT8/jvRL79ibh/y/nt49++PUTEyYeME/rn8DwCOakcmNZnEwJoD4fAvsHxk3gtVfhAenQ/OXkV1a0IIIeysWNahGjx4sEzBIazi8JVkFu28xKXETHacT7ht/2s9avCIezrn+/Uj5/gJAFwaN6bi/HmoHB0B+GDXB+ZkanSD0TxZ80k8tZ7w53jYN890Ip8I6DQFqncHB6eiuDUhhBBlVIETqvnz59swDFEWZOUaePuPo/yy90qe7R7ODtSv4E2VQHd61A2hTmIUFx57ylxLyufJJwmc/BIqR0cWHFvAwuMLic2MBWDyA5N5Ci+Y0wOSoiD3xhuB9QZA7xkgxTqFEEIUAXnVSRSJg5eTGb1oP1eTTWOd2lYL4MHqAYT7u9G2agDqm1XOT5/mwvARoChow8MJnT0bbYXy6Iw6Fh//kU/2fmI+ZzejC08tnQBGXd6LNXsWun9cZPcmhBBCSEIlbG7ZvitM+vWQef2bgY3oXjckT5uUP1eSvvEfUv9aDYC2UiXCf/kZjbs7my5v4rWtr5GamwpAW72asdevUjn3X4lUg4HQeCi4B4JPuK1vSQghhMhDEiphU2uPxZiTqSqB7swY0IDa5fIODL/+8TQS580zrzsEBVFx4QI07u5s2D2TCSe+5+abE50zMvlfbDwagBZjIKyF6cvNr2huSAghhMiHJFTCJrJ1Bj5bf5pvN50HoHKAG3+Pa4ODJu+8eRm7dpuTKZd2bcio5UN0PW8unJzFn+dWspEMAGrn5DD7WizunhUgpAE0HARN//MmnxBCCGEnklAJqzoZk8r7q06w5Uy8eVubqv58PbBRnmRK0emIm/kFCd9/D8DBmk580HKHaeelvOfsrvbi7Wo9cevcGiKkkKwQQojiRxIqYTVzt0bx/l8nMBhND+hcHDU8174yYx+sclvJjSs/zCH9RjKVqYWvOusBFQ6KQoRRjYtKg5fGmVFNJtCg1mNFfStCCCGERSShElbx+frTfL7+DACNwryZ8lBtagR7onXI+4hPp89l/ZevED7LNPj8aEUV33ZX4++iYcWlS3jV6IPjo/NuO78QQghRnElCJe7b/9ac5KuNpjn22lT1Z+HwpuYeqewTJ0j75x+uXj/LpUNbCbiSTrhpWBQxvirOd8vk1dxs2l7MwlH9//buPS6qOv0D+GcGmBFELnJHuagQcvWCSqiVBYWXyNhNyaYWtXVXw81b3jK12nV1N1+mmWlXzDRRWzU1xQhv5RomiooQmqK4BmIqV5HLzPP7gzy/JsyyEQaHz/v1mtfL+X6fOed5/OLweOacM9ZA7FxzlUFERPS7saGi30VE8Nae03gj4xRq6g0AgKfv9cUrj4XBUFWFa5mZqMjYhbIfv4pICyDwx9fWWQHFkcBDfhfwYBkAzwigZwzg14+3PCAiorsSGyq6bZlnLmPulhP4trhCGRv/YAAmPxyIsvXrUbJoEQzl5crcOTcg11cFOxcPRDrboGvtIURo9Q2T/vcBz2wCrGyauwwiIqI7hg0V/Wb/u3oN8z7Lw46cYgCAlVqF5wZ0wZj7O8O27AoKn9KhOjsbAKB2aIdCLxvs9C3Fzl5qPODdD4vzD8H6h1MNh6tChgKDFzbciJOIiOgux4aKfpNPsy9g4rrsG1+vh4dDPDBrcDD8XduirqgIBQl/gL60FADgMmYMPgz+DqsufQlAjT+qnDDrwAZYG+obXjx8VUNDRUREZCHYUNEtFZVVY95nedh2rAgA0MHJFq8ndkefTu2hLy9H+eefo+jFWTBUVgI2NuiweBE+qUjBqkvHAADPXynFmLKf3Fhq+EdAyGPmKIWIiKjJsKGiX5SeexHPrclCnb7hsFSknzM+HhMF9eXLKJozF6UbNuDGIau6djbY+HRbfHluIkqsGq7w62/Q4M/dnwO07QA7F8AlAPDpbbZ6iIiImgobKmqkTm/AlPVHseXo9wAAV3stxj/YBcPDXFH25lJc+SAFUlvbEGsF5Pmo8Ga8AaX2lQBUsBHBOLd7MTpuOVTWPNmciIgsHxsqMnLg9GW8sOEoLpRWAwAeCfHAGyN6wHAsG/8bqIP+6lUAwIX2wMcPqvHNPWp0rKtDJ5UV3K2c0du9J2J6jYdr+wBzlkFERNSs2FARgIb7Su08cRHjPz6MeoOgrcYKLz8Wike1pSgZ/xyq9u4DAFy3AT6MVWNXNxVCa2vxyYUrCPLqA4zabuYKiIiIzIcNFaGk4jr+sioL2edLAQAObayxc+L9+N9H/8LZZeuUuEJXYM4zVnCwEfzr0mUMrLoGlXsIkPC2mTInIiJqGdhQtXIXSqsxeMmXKKuuAwD0D3DFM/aZyHt8GryKG86TKnQD/tNPjUP3ACMryjGuqAw2dq5A/Hygx58AtfpWuyAiIrJ4bKhasepaPUanfINrldUYcuEwxlWfgNXmE0Yxh/u0Q27vS3j8+nUsPl8NO0074IHpQL+JgMbOPIkTERG1MGyoWqmc/dnYP38Jxl6+gMCr/4MVRJkzqIDCcCcEhNdDp88HLgPwCAfGb2u4BYLaynyJExERtUBsqFqZ+no9Ph3/EkL2bMb9Px23tcG+wHrsD1Eh3scTI84fAn78uj2EDweGvglYa82RMhERUYvHhqoVObI7E5dfnIWQqxcAABc8/KGPtsFhbSHW+dVDb2WFkaXlDc0UAAQNAfpPBHz6mC9pIiKiuwAbqlag8NQ5HHppHoKPfokOAPQqFU4P7I/1YV/jmPWNj/pUGFBvjQk+A4EgF+CeR4DOA8yYNRER0d2DDZUFO1x4Fcc+WIue699CsKHh87sc/zZ4/+E6XHA9oMQN0njgifDR6BP2lLlSJSIiuquxobJQi9JyoV+yEI8WNDROl+2tsWKwAUe71ANQwVGvRxexwbT7/oHQe+LNmywREdFdjg2VhblYfh1T1x3GfR8vRv+i4wCAvWEqrBgs0Fup8WhlFZJLy9Gx9zjg4VcBlcrMGRMREd392FBZkKxzV/HqgrWYdPAjeFSVAgBWPaTGtig1Qmpq8GrxFQQFPgoMmw64B5s3WSIiIgvChsoCVFyvwxvp30Kz6QUs+G8hgIbv3Hs3To0vw9V4tsYaEx0igEenAt7dzZssERGRBWJDdRer1xvw0dfnkPeflxGTdRC+FxvGCzyAd4dZ4aGO4RjXdTjCuyaYN1EiIiILx4bqLpWWU4St65chJmsTRp02AAAMAL6LdoD9qASs7zsJNrwRJxERUbNgQ3WXKS2/hvdn/QMdz2/Dc/l1UP94G6mzQVpEzH0NQ3s+bN4EiYiIWiE2VHeRgx8tg3bRmxhc/f9j33up4TVxHAYNHW++xIiIiFo5NlR3gVO7PkDe668j8FQ9gIaP9g4Gq3BPzEN4KHkpVLz1ARERkVmxoWrBrhQex75pf0Jg9nUE/jh22hPYNSoEs55YAZe2bmbNj4iIiBqwoWqB9PV6rP/nn9Dp08MIqmoYO+epwpl7IzH2Hyl41JrLRkRE1JKozZ1Ac1q2bBn8/f3Rpk0bREVF4eDBg+ZOqZEt783FzrgwdP/4MByrGu4ntX1AEOoWp2Pc/FVQs5kiIiJqcVrNb+d169Zh8uTJWLFiBaKiorB48WLExcUhPz8f7u7u5k4P+z9fg+Jl8xGSr1fG8sLbInLeSkwODOV5UkRERC2YSkTE3Ek0h6ioKPTu3RtvvvkmAMBgMMDHxwd/+9vfMGPGjFu+try8HI6OjigrK4ODg8Mdy6m+phqfb1uNsvXLEHa8BtYNt5PCGX9r+LzwEiJiE+/YvoiIiFqbpvr9fTOt4ghVbW0tsrKyMHPmTGVMrVYjNjYWBw4caBRfU1ODmpoa5Xl5eXmT5HU6NxOdZi1Snhe7qiCjEjHk2blNsj8iIiJqGq2iofrhhx+g1+vh4eFhNO7h4YFvv/22Ufz8+fPxyiuvNHleZdV10FoDejVQ0rsjHnl3J9TqVnVaGxERkUVoFQ3V7Zo5cyYmT56sPC8vL4ePj88d30+fvg+jJjsHP1TWoqeT7R3fPhERETWPVtFQubq6wsrKChcvXjQav3jxIjw9PRvFa7VaaLXN8z14WmsrdGAzRUREdFdrFZ8vaTQaREZGIiMjQxkzGAzIyMhAdHS0GTMjIiIiS9AqjlABwOTJk5GUlIRevXqhT58+WLx4MaqqqjBq1Chzp0ZERER3uVbTUCUmJuLSpUuYM2cOiouL0b17d6SlpTU6UZ2IiIjodrWa+1CZojnvY0FERER3RnP+/m4V51ARERERNSU2VEREREQmYkNFREREZCI2VEREREQmYkNFREREZCI2VEREREQmYkNFREREZCI2VEREREQmYkNFREREZKJW89UzprhxM/ny8nIzZ0JERES/1Y3f283xpTBsqH6DiooKAICPj4+ZMyEiIqLbVVFRAUdHxybdB7/L7zcwGAz4/vvv0a5dO6hUqju67fLycvj4+OD8+fMW/z2BrNUysVbLxFotU2usNTc3F0FBQVCrm/YsJx6h+g3UajU6duzYpPtwcHCw+B/uG1irZWKtlom1WqbWVGuHDh2avJkCeFI6ERERkcnYUBERERGZiA2VmWm1WsydOxdardbcqTQ51mqZWKtlYq2WibU2HZ6UTkRERGQiHqEiIiIiMhEbKiIiIiITsaEiIiIiMhEbKiIiIiITsaEyo2XLlsHf3x9t2rRBVFQUDh48aO6UftW+ffsQHx8Pb29vqFQqbN682WheRDBnzhx4eXnB1tYWsbGxOHXqlFHMlStXoNPp4ODgACcnJzz77LOorKw0ijl27Bjuu+8+tGnTBj4+Pvj3v//d1KU1Mn/+fPTu3Rvt2rWDu7s7Hn/8ceTn5xvFXL9+HcnJyXBxcYG9vT3++Mc/4uLFi0YxhYWFGDJkCOzs7ODu7o6pU6eivr7eKGbPnj3o2bMntFotAgICsHLlyqYuz8jy5csRERGh3OwvOjoaO3bsUOYtpc6fW7BgAVQqFSZOnKiMWVKtL7/8MlQqldGja9euyrwl1QoAFy5cwNNPPw0XFxfY2toiPDwchw4dUuYt5f3J39+/0bqqVCokJycDsKx11ev1mD17Njp16gRbW1t06dIFf//7342+m6/FrKuQWaSmpopGo5EPPvhATpw4IWPGjBEnJye5ePGiuVO7pe3bt8usWbNk48aNAkA2bdpkNL9gwQJxdHSUzZs3y9GjR+Wxxx6TTp06SXV1tRIzcOBA6datm3z99dfy5ZdfSkBAgIwYMUKZLysrEw8PD9HpdJKTkyNr164VW1tbefvtt5urTBERiYuLk5SUFMnJyZHs7GwZPHiw+Pr6SmVlpRIzduxY8fHxkYyMDDl06JDce++90rdvX2W+vr5ewsLCJDY2Vo4cOSLbt28XV1dXmTlzphJz5swZsbOzk8mTJ0tubq4sXbpUrKysJC0trdlq3bJli3z22Wdy8uRJyc/PlxdffFFsbGwkJyfHour8qYMHD4q/v79ERETIhAkTlHFLqnXu3LkSGhoqRUVFyuPSpUsWWeuVK1fEz89PRo4cKZmZmXLmzBnZuXOnfPfdd0qMpbw/lZSUGK1penq6AJDdu3eLiGWt67x588TFxUW2bdsmBQUFsmHDBrG3t5clS5YoMS1lXdlQmUmfPn0kOTlZea7X68Xb21vmz59vxqxuz88bKoPBIJ6envLaa68pY6WlpaLVamXt2rUiIpKbmysA5JtvvlFiduzYISqVSi5cuCAiIm+99ZY4OztLTU2NEjN9+nQJCgpq4opuraSkRADI3r17RaShNhsbG9mwYYMSk5eXJwDkwIEDItLQgKrVaikuLlZili9fLg4ODkp906ZNk9DQUKN9JSYmSlxcXFOXdEvOzs7y3nvvWWSdFRUVEhgYKOnp6fLAAw8oDZWl1Tp37lzp1q3bTecsrdbp06dL//79f3Hekt+fJkyYIF26dBGDwWBx6zpkyBAZPXq00dgf/vAH0el0ItKy1pUf+ZlBbW0tsrKyEBsbq4yp1WrExsbiwIEDZszMNAUFBSguLjaqy9HREVFRUUpdBw4cgJOTE3r16qXExMbGQq1WIzMzU4m5//77odFolJi4uDjk5+fj6tWrzVRNY2VlZQCA9u3bAwCysrJQV1dnVG/Xrl3h6+trVG94eDg8PDyUmLi4OJSXl+PEiRNKzE+3cSPGXD8Ler0eqampqKqqQnR0tEXWmZycjCFDhjTKxxJrPXXqFLy9vdG5c2fodDoUFhYCsLxat2zZgl69emHYsGFwd3dHjx498O677yrzlvr+VFtbi9WrV2P06NFQqVQWt659+/ZFRkYGTp48CQA4evQovvrqKwwaNAhAy1pXNlRm8MMPP0Cv1xv9MAOAh4cHiouLzZSV6W7kfqu6iouL4e7ubjRvbW2N9u3bG8XcbBs/3UdzMxgMmDhxIvr164ewsDAlF41GAycnJ6PYn9f7a7X8Ukx5eTmqq6ubopybOn78OOzt7aHVajF27Fhs2rQJISEhFldnamoqDh8+jPnz5zeas7Rao6KisHLlSqSlpWH58uUoKCjAfffdh4qKCour9cyZM1i+fDkCAwOxc+dOjBs3Ds8//zw+/PBDo3wt7f1p8+bNKC0txciRI5UcLGldZ8yYgSeffBJdu3aFjY0NevTogYkTJ0Kn0xnl2xLW1fo2ayNqlZKTk5GTk4OvvvrK3Kk0maCgIGRnZ6OsrAyffPIJkpKSsHfvXnOndUedP38eEyZMQHp6Otq0aWPudJrcjf/FA0BERASioqLg5+eH9evXw9bW1oyZ3XkGgwG9evXCP//5TwBAjx49kJOTgxUrViApKcnM2TWd999/H4MGDYK3t7e5U2kS69evx5o1a/Dxxx8jNDQU2dnZmDhxIry9vVvcuvIIlRm4urrCysqq0VUXFy9ehKenp5myMt2N3G9Vl6enJ0pKSozm6+vrceXKFaOYm23jp/toTuPHj8e2bduwe/dudOzYURn39PREbW0tSktLjeJ/Xu+v1fJLMQ4ODs36S0+j0SAgIACRkZGYP38+unXrhiVLllhUnVlZWSgpKUHPnj1hbW0Na2tr7N27F2+88Qasra3h4eFhMbXejJOTE+655x589913FrWuAODl5YWQkBCjseDgYOUjTkt8fzp37hy++OIL/PnPf1bGLG1dp06dqhylCg8PxzPPPINJkyYpR5hb0rqyoTIDjUaDyMhIZGRkKGMGgwEZGRmIjo42Y2am6dSpEzw9PY3qKi8vR2ZmplJXdHQ0SktLkZWVpcTs2rULBoMBUVFRSsy+fftQV1enxKSnpyMoKAjOzs7NVE3Dpbjjx4/Hpk2bsGvXLnTq1MloPjIyEjY2Nkb15ufno7Cw0Kje48ePG/1jTk9Ph4ODg/LmHx0dbbSNGzHm/lkwGAyoqamxqDpjYmJw/PhxZGdnK49evXpBp9Mpf7aUWm+msrISp0+fhpeXl0WtKwD069ev0W1NTp48CT8/PwCW9/4EACkpKXB3d8eQIUOUMUtb12vXrkGtNm5VrKysYDAYALSwdb3tU+7pjkhNTRWtVisrV66U3Nxc+ctf/iJOTk5GV120RBUVFXLkyBE5cuSIAJBFixbJkSNH5Ny5cyLScPmqk5OTfPrpp3Ls2DEZOnToTS9f7dGjh2RmZspXX30lgYGBRpevlpaWioeHhzzzzDOSk5MjqampYmdn1+y3TRg3bpw4OjrKnj17jC5RvnbtmhIzduxY8fX1lV27dsmhQ4ckOjpaoqOjlfkblyc/8sgjkp2dLWlpaeLm5nbTy5OnTp0qeXl5smzZsma/PHnGjBmyd+9eKSgokGPHjsmMGTNEpVLJ559/blF13sxPr/ITsaxap0yZInv27JGCggLZv3+/xMbGiqurq5SUlFhcrQcPHhRra2uZN2+enDp1StasWSN2dnayevVqJcaS3p/0er34+vrK9OnTG81Z0romJSVJhw4dlNsmbNy4UVxdXWXatGlKTEtZVzZUZrR06VLx9fUVjUYjffr0ka+//trcKf2q3bt3C4BGj6SkJBFpuIR19uzZ4uHhIVqtVmJiYiQ/P99oG5cvX5YRI0aIvb29ODg4yKhRo6SiosIo5ujRo9K/f3/RarXSoUMHWbBgQXOVqLhZnQAkJSVFiamurpbnnntOnJ2dxc7OThISEqSoqMhoO2fPnpVBgwaJra2tuLq6ypQpU6Surs4oZvfu3dK9e3fRaDTSuXNno300h9GjR4ufn59oNBpxc3OTmJgYpZkSsZw6b+bnDZUl1ZqYmCheXl6i0WikQ4cOkpiYaHRfJkuqVURk69atEhYWJlqtVrp27SrvvPOO0bwlvT/t3LlTADTKX8Sy1rW8vFwmTJggvr6+0qZNG+ncubPMmjXL6PYGLWVdVSI/ud0oEREREd02nkNFREREZCI2VEREREQmYkNFREREZCI2VEREREQmYkNFREREZCI2VEREREQmYkNFREREZCI2VEREREQmYkNFRBbh7NmzUKlUyM7ObvJ9rVy5Ek5OTk2+HyK6e7ChIqJmMXLkSKhUqkaPgQMHmju1W/L398fixYuNxhITE3Hy5EnzJERELZK1uRMgotZj4MCBSElJMRrTarVmyub3s7W1ha2trbnTIKIWhEeoiKjZaLVaeHp6Gj2cnZ3x1FNPITEx0Si2rq4Orq6uWLVqFQAgLS0N/fv3h5OTE1xcXPDoo4/i9OnTv7ivm30st3nzZqhUKuX56dOnMXToUHh4eMDe3h69e/fGF198ocwPGDAA586dw6RJk5Qjar+07eXLl6NLly7QaDQICgrCRx99ZDSvUqnw3nvvISEhAXZ2dggMDMSWLVuU+atXr0Kn08HNzQ22trYIDAxs1HwSUcvFhoqIzE6n02Hr1q2orKxUxnbu3Ilr164hISEBAFBVVYXJkyfj0KFDyMjIgFqtRkJCAgwGw+/eb2VlJQYPHoyMjAwcOXIEAwcORHx8PAoLCwEAGzduRMeOHfHqq6+iqKgIRUVFN93Opk2bMGHCBEyZMgU5OTn461//ilGjRmH37t1Gca+88gqGDx+OY8eOYfDgwdDpdLhy5QoAYPbs2cjNzcWOHTuQl5eH5cuXw9XV9XfXRkTNTIiImkFSUpJYWVlJ27ZtjR7z5s2Turo6cXV1lVWrVinxI0aMkMTExF/c3qVLlwSAHD9+XERECgoKBIAcOXJERERSUlLE0dHR6DWbNm2SX3vbCw0NlaVLlyrP/fz85PXXXzeK+fm2+/btK2PGjDGKGTZsmAwePFh5DkBeeukl5XllZaUAkB07doiISHx8vIwaNeqWuRFRy8UjVETUbB588EFkZ2cbPcaOHQtra2sMHz4ca9asAdBwNOrTTz+FTqdTXnvq1CmMGDECnTt3hoODA/z9/QFAOZr0e1RWVuKFF15AcHAwnJycYG9vj7y8vNveZl5eHvr162c01q9fP+Tl5RmNRUREKH9u27YtHBwcUFJSAgAYN24cUlNT0b17d0ybNg3//e9/f2dVRGQOPCmdiJpN27ZtERAQcNM5nU6HBx54ACUlJUhPT4etra3RFYDx8fHw8/PDu+++C29vbxgMBoSFhaG2tvam21Or1RARo7G6ujqj5y+88ALS09OxcOFCBAQEwNbWFk888cQvbtNUNjY2Rs9VKpXykeWgQYNw7tw5bN++Henp6YiJiUFycjIWLlzYJLkQ0Z3FI1RE1CL07dsXPj4+WLduHdasWYNhw4YpDcjly5eRn5+Pl156CTExMQgODsbVq1dvuT03NzdUVFSgqqpKGfv5Par279+PkSNHIiEhAeHh4fD09MTZs2eNYjQaDfR6/S33FRwcjP379zfadkhIyK9U3TjnpKQkrF69GosXL8Y777xzW68nIvPhESoiajY1NTUoLi42GrO2tlZOvn7qqaewYsUKnDx50uiEbmdnZ7i4uOCdd96Bl5cXCgsLMWPGjFvuKyoqCnZ2dnjxxRfx/PPPIzMzEytXrjSKCQwMxMaNGxEfHw+VSoXZs2c3Osnd398f+/btw5NPPgmtVnvTE8WnTp2K4cOHo0ePHoiNjcXWrVuxceNGoysGf82cOXMQGRmJ0NBQ1NTUYNu2bQgODv7Nryci8+IRKiJqNmlpafDy8jJ69O/fX5nX6XTIzc1Fhw4djM5JUqvVSE1NRVZWFsLCwjBp0iS89tprt9xX+/btsXr1amzfvh3h4eFYu3YtXn75ZaOYRYsWwdnZGX379kV8fDzi4uLQs2dPo5hXX30VZ8+eRZcuXeDm5nbTfT3++ONYsmQJFi5ciNDQULz99ttISUnBgAEDfvPfjUajwcyZMxEREYH7778fVlZWSE1N/c2vJyLzUsnPTzIgIiIiotvCI1REREREJmJDRURERGQiNlREREREJmJDRURERGQiNlREREREJmJDRURERGQiNlREREREJmJDRURERGQiNlREREREJmJDRURERGQiNlREREREJvo/P9OIjrawb14AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", + "if __name__ == '__main__':\n", + " import pandas as pd\n", + " from brush import BrushRegressor\n", + " \n", + " import warnings\n", + " warnings.filterwarnings(\"ignore\")\n", + "\n", + " # data = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", + " # X = data.drop(columns='label')\n", + " # y = data['label']\n", + "\n", + " # data = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", + " # X = data.drop(columns='target')\n", + " # y = data['target']\n", + "\n", + " data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", + " X = data.drop(columns='target')\n", + " y = data['target']\n", + "\n", + " kwargs = {\n", + " 'pop_size' : 200,\n", + " 'max_gen' : 40,\n", + " 'max_depth' : 10,\n", + " 'max_size' : 20,\n", + " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", + " }\n", + "\n", + " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " [('Original', 'score'), ('Original', 'best model'), \n", + " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'point mutation calls'),\n", + " ('Modified', 'insert mutation calls'),\n", + " ('Modified', 'delete mutation calls'),\n", + " ('Modified', 'toggle_weight mutation calls')],\n", + " names=('Brush version', 'metric')))\n", + " \n", + " est_mab = None\n", + " for i in range(30):\n", + " try:\n", + " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", + " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", + "\n", + " est = BrushRegressor(**kwargs).fit(X,y)\n", + " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", + "\n", + " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " \n", + " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", + " except Exception as e:\n", + " print(e)\n", + "\n", + " display(df)\n", + " display(df.describe())\n", + "\n", + " if True: # plot the cumulative history of pulls\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + "\n", + " # Plot for evaluations, not generations\n", + " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", + " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", + " data[i+1, :] = data[i]\n", + " data[i+1, arm] += 1\n", + " \n", + " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", + " plt.xlabel(\"Evaluations\")\n", + " plt.ylabel(\"Number of times mutation was used\")\n", + "\n", + " for x in est_mab.learner_.reset_history:\n", + " plt.axvline(x=x, color='k')\n", + " \n", + " plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification problem" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[25.075 20.825]\t[1.23667902 0.9188988 ]\t[20. 20.]\n", + "1 \t200 \t[24.72 13.995]\t[1.24161186 6.44243549]\t[18. 2.]\n", + "2 \t200 \t[24.34 5.71] \t[1.78728845 5.45306336]\t[16. 1.]\n", + "3 \t200 \t[24.37 1.995]\t[2.01571327 1.34349358]\t[16. 1.]\n", + "4 \t200 \t[22.695 1.57 ]\t[3.33945729 1.79027931]\t[15. 1.]\n", + "5 \t200 \t[20.4 1.325]\t[3.4278273 1.63687965]\t[15. 1.]\n", + "6 \t200 \t[18.27 1.31] \t[1.68733518 1.49796529]\t[14. 1.]\n", + "7 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "8 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "9 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "10 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "11 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "12 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "13 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "14 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "15 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "16 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "17 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "18 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "19 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "20 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "21 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "22 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "23 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "24 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "25 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "26 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "27 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "28 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "29 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "30 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "31 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "32 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "33 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "34 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "35 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "36 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "37 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "38 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "39 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", + "Final population hypervolume is 48792.000000\n", + "best model: Logistic(Sin(Median(Mean(If(AIDS>68817.00,-0.00*AIDS,15.54),1.00*Total),-8.65,Total,Sqrtabs(0.00*AIDS))))\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[25.115 20.785]\t[1.50391988 0.94803745]\t[17. 20.]\n", + "1 \t0 \t[24.375 15.865]\t[1.94791555 5.67157606]\t[16. 2.]\n", + "2 \t0 \t[23.885 8.92 ]\t[2.49635234 6.24368481]\t[16. 1.]\n", + "3 \t0 \t[23.97 4.275]\t[2.66816416 3.86385494]\t[14. 1.]\n", + "4 \t0 \t[23.735 2.835]\t[2.85390522 2.93049057]\t[14. 1.]\n", + "5 \t0 \t[23.43 2.155]\t[3.09759584 2.40436582]\t[14. 1.]\n", + "6 \t0 \t[22.235 1.84 ]\t[3.72421468 2.53069161]\t[14. 1.]\n", + "7 \t0 \t[22.425 1.34 ]\t[3.55870412 2.29224781]\t[14. 1.]\n", + "8 \t0 \t[20.87 1.365]\t[3.64322659 2.31555069]\t[14. 1.]\n", + "9 \t0 \t[18.945 1.435]\t[2.89861605 2.31857176]\t[14. 1.]\n", + "10 \t0 \t[18.435 1.295]\t[2.40120282 1.43107477]\t[12. 1.]\n", + "11 \t0 \t[17.685 1.41 ]\t[1.18986344 1.89786722]\t[12. 1.]\n", + "12 \t0 \t[17.865 1.1 ]\t[0.64558113 0.52915026]\t[12. 1.]\n", + "13 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "14 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "15 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "16 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "17 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "18 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "19 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "20 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "21 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "22 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "23 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "24 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "25 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "26 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "27 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "28 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "29 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "30 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "31 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "32 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "33 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "34 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "35 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "36 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "37 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "38 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "39 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", + "Final population hypervolume is 48896.000000\n" + ] + }, + { + "data": { + "text/html": [ + "
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Brush versionOriginalModified
metricscorebest modelscorebest modelpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
run 00.76Sub(Max(0.03*AIDS,0.57*AIDS,0.28*AIDS),Abs(0.0...0.76Logistic(Sin(Mean(1.00*AIDS,Max(Mean(1.00*Tota...1937221816111803
run 10.72Logistic(Cos(Sum(0.00*AIDS,1.00*AIDS,1.00*Tota...0.76Logistic(Cos(Sqrtabs(Mean(Cos(0.92*AIDS),1.15*...2187184917681816
run 20.80Logistic(Tan(Abs(Max(1.01*AIDS,Max(0.64*AIDS,I...0.78Div(Mean(1.00*Sum(Median(1.00,3.41*Total,3961....1472176318332481
run 30.78Sinh(Mean(Max(Atan(1.00*Total),1.00,Add(0.31*A...0.82Atan(Median(Asin(0.99*Sin(Mean(0.00*AIDS,1.00*...1765252417581578
run 40.82Median(Median(-0.00*Total,Log(0.00*AIDS),4.19,...0.70Logistic(If(If(0.00*AIDS>1.48,1.00*Age,If(Race...1939246918391329
run 50.86Median(Min(Cos(Prod(Logabs(0.70*AIDS),Sum(1.00...0.70Ceil(Sub(0.00*AIDS,Exp(Square(Sin(Median(AIDS,...1816247819651331
run 60.86Mean(Sin(Sum(-2.88,Total,Abs(Log1p(Mean(1659.0...0.76Logistic(Log(0.89*Div(0.89*Total,Sum(-0.21*AID...1544226919141889
run 70.78Logistic(Cos(Sum(1.39,Sqrtabs(Sum(1.00*Total,-...0.84Atan(1.00*Max(Tanh(-0.45*Tan(1.00*AIDS)),0.00*...1821234619151561
run 80.68Mean(0.00*AIDS,0.69,0.50)0.68Sqrt(0.00*AIDS)1415241221111686
run 90.86Max(Atan(0.00*AIDS),Sqrt(Sin(Sum(Sqrtabs(1.00*...0.78Logistic(Median(-0.00*Total,0.01*AIDS))1431309214201633
run 100.84Mean(Max(0.00*AIDS,Min(Total,Total,Cos(Max(Sum...0.78Mean(0.63*Sin(1.00*Median(Div(Total,1.00*AIDS)...1682251515591810
run 110.84Log1p(Median(Tan(1.00*AIDS),-0.00*AIDS,Ceil(0....0.74Sin(1.00*Median(0.70*AIDS,23.09,1.53*AIDS,1.00...2045212919251494
run 120.74Logistic(Cos(Mean(Tan(1.42),Sqrtabs(Add(Mean(0...0.78Logistic(Log1p(Logabs(Div(0.73*Sum(325.00*AIDS...1269197528091540
run 130.68Logistic(0.00*Median(Mean(-1313.13,-1082.38*AI...0.76Logistic(Sin(1.00*Mean(Logabs(Tan(1.00*AIDS)),...1705252417821573
run 140.70Median(Mean(0.00*AIDS,-1.00*Total,2.57),0.33*T...0.741.01*Div(4.83*AIDS,1.00*Max(6.57*AIDS,-150.51*...1612313117161133
run 150.72Sin(Log(Add(-1207.99,Mean(0.07*AIDS,Abs(0.54),...0.74Logistic(1.15*Median(-1.98,Tan(-0.00*Total),8....1471269618271630
run 160.68Median(0.00*AIDS,0.79)0.78Ceil(Mean(1498.01*AIDS,1500.15*AIDS,Ceil(Sum(-...1335300119641247
run 170.68Logistic(Mean(-1.81,0.06,0.00*AIDS))0.68Sqrt(0.00*AIDS)1375243519921807
run 180.80Logistic(Tan(Cos(Sum(Prod(Log(Tanh(0.01*AIDS))...0.76Logistic(Sin(Sum(2.05,1.00*AIDS,Sum(Min(1.00*A...1848252219041260
run 190.86Logistic(Sqrt(Atan(Sin(Max(Mean(0.64*AIDS,Sum(...0.70Ceil(Tan(1.00*AIDS))1420239517971957
run 200.68Mean(1.19,-0.50*AIDS,0.50*AIDS)0.70Logistic(0.82*Sum(-0.50*AIDS,-0.00*Total,0.50*...1687273815291661
run 210.80Logistic(Sin(Median(1.00,Mean(Prod(Tanh(Add(AI...0.72Logistic(Max(-6.74*AIDS,Mean(0.00*AIDS,Sinh(-0...1602241115531992
run 220.80Sqrtabs(Sin(Log1p(Min(Sum(Abs(0.16*AIDS),-0.00...0.76Max(Sin(1.00*Sum(-13.76,1.00*AIDS,Total,1.00*T...1627267220381254
run 230.78Logistic(Log(Sum(Ceil(1.00),Cos(Mean(Sqrtabs(M...0.78Sub(Logabs(0.11*AIDS),Log(0.00*Total))1878259617961325
run 240.78Logistic(Prod(1.00,Sin(Min(1.00,1.00,Sub(Log(S...0.761.00*Mean(0.76*Median(-12.63,Abs(Abs(-0.00*AID...1772273113771704
run 250.80Min(Median(Tan(0.62*Total),If(AIDS>68817.00,0....0.78Mul(Prod(Ceil(Cos(1.25*Total)),0.00*Total,0.00...1888288116341146
run 260.82Mean(Sum(Tan(Add(-269.50*AIDS,0.00*Total)),-0....0.78Prod(-1.59,Log(1.00),-3.20,Sinh(1.18*Sum(-5.00...1791269214881615
run 270.70Cos(If(AIDS>68817.00,AIDS,Sum(Mean(1.00*Total,...0.82Min(1.62*Max(1.12*Sin(Median(-120.03,0.98*AIDS...1560235016501985
run 280.78Add(Median(Ceil(0.00*AIDS),-0.00*AIDS,0.01*AID...0.70Logistic(1.00*Sin(Mean(1.00*Total,Sub(Total,Me...1790211721321565
run 290.72Logistic(Sin(Median(Mean(If(AIDS>68817.00,-0.0...0.76Logistic(Min(Sub(-0.00*Total,-0.00*AIDS),0.00*...1105210823382020
\n", + "
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if __name__ == '__main__':\n", + " import pandas as pd\n", + " from brush import BrushClassifier\n", + " \n", + " import warnings\n", + " warnings.filterwarnings(\"ignore\")\n", + "\n", + " from pmlb import fetch_data\n", + "\n", + " # X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", + "\n", + " data = pd.read_csv('../../docs/examples/datasets/d_analcatdata_aids.csv')\n", + " X = data.drop(columns='target')\n", + " y = data['target']\n", + "\n", + " kwargs = {\n", + " 'pop_size' : 200,\n", + " 'max_gen' : 40,\n", + " 'max_depth' : 10,\n", + " 'max_size' : 20,\n", + " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", + " }\n", + "\n", + " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " [('Original', 'score'), ('Original', 'best model'), \n", + " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'point mutation calls'),\n", + " ('Modified', 'insert mutation calls'),\n", + " ('Modified', 'delete mutation calls'),\n", + " ('Modified', 'toggle_weight mutation calls')],\n", + " names=('Brush version', 'metric')))\n", + " \n", + " est_mab = None\n", + " for i in range(30):\n", + " try:\n", + " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", + " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", + "\n", + " est = BrushClassifier(**kwargs).fit(X,y)\n", + "\n", + " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", + "\n", + " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " \n", + " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", + " except Exception as e:\n", + " print(e)\n", + "\n", + " display(df)\n", + " display(df.describe())\n", + "\n", + " if True: # plot the cumulative history of pulls\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + "\n", + " # Plot for evaluations, not generations\n", + " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", + " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", + " data[i+1, :] = data[i]\n", + " data[i+1, arm] += 1\n", + "\n", + " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", + " plt.xlabel(\"Evaluations\")\n", + " plt.ylabel(\"Number of times mutation was used\")\n", + "\n", + " for x in est_mab.learner_.reset_history:\n", + " plt.axvline(x=x, color='k')\n", + "\n", + " plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb new file mode 100644 index 00000000..018993c6 --- /dev/null +++ b/src/brush/D_TS_experiments.ipynb @@ -0,0 +1,2030 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**(Dynamic) Thompson Sampling, as described in Gupta et al. - 2011 - Thompson sampling for dynamic multi-armed bandits**\n", + "\n", + "> Thompson sampling is a probabilistic approach to solve the Multi-Armed Bandit. This paper modifies the original algorithm to make it handle distribution changes during the execution\n", + "\n", + "The Thompson Sampling in this paper considers that each arm is a Bernoulli trial, having the output set ${0, 1}$, with $\\theta^k$ denoting the probability of success for arm $k$.\n", + "\n", + "The probability distribution of successes $S$ obtained in $n^k$ trials is a Binomial distribution:\n", + "\n", + "$$p(S = s|\\theta^k) = \\binom{n^k}{s} (1-\\theta^k)^{n-s}(\\theta^k)^s.$$\n", + "\n", + "The Beta distribution is a conjugate prior (is of the same probability distribution family as the prior probability, which is the Binomial distribution), parameterized by $\\alpha_0$ and $\\beta_0$:\n", + "\n", + "$$p(\\widehat{\\theta}^k; \\alpha_0, \\beta_0) = \\frac{x^{\\alpha_0-1}(1-x)^{\\beta_0-1}}{B(\\alpha_0, \\beta_0)},$$\n", + "\n", + "with $B$ being a binonial distribution.\n", + "\n", + "> We use conjugate prior to derive a closed-form expression for the posterior distribution, usually easier to interpret, manipulate and update. In Bayesian statistics, we adjust the hyperparameters of the posterior distribution to optimize the likelihood with the prior distribution.\n", + "\n", + "The **original Thompson sampling** updates $\\alpha_n$ and $\\beta_n$ for the $n$-th trial, with reward $r_n$ as:\n", + "\n", + "$$\\alpha^k_ n = \\alpha^k_{n-1} + r_n,$$\n", + "$$\\beta^k_ n = \\beta^k_{n-1} + (1-r_n).$$\n", + "\n", + "The proposed method extends the original algorithm by inserting a new update rule based on an hyperparameter $C$. $C$ is a threshold that provides exponential weighting of the outcomes of the trials, making more recent rewards getting more weight. This way, if prior distributions change during the execution, the learned posterior distributions would respond to it.\n", + "\n", + "We update $\\alpha_n$ and $\\beta_n$ conditionally based on $C$:\n", + "\n", + "If $\\alpha_{n-1}+\\beta_{n-1} The paper suggest initializing all $\\alpha$ and $\\beta$ with the value $2$ for all arms.\n", + "\n", + "The remaining of the paper performs an sensitivity analysis and some experiments to check how well the Dynamic Thompson Sampling performs.\n", + "\n", + "> In our work, the mutations would be the arms, and this update would be used during the evolution to adjust the mutation probabilities.\n", + "\n", + "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# TODO: Maybe I could create a base class to be inherited by different learners\n", + "class D_TS:\n", + " def __init__(self, num_bandits, C=100):\n", + " self.num_bandits = num_bandits\n", + "\n", + " # Store tuples when update is called. Tuples will have 3 values:\n", + " # (time instant t, arm idx, reward)\n", + " self.pull_history = [] \n", + "\n", + " # This is the probability that should be used to update brush probs\n", + " self._probabilities = np.ones(num_bandits)/num_bandits\n", + "\n", + " self._alphas = 2*np.ones(num_bandits) # Paper suggests starting with 2's\n", + " self._betas = 2*np.ones(num_bandits)\n", + " self.C = C # how to define this value???\n", + "\n", + " @property\n", + " def probabilities(self):\n", + " # How to transform our Beta distributions into node probabilities?\n", + " return self._probabilities\n", + " \n", + " @probabilities.setter\n", + " def probabilities(self, new_probabilities):\n", + " if len(self._probabilities)==len(new_probabilities):\n", + " self._probabilities = new_probabilities\n", + " else:\n", + " print(f\"New probabilities must have size {self.num_bandits}\")\n", + "\n", + " def choose_arm(self):\n", + " \"\"\"Uses the learned distributions to randomly choose an arm to pull. \n", + " \n", + " Returns the index of the arm that was choosen based on the Beta\n", + " probabilities of previous successes and fails.\n", + " \"\"\"\n", + " \n", + " # probability estimates from the beta distribution\n", + " thetas = np.random.beta(self._alphas, self._betas)\n", + " \n", + " arm_idx = np.argmax(thetas)\n", + " \n", + " return arm_idx\n", + " \n", + " def update(self, arm_idx, reward):\n", + " self.pull_history.append( (len(self.pull_history), arm_idx, reward) )\n", + "\n", + " if self._alphas[arm_idx] + self._betas[arm_idx] < self.C:\n", + " # This is the pure thompson scheme\n", + " self._alphas[arm_idx] = self._alphas[arm_idx]+reward\n", + " self._betas[arm_idx] = self._betas[arm_idx] + (1-reward)\n", + " else:\n", + " # This is the dynamic adjust\n", + " self._alphas[arm_idx] = (self._alphas[arm_idx]+reward)*(self.C/(self.C+1))\n", + " self._betas[arm_idx] = (self._betas[arm_idx] + (1-reward))*(self.C/(self.C+1))\n", + "\n", + " return self" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 10.0, 1: 46.0, 2: 46.0, 3: 54.0}\n", + "number of pulls for each arm: {0: 94, 1: 298, 2: 305, 3: 303}\n", + "(it was expected: similar amount of pulls for each arm)\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 812.0, 1: 2.0, 2: 3.0, 3: 1.0}\n", + "number of pulls for each arm: {0: 977, 1: 9, 2: 9, 3: 5}\n", + "(it was expected: more pulls for first arm, less pulls for last)\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 4.0, 1: 318.0, 2: 2.0, 3: 543.0}\n", + "number of pulls for each arm: {0: 8, 1: 364, 2: 5, 3: 623}\n", + "(it was expected: 2nd approx 4th > 1st > 3rd)\n" + ] + } + ], + "source": [ + "# Sanity checks\n", + "\n", + "class Bandits:\n", + " def __init__(self, reward_prob):\n", + " # Implementing simple bandits.\n", + " self.reward_prob = reward_prob # True reward prob., which learner shoudn't know\n", + " self.n_bandits = len(reward_prob) \n", + "\n", + " def pull(self, arm_idx):\n", + " # Sampling over a normal distr. with mu=0 and var=1\n", + " result = np.random.randn()\n", + " \n", + " # return a positive or nullary reward (Bernoulli random variable).\n", + " return 1.0 if result > self.reward_prob[arm_idx] else 0.0\n", + "\n", + "for probs, descr, expec in [\n", + " (np.array([ 1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs' , 'similar amount of pulls for each arm' ),\n", + " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob' , 'more pulls for first arm, less pulls for last'),\n", + " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd approx 4th > 1st > 3rd' ),\n", + "]:\n", + " bandits = Bandits(probs)\n", + "\n", + " print(\"------------------------ optimizing ------------------------\")\n", + "\n", + " learner = D_TS(4)\n", + " for i in range(1000):\n", + " arm_idx = learner.choose_arm()\n", + " reward = bandits.pull(arm_idx)\n", + "\n", + " learner.update(arm_idx, reward) \n", + "\n", + " total_rewards = {arm_idx : sum([r for (t, i, r) in learner.pull_history if i==arm_idx])\n", + " for arm_idx in range(learner.num_bandits)}\n", + "\n", + " total_pulls = {arm_idx : sum([1 for (t, i, r) in learner.pull_history if i==arm_idx])\n", + " for arm_idx in range(learner.num_bandits)}\n", + "\n", + " print(\"cum. reward for each arm : \", total_rewards)\n", + " print(\"number of pulls for each arm: \", total_pulls)\n", + " print(f\"(it was expected: {expec})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from brush.estimator import BrushEstimator\n", + "from sklearn.base import ClassifierMixin, RegressorMixin\n", + "from deap import creator\n", + "import _brush\n", + "from deap_api import nsga2 \n", + "\n", + "class BrushEstimatorMod(BrushEstimator): # Modifying brush estimator\n", + " def __init__(self, **kwargs):\n", + " super().__init__(**kwargs)\n", + "\n", + " def _mutate(self, ind1):\n", + " # Overriding the mutation so it updates our sampling method. Doing the\n", + " # logic on the python-side for now.\n", + "\n", + " # Creating a wrapper for mutation to be able to control what is happening\n", + " # in the C++ code (this should be prettier in a future implementation)\n", + " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", + " params = self.get_params()\n", + " \n", + " ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", + " or ind1.prg.depth()+1>=self.max_depth) else False\n", + "\n", + " # Insert Mutation will not work, even if we force it, when the expression\n", + " # is already at maximum size.\n", + " # In this case, we'll do the mutation without controlling the probabilities.\n", + " if ignore_this_time:\n", + " for i, m in enumerate(mutations):\n", + " params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", + " else:\n", + " mutation_idx = self.learner_.choose_arm()\n", + "\n", + " for i, m in enumerate(mutations):\n", + " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", + "\n", + " _brush.set_params(params)\n", + " \n", + " # ind1.prg.mutate is a convenient interface that uses the current search \n", + " # space to sample mutations\n", + " offspring = creator.Individual(ind1.prg.mutate())\n", + "\n", + " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", + " \n", + " # We compare fitnesses using the deap overloaded operators\n", + " # from the docs: When comparing fitness values that are **minimized**,\n", + " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", + " # (this means that this comparison should work agnostic of min/max problems,\n", + " # or even a single-objective or multi-objective problem)\n", + " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", + " \n", + " if not ignore_this_time:\n", + " self.learner_.update(mutation_idx, reward)\n", + " \n", + " return offspring\n", + " \n", + " def fit(self, X, y):\n", + "\n", + " _brush.set_params(self.get_params())\n", + "\n", + " self.data_ = self._make_data(X,y)\n", + " # self.data_.print()\n", + "\n", + " # set n classes if relevant\n", + " if self.mode==\"classification\":\n", + " self.n_classes_ = len(np.unique(y))\n", + "\n", + " # We have 4 different mutations, and the learner will learn to choose\n", + " # between these options by maximizing the reward when using each one\n", + " self.learner_ = D_TS(4)\n", + "\n", + " if isinstance(self.functions, list):\n", + " self.functions_ = {k:1.0 for k in self.functions}\n", + " else:\n", + " self.functions_ = self.functions\n", + "\n", + " self.search_space_ = _brush.SearchSpace(self.data_, self.functions_)\n", + "\n", + " self.toolbox_ = self._setup_toolbox(data=self.data_)\n", + "\n", + " archive, logbook = nsga2(\n", + " self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", + "\n", + " self.archive_ = archive\n", + " self.best_estimator_ = self.archive_[0].prg\n", + "\n", + " return self\n", + " \n", + "\n", + "class BrushClassifierMod(BrushEstimatorMod,ClassifierMixin):\n", + " def __init__( self, **kwargs):\n", + " super().__init__(mode='classification',**kwargs)\n", + "\n", + " def _fitness_function(self, ind, data: _brush.Dataset):\n", + " ind.prg.fit(data)\n", + " return (\n", + " np.abs(data.y-ind.prg.predict(data)).sum(), \n", + " ind.prg.size()\n", + " )\n", + " \n", + " def _make_individual(self):\n", + " return creator.Individual(\n", + " self.search_space_.make_classifier(self.max_depth, self.max_size)\n", + " if self.n_classes_ == 2 else\n", + " self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size)\n", + " )\n", + "\n", + " def predict_proba(self, X):\n", + " data = self._make_data(X)\n", + " return self.best_estimator_.predict_proba(data)\n", + "\n", + "\n", + "class BrushRegressorMod(BrushEstimatorMod, RegressorMixin):\n", + " def __init__(self, **kwargs):\n", + " super().__init__(mode='regressor',**kwargs)\n", + "\n", + " def _fitness_function(self, ind, data: _brush.Dataset):\n", + " ind.prg.fit(data)\n", + " return (\n", + " np.sum((data.y- ind.prg.predict(data))**2),\n", + " ind.prg.size()\n", + " )\n", + "\n", + " def _make_individual(self):\n", + " return creator.Individual(\n", + " self.search_space_.make_regressor(self.max_depth, self.max_size)\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regression problem" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[ nan 20.715]\t[ nan 0.85074967]\t[nan 20.]\n", + "1 \t200 \t[ nan 11.905]\t[ nan 7.09408028]\t[nan 1.]\n", + "2 \t200 \t[ nan 2.655] \t[ nan 2.14615354]\t[nan 1.]\n", + "3 \t200 \t[5.25970482 1.03 ]\t[1.21317177 0.17058722]\t[2.73836112 1. ]\n", + "4 \t200 \t[4.47344586 1.02 ]\t[1.05184037 0.14 ]\t[2.73836112 1. ]\n", + "5 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "6 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "7 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "8 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "9 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "10 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "11 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "12 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "13 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "14 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "15 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "16 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "17 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "18 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "19 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "20 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "21 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "22 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "23 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "24 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "25 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "26 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "27 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "28 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "29 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "30 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "31 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "32 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "33 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "34 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "35 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "36 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "37 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "38 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "39 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "Final population hypervolume is 49363.883813\n", + "best model: Square(0.96*x1)\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[ nan 20.755]\t[ nan 0.92464858]\t[nan 19.]\n", + "1 \t0 \t[ nan 15.015]\t[ nan 5.92408432]\t[nan 1.]\n", + "2 \t0 \t[ nan 7.255] \t[ nan 4.40567532]\t[nan 1.]\n", + "3 \t0 \t[ nan 2.905] \t[ nan 1.60498442]\t[nan 1.]\n", + "4 \t0 \t[ nan 1.51] \t[ nan 0.66324958]\t[nan 1.]\n", + "5 \t0 \t[4.98724882 1.035 ]\t[1.25116115 0.18377976]\t[2.61403799 1. ]\n", + "6 \t0 \t[4.35035535 1.02 ]\t[0.98937437 0.14 ]\t[2.60900354 1. ]\n", + "7 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "8 \t0 \t[3.8520625 1.02 ] \t[0.17104111 0.17204651]\t[2.21670485 1. ]\n", + "9 \t0 \t[3.8520625 1.02 ] \t[0.17104111 0.17204651]\t[2.21670485 1. ]\n", + "10 \t0 \t[3.8520625 1.02 ] \t[0.17104111 0.17204651]\t[2.21670485 1. ]\n", + "11 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "12 \t0 \t[3.85330723 1.02 ]\t[0.15966286 0.17204651]\t[2.46565056 1. ]\n", + "13 \t0 \t[3.8293101 1.065 ] \t[0.25177144 0.41324932]\t[1.90340519 1. ]\n", + "14 \t0 \t[3.82658499 1.065 ]\t[0.27452376 0.41324932]\t[1.3583827 1. ] \n", + "15 \t0 \t[3.81947242 1.08 ]\t[0.29115356 0.4621688 ]\t[1.3583827 1. ] \n", + "16 \t0 \t[3.79071911 1.13 ]\t[0.40600859 0.68051451]\t[0.63692158 1. ]\n", + "17 \t0 \t[3.80689942 1.1 ]\t[0.33894367 0.53851648]\t[1.3583827 1. ] \n", + "18 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "19 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "20 \t0 \t[3.85375515 1.02 ]\t[0.15584938 0.17204651]\t[2.55523491 1. ]\n", + "21 \t0 \t[3.84619466 1.035 ]\t[0.18782827 0.27161554]\t[2.45047045 1. ]\n", + "22 \t0 \t[3.83204543 1.06 ]\t[0.23311332 0.36932371]\t[2.45047045 1. ]\n", + "23 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "24 \t0 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", + "25 \t0 \t[3.83913395 1.045 ]\t[0.21171389 0.30491802]\t[2.44566083 1. ]\n", + "26 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "27 \t0 \t[3.86003045 1.02 ]\t[0.12892129 0.22271057]\t[2.54631495 1. ]\n", + "28 \t0 \t[3.85962713 1.015 ]\t[0.13308933 0.15740076]\t[2.46565056 1. ]\n", + "29 \t0 \t[3.85259046 1.025 ]\t[0.16546364 0.21065374]\t[2.46565056 1. ]\n", + "30 \t0 \t[3.83358411 1.045 ]\t[0.31383125 0.35067791]\t[0.07171333 1. ]\n", + "31 \t0 \t[3.8272642 1.05 ] \t[0.32548479 0.35707142]\t[0.07171333 1. ]\n", + "32 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", + "33 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", + "34 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", + "35 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "36 \t0 \t[3.85331105 1.025 ]\t[0.1596296 0.23318448]\t[2.46641684 1. ]\n", + "37 \t0 \t[3.84627056 1.035 ]\t[0.18726651 0.27161554]\t[2.46565032 1. ]\n", + "38 \t0 \t[3.84621458 1.035 ]\t[0.18768039 0.27161554]\t[2.45445561 1. ]\n", + "39 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "Final population hypervolume is 49370.222358\n" + ] + }, + { + "data": { + "text/html": [ + "
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Brush versionOriginalModified
metricscorebest modelscorebest modelpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
run 00.3263581.04*Cos(1.73*x2)0.5338200.99*Median(2.01,1.24,-2.40*x2,1.25*x1)149639191862372
run 10.3263581.04*Cos(1.73*x2)0.3776490.49*Tan(If(x1>0.91,4.41,-0.83*x1))806234715012987
run 20.3385040.01*Cosh(5.22*x1)0.3263581.04*Cos(1.73*x2)962304921501465
run 30.325058Cos(1.72*x2)0.454707Abs(Median(If(x1>0.91,3.06,-1.00*x1),0.16))650313621341696
run 40.3388830.02*Cosh(5.01*x1)0.3672911.04*Cos(Mean(2.51*x2,0.83,1.78*x2))80432852575992
run 50.325058Cos(-1.72*x2)0.366009Mean(3.98*Cos(1.36*x2),-0.67*x2,0.22*x1,-0.66*x2)197394019721512
run 60.3263581.04*Cos(-1.73*x2)0.362638Sum(0.98*Cos(1.36*x2),-0.36*x2)150740586761397
run 70.397507Square(Sin(4.25*x2))0.363372If(x1>0.91,1.61,-0.52*x1)174342451449180
run 80.292958Square(0.96*x1)0.3263581.04*Cos(1.73*x2)1623226523291445
run 90.350809If(x1>0.91,5.00*x2,0.38)0.551571Logistic(218.18*Cos(3.35*x2))108743779641211
run 100.431209Logistic(156.42*Logabs(-1.12*x1))0.473042Logabs(If(x1>0.91,5.00,-2.10*x1))92843881710633
run 110.325058Cos(1.72*x2)0.3263581.04*Cos(1.73*x2)116242469761264
run 120.3149720.51*Acos(1.10*x2)0.3263581.04*Cos(1.73*x2)2277253917691022
run 130.397507Square(Sin(-4.25*x2))0.363372If(x1>0.91,1.61,-0.52*x1)1145331810582098
run 140.397507Square(Sin(-4.25*x2))0.508543Median(2.01,1.27*x1,1.27,-1.94*x2)215531521505827
run 150.397507Square(Sin(4.25*x2))0.363372If(x1>0.91,1.61,-0.52*x1)134641535681559
run 160.363372If(x1>0.91,5.00*x2,-0.52*x1)0.972920If(x1>0.91,1.61,Median(2.00*x1,-3.75*x2,-2.11*...1405230522291700
run 170.397507Square(Sin(4.25*x2))0.675277Sum(Median(1.20,1.38,-2.49*x2,2.80*x1),-0.33*x1)1193425012021016
run 180.397507Square(Sin(4.25*x2))0.3263581.04*Cos(1.73*x2)195333367491589
run 190.325058Cos(1.72*x2)0.670912Mean(Median(2.85,4.99*x1,-4.75*x2,2.29),-0.86*x1)3885667654939
run 200.397507Square(Sin(-4.25*x2))0.363372If(x1>0.91,1.61,-0.52*x1)73344636781730
run 210.3149720.51*Acos(1.10*x2)0.3263581.04*Cos(1.73*x2)127134406292301
run 220.397507Square(Sin(4.25*x2))0.350809If(x1>0.91,1.61,0.38)667394618861143
run 230.397507Square(Sin(4.25*x2))0.363372If(x1>0.91,1.61,-0.52*x1)137444001076778
run 240.397507Square(Sin(4.25*x2))0.3263581.04*Cos(1.73*x2)241837521319142
run 250.198205Abs(0.74*x1)0.363372If(x1>0.91,1.61,-0.52*x1)2025308611721353
run 260.397507Square(Sin(-4.25*x2))0.742135Median(2.77,0.82,1.07*x1,Median(-7.40*x2,-2.47...769410112821491
run 270.3263581.04*Cos(1.73*x2)0.9991292.04*Cos(Mean(3.35*x2,0.37*x1,-3.62*x1))1212337514031645
run 280.292958Square(0.96*x1)0.350809If(x1>0.91,1.61,0.38)596413712031706
run 290.292958Square(0.96*x1)0.3263581.04*Cos(1.73*x2)1597349414291144
\n", + "
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metricscorescorepoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", + "if __name__ == '__main__':\n", + " import pandas as pd\n", + " from brush import BrushRegressor\n", + " \n", + " import warnings\n", + " warnings.filterwarnings(\"ignore\")\n", + "\n", + " # data = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", + " # X = data.drop(columns='label')\n", + " # y = data['label']\n", + "\n", + " # data = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", + " # X = data.drop(columns='target')\n", + " # y = data['target']\n", + "\n", + " data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", + " X = data.drop(columns='target')\n", + " y = data['target']\n", + "\n", + " kwargs = {\n", + " 'pop_size' : 200,\n", + " 'max_gen' : 40,\n", + " 'max_depth' : 10,\n", + " 'max_size' : 20,\n", + " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", + " }\n", + "\n", + " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " [('Original', 'score'), ('Original', 'best model'), \n", + " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'point mutation calls'),\n", + " ('Modified', 'insert mutation calls'),\n", + " ('Modified', 'delete mutation calls'),\n", + " ('Modified', 'toggle_weight mutation calls')],\n", + " names=('Brush version', 'metric')))\n", + " \n", + " est_mab = None\n", + " for i in range(30):\n", + " try:\n", + " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", + " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", + "\n", + " est = BrushRegressor(**kwargs).fit(X,y)\n", + " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", + "\n", + " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " \n", + " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", + " except Exception as e:\n", + " print(e)\n", + "\n", + " display(df)\n", + " display(df.describe())\n", + "\n", + " if True: # plot the cumulative history of pulls\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + "\n", + " # Plot for evaluations, not generations\n", + " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", + " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", + " data[i+1, :] = data[i]\n", + " data[i+1, arm] += 1\n", + " \n", + " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", + " plt.xlabel(\"Evaluations\")\n", + " plt.ylabel(\"Number of times mutation was used\")\n", + " plt.legend()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Classification problem" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[24.94 20.7 ]\t[0.84047606 1.05356538]\t[19. 14.]\n", + "1 \t200 \t[24.58 16.665]\t[1.44346805 5.34441531]\t[12. 2.]\n", + "2 \t200 \t[24.215 10.995]\t[1.92321996 6.51037441]\t[12. 2.]\n", + "3 \t200 \t[23.785 6.02 ]\t[2.59784045 5.73756046]\t[12. 2.]\n", + "4 \t200 \t[23.49 3.855]\t[3.04136483 4.1765985 ]\t[12. 2.]\n", + "5 \t200 \t[24.655 2.235]\t[1.79331397 1.43170353]\t[12. 2.]\n", + "6 \t200 \t[24.605 2.275]\t[1.91806543 1.53276711]\t[12. 2.]\n", + "7 \t200 \t[24.56 2.31] \t[2.01156655 1.60433787]\t[12. 2.]\n", + "8 \t200 \t[24.725 2.15 ]\t[1.52294944 0.94207218]\t[12. 2.]\n", + "9 \t200 \t[24.725 2.15 ]\t[1.52294944 0.94207218]\t[12. 2.]\n", + "10 \t200 \t[24.725 2.15 ]\t[1.52294944 0.94207218]\t[12. 2.]\n", + "11 \t200 \t[24.725 2.125]\t[1.66414392 0.87142125]\t[12. 2.]\n", + "12 \t200 \t[24.675 2.145]\t[1.79982638 0.91322232]\t[12. 2.]\n", + "13 \t200 \t[24.65 2.155]\t[1.88613361 0.93326041]\t[12. 2.]\n", + "14 \t200 \t[24.65 2.155]\t[1.88613361 0.93326041]\t[12. 2.]\n", + "15 \t200 \t[24.65 2.155]\t[1.88613361 0.93326041]\t[12. 2.]\n", + "16 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", + "17 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", + "18 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", + "19 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", + "20 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", + "21 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", + "22 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "23 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "24 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "25 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "26 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "27 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "28 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "29 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "30 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", + "31 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "32 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "33 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "34 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "35 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "36 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "37 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "38 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "39 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", + "Final population hypervolume is 48437.500000\n", + "best model: Logistic(1.78*Sin(Sum(Pow(1.00*AIDS,1.00),AIDS,AIDS,1.22*Atan(0.01*AIDS))))\n", + "gen\tevals\tave \tstd \tmin \n", + "0 \t200 \t[24.83 20.695]\t[1.68852006 1.15843645]\t[16. 11.]\n", + "1 \t0 \t[24.275 14.14 ]\t[1.79704619 6.70450595]\t[16. 2.]\n", + "2 \t0 \t[23.7 6.92] \t[2.310844 6.51257246]\t[16. 1.]\n", + "3 \t0 \t[24.06 2.565]\t[2.42619043 2.65250353]\t[16. 1.]\n", + "4 \t0 \t[22.83 2.57] \t[3.31528279 3.72090043]\t[16. 1.]\n", + "5 \t0 \t[22.12 1.29] \t[3.58686493 1.05636168]\t[11. 1.]\n", + "6 \t0 \t[21.9 1.095]\t[3.66060104 0.55315007]\t[11. 1.]\n", + "7 \t0 \t[20.135 1.125]\t[3.48522237 0.58255901]\t[11. 1.]\n", + "8 \t0 \t[18.8 1.165]\t[2.71845544 0.60644456]\t[11. 1.]\n", + "9 \t0 \t[17.74 1.135]\t[0.89576783 0.48659531]\t[11. 1.]\n", + "10 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "11 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "12 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "13 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "14 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "15 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "16 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "17 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "18 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "19 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "20 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "21 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "22 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "23 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "24 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "25 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "26 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "27 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "28 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "29 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "30 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "31 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "32 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "33 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "34 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "35 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "36 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "37 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "38 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "39 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", + "Final population hypervolume is 48944.500000\n" + ] + }, + { + "data": { + "text/html": [ + "
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Brush versionOriginalModified
metricscorebest modelscorebest modelpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
run 00.68Sqrt(0.00*AIDS)0.76Median(0.00*AIDS,Square(0.00*AIDS),3.97*Tan(1....153479714451194
run 10.82Abs(Sinh(Log1p(Min(Median(0.01*AIDS,Total),Div...0.78Logistic(1.01*Mean(-0.00*Total,0.02*AIDS,0.48))195635571191823
run 20.68Logistic(Add(Ceil(Sqrtabs(Prod(6.09*AIDS,-0.00...0.72Logistic(Tan(1.32*Logabs(Sqrt(0.01*AIDS))))1304386313681057
run 30.82Logistic(Min(Sum(0.01*AIDS,-0.72,-0.72),2.30,D...0.78Logistic(Div(-0.57*Total,Mean(546.40*AIDS,1.27...116076031232561
run 40.78Logistic(Min(Cos(Sub(Mean(If(AIDS>68817.00,Tan...0.86Logistic(Atan(Cos(1.00*Mean(4.94,0.64*AIDS,Tot...144035697651835
run 50.86Sum(Sin(-0.48*AIDS),Abs(Sin(-0.49*AIDS)),-5.57...0.68Atan(0.00*AIDS)1997188720891634
run 60.88Median(Sqrt(Tan(Atan(Cos(Sum(0.48*AIDS,0.92)))...0.86Median(2.06*Sum(-0.00*AIDS,Tan(1.00*AIDS)),0.8...114430817901366
run 70.88Mean(Sin(Mean(1.00*Total,1.00*Total,1.00*Total...0.74Logistic(Sum(1.00*Sum(242.90,Total),3.38*AIDS,...863394516101154
run 80.84Add(Sum(Sub(Max(Cos(Sum(Median(1.00*Total,Sqrt...0.68Atan(0.00*AIDS)15443612240821
run 90.78Div(Sum(Cos(2.88*Total),Mean(Logabs(133518.33*...0.68Tanh(0.00*AIDS)44355951351219
run 100.82Mean(Exp(Sin(Sum(0.00*AIDS,1.09,Total))),0.00*...0.76Logistic(Sum(-0.47*Total,726.97*AIDS,AIDS,3499...89941111870745
run 110.80Sqrt(Log1p(Max(0.00*AIDS,Sub(Sub(Median(Total,...0.68Sqrtabs(0.00*AIDS)1140342714501592
run 120.84Add(Cos(Add(Cos(Mul(-1.21,Tan(Mean(-0.00*Total...0.78Logistic(2.08*Sin(Median(1.00*Sub(1.00*AIDS,-1...2551274411721137
run 130.80Median(Pow(Cos(Sqrtabs(0.87*AIDS)),6.44),0.00*...0.76Sqrt(0.06*Mean(Square(-0.13*AIDS),-0.30*Total,...486473911421255
run 140.70Logistic(Sin(Add(Max(1.00*Total,1.00,1.00,1.00...0.68Atan(0.00*AIDS)1280373715341057
run 150.84Median(Mean(Sum(Sin(Tan(1.00*AIDS)),0.72,0.72,...0.68Sqrt(0.00*AIDS)1450355610631517
run 160.78Mean(Atan(0.19*AIDS),-0.00*Total,0.00*AIDS)0.78Logistic(Mean(1.33*Sqrt(Div(1.44*Max(-2.70*Tot...157533041864766
run 170.72Logistic(1.00*Sum(Mean(393.50,Total,393.43,1.0...0.68Sqrtabs(0.00*AIDS)155540401095886
run 180.72Logistic(Sin(Div(Tan(1.00*AIDS),1.85*AIDS)))0.74Logistic(Cos(Sum(If(AIDS>68817.00,1.00*AIDS,1....735377412131814
run 190.80Mean(Cos(Sqrtabs(Median(1.00,1.09*AIDS,Median(...0.76Ceil(0.87*Sum(6927.20*AIDS,-345.95,-5.24*Total...113939701808682
run 200.76Logistic(Sub(0.82*Logabs(2.64*Median(1.50*Sum(...0.781.00*Pow(1.00,Sum(Sum(2041.16,-2073.40*AIDS,2....20648482153386
run 210.78Atan(Atan(Sinh(Div(116.98*AIDS,0.14*Total))))0.76Logistic(Sin(Tan(1.00*AIDS)))10725210122569
run 220.84Mean(Cos(Abs(0.67*AIDS)),Tanh(0.00*AIDS),Mean(...0.78Exp(Mean(-3.55*Total,5013.83*AIDS,7295.95))191931002184404
run 230.78Logistic(Tan(Sum(1.05*Mul(1.12*Div(-390.05*AID...0.74Logistic(Tan(Add(1.00*Min(Sinh(1.00*AIDS),2.93...139735325221700
run 240.84Abs(Sub(Abs(Max(Sin(Sum(0.00*Total,1.00*AIDS,1...0.74Logistic(Sin(Sum(Prod(1.00,Sub(Sum(Total,0.23,...1003335813091911
run 250.86Sqrtabs(Sinh(Ceil(Cos(Mean(0.64*AIDS,1.00*Tota...0.70Logistic(Cos(Mean(Median(1.00*Total,13.21),-0....991352214881614
run 260.80Logistic(Sin(Mean(Log(Total),1.29,Mean(0.94*Lo...0.68Atan(0.00*AIDS)155332462160592
run 270.68Median(Total,0.00*AIDS,-3.57,0.79)0.74Mean(Atan(0.05*AIDS),0.00*AIDS,-0.00*Total)295428915621452
run 280.82Max(Tanh(0.00*AIDS),Asin(Mean(Square(Sin(Media...0.68Sqrtabs(0.00*AIDS)985319111552223
run 290.78Logistic(1.78*Sin(Sum(Pow(1.00*AIDS,1.00),AIDS...0.78Ceil(Mean(-0.00*Total,0.02*AIDS,0.41))164335666721722
\n", + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "if __name__ == '__main__':\n", + " import pandas as pd\n", + " from brush import BrushClassifier\n", + " \n", + " import warnings\n", + " warnings.filterwarnings(\"ignore\")\n", + "\n", + " from pmlb import fetch_data\n", + "\n", + " # X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", + "\n", + " data = pd.read_csv('../../docs/examples/datasets/d_analcatdata_aids.csv')\n", + " X = data.drop(columns='target')\n", + " y = data['target']\n", + "\n", + " kwargs = {\n", + " 'pop_size' : 200,\n", + " 'max_gen' : 40,\n", + " 'max_depth' : 10,\n", + " 'max_size' : 20,\n", + " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", + " }\n", + "\n", + " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " [('Original', 'score'), ('Original', 'best model'), \n", + " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'point mutation calls'),\n", + " ('Modified', 'insert mutation calls'),\n", + " ('Modified', 'delete mutation calls'),\n", + " ('Modified', 'toggle_weight mutation calls')],\n", + " names=('Brush version', 'metric')))\n", + " \n", + " est_mab = None\n", + " for i in range(30):\n", + " try:\n", + " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", + " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", + "\n", + " est = BrushClassifier(**kwargs).fit(X,y)\n", + "\n", + " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", + "\n", + " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", + " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " \n", + " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", + " except Exception as e:\n", + " print(e)\n", + "\n", + " display(df)\n", + " display(df.describe())\n", + "\n", + " if True: # plot the cumulative history of pulls\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + "\n", + " # Plot for evaluations, not generations\n", + " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", + " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", + " data[i+1, :] = data[i]\n", + " data[i+1, arm] += 1\n", + "\n", + " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", + " plt.xlabel(\"Evaluations\")\n", + " plt.ylabel(\"Number of times mutation was used\")\n", + " plt.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From dd69ea2837971ef42b9cc8b078bc394c90ae7cfd Mon Sep 17 00:00:00 2001 From: gAldeia Date: Mon, 22 May 2023 20:57:58 -0300 Subject: [PATCH 012/102] Rename node_weights to node_map_weights --- src/brush/deap_api/nsga2.py | 2 +- src/program/dispatch_table.h | 2 +- src/search_space.cpp | 2 +- src/search_space.h | 18 +++++++++--------- src/variation.h | 4 ++-- 5 files changed, 14 insertions(+), 14 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 21e4da86..8aff555d 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -49,7 +49,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): off1 = toolbox.mutate(ind1) off2 = toolbox.mutate(ind2) - # del ind1.fitness.values, ind2.fitness.values + offspring.extend([off1, off2]) # archive.update(offspring) diff --git a/src/program/dispatch_table.h b/src/program/dispatch_table.h index 8a6f3470..f4217057 100644 --- a/src/program/dispatch_table.h +++ b/src/program/dispatch_table.h @@ -216,7 +216,7 @@ extern DispatchTable dtable_predict; // ArgsName[args_type], // node_type, // node, -// SS.node_weights.at(ret_type).at(args_type).at(node_type) +// SS.node_map_weights.at(ret_type).at(args_type).at(node_type) // ); // } // } diff --git a/src/search_space.cpp b/src/search_space.cpp index 39da28cc..e0ec7619 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -46,7 +46,7 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user { // fmt::print("constructing search space...\n"); this->node_map.clear(); - this->node_weights.clear(); + this->node_map_weights.clear(); this->terminal_map.clear(); this->terminal_types.clear(); this->terminal_weights.clear(); diff --git a/src/search_space.h b/src/search_space.h index 10c8ba9c..f196d609 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -93,7 +93,7 @@ struct SearchSpace Map node_map; /// @brief A map of weights corresponding to elements in @ref node_map, used to weight probabilities of each node being sampled from the map. - Map node_weights; + Map node_map_weights; /** * @brief Maps return types to terminals. @@ -117,7 +117,7 @@ struct SearchSpace NLOHMANN_DEFINE_TYPE_INTRUSIVE(SearchSpace, node_map, - node_weights, + node_map_weights, terminal_map, terminal_weights, terminal_types @@ -261,7 +261,7 @@ struct SearchSpace if (node_type_map.find(type) != node_type_map.end()) { matches.push_back(node_type_map.at(type)); - weights.push_back(node_weights.at(R).at(arg_hash).at(type)); + weights.push_back(node_map_weights.at(R).at(arg_hash).at(type)); } } @@ -303,7 +303,7 @@ struct SearchSpace vector get_weights() const { vector v; - for (auto& [ret, arg_w_map]: node_weights) + for (auto& [ret, arg_w_map]: node_map_weights) { v.push_back(0); for (const auto& [arg, name_map] : arg_w_map) @@ -324,7 +324,7 @@ struct SearchSpace vector get_weights(DataType ret) const { vector v; - for (const auto& [arg, name_map] : node_weights.at(ret)) + for (const auto& [arg, name_map] : node_map_weights.at(ret)) { v.push_back(0); for (const auto& [name, w]: name_map) @@ -343,7 +343,7 @@ struct SearchSpace vector get_weights(DataType ret, ArgsHash sig_hash) const { vector v; - for (const auto& [name, w]: node_weights.at(ret).at(sig_hash)) + for (const auto& [name, w]: node_map_weights.at(ret).at(sig_hash)) v.push_back(w); return v; @@ -417,7 +417,7 @@ struct SearchSpace } // if we made it this far, include the node as a match! matches.push_back(node); - weights.push_back(node_weights.at(ret).at(args_type).at(name)); + weights.push_back(node_map_weights.at(ret).at(args_type).at(name)); // saving for future checking // has no size limit (max_arg is 0) or the number of @@ -504,7 +504,7 @@ struct SearchSpace node_map[n.ret_type][n.args_type()][n.node_type] = n; // sampling probability map float w = use_all? 1.0 : user_ops.at(name); - node_weights[n.ret_type][n.args_type()][n.node_type] = w; + node_map_weights[n.ret_type][n.args_type()][n.node_type] = w; } } @@ -706,7 +706,7 @@ template <> struct fmt::formatter: formatter { ArgsName[args_type], node_type, node, - SS.node_weights.at(ret_type).at(args_type).at(node_type) + SS.node_map_weights.at(ret_type).at(args_type).at(node_type) ); } } diff --git a/src/variation.h b/src/variation.h index 5cfc2485..51a17599 100644 --- a/src/variation.h +++ b/src/variation.h @@ -32,7 +32,7 @@ inline void point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "point mutation\n"; - // get_node_like will sample a similar node based on node_weights or terminal_weights + // get_node_like will sample a similar node based on node_map_weights or terminal_weights auto newNode = SS.get_node_like(spot.node->data); Tree.replace(spot, newNode); } @@ -44,7 +44,7 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) auto spot_type = spot.node->data.ret_type; // pick a random compatible node to insert (with probabilities given by - // node_weights). The `-1` represents the node being inserted. + // node_map_weights). The `-1` represents the node being inserted. // Ideally, it should always find at least one match (the same node // used as a reference when calling the function). However, we have a // size restriction, which will be relaxed here (just as it is in the PTC2 From ac559378072853a139e6e2f6405d93f12591b7f3 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Tue, 23 May 2023 08:32:16 -0300 Subject: [PATCH 013/102] Saving logbook into an attribute --- src/brush/estimator.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index d01e2b61..8fddb091 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -154,6 +154,7 @@ def fit(self, X, y): archive, logbook = nsga2(self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity) self.archive_ = archive + self.logbook_ = logbook self.best_estimator_ = self.archive_[0].prg if self.verbosity > 0: From 3920413cc3cefce555a3dfaebf7492fc0acf093a Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 25 May 2023 08:52:18 -0300 Subject: [PATCH 014/102] Improved visualization of learners historic --- src/brush/D_MAB_experiments.ipynb | 2642 ++++++++++++++++------------- src/brush/D_TS_experiments.ipynb | 2599 ++++++++++++++++------------ 2 files changed, 2992 insertions(+), 2249 deletions(-) diff --git a/src/brush/D_MAB_experiments.ipynb b/src/brush/D_MAB_experiments.ipynb index 3dd7a6fa..53a39184 100644 --- a/src/brush/D_MAB_experiments.ipynb +++ b/src/brush/D_MAB_experiments.ipynb @@ -58,10 +58,11 @@ " def __init__(self, num_bandits, delta=0.15, lmbda=0.25):\n", " self.num_bandits = num_bandits\n", "\n", - " # Store tuples when update is called. Tuples will have 3 values:\n", - " # (time instant t, arm idx, reward)\n", - " self.pull_history = []\n", - " self.reset_history = []\n", + " # Store learner status when the update function is called\n", + " self.pull_history = {\n", + " c:[] for c in ['t', 'arm idx', 'reward', 'update'] + \n", + " [f'UCB1 {i}' for i in range(num_bandits)]} \n", + "\n", "\n", " # This is the probability that should be used to update brush probs\n", " self._probabilities = np.ones(num_bandits)/num_bandits\n", @@ -77,6 +78,17 @@ " self._avg_deviations = np.zeros(self.num_bandits)\n", " self._max_deviations = np.zeros(self.num_bandits)\n", "\n", + " def _calculate_UCB1s(self):\n", + " # We need that the reward is in [0, 1] (not avg_reward, as it seems to\n", + " # render worse results). It looks like normalizing the rewards is a\n", + " # problem: reward should be [0, 1], but not necessarely avg_rewards too\n", + " rs = self._avg_rewards\n", + " ns = self._num_pulls\n", + " \n", + " UCB1s = rs + np.sqrt(2*np.log1p(sum(ns))/(ns+1))\n", + "\n", + " return UCB1s\n", + "\n", " @property\n", " def probabilities(self):\n", " # How to transform our UCB1 scores into node probabilities?\n", @@ -94,13 +106,7 @@ " the UCB1 function. The choice is made in a deterministic way.\n", " \"\"\"\n", "\n", - " # We need that the reward is in [0, 1] (not avg_reward, as it seems to\n", - " # render worse results). It looks like normalizing the rewards is a\n", - " # problem: reward should be [0, 1], but not necessarely avg_rewards too\n", - " rs = self._avg_rewards\n", - " ns = self._num_pulls\n", - " \n", - " UCB1s = rs + np.sqrt(2*np.log1p(sum(ns))/(ns+1))\n", + " UCB1s = self._calculate_UCB1s()\n", "\n", " return np.nanargmax(UCB1s)\n", "\n", @@ -109,7 +115,12 @@ " # interval [0, 1] (in the original paper, they sugest using a scaling\n", " # factor as an hyperparameter).\n", "\n", - " self.pull_history.append( (len(self.pull_history), arm_idx, reward) )\n", + " self.pull_history['t'].append( len(self.pull_history['t']) )\n", + " self.pull_history['arm idx'].append( arm_idx )\n", + " self.pull_history['reward'].append( reward )\n", + "\n", + " for i, UCB1 in enumerate(self._calculate_UCB1s()):\n", + " self.pull_history[f'UCB1 {i}'].append( UCB1 )\n", "\n", " if np.isfinite(reward):\n", " self._avg_rewards[arm_idx] = \\\n", @@ -123,7 +134,9 @@ "\n", " if (self._max_deviations[arm_idx] - self._avg_deviations[arm_idx] > self.lmbda):\n", " self._reset_indicators()\n", - " self.reset_history.append(len(self.pull_history))\n", + " self.pull_history['update'].append( 1 )\n", + " else:\n", + " self.pull_history['update'].append( 0 )\n", "\n", " return self" ] @@ -146,22 +159,23 @@ "output_type": "stream", "text": [ "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 44.0, 1: 41.0, 2: 28.0, 3: 48.0}\n", - "number of pulls for each arm: {0: 273, 1: 257, 2: 230, 3: 240}\n", + "cum. reward for each arm : {0: 40.0, 1: 41.0, 2: 37.0, 3: 33.0}\n", + "number of pulls for each arm: {0: 278, 1: 255, 2: 247, 3: 220}\n", "(it was expected: similar amount of pulls for each arm)\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 462.0, 1: 78.0, 2: 79.0, 3: 19.0}\n", - "number of pulls for each arm: {0: 542, 1: 178, 2: 175, 3: 105}\n", + "cum. reward for each arm : {0: 441.0, 1: 73.0, 2: 103.0, 3: 16.0}\n", + "number of pulls for each arm: {0: 514, 2: 207, 1: 179, 3: 100}\n", "(it was expected: more pulls for first arm, less pulls for last)\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 107.0, 1: 235.0, 2: 89.0, 3: 321.0}\n", - "number of pulls for each arm: {0: 177, 1: 292, 2: 163, 3: 368}\n", + "cum. reward for each arm : {0: 105.0, 1: 266.0, 2: 61.0, 3: 295.0}\n", + "number of pulls for each arm: {3: 358, 1: 324, 0: 186, 2: 132}\n", "(it was expected: 2nd approx 4th > 1st > 3rd)\n" ] } ], "source": [ "# Sanity checks\n", + "import pandas as pd\n", "\n", "class Bandits:\n", " def __init__(self, reward_prob):\n", @@ -192,11 +206,10 @@ "\n", " learner.update(arm_idx, reward) \n", "\n", - " total_rewards = {arm_idx : sum([r for (t, i, r) in learner.pull_history if i==arm_idx])\n", - " for arm_idx in range(learner.num_bandits)}\n", + " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", "\n", - " total_pulls = {arm_idx : sum([1 for (t, i, r) in learner.pull_history if i==arm_idx])\n", - " for arm_idx in range(learner.num_bandits)}\n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", "\n", " print(\"cum. reward for each arm : \", total_rewards)\n", " print(\"number of pulls for each arm: \", total_pulls)\n", @@ -231,13 +244,26 @@ " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", "\n", + " # mutations optimized by the learner. Learner arms correspond to\n", + " # these mutations in the order they appear here\n", + " self.mutations_ = ['point', 'insert', 'delete', 'toggle_weight']\n", + "\n", + " # Whether the learner should update after each mutation, or if it should\n", + " # update only after a certain number of evaluations.\n", + " # Otherwise, it will\n", + " # store all rewards in gen_rewards_ (which is reseted at the beggining\n", + " # of every generation) and do a batch of updates only after finishing\n", + " # mutating the solutions.\n", + " self.batch_size_ = self.pop_size*2 #\n", + " self.batch_rewards_ = []\n", + "\n", " def _mutate(self, ind1):\n", " # Overriding the mutation so it updates our sampling method. Doing the\n", " # logic on the python-side for now.\n", "\n", " # Creating a wrapper for mutation to be able to control what is happening\n", " # in the C++ code (this should be prettier in a future implementation)\n", - " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", + " \n", " params = self.get_params()\n", " \n", " ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", @@ -247,12 +273,12 @@ " # is already at maximum size.\n", " # In this case, we'll do the mutation without controlling the probabilities.\n", " if ignore_this_time:\n", - " for i, m in enumerate(mutations):\n", + " for i, m in enumerate(self.mutations_):\n", " params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", " else:\n", " mutation_idx = self.learner_.choose_arm()\n", "\n", - " for i, m in enumerate(mutations):\n", + " for i, m in enumerate(self.mutations_):\n", " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", "\n", " _brush.set_params(params)\n", @@ -271,8 +297,13 @@ " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", " \n", " if not ignore_this_time:\n", - " self.learner_.update(mutation_idx, reward)\n", + " self.batch_rewards_.append( (mutation_idx, reward) )\n", " \n", + " if len(self.batch_rewards_) > self.batch_size_:\n", + " for (mutation_idx, reward) in self.batch_rewards_:\n", + " self.learner_.update(mutation_idx, reward)\n", + " self.batch_rewards_ = []\n", + " \n", " return offspring\n", " \n", " def fit(self, X, y):\n", @@ -303,6 +334,7 @@ " self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", "\n", " self.archive_ = archive\n", + " self.logbook_ = logbook\n", " self.best_estimator_ = self.archive_[0].prg\n", "\n", " return self\n", @@ -365,92 +397,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[ nan 20.925]\t[ nan 0.97435876]\t[nan 20.]\n", - "1 \t200 \t[ nan 16.925]\t[ nan 4.98491474]\t[nan 1.]\n", - "2 \t200 \t[ nan 10.495]\t[ nan 5.4488508] \t[nan 1.]\n", - "3 \t200 \t[ nan 4.73] \t[ nan 2.58400851]\t[nan 1.]\n", - "4 \t200 \t[ nan 2.425] \t[ nan 1.16377618]\t[nan 1.]\n", - "5 \t200 \t[ nan 1.725] \t[ nan 0.69955343]\t[nan 1.]\n", - "6 \t200 \t[ nan 1.43] \t[ nan 0.62056426]\t[nan 1.]\n", - "7 \t200 \t[5.15006834 1.05 ]\t[1.27027425 0.21794495]\t[2.73836088 1. ]\n", - "8 \t200 \t[4.4173494 1.025 ] \t[1.03668311 0.15612495]\t[2.73836088 1. ]\n", - "9 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "10 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "11 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "12 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "13 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "14 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "15 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "16 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "17 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "18 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "19 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "20 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "21 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "22 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "23 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "24 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "25 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "26 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "27 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "28 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "29 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "30 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "31 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "32 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "33 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "34 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "35 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "36 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "37 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "38 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "39 \t200 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "Final population hypervolume is 49363.883825\n", - "best model: Square(0.96*x1)\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[ nan 20.695]\t[ nan 1.0686323]\t[nan 13.]\n", - "1 \t0 \t[ nan 15.36] \t[ nan 6.07950656]\t[nan 1.]\n", - "2 \t0 \t[ nan 8.325] \t[ nan 5.0704413] \t[nan 1.]\n", - "3 \t0 \t[ nan 2.915] \t[ nan 1.83242326]\t[nan 1.]\n", - "4 \t0 \t[ nan 1.45] \t[ nan 0.65383484]\t[nan 1.]\n", - "5 \t0 \t[5.69480249 1.035 ]\t[1.72669191 0.18377976]\t[2.61403799 1. ]\n", - "6 \t0 \t[4.74933375 1.01 ]\t[1.15020605 0.09949874]\t[2.61403799 1. ]\n", - "7 \t0 \t[3.86668897 1.005 ]\t[0.08879807 0.07053368]\t[2.61403799 1. ]\n", - "8 \t0 \t[3.86668897 1.005 ]\t[0.08879807 0.07053368]\t[2.61403799 1. ]\n", - "9 \t0 \t[3.85004769 1.035 ]\t[0.18980221 0.32214127]\t[1.90340519 1. ]\n", - "10 \t0 \t[3.8392059 1.06 ] \t[0.24252853 0.47581509]\t[1.80252552 1. ]\n", - "11 \t0 \t[3.84267018 1.045 ]\t[0.21723216 0.35067791]\t[1.79161644 1. ]\n", - "12 \t0 \t[3.86666379 1.005 ]\t[0.08915317 0.07053368]\t[2.60900354 1. ]\n", - "13 \t0 \t[3.86666379 1.005 ]\t[0.08915317 0.07053368]\t[2.60900354 1. ]\n", - "14 \t0 \t[3.86666379 1.005 ]\t[0.08915317 0.07053368]\t[2.60900354 1. ]\n", - "15 \t0 \t[3.85962713 1.015 ]\t[0.13308931 0.15740076]\t[2.46565056 1. ]\n", - "16 \t0 \t[3.84274257 1.045 ]\t[0.21511804 0.35067791]\t[1.90340519 1. ]\n", - "17 \t0 \t[3.84274257 1.045 ]\t[0.21511804 0.35067791]\t[1.90340519 1. ]\n", - "18 \t0 \t[3.83358411 1.045 ]\t[0.31383125 0.35067791]\t[0.07171333 1. ]\n", - "19 \t0 \t[3.80455297 1.095 ]\t[0.4347226 0.63716167]\t[0.00325559 1. ]\n", - "20 \t0 \t[3.82355932 1.06 ]\t[0.34492818 0.40792156]\t[0.00325559 1. ]\n", - "21 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", - "22 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", - "23 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", - "24 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", - "25 \t0 \t[3.81723942 1.065 ]\t[0.35538575 0.41324932]\t[0.00325559 1. ]\n", - "26 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", - "27 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", - "28 \t0 \t[3.83915799 1.045 ]\t[0.21155585 0.30491802]\t[2.45047045 1. ]\n", - "29 \t0 \t[3.8293101 1.065 ] \t[0.25177146 0.41324932]\t[1.90340519 1. ]\n", - "30 \t0 \t[3.83915799 1.045 ]\t[0.21155585 0.30491802]\t[2.45047045 1. ]\n", - "31 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "32 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "33 \t0 \t[3.84672464 1.045 ]\t[0.1840279 0.36465737]\t[2.45543599 1. ]\n", - "34 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "35 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "36 \t0 \t[3.84675711 1.03 ]\t[0.18370814 0.2215852 ]\t[2.51430631 1. ]\n", - "37 \t0 \t[3.84675711 1.03 ]\t[0.18370814 0.2215852 ]\t[2.51430631 1. ]\n", - "38 \t0 \t[3.83317034 1.06 ]\t[0.22652033 0.36932371]\t[2.51430607 1. ]\n", - "39 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "Final population hypervolume is 49370.222358\n" + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" ] }, { @@ -474,15 +421,19 @@ " \n", " \n", " Brush version\n", - " Original\n", - " Modified\n", + " Original\n", + " Modified\n", " \n", " \n", " metric\n", " score\n", " best model\n", + " size\n", + " depth\n", " score\n", " best model\n", + " size\n", + " depth\n", " point mutation calls\n", " insert mutation calls\n", " delete mutation calls\n", @@ -492,470 +443,590 @@ " \n", " \n", " run 0\n", - " 0.292958\n", - " 1.00*Square(0.96*x1)\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 1836\n", - " 2244\n", - " 1966\n", - " 1595\n", + " 0.490733\n", + " Square(If(x1>0.91,1.34*x1,-0.85*x1))\n", + " 4\n", + " 2\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3417\n", + " 2211\n", + " 2211\n", + " 1809\n", " \n", " \n", " run 1\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 0.350809\n", - " If(x1>0.91,1.61,0.38)\n", - " 1142\n", - " 3008\n", - " 1563\n", - " 1907\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3819\n", + " 2412\n", + " 2010\n", + " 1407\n", " \n", " \n", " run 2\n", - " 0.314972\n", - " 0.51*Acos(1.10*x2)\n", - " 0.835548\n", - " Mean(If(x1>0.91,9.83,1.69),-1.75*x2,-2.39*x1,-...\n", - " 1876\n", - " 3065\n", - " 1030\n", - " 1661\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", + " 0.306978\n", + " Sum(0.79,-0.70*x2)\n", + " 3\n", + " 1\n", + " 2814\n", + " 2412\n", + " 2211\n", + " 2211\n", " \n", " \n", " run 3\n", - " 0.363372\n", - " If(x1>0.91,5.00*x2,-0.52*x1)\n", - " 0.292958\n", - " 0.91*Square(x1)\n", - " 1627\n", - " 2514\n", - " 1763\n", - " 1731\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.508543\n", + " Median(2.01,1.27,-1.94*x2,1.27*x1)\n", + " 5\n", + " 1\n", + " 3417\n", + " 3216\n", + " 1809\n", + " 1206\n", " \n", " \n", " run 4\n", - " 0.292958\n", - " Square(0.96*x1)\n", - " 1.000000\n", - " Square(Median(-2.00*x1,-2.00*x2))\n", - " 2001\n", - " 2585\n", - " 1602\n", - " 1439\n", + " 0.198205\n", + " Abs(0.74*x1)\n", + " 2\n", + " 1\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3618\n", + " 2412\n", + " 2010\n", + " 1608\n", " \n", " \n", " run 5\n", - " 0.325058\n", - " Cos(1.72*x2)\n", - " 0.350809\n", - " If(x1>0.91,1.61,0.38)\n", - " 1930\n", - " 2135\n", - " 1437\n", - " 2113\n", + " 0.314972\n", + " 0.51*Acos(1.10*x2)\n", + " 2\n", + " 1\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3618\n", + " 2010\n", + " 2010\n", + " 2010\n", " \n", " \n", " run 6\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 0.490733\n", - " Square(If(x1>0.91,1.27,-0.85*x1))\n", - " 2164\n", - " 2089\n", - " 1854\n", - " 1552\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 1.000000\n", + " 0.60*Square(Sum(0.63*x1,-0.00,0.65*x1,Median(1...\n", + " 9\n", + " 3\n", + " 3417\n", + " 2412\n", + " 2211\n", + " 1608\n", " \n", " \n", " run 7\n", " 0.325058\n", - " Cos(1.72*x2)\n", - " 0.508543\n", - " Median(2.01,-1.94*x2,1.27*x1,1.27)\n", - " 1555\n", - " 2453\n", - " 1857\n", - " 1768\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3819\n", + " 2814\n", + " 1608\n", + " 1407\n", " \n", " \n", " run 8\n", - " 0.198205\n", - " Abs(0.74*x1)\n", - " 0.292958\n", - " 0.96*Square(-0.98*x1)\n", - " 1386\n", - " 2389\n", - " 1922\n", - " 1953\n", + " 0.306978\n", + " Sum(-0.70*x2,0.79)\n", + " 3\n", + " 1\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3819\n", + " 2412\n", + " 1809\n", + " 1608\n", " \n", " \n", " run 9\n", - " 0.363372\n", - " If(x1>0.91,1.70*x1,-0.52*x1)\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 1457\n", - " 2228\n", - " 1804\n", - " 2160\n", + " 0.507152\n", + " Logistic(243.34*Logabs(-1.13*x1))\n", + " 3\n", + " 2\n", + " 0.350809\n", + " If(x1>0.91,1.61,0.38)\n", + " 3\n", + " 1\n", + " 3216\n", + " 2412\n", + " 2211\n", + " 1809\n", " \n", " \n", " run 10\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", + " 0.292958\n", + " Square(0.96*x1)\n", + " 2\n", + " 1\n", " 0.350809\n", " If(x1>0.91,1.61,0.38)\n", - " 1591\n", - " 2011\n", - " 1814\n", - " 2217\n", + " 3\n", + " 1\n", + " 3015\n", + " 2613\n", + " 2412\n", + " 1608\n", " \n", " \n", " run 11\n", - " 0.325058\n", - " Cos(1.72*x2)\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 1594\n", - " 2426\n", - " 1783\n", - " 1834\n", + " 0.264208\n", + " Atan(Square(1.02*x1))\n", + " 3\n", + " 2\n", + " 0.993012\n", + " 1.75*Cos(Square(Mean(2.60*x2,0.02,-2.76*x1)))\n", + " 6\n", + " 3\n", + " 3216\n", + " 2613\n", + " 2613\n", + " 1206\n", " \n", " \n", " run 12\n", - " 0.425247\n", - " Logistic(50.64*Logabs(-1.15*x1))\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", " 0.326358\n", " 1.04*Cos(1.73*x2)\n", - " 1795\n", - " 2712\n", - " 1571\n", - " 1561\n", + " 2\n", + " 1\n", + " 4422\n", + " 2010\n", + " 1809\n", + " 1407\n", " \n", " \n", " run 13\n", - " 0.325058\n", - " Cos(1.72*x2)\n", - " 0.624433\n", - " Add(If(x1>0.91,1.82,0.65),-0.67*x2)\n", - " 1686\n", - " 2825\n", - " 1587\n", - " 1535\n", + " 0.292958\n", + " Square(0.96*x1)\n", + " 2\n", + " 1\n", + " 0.363372\n", + " If(x1>0.91,1.61,-0.52*x1)\n", + " 3\n", + " 1\n", + " 3417\n", + " 2613\n", + " 2010\n", + " 1608\n", " \n", " \n", " run 14\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.350809\n", - " If(x1>0.91,1.61,0.38)\n", - " 1602\n", - " 2900\n", - " 1790\n", - " 1331\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 3015\n", + " 2412\n", + " 2211\n", + " 2010\n", " \n", " \n", " run 15\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", " 0.363372\n", - " If(x1>0.91,5.00*x2,-0.52*x1)\n", - " 0.308425\n", - " Sum(0.79,0.02*x1,-0.69*x2,0.02*x1)\n", - " 1603\n", - " 2386\n", - " 1833\n", - " 1809\n", + " If(x1>0.91,1.61,-0.52*x1)\n", + " 3\n", + " 1\n", + " 2814\n", + " 2412\n", + " 2211\n", + " 2211\n", " \n", " \n", " run 16\n", - " 0.324176\n", - " 0.05*Cosh(3.63*x1)\n", - " 1.000000\n", - " 1.22*Square(Mean(1.19*x1,3.62*x2,1.21*x1,1.21*...\n", - " 1965\n", - " 2586\n", - " 1735\n", - " 1378\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.964813\n", + " 1.65*Cos(Mean(2.12*x2,1.39*x2,-3.00*x1))\n", + " 5\n", + " 2\n", + " 2814\n", + " 2814\n", + " 2211\n", + " 1809\n", " \n", " \n", " run 17\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", - " 0.508543\n", - " Median(2.01,-1.94*x2,1.27,1.27*x1)\n", - " 1804\n", - " 2326\n", - " 1953\n", - " 1545\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.828817\n", + " Mean(If(x1>0.91,8.33,Sqrtabs(0.05*x1)),1.55,-2...\n", + " 8\n", + " 3\n", + " 3015\n", + " 2412\n", + " 2211\n", + " 2010\n", " \n", " \n", " run 18\n", - " 0.292958\n", - " Square(0.96*x1)\n", - " 0.999129\n", - " 2.04*Cos(Sum(0.43*x2,-0.31*x2,-1.08*x1,x2))\n", - " 1158\n", - " 3363\n", - " 1766\n", - " 1354\n", + " 0.306978\n", + " Median(Median(-2.81*x2,0.79),1.18)\n", + " 5\n", + " 2\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 2613\n", + " 2412\n", + " 2412\n", + " 2211\n", " \n", " \n", " run 19\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", - " 0.508543\n", - " Median(2.01,1.27*x1,-1.94*x2,1.27)\n", + " 0.326358\n", + " 1.04*Cos(1.73*x2)\n", + " 2\n", + " 1\n", + " 0.500913\n", + " Add(If(x1>0.91,1.12,0.20),0.61*Cos(1.99*x2))\n", + " 6\n", + " 2\n", + " 3618\n", + " 2010\n", + " 2010\n", " 2010\n", - " 2539\n", - " 1608\n", - " 1479\n", " \n", " \n", " run 20\n", + " 0.314972\n", + " 0.51*Acos(1.10*x2)\n", + " 2\n", + " 1\n", " 0.292958\n", - " Square(0.96*x1)\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 1915\n", - " 2775\n", - " 1387\n", - " 1556\n", + " 0.91*Square(x1)\n", + " 2\n", + " 1\n", + " 3015\n", + " 2613\n", + " 2412\n", + " 1608\n", " \n", " \n", " run 21\n", - " 0.363372\n", - " If(x1>0.91,5.00*x2,-0.52*x1)\n", - " 0.350809\n", - " If(x1>0.91,1.61,0.38)\n", - " 1495\n", - " 2570\n", - " 1844\n", - " 1733\n", + " 0.292958\n", + " Square(0.96*x1)\n", + " 2\n", + " 1\n", + " 0.921552\n", + " Mean(If(x1>0.91,5.58,-2.98*x1),Max(-6.53*x2,0....\n", + " 12\n", + " 2\n", + " 3216\n", + " 2412\n", + " 2010\n", + " 2010\n", " \n", " \n", " run 22\n", - " 0.363372\n", - " If(x1>0.91,5.00*x2,-0.52*x1)\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", " 0.363372\n", " If(x1>0.91,1.61,-0.52*x1)\n", - " 1919\n", - " 2982\n", - " 1100\n", - " 1631\n", + " 3\n", + " 1\n", + " 4221\n", + " 2010\n", + " 2010\n", + " 1407\n", " \n", " \n", " run 23\n", 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Logabs(2.31*x1) 0.326358 \n", - "run 26 0.325058 Cos(1.72*x2) 0.508543 \n", - "run 27 0.326358 1.04*Cos(1.73*x2) 0.350809 \n", - "run 28 0.397507 Square(Sin(4.25*x2)) 0.573936 \n", - "run 29 0.292958 Square(0.96*x1) 0.326358 \n", + "Brush version Original \n", + "metric score best model size depth \n", + "run 0 0.490733 Square(If(x1>0.91,1.34*x1,-0.85*x1)) 4 2 \\\n", + "run 1 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 2 0.325058 Cos(1.72*x2) 2 1 \n", + "run 3 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 4 0.198205 Abs(0.74*x1) 2 1 \n", + "run 5 0.314972 0.51*Acos(1.10*x2) 2 1 \n", + "run 6 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 7 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 8 0.306978 Sum(-0.70*x2,0.79) 3 1 \n", + "run 9 0.507152 Logistic(243.34*Logabs(-1.13*x1)) 3 2 \n", + "run 10 0.292958 Square(0.96*x1) 2 1 \n", + "run 11 0.264208 Atan(Square(1.02*x1)) 3 2 \n", + "run 12 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 13 0.292958 Square(0.96*x1) 2 1 \n", + "run 14 0.326358 1.04*Cos(1.73*x2) 2 1 \n", + "run 15 0.325058 Cos(1.72*x2) 2 1 \n", + "run 16 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 17 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 18 0.306978 Median(Median(-2.81*x2,0.79),1.18) 5 2 \n", + "run 19 0.326358 1.04*Cos(1.73*x2) 2 1 \n", + "run 20 0.314972 0.51*Acos(1.10*x2) 2 1 \n", + "run 21 0.292958 Square(0.96*x1) 2 1 \n", + "run 22 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 23 0.198205 Abs(0.74*x1) 2 1 \n", + "run 24 0.326358 1.04*Cos(1.73*x2) 2 1 \n", + "run 25 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 26 0.317954 Mul(0.72*x1,Add(1.36*x1,0.19)) 5 2 \n", + "run 27 0.325058 Cos(1.72*x2) 2 1 \n", + "run 28 0.325058 Cos(-1.72*x2) 2 1 \n", + "run 29 0.325058 Cos(1.72*x2) 2 1 \n", "\n", - "Brush version \n", - "metric best model \n", - "run 0 If(x1>0.91,1.61,-0.52*x1) \\\n", - "run 1 If(x1>0.91,1.61,0.38) \n", - "run 2 Mean(If(x1>0.91,9.83,1.69),-1.75*x2,-2.39*x1,-... \n", - "run 3 0.91*Square(x1) \n", - "run 4 Square(Median(-2.00*x1,-2.00*x2)) \n", - "run 5 If(x1>0.91,1.61,0.38) \n", - "run 6 Square(If(x1>0.91,1.27,-0.85*x1)) \n", - "run 7 Median(2.01,-1.94*x2,1.27*x1,1.27) \n", - "run 8 0.96*Square(-0.98*x1) \n", - "run 9 1.04*Cos(1.73*x2) \n", - "run 10 If(x1>0.91,1.61,0.38) \n", - "run 11 If(x1>0.91,1.61,-0.52*x1) \n", - "run 12 1.04*Cos(1.73*x2) \n", - "run 13 Add(If(x1>0.91,1.82,0.65),-0.67*x2) \n", - "run 14 If(x1>0.91,1.61,0.38) \n", - "run 15 Sum(0.79,0.02*x1,-0.69*x2,0.02*x1) \n", - "run 16 1.22*Square(Mean(1.19*x1,3.62*x2,1.21*x1,1.21*... \n", - "run 17 Median(2.01,-1.94*x2,1.27,1.27*x1) \n", - "run 18 2.04*Cos(Sum(0.43*x2,-0.31*x2,-1.08*x1,x2)) \n", - "run 19 Median(2.01,1.27*x1,-1.94*x2,1.27) \n", - "run 20 1.04*Cos(1.73*x2) \n", - "run 21 If(x1>0.91,1.61,0.38) \n", - "run 22 If(x1>0.91,1.61,-0.52*x1) \n", - "run 23 If(x1>0.91,1.61,Square(-0.85*x1)) \n", - "run 24 If(x1>0.91,1.61,Sum(-0.89*x1,-0.34*x2,-0.34*x2)) \n", - "run 25 1.04*Cos(1.73*x2) \n", - "run 26 Median(2.01,1.27,1.27*x1,-1.94*x2) \n", - "run 27 If(x1>0.91,1.61,0.38) \n", - "run 28 Median(Median(1.89,2.61*x1,-5.16*x2,4.11),0.22... \n", - "run 29 1.04*Cos(1.73*x2) \n", + "Brush version Modified \n", + "metric score best model \n", + "run 0 0.326358 1.04*Cos(1.73*x2) \\\n", + "run 1 0.326358 1.04*Cos(1.73*x2) \n", + "run 2 0.306978 Sum(0.79,-0.70*x2) \n", + "run 3 0.508543 Median(2.01,1.27,-1.94*x2,1.27*x1) \n", + "run 4 0.326358 1.04*Cos(1.73*x2) \n", + "run 5 0.326358 1.04*Cos(1.73*x2) \n", + "run 6 1.000000 0.60*Square(Sum(0.63*x1,-0.00,0.65*x1,Median(1... \n", + "run 7 0.326358 1.04*Cos(1.73*x2) \n", + "run 8 0.326358 1.04*Cos(1.73*x2) \n", + "run 9 0.350809 If(x1>0.91,1.61,0.38) \n", + "run 10 0.350809 If(x1>0.91,1.61,0.38) \n", + "run 11 0.993012 1.75*Cos(Square(Mean(2.60*x2,0.02,-2.76*x1))) \n", + "run 12 0.326358 1.04*Cos(1.73*x2) \n", + "run 13 0.363372 If(x1>0.91,1.61,-0.52*x1) \n", + "run 14 0.326358 1.04*Cos(1.73*x2) \n", + "run 15 0.363372 If(x1>0.91,1.61,-0.52*x1) \n", + "run 16 0.964813 1.65*Cos(Mean(2.12*x2,1.39*x2,-3.00*x1)) \n", + "run 17 0.828817 Mean(If(x1>0.91,8.33,Sqrtabs(0.05*x1)),1.55,-2... \n", + "run 18 0.326358 1.04*Cos(1.73*x2) \n", + "run 19 0.500913 Add(If(x1>0.91,1.12,0.20),0.61*Cos(1.99*x2)) \n", + "run 20 0.292958 0.91*Square(x1) \n", + "run 21 0.921552 Mean(If(x1>0.91,5.58,-2.98*x1),Max(-6.53*x2,0.... \n", + "run 22 0.363372 If(x1>0.91,1.61,-0.52*x1) \n", + "run 23 0.573010 1.03*Median(1.10,1.17*x1,1.05,Add(-3.56*x2,0.85)) \n", + "run 24 0.508543 Median(2.01,1.27,-1.94*x2,1.27*x1) \n", + "run 25 0.624433 Sub(If(x1>0.91,1.82,0.65),0.67*x2) \n", + "run 26 0.326358 1.04*Cos(1.73*x2) \n", + "run 27 0.624433 Sum(If(x1>0.91,1.82,0.65),-0.33*x2,-0.33*x2) \n", + "run 28 0.363372 If(x1>0.91,1.70*x1,-0.52*x1) \n", + "run 29 0.363372 If(x1>0.91,1.61,-0.52*x1) \n", "\n", - "Brush version \n", - "metric point mutation calls insert mutation calls \n", - "run 0 1836 2244 \\\n", - "run 1 1142 3008 \n", - "run 2 1876 3065 \n", - "run 3 1627 2514 \n", - "run 4 2001 2585 \n", - "run 5 1930 2135 \n", - "run 6 2164 2089 \n", - "run 7 1555 2453 \n", - "run 8 1386 2389 \n", - "run 9 1457 2228 \n", - "run 10 1591 2011 \n", - "run 11 1594 2426 \n", - "run 12 1795 2712 \n", - "run 13 1686 2825 \n", - "run 14 1602 2900 \n", - "run 15 1603 2386 \n", - "run 16 1965 2586 \n", - "run 17 1804 2326 \n", - "run 18 1158 3363 \n", - "run 19 2010 2539 \n", - "run 20 1915 2775 \n", - "run 21 1495 2570 \n", - "run 22 1919 2982 \n", - "run 23 1542 2612 \n", - "run 24 1571 2719 \n", - "run 25 1552 2810 \n", - "run 26 1699 2815 \n", - "run 27 1845 2697 \n", - "run 28 1667 2622 \n", - "run 29 1821 2569 \n", + "Brush version \n", + "metric size depth point mutation calls insert mutation calls \n", + "run 0 2 1 3417 2211 \\\n", + "run 1 2 1 3819 2412 \n", + "run 2 3 1 2814 2412 \n", + "run 3 5 1 3417 3216 \n", + "run 4 2 1 3618 2412 \n", + "run 5 2 1 3618 2010 \n", + "run 6 9 3 3417 2412 \n", + "run 7 2 1 3819 2814 \n", + "run 8 2 1 3819 2412 \n", + "run 9 3 1 3216 2412 \n", + "run 10 3 1 3015 2613 \n", + "run 11 6 3 3216 2613 \n", + "run 12 2 1 4422 2010 \n", + "run 13 3 1 3417 2613 \n", + "run 14 2 1 3015 2412 \n", + "run 15 3 1 2814 2412 \n", + "run 16 5 2 2814 2814 \n", + "run 17 8 3 3015 2412 \n", + "run 18 2 1 2613 2412 \n", + "run 19 6 2 3618 2010 \n", + "run 20 2 1 3015 2613 \n", + "run 21 12 2 3216 2412 \n", + "run 22 3 1 4221 2010 \n", + "run 23 7 2 3417 2211 \n", + "run 24 5 1 3015 2412 \n", + "run 25 5 2 3618 2613 \n", + "run 26 2 1 3819 2010 \n", + "run 27 6 2 3618 2412 \n", + "run 28 3 1 2814 2613 \n", + "run 29 3 1 4221 2010 \n", "\n", "Brush version \n", "metric delete mutation calls toggle_weight mutation calls \n", - "run 0 1966 1595 \n", - "run 1 1563 1907 \n", - "run 2 1030 1661 \n", - "run 3 1763 1731 \n", - "run 4 1602 1439 \n", - "run 5 1437 2113 \n", - "run 6 1854 1552 \n", - "run 7 1857 1768 \n", - "run 8 1922 1953 \n", - "run 9 1804 2160 \n", - "run 10 1814 2217 \n", - "run 11 1783 1834 \n", - "run 12 1571 1561 \n", - "run 13 1587 1535 \n", - "run 14 1790 1331 \n", - "run 15 1833 1809 \n", - "run 16 1735 1378 \n", - "run 17 1953 1545 \n", - "run 18 1766 1354 \n", - "run 19 1608 1479 \n", - "run 20 1387 1556 \n", - "run 21 1844 1733 \n", - "run 22 1100 1631 \n", - "run 23 1782 1709 \n", - "run 24 2055 1300 \n", - "run 25 1441 1844 \n", - "run 26 1634 1487 \n", - "run 27 1329 1763 \n", - "run 28 1899 1465 \n", - "run 29 1359 1889 " + "run 0 2211 1809 \n", + "run 1 2010 1407 \n", + "run 2 2211 2211 \n", + "run 3 1809 1206 \n", + "run 4 2010 1608 \n", + "run 5 2010 2010 \n", + "run 6 2211 1608 \n", + "run 7 1608 1407 \n", + "run 8 1809 1608 \n", + "run 9 2211 1809 \n", + "run 10 2412 1608 \n", + "run 11 2613 1206 \n", + "run 12 1809 1407 \n", + "run 13 2010 1608 \n", + "run 14 2211 2010 \n", + "run 15 2211 2211 \n", + "run 16 2211 1809 \n", + "run 17 2211 2010 \n", + "run 18 2412 2211 \n", + "run 19 2010 2010 \n", + "run 20 2412 1608 \n", + "run 21 2010 2010 \n", + "run 22 2010 1407 \n", + "run 23 2010 2010 \n", + "run 24 2412 1809 \n", + "run 25 2010 1407 \n", + "run 26 2010 1809 \n", + "run 27 2211 1407 \n", + "run 28 2211 2010 \n", + "run 29 1809 1608 " ] }, "metadata": {}, @@ -982,13 +1053,17 @@ " \n", " \n", " Brush version\n", - " Original\n", - " Modified\n", + " Original\n", + " Modified\n", " \n", " \n", " metric\n", " score\n", + " size\n", + " depth\n", " score\n", + " size\n", + " depth\n", " point mutation calls\n", " insert mutation calls\n", " delete mutation calls\n", @@ -1000,121 +1075,143 @@ " count\n", " 30.000000\n", " 30.000000\n", - " 30.0000\n", " 30.000000\n", " 30.000000\n", " 30.000000\n", + " 30.000000\n", + " 30.000000\n", + " 30.00000\n", + " 30.000000\n", + " 30.000000\n", " \n", " \n", " mean\n", - " 0.335993\n", - " 0.480635\n", - " 1693.6000\n", - " 2598.500000\n", - " 1668.933333\n", - " 1676.633333\n", + " 0.320971\n", + " 2.366667\n", + " 1.166667\n", + " 0.481002\n", + " 4.000000\n", + " 1.400000\n", + " 3396.900000\n", + " 2412.00000\n", + " 2110.500000\n", + " 1728.600000\n", " \n", " \n", " std\n", - " 0.047602\n", - 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2011.000000 1030.000000 \n", - "25% 2398.250000 1565.000000 \n", - "50% 2585.500000 1764.500000 \n", - "75% 2801.250000 1841.250000 \n", - "max 3363.000000 2055.000000 \n", + "Brush version \n", + "metric depth point mutation calls insert mutation calls \n", + "count 30.000000 30.000000 30.00000 \\\n", + "mean 1.400000 3396.900000 2412.00000 \n", + "std 0.674665 461.228969 279.31295 \n", + "min 1.000000 2613.000000 2010.00000 \n", + "25% 1.000000 3015.000000 2261.25000 \n", + "50% 1.000000 3417.000000 2412.00000 \n", + "75% 2.000000 3618.000000 2613.00000 \n", + "max 3.000000 4422.000000 3216.00000 \n", "\n", - "Brush version \n", - "metric toggle_weight mutation calls \n", - "count 30.000000 \n", - "mean 1676.633333 \n", - "std 242.443663 \n", - "min 1300.000000 \n", - "25% 1499.000000 \n", - "50% 1646.000000 \n", - "75% 1827.750000 \n", - "max 2217.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "Brush version \n", + "metric delete mutation calls toggle_weight mutation calls \n", + "count 30.000000 30.000000 \n", + "mean 2110.500000 1728.600000 \n", + "std 222.387942 296.725997 \n", + "min 1608.000000 1206.000000 \n", + "25% 2010.000000 1457.250000 \n", + "50% 2110.500000 1708.500000 \n", + "75% 2211.000000 2010.000000 \n", + "max 2613.000000 2211.000000 " ] }, "metadata": {}, @@ -1124,7 +1221,6 @@ "source": [ "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", "if __name__ == '__main__':\n", - " import pandas as pd\n", " from brush import BrushRegressor\n", " \n", " import warnings\n", @@ -1143,16 +1239,19 @@ " y = data['target']\n", "\n", " kwargs = {\n", - " 'pop_size' : 200,\n", - " 'max_gen' : 40,\n", + " 'verbosity' : False,\n", + " 'pop_size' : 100,\n", + " 'max_gen' : 100,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", " }\n", "\n", - " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", + " ('Original', 'size'), ('Original', 'depth'), \n", " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), \n", " ('Modified', 'point mutation calls'),\n", " ('Modified', 'insert mutation calls'),\n", " ('Modified', 'delete mutation calls'),\n", @@ -1163,42 +1262,171 @@ " for i in range(30):\n", " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", "\n", " est = BrushRegressor(**kwargs).fit(X,y)\n", " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", "\n", - " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", - " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", + " \n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " \n", + " results.loc[f'run {i}'] = [\n", + " # Original implementation\n", + " est.score(X,y), est.best_estimator_.get_model(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + "\n", + " # Implementation using Dynamic Thompson Sampling\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " \n", + " # Mutation count\n", + " *total_pulls.values()]\n", " \n", - " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", " except Exception as e:\n", " print(e)\n", "\n", - " display(df)\n", - " display(df.describe())\n", + " # Showing results and statistics\n", + " display(results)\n", + " display(results.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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J2rSRGjWSiovN2KSq0JIEAABQf1nVXWATZfjj7+bokDRo0CDNnDlTn376qV544QXt3LlTv/nNb5SVlVXpbaZNm6aUlBTvlpaWFsSKT87lknr0MN+frMsdIQkAAKD+8Sy8WsgA9VrJzc2VJEVHR9f6Phzd3W7UqFHe73v37q1BgwapXbt2evfdd3XTTTdVeJvJkydr0qRJ3p8zMzMdF5R69pSWLJG2bZPy86UGDSrej5AEAABQ/0RFRSk+Pl5HjhxRdHS0IiIc3a7hGJZlKTc3V4cPH1Zqaqo3bNaGo0PSiVJTU9WlSxdt27at0n1iY2MVGxsbxKpqrlkzqWlTs7Ds5s1mnFJFCEkAAAD1j8vlUsuWLbVz507t3r3b7nJCTmpqap3nNAipkJSdna3t27fruuuus7uUOuvRQ1q40HS5qywksaAsAABA/RQTE6POnTvT5a6GoqOj69SC5OHokHTvvffqkksuUbt27bR//35NmTJFkZGRuuaaa+wurc569jQhaccOKTdXio8vv09RkZSdLSUmBr08AAAA2CwiIkINKhuXgYBydAfHffv26ZprrlHXrl115ZVXqnHjxlq+fLmaNm1qd2l11rix1KKF5HZLmzZVvh9d7gAAAIDgcnRL0qxZs+wuIaB69pQOHjRd7vr3r3if48clh807AQAAAIQ1R7ckhTvPVOC7dpludRVhXBIAAAAQXIQkG6WmmsVlLUvauLHifehuBwAAAAQXIclmntak9esrvp6QBAAAAAQXIclm3bubr3v3ShkZ5a8nJAEAAADBRUiyWXKy1K6d+X7DhvLXZ2ZKJSXBrQkAAACozwhJDuDpcldRSLKsiluYAAAAAAQGIckBuneXXC5p/37p2LHy19PlDgAAAAgeQpIDJCRIHTqY7ytqTSIkAQAAAMFDSHIIT5e7zZvLX0dIAgAAAIKHkOQQnTubr/v3S7m5Za9jQVkAAAAgeAhJDpGUJDVrZr7fubPsdbQkAQAAAMFDSHKQjh3N1+3by15OSAIAAACCh5DkIJ06ma/bt5upvz3y8qT8fHtqAgAAAOobQpKDtGsnRUaaBWSPHi17HeOSAAAAgOAgJDlIdLQJShJd7gAAAAC7EJIcxjMuaceOspcTkgAAAIDgICQ5jGdc0s6dUklJ6eWEJAAAACA4CEkO07y5lJAgFRVJe/eWXk5IAgAAAIKDkOQwLlfFU4EzcQMAAAAQHIQkB/J0ufMdl5SeXnZacAAAAACBQUhyIE9L0v79Um6u+b64WMrKsq8mAEDlSkqkI0ekTZukJUvMUg4AgNAVZXcBKC8pSWrWTDp82Ezg0KOHufzYMSk52d7aAKA+y84278XHjkm//GK2I0fMuFG3u3S/1aulCROk1FTbSgUA1AEhyaE6djQhafv20pC0cKH50HW5bC0NqHeKiqTCQrMVFJifndj9NTFRatTI7ipCx88/m1Z6yzIBx7JKv3e7Tev98eMmEB0/brbCwurd9/Hj0owZ5j2b5wQAQg8hyaE6dZKWLzchybJMMNq1y1w2eLDd1QHhq6RE+vprad260mDk20LgZJGR0vDh0hlncDKlKpYlffqptGJFYB8nI8MEpd/+VmraNLCPBQDwL8YkOVS7duaAJzNTOnq09PIFC0wLEwD/O3RIeukl6ZtvzP9efn7oBCTJBLzPPpPefrt0PCPKcrulefMCH5A8srKkmTPNawsAEDoISQ4VHW2CklR2KvDiYmn27LILzQKoG8syweill8LjYHbrVunFF6Xdu+2uxFlKSqQPPpDWrAnu4+bkmKC0f39wHxcAUHt0t3Owjh3NNOA7dkiDBpVefvCgGZ80bJhtpQWFp5sh4FFSYlp4MjLM15yc0rEkUumYEs/PTZqYkw2JiZXf5/Hj0pw50p49ga8/mDIzpVdflc45Rzr7bP6Xioqkd9+VfvrJnsfPy5Nee00aP15q08aeGuAfR45IP/xgurVW9d4CILQRkhysUyfpyy/NDHclJab7nce330pdukhpafbVF0iZmdL770sREdL550utW9tdEYLp+HEzqP7AATNo3hOMcnJqN2GCJyy1b2++emaJXLXKdE+r7mD8UON2m/FVu3ZJl19uZs6sjwoKTBfEXbvsrSM/3wSlq64qXQ8PoSEnR/rxRxOODhwwl/Xvb29NQG243ebYyh8KC83/hmdzu6WEhNKtQQP/PI5dQiokPfnkk5o8ebLuuusu/f3vf7e7nIBr3ty8yHJypL17zQGeh9ttzn7feqsUE1P7x8jKktavP/k4p9rO5BUZKZ1+utSiRfVvs2OH6RKTk2N+fvllqXt303LWuHHt6oBzZWebQLR/f+lXf4+n8UzVvGqV+blRIyk+Xtq3z7+P41Q7d5rud506lZ3F7cSWt/h4c2b8xC0pqW7vM3bKy5PeeMO8tpygsFB6/XUT1ocMkTp3tr+Vb88eMylQt25S79721uIkxcXSli0mGG3bFlrjE4ET7d5t/s+3bDHHlo0aSQ0bmq++m8tlPpd9t5ycst97tqKiqh8zMrI0MHXrZno2hJKQCUnff/+9/v3vf6t3PXoHd7lMl7sffzTBwTckSeYM+2efSZdcUrP7zcuTNm4097t7d+CnMl69Wurb14SchITK97Msswjj11+Xr2njRmnzZqlfP2noUHu7OLjdUnq62TIySr/3bCNGmFCHirndJvRv3Wq2I0eCX4NnnZv6JCfHzNhXW1WdeezQQbruutrfd6BkZZmA5MRxZrt3m61pUzNjae/eUlQQP5FLSswJsuXLS1tGunQJ7uPv2WPG3O7aZQ6mkpOllJTyX2NizHurZxr248dLf05PD1xLsO8JBNRccbF5jf/0k9lycqTYWPN8xsaW/b5Xr/BrXXXCkIHi4tL/84MHSy/PyjJboMeterrIZ2aG5ppxIRGSsrOzNW7cOL388st6/PHH7S4nqDwhaft26bzzyl+/apXUtevJP9wKC83ZA899BXPiB8syQWnDBnMWYdCgsl0HJRPc5swxB82VcbullSvNgd4ZZ5izsLGxgavb7TYH0ocPmwP5w4fNduxY1X8/zjaWl5dnzsRu3Wq+5uXZXRFqqqrXtd0HksXFpYu6ev5PDx82B9B213YyR46Y2fa++sq8Nw4YIMXFBe7xsrPN++jKleb7YDp61Pz/e4JRuHZzrc8yM00g2rrVtGCf+Bzn51d8uyZNQjckFRWVf+85csSc1LWr5SQ7W/r+e/N/7umVg5oLiZA0ceJEXXTRRRo+fPhJQ1JBQYEKCgq8P2dmZga6vIDyvGl4uiDFx5ffZ9486fbby16Xm2vO1u/ZY77u328OJOxUUCB9/rn5px0xwoQ7ydT27rvmgKY6CgulxYtNq1NUVNktMrL0+7r0uS0oMB/odv/NTvTmm6a2UFJcbM5gER5RkZwc6Z13an/73Fxz4iLUX1/Z2WaJhyVLatY9uSYsy7zfBuIk2bp15r29MpmZ1X+PR9VWrDAnHZ0mP9+ZS5R8+GHZpVT8KSsrMCdjtm+XFi2q3W0D+X9e3zg+JM2aNUurV6/W999/X639p02bpkceeSTAVQVPUpLUrJl549m5U+rRo/w+2dnmTaBr19JQFKg3BH84dswMou7UyXTTWbiwdmHEsswZnJP1iQ0n+/bRCoPwUlwcfjML1kVhYWj+PTIyQrPuUHTsGH/rmjhwoGxXs1CQk8Nz7ASOXidp7969uuuuu/Tmm2+qQTWnyJg8ebIyMjK82969ewNcZeB17Gi++q6XdKItW0yL0tq1zg5IvrZvN7P3Oa21BgAAAPWbo1uSVq1apcOHD6tfv37ey0pKSrR48WI999xzKigoUOQJg1tiY2MVG8iBKjbo1MkMutuxwxkDAQEAAIBw5uiQNGzYMP34449lLrvhhhvUrVs33X///eUCUrhq186MtcnIkObPl1q1Mn3WmzUL3Wl5AQAAAKdydEhKSkpSz549y1yWkJCgxo0bl7s8nEVHm9akrVulNWvM5tGokVlPqXlzM71iXFzZrUGD8jPJOYHbbdYtSU83C+KG4tSQAAAACE+ODkkodfnlZtzRoUOlW3Z26XovmzZVftuYGLM+UfPmUuvWpiWqVavgr4ScmWnGIW3bZroO+k4F2rixGXvVqZNZD6qiHpNZWWYA5v79Zvvll9LZW3zXs/DHLDMREaVBMz7e/K083/uGUN+fA73GSVpaaExpfCKXy6x10qiReZ59v4b6atyom23bpDPPrNviwZGR5f8vPVtCgnlfSUryX82B4Hab97SffjJ/k4yMwD2WZzIg3y05uW7duJ94QvrrX6ue0js6uvyilZ7FLH3fO91u89lQUGC2/HzzPt+gQekWGxvcNaWcZNIkacYMZ05YFBlpTnhW9F4fHV26n2WZscie5zk/X/ruO9ND5pRT/FvT4MFmJsBAzX4ZFVX5+09hofnsO+20mt3nG29Id91Vt5lsU1PNMUNamtS2rb1rS4aykHubWbhwod0l2CI2tvxK6Dk55UNTXl7p5gkhhYVmO37cLMjq0aRJaWhKTQ3MWKfiYjPb3vbt5acGbdDAfEAePGgmmzh61MzrHxEhtWlTOmGFJxhlZfm/vqrU9PGio80b46xZ0uOPS//zP/6t5/Dh0F1XJDvbtByeKCHBvAaSksybuO9XzxYX59/Xpttt/hcyM81jpaaW/QAPN5ZVOk2t50PX9+/p+d6yzHuG73tIXp4JMHl5VR+UxcRIX3whvf56zWorKfHPYsJVrfQQEWEWqjzjjMBNrV0bOTnmffGnn8zXYM1amZNTfqav2FgTltavl+64wyzYXdP7PH785Pulp5sTZCdKSjKvv4KC6h/8R0UFPjC5XKa21FSzNWxY+tWuEzxOn0o9M7PiWdmSkszz5AlGFYWWnBzpoYf8W8/Ro4E/dqjspMbnn5vftaYhKS+v7oud5+SYz9zly83PDRuWhqYWLczfv6Cg9PjQ93vJPFfR0aWb78+eE8Tx8ebnqj6fi4pKP0eKi82MxgMH1u13C6aQC0mhrlUr/73Bec6SesLEiTxn5PLyzJuEpwXG083tl1/M9sMP/qmnOlq3Nq1Fp5xivo+IMDXu2mUOFLZvNx+2e/aUf6N1uUyw87SENW9eenDrcpX+o574tTaKi8sfLJ540Oi7+U5HnplZtzPjlVm9WvrPf0JvnaSSEvOcHjtmPrA8X3NySreqxMRUfGayceOTB6icHBMuPScSPAv9nTijYny8OeOXmlr61XPfvq8n382JSkrM/3Z6uvmbe74PxnoZ27bV/Dbt20tLl9Y8XPnyfAhX9P959Kh5z/vhB7O1b2/CUufOdVtHrbaKi816QqtXlz9pEBtr3hs7d5ZatgzMa8yyzOvCd9HLo0fNe8revWY799yah6Tf/94saL5iReX75OWVvg/4bgUFFR/E+oagyMjSFgfP+19xsTn5EugFcSubOrpBA2nuXOnVV6U+fQJbg69HHpH69zcLwztNYWH55/foUfO8VfQcu1zm+Y2NNZ/tDRv6v6aPPjLvL4GY9dfzuV/ZsUFcnFSbkSFjx5rPuAULaleX223+tz1Lwhw+bP73jh837z/+5NuSHx9v3lc9f4fc3PInPX74wYytDxWEpCAbNsy05gRj4cOIiNK037ixOUDwyMkpDUw//1y3g/qqun+5XFLTpiYUdexY8WK4DRpI3bqZTTL/yJ4V2SMiSkNRixbOnKjCcwbU88bQv795nv2tRw/zNwjFdZJatix/mWfB3owM8wGanV32a1aW+ZsWFpoDlYoOVqKiKg/Fng+wikRFmTCUnV363OXmmlbLcOTp8hgXV3qZ7/+t53tPt9KKupRW9b/XsmXtWk5jY6Xu3U0rRqB4zqZu3GjeU3btMgdjgwZJffsG5z0lP19atcrU4XtQ7+le1LmzaT0PRnBr3rz0vVYyAfqXX8yBVGqqdPbZtbvPjh3NWn5Vadeu7M+WVRqeIiJKQ5EnGFXEcwbcE5o83fECwe02J718TzgcP27eK/LzzQFfRZ9pgdS6tfksP3QouI9bXW3alL/Md8Fn3y6TMTGl79vDh0tnneX/erp0MX8zO8Zmn3uudM45Nb9do0bmPaEuQbhFi9LeR/n5Zo1Fz4mQX34xJ5hjYkqfB9/N5TKfncXFpSd/PT8XFpYGoJISs3k+ryvjGb7gac0KJYSkIGvcWBowwPS/tVNCgvkn7NzZ3joq0rCh+RsNGGB3JdXjcpW+8Uvmjb51a3trCgWxsaUBuDLFxebAxNMd07c1KjOzemtsNWxoDuKaNSv92qhR6QFpfr55jIyM0gOhjIzSM9ae8W4nbk4UEVHaEubbPSglJbAH4B07OrcLRevW5szs+eeb991Vq8yB7qefmq1Bg7JjCjxjEOPjzVgdz98xObnmf8PMTNO6snJlaTeW5GQT0Hr1csZYqcjI0sl/xowxdQWLy1V6Iq+6fMeL2qWw0LxP/OY35YMfyqvpcwz/atDAnIzx53gvz0lI31aj3FwThD3Pt+d9NTbW/K+feqp01VX+qyEYCEk2OOcccwbKn92mmjUzL8jdu517AHciTyvTiWOVAI+oKNMNo0mT8tcVFZXvanNi60hiYsWTgPhq0MCcdXPSeBX4X3KyOVt99tnm/XfFitKuQL6TyFQmIsLchyd4Jiebs65RUaWbp+++y2UGi69bV9proGlTacgQ0/3GiTOOovpiYsxn7siRzuzdAASay1Xa8hTOsxMHPCQVFhZq586d6tSpk6Lq63Q0J0hIMB+WX33ln/tr3FiaMMHcb3q6OQBYty4wfXD9afhwqV8/6cUXAzubE8JTdHRg+rAjvMXESKefblqqPWc/KxpTkJtbtnXRd7xXTbRrZ2bw69zZuWPZAADlBSy15Obm6s4779Srr74qSdq6das6duyoO++8U61bt9YDDzwQqIcOCYMHmy4YVc3KVB3JydJ115mAJJlEf845Ztu71wSm9eurd6Y0mAYONEFRkq64wkxpGozB5QAgmcCSkFD63lkV3xkCPQOgs7JMd0/fzdNvv7jYtBwNHlzxGA0AgPMFLCRNnjxZP/zwgxYuXKiRI0d6Lx8+fLimTp1a70NSdLQZ1Pfhh7W/j/h4E5Aqa+r0TPc4cqQJS59+6oy1Fbp1k0aNKv25TRvpggukTz6p/n1ERUkjRpiWBN9pKn23unRpycoy61Jt2mRmiAnGRBvVce+9dldQc/n50tdfm7EgodIVFMGTklK3aX8968Rt3mwmowkEl8uckEpONmuOBFL//tKFFwbmvt1u6csvq56FrrbOOsu0mFVm714zJXKgnqP6ZMQI85npNMePS599Zqa1d5JbbgncZ8+hQ6XvP/5YzsCjVy8zWVNtuN3SvHnOnAEx1AQsJM2dO1fvvPOOzjjjDLl8+hj06NFD27dvD9TDhpTTTjOzHdVmlpqYGGncOHO28mSioswHb1qa9O67ZmYTu7RpYwZRn9jtZNAgM55q48aT30dMjHTNNWa+/UBJTTU1DRpkut1s3WreBLdvtzdohuJYhoQE6eKLzWvwk08qXkMD9VtdXtdNm5rt7LNN97jNm822e7dzTm5U12mnmf+VQHXLi4w0J6jatDEHUf58L3O5qn4e27eXbr7ZHLgtWEAX67qwY/r66mjSxByX/PSTCUt2Hmv4CuTfyzP50LBhZojD5s0mNFW0LmBNnOz/qSqRkdLll5uJYZYurVsd9V3AQtKRI0fUrIJ5XXNycsqEpvrM5TIzLr3xRs1uFxkpXX11zWdQa9bMnFH58EMzqDjYGjeWrr228oU7R482gbGqsVQNGpg34WBOIxkfbw5eTjvNHFRs327WhPGs6YTqadlSuvFGM17uiy+Cvzgwwl9KSunJjbw806W5tmuNBFuvXuY9MBgfj716mc+Dd96p+6KVNeFymWmJu3c3JwiXLKl6AqPERPNen5hoQi+T/ISGzp3NjJfffSctWuS87v6B0rixGUYwZIj5fLPz93a5TGtjcrIJrPTiqJ2AhaQBAwbo448/1p133ilJ3mD0n//8R4MHDw7Uw4Ycz/pBFa1CXpGICNMSU9kCsicTE2PGALVta7o+BGscUEKCCTdVTQMaGytdeaX08ssVT+2ckGC6F9o5C1l0dPk1nXbsMIFp587QXMMo2Hr3Nn+/xYulZcsYi4bAiIszUzRv3lz3s7qB1r27dNllwZ3YoXlzc9JszhzTtTiYoqJM97y+fc1B9MqV5iCuWbPSbuJt25afmCUnx6xztXOn+eqUlgqUFxlpxuT17m0mqVq9uvRAPSbGtDo1a2a2pk3Db9mMpCRnTPF/xhmmjjlzqrdkBspyWVZg8uU333yjUaNGafz48Zo5c6Z+97vfaePGjVq6dKkWLVqk/v37B+Jhy8nMzFRKSooyMjKUnJwclMesqYMHpX//u3pJ/9JLzYxw/rBvn/Tee4Hv9hAdLV1/ffXfBFevNl1BfKWkmIBU0VTQTmFZpr99fDyzrlXX0aMmLG3YwBu4E7lc5n+uXbuq//dSUswaGE60e7eZGMapunY1J4fs6kprWdI335gD2dGjTYt5sGVklC4wWhNZWSYs5eQEpCzl55sWr8paBO66i/f66jp40DzPzZqZ7ux0KAquXbukWbPsbd1y0jpJ1c0GAQtJkrRjxw5NmzZNP/zwg7Kzs9WvXz/df//96hXE1epCISRJJuX/8EPl18fHmxnrBg3y7+Pm5koffGBaQgIhIsL8U3TtWrPbzZ0rrV1rvm/USPrtb8N7Lv76LjfXPN+rVjl/6vpwFhFhukW2bWuCUdu24bEI5NtvB7+1pDpOOcWMr3TCWMMdO8yJii5d7K7EWbKzzaRH69eXv46QhFBy6JAZ3uHb1T0iwrQqe97v27Uzlx0+bCaiOHKk9PsT1yWsKULSr4qKivS73/1ODz30kDoEcnR9NYRKSMrIkP75z7Jn01u0MB9YnTubgbaBOvNiWWYq8pq+Eqqzf1RU7Zqci4pMtzvJBKTExJrfB0KPZZmuNCtXmoPaQHXF69bNvGF//LFUWBiYx3Ca3r3N5AaWZSY08Gyen10u82EZjotjHjkivfCCsyZy6NDBdEFm+cDQsG2beb/wHYdKSEKoycgwob9pUxOK0tKq34Kbm2sC0549Ztu7t+oxhZJ5f2vVyjxW167BHU9eFdtbklJSUrR27VpCUg0sXCgdOGBCUZcuZsBdfXbsmOmCEQ5nslFz2dmm6+Xq1TVfwLMyjRqZ2b06dzY/79snvflm+I8l6907+GNenGb+fNNS6QRt25ruw5VNYgNnKioyY6iWLjWBm5CE+sztNq1Te/aYbs179pj/kbS00pap1q2deSLI9pA0YcIEnXbaabrnnnsCcffVFkohCUB5lmWmlF250nytzTtWdLQZKD5kSPk37EOHpNdfr3tXAqciIBlZWaa13u6Ww4gIaeJEMxMWQtOhQ9JHH5lplglJQCnLCo3Pmupmg4Dlu86dO+vRRx/Vt99+q/79+yvhhGXNf//73wfqoQGEEZfLtKx26WK6Cnhal6o7hXjXrmZB5coOZpo3N1OTv/aa/1qsnKJPH2nMmND40Aq0pCQz29aiRfbW0bcvASnUed4zmFYZKCvcPmsC1pJUVTc7l8ulHdWd87qOaEkCwo/bbcYsbd1admyNZ3yN512tX7/qD0TPyjJByZ+rptuJgFReYaH0//5f4GZDO5moKOn3v6crNQDYyfbudk5BSAJQXbm5Zvaf/fvtrqRuCEiV+/57MwDfDmeeaRZ4BADYp7rZICIYxViWpTDPYgDCQHy8NGGC1L693ZXU3mmnEZCq0r+/Pd3dYmPNuDgAQGgI6JwTr732mp5++mn99NNPkqQuXbroj3/8o6677rpAPiwA1FpsrDR+vJneND+/8s2J532aNpWGDycgVSUiwvyN3nknuI975pnM1AkAoSRgIelvf/ubHnroId1xxx0aMmSIJOmbb77Rrbfeql9++cX2We8AoDJRUWYdG4SnU08109Tu3Rucx0tIMJNGAABCR0AnbnjkkUf029/+tszlr776qqZOnaqdO3cG4mHLYUwSAOBEe/dK06cH57FGjZIGDQrOYwEAqmb7FOAHDhzQmWeeWe7yM888UwcOHAjUwwIAcFJpaaZb5cGD0i+/lG75+f59nNRUacAA/94nACDwAhaSTjnlFL377rt68MEHy1z+zjvvqLNnuXsAAGxyyilm85WVZcLSkSPSxo3Srl11e4yhQ6XIyLrdBwAg+AIWkh555BFdddVVWrx4sXdM0rfffqsFCxbo3XffDdTDAgBQa0lJZuvQQRo4UDp61CxevHZtzddXatrUTMcOAAg9AV0nadWqVXr22We1adMmSdKpp56qP/zhD+rbt2+gHrIcxiQBAOqqpMQsYLxqlbRjR/VmN7zqKjNJBADAOVhM9leEJACAP6WnSz/+KO3ZI+3bJ+Xlld+nTRvpf/836KUBAE7C9okb/vvf/yoyMlIjRowoc/lnn30mt9utUaNGBeqhAQAImNRU6Te/Md9blumSt3ev2fbtM+OZhg2ztUQAQB1FBOqOH3jgAZWUlJS73LIsPfDAA9W6jxdeeEG9e/dWcnKykpOTNXjwYH3yySf+LhUAgFpxuaQmTaS+faVLL5Vuv1164AHW2QKAUBewkPTTTz+pe/fu5S7v1q2btm3bVq37aNOmjZ588kmtWrVKK1eu1HnnnafRo0drw4YN/i4XAAC/iI21uwIAQF0FLCSlpKRox44d5S7ftm2bEhISqnUfl1xyiS688EJ17txZXbp00Z///GclJiZq+fLl/i4XAAAAACQFMCSNHj1ad999t7Zv3+69bNu2bfrDH/6gSy+9tMb3V1JSolmzZiknJ0eDBw+udL+CggJlZmaW2QAAAACgugIWkp566iklJCSoW7du6tChgzp06KBu3bqpcePG+utf/1rt+/nxxx+VmJio2NhY3XrrrZozZ06F3fg8pk2bppSUFO+Wlpbmj18HAAAAQD0R0CnALcvSF198oR9++EFxcXHq06ePfuOZEqiaCgsLtWfPHmVkZOj999/Xf/7zHy1atKjSoFRQUKCCggLvz5mZmUpLS2MKcAAAAKCes22dpGXLluno0aO6+OKLvZe9+uqrmjJlinJzczVmzBj985//VGwtR7YOHz5cnTp10r///e9q7c86SQAAAACk6mcDv3e3e/TRR8vMPvfjjz/q5ptv1vnnn68HHnhA8+fP17Rp02p9/263u0xLEQAAAAD4k98Xk127dq0ee+wx78+zZs3SwIED9fLLL0uS0tLSNGXKFE2dOvWk9zV58mSNGjVKbdu2VVZWlt566y0tXLhQn332mb/LBgAAAABJAQhJx48fV/Pmzb0/L1q0SKNGjfL+fPrpp2vv3r3Vuq/Dhw/rt7/9rQ4cOKCUlBT17t1bn332mc4//3x/lw0AAAAAkgIQkpo3b66dO3cqLS1NhYWFWr16tR555BHv9VlZWYqOjq7WfU2fPt3f5QEAAABAlfw+JunCCy/UAw88oCVLlmjy5MmKj48vM6PdunXr1KlTJ38/LAAAAAD4hd9bkh577DFdfvnlOuecc5SYmKhXX31VMTEx3utfeeUVXXDBBf5+WAAAAADwi4Ctk5SRkaHExERFRkaWufzYsWNKTEwsE5wCiSnAAQAAAEjVzwZ+b0nySElJqfDyRo0aBeohAQAAAKDO/D4mCQAAAABCGSEJAAAAAHwQkgAAAADAR8DGJKG8QYMG6ejRowG7/4EDB+qhhx7SqaeeGrDH8JfCwkK98cYb2rp1q6ZOnaoGDRr47b6PHDmi559/Xhs3bqz1fbRp00Z/+tOf1LhxY7/VVVfXXnutiouL7S6jxuLj43XnnXeqf//+dpcCh9m/f7/uvvvuOt1Hz549dd999/n1PcQuJSUl+uc//6mlS5cG5P5dLpeuu+46XXzxxX6937fffltz5sypcp9u3bpp8uTJiouL8+tj1zcvvviivvrqK7vLqFDTpk31pz/9Sa1atbK7FK+77rpLBw4cCNj9DxkyRHfccUe5Scrq4ssvv9RLL71U69u7XC5dc801GjNmjN9qqresMJeRkWFJsjIyMuwuxWrWrJklKaBbRESENX78eOunn36y+9etUF5envX8889bbdu29db8j3/8wy/3vX//fusPf/iDFR8f75e/ZatWrayvvvrKL7X5Q0xMTMBfP4HaYmNjrddff93uPyEcZvPmzX55ffXp08favHmz3b9Onezbt886++yzA/6/2Lx5c6ukpMSvtT/44IPVeuwBAwZYe/fu9etj1zc33XST7e/nVW0tW7a0li9fbvefyatz584B/53PPfdc6+eff/ZbzS+99FKda0pMTLQOHz7st5rCTXWzQcCmAHcKJ00BvnLlShUVFQXkvnNycvSvf/3LezYvMjJSEyZM0EMPPaT27dsH5DFrIicnRy+99JKefvpp71mdqKgoFRcX66KLLtJHH31U6/veu3evnnrqKb388ssqKCiQJA0YMEDjxo1TdHR0je/P7Xbr+eef15YtW+RyuTR58mRNnTq1VvflTy+88ILcbretNdTGf//7X/33v/+VJN1777168skn/XrWDaHr+PHjeuutt2p9+7y8PD311FM6cuSIEhIS9K9//Uu//e1v/VhhcHz88ceaMGGCjh49qsTERD3wwANKTU31++Pcf//9ysnJ0dq1a9WnTx+/3e/y5cu1atWqSq/Pz8/XtGnTdPToUTVv3lyzZ8/WmWee6bfHr0+++uorbdq0ye4yyrEsSy+++KI2bNigmJgYvfTSS5owYYLdZemNN95QRkZGQO772LFj+stf/qKcnBw1bdpUr776qkaNGlXn+92wYYMWLlxY69u/+OKLWr9+ve699149/fTTda4nHFU7GwQjsdnJSS1JwbBy5Urrwgsv9J5NiI6Otm677TZr3759ttSTkZFhTZs2zWratKm3prS0NOu5556zli1bZkmy4uPjrfz8/Brf9/bt262bb77Zio6O9t73mWeeaX3yySeW2+2uU93Z2dnW//7v/3rvd9CgQdb27dvrdJ/1VUlJSZkzzSNGjLCOHTtmd1kIEz///LN17rnnel9f1113nZWVlWV3WdVSUFBg3XPPPd7a+/XrF9BeABdffLElyfrLX/4SsMeozI4dO6xevXpZkqyYmBhr+vTpQa8BgZWZmWmNGTPG+3q+5557rKKiIrvLCqjNmzdbffr08f7O9957r1VQUGBrTR9//LElyYqLi7MOHDhgay1OVd1sQEgKU0uXLrWGDRvm/cd1uVxWdHR00LeIiAhvDR07drRefvll7xtISUmJ1bx5c0tSjbu1ffjhh1ZkZGSZ5u4FCxbUORyd6N1337VSUlIsSVZSUpL15ptv+vX+65NZs2ZZcXFxliSrc+fO1qZNm+wuCWGiuLjYevTRR73vN126dLHWrFljd1lV2rZtmzVgwADve9hdd91Vq5NFNfGPf/zDkmSdd955AX2cymRlZVmXX36593f+/e9/H/YH0fVNSUmJ9fDDD3uf4/PPP986evSo3WUFVF5ennXHHXd4f+eBAwfaelLV7XZbZ5xxhvd/DOXR3e5XTupuZ4dFixbpoYce0pIlS2yroWvXrvrTn/6ka665RlFRZecKue666/TGG2/ogQce0LRp06p9n0OGDNHSpUt1zjnn6M9//rOGDBni77K9du/erXHjxunbb7+VZGqeNGmSoqOjFRkZqaioKO/m+TkpKSksBpL729q1azV69Gjt2bNHycnJeuutt3TRRRfZUotlWXK5XLY8th1KSkp07NixgN1/VFSUGjZsGLD7r44lS5bo2muv1b59+xQTE6NnnnlGEydOdNzzPGvWLN1yyy3KyspSo0aNNGPGDF166aUBf9ytW7eqa9euiomJ0bFjx5SQkBDwxzyR2+3W448/rilTpkiSzjvvPL377ruVTpKTn5+vrKysYJboFRsbWy+PG/zhgw8+0G9/+1vl5uaqU6dO+vDDD9WjRw+7ywqoOXPm6MYbb1R6erqSk5P1n//8R1dccYUttXz55Zc6//zzFRMTo23btiktLa1atzvZ50Rd3ucPHz6s3NxcRwwBobvdr+prS9KJDh06ZO3bty/o2/79+6scJPz6669bkqy+fftW+3fZv3+/5XK5LElB60ZYVFRkTZkypUzLWFXbf/7zn6DUFYoOHTpk/eY3v/G2cD799NNBr+G5556zYmNjraVLlwb9se1QUlJi9evXL6CDl8844wy7f03Lsizrl19+sS655BJvXX/729/sLqmM9957z1vbWWedZe3Zsydoj+12u6127dpZkqyPP/44aI9bkdmzZ1sJCQmWJKtDhw7W3XffbY0fP94aOXKkNWDAAKt9+/ZWYmJiQF+zJ9tcLpf18ssv2/p3CmU//PCD1b59e+9EAvPnz7e7pIDbtWuXdeaZZ3pfQ3fffbctdbjdbuucc86xJFm/+93vqnWb3bt3V2uii27dulmTJk2yvvzyyypbv0tKSqzvvvvOmjJlinX66adbLpfLmjBhgp9+w7qhu92vCEnOdvDgQe8/3qFDh6p1mxdeeME7TijYFi9ebPXv399q2bKl1bRpU6thw4ZWUlKSFR8fb8XExHhD1IwZM4JeWygpKCiwfve733mf+9WrVwftsUtKSqw2bdpYkqxHHnkkaI9rp8OHDwf8gNIpIcmyzAHCH/7wB0uSdcEFF9hdThme1/3VV19tS1ezW265xZJM9z67rVu3zurQoYOtQehk24UXXmj3nymkHTlyxBo6dKglmbFo3377rd0lBVxhYaE1efJky+Vy2XrCdPHixZYkKyoqytqxY0eV+2ZnZ5cZW1XdLTEx0RozZoz10ksvWXv37rWOHTtmzZo1y/rtb39bZiy6ZxsxYkSQfvuqEZJ+RUhyvtNOO82SZL3xxhvV2v+CCy6wJHsGH1eH2+32+xS74Wrs2LGWJOuOO+4I2mMuWbLE+4Y9ceLEoD2unTZu3GhJslJTU+0uJWg+//xzS5LVo0cPu0spw/Oaf+6552x5/Pfff997NtgJfvnlF2vKlCnW/fffbz399NPWjBkzrPnz51vLly+3tm3bZqWnp/t9rGl1eN4n0tLSgv7Y4aawsNA7Fq1p06bWrl277C4pKNauXWvLa9fX+eefb0mybrjhhkr3KSkp8b4vnez5SU9Pt95//33rhhtusFq0aFEuBJ3Y2yYpKckaO3asNX36dL9Ok15XhKRfEZKc7/7777ckMyvVyRw7dsyKioqyJFlbt24NQnUIpE8//dSSZDVq1Cjgg9Y9br/9du8b+JVXXhmUx7TbokWLLMlMmFFfbNiwwZHB0LMW0qxZs2x5/OPHj3sPZHbv3m1LDaHg2LFj3veJ48eP211OyMvOzrb69u1rSbJ69+4dMjNQhrrly5d7w8uWLVsq3Gfq1KmWZGZD/uabb6p93yUlJdaqVausxx57zBo8eLB3GET37t2te++91/rqq69sn+mvMtXNBhECbHbBBRdIkj7//HNZJ5lH5OOPP1ZxcbF69uypzp07B6M8BNDw4cPVpk0bHTt2TPPmzQv44xUXF+u9997z/nzkyJGAP6YT/PLLL5KkJk2a2FxJ8LRu3VqSlJ6ertzcXJurKeV5Lpo2bWrL46empmrQoEGSzHsuKtawYUO1adNGkrR+/Xqbqwl9CQkJ+vDDD9W8eXOtW7dO48ePD8l1/0LNoEGDdNFFF8ntduuRRx4pd/0HH3ygqVOnSjLrK9VkEqyIiAj169dP//d//6elS5fqyJEj2rdvnzZs2KCnn35a5557rmJiYvz1q9iCkATbDRkyRPHx8Tp06JDWrVtX5b6zZ8+WJF122WXBKA0BFhkZ6V38c8aMGQF/vK+++qpMMDp8+HDAH9MJ6mNISk5O9s7e9vPPP9tcTSknPBcjRoyQJH322We21RAKevXqJUn68ccfba4kPKSlpenDDz9UbGysPvzwQ/3pT3+yu6R64dFHH5Ukvf3229qwYYP38rVr13o/f++++27deOONdXqcxo0be09OhQtCEmwXGxuroUOHSqr6zGZubq4+/fRTSdLll18ejNIQBNdff70kc8AW6IPZWbNmSZLOPPNMSbQkhTOXy+VtCdi3b5/N1Rhut1tHjx6V5IyQ9OWXX6qkpMS2OpyOkOR/gwYN0iuvvCJJevLJJ/X666/bXFH469evny6//HJZluWdev/QoUO69NJLlZubqxEjRujpp5+2uUpnIiTBEapzZvOzzz5TXl6eOnTooD59+gSrNARY586dddZZZ8ntdgf0A7OgoMDbEvn73/9ekgkP9aHLh91dvOziOavplJakjIwMbyipbF2gYBgwYIBSU1OVnp6u77//3rY6nI6QFBjXXnutHnzwQUnS//7v/2rZsmU2VxT+HnnkEblcLn3wwQdasWKFLr/8cu3du1ddunTRrFmzyq1hCYOQBEfwjEtasmSJcnJyKtzHt6ud0xaHRN3ccMMNkkyXu5ONS6utTz/9VBkZGWrTpo3GjBkjyZzZD+QCq07haTGrTy1JkvNCkud5SEpKUmxsrG11REVFafjw4ZIYl1QV35AUqPel+uqxxx7TmDFjVFhYqDFjxmjPnj12lxTWevbsqauuukqSNGzYMC1dulQpKSmaN2+eUlNT7S3OwQhJcISuXbuqbdu2Kiws1OLFi8tdX1hYqI8++kgSXe3C0RVXXKH4+Hht3bpVy5cvD8hjvP3225Kkq666SrGxsd4PhvrQ5a4+dreTnBeSnNSi5zkxxbikynXr1k2RkZHKyMhwTJfNcBEREaHXX39dffr00eHDh3XppZcqOzvb7rLC2tSpUxUREaGcnBxFRETo3XffVdeuXe0uy9EISXAEl8tVZZe7hQsXKj09Xc2bN9fgwYODXR4CLCkpSVdccYWkwEzgkJ2d7Z097+qrr5ZUeqBaHyZvqO8hySkHuE56HjwhacWKFUpPT7e3GIeKjY31HkTS5c7/EhMTNW/ePDVr1kw//PCDfve739ldUljr2rWrbrnlFknSs88+630PQOUISXAM36nATzRnzhxJ0pgxYxQRwcs2HHm63M2aNcvvUzbPnz9feXl5OuWUU9S/f39JUrNmzSTRkhTOPBM3OK0lyQnPQ7t27dS1a1eVlJToq6++srscx2JcUmC1bdtWc+fOVadOnXTvvffaXU7Y++c//6m9e/d6x+WiahxtwjGGDRumiIgIbdq0SXv37vVe7na7NXfuXElM/R3Ozj77bHXs2FFZWVne8Wf+4ulqd/XVV3vHs3lakupDSPL8jk7o5hVMTu1u54SQJDEVeHUQkgJv8ODB2rRpk/r27Wt3KWEvKirKe/IIJ0dIgmM0bNhQAwcOlFS2NWn58uU6ePCgUlJSdO6559pVHgLM5XJ5pwP3Z5e748ePe6eOv+aaa7yX15fudnl5ed7JUJxycB4snpB08OBBFRcX21yN80KS77gkJiaoGCEpOKKjo+0uASiHkARHqejMpqdV4eKLLw751ZtRtQkTJsjlcumrr77Srl27/HKfs2fPVlFRkXr16qXu3bt7L68v3e086/JERkYqJSXF5mqCq3nz5oqMjFRJSYkOHTpkdzmOa9EbOnSoYmJitHv3bv300092l+NInpC0adMmFRUV2VwNgGBydEiaNm2aTj/9dCUlJalZs2YaM2aMtmzZYndZCCDPmU3PIoeWZXnHIzGrXfhr27athg0bJkl69dVX/XKfngVkfVuRpPrT3c639aK+TZ0fGRmpli1bSnJGlzuntSQlJCTorLPOksRU4JVp166dEhMTVVRUpK1bt9pdDoAgcnRIWrRokSZOnKjly5friy++UFFRkS644IJK19FB6Bs4cKBSUlJ0/PhxrVy5UuvWrdOOHTvUoEEDbysTwptnAoeZM2fWeaHXgwcPegele9aI8PC0JIV7dzsnTTttByeNS3JaSJKYCvxkIiIi1LNnT0l0uQPqG0eHpE8//VTXX3+9evTooT59+mjmzJnas2ePVq1aZXdpCJCoqChvS8Jnn33mbUUaOXKkEhIS7CwNQXLZZZcpJSVFu3bt0qJFi+p0X++//77cbrcGDRqkjh07lrmuvrQk1deFZD0ISVXznHz6+uuvVVhYaHM1zsS4JKB+cnRIOlFGRoYkqVGjRpXuU1BQoMzMzDIbQovnQ/vzzz/3jkdiVrv6Iy4uzruWUV0ncPCd1e5E9WXiBicemAcTIalqvXv3VrNmzZSTk6OlS5faXY4jEZKA+ilkQpLb7dbdd9+tIUOGeJu+KzJt2jSlpKR4t7S0tCBWCX/wdP9YunSpfvzxR0VFReniiy+2uSoEk2eWu/fff7/WJzp2796tpUuXyuVy6corryx3vae73dGjR+vcrc/JnHhgHkxOWVC2qKjIu2irk7o+RkREVLlGHQhJQH0VZXcB1TVx4kStX79e33zzTZX7TZ48WZMmTfL+nJmZSVAKMe3bt1eXLl28g2SHDh1aZeshws+gQYPUrVs3bd68Wbfddpt31fsTxcXFqXv37urZs6fatm1bZmKCd955R5J0zjnnqFWrVuVu6wkNbrdbx44dC9sQUd9DklMWlPXMMhgREaHU1FRbaznRBRdcoDfeeEOfffaZnnjiCbvLcRxPSNq1a5eysrKUlJRkc0UAgiEkQtIdd9yhjz76SIsXLz7pIlixsbGKjY0NUmUIlBEjRnhDErPa1T8ul0s33nij7rvvPr311lvVuk1SUpJ69uzp3Tyz4504q51HdHS0UlNTlZ6ersOHD4dtiHDatNPB5pTudp6w2qhRI0VGRtpay4nOP/98SdLq1at1+PBhbysrjMaNG6tly5Y6cOCANmzYoDPOOMPukgAEgaNDkmVZuvPOOzVnzhwtXLhQHTp0sLskBMkFF1ygf/7zn5Kk0aNH21wN7DBx4kSlp6fr2LFjle6Tnp6uDRs2aPPmzcrKytKyZcu0bNky7/VRUVEaO3Zspbdv1qyZ0tPTw3ryhvrekuQbkizLsm0adCc/Dy1atFCfPn30ww8/6Msvv9S1115rd0mO06tXLx04cEA//vgjIQmoJxwdkiZOnKi33npLH374oZKSknTw4EFJUkpKiuLi4myuDoF0/vnna/To0erSpUuFXaUQ/uLj4/XnP/+5Wvt61jBZv3691q9frx9//FFbtmzRFVdcocaNG1d6u6ZNm2rr1q1hPXmDkw/Og8ETknJycpSRkWFbVzenPw8jRozQoUOHWGKjEr169dLnn3/OuCSgHnF0SHrhhRckmTEpvmbMmOEd2I3wFBsbq7lz59pdBkJEdHS0evTooR49epRbD6kqnm5FtCSFr/j4eDVs2FDHjx/Xzz//bFtIcnq3x6lTp+rJJ5+sdwsOVxeTNwD1j6NDkmVZdpcAIIyF+1pJlmXV+8VkJdOa5AlJPXr0sKUGp4dVemdUzTck2dltE0DwhMwU4ADgb+G+VlJmZqaKiookqcpuh+HOCZM3OD0koWqnnnqqIiIidPToUW/XfwDhjZAEoN4K9+52ngPz+Ph4xcfH21yNfQhJqKu4uDh17txZEl3ugPqCkASg3gr37nYcmBuepSPsXFCW5yL0MS4JqF8ISQDqLU9LUrh2t+PA3HBCS5LTJ27AyRGSgPqFkASg3gr3liQOzA0nhCQCa+gjJAH1CyEJQL3lCQ+//PKLSkpKbK7G/zgwNwhJ8AdPSNq4cWNYvl8AKIuQBKDe8hywWpalY8eO2VyN/3FgbnhC0uHDh1VQUBD0x8/NzVVeXp4knotQ1rFjR8XHxys/P1/btm2zuxwAAUZIAlBvRUdHq2HDhpLCs8sdIclo0qSJYmJiJEkHDhwI+uN7noeYmBglJiYG/fHhHxEREd51tuhyB4Q/QhKAei2c10piIVnD5XLZ2uXOd2wYi5CGNsYlAfUHIQlAvRbOayV5fqf63pIk2TsuiRa98EFIAuoPQhKAei2cZ7jj4LyUJyTZsVYSz0P4ICQB9QchCUC9Fs5rJXFwXsqzoCwtSagLT0javn27cnJybK4GQCARkgDUa+HaklRcXKzjx49L4uBcorsd/KNZs2Zq1qyZLMvSxo0b7S4HQAARkgDUa+E6ccOxY8dkWZYkqXHjxjZXYz+nTNyA0NezZ09JdLkDwh0hCUC9Fq4TN3haLxo2bKioqCibq7EfLUnwF8YlAfUDIQlAvRau3e04MC/Ld0ySp4UtWHguwgshCagfCEkA6rVwnbiBA/OyWrZsKUkqLCz0/m2ChecivBCSgPqBkASgXvO0JB09elQlJSU2V+M/LCRbVkxMjDcQB7vLHSEpvPTo0UMul0uHDx8Ou5MrAEoRkgDUa55JDSzL0rFjx2yuxn9YSLY8O8Ylud1uAmuYSUhIUMeOHSWJGe6AMEZIAlCvRUdHq1GjRpLCq8sdrRfl2bGgbEZGhreFklkGw8fcuXN1+PBhDR061O5SAAQIUx4BqPeaNm2qY8eOhdXkDYSk8uxYUNbzPCQlJSk2NjZoj4vA8kwDDiB80ZIEoN4Lx7WSCEnl2dHdjucBAEITIQlAvReOayWxgGl5hCQAQHURkgDUe+G4VhIH5+XZMSaJsAoAoYmQBKDeC8e1kghJ5dk5JonnAQBCCyEJQL0Xbi1JeXl5ysnJkcTBuS9PS1J6erpyc3OD8piEJAAITYQkAPVeuIWko0ePSpKioqKUkpJiczXOkZycrISEBEnBa00iJAFAaCIkAaj3wq27ne9Csi6Xy+ZqnMPlcgV98gZCEgCEJseHpMWLF+uSSy5Rq1at5HK5NHfuXLtLAhBmwq0liQPzynnGJQVr8gbfwAoACB2OD0k5OTnq06ePnn/+ebtLARCmPC1JR48eVUlJic3V1B0hqXJ2tSQxux0AhJYouws4mVGjRmnUqFF2lwEgjDVu3FiSZFmWjh496g1NoYqQVDm62wEAqsPxLUk1VVBQoMzMzDIbAFQlKipKjRo1khQeXe5Ym6dywQxJRUVFSk9Pl0RIAoBQE3Yhadq0aUpJSfFuaWlpdpcEIASE0+QNtF5ULpgLyh47dkySmTCiYcOGAX88AID/hF1Imjx5sjIyMrzb3r177S4JQAgIp8kbCEmVC+aCsp7XUqNGjRQZGRnwxwMA+I/jxyTVVGxsrGJjY+0uA0CI8YQkWpLCm6cl6eDBgyopKQloeGHSBgAIXWHXkgQAteHpbhdOLUkcnJfXvHlzRUZGqqSkRIcOHQroYxFWASB0OT4kZWdna+3atVq7dq0kaefOnVq7dq327Nljb2EAwko4dbdjbZ7KRUZGqkWLFpICPy6JkAQAocvxIWnlypXq27ev+vbtK0maNGmS+vbtq4cfftjmygCEk3CZuMGyLA7OTyJY45J4HgAgdDl+TNLQoUNlWZbdZQAIc+HSkpSZmani4mJJpes/oaxgTQNOSAKA0OX4liQACIZwCUmeA/P4+HjFx8fbXI0zBSsksV4VAIQuQhIAKHy623FgfnK0JAEAToaQBAAqDRXHjh3zdlcLRRyYn1ywFpTluQCA0EVIAgCZ8Tsul0uWZeno0aN2l1NrHJifHBM3AABOhpAEAJKioqLUqFEjSaE9LokD85Pz7W4XyImBeC4AIHQRkgDgV+EweQMLyZ6cJyTl5OQoMzMzII+Rm5ur3NxcSTwXABCKHD8FOAAES7NmzbR58+aQnryBhWRPLj4+XqmpqUpPT1efPn0UHR1d4/to1KiR3nnnHbVv377C6z1hNSYmRomJiXUpFwBgA0ISAPwqnFqSCElVGzhwoD7//HPt3r271vfx73//W9OmTavwOt/nweVy1foxAAD2ICQBwK8ISfXH3LlztWbNmlqNSVq8eLEefPBBzZs3r1ohCQAQeghJAPCrcFgriYPz6omLi9OZZ55Zq9t2795dDz/8sDZu3Kht27bplFNOKbcPzwMAhDYmbgCAX4VDSxKLyQZew4YNdfbZZ0uS5s+fX+E+PA8AENoISQDwq1BvSSouLtbx48cl0YIRaJdeeqkk6cMPP6zwelqSACC0EZIA4Feh3pJ07Ngx7/eeNZ8QGJdccokk6Ztvvqlw8WFCEgCENkISAPwq1EOS58C8YcOGiopiyGkgdezYUT179lRJSYk++eSTctcTkgAgtBGSAOBXnu52R48eVXFxsc3V1BwLyQbX6NGjJUnz5s0rdx0hCQBCGyEJAH7VuHFj75o2FXWhcjoWkg0uz7ikTz/9VAUFBWWuY+IGAAhthCQA+FVkZKQaN24sKTS73NF6EVwDBgxQixYtlJWVpUWLFpW5jucCAEIbIQkAfHjO/IfiDHccmAdXRESEdwIH31nuLMviuQCAEEdIAgAfoTx5Awfmwefpcjdv3jxZliVJysjIUElJiSR5WyYBAKGFkAQAPkJ5rSTGwQTfsGHDFB8fr3379mnt2rWSSsNqYmKiGjRoYGN1AIDaIiQBgA9aklATcXFxuuCCCySVznJHWAWA0EdIAgAfhCTUlG+XO4nnAQDCASEJAHyEcnc7Ds7tcdFFF8nlcmn16tXat28fzwMAhAFCEgD4CIeWJLp5BVezZs00ePBgSaY1iZAEAKGPkAQAPjwtSaEWkvLy8pSTkyOJg3M7+Ha5IyQBQOgjJAGAj1BdJ8lzYB4VFaXk5GSbq6l/Ro8eLUn66quvtGPHDkm06AFAKCMkAYCPFi1aSJKOHTum++67T/n5+TZXVD2+rRcul8vmauqfrl27qnPnzioqKtJHH30kiZYkAAhlhCQA8NG4cWNNmjRJkvT0009rwIABWrNmjc1VnRxdvOzlcrm8Xe48wZrnAgBCV0iEpOeff17t27dXgwYNNGjQIH333Xd2lwQgjD3zzDOaO3eumjVrpg0bNmjgwIF6/PHHVVxcbHdplWJtHvt5QpIHIQkAQpfjQ9I777yjSZMmacqUKVq9erX69OmjESNGhNx4AQChZfTo0Vq/fr0uv/xyFRcX66GHHtKQIUO0ZcsWu0urEC1J9jvzzDPVqFEj7888FwAQuqLsLuBk/va3v+nmm2/WDTfcIEl68cUX9fHHH+uVV17RAw88YHN1AMJZ06ZN9f777+vNN9/UHXfcoe+++06nnXaapk2bpuHDh9tdXhmbN2+WxIG5naKionTxxRfrtddek0SrHgCEMkeHpMLCQq1atUqTJ0/2XhYREaHhw4dr2bJlFd6moKBABQUF3p8zMzMDXieA8OVyuTR+/HgNHTpUN910kz7//HPdc889dpdVKUKSvS699FK99tprcrlcatiwod3lAABqydEh6ZdfflFJSYmaN29e5vLmzZt7z5qeaNq0aXrkkUeCUR6AeqRNmzb69NNP9eKLL+qvf/2rsrOz7S6pnNTUVF122WV2l1GvjRw5Uv3791enTp0UGRlpdzkAgFpyWZZl2V1EZfbv36/WrVtr6dKl3tXMJem+++7TokWLtGLFinK3qaglKS0tTRkZGawdAgAAANRjmZmZSklJOWk2cHRLUpMmTRQZGalDhw6VufzQoUPetUxOFBsbq9jY2GCUBwAAACAMOXp2u5iYGPXv318LFizwXuZ2u7VgwYIyLUsAAAAA4C+ObkmSpEmTJmnChAkaMGCABg4cqL///e/KycnxznYHAAAAAP7k+JB01VVX6ciRI3r44Yd18OBBnXbaafr000/LTeYAAAAAAP7g6Ikb/KG6g7MAAAAAhLfqZgNHj0kCAAAAgGAjJAEAAACAD0ISAAAAAPggJAEAAACAD0ISAAAAAPhw/BTgdeWZvC8zM9PmSgAAAADYyZMJTjbBd9iHpKysLElSWlqazZUAAAAAcIKsrCylpKRUen3Yr5Pkdru1f/9+JSUlyeVy2V0OQkBmZqbS0tK0d+9e1taC3/H6QqDw2kIg8fpCoAT7tWVZlrKystSqVStFRFQ+8ijsW5IiIiLUpk0bu8tACEpOTuaDAAHD6wuBwmsLgcTrC4ESzNdWVS1IHkzcAAAAAAA+CEkAAAAA4IOQBJwgNjZWU6ZMUWxsrN2lIAzx+kKg8NpCIPH6QqA49bUV9hM3AAAAAEBN0JIEAAAAAD4ISQAAAADgg5AEAAAAAD4ISQAAAADgg5CEemnatGk6/fTTlZSUpGbNmmnMmDHasmVLmX3y8/M1ceJENW7cWImJiRo7dqwOHTpkU8UIZU8++aRcLpfuvvtu72W8vlBbP//8s8aPH6/GjRsrLi5OvXr10sqVK73XW5alhx9+WC1btlRcXJyGDx+un376ycaKESpKSkr00EMPqUOHDoqLi1OnTp302GOPyXeOL15fqK7FixfrkksuUatWreRyuTR37twy11fntXTs2DGNGzdOycnJSk1N1U033aTs7Oyg1E9IQr20aNEiTZw4UcuXL9cXX3yhoqIiXXDBBcrJyfHuc88992j+/Pl67733tGjRIu3fv1+XX365jVUjFH3//ff697//rd69e5e5nNcXauP48eMaMmSIoqOj9cknn2jjxo165pln1LBhQ+8+Tz31lP7xj3/oxRdf1IoVK5SQkKARI0YoPz/fxsoRCv7yl7/ohRde0HPPPadNmzbpL3/5i5566in985//9O7D6wvVlZOToz59+uj555+v8PrqvJbGjRunDRs26IsvvtBHH32kxYsX65ZbbgnOL2ABsA4fPmxJshYtWmRZlmWlp6db0dHR1nvvvefdZ9OmTZYka9myZXaViRCTlZVlde7c2friiy+sc845x7rrrrssy+L1hdq7//77rbPOOqvS691ut9WiRQvr6aef9l6Wnp5uxcbGWm+//XYwSkQIu+iii6wbb7yxzGWXX365NW7cOMuyeH2h9iRZc+bM8f5cndfSxo0bLUnW999/793nk08+sVwul/Xzzz8HvGZakgBJGRkZkqRGjRpJklatWqWioiINHz7cu0+3bt3Utm1bLVu2zJYaEXomTpyoiy66qMzrSOL1hdqbN2+eBgwYoCuuuELNmjVT37599fLLL3uv37lzpw4ePFjmtZWSkqJBgwbx2sJJnXnmmVqwYIG2bt0qSfrhhx/0zTffaNSoUZJ4fcF/qvNaWrZsmVJTUzVgwADvPsOHD1dERIRWrFgR8BqjAv4IgMO53W7dfffdGjJkiHr27ClJOnjwoGJiYpSamlpm3+bNm+vgwYM2VIlQM2vWLK1evVrff/99uet4faG2duzYoRdeeEGTJk3Sgw8+qO+//16///3vFRMTowkTJnhfP82bNy9zO15bqI4HHnhAmZmZ6tatmyIjI1VSUqI///nPGjdunCTx+oLfVOe1dPDgQTVr1qzM9VFRUWrUqFFQXm+EJNR7EydO1Pr16/XNN9/YXQrCxN69e3XXXXfpiy++UIMGDewuB2HE7XZrwIABeuKJJyRJffv21fr16/Xiiy9qwoQJNleHUPfuu+/qzTff1FtvvaUePXpo7dq1uvvuu9WqVSteX6h36G6Heu2OO+7QRx99pK+//lpt2rTxXt6iRQsVFhYqPT29zP6HDh1SixYtglwlQs2qVat0+PBh9evXT1FRUYqKitKiRYv0j3/8Q1FRUWrevDmvL9RKy5Yt1b179zKXnXrqqdqzZ48keV8/J86UyGsL1fHHP/5RDzzwgK6++mr16tVL1113ne655x5NmzZNEq8v+E91XkstWrTQ4cOHy1xfXFysY8eOBeX1RkhCvWRZlu644w7NmTNHX331lTp06FDm+v79+ys6OloLFizwXrZlyxbt2bNHgwcPDna5CDHDhg3Tjz/+qLVr13q3AQMGaNy4cd7veX2hNoYMGVJuuYKtW7eqXbt2kqQOHTqoRYsWZV5bmZmZWrFiBa8tnFRubq4iIsoeGkZGRsrtdkvi9QX/qc5rafDgwUpPT9eqVau8+3z11Vdyu90aNGhQ4IsM+NQQgAPddtttVkpKirVw4ULrwIED3i03N9e7z6233mq1bdvW+uqrr6yVK1dagwcPtgYPHmxj1QhlvrPbWRavL9TOd999Z0VFRVl//vOfrZ9++sl68803rfj4eOuNN97w7vPkk09aqamp1ocffmitW7fOGj16tNWhQwcrLy/PxsoRCiZMmGC1bt3a+uijj6ydO3das2fPtpo0aWLdd9993n14faG6srKyrDVr1lhr1qyxJFl/+9vfrDVr1li7d++2LKt6r6WRI0daffv2tVasWGF98803VufOna1rrrkmKPUTklAvSapwmzFjhnefvLw86/bbb7caNmxoxcfHW5dddpl14MAB+4pGSDsxJPH6Qm3Nnz/f6tmzpxUbG2t169bNeumll8pc73a7rYceeshq3ry5FRsbaw0bNszasmWLTdUilGRmZlp33XWX1bZtW6tBgwZWx44drT/96U9WQUGBdx9eX6iur7/+usJjrQkTJliWVb3X0tGjR61rrrnGSkxMtJKTk60bbrjBysrKCkr9LsvyWUYZAAAAAOo5xiQBAAAAgA9CEgAAAAD4ICQBAAAAgA9CEgAAAAD4ICQBAAAAgA9CEgAAAAD4ICQBAAAAgA9CEgAANTBz5kylpqbaXQYAIIAISQCAgDh48KDuuusunXLKKWrQoIGaN2+uIUOG6IUXXlBubq7d5VVL+/bt9fe//73MZVdddZW2bt1qT0EAgKCIsrsAAED42bFjh4YMGaLU1FQ98cQT6tWrl2JjY/Xjjz/qpZdeUuvWrXXppZfaUptlWSopKVFUVO0+AuPi4hQXF+fnqgAATkJLEgDA726//XZFRUVp5cqVuvLKK3XqqaeqY8eOGj16tD7++GNdcsklkqT09HT97//+r5o2bark5GSdd955+uGHH7z3M3XqVJ122ml6/fXX1b59e6WkpOjqq69WVlaWdx+3261p06apQ4cOiouLU58+ffT+++97r1+4cKFcLpc++eQT9e/fX7Gxsfrmm2+0fft2jR49Ws2bN1diYqJOP/10ffnll97bDR06VLt379Y999wjl8sll8slqeLudi+88II6deqkmJgYde3aVa+//nqZ610ul/7zn//osssuU3x8vDp37qx58+b57e8NAPAvQhIAwK+OHj2qzz//XBMnTlRCQkKF+3gCxxVXXKHDhw/rk08+0apVq9SvXz8NGzZMx44d8+67fft2zZ07Vx999JE++ugjLVq0SE8++aT3+mnTpum1117Tiy++qA0bNuiee+7R+PHjtWjRojKP+cADD+jJJ5/Upk2b1Lt3b2VnZ+vCCy/UggULtGbNGo0cOVKXXHKJ9uzZI0maPXu22rRpo0cffVQHDhzQgQMHKvxd5syZo7vuukt/+MMftH79ev3ud7/TDTfcoK+//rrMfo888oiuvPJKrVu3ThdeeKHGjRtX5vcEADiIBQCAHy1fvtySZM2ePbvM5Y0bN7YSEhKshIQE67777rOWLFliJScnW/n5+WX269Spk/Xvf//bsizLmjJlihUfH29lZmZ6r//jH/9oDRo0yLIsy8rPz7fi4+OtpUuXlrmPm266ybrmmmssy7Ksr7/+2pJkzZ0796S19+jRw/rnP//p/bldu3bWs88+W2afGTNmWCkpKd6fzzzzTOvmm28us88VV1xhXXjhhd6fJVn/93//5/05OzvbkmR98sknJ60JABB8jEkCAATFd999J7fbrXHjxqmgoEA//PCDsrOz1bhx4zL75eXlafv27d6f27dvr6SkJO/PLVu21OHDhyVJ27ZtU25urs4///wy91FYWKi+ffuWuWzAgAFlfs7OztbUqVP18ccf68CBAyouLlZeXp63Jam6Nm3apFtuuaXMZUOGDNH/+3//r8xlvXv39n6fkJCg5ORk7+8BAHAWQhIAwK9OOeUUuVwubdmypczlHTt2lCTvpAfZ2dlq2bKlFi5cWO4+fMf8REdHl7nO5XLJ7XZ770OSPv74Y7Vu3brMfrGxsWV+PrHr37333qsvvvhCf/3rX3XKKacoLi5O//M//6PCwsJq/qY1U9XvAQBwFkISAMCvGjdurPPPP1/PPfec7rzzzkrHJfXr108HDx5UVFSU2rdvX6vH6t69u2JjY7Vnzx6dc845Nbrtt99+q+uvv16XXXaZJBO4du3aVWafmJgYlZSUVHk/p556qr799ltNmDChzH137969RvUAAJyDkAQA8Lt//etfGjJkiAYMGKCpU6eqd+/eioiI0Pfff6/Nmzerf//+Gj58uAYPHqwxY8boqaeeUpcuXbR//359/PHHuuyyy8p1j6tIUlKS7r33Xt1zzz1yu90666yzlJGRoW+//VbJycllgsuJOnfurNmzZ+uSSy6Ry+XSQw89VK5lp3379lq8eLGuvvpqxcbGqkmTJuXu549//KOuvPJK9e3bV8OHD9f8+fM1e/bsMjPlAQBCCyEJAOB3nTp10po1a/TEE09o8uTJ2rdvn2JjY9W9e3fde++9uv322+VyufTf//5Xf/rTn3TDDTfoyJEjatGihc4++2w1b9682o/12GOPqWnTppo2bZp27Nih1NRU9evXTw8++GCVt/vb3/6mG2+8UWeeeaaaNGmi+++/X5mZmWX2efTRR/W73/1OnTp1UkFBgSzLKnc/Y8aM0f/7f/9Pf/3rX3XXXXepQ4cOmjFjhoYOHVrt3wEA4Cwuq6J3fAAAAACop1gnCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwEeU3QUEmtvt1v79+5WUlCSXy2V3OQAAAABsYlmWsrKy1KpVK0VEVN5eFPYhaf/+/UpLS7O7DAAAAAAOsXfvXrVp06bS68M+JCUlJUkyf4jk5GSbqwEAAABgl8zMTKWlpXkzQmXCPiR5utglJycTkgAAAACcdBgOEzcAAAAAgA9CEgAAAAD4ICQBAAAAgI+wH5MEAAAAhKKSkhIVFRXZXUZIiYyMVFRUVJ2X/iEkAQAAAA6TnZ2tffv2ybIsu0sJOfHx8WrZsqViYmJqfR+EJAAAAMBBSkpKtG/fPsXHx6tp06Z1bhWpLyzLUmFhoY4cOaKdO3eqc+fOVS4YWxVCEgAAAOAgRUVFsixLTZs2VVxcnN3lhJS4uDhFR0dr9+7dKiwsVIMGDWp1P0zcAAAAADgQLUi1U9vWozL34Yc6AAAAACBsEJKCbNcuuysAAAAAUBVCUpDNnSu9+aZ0/LjdlQAAAAD+t2zZMkVGRuqiiy6yu5RaIyTZ4KefpH/9S1q8WCopsbsaAAAAwH+mT5+uO++8U4sXL9b+/fvtLqdWCEk2KSqSvvrKhKUdO+yuBgAAAKi77OxsvfPOO7rtttt00UUXaebMmZKka6+9VldddVWZfYuKitSkSRO99tprkqSsrCyNGzdOCQkJatmypZ599lkNHTpUd999d5B/C0KS7Y4elV57TfrgAykvz+5qAAAA4DSWJeXk2LPVdC3bd999V926dVPXrl01fvx4vfLKK7IsS+PGjdP8+fOVnZ3t3fezzz5Tbm6uLrvsMknSpEmT9O2332revHn64osvtGTJEq1evdqff8pqY50kh/jxR6lXL6lLF7srAQAAgJPk5kqJifY8dna2lJBQ/f2nT5+u8ePHS5JGjhypjIwMLVq0SCNGjFBCQoLmzJmj6667TpL01ltv6dJLL1VSUpKysrL06quv6q233tKwYcMkSTNmzFCrVq38/jtVBy1JDkJLEgAAAELVli1b9N133+maa66RJEVFRemqq67S9OnTFRUVpSuvvFJvvvmmJCknJ0cffvihxo0bJ0nasWOHioqKNHDgQO/9paSkqGvXrsH/RURLkqMQkgAAAHCi+HjTomPXY1fX9OnTVVxcXKb1x7IsxcbG6rnnntO4ceN0zjnn6PDhw/riiy8UFxenkSNHBqDquiMkOQghCQAAACdyuWrW5c0OxcXFeu211/TMM8/oggsuKHPdmDFj9Pbbb+vWW29VWlqa3nnnHX3yySe64oorFB0dLUnq2LGjoqOj9f3336tt27aSpIyMDG3dulVnn3120H8fQlKQWJa0apUZe9SypRRVwV+ekAQAAIBQ9NFHH+n48eO66aablJKSUua6sWPHavr06br11lt17bXX6sUXX9TWrVv19ddfe/dJSkrShAkT9Mc//lGNGjVSs2bNNGXKFEVERMjlcnn3mzx5sn7++WfvjHiBwpikIDr3XOmVV6SMjIqvJyQBAAAgFE2fPl3Dhw8vF5AkE5JWrlypdevWady4cdq4caNat26tIUOGlNnvb3/7mwYPHqyLL75Yw4cP15AhQ3TqqaeqQYMG3n0OHDigPXv2BPz3oSUpSFwuqU0bafNmE5IaNy6/DyEJAAAAoWj+/PmVXjdw4EBZPnOJW5XMK56UlOSd2EEykzs88sgjuuWWW7yXedZdCjRCUhClpZmQlJlZ8fWEJAAAANRXa9as0ebNmzVw4EBlZGTo0UcflSSNHj066LUQkoIoLc18JSQBAAAA5f31r3/Vli1bFBMTo/79+2vJkiVq0qRJ0OsgJAVRmzbmK2OSAAAAgLL69u2rVatW2V2GJJsnbpg2bZpOP/10JSUlqVmzZhozZoy2bNlSZp/8/HxNnDhRjRs3VmJiosaOHatDhw7ZVHHdeFqSsrIqvj4/38yCBwAAAMA+toakRYsWaeLEiVq+fLm++OILFRUV6YILLlBOTo53n3vuuUfz58/Xe++9p0WLFmn//v26/PLLbay69jwhqbKWJMsyQQkAAACAfWztbvfpp5+W+XnmzJlq1qyZVq1apbPPPlsZGRmaPn263nrrLZ133nmSpBkzZujUU0/V8uXLdcYZZ9hRdq1lZKySlKuMjP6SKl6+OC9PiosLalkAAAAAfDhqnaSMX5tYGjVqJElatWqVioqKNHz4cO8+3bp1U9u2bbVs2bIK76OgoECZmZllNqe49dYLJJ2tgoJdKiyseB/GJQEAAAD2ckxIcrvduvvuuzVkyBD17NlTknTw4EHFxMQoNTW1zL7NmzfXwYMHK7yfadOmKSUlxbulefq4OUBcnGchrHxmuAMAAAAcyjEhaeLEiVq/fr1mzZpVp/uZPHmyMjIyvNvevXv9VGHdla4WnM8MdwAAAIBDOSIk3XHHHfroo4/09ddfq41nnmxJLVq0UGFhodLT08vsf+jQIbVo0aLC+4qNjVVycnKZzSl8QxItSQAAAEDFpk6dqtNOO822x7d14gbLsnTnnXdqzpw5WrhwoTp06FDm+v79+ys6OloLFizQ2LFjJUlbtmzRnj17NHjwYDtKrhNCEgAAAGpr6lTnP96RI0f08MMP6+OPP9ahQ4fUsGFD9enTRw8//LCGDBkil8ulOXPmaMyYMf4u169sDUkTJ07UW2+9pQ8//FBJSUnecUYpKSmKi4tTSkqKbrrpJk2aNEmNGjVScnKy7rzzTg0ePDjkZraT6G4HAACA8DZ27FgVFhbq1VdfVceOHXXo0CEtWLBAR48etbu0GrE1JL3wwguSpKFDh5a5fMaMGbr++uslSc8++6wiIiI0duxYFRQUaMSIEfrXv/4V5Er9g5YkAAAAhKv09HQtWbJECxcu1DnnnCNJateunQYOHChJat++vSTpsssu8163a9cuSdKTTz6pZ599Vrm5ubryyivVtGnToNfvy9YxSZZlVbh5ApJkgsXzzz+vY8eOKScnR7Nnz650PJLTlYakAkISAAAAwkpiYqISExM1d+5cFRQUlLv++++/l2QaRA4cOOD9+d1339XUqVP1xBNPaOXKlWrZsqXtjSKOmLihvqAlCQAAAOEqKipKM2fO1KuvvqrU1FQNGTJEDz74oNatWydJ3tah1NRUtWjRwvvz3//+d91000266aab1LVrVz3++OPq3r27bb+HREgKKt+QVFAgVRCwCUkAAAAIWWPHjtX+/fs1b948jRw5UgsXLlS/fv00c+bMSm+zadMmDRo0qMxldk/SRkgKIk9IiorKl6QKJ28gJAEAACCUNWjQQOeff74eeughLV26VNdff72mTJlid1k1QkgKIk9IiokxIamiLnf5+cGsCAAAAAis7t27KycnR5IUHR2tkpKSMtefeuqpWrFiRZnLli9fHrT6KkJICiJPSIqNrTwklZRU3A0PAAAAcLKjR4/qvPPO0xtvvKF169Zp586deu+99/TUU09p9OjRkswMdwsWLNDBgwd1/PhxSdJdd92lV155RTNmzNDWrVs1ZcoUbdiwocx9z5kzR926dQva72LrFOD1TXW620mmy11sbLCqAgAAAOouMTFRgwYN0rPPPqvt27erqKhIaWlpuvnmm/Xggw9Kkp555hlNmjRJL7/8slq3bq1du3bpqquu0vbt23XfffcpPz9fY8eO1W233abPPvvMe98ZGRnasmVL0H4Xl2VZVtAezQaZmZlKSUlRRkaGkpOTba3lscce08MPP6xWrW7R/v3/1mmnSb+G6jJ+9zupZcuglwcAAAAHyM/P186dO9WhQwefib9QXVX9/aqbDehuF0SeJykysvLudhKTNwAAAAB2IiQFkSckRUQQkgAAAACnIiQF0YkhKSNDqqizIyEJAAAAsA8hKYg8IcnlMiGpqIgFZQEAAACnISQFkSckWVa+4uLMZSwoCwAAADgLISmIPCGppCRfnsk0KhqXREgCAAAA7ENICiLfkJSSYi4jJAEAAADOQkgKoopakuhuBwAAADgLISmIPCGpuJjudgAAAIBTEZKCyBOSiooISQAAAKjfhg4dqrvvvtvuMipESAoi35DEmCQAAACEm+uvv14ul0u33npruesmTpwol8ul66+/XpI0e/ZsPfbYY0GusHoISUFUUUtSRQvKFhebNZQAAACAUJOWlqZZs2Ypz+fMf35+vt566y21bdvWe1mjRo2UlJRkR4knRUgKotKQVKikJLckE4gqajmiNQkAAAChqF+/fkpLS9Ps2bO9l82ePVtt27ZV3759vZed2N2uffv2euKJJ3TjjTcqKSlJbdu21UsvvRTM0r0ISUHkCUlGgeLjzXd0uQMAAEBlLMtSTk6OLZt1Ypenarrxxhs1Y8YM78+vvPKKbrjhhpPe7plnntGAAQO0Zs0a3X777brtttu0ZcuWWtVQF1FBf8R6zDckFRfnKyUlTrm5JiS1aFF2X0ISAAAAJCk3N1eJiYm2PHZ2drYSEhJqfLvx48dr8uTJ2r17tyTp22+/1axZs7Rw4cIqb3fhhRfq9ttvlyTdf//9evbZZ/X111+ra9euNa6hLghJQRQVFaWIiAi53W7vNOAHDrBWEgAAAMJL06ZNddFFF2nmzJmyLEsXXXSRmjRpctLb9e7d2/u9y+VSixYtdPjw4UCWWiFCUhC5XC41aNBAubm5rJUEAACAaomPj1d2drZtj11bN954o+644w5J0vPPP1+t20RHR5f52eVyye1217qG2iIkBRkhCQAAADXhcrlq1eXNbiNHjlRhYaFcLpdGjBhhdzk1wsQNQeYZl0RIAgAAQDiLjIzUpk2btHHjRkVGRvrlPocNG6bnnnvOL/dVFVqSgsw3JLGgLAAAAMJZsqdVwE+2b9+uX375xa/3WRFCUpB5QlJJSdmQZFmSy1W6HyEJAAAAoWbmzJlVXj937lzv9yfOdLdr165y+69du/ak+wQC3e2CzBOSXK58eRYYLi6WcnPL7kdIAgAAAOxBSAoy35AUFSV5xuCd2OWOkAQAAADYg5AUZKULyuZLUqXjkghJAAAAgD0ISUF2YkjyjGU7cUFZQhIAAABgD0JSkFUWkk5sSSoslEpKglcXAAAAAIOQFGSekGRZVYckidYkAACA+syyLLtLCEn++LsRkoLME5Lc7qrHJEmEJAAAgPrIs/BqYWGhzZWEptxfp42Ojo6u9X2wTlKQnRiSKhuTJBGSAAAA6qOoqCjFx8fryJEjio6OVkQE7RrVYVmWcnNzdfjwYaWmpnrDZm0QkoKsspCUlcWCsgAAAJBcLpdatmypnTt3avfu3XaXE3JSU1PVokWLOt0HISnIPCGppMSEJM+CsiUlUk6OlJhYui8hCQAAoH6KiYlR586d6XJXQ9HR0XVqQfIgJAXZiSEpMtIEo+xsMy6JkAQAAABJioiI8JkZGcFEB8cg87zQi4vzvZd5Jm9grSQAAADAfoSkIKsoJFU2DTghCQAAAAg+QlKQeUJSUREhCQAAAHAiQlKQEZIAAAAAZyMkBZknJBUWlh+TREgCAAAA7EdICjLfkORZE6myBWUJSQAAAEDwEZKCzBOS8vPzFRNjLvNdUNbtLt2XkAQAAAAEHyEpyHxDUmysuSwpSXK5TEDKySndt6CgbGgCAAAAEHiEpCDzDUmetcEiIkxQksqOS7IsKT9fAAAAAIKIkBRkFbUkSaUhKSur7P50uQMAAACCy9aQtHjxYl1yySVq1aqVXC6X5s6dW+b666+/Xi6Xq8w2cuRIe4r1k8pCUkKC+erb3U4iJAEAAADBZmtIysnJUZ8+ffT8889Xus/IkSN14MAB7/b2228HsUL/q6i7nSTFx5uvubll9yckAQAAAMEVZeeDjxo1SqNGjapyn9jYWLVo0SJIFQVe6RTghYqOdsuTUwlJAAAAgDM4fkzSwoUL1axZM3Xt2lW33Xabjh49WuX+BQUFyszMLLM5SQOf5qPIyALv94QkAAAAwBkcHZJGjhyp1157TQsWLNBf/vIXLVq0SKNGjVJJSUmlt5k2bZpSUlK8W1paWhArPjnfkORylU5dR0gCAAAAnMHW7nYnc/XVV3u/79Wrl3r37q1OnTpp4cKFGjZsWIW3mTx5siZNmuT9OTMz01FBKSoqShEREXK73WVCkmfiBkISAAAAYC9HtySdqGPHjmrSpIm2bdtW6T6xsbFKTk4uszmJy+XyaU0q35LE7HYAAACAvUIqJO3bt09Hjx5Vy5Yt7S6lTjwhybLobgcAAAA4ja3d7bKzs8u0Cu3cuVNr165Vo0aN1KhRIz3yyCMaO3asWrRooe3bt+u+++7TKaecohEjRthYdd1V1ZJUVGS26GjzMyEJAAAACC5bW5JWrlypvn37qm/fvpKkSZMmqW/fvnr44YcVGRmpdevW6dJLL1WXLl100003qX///lqyZIlifVdhDUGekOR2l4ak2Fgp4tdnw7c1iZAEAAAABJetLUlDhw6VZVmVXv/ZZ58FsZrgqai7nctlWpOys01ISkkxlxOSAAAAgOAKqTFJ4cITkkpK8stcXtEMd/n5UhU5EgAAAICfEZJsUFF3O6niGe7cbqmgQAAAAACChJBkA09IKizM907QIDHDHQAAAOAEhCQbeEJSfn6+vBPdiZAEAAAAOAEhyQa+Icl3oj4WlAUAAADsR0iyQWUhyTNxw4mhiJAEAAAABA8hyQYn625HSxIAAABgH0KSDU7W3Y4xSQAAAIB9CEk2YOIGAAAAwLkISTaozpgkt7v0ckISAAAAEDyEJBtU1pIUF2e+WpaU77POLCEJAAAACB5Ckg0qa0mKjJQ3NPl2uSMkAQAAAMFDSLJBZSFJqniGO0ISAAAAEDyEJBtU1t1OqnjyhhMncgAAAAAQOIQkG1TVkuSZvME3GPmOTwIAAAAQWIQkG1TVkuSZvMG3u11JiVRQEKTiAAAAgHqOkGSDmrYkSYxLAgAAAIKFkGSDmo5JkghJAAAAQLAQkmxQndntCEkAAACAPQhJNvANSVFRZn0kD0ISAAAAYC9Ckg18Q5L5ufQ6xiQBAAAA9iIk2eDEkOTb5a6ixWQl1koCAAAAgoWQZIOqWpI8Iam4WCoqKr2ckAQAAAAEByHJBp6QVFhYKLfbXaYlKSamdIySb2vSiS1LAAAAAAKDkGSDBj5NRwUFBWVaklyuiidvoCUJAAAACA5Ckg18Q1J1F5QlJAEAAADBQUiyQVRUlCIizJ++qrWSfLvYEZIAAACA4CAk2cDlcpWZvMG3u51EdzsAAADAToQkm/iGpMpaknyDUVFR2dnuAAAAAAQGIckmNW1JquhnAAAAAP5X65C0ZMkSjR8/XoMHD9bPP/8sSXr99df1zTff+K24cFbTliSJacABAACAYKhVSPrggw80YsQIxcXFac2aNSooKJAkZWRk6IknnvBrgeGqqpakima3q+hnAAAAAP5Xq5D0+OOP68UXX9TLL7+s6Oho7+VDhgzR6tWr/VZcOKtOS9KJLUeEJAAAACDwahWStmzZorPPPrvc5SkpKUpPT69rTfVCbbrbEZIAAACAwKtVSGrRooW2bdtW7vJvvvlGHTt2rHNR9UF1utvl5Ulud+nlhCQAAAAg8GoVkm6++WbdddddWrFihVwul/bv368333xT9957r2677TZ/1xiWqmpJiosr/T4vr/R7QhIAAAAQeFG1udEDDzwgt9utYcOGKTc3V2effbZiY2N177336s477/R3jWHJNyTFxEgREaWtRhERJijl5ZlgVNlEDgAAAAD8r1YhyeVy6U9/+pP++Mc/atu2bcrOzlb37t2VmJjo7/rClm9IkqTY2LKtRvHxpSHJgynAAQAAgMCrVXe71157TZs2bVJMTIy6d++ugQMHKjExUfn5+Xrttdf8XWNYOjEkVbagrG8woiUJAAAACLxahaTrr79eAwcO1AcffFDm8oyMDN1www1+KSzcVdSS5KuiGe4ISQAAAEDg1SokSdIjjzyi6667TlOnTvVjOfVHbUJSXp5kWcGoDgAAAKi/ah2Sxo8fr6+++kr//ve/9T//8z/K8x1Qg5OqTXc7t1v6dXcAAAAAAVKrkORyuSRJZ5xxhlasWKFt27bpzDPP1K5du/xZW1g7WUuS71pJvuhyBwAAAARWrUKS5dPnq23btlq6dKnat2+v888/32+FhbvatCRJhCQAAAAg0GoVkqZMmVJmuu/4+HjNmTNH99xzj84++2y/FRfOajMmSWIacAAAACDQarVO0pQpUyq8/JFHHqlTMfXJyVqSKltAlpYkAAAAILCqHZLmzZunUaNGKTo6WvPmzat0P5fLpUsuucQvxYWzmrQkWZb06zAwQhIAAAAQYNUOSWPGjNHBgwfVrFkzjRkzptL9XC6XSkpK/FFbWKvumKTiYqmoSIqJMT8TkgAAAIDAqnZIcrvdFX6P2jlZS1J0tBQVZUJSbi4hCQAAAAiWGk3csGzZMn300UdlLnvttdfUoUMHNWvWTLfccosKCgqqfX+LFy/WJZdcolatWsnlcmnu3LllrrcsSw8//LBatmypuLg4DR8+XD/99FNNSnas2F9TUWUhyeWqeIY7QhIAAAAQWDUKSY8++qg2bNjg/fnHH3/UTTfdpOHDh+uBBx7Q/PnzNW3atGrfX05Ojvr06aPnn3++wuufeuop/eMf/9CLL76oFStWKCEhQSNGjPAGi1B2su52UsUz3BGSAAAAgMCq0ex2a9eu1WOPPeb9edasWRo0aJBefvllSVJaWpqmTJmiqVOnVuv+Ro0apVGjRlV4nWVZ+vvf/67/+7//0+jRoyWZVqvmzZtr7ty5uvrqq2tSuuOcrLudVPEMd0wBDgAAAARWjVqSjh8/rubNm3t/XrRoUZmQc/rpp2vv3r1+KWznzp06ePCghg8f7r0sJSVFgwYN0rJlyyq9XUFBgTIzM8tsTlSTliS62wEAAADBU6OQ1Lx5c+3cuVOSVFhYqNWrV+uMM87wXp+VlaXo6Gi/FHbw4EHvY55Yg+e6ikybNk0pKSneLS0tzS/1+NuJISkmpnSab4+KutsVFEhMHggAAAAETo1C0oUXXqgHHnhAS5Ys0eTJkxUfH6/f/OY33uvXrVunTp06+b3Impg8ebIyMjK8m79atvztxJDkclW9VpIvWpMAAACAwKnRmKTHHntMl19+uc455xwlJibq1VdfVYxnbmpJr7zyii644AK/FNaiRQtJ0qFDh9SyZUvv5YcOHdJpp51W6e1iY2O9M8c5mSckFRYWyrIsuVwuxcZKvnNSVBWSkpKCVCgAAABQz9QoJDVp0kSLFy9WRkaGEhMTFRkZWeb69957T4mJiX4prEOHDmrRooUWLFjgDUWZmZlasWKFbrvtNr88hp0a+AxCKigoUIMGDcq1JFU0cUNFPwMAAADwnxqFJI+UlJQKL2/UqFGN7ic7O1vbtm3z/rxz506tXbtWjRo1Utu2bXX33Xfr8ccfV+fOndWhQwc99NBDatWqlcaMGVObsh3FNyTl5+erQYMG5SZvoLsdAAAAEHy1Ckn+snLlSp177rnenydNmiRJmjBhgmbOnKn77rtPOTk5uuWWW5Senq6zzjpLn376aZmAEaqio6PlcrlkWZZ3XJKn5cijotntKvoZAAAAgP/YGpKGDh0qy7Iqvd7lcunRRx/Vo48+GsSqgsPlcqlBgwbKy8vzhqQTeyp6QlJ+vuR2SxG/TrNBSxIAAAAQODWa3Q7+deIMdyeGpLi40u99gxEhCQAAAAgcQpKNThaSIiJKgxIhCQAAAAgOQpKNThaSpIpnuCMkAQAAAIFDSLJRdUJSRTPcEZIAAACAwCEk2ejEkHTi7HZSxTPcEZIAAACAwCEk2YiWJAAAAMB5CEk2OjEkRUWpWgvKFhdLBQXBqBAAAACofwhJNjoxJEnlW5Mqmrihop8BAAAA+AchyUbVCUkVtSRV9DMAAAAA/yAk2agmIcl34gaJkAQAAAAECiHJRrQkAQAAAM5DSLJRTcckWVbp5YQkAAAAIDAISTaqSUtSSYlUWFh6+Ynd7wAAAAD4ByHJRtUJSdHRZpNYKwkAAAAIBkKSjaoTkiQWlAUAAACCiZBko5qGJN8udoQkAAAAIDAISTaqKCTFx0suV9n9aEkCAAAAgoeQZKOKQlJEROmMdh5JSeZrRkbpZYQkAAAAIDAISTaqKCRJ5bvcNWxovqanl16Wl1d2SnAAAAAA/kFIslF1Q1JqqvnqG5Isi9YkAAAAIBAISTaqaUvS8eNlLyckAQAAAP5HSLJRTUNSZqZZVNaDkAQAAAD4HyHJRtUNSQkJUlSU6WLH5A0AAABAYBGSbFTdkORyVTwuiZAEAAAA+B8hyUbVDUlSxeOSCEkAAACA/xGSbFSTkORpSfINSTk5ASoMAAAAqMcISTaqLCSduJisVPFaSbQkAQAAAP5HSLKRJyQVFBTI8lkZNi7OTNTgizFJAAAAQHAQkmzkCUmSCUq+TmxNYkwSAAAAEByEJBv5hqSTjUvytCTl5kqFhaXfAwAAAPAvQpKNoqOj5XK5JJ08JDVoYLrhSaWtSYQkAAAAwP8ISTZyuVy1muHOMy6psFAqLg5cfQAAAEB9REiyWV3XSmIacAAAAMC/CEk2q+taSXS5AwAAAPyLkGSz2rQkMQ04AAAAEDiEJJvVZUySREgCAAAA/I2QZLPajknyrD1LSAIAAAD8i5Bks5qEpJQU87WoqDQcEZIAAAAA/yIk2ayykBQdLcXGlt03KkpKSjLfs1YSAAAAEBiEJJtVFpKk6k3ewBTgAAAAgH8RkmxW25BESxIAAAAQGIQkm9U0JJ24VhIhCQAAAPAvQpLNahuSPN3tCEkAAACAfxGSbFbXMUmEJAAAAMC/CEk2q21IysiQ3G6zVXBTAAAAALVESLJZTUNSUpIUGWnCUWamuYzWJAAAAMB/CEk2q2lIcrlKF5X1TN7ANOAAAACA/xCSbFbTkCQxLgkAAAAIJEKSzaoKSQkJpuXoREwDDgAAAASOo0PS1KlT5XK5ymzdunWzuyy/qiokRURIcXHlb3NiSxLd7QAAAAD/ibK7gJPp0aOHvvzyS+/PUVGOL7lGqgpJkulyd2JL0YktSRs3SmedFaACAQAAgHrG8YkjKipKLVq0sLuMgKlOSDp8uOxlJ7Yk7d8v/fyz1Lp1gIoEAAAA6hFHd7eTpJ9++kmtWrVSx44dNW7cOO3Zs6fK/QsKCpSZmVlmc7LqhKQTeVqSsrOloiLz/cqVASgOAAAAqIccHZIGDRqkmTNn6tNPP9ULL7ygnTt36je/+Y2ysrIqvc20adOUkpLi3dLS0oJYcc3VJiTFxUmxseZ7T2vS+vVSXl4ACgQAAADqGUeHpFGjRumKK65Q7969NWLECP33v/9Venq63n333UpvM3nyZGVkZHi3vXv3BrHimqtNSHK5yo9LKiqS1q71f30AAABAfePokHSi1NRUdenSRdu2bat0n9jYWCUnJ5fZnKw2IUkqPy5JMl3uLMuPxQEAAAD1UEiFpOzsbG3fvl0tW7a0uxS/qW1IOrElSZKOHpV27PBjcQAAAEA95OiQdO+992rRokXatWuXli5dqssuu0yRkZG65ppr7C7Nb/zZkiRJ33/vp8IAAACAesrRU4Dv27dP11xzjY4ePaqmTZvqrLPO0vLly9W0aVO7S/OburYknRiStm6VMjKklBT/1AcAAADUN44OSbNmzbK7hIDzhKSCggJZliWXy1Xm+rg4KTJSKikpeztPS9Lx42Yckudmbre0apV03nmBrhwAAAAIT47ublcfeEKSZILSiVwuKSGh/O08LUkFBdKJjVCrV5cPVQAAAACqh5BkM9+QVJMud9HRpeHJd/IGySwyu2mTvyoEAAAA6hdCks2io6O9Xez8NXmDxAQOAAAAQG0RkmzmcrnqPMPdiS1JkrR7t3T4sD8qBAAAAOoXQpID+HOtJF+0JgEAAAA1R0hyAH9PA+6xbp1UWFi32gAAAID6hpDkAP5eUNajoED64Yc6FgcAAADUM4QkB/BHSLKsivdZupTpwAEAAICaICQ5QG1DUnKyWUeppETKyqp4n+PHpe++80eVAAAAQP1ASHKA2oakiAgpJcV8X9nkDZK0eLGUl1eXCgEAAID6g5DkACcLSTExZqvIycYlSSYgLVlShwIBAACAeoSQ5AAnC0mSlJBQ8eWeGe62b6/6Mb77ruogBQAAAMAgJDlAdUJSZV3u+vQxX3/8UVqzpvLHKC6WFiyobYUAAABA/UFIcoDqhKSkpIovb9dOOvdc8/1//ysdPFj546xfL+3fX9sqAQAAgPqBkOQA1QlJ7dtXfvvf/Ebq3Nm0Fr37rlTZ3ViW9PnndSgUAAAAqAcISQ5QnZDUs6eZza4iLpd02WVmprvjx6UPP6x83aRdu6StW+tYMAAAABDGCEkOUJ2QFB8vnXJK5fcRFyddeaUUGSlt3mwWka3MF19IbndtqwUAAADCGyHJAaoTkiSpd++q76dVK2nkSPP9ggWm1agiR45UPckDAAAAUJ8RkhyguiGpa1cpNrbq++rf34Qpy5Lef1/Kyqp4v6+/lgoLa1MtAAAAEN4ISQ5Q3ZAUHS2demrV9+VySRdfLDVrJuXkSB98UHHXuuzsqrvkAQAAAPUVIckBqhuSJKlXr5PfX3S0GZ8UEyPt3m3GIFVkxQqpqKgmlQIAAADhj5DkADUJSR07Vr5mkq/GjaXRo833y5dLP/xQfp+8PGnt2hoUCgAAANQDhCQHqElIcrnMdODV0b27WUNJkubPl37+ufw+K1ZUPl04AAAAUB8RkhygJiFJOvksd77OPVfq0kUqKZHeeaf8RA6//CJt3179+wMAAADCHSHJAWoaklq2lJo2rd59u1zS5ZdLTZqYgPTuu1Jxcdl9VqyoSbUAAABAeCMkOUBNQ5JUs9ak2Fjp6qulBg2kffuk//63bBe7bdtMixIAAAAAQpIj1CYk9eplWomqq3FjaexYc5s1a6Tvvy+9zrJoTQKAYMvLM92dv/lGmjNH+vRTszTDhg3S3r1SRkbFSzgAAAIvyu4CULuQlJoqpaVJe/ZU/3FOOUUaPtxMCf7pp6bLXocO5roffpCGDTOtTQAA/ykpkdLTpePHpQMHzLZ/v7nsZCIizEmuq64y3aYBAMFBSHKA2oQkyXS5q0lIkqTBg6WDB6Uff5Tee0+6+WapYUOpsFBavVo688ya3R8AoNSmTdKhQyYQeYJRVlbtZxF1u6UjR6SZM6XrrpOaN/dntQCAytDdzgFqG5J69JAiI2v2WC6XdMklZvKHvDwTlEpKzHXffUfXDgCorTVrzCyiCxea1vndu6XMTP8ss5CdbYLS/v11vy8AwMkRkhygtiEpLk7q3LnmjxcdbbpuxMWZbh9ffWUuT0+Xtmyp+f0BQH23b5/08ceBfYy8POnVV2vegwAAUHN0t3MAT0gqKCiQZVly1WBGht69pc2ba/6YKSmmRendd81A4VNOMeOTli+XTj215vcXaHv3Srm5/r3PqCipUyf/3ifCk9ttzuDv2mW2Y8cqbx1o1sysT9aiRTArhJ2yskwL0onLKwRCQYH0xhtmxtKOHQP/eABQXxGSHKCBz2wJBQUFZX4+mS5dzGQLNWyEkmTCUL9+ZizSnDnSrbea7iEHDzrnAM+yTEvXkiWBuf9zz5XOOadmt9m50wS2Hj0CUxPs53ZLP/9s/h927TJn7gsLq3fb48elrVvNDJTnnmvG/CF8FRdLs2aVX6g7kAoLpbfekq680nwG1Gd79khJSfyfAXbbvduMd+/YUerWzUw6E+oISQ7gG4ry8/NrFJKioqSBA82U3nl5NX/sESPMC/voUWn+fPOhu3y5NGZMze/L34qKpLlzzXS4gfL111J8vHT66dXbf+dOc3BSVGRat84/v+bjwuBMlmWe0/XrzWsuJ6du97Vunbmf/v1NEE9IqHjfggJzYuKXX0rHBzpdUpLUvr3pslvfzZ9vAnWwFReb1quxY6Xu3YP/+E6wfr05wRcZaU5InHFGzZbGCKb8fPNZe8opfGZUZPduM3tjZe+T8K/CQnMCsGNHcxxZWyUl5nNu+fLS8ZIrV5rPiL59zedfSopfSraFy7L8MaTUuTIzM5WSkqKMjAwlJyfbXU6FLMtSZGSkLMvSgQMH1KKWzTiZmWZWpcOHzddDh6p34HXggPSf/5iz5xdfLA0aJN1zj71vVtnZ0ttvB+fgw+WS/ud/Tt4ytGuX9OabJiB5pKVJV1whOfSlhWo4cMCc/dqwwaxLEwgxMWZmyT59zAmJAwdMMDp40LQ8heK7sMtlWpw7dDBbu3bm96xPli2TPvvM3hpcLhMOhg2r28GOPxw8aHomDBwY+OnKly83f3vf/53WraVLL3XWDIC7dpkJPTZuNJ8djRubk2vdutldmTOUlEhffmn+l6KjpQEDpCFDpMREuysLXwcPSu+/b44PGzQwr8WePU1gqm7rT26utGqVmfCrqlZ0l8uMnR8wwHx1ykmM6mYDQpJDxMfHKy8vTzt37lT79u39dr/5+eYNaNWqqg/Evv3W7BcdLd1yiznwr2k3NH85fNi01lRnDRF/iYyUrr228jFKu3ebgFRRl6uEBHM2l/EBoaOw0JztWrXKhBbUXWSkOUht0aLyD8KoKHNCoWFDs9ZbamroBqvt2817glNmBG3WTLrsMjNzabAVF5sZ/ZYuNX+PiAgzXnboUPMc+5Nlmc+qb7+t+PrISHOQffbZ9oXGrCxp7VoTjo4dq3if9u2lCy6QWrUKZmXOkp5uZtg98WRodLQZCnDWWaZFAv7z/ffm5EJF4ycTEkyrdK9e5gRwYaHpUeG7ZWebE3sbNpQ9YVwdffqY9ygnICT9KlRCUqNGjXT8+HFt2rRJ3QJwimn3bmnevMoPCC1Lev11052sZUvp9ttNi1KXLuafJVh9S7dtM2+aBQXBeTxfMTHShAnmQM/Xnj1moHRVY1JcLtPd4ze/Cd6ZkqIisybLhg2mNpfLPE8REaXfR0aag6e0NPN7heoBqb8UFJgzX8uW+X8iENROfLw5kE5Jqfx9JiLCtA54Xsd2t5gcOya9/HLtujhblvnfzcszW36++VpQUPZ/2HeLjDQtEMnJVb+/REaak1tnnRW89+xdu0yXw4o+WyIjTZebs8/2T2t7SYn04YemK+vJNG1qJidKSzMHdllZ5bdAfM7k5Zm/SXXCs8tlwuSwYfWvN8KmTea5rGo8dVSUef2cdVZod9lygvx8cwy4cWP19o+I8P8JoFNPNTMrOwEh6VehEpJatWqlAwcOaM2aNTrttNMC8hjFxdKiReYMXEUv/sxM6cUXzZv8mWeaLgGSGXdwyikmMJ1ySmDGIeTnmzNvn39u75nZ+HjpxhtLu4rs3WsCUnU/TLt0MR94jRsH5kDOsswH8A8/mDe76k4mIJU90PRslT2XLpc5m+eUpvG6ys+XVqwwXXRqc2Armb+958xaVWfQIiNNV5HY2PD5+zlJZKRprfJ9HQfjrb2gwPS537/fdCk7MRRkZJiTTIcOmX0LC83m+d7zNS+v9u9x8fGm5cF3q+hMe5s25oxt48a1e5zqyM+XvvjC/C1OdhQRFWXGfdalG1VhoRmDtX179W/jcpnNKa19lYmONn+f2ryOXS5zu+bNTQut099ziovN5/x331X/NhER5ndLSSltgfZsDRtWffIvOtreCQQsy3Rr27/fvDfExZmaGzUyX4MxrvPnn033uuPHA/9YVSEkOVCohKSOHTtq586dWrZsmc4444yAPtbBg+aMQkWLEm7ebD6IJHMWrnVr8yEcF1d6ljMtzZyli4sr3Ro0KP0+Pt5sVQ1Ozc42rVt79pivhw6VftAWF5sDkMOHzXbkiGmWD8QrNTW19GCjZUvzAZ6SIt10kzno8QQk35qOHDG1NGxYuiUn///27j26yfr+A/g7aZr03kILKfeWwuReWiqcUn/TSR0ODwPcQDh1p0MnbtStwEBRhzA9UkRlKjLQ7Qw35yZzK0zK6hkrUFYupRQEgVIQkGvTCrVN75fk+/vjuyd5kl4JbZKW9+uc5+T2JPkm+eR5ns/39jhuiDUa+ToREXIJD5eXHdUEt6WhQSZFJ09237gZZzqdfaek7h7lrg17V7Ba5Xd25IhjjaUQ9pp85xp95XptrVzUXQ1uZ2IFX1/531GWoCDZncHbD2I6Q6OR8aHTyc+pvt7e/179POfn3slgdr2+9Ti9k+58NTXy4EJJjG7dctwGmc2ywuLSJXl5u92DtVrHbaefn3x9q7Xl0tTU8v0VQUFy22U0yuQxMlJ+doNBdufqjtMcmExAdrbjWITGRrldr6uTn0X5XM4HqErLofNvFRradgw0NclWB2Wf1dwsD/iURauV/63AQPn6gYH2fZa7CSG31+ptinLZ2Ci/F6WMytJVFVJ6vew5EBkp48Fo9K6xPXV1QFaWHJMJyO+qpkbu08xm+fs7H1Pc6SQXPj5yv6uOOeWyO8ZdWyzyeEbZdpSUtF+ZqSRNffrY49b5mOpOvofiYtk91WKR33dVlfzPlJfL30Ovl9sK50tf35bbIYtFXjY3t9w/Ktfr6uRz1Z9D+SwxMcCCBbKyxNM6mxtwdjsvocxod/DgQdTcybRanRQdLTfkJ086tpLo9bK16MsvZRcKhbJDV5IgZaOu7hKi1Nop1/V6+2IwyMVqlQcT1dXyD6cszc1yQ1JRITeW7lJWJqdrVgQEyIRm2zb5vZSXd65MGo3cGQUFyddQfw/O348rO0Nlw9TcLA8Ymprs15ub7RsxIewbQ+W20l3Hx8d+MKos7ZVF/Tzn57v6OdxNaf2pr5e/Z329fXGVj4+M/7ZYLPbfqLy87TEJ1FJn4tE5hluLT/Xiai2yclCg3kYp15XuW86UShHlIENJAvV6e1KoHIh09P9z1twst0U3b8qYunXLvi09f14uCh8fedD1xz92T4VGc7O9QqG2Vl621bqq09m/D4Oh/d+vre/DapXvoXST62xrsJ+ffF1lW+h82R0Vb668plYry+pqrCqxr8SZEmtKK4q3bKuV31Fd8dRRK5+Pj4wbJT6cu5R3tC9SthtabcvY647vRtkHt7U4Hye19nnaunRFQ4N9e1Vd7flW1YKCvsjPj/NsIW4DW5K8REJCAgoLCz1dDCIiIiKiLhcdPR0XL37m6WKwJamnWbJkCd58801YPHSiFCHszaet1TSom1qda+HUaXZ796k5t6oo15VaME/0IRbCsXUGcOwa1F6Z1DVFHX03rlJ/Z63VojlfKtedy9NW+VrT3nN7irYGw/eGE931Bl0Rix3ddlVb2ymt1rvH7Cmtmc3N3fd/VWri1TX1zt9Hay04gOPv09nf37llu6P/r3pf5bxNVF92h9vZtqi7WLrK+ftUFk+3GrRG/Tsq19vSWqtfW5ftcfd+zHl70VrMdfa4qSu2Yc4ttp7i5wdMnx7tuQK4gC1J5KCxUY7fOHhQJkzKIHTnxc/PcUyB+lLdpUTpZqFWU2Mfb6Qed3QnXaC60qBBctBzZaUcu+XKGCBlbJMya5dy3dU+0E1NcvzWxYvAtWt3dtJRjUZ2xYmIaHu8hpKwKv2JDQb7deW37wmEkN9bUZHsI967t3Z3F61WDn5WTkDpHKNK3Lp6UKB0ty0vt/fh/+ab25/21hP0enlOktGju34KbkB288vPlxPbdJavrxyTEBzc9m/VVuJisQB5eY5dCnubsDDZVdsVBoN9LLB6XJbSNd5bKMcXZ8/e2ev4+trHeAYFtb8/Uu/HnPdl3fHdWK3ymMF521FdbV9Ho5FlDw6W46WUcatKd1Tn46o7qTguKZHHc56etAHgxA1eiUmSa5RaSHcO0C8pkefaKC5233uqtTZ9bkMD8Nln8nwXHenTR051O25c9+6YlDNlX7wol7Iy+2M+PvZxYEqyqiRE/frZJ5HoKUlOVzKbZbJUVCQTJ2+sZb3b+PraD1wMhrbXU2a3Usdx377urxUVQh7snDkjZ3Zr7VwjnhIQIGfXHD1aJiPu+I9fuyZnjDxzpu3/06BBchrncePkb30nTp+WE0aoDzjb4u8vT2rbv3/LiVmU201NXV9x0tRkn9ynI76+8rw08fHyZMx3ixs3gL17O056NRo5UdT48fZJKJQKkZ6msVEmTwaD/Bzu7Mlgtcr/Tl6enFSiI8r2tqnJPitnV/xPmCR5ISZJPc/168CePbc31eud6uhEjOfPy4ksWpvAQUmOYmM904VLGcSsDAanjtXUyGT80iUZb909uUL//vJgaNAgOfC+pETOEHbzZs9P1vz95f8mMlJe9u3b9rparb0m905aebxBaSnwj384VlK4m0Yjkw/lINtTXUgrK2XL0rFjMvkIDJTbw7g4mdR2pY5OkB4UBCQmAgkJnjuYrquTs7Z+9ZVc1LO3AvJ/Eh8vD/7vNHHsya5dk8mS876+Xz95/qjx47unJfRuJYQ8lsnLkxWFCuVE4MOGyZMcDxni2MtEObdbQ4NcampkXF+4IH/D9vZhGo3cN8TEyCTJ+TyUnsIk6X+YJPVcly/LZOny5e57D41G7lAffLDjmtf6elmLeeKEvO3p5Ii6Tl2drN28ft2+dKa2uj39+wNjx8rkqK0DxeZmeQBlMsna556SMKkTo7v5IKapSZ69/uhR9793VJSc4nvgQPe/d1saG+V/xx0J29WrsuJKSVL79JFTC0+c6H0t5coJZktLgXvuabsy7m515Yo8cI+IkIkRv5/upyTxQ4fK86q52vuloUFWNl64IJfycjm8YPhwmRhFR3fPVOt3iknS/zBJ6vkuXJD9mG/nxKmd4eMD/N//3X43h+JiOV6LyVHvppw4Vn0+COWyvVgMDpaJkXJCYur9iork+EVXT1R8OyIi5Im+77mn+9/L21kssrtfcLBsUeP2mMizamtdH1vnTkyS/odJEhERdTezGcjMlLWz3SEwEPjOd2Q3LSYDRESu4xTgREREbhISAqSmypm76usdT0CrvlSf0FiZQKC+XrZOGgz2WcnUS0iIHKPREwesExH1VD0iSdq0aRNef/11mEwmxMbGYuPGjZg8ebKni0VERGSj0cjByURE1PN5faP9tm3bsGzZMqxevRrHjh1DbGwspk+fjjJPTilERERERES9ltcnSRs2bMBTTz2FhQsXYsyYMdiyZQsCAgLwhz/8wdNFIyIiIiKiXsirk6TGxkYUFhYiOTnZdp9Wq0VycjIOHTrU6nMaGhpgNpsdFiIiIiIios7y6iTp5s2bsFgsMBqNDvcbjUaYTKZWn5ORkYHQ0FDbMmTIEHcUlYiIiIiIegmvTpJc8fzzz6OystK2XL161dNFIiIiIiKiHsSrZ7eLiIiAj48PSktLHe4vLS1FZGRkq88xGAwwcJ5UIiIiIiJykVe3JOn1ekyaNAk5OTm2+6xWK3JycpCYmOjBkhERERERUW/l1S1JALBs2TKkpqYiISEBkydPxltvvYWamhosXLjQ00UjIiIiIqJeyOuTpMceewxff/01XnrpJZhMJkycOBGfffZZi8kciIiIiIiIuoJGCCE8XYjuZDabERoaisrKSoSEhHi6OERERERE5CGdzQ28ekwSERERERGRu3l9d7s7pTSU8aSyRERERER3NyUn6KgzXa9PkqqqqgCAJ5UlIiIiIiIAMkcIDQ1t8/FePybJarXixo0bCA4Ohkaj8WhZzGYzhgwZgqtXr3J8FHUa44ZcxdghVzBuyBWMG3KVu2NHCIGqqioMHDgQWm3bI496fUuSVqvF4MGDPV0MByEhIdyA0G1j3JCrGDvkCsYNuYJxQ65yZ+y014Kk4MQNREREREREKkySiIiIiIiIVJgkuZHBYMDq1athMBg8XRTqQRg35CrGDrmCcUOuYNyQq7w1dnr9xA1ERERERES3gy1JREREREREKkySiIiIiIiIVJgkERERERERqTBJIiIiIiIiUmGS5EabNm1CVFQU/Pz8MGXKFBw5csTTRSIvkpGRgXvvvRfBwcHo378/Zs+ejeLiYod16uvrkZaWhvDwcAQFBeEHP/gBSktLPVRi8kbr1q2DRqPBkiVLbPcxbqg1169fx+OPP47w8HD4+/tj/PjxOHr0qO1xIQReeuklDBgwAP7+/khOTsb58+c9WGLyBhaLBatWrUJ0dDT8/f0RExODV155Bep5wBg7tH//fsycORMDBw6ERqPBjh07HB7vTIyUl5cjJSUFISEhCAsLw5NPPonq6mq3fQYmSW6ybds2LFu2DKtXr8axY8cQGxuL6dOno6yszNNFIy+Rm5uLtLQ0HD58GLt370ZTUxO++93voqamxrbO0qVLsXPnTnzyySfIzc3FjRs38Oijj3qw1ORNCgoK8N5772HChAkO9zNuyNk333yDpKQk+Pr6Ijs7G2fOnMGbb76JPn362NZZv3493nnnHWzZsgX5+fkIDAzE9OnTUV9f78GSk6e99tpr2Lx5M959910UFRXhtddew/r167Fx40bbOowdqqmpQWxsLDZt2tTq452JkZSUFJw+fRq7d+9GVlYW9u/fj0WLFrnrIwCC3GLy5MkiLS3NdttisYiBAweKjIwMD5aKvFlZWZkAIHJzc4UQQlRUVAhfX1/xySef2NYpKioSAMShQ4c8VUzyElVVVWLkyJFi9+7d4v777xfp6elCCMYNte65554T9913X5uPW61WERkZKV5//XXbfRUVFcJgMIi//vWv7igiealHHnlEPPHEEw73PfrooyIlJUUIwdihlgCI7du32253JkbOnDkjAIiCggLbOtnZ2UKj0Yjr16+7pdxsSXKDxsZGFBYWIjk52XafVqtFcnIyDh065MGSkTerrKwEAPTt2xcAUFhYiKamJoc4GjVqFIYOHco4IqSlpeGRRx5xiA+AcUOt+/TTT5GQkIC5c+eif//+iIuLw+9+9zvb45cuXYLJZHKIm9DQUEyZMoVxc5ebOnUqcnJycO7cOQDAiRMnkJeXh+9973sAGDvUsc7EyKFDhxAWFoaEhATbOsnJydBqtcjPz3dLOXVueZe73M2bN2GxWGA0Gh3uNxqNOHv2rIdKRd7MarViyZIlSEpKwrhx4wAAJpMJer0eYWFhDusajUaYTCYPlJK8xccff4xjx46hoKCgxWOMG2rNxYsXsXnzZixbtgwvvPACCgoK8Itf/AJ6vR6pqam22Ghtv8W4ubutXLkSZrMZo0aNgo+PDywWC1599VWkpKQAAGOHOtSZGDGZTOjfv7/D4zqdDn379nVbHDFJIvJCaWlpOHXqFPLy8jxdFPJyV69eRXp6Onbv3g0/Pz9PF4d6CKvVioSEBKxduxYAEBcXh1OnTmHLli1ITU31cOnIm/3tb3/DRx99hL/85S8YO3YsPv/8cyxZsgQDBw5k7FCvwu52bhAREQEfH58Ws0mVlpYiMjLSQ6Uib/XMM88gKysLe/fuxeDBg233R0ZGorGxERUVFQ7rM47uboWFhSgrK0N8fDx0Oh10Oh1yc3PxzjvvQKfTwWg0Mm6ohQEDBmDMmDEO940ePRpXrlwBAFtscL9FzlasWIGVK1di/vz5GD9+PH70ox9h6dKlyMjIAMDYoY51JkYiIyNbTG7W3NyM8vJyt8URkyQ30Ov1mDRpEnJycmz3Wa1W5OTkIDEx0YMlI28ihMAzzzyD7du3Y8+ePYiOjnZ4fNKkSfD19XWIo+LiYly5coVxdBebNm0avvjiC3z++ee2JSEhASkpKbbrjBtylpSU1OIUA+fOncOwYcMAANHR0YiMjHSIG7PZjPz8fMbNXa62thZarePho4+PD6xWKwDGDnWsMzGSmJiIiooKFBYW2tbZs2cPrFYrpkyZ4p6CumV6CBIff/yxMBgM4oMPPhBnzpwRixYtEmFhYcJkMnm6aOQlfvazn4nQ0FCxb98+UVJSYltqa2tt6/z0pz8VQ4cOFXv27BFHjx4ViYmJIjEx0YOlJm+knt1OCMYNtXTkyBGh0+nEq6++Ks6fPy8++ugjERAQIP785z/b1lm3bp0ICwsT//znP8XJkyfFrFmzRHR0tKirq/NgycnTUlNTxaBBg0RWVpa4dOmSyMzMFBEREeLZZ5+1rcPYoaqqKnH8+HFx/PhxAUBs2LBBHD9+XFy+fFkI0bkYefjhh0VcXJzIz88XeXl5YuTIkWLBggVu+wxMktxo48aNYujQoUKv14vJkyeLw4cPe7pI5EUAtLps3brVtk5dXZ1YvHix6NOnjwgICBBz5swRJSUlnis0eSXnJIlxQ63ZuXOnGDdunDAYDGLUqFHi/fffd3jcarWKVatWCaPRKAwGg5g2bZooLi72UGnJW5jNZpGeni6GDh0q/Pz8xPDhw8WLL74oGhoabOswdmjv3r2tHtOkpqYKIToXI7du3RILFiwQQUFBIiQkRCxcuFBUVVW57TNohFCdIpmIiIiIiOguxzFJREREREREKkySiIiIiIiIVJgkERERERERqTBJIiIiIiIiUmGSREREREREpMIkiYiIiIiISIVJEhERERERkQqTJCIiotvwwQcfICwszNPFICKibsQkiYiIuoXJZEJ6ejpGjBgBPz8/GI1GJCUlYfPmzaitrfV08TolKioKb731lsN9jz32GM6dO+eZAhERkVvoPF0AIiLqfS5evIikpCSEhYVh7dq1GD9+PAwGA7744gu8//77GDRoEL7//e97pGxCCFgsFuh0ru0C/f394e/v38WlIiIib8KWJCIi6nKLFy+GTqfD0aNHMW/ePIwePRrDhw/HrFmzsGvXLsycORMAUFFRgZ/85Cfo168fQkJC8OCDD+LEiRO211mzZg0mTpyIDz/8EFFRUQgNDcX8+fNRVVVlW8dqtSIjIwPR0dHw9/dHbGws/v73v9se37dvHzQaDbKzszFp0iQYDAbk5eXhwoULmDVrFoxGI4KCgnDvvffiP//5j+15DzzwAC5fvoylS5dCo9FAo9EAaL273ebNmxETEwO9Xo977rkHH374ocPjGo0Gv//97zFnzhwEBARg5MiR+PTTT7vs+yYioq7FJImIiLrUrVu38O9//xtpaWkIDAxsdR0l4Zg7dy7KysqQnZ2NwsJCxMfHY9q0aSgvL7ete+HCBezYsQNZWVnIyspCbm4u1q1bZ3s8IyMDf/rTn7BlyxacPn0aS5cuxeOPP47c3FyH91y5ciXWrVuHoqIiTJgwAdXV1ZgxYwZycnJw/PhxPPzww5g5cyauXLkCAMjMzMTgwYPx8ssvo6SkBCUlJa1+lu3btyM9PR2//OUvcerUKTz99NNYuHAh9u7d67Der3/9a8ybNw8nT57EjBkzkJKS4vA5iYjIiwgiIqIudPjwYQFAZGZmOtwfHh4uAgMDRWBgoHj22WfFf//7XxESEiLq6+sd1ouJiRHvvfeeEEKI1atXi4CAAGE2m22Pr1ixQkyZMkUIIUR9fb0ICAgQBw8edHiNJ598UixYsEAIIcTevXsFALFjx44Oyz527FixceNG2+1hw4aJ3/zmNw7rbN26VYSGhtpuT506VTz11FMO68ydO1fMmDHDdhuA+NWvfmW7XV1dLQCI7OzsDstERETuxzFJRETkFkeOHIHVakVKSgoaGhpw4sQJVFdXIzw83GG9uro6XLhwwXY7KioKwcHBttsDBgxAWVkZAODLL79EbW0tHnroIYfXaGxsRFxcnMN9CQkJDrerq6uxZs0a7Nq1CyUlJWhubkZdXZ2tJamzioqKsGjRIof7kpKS8PbbbzvcN2HCBNv1wMBAhISE2D4HERF5FyZJRETUpUaMGAGNRoPi4mKH+4cPHw4AtkkPqqurMWDAAOzbt6/Fa6jH/Pj6+jo8ptFoYLVaba8BALt27cKgQYMc1jMYDA63nbv+LV++HLt378Ybb7yBESNGwN/fHz/84Q/R2NjYyU96e9r7HERE5F2YJBERUZcKDw/HQw89hHfffRc///nP2xyXFB8fD5PJBJ1Oh6ioKJfea8yYMTAYDLhy5Qruv//+23rugQMH8OMf/xhz5swBIBOur776ymEdvV4Pi8XS7uuMHj0aBw4cQGpqqsNrjxkz5rbKQ0RE3oNJEhERdbnf/va3SEpKQkJCAtasWYMJEyZAq9WioKAAZ8+exaRJk5CcnIzExETMnj0b69evx7e+9S3cuHEDu3btwpw5c1p0j2tNcHAwli9fjqVLl8JqteK+++5DZWUlDhw4gJCQEIfExdnIkSORmZmJmTNnQqPRYNWqVS1adqKiorB//37Mnz8fBoMBERERLV5nxYoVmDdvHuLi4pCcnIydO3ciMzPTYaY8IiLqWZgkERFRl4uJicHx48exdu1aPP/887h27RoMBgPGjBmD5cuXY/HixdBoNPjXv/6FF198EQsXLsTXX3+NyMhIfPvb34bRaOz0e73yyivo168fMjIycPHiRYSFhSE+Ph4vvPBCu8/bsGEDnnjiCUydOhURERF47rnnYDabHdZ5+eWX8fTTTyMmJgYNDQ0QQrR4ndmzZ+Ptt9/GG2+8gfT0dERHR2Pr1q144IEHOv0ZiIjIu2hEa1t8IiIiIiKiuxTPk0RERERERKTCJImIiIiIiEiFSRIREREREZEKkyQiIiIiIiIVJklEREREREQqTJKIiIiIiIhUmCQRERERERGpMEkiIiIiIiJSYZJERERERESkwiSJiIiIiIhIhUkSERERERGRCpMkIiIiIiIilf8H2LtGLzDl33kAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generate_plots():\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + " \n", + " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", + " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", + " for i, row in learner_log.iterrows():\n", + " data[i+1, :] = data[i]\n", + " data[i+1, row['arm idx'].astype(int)] += 1\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + "\n", + " plt.plot(data, label=est_mab.mutations_)\n", + " plt.xlabel(\"Evaluations\")\n", + " plt.ylabel(\"Number of times mutation was used\")\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # --------------------------------------------------------------------------\n", + " fig, axs = plt.subplots(1, 1, figsize=(10, 4))\n", + "\n", + " columns = learner_log.columns[learner_log.columns.str.startswith('UCB1 ')]\n", + " labels = [columns[i].replace(str(i), est_mab.mutations_[i]) for i in range(4)] \n", + " data = learner_log.loc[:, columns]\n", + "\n", + " axs.plot(data, label=labels)\n", + " axs.set_xlabel(\"Evaluations\")\n", + " axs.set_ylabel(f\"UCB1s\")\n", + " axs.legend()\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " axs.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + "\n", + " plt.show()\n", + "\n", + " # Approximating the percentage of usage for each generation ----------------\n", + " data = np.zeros( (kwargs['max_gen'], 4) )\n", + " for g in range(kwargs['max_gen']):\n", + " idx_start = g*(learner_log.shape[0]%kwargs['max_gen'])\n", + " idx_end = (g+1)*(learner_log.shape[0]%kwargs['max_gen'])\n", + "\n", + " df_in_range = learner_log.iloc[idx_start:idx_end]\n", + " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", + " for k, v in g_data.items():\n", + " data[g, k] = v\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + "\n", + " #plt.plot(data, label=est_mab.mutations_)\n", + " plt.stackplot(range(kwargs['max_gen']), data.T, labels=est_mab.mutations_)\n", + " plt.xlabel(\"Generations\")\n", + " plt.ylabel(\"Percentage of usage\")\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # --------------------------------------------------------------------------\n", + " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", + " 'std m1', 'std m2', 'min m1', 'min m2'])\n", + " for item in est_mab.logbook_:\n", + " # I'll store the calculate\n", + " logbook.loc[item['gen']] = (\n", + " item['gen'], item['evals'], *item['ave'], *item['std'], *item['min']\n", + " )\n", "\n", - " if True: # plot the cumulative history of pulls\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", + " fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", + " x = logbook['gen']\n", + " for i, metric in enumerate(['m1', 'm2']):\n", + " y = logbook[f'ave {metric}']\n", + " y_err = logbook[f'std {metric}']\n", + " y_min = logbook[f'min {metric}']\n", "\n", - " # Plot for evaluations, not generations\n", - " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", - " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", - " data[i+1, :] = data[i]\n", - " data[i+1, arm] += 1\n", - " \n", - " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", - " plt.xlabel(\"Evaluations\")\n", - " plt.ylabel(\"Number of times mutation was used\")\n", + " axs[i].plot(x, y, 'b', label='Avg.')\n", + " axs[i].fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", + " axs[i].plot(x, y_min, 'k', label='Min.')\n", "\n", - " for x in est_mab.learner_.reset_history:\n", - " plt.axvline(x=x, color='k')\n", - " \n", - " plt.legend()" + " axs[i].set_xlabel(\"Generation\")\n", + " axs[i].set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", + " axs[i].legend()\n", + "\n", + " plt.show()\n", + "\n", + "generate_plots()" ] }, { @@ -1211,99 +1439,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[25.075 20.825]\t[1.23667902 0.9188988 ]\t[20. 20.]\n", - "1 \t200 \t[24.72 13.995]\t[1.24161186 6.44243549]\t[18. 2.]\n", - "2 \t200 \t[24.34 5.71] \t[1.78728845 5.45306336]\t[16. 1.]\n", - "3 \t200 \t[24.37 1.995]\t[2.01571327 1.34349358]\t[16. 1.]\n", - "4 \t200 \t[22.695 1.57 ]\t[3.33945729 1.79027931]\t[15. 1.]\n", - "5 \t200 \t[20.4 1.325]\t[3.4278273 1.63687965]\t[15. 1.]\n", - "6 \t200 \t[18.27 1.31] \t[1.68733518 1.49796529]\t[14. 1.]\n", - "7 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "8 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "9 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "10 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "11 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "12 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "13 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "14 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "15 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "16 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "17 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "18 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "19 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "20 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "21 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "22 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "23 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "24 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "25 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "26 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "27 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "28 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "29 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "30 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "31 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "32 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "33 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "34 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "35 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "36 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "37 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "38 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "39 \t200 \t[17.94 1.15] \t[0.4431704 1.16081868]\t[14. 1.]\n", - "Final population hypervolume is 48792.000000\n", - "best model: Logistic(Sin(Median(Mean(If(AIDS>68817.00,-0.00*AIDS,15.54),1.00*Total),-8.65,Total,Sqrtabs(0.00*AIDS))))\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[25.115 20.785]\t[1.50391988 0.94803745]\t[17. 20.]\n", - "1 \t0 \t[24.375 15.865]\t[1.94791555 5.67157606]\t[16. 2.]\n", - "2 \t0 \t[23.885 8.92 ]\t[2.49635234 6.24368481]\t[16. 1.]\n", - "3 \t0 \t[23.97 4.275]\t[2.66816416 3.86385494]\t[14. 1.]\n", - "4 \t0 \t[23.735 2.835]\t[2.85390522 2.93049057]\t[14. 1.]\n", - "5 \t0 \t[23.43 2.155]\t[3.09759584 2.40436582]\t[14. 1.]\n", - "6 \t0 \t[22.235 1.84 ]\t[3.72421468 2.53069161]\t[14. 1.]\n", - "7 \t0 \t[22.425 1.34 ]\t[3.55870412 2.29224781]\t[14. 1.]\n", - "8 \t0 \t[20.87 1.365]\t[3.64322659 2.31555069]\t[14. 1.]\n", - "9 \t0 \t[18.945 1.435]\t[2.89861605 2.31857176]\t[14. 1.]\n", - "10 \t0 \t[18.435 1.295]\t[2.40120282 1.43107477]\t[12. 1.]\n", - "11 \t0 \t[17.685 1.41 ]\t[1.18986344 1.89786722]\t[12. 1.]\n", - "12 \t0 \t[17.865 1.1 ]\t[0.64558113 0.52915026]\t[12. 1.]\n", - "13 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "14 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "15 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "16 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "17 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "18 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "19 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "20 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "21 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "22 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "23 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "24 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "25 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "26 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "27 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "28 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "29 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "30 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "31 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "32 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "33 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "34 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "35 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "36 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "37 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "38 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "39 \t0 \t[17.92 1.065]\t[0.55099909 0.469867 ]\t[12. 1.]\n", - "Final population hypervolume is 48896.000000\n" + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" ] }, { @@ -1327,15 +1470,19 @@ " \n", " \n", " Brush version\n", - " Original\n", - " Modified\n", + " Original\n", + " Modified\n", " \n", " \n", " metric\n", " score\n", " best model\n", + " size\n", + " depth\n", " score\n", " best model\n", + " size\n", + " depth\n", " point mutation calls\n", " insert mutation calls\n", " delete mutation calls\n", @@ -1346,332 +1493,452 @@ " \n", " run 0\n", " 0.76\n", - " Sub(Max(0.03*AIDS,0.57*AIDS,0.28*AIDS),Abs(0.0...\n", - " 0.76\n", - " Logistic(Sin(Mean(1.00*AIDS,Max(Mean(1.00*Tota...\n", - " 1937\n", - " 2218\n", - " 1611\n", - " 1803\n", + " Logistic(1.00*Sub(1.00*Sum(0.08*AIDS,2.24,2.24...\n", + " 8\n", + " 3\n", + " 0.78\n", + " Median(Prod(14.57,0.00*AIDS),Sqrtabs(-0.00*AID...\n", + " 12\n", + " 2\n", + " 3216\n", + " 2211\n", + " 2211\n", + " 2010\n", " \n", " \n", " run 1\n", + " 0.68\n", + " Sum(0.22,0.00*AIDS,0.18)\n", + " 4\n", + " 1\n", " 0.72\n", - " Logistic(Cos(Sum(0.00*AIDS,1.00*AIDS,1.00*Tota...\n", - " 0.76\n", - " Logistic(Cos(Sqrtabs(Mean(Cos(0.92*AIDS),1.15*...\n", - " 2187\n", - " 1849\n", - " 1768\n", - " 1816\n", + " Logistic(Cos(Sqrtabs(If(AIDS>68817.00,-0.00*AI...\n", + " 11\n", + " 7\n", + " 4020\n", + " 2211\n", + " 2010\n", + " 1407\n", " \n", " \n", " run 2\n", - " 0.80\n", - " Logistic(Tan(Abs(Max(1.01*AIDS,Max(0.64*AIDS,I...\n", + " 0.64\n", + " Logistic(0.63*Sin(-0.18*AIDS))\n", + " 3\n", + " 2\n", " 0.78\n", - " Div(Mean(1.00*Sum(Median(1.00,3.41*Total,3961....\n", - " 1472\n", - " 1763\n", - " 1833\n", - " 2481\n", + " Sin(Sqrtabs(Log1p(Sqrt(Div(6.43,Div(0.01*AIDS,...\n", + " 9\n", + " 6\n", + " 2814\n", + " 2613\n", + " 2211\n", + " 2010\n", " \n", " \n", " run 3\n", - " 0.78\n", - " Sinh(Mean(Max(Atan(1.00*Total),1.00,Add(0.31*A...\n", - " 0.82\n", - " Atan(Median(Asin(0.99*Sin(Mean(0.00*AIDS,1.00*...\n", - " 1765\n", - " 2524\n", - " 1758\n", - " 1578\n", + " 0.68\n", + " Mean(0.32,0.00*AIDS,0.87)\n", + " 4\n", + " 1\n", + " 0.68\n", + " Tanh(0.00*AIDS)\n", + " 2\n", + " 1\n", + " 3216\n", + " 2814\n", + " 2010\n", + " 1608\n", " \n", " \n", " run 4\n", - " 0.82\n", - " Median(Median(-0.00*Total,Log(0.00*AIDS),4.19,...\n", - " 0.70\n", - " Logistic(If(If(0.00*AIDS>1.48,1.00*Age,If(Race...\n", - " 1939\n", - " 2469\n", - " 1839\n", - " 1329\n", + " 0.84\n", + " Sqrtabs(Median(Cos(Mean(2.13*AIDS,-863.26)),0....\n", + " 8\n", + " 4\n", + " 0.78\n", + " Logistic(1.89*Min(0.00*AIDS,Add(-0.00*Total,0....\n", + " 6\n", + " 3\n", + " 3015\n", + " 2613\n", + " 2211\n", + " 1809\n", " \n", " \n", " run 5\n", - " 0.86\n", - " Median(Min(Cos(Prod(Logabs(0.70*AIDS),Sum(1.00...\n", - " 0.70\n", - " Ceil(Sub(0.00*AIDS,Exp(Square(Sin(Median(AIDS,...\n", - " 1816\n", - " 2478\n", - " 1965\n", - " 1331\n", + " 0.64\n", + " Logistic(Tan(1.00*AIDS))\n", + " 3\n", + " 2\n", + " 0.68\n", + " Tanh(0.00*AIDS)\n", + " 2\n", + " 1\n", + " 3015\n", + " 2814\n", + " 2010\n", + " 1809\n", " \n", " \n", " run 6\n", - " 0.86\n", - " Mean(Sin(Sum(-2.88,Total,Abs(Log1p(Mean(1659.0...\n", - " 0.76\n", - " Logistic(Log(0.89*Div(0.89*Total,Sum(-0.21*AID...\n", - " 1544\n", - " 2269\n", - " 1914\n", - " 1889\n", + " 0.80\n", + " Tanh(Median(Sin(Ceil(Sin(-0.13*AIDS))),Log(0.0...\n", + " 9\n", + " 5\n", + " 0.80\n", + " Logistic(2.06*Cos(Add(Cos(1.00*Mean(1.37,1.00*...\n", + " 15\n", + " 6\n", + " 3618\n", + " 2814\n", + " 1608\n", + " 1608\n", " \n", " \n", " run 7\n", + " 0.74\n", + " Logistic(-1.41*Sin(1.00*Min(531.53*Tan(Total),...\n", + " 6\n", + " 4\n", " 0.78\n", - " Logistic(Cos(Sum(1.39,Sqrtabs(Sum(1.00*Total,-...\n", - " 0.84\n", - " Atan(1.00*Max(Tanh(-0.45*Tan(1.00*AIDS)),0.00*...\n", - " 1821\n", - " 2346\n", - " 1915\n", - " 1561\n", + " Log(Div(1580.85*AIDS,0.66*Total))\n", + " 4\n", + " 2\n", + " 3216\n", + " 2412\n", + " 2211\n", + " 1809\n", " \n", " \n", " run 8\n", - " 0.68\n", - " Mean(0.00*AIDS,0.69,0.50)\n", - " 0.68\n", - " Sqrt(0.00*AIDS)\n", - " 1415\n", + " 0.78\n", + " Logistic(Exp(Mean(Add(-8.01*Total,11667.90*AID...\n", + " 7\n", + " 4\n", + " 0.78\n", + " 1.04*Logistic(Sin(Sum(0.49,6.36*Sqrt(0.00*AIDS...\n", + " 8\n", + " 4\n", + " 3015\n", " 2412\n", - " 2111\n", - " 1686\n", + " 2211\n", + " 2010\n", " \n", " \n", " run 9\n", - " 0.86\n", - " Max(Atan(0.00*AIDS),Sqrt(Sin(Sum(Sqrtabs(1.00*...\n", - " 0.78\n", - " Logistic(Median(-0.00*Total,0.01*AIDS))\n", - " 1431\n", - " 3092\n", - " 1420\n", - " 1633\n", + " 0.68\n", + " Mean(0.00*AIDS,1.06,0.66,-0.14)\n", + " 5\n", + " 1\n", + " 0.68\n", + " Tanh(0.00*AIDS)\n", + " 2\n", + " 1\n", + " 3015\n", + " 2412\n", + " 2211\n", + " 2010\n", " \n", " \n", " run 10\n", - " 0.84\n", - " Mean(Max(0.00*AIDS,Min(Total,Total,Cos(Max(Sum...\n", " 0.78\n", - " Mean(0.63*Sin(1.00*Median(Div(Total,1.00*AIDS)...\n", - " 1682\n", - " 2515\n", - " 1559\n", - " 1810\n", + " Logabs(Div(-1317.10*AIDS,0.55*Total))\n", + " 4\n", + " 2\n", + " 0.70\n", + " Logistic(Cos(Mean(3.07,3.07,1.00*AIDS)))\n", + " 6\n", + " 3\n", + " 3216\n", + " 2613\n", + " 2211\n", + " 1608\n", " \n", " \n", " run 11\n", - " 0.84\n", - " Log1p(Median(Tan(1.00*AIDS),-0.00*AIDS,Ceil(0....\n", + " 0.72\n", + " Logistic(Add(Sin(Median(0.64*AIDS,0.00*AIDS)),...\n", + " 8\n", + " 4\n", " 0.74\n", - " Sin(1.00*Median(0.70*AIDS,23.09,1.53*AIDS,1.00...\n", - " 2045\n", - " 2129\n", - " 1925\n", - " 1494\n", + " Mean(Atan(0.05*AIDS),0.00*AIDS,-0.00*Total)\n", + " 5\n", + " 2\n", + " 3417\n", + " 2613\n", + " 2211\n", + " 1407\n", " \n", " \n", " run 12\n", - " 0.74\n", - " Logistic(Cos(Mean(Tan(1.42),Sqrtabs(Add(Mean(0...\n", + " 0.88\n", + " Median(0.00*AIDS,Min(Sinh(Sinh(Max(Sin(-0.91*T...\n", + " 20\n", + " 6\n", " 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1bRsOIgmRHWQZsmgxv4VD2t45aCdu/130qhDp6enMmDGDX375hY4dOwIwZ84cqlSpAkB0dDSzZs0iOjqaSpUqAfDqq6+yatUqZs2axcSJE/PVOWHCBN544w2GDRsGQLVq1fjwww8ZN24c48ePJyAgAAAfHx+Cg4OL/JwQQoii2XBxA1czr+Lr7EvXsK62rfzcZvNaQ1o36DwBXH1tW78DFSkh6tu3b5ErXLJkSbGDEaUrKiqK3NxcWrS4mbz5+fkREREBwOHDhzEajdSqlXeqZk5ODhUqFDxA7+DBg2zdupWPP/7Ycs1oNJKdnU1mZiZubgUna8V9TgghRF43BlM/WutRnDXOtq38Ru9Qo0GFJkOKomCIiUExGDBcvYopJzfPfVN6OoaEa/meU7u44tOv6PmGrRUpIfL29rYcK4rCH3/8gbe3N82aNQNg7969JCcnW5U4AUyaNIklS5Zw4sQJXF1dad26NZMnT7b8QgbIzs7mlVde4ddffyUnJ4euXbvyf//3fwQFBVnKREdH88wzz7BhwwY8PDwYNmwYkyZNwsnp5sfbuHEjY8eO5ejRo4SEhPDOO+8wfPhwq+ItKlcnV3YO2mmXuovStq2kp6ej0WjYu3ev5dXWDR4eHoU+M2HChAK/F1xcXG7bVnGeE0IIcdPppNPsjt2NRqXh8YjHbVPpvp9hzwyIPQImvflai2cKLX510iSS5v5sdTNOAQFlPyGaNWuW5fj111/n8ccf57vvvrP8kjQajTz77LN4eXlZ1fimTZt47rnnuP/++zEYDLz11lt06dKFY8eO4e7uDsDLL7/M33//ze+//463tzdjxoyhb9++bN261dJ2z549CQ4OZtu2bcTExDB06FC0Wq3llc65c+fo2bMnTz/9NPPmzWPdunU89dRTVKxYka5dbdydiHlaeVFfWzlS9erV0Wq17Ny5k9DQUACSkpI4deoU7du3p3HjxhiNRuLi4mjbtm2R6mzSpAknT56kRo0ahZbRarUYjUarnxNCCHF7N3qHHgx9kGD34NsXPvMPJF24fRmjHta8DaZbJrg0eBz8C/5Zrb8aR/KCXwFQubqi1ulwCgwEp7zphlOFCqj/84e1xsocwtasHkM0c+ZMtmzZkqfHQKPRMHbsWFq3bs2nn35a5LpWrVqV53z27NkEBgayd+9e2rVrR0pKCjNmzGD+/Pk8+OCDgDk5q1OnDjt27KBly5asWbOGY8eO8c8//xAUFESjRo348MMPef3113n//ffR6XR89913hIeHW2ZQ1alThy1btjBt2rQCE6KcnBxycnIs56mpqVb9O7pbeHh4MHLkSF577TUqVKhAYGAgb7/9Nmq1efJhrVq1GDx4MEOHDmXq1Kk0btyY+Ph41q1bR8OGDenZs2e+Ot977z169epFaGgojz76KGq1moMHD3LkyBE++si8PkVYWBjr1q2jTZs2ODs74+vrW6TnhBBC5Hcg7gDjt40nOi0aw/XE5Y5T7aN3wC/9it6Imz88Nhv8a4JHUL7buZcuk7JkCVkH9qPo9bg2bUrYvLtrKr7VCZHBYODEiRN5XmsBnDhxApPJVKJgUlLMO+76+fkB5ldxer2eTp06WcrUrl2b0NBQtm/fTsuWLdm+fTsNGjTI8wqta9euPPPMMxw9epTGjRuzffv2PHXcKFPYasyTJk1iwoQJJfosd4tPP/2U9PR0HnroITw9PXnllVcs/x3AnIB+9NFHvPLKK1y+fBl/f39atmxJr169Cqyva9euLF++nA8++IDJkyej1WqpXbs2Tz31lKXM1KlTGTt2LD/++COVK1fm/PnzRXpOCCHKi2tZ17iYdtGS4BTGqBh5bdNrJOckW641DmxMs6Bmt29g53fmr4F1oUL125d1coXINwotpygKl8aMIefECcu1CiOfvH2dZZBKURTFmgfGjh3L3Llzeeutt2jevDkAO3fu5JNPPuGJJ57g888/L1YgJpOJhx9+mOTkZLZs2QLA/PnzGTFiRJ7eGoDmzZvToUMHJk+ezOjRo7lw4QKrV6+23M/MzMTd3Z0VK1bQvXt3atWqxYgRI3jzzTctZVasWEHPnj3JzMzE1TXvuJuCeohCQkJISUnJ91owOzubc+fOER4eLmNdRInJ95MQ4s8zf/LO1nesfm5y28k0CWpCgGsAGrWm8IKpV2BafVCM8PRWCK5fgmghfetWLo40//HqM6A/LrVr49O/f8k3i7WB1NRUvL29C/z9/V9W9xB99tlnBAcHM3XqVGJiYgCoWLEir732Gq+88krxIgaee+45jhw5YkmGHMnZ2RlnZxuPzBdCCCHuwGAy8O3Bby3n1byrFbpD/Q06jY6xzcbSsmLLojWyZ5Y5GarapsTJEEDinDkA+A4ZQvA7b5e4PkexOiFSq9WMGzeOcePGWcbWWDuY+r/GjBnD8uXL2bx5s2UNHIDg4GByc3NJTk7Os4Ho1atXLWvYBAcHs2vXrjz1Xb161XLvxtcb124t4+Xlla93SAghhChtmfpMMg2ZbLm8hcvpl/Fx9mHNo2tsOnMYAEOOeZVpgOajSlxd9okTZGz+F1Qq/IY+UeL6HKlYCzMaDAY2btxIVFQUgwYNAuDKlSt4eXkVOh27IIqi8Pzzz/PHH3+wceNGwsPD89xv2rQpWq2WdevW0a+fefDXyZMniY6OplWrVgC0atWKjz/+mLi4OAIDAwFYu3YtXl5e1K1b11JmxYoVeepeu3atpQ4hhBDCEb7a9xXzT8wnQ5+R5/qA2gNsnwwBHPsTMuLBs5J5M1Yr6WNi0MfEEP/112QfOowpwxy3x4MPors+W/luZXVCdOHCBbp160Z0dDQ5OTl07twZT09PJk+eTE5ODt99912R63ruueeYP38+f/75J56ensTGxgLmdY9cXV3x9vZm5MiRjB07Fj8/P7y8vHj++edp1aoVLVuauwa7dOlC3bp1eeKJJ5gyZQqxsbG88847PPfcc5bXXk8//TTffPMN48aN48knn2T9+vUsXLiQv//+29qPL4QQQthEbEYsM4/MxKjcXIZEhYoQzxDbb8h6w64fzF+bPQkarVWP5p4/z9nej6D8Z1yvysUF/2cKX5fobmF1QvTiiy/SrFkzDh48mGe14j59+jBqlHXdb99+a35PGhkZmef6rFmzLIsmTps2DbVaTb9+/fIszHiDRqNh+fLlPPPMM7Rq1Qp3d3eGDRvGBx98YCkTHh7O33//zcsvv8yXX35JlSpV+Omnn+yyBpEQQghRFL+d/A2jYqRJYBOmRk7F39Xfvg1e3geXdoNGB02HWy4rRiOmtLRCHzPl5pKxbRsxb5gnJmm8vdH4+uLZtSu+jz+GU6VKZWIAdUlZnRD9+++/bNu2DZ1Ol+d6WFgYly9ftqquokxwc3FxYfr06UyfPr3QMlWrVs33Suy/IiMj2b9/v1XxCSGEEPaQbchm0alFAAytO9T+yRDArh/NX+v1AQ/zvpKm7GzOPdKH3PPni1xN6OxZuNSpY4cAHUtt7QMmkynfKsMAly5dwtPT0yZBCSGEEPeyv8/+TXJOMpU9KhMZEmn/BjOuwZHF5uPmoy2XU/76q0jJkNrTE+c6dQhf+sc9mQxBMXqIunTpwhdffMEPP5jfQ6pUKtLT0xk/fjw9evSweYBCCCHEvURRFH45bl7FeWDtgbdfM8hW9s0BYw5UagJVmlniSJo7F4DA117F73b7e6rV98RrsduxOiGaOnUqXbt2pW7dumRnZzNo0CBOnz6Nv78/CxYssEeMQgghxF3vcPxhxm0eR4Y+g6ScJFydXOlTs4/9GzYaYPdM8/EtvUOZO3aQc/oMKjc3fB57DJWmFBKzMszqhKhKlSocPHiQ3377jYMHD5Kens7IkSMZPHiwrOkjikylUvHHH3/wyCOPODqUfCIjI2nUqBFffPFFkZ8py59HCFE2fLHvCy6lX7KcD4gYgJfORhua5qRD0nmggLG5F3dC6iVwq2AeP3Rd4hxz75BPnz4O31i1LCjWOkROTk4MHjyYwYMH2zoeUYqK84u/PFiyZAlarXXTUe9k48aNdOjQgaSkpDyLjAoh7h3J2cl8f+h7Mg2Z+e7lGnPZFbsLJ5UTM7rOwNfFl6peVYvfWMY12PwZ5KabZ4/FHUVR4PJWX7Ku6Qp4IAicPeCvLpYrhvh4APyeGFL8OO4hVidEc+bMwd/f37LT+bhx4/jhhx+oW7cuCxYsoGrVEvwHFqIMuLG5sBBCWOO7Q98x7/i825bpWa0nTYKalLyxjZNg9095LqVfcSHt0m3e1GRnAVl5Lnl274YuLKzk8dwDrJ5lNnHiRMurse3bt/PNN98wZcoU/P39efnll20eoLCP4cOHs2nTJr788ktUKhUqlYrz58+zadMmmjdvjrOzMxUrVuSNN97AYLi523JaWhqDBw/G3d2dihUrMm3aNCIjI3nppZcsZWJiYujZsyeurq6Eh4czf/58wsLCbtsTdfHiRR5//HF8fHzw8/Ojd+/enC/CzIcjR46gVquJv/6XTmJiImq1mgEDBljKfPTRRzzwwAN5nunevTseHh4EBQXxxBNPcO3aNcv94n6ea9eu0adPH9zc3KhZsybLli0D4Pz583To0AEAX19fVCqVZZ0tIcS9IUOfwdIzSwEYXGcwLzZ5Md8/4+4fx2v3v1byxrJT4MD1Mbstn4WO78Gjs0jM7QaYN1gNX/rHnf9Z9ieVp0wpeTz3CKt7iC5evEiNGjUAWLp0KY8++iijR4+mTZs2+RZYLK8URUHJyrpzQTtQuboWaSbAl19+yalTp6hfv75lEUuj0UiPHj0YPnw4c+fO5cSJE4waNQoXFxfef/99AMaOHcvWrVtZtmwZQUFBvPfee+zbt49GjRpZ6h46dCjXrl1j48aNaLVaxo4dS1xcXKGx6PV6unbtSqtWrfj3339xcnLio48+olu3bhw6dCjfmle3qlevHhUqVGDTpk08+uij/Pvvv5bzGzZt2mT53kxOTubBBx/kqaeeYtq0aWRlZfH666/z+OOPs379+gLbKOrnmTBhAlOmTOHTTz/l66+/ZvDgwVy4cIGQkBAWL15Mv379OHnypOyhJ8Q9aFnUMjL0GYR5hTHu/nGoVVb3NxTdwV9BnwH+EdB1IqhUZB8/TubOt0Gjwf9//0NbsaL92r9HWZ0QeXh4kJCQQGhoKGvWrGHs2LGAeQHFLAclAWWNkpXFySZNHdJ2xL69qNzc7ljO29sbnU6Hm5ubZRPct99+m5CQEL755htUKhW1a9fmypUrvP7667z33ntkZGQwZ84c5s+fT8eOHQHzquKVKlWy1HvixAn++ecfdu/eTbNm5qmdP/30EzVr1iw0lt9++w2TycRPP/1kSeZmzZqFj48PGzdupEuXLoU+q1KpaNeuHRs3buTRRx9l48aNjBgxgp9++okTJ05QvXp1tm3bxrhx4wD45ptvaNy4MRMnTrTUMXPmTEJCQjh16hS1atXKU781n2f48OEMHGhebn/ixIl89dVX7Nq1i27dullewwUGBsoYIiHuMSbFxIIT5h6bAbUH2DcZUpSbCyw2HwUqFYqicOX6zzivrl0lGSomqxOizp0789RTT9G4cWNOnTplWXvo6NGjhMl7yLva8ePHadWqVZ4epjZt2pCens6lS5dISkpCr9fTvHlzy31vb28iIiIs5ydPnsTJyYkmTW6+I69Rowa+vr6Ftnvw4EHOnDmTb2HP7OxsoqKi7hh3+/btLetibdq0iYkTJ3Lq1Ck2btxIYmIier2eNm3aWNrasGFDgZsQR0VF5UuIrPk8DRs2tBy7u7vj5eV1254xIcS9YceVHZxLOYe71p3e1Xvbt7GzGyHhNOg84b4BKIrCtf/7P3JOnwHAb/gw+7Z/D7M6IZo+fTrvvPMOFy9eZPHixZb9zPbu3Wv567i8U7m6ErFvr8Pavtukp6fTtGlT5s3LPxgxICDgjs/fGPNz+vRpjh07xgMPPMCJEyfYuHEjSUlJNGvWDLfrvWbp6ek89NBDTJ48OV89FUv4V9V/Z6apVCpMJlOJ6hRClH3zT8wHoHf13njo8v+xZVM3BlLfNwCcPUld/jfXvv4GAPcHHsD1lj/MhHWsToh8fHz45ptv8l2fMGGCTQK6F6hUqiK9tnI0nU6XZxuWOnXqsHjxYhRFsfQSbd26FU9PT6pUqYKvry9arZbdu3cTGhoKQEpKCqdOnaJdu3YAREREYDAY2L9/P02bml8bnjlzhqSkpELjaNKkCb/99huBgYF4FWMtjAYNGuDr68tHH31Eo0aN8PDwIDIyksmTJ5OUlJRnbFuTJk1YvHgxYWFhODnd+du/OJ+nIDfGQRW07Y0Q4u51Me0imy9tBrDfDvU3JF+Ek9f37bz/KRRFIWHmDMC8tUalT2WAdElY/aJz8+bNt/1H3D3CwsLYuXMn58+f59q1azz77LNcvHiR559/nhMnTvDnn38yfvx4xo4di1qtxtPTk2HDhvHaa6+xYcMGjh49ysiRI1HfsqR77dq16dSpE6NHj2bXrl3s37+f0aNH43qbwd6DBw/G39+f3r178++//3Lu3Dk2btzICy+8wKVLlwp85lY3xhHNmzfPkvw0bNiQnJwc1q1bR/v27S1ln3vuORITExk4cCC7d+8mKiqK1atXM2LEiAKTleJ8noJUrVoVlUrF8uXLiY+PJz09vcjPCiHKrl9P/IqCQptKbQjzDrNvY3tmgmKC8HYQWJvM3bvJOXYclYsL1desxuk2QxPEnVmdEEVGRub7p0OHDpZ/xN3j1VdfRaPRULduXQICAtDr9axYsYJdu3Zx33338fTTTzNy5EjeeecdyzOff/45rVq1olevXnTq1Ik2bdpQp04dXFxcLGXmzp1LUFAQ7dq1o0+fPowaNQpPT888ZW7l5ubG5s2bCQ0NpW/fvtSpU4eRI0eSnZ1d5B6j9u3bYzQaLQmRWq2mXbt2qFQqy/ghgEqVKrF161aMRiNdunShQYMGvPTSS/j4+KBWF/y/g7WfpyCVK1dmwoQJvPHGGwQFBTFmzJgiPyuEKJsy9Zn8ceYPAAbVGWTfxvTZ5v3IAO4fBUDi9X3IvHv3lmTIBlSKohSwznfhUlJS8pzr9Xr279/Pu+++y8cff2yZfXQvSU1Nxdvbm5SUlHy/oLOzszl37hzh4eFW/YK8V2RkZFC5cmWmTp3KyJEjCyxz6dIlQkJC+Oeff+6J7w97fp7y/v0kRFmVbchmztE5pOamWq5dTr/Muuh1hHiGsLzP8hLPLkvfshX9pYsF37y0Bw7MAxcfePBdFL2eq59MBkWh2t/Lca5evURt36tu9/v7v6weQ+Tt7Z3vWufOndHpdIwdO5a9ex0zmFiUjv3793PixAmaN29OSkqKZQ2j3r1vzqxYv3496enpNGjQgJiYGMaNG0dYWJhlnNHd5l77PEII6805OodvDuQfPwvmsUMlTYYy9+7l4lNP3aGUj/nLlg8tV9zbtpVkyEaKtZdZQYKCgjh58qStqhNl2GeffcbJkyfR6XQ0bdqUf//9F39/f8t9vV7PW2+9xdmzZ/H09KR169bMmzev2PuDFTRF/oaVK1fStm3bYtVbVLb+PEKIu0u2Idsyk6xntZ4EuQVZ7vk4+zAgYkBhjxZZwk/mwdHOtWqh++8WWNnJcG4zqNRQozM4OQOgcnbG/9lnSty2MLP6ldmhQ4fynCuKQkxMDJ988gkGg4EtW7bYNMCyQF6ZOdaZM2cKvVe5cuV7atVn+X4SwnGMJiMLTiwgPivech6XFcfVjKvsi9tHRfeK/N33b7Tqkv0xZEhKIn3jJkyZGRji41GyskmcMwdUKqqt+Bvn8PC8Dyx91vy6rMHj0O/HErVd3tj1lVmjRo1QXV8Z81YtW7Zk5syZ1lYnxB3d2CpGCCHsaVnUMibvzr9G2Q3D6g0rcTIEEPPmW6Rv3JjvukfHB/MnQxkJcHiR+bj56BK3LQpndUJ07ty5POdqtZqAgIBy/9eslR1tQhRIvo+EcAxFUfj5+M8AtKvSjjCvMACcNc4EuAXg4+xDl6qFbyNUVDlRUeZkSKXCo3171G5uOAUEoHJxwXdQAesY7f8ZjDlQ8T6o0qzE7YvCWZ0QVf3vu81y7sY4kszMzHvq1Y1wjNzcXAA0Go2DIxGifNkRs4PTSadxdXJlUttJeOmsXyS2KBLnmpMujwcfJGR6wYO0LUxG2G0eW0Tz0WDF2mfCejYbVF1eaTQafHx8LHtWubm5WbVgnxA3mEwm4uPjcXNzK9Iq2kII21AUhal7pgLwSI1H7JYM6WNiSF64EAC/YUPv/MDpNZASDa6+UL+fXWISN8lPXRu4sVu8bOQpSkqtVhMaGipJtRCl6NeTv3Iy6SQqVAypM8QubWTs2EH08BEAONetg9v999/5oV3mTatp/ARo5Q2EvUlCZAMqlYqKFSsSGBiIXq93dDjiLqbT6QpdMVsIYXsmxcTPx8yvsdpXaU+oV6hd2rn2/feW44AXXrjzHz3XzkDUekAF9xe86K2wLUmIbEij0cjYDyGEuItsu7KNi2kX8dR6Mrld4TPMSiInKorM7TtArabG2jVoK1e+80M3drWv1RV8w+wSl8jL6j9F9+3bx+HDhy3nf/75J4888ghvvfWWZUCoEEIIcTdYcGIBAL1r9MZN62aXNpLmmRd19HiwQ9GSoZx0OGB+5sa+ZcL+rE6I/ve//3Hq1CkAzp49y4ABA3Bzc+P3339n3LhxNg9QCCGEsIdLaZf499K/AAyoXfLVpgtiTE8nZelSAPwGD77zA/t/gU9CIScF/KpB9QftEpfIz+qE6NSpUzRq1AiA33//nXbt2jF//nxmz57N4sWLbR2fEEIIYRcLTy5EQaF1pdZU9bLPkjIpfyzFlJmJrnp13Fq2vH3hzET4+1VQjObzFs+AjCksNVaPIVIUBZPJBMA///xDr169AAgJCeHatWu2jU4IIYSwg2xDNkvOLAHMm7Pag2IykTRvHgC+gwfdeSD1nhlgyAKNDgYthPD2dolLFMzqhKhZs2Z89NFHdOrUiU2bNvHtt98C5hWsg4KC7vC0EEII4Xgrz60kJSeFSu6VaFvZPhtEZ2zbTu7586g9PPDp3bvwgvps2DMT1n9kPu89Hap3sEtMonBW98V98cUX7Nu3jzFjxvD2229b9platGgRrVu3tnmAQgghhC0pimIZTP14xONo1PaZHXyjd8i7Tx/U7u6FF/z3M1j9pvnYqzLU62OXeMTtWd1D1LBhwzyzzG749NNPZcq5EEKIMu/QtUMcTzyOTq2jb82+dmkj9+JFywauBe5RZimYAbuu72AfUBt6TQNNyTeQFdaz2TpE5X1zVyGEEHeHX0/8CkC38G74uvjapY2kBb+CouD+wAP5d7BXFIjeAdnJcH6L+atvODyzDezUWyXuzOqEyGg0Mm3aNBYuXEh0dHS+tYcSExNtFpwQQghhSwlZCaw+vxqw32Bq05EVJC8wr37tG5EFf7+St8DFnRD7nzctrZ6TZMjBrE6IJkyYwE8//cQrr7zCO++8w9tvv8358+dZunQp7733nj1iFEIIIWxiyekl6E16Gvg3oL5/fds3kJVEymfPYMpyQ+tuwCPtL9hdSFmtOwTWNq9E3dg+e6iJorM6IZo3bx4//vgjPXv25P3332fgwIFUr16dhg0bsmPHDl544QV7xCmEEEKUiMFkYOEp827zRVmI0ZSRQeK8+Riub9xtTErCmJZaaHklJxfDhZMYrpmHkPh2a4GqQ92CC3sEQtMR0itUhlidEMXGxtKgQQMAPDw8SElJAaBXr168++67to1OCCGEsJFNlzYRmxGLr7MvXcO63rH81U8/JfnX34rRkhq1uws+r30DPj7FeF44gtUJUZUqVYiJiSE0NJTq1auzZs0amjRpwu7du3F2drZHjEIIIUSR5V66RPr69eSev4A+NhauLyacFX+Q13OMhHi6ErfxpTzPGJOTMV7/A99ST3Q0AB6RkTjXjkClVuMUEIDKxdVc4ORKOP5nnmfUTgpOft7oXvkbjSRDdxWrE6I+ffqwbt06WrRowfPPP8+QIUOYMWMG0dHRvPzyy/aIUQghhCgSxWTi4qjR5J47l+9edctRNOlEF6k+99atCfnu2/w39Flw6iUIzwInV1BdX9ZPo4Wu4yG4UnHCFw5kdUL0ySefWI779+9P1apV2bZtGzVr1uShhx6yaXBCCCGENdI3b7YkQ149uuNSty4aX1/+Pvs3O2N2EuEXweA6BW2yqsIpwB+12y073qvVuNSpU3BDhxdBVhJ4h8KLB2Qs0D2gxOsQtWzZkpZ32rBOCCGEKAVJc83T3f1GjCDo9XEAZOgz+Pr3T8kIVDOg86v4VCrhrgqKAru+Nx/fP1KSoXuE1QlRaGgokZGRtG/fnsjISKpXr37nh4QQQgg7yzlzhoxt20CtxnfwzV6g5VHLydBnEOYVRsuKNvgDPnqHeR0hJxdoMrTk9Ykyweq9zCZOnIiLiwuTJ0+mZs2ahISEMGTIEH788UdOnz5tjxiFEEKIO0r8+RcAPDs+iK5KZSDvvmUDag9ArbL6115+N3qHGjwGbn4lr0+UCVb3EA0ZMoQhQ8wLSMXExLBp0yaWL1/Os88+i8lkwmg02jxIIYQQoiCKopDx778Y4uNJ+dM848tv6M1emz1X9xCVEoWrkysPV3+45A2mXoFjy8zHLf5X8vpEmVGsMUSZmZls2bKFjRs3smHDBvbv30/9+vWJjIy0cXhCCCHKA31cHFcnTUJ/4fazv4xpaRhv2SLKlJGR575znTq4NmtmOb/RO9SrWi88dZ4lD3TPTFCMENoaghuUvD5RZlidELVu3Zr9+/dTp04dIiMjeeONN2jXrh2+vvbZIE8IIcS9L37q56StXFXs57VVQ3GuWRP/p55CpVIBEJsRy/ro9UDRVqa+I0MO7J1tPm4xuuT1iTLF6oToxIkTuLu7U7t2bWrXrk2dOnUkGRJCCFEkiqJgumUBRP3VOFKXL7e87qowejRu9zcr7HEAnAIDUbu6Ws5Vzi5ogwLztfPWlrcwKkaaBjWllm+tkgd/dClkxINnJajdq+T1iTLF6oQoISGBw4cPs3HjRlavXs3bb7+NTqejffv2dOjQgVGjRtkjTiGEEGWMKSsLJTc3//X0dAwJCde/JqLo9WQdPEjaunUYk5PBYCiwPvc2bQgca5sFfhedXsTuWPOuqiXuHTKZIO4Y7Pg/8/n9T5oXYBT3FJWiKEpxH1YUhb179/LNN98wb968e3ZQdWpqKt7e3qSkpODl5eXocIQQwuGufvopiTNmlrwiJye0lSrhXKsmwe+9hzYw8M7PFEJv1JNtzMakmOi/vD+X0y9Tx68O83vOx0ldgmX3Nk2BDR+bjzU6ePkYeAQUvz5Raqz5/W31d8i+ffvYuHEjGzduZMuWLaSlpdGgQQOef/552rdvX+yghRBClG3G9HRi3n6HrH37MMTH37asxtcXtasrGn9/NB7uqFxc0YVUwevhh3GuXh2V9noPi0qFSl3yqfCnkk4xZMUQsgxZlmu+zr7M6T6nZMlQbgZsn24+9qoCzZ+SZOgeZfV3SfPmzWncuDHt27dn1KhRtGvXDm9vb3vEJoQQogxJWrCAtNWrLeeeXbpQeepn+QuqVKicSrwRglVmHJ6RJxnSqDQ82+hZXJ1cb/NUERyYD9nJ4BsOz++VVanvYVZ/xyYmJsprIyGEKGcUg4Gk+eYp7F49ulNh1Cica9VCpXF8gnA14yprzq8BYEHPBUT4RqBSqYrXM6QocGQxJF8wn9+YVdbyWUmG7nFWf7dIMiSEEOVP2j/rMMTEoPHzo+KkSaidnR0dksWCEwswKAaaBTWjvn/9klV25h9YPDLvNRdvaDSoZPWKMq90+zSFEELclRJ/Nm+a6tP/8TKVDMVmxDLjyAwAhtQdUvIKt3xh/hrSAvxrgUoF9fqAs0fJ6xZlmiREQgghbivr6FGy9u4FJyd8Bwx0dDh5fL7ncwCqeFQhskpk3pv7fobDC82vwW5HMUFaDOizIe0KqLXw6CzwrmyfoEWZJAmREEKI20q6vmmqV9eu+RZAdKRrWddYG70WgGcbPYvmxhif7FRY8zbsm1u8ips8IclQOSQJkRBClCO558+TvmkTppxcDPHxKAY9AKb0DIwJ1yzlFEUx38/MQn/1KgB+Q59wSMx5JJ6D2EOQHsfvcdswmAzcp/XjoeMb4PgGc5mjSyAryXxcpTm0fPrO9WqcwTMYnFwgoLb94hdlltUJkdFoZPbs2axbt464uDhMJlOe++vXr7dZcEIIIWxHMZm4+L+nyb1wwepn3e6/H9f77rNDVFbITITv20FOKnrg95BK4OTEoMun4NSB/OXr9oZun4BXpdKOVNyFrE6IXnzxRWbPnk3Pnj2pX7++ZRM9IYQQZVv65s3kXriA2sMDjw4dULu44BQYaB44DDhV8EPtcXNHeJXWCSd/f1TOzjjXrOmosG/aNxdyUsHVl38Cw4hXJ+Cv0tK5+sPm3h3tLWsO+deC+n0dF6u461idEP36668sXLiQHj162CMeIYQQdpL0yzwAfB57jKDXxzk4GiuZjLDHPJuMzh8yP3YNxCfweMNRaBs949jYxD3B6vXSdTodNWrUsEcsQggh7CTn7DkytmwBlQrfQWVrpliRnF4DydHg6suxyvU5EH8AJ7UTj0U85ujIxD3C6oTolVde4csvv6QEe8JabN68mYceeohKlSqhUqlYunRpnvvDhw9HpVLl+adbt255yiQmJjJ48GC8vLzw8fFh5MiRpKen5ylz6NAh2rZti4uLCyEhIUyZMqXEsQshxN0kaZ65d8ijQwd0ISEOjqYYdv1g/tr4CeafXgxAl6pd8Hf1d2BQ4l5i9SuzLVu2sGHDBlauXEm9evXQ3tig77olS5YUua6MjAzuu+8+nnzySfr2Lfhdb7du3Zg1a5bl3Pk/C4INHjyYmJgY1q5di16vZ8SIEYwePZr58+cD5p1uu3TpQqdOnfjuu+84fPgwTz75JD4+PowePbrIsQohxN3KmJ5Oyh9/AOA3ZLCDoymGa2cgaj2gIrHho6xc+yQAg+rI6tHCdqxOiHx8fOjTp49NGu/evTvdu3e/bRlnZ2eCg4MLvHf8+HFWrVrF7t27adasGQBff/01PXr04LPPPqNSpUrMmzeP3NxcZs6ciU6no169ehw4cIDPP/+80IQoJyeHnJwcy3lqamoxP6EQQjheyh9LMWVmoqteHbdWrRwdjvV2/2T+WqsrS+J3k2vKpW6FujT0b+jYuMQ9xeqE6NbemtKwceNGAgMD8fX15cEHH+Sjjz6iQoUKAGzfvh0fHx9LMgTQqVMn1Go1O3fupE+fPmzfvp127dqh0+ksZbp27crkyZNJSkrC19c3X5uTJk1iwoQJ9v9wQghhZ4rJRNIv5oUV/YYMLnMzgzP1mcw9NpfTSae5nH6ZHGNO3gKKCa6dhsrBoE3i8iHzq7NBtQeVuc8i7m7FXpgxPj6ekydPAhAREUFAQIDNgrqhW7du9O3bl/DwcKKionjrrbfo3r0727dvR6PREBsbS2Bg3lVTnZyc8PPzIzY2FoDY2FjCw8PzlAkKCrLcKyghevPNNxk7dqzlPDU1lZC78Z27EKLcS1v7j3mqvacn3g8/7Ohw8kjISqDb4m5kG7NvX1CrATSQGQNAkFsQ3cK73f4ZIaxkdUKUkZHB888/z9y5cy2LMmo0GoYOHcrXX3+Nm5ubzYIbMGCA5bhBgwY0bNiQ6tWrs3HjRjp27Gizdv7L2dk531glIYQoq0xZWcSMH0/OqdOWa8aUFIxJSSjZ5mTDp28f1O7ujgqxQPOOz7MkQ51CO9G2SluC3IJwUl//1aQosOx5SL4A94+CuuaEroZPDZw18jNa2JbVCdHYsWPZtGkTf/31F23atAHMA61feOEFXnnlFb799lubB3lDtWrV8Pf358yZM3Ts2JHg4GDi4uLylDEYDCQmJlrGHQUHB3P1+rLzN9w4L2xskhBC3E2S5s0jddlfhd7XBPjjN2xYKUZUuLjMON7a8hYpOSmcTzkPwLTIaXSq2il/4fNbIPYUaN2g9Wvg6lOqsYryxeqEaPHixSxatIjIyEjLtR49euDq6srjjz9u14To0qVLJCQkULFiRQBatWpFcnIye/fupWnTpoB56xCTyUSLFi0sZd5++230er1lRtzatWuJiIgo8HWZEELcTUy5uSTO/RkAr1698O7zCAAqlQqnoCA03t44+Zedqekzj8xkZ8xOy3kNnxp0COlQcOEbU+0b9pdkSNid1QlRZmamZQzOrQIDA8nMzLSqrvT0dM6cOWM5P3fuHAcOHMDPzw8/Pz8mTJhAv379CA4OJioqinHjxlGjRg26du0KQJ06dejWrRujRo3iu+++Q6/XM2bMGAYMGEClSua9awYNGsSECRMYOXIkr7/+OkeOHOHLL79k2rRp1n50IYRwiNiPJ5KydKn5FdJ/KCYTSmYmTkFBVJr4MapbJpCUNWm5afxx2jz9/43mbxDuHU5dv7o3d6m/VcplOL7cfNx8VClGKcorqxOiVq1aMX78eObOnYuLiwsAWVlZTJgwgVZWTufcs2cPHTrc/MvgxkDmYcOG8e2333Lo0CHmzJlDcnIylSpVokuXLnz44Yd5xvfMmzePMWPG0LFjR9RqNf369eOrr76y3Pf29mbNmjU899xzNG3aFH9/f9577z1Zg0gIcVfIOXOGpJ9/vmO5Cv8bXaaTIYAlp5eQacikhk+NO88S2zsLFCNUfQCC6pVekKLcUilWLjl95MgRunbtSk5ODvdd3/n44MGDuLi4sHr1aurVu/e+cVNTU/H29iYlJQUvLy9HhyOEKEeuvPMOKYsW4xEZSdAbrxdYRuXigraMj4k0mAz0XNKTKxlXeL/V+/Sr1e82hXNgWj3IiIfH5kC9R0otTnFvseb3t9U9RPXr1+f06dPMmzePEydOADBw4EAGDx6Mq6vrHZ4WQghRVLmXLpGyyLxNRYX/jUYXFubYgIph3vF5LI9azpGEIwD4OvvSs1rP2z90bJk5GfKsCLXvUFYIGynWOkRubm6MGiXvdIUQwp4SZ80GQBcejlvjxo4NphjiMuP4bPdnGBSD5drQekNxcXK5/YM3BlM3exI02tuXFcJGipQQLVu2jO7du6PValm2bNltyz5cxhb+EkKIu5EhKYnk63tDBr31poOjKZ6FJxdiUAzUq1CPFxq/QBXPKoR43maRW0WBS7vh0i5Qa6FJ2VgqQJQPRUqIHnnkEcuq0I888kih5VQqFUaj0VaxCSFEuXXllVdRsrJwrlMH9wcecHQ4Vss15vL7qd8BGFF/BK0rt779A4YcmNEZYg6az+s9Ap75ZzQLYS9FSohurEj932MhhBC2l338OBnbtgHgP+qpu3LPrlXnV5GYnUiQWxAPhj54+8IXtsGsWzb61nlC6xfsG6AQ/6G29oG5c+fm2Qn+htzcXObOnWuToIQQorxSTCYuPW9OBlwaNMCze/c7PFH2KIrCvOPzABhQewBadSHjgLKSzatRz3no5rUen8Hr56Gi7GQvSpfVCdGIESNISUnJdz0tLY0RI0bYJCghhCivMrZuRX/pEgAVP/rwruwdOhh/kGMJx3DWONOvZiHT6zMT4esmMLsnmK4Puh6zx7wIo6bY+44LUWxWf9cpilLg/6CXLl3C29vbJkEJIUR5lfjLLwB4dOyIS0SEg6MpOoPJgIJ5Wbtfjps/Q89qPfF1KWSLpH1zIDPBfFzxPmj/OvjXLI1QhShQkROixo0bo1KpUKlUdOzYESenm48ajUbOnTtHt27d7BKkEEKUB7nnz5OxaTOoVASNe83R4RTZ5F2TLUnQrQbVHlTwA0Y97PrRfPzIt9CokHJClKIiJ0Q3ZpcdOHCArl274uHhYbmn0+kICwujX7/brDwqhBDithLnzwfAvV1bdFWrOjiaoonNiGXBiQX5rncK7USEXyE9XMeXQeplcA+A+vJ7Q5QNRU6Ixo8fD0BYWBj9+/e37GMmhBCi5EwZGaQsMW986jfkCQdHU3S/nfwNo2KkaVBTvuzwJWBegsVT61n4Qzu+NX+9/ylwci68nBClyOoxRMOGyUJZQghha8l//okpPR1dWBjube6wZk8ZkW3IZtGpRQA8UecJvJ1vM470xAr443+Qk2o+1+jMK1ELUUZYnRAZjUamTZvGwoULiY6OJjc3N8/9xMREmwUnhBDlgaIoJP1inqbuO3gwKrXVE4AdYsW5FSTnJFPZozKRIZGFFzQaYNUbN5MhgPtHgUeg3WMUoqis/r9uwoQJfP755/Tv35+UlBTGjh1L3759UavVvP/++3YIUQgh7m0Z27aRe/Ysajc3vPs84uhwikRRFMtA6oG1B6JRawovvOP/IPmCeTuOFw/BuHPQbWIpRSpE0VjdQzRv3jx+/PFHevbsyfvvv8/AgQOpXr06DRs2ZMeOHbzwgqwuKoQQ1rjRO+Tdpw+aWyaslAWZ+kySc5LzXEvJSWHl+ZWcTjqNq5MrfWr2uXkzJw1+Hw7RO8GQZb52Y52hdq+C790xWFyUP1YnRLGxsTRo0AAADw8PyyKNvXr14t1337VtdEIIcRdJXbWKlKV/or98GUXJu82RkpOLIT4epaD9HvV6wPy6rKw4m3KWUatHEZcVd9tyD1d/GC+d180L+36GM//kL+gTCg+8bOMohbAdqxOiKlWqEBMTQ2hoKNWrV2fNmjU0adKE3bt34+wsswWEEOVTzrlzXH55rHnH9mLw7N4N52rhNo7KOscSjnEs4RgAK8+tzJMMOWtu/nxXq9T4u/oT7h3O0/c9fbMCkwl2/WA+bvwERL4JqusjM9wDZAVqUaZZ/d3Zp08f1q1bR4sWLXj++ecZMmQIM2bMIDo6mpdfluxfCFE+JfzwIygKKp2OSp9MQlPBP18ZtYc7ThUq5H9YpcYpMKAUoixcfGY8Q1cOJceYd6/KHzr/QKtKrYpWyZm1kHQOXLyh+2TQudshUiHsw+qE6JNPPrEc9+/fn6pVq7Jt2zZq1qzJQw89dJsnhRDi7qfo9cR9Po3so0fRx8RguHYNFAUlOxuA0FkzcWva1MFRWsdoMjJh+wRyjDlUdK9Ibb/aALSq1KroyRDAzu/MX5sMlWRI3HWsTog2b95M69atLVt3tGzZkpYtW2IwGNi8eTPt2rWzeZBCCFFWpCxbRuKsWQXe8+jU8a5LhgCmH5jOpkubAHi7xdu0D2lvfSXxpyBqPaAyL7goxF3G6oSoQ4cOxMTEEBiYd/2IlJQUOnTogLGgAYNCCHEPUEwmEmaakyGXBg3w/99otCEhqN09UOm0aAPvrnV1UnJSSMtNY95x8yy35sHNaVulbfEquzF2KKIH+IbZJkAhSpHNdrtPSEjA3V26SIUQ9yZTdjYXR40mNyoKtYcHobNmlrkp8taYfmA63x38znJe07cmP3X5qcCf73eUnQIHzPuw0WK0jSIUonQVOSHq27cvYN6jZvjw4XlmlBmNRg4dOkTr1nfHcvNCCGGtpHnzydy9GwCf/o/f1clQUnYSs4/Mtpy7OrnyUpOXrEuGTEa4esS8c/3JFaDPgIDaEF6M121ClAFFToi8vc171CiKgqenJ66urpZ7Op2Oli1bMmrUKNtHKIQQDqYYDCTNM79Wcm/dmoDnn7d7mznGHDZf2kyuMfeOZRUUErISuJh2kXR9uuW6yWQiLiuOXGMuidmJpOWmAaA36ck2ZlPHrw6/9frNukRInwV/joETf99cePGG5qOhOD1MQpQBRU6IZl0fRBgWFsarr74qr8eEEOVG2vr16K9cQePjQ5X/m47axcVmdeuNeqYfmE5c5s01f3JNuayLXofhxgrPdjKm8RjrX5Ht/wWOLLp57uZvnlFWoQbcN9C2AQpRiqweQzR+/Hh7xCGEEGVW0tyfAfDp39+myRDA0qilzDgyo9D7dfzq3H4X+es0ag3BbsFU9qicZxFFrUZLgGsAOo2OANcAnJ3M9zy1ngS4FXHto/R4SL1kXnRy+zfma63GwH0DILhB0eoQooyzOiEKDw+/7V8UZ8+eLVFAQghRlmQfO0bmnj3g5ITvINv2gCiKwq8nfgWgR3gP6laoa7nn6uTKfQH3EeEXYdM2rZYaA9Ob592p3tUPOrwNOjfHxSWEjVmdEL300kt5zvV6Pfv372fVqlW89tprtopLCCEczpSRwblHHwPAq2tXtEFBNq1/f9x+TiWdwkXjwlst3ipST1Cp2/mtORnSuoOrD6g10P4NSYbEPcfqhOjFF18s8Pr06dPZs2dPiQMSQoiyIum3heb9uQC/J0fYvP4FJxYA0LNaz7KZDMWdgK1fmo8fnQkR3RwbjxB2pLZVRd27d2fx4sW2qk4IIRxCyc0laeFCznTtSvwXXwAQ8PLLuNarZ9N24jPj+eeCeVf4AbUH2LRum1n7rvmrfwTU7OLYWISwM5ttPbxo0SL8/PxsVZ0QQtiVYjCQsX0HSo55DzJFUUhbvYbUFSssvUIA2qqhVBgx3ObtLzq9CINioFFAI8veYWWGosDCoXB6jfm8xxRQ2+zvZyHKJKsTosaNG+cZVK0oCrGxscTHx/N///d/Ng1OCCFsIefMGeKmfo4hIQHDtXgADFdibvuMc61aBL31Fi7166PS6Wwaj96kZ9FJ89T1Mtk7dHEnHF9mPm48BKpFOjQcIUqD1QnRI488kudcrVYTEBBAZGQktWuXsb9yhBACuDp5Chn//lvgPV21ami8vMwnKhWuDRsQMHYs6ltW47e1DdEbiMuKw8/Fj85VO9utnWK7sWt91TbQ60vHxiJEKZF1iIQQ97TskyfNyZBaTfCE99FVqYLawxMAJz9ftJUrl3pMv540T7XvV7MfOo1te59KLOUSHLveO9R9CmhsNrJCiDKt2N/pcXFxxMXFYbrlXTtAw4YNSxyUEELYSuwHHwLg2aULvo895uBo4EzSGXbH7kaj0vB4xOOODie/3TNAMUJYWwiu7+hohCg1VidEe/fuZdiwYRw/fhxFUfLcU6lUGI1GmwUnhBAlkXvpEln79gFQYeSTDo7G7EbvUIeQDgS7Bzs4mv/QZ8He2ebjFv9zaChClDarE6Inn3ySWrVqMWPGDIKCgqzfB0cIIUpJ0oIFoCi4tWqJawPHbzGRnpvOX1F/AWV0MPXhRZCVCN6hUKu7o6MRolRZnRCdPXuWxYsXU6NGDXvEI4QQNmHKyiJ5kXltNL8nhjo4GrNlUcvINGRSzbsazYObOzqcvE6sgGVjzMfNn5KxQ6Lcsfo7vmPHjhw8eFASIiFEmZb699+YUlLQVq6MR/t2jg4HRVH47eRvAPSP6O/Y3nWjAS7vAYN5DSZy0mDhMPOx1g0aP+G42IRwEKsTop9++olhw4Zx5MgR6tevj1arzXP/4YcftllwQghRHIqikDhvPgC+gwah0mgcHBHsit3F2ZSzuDm58XB1B/+c3DgJ/v0s/3WVBgb+Cm6yyK4of6xOiLZv387WrVtZuXJlvnsyqFoIURZk7d9PzvHjqJyd8enX19HhAFh2tX+o+kN46DwcF0h2Cuz83nzsXwvU1/+o1bnDw19DoKwnJ8onqxOi559/niFDhvDuu+8SZOOdn4UQwhaSfpkHgNdDvdD4+Dg2GCA2I5YNFzcAMCCilAdTp12FM2vBdP2P1Uu7IDfNvD/ZsztkSw4hrrM6IUpISODll1+WZEgIUSbpr8aRusa8B5ff4MEOjsZs4cmFGBUj9wffTw3fUhx/qSjw60C4vDf/vdbPSzIkxC2sToj69u3Lhg0bqF69uj3iEUKIEkleuBAMBlybNsWlTh1Hh0OuMZfFp82z3Uqtd8iohz/HwIm/zb1BTq559yPzDYP7yuC0fyEcyOqEqFatWrz55pts2bKFBg0a5BtU/cILL9gsOCGEsIaSm0vSQvNMLr/BgxwcjdnaC2tJzE4k0DWQDqEdSqfR48vg0K83z9u8AB3eKp22hbhLFWuWmYeHB5s2bWLTpk157qlUKkmIhBAOk7p2Lcb4azgFBODZqZOjwwFuDqZ+NOJRtGrtHUrbyI1B0+HtoPunEBBROu0KcRezOiE6d+6cPeIQQogSS7o+1d6nf39UOsdvmnoi8QQH4g/gpHLi0ZqPlk6jl/fBxZ3m2WN9fwTPMrY9iBBllCxFKoS46ykmE1kHDpj3LdNq8e1fNjZNvdE71KlqJwLcAkqn0V0/mL/W6yPJkBBWkIRICHFXU3JzOdfvUXJOnwbAq0sXnAJKKfm4jZScFP4++zdQivuWpcfBEfMAblo8XTptCmGF9/48wl8HrxR4z9/DmbVj25dyRDdJQiSEuKulrl5tSYbUnp5UGPWUgyMy+/PMn2Qbs6npW5MmgU1Kp9E9s8CYC5WbQZWmpdOmEEV07Eoqc7dfKPS+VuPYZSAkIRJC3NVuLMLo/9xz+D/3LKoysLaOSTHx60nz67KBtQfaf98yQy6cWA4bJ5rPWz5j3/aEuINsvRGjSclz7fvNUQB0rRfEa13zD/TXOPj/XUmIhBB3razDR8g6eNA8bmhA/zKRDAFsu7KNi2kX8dR60jO8p30bO/cvzO0NyvWVqD2CoY7sKSlsb8/5RH7fcwmTopCZayQ+LQcFc9KTrTcRn5aDUVHI0RtJzTYUWs+YDjWpEehZWmEXmdUJ0apVq/Dw8OCBBx4AYPr06fz444/UrVuX6dOn4+vra/MghRCiIEm//AKAV7duZWLc0A03BlP3rtEbN62bfRv797ObyVBAbeg1DZwcP8NO3Fvi03J49LvtJa6nV8OKNKjibYOIbM/qhOi1115j8uTJABw+fJhXXnmFsWPHsmHDBsaOHcusWbNsHqQQQvyXISGB1BUrAPAbUja26AC4lHaJTZfMa7T1j+hv38biT8HZjebjMXvBvxS3BRHlhsFo4plfzNu/BHu5MLR1VQB83XT4uN5cW8vTRUsFD3MyXsFdh5dr/nW3XLSaUoi4eIq1DlHdunUBWLx4Mb169WLixIns27ePHj162DxAIYQoSPLvv6Po9bg0aIDrffc5OhyLcZvHAdCqYivCvMPs29jun8xfI3pKMiTsYntUAgN/3GE5f//hunSrX9GBEdmP1QmRTqcjMzMTgH/++YehQ4cC4OfnR2pqqm2jE0KIAih6PUkLzK+lykrv0P64/Wy4uIHD1w4DMKL+CPs2mJMGB8wLUdJ8lH3bEuXW1+tPW46fbl/9nk2GoBgJ0QMPPMDYsWNp06YNu3bt4rffzPsGnTp1iipVqtg8QCGE+K+0deswXL2KpkIFPLt3d1gcf5/9m/1x+zl67ShHEo5YrncP706rSq3s2/jBX80bt1aomXfjViFs5PTVNLZFJQDwz9h2ZXIgtC1ZnRB98803PPvssyxatIhvv/2WypUrA7By5Uq6detm8wCFEOK/bky193n8MdQO2qLjXMo53vz3TcssGwAXjQsdq3bklaav2LdxRYFdP5qPm48Ce0/rF+XSjTWDutYLuueTIShGQhQaGsry5cvzXZ82bZpNAhJCiP/KPnGC3PMXMFy7hik9jcw9e8DJCd8BpbQCdAHmHJ2DgkL9CvVpV6UdPi4+PFbrMZzUpbCaybnNcO0k6DzgvoH2b0+UO6nZehbvuwTAsFZhjg2mlBTr/9yoqChmzZpFVFQUX375JYGBgaxcuZLQ0FDq1atn6xiFEOVY5r79XBg82NwrcgvPzp3QBgXZvf05R+ew6NQiTIoJo2IkLjMOvUlvuf9689dpFNjI7nHksft679B9A8DFq3TbFuXC4r2XyMw1UjPQg1bVKzg6nFJhdUK0adMmunfvTps2bdi8eTMff/wxgYGBHDx4kBkzZrBo0SJ7xCmEKKcSZs4ARcGpYkWcw8PR+PigcnXB/5lnbd7W2gtrWXRqEWeSz5CQlYDxxvo+hWhXpV3pJ0PJF+GEeY807i8b25SIe4vJpPDz9ddlQ1uH2X+l9TLC6mVd33jjDT766CPWrl2L7pZ39w8++CA7duy4zZP5bd68mYceeohKlSqhUqlYunRpnvuKovDee+9RsWJFXF1d6dSpE6dPn85TJjExkcGDB+Pl5YWPjw8jR44kPT09T5lDhw7Rtm1bXFxcCAkJYcqUKdZ9aCGEQ+ReuED6uvUAhM74idCZM6j8+VQqffwxuiqVbdrW0WtHGbtxLNuubCMuMy5PMtQksAlzu8/l5+4/8/tDv7Px8Y1s7r+Zbx78xqYxFMneWaCYIKwtBNYp/fbFPe/fM9c4ey0DT2cn+ja27f9nZZnVPUSHDx9m/vz5+a4HBgZy7do1q+rKyMjgvvvu48knn6Rv37757k+ZMoWvvvqKOXPmEB4ezrvvvkvXrl05duwYLi4uAAwePJiYmBjWrl2LXq9nxIgRjB492hJjamoqXbp0oVOnTnz33XccPnyYJ598Eh8fH0aPHm3txxdC2JEpN5fsQ4dQ9OZXUsmLl4Ci4N6+Hc7Vqtm17XnHzQO1vXRefNjmQ8K9w/HUeeKiccFD52HXtotMnw17Z5uPm8vPL2Efc7edB6Bf0yq4O5efHb6s/qQ+Pj7ExMQQHh6e5/r+/fstM86Kqnv37nQvZMqsoih88cUXvPPOO/Tu3RuAuXPnEhQUxNKlSxkwYADHjx9n1apV7N69m2bNmgHw9ddf06NHDz777DMqVarEvHnzyM3NZebMmeh0OurVq8eBAwf4/PPPC02IcnJyyMnJsZzL+kpCWC/rwAGyjh3DEB+Pkp2D4do1DNfiMcReRX/1qiXpyaOga0CFYcPsGmtCVgKrzq8C4PvO31Pfv75d2yu2Y0shMwG8KkOELIQrbC86IZP1J+MAGNqqqoOjKV1WJ0QDBgzg9ddf5/fff0elUmEymdi6dSuvvvqqZZFGWzh37hyxsbF06tTJcs3b25sWLVqwfft2BgwYwPbt2/Hx8bEkQwCdOnVCrVazc+dO+vTpw/bt22nXrl2e13tdu3Zl8uTJJCUlFbj32qRJk5gwYYLNPosQ5U3upUucf2JooQnO7ai0WnRhYZap5K6NG+PWyr5r+iw6tQi9SU9D/4ZlNxmCm1Ptmz0JmvLzl7soPT/vOI+iQLtaAVQLKCM9o6XE6v+jJk6cyHPPPUdISAhGo5G6detiNBoZNGgQ77zzjs0Ci42NBSDoP7NIgoKCLPdiY2MJDAzMc9/JyQk/P788Zf7bm3WjztjY2AITojfffJOxY8dazlNTUwkJCSnhJxKi/EicNRv0erQhIbg2qI9TQAAqF1ec/P1xCgxEWzHYvBlrAYM1nfz9UTmV3i97vUnPwpMLARhYpwxPYb+8Fy7vAY0Omti3x0yUT1m5Rn7bfRGAYeWsdwiKuXXHjz/+yLvvvsuRI0dIT0+ncePG1KxZ0x7xOYSzszPOzs6ODkOIu1LWoUMkzTOPx6n44Qe4t2zp4Ihub92FdcRlxVHBpQJdq3Z1dDiF23V937J6fcAjwLGxiHvSnwcuk5ptINTPjciIwDs/cI8p9p9hoaGhhIaG2jKWPIKDgwG4evUqFSve3Dvl6tWrNGrUyFImLi4uz3MGg4HExETL88HBwVy9ejVPmRvnN8oIIWwn/osvAdBVq4ZbixYOjubO5p8wT8B4LOIxtJr8u3OXCRnX4Mhi87EMpi7TsnKNpOcY7lguPi2HjNz85bJyjcSn5WD6z7pbd5KSpScpM9eqZ/5r+aEYAJ5oWRWNunxMtb+V1QmRoigsWrSIDRs2EBcXh8lkynN/yZIlNgksPDyc4OBg1q1bZ0mAUlNT2blzJ8888wwArVq1Ijk5mb1799K0aVMA1q9fj8lkosX1H8StWrXi7bffRq/Xo9Waf9itXbuWiIiIAl+XCSGKL/vkSTK2bQOg8udTy/z6JccTjrM/bj9OKiceq/WYo8PJLycdDNnmXe2NOVCpMVRu6uioyg1FUVi87zLnr2WQkWvg70MxZOYWvjaVoihk3Ob+3cBFq+axZuVzX1KrE6KXXnqJ77//ng4dOhAUFFSiH3jp6emcOXPGcn7u3DkOHDiAn58foaGhvPTSS3z00UfUrFnTMu2+UqVKPPLIIwDUqVOHbt26MWrUKL777jv0ej1jxoxhwIABVKpUCYBBgwYxYcIERo4cyeuvv86RI0f48ssvZasRIYpBHxdH5s6d8J8/hG5I+du8YKBn92641K5dmqEVy43eoc5VOxPoZoNXBIYcOPoHRK0H03/++jfmQlosGIsw0NyQA6mXIec/M1zvl33LSku23sjgn3ay90JSsZ6/038mrVpNgKczOqf8ywF6uTjh527dHn0qlYoAD2fcnDVWPfdfkRGB+Lg5Zn9AR7M6Ifr5559ZsmQJPXqUfMrnnj176NChg+X8xkDmYcOGMXv2bMaNG0dGRgajR48mOTmZBx54gFWrVlnWIAKYN28eY8aMoWPHjqjVavr168dXX31lue/t7c2aNWt47rnnaNq0Kf7+/rz33nuyBpEQVlIUhUvPPEv20aN3LFthxIhSiKhkkrKT+CvqLwAG1RlUsspSr8D26bBnJugzbRBdASreB/Xzr9cm7GPJvsuWZKhqBTc6RASiUat4sHYgVXxdC33ORashyMul0Pui7FIpinUvKsPDw1m5ciW174K//mwlNTUVb29vUlJS8PKSfYNE+ZS5Zw8XhjyBSqfDrXnzQsu5NWuK/9NPl2JkN2XoMzCYDCRkJ5CRm2G5blSMxGfFk2u8OcZi0q5JpOSkUMevDr/1+q1kr/d+eRTOrL15HlAbGj4OWve85bSu4BkMFKEtZ0+oUEMGUDuAoih0nraZM3Hp9GxYka8HNEZdDsfU3Aus+f1tdQ/R+++/z4QJE5g5cyauroVnyUKIe0viL+aZY969H6bihx86OJr8vj/4Pd8csH4rjVENR5UsGYo/dTMZavYk3DcIQu4vfn3C4TafvsaZuHQ8nJ34pG8DSYbKCasToscff5wFCxYQGBhIWFiYZaDyDfv27bNZcEKIskEfG0vaWvMvfd8hQxwcTX6puanMPDLTcq5RafBz8UOrvvnzSafREeAWgEZ1c4xFh5AOdK7auWSN7/zO/DWiJ/SSsYn3gplbzgHwWLMqeLqU0ZmHwuasToiGDRvG3r17GTJkSIkHVQsh7g5Jv/4KRiNuzZrhEhHh6HDyWXxqMZmGTKp7V+f3h35HrVKjUZdscGmRZCbCwQXm45bP2L89kUe23ojRVPCoj4T0XFKzi7ZSepbeyPg/jxKXlg3AtfRcVCoY3jrMVqGKu4DVCdHff//N6tWreeCBB+wRjxCijDHl5JC88HegbPQOHU04yhd7v8gzHuh00mkAhtUbVjprCZmM5unwNwZRB9WHMPmZaAsrD8fw0d/HyTUWPJPxhlyDiZQs67eGKaqH76tE1Qrudy4o7hlWJ0QhISEysFiIciR15UqMiYk4BQfj2amjo8Ph092fsvfq3nzXg9yC6Fmtp30bP7sJ/noBUi6D6ZZfxi2fkenwNmAwmpi48jiXk7NKXJdKBX5uOrSa/NPaC+KsVTOidRitqvujUUOYJEPljtUJ0dSpUxk3bhzfffcdYWFhdghJCFFWKIpC0vXB1L4DBpTqHmMFOZ5wnL1X9+KkcuLDBz7EWXNzi50G/g3QaeywfoqimNcFQoHVb0HS+bz3A+tC/Udt3245tProVS4mZuHrpuXnkS3uuFqyt6u20PV61CpVgWv8CFEYq3+6DRkyhMzMTKpXr46bm1u+QdWJiYk2C04I4VjZBw+SfeQIKp0On8cdv5LzL8d/AaBzWGd6Vetl38ZMRlg0Ak7/A/qMvPdGrDS/JlNrwMkV1PKLt7iupecwbe0pLiRkcvJqGgBPtAqjfmVvB0cmyhurE6IvvvjCDmEIIcqiG1PtvXr0wMnPz6GxXMu6xspzKwEYUqcUxjKdXAHH/sx/veN4qNra/u3fQxRF4XJyFtl687YWcWk5ZOSYj3/dFc26Ezf3pHTTaRhaDndaF45XrFlmQoh7X+6FC6QuXw44fjD1hdQLvLf1PfQmPQ0DGtIwoKH9G912fU2jiB7w8Deg0YLaCXRu9m/7HmI0KfT/fjt77rAFxpCWobQIr0Cdip74ezjftqwQ9lCkhCg1NdUykDo1NfW2ZWXAtRB3J8VoJOWvvzClpGDKzeXat+b1dXQ1quNav55d247NiOVU0iky9Zlcy7qGgkJidiLro9eToc/gauZVS9kn6jxh11gAOLwILu4AtRZ6fg7uFezf5l3mdpscpOcYiE/LAWDH2URLMuTtqkWtAo1aTaCnM85a86vGjrUDGfNgTfsHLcRtFCkh8vX1JSYmhsDAQHx8fApce0hRFFQqFUbj3b3TrxDlVfLChcRO+CDf9UqTPrFru5n6TPov709i9u3HH6pQ0btGbzpWtfNMN0MurH7bfNzgMfCqaN/27kJXkrPo+3/biE3NLvIzz0ZWZ1y38rPlk7j7FCkhWr9+PX7Xxw9s2LDBrgEJIUqfoigk/mwesOx2//04BQaicnXBs2NHXBvUt2vbf0X9RWJ2Ip46T6p6VsXbxRsvnbmnWavW0jG0I0FuQdTyrVU6awwdWQzpsebjDm/Zv7270Hebou6YDOk0alyu9wCFVnBjdLtqpRGaEMVWpISoffv2luPw8HBCQkLy9RIpisLFixdtG50QolRkbNtG7tmzqN3dqfLtt2g8SmcNFkVRmHfCPHB7TKMxJd913hp758DpNXDLAo+YDBC13nzc6X3wCSm9eBwoLjWbqPgM9P9ZDPFiUiafrj6ZbwHEG2/LZgxrRuNQ33z1aVQqvN1kywtxd7F6UHV4eLjl9dmtEhMTCQ8Pl1dmQtyFkq73Dnn36VNqyRDA9pjtnEs5h7vWnd41epdau1zaY15gsTAewdB0ROnF40DLD11hzPz9Vj/XtqY/D9YOlO2bxD3D6oToxlih/0pPT8fFxcUmQQkhSk/uxYukb9oEgO/gUuyhAeYfnw/AIzUewV1biisD7/ze/FXnAV0nmtcTukHrCjU6gcu9vw6Ooih8s/6M5bx2sCfq//x8d9NpeLNHbUL98v738ffQSTIk7ilFTojGjh0LgEql4t1338XN7ebUU6PRyM6dO2nUqJHNAxRC2FfSvPmgKLi3bYtzeHiptRudGs3mS5sBGFh7YKm1S9pVOPqH+XjYX1C5Sem1XcZsj0rgRGwabjoN29/oKK+5RLlW5IRo/35zl6qiKBw+fBid7uZy6Tqdjvvuu49XX33V9hEKIezGlJlJ8pIlAPgNGVyqbS84sQAFhbaV21LVqxQX4ts727wPWZX7y3UyBDBz6zkAHm1aRZIhUe4VOSG6MbtsxIgRfPnll7LekBD3gJRlf2FKTUVbNRT3tm1LpU29Uc/u2N2WbTgG1ynFRMyQa96hHqD5/0qv3TLo3LUMywrRw1uHOTYYIcoAq8cQzZo1yx5xCCFKmaIoJM0zJyV+gwahKqX9uCbvnsxvJ38DIMwrjFaVWpVKuwCc+Ms8pd4jCOqW4iBuBzl8KYU3lhwiOiETgynvQopGk4KiwIO1A6kW4OGgCIUoOxy7dbUQotSYcnMxpaZiysgg+8RJcs9GkXP6DCo3N7z79i2VGBKzE/njtHn8jo+zD6/d/xpqVSlujLrzB/PXpiPAqeBd0u8lE1cc5+iVwncX0KhVPBtZvRQjEqLskoRIiHJAHxfHuYd7Y0xOznfPu/fDaDw9SyWORacWkWvKpV6FeizouaB0ZynFHLy+HYcTNLv3p9QfuZzC9rMJAHw3pAn1KuWfNeflopWxQ0JcJwmREOVA0oIFN5MhtRqnoCB0oaE4VaiA/zPPlEoMeqOe306YX5UNrjO49Kds3+gdqvsIeAaXbtulJMdgtOwi/8PmswA8fF8lutWX7UeEuJMiJURNmjRh3bp1+Pr68sEHH/Dqq6/mmXYvhCi7TLm5JC/8HYDKX0zDq1s3h8Sx5sIa4rLi8Hf1p1tYKceQeBYOmMdL0eLeHEx9MTGTXl9vybeq9FNtS28pBSHuZkVKiI4fP05GRga+vr5MmDCBp59+WhIiIe4SaStXYkxIwCkoCM+Odt4YtRBGk5EJ2ycA0D+if9H3JDu8CE6thuxkyDS//sFkhLRYMBRxY1F9FhjNO69TsZF5uv1dIjVbT0qmnoSM3DuWnb31XL5k6KH7KtGwio+dohPi3lKkhKhRo0aMGDGCBx54AEVR+Oyzz/DwKHhWwnvvvWfTAIUQJZP4i3mvMN+BA1BpHTNe5LeTv5FlyEKr1vJYrceK9lD0Tlg80nZBOLlA5Jtg51d1WblGLiRmkGsw3blwIWZtPc+qI7Fk6a3fCum30S1pHm7ejFtWkhai6IqUEM2ePZvx48ezfPlyVCoVK1euxMkp/6MqlUoSIiHKkKyDB8k+fBiVVovPY0VMRGxs8anFTNo1CYC+NftSwbVC0R7cPMX8VaODThPAIxB07jeveVUClabw52/lGQQ6T7Dh0gJGk8KFhAwycowoKCw/FMP2qAQOX06xWRs3+LnrcNXe+bN2qB1Ai2pF/PcrhMijSAlRREQEv/76KwBqtZp169bl29xVCFH2JM4z9w559eiBU4XS/0WZbcjmi31fADen2RfJtdNw5h/z8agNEFzfPgEWU1JGLj2++peYlMJf21X0dsm3L5g1WlevwDu96uKu0+CkKcWlCYQop6yeZWYyFb8bWAhRegzXrpG6chUAvkOGOCSGFedWkJyTjIvGhVX9VuGscS7ag7uuzwir1b3MJUMAc7aftyRD/h7OaDUq1CoVDSp7071BML0aVkKjltdVQtxNijXtPioqii+++ILjx48DULduXV588UWqV5cFvoQoK5IWLgS9Hpf7GuLaoPSTCkVR+PnYzwCMaTym6LvZZ6fAgfnmYwfMCLuYmMnRKykoinlQc0xKNnFpORiNN1d6XnU0FoAv+jfikcaVSz1GIYTtWZ0QrV69mocffphGjRrRpk0bALZu3Uq9evX466+/6Ny5s82DFEJYR9HrSf7VvOaPn4N6h3bG7uRM8hlcnVzpU7NP0R/cPw9y0yGgNlSLtFt8BTl0KZmHv9lapLKhfm70aijr+whxr7A6IXrjjTd4+eWX+eSTT/Jdf/311yUhEqIMSFm2DENcHBp/f7y6di29dnNSOBB3gPiseD7e8TEAvav3xktXxM2gTUbY9b35uMX/8swIyzEYuZCQSY7eRFxadr69uXINJuLTcjD857W+uXxOvvI5eiPx6TkYbun5ubGyM8D9Yb4ABHg6E+jpQoDnzdd9apWKznWDZGyPEPcQqxOi48ePs3DhwnzXn3zySb744gtbxCSEKAFTTg4x75pne/o+/jgqXens2ZVrzGXA8gFcSr+U57pVu9nvmwNJ58HFGxr2t1xWFIUBP+xgf3SybYK9gw2vRhLuX8RXfEKIe4LVCVFAQAAHDhygZs2aea4fOHBAZp4JUQakLFsG13tJfAcPKrV2V51fZUmGmgQ2oYJrBdpWbkuYd1jRKsjNhFVvmo+bDL05xR7YfPoa+6OT0ahV+LhqcXPW4O/hzH+HLbvpnAj0dOa/N9x1TgR45i/v7uyUp+cHoE5FT0mGhCiHrE6IRo0axejRozl79iytW7cGzGOIJk+ezNixY20eoBCi6BSTicTZcwAIfP31UptqrygKc4/OBeDFJi/yVIOnrK/k4ALL6tP/Bg4k9/hV4tJyyMw1snT/ZQCGtQrjvYfq2ixuIYS4weqE6N1338XT05OpU6fy5pvmv+YqVarE+++/zwsvvGDzAIUQRZPy13LSN20iNyoKtbs7Po/2K7W2d8bs4mTSSZw1Ltzv151/T8dzISETvdGE3mgiLjWHa+k5JGaat5ZQFIW41BzLSswqxcTc7M+pCkzQP8GsX88D5/O0oVGrePKBsFL7TEKI8kWlKIpy52IFS0tLA8DT09NmAZVFqampeHt7k5KSgpdXEQeHClGKcqKiONuzl+Xcb/hwgt543e7tLjq1iI93fozBZAAgN7ElOVcfsbqe9uqDzNFNJlVxpVXON4QEB+LspMZJoybAwxmdk5oOtQPo07iKjT+BEOJeZs3v72KtQ3TDvZ4ICXG3SLq+IrVL/fp4deuK78CBdm9Tb9Tzfwf+z5IMKUYXtOmR6Jyd0DqpqVrBjUo+rqhVKlyc1FT0diHAywV3nQYUI1VjVuGnzkSjVlPhzCq4Bln1B7G+ay+CvFzsHr8QQtyqRAmREMLxjGlpJC/9E4DAV1/BvWXLUmn3n+h/iM+Kx1XtQ/zJZ2lapTKL3m1XtIe3/x/seTPvNZWaoE4vgCRDQggHkIRIiLtcypIlKJmZONesgVuLFqXW7vzj5tWksxOboxi8eOqBWkV70KiH7dPNx2Ftwe36wO8aHcE3zPaBCiFEEUhCJMRdTDGZSJxnTkx8Bw9GVYLNRP/r/LUMlh+6Qmq2Id+9RP1ZDiQfQIWGtPhmhPq50bluUOGVpV2F9R9CZqJ5a47US+AeCIMXgVZ6hIQQjmdVQqTX6+nWrRvfffddvnWIhBClL33zZvTR0ag9PfF++GGb1XstPYfIzzYWet+l4u9ofSA3pT6KwYvhrcMK38w0NwO+awMZ8Xmvt3xakiEhRJlhVUKk1Wo5dOiQvWIRQlgp6RfzYGqffv1Qu7nZrN7vNkYB4ISB0U29cNVqLPeyTGnMTzuIEXi0Uidq1a7AwPo6SI0BfSYkX4BDCyH1CmRcM+9LdiMZajQYQpqDzgPqPmKzeIUQoqSsfmU2ZMgQZsyYkW8vMyFE6co5e46MLVtApSreitSpV2DvHDDmQE4apMcBYDAptDh5lT66q9RTX4CjeR/7ydsLo58PdXNy+PDci+bVn7cVob2+P0HDx6yPUwghSoHVCZHBYGDmzJn8888/NG3aFHf3vEvcf/755zYLTghRuKsTJwLgERmJLiTE+gr+fgVOrsh32QnoDHDrvqVq848KA/CblwcAg9IyUakL+BHiHgCufhDaEqp3AGdP895klRpbH6MQQpQSqxOiI0eO0KRJEwBOnTqV554tB3QKIQqXdfSouXcI8B1ixeapN1w7AydXmo+bjzYnPG4VMDl78/X608Sn59K9YRXatOkAlZtYdp3fcGEtsRvH4ufiR7eX9oDG+TaNCCHE3cPqhGjDhg32iEMIcQf6mBgyd+/GlJHB1U8mA6ALC8P9+p6CN2TqM8kx5ljO03PTSchOuHmuTydq2zTSfLygQg2oWBUABYUjsWfY5H4FJy8VF71SGL99FRn6DMuz2UbzXmP9avbDWZIhIcQ9pNjT7s+cOUNUVBTt2rXD1dUVRVGkh0gIO1GMRqJHPEnu+fN5rn/5qDNX/x5AfGY82cZsDCYDWYasolXq6w2meDj0fZ7LWm/z131xBT/mqfWkf0R/Kz+BEEKUbVYnRAkJCTz++ONs2LABlUrF6dOnqVatGiNHjsTX15epU6faI04hyrW09evJPX8etZsbOfWqsSPzKPuqq9iqjYKE2z+rQoWfix+uTq7m3eTTYgg0Gqlgcme3ti2mW3YzvJamRzF6MSayNoGebgS5BVHFswo6jc5SpoJLBTx0Hnb6pEII4RhWJ0Qvv/wyWq2W6Oho6tSpY7nev39/xo4dKwmREMW0M2YnCVnm7MaoGInPiicpO4m4zDg6TttACLC5tSfz2saTkK2hikcVPm36Im5ObjipnQhyC0Kj0uCh86CCS4U8datUKkwmBeOCgWgv7eOoqSoDc98klfyJTZe6Qbxwf7PS+MhCCFFmWJ0QrVmzhtWrV1OlSt5dp2vWrMmFCxdsFpgQ5cmG6A28sOGFAu91320i5KwJgxp+rn2NpGwVLhoXZnWbRbB7cJ6yBy8m8+bKEyRkHCUhPTfPPZ/sS6x1WgUqeEE/hh7316F+ZW8CPZ1RX3/drdGoaB7mZ58PKYQQZZjVCVFGRgZuBSwAl5iYiLOzDLIUojhmH50NQHXv6vi7+gOg1WipmuPBQxvMU+NzO9zPhIdHABDuHZ4vGQL4cPkx9lxIKrCNMU4rUasUNhjvw61SXSb0roezk6bAskIIUd5YnRC1bduWuXPn8uGHHwI3uuJNTJkyhQ4dOtg8QCHudUeuHWFf3D6c1E780OUHAt0CLfeiR48mw2gC4L73p+IUEHDzwdwMSDxrOT0ZHUPAxS085KTi9bAzBCbuQY3Jcl+TlQAKPDDkPSJrtZFJEEIIcQurE6IpU6bQsWNH9uzZQ25uLuPGjePo0aMkJiaydetWe8QoxD0n25BNdFo0iqLw0+GfAOge1j1PMpS2YQMZm/8FIOSH783J0Jl15F4+QGr0EfyjluSpMwL49sbY50uFNFypMdpaHS3rCgkhhDCzOiGqX78+p06d4ptvvsHT05P09HT69u3Lc889R8WKFe0RoxB3NUVRyDXdHM+zIXoDr21+LV+5J+o+kec84YcfAXCuVQv3tm0h7gTKL/3QoeB/S7mris/NZxRvwioH46ZzAr9q0OJ/wC3JT4XqkgwJIUQBVIqiKHcuVr6lpqbi7e1NSkoKXl5ejg5H3AX2Xd3HNwe+4Ur6FRKzE8nNySQ4CVSK+R+/NAWdEby0Xnilm6jvVp327o0wZWRA+lVMGWmkbtqHyklD9Q97o3VKhysHIOYAJ0whHDBVJ80jnJOB3VB53vxDpFmYL/3vD3XcBxdCiDLEmt/fxVqYMSkpiRkzZnD8+HEA6taty4gRI/Dzk9kpQiiKwkc7P+J00mnLtTcXmWh8tqC/PZKvf91PIvvz3fUOTUV78Js818bpR/Ngx2681KmW7YIWQohyzuqEaPPmzTz00EN4e3vTrJl5rZKvvvqKDz74gL/++ot27drZPEgh7iY7YnZYkqGJD0yk+mUjqrNvgkqF2scHALWzM04BAag0GlROTmgC/FG7ueF0eS3qjMvg6ofK1R1tnUqsS3XmTK4PaYobJ5UQTjnVZHarMMd9QCGEuAdZnRA999xz9O/fn2+//RaNxjxl12g08uyzz/Lcc89x+PBhmwcpxN3k52M/AzCw9kB6Bj9I9NtPkQV49+5NpU8mFf7gxd0w4xvQ6Ige9hfvrYtj48l4y+37w3wJ8XXjy/rB+LnrCq9HCCGE1axOiM6cOcOiRYssyRCARqNh7NixzJ0716bBCXE3SclJYcW5Ffx7+V9UqBhc8WHOdO6CMTERAL8nR9y+gh3TzV8bPMbETQl5kqEPe9fjCekVEkIIu7E6IWrSpAnHjx8nIiIiz/Xjx49z33332SwwIe4Gf0X9xT8X/mHHle103JpB7UsKbxghQOtL5qTHLOV8Bw3CpdYtY34yrpkHSSsmOPw7ZCVC1AYAYuuMYM3sWACef7AGT7YJx1d6hIQQwq6KlBAdOnTIcvzCCy/w4osvcubMGVq2bAnAjh07mD59Op988olNg3v//feZMGFCnmsRERGcOHECgOzsbF555RV+/fVXcnJy6Nq1K//3f/9HUFCQpXx0dDTPPPMMGzZswMPDg2HDhjFp0iScnIo1nlwIi2MJx3hry1sAtDhh4okNplvuJlqOQr7/Do/27W/eMuphRhdIjMpfabUO/HDKHZMCbWv680qXiPxlhBBC2FyRsoJGjRqhUqm4dYb+uHHj8pUbNGgQ/fv3t110QL169fjnn38s57cmMi+//DJ///03v//+O97e3owZM4a+fftaFog0Go307NmT4OBgtm3bRkxMDEOHDkWr1TJx4kSbxinuTUaTkdjMWDL0GVzLuma5bjAZGL9tPAAuameeO1YBiEYbGor///4HajVqVxecq4XhvP9j+PxZSL8KihEUBVBA5wEVqpNrUpHgVZeUgKYkBD/AwsXmPQFHPhDugE8shBDlU5ESonPnztk7jkI5OTkRHJx/z6aUlBRmzJjB/PnzefDBBwGYNWsWderUYceOHbRs2ZI1a9Zw7Ngx/vnnH4KCgmjUqBEffvghr7/+Ou+//z46nbyGEHAg7gBzjs5Bb9ITnxXP5fTLZORmAGBQDHd8/uf4R1BOzkOl0xE2fx5O/rcsm7hvLhxfVvCDXSeyP+Bh+n27DZPlbw1zr1GNQA/a1woo+DkhhBA2V6SEqGrVqvaOo1CnT5+mUqVKuLi40KpVKyZNmkRoaCh79+5Fr9fTqVMnS9natWsTGhrK9u3badmyJdu3b6dBgwZ5XqF17dqVZ555hqNHj9K4ceMC28zJySEnJ8dynpqaar8PKBzKpJgYv208Z1PO3racq5Mrfi5+eGg9LNecnZzp494GZcr/AeDd++G8ydCF7bDsefNxvT4Q+Sa4+JjPnZzB1Ydv5+6xJEN1KpoXDdNpVLzaNUL2GhNCiFJUrIE0V65cYcuWLcTFxWEymfLce+GFF2wSGECLFi2YPXs2ERERxMTEMGHCBNq2bcuRI0eIjY1Fp9Phc31dlxuCgoKIjTUPSI2Njc2TDN24f+NeYSZNmpRv7JK4eymKQmxGLDtjd3I2OW/ik5CdwNmUs3hoPXil2Sto1VqC3YMJ8QzBSW3+36OCSwU06vy7wit6PZfHjSPNaESl1RL4yis3b5qM8Odz5mOdJzz8NTh75nn+YmIma49fBWDty+2oGZT3vhBCiNJjdUI0e/Zs/ve//6HT6ahQoUKev2JVKpVNE6Lu3btbjhs2bEiLFi2oWrUqCxcuxNXV1Wbt/Nebb77J2LFjLeepqamEhITYrT1hX69uepU1F9bctkz/iP48WuvRItdpys7m3CN9yD1/HjAPnNb4+Jhnj+3+CZLO3xw0PezPfMkQwJxt51GuD56WZEgIIRzL6oTo3Xff5b333uPNN99ErVbbI6ZC+fj4UKtWLc6cOUPnzp3Jzc0lOTk5Ty/R1atXLWOOgoOD2bVrV546rl69arlXGGdnZ5ydnW3/AUSpO5N0xpIMeWg9iAyJxN/VP08ZD61Hvo1VC6IYDMS8/TbZJ09hyspEfyEaAM/OnXBr1cpcaNUb5mn0N7QbB5Wb5qsrI8fAb3suAjCiTVgxPpkQQghbsjohyszMZMCAAaWeDAGkp6cTFRXFE088QdOmTdFqtaxbt45+/foBcPLkSaKjo2l1/ZdTq1at+Pjjj4mLiyMwMBCAtWvX4uXlRd26dUs9flH65p2YB0DH0I580eGLEtWVtnYtKX/mHSBd8eOP8enX13ySdOFmMnT/U+ARBK2fz1ePyaTw/eazpGUbCPd3J7JWYIniEkIIUXJWJ0QjR47k999/54033rBHPHm8+uqrPPTQQ1StWpUrV64wfvx4NBoNAwcOxNvbm5EjRzJ27Fj8/Pzw8vLi+eefp1WrVpb1kbp06ULdunV54oknmDJlCrGxsbzzzjs899xz0gNUDiRnJ7M8ajkAQ+oMKXF9ibPnAODTvz+eXTqj8fTEpUED882sZFgyynxcrQP0nFpgHYqiMGTGTrZFJQAwvHUYarUMnhZCCEezOiGaNGkSvXr1YtWqVTRo0ACtVpvn/ueff26z4C5dusTAgQNJSEggICCABx54gB07dhAQYJ6OPG3aNNRqNf369cuzMOMNGo2G5cuX88wzz9CqVSvc3d0ZNmwYH3zwgc1iFGXXotOLyDZmU8evDk2D8r+2skbWgQNkHTyISqsl4PkxOPn6QPQ2OPQbHJgH5zbfLNz2lULr2X0+yZIM1QryoF/TKiWKSwghhG0UKyFavXq1ZeuO/w6qtqVff/31tvddXFyYPn0606dPL7RM1apVWbFihU3jEmWPoiicSjrF+dTzJGUnkZCdwOJTiwEYXGdwib43c86e4+IY86svr169zFPr17wL277KX7jZSAhvW2hds7eZ1/Qa2DyESX0bFjsmIYQQtmV1QjR16lRmzpzJ8OHD7RCOEMWz8ORCPtr5Ub7rfi5+dA/vXsATRWNMS+P8wIGYUlLM9Q0bCtkpsGemuYBnJQisA6Etod1rcJvE63JyFquPmgf1D2sdVuyYhBBC2J7VCZGzszNt2rSxRyxCFIvBZGDGkRmW8xYVW+Dv6o+n1pPu4d3RaYq3IrliNJLw40+WZCjglbG41K4N26dDbjoE1IFnt982CbrVz9svYDQptK5egdrBXsWKSQghhH1YnRC9+OKLfP3113z1VQGvC4RwgLUX1hKTEYOfix9rHl2Ds6bkA+azT54keugwjNeToYqfTMLnkUfMCy7u/N5cqMX/ipwMZeUa+XW3eZr+cOkdEkKIMsfqhGjXrl2sX7+e5cuXU69evXyDqpcsWWKz4IS4k50xOxm32bzR8IDaA2ySDAFc+2a6JRlyadAA7549zTdOrYLkC+DqCw0L3sg4x2DEYDTvx5GlNxKbks2mU/EkZ+oJ8XOlY52gAp8TQgjhOFYnRD4+PvTt29cesQhhlcPxh3lqzVOW8/4RBSco1sq9dJm0desACFrwK1E+VYjevRvPlJOEnpiJP7DXvzc7tl4hNVvP1ZRs4tJySM8xEJOSTXxaTqF1D2sVhkam2QshRJljdUI0a9Yse8QhhNU+33tziYfV/Vbj5+Jnk3qT5s8HkwnXli3pvTaB9MRjbHJ+GS9VFgAGRc2Y002JOX2ySPV5ujjhrnMitIIb/e+XLWCEEKIsKtbmrkI4Uo4xh4k7J7Ln6h4AlvZeSiWPSjap25SZSfKiRQCcbvYArpdPMcxpI16qLJLVPlxxCuGodyTtghoB5iFE/h7OVPDQEeTlgoezE+H+7gR43nx156LNvzGsEEKIssXqhCg8PPy2a7qcPXu20HtClJRJMfHEiic4nngcgH41+1Hdp3rxKruyHxLPYUq9Qsz5U5gM2ei3n8SUmoraS02n+LF0vmVIkk+/L/Gp9wh1gcdK/lGEEEKUIVYnRC+99FKec71ez/79+1m1ahWvvfaareISokD/XvrXkgy1q9KOd1u+W+Rnz8anExWfAUDWoaU8fMI8GFsNVAYUBU5vCwI0BFRPskwgM7kFoA5pDnUesuEnEUIIUZYUa9p9QaZPn86ePXtKHJAQ/2U0GQFIykni0z2fAjCi/gjGNh1b5DpiU7Lp8dW/ZOtNgMLfuunmTAjYaqxHtBKI+1U1NXOOY9Q6sbLNC+hd3Ahv1Zv29ara+iMJIYQoY2w2hqh79+68+eabMuha2MTeq3tZdW4VxxKPcTj+MAqK5Z6TyonBtQdbVd+c7efJ1pvw93Cmi0cU9ZIvmNt5dBuePpV5wE2HccwosgC/Hj0YPMb+mxcLIYQoO2yWEC1atAg/P9vM8hHl2+7Y3Ty5+skC77lr3RladyhB7kVfyycz18D8neZFESf2qU+XI3MgGWg6nKb16wGQdfgw5/ftA62WgFcK35xVCCHEvcnqhKhx48Z5BlUrikJsbCzx8fF5dpoXwlpXM67yyqZXOBh/EIDKHpV5uPrDNAtqRoRfBFq1FjetW5Hqysw1WNYD+vtwDClZeqpWcKNjpVxYtNxcqPn/LOUT5/4MgHeP7mgDA234qYQQQtwNrE6IHnnkkTznarWagIAAIiMjqV27tq3iEuXM2eSzDF81nKScJAAquFTgs/afUd+/vtV1pWTq6fj5Jq6l510g8ck24Wj2zADFBOHtIKguSm4uqWvWkvrXXwD4Dh1a8g8jhBDirmN1QjR+/Hh7xCHKsZ0xO3nmn2fQm/R46bz4+sGvaRzY+LbLOxQmPi2HmVvPcS09Bye1yrIGUI1ADx5t6AfT55gLtngagKtTPiXpl18AcGvWDNd69WzzoYQQQtxVZGFG4TBf7vuSecfnkWUwrwAd5BbE3O5zi73I4i87LvDO0iOW86mP30fvRpVvFtg7B7KSwCcUanXDkJRE8u+/A+AUHEzga68W/8MIIYS4qxU5IVKr1Xf8i12lUmEwGEoclLi3XUq7xOd7P2fthbWWa/cH38+XHb7EU+dZrDpzDEa+WncaAGcnNa3CPHno5Fuw9cTNQqlXzF+bjwa1hqQFC1BycnCpV4+wRb8Xq0dKCCHEvaHICdEff/xR6L3t27fz1VdfYTKZbBKUuHfNPDKTaXunWc5bVGzBlHZTir0P2YWrqUz64g+uXUumfkYK/joY1y0C1cm/iN+/Pv8DTgHgnAJbPyXlj6UA+A0fLsmQEEKUcypFUZQ7FyvYyZMneeONN/jrr78YPHgwH3zwAVWr3nuL2KWmpuLt7U1KSgpeXl6ODueu9dnuz5hzzDyGJ8gtiBebvEi3sG5oNVqr69q//xTHPv8/Guz5B61iLHZMTsHB1Fi7BpXW+hiEEEKUbdb8/i7WGKIrV64wfvx45syZQ9euXTlw4AD161s/G0iUH7tidlmSocgqkXzR4Qs06qJvehqbks2nq09yMSkTY2wMHy14mya33M+pWAV3Lw9c/Dwg+l8AnDy0qFsOB42uwDpVahWeXbtJMiSEEMK6hCglJYWJEyfy9ddf06hRI9atW0fbtm3tFZu4R5xKOsXINSMBCPUM5csHv0StUltVx5RVJ1iy7xLdLuyk/aX9luuXW3emyTuv4VctxHzhr5dgbxLoPOGplRBYx1YfQwghxD2syAnRlClTmDx5MsHBwSxYsIDevXvbMy5xjzifcp6hK81r+7g5uTGn+xyrk6FLSZns27yHR66e5n9HllmuB8+cRZ3WLcFkgoxrkJ0CBxeYbw76TZIhIYQQRVbkMURqtRpXV1c6deqERlP4q44lS5bYLLiyQsYQFc/VjKt0W9INg8mAh9aDn7r8RD1/69b5SZw3j1NffYd3yjXLNfcHHsArsjU+kQ0g6TysehMy4m4+VKkxjNoAMlBaCCHKNbuMIRo6dKjMxBFFtixqGeO3jcdgMqBT65jTfQ61fGtZVUfW0aNc/fAjvG9c8HTF3TOdyhX/QHNmIZwp4CGtG3R4R5IhIYQQVilyQjR79mw7hiHuJedSzvH2lrcB0Kl1/NjlR6uSoZSkVLZ9Mxuvf9fiB+TqnLjSoR7d/deD8ZbtOLRu4B0CXpWgxf8goruNP4kQQojyQlaqFja14uwKXv/3dQAC3QL565G/irwhK8Dl81c42+9RwjKSLNciHrzCfT7RYARCWkLv6eYkSOsqPUFCCCFsQhIiUWyxGbEkZCeYTxTYHbubqXunWu5/8+A3d0yGVk3+DmXNSlRG81pCVWOj8L9+z7NKFp6Vs8mq3hCX8EagcYbmo6BCdTt8GiGEEOWZJESiWA7HH+aJlU9gLGBRxEDXQP7qc/ueodjT54l64WWqnjtR4P3KrRPxCs2G7p9Ci9E2i1sIIYQoiCREolhmHJmBUTHiqfPEzcmc+Lg4udAhpAOjGo4qNBkymUz8Nf4Lqv/+E36YJzie86+K+/AniTgyBff0i+i89Dj7u0LIg9DkiVL7TEIIIcovSYiE1aJTo1kfbd4n7Jfuv1DNp1qhZbP15h6kbzdGsT3qGn0XfsZ9V44DEO/ux+Hug3j89acIODAdLp0GH2DonxDeXsYHCSGEKDWSEAmrfbzzYxQU2lZuW2gy9O/peCatOMGxmFTLtSePLrckQ+fa96Lb9Em0c3KC5IuwboK5UKMhUC3S3h9BCCGEyEMSInFHKTkpzD02l1NJp7iacZXjieak5sn6T+Yru+FkHM/N20dm7s2xRR65mYw8upxuF3YBoO7zKD0mfXjzoT+eNn919YNe0+z3QYQQQohCSEIkbivbkE3fP/sSlxWX53pklUiaBTfLc23zqXhGzNptOW9b05/3e9XBOLAPxitXANBWqkT1iR/cfOjoUriwxXz86ExwKngjViGEEMKeJCEShUrNTWXIiiGWZGh4veFU96lOoGsgLSu1tJTLyjXy3p9H+H3vJcu1f8d1IMTPjZh33yX5ejLkN2IE/k//z7zi+cXd8OtAyIg3P1CzC1TvUHofTgghhLiFJESiQCk5KTy99mnOpZwD4NN2n9ItvFu+cnvOJ/L0L3u5lp4LQJsaFfjssfuo6O1K2oqlJP++CIAKvR8gsJ0HHJsHaVdg29c3K/GpCj0+s/+HEkIIIQohCZHIJ1OfyUN/PERSjnm16HdbvpsnGVIUhUtJWczdfp4f/zUnTFqNik/6NqRf0yokzvyRqDkzyb2aDICzt54Al4WwemHehrTu8NRa/r+9O4+Lqt77AP6ZAWcYZFV2ZXHBXdxQQrTlSuESLreUjNuDdq+lUVqYpt3Ullv4pE+ZppjaxRIfabmupRghankVk0BECVwQvCZiIrIo63yfP8jzNKKmJozO+bxfr3m9OL/fd875fjm8Dt/XmXPmwP3WHvhKRER0p7EhIhNbT2xF7P5YlFaXAgD+EfIPjOo4SpkvqazBkyv34aeicmWsq6cDVkUFwuFfC3Bi6ueoPv//F1RrdUb4jG8Dja030NIF0P/6tGHHNsD9MwC9fbPURUREdCNsiEixMmslFmcsBgDYWNlgbvBchHcIV+aNRsHID7/Hfy5cBgDorbUYEeCJBSM742zUI/jPwV+UWL1zDTz/3AU2U7+ARn/zzzIjIiIyBzZEhIKyAiw8sBA7T+0EAAS6B2LJn5bATmenxOT/UolX/pWlNEMLHg/A2EBv1J8tROEj/VFV3HBWyL5bazg//SxsHx4HjV7f3KUQERHdFjZEKldUWYTRm0ajzlgHAAjxCsH7D70Pg7UBQMMdZLPWZ2FT5s9wRAXa4DKe72+HsVa7UP15Hgrnf4a6S1oAgMezo+D80nyz1UJERHS72BCpVHV9NT45/AniDsYpzdCqR1YhyDNIidly8GfM+lcWKmvqEaI9hDW6+ai5YIXS1S1x6pIWFacNALTQaAXe82eh5cgJ5imGiIjoD2JDpEJGMeKJr57AsdJjAADPlp54Y+AbSjNUcL4ST65Mw+nSho/HrLTAIretqMm3Qv52N9OVWWnQ7n9XQd8rpFlrICIiupPYEKnM/jP7MWP3DJRUlQAAontHY1LPSbDSWqGksgYf7TqO+D0nUVNvBABEdQKm5i1B3c585P/0azNkZQW3mBhoHezRMigIOh8fc5VDRER0R7AhUpG4g3FYlrlMWX6hzwt4JuAZAMD2w0WYnJAOEQAQPFf7b4zN/QZVGyvR8D3V/3+BdfuvtkDfrl1zpk5ERNSk2BCpQNa5LCw/uBzfnf4OQMOF0+8MfgetbFoh50wZJieko+D8JUAE91Ucxls/rUXd6VpU/fp+jTVg29UH+r4PwGH4CDZDRERkcdgQWbALVRew4IcF2HJiizLm7+yPZaHLANFg+a7jeC85DzV1Rtx36TDmHUgASmpR92usjaceLhOfgl3ki9BYWZmnCCIiombAhsgCVddXI+FIApZlLkONseEZY8GewQjzC8PIDiPxY0Gp8vyx+0szEJP1JfQl1cr7bVw18PhHLAwPjLreJoiIiCwKGyILU1pVivCN4cqjN+x19vjvwf8NB+mJlJ+K8dTOA9h3ogT3VWbj7VPb4JNbBIgGAKBz0qLtWy9DHzoB0GjMVwQREVEzY0NkQWqNtXhq21NKMxTROQIx/WKwI6cUT/3vHgCAjbEK67Lnw+lExa/v0qCFoxbuE0fB7pl/QKPVmid5IiIiM2JDZAFEBGuOrMGanDUoqiwCACx+aDF8Df3xXEI2duaew9SMLzDoTBbsay8pZ4SsbYHWjw2B86wPeI0QERGpGhsiC/DuD+8iISdBWX4t6DVk5bXFxG93oWPpf/Dhj5+jQ9nPv85qoLEywjXiT2g1Zxk0/GiMiIiIDdG97uNDHyvNUJBHEGYOmIkz55zxxrbdmJ61AaGn0pVYe+/LcO1RjhYv74a2TXdzpUxERHTXYUN0jzp36Rze2vcWUk+lAgDu87wPH/jEoDghBT8k/Yh/Hf9eiW3pUQWPfhehs68HHvsYYDNERERkgg3RPeiHoh8QnRKNy3UNzxob3W4Eph12RMHUPwMARvwm1iu4BI6+VYBLZ2DsasC9W/MnTEREdJdjQ3QPKSgrwNw9c/Fj8Y8AAPsW9ljsHIQOH32ConQnAIC1bR3sPKthbaiHg3cV9C464OGFQP+/8VZ6IiKi62BDdI8oqizCoxseVZb72vvh/eN5qEn8EkV5Tg2DOqBwqBc6hITDs1MvwN4DaNUe0LU0T9JERET3CDZE94BNxzbhrX1vAQBsrGwwp6YzHk5Nws9pTqgpa3joalFLZ8QMfgGfPDcMfm2dzJgtERHRvYcN0V1u3U/r8E7aOwCANpU2mHrQBj3y9uDkGVcAQK3WGsfDI+E+6a/41sMBre305kyXiIjonsSG6C729Ymv8c6+t+F/Ghj/nR49TlYAqEAlbAAA5e06wWfxYgT4+5o3USIionscG6K7VGZxJjatmIkPU41wuwgAlwAA1jb1qOrUDj7/9QIcRozgFysSERHdAWyI7kKffrMSLd5/Hy/lizJm61oNtz5lMET9D9AvyozZERERWR5VPclz6dKl8PPzg42NDYKCgrB//35zp2TiUtUlfBL9J/Sf+h56/9oM6byr0H54MXz/4g3DG+lshoiIiJqAas4QffbZZ4iJicHy5csRFBSERYsWISwsDLm5uXBzczN3etjy8evQrfkMAxqezYrLeoHzw97wefwxWLXpC7QJBPgkeiIioiahERH5/bB7X1BQEPr3748PP/wQAGA0GuHt7Y0XXngBs2bNuuF7y8rK4OjoiIsXL8LBweGO5VRXfRlfb/wnqhKXISDH2JCXBsgd6IqRsatg7dbpjm2LiIhIbW7l/7cqzhDV1NQgPT0ds2fPVsa0Wi1CQ0Oxd+/eRvHV1dWorq5WlsvKypokrxM5aeg070Nl+aSPFXznzsefBz16g3cRERHRnaaKz2B++eUX1NfXw93d3WTc3d0dRUVFjeJjY2Ph6OiovLy9vZskrwuVtaixBi7rgP/8qQOGfZONbmyGiIiImp0qzhDdqtmzZyMmJkZZLisra5KmKCjkYdRkHkZxeRX6Otve8fUTERHRzVFFQ+Ti4gIrKyucPXvWZPzs2bPw8PBoFK/X66HXN883PuustWjLZoiIiMisVPGRmU6nQ79+/ZCSkqKMGY1GpKSkIDg42IyZERER0d1AFWeIACAmJgZRUVEIDAzEgAEDsGjRIlRWVmLixInmTo2IiIjMTDUNUUREBM6dO4e5c+eiqKgIvXv3RlJSUqMLrYmIiEh9VPM9RH9EU30PERERETWdW/n/rYpriIiIiIhuhA0RERERqR4bIiIiIlI9NkRERESkemyIiIiISPXYEBEREZHqsSEiIiIi1WNDRERERKrHhoiIiIhUTzWP7vgjrnyZd1lZmZkzISIiopt15f/2zTyUgw3RTSgvLwcAeHt7mzkTIiIiulXl5eVwdHS8YQyfZXYTjEYjfv75Z9jb20Oj0dzRdZeVlcHb2xunTp1SxXPS1FSvmmoF1FWvmmoF1FWvmmoFLL9eEUF5eTm8vLyg1d74KiGeIboJWq0Wbdu2bdJtODg4WOQf4/WoqV411Qqoq1411Qqoq1411QpYdr2/d2boCl5UTURERKrHhoiIiIhUjw2Rmen1esybNw96vd7cqTQLNdWrploBddWrploBddWrploB9dV7I7yomoiIiFSPZ4iIiIhI9dgQERERkeqxISIiIiLVY0NEREREqseGyIyWLl0KPz8/2NjYICgoCPv37zd3Sjdl9+7dCA8Ph5eXFzQaDTZu3GgyLyKYO3cuPD09YTAYEBoaiqNHj5rElJSUIDIyEg4ODnBycsJf//pXVFRUmMRkZWVh8ODBsLGxgbe3N959992mLq2R2NhY9O/fH/b29nBzc8Po0aORm5trElNVVYXo6Gi0bt0adnZ2eOyxx3D27FmTmMLCQowYMQK2trZwc3PDjBkzUFdXZxKzc+dO9O3bF3q9Hh07dsTq1aubujwTcXFxCAgIUL6gLTg4GNu2bVPmLaXOa5k/fz40Gg1efPFFZcyS6n399deh0WhMXl26dFHmLanWK06fPo2//OUvaN26NQwGA3r27IkDBw4o85ZynPLz82u0bzUaDaKjowFY5r5tMkJmkZiYKDqdTv75z3/K4cOHZdKkSeLk5CRnz541d2q/a+vWrfL3v/9d1q9fLwBkw4YNJvPz588XR0dH2bhxoxw8eFBGjhwp7dq1k8uXLysxQ4cOlV69esm+ffvku+++k44dO8r48eOV+YsXL4q7u7tERkZKdna2rFu3TgwGg3z00UfNVaaIiISFhUl8fLxkZ2dLZmamDB8+XHx8fKSiokKJmTx5snh7e0tKSoocOHBA7rvvPhk4cKAyX1dXJz169JDQ0FDJyMiQrVu3iouLi8yePVuJOXHihNja2kpMTIwcOXJElixZIlZWVpKUlNRstW7evFm+/vprycvLk9zcXHn11VelRYsWkp2dbVF1Xm3//v3i5+cnAQEBMm3aNGXckuqdN2+edO/eXc6cOaO8zp07Z5G1ioiUlJSIr6+vTJgwQdLS0uTEiROyfft2OXbsmBJjKcep4uJik/2anJwsACQ1NVVELG/fNiU2RGYyYMAAiY6OVpbr6+vFy8tLYmNjzZjVrbu6ITIajeLh4SELFixQxkpLS0Wv18u6detEROTIkSMCQH744QclZtu2baLRaOT06dMiIrJs2TJxdnaW6upqJeaVV16Rzp07N3FFN1ZcXCwAZNeuXSLSUFuLFi3kiy++UGJycnIEgOzdu1dEGhpIrVYrRUVFSkxcXJw4ODgo9c2cOVO6d+9usq2IiAgJCwtr6pJuyNnZWVatWmWxdZaXl4u/v78kJyfLAw88oDREllbvvHnzpFevXtecs7RaRRqOFYMGDbruvCUfp6ZNmyYdOnQQo9Fokfu2KfEjMzOoqalBeno6QkNDlTGtVovQ0FDs3bvXjJn9cfn5+SgqKjKpzdHREUFBQUpte/fuhZOTEwIDA5WY0NBQaLVapKWlKTH3338/dDqdEhMWFobc3FxcuHChmapp7OLFiwCAVq1aAQDS09NRW1trUm+XLl3g4+NjUm/Pnj3h7u6uxISFhaGsrAyHDx9WYn67jisx5vp7qK+vR2JiIiorKxEcHGyxdUZHR2PEiBGNcrLEeo8ePQovLy+0b98ekZGRKCwsBGCZtW7evBmBgYEYO3Ys3Nzc0KdPH6xcuVKZt9TjVE1NDRISEvD0009Do9FY5L5tSmyIzOCXX35BfX29yR8gALi7u6OoqMhMWd0ZV/K/UW1FRUVwc3Mzmbe2tkarVq1MYq61jt9uo7kZjUa8+OKLCAkJQY8ePZRcdDodnJycTGKvrvf3arleTFlZGS5fvtwU5VzToUOHYGdnB71ej8mTJ2PDhg3o1q2bxdUJAImJifjxxx8RGxvbaM7S6g0KCsLq1auRlJSEuLg45OfnY/DgwSgvL7e4WgHgxIkTiIuLg7+/P7Zv344pU6Zg6tSp+OSTT0xytrTj1MaNG1FaWooJEyYoOVjavm1KfNo90U2Kjo5GdnY2vv/+e3On0mQ6d+6MzMxMXLx4EV9++SWioqKwa9cuc6d1x506dQrTpk1DcnIybGxszJ1Okxs2bJjyc0BAAIKCguDr64vPP/8cBoPBjJk1DaPRiMDAQLzzzjsAgD59+iA7OxvLly9HVFSUmbNrOh9//DGGDRsGLy8vc6dyT+IZIjNwcXGBlZVVoyv9z549Cw8PDzNldWdcyf9GtXl4eKC4uNhkvq6uDiUlJSYx11rHb7fRnJ5//nl89dVXSE1NRdu2bZVxDw8P1NTUoLS01CT+6np/r5brxTg4ODTrPyydToeOHTuiX79+iI2NRa9evfDBBx9YXJ3p6ekoLi5G3759YW1tDWtra+zatQuLFy+GtbU13N3dLareqzk5OaFTp044duyYxe1bAPD09ES3bt1Mxrp27ap8TGiJx6mCggJ8++23+Nvf/qaMWeK+bUpsiMxAp9OhX79+SElJUcaMRiNSUlIQHBxsxsz+uHbt2sHDw8OktrKyMqSlpSm1BQcHo7S0FOnp6UrMjh07YDQaERQUpMTs3r0btbW1SkxycjI6d+4MZ2fnZqqm4dbc559/Hhs2bMCOHTvQrl07k/l+/fqhRYsWJvXm5uaisLDQpN5Dhw6ZHFyTk5Ph4OCgHLSDg4NN1nElxtx/D0ajEdXV1RZX55AhQ3Do0CFkZmYqr8DAQERGRio/W1K9V6uoqMDx48fh6elpcfsWAEJCQhp9PUZeXh58fX0BWN5xCgDi4+Ph5uaGESNGKGOWuG+blLmv6larxMRE0ev1snr1ajly5Ig888wz4uTkZHKl/92qvLxcMjIyJCMjQwDIe++9JxkZGVJQUCAiDbezOjk5yaZNmyQrK0tGjRp1zdtZ+/TpI2lpafL999+Lv7+/ye2spaWl4u7uLk899ZRkZ2dLYmKi2NraNvtt91OmTBFHR0fZuXOnya2tly5dUmImT54sPj4+smPHDjlw4IAEBwdLcHCwMn/lttZHHnlEMjMzJSkpSVxdXa95W+uMGTMkJydHli5d2uy3tc6aNUt27dol+fn5kpWVJbNmzRKNRiPffPONRdV5Pb+9y0zEsuqdPn267Ny5U/Lz82XPnj0SGhoqLi4uUlxcbHG1ijR8lYK1tbW8/fbbcvToUVm7dq3Y2tpKQkKCEmNJx6n6+nrx8fGRV155pdGcpe3bpsSGyIyWLFkiPj4+otPpZMCAAbJv3z5zp3RTUlNTBUCjV1RUlIg03NI6Z84ccXd3F71eL0OGDJHc3FyTdZw/f17Gjx8vdnZ24uDgIBMnTpTy8nKTmIMHD8qgQYNEr9dLmzZtZP78+c1VouJadQKQ+Ph4Jeby5cvy3HPPibOzs9ja2sqYMWPkzJkzJus5efKkDBs2TAwGg7i4uMj06dOltrbWJCY1NVV69+4tOp1O2rdvb7KN5vD000+Lr6+v6HQ6cXV1lSFDhijNkIjl1Hk9VzdEllRvRESEeHp6ik6nkzZt2khERITJd/JYUq1XbNmyRXr06CF6vV66dOkiK1asMJm3pOPU9u3bBUCj/EUsc982FY2IiFlOTRERERHdJXgNEREREakeGyIiIiJSPTZEREREpHpsiIiIiEj12BARERGR6rEhIiIiItVjQ0RERESqx4aIiIiIVI8NERFZhJMnT0Kj0SAzM7PJt7V69Wo4OTk1+XaIqPmwISKiZjFhwgRoNJpGr6FDh5o7tRvy8/PDokWLTMYiIiKQl5dnnoSIqElYmzsBIlKPoUOHIj4+3mRMr9ebKZvbZzAYYDAYzJ0GEd1BPENERM1Gr9fDw8PD5OXs7Iwnn3wSERERJrG1tbVwcXHBp59+CgBISkrCoEGD4OTkhNatW+PRRx/F8ePHr7uta32stXHjRmg0GmX5+PHjGDVqFNzd3WFnZ4f+/fvj22+/VeYffPBBFBQU4KWXXlLOaF1v3XFxcejQoQN0Oh06d+6MNWvWmMxrNBqsWrUKY8aMga2tLfz9/bF582Zl/sKFC4iMjISrqysMBgP8/f0bNY9E1HTYEBGR2UVGRmLLli2oqKhQxrZv345Lly5hzJgxAIDKykrExMTgwIEDSElJgVarxZgxY2A0Gm97uxUVFRg+fDhSUlKQkZGBoUOHIjw8HIWFhQCA9evXo23btnjzzTdx5swZnDlz5prr2bBhA6ZNm4bp06cjOzsbzz77LCZOnIjU1FSTuDfeeAPjxo1DVlYWhg8fjsjISJSUlAAA5syZgyNHjmDbtm3IyclBXFwcXFxcbrs2IrpFQkTUDKKiosTKykpatmxp8nr77beltrZWXFxc5NNPP1Xix48fLxEREddd37lz5wSAHDp0SERE8vPzBYBkZGSIiEh8fLw4OjqavGfDhg3ye4e97t27y5IlS5RlX19fef/9901irl73wIEDZdKkSSYxY8eOleHDhyvLAOS1115TlisqKgSAbNu2TUREwsPDZeLEiTfMjYiaDs8QEVGzeeihh5CZmWnymjx5MqytrTFu3DisXbsWQMPZoE2bNiEyMlJ579GjRzF+/Hi0b98eDg4O8PPzAwDlbM7tqKiowMsvv4yuXbvCyckJdnZ2yMnJueV15uTkICQkxGQsJCQEOTk5JmMBAQHKzy1btoSDgwOKi4sBAFOmTEFiYiJ69+6NmTNn4t///vdtVkVEt4MXVRNRs2nZsiU6dux4zbnIyEg88MADKC4uRnJyMgwGg8kdaOHh4fD19cXKlSvh5eUFo9GIHj16oKam5prr02q1EBGTsdraWpPll19+GcnJyVi4cCE6duwIg8GAxx9//Lrr/KNatGhhsqzRaJSP/IYNG4aCggJs3boVycnJGDJkCKKjo7Fw4cImyYWITPEMERHdFQYOHAhvb2989tlnWLt2LcaOHas0EOfPn0dubi5ee+01DBkyBF27dsWFCxduuD5XV1eUl5ejsrJSGbv6O4r27NmDCRMmYMyYMejZsyc8PDxw8uRJkxidTof6+vobbqtr167Ys2dPo3V369btd6punHNUVBQSEhKwaNEirFix4pbeT0S3j2eIiKjZVFdXo6ioyGTM2tpauXj4ySefxPLly5GXl2dyQbKzszNat26NFStWwNPTE4WFhZg1a9YNtxUUFARbW1u8+uqrmDp1KtLS0rB69WqTGH9/f6xfvx7h4eHQaDSYM2dOo4u0/fz8sHv3bjzxxBPQ6/XXvNB5xowZGDduHPr06YPQ0FBs2bIF69evN7lj7ffMnTsX/fr1Q/fu3VFdXY2vvvoKXbt2ven3E9EfwzNERNRskpKS4OnpafIaNGiQMh8ZGYkjR46gTZs2JtfkaLVaJCYmIj09HT169MBLL72EBQsW3HBbrVq1QkJCArZu3YqePXti3bp1eP31101i3nvvPTg7O2PgwIEIDw9HWFgY+vbtaxLz5ptv4uTJk+jQoQNcXV2vua3Ro0fjgw8+wMKFC9G9e3d89NFHiI+Px4MPPnjTvxudTofZs2cjICAA999/P6ysrJCYmHjT7yeiP0YjV3/ITkRERKQyPENEREREqseGiIiIiFSPDRERERGpHhsiIiIiUj02RERERKR6bIiIiIhI9dgQERERkeqxISIiIiLVY0NEREREqseGiIiIiFSPDRERERGp3v8By/kLyNs5UhsAAAAASUVORK5CYII=", - "text/plain": [ - "
" + "Brush version \n", + "metric delete mutation calls toggle_weight mutation calls \n", + "count 30.000000 30.000000 \n", + "mean 2070.300000 1701.800000 \n", + "std 183.992532 240.351381 \n", + "min 1608.000000 1206.000000 \n", + "25% 2010.000000 1608.000000 \n", + "50% 2010.000000 1809.000000 \n", + "75% 2211.000000 1809.000000 \n", + "max 2412.000000 2010.000000 " ] }, "metadata": {}, @@ -1976,7 +2302,6 @@ ], "source": [ "if __name__ == '__main__':\n", - " import pandas as pd\n", " from brush import BrushClassifier\n", " \n", " import warnings\n", @@ -1991,63 +2316,106 @@ " y = data['target']\n", "\n", " kwargs = {\n", - " 'pop_size' : 200,\n", - " 'max_gen' : 40,\n", + " 'verbosity' : False,\n", + " 'pop_size' : 100,\n", + " 'max_gen' : 100,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", " }\n", "\n", - " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'point mutation calls'),\n", - " ('Modified', 'insert mutation calls'),\n", - " ('Modified', 'delete mutation calls'),\n", - " ('Modified', 'toggle_weight mutation calls')],\n", + " ('Original', 'size'), ('Original', 'depth'), \n", + " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), \n", + " ('Modified', 'point mutation calls'),\n", + " ('Modified', 'insert mutation calls'),\n", + " ('Modified', 'delete mutation calls'),\n", + " ('Modified', 'toggle_weight mutation calls')],\n", " names=('Brush version', 'metric')))\n", " \n", " est_mab = None\n", " for i in range(30):\n", " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", "\n", " est = BrushClassifier(**kwargs).fit(X,y)\n", - "\n", " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", "\n", - " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", - " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", + " \n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " \n", + " results.loc[f'run {i}'] = [\n", + " # Original implementation\n", + " est.score(X,y), est.best_estimator_.get_model(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + "\n", + " # Implementation using Dynamic Thompson Sampling\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " \n", + " # Mutation count\n", + " *total_pulls.values()]\n", " \n", - " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", " except Exception as e:\n", " print(e)\n", "\n", - " display(df)\n", - " display(df.describe())\n", - "\n", - " if True: # plot the cumulative history of pulls\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", - "\n", - " # Plot for evaluations, not generations\n", - " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", - " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", - " data[i+1, :] = data[i]\n", - " data[i+1, arm] += 1\n", - "\n", - " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", - " plt.xlabel(\"Evaluations\")\n", - " plt.ylabel(\"Number of times mutation was used\")\n", - "\n", - " for x in est_mab.learner_.reset_history:\n", - " plt.axvline(x=x, color='k')\n", - "\n", - " plt.legend()" + " # Showing results and statistics\n", + " display(results)\n", + " display(results.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ORERERFwkLRUWPQEx/0GxiioAkotcmpyVLVuWKVOmsGXLFjZv3sy9995L586d2bNnj7VN//79iYiIsL6mTp1q3ZeWlkbHjh1JTk5m3bp1fP7558ydO5fx48db2xw5coSOHTvSqlUrtm/fztChQ3nyySdZvnx5rp5rfufn58fUqVO58847ueuuuzh69Ci//PILbm5umEwmfvnlF5o3b07fvn2pVq0a3bp147///iM4OPi6xx05ciTu7u7UrFmTwMBAjh07lktnJCIiIiLZWvUK/LvaUsug23zwcd3cuAWNyTDsmBgrFxQvXpw333yTfv360bJlS+rVq8eMGTOybbts2TI6derEqVOnrEnABx98wJgxYzhz5gxeXl6MGTOGpUuXsnv3bmu/bt26ERMTw6+//prtcZOSkkhKSrKux8XFUa5cOWJjY/H398/SNjExkSNHjljn7srrMm4hdHfXpx95UX77eRIREZFbzO7FlrL5AA/NgVpdXBtPHhQXF0dAQEC2ucHNyjOTUKelpfHNN99w8eJFQkNDrdvnzZtHyZIlqVWrFmPHjuXSpUvWfevXr6d27dpZrs6EhYURFxdnvfq2fv162rRpk2WssLAw1q9ff81YJk+eTEBAgPVVrlw5R52miIiIiEjeFLkbfhxoWW4yRImZC7i8IMiuXbsIDQ0lMTERX19fvv/+e2rWtJTo7N69OxUqVKB06dLs3LmTMWPGcODAARYvXgxAZGTkVbfNZaxHRkZet01cXBwJCQkULlz4qpjGjh3L8OHDresZV85ERERERG5Jl87Dgh6QcgluawWtJ7g6ogLJ5clZ9erV2b59O7GxsSxatIjevXuzZs0aatasaZ28GKB27dqUKlWK1q1bc/jwYSpXruy0mLy9va2TKYuIiIiI3NLMafDdkxB9FIpWgIc+UwEQF3H5bY1eXl5UqVKFhg0bMnnyZOrWrcvMmTOzbXvPPfcAcOjQIQBCQkI4ffp0ljYZ6yEhIddt4+/vn+1VMxERERGRAmXVq3B4JXgUhm7zVADEhVyenF3JbDZnKcaR2fbt2wEoVaoUAKGhoezatYuoqChrmxUrVuDv72+9NTI0NJSVK1dmOc6KFSuyPNcmIiIiIlIg7fke/ppuWe48G0JquzaeAs6ltzWOHTuW9u3bU758eS5cuMD8+fMJDw9n+fLlHD58mPnz59OhQwdKlCjBzp07GTZsGM2bN6dOnToAtG3blpo1a9KzZ0+mTp1KZGQkL730EgMHDrTelvj0008ze/ZsRo8ezRNPPMGqVav49ttvWbp0qStPXURERETEtU7vhR/SC4A0Hgy1H3JtPOLa5CwqKopevXoRERFBQEAAderUYfny5dx3330cP36c33//nRkzZnDx4kXKlStH165deemly7OTu7u7s2TJEp555hlCQ0MpUqQIvXv35pVXXrG2qVSpEkuXLmXYsGHMnDmTsmXL8sknnxAWFuaKUxYRERERcb2EaPimO6RchEotoPXLro5IyIPznOVF15vLIL/NS6V5zvK2/PbzJCIiIvmQOQ3mPwqHVkBAeRgQDkVKuDqqfKNAzHMmIiIiIiK5YPUkS2KWUQBEiVmeoeRMrtKyZUuGDh1qU9u5c+dStGhRp8YjIiIiIg6y9yf48y3L8v/egVJ1XBuPZKHkTHKdyWTihx9+cHUYIiIiIgVL1D74/mnLcqOBUOdh18YjV1FyJiIiIiJyq0uIgW96pBcAaQ73vXLDLpL7lJw5gWEYXEq55JKXvfVdLl68SK9evfD19aVUqVJMmzYty/6kpCRGjhxJmTJlKFKkCPfccw/h4eHXPeaPP/5IgwYNKFSoELfddhsTJ04kNTUVgIoVKwLw4IMPYjKZrOs36iciIiIiOWQ2w+IBcP4wBJSDh+aAu0uLtss16LviBAmpCdwz/x6XjL2x+0Z8PH1sbj9q1CjWrFnDjz/+SFBQEC+88AJbt26lXr16AAwaNIi9e/fyzTffULp0ab7//nvatWvHrl27qFq16lXH+/PPP+nVqxezZs2iWbNmHD58mAEDBgAwYcIE/v77b4KCgpgzZw7t2rWzVo28UT8RERERyaHwyfDPcvAoBI9+BUVKujoiuQZdOSvA4uPj+fTTT3nrrbdo3bo1tWvX5vPPP7derTp27Bhz5sxh4cKFNGvWjMqVKzNy5EiaNm3KnDlzsj3mxIkTef755+nduze33XYb9913H6+++ioffvghAIGBgQAULVqUkJAQ6/qN+omIiIhIDuxbAn9MtSzfPxNK13NpOHJ9unLmBIU9CrOx+0aXjW2rw4cPk5yczD33XL7KV7x4capXrw7Arl27SEtLo1q1aln6JSUlUaJE9iVXd+zYwdq1a3n99det29LS0khMTOTSpUv4+GR/VS+n/URERETkGs4cgO+fsizf8wzU7ebaeOSGlJw5gclksuvWwrwqPj4ed3d3tmzZctWk1b6+vtfsM3HiRLp06XLVvutNqpzTfiIiIiKSjcRY+KY7JMdDhabQ9lVXRyQ2UHJWgFWuXBlPT082btxI+fLlAYiOjubgwYO0aNGC+vXrk5aWRlRUFM2aNbPpmA0aNODAgQNUqVLlmm08PT1JS0uzu5+IiIiI2MBshsVPwblD4F8WHp4L7p6ujkpsoOSsAPP19aVfv36MGjWKEiVKEBQUxIsvvoibm+VRxGrVqtGjRw969erFtGnTqF+/PmfOnGHlypXUqVOHjh07XnXM8ePH06lTJ8qXL89DDz2Em5sbO3bsYPfu3bz22muApWLjypUradKkCd7e3hQrVsymfiIiIiJigzVvwMFl4O4Nj34JvoGujkhspIIgBdybb75Js2bNuP/++2nTpg1NmzalYcOG1v1z5syhV69ejBgxgurVq/PAAw/w999/W6+0XSksLIwlS5bw22+/cdddd9GoUSOmT59OhQoVrG2mTZvGihUrKFeuHPXr17e5n4iIiIjcwP5fYM0Uy3Kn6VCmgWvjEbuYDHsnxiqA4uLiCAgIIDY2Fn9//yz7EhMTOXLkCJUqVcoXz0Zl3E545TNkkjfkt58nERERyUPOHISP74XkC3D3AOjwpqsjuiVdLze4WbpyJiIiIiKS3yXGwYIelsSsfGMIm+TqiCQHlJyJiIiIiORnZjN8/zScPQh+peGRz1UAJJ+yqSBIduXNr2Xx4sU5DkZEREREROz051twYGl6AZCvwDfI1RFJDtl05SwgIMD68vf3Z+XKlWzevNm6f8uWLaxcuZKAgACnBSoiIiIiIlc48CusTr+FsdPbULbh9dtLnmbTlbM5c+ZYl8eMGcMjjzzCBx98YC0qkZaWxrPPPuvwB+JERERERHJNfBRs/swygbO1Zp6RdRmuv25TWyPLppwfB/jnN8u2u56E+o/bfKqSN9k9z9lnn33GX3/9laXan7u7O8OHD6dx48a8+aaqwoiIiIhIPpN8Cb7qApG7XB2J/cqHQthkV0chDmB3cpaamsr+/fupXr16lu379+/HbDY7LDARERERkVxhGPDTYEti5lMSGvQETGAypTdIf892/Xr70tczFm1qa+eYXr5wx4Pg4WXXKUveZHdy1rdvX/r168fhw4e5++67Adi4cSNTpkyhb9++Dg9QRERERMSp1s+G3YvAzcNS6bBiU1dHJAWU3cnZW2+9RUhICNOmTSMiIgKAUqVKMWrUKEaMGOHwAEVEREREnObwalgx3rIcNlmJmbiU3fOcubm5MXr0aE6ePElMTAwxMTGcPHmS0aNHZ3kOTeR6TCYTP/zwg6vDyFbLli0ZOnSoXX3y8vmIiIjINUQfhUV9wTBDvR5wd39XRyQFXI4moU5NTeX333/n66+/xpR+H+ypU6eIj493aHDiXDlJQgqCxYsX8+qrrzr0mOHh4ZhMJmJiYhx6XBEREcmh5EvwzeOQEA2lG0DHtzM93yXiGnbf1vjff//Rrl07jh07RlJSEvfddx9+fn688cYbJCUl8cEHHzgjTpFcU7x4cVeHICIiIs5kGPDTIDi9C4oEWiZu9izk6qhE7L9yNmTIEO68806io6MpXLiwdfuDDz7IypUrHRpcfmUYBuZLl1zyMqxzYFxfnz59WLNmDTNnzsRkMmEymTh69Chr1qzh7rvvxtvbm1KlSvH888+Tmppq7XfhwgV69OhBkSJFKFWqFNOnT7/qClxERAQdO3akcOHCVKpUifnz51OxYkVmzJhxzXiOHz/OI488QtGiRSlevDidO3fm6NGjNzyP3bt34+bmxpkzZwA4f/48bm5udOvWzdrmtddeo2nTpln6tG/fHl9fX4KDg+nZsydnz5617s/p+Zw9e5YHH3wQHx8fqlatyk8//QTA0aNHadWqFQDFihXDZDLRp0+fG56biIiIOMm6d2D3d+kFQL6AgDKujkgEyMGVsz///JN169bh5ZW1XGfFihU5efKkwwLLz4yEBA40cM3s7NW3bsHk43PDdjNnzuTgwYPUqlWLV155BbBMJt6hQwf69OnDF198wf79++nfvz+FChXi5ZdfBmD48OGsXbuWn376ieDgYMaPH8/WrVupV6+e9di9evXi7NmzhIeH4+npyfDhw4mKirpmLCkpKYSFhREaGsqff/6Jh4cHr732Gu3atWPnzp1X/axldscdd1CiRAnWrFnDQw89xJ9//mldz7BmzRpatmwJQExMDPfeey9PPvkk06dPJyEhwTqx+qpVq7Idw9bzmThxIlOnTuXNN9/knXfeoUePHvz333+UK1eO7777jq5du3LgwAH8/f2zfLAhIiIiuejwKvh9gmW53RSo0Ni18YhkYveVM7PZTFpa2lXbT5w4gZ+fn0OCEucLCAjAy8sLHx8fQkJCCAkJ4b333qNcuXLMnj2bGjVq8MADDzBx4kSmTZuG2WzmwoULfP7557z11lu0bt2aWrVqMWfOnCw/D/v37+f333/n448/5p577qFBgwZ88sknJCQkXDOWBQsWYDab+eSTT6hduza33347c+bM4dixY4SHh1/3PEwmE82bN7e2Cw8Pp2/fviQlJbF//35SUlJYt24dLVq0AGD27NnUr1+fSZMmUaNGDerXr89nn33G6tWrOXjw4FXHt+d8+vTpw2OPPUaVKlWYNGkS8fHxbNq0CXd3d+utkkFBQYSEhBAQEHCjb5GIiIg4WvRRWPREegGQx+GuJ10dkUgWdl85a9u2LTNmzOCjjz4CLH8cx8fHM2HCBDp06ODwAPMjU+HCVN+6xWVj59S+ffsIDQ21FnkBaNKkCfHx8Zw4cYLo6GhSUlKs89uBJcnLPCH5gQMH8PDwoEGDBtZtVapUoVixYtccd8eOHRw6dOiq5D4xMZHDhw/fMO4WLVpYfx7XrFnDpEmTOHjwIOHh4Zw/f56UlBSaNGliHWv16tX4+vpedZzDhw9TrVq1LNvsOZ86depYl4sUKYK/v/91rxiKiIhILkq+CN/0sBQAKdMQOk5TARDJc+xOzqZNm0ZYWBg1a9YkMTGR7t27888//1CyZEm+/vprZ8SY75hMJptuLRSL+Ph4GjZsyLx5867aFxgYeMP+Gc+I/fPPP+zdu5emTZuyf/9+wsPDiY6O5s4778Qn/fsRHx/P/fffzxtvvHHVcUqVKnVT5+Hp6Zll3WQyYTabb+qYIiIi4gCGAT8OgtO7LQVAHvlSBUAkT7I7OStbtiw7duxgwYIF7Nixg/j4ePr160ePHj30HE0+4+XlleWWxNtvv53vvvsOwzCsV8/Wrl2Ln58fZcuWpVixYnh6evL3339Tvnx5AGJjYzl48CDNmzcHoHr16qSmprJt2zYaNrQ8d3fo0CGio6OvGUeDBg1YsGABQUFB+Pv7230etWvXplixYrz22mvUq1cPX19fWrZsyRtvvEF0dLT1ebOMsb777jsqVqyIh8eNf/xzcj7ZyXhuLrtbgkVERMTJ1s2CPYtVAETyvBzNc+bh4UGPHj2YOnUq7733Hk8++aQSs3yoYsWKbNy4kaNHj3L27FmeffZZjh8/zuDBg9m/fz8//vgjEyZMYPjw4bi5ueHn50fv3r0ZNWoUq1evZs+ePfTr1w83NzdrMlejRg3atGnDgAED2LRpE9u2bWPAgAEULlw4y+2SmfXo0YOSJUvSuXNn/vzzT44cOUJ4eDjPPfccJ06cuOF5ZDx3Nm/ePGsiVqdOHZKSkli5cqX1eTOAgQMHcv78eR577DH+/vtvDh8+zPLly+nbt2+2iVNOzic7FSpUwGQysWTJEs6cOaM5AUVERHLL4VXw+8uWZRUAkTzO7uTs888/Z+nSpdb10aNHU7RoURo3bsx///3n0ODEuUaOHIm7uzs1a9YkMDCQlJQUfvnlFzZt2kTdunV5+umn6devHy+99JK1z9tvv01oaCidOnWiTZs2NGnShNtvv51ChS7fGvDFF18QHBxM8+bNefDBB+nfvz9+fn5Z2mTm4+PDH3/8Qfny5enSpQu33347/fr1IzEx0eYraS1atCAtLc2anLm5udG8eXNMJpP1eTOA0qVLs3btWtLS0mjbti21a9dm6NChFC1aFDe37P93sPd8slOmTBkmTpzI888/T3BwMIMGDbK5r4iIiOTQ+SOwsK+lAEh9FQCRvM9k2DoxVrrq1avz/vvvc++997J+/Xpat27NjBkzWLJkCR4eHixevNhZsbpMXFwcAQEBxMbGXpUsJCYmcuTIESpVqmTXH+uuknF1yN3d3SHHu3jxImXKlGHatGn069cv2zYnTpygXLly/P7777Ru3doh47qSM88nv/08iYiI5FnJF+GT+yBqj6UASJ9f9JyZOMT1coObZfczZ8ePH6dKlSoA/PDDDzz00EMMGDCAJk2aZHm2R25N27ZtY//+/dx9993ExsZa50jr3Lmztc2qVauIj4+ndu3aREREMHr0aCpWrGh9Li2/udXOR0RE5JZnGPDjQEtiViQIHv1KiZnkC3bf1ujr68u5c+cA+O2337jvvvsAKFSo0HXnssrO+++/T506dfD398ff35/Q0FCWLVtm3Z+YmMjAgQMpUaIEvr6+dO3aldOnT2c5xrFjx+jYsSM+Pj4EBQUxatQoUlNTs7QJDw+nQYMGeHt7U6VKFebOnWvvaUsmb731FnXr1qVNmzZcvHiRP//8k5IlS1r3p6Sk8MILL3DHHXfw4IMPEhgYaJ3AOSd8fX2v+frzzz8ddVrX5OjzERERERuYzXD+Xzh7CM7+A2cOQNR+OL0XIndD5C6I2AGntsPJrXByC5zYDMf/htWvw57vLxcA8S/t6rMRsYndV87uu+8+nnzySerXr8/Bgwetc5vt2bOHihUr2nWssmXLMmXKFKpWrYphGHz++ed07tyZbdu2cccddzBs2DCWLl3KwoULCQgIYNCgQXTp0oW1a9cCllv0OnbsSEhICOvWrSMiIoJevXrh6enJpEmTADhy5AgdO3bk6aefZt68eaxcuZInn3ySUqVKERYWZu/pF3j169dny5brz+EWFhbm0K/t9u3br7mvTBnnV1ty9PmIiIjIDSRfhM//Byc339xx2r8BFUIdE5NILrD7mbOYmBheeukljh8/zjPPPEO7du0AmDBhAl5eXrz44os3FVDx4sV58803eeihhwgMDGT+/Pk89NBDAOzfv5/bb7+d9evX06hRI5YtW0anTp04deoUwcHBAHzwwQeMGTOGM2fO4OXlxZgxY1i6dCm7d++2jtGtWzdiYmL49ddfs40hKSmJpKQk63pcXBzlypXTM2fidPnt50lERMThDAMW9b185cuzCJgAk5vlhSl92XSNbSYwuUO9x6DZSE00LQ6Xp545K1q0KLNnz75q+8SJE28qkLS0NBYuXMjFixcJDQ1ly5YtpKSk0KZNG2ubGjVqUL58eWtytn79emrXrm1NzMByleOZZ55hz5491K9fn/Xr12c5RkaboUOHXjOWyZMn230+dua4ItnSz5GIiBR4a2emJ2ae0GcJlG/k6ohEco3dydkff/xx3f32FknYtWsXoaGhJCYm4uvry/fff0/NmjXZvn07Xl5eFC1aNEv74OBgIiMjAYiMjMySmGXsz9h3vTZxcXEkJCRkOz/b2LFjGT58uHU948pZdjKeO7p06ZLmepOblpycDOjKpoiIFFCHVsLK9A/I27+hxEwKHLuTs+wqMmaejDe7iXyvp3r16mzfvp3Y2FgWLVpE7969WbNmjb1hOZS3tzfe3t42tXV3d6do0aJERUUBljm77JmcOLfptsa8y2w2c+bMGXx8fPDwsPt/TRERkfzt/BFY9ET6nGQ94c4nXB2RSK6z+y/A6OjoLOspKSls27aNcePG8frrr9sdgJeXl7U0f8OGDfn777+ZOXMmjz76KMnJycTExGS5enb69GlCQkIACAkJYdOmTVmOl1HNMXObKys8nj59Gn9/f4dd6coYKyNBy8vMZjPANSdcFtdyc3OjfPnyeTrBFxERcbjki7DgcUiMgTJ3QsdpelZMCiS7k7OAgICrtt133314eXkxfPjwG1byuxGz2UxSUhINGzbE09OTlStX0rVrVwAOHDjAsWPHCA21VN0JDQ3l9ddfJyoqiqCgIABWrFiBv78/NWvWtLb55ZdfsoyxYsUK6zEcwWQyUapUKYKCgkhJSXHYcZ3h4sWLABQpUsTFkUh2vLy8lDiLiEjBYhjw02A4vTt9TrIvwcO2O5hEbjUOu3cqODiYAwcO2NVn7NixtG/fnvLly3PhwgXmz59PeHg4y5cvJyAggH79+jF8+HCKFy+Ov78/gwcPJjQ0lEaNLPcft23blpo1a9KzZ0+mTp1KZGQkL730EgMHDrTelvj0008ze/ZsRo8ezRNPPMGqVav49ttvWbp0qaNO3crd3T3P3y6YMQecKgGKiIhInrB+Nuz+Ln1Oss81J5kUaHYnZzt37syybhgGERERTJkyhXr16tl1rKioKHr16kVERAQBAQHUqVOH5cuXWye2nj59Om5ubnTt2pWkpCTCwsJ47733rP3d3d1ZsmQJzzzzDKGhoRQpUoTevXvzyiuvWNtUqlSJpUuXMmzYMGbOnEnZsmX55JNPNG+ViIiIiKsdXg0rxluW202BCo1dG4+Ii9k9z5mbmxsmk+mqkt+NGjXis88+o0aNGg4NMC9w5lwGuS0+Ph4AX19fF0ciIiIiBVr0UfioJSREQ73HofNsPWcm+UKemufsyJEjWdbd3NwIDAzUbXIiIiIiYpvkS5YCIAnRULqBCoCIpLM7OatQoYIz4hARERGRgsAw4OfnIHIXFAm0FADx1If8IgAqCyciIiIiuWfDe7BroaUAyMOfQ0BZV0ckkmcoORMRERGR3PHvGvhtnGU5bBJUbOLaeETyGCVnIiIiIuJ8McdgUV8w0qBud7h7gKsjEslzlJyJiIiIiHOlJMA3PeDSOShVDzq9rQIgItmwOznbunUru3btsq7/+OOPPPDAA7zwwgskJyc7NDgRERERyecMA34eApE7wacEPPoVeBZ2dVQieZLdydlTTz3FwYMHAfj333/p1q0bPj4+LFy4kNGjRzs8QBERERHJxzZ+ADsXgMndUgCkaDlXRySSZ9mdnB08eJB69eoBsHDhQpo3b878+fOZO3cu3333naPjExEREZH86sifsPxFy3LY61CpmWvjEcnj7E7ODMPAbDYD8Pvvv9OhQwcAypUrx9mzZx0bnYiIiIjkTzHHYWEfSwGQOo/CPU+7OiKRPM/u5OzOO+/ktdde48svv2TNmjV07NgRgCNHjhAcHOzwAEVEREQkn0lJgAWPw6WzEFIHOs1QARARG9idnM2YMYOtW7cyaNAgXnzxRapUqQLAokWLaNy4scMDFBEREZF8xDBgyTCI2A6Fi0O3eeDl4+qoRPIFk2EYhiMOlJiYiLu7O56eno44XJ4SFxdHQEAAsbGx+Pv7uzqcmxIfHw+Ar6+viyMRERGRW9LGj2DZKDC5Qc8f4LYWro5IxKGcmRt4OOpAhQoVctShRERERCQ/OroWlo+1LN/3qhIzETvZnZylpaUxffp0vv32W44dO3bV3Gbnz593WHAiIiIikk/EnoBve4E5FWo/DKEDXR2RSL5j9zNnEydO5O233+bRRx8lNjaW4cOH06VLF9zc3Hj55ZedEKKIiIiI5GkpibCgp6UASHBtuH+WCoCI5IDdydm8efP4+OOPGTFiBB4eHjz22GN88sknjB8/ng0bNjgjRhERERHJqwwDlo6AU1uhcDHo9pUKgIjkkN3JWWRkJLVr1wYsRSViY2MB6NSpE0uXLnVsdCIiIiKSt/39CWz/ylIA5KE5UKyiqyMSybfsTs7Kli1LREQEAJUrV+a3334D4O+//8bb29ux0YmIiIhI3vXfevj1ectym4lQuZVr4xHJ5+xOzh588EFWrlwJwODBgxk3bhxVq1alV69ePPHEEw4PUERERETyoLhTlwuA1OoKjQe7OiKRfO+m5znbsGED69ato2rVqtx///2OiitP0TxnIiIiIpmkJsGcDnByMwTXgn6/gVcRV0clkivy9DxnjRo1olGjRo6IRURERETyuowCICc3Q6Gi8OhXSsxEHMTu5Kx8+fK0bNmSFi1a0LJlSypXruyMuEREREQkL9oyB7Z9mV4A5DMoXsnVEYncMux+5mzSpEkUKlSIN954g6pVq1KuXDkef/xxPv74Y/755x9nxCgiIiIiecGxDfDLaMty6wlQpbVr4xG5xdzUM2cRERGsWbOGJUuWsGDBAsxmM2lpaY6ML0/QM2ciIiJS4MVFwEctIP401HwAHp6riaalQMpzz5xdunSJv/76i/DwcFavXs22bduoVasWLVu2dGhwIiIiIpIHpCZZKjPGn4agO6Dzu0rMRJzA7uSscePGbNu2jdtvv52WLVvy/PPP07x5c4oVK+aM+ERERETE1ZaNhhOboFAAdPsKvHUHjogz2P3M2f79+ylSpAg1atSgRo0a3H777UrMRERERG5Vm+fAlrmACbp+BsVvc3VEIrcsu5Ozc+fOsWrVKho1asTy5ctp0qQJZcqUoXv37nz88cfOiFFEREREXOH4JvhllGW59Tio2sa18Yjc4m6qIIhhGGzZsoXZs2czb948FQTJB1QQRERERGxyIRI+bAHxkVCzMzz8uZ4zEyGPFQTZunUr4eHhhIeH89dff3HhwgVq167N4MGDadGihUODExEREREXSE1OLwASCYG3Q+f3lJiJ5AK7k7O7776b+vXr06JFC/r370/z5s0JCAhwRmwiIiIi4gq/Pg/HN4J3AHSbpwIgIrnE7uTs/Pnz+f7WPhERERG5hq1fwOZPsRQA+QRKVHZ1RCIFht0FQZSYiYiIiNyiTmyGpSMsy/e+CNXaujYekQLG7uTMkSZPnsxdd92Fn58fQUFBPPDAAxw4cCBLm5YtW2IymbK8nn766Sxtjh07RseOHfHx8SEoKIhRo0aRmpqapU14eDgNGjTA29ubKlWqMHfuXGefnoiIiEj+ceE0LOgJaclQoxM0HeHqiEQKHJcmZ2vWrGHgwIFs2LCBFStWkJKSQtu2bbl48WKWdv379yciIsL6mjp1qnVfWloaHTt2JDk5mXXr1vH5558zd+5cxo8fb21z5MgROnbsSKtWrdi+fTtDhw7lySefZPny5bl2riIiIiJ5VmoyLOwNF05Byerw4Afg5tI/E0UKpJsqpe9oZ86cISgoiDVr1tC8eXPAcuWsXr16zJgxI9s+y5Yto1OnTpw6dYrg4GAAPvjgA8aMGcOZM2fw8vJizJgxLF26lN27d1v7devWjZiYGH799dcbxqVS+iIiInJLWzoS/v4YvP2h/2ooWcXVEYnkWc7MDfLURyKxsbEAFC9ePMv2efPmUbJkSWrVqsXYsWO5dOmSdd/69eupXbu2NTEDCAsLIy4ujj179ljbtGmTddLEsLAw1q9fn20cSUlJxMXFZXmJiIiI3JK2fWVJzAC6fKzETMSF7K7WmJaWxty5c1m5ciVRUVGYzeYs+1etWpWjQMxmM0OHDqVJkybUqlXLur179+5UqFCB0qVLs3PnTsaMGcOBAwdYvHgxAJGRkVkSM8C6HhkZed02cXFxJCQkULhw4Sz7Jk+ezMSJE3N0HiIiIiL5xsktsGS4ZbnlC1C9nWvjESng7E7OhgwZwty5c+nYsSO1atXC5KAJCQcOHMju3bv566+/smwfMGCAdbl27dqUKlWK1q1bc/jwYSpXdk5p17FjxzJ8+HDrelxcHOXKlXPKWCIiIiIuER8F3zwOaUlQvSM0H+XqiEQKPLuTs2+++YZvv/2WDh06OCyIQYMGsWTJEv744w/Kli173bb33HMPAIcOHaJy5cqEhISwadOmLG1Onz4NQEhIiPU9Y1vmNv7+/lddNQPw9vbG29s7x+cjIiIikqelpcDCPpYCICWqqgCISB5h9/+FXl5eVKnimHuRDcNg0KBBfP/996xatYpKlSrdsM/27dsBKFWqFAChoaHs2rWLqKgoa5sVK1bg7+9PzZo1rW1WrlyZ5TgrVqwgNDTUIechIiIikq8sfxH+WwteftBtPhTK3wXPRG4VdidnI0aMYObMmTiiyOPAgQP56quvmD9/Pn5+fkRGRhIZGUlCQgIAhw8f5tVXX2XLli0cPXqUn376iV69etG8eXPq1KkDQNu2balZsyY9e/Zkx44dLF++nJdeeomBAwdar349/fTT/Pvvv4wePZr9+/fz3nvv8e233zJs2LCbPgcRERGRfGX7fNj0oWW5y0cQWM218YiIld2l9B988EFWr15N8eLFueOOO/D09MyyP6NQh02DX+N5tTlz5tCnTx+OHz/O448/zu7du7l48SLlypXjwQcf5KWXXspStvK///7jmWeeITw8nCJFitC7d2+mTJmCh8fluzbDw8MZNmwYe/fupWzZsowbN44+ffrYFKdK6YuIiMgt4dQ2+DTM8pxZi+eh1VhXRySS7zgzN7A7Oevbt+9198+ZM+emAsqLlJyJiIhIvhd/Bj5qCXEnoFp7y+2Mes5MxG7OzA3sLghyKyZfIiIiIvnCviWw6lVIvgiGOf1lXF7GyLTNyLotLQXMKVCiCnT5UImZSB5kd3KW4cyZMxw4cACA6tWrExgY6LCgREREROQKJ7fCoicstyTmlG9wegGQAMfFJSIOY3dydvHiRQYPHswXX3xhnYDa3d2dXr168c477+Dj4+PwIEVEREQKtPgzsCB9TrJq7aDFGDC5gclkeceUdd26zZS1jW8weBZy8cmIyLXYnZwNHz6cNWvW8PPPP9OkSRMA/vrrL5577jlGjBjB+++/7/AgRURERAqstBRY2BviTlrmJOvyka58idyi7E7OvvvuOxYtWkTLli2t2zp06EDhwoV55JFHlJyJiIiIONJVc5IpMRO5Vdn9JOilS5cIDg6+antQUBCXLl1ySFAiIiIiAmybpznJRAoQu5Oz0NBQJkyYQGJionVbQkICEydOJDQ01KHBiYiIiBRYJ7fAkmGW5RbPQ40Oro1HRJzO7tsaZ86cSVhYGGXLlqVu3boA7Nixg0KFCrF8+XKHBygiIiJS4MRHwYKelgIg1TtYCoCIyC3P7uSsVq1a/PPPP8ybN4/9+/cD8Nhjj9GjRw8KFy7s8ABFRERECpS0FFjY53IBkAc1J5lIQZGjec58fHzo37+/o2MRERERkeUvXFEAxN/VEYlILrEpOfvpp59o3749np6e/PTTT9dt+7///c8hgYmIiIgUONvmwaaPLMsqACJS4NiUnD3wwANERkYSFBTEAw88cM12JpOJtLQ0R8UmIiIiUnCcyFQApOVYFQARKYBsSs7MZnO2yyIiIiLiABdOw4LH0wuAdITmo10dkYi4gN1Pl37xxRckJSVdtT05OZkvvvjCIUGJiIiIFBipybCwN1w4BSWrwYMfqACISAFl9//5ffv2JTY29qrtFy5coG/fvg4JSkRERKTAWD4Wjq0Hb38VABEp4OxOzgzDwGQyXbX9xIkTBAQEOCQoERERkQJh65fw9yeW5S4fQcmqro1HRFzK5lL69evXx2QyYTKZaN26NR4el7umpaVx5MgR2rVr55QgRURERG45JzbD0uGW5ZYvQPX2ro1HRFzO5uQso0rj9u3bCQsLw9fX17rPy8uLihUr0rVrV4cHKCIiInLLsRYASYYanaD5KFdHJCJ5gM3J2YQJEwCoWLEijz76KIUKFXJaUCIiIiK54tJ52P0dpFwCw0jfaKQvZ3on4+3Kfddrb1y7/b9r4EKEpQDIA++rAIjIDVxYvZrEffso+fTTmG7h/19sTs4y9O7d2xlxiIiIiOSupHiY2xGi9rpmfBUAEbFJ0pEjnBo1GnN8PB4lS1LskUdcHZLT2J2cpaWlMX36dL799luOHTtGcnJylv3nz593WHAiIiIiTmEY8OOzlsSsSCBUuc+y3WQCTGCC9P9k2ma6yW1c3ubmAbUfUQEQkRtIi7/IiUGDMcfHU7hhQ4qmP2p1q7I7OZs4cSKffPIJI0aM4KWXXuLFF1/k6NGj/PDDD4wfP94ZMYqIiIg41l/TYe+P4OYJj86D8ve4OiIRuYJhNhMx9nmSDx/GIyiIsjOmY/LycnVYTmX3DZvz5s3j448/ZsSIEXh4ePDYY4/xySefMH78eDZs2OCMGEVEREQc55/fYeUrluUOU5WYieRR5z76mAsrfsfk6UnZWTPxCAx0dUhOZ3dyFhkZSe3atQHw9fW1TkjdqVMnli5d6tjoRERERBzp3GH47gnAgAa94c4nXB2RiGQj/o8/ODNzJgDB48dRuF491waUS+xOzsqWLUtERAQAlStX5rfffgPg77//xtvb27HRiYiIiDhKUjx80wMSY6Hs3dDhTVdHJCLZSP7vP06OHAWGQdFHHqHYww+7OqRcY3dy9uCDD7Jy5UoABg8ezLhx46hatSq9evXiiSf06ZOIiIjkQRkFQM7sA99geOQL8NCHyiJ5jfliegGQuDgK161L8EsvujqkXGV3QZApU6ZYlx999FEqVKjAunXrqFq1Kvfff79DgxMRERFxiL/evlwA5JEvwb+UqyMSkSsYhsGpF18i6Z9/cA8sSZlZs3C7xQuAXMnu5OyPP/6gcePGeHhYujZq1IhGjRqRmprKH3/8QfPmzR0epIiIiEiO/bMCVr5qWe7wpgqAiORR5z/9lAu//goeHpSdORPP4CBXh5Tr7L6tsVWrVtnOZRYbG0urVq0cEpSIiIiIQ5w7DN/1Awxo2Afu7OvqiEQkG/F/rSXq7ekAhLz4Aj4NGrg4ItewOzkzDANT5okU0507d44iRYo4JCgRERGRm5Z0IWsBkPZTXR2RiGQj+fhxTo4YAWYzAV27ULRbN1eH5DI239bYpUsXAEwmE3369MlSmTEtLY2dO3fSuHFjx0coIiIiYi/DgB8yCoCEwKNfqgCISB5kvnTJUgAkNpZCtWsTMn58theCCgqbk7OAgADAcuXMz8+PwoULW/d5eXnRqFEj+vfv7/gIRUREROz15zTY95OlAMijX4JfiKsjEpErGIZBxLjxJB04gHuJEpSdNRO3Aj41l83J2Zw5cwCoWLEiI0eO1C2MIiIikjcd/A1WvWZZ7vgWlLvbtfGISLbOz/2cuKVLLQVAZkzHs5SqqNpdrXHChAnOiENERETk5p07DN89iaUASF9LERARyXMubthA1JuWieCDx4zB5667XBxR3mB3clapUqXr3gf677//3lRAIiIiIjmSdAG+6Q5JsVDuHhUAEcmjUk6e5OTQYZYCIJ07U+zxHq4OKc+wu1rj0KFDGTJkiPX17LPPEhoaSmxsLAMGDLDrWJMnT+auu+7Cz8+PoKAgHnjgAQ4cOJClTWJiIgMHDqREiRL4+vrStWtXTp8+naXNsWPH6NixIz4+PgQFBTFq1ChSU1OztAkPD6dBgwZ4e3tTpUoV5s6da++pi4iISF5lGPDDM3BmP/iVgke+AI+CNXmtSH5gTkzk+ODBpMXEUKhmTUImvlygC4Bcye4rZ0OGDMl2+7vvvsvmzZvtOtaaNWsYOHAgd911F6mpqbzwwgu0bduWvXv3Wp9pGzZsGEuXLmXhwoUEBAQwaNAgunTpwtq1awFLpciOHTsSEhLCunXriIiIoFevXnh6ejJp0iQAjhw5QseOHXn66aeZN28eK1eu5Mknn6RUqVKEhYXZ+yUQERERZzAMSE0Ew5z+MizvGOnLmdevaLPtK9j3M7h7wSMqACKukZSahmHk7phGSgpGWhrWga8MIOP/nUz7jOu1vcG7YWvbjJbWzZaF6HdmkbR3H27FilFy+gyS3T0hJc3m8/Vyd8PN7dZN5kyG4ZgfoX///Zd69eoRFxeX42OcOXOGoKAg1qxZQ/PmzYmNjSUwMJD58+fz0EMPAbB//35uv/121q9fT6NGjVi2bBmdOnXi1KlTBAcHA/DBBx8wZswYzpw5g5eXF2PGjGHp0qXs3r3bOla3bt2IiYnh119/vWFccXFxBAQEEBsbi7+/f47PLy+Ij48HwNfX18WRiIiIZJJ0AeZ3g//+urnj3D8LGvZ2TEwiNkpNMzN0wXaW7IzI1XHvPbaFgTsX45OalKvj3qw0kxsvNB7AzsAqdvdd/GxjGpQv5oSobOfM3MDu2xqvZdGiRRQvXvymjhEbGwtgPc6WLVtISUmhTZs21jY1atSgfPnyrF+/HoD169dTu3Zta2IGEBYWRlxcHHv27LG2yXyMjDYZx7hSUlIScXFxWV4iIiLiJGYzfP+0HYmZCUxu4OZhuVLm7g1eftBijBIzcYkpy/bnemJW4/xRhm77NtcTMzMmzJhIy3iZ3EgzuZGa/koxuVtebu4ku3mQ7OZBUvor0d2TWK8izKrbNUeJWUFg922N9evXz3JfqGEYREZGcubMGd57770cB2I2mxk6dChNmjShVq1aAERGRuLl5UXRokWztA0ODiYyMtLaJnNilrE/Y9/12sTFxZGQkJBlzjawPAs3ceLEHJ+LiIiI2OGvabB/iSXR6vUjlKqLNQEzuYHpimWRPOTH7Sf55K8jAMx6rD6tqgc6fcy0qChO93gDs5FG4datKfbKa+n/n2S0sCxY/2a/1ru1eabtV7RxxvNg026ib2FPd4fFkRfZnZw98MADWdbd3NwIDAykZcuW1KhRI8eBDBw4kN27d/PXXzd5O4MDjB07luHDh1vX4+LiKFeunAsjEhERuUUd/A1WvW5Z7jgNKjR2bTwidth9MpbRi3YC8GzLyvyvbmmnj2lOTubY6JGYz5zBq0plyr3xBu6+mn/4VpEn5jkbNGgQS5Ys4Y8//qBs2bLW7SEhISQnJxMTE5Pl6tnp06cJCQmxttm0aVOW42VUc8zc5soKj6dPn8bf3/+qq2YA3t7eeBfw2clFREScLvOcZHc+AQ16uToiEZudv5jMU19uISnVTMvqgYxoWz1Xxj392uskbN+Om58f5WbPVmJ2i8nxM2dRUVHs3r2bnTt3ZnnZwzAMBg0axPfff8+qVauoVKlSlv0NGzbE09OTlStXWrcdOHCAY8eOERoaCkBoaCi7du0iKirK2mbFihX4+/tTs2ZNa5vMx8hok3EMERERyWVXzknW7g1XRyRis9Q0M4Pmb+VkTAIVSvgw89H6uOdCBcHoBd8S8+23YDJRZtpbeFWs6PQxJXfZfeVsy5Yt9O7dm3379nFloUeTyURamu2lMAcOHMj8+fP58ccf8fPzsz4jFhAQQOHChQkICKBfv34MHz6c4sWL4+/vz+DBgwkNDaVRo0YAtG3blpo1a9KzZ0+mTp1KZGQkL730EgMHDrRe/Xr66aeZPXs2o0eP5oknnmDVqlV8++23LF261N7TFxERkZtlGPDDs5qTTPKtycv2s+7wOXy83Pmo550E+Hg6fcxLW7cR+dprAAQOGYJv8+ZOH1Nyn92l9OvWrUvlypUZM2YMwcHBVz0kWKFCBdsHv8YDhnPmzKFPnz6AZRLqESNG8PXXX5OUlERYWBjvvfee9ZZFgP/++49nnnmG8PBwihQpQu/evZkyZQoeHpdzz/DwcIYNG8bevXspW7Ys48aNs45xIyqlLyIi4kB/ToOVr4CbJ/RdBuXucnVEIjb7ftsJhi3YAcD7PRrQvnYpp4+ZcjqKIw91Je3MWfzatqXMzBmauNmFnJkb2J2c+fn5sW3bNqpUKTjlL5WciYiIOMg/K2Dew4AB98+Ehn1cHZGIzXafjKXr++tISjUzsFVlRoXlvBierczJyRzr2YuEHTvwrlqFit98g1sRPWfmSnlqnrPWrVuzY8cOhwYhIiIiBcC5w/BdP8CAhn2VmEm+ci4+KUsBkOH35VIBkFdfI2HHDtz8/Sk7e7YSs1uc3c+cffLJJ/Tu3Zvdu3dTq1YtPD2z3mP7v//9z2HBiYiIyC0iKR6+6QGJsVD2bmivAiCSf1gKgGzjZEwCFUv4MLNbLhUA+WYBMQsXXi4AYsfjQ5I/2Z2crV+/nrVr17Js2bKr9tlbEEREREQKAMOAH5+FM/vANwQe/RI8NGWN5B+TftnP+n/PUcTLnY963UlA4dwoALKVyNctcwAGDh2Kb7NmTh9TXM/u2xoHDx7M448/TkREBGazOctLiZmIiIhc5a/psPdHSwGQR74Av5Ab9xHJIxZvPcFna48AMO2RulQL9nP6mCmnozgxZAikpOAXFkaJAf2dPqbkDXYnZ+fOnWPYsGEEBwc7Ix4RERG5lfzzu6UyI0CHN6H8Pa6NR8QOu0/GMnbxLgAGtapCu1rOr8xoTk7m5HPPkXbmLN5Vq1J60uuqzFiA2J2cdenShdWrVzsjFhEREbmVnP8XvnsCMKBBb7izr6sjErFZ5gIgraoHMuy+ak4f0zAMTr/66uUCIO+qAEhBY/czZ9WqVWPs2LH89ddf1K5d+6qCIM8995zDghMREZF8KksBkLssV81E8omUNDMD52/lZEwClUoWYUYuFQCJWbCAmIWLLhcAKV/e6WNK3mL3PGeVKlW69sFMJv7999+bDiqv0TxnIiIidjAMWNQX9nwPvsEwYA34O/92MBFHmfjzHuasPUoRL3d+GNiEqrnwnNmlrVv5r3cfSEkhcPhwSuo5szzLmbmB3VfOjhw54tAARERE5BazdqYlMcsoAKLETPKRxVtPMGftUQCmPVIvVxKzlNOnOfFcegGQdu0o0f9Jp48peZPdyZmIiIjINR1aCSsnWpbbvwHlG7k2njwoMSWNT/86wn/nLmbZfuW9TNnd2nR1mxt3unJTdjdNXd3GMce5Orxs+lzZJtuxDRvaWLilpVJ/828UP3MS01UdLO+mjHUja28TcCYukdGGQfVgX2rMWc3JK/oaV/a1rmd+t6ctJP37L2lnz+JdrRqlX39NBUAKMCVnIiIi4hjnj8CiJ8AwQ/2ecOcTro4ozzEMg7GLd/H9tpOuDuWW9dTOH2jx7183f6ATEHfzR7GZW0AAZWe/owIgBZySMxEREbl5yRdhweOQGANl7oSO00Cf/l/ls7VH+X7bSdzdTDzTojI+3u5Z9pu4+mt25Zcxu6/q1W1M192fneyu1ly5JbvjXN0mB+eQ47Gzbiy+dgUV0hOz0x0eJq2IHybAyGhmSu9hMl2+aGYyWY6T3sbb052qwX54uLtdMbAp67rpcp+MbSaT6QZ9smlvMgEmijRtgqemqirwlJyJiIjIzTEM+HEQnN4NRYLg0S/Bw9vVUeU56w6fZdIv+wB4scPtPNH02kXWxH4Ju3bz39xZGEDJgQO5ffAgV4ckYje75zkTERERyWLdO7BnMbh5pBcAKe3qiPKcE9GXGDR/G2lmgy4NytC3SUVXh3RLST13jhODB2MkJ+PbqhUlBz7r6pBEcsTu5OzXX3/lr78u38f77rvvUq9ePbp37050dLRDgxMREZE87vAq+H2CZbndFKgQ6tp48qCE5DSe+nIL5y8mU7tMAJMerK2CDw5kpKRwcshQUiMj8apUidJT38DkpusPkj/Z/ZM7atQo4uIsj0fu2rWLESNG0KFDB44cOcLw4cMdHqCIiIjkUdFHMxUAeRzuUvnvK1kKgOxkz6k4ShTx4oOeDSnk6X7jjmKz029M5dLmzbgVKULZd2fj7uf80vcizpKjec5q1qwJwHfffUenTp2YNGkSW7dupUOHDg4PUERERPKg5EvwzeOQEA1lGkIHFQDJzqd/HeGH7adwdzMxu3sDyhQt7OqQbikxi78n+quvACj95lS8b7vNxRGJ3By7r5x5eXlx6dIlAH7//Xfatm0LQPHixa1X1EREROQWZhjw02A4vQuKBMIjX4JnIVdHleesO3SWycv2A/BSx9sJrVzCxRHdWhJ27SLy5ZcBSwEQv3vvdW1AIg5g95Wzpk2bMnz4cJo0acKmTZtYsGABAAcPHqRs2bIOD1BERETymPWzYfeiywVAAsq4OqI85/j5Swycv9VaAKRP44quDumWknr2LCcGP2cpAHLvvSoAIrcMu6+czZ49Gw8PDxYtWsT7779PmTKWf5CXLVtGu3btHB6giIiI5CGHV8OK8ZbldlOgQmPXxpMHZRQAib6UogIgTmCkpHBy6DAVAJFbkt1XzsqXL8+SJUuu2j59+nSHBCQiIiJ5VPR/lwuA1OuhAiDZMAyD5xfvZG+EpQDIhyoA4nCnp7yRtQCIr6+rQxJxmBx9zHD48GFeeuklHnvsMaKiogDLlbM9e/Y4NDgRERHJI5IvwYIekHAeSteHjm+rAEg2Pv3rCD9uP4WHm4l3ezSgtAqAOFTM4u+JnjcPUAEQuTXZnZytWbOG2rVrs3HjRhYvXkx8fDwAO3bsYMKECQ4PUERERFzMMODn5yAyvQDIo1+pAEg21h46y6Rf9gGWAiCNblMBEEfKUgBk0CAVAJFbkt3J2fPPP89rr73GihUr8PLysm6/99572bBhg0ODExERkTxgw3uwa6GlAMjDn0OACoBd6fj5SwyavxWzAV0blKW3CoA4VOrZs5wYNNhSAKR1a0o++4yrQxJxCruTs127dvHggw9etT0oKIizZ886JCgRERHJI/5dA7+NsyyHTYKKTVwbTx6UuQBInbIBvP5gLRUAcSAjOZkTQ4eSevo0XrfdRuk3pqgAiNyy7P7JLlq0KBEREVdt37Ztm7Vyo4iIiNwCYo7Bwj5gpEHd7nD3AFdHlOdkLgBS0teLDx5XARBHOz3lDRI2b8HN15eys1UARG5tdidn3bp1Y8yYMURGRmIymTCbzaxdu5aRI0fSq1cvZ8QoIiIiuS35EnyTXgCkVD3opAIg2clSAKS7CoA4Wsx3i4mePx+A0lOn4n1bJRdHJOJcdpfSnzRpEgMHDqRcuXKkpaVRs2ZN0tLS6N69Oy+99JIzYhQRESm4DAPOHIDURMC4vA0jffXKbYZ1W1JKGiejL2YcCABTpnaGYVj3mQwDg8t9ix/4lqKRO0kpVIJ/mr9PSmQSBkmZwjLILOtaehjXaZF5/4362jOWceXe669ePVamFlfvyyoiJsFaAGRcp5rck8cLgBgpKSQdOnT5e2/99l/+OciyfsW79ftg/UIYtre9zhjXapsWff5yAZDBg/C7t5V9J5yPxCbFEnHx6jvT5Grl/crj4+nj6jCcxmRc+S+ejY4dO8bu3buJj4+nfv36VK1a1dGx5RlxcXEEBAQQGxuLv7+/q8O5KRnVNX11S4CISN5nToMFPeHAUpcMn2q48XjKC2ww13TJ+PnFQw3L8uZDdfL0c2ap0dH8170HyUeOuDoUu/m2aU3ZWbNu2efMtkdt56kVT3Ep9ZKrQ8kXvurwFXUD67o0BmfmBnZfOctQvnx5ypcv78hYREREJLNVr1kSMzcPKBJk2WYyAabL72S8Xd5mBiJiE0lKNTCZTJhMl/+oNTBhmEyZrgKZLNvS95nS31Pw5BvP/3G8cEMyP1F+Zf5x1Tqm6+y7sq/puvuv3JB59UZ9rxdXdvvtiSvz7jplizLh/pp5OjEzUlM5NWIEyUeOYCpcGPeMPyYzYjZl/jnK9LUymbJpc522133P/HW1vU+hqlUJHjfulk3Moi5FMTx8OJdSL+Hv5U8hd01RcSMebjlOX/IFu8/OMAwWLVrE6tWriYqKwmw2Z9m/ePFihwUnIiJSYO35Af5627L84IdQ+yGbuhmGwdBvtvNTxClKFPHi58FNc/wc1KvpL8nfot6ezsV16zH5+FDx668pVL2aq0MSIDktmeHhwzmTcIYqRaswr8O8W/p2PbGN3cnZ0KFD+fDDD2nVqhXBwcF5+pMiERGRfOn0XvjhWcty6CCbEzOAT/48wk870gtU9FCBioIudslSzn/2GQClJ72uxCwPmbxpMjvO7MDPy4+ZrWYqMRMgB8nZl19+yeLFi+nQoYMz4hERESnYEqLhm+6QchEqtYA2E23u+tc/Z5m8zFKg4qWOt9MojxeoEOdK3LePiPRibSX698e/XTsXRyQZFh5cyKKDizBhYmrzqZT316NCYmH3DbwBAQHcdtttzohFRESkYDOnwaJ+EH0EipaHh+eCu22fox4/f4lBX2/FbEDXBmXp3biiU0OVvC01OpoTgwZjJCZSpFkzAocOcXVIkm571HYmbZwEwHMNnqNpmaYujkjyEruTs5dffpmJEyeSkJDgjHhEREQKrlWvwuGV4FEYus0Hn+I2dUtITmPAl1uIuZRCnbIBvP5gLT12UIAZqamcHD6clJMn8SxfnjJvvYnJXRNj5wVRl6IYFj6MVHMq91W4j361+rk6JMlj7E7OHnnkEaKjowkKCqJ27do0aNAgy8sef/zxB/fffz+lS5fGZDLxww8/ZNnfp0+f9CpTl1/trrgkf/78eXr06IG/vz9FixalX79+1nLxGXbu3EmzZs0oVKgQ5cqVY+rUqfaetoiIiHPt+R7+mm5Z7jwbQmrb1M0wDMZ8t5N9EXGU9PXig8cbUshTf4gXZFHT3ubS+g2YfHwoO/sd3AMCXB2SYCkAMix8GGcTzlKlaBVea/KaPkSRq9j9zFnv3r3ZsmULjz/++E0XBLl48SJ169bliSeeoEuXLtm2adeuHXPmzLGue3t7Z9nfo0cPIiIiWLFiBSkpKfTt25cBAwYwP302+bi4ONq2bUubNm344IMP2LVrF0888QRFixZlwIABOY5dRETEYU7vuVwApPHgnBcA6a4CIAVd7M9LOJ/+d1PpSZMoVE0FQPKKSRsnsfPMTvy8/JjVapYKgEi27E7Oli5dyvLly2na9Obvj23fvj3t27e/bhtvb29CQkKy3bdv3z5+/fVX/v77b+68804A3nnnHTp06MBbb71F6dKlmTdvHsnJyXz22Wd4eXlxxx13sH37dt5++20lZyIi4nqXzqcXALkEt7WE1i/b3DVzAZBxnWpyjwqAFGiJ+/YRMW4cACUGDMC/XZiLI5IM3x74lu/++c5aAKScfzlXhyR5lN23NZYrV87hM2FfT3h4OEFBQVSvXp1nnnmGc+fOWfetX7+eokWLWhMzgDZt2uDm5sbGjRutbZo3b46Xl5e1TVhYGAcOHCA6OjrbMZOSkoiLi8vyEhERcThzGnzXD6KPQtEK8NCcHBUAeahhWXqFVnBurJKnpUZHc2LgIEsBkObNCBzynKtDknTborYxedNkQAVA5MbsTs6mTZvG6NGjOXr0qBPCyapdu3Z88cUXrFy5kjfeeIM1a9bQvn170tLSAIiMjCQoKChLHw8PD4oXL05kZKS1TXBwcJY2GesZba40efJkAgICrK9y5fTphoiIOMHKV+DwqvQCIPNyVACkbtkAXntABUAKMiM1lZPDhpNy6pSlAMibKgCSV0RdimJ4+HBSzam0rdBWBUDkhuy+rfHxxx/n0qVLVK5cGR8fHzw9PbPsP3/+vMOC69atm3W5du3a1KlTh8qVKxMeHk7r1q0dNs6Vxo4dy/Dhw63rcXFxStBERMSxdi+GtTMsy3YWABmduQBITxUAKeii3prGpQ0qAJLXZC4AUrVYVV5t8qo+RJEbsjs5mzFjhhPCsM1tt91GyZIlOXToEK1btyYkJISoqKgsbVJTUzl//rz1ObWQkBBOnz6dpU3G+rWeZfP29r6q8IiIiIjDRO6GHwdalhs/Z1cBkI///Jef0wuAvNejIaUCVACkIIv9eQnn584FoPTkySoAkkcYhmEtAOLv5c/MljNVAERskqNqja5y4sQJzp07R6lSpQAIDQ0lJiaGLVu20LBhQwBWrVqF2WzmnnvusbZ58cUXSUlJsV7lW7FiBdWrV6dYsWKuORERESm4rioAMsHmrn/+c4Ypy/YDMP7+mtxdybbbIOXWlLBnDxEvvQRAiaeewj+srYsjkgwLDy7ku3++w83kpgIgYhebnjnLXBDjykIZN1M4Iz4+nu3bt7N9+3YAjhw5wvbt2zl27Bjx8fGMGjWKDRs2cPToUVauXEnnzp2pUqUKYWGW6kO333477dq1o3///mzatIm1a9cyaNAgunXrRunSpQHo3r07Xl5e9OvXjz179rBgwQJmzpyZ5bZFERGRXJFRACTmvxwVABn89TbMBjzcsCw9G6kASEGWev48JwYPxkhKshQAeW6wq0OSdFtPb2XyxvQCIPWfo0mZJi6OSPITm34jFCtWjIiICIKCgihatGi298sahoHJZLIW67DF5s2badWqlXU9I2Hq3bs377//Pjt37uTzzz8nJiaG0qVL07ZtW1599dUstxzOmzePQYMG0bp1a9zc3OjatSuzZs2y7g8ICOC3335j4MCBNGzYkJIlSzJ+/HiV0RcRkdy3cmKOCoBcSk6l/xebLQVAyhXlVRUAKdAyCoCknorAs0J5yrz1lgqA5BGRFyMtBUCMVMIqhvFErSdcHZLkMybDMIwbNVqzZg1NmjTBw8ODNWvWXLdtixYtHBZcXhEXF0dAQACxsbG5Oo2AM8THxwPg6+vr4khERAqY3d/BovQ/1Lp+avNzZoZhMPjrbSzZGUFJXy9+HtxUz5kVcKcnT+b851/g5uNDxQXf4F21qqtDEiApLYm+v/Zl19ldVC1Wla/af6XnzG5RzswNbLpyljnhqlSpEuXKlbvqEzvDMDh+/LhDgxMREcnWucPw+wSIjwLrZ4zp79muX29f5nWuud8wDM7GJ5GYnIoBmMj62abpij6X91veg8xn8AQWenfhk9+DMH5fY21uYPk9mnn4jG0paQYnYxJUAOQ6Us+c4fQbU0k5edKywcj6vTAyf6+s35bM2678fmfTN/O3O/1n6nzCeeKTLR96Wv8qMq5sDKZsPgY3GVcfN+sxrtEeKH4uGYBvHg5i34FxcODq9kY2B7Hh8/jr9r/WMa7Z9mZjuEbbbI9rR7zXHM+OeLNrm5iayJmEM5YCIK1UAERyxu6CIJUqVbLe4pjZ+fPnqVSpkl23NYqIiNgtMQ6+7gZnD+bqsCYg8CaPsTqtLmNiu2COvWDf2CZ4+X93qABINozkZE48N4SEbdtyfewi6S9XWNjUxKLSJ+DcCRdFINnxdPPkzeZvUs5PBUAkZ+xOzjKeLbtSfHw8hQoVckhQIiIi2TKb4funLImZfxloNxlM6c/amExYrz1Yf0/Zus7l9Wzarjt8jndWHwag+93lCQkoBJjSfx+a0puZMJksFz1MJrf0zZfbGO5eeBS/gy9MbtYhTBlDYsqyLeNYGZEE+RWifAl9Cp+dyNcnkbBtG27+/oSMH49bofTn0jP/rXLl9zTzYqZ21r9vsu17udOB6APM2DoTs5FGu9vaU8m/IkY2fUxkcxzT5e1Gpp87U5YfiuyPkTGG4V+EluVL0RLbZIkj8/ZrPLeYXftrHePam7M5hh3jXXe7Hc9bOuTc7WhbxrcMgT43+zGOFGQ2J2cZxTpMJhPjxo3Dx+fyL4m0tDQ2btxIvXr1HB6giIiI1R9T4cAv4O4Nj34FZRo4fci9p+J44o+1JJrv4JmWlbm/XQ2njym2iV7wLTELFoDJRJlpb+HbrJnTxzwZf5KhS8YSU8ngf5Uf4PEmr6k4i4g4jM3J2bb02wUMw2DXrl14eXlZ93l5eVG3bl1Gjhzp+AhFREQA9i+FcEt5au6fkSuJWfTFZAZ8uZnEFDMtqgUysm11p48ptrm0dRuRr70GQODQobmSmCWkJjB09VBikmKoWaIm4xqNU2ImIg5lc3K2evVqAPr27cvMmTPzfdVCERHJR84chMVPWZbvfgrqdXf6kKlpZgZ9vZUT0QlUKOHDrG71cXfTH+J5QcrpKE4MeQ5SUvALC6PEgP5OH9MwDF5e9zL7z++neKHizGw1k0IeepxDRBzL7mfO5syZ44w4REREspcYC988BskXoEJTCHs9V4Z949f9rD10Dh8vdz7qeScBPp65Mq5cnzk5mZPPPUfambN4V61K6Umv58rVqy/2fsEvR37Bw+TBWy3eIqRIiNPHFJGCx83VAYiIiFyT2QyLB8C5Q+BfFh6eC+7OT5J+3H6Sj/88AsBbD9eleoif08cU25x+9TUSduzAzd+fsu/Oxq2I8+slbojYwNtb3gZg5F0juSvkLqePKSIFk5IzERHJu9ZMgYO/phcA+RJ8nV8Fbc+pWMZ8txOAZ1tWpkPtUk4fU2wT/c0CYhYutBYA8Spf3uljnow/yag1ozAbZv5X+X90r+H8W2pFpOBSciYiInnTviWw5g3L8v0zc6UAyPmLyQz4YguJKWZaVg9khAqA5BmXtm4l8nXLLa2Bw4blegGQO0rcwfjQ8SoAIiJOZVNy1qBBA6KjowF45ZVXuHTpklODEhGRAu7MAct8ZgD3PA31HnP6kKlpZgbN38rJGEsBkJmPqgBIXmEpADLEUgCkXTtK9H/S6WMahsGEdROsBUBmtJqBt7u308cVkYLNpuRs3759XLx4EYCJEycSHx/v1KBERKQAS4yFb7pDcrylAEjb13Jl2CnL9rPusAqA5DVZCoBUq0bp13NnXrEv9n7BsiPL8DB5MK3FNBUAEZFcYVO1xnr16tG3b1+aNm2KYRi89dZb+Pr6Ztt2/PjxDg1QREQKEBcWAPnkL0sBkGkqAJJnGIbB6VdftRQACQig7Ox3cqUAyPpT660FQEbdNYo7Q+50+pgiImBjcjZ37lwmTJjAkiVLMJlMLFu2DA+Pq7uaTCYlZyIiknPhky0FQDwKQbevcqUAyO6TsYxeZCkAMrBVZdqrAEieEbNgATELF4GbG2Xeyp0CICcunGDUH5YCIJ0rd+axGs6/pVZEJINNyVn16tX55ptvAHBzc2PlypUEBQU5NTARESlg9v0Mf0y1LN8/E0rXd/qQ5y8m89SXW0hKtRQAGX6fCoDkFZYCIJMACBw2FN9mTZ0+ZkYBkNikWGqVqMW40HEqACIiucruSajNZrMz4hARkYIsaj98/7Rl+Z5noG43pw+ZuQBIxRI+zOymAiB5Rcrp05x4Lr0ASPt2lHgylwqArJ3AgegDFC9UnOmtpqsAiIjkOruTM4DDhw8zY8YM9u3bB0DNmjUZMmQIlStXdmhwIiJSACTEXC4AUrEZtH01V4adnF4ApIiXOx/1upOAwioAkheYk5M58dxzpJ3NKADyeq5cvfp8z+csO6oCICLiWnbPc7Z8+XJq1qzJpk2bqFOnDnXq1GHjxo3ccccdrFixwhkxiojIrSqjAMj5wxBQLtcKgHy/7QSfZhQAeaQu1YJVACQvMAyDyFdeIXHHTksBkHdn4+bj4/Rx151ax/St0wEVABER1zIZhmHY06F+/fqEhYUxZcqULNuff/55fvvtN7Zu3erQAPOCuLg4AgICiI2Nxd/f39Xh3JSMaRCuVW1TRAoosxmOhMPFs5Z1wwCMy+/X2ZaYksrB0xcu3/Z+RTvDABMGBmAyDAzj8u3xxeL2UfHET6S6ebOq8RdEB9Qk47eSkXEoy1EuH/ry6NYNRtZVjGy2Zywnp5qZ8ftBklLNDGpVhZFhzn/OzDAMLm36m9SoqIwtmXde2ThLv8sr2be5qv81jm1cq082xzUwOH7hGLGJsVmOa1z5hb7yAObL/a8aK8uQmX6mMn2PvU5HU2LJBgw3E/9N6MmFelffkZP5PKw/F1x9nlliuE5fs2Hm410fE5sUS+fKnXm1yat6zkxErsuZuYHdyVmhQoXYtWsXVatWzbL94MGD1KlTh8TERIcGmBcoORORW97PQ2HLHJcNPzT5WX4wO7/gQ2atqgfySe+7cuU5s6hpb3Pu44+dPs6t4qtWbvzUyO6be25KrRK1mNt+rp4zE5EbcmZuYPczZ4GBgWzfvv2q5Gz79u2q4Cgikh9tnpOemJmgUjMwuVmWTSbLO1xezrTNALafiOVsfAqe7m74FvLAwLLfyHLlwWTZbkp/T5fRdmfhu4nza8O96Ue+3NV0eejLkWAygenKfdY2WRtbjme6qn/pooV5pmXlXEnM4pYtsyZmPnfeCZ6Xf/Warvg6ZdphwzLZbjfZfZysxzx76Sw7z+7EAIp6F8XNZPl+ZrQyMg1tZPraGteKLfOGa8SceT3ytgAuNC9HK5Mpy7lc/j6asqxnHeXa7bMsX/HzUrxQcfrV7qfETERczu7krH///gwYMIB///2Xxo0bA7B27VreeOMNhg8f7vAARUTEiY5thF9GWZZbj4NmI2zu+uav+3lv12G8Pdz4rn9japUJyFEIocBTOeqZ9yUeOMCpF14EoHjfvgSPGe3iiK7v39h/eWZpdy6muNOtejf6NHrR1SGJiBQodidn48aNw8/Pj2nTpjF27FgASpcuzcsvv8xzzz3n8ABFRMRJ4iLg255gToGanaGp7R+w/bIrgvfCDwPwRtc6OU7MbmVpMTGcGDQYIyEBn9BGBI3I2x9gXki+wJBVQ7iYcpEGQQ0YfXfeTiRFRG5Fdj9zltmFCxcA8PO7tatc6ZkzEbnlpCbB3I5w4m8Iqgn9VoC3bf8uHIi8wIPvreVSchr9mlZiXKeaTg42/zHS0jg+4Ckurl2LZ5kyVFy0EI9ixVwd1jWZDTNDVg0h/EQ4wT7BfNPpG0oWLunqsERE8qQ89cxZZrd6UiYicsv6ZZQlMSsUAN3m2ZyYxV5KYcCXm7mUnEbjyiUY276GkwPNn87MmMHFtWsxFSpE2Xdn5+nEDOCDHR8QfiIcLzcvZrSaocRMRMRFcrcUkoiIuN7mz2Dr54AJun4GxW+zqVua2eC5b7bx37lLlClamNndG+Dhrl8jV7IUAPkEgFKvv0ahGnk7gV11bBXv73gfgHGh46hVspaLIxIRKbj0W1VEpCA5tgF+SX+WqPV4qNrG5q7TfjvAmoNnKOTpxoc9G1K8iJeTgsy/shQAeeIJAjp2dHFE1/dv7L+88NcLADxW4zEeqPKAawMSESnglJyJiBQUcRHwba/0AiAPQNNhNndVAZAbS4uJ4cTAQRgJCRRpHErQcNu/vq6QuQBIw+CGjLprlKtDEhEp8OxKzlJSUmjdujX//POPs+IRERFnSE2yVGaMP20pANL53avnmbqGA5EXGLlwBwD9m1Wic70yzow0XzLS0jg5YiQpJ07gWbYspadNw+RxU491O5XZMDP2z7EcjTtKsE8w01pMw9PN09VhiYgUeHYlZ56enuzcudNZsYiIiDMYBvwy8qYLgDSpUoIx7fL281Oucmb6dEsBkMKF80UBkPd3vM+aE2vwcvNiZquZlChcwtUhiYgIObit8fHHH+fTTz91RiwiIuIMmz+DrV+QkwIgg9MLgJQtVpjZj6kASHbifvmFc59Yfi+WnvQ6hapXd3FE17fy2Eo+2PEBAONDx3NHyTtcHJGIiGSw+56L1NRUPvvsM37//XcaNmxIkSJFsux/++23HRaciIjcpGMbYNkYy7KdBUDe+u0Af6QXAPmo550UUwGQqyTu38+pF18CoMST/fBv397FEV3fvzH/8sKflgIg3Wt0p3OVzi6OSEREMrM7Odu9ezcNGjQA4ODBg1n2mWx8fkFERHJB3ClY0DNHBUCW7ozg/fQCIFMfqkvN0o6dZPNWkBodzYlBgy0FQJo0IXBYPigAsnoIl1IvcWfwnYy8a6SrQxIRkSvYnZytXr3aGXGIiIgjpSZZErOLURB0h10FQPZHxlkLgAxofhv/q1vamZHmS0ZqKqcyCoCUK0eZaW9hcnd3dVjXlLkASEiREN5q8ZYKgIiI5EE5fnjg0KFDLF++nISEBAAMw3BYUCIichMMA5aOgJOboVBR6PaVzQVAYi4lM+CLLSSkpNG0SklGh+Xt56dcJWr6dC6uW2cpADJ7Nu5Fi7o6pOt6b/t71gIgM1rOUAEQEZE8yu7k7Ny5c7Ru3Zpq1arRoUMHIiIiAOjXrx8jRoyw61h//PEH999/P6VLl8ZkMvHDDz9k2W8YBuPHj6dUqVIULlyYNm3aXFXG//z58/To0QN/f3+KFi1Kv379iI+Pz9Jm586dNGvWjEKFClGuXDmmTp1q72mLiOQfmz+FbV+CyQ0e+tS+AiBfb+PY+UuUK16Ydx6rrwIg2YhdupTzn34GZBQAqebiiK5v5X8r+XDnhwBMaDxBBUBERPIwu3/rDhs2DE9PT44dO4aPj491+6OPPsqvv/5q17EuXrxI3bp1effdd7PdP3XqVGbNmsUHH3zAxo0bKVKkCGFhYSQmJlrb9OjRgz179rBixQqWLFnCH3/8wYABA6z74+LiaNu2LRUqVGDLli28+eabvPzyy3z00Ud2nrmISB5jGGA2gznN8kpLhf/WZS0AUuXGBUAMwyDNbPDm8gP8+c9ZCnm68eHjBbMAiGEYlpfZbHmlpVleqakYqakk7ttHREYBkP5PuqwASEacZsNsfaWZ00gzp5FqTrW+DkUf4oW/LAVAetzeg/9V/p9L4hUREduYDDvvRwwJCWH58uXUrVsXPz8/duzYwW233ca///5LnTp1rrpqZXMgJhPff/89DzzwAGD5xVO6dGlGjBjByJGWh5ZjY2MJDg5m7ty5dOvWjX379lGzZk3+/vtv7rzzTgB+/fVXOnTowIkTJyhdujTvv/8+L774IpGRkXh5Wf7QeP755/nhhx/Yv3+/TbHFxcUREBBAbGws/v75+6H4jO+Pr69ttziJSA4dWmmZW+zSOcu6kf4fw8hYubyck2038CuhjDSGWvI3AwwMy7thpG8zMDIOfYVZj9XPlefM0i5c4OTIkSRs33E5kBu8W8PNvN2etoBhmC234qdvzsm1wZ2V3Xm7W2EMN1P6MS9/IQ2Ma2/L/P0zuGrb9frejDuD7+Sjth/pOTMREQdwZm5gd0GQixcvZrliluH8+fN4e3s7JCiAI0eOEBkZSZs2lz/1DQgI4J577mH9+vV069aN9evXU7RoUWtiBtCmTRvc3NzYuHEjDz74IOvXr6d58+bWxAwgLCyMN954g+joaIplM1FoUlISSUlJ1vW4uDiHnZeIFABn/4GFfSDJNf92bDTXYFhyfxJIs6ufu5uJ5+6tmiuJmWE2c2rUaC6u+cPpY13JlP7KqcMhMP1+uGROBLOjonKeasWqqQCIiEg+YXdy1qxZM7744gteffVVwHLFy2w2M3XqVFq1auWwwCIjIwEIDg7Osj04ONi6LzIykqCgoCz7PTw8KF68eJY2lSpVuuoYGfuyS84mT57MxIkTHXMiIlKwJMbBN90tiVn5ULh/VnqVRFPWaolZtpluetuM3/9hzvr/KOTpztu9WrC0aGHcTCZMJqzvJpMJNxOYSH/PtN/NBJ7ubhTxtvvXQo6cnf0u8eHhmLy8KPvuu3iWKZN+bhmnmPlc09+v3Jbe+PJqNn0yvR+KOczgVYNJMifxSLVH6Fi5Y6ZjWK6fmdwu9zGlp3EmTJezOTcTdfx8WWS6fL0t8zQypozjZUr/sptmxtouu77XaZ95f5Zt1+nr5+WHm0nPDoqI5Ad2/xaeOnUqrVu3ZvPmzSQnJzN69Gj27NnD+fPnWbt2rTNizHVjx45l+PDh1vW4uDjKlSvnwohEJF8wm+H7p+DsQfArDY98Ab5BN+53k37acYoZ688Bvrz+cH2aVA10+pg348Lvv3P2vfcACHllIr7Nmjp9zOjEaIb+OZHTPsk0LdOMZ+99EXe3vFv6XkRECia7P0qrVasWBw8epGnTpnTu3JmLFy/SpUsXtm3bRuXKlR0WWEhICACnT5/Osv306dPWfSEhIURFRWXZn5qayvnz57O0ye4Ymce4kre3N/7+/lleIiI39MdUOPALuHvDo1/lSmK291QcoxdZ5iR7qsVtdKqTt+ckSzp8mFOjLQVLivXsSdH054ydKdWcyqg1ozh18RTl/MoxpdkUJWYiIpIn5ej+lYCAAF588UVHx5JFpUqVCAkJYeXKldSrVw+wXMHauHEjzzzzDAChoaHExMSwZcsWGjZsCMCqVaswm83cc8891jYvvvgiKSkpeHpa7rdfsWIF1atXz/aWRhGRHNn/C4RPtix3ehvKNnT6kNEXkxnw5WYSU8w0q1qS0WE1nD7mzUi7cIETAwdhvnQJn7vuInj0qFwZd/qW6WyM3Ehhj8LMbDWTAO+AXBlXRETEXjlKzqKjo/n000/Zt28fADVr1qRv374UL17cruPEx8dz6NAh6/qRI0fYvn07xYsXp3z58gwdOpTXXnuNqlWrUqlSJcaNG0fp0qWtFR1vv/122rVrR//+/fnggw9ISUlh0KBBdOvWjdKlLZ8ed+/enYkTJ9KvXz/GjBnD7t27mTlzJtOnT8/JqYuIXO3MQVicPoXHXf2h/uNOHzI1zczgr7dxIjqB8sV9eOex+ri7Xf28UV6RUQAk+ehRPEqVosyM6Zg8nV+gYum/S/li7xcAvN70daoWq+r0MUVERHLK7lL6GRNHBwQEWKskbtmyhZiYGH7++WeaN29u87HCw8OzLSLSu3dv5s6di2EYTJgwgY8++oiYmBiaNm3Ke++9R7Vqlyf8PH/+PIMGDeLnn3/Gzc2Nrl27MmvWrCyl4nfu3MnAgQP5+++/KVmyJIMHD2bMmDE2x6lS+iJyTYmx8HFrOPcPlG8MvX8Cd+cnHZN+2cdHf/xLYU93vh/YmBohefvfpjOzZnH2vfcxeXtTYd48Ctdy/kTI+87to+eyniSlJdG/dn+ea/Cc08cUEZFbnzNzA7uTs9q1axMaGsr777+Pu7vlnv20tDSeffZZ1q1bx65duxwaYF6g5ExEsmU2WyozHlwG/mVgQHiuPGf24/aTDPlmOwCzu9fP88+Zxa1YwcnBlsSo9BtTCOjc2eljRidG021JN05dPEXTMk2Zfe9sPWcmIiIO4czcwO6CIIcOHWLEiBHWxAzA3d2d4cOHZ7lFUUTklrfmDUti5u4Nj36ZK4nZnlOxjPluJwBPt6ic5xOzpEOHiBjzPADFevXMlcQscwGQ8n7leaP5G0rMREQkX7A7OWvQoIH1WbPM9u3bR926dR0SlIhInrd/KayZYlnuNB3K5E4BkKe+3EJiipnm1QIZFVbd6WPejLS4uMsFQO6+m+BRuVMA5O0tb7MxciM+Hj7MbDUTf6/8fceDiIgUHDYVBNm5c6d1+bnnnmPIkCEcOnSIRo0aAbBhwwbeffddpkyZ4pwoRUTykjMHYfFTluW7B0D9Hk4fMjXNzKCvt1oLgMzqVi9/FAD57z88SudeAZCfD//Ml3u/BCwFQKoUq+L0MUVERBzFpmfO3NzcMJlM3KipyWQiLS3NYcHlFXrmTESsEmPh43vh3CGo0AR6/ZgrBUBeX7qXj/88go+XO4ufzfsFQKJmzuTc+x9YCoDMn0fhO5xfAGTvub30WtZLBUBERMSpnJkb2HTl7MiRIw4dVEQkXzKbLSXzzx2yFAB5+PNcScx+3H6Sj/+0/Dv85kN183xiFvfbb5x7/wMASr36Sq4kZucTzzN09VCS0pJoVqYZA+sNdPqYIiIijmZTclahQgVnxyEikvetmQIHf00vAPIV+AY6fcjMBUCeaVmZjnVKOX3Mm5F06BARz48FoHjvXgT8739OHzOjAEjExQgq+FdgSvMpKgAiIiL5Uo4moT516hR//fUXUVFRmM3mLPuee063kYjILWjfEkt1RoD7Z0CZBk4f8vzFZAZ8YSkA0qJaICPb5qMCIPfcQ1AuFQCZtnkamyI3qQCIiIjke3YnZ3PnzuWpp57Cy8uLEiVKYDJdfiDdZDIpORORW8+ZA/B9RgGQp6Bed6cPmZpmZtD8rZyMSaBCCR9mdauftwuApKVxctSoywVApr+NySNHn//Z5efDP/PVvq8AmNR0EpWLVnb6mCIiIs5i92/OcePGMX78eMaOHYubm92V+EVE8pfEWMtE08nxUKEphL2eK8NOWbafdYfP4ePlzkc97yTAx/nPtt2MM++8w8U1f2Dy9qbsO+/gUby408fce24vE9dPBGBAnQG0rtDa6WOKiIg4k03VGjMrUaIEmzZtonLlgvPppKo1iuQBCTGwfR4kRFvWDQMwLr9fZ9v5i8kcORsPRvpt2EbGfwxMGBgGmLjyOGBg5lDqXs6YT5PkXoTtgf8jxb1wlsq1GS2tXa3bs/7Tmt0/tRltLu+yLAScjqXcpiO4Gwa3BRahmI/X5UYGmK5oz5XHNl+xblyOytr3yj7GlSdgWbBeq8v4mhmZGqW/uSelUn7dvwD8/VQTTjS+zabzvp4r+2XX548Tf3D60mmal23OO/e+g5tJHxiKiIjzubxaY2b9+vVj4cKFPP/88w4NRETkmlKTYf4jcHxjjroXT3/Z60t/P6aWKAYEWDYk/ZKj8e0Rct7gxblpFEly+lAOt+QuE18U3wj7c/Z9yokK/hWY3GyyEjMREbkl2H3lLC0tjU6dOpGQkEDt2rXxvGJS0bffftuhAeYFunIm4mI/D4Utc8A7AOo8AiYTYLr8Dldss0hKM/h+2yliElIp4etNiH+h9OZulusw1rYmDOuxLNsOukUz23MLZpNBVfc7wLNcektrl/S3rM+BZd5vynQ8677L/+HKJ8i8k1J54sM1BEVd4EypAOLvKJ2lvbWDyZT1ncvbjSvbXDn41ScAkH7+WJ8jzvj6mODyMa8TR0KgH6cbV8Vwd7vqa5L5uJePlE2bK7dl84hd5jbe7t7cX/l+ShYueXVDERERJ8lTV84mT57M8uXLqV7dUjXsyoIgIiIOtWWuJTHDBF0/gWptbeqWZjZ4+vO/WX3hDGWKFuanQU0o4ettU99T8acYt6Qb5iSDTrd1YlLTSU7/980wDE4+N4QLURfwCAoidMFCPIOCnDqmiIiI5C12J2fTpk3js88+o0+fPk4IR0Qkk+ObYOlIy/K9L9qcmAFMX3GQ1QfO4O3hxoc9G9qcmCWmJjJ09VCik6K5vfjtTAidkCsfPJ378EMurFiBydOTsrNmKjETEREpgOy+Sd/b25smTZo4IxYRkcviImBBTzCnwO33Q7ORNnf9dXcEs1cfAuCNrnWoVSbApn6GYTBx/UT2nd9HMe9izGw1k0IehXIUvj0uhIdzZuYsAILHj6NwvXpOH1NERETyHruTsyFDhvDOO+84IxYREYvUJPi2F8RHQmANeOD9q5+huoaDpy8w/NsdAPRrWokH6pexediv9n3Fkn+X4G5yZ1rLaZTyLZWj8O2RfPQop0aNBsOgaLdHKfbww04fU0RERPImu29r3LRpE6tWrWLJkiXccccdVxUEWbx4scOCE5ECatloOLHJUgCk23zw9rOpW+ylFAZ8sZlLyWk0rlyCse1r2DzkpohNTNs8DYCRd47krpC7chS6PdLiL3J80CDMFy5QuEEDQl54weljioiISN5ld3JWtGhRunTp4oxYRERg8xxLERBM8NCnUMK2ORXTzAZDFmzj6LlLlClamNndG+DhbtvNAafiTzFyzUjSjDT+V/l/9Li9R87jt5FhNhMx9nmSDx3GIyiIsjNnYPLycvq4IiIiknfZnZzNmTPHGXGIiMCxjfDLKMvyvS9B1fts7vr2igOEHzhDIU9LAZDiRWxLdBJSE6wFQGqWqMm4RuNysQDI75YCIO/MwiMw0OljioiISN6mWTtFJG+Ii4BvMwqA/A+ajbC567JdEby7+jCQ8wIgxQsVZ0bLGblXAGSW5dndkAnjKVy3rtPHFBERkbzP7itnlSpVuu6nyv/+++9NBSQiBVBqkiUxiz8NgbfbVQDkQOQFRiy0FAB5smklOtezvQDIl3u/ZOm/S3E3ufNWi7dypQBI0pEjnBo5ylIA5LFuFH3oIaePKSIiIvmD3cnZ0KFDs6ynpKSwbds2fv31V0aNGuWouESkIPllFJz4GwoFQLd54O1rU7fYSykM+PJyAZDn7SgAsjFiI29veRuAUXeNyqUCIPGcGDQYc3w8hRs2JGTsWKePKSIiIvmH3cnZkCFDst3+7rvvsnnz5psOSEQKmM2fwdbPARN0/cyuAiDPfbON/3JQAORk/MksBUC61+h+EydgG8Ns5tTzz5N8+DAewcGUnTFdBUBEREQkC4c9c9a+fXu+++47Rx1ORAqCYxvgl9GW5dbjoGobm7tO++0Aaw7mvABITFJMrhYAOfvBB8T/vtJSAGTWTBUAERERkas4LDlbtGgRxYsXd9ThRORWFxdhmWjanAI1O0PT4TZ3/WVXBO+F56wAyMvrXmb/+f0UL1Scma1m5k4BkNWrOfvObABCXp6gAiAiIiKSLbtva6xfv36WT5kNwyAyMpIzZ87w3nvvOTQ4EblFZS4AElQTOr9nVwGQkTksAPLF3i/45cgveJg8eKvFW4QUCclR+PZI+vcIp0aNBsOgWPfHKNq1q9PHFBERkfzJ7uTsgQceyLLu5uZGYGAgLVu2pEYN2x/GF5ECyjDgl5E3XQCkSRX7CoBsiNhgLQAy8q6RuVgAZJC1AEjw8887fUwRERHJv+xOziZMmOCMOEQkh5JS07iQmApY8h4AA+NyAyPLW5Z2JF/AlJJg6WGktzKMy/3N5svbjMtHMMxm4lLjMMxmDAzMhjn9GOb0/ulRmI30d3P6FjOFjq0lYON8DDw5c89YEo+dhaNnMNJjMDBnCdISguU4X2z4j4unz3O7byFG3V2UY4c3W2K1dLSESkashnXfpZRLTFk/kZJJabSp0IaHfJqSfOxY1i9G5vMzsvmicXW76/YFzsycRfK//1oKgMycoQIgIiIicl0mI/NfEpKtuLg4AgICiI2Nxd/f39Xh3JT4+HgAfH1tu1IheduuE7H0nfs3Z+OT7O7bz30pz3t8g6cpza5+ycDg4EDW+RS2e8xyUQYvLkijeLzdXfMtk5cXFb76ksJ16rg6FBEREXEAZ+YGNl85c3Nzu2FFM5PJRGpq6k0HJSI3djY+iae+3HxVYpb5f1OTdZspy7aWpq286DEfN9Plz2bMhin94pOlrYEp/VqRybrNDLxWMuCqxMxkZLTMOm7mMYskGIz+znxVYma+siFXXOXL5p+dnGwzmdzwcvfCzeSWeWPW9xtsyzLE9fqmL7v5+BA0YoQSMxEREbGJzcnZ999/f81969evZ9asWZjN5mu2ERHHSUkzM3DeVk7FJnJbySL8MKgJ/oU8bet89hB8/BQkGXDXk9BxGmBb6dYF+xfw08bXcDO58V7r92hSpolNQxppaRwf8BQXY9biWbYsFRd+i0exYrbFKyIiIlJA2Jycde7c+aptBw4c4Pnnn+fnn3+mR48evPLKKw4NTkSy9/rSfWw8ch5fbw8+6tXQ9sQsMQ6+6Q5JcVA+FMIm2zzm1tNbmbJpCgBDGgyxOTEDODNjBhfXrsVUuDBl352txExEREQkGzma5+zUqVP079+f2rVrk5qayvbt2/n888+pUKGCo+MTkSt8t+UEc9cdBeDtR+pSJcjPto5mM/zwDJw9AH6l4ZEvwMO2AhWRFyMZHj6cVCOVdhXb0feOvjbHG7dsGec+/gSA0pNep1D16jb3FRERESlI7ErOYmNjGTNmDFWqVGHPnj2sXLmSn3/+mVq1ajkrPhHJZOeJGMZ+vwuA51pXpe0ddszT9edbsH8JuHvDo1+Bb5BN3ZLSkhgePpxzieeoVqwaExtPvOHzpxkSDxzg1AsvAlDiyX74t29ve7wiIiIiBYzNtzVOnTqVN954g5CQEL7++utsb3MUEec5cyGJp77cQnKqmTa3BzG0dVXbOx9YBqsnWZY7vQ1lG9rUzTAMXt/wOrvO7sLfy58ZrWbg4+ljU9+0mBhODByEkZBAkSZNCBw2zPZ4RURERAogm6+cPf/88yQmJlKlShU+//xzunTpku3LkV5++WVMJlOWV+aJrhMTExk4cCAlSpTA19eXrl27cvr06SzHOHbsGB07dsTHx4egoCBGjRqlipKS76SkmRk4fysRsYncFliEtx+th5ubbVevOPsPLB4AGHBXf6j/uM3jfnPgG74/9D1uJjfebPEm5fzK2dTPSE3l5PARpJw4gWe5cpSZ9hYmd3ebxxUREREpiGy+ctarVy+bb2VypDvuuIPff//duu7hcTnkYcOGsXTpUhYuXEhAQACDBg2iS5curF27FoC0tDQ6duxISEgI69atIyIigl69euHp6cmkSZNy/VxEcuq1JXvZlFEApOedOSwA0hja2V4AZHPkZqZumgrA0AZDaVy6sc19o6ZP5+K6dZYCILNn4160qM19RURERAoqm5OzuXPnOjGMa/Pw8CAk5OrnamJjY/n000+ZP38+9957LwBz5szh9ttvZ8OGDTRq1IjffvuNvXv38vvvvxMcHEy9evV49dVXGTNmDC+//DJeXtkXQ0hKSiIp6fLcUXFxcc45OREbLNx8nM/X/wfA9EfrUSXIxgnEzWb4/ik4exD8y8Ajn4O7bUld5MVIRqwZQaqRSvuK7elzRx+b441dupTzn34GZBQAqWZzXxEREZGCLEfVGnPTP//8Q+nSpbntttvo0aMHx44dA2DLli2kpKTQpk0ba9saNWpQvnx51q9fD1jmX6tduzbBwcHWNmFhYcTFxbFnz55rjjl58mQCAgKsr3LlbLuVS8TRdhyP4cUfdgMwpHVV7qsZfIMemfwxFQ78kl4A5Eu7CoAMWz2M84nnqVasGi83ftn2AiD79xPx4ksAlOj/pAqAiIiIiNghTydn99xzD3PnzuXXX3/l/fff58iRIzRr1owLFy4QGRmJl5cXRa+4XSo4OJjIyEgAIiMjsyRmGfsz9l3L2LFjiY2Ntb6OHz/u2BMTsUHWAiDBDLGnAMj+XyA8/RbGTtOhjO0FQF5d/yq7z+0mwDuAma1m2lwAJDU62lIAJDGRIk2bEjh0qO3xioiIiIjttzW6QvtMn7rXqVOHe+65hwoVKvDtt99SuHBhp43r7e2Nt7e3044vciPJqWYGzttKZJylAMj0R+vaXgDkzMH0AiDA3QOgfg+bx/16/9f8ePhHSwGQ5m9S1q+sTf2M1FROjRhBysmTlgIgb72pAiAiIiIidsrTV86uVLRoUapVq8ahQ4cICQkhOTmZmJiYLG1Onz5tfUYtJCTkquqNGevZPccmkle8tnQvm45eLgDiZ3MBkFhLAZDkC1ChCYTZXvhmc+Rm3vz7TQCGNRhGaOlQm/tGvT2di+vWY/LxUQEQERERkRzKV8lZfHw8hw8fplSpUjRs2BBPT09Wrlxp3X/gwAGOHTtGaKjlj8rQ0FB27dpFVFSUtc2KFSvw9/enZs2auR6/iC2+3XycL3JaAGTxU3DuH0sBkIdzWACkUnt639Hb5nhjlyzl/GcqACIiIiJys/L0bY0jR47k/vvvp0KFCpw6dYoJEybg7u7OY489RkBAAP369WP48OEUL14cf39/Bg8eTGhoKI0aNQKgbdu21KxZk549ezJ16lQiIyN56aWXGDhwoG5blDxp+/EYXvreUgBkaBs7C4CseQMOLksvAPIV+Aba1C0xNZGhq4dyPvE81YtVZ2LjibYXANm3j4iXMgqA9Me/XTvb4xURERGRLPJ0cnbixAkee+wxzp07R2BgIE2bNmXDhg0EBlr+6Jw+fTpubm507dqVpKQkwsLCeO+996z93d3dWbJkCc888wyhoaEUKVKE3r1788orr7jqlESuKepCIk9/uYXkNDP31QzmuXvtKQCyFNZMsSzfPwPKNLCpm2EYvLrhVfac20OAdwAzWs2gsIdtz3OmRkdzYtBgSwGQZs0IHDrE9nhFRERE5ComwzAMVweR18XFxREQEEBsbCz+/v6uDuemxMfHA+Dra+OtcpIrklPN9PhkA38fjaZyYBF+GNjE9ufMzhyEj++1PGd291PQYarN487bN48pm6bgZnLjgzYf2PycmZGayrH+/bm0fgOe5ctTaeG3uAcE2DyuiIiISH7lzNxAyZkNlJzl3IXEFGavPsSJ8wkA/L+9O4+Lqt7/B/46wzDDIPu+iympCSqKImrbT+51y8q6acb1qi22aC6YpqV2M7eKzDIz7f7SW5qopaZe0xC39LpcUVyCwHKtBNzYRAFn3t8/BkZGUQcCZoDX8/GYx8z5nM/nnPeRt8r7cWbeI7iRbhUzz+z17eag8vk37739GrnN+I3X9oareDR/OXyv/w7FtMP4rFRYoODmfYLf1aXY1KQQ1xTjFShmV2IW4S0x3Jt5HS2PX4cigL2dCre8qbA8lrInpeIBDHoABkCxBxxcTPOUmy+uwrYixj/nwpICQICmLk3h38TPPFDTOSs8l73WFxSgOCMDiqMjQhOXw+Fefs6MiIiIGofarA1s+m2NVL8ZDIKxKw5jS3r23SfbBMEn9vPwiN3eKq+8YKfCBG8/5Kir/leqU4YBT2w0VBgx3Hbu7akA6AFcrsZaADiFIpyq8qqAmTNZmBERERHVEBZnVGs+3nocW9KzoVGrMO4v90KnufG9V2Z3hio0n1AqHy7bp1S673ZrKs6/+VaU+RrjVstf/oWI9L0wKGr81HIk9PZNbsxWjM9SftSyNQIFeugx68om5Oiz4a9yxV80bUzHlfI4lBvx3IhLgVNOIWI2HAAAXGjvj9KWXmXzFfNzK+VrK1ykopS9VAAXf4id1ngtZecqP7eiqnAsAKiwrVM7oLl7GFQqlfkc0znM/4wqnlvbvDm0YVX4XBwRERER3RGLM6oVSWnZmLvlOABgxuPheCoq2MoR3cUvW4D0uQAAVZ93EdHpeYuXTt87HZkZ2XC2d8aivksR6hpq0Tp9fj5OPTUAJSV6OHbqhO5f/H8o9hZ+zoyIiIiIGpx69T1nVD/8klOIsStSAQBDYprafmF26QTwzbMABIgcDEQ9Z/HS1cdXY0XGCihQMPuB2RYXZmIw4I/xE1By+jTU/v4InPshCzMiIiKiRo7FGdWo/GulGP7VARQWX0fnZh6Y/IiNf9l3cSGQGAdcywMCo4C+H9z6fsrbOHz+MKbvnQ4AGNF+BB4IesDi056fNw+FO3ZA0WoRNG8e1J6e1QqfiIiIiBoOFmdUYwwGQfyKVJw4fwX+rg74NK4D7O1sOMVEgO9eAXLSACdfYOBXgNqyLye/cPUC4rfFo9RQih4hPfBC2xcsPm3+Dz/g4oLPAAD+096GLrxNtcInIiIioobFhn9zpvrmo+Tj2JKeA41ahYWDO8LLybJCx2p2fQikfQeo7IEBXwIuARYtK9WXIn57PHKu5qC5a3PM6D4DKsWyv0rFv/yCcxMnAQDc/zEYro89Vu3wiYiIiKhhYXFGNeKHn7LwUbKxAcjM/hFoG+Rm3YDu5vgWIHma8XXvd4GQLhYvnb1/Ng7lHIKzvTM++n8foYmpq+Od6fPz8duIkTAUFcGxc2f4jh9fnciJiIiIqIFicUZ/2i85hYhfeRgAMLRrKP7WMcjKEd3FxV+Bb8sagHT4BxD1rMVLv838FiszV5oagDR1aWrROtHr8fv48cYGIAFsAEJEREREt2JxRn9K/rVSDP/S2AAkupkH3uzb2toh3VnFBiBBnYA+CVVqADJj3wwAwMjIkVVuAHJlx84bDUA8PKoVPhERERE1XCzOqNoMBsHYxFScuHAFAa4OmF9fGoCcTzc2ABlgeQOQ80XnMXbbWJQaShEbEosXIqrYAOSzhQAA/3emQdeGDUCIiIiI6FY2/Js02bq5yceR/LOxAchn9aIByJwKDUC+Alz8LVpW3gDk/NXzaO7aHNO7T4di4d224uPH8UdZAxCPIf+A66OPVjt8IiIiImrYWJxRtWz+KQsflzUAmVUvGoAkAcnvGF/3eR8IibZ46az9s5B6PrXqDUDy8nB25EhIUREcu3SBDxuAEBEREdEdsDijKjueXYD4FakAgGHdQvFkvWgA8hwAAToOBaKGWbz0m8xvsCpzFRQoePeBd6vcAKT09BnYBwQg8MM5UNTq6sVPRERERI0CizOqkryrpRj+VQqulOjR5R4PvNHH1huAFFRoANIZ6P2exUtTc1JNDUBejXwV9wfdb/Ha8x/Pw5WdP0JxcEDQJ/OgdnevcuhERERE1LiwOCOLGQyCsStScfLCFQS66TD/mXrQAGRteQMQP2Cg5Q1AcopyEL89HtcN1/GXpn/B8xHPW3za/M0/4OLC8gYg78DhvvuqFT4RERERNS42/Js12ZoPt2Ri68850KpVWDi4IzxtvQHIjx8A6euMDUAGfgU4+1m0rERfYmoA0sKtBaZ3s7wByLXMTPwxqawByNChcO33SLXDJyIiIqLGhR+CaUQMBkHq2VxcK9HDsck1GAQQiPFZBFK+bQAEgEHENH4u5zz2b/sBXVSCEQ82R3jJYeAkjHenABhXWLJt+VwxGPDTld9w1VBctksgAEQMZc9SNrcsTgACg3HtpRzYJ6+AiBOk1SPAoYPAwYMQGCAiUAxl5yhbi7KHiODw+cNQZR1CN7UOE6IGQf/jXhSUxycV5peHWh6HCHI+nGtsABLTBT6vjav2z4qIiIiIGh9FxPQbMd1Gfn4+XF1dkZeXBxcXF2uHUy16g+DFrw5g8+HTAAA7jc7itYE4j7XaqfBW8morvFsYAIz39sQPTpZ1RqzIvUAw8996eBbUfFyWsA8IQOi33/BzZkREREQNUG3WBrxz1kjMScrAlvQcaOxUCHLTQevoCFXZW/VUigJFMT6rFABlzypFgVauYfqlj+F9PQ9Falfo3PygwDjHqOy50m3FbOjOc823P1ddwQ/qIqgFCIFd2V4FN82EUmFcAWB3XTBsdTE8C4AinYIrzhqIYtwvZYuk4lsUy65XzA6qwN3BA64ObjdiU4zngaJUGCvfhikyO3c3eI8ezcKMiIiIiKqMxVkj8P3Rc5i/7VcAwPRH26BfZCCcnJzuvlAEWD0cyPkVcPSC4/DtgFtw7QYLYOdvOzE/eSQAYHLXf+LJe5+0aJ2IIGvqVOT+8Q1Urq6IWLUSmpCQ2gyViIiIiKjGsCFIA5eRVYBxqw4DAJ7r3gz9IgMtX7z3U+DoSkCxA55aUieF2am8U3h95+sQCAbcO8DiwgwAclesQO6qbwCVCoEJCSzMiIiIiKheYXHWgOUVlWL4VwdQVKJH1+aemNS7leWLT+wAfphifN1zJtDM8u/4qq7CkkKM3jYahaWFiPSJxMTOEy1eW3TwILJmzAQAeI8dA6f7u9dWmEREREREtYLFWQOlNwhGJR7C6YtFCHTT4ZNnOkBt6XeSXT4NrBoKiB5oNwiIfrFWYwUAgxjw5q43cSLvBHx0Ppjz0BzY29lbtLY0Oxu/jRoNlJbCuXcveD5v+XeSERERERHZChZnDdQHP2RgR+Z5ONgbv5PMo4nGsoUlRcCKOODqJcC/PfDIhxUadtSeRUcWYevZrbBX2ePDhz+El87LonWGkhL8NmoU9BcuQHvvvQiYMcPi7yQjIiIiIrIlLM4aoI1Hz+HT7cYGIO8+2Rbhga6WLRQB1o8Cso4Cjl7A08sAe8tb7lfXjrM78GnqpwCAyV0mo613W4vWiQiypk3DtcNHoHJ1RdD8T6BydKzNUImIiIiIag2LswYmI6sAr5U1AHm+ezM81r4KDUD2zAeOrgJUamDAvwHXoFqK8oaTeScx8ceJEAgGthyIJ8KesHhtbmIi8r751tgA5IMPoAmu/YYlRERERES1hcVZA1KxAUi3Fp6YWKUGINuBpAoNQEJrv6FGxQYgHXw64PVOr1u8tiglxdQAxCd+LJy6d6utMImIiIiI6gSLswZCbxC8WqEByLxBVW0AMgwQA9DuGaDz8NoNFsYGIG/segMn807Cx9EHHzz0geUNQLKy8NvoMcD163Dp0xsezz1Xu8ESEREREdUBFmcNRMIPGdhZ1gBk0T+q2QAkILLOGoAsPLIQ285uMzYAeagKDUCKi/HbqNHGBiAtW8J/+nQ2ACEiIiKiBoHFWQPwnyPnsKBCA5A2AVVoALLuVWMDkCbewMClgL1DLUZqtP3sdlMDkCldplS9AciRsgYgn8xjAxAiIiIiajBYnNVzP2flmxqAvHB/NRqAHPvG2ADkqbprADLpx0kAgIEtB6J/WH+L115evhx53642NgCZwwYgRERERNSwsDirx3KLSjD8yxRcLTU2AHm9V3UbgMwCQmu/ocafagBy4ACyZ84CAPiMi4dTNzYAISIiIqKGhcVZPaU3CF5dfghnLhUhyF2HT6rbAKR9HND5hdoNFsYGIJN2TaqBBiB94PHss7UbLBERERGRFTSq4mz+/PkIDQ2Fg4MDoqOjsX//fmuHVG3vb87Aj8cvGBuADI6Ce1UagHzz/I0GIH3n1E0DkMMLsf3sdmhUGsx9aG7VGoC8Ogr6ixfLGoC8wwYgRERERNQgNZribMWKFYiPj8dbb72FgwcPol27dujZsydycnKsHVqVbTjyBz7bYWwA8t7f2uG+ABfLFooA/xkHnK/bBiDbzmzDp4eNDUAmd5mMCO8Ii9aJCLLenoZrR4/CztUVQfM/YQMQIiIiImqwFBERawdRF6Kjo9GpUyd88sknAACDwYDg4GC8+uqrmDhxotnc4uJiFBcXm7bz8/MRHByMvLw8uLhYWAjVko0zh0C70XjHT6nwKP8hKjf/NCtsKwCK9NcBUeBorwEUFRQRSNm+m9cYj2V+wPLjV1xzY0xuHKfCMhEDAMDRTgdnjXP5oGnNzfPL98FggP7yZUClQsi/PkeTrl1BRERERGRN+fn5cHV1rZXaQF2jR7NRJSUlSElJwaRJk0xjKpUKsbGx2LNnzy3zZ82ahbfffrsuQ7TYhXPZ6HThzxyh/EeuL3vUpau4jqtVW6JSwXfi6yzMiIiIiKjBaxTF2YULF6DX6+Hr62s27uvri59//vmW+ZMmTUJ8fLxpu/zOmS3o8cJr2BGyHk09m0CrtgMUBYqiMj5DMX5+TKUCoAAqBQrK9ill+7ROgNa1bG7ZQcvXAjc+z6WoTB9FU8rf/aoqn64qGy8/hmJ2DOPmjTGVokKgU+CNGMqZTlDxvp1i9mTn6gr7m35uREREREQNUaMozqpKq9VCq9VaO4xKBbaNxTNtY60dBhERERER1bBG0RDEy8sLdnZ2yM7ONhvPzs6Gn5+flaIiIiIiIiK6oVEUZxqNBh07dkRycrJpzGAwIDk5GTExMVaMjIiIiIiIyKjRvK0xPj4eQ4YMQVRUFDp37oy5c+fiypUrGDZsmLVDIyIiIiIiajzF2cCBA3H+/HlMnToVWVlZaN++PTZt2nRLkxAiIiIiIiJraDTfc/Zn1OZ3GRARERERUf1Rm7VBo/jMGRERERERka1jcUZERERERGQDWJwRERERERHZABZnRERERERENoDFGRERERERkQ1gcUZERERERGQDWJwRERERERHZABZnRERERERENoDFGRERERERkQ1QWzuA+kBEABi/DZyIiIiIiBqv8pqgvEaoSSzOLFBQUAAACA4OtnIkRERERERkCwoKCuDq6lqjx1SkNkq+BsZgMOCPP/6As7MzFEWxdjjIz89HcHAwzp49CxcXF2uHQ/UU84hqAvOIagLziGoC84hqgiV5JCIoKChAQEAAVKqa/ZQY75xZQKVSISgoyNph3MLFxYX/+NCfxjyimsA8oprAPKKawDyimnC3PKrpO2bl2BCEiIiIiIjIBrA4IyIiIiIisgEszuohrVaLt956C1qt1tqhUD3GPKKawDyimsA8oprAPKKaYO08YkMQIiIiIiIiG8A7Z0RERERERDaAxRkREREREZENYHFGRERERERkA1icERERERER2QAWZ/XM/PnzERoaCgcHB0RHR2P//v3WDomsZNasWejUqROcnZ3h4+ODxx9/HBkZGWZzrl27hhEjRsDT0xNOTk548sknkZ2dbTbnzJkz6Nu3LxwdHeHj44Px48fj+vXrZnO2b9+ODh06QKvVokWLFliyZEltXx5ZyezZs6EoCsaMGWMaYx6RJX7//Xf8/e9/h6enJ3Q6HSIiInDgwAHTfhHB1KlT4e/vD51Oh9jYWBw/ftzsGJcuXUJcXBxcXFzg5uaG5557DoWFhWZzjhw5gvvvvx8ODg4IDg7Ge++9VyfXR7VPr9djypQpaNasGXQ6HZo3b4533nkHFXvXMY+oMjt37kS/fv0QEBAARVGwdu1as/11mTerVq1Cq1at4ODggIiICGzcuLFqFyNUbyQmJopGo5EvvvhCfvrpJ3nhhRfEzc1NsrOzrR0aWUHPnj1l8eLFcuzYMUlNTZU+ffpISEiIFBYWmua89NJLEhwcLMnJyXLgwAHp0qWLdO3a1bT/+vXrEh4eLrGxsXLo0CHZuHGjeHl5yaRJk0xzTpw4IY6OjhIfHy9paWkyb948sbOzk02bNtXp9VLt279/v4SGhkrbtm1l9OjRpnHmEd3NpUuXpGnTpjJ06FDZt2+fnDhxQjZv3iy//PKLac7s2bPF1dVV1q5dK4cPH5ZHH31UmjVrJlevXjXN6dWrl7Rr10727t0rP/74o7Ro0UIGDRpk2p+Xlye+vr4SFxcnx44dk+XLl4tOp5OFCxfW6fVS7ZgxY4Z4enrKhg0b5OTJk7Jq1SpxcnKSjz76yDSHeUSV2bhxo7z55puyevVqASBr1qwx219XebN7926xs7OT9957T9LS0mTy5Mlib28vR48etfhaWJzVI507d5YRI0aYtvV6vQQEBMisWbOsGBXZipycHAEgO3bsEBGR3Nxcsbe3l1WrVpnmpKenCwDZs2ePiBj/MVOpVJKVlWWas2DBAnFxcZHi4mIREZkwYYK0adPG7FwDBw6Unj171vYlUR0qKCiQsLAwSUpKkgcffNBUnDGPyBKvv/66dO/e/bb7DQaD+Pn5yfvvv28ay83NFa1WK8uXLxcRkbS0NAEg//vf/0xzvv/+e1EURX7//XcREfn000/F3d3dlFfl527ZsmVNXxJZQd++feXZZ581G3viiSckLi5ORJhHZJmbi7O6zJsBAwZI3759zeKJjo6WF1980eL4+bbGeqKkpAQpKSmIjY01jalUKsTGxmLPnj1WjIxsRV5eHgDAw8MDAJCSkoLS0lKznGnVqhVCQkJMObNnzx5ERETA19fXNKdnz57Iz8/HTz/9ZJpT8Rjlc5h3DcuIESPQt2/fW37WzCOyxLp16xAVFYWnnnoKPj4+iIyMxOeff27af/LkSWRlZZnlgKurK6Kjo83yyM3NDVFRUaY5sbGxUKlU2Ldvn2nOAw88AI1GY5rTs2dPZGRk4PLly7V9mVTLunbtiuTkZGRmZgIADh8+jF27dqF3794AmEdUPXWZNzXxfx2Ls3riwoUL0Ov1Zr/8AICvry+ysrKsFBXZCoPBgDFjxqBbt24IDw8HAGRlZUGj0cDNzc1sbsWcycrKqjSnyvfdaU5+fj6uXr1aG5dDdSwxMREHDx7ErFmzbtnHPCJLnDhxAgsWLEBYWBg2b96Ml19+GaNGjcK///1vADfy4E7/h2VlZcHHx8dsv1qthoeHR5VyjeqviRMn4umnn0arVq1gb2+PyMhIjBkzBnFxcQCYR1Q9dZk3t5tTlbxSWzyTiGzWiBEjcOzYMezatcvaoVA9c/bsWYwePRpJSUlwcHCwdjhUTxkMBkRFRWHmzJkAgMjISBw7dgyfffYZhgwZYuXoqL5YuXIlli1bhq+//hpt2rRBamoqxowZg4CAAOYRNRq8c1ZPeHl5wc7O7pYOadnZ2fDz87NSVGQLRo4ciQ0bNmDbtm0ICgoyjfv5+aGkpAS5ublm8yvmjJ+fX6U5Vb7vTnNcXFyg0+lq+nKojqWkpCAnJwcdOnSAWq2GWq3Gjh078PHHH0OtVsPX15d5RHfl7++P++67z2ysdevWOHPmDIAbeXCn/8P8/PyQk5Njtv/69eu4dOlSlXKN6q/x48eb7p5FRERg8ODBGDt2rOmuPvOIqqMu8+Z2c6qSVyzO6gmNRoOOHTsiOTnZNGYwGJCcnIyYmBgrRkbWIiIYOXIk1qxZg61bt6JZs2Zm+zt27Ah7e3uznMnIyMCZM2dMORMTE4OjR4+a/YOUlJQEFxcX0y9aMTExZscon8O8axh69OiBo0ePIjU11fSIiopCXFyc6TXziO6mW7dut3yVR2ZmJpo2bQoAaNasGfz8/MxyID8/H/v27TPLo9zcXKSkpJjmbN26FQaDAdHR0aY5O3fuRGlpqWlOUlISWrZsCXd391q7PqobRUVFUKnMfzW1s7ODwWAAwDyi6qnLvKmR/+ssbh1CVpeYmCharVaWLFkiaWlpMnz4cHFzczPrkEaNx8svvyyurq6yfft2OXfunOlRVFRkmvPSSy9JSEiIbN26VQ4cOCAxMTESExNj2l/eAv2vf/2rpKamyqZNm8Tb27vSFujjx4+X9PR0mT9/PlugN3AVuzWKMI/o7vbv3y9qtVpmzJghx48fl2XLlomjo6MsXbrUNGf27Nni5uYm3333nRw5ckQee+yxSltZR0ZGyr59+2TXrl0SFhZm1so6NzdXfH19ZfDgwXLs2DFJTEwUR0dHtkBvIIYMGSKBgYGmVvqrV68WLy8vmTBhgmkO84gqU1BQIIcOHZJDhw4JAJkzZ44cOnRITp8+LSJ1lze7d+8WtVotCQkJkp6eLm+99RZb6Td08+bNk5CQENFoNNK5c2fZu3evtUMiKwFQ6WPx4sWmOVevXpVXXnlF3N3dxdHRUfr37y/nzp0zO86pU6ekd+/eotPpxMvLS8aNGyelpaVmc7Zt2ybt27cXjUYj99xzj9k5qOG5uThjHpEl1q9fL+Hh4aLVaqVVq1ayaNEis/0Gg0GmTJkivr6+otVqpUePHpKRkWE25+LFizJo0CBxcnISFxcXGTZsmBQUFJjNOXz4sHTv3l20Wq0EBgbK7Nmza/3aqG7k5+fL6NGjJSQkRBwcHOSee+6RN99806x1OfOIKrNt27ZKfycaMmSIiNRt3qxcuVLuvfde0Wg00qZNG/nPf/5TpWtRRCp87ToRERERERFZBT9zRkREREREZANYnBEREREREdkAFmdEREREREQ2gMUZERERERGRDWBxRkREREREZANYnBEREREREdkAFmdEREREREQ2gMUZERERERGRDWBxRkREjc6pU6egKApSU1Nr/VxLliyBm5tbrZ+HiIjqPxZnRERkc4YOHQpFUW559OrVy9qh3VFoaCjmzp1rNjZw4EBkZmZaJyAiIqpX1NYOgIiIqDK9evXC4sWLzca0Wq2Voqk+nU4HnU5n7TCIiKge4J0zIiKySVqtFn5+fmYPd3d3PPPMMxg4cKDZ3NLSUnh5eeHLL78EAGzatAndu3eHm5sbPD098cgjj+DXX3+97bkqe+vh2rVroSiKafvXX3/FY489Bl9fXzg5OaFTp07YsmWLaf9DDz2E06dPY+zYsaY7fbc79oIFC9C8eXNoNBq0bNkSX331ldl+RVHwr3/9C/3794ejoyPCwsKwbt060/7Lly8jLi4O3t7e0Ol0CAsLu6WQJSKi+ofFGRER1StxcXFYv349CgsLTWObN29GUVER+vfvDwC4cuUK4uPjceDAASQnJ0OlUqF///4wGAzVPm9hYSH69OmD5ORkHDp0CL169UK/fv1w5swZAMDq1asRFBSEadOm4dy5czh37lylx1mzZg1Gjx6NcePG4dixY3jxxRcxbNgwbNu2zWze22+/jQEDBuDIkSPo06cP4uLicOnSJQDAlClTkJaWhu+//x7p6elYsGABvLy8qn1tRERkG/i2RiIiskkbNmyAk5OT2dgbb7yBCRMmoEmTJlizZg0GDx4MAPj666/x6KOPwtnZGQDw5JNPmq374osv4O3tjbS0NISHh1crnnbt2qFdu3am7XfeeQdr1qzBunXrMHLkSHh4eMDOzg7Ozs7w8/O77XESEhIwdOhQvPLKKwCA+Ph47N27FwkJCXj44YdN84YOHYpBgwYBAGbOnImPP/4Y+/fvR69evXDmzBlERkYiKioKgPGzbkREVP/xzhkREdmkhx9+GKmpqWaPl156CWq1GgMGDMCyZcsAGO+Sfffdd4iLizOtPX78OAYNGoR77rkHLi4upuKl/C5XdRQWFuK1115D69at4ebmBicnJ6Snp1f5mOnp6ejWrZvZWLdu3ZCenm421rZtW9PrJk2awMXFBTk5OQCAl19+GYmJiWjfvj0mTJiA//73v9W8KiIisiW8c0ZERDapSZMmaNGiRaX74uLi8OCDDyInJwdJSUnQ6XRmnRz79euHpk2b4vPPP0dAQAAMBgPCw8NRUlJS6fFUKhVExGystLTUbPu1115DUlISEhIS0KJFC+h0Ovztb3+77TH/LHt7e7NtRVFMb8vs3bs3Tp8+jY0bNyIpKQk9evTAiBEjkJCQUCuxEBFR3eCdMyIiqne6du2K4OBgrFixAsuWLcNTTz1lKmYuXryIjIwMTJ48GT169EDr1q1x+fLlOx7P29sbBQUFuHLlimns5u9A2717N4YOHYr+/fsjIiICfn5+OHXqlNkcjUYDvV5/x3O1bt0au3fvvuXY9913312u+taYhwwZgqVLl2Lu3LlYtGhRldYTEZHt4Z0zIiKyScXFxcjKyjIbU6vVpsYXzzzzDD777DNkZmaaNdNwd3eHp6cnFi1aBH9/f5w5cwYTJ06847mio6Ph6OiIN954A6NGjcK+ffuwZMkSszlhYWFYvXo1+vXrB0VRMGXKlFsajISGhmLnzp14+umnodVqK23SMX78eAwYMACRkZGIjY3F+vXrsXr1arPOj3czdepUdOzYEW3atEFxcTE2bNiA1q1bW7yeiIhsE++cERGRTdq0aRP8/f3NHt27dzftj4uLQ1paGgIDA80+w6VSqZCYmIiUlBSEh4dj7NixeP/99+94Lg8PDyxduhQbN25EREQEli9fjn/+859mc+bMmQN3d3d07doV/fr1Q8+ePdGhQwezOdOmTcOpU6fQvHlzeHt7V3quxx9/HB999BESEhLQpk0bLFy4EIsXL8ZDDz1k8Z+NRqPBpEmT0LZtWzzwwAOws7NDYmKixeuJiMg2KXLzm+yJiIiIiIiozvHOGRERERERkQ1gcUZERERERGQDWJwRERERERHZABZnRERERERENoDFGRERERERkQ1gcUZERERERGQDWJwRERERERHZABZnRERERERENoDFGRERERERkQ1gcUZERERERGQDWJwRERERERHZgP8DcNGLbzYb2VcAAAAASUVORK5CYII=", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "generate_plots()" ] } ], diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index 018993c6..2d739bf8 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -43,7 +43,7 @@ "\n", "> In our work, the mutations would be the arms, and this update would be used during the evolution to adjust the mutation probabilities.\n", "\n", - "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user.\n" + "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user." ] }, { @@ -54,14 +54,15 @@ "source": [ "import numpy as np\n", "\n", - "# TODO: Maybe I could create a base class to be inherited by different learners\n", "class D_TS:\n", " def __init__(self, num_bandits, C=100):\n", " self.num_bandits = num_bandits\n", "\n", - " # Store tuples when update is called. Tuples will have 3 values:\n", - " # (time instant t, arm idx, reward)\n", - " self.pull_history = [] \n", + " # Store learner status when the update function is called\n", + " self.pull_history = {\n", + " c:[] for c in ['t', 'arm idx', 'reward', 'update'] + \n", + " [f'alpha {i}' for i in range(num_bandits)] + \n", + " [f'beta {i}' for i in range(num_bandits)]} \n", "\n", " # This is the probability that should be used to update brush probs\n", " self._probabilities = np.ones(num_bandits)/num_bandits\n", @@ -97,16 +98,26 @@ " return arm_idx\n", " \n", " def update(self, arm_idx, reward):\n", - " self.pull_history.append( (len(self.pull_history), arm_idx, reward) )\n", + " self.pull_history['t'].append( len(self.pull_history['t']) )\n", + " self.pull_history['arm idx'].append( arm_idx )\n", + " self.pull_history['reward'].append( reward )\n", + " \n", + " for i in range(self.num_bandits):\n", + " self.pull_history[f'alpha {i}'].append( self._alphas[i] )\n", + " self.pull_history[f'beta {i}'].append( self._betas[i] )\n", "\n", " if self._alphas[arm_idx] + self._betas[arm_idx] < self.C:\n", " # This is the pure thompson scheme\n", " self._alphas[arm_idx] = self._alphas[arm_idx]+reward\n", - " self._betas[arm_idx] = self._betas[arm_idx] + (1-reward)\n", + " self._betas[arm_idx] = self._betas[arm_idx]+(1-reward)\n", + "\n", + " self.pull_history['update'].append( 0 )\n", " else:\n", " # This is the dynamic adjust\n", " self._alphas[arm_idx] = (self._alphas[arm_idx]+reward)*(self.C/(self.C+1))\n", - " self._betas[arm_idx] = (self._betas[arm_idx] + (1-reward))*(self.C/(self.C+1))\n", + " self._betas[arm_idx] = (self._betas[arm_idx]+(1-reward))*(self.C/(self.C+1))\n", + "\n", + " self.pull_history['update'].append( 1 )\n", "\n", " return self" ] @@ -121,22 +132,23 @@ "output_type": "stream", "text": [ "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 10.0, 1: 46.0, 2: 46.0, 3: 54.0}\n", - "number of pulls for each arm: {0: 94, 1: 298, 2: 305, 3: 303}\n", + "cum. reward for each arm : {0: 448, 1: 366, 2: 427, 3: 303}\n", + "number of pulls for each arm: {0: 2821, 2: 2682, 1: 2461, 3: 2036}\n", "(it was expected: similar amount of pulls for each arm)\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 812.0, 1: 2.0, 2: 3.0, 3: 1.0}\n", - "number of pulls for each arm: {0: 977, 1: 9, 2: 9, 3: 5}\n", + "cum. reward for each arm : {0: 8406, 1: 7, 2: 4, 3: 0}\n", + "number of pulls for each arm: {0: 9969, 1: 16, 2: 10, 3: 5}\n", "(it was expected: more pulls for first arm, less pulls for last)\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 4.0, 1: 318.0, 2: 2.0, 3: 543.0}\n", - "number of pulls for each arm: {0: 8, 1: 364, 2: 5, 3: 623}\n", + "cum. reward for each arm : {0: 25, 1: 5012, 2: 11, 3: 3361}\n", + "number of pulls for each arm: {1: 5919, 3: 4022, 0: 39, 2: 20}\n", "(it was expected: 2nd approx 4th > 1st > 3rd)\n" ] } ], "source": [ "# Sanity checks\n", + "import pandas as pd\n", "\n", "class Bandits:\n", " def __init__(self, reward_prob):\n", @@ -149,7 +161,7 @@ " result = np.random.randn()\n", " \n", " # return a positive or nullary reward (Bernoulli random variable).\n", - " return 1.0 if result > self.reward_prob[arm_idx] else 0.0\n", + " return 1 if result > self.reward_prob[arm_idx] else 0\n", "\n", "for probs, descr, expec in [\n", " (np.array([ 1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs' , 'similar amount of pulls for each arm' ),\n", @@ -161,17 +173,16 @@ " print(\"------------------------ optimizing ------------------------\")\n", "\n", " learner = D_TS(4)\n", - " for i in range(1000):\n", + " for i in range(10000):\n", " arm_idx = learner.choose_arm()\n", " reward = bandits.pull(arm_idx)\n", "\n", " learner.update(arm_idx, reward) \n", "\n", - " total_rewards = {arm_idx : sum([r for (t, i, r) in learner.pull_history if i==arm_idx])\n", - " for arm_idx in range(learner.num_bandits)}\n", + " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", "\n", - " total_pulls = {arm_idx : sum([1 for (t, i, r) in learner.pull_history if i==arm_idx])\n", - " for arm_idx in range(learner.num_bandits)}\n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", "\n", " print(\"cum. reward for each arm : \", total_rewards)\n", " print(\"number of pulls for each arm: \", total_pulls)\n", @@ -194,13 +205,26 @@ " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", "\n", + " # mutations optimized by the learner. Learner arms correspond to\n", + " # these mutations in the order they appear here\n", + " self.mutations_ = ['point', 'insert', 'delete', 'toggle_weight']\n", + "\n", + " # Whether the learner should update after each mutation, or if it should\n", + " # update only after a certain number of evaluations.\n", + " # Otherwise, it will\n", + " # store all rewards in gen_rewards_ (which is reseted at the beggining\n", + " # of every generation) and do a batch of updates only after finishing\n", + " # mutating the solutions.\n", + " self.batch_size_ = self.pop_size*2 #\n", + " self.batch_rewards_ = []\n", + "\n", " def _mutate(self, ind1):\n", " # Overriding the mutation so it updates our sampling method. Doing the\n", " # logic on the python-side for now.\n", "\n", " # Creating a wrapper for mutation to be able to control what is happening\n", " # in the C++ code (this should be prettier in a future implementation)\n", - " mutations = ['point', 'insert', 'delete', 'toggle_weight']\n", + " \n", " params = self.get_params()\n", " \n", " ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", @@ -210,12 +234,12 @@ " # is already at maximum size.\n", " # In this case, we'll do the mutation without controlling the probabilities.\n", " if ignore_this_time:\n", - " for i, m in enumerate(mutations):\n", + " for i, m in enumerate(self.mutations_):\n", " params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", " else:\n", " mutation_idx = self.learner_.choose_arm()\n", "\n", - " for i, m in enumerate(mutations):\n", + " for i, m in enumerate(self.mutations_):\n", " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", "\n", " _brush.set_params(params)\n", @@ -234,7 +258,12 @@ " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", " \n", " if not ignore_this_time:\n", - " self.learner_.update(mutation_idx, reward)\n", + " self.batch_rewards_.append( (mutation_idx, reward) )\n", + "\n", + " if len(self.batch_rewards_) > self.batch_size_:\n", + " for (mutation_idx, reward) in self.batch_rewards_:\n", + " self.learner_.update(mutation_idx, reward)\n", + " self.batch_rewards_ = []\n", " \n", " return offspring\n", " \n", @@ -251,7 +280,7 @@ "\n", " # We have 4 different mutations, and the learner will learn to choose\n", " # between these options by maximizing the reward when using each one\n", - " self.learner_ = D_TS(4)\n", + " self.learner_ = D_TS(4, C=self.pop_size*3)\n", "\n", " if isinstance(self.functions, list):\n", " self.functions_ = {k:1.0 for k in self.functions}\n", @@ -266,6 +295,7 @@ " self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", "\n", " self.archive_ = archive\n", + " self.logbook_ = logbook\n", " self.best_estimator_ = self.archive_[0].prg\n", "\n", " return self\n", @@ -287,7 +317,7 @@ " self.search_space_.make_classifier(self.max_depth, self.max_size)\n", " if self.n_classes_ == 2 else\n", " self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size)\n", - " )\n", + " )\n", "\n", " def predict_proba(self, X):\n", " data = self._make_data(X)\n", @@ -328,92 +358,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[ nan 20.715]\t[ nan 0.85074967]\t[nan 20.]\n", - "1 \t200 \t[ nan 11.905]\t[ nan 7.09408028]\t[nan 1.]\n", - "2 \t200 \t[ nan 2.655] \t[ nan 2.14615354]\t[nan 1.]\n", - "3 \t200 \t[5.25970482 1.03 ]\t[1.21317177 0.17058722]\t[2.73836112 1. ]\n", - "4 \t200 \t[4.47344586 1.02 ]\t[1.05184037 0.14 ]\t[2.73836112 1. ]\n", - "5 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "6 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "7 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "8 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "9 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "10 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "11 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "12 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "13 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "14 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "15 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "16 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "17 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "18 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "19 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "20 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "21 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "22 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "23 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "24 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "25 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "26 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "27 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "28 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "29 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "30 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "31 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "32 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "33 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "34 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "35 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "36 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "37 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "38 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "39 \t200 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "Final population hypervolume is 49363.883813\n", - "best model: Square(0.96*x1)\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[ nan 20.755]\t[ nan 0.92464858]\t[nan 19.]\n", - "1 \t0 \t[ nan 15.015]\t[ nan 5.92408432]\t[nan 1.]\n", - "2 \t0 \t[ nan 7.255] \t[ nan 4.40567532]\t[nan 1.]\n", - "3 \t0 \t[ nan 2.905] \t[ nan 1.60498442]\t[nan 1.]\n", - "4 \t0 \t[ nan 1.51] \t[ nan 0.66324958]\t[nan 1.]\n", - "5 \t0 \t[4.98724882 1.035 ]\t[1.25116115 0.18377976]\t[2.61403799 1. ]\n", - "6 \t0 \t[4.35035535 1.02 ]\t[0.98937437 0.14 ]\t[2.60900354 1. ]\n", - "7 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "8 \t0 \t[3.8520625 1.02 ] \t[0.17104111 0.17204651]\t[2.21670485 1. ]\n", - "9 \t0 \t[3.8520625 1.02 ] \t[0.17104111 0.17204651]\t[2.21670485 1. ]\n", - "10 \t0 \t[3.8520625 1.02 ] \t[0.17104111 0.17204651]\t[2.21670485 1. ]\n", - "11 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "12 \t0 \t[3.85330723 1.02 ]\t[0.15966286 0.17204651]\t[2.46565056 1. ]\n", - "13 \t0 \t[3.8293101 1.065 ] \t[0.25177144 0.41324932]\t[1.90340519 1. ]\n", - "14 \t0 \t[3.82658499 1.065 ]\t[0.27452376 0.41324932]\t[1.3583827 1. ] \n", - "15 \t0 \t[3.81947242 1.08 ]\t[0.29115356 0.4621688 ]\t[1.3583827 1. ] \n", - "16 \t0 \t[3.79071911 1.13 ]\t[0.40600859 0.68051451]\t[0.63692158 1. ]\n", - "17 \t0 \t[3.80689942 1.1 ]\t[0.33894367 0.53851648]\t[1.3583827 1. ] \n", - "18 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "19 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "20 \t0 \t[3.85375515 1.02 ]\t[0.15584938 0.17204651]\t[2.55523491 1. ]\n", - "21 \t0 \t[3.84619466 1.035 ]\t[0.18782827 0.27161554]\t[2.45047045 1. ]\n", - "22 \t0 \t[3.83204543 1.06 ]\t[0.23311332 0.36932371]\t[2.45047045 1. ]\n", - "23 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "24 \t0 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", - "25 \t0 \t[3.83913395 1.045 ]\t[0.21171389 0.30491802]\t[2.44566083 1. ]\n", - "26 \t0 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "27 \t0 \t[3.86003045 1.02 ]\t[0.12892129 0.22271057]\t[2.54631495 1. ]\n", - "28 \t0 \t[3.85962713 1.015 ]\t[0.13308933 0.15740076]\t[2.46565056 1. ]\n", - "29 \t0 \t[3.85259046 1.025 ]\t[0.16546364 0.21065374]\t[2.46565056 1. ]\n", - "30 \t0 \t[3.83358411 1.045 ]\t[0.31383125 0.35067791]\t[0.07171333 1. ]\n", - "31 \t0 \t[3.8272642 1.05 ] \t[0.32548479 0.35707142]\t[0.07171333 1. ]\n", - "32 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", - "33 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", - "34 \t0 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", - "35 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "36 \t0 \t[3.85331105 1.025 ]\t[0.1596296 0.23318448]\t[2.46641684 1. ]\n", - "37 \t0 \t[3.84627056 1.035 ]\t[0.18726651 0.27161554]\t[2.46565032 1. ]\n", - "38 \t0 \t[3.84621458 1.035 ]\t[0.18768039 0.27161554]\t[2.45445561 1. ]\n", - "39 \t0 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "Final population hypervolume is 49370.222358\n" + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" ] }, { @@ -437,15 +382,19 @@ " \n", " \n", " Brush version\n", - " Original\n", - " Modified\n", + " Original\n", + " Modified\n", " \n", " \n", " metric\n", " score\n", " best model\n", + " size\n", + " depth\n", " score\n", " best model\n", + " size\n", + " depth\n", " point mutation calls\n", " insert mutation calls\n", " delete mutation calls\n", @@ -455,470 +404,590 @@ " \n", " \n", " run 0\n", + " 0.350809\n", + " If(x1>0.91,Abs(1.61),0.38)\n", + " 4\n", + " 2\n", " 0.326358\n", " 1.04*Cos(1.73*x2)\n", - " 0.533820\n", - " 0.99*Median(2.01,1.24,-2.40*x2,1.25*x1)\n", - " 1496\n", - " 3919\n", - " 1862\n", - " 372\n", + " 2\n", + " 1\n", + " 7128\n", + " 1417\n", + " 589\n", + " 514\n", " \n", " \n", " run 1\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", " 0.326358\n", " 1.04*Cos(1.73*x2)\n", - " 0.377649\n", - " 0.49*Tan(If(x1>0.91,4.41,-0.83*x1))\n", - " 806\n", - " 2347\n", - " 1501\n", - " 2987\n", + " 2\n", + " 1\n", + " 5227\n", + " 2662\n", + " 1219\n", + " 540\n", " \n", " \n", " run 2\n", - " 0.338504\n", - " 0.01*Cosh(5.22*x1)\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 962\n", - " 3049\n", - " 2150\n", - " 1465\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", + " 0.363372\n", + " If(x1>0.91,1.26*Square(1.20*x1),-0.52*x1)\n", + " 4\n", + " 2\n", + " 4775\n", + " 2383\n", + " 1520\n", + " 970\n", " \n", " \n", " run 3\n", " 0.325058\n", - " Cos(1.72*x2)\n", - " 0.454707\n", - " Abs(Median(If(x1>0.91,3.06,-1.00*x1),0.16))\n", - " 650\n", - " 3136\n", - " 2134\n", - " 1696\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.386766\n", + " Mean(1.30*Cos(3.86*x2),-1.40*x2,3.38,0.21*x1)\n", + " 6\n", + " 2\n", + " 3944\n", + " 2698\n", + " 1804\n", + " 1202\n", " \n", " \n", " run 4\n", - " 0.338883\n", - " 0.02*Cosh(5.01*x1)\n", - " 0.367291\n", - " 1.04*Cos(Mean(2.51*x2,0.83,1.78*x2))\n", - " 804\n", - " 3285\n", - " 2575\n", - " 992\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.508543\n", + " Median(2.01,-1.94*x2,1.27*x1,1.27)\n", + " 5\n", + " 1\n", + " 5094\n", + " 3135\n", + " 727\n", + " 692\n", " \n", " \n", " run 5\n", " 0.325058\n", " Cos(-1.72*x2)\n", - " 0.366009\n", - " Mean(3.98*Cos(1.36*x2),-0.67*x2,0.22*x1,-0.66*x2)\n", - " 197\n", - " 3940\n", - " 1972\n", - " 1512\n", + " 2\n", + " 1\n", + " 0.421938\n", + " Mean(2.23*Cos(Max(1.63*x2,-16.06*x2,x2,x2)),-0...\n", + " 8\n", + " 3\n", + " 4948\n", + " 2422\n", + " 1382\n", + " 896\n", " \n", " \n", " run 6\n", - " 0.326358\n", - " 1.04*Cos(-1.73*x2)\n", - " 0.362638\n", - " Sum(0.98*Cos(1.36*x2),-0.36*x2)\n", - " 1507\n", - " 4058\n", - " 676\n", - " 1397\n", + " 0.198205\n", + " Abs(0.74*x1)\n", + " 2\n", + " 1\n", + " 0.480289\n", + " If(x1>0.91,1.61,0.81*Logabs(-2.30*x1))\n", + " 4\n", + " 2\n", + " 5758\n", + " 1768\n", + " 1290\n", + " 832\n", " \n", " \n", " run 7\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 1743\n", - " 4245\n", - " 1449\n", - " 180\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.367344\n", + " 0.95*Cos(Max(1.75*x2,Acos(1.25*Cos(1.52*x2))))\n", + " 6\n", + " 4\n", + " 5476\n", + " 2085\n", + " 1068\n", + " 1019\n", " \n", " \n", " run 8\n", + " 0.113124\n", + " Sqrtabs(0.40*x1)\n", + " 2\n", + " 1\n", " 0.292958\n", - " Square(0.96*x1)\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 1623\n", - " 2265\n", - " 2329\n", - " 1445\n", + " 0.91*Square(x1)\n", + " 2\n", + " 1\n", + " 5025\n", + " 1906\n", + " 1760\n", + " 957\n", " \n", " \n", " run 9\n", - " 0.350809\n", - " If(x1>0.91,5.00*x2,0.38)\n", - " 0.551571\n", - " Logistic(218.18*Cos(3.35*x2))\n", - " 1087\n", - " 4377\n", - " 964\n", - " 1211\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.363372\n", + " If(x1>0.91,1.61,-0.52*x1)\n", + " 3\n", + " 1\n", + " 3751\n", + " 2401\n", + " 2126\n", + " 1370\n", " \n", " \n", " run 10\n", - " 0.431209\n", - " Logistic(156.42*Logabs(-1.12*x1))\n", - " 0.473042\n", - " Logabs(If(x1>0.91,5.00,-2.10*x1))\n", - " 928\n", - " 4388\n", - " 1710\n", - " 633\n", + " 0.289662\n", + " 1.54*Logistic(-1.98*x2)\n", + " 2\n", + " 1\n", + " 0.550445\n", + " Logistic(Add(18.67*Cos(3.17*x2),-3.09*x1))\n", + " 5\n", + " 3\n", + " 4823\n", + " 3227\n", + " 1270\n", + " 328\n", " \n", " \n", " run 11\n", " 0.325058\n", " Cos(1.72*x2)\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 1162\n", - " 4246\n", - " 976\n", - " 1264\n", + " 2\n", + " 1\n", + " 0.418151\n", + " Square(Sum(1.20*Cos(1.82*x2),-0.37*x2))\n", + " 5\n", + " 3\n", + " 5260\n", + " 2472\n", + " 988\n", + " 928\n", " \n", " \n", " run 12\n", " 0.314972\n", " 0.51*Acos(1.10*x2)\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 2277\n", - " 2539\n", - " 1769\n", - " 1022\n", + " 2\n", + " 1\n", + " 0.356233\n", + " 1.02*Cos(Max(1.77*x2,0.55,x2))\n", + " 5\n", + " 2\n", + " 4603\n", + " 2260\n", + " 2043\n", + " 742\n", " \n", " \n", " run 13\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 1145\n", - " 3318\n", - " 1058\n", - " 2098\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.964814\n", + " 1.65*Cos(Mean(2.12*x2,1.39*x2,-3.00*x1))\n", + " 5\n", + " 2\n", + " 4554\n", + " 2295\n", + " 1636\n", + " 1163\n", " \n", " \n", " run 14\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", - " 0.508543\n", - " Median(2.01,1.27*x1,1.27,-1.94*x2)\n", - " 2155\n", - " 3152\n", - " 1505\n", - " 827\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", + " 0.363372\n", + " If(x1>0.91,1.61,-0.52*x1)\n", + " 3\n", + " 1\n", + " 4269\n", + " 2344\n", + " 1757\n", + " 1278\n", " \n", " \n", " run 15\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 1346\n", - " 4153\n", - " 568\n", - " 1559\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", + " 0.999862\n", + " Square(Mean(1.99*x1,2.03*x1,3.96*x2,0.02))\n", + " 6\n", + " 2\n", + " 5829\n", + " 1868\n", + " 1223\n", + " 728\n", " \n", " \n", " run 16\n", - " 0.363372\n", - " If(x1>0.91,5.00*x2,-0.52*x1)\n", - " 0.972920\n", - " If(x1>0.91,1.61,Median(2.00*x1,-3.75*x2,-2.11*...\n", - " 1405\n", - " 2305\n", - " 2229\n", - " 1700\n", + " 0.028410\n", + " Logistic(0.57*x1)\n", + " 2\n", + " 1\n", + " 0.958755\n", + " 1.63*Cos(Mean(2.82*x2,2.08*x2,-0.15,-4.02*x1))\n", + " 6\n", + " 2\n", + " 5981\n", + " 1783\n", + " 1083\n", + " 801\n", " \n", " \n", " run 17\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.675277\n", - " Sum(Median(1.20,1.38,-2.49*x2,2.80*x1),-0.33*x1)\n", - " 1193\n", - " 4250\n", - " 1202\n", - " 1016\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.292958\n", + " 0.91*Square(x1)\n", + " 2\n", + " 1\n", + " 4637\n", + " 2988\n", + " 1206\n", + " 817\n", " \n", " \n", " run 18\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", " 0.326358\n", " 1.04*Cos(1.73*x2)\n", - " 1953\n", - " 3336\n", - " 749\n", - " 1589\n", + " 2\n", + " 1\n", + " 5396\n", + " 2485\n", + " 1466\n", + " 301\n", " \n", " \n", " run 19\n", " 0.325058\n", " Cos(1.72*x2)\n", - " 0.670912\n", - " Mean(Median(2.85,4.99*x1,-4.75*x2,2.29),-0.86*x1)\n", - " 388\n", - " 5667\n", - " 654\n", - " 939\n", + " 2\n", + " 1\n", + " 0.367291\n", + " 1.04*Cos(Add(1.43*x2,0.28))\n", + " 4\n", + " 2\n", + " 4867\n", + " 2052\n", + " 1463\n", + " 1266\n", " \n", " \n", " run 20\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 733\n", - " 4463\n", - " 678\n", - " 1730\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", + " 0.350809\n", + " If(x1>0.91,1.61,0.38)\n", + " 3\n", + " 1\n", + " 5582\n", + " 2007\n", + " 1076\n", + " 983\n", " \n", " \n", " run 21\n", - " 0.314972\n", - " 0.51*Acos(1.10*x2)\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", " 0.326358\n", " 1.04*Cos(1.73*x2)\n", - " 1271\n", - " 3440\n", - " 629\n", - " 2301\n", + " 2\n", + " 1\n", + " 5669\n", + " 1997\n", + " 1594\n", + " 388\n", " \n", " \n", " run 22\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.350809\n", - " If(x1>0.91,1.61,0.38)\n", - " 667\n", - " 3946\n", - " 1886\n", - " 1143\n", + " 0.325058\n", + " Cos(1.72*x2)\n", + " 2\n", + " 1\n", + " 0.363372\n", + " If(x1>0.91,1.61,-0.52*x1)\n", + " 3\n", + " 1\n", + " 5321\n", + " 1654\n", + " 1343\n", + " 1330\n", " \n", " \n", " run 23\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 1374\n", - " 4400\n", - " 1076\n", - " 778\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.490733\n", + " Prod(If(x1>0.91,2.24,0.94*x1),0.76*x1)\n", + " 5\n", + " 2\n", + " 4147\n", + " 2287\n", + " 2216\n", + " 998\n", " \n", " \n", " run 24\n", - " 0.397507\n", - " Square(Sin(4.25*x2))\n", - " 0.326358\n", - " 1.04*Cos(1.73*x2)\n", - " 2418\n", - " 3752\n", - " 1319\n", - " 142\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.585066\n", + " Logistic(-102.47*Cos(Sum(4.53*x2,-4.33*x1)))\n", + " 5\n", + " 3\n", + " 5467\n", + " 2174\n", + " 1294\n", + " 713\n", " \n", " \n", " run 25\n", - " 0.198205\n", - " Abs(0.74*x1)\n", - " 0.363372\n", - " If(x1>0.91,1.61,-0.52*x1)\n", - " 2025\n", - " 3086\n", - " 1172\n", - " 1353\n", + " 0.325058\n", + " Cos(-1.72*x2)\n", + " 2\n", + " 1\n", + " 0.406220\n", + " Sqrtabs(If(x1>0.91,2.59,-0.23*x1))\n", + " 4\n", + " 2\n", + " 7325\n", + " 1493\n", + " 448\n", + " 382\n", " \n", " \n", " run 26\n", - " 0.397507\n", - " Square(Sin(-4.25*x2))\n", - " 0.742135\n", - " Median(2.77,0.82,1.07*x1,Median(-7.40*x2,-2.47...\n", - " 769\n", - " 4101\n", - " 1282\n", - " 1491\n", + " 0.275650\n", + " Logabs(2.31*x1)\n", + " 2\n", + " 1\n", + " 0.452430\n", + " 0.95*Logistic(60.19*Cos(3.29*x2))\n", + " 3\n", + " 2\n", + " 5717\n", + " 1610\n", + " 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", - "text/plain": [ - "
" + "Brush version \n", + "metric delete mutation calls toggle_weight mutation calls \n", + "count 30.000000 30.000000 \n", + "mean 1373.066667 825.533333 \n", + "std 425.113529 318.965000 \n", + "min 448.000000 153.000000 \n", + "25% 1113.750000 697.250000 \n", + "50% 1319.000000 846.500000 \n", + "75% 1625.500000 994.250000 \n", + "max 2216.000000 1370.000000 " ] }, "metadata": {}, @@ -1087,7 +1182,6 @@ "source": [ "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", "if __name__ == '__main__':\n", - " import pandas as pd\n", " from brush import BrushRegressor\n", " \n", " import warnings\n", @@ -1106,16 +1200,19 @@ " y = data['target']\n", "\n", " kwargs = {\n", - " 'pop_size' : 200,\n", - " 'max_gen' : 40,\n", + " 'verbosity' : False,\n", + " 'pop_size' : 100,\n", + " 'max_gen' : 100,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", " }\n", "\n", - " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", + " ('Original', 'size'), ('Original', 'depth'), \n", " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), \n", " ('Modified', 'point mutation calls'),\n", " ('Modified', 'insert mutation calls'),\n", " ('Modified', 'delete mutation calls'),\n", @@ -1126,38 +1223,172 @@ " for i in range(30):\n", " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", "\n", " est = BrushRegressor(**kwargs).fit(X,y)\n", " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", "\n", - " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", - " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", + " \n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " \n", + " results.loc[f'run {i}'] = [\n", + " # Original implementation\n", + " est.score(X,y), est.best_estimator_.get_model(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + "\n", + " # Implementation using Dynamic Thompson Sampling\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " \n", + " # Mutation count\n", + " *total_pulls.values()]\n", " \n", - " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", " except Exception as e:\n", " print(e)\n", "\n", - " display(df)\n", - " display(df.describe())\n", + " # Showing results and statistics\n", + " display(results)\n", + " display(results.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def generate_plots():\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + " \n", + " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", + " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", + " for i, row in learner_log.iterrows():\n", + " data[i+1, :] = data[i]\n", + " data[i+1, row['arm idx'].astype(int)] += 1\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + "\n", + " plt.plot(data, label=est_mab.mutations_)\n", + " plt.xlabel(\"Evaluations\")\n", + " plt.ylabel(\"Number of times mutation was used\")\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # --------------------------------------------------------------------------\n", + " fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", + "\n", + " for i, col in enumerate(['alpha', 'beta']):\n", + " columns = learner_log.columns[learner_log.columns.str.startswith(f'{col} ')]\n", + " labels = [columns[i].replace(str(i), est_mab.mutations_[i]) for i in range(4)] \n", + " data = learner_log.loc[:, columns]\n", + "\n", + " axs[i].plot(data, label=labels)\n", + " axs[i].set_xlabel(\"Evaluations\")\n", + " axs[i].set_ylabel(f\"{col}s\")\n", + " axs[i].legend()\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " axs[i].vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + "\n", + " plt.show()\n", + "\n", + " # Approximating the percentage of usage for each generation ----------------\n", + " data = np.zeros( (kwargs['max_gen'], 4) )\n", + " for g in range(kwargs['max_gen']):\n", + " idx_start = g*(learner_log.shape[0]%kwargs['max_gen'])\n", + " idx_end = (g+1)*(learner_log.shape[0]%kwargs['max_gen'])\n", + "\n", + " df_in_range = learner_log.iloc[idx_start:idx_end]\n", + " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", + " for k, v in g_data.items():\n", + " data[g, k] = v\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + "\n", + " #plt.plot(data, label=est_mab.mutations_)\n", + " plt.stackplot(range(kwargs['max_gen']), data.T, labels=est_mab.mutations_)\n", + " plt.xlabel(\"Generations\")\n", + " plt.ylabel(\"Percentage of usage\")\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " # --------------------------------------------------------------------------\n", + " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", + " 'std m1', 'std m2', 'min m1', 'min m2'])\n", + " for item in est_mab.logbook_:\n", + " # I'll store the calculate\n", + " logbook.loc[item['gen']] = (\n", + " item['gen'], item['evals'], *item['ave'], *item['std'], *item['min']\n", + " )\n", "\n", - " if True: # plot the cumulative history of pulls\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", + " fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", + " x = logbook['gen']\n", + " for i, metric in enumerate(['m1', 'm2']):\n", + " y = logbook[f'ave {metric}']\n", + " y_err = logbook[f'std {metric}']\n", + " y_min = logbook[f'min {metric}']\n", "\n", - " # Plot for evaluations, not generations\n", - " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", - " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", - " data[i+1, :] = data[i]\n", - " data[i+1, arm] += 1\n", - " \n", - " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", - " plt.xlabel(\"Evaluations\")\n", - " plt.ylabel(\"Number of times mutation was used\")\n", - " plt.legend()" + " axs[i].plot(x, y, 'b', label='Avg.')\n", + " axs[i].fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", + " axs[i].plot(x, y_min, 'k', label='Min.')\n", + "\n", + " axs[i].set_xlabel(\"Generation\")\n", + " axs[i].set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", + " axs[i].legend()\n", + "\n", + " plt.show()\n", + "\n", + "generate_plots()" ] }, { @@ -1170,99 +1401,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[24.94 20.7 ]\t[0.84047606 1.05356538]\t[19. 14.]\n", - "1 \t200 \t[24.58 16.665]\t[1.44346805 5.34441531]\t[12. 2.]\n", - "2 \t200 \t[24.215 10.995]\t[1.92321996 6.51037441]\t[12. 2.]\n", - "3 \t200 \t[23.785 6.02 ]\t[2.59784045 5.73756046]\t[12. 2.]\n", - "4 \t200 \t[23.49 3.855]\t[3.04136483 4.1765985 ]\t[12. 2.]\n", - "5 \t200 \t[24.655 2.235]\t[1.79331397 1.43170353]\t[12. 2.]\n", - "6 \t200 \t[24.605 2.275]\t[1.91806543 1.53276711]\t[12. 2.]\n", - "7 \t200 \t[24.56 2.31] \t[2.01156655 1.60433787]\t[12. 2.]\n", - "8 \t200 \t[24.725 2.15 ]\t[1.52294944 0.94207218]\t[12. 2.]\n", - "9 \t200 \t[24.725 2.15 ]\t[1.52294944 0.94207218]\t[12. 2.]\n", - "10 \t200 \t[24.725 2.15 ]\t[1.52294944 0.94207218]\t[12. 2.]\n", - "11 \t200 \t[24.725 2.125]\t[1.66414392 0.87142125]\t[12. 2.]\n", - "12 \t200 \t[24.675 2.145]\t[1.79982638 0.91322232]\t[12. 2.]\n", - "13 \t200 \t[24.65 2.155]\t[1.88613361 0.93326041]\t[12. 2.]\n", - "14 \t200 \t[24.65 2.155]\t[1.88613361 0.93326041]\t[12. 2.]\n", - "15 \t200 \t[24.65 2.155]\t[1.88613361 0.93326041]\t[12. 2.]\n", - "16 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", - "17 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", - "18 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", - "19 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", - "20 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", - "21 \t200 \t[24.64 2.155]\t[1.92104138 0.93326041]\t[12. 2.]\n", - "22 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "23 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "24 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "25 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "26 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "27 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "28 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "29 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "30 \t200 \t[24.7 2.115]\t[1.73781472 0.74951651]\t[11. 2.]\n", - "31 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "32 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "33 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "34 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "35 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "36 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "37 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "38 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "39 \t200 \t[24.63 2.155]\t[1.98824043 0.93326041]\t[11. 2.]\n", - "Final population hypervolume is 48437.500000\n", - "best model: Logistic(1.78*Sin(Sum(Pow(1.00*AIDS,1.00),AIDS,AIDS,1.22*Atan(0.01*AIDS))))\n", - "gen\tevals\tave \tstd \tmin \n", - "0 \t200 \t[24.83 20.695]\t[1.68852006 1.15843645]\t[16. 11.]\n", - "1 \t0 \t[24.275 14.14 ]\t[1.79704619 6.70450595]\t[16. 2.]\n", - "2 \t0 \t[23.7 6.92] \t[2.310844 6.51257246]\t[16. 1.]\n", - "3 \t0 \t[24.06 2.565]\t[2.42619043 2.65250353]\t[16. 1.]\n", - "4 \t0 \t[22.83 2.57] \t[3.31528279 3.72090043]\t[16. 1.]\n", - "5 \t0 \t[22.12 1.29] \t[3.58686493 1.05636168]\t[11. 1.]\n", - "6 \t0 \t[21.9 1.095]\t[3.66060104 0.55315007]\t[11. 1.]\n", - "7 \t0 \t[20.135 1.125]\t[3.48522237 0.58255901]\t[11. 1.]\n", - "8 \t0 \t[18.8 1.165]\t[2.71845544 0.60644456]\t[11. 1.]\n", - "9 \t0 \t[17.74 1.135]\t[0.89576783 0.48659531]\t[11. 1.]\n", - "10 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "11 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "12 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "13 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "14 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "15 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "16 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "17 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "18 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "19 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "20 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "21 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "22 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "23 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "24 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "25 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "26 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "27 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "28 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "29 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "30 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "31 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "32 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "33 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "34 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "35 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "36 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "37 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "38 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "39 \t0 \t[17.91 1.05] \t[0.72242647 0.40926764]\t[11. 1.]\n", - "Final population hypervolume is 48944.500000\n" + "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" ] }, { @@ -1286,15 +1432,19 @@ " \n", " \n", " Brush version\n", - " Original\n", - " Modified\n", + " Original\n", + " Modified\n", " \n", " \n", " metric\n", " score\n", " best model\n", + " size\n", + " depth\n", " score\n", " best model\n", + " size\n", + " depth\n", " point mutation calls\n", " insert mutation calls\n", " delete mutation calls\n", @@ -1304,333 +1454,453 @@ " \n", " \n", " run 0\n", - " 0.68\n", - " Sqrt(0.00*AIDS)\n", " 0.76\n", - " Median(0.00*AIDS,Square(0.00*AIDS),3.97*Tan(1....\n", - " 153\n", - " 4797\n", - " 1445\n", - " 1194\n", + " Logistic(Add(Min(Sinh(-0.00*Total),AIDS,1.00),...\n", + " 8\n", + " 4\n", + " 0.82\n", + " Log1p(Min(Median(Logistic(4.97*Sin(Median(1.00...\n", + " 15\n", + " 7\n", + " 5899\n", + " 1336\n", + " 1299\n", + " 1114\n", " \n", " \n", " run 1\n", " 0.82\n", - " Abs(Sinh(Log1p(Min(Median(0.01*AIDS,Total),Div...\n", + " Logistic(2.21*Cos(0.98*Sub(2.26*Sin(Median(1.9...\n", + " 13\n", + " 5\n", " 0.78\n", - " Logistic(1.01*Mean(-0.00*Total,0.02*AIDS,0.48))\n", - " 1956\n", - " 3557\n", - " 1191\n", - " 823\n", + " Mean(Max(Cos(18.50*Mean(Sin(1.00*AIDS),0.52)),...\n", + " 11\n", + " 5\n", + " 5847\n", + " 2192\n", + " 936\n", + " 673\n", " \n", " \n", " run 2\n", - " 0.68\n", - " Logistic(Add(Ceil(Sqrtabs(Prod(6.09*AIDS,-0.00...\n", - " 0.72\n", - " Logistic(Tan(1.32*Logabs(Sqrt(0.01*AIDS))))\n", - " 1304\n", - " 3863\n", - " 1368\n", - " 1057\n", + " 0.76\n", + " Logistic(Sin(Median(1.00*Total,-4.24)))\n", + " 5\n", + " 3\n", + " 0.74\n", + " Mean(Atan(0.05*AIDS),-0.00*Total,0.00*AIDS)\n", + " 5\n", + " 2\n", + " 4703\n", + " 1969\n", + " 1688\n", + " 1288\n", " \n", " \n", " run 3\n", - " 0.82\n", - " Logistic(Min(Sum(0.01*AIDS,-0.72,-0.72),2.30,D...\n", " 0.78\n", - " Logistic(Div(-0.57*Total,Mean(546.40*AIDS,1.27...\n", - " 1160\n", - " 760\n", - " 3123\n", - " 2561\n", + " Logistic(Cos(1.00*Mean(Total,0.64*AIDS,606.78)))\n", + " 6\n", + " 3\n", + " 0.74\n", + " Mean(Atan(0.05*AIDS),-0.00*Total,0.00*AIDS)\n", + " 5\n", + " 2\n", + " 5107\n", + " 2137\n", + " 1483\n", + " 921\n", " \n", " \n", " run 4\n", - " 0.78\n", - " Logistic(Min(Cos(Sub(Mean(If(AIDS>68817.00,Tan...\n", - " 0.86\n", - " Logistic(Atan(Cos(1.00*Mean(4.94,0.64*AIDS,Tot...\n", - " 1440\n", - " 3569\n", - " 765\n", - " 1835\n", + " 0.76\n", + " Logistic(Div(Sum(10407.20,162.04*AIDS,-0.12*To...\n", + " 7\n", + " 3\n", + " 0.84\n", + " 1.58*Median(Mul(Sin(Tan(1.00*AIDS)),1.45),0.55...\n", + " 8\n", + " 4\n", + " 5772\n", + " 2293\n", + " 1362\n", + " 221\n", " \n", " \n", " run 5\n", - " 0.86\n", - " Sum(Sin(-0.48*AIDS),Abs(Sin(-0.49*AIDS)),-5.57...\n", - " 0.68\n", - " Atan(0.00*AIDS)\n", - " 1997\n", - " 1887\n", - " 2089\n", - " 1634\n", + " 0.82\n", + " Logistic(Cos(Median(1.00*Div(1.00*Total,Median...\n", + " 14\n", + " 8\n", + " 0.78\n", + " Log(1.00*Div(1426.88*AIDS,0.59*Total))\n", + " 4\n", + " 2\n", + " 4655\n", + " 2445\n", + " 1771\n", + " 777\n", " \n", " \n", " run 6\n", - " 0.88\n", - " Median(Sqrt(Tan(Atan(Cos(Sum(0.48*AIDS,0.92)))...\n", - " 0.86\n", - " Median(2.06*Sum(-0.00*AIDS,Tan(1.00*AIDS)),0.8...\n", - " 114\n", - " 4308\n", - " 1790\n", - " 1366\n", + " 0.78\n", + " Sqrt(Median(-0.93*Total,1313.06*AIDS))\n", + " 4\n", + " 2\n", + " 0.68\n", + " Sqrt(0.00*AIDS)\n", + " 2\n", + " 1\n", + " 6324\n", + " 1691\n", + " 1257\n", + " 376\n", " \n", " \n", " run 7\n", - " 0.88\n", - " Mean(Sin(Mean(1.00*Total,1.00*Total,1.00*Total...\n", - " 0.74\n", - " Logistic(Sum(1.00*Sum(242.90,Total),3.38*AIDS,...\n", - " 863\n", - " 3945\n", - " 1610\n", - " 1154\n", + " 0.84\n", + " Abs(Sin(Prod(Square(Atan(Median(0.00*AIDS,1.00...\n", + " 16\n", + " 8\n", + " 0.68\n", + " Tanh(0.00*AIDS)\n", + " 2\n", + " 1\n", + " 4410\n", + " 2113\n", + " 1992\n", + " 1133\n", " \n", " \n", " run 8\n", - " 0.84\n", - " Add(Sum(Sub(Max(Cos(Sum(Median(1.00*Total,Sqrt...\n", + " 0.86\n", + " Abs(Median(Square(Min(Median(Floor(-0.00*Total...\n", + " 16\n", + " 8\n", " 0.68\n", - " Atan(0.00*AIDS)\n", - " 1544\n", - " 3612\n", - " 2408\n", - " 21\n", + " Sqrt(0.00*AIDS)\n", + " 2\n", + " 1\n", + " 6259\n", + " 1479\n", + " 1427\n", + " 483\n", " \n", " \n", " run 9\n", - " 0.78\n", - " Div(Sum(Cos(2.88*Total),Mean(Logabs(133518.33*...\n", - " 0.68\n", - " Tanh(0.00*AIDS)\n", - " 443\n", - " 5595\n", - " 1351\n", - " 219\n", + " 0.80\n", + " Logistic(Mean(Median(Sqrtabs(-0.13),Square(Tot...\n", + " 15\n", + " 4\n", + " 0.74\n", + " Mean(Atan(0.05*AIDS),0.00*AIDS,-0.00*Total)\n", + " 5\n", + " 2\n", + " 3798\n", + " 2466\n", + " 1866\n", + " 1518\n", " \n", " \n", " run 10\n", - " 0.82\n", - " Mean(Exp(Sin(Sum(0.00*AIDS,1.09,Total))),0.00*...\n", " 0.76\n", - " Logistic(Sum(-0.47*Total,726.97*AIDS,AIDS,3499...\n", - " 899\n", - " 4111\n", - " 1870\n", - " 745\n", + " Sum(-0.00*Total,Logistic(0.00*AIDS))\n", + " 4\n", + " 2\n", + " 0.72\n", + " Sin(Mean(5.87,Add(1.00*AIDS,1.00*Total)))\n", + " 6\n", + " 3\n", + " 4297\n", + " 3215\n", + " 1436\n", + " 700\n", " \n", " \n", " run 11\n", - " 0.80\n", - " Sqrt(Log1p(Max(0.00*AIDS,Sub(Sub(Median(Total,...\n", " 0.68\n", - " Sqrtabs(0.00*AIDS)\n", - " 1140\n", - " 3427\n", - " 1450\n", - " 1592\n", + " Sum(0.40,-0.25*AIDS,0.25*AIDS)\n", + " 4\n", + " 1\n", + " 0.74\n", + " Sin(Mean(4.36,1.00*Total,Div(Sub(101.87*AIDS,1...\n", + " 12\n", + " 5\n", + " 6391\n", + " 1934\n", + " 931\n", + " 392\n", " \n", " \n", " run 12\n", - " 0.84\n", - " Add(Cos(Add(Cos(Mul(-1.21,Tan(Mean(-0.00*Total...\n", - " 0.78\n", - " Logistic(2.08*Sin(Median(1.00*Sub(1.00*AIDS,-1...\n", - " 2551\n", - " 2744\n", - " 1172\n", - " 1137\n", + " 0.82\n", + " Median(Median(-0.31*AIDS,-0.00*Total,4.42,0.02...\n", + " 11\n", + " 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version \n", + "metric depth point mutation calls insert mutation calls \n", + "count 30.000000 30.000000 30.000000 \\\n", + "mean 3.133333 5435.100000 2105.333333 \n", + "std 1.925032 1062.467471 599.846724 \n", + "min 1.000000 3308.000000 1044.000000 \n", + "25% 1.250000 4632.500000 1751.750000 \n", + "50% 3.000000 5677.000000 2151.500000 \n", + "75% 4.000000 6093.750000 2411.500000 \n", + "max 7.000000 7535.000000 3895.000000 \n", "\n", - "Brush version \n", - "metric toggle_weight mutation calls \n", - "count 30.000000 \n", - "mean 1179.600000 \n", - "std 633.857082 \n", - "min 21.000000 \n", - "25% 750.250000 \n", - "50% 1174.000000 \n", - "75% 1629.000000 \n", - "max 2561.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + "Brush version \n", + "metric delete mutation calls toggle_weight mutation calls \n", + "count 30.00000 30.000000 \n", + "mean 1389.80000 717.766667 \n", + "std 405.52243 462.035055 \n", + "min 680.00000 67.000000 \n", + "25% 1050.75000 300.750000 \n", + "50% 1408.50000 686.500000 \n", + "75% 1665.25000 1096.500000 \n", + "max 2267.00000 1524.000000 " ] }, "metadata": {}, @@ -1935,7 +2264,6 @@ ], "source": [ "if __name__ == '__main__':\n", - " import pandas as pd\n", " from brush import BrushClassifier\n", " \n", " import warnings\n", @@ -1950,59 +2278,106 @@ " y = data['target']\n", "\n", " kwargs = {\n", - " 'pop_size' : 200,\n", - " 'max_gen' : 40,\n", + " 'verbosity' : False,\n", + " 'pop_size' : 100,\n", + " 'max_gen' : 100,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", " }\n", "\n", - " df = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", + " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'point mutation calls'),\n", - " ('Modified', 'insert mutation calls'),\n", - " ('Modified', 'delete mutation calls'),\n", - " ('Modified', 'toggle_weight mutation calls')],\n", + " ('Original', 'size'), ('Original', 'depth'), \n", + " ('Modified', 'score'), ('Modified', 'best model'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), \n", + " ('Modified', 'point mutation calls'),\n", + " ('Modified', 'insert mutation calls'),\n", + " ('Modified', 'delete mutation calls'),\n", + " ('Modified', 'toggle_weight mutation calls')],\n", " names=('Brush version', 'metric')))\n", " \n", " est_mab = None\n", " for i in range(30):\n", " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - " kwargs['verbosity'] = (i==29) #verbosity only on last one\n", "\n", " est = BrushClassifier(**kwargs).fit(X,y)\n", - "\n", " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", "\n", - " total_rewards = {arm_idx : sum([r for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", - " total_pulls = {arm_idx : sum([1 for (t, i, r) in est_mab.learner_.pull_history if i==arm_idx])\n", - " for arm_idx in range(est_mab.learner_.num_bandits)}\n", + " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", + " \n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " \n", + " results.loc[f'run {i}'] = [\n", + " # Original implementation\n", + " est.score(X,y), est.best_estimator_.get_model(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + "\n", + " # Implementation using Dynamic Thompson Sampling\n", + " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " \n", + " # Mutation count\n", + " *total_pulls.values()]\n", " \n", - " df.loc[f'run {i}'] = [est.score(X,y), est.best_estimator_.get_model(),\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), *total_pulls.values()]\n", " except Exception as e:\n", " print(e)\n", "\n", - " display(df)\n", - " display(df.describe())\n", - "\n", - " if True: # plot the cumulative history of pulls\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", - "\n", - " # Plot for evaluations, not generations\n", - " data = np.zeros( (len(est_mab.learner_.pull_history)+1, 4) )\n", - " for i, (t, arm, r) in enumerate(est_mab.learner_.pull_history):\n", - " data[i+1, :] = data[i]\n", - " data[i+1, arm] += 1\n", - "\n", - " plt.plot(data, label=['point', 'insert', 'delete', 'toggle_weight'])\n", - " plt.xlabel(\"Evaluations\")\n", - " plt.ylabel(\"Number of times mutation was used\")\n", - " plt.legend()" + " # Showing results and statistics\n", + " display(results)\n", + " display(results.describe())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "generate_plots()" ] } ], From f7b0c61fdc29154a926910a8218afaada143b180 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 25 May 2023 11:01:59 -0300 Subject: [PATCH 015/102] Makes `get_op_with_arg` and `get_node_like` return optional --- src/search_space.h | 9 +++++++-- src/variation.h | 19 +++++++++++++------ 2 files changed, 20 insertions(+), 8 deletions(-) diff --git a/src/search_space.h b/src/search_space.h index f196d609..c7f03c05 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -68,6 +68,11 @@ extern std::unordered_map ArgsName; * - assertion check to make sure there is at least one operator that * returns the output type of the model. * + * When sampling in the search space, some methods can fail to return a + * value --- given a specific set of parameters to a function, the candidate + * solutions set may be empty --- and, for these methods, the return type is + * either a valid value, or a `std::nullopt`. This is controlled wrapping + * the return type with `std::optional`. * * Parameters * ---------- @@ -379,7 +384,7 @@ struct SearchSpace /// @param terminal_compatible if true, the other args the returned operator takes must exist in the terminal types. /// @param max_args if zero, there is no limit on number of arguments of the operator. If not, the operator can have at most `max_args` arguments. /// @return a matching operator. - Node get_op_with_arg(DataType ret, DataType arg, + std::optional get_op_with_arg(DataType ret, DataType arg, bool terminal_compatible=true, int max_arg=0) const { @@ -446,7 +451,7 @@ struct SearchSpace /// @brief get a node with a signature matching `node` /// @param node the node to match /// @return a Node - Node get_node_like(Node node) const + std::optional get_node_like(Node node) const { if (Is(node.node_type)){ return get_terminal(node.ret_type); diff --git a/src/variation.h b/src/variation.h index 51a17599..1fff2fea 100644 --- a/src/variation.h +++ b/src/variation.h @@ -32,9 +32,12 @@ inline void point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "point mutation\n"; - // get_node_like will sample a similar node based on node_map_weights or terminal_weights - auto newNode = SS.get_node_like(spot.node->data); - Tree.replace(spot, newNode); + // get_node_like will sample a similar node based on node_map_weights or + // terminal_weights, and maybe will return a Node. + std::optional newNode = SS.get_node_like(spot.node->data); + + if (newNode) // if optional contains a Node, we access its contained value + Tree.replace(spot, *newNode); } /// insert a node with spot as a child @@ -50,15 +53,18 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) // size restriction, which will be relaxed here (just as it is in the PTC2 // algorithm). This mutation can create a new expression that exceeds the // maximum size by the highest arity among the operators. - auto n = SS.get_op_with_arg(spot_type, spot_type, true, + std::optional n = SS.get_op_with_arg(spot_type, spot_type, true, PARAMS["max_size"].get()-Tree.size()-1); + if (!n) // there is no operator with compatible arguments + return; + // make node n wrap the subtree at the chosen spot - auto parent_node = Tree.wrap(spot, n); + auto parent_node = Tree.wrap(spot, *n); // now fill the arguments of n appropriately bool spot_filled = false; - for (auto a: n.arg_types) + for (auto a: (*n).arg_types) { if (spot_filled) { @@ -81,6 +87,7 @@ inline void delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) // get_terminal will sample based on terminal_weights auto terminal = SS.get_terminal(spot.node->data.ret_type); + Tree.erase_children(spot); Tree.replace(spot, terminal); }; From 06270580d29641f31edaecb27d1e1defc9ce753d Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sat, 27 May 2023 16:59:13 -0300 Subject: [PATCH 016/102] Adds array type in dataset.print() --- src/data/data.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/data/data.h b/src/data/data.h index 52a37f20..505a20a7 100644 --- a/src/data/data.h +++ b/src/data/data.h @@ -123,11 +123,11 @@ class Dataset for (auto& [key, value] : this->features) { if (std::holds_alternative(value)) - fmt::print("{}: {}\n", key, std::get(value)); + fmt::print("{} : {}\n", key, std::get(value)); else if (std::holds_alternative(value)) - fmt::print("{}: {}\n", key, std::get(value)); + fmt::print("{} : {}\n", key, std::get(value)); else if (std::holds_alternative(value)) - fmt::print("{}: {}\n", key, std::get(value)); + fmt::print("{} : {}\n", key, std::get(value)); } }; From d35edcdd17863d5b35e49525d2859e190485943e Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sat, 27 May 2023 17:08:35 -0300 Subject: [PATCH 017/102] Create tests to check dispatch_table behavior It seems that the fit method is not working properly when we have terminals of type ArrayXb. The sig_hash of the node is different from all of the available nodes in the dispatch_table, raising an error in dispatch_table.h:172. This commit introduces a simple test case to reproduce the error that I am getting. Ideally, We should fix this bug so this new test case does not get a core dump. --- src/variation.h | 2 +- tests/cpp/test_data.cpp | 81 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 82 insertions(+), 1 deletion(-) diff --git a/src/variation.h b/src/variation.h index 1fff2fea..7439e675 100644 --- a/src/variation.h +++ b/src/variation.h @@ -260,5 +260,5 @@ Program cross(const Program& root, const Program& other) return child; }; -} //namespace vary +} //namespace variation #endif \ No newline at end of file diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index e69de29b..d2103277 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -0,0 +1,81 @@ +#include "testsHeader.h" +#include "../../src/search_space.h" +#include "../../src/program/program.h" +#include "../../src/program/dispatch_table.h" + +TEST(Data, MixedVariableTypes) +{ + // We need to set at least the mutation options (and respective + // probabilities) in order to call PRG.predict() + PARAMS["mutation_options"] = { + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} + }; + + MatrixXf X(5,3); + X << 0 , 1, 0 , // binary with integer values + 0.0, 1.0, 1.0, // binary with float values + 2 , 1.0, -3.0, // integer with float and negative values + 2 , 1 , 3 , // integer with integer values + 2.1, 3.7, -5.2; // float values + + X.transposeInPlace(); + + ArrayXf y(5); + + y << 6.1, 7.7, -4.2; // y = x_0 + x_1 + x_2 + + unordered_map user_ops = { + {"Add", 1}, + {"Sub", 1}, + {"SplitOn", 1} + }; + + Dataset dt(X, y); + SearchSpace SS; + SS.init(dt, user_ops); + + dt.print(); + SS.print(); + + for (int d = 1; d < 5; ++d) + for (int s = 1; s < 5; ++s) + { + + PARAMS["max_size"] = s; + PARAMS["max_depth"] = d; + + RegressorProgram PRG = SS.make_regressor(d, s); + fmt::print( + "=================================================\n" + "Tree model for depth = {}, size= {}: {}\n", + d, s, PRG.get_model("compact", true) + ); + + auto Child = PRG.mutate(); + fmt::print("Child model: {}\n", Child.get_model("compact", true)); + + std::for_each(PRG.Tree.begin(), PRG.Tree.end(), + [](const auto& n) { + fmt::print("Name {}, node {}, feature {}, sig_hash {}\n", + n.name, n.node_type, n.get_feature(), n.sig_hash); + }); + + std::cout << std::endl; + + PRG.fit(dt); + fmt::print( "PRG predict\n"); + ArrayXf y_pred = PRG.predict(dt); + fmt::print( "y_pred: {}\n", y_pred); + + Child.fit(dt); + fmt::print( "Child predict\n"); + ArrayXf y_pred_child = Child.predict(dt); + fmt::print( "y_pred: {}\n", y_pred); + } + + // Brush exports two DispatchTable structs named dtable_fit and dtable_predict. + // These structures holds the mapping between nodes and its corresponding + // operations, and are used to resolve the evaluation of an expression. + // dtable_fit.print(); + // dtable_predict.print(); +} \ No newline at end of file From a9d5bbb47c4e472d1b652ec8bbb9a05525d95a2c Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 28 May 2023 13:31:23 -0300 Subject: [PATCH 018/102] Mutation returns std::optional> This commit changes the behavior of variation functions to return an `std::optional` value. Since variation is done stochastically, and sometimes the Search Space will have an empty set of nodes to use in the mutation, I've made this function to return an std::nullopt if it fails to change the expression. This is also applied when it succeeds to change the expression, but the new expression exceeds `max_size` or `max_depth`. I moved the check for max_size and depth for the end of the mutaiton function. This makes the mutation work more generically and regardless of mutation type (thus there is no need to manually write these checks for future mutation implementations). While this could make mutations less effective (since there is a greater chance of applying a mutation that will render an invalid individual), i think this design is more robust and can be integrated with the multi armed bandit learner. --- src/bindings/bind_programs.h | 3 +- src/program/program.h | 4 +- src/search_space.h | 1 + src/variation.h | 76 +++++++++++++++++++++++------------- 4 files changed, 54 insertions(+), 30 deletions(-) diff --git a/src/bindings/bind_programs.h b/src/bindings/bind_programs.h index 8ee9b3ef..41592b0d 100644 --- a/src/bindings/bind_programs.h +++ b/src/bindings/bind_programs.h @@ -48,7 +48,8 @@ void bind_program(py::module& m, string name) .def("size", &T::size) .def("depth", &T::depth) .def("cross", &T::cross) - .def("mutate", &T::mutate) // static_cast(&T::mutate)) + .def("mutate", &T::mutate, py::return_value_policy::automatic, + "Performs one attempt to stochastically mutate the program and generate a child") .def("set_search_space", &T::set_search_space) .def(py::pickle( [](const T &p) { // __getstate__ diff --git a/src/program/program.h b/src/program/program.h index 7393956f..438acfa3 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -419,7 +419,7 @@ template struct Program /// @brief convenience wrapper for :cpp:func:`variation:mutate()` in variation.h /// @return a mutated version of this program - Program mutate() const; + std::optional> mutate() const; /** * @brief convenience wrapper for :cpp:func:`variation:cross` in variation.h @@ -459,7 +459,7 @@ void Program::update_weights(const Dataset& d) // mutation and crossover #include "../variation.h" template -Program Program::mutate() const +std::optional> Program::mutate() const { return variation::mutate(*this, this->SSref.value().get()); }; diff --git a/src/search_space.h b/src/search_space.h index c7f03c05..9e655f4e 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -388,6 +388,7 @@ struct SearchSpace bool terminal_compatible=true, int max_arg=0) const { + // TODO: take out the size limit here and add the return std::nullopt when it fails // thoughts (TODO): // this could be templated by return type and arg. although the lookup in the map should be // fairly fast. diff --git a/src/variation.h b/src/variation.h index 7439e675..02411266 100644 --- a/src/variation.h +++ b/src/variation.h @@ -11,6 +11,8 @@ license: GNU/GPL v3 // #include "program/tree_node.h" // #include "node.h" +#include + // namespace Brush{ // typedef tree::pre_order_iterator Iter; @@ -28,7 +30,7 @@ namespace variation { typedef tree::pre_order_iterator Iter; /// point mutation: replace node with same typed node -inline void point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +inline bool point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "point mutation\n"; @@ -36,12 +38,18 @@ inline void point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) // terminal_weights, and maybe will return a Node. std::optional newNode = SS.get_node_like(spot.node->data); - if (newNode) // if optional contains a Node, we access its contained value + // if optional contains a Node, we access its contained value + if (newNode) { Tree.replace(spot, *newNode); + return true; + } + + // in case mutation fails + return false; } /// insert a node with spot as a child -inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "insert mutation\n"; auto spot_type = spot.node->data.ret_type; @@ -57,7 +65,7 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) PARAMS["max_size"].get()-Tree.size()-1); if (!n) // there is no operator with compatible arguments - return; + return false; // make node n wrap the subtree at the chosen spot auto parent_node = Tree.wrap(spot, *n); @@ -78,10 +86,12 @@ inline void insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) else Tree.insert(spot, SS.get_terminal(a)); } + + return true; } /// delete subtree and replace it with a terminal of the same return type -inline void delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "delete mutation\n"; @@ -89,16 +99,22 @@ inline void delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) auto terminal = SS.get_terminal(spot.node->data.ret_type); Tree.erase_children(spot); + + // TODO: this may fail. I need to return optional here as well Tree.replace(spot, terminal); + + return true; }; /// @brief toggle the node's weight on or off. /// @param Tree the program tree /// @param spot an iterator to the node that is being mutated /// @param SS the search space (unused) -inline void toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { spot.node->data.is_weighted = !spot.node->data.is_weighted; + + return true; } /** @@ -125,10 +141,12 @@ inline void toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpac * @return `child`, the mutated program */ template -Program mutate(const Program& parent, const SearchSpace& SS) +std::optional> mutate(const Program& parent, const SearchSpace& SS) { Program child(parent); + // TODO: update documentation + // choose location by weighted sampling of program vector weights(child.Tree.size()); std::transform(child.Tree.begin(), child.Tree.end(), @@ -141,32 +159,36 @@ Program mutate(const Program& parent, const SearchSpace& SS) auto options = PARAMS["mutation_options"].get>(); - // Setting to zero the weight of variations that increase the expression - // if the expression is already at the maximum size or depth - if (child.Tree.size()+1 >= PARAMS["max_size"].get() - || child.Tree.depth(spot)+child.Tree.max_depth(spot)+1 >= PARAMS["max_depth"].get()) - { - // avoid using mutations that increase size/depth - options["insert"] = 0.0; - } - // choose a valid mutation option string choice = r.random_choice(options); - if (choice == "insert") - insert_mutation(child.Tree, spot, SS); - else if (choice == "delete") - delete_mutation(child.Tree, spot, SS); - else if (choice == "point") - point_mutation(child.Tree, spot, SS); - else if (choice == "toggle_weight") - toggle_weight_mutation(child.Tree, spot, SS); - else{ - string msg = fmt::format("{} not a valid mutation choice", choice); + // Every mutation here works inplace, so they return bool instead of + // std::optional to indicare the result of their manipulation over the + // program tree. Here we call the mutation function and return the result + using MutationFunc = std::function&, Iter, const SearchSpace&)>; + + std::map mutations{ + {"insert", insert_mutation}, + {"delete", delete_mutation}, + {"point", point_mutation}, + {"toggle_weight", toggle_weight_mutation} + }; + + // Try to find the mutation function based on the choice + auto it = mutations.find(choice); + if (it == mutations.end()) { + std::string msg = fmt::format("{} not a valid mutation choice", choice); HANDLE_ERROR_THROW(msg); } - return child; + bool success = it->second(child.Tree, spot, SS); + if (success + && ((child.Tree.size() <= PARAMS["max_size"].get()) + && (child.Tree.max_depth() <= PARAMS["max_depth"].get())) ){ + return child; + } else { + return std::nullopt; + } }; /// @brief swaps subtrees between root and other, returning new program From fc6a694c5f68d5992ca4e079682689ff8566d874 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 28 May 2023 13:34:55 -0300 Subject: [PATCH 019/102] Updated the python wrapper to handle mutation returning None --- src/brush/D_TS_experiments.ipynb | 1968 +++++++++++++----------------- src/brush/deap_api/nsga2.py | 23 +- src/brush/estimator.py | 6 +- 3 files changed, 864 insertions(+), 1133 deletions(-) diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index 2d739bf8..cd7f23cd 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -132,16 +132,16 @@ "output_type": "stream", "text": [ "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 448, 1: 366, 2: 427, 3: 303}\n", - "number of pulls for each arm: {0: 2821, 2: 2682, 1: 2461, 3: 2036}\n", + "cum. reward for each arm : {0: 372, 1: 473, 2: 395, 3: 297}\n", + "number of pulls for each arm: {1: 2976, 2: 2617, 0: 2384, 3: 2023}\n", "(it was expected: similar amount of pulls for each arm)\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 8406, 1: 7, 2: 4, 3: 0}\n", - "number of pulls for each arm: {0: 9969, 1: 16, 2: 10, 3: 5}\n", + "cum. reward for each arm : {0: 8346, 1: 4, 2: 25, 3: 1}\n", + "number of pulls for each arm: {0: 9940, 2: 42, 1: 12, 3: 6}\n", "(it was expected: more pulls for first arm, less pulls for last)\n", "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 25, 1: 5012, 2: 11, 3: 3361}\n", - "number of pulls for each arm: {1: 5919, 3: 4022, 0: 39, 2: 20}\n", + "cum. reward for each arm : {0: 9, 1: 4841, 2: 13, 3: 3552}\n", + "number of pulls for each arm: {1: 5715, 3: 4242, 2: 24, 0: 19}\n", "(it was expected: 2nd approx 4th > 1st > 3rd)\n" ] } @@ -244,9 +244,15 @@ "\n", " _brush.set_params(params)\n", " \n", - " # ind1.prg.mutate is a convenient interface that uses the current search \n", - " # space to sample mutations\n", - " offspring = creator.Individual(ind1.prg.mutate())\n", + " opt, attempts = ind1.prg.mutate(), 0\n", + " while attempts < 10 and opt is None:\n", + " opt = ind1.prg.mutate()\n", + " attempts += 1\n", + " \n", + " if opt is None:\n", + " return None\n", + " \n", + " offspring = creator.Individual(opt)\n", "\n", " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", " \n", @@ -358,7 +364,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" + "0, 1, 2, Input contains NaN.\n", + "3, Input contains NaN.\n", + "4, Input contains NaN.\n", + "5, Input contains NaN.\n", + "6, Input contains NaN.\n", + "7, Input contains NaN.\n", + "8, 9, Input contains NaN.\n", + "10, 11, Input contains NaN.\n", + "12, Input contains NaN.\n", + "13, Input contains NaN.\n", + "14, 15, Input contains NaN.\n", + "16, 17, 18, Input contains NaN.\n", + "19, Input contains NaN.\n", + "20, 21, 22, 23, Input contains NaN.\n", + "24, 25, 26, Input contains NaN.\n", + "27, Input contains NaN.\n", + "28, 29, 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1294 713 \n", - "run 25 448 382 \n", - "run 26 1450 871 \n", - "run 27 950 861 \n", - "run 28 1295 153 \n", - "run 29 1906 743 " + "run 0 1495 1348 \n", + "run 1 1084 904 \n", + "run 8 610 529 \n", + "run 10 1509 246 \n", + "run 14 751 274 \n", + "run 16 1361 501 \n", + "run 17 1825 1081 \n", + "run 20 1528 1350 \n", + "run 21 1523 944 \n", + "run 22 1405 862 \n", + "run 24 1435 817 \n", + "run 25 1622 419 \n", + "run 28 1075 486 \n", + "run 29 1633 946 " ] }, "metadata": {}, @@ -1034,107 +752,107 @@ " \n", " \n", " count\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", - " 30.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", + " 14.000000\n", " \n", " \n", " mean\n", - " 0.309137\n", - " 2.100000\n", - " 1.066667\n", - " 0.447554\n", - " 3.900000\n", - " 1.733333\n", - " 5247.466667\n", - " 2201.933333\n", - " 1373.066667\n", - " 825.533333\n", + " 0.330077\n", + " 2.214286\n", + " 1.071429\n", + " 0.440539\n", + " 3.714286\n", + " 1.571429\n", + " 5679.142857\n", + " 1857.214286\n", + " 1346.857143\n", + " 764.785714\n", " \n", " \n", " std\n", - " 0.082495\n", - " 0.402578\n", - " 0.253708\n", - " 0.192888\n", - " 1.626293\n", - " 0.827682\n", - " 810.890091\n", - " 454.865871\n", - " 425.113529\n", - " 318.965000\n", + " 0.068610\n", + " 0.425815\n", + " 0.267261\n", + " 0.184104\n", + " 2.267787\n", + " 0.755929\n", + " 783.515535\n", + " 407.624484\n", + " 345.368445\n", + " 362.851565\n", " \n", " \n", " min\n", - " 0.028410\n", + " 0.275650\n", " 2.000000\n", " 1.000000\n", " 0.292958\n", " 2.000000\n", " 1.000000\n", - " 3751.000000\n", - " 1417.000000\n", - " 448.000000\n", - " 153.000000\n", + " 4424.000000\n", + " 1201.000000\n", + " 610.000000\n", + " 246.000000\n", " \n", " \n", " 25%\n", - " 0.325058\n", + " 0.292958\n", " 2.000000\n", " 1.000000\n", - " 0.332471\n", - " 2.250000\n", + " 0.326358\n", + " 2.000000\n", " 1.000000\n", - " 4787.000000\n", - " 1877.500000\n", - " 1113.750000\n", - " 697.250000\n", + " 5088.250000\n", + " 1623.000000\n", + " 1153.250000\n", + " 489.750000\n", " \n", " \n", " 50%\n", - " 0.325058\n", + " 0.319994\n", " 2.000000\n", " 1.000000\n", - " 0.365332\n", - " 4.000000\n", - " 2.000000\n", - " 5243.500000\n", - " 2217.000000\n", - " 1319.000000\n", - " 846.500000\n", + " 0.344865\n", + " 2.500000\n", + " 1.000000\n", + " 5568.500000\n", + " 1837.000000\n", + " 1465.000000\n", + " 839.500000\n", " \n", " \n", " 75%\n", - " 0.325058\n", + " 0.326358\n", " 2.000000\n", " 1.000000\n", - " 0.473324\n", - " 5.000000\n", + " 0.481157\n", + " 5.500000\n", " 2.000000\n", - " 5705.000000\n", - " 2416.750000\n", - " 1625.500000\n", - " 994.250000\n", + " 6432.500000\n", + " 1997.750000\n", + " 1526.750000\n", + " 945.500000\n", " \n", " \n", " max\n", " 0.551982\n", - " 4.000000\n", + " 3.000000\n", " 2.000000\n", - " 0.999862\n", + " 0.948103\n", " 8.000000\n", - " 4.000000\n", - " 7325.000000\n", - " 3227.000000\n", - " 2216.000000\n", - " 1370.000000\n", + " 3.000000\n", + " 6962.000000\n", + " 2957.000000\n", + " 1825.000000\n", + " 1350.000000\n", " \n", " \n", "\n", @@ -1143,36 +861,36 @@ "text/plain": [ "Brush version Original Modified \n", "metric score size depth score size \n", - "count 30.000000 30.000000 30.000000 30.000000 30.000000 \\\n", - "mean 0.309137 2.100000 1.066667 0.447554 3.900000 \n", - "std 0.082495 0.402578 0.253708 0.192888 1.626293 \n", - "min 0.028410 2.000000 1.000000 0.292958 2.000000 \n", - "25% 0.325058 2.000000 1.000000 0.332471 2.250000 \n", - "50% 0.325058 2.000000 1.000000 0.365332 4.000000 \n", - "75% 0.325058 2.000000 1.000000 0.473324 5.000000 \n", - "max 0.551982 4.000000 2.000000 0.999862 8.000000 \n", + "count 14.000000 14.000000 14.000000 14.000000 14.000000 \\\n", + "mean 0.330077 2.214286 1.071429 0.440539 3.714286 \n", + "std 0.068610 0.425815 0.267261 0.184104 2.267787 \n", + "min 0.275650 2.000000 1.000000 0.292958 2.000000 \n", + "25% 0.292958 2.000000 1.000000 0.326358 2.000000 \n", + "50% 0.319994 2.000000 1.000000 0.344865 2.500000 \n", + "75% 0.326358 2.000000 1.000000 0.481157 5.500000 \n", + "max 0.551982 3.000000 2.000000 0.948103 8.000000 \n", "\n", "Brush version \n", "metric depth point mutation calls insert mutation calls \n", - "count 30.000000 30.000000 30.000000 \\\n", - "mean 1.733333 5247.466667 2201.933333 \n", - "std 0.827682 810.890091 454.865871 \n", - "min 1.000000 3751.000000 1417.000000 \n", - "25% 1.000000 4787.000000 1877.500000 \n", - "50% 2.000000 5243.500000 2217.000000 \n", - "75% 2.000000 5705.000000 2416.750000 \n", - "max 4.000000 7325.000000 3227.000000 \n", + "count 14.000000 14.000000 14.000000 \\\n", + "mean 1.571429 5679.142857 1857.214286 \n", + "std 0.755929 783.515535 407.624484 \n", + "min 1.000000 4424.000000 1201.000000 \n", + "25% 1.000000 5088.250000 1623.000000 \n", + "50% 1.000000 5568.500000 1837.000000 \n", + "75% 2.000000 6432.500000 1997.750000 \n", + "max 3.000000 6962.000000 2957.000000 \n", "\n", "Brush version \n", "metric delete mutation calls toggle_weight mutation calls \n", - "count 30.000000 30.000000 \n", - "mean 1373.066667 825.533333 \n", - "std 425.113529 318.965000 \n", - "min 448.000000 153.000000 \n", - "25% 1113.750000 697.250000 \n", - "50% 1319.000000 846.500000 \n", - "75% 1625.500000 994.250000 \n", - "max 2216.000000 1370.000000 " + "count 14.000000 14.000000 \n", + "mean 1346.857143 764.785714 \n", + "std 345.368445 362.851565 \n", + "min 610.000000 246.000000 \n", + "25% 1153.250000 489.750000 \n", + "50% 1465.000000 839.500000 \n", + "75% 1526.750000 945.500000 \n", + "max 1825.000000 1350.000000 " ] }, "metadata": {}, @@ -1243,7 +961,6 @@ " \n", " # Mutation count\n", " *total_pulls.values()]\n", - " \n", " except Exception as e:\n", " print(e)\n", "\n", @@ -1259,7 +976,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", + "image/png": 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", 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", 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", "text/plain": [ "
" ] @@ -1454,453 +1171,453 @@ " \n", " \n", " run 0\n", - " 0.76\n", - " Logistic(Add(Min(Sinh(-0.00*Total),AIDS,1.00),...\n", - " 8\n", - " 4\n", - " 0.82\n", - " Log1p(Min(Median(Logistic(4.97*Sin(Median(1.00...\n", - " 15\n", + " 0.72\n", + " 0.02*Div(If(AIDS>68817.00,1445.51,0.01*Total),...\n", + " 6\n", + " 2\n", + " 0.72\n", + " Logistic(Cos(Mean(Sub(1.00*Total,-3.58),1.00*A...\n", " 7\n", - " 5899\n", - " 1336\n", - " 1299\n", - " 1114\n", + " 4\n", + " 3147\n", + " 2563\n", + " 2148\n", + " 1790\n", " \n", " \n", " run 1\n", - " 0.82\n", - " Logistic(2.21*Cos(0.98*Sub(2.26*Sin(Median(1.9...\n", + " 0.84\n", + " Logistic(Max(Prod(Sin(0.26*AIDS),-2.70),-3.12,...\n", " 13\n", - " 5\n", - " 0.78\n", - " Mean(Max(Cos(18.50*Mean(Sin(1.00*AIDS),0.52)),...\n", - " 11\n", - " 5\n", - " 5847\n", - " 2192\n", - " 936\n", - " 673\n", + " 6\n", + " 0.76\n", + " Logistic(1.64*Cos(1.00*Median(1.00*AIDS,1.00*P...\n", + " 13\n", + " 6\n", + " 4742\n", + " 3869\n", + " 820\n", + " 217\n", " \n", 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5.750000\n", + " 0.810000\n", + " 12.000000\n", + " 5.000000\n", " 0.780000\n", - " 8.750000\n", + " 9.000000\n", " 4.000000\n", - " 6093.750000\n", - " 2411.500000\n", - " 1665.25000\n", - " 1096.500000\n", + " 3894.750000\n", + " 2897.750000\n", + " 2375.750000\n", + " 1604.000000\n", " \n", " \n", " max\n", - " 0.920000\n", - " 20.000000\n", - " 8.000000\n", " 0.880000\n", " 20.000000\n", - " 7.000000\n", - " 7535.000000\n", - " 3895.000000\n", - " 2267.00000\n", - " 1524.000000\n", + " 10.000000\n", + " 0.860000\n", + " 16.000000\n", + " 6.000000\n", + " 6644.000000\n", + " 3869.000000\n", + " 2600.000000\n", + " 1861.000000\n", " \n", " \n", "\n", @@ -2227,35 +1944,35 @@ "Brush version Original Modified \n", "metric score size depth score size \n", "count 30.000000 30.000000 30.000000 30.000000 30.000000 \\\n", - "mean 0.792000 9.466667 4.100000 0.746667 6.600000 \n", - "std 0.060252 5.217565 2.056948 0.052347 4.399059 \n", - "min 0.680000 4.000000 1.000000 0.680000 2.000000 \n", - "25% 0.760000 5.000000 3.000000 0.690000 2.500000 \n", - "50% 0.780000 7.500000 4.000000 0.740000 5.500000 \n", - "75% 0.820000 14.750000 5.750000 0.780000 8.750000 \n", - "max 0.920000 20.000000 8.000000 0.880000 20.000000 \n", + "mean 0.755333 8.400000 4.033333 0.739333 7.066667 \n", + "std 0.064259 4.716808 2.413801 0.057412 3.777733 \n", + "min 0.640000 1.000000 0.000000 0.680000 2.000000 \n", + "25% 0.700000 5.000000 2.250000 0.680000 4.000000 \n", + "50% 0.760000 6.000000 3.500000 0.740000 5.500000 \n", + "75% 0.810000 12.000000 5.000000 0.780000 9.000000 \n", + "max 0.880000 20.000000 10.000000 0.860000 16.000000 \n", "\n", "Brush version \n", "metric depth point mutation calls insert mutation calls \n", "count 30.000000 30.000000 30.000000 \\\n", - "mean 3.133333 5435.100000 2105.333333 \n", - "std 1.925032 1062.467471 599.846724 \n", - "min 1.000000 3308.000000 1044.000000 \n", - "25% 1.250000 4632.500000 1751.750000 \n", - "50% 3.000000 5677.000000 2151.500000 \n", - "75% 4.000000 6093.750000 2411.500000 \n", - "max 7.000000 7535.000000 3895.000000 \n", + "mean 3.366667 3707.366667 2702.500000 \n", + "std 1.425950 855.867355 394.091513 \n", + "min 1.000000 2694.000000 1976.000000 \n", + "25% 2.000000 3125.250000 2532.500000 \n", + "50% 3.000000 3496.500000 2674.000000 \n", + "75% 4.000000 3894.750000 2897.750000 \n", + "max 6.000000 6644.000000 3869.000000 \n", "\n", "Brush version \n", "metric delete mutation calls toggle_weight mutation calls \n", - "count 30.00000 30.000000 \n", - "mean 1389.80000 717.766667 \n", - "std 405.52243 462.035055 \n", - "min 680.00000 67.000000 \n", - "25% 1050.75000 300.750000 \n", - "50% 1408.50000 686.500000 \n", - "75% 1665.25000 1096.500000 \n", - "max 2267.00000 1524.000000 " + "count 30.000000 30.000000 \n", + "mean 1999.566667 1238.566667 \n", + "std 483.568965 513.808275 \n", + "min 609.000000 85.000000 \n", + "25% 1765.500000 988.500000 \n", + "50% 2033.500000 1399.000000 \n", + "75% 2375.750000 1604.000000 \n", + "max 2600.000000 1861.000000 " ] }, "metadata": {}, @@ -2321,7 +2038,6 @@ " \n", " # Mutation count\n", " *total_pulls.values()]\n", - " \n", " except Exception as e:\n", " print(e)\n", "\n", @@ -2337,7 +2053,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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", + "image/png": 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" ] diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 8aff555d..81f52f7d 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -43,14 +43,27 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # offspring = [toolbox.clone(ind) for ind in offspring] offspring = [] - for ind1, ind2 in zip(parents[::2], parents[1::2]): + # Since crossover/mutation can fail, we'll cycle through the parents + # until we have an offspring big enough + index, num_attempts = 0, 0 + + # iterate over the array until a criterion is met + while num_attempts < len(parents) and len(offspring) < len(parents): + index1 = (index + 1) % len(parents) + index2 = (index + 2) % len(parents) + + ind1, ind2 = parents[index1], parents[index2] + if random.random() <= CXPB: ind1, ind2 = toolbox.mate(ind1, ind2) - off1 = toolbox.mutate(ind1) - off2 = toolbox.mutate(ind2) - - offspring.extend([off1, off2]) + if ind1 is not None: ind1 = toolbox.mutate(ind1) + if ind1 is not None: offspring.append(ind1) + + if ind2 is not None: ind2 = toolbox.mutate(ind2) + if ind2 is not None: offspring.append(ind2) + + index += 2 # archive.update(offspring) # Evaluate the individuals with an invalid fitness diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 8fddb091..1847b6c8 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -123,8 +123,10 @@ def _crossover(self, ind1, ind2): def _mutate(self, ind1): # offspring = (creator.Individual(ind1.prg.mutate(self.search_space_)),) - offspring = creator.Individual(ind1.prg.mutate()) - return offspring + opt = ind1.prg.mutate() + if opt is not None: + return creator.Individual(opt) + return None def fit(self, X, y): """ From 34fa9858e267869826d7600876a0b8cfc6686a34 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 28 May 2023 13:36:11 -0300 Subject: [PATCH 020/102] Update C++ tests to work with mutation returning std::optional --- tests/cpp/test_variation.cpp | 291 ++++++++++++++++------------------- 1 file changed, 135 insertions(+), 156 deletions(-) diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index 1f505754..b75802ba 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -6,19 +6,24 @@ TEST(Operators, Mutation) { + // test mutation + // TODO: set random seed + PARAMS["mutation_options"] = { {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} }; - // test mutation - // TODO: set random seed + MatrixXf X(10,2); ArrayXf y(10); X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, + 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; + y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, - 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; + 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; + Dataset data(X,y); SearchSpace SS; @@ -30,36 +35,144 @@ TEST(Operators, Mutation) { fmt::print("d={},s={}\n",d,s); fmt::print("make_regressor\n"); + + // if we set max_size and max_depth to zero, it will use the + // values in the global PARAMS. Otherwise, it will respect the + // values passed as argument. RegressorProgram PRG = SS.make_regressor(d, s); + fmt::print("PRG.fit(data);\n"); PRG.fit(data); ArrayXf y_pred = PRG.predict(data); - fmt::print("auto Child = PRG.mutate(SS);\n"); - auto Child = PRG.mutate(); + + // applying mutation and checking if the optional result is non-empty + fmt::print("auto Child = PRG.mutate();\n"); + auto opt = PRG.mutate(); + + if (!opt){ + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model: {}\n" + "Mutation failed to create a child", + d, s, + PRG.get_model("compact", true) + ); + } + else { + auto Child = opt.value(); + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model: {}\n" + "Mutated Model: {}\n", + d, s, + PRG.get_model("compact", true), + Child.get_model("compact", true) + ); + + fmt::print("child fit\n"); + Child.fit(data); + y_pred = Child.predict(data); + } + } + } +} - fmt::print("print\n"); - fmt::print( - "=================================================\n" - "depth = {}, size= {}\n" - "Initial Model: {}\n" - "Mutated Model: {}\n", - d, s, - PRG.get_model("compact", true), - Child.get_model("compact", true) - ); +TEST(Operators, MutationSizeAndDepthLimit) +{ + PARAMS["mutation_options"] = { + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} + }; + + MatrixXf X(10,2); + ArrayXf y(10); + X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, + 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, - fmt::print("child fit\n"); - Child.fit(data); - y_pred = Child.predict(data); + 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, + 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; + + y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, + 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; + + Dataset data(X,y); + + SearchSpace SS; + SS.init(data); + + // split operator --> arity 3 + // prod operator --> arity 4 + int max_arity = 4; + + for (int d = 5; d < 15; ++d) + { + for (int s = 5; s < 15; ++s) + { + PARAMS["max_size"] = s; + PARAMS["max_depth"] = d; + + fmt::print("d={},s={}\n",d,s); + fmt::print("make_regressor\n"); + + // Enforcing that the parents does not exceed max_size by + // taking into account the highest arity of the function nodes; + // and the max_depth+1 that PTC2 can generate + RegressorProgram PRG = SS.make_regressor(d-1, s - max_arity); + + auto PRG_model = PRG.get_model("compact", true); + + auto opt = PRG.mutate(); + + if (!opt){ + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model: {}\n" + "Mutation failed to create a child", + d, s, + PRG.get_model("compact", true) + ); + } + else { + // Extracting the child from the std::optional and checking + // if it is within size and depth restrictions. There is no + // margin for having slightly bigger expressions. + auto Child = opt.value(); + + fmt::print("print\n"); + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model: {}\n" + "Mutated Model: {}\n" + "Mutated depth: {}\n" + "Mutated size : {}\n", + d, s, + PRG.get_model("compact", true), + Child.get_model("compact", true), + Child.Tree.max_depth(), + Child.Tree.size() + ); + + // Original didn't change + ASSERT_TRUE(PRG_model == PRG.get_model("compact", true)); + + ASSERT_TRUE(Child.size() > 0); + ASSERT_TRUE(Child.size() <= s); + + ASSERT_TRUE(Child.Tree.size() > 0); + ASSERT_TRUE(Child.Tree.size() <= s); + + ASSERT_TRUE(Child.Tree.max_depth() >= 0); + ASSERT_TRUE(Child.Tree.max_depth() <= d); + } } } } TEST(Operators, Crossover) { - // test mutation - // TODO: set random seed - MatrixXf X(10,2); ArrayXf y(10); X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, @@ -122,85 +235,6 @@ TEST(Operators, Crossover) } } -TEST(Operators, MutationSizeAndDepthLimit) -{ - PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} - }; - - MatrixXf X(10,2); - ArrayXf y(10); - X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, - 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, - - 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, - 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; - - y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, - 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; - - Dataset data(X,y); - - SearchSpace SS; - SS.init(data); - - // split operator --> arity 3 - // prod operator --> arity 4 - int max_arity = 4; - - for (int d = 5; d < 15; ++d) - { - for (int s = 5; s < 15; ++s) - { - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; - - fmt::print("d={},s={}\n",d,s); - fmt::print("make_regressor\n"); - - // Enforcing that the parents does not exceed max_size by - // taking into account the highest arity of the function nodes; - // and the max_depth+1 that PTC2 can generate - RegressorProgram PRG = SS.make_regressor(d-1, s - max_arity); - - auto PRG_model = PRG.get_model("compact", true); - - auto Child = PRG.mutate(); - - fmt::print("print\n"); - fmt::print( - "=================================================\n" - "depth = {}, size= {}\n" - "Initial Model: {}\n" - "Mutated Model: {}\n" - "Mutated depth: {}\n" - "Mutated size : {}\n", - d, s, - PRG.get_model("compact", true), - Child.get_model("compact", true), - Child.Tree.max_depth(), - Child.Tree.size() - ); - - // Original didn't change - ASSERT_TRUE(PRG_model == PRG.get_model("compact", true)); - - // Child is within restrictions. Here we expect the generated - // expression to have at most max_size nodes (there is no tolerance - // gap as PTC2 has). Notice that this is only valid if the original - // parent is already respecting the max_size - ASSERT_TRUE(Child.size() > 0); - ASSERT_TRUE(Child.size() <= s); - - ASSERT_TRUE(Child.Tree.size() > 0); - ASSERT_TRUE(Child.Tree.size() <= s); - - ASSERT_TRUE(Child.Tree.max_depth() >= 0); - ASSERT_TRUE(Child.Tree.max_depth() <= d); - } - } -} - TEST(Operators, CrossoverSizeAndDepthLimit) { MatrixXf X(10,2); @@ -298,62 +332,7 @@ TEST(Operators, CrossoverSizeAndDepthLimit) } } -TEST(Operators, MutationSizeAndDepthPARAMS) -{ - PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} - }; - - MatrixXf X(10,2); - ArrayXf y(10); - X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, - 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, - - 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, - 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; - - y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, - 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; - - Dataset data(X,y); - - SearchSpace SS; - SS.init(data); - - // split operator --> arity 3 - // prod operator --> arity 4 - int max_arity = 4; - - for (int d = 1; d < 10; ++d) - { - for (int s = 1; s < 10; ++s) - { - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; - - fmt::print("d={},s={}\n",d,s); - fmt::print("make_regressor\n"); - - RegressorProgram PRG = SS.make_regressor(0, 0); - - auto PRG_model = PRG.get_model("compact", true); - - auto Child = PRG.mutate(); - - // Child is within restrictions. Here we allow the mutation - // to generate slightly bigger expressions (because the original - // parents can also have this offset due to PTC2 generation method) - ASSERT_TRUE(Child.size() > 0); - ASSERT_TRUE(Child.size() <= s+max_arity); - - ASSERT_TRUE(Child.Tree.size() > 0); - ASSERT_TRUE(Child.Tree.size() <= s+max_arity); - - ASSERT_TRUE(Child.Tree.max_depth() >= 0); - ASSERT_TRUE(Child.Tree.max_depth() <= d+1); - } - } -} +// TODO: make a test that will always choose one mutation and check for errors TEST(Operators, CrossoverSizeAndDepthPARAMS) { From ff663ec1a1312315cbcc8ce71f621c0159a9ed15 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 28 May 2023 13:37:56 -0300 Subject: [PATCH 021/102] Update `test_data` to work with mutation returning optional --- tests/cpp/test_data.cpp | 28 ++++++++++++++++++++-------- 1 file changed, 20 insertions(+), 8 deletions(-) diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index d2103277..d40866e7 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -51,9 +51,7 @@ TEST(Data, MixedVariableTypes) d, s, PRG.get_model("compact", true) ); - auto Child = PRG.mutate(); - fmt::print("Child model: {}\n", Child.get_model("compact", true)); - + // visualizing detailed information for the model std::for_each(PRG.Tree.begin(), PRG.Tree.end(), [](const auto& n) { fmt::print("Name {}, node {}, feature {}, sig_hash {}\n", @@ -61,16 +59,30 @@ TEST(Data, MixedVariableTypes) }); std::cout << std::endl; - + + fmt::print( "PRG fit\n"); PRG.fit(dt); fmt::print( "PRG predict\n"); ArrayXf y_pred = PRG.predict(dt); fmt::print( "y_pred: {}\n", y_pred); - Child.fit(dt); - fmt::print( "Child predict\n"); - ArrayXf y_pred_child = Child.predict(dt); - fmt::print( "y_pred: {}\n", y_pred); + // creating and fitting a child + auto opt = PRG.mutate(); + + if (!opt){ + fmt::print("Mutation failed to create a child\n"); + } + else { + auto Child = opt.value(); + + fmt::print("Child model: {}\n", Child.get_model("compact", true)); + + fmt::print( "Child fit\n"); + Child.fit(dt); + fmt::print( "Child predict\n"); + ArrayXf y_pred_child = Child.predict(dt); + fmt::print( "y_pred: {}\n", y_pred); + } } // Brush exports two DispatchTable structs named dtable_fit and dtable_predict. From 6a72faeb14b6d66a8366c169f26f61aa4ad10e7a Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 1 Jun 2023 21:27:57 -0400 Subject: [PATCH 022/102] Avoiding unwanted mutations can speed up the algorithm --- src/variation.h | 26 ++++++++++++++++++++++++-- tests/cpp/test_variation.cpp | 3 +++ 2 files changed, 27 insertions(+), 2 deletions(-) diff --git a/src/variation.h b/src/variation.h index 02411266..5fa5d9df 100644 --- a/src/variation.h +++ b/src/variation.h @@ -76,7 +76,8 @@ inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { if (spot_filled) { - // if spot is in its child position, append children + // if spot is in its child position, append children. + // reminding that get_terminal may fail as well Tree.append_child(parent_node, SS.get_terminal(a)); } // if types match, treat this spot as filled by the spot node @@ -159,6 +160,27 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS auto options = PARAMS["mutation_options"].get>(); + // these restrictions below increase the performance + + // don't increase an expression already at its maximum size!! + // Setting to zero the weight of variations that increase the expression + // if the expression is already at the maximum size or depth + if (child.Tree.size()+1 >= PARAMS["max_size"].get() + || child.Tree.max_depth()+1 >= PARAMS["max_depth"].get()) + { + // avoid using mutations that increase size/depth. New mutations that + // has similar behavior should be listed here. + options["insert"] = 0.0; + } + + // don't shrink an expression already at its minimum size + if (child.Tree.size() <= 1 || child.Tree.max_depth() <= 1) + { + // avoid using mutations that decrease size/depth. New mutations that + // has similar behavior should be listed here. + options["delete"] = 0.0; + } + // choose a valid mutation option string choice = r.random_choice(options); @@ -183,7 +205,7 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS bool success = it->second(child.Tree, spot, SS); if (success - && ((child.Tree.size() <= PARAMS["max_size"].get()) + && ((child.Tree.size() <= PARAMS["max_size"].get()) && (child.Tree.max_depth() <= PARAMS["max_depth"].get())) ){ return child; } else { diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index b75802ba..ed508429 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -124,6 +124,9 @@ TEST(Operators, MutationSizeAndDepthLimit) auto opt = PRG.mutate(); + // TODO: count the number of fails and assert that it is not equal to + // the number of mutations applied (there is no point in having mutation + // if it doesn't work) if (!opt){ fmt::print( "=================================================\n" From b0f9593afc02bf0870252fa46c5bcb86f0f7c487 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Thu, 1 Jun 2023 21:29:41 -0400 Subject: [PATCH 023/102] Update wrapper to as before std::optional and added offspring size --- src/brush/D_MAB_experiments.ipynb | 1 + src/brush/D_TS_experiments.ipynb | 1654 ++--------------------------- src/brush/D_TS_experiments.py | 12 + src/brush/deap_api/nsga2.py | 29 +- src/brush/estimator.py | 10 +- 5 files changed, 128 insertions(+), 1578 deletions(-) create mode 100644 src/brush/D_TS_experiments.py diff --git a/src/brush/D_MAB_experiments.ipynb b/src/brush/D_MAB_experiments.ipynb index 53a39184..486ba7c1 100644 --- a/src/brush/D_MAB_experiments.ipynb +++ b/src/brush/D_MAB_experiments.ipynb @@ -54,6 +54,7 @@ "source": [ "import numpy as np\n", "\n", + "# TODO: update this to work with optional mutation\n", "class D_MAB:\n", " def __init__(self, num_bandits, delta=0.15, lmbda=0.25):\n", " self.num_bandits = num_bandits\n", diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index cd7f23cd..8eeecd0c 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -61,8 +61,9 @@ " # Store learner status when the update function is called\n", " self.pull_history = {\n", " c:[] for c in ['t', 'arm idx', 'reward', 'update'] + \n", - " [f'alpha {i}' for i in range(num_bandits)] + \n", - " [f'beta {i}' for i in range(num_bandits)]} \n", + " [f'alpha {i}' for i in range(num_bandits)] + \n", + " [f'beta {i}' for i in range(num_bandits)] + \n", + " [f'weight {i}' for i in range(num_bandits)] } \n", "\n", " # This is the probability that should be used to update brush probs\n", " self._probabilities = np.ones(num_bandits)/num_bandits\n", @@ -73,7 +74,6 @@ "\n", " @property\n", " def probabilities(self):\n", - " # How to transform our Beta distributions into node probabilities?\n", " return self._probabilities\n", " \n", " @probabilities.setter\n", @@ -98,14 +98,12 @@ " return arm_idx\n", " \n", " def update(self, arm_idx, reward):\n", + " # There are informations about state. we'll save the pull history of\n", + " # other stuff after updating their values\n", " self.pull_history['t'].append( len(self.pull_history['t']) )\n", " self.pull_history['arm idx'].append( arm_idx )\n", " self.pull_history['reward'].append( reward )\n", " \n", - " for i in range(self.num_bandits):\n", - " self.pull_history[f'alpha {i}'].append( self._alphas[i] )\n", - " self.pull_history[f'beta {i}'].append( self._betas[i] )\n", - "\n", " if self._alphas[arm_idx] + self._betas[arm_idx] < self.C:\n", " # This is the pure thompson scheme\n", " self._alphas[arm_idx] = self._alphas[arm_idx]+reward\n", @@ -119,33 +117,28 @@ "\n", " self.pull_history['update'].append( 1 )\n", "\n", + " # How to transform our Beta distributions into node probabilities?\n", + " # onde idea is to return the expected value of this distribution as\n", + " # the weight that will be given to each arm. In the case of our prior\n", + " # (which is a beta distribution), the expected value is given by\n", + " # 1 / (1 + beta/alpha)\n", + " self._probabilities = 1 / (1 + (self._betas/self._alphas))\n", + "\n", + " # Now that we finished updating the values we save them to the logs\n", + " for i in range(self.num_bandits):\n", + " self.pull_history[f'alpha {i}'].append( self._alphas[i] )\n", + " self.pull_history[f'beta {i}'].append( self._betas[i] )\n", + " self.pull_history[f'weight {i}'].append( self.probabilities[i] )\n", + "\n", + "\n", " return self" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 372, 1: 473, 2: 395, 3: 297}\n", - "number of pulls for each arm: {1: 2976, 2: 2617, 0: 2384, 3: 2023}\n", - "(it was expected: similar amount of pulls for each arm)\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 8346, 1: 4, 2: 25, 3: 1}\n", - "number of pulls for each arm: {0: 9940, 2: 42, 1: 12, 3: 6}\n", - "(it was expected: more pulls for first arm, less pulls for last)\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 9, 1: 4841, 2: 13, 3: 3552}\n", - "number of pulls for each arm: {1: 5715, 3: 4242, 2: 24, 0: 19}\n", - "(it was expected: 2nd approx 4th > 1st > 3rd)\n" - ] - } - ], + "outputs": [], "source": [ "# Sanity checks\n", "import pandas as pd\n", @@ -191,7 +184,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -215,7 +208,7 @@ " # store all rewards in gen_rewards_ (which is reseted at the beggining\n", " # of every generation) and do a batch of updates only after finishing\n", " # mutating the solutions.\n", - " self.batch_size_ = self.pop_size*2 #\n", + " self.batch_size_ = self.pop_size #\n", " self.batch_rewards_ = []\n", "\n", " def _mutate(self, ind1):\n", @@ -227,52 +220,60 @@ " \n", " params = self.get_params()\n", " \n", - " ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", - " or ind1.prg.depth()+1>=self.max_depth) else False\n", + " # if the mutation returns an invalid expression, this should count as reward=0\n", + " # ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", + " # or ind1.prg.depth()+1>=self.max_depth) else False\n", "\n", " # Insert Mutation will not work, even if we force it, when the expression\n", " # is already at maximum size.\n", " # In this case, we'll do the mutation without controlling the probabilities.\n", - " if ignore_this_time:\n", - " for i, m in enumerate(self.mutations_):\n", - " params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", - " else:\n", - " mutation_idx = self.learner_.choose_arm()\n", + " # if ignore_this_time:\n", + " # for i, m in enumerate(self.mutations_):\n", + " # params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", + " # else:\n", + " # mutation_idx = self.learner_.choose_arm()\n", + "\n", + " # for i, m in enumerate(self.mutations_):\n", + " # params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", + "\n", + " mutation_idx = self.learner_.choose_arm()\n", "\n", - " for i, m in enumerate(self.mutations_):\n", - " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", + " for i, m in enumerate(self.mutations_):\n", + " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", "\n", " _brush.set_params(params)\n", " \n", - " opt, attempts = ind1.prg.mutate(), 0\n", - " while attempts < 10 and opt is None:\n", - " opt = ind1.prg.mutate()\n", - " attempts += 1\n", + " opt = ind1.prg.mutate()\n", + "\n", + " if opt:\n", + " offspring = creator.Individual(opt)\n", + " # print(\"mutation\")\n", + " # print(ind1.prg.get_model())\n", + " # print(offspring.prg.get_model())\n", + "\n", + " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", " \n", - " if opt is None:\n", - " return None\n", - " \n", - " offspring = creator.Individual(opt)\n", + " # We compare fitnesses using the deap overloaded operators\n", + " # from the docs: When comparing fitness values that are **minimized**,\n", + " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", + " # (this means that this comparison should work agnostic of min/max problems,\n", + " # or even a single-objective or multi-objective problem)\n", + " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", + " \n", + " # if not ignore_this_time:\n", + " # self.batch_rewards_.append( (mutation_idx, reward) )\n", "\n", - " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", - " \n", - " # We compare fitnesses using the deap overloaded operators\n", - " # from the docs: When comparing fitness values that are **minimized**,\n", - " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", - " # (this means that this comparison should work agnostic of min/max problems,\n", - " # or even a single-objective or multi-objective problem)\n", - " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", - " \n", - " if not ignore_this_time:\n", " self.batch_rewards_.append( (mutation_idx, reward) )\n", "\n", - " if len(self.batch_rewards_) > self.batch_size_:\n", - " for (mutation_idx, reward) in self.batch_rewards_:\n", - " self.learner_.update(mutation_idx, reward)\n", - " self.batch_rewards_ = []\n", - " \n", - " return offspring\n", - " \n", + " if len(self.batch_rewards_) >= self.batch_size_:\n", + " for (mutation_idx, reward) in self.batch_rewards_:\n", + " self.learner_.update(mutation_idx, reward)\n", + " self.batch_rewards_ = []\n", + " \n", + " return offspring\n", + "\n", + " return None\n", + "\n", " def fit(self, X, y):\n", "\n", " _brush.set_params(self.get_params())\n", @@ -286,7 +287,7 @@ "\n", " # We have 4 different mutations, and the learner will learn to choose\n", " # between these options by maximizing the reward when using each one\n", - " self.learner_ = D_TS(4, C=self.pop_size*3)\n", + " self.learner_ = D_TS(4, C=self.pop_size) # C=self.pop_size\n", "\n", " if isinstance(self.functions, list):\n", " self.functions_ = {k:1.0 for k in self.functions}\n", @@ -357,546 +358,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0, 1, 2, Input contains NaN.\n", - "3, Input contains NaN.\n", - "4, Input contains NaN.\n", - "5, Input contains NaN.\n", - "6, Input contains NaN.\n", - "7, Input contains NaN.\n", - "8, 9, Input contains NaN.\n", - "10, 11, Input contains NaN.\n", - "12, Input contains NaN.\n", - "13, Input contains NaN.\n", - "14, 15, Input contains NaN.\n", - "16, 17, 18, Input contains NaN.\n", - "19, Input contains NaN.\n", - "20, 21, 22, 23, Input contains NaN.\n", - "24, 25, 26, Input contains NaN.\n", - "27, Input contains NaN.\n", - "28, 29, \n" - ] - }, - { - "data": { - "text/html": [ - 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6962.000000 2957.000000 \n", - "\n", - "Brush version \n", - "metric delete mutation calls toggle_weight mutation calls \n", - "count 14.000000 14.000000 \n", - "mean 1346.857143 764.785714 \n", - "std 345.368445 362.851565 \n", - "min 610.000000 246.000000 \n", - "25% 1153.250000 489.750000 \n", - "50% 1465.000000 839.500000 \n", - "75% 1526.750000 945.500000 \n", - "max 1825.000000 1350.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", "if __name__ == '__main__':\n", @@ -918,7 +382,7 @@ " y = data['target']\n", "\n", " kwargs = {\n", - " 'verbosity' : False,\n", + " 'verbosity' : True,\n", " 'pop_size' : 100,\n", " 'max_gen' : 100,\n", " 'max_depth' : 10,\n", @@ -942,8 +406,15 @@ " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", + " print(f\"est, \", end='\\n' if (i==29) else '')\n", " est = BrushRegressor(**kwargs).fit(X,y)\n", + " print(f\"fit, \", end='\\n' if (i==29) else '')\n", + " est.score(X,y)\n", + "\n", + " print(f\"est_mab, \", end='\\n' if (i==29) else '')\n", " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", + " print(f\"fit, \", end='\\n' if (i==29) else '')\n", + " est_mab.score(X,y)\n", "\n", " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", " \n", @@ -971,50 +442,23 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# print(est.best_estimator_.get_model())\n", + "\n", + "# mut = est.best_estimator_.mutate()\n", + "# if mut:\n", + "# print(est.best_estimator_.get_model())\n", + "# print(mut.get_model())" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def generate_plots():\n", " !pip install matplotlib > /dev/null\n", @@ -1059,10 +503,11 @@ " plt.show()\n", "\n", " # Approximating the percentage of usage for each generation ----------------\n", + " # TODO: test if different batch sizes will produce different plots here\n", " data = np.zeros( (kwargs['max_gen'], 4) )\n", " for g in range(kwargs['max_gen']):\n", - " idx_start = g*(learner_log.shape[0]%kwargs['max_gen'])\n", - " idx_end = (g+1)*(learner_log.shape[0]%kwargs['max_gen'])\n", + " idx_start = g*(learner_log.shape[0]//kwargs['max_gen'])\n", + " idx_end = (g+1)*(learner_log.shape[0]//kwargs['max_gen'])\n", "\n", " df_in_range = learner_log.iloc[idx_start:idx_end]\n", " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", @@ -1118,867 +563,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" - ] - }, - { - "data": { - "text/html": [ - "
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6.000000 6644.000000 3869.000000 \n", - "\n", - "Brush version \n", - "metric delete mutation calls toggle_weight mutation calls \n", - "count 30.000000 30.000000 \n", - "mean 1999.566667 1238.566667 \n", - "std 483.568965 513.808275 \n", - "min 609.000000 85.000000 \n", - "25% 1765.500000 988.500000 \n", - "50% 2033.500000 1399.000000 \n", - "75% 2375.750000 1604.000000 \n", - "max 2600.000000 1861.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if __name__ == '__main__':\n", " from brush import BrushClassifier\n", @@ -2048,50 +635,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "generate_plots()" ] diff --git a/src/brush/D_TS_experiments.py b/src/brush/D_TS_experiments.py new file mode 100644 index 00000000..899aeaa7 --- /dev/null +++ b/src/brush/D_TS_experiments.py @@ -0,0 +1,12 @@ + +from brush import BrushRegressor +import pandas as pd + +if __name__ == '__main__': + + data = pd.read_csv('docs/examples/datasets/d_example_patients.csv') + X = data.drop(columns='target') + y = data['target'] + + est = BrushRegressor().fit(X,y) + \ No newline at end of file diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 81f52f7d..9c6aeed6 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -15,7 +15,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # stats.register("max", np.max, axis=0) logbook = tools.Logbook() - logbook.header = "gen", "evals", "ave", "std", "min" + logbook.header = "gen", "evals", "offspring", "ave", "std", "min" pop = toolbox.population(n=MU) @@ -43,27 +43,16 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # offspring = [toolbox.clone(ind) for ind in offspring] offspring = [] - # Since crossover/mutation can fail, we'll cycle through the parents - # until we have an offspring big enough - index, num_attempts = 0, 0 - - # iterate over the array until a criterion is met - while num_attempts < len(parents) and len(offspring) < len(parents): - index1 = (index + 1) % len(parents) - index2 = (index + 2) % len(parents) - - ind1, ind2 = parents[index1], parents[index2] - + for ind1, ind2 in zip(parents[::2], parents[1::2]): if random.random() <= CXPB: ind1, ind2 = toolbox.mate(ind1, ind2) + + off1 = toolbox.mutate(ind1) + off2 = toolbox.mutate(ind2) - if ind1 is not None: ind1 = toolbox.mutate(ind1) - if ind1 is not None: offspring.append(ind1) - - if ind2 is not None: ind2 = toolbox.mutate(ind2) - if ind2 is not None: offspring.append(ind2) - - index += 2 + # avoid inserting empty solutions + if off1: offspring.extend([off1]) + if off2: offspring.extend([off2]) # archive.update(offspring) # Evaluate the individuals with an invalid fitness @@ -75,7 +64,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # Select the next generation population pop = toolbox.survive(pop + offspring, MU) record = stats.compile(pop) - logbook.record(gen=gen, evals=len(invalid_ind), **record) + logbook.record(gen=gen, evals=len(invalid_ind), offspring=len(offspring), **record) if verbosity > 0: print(logbook.stream) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 1847b6c8..16bb3014 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -123,9 +123,11 @@ def _crossover(self, ind1, ind2): def _mutate(self, ind1): # offspring = (creator.Individual(ind1.prg.mutate(self.search_space_)),) - opt = ind1.prg.mutate() - if opt is not None: - return creator.Individual(opt) + offspring = ind1.prg.mutate() + + if offspring: + return creator.Individual(offspring) + return None def fit(self, X, y): @@ -279,7 +281,7 @@ def __init__(self, **kwargs): def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) return ( - np.sum((data.y- ind.prg.predict(data))**2), + np.mean((data.y- ind.prg.predict(data))**2), ind.prg.size() ) From 3f91d4ffd42a51fee5ab71907e05b9895e41f5d2 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Fri, 2 Jun 2023 11:00:11 -0400 Subject: [PATCH 024/102] Code cleaning --- src/brush/D_TS_experiments.ipynb | 639 +++++++++++++++++++++++++++++-- src/program/program.h | 2 + tests/cpp/test_program.cpp | 6 - tests/cpp/test_variation.cpp | 10 +- 4 files changed, 619 insertions(+), 38 deletions(-) diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index 8eeecd0c..5703f0a8 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -43,12 +43,18 @@ "\n", "> In our work, the mutations would be the arms, and this update would be used during the evolution to adjust the mutation probabilities.\n", "\n", - "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user." + "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user.\n", + "\n", + "My suggestion is that we use the expected value for each Beta distribution:\n", + "\n", + "$$\\mathop{\\mathbb{E}}[X] = \\int_{0}^{\\infty} x,$$\n", + "\n", + "which, in our case, would be calculated for each arm $k$, so we replace $f(x; \\cdot)$ by $p(\\theta^k; \\cdot)$ and we can get these weights. Since the C++ does not expect the weights to have their sum equals to 1, as long as the proportions are representative, we can work with this. Execution logs shows us some empirical evidence that the expected value is proportional to the average number of times each mutation was used (plots 3 and 4)." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -122,7 +128,8 @@ " # the weight that will be given to each arm. In the case of our prior\n", " # (which is a beta distribution), the expected value is given by\n", " # 1 / (1 + beta/alpha)\n", - " self._probabilities = 1 / (1 + (self._betas/self._alphas))\n", + " #self._probabilities = 1 / (1 + (self._betas/self._alphas))\n", + " self._probabilities = (self._alphas-1)/(self._alphas+self._betas-2)\n", "\n", " # Now that we finished updating the values we save them to the logs\n", " for i in range(self.num_bandits):\n", @@ -136,9 +143,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 361, 1: 366, 2: 338, 3: 497}\n", + "number of pulls for each arm: {3: 3077, 1: 2398, 0: 2370, 2: 2155}\n", + "(it was expected: similar amount of pulls for each arm)\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 8293, 1: 11, 2: 21, 3: 0}\n", + "number of pulls for each arm: {0: 9933, 2: 39, 1: 23, 3: 5}\n", + "(it was expected: more pulls for first arm, less pulls for last)\n", + "------------------------ optimizing ------------------------\n", + "cum. reward for each arm : {0: 50, 1: 3940, 2: 1, 3: 4384}\n", + "number of pulls for each arm: {3: 5226, 1: 4694, 0: 74, 2: 6}\n", + "(it was expected: 2nd approx 4th > 1st > 3rd)\n" + ] + } + ], "source": [ "# Sanity checks\n", "import pandas as pd\n", @@ -184,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -358,9 +384,488 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.75]\t[ nan 0.92059763]\t[nan 20.]\n", + "1 \t94 \t94 \t[ nan 15.6] \t[ nan 6.08604962]\t[nan 1.]\n", + "2 \t97 \t97 \t[ nan 9.28] \t[ nan 5.50105444]\t[nan 1.]\n", + "3 \t99 \t99 \t[ nan 3.33] \t[ nan 2.34117492]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 1.59] \t[ nan 0.70844901]\t[nan 1.]\n", + "5 \t100 \t100 \t[0.61577302 1.18 ]\t[0.33362003 0.38418745]\t[0.26090035 1. ]\n", + "6 \t100 \t100 \t[0.44268739 1.15 ]\t[0.12072349 0.35707142]\t[0.26090035 1. ]\n", + "7 \t100 \t100 \t[0.38106325 1.09 ]\t[0.04378987 0.28618176]\t[0.26090035 1. ]\n", + "8 \t100 \t100 \t[0.38200725 1.07 ]\t[0.02594982 0.38091994]\t[0.24684934 1. ]\n", + "9 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", + "10 \t100 \t100 \t[0.38211246 1.07 ]\t[0.03103629 0.45287967]\t[0.13583826 1. ]\n", + "11 \t100 \t100 \t[0.37959786 1.15 ]\t[0.03953694 0.90967027]\t[0.13583824 1. ]\n", + "12 \t100 \t100 \t[0.37819053 1.17 ]\t[0.04168421 0.92795474]\t[0.13583824 1. ]\n", + "13 \t100 \t100 \t[0.37567592 1.25 ]\t[0.04814319 1.21140414]\t[0.13583824 1. ]\n", + "14 \t100 \t100 \t[0.37567592 1.25 ]\t[0.04814319 1.21140414]\t[0.13583824 1. ]\n", + "15 \t100 \t100 \t[0.37280568 1.3 ]\t[0.05723907 1.37477271]\t[0.06369215 1. ]\n", + "16 \t100 \t100 \t[0.37604174 1.2 ]\t[0.0480877 0.96953597]\t[0.06369214 1. ]\n", + "17 \t100 \t100 \t[0.37744907 1.18 ]\t[0.04630412 0.95268043]\t[0.06369214 1. ]\n", + "18 \t100 \t100 \t[0.37744907 1.18 ]\t[0.04630412 0.95268043]\t[0.06369214 1. ]\n", + "19 \t100 \t100 \t[0.37282535 1.27 ]\t[0.05713309 1.18198985]\t[0.06369214 1. ]\n", + "20 \t100 \t100 \t[0.37282535 1.27 ]\t[0.05713309 1.18198985]\t[0.06369214 1. ]\n", + "21 \t100 \t100 \t[0.36827331 1.39 ]\t[0.06597425 1.59307878]\t[0.05849276 1. ]\n", + "22 \t100 \t100 \t[0.36480056 1.45 ]\t[0.07359937 1.65151446]\t[0.03482345 1. ]\n", + "23 \t100 \t100 \t[0.36480056 1.45 ]\t[0.07359937 1.65151446]\t[0.03482345 1. ]\n", + "24 \t100 \t100 \t[0.36480056 1.45 ]\t[0.07359937 1.65151446]\t[0.03482345 1. ]\n", + "25 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "26 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "27 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", + "28 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", + "29 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", + "30 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656503 1. ]\n", + "31 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "32 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "33 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "34 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "35 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", + "36 \t100 \t100 \t[0.37866097 1.13 ]\t[0.03470452 0.57714816]\t[0.19034052 1. ]\n", + "37 \t100 \t100 \t[0.37866097 1.13 ]\t[0.03470452 0.57714816]\t[0.19034052 1. ]\n", + "38 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "39 \t100 \t100 \t[0.38189896 1.11 ]\t[0.02650796 0.64645185]\t[0.24240461 1. ]\n", + "40 \t100 \t100 \t[0.38189883 1.11 ]\t[0.02650865 0.64645185]\t[0.24239156 1. ]\n", + "41 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", + "42 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", + "43 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", + "44 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", + "45 \t100 \t100 \t[0.37997235 1.1 ]\t[0.03248637 0.5 ]\t[0.1889583 1. ] \n", + "46 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", + "47 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", + "48 \t100 \t100 \t[0.37998617 1.1 ]\t[0.03240528 0.5 ]\t[0.19034052 1. ]\n", + "49 \t100 \t100 \t[0.37998617 1.1 ]\t[0.03240528 0.5 ]\t[0.19034052 1. ]\n", + "50 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", + "51 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", + "52 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", + "53 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", + "54 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", + "55 \t100 \t100 \t[0.38027484 1.1 ]\t[0.0308036 0.5 ] \t[0.21920769 1. ]\n", + "56 \t100 \t100 \t[0.38027484 1.1 ]\t[0.0308036 0.5 ] \t[0.21920769 1. ]\n", + "57 \t100 \t100 \t[0.38005515 1.1 ]\t[0.03200642 0.5 ]\t[0.19723824 1. ]\n", + "58 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", + "59 \t100 \t100 \t[0.38022217 1.09 ]\t[0.03108221 0.42649736]\t[0.21394053 1. ]\n", + "60 \t100 \t100 \t[0.37835398 1.13 ]\t[0.03584952 0.57714816]\t[0.20047927 1. ]\n", + "61 \t100 \t100 \t[0.37835398 1.13 ]\t[0.03584952 0.57714816]\t[0.20047927 1. ]\n", + "62 \t100 \t100 \t[0.37835398 1.13 ]\t[0.03584952 0.57714816]\t[0.20047927 1. ]\n", + "63 \t100 \t100 \t[0.37475221 1.2 ]\t[0.04309672 0.74833148]\t[0.20047927 1. ]\n", + "64 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "65 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "66 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "67 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", + "68 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", + "69 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", + "70 \t100 \t100 \t[0.3817898 1.07 ] \t[0.02710657 0.38091994]\t[0.2299699 1. ] \n", + "71 \t100 \t100 \t[0.38021321 1.12 ]\t[0.03103986 0.62096699]\t[0.22963998 1. ]\n", + "72 \t100 \t100 \t[0.38021321 1.12 ]\t[0.03103986 0.62096699]\t[0.22963998 1. ]\n", + "73 \t100 \t100 \t[0.3768396 1.16 ] \t[0.03859054 0.64373908]\t[0.19034052 1. ]\n", + "74 \t100 \t100 \t[0.3768396 1.16 ] \t[0.03859054 0.64373908]\t[0.19034052 1. ]\n", + "75 \t100 \t100 \t[0.37442131 1.22 ]\t[0.04491781 0.86694867]\t[0.14546938 1. ]\n", + "76 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "77 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "78 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "79 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", + "80 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "81 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "82 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "83 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", + "84 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "85 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "86 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "87 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "88 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "89 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "90 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", + "91 \t100 \t100 \t[0.3833479 1.05 ] \t[0.02250061 0.32787193]\t[0.24504705 1. ]\n", + "92 \t100 \t100 \t[0.38001964 1.11 ]\t[0.03227117 0.54580216]\t[0.19034052 1. ]\n", + "93 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", + "94 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", + "95 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", + "96 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", + "97 \t100 \t100 \t[0.37818345 1.09 ]\t[0.04585887 0.42649736]\t[3.37517238e-04 1.00000000e+00]\n", + "98 \t100 \t100 \t[0.37818345 1.09 ]\t[0.04585887 0.42649736]\t[3.37517238e-04 1.00000000e+00]\n", + "99 \t100 \t100 \t[0.37818345 1.09 ]\t[0.04585887 0.42649736]\t[3.37517238e-04 1.00000000e+00]\n", + "Final population hypervolume is 49499.533985\n", + "best model: 2.04*Cos(Sub(1.12*x2,1.08*x1))\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.84]\t[ nan 0.91345498]\t[nan 18.]\n", + "1 \t0 \t91 \t[ nan 14.52]\t[ nan 6.47993827]\t[nan 1.]\n", + "2 \t0 \t100 \t[ nan 6.36] \t[ nan 4.80524713]\t[nan 1.]\n", + "3 \t0 \t100 \t[ nan 2.04] \t[ nan 1.67284189]\t[nan 1.]\n", + "4 \t0 \t100 \t[5.27745839 1.04 ]\t[1.22288142 0.19595918]\t[2.73843455 1. ]\n", + "5 \t0 \t100 \t[4.41301567 1.02 ]\t[1.02084156 0.14 ]\t[2.73843455 1. ]\n", + "6 \t0 \t100 \t[3.8616382 1.01 ] \t[0.11288621 0.09949874]\t[2.73843455 1. ]\n", + "7 \t0 \t100 \t[3.83780377 1.07 ]\t[0.20009637 0.45287967]\t[2.67845964 1. ]\n", + "8 \t0 \t100 \t[3.82591384 1.09 ]\t[0.230646 0.49183331]\t[2.67845964 1. ]\n", + "9 \t0 \t100 \t[3.82591384 1.08 ]\t[0.230646 0.41665333]\t[2.67845964 1. ]\n", + "10 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ]\n", + "11 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ]\n", + "12 \t0 \t100 \t[3.80922492 1.1 ]\t[0.27911144 0.5 ]\t[2.48370647 1. ]\n", + "13 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", + "14 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", + "15 \t0 \t100 \t[3.80953091 1.11 ]\t[0.27767113 0.58129167]\t[2.51430607 1. ]\n", + "16 \t0 \t100 \t[3.82006443 1.11 ]\t[0.26238577 0.73341666]\t[2.25763559 1. ]\n", + "17 \t0 \t100 \t[3.82006443 1.08 ]\t[0.26238577 0.4621688 ]\t[2.25763559 1. ]\n", + "18 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[2.88191373e-08 1.00000000e+00]\n", + "19 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572203 0.4621688 ]\t[2.88191373e-08 1.00000000e+00]\n", + "20 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[2.74334333e-10 1.00000000e+00]\n", + "21 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[2.74334333e-10 1.00000000e+00]\n", + "22 \t0 \t100 \t[3.80883429 1.06 ]\t[0.42256887 0.36932371]\t[2.74334333e-10 1.00000000e+00]\n", + "23 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "24 \t0 \t100 \t[3.84805069 1.03 ]\t[0.17524863 0.2215852 ]\t[2.51430631 1. ] \n", + "25 \t0 \t100 \t[3.83446392 1.05 ]\t[0.21979537 0.29580399]\t[2.51430631 1. ] \n", + "26 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "27 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "28 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269327 0.2215852 ]\t[2.68406439 1. ] \n", + "29 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566732 0.29580399]\t[2.46565056 1. ] \n", + "30 \t0 \t100 \t[3.82786834 1.07 ]\t[0.26350819 0.45287967]\t[1.90340519 1. ] \n", + "31 \t0 \t100 \t[3.80817256 1.11 ]\t[0.32567449 0.59824744]\t[1.90340519 1. ] \n", + "32 \t0 \t100 \t[3.80642941 1.12 ]\t[0.33596219 0.66753277]\t[1.78690541 1. ] \n", + "33 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "34 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566732 0.29580399]\t[2.46565056 1. ] \n", + "35 \t0 \t100 \t[3.82241812 1.08 ]\t[0.30554029 0.54184869]\t[1.3583827 1. ] \n", + "36 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", + "37 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", + "38 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", + "39 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", + "40 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "41 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "42 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "43 \t0 \t100 \t[3.81775795 1.08 ]\t[0.27237438 0.41665333]\t[2.29969907 1. ] \n", + "44 \t0 \t100 \t[3.79806216 1.12 ]\t[0.3322903 0.5706137] \t[1.90340519 1. ] \n", + "45 \t0 \t100 \t[3.79701344 1.12 ]\t[0.33837801 0.5706137 ]\t[1.79853261 1. ] \n", + "46 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "47 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "48 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "49 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "50 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "51 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "52 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", + "53 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "54 \t0 \t100 \t[3.80865912 1.11 ]\t[0.32369874 0.59824744]\t[1.90340519 1. ] \n", + "55 \t0 \t100 \t[3.80817256 1.11 ]\t[0.32567448 0.59824744]\t[1.90340519 1. ] \n", + "56 \t0 \t100 \t[3.80817256 1.11 ]\t[0.32567448 0.59824744]\t[1.90340519 1. ] \n", + "57 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "58 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897879 0.2215852 ]\t[2.46565056 1. ] \n", + "59 \t0 \t100 \t[3.82241812 1.07 ]\t[0.30554029 0.45287967]\t[1.35838258 1. ] \n", + "60 \t0 \t100 \t[3.82241812 1.07 ]\t[0.30554029 0.45287967]\t[1.35838258 1. ] \n", + "61 \t0 \t100 \t[3.75697815 1.18 ]\t[0.49305594 0.81706793]\t[0.63692158 1. ] \n", + "62 \t0 \t100 \t[3.75697815 1.18 ]\t[0.49305596 0.81706793]\t[0.63692147 1. ] \n", + "63 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "64 \t0 \t100 \t[3.83774699 1.08 ]\t[0.20042404 0.50358713]\t[2.67846012 1. ] \n", + "65 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "66 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269327 0.2215852 ]\t[2.68406439 1. ] \n", + "67 \t0 \t100 \t[3.82786835 1.07 ]\t[0.26350818 0.45287967]\t[1.90340519 1. ] \n", + "68 \t0 \t100 \t[3.82786835 1.07 ]\t[0.26350818 0.45287967]\t[1.90340519 1. ] \n", + "69 \t0 \t100 \t[3.81053256 1.1 ]\t[0.3124496 0.53851648]\t[1.90340519 1. ] \n", + "70 \t0 \t100 \t[3.79079968 1.15 ]\t[0.3656629 0.72629195]\t[1.89969528 1. ] \n", + "71 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "72 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269326 0.2215852 ]\t[2.68406463 1. ] \n", + "73 \t0 \t100 \t[3.83780304 1.06 ]\t[0.20010039 0.36932371]\t[2.67846036 1. ] \n", + "74 \t0 \t100 \t[3.81357457 1.12 ]\t[0.25905493 0.5706137 ]\t[2.63905644 1. ] \n", + "75 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "76 \t0 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0.94207218]\t[0.63692141 1. ] \n", + "88 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", + "89 \t0 \t100 \t[3.84805069 1.03 ]\t[0.17524863 0.2215852 ]\t[2.51430631 1. ] \n", + "90 \t0 \t100 \t[3.84805069 1.03 ]\t[0.17524865 0.2215852 ]\t[2.51430607 1. ] \n", + "91 \t0 \t100 \t[3.84805069 1.03 ]\t[0.17524865 0.2215852 ]\t[2.51430607 1. ] \n", + "92 \t0 \t100 \t[3.84805069 1.03 ]\t[0.17524865 0.2215852 ]\t[2.51430607 1. ] \n", + "93 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897879 0.2215852 ]\t[2.46565056 1. ] \n", + "94 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", + "95 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "96 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "97 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "98 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "99 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "Final population hypervolume is 49377.110287\n", + "fit, " + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "Brush version Original Modified \n", + "metric score size depth score size depth point mutation calls \n", + "count 1.000000 1.0 1.0 1.000000 1.0 1.0 1.0 \\\n", + "mean 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", + "std NaN NaN NaN NaN NaN NaN NaN \n", + "min 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", + "25% 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", + "50% 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", + "75% 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", + "max 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", + "\n", + "Brush version \n", + "metric insert mutation calls delete mutation calls \n", + "count 1.0 1.0 \\\n", + "mean 2756.0 2437.0 \n", + "std NaN NaN \n", + "min 2756.0 2437.0 \n", + "25% 2756.0 2437.0 \n", + "50% 2756.0 2437.0 \n", + "75% 2756.0 2437.0 \n", + "max 2756.0 2437.0 \n", + "\n", + "Brush version \n", + "metric toggle_weight mutation calls \n", + "count 1.0 \n", + "mean 966.0 \n", + "std NaN \n", + "min 966.0 \n", + "25% 966.0 \n", + "50% 966.0 \n", + "75% 966.0 \n", + "max 966.0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", "if __name__ == '__main__':\n", @@ -402,7 +907,7 @@ " names=('Brush version', 'metric')))\n", " \n", " est_mab = None\n", - " for i in range(30):\n", + " for i in range(1):\n", " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", @@ -442,23 +947,60 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# print(est.best_estimator_.get_model())\n", - "\n", - "# mut = est.best_estimator_.mutate()\n", - "# if mut:\n", - "# print(est.best_estimator_.get_model())\n", - "# print(mut.get_model())" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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BcTBiKrgbXBuTaFMdbg1Xl2Cuh+fDXfPsPxeCzvu8Lr3yyiu59957URSF6dOn8/rrr7NmzRqSkpI4duwY8fHxDBs2DIDIyEjHdZmZmWzbto39+/fj4eGBoijMnj2bhQsX8sMPPzBlyhTAPkr29ttvExAQ4Fg7ptPp8PT0JCQkBJvN5vI1ZUIIIYQQF03ellPJVvRgGHQHaD2lEEYXIwmXcOjTp4/jtUqlIiQkhLKyMsC+J9oNN9zAkCFDGDNmDOPGjeOyyy4DYM+ePdTV1dGzZ09UKhWKYt8jorGxkby8PMc9o6KiCAwMdMmaMSGEEEKIDuXweth+YvlG0mjod9upyoOiS5GEqz1ovewjTa569vleqtU2O1apVI7kqF+/fmzbto1ly5axatUq7r77bkaNGsXHH39MXV0dISEhfPvtt80SLgCD4dSQuJfX+ccmhBBCCNFl5KyG7Z/YX0f0hz4TXRuPaFeScLUHleq8p/V1ZL6+vtx4443ccMMNXHvttdx8881UVlbSr18/SktLcXNzIyYmplnCdS5arVZGvIQQQgjRPSgKZK+wl34HiB4Cg+857/X3onOQhEu0yhtvvEFwcDBpaWmoVCq+++47goODMRgMjB49msGDBzN58mSeeeYZ4uPjKS4uZunSpUyYMIH+/fuf9b7R0dFs3bqV/Px8PD09CQgIQK12afFMIYQQQoi2Z7PCrnmQvdp+nHAppN8uyVY3IAmXaBW9Xs9//vMfDh8+jFqtpn///nz22Weo1WpUKhWfffYZf//735k2bRrl5eUEBweTkZFxzqqDDz30EA8++CAjRoygoaGBrVu3Eh0dfZHelRBCCCFEO2uohuwl9j+mE8UwUq+FlGsl2eomVIoz87+6KaPRiMFgoLq6Gl9f32bnGhsbyc3NJS4uDg8PDxdFeH4URXFsfKxWq1EUBZPJhKIoqNVqbDYb1tOq5JysIqgoimPa4MmEy2azNTt/OpVKhVqtdjzjTNUIT7/+lzGey8lrukJfs9nMsWPHOHjwIE1NTWhOLJ61tqJakTN9ne0vfaXv+fbtKHFI367ft6PEIX07b1+NRoPVam27e5vqIWsxmpxFYFGw2gAN0HcSJI5qk5i7U1+DwUD//v1JTEw85/Xt7ddygzORES4hhBBCCCHaUsUR2PwB1BSBGtCHQvzlEDsM3NxdHZ24yCThEkIIIYQQoq3kb4ctb4IN0Gkh5QaIHwkaSbS6K0m4hBBCCCGEuFCmOtjzJRxeZz8O7AWDJoMhzH4smxl3W5JwCSGEEEIIcSEq8mDbPKjKtR9HD4GBU0Cjc2lYomOQhEsIIYQQQojzYbXCns8ha4X92A0YMBWi010ZlehgJOESQgghhBDCWaZayHwfinfZj4N7Q9qN4Cfb24jmJOESQgghhBDCGXVlsPJFqK0AFTDwXogd4uqoRAclCZcQQgghhBCt1WiENa9AfQV4+UG/OyEi1dVRiQ5MEi4hhBBCCCFao/Y4bPrQvr+Whw9c+hj4hLo6KtHBqV0dgOh4rr76ambMmNGqvvPmzSMmJqadIxJCCCGEcLHa47Dyb1CRDVoNjHhUki3RKpJwiYuuR48eLFiwwNVhCCGEEEK0jqkONrwLjQ32JGvULAiIdXVUopOQKYVCCCGEEEKcjaLAlo+gKg90bjB8KvhFuDoq0YnICFc3V1dXx1133YW/vz8xMTH83//9X7PzTU1NPP3006SmphIVFcXll1/O2rVrf/WeixYtYsyYMURGRjJo0CD++c9/YrFYAEhLSwPg9ttvJyAggH79+p31updeeslxnRBCCCGES+z7Fgp32KsRDpsu0wiF02SEqx0oikKDpcElz/Z080SlUrW6/5/+9CfWrFnD/PnzCQoK4qmnnmLnzp2OxGjGjBkcOHCA999/n5CQEBYsWMDNN9/M6tWrSUhIaHG/zMxMpk6dyvPPP8+wYcPIy8vjscceQ6VSMXPmTJYvX05SUhKvv/46Y8eORaPROK576KGHWlynKApPPPFE23xxhBBCCCGckb0S9i+0v+53KwQnuTQc0TlJwtUOGiwNDJ031CXP3njrRry0Xq3qW1tby5w5c5gzZw5jxoxBURTeeecdkpOTATh69Chz585l9+7dhIWFYbPZmDZtGsuWLePTTz9l1qxZLe750ksv8fDDDzNx4kQAYmNj+dOf/sSzzz7LzJkzCQwMBMBgMBASEnLO62bPni0JlxBCCCEuvgMLYM+39tfJ4yFhtCujEZ2YJFzdWE5ODiaTicGDBzvaAgICSEqy//Zm7969WK3WZufBPs3Q39//jPfcu3cvmzZt4pVXXnG02Ww2Ghsbqa+vx9PT87yu8/JqXRIphBBCCHHBDq8/lWwlXAK9bwInZhAJcTpJuNqBp5snG2/d6LJnt5W6ujo0Gg0rVqxAo9Fgs9lQqVQoinLWBKiuro4ZM2YwYcIER5tKpUKtVuPh4fGrz3riiSe45pprmrUrivKr1wkhhBBCtKmKPNgxx/661zjo+zuXhiM6P0m42oFKpWr1tD5XSkhIQKvVsnnzZmJjYwGorKwkOzubSy65hLS0NKxWK8ePH2f48OHNEi5FUc54z7S0NLKzs4mPj3e0nUy41Go1iqKg1WqxWq0trsvJyWl2HXDW5wghhBBCtDlTLWx6B2xAaB/7yJYQF0gSrm5Mr9dz5513OtZWBQUFMWvWLNRqe/HKpKQkfve73/HAAw/w97//nT59+lBeXs6qVatITU1l3LhxLe75+OOPc+uttxIZGcm1116LWq1m7969HDx4kKeffhqA6OhoVq9ezdChQ3F3d8fPz++s1+3fv5+ZM2de1K+LEEIIIbohqxU2vg+1ZeBlgEF3gloKeosLJz9F3dyLL77IiBEjuOGGG7jqqqvIyMggPT3dcf6NN95g4sSJzJo1i6FDh3L77bezfft2IiMjz3i/MWPGMHfuXFasWMG4ceO48sorefvtt5v1f/bZZ1m5ciV9+/Zl1KhRjuv+97///ep1QgghhBDtQlFg2wdQvMv+6Xjw/eDh6+qoRBehUmTO1jkZjUYMBgPV1dX4+jb/y9fY2Ehubi5xcXGdbq2RoihYLBZsNptjup/JZEJRFNRqNTabrdnUvzNNKVSr1ahUKmw2W7Pzp/vllMKT/U53+vW/jPFcTl7TFfqazWaOHTvGwYMHaWpqcpTN/+UUzDNxpq+z/aWv9D3fvh0lDunb9ft2lDikbyftm70CzZ55WG1gHfQARA9wTRzS96x9DQYD/fv3JzEx8ZzXt7dfyw3OREa4hBBCCCFE95W/E3bPs7/ue/M5ky0hnCUJlxBCCCGE6J6OZ8Pm10EBIgdDr/Gujkh0Qa0qmnHjjTe2+oZff/31eQcjhBBCCCHERdFQBZvetCdbEf1h6B8A2WtLtL1WjXAZDAbHH19fX5YtW8aWLVsc57du3cqyZcswGAztFqgQQgghhBBtovY4rHkVGmpAHwKD7waN1tVRiS6qVSNcc+bMcbx+8sknufnmm3n77bebLWh78MEHW7VoTAghhBBCCJepr4C1/7aXf/fwhGH3ga7j758qOi+n13B9+OGHPP74445kC+yVRKZPn86HH37YpsEJIYQQQgjRZkoOwep/ndhryw9GzwK/aFdHJbo4pxMui8XCgQMHWrQfOHAAm83WJkEJIYQQQgjRZhQF9v0Aa16yTyf08IZLHgV9sKsjE91Aq6YUnu7OO+/k7rvvJicnhyFDhgCwceNGXnzxRe688842D1AIIYQQQojzpiiw61PIWmE/DkuDgVNkY2Nx0TidcP3rX/8iNDSUl19+maKiIgDCwsJ44okneOyxx9o8QCGEEEIIIc7brnmQtdL+OvVaSL4G1LIzkrh4nP5pU6vVzJgxg4KCAqqqqqiqqqKgoIAZM2Y0W9fVGm+99RZpaWn4+vri6+tLRkYGixYtcpxvbGxk6tSp9OjRA71ez0033URJSUmze+Tn5zNhwgS8vLwIDg7miSeewGKxNOuzcuVKBgwYgLu7O4mJiXz00UfOvm3RxgICAliwYIGrwzij66+/nlmzZjl1TVBQEAsXLmyniIQQQgjhtPoKyHzzVLLV/3ZIvU6SLXHRnddPnMVi4eeff+bTTz9FpbLvV1BYWEhtba1T94mMjOTFF19k69atbNmyhTFjxvCb3/yGvXv3AvDoo4/yww8/8OWXX7Jq1SoKCwub7QlmtVqZMGECJpOJ9evX8/HHH/PRRx/xl7/8xdEnNzeXCRMmcNlll7Fjxw4eeeQR7rnnHhYvXnw+b71Lueyyy2RU8gzmzJnDn/70pza957p16wgODqa6urpN7yuEEEKIM8jfDkuehGMntjHqcyMkjHRtTKLbcnpK4ZEjR7jyyivJz8+nqamJK664Ah8fH/7xj3/Q1NTE22+/3ep7XXvttc2On3vuOd566y02bNhAZGQkH3zwAfPmzWPMmDGA/YNwSkoKGzZsYNiwYSxZsoR9+/bx888/ExISQnp6On/729948skneeaZZ9DpdLz99tvExcXx8ssvA5CSksLatWt55ZVXGD9edhMXLfn7+7s6BCGEEEKcD3MD7P0KslfbhxW8/KHvZIjo4+rIRDfm9AjXH//4RwYNGkRlZSWenp6O9htuuIFly5addyBWq5XPPvuMuro6MjIy2Lp1K2azmcsvv9zRJzk5mejoaDIzMwHIzMykb9++hISEOPqMHz8eo9HoGCXLzMxsdo+TfU7e40yampowGo3N/nQ1d9xxB6tWreI///kPHh4e6HQ68vLyWLt2LaNHjyYgIICkpCSeeeaZZlM0a2pq+MMf/kB0dDSpqam8+eabXHPNNfz5z3929CkuLub3v/89UVFRDBw4kK+++op+/frx5ptvnjWegoIC7rnnHhISEkhKSuL2228nPz//nO9j//79BAcHU1ZWBkBlZSUhISHcd999jj7//ve/ueaaa5pdM3HiRGJjY0lNTeXBBx+kvLzccf6XUwpLSkq49dZbiY6OZtCgQcyfP5+BAwfyzjvvNIulvLycKVOmEBMTw9ChQ/npp58A+7TXG264AYCkpCSCgoJ46KGHzvnehBBCCOGE2hJY/oI92QKIHgpXPCvJlnA5pxOuNWvWMGvWLHQ6XbP22NhYCgoKnA5g9+7d6PV63N3duf/++/nmm29ITU2luLgYnU6Hn59fs/4hISEUFxcD9g/2pydbJ8+fPPdrfYxGIw0NDWeM6YUXXsBgMDj+REVFOfWeFEXBVl/vkj+KorQqxtdee42MjAzuvvtu8vLyyM/PR6vVctNNNzFgwAAyMzN55ZVX+N///se//vUvx3WzZs1i48aN/O9//2P+/Pls2LCBXbt2Nbv3Qw89RHFxMd9++y0ffvgh//3vfx0J0ZmYzWZ+97vfodfr+eGHH1iwYAHe3t7ccsstmEymX30fycnJBAQEsH79egA2bNjQ7Bhg/fr1jBgxAoDq6mpuvPFG+vbty9KlS/n88885fvw4995771mfMXXqVIqLi/nmm2/48MMP+eSTT874fv71r3/xm9/8hhUrVnD55ZfzwAMPUFlZSUREhGOPuszMTPbs2cPzzz//q+9LCCGEEE44sAiWzIKaItBpYfAfYOgfQOft6siEcH5Koc1mw2q1tmg/duwYPj4+TgfQq1cvduzYQXV1NV999RVTpkxh1apVTt+nLc2cOZPp06c7jo1Go1NJl9LQwMEBA9sjtHPqtW0rKq9z75ZuMBjQ6XR4eXkRGhqKWq3m6aefJiIigpdffhmNRkNSUhIFBQXMnj2bGTNmUFNTw2effca7777LyJH2edCvv/46qampjvtmZWWxatUqli5dSnp6OgCvvvqqYwuBM/nmm2+w2Wy88sorqE8sZP2///s/EhMTWbduHZdddtlZr1WpVAwbNox169Zx7bXXsn79eiZOnMjcuXPJysoiNjaWLVu2OEaU3n//ffr06cNTTz3luMdrr71Geno6OTk5JCQkNLt/VlYWq1evZsmSJY738+9//5thw4a1iGXixImONYZ//vOfee+999i+fTtjxoxxTFMMDAx0/BKhtcmxEEIIIX7FgUWw52v76x49YfBk0If8+jVCXEROJ1zjxo3j1Vdf5d133wXsH3hra2v561//ytVXX+10ADqdjsTERAAGDhzI5s2bee211xyjG1VVVc1GuUpKSggNDQUgNDSUTZs2NbvfySqGp/f5ZWXDkpISfH19m02JPJ27uzvu7u5Ov5fO7sCBAwwZMsRRCAVg6NCh1NbWUlBQQGVlJWazmQEDBjjO+/r6Or5/YE9Q3NzcSEtLc7TFx8e3GKk83Z49e8jNzSUuLq5Ze2NjI3l5eeeMe/jw4XzyySeAfTTrqaeeIicnh3Xr1jliPpnw7d27l3Xr1hEbG9viPnl5eS0Sruzs7Fa/n9MTT29vb3x8fDh+/Pg54xdCCCHEecrbdCrZSr4Set8Ip32OEaIjcDrhevnllxk/fjypqak0NjZy6623kpWVRWBgIJ9++ukFB2Sz2WhqamLgwIFotVqWLVvGTTfdBMDBgwfJz88nIyMDgIyMDJ577jlKS0sJDrbvFL506VJ8fX0dH34zMjJalOteunSp4x7tQeXpSa9tW9vt/ud6dmdTV1fnWOOl+sX/JHv06HHO60eMGMGsWbM4fPgwhw4dYsiQIWRlZbF+/Xqqq6vp168fXidG/erq6hg3bhxPP/10i/v8cuqps7RabbNjlUolo1hCCCFEe6nMh+3v2V8njpRkS3RYTidckZGR7Ny5k88//5ydO3dSW1vL3XffzW233XbWEaOzmTlzJldddRXR0dHU1NQwb948Vq5cyeLFizEYDNx9991Mnz6dgIAAfH19mTZtGhkZGY7pXOPGjSM1NZXbb7+df/7znxQXFzNr1iymTp3qGKG6//77ef3115kxYwZ33XUXy5cv54svvmjXPaBUKlWrpvW5mk6nazY9NDk5ma+//rpZkrBx40Z8fHyIiIjA19cXrVbLtm3biIiIAOzTLXNychwJbFJSEhaLhd27d9OvXz8ADh8+TFVV1VnjSEtL45tvviEoKAhf3+a7vrcmYUlNTcXPz49///vf9OnTB71ez/Dhw/nPf/5DVVWVY/3WyWf9+OOPREdH4+Z27h//xMREp9/PmZxMxs40HVcIIYQQTmqoho1vgRUIToG+t0qyJTqs89qHy83Njdtuu41//vOfvPnmm9xzzz1OJ1sApaWlTJ48mV69ejF27Fg2b97M4sWLueKKKwB45ZVXuOaaa7jpppsYOXIkoaGhfP31147rNRoNP/74IxqNhoyMDCZNmsTkyZN59tlnHX3i4uJYsGABS5cupV+/frz88su8//77UhIeiImJYdOmTeTl5VFWVsYf/vAHCgoKePzxxzl48CALFizgxRdf5MEHH0StVuPj48PEiRN55plnWLNmDQcOHGDatGmo1WrHyFRSUhKjRo1i+vTpbNu2jV27djF9+nQ8PT1bjF6d9Lvf/Y6AgAAmT55MZmYmR44cYd26dcycOZPCwsJzvg+VSkVGRgbz589n+PDhAPTu3RuTycSaNWuajWbefffdVFVV8Yc//IHt27eTm5vL8uXLefjhh8+YDCUlJTFy5Egee+wxtm3bxu7du3n88cd/9f2cSVRUFCqViiVLllBWVub0nnVCCCGEOMFmhc1zoLYMPH1g8F2g0bg6KiHOyumE6+OPP242OjRjxgz8/PwYPnw4R44ccepeH3zwAXl5eTQ1NVFaWsrPP//sSLYAPDw8eOONN6ioqKCuro6vv/7asTbrpJiYGBYuXEh9fT3Hjx/nX//6V4uRi9GjR7N9+3aamprIycnhjjvucPZtd0mPP/44Go2G/v37Ex4ejtlsZv78+WzdupWMjAweeeQRJk2axOOPP+645u9//zuDBw/mtttu48Ybb2To0KH07Nmz2Zq3119/naCgIK677jruuOMObr/9dvR6PR4eHmeMw8vLix9++IGIiAjuvPNORowYwSOPPEJTU1OrC7FkZGRgtVodo1lqtZphw4ahUqmaFewIDQ3lxx9/xGq1cvPNNzN69GiefvppDAaDo2DHL73xxhsEBQXxm9/8hjvuuINJkybh7e3t1Dq/sLAwZsyYwd///ndSU1PbfGNlIYQQotvY+y2U7gUNkPEwePq5OCAhfp1KcXKRSa9evXjrrbcYM2YMmZmZjB07lldffZUff/wRNze3ZiNQXYXRaMRgMFBdXd1iyltjY6Oj4MPZEoqOSlEULBYLNpsNtVqNoiiYTCYURUGtVreoSGmz2Rzrkk7+2DQ0NNC7d2/+9re/ceutt55x3VJRURFpaWl89913jBw50nGf09lstjO2t+bH8+Q1F6tvYWEh6enpfPXVV45qjW0Vg9ls5tixYxw8eJCmpiY0J35j15qpiM70dba/9JW+59u3o8Qhfbt+344Sh/Rt5755G2Hb+1htwMC7Ia5l1eDzjUGj0WC1WjvP16Kb9TUYDPTv379ZsTZX+bXc4EycXsN19OhRxxv99ttv+e1vf8t9993HiBEjGD16tNMBi85l165dHDp0iP79+2M0Gnn55ZcBuOqqqxx91qxZQ11dHSkpKZSUlPDss88SHR3tmO7X2Zzt/bRn4RUhhBBC/EJFHux43/6655hfTbaE6EicTrj0ej3l5eVER0ezZMkSx35VHh4eZ91IWHQtb7zxBtnZ2Wi1Wvr168fChQvp0aMHNpsNsI/SPPfccxw5cgS9Xs/gwYN599130Wq151W170wl3E/67LPP2j3xMZvNPP/88xw5cgRvb28GDx7MW2+91aIqoRBCCCHaSX0FbHgdbEBIX+hzs6sjEqLVnE64rrjiCu655x769+/PoUOHHHtv7d2791c/GIuuIS0tjeXLlzsSp5MFM04mWwBjxoxhzJgxjmOVSnXW9VGtsXz58rOeCwsLO+/7ttYv348QQgghLqKmWsh8F+qrwacHDLkb1FIkQ3QeTidcb7zxBrNmzeLo0aPMnz/fsU/S1q1b+f3vf9/mAQoRHx/v6hCEEEII4Qr1FbDhPajMAa0KRjwCHr4g26yITsTphMvPz4/XX3+9Rfvs2bPbJCAhhBBCCCHI3wFb37DvteUGDJ8BhggXByWE85xOuFavXv2r509WbetuzmdtkhC/JD9HQgghBFB0ALa8YV+z5d0Dht4PAbGujkqI8+J0wnWmSoSnl/JubenWruJk4YT6+vrz2vxZiNNZrVYURel2f4+EEEIIh4K9sOlVe7IV1g+G/QE0UqhKdF5OJ1yVlZXNjs1mM9u3b+fpp5/mueeea7PAOguNRoOfnx+lpaWAfRPfX+4l1VGd3IdLURTH/llmsxnAcXyufbhOL5rxa6MzarXasdfX2fbhOhnHL2M8l4u9D1d79VUUhaqqKqqrq7FYLOe8jxBCCNHlFB+wJ1tWIDAZht4ryZbo9JxOuAwGQ4u2K664Ap1Ox/Tp09m6dWubBNaZhIaGAjiSrs7iZPJzesJ1coTl5PHp1QdPbz9JpVI1S7jOtPHxyT6nJ2tnSqw6y8bH7dnXZDJRVFR0znsIIYQQXYqiwIGFsOcb+3FIb8h4ANzcXRuXEG3A6YTrbEJCQjh48GBb3a5TUalUhIWFERwc7Bgh6gwsFguVlZWYTCa8vLxoamqiuLgYq9WKu7s79fX11NTUOPqbzWbUajVms9mRiLm7u6PVaqmvr8dsNjt2aT/JZrOh1Wpxd3d3PKOxsbHFHlYn20/uLH5SR9vlvD37nhxhlHVcQgghuhVFgV2fw4El9uOQPvZphJJsiS7C6YRr165dzY4VRaGoqIgXX3yR9PT0toqrU9JoNC0Sho7MYrGg1WqxWq2O/8KpkRebzdZsatvJhMpsNjv6ajQaRxJmMplaJFwnR8w0Go3jfmazucVIlslkoqmpqVsnXEIIIUS3U1sCu7+Eop324z6/gV4ToJMszxCiNZxOuNLT0884bWzYsGF8+OGHbRaYEEIIIYTowspzYc0LYDvxmTL995A4xrUxCdEOnE64cnNzmx2r1WqCgoLw8PBos6CEEEIIIUQXZbPCocWw7xt7JUJDOAyYAj3iXR2ZEO3C6YQrJiamPeIQQgghhBBdnaLArnmQfWJf18BecMmD4O4DMtVedFFqVwcghBBCCCG6AavZPqp1MtlKHg8jHrInW0L8CptiY2nuUh5f/Tg5VTmuDsdpbValUAghhBBCiDMqPgg7PoLaMvtx35ug15UuDUl0DmarmXkH53Gw/iC+Fl9qzbWuDslpknAJIYQQQoj2k7cJtrxnf+2mgj63QcJI18YkOoW8qjx+PPwjR2qP4OXtxc0pNxPj0/mWN0nCJYQQQggh2p7VDHu+hgOL7ceByTB4CngHujYu0eHVmmr5JucbthdvB8BL7cU9afdwe9/b8fPwc21w58HphGvbtm1otVr69u0LwHfffcecOXNITU3lmWeeQafTtXmQQgghhBCiEzEWQua7UJVvPw7rBxkPgLrz7FcqLj6bYmPlkZWsKlxFg60BgLSgNMbHjCcpMMnF0Z0/p4tm/OEPf+DQoUMAHD58mIkTJ+Ll5cWXX37JjBkz2jxAIYQQQgjRiRQfgp9ngTHf/kkz/VbIeFCSLfGrLFYLXx78kkX5i2iwNRCgDeDe1Hu5PeV2gr2CXR3eBXF6hOvQoUOkp6cD8OWXXzJy5EjmzZvHunXrmDhxIq+++mobhyiEEEIIITo8RbHvr7V7vj3R8ouHgZPBEOHqyEQHV1JXwvys+eQac1GhYnzkeC6NvhSdumvMnHM64VIUBZvNBsDPP//MNddcA0BUVBRlZWVtG50QQgghhOj4rBbY/jHkbbAfh6bB0HtB4+HauESHZrFZWJ+/nsVHF2NSTHiqPZmYNJG+4falS9Yusjeb0wnXoEGD+Pvf/87ll1/OqlWreOuttwDIzc0lJCSkzQMUQgghhBAdmKLAjk9OJVt9boCUCaBWy2bG4qx2FO5gecFyihqKAIj0juS3PX9LhE/XGxF1OuF69dVXue222/j222956qmnSExMBOCrr75i+PDhbR6gEEIIIYTogCxNkJcJhxZCfaW9rf9kSLjUnmwJcQYmq4mFOQtZV7wOAA+1B5dFXMYl0Zeg03SNKYS/5HTClZaWxu7du1u0v/TSS2g0shhSCCGEEKLLqy6AjR+A8aj9WA2k3WpPtoQ4g8rGSjYXbGZ76XZKGksAGBw8mPFx4zG4G1wcXftqs324PDxkjq4QQgghRJdWfQyyl8HhVWADtGpIugpiLgXvHq6OTnRAFQ0V7CnZw/LC5dTb6lFsCr5uvlwXdx39wvq5OryLwumEy2q18sorr/DFF1+Qn5+PyWRqdr6ioqLNghNCCCGEEC6mKFCZDzmL4ehme6IF4BcDw+4Dfecu2S3aR3VTNd8f/p5dx3cBoFKrCPEIYUjQENLD0vHR+bg4wovH6YRr9uzZvP/++zz22GPMmjWLp556iry8PL799lv+8pe/tEeMQgghhBDiYmuqgaNb7Ou0qnJP7d4alAJRwyBmCGjabLKU6EL2H9/PN1nfUGmxr+2L9I6kf1B/Lom+BLXz2wB3ek7/LZk7dy7vvfceEyZM4JlnnuH3v/89CQkJpKWlsWHDBh5++OH2iFMIIYQQQrS3phooyYbizZC/ufm5gCTofT0E9XRJaKJz2HxsM18c/gKAUI9QJsRNIDko2VHroauUeneG0wlXcXExffvaa+Pr9Xqqq6sBuOaaa3j66afbNjohhBBCCNG+bFY4vNa+Nst4rPk5dw+IGAQ9rwJDmL2tG35gFq2zo2gH8w/PByAtKI3fJv4WT62ni6NyPacTrsjISIqKioiOjiYhIYElS5YwYMAANm/ejLu7e3vEKIQQQggh2prVAsX74MACKDt0qt3DCwKTIfYyCE4CtVShFr+u3lzP2qNrWXZsGTZsDAweyC29bkGlUrk6tA7B6YTrhhtuYNmyZQwdOpRp06YxadIkPvjgA/Lz83n00UfbI0YhhBBCCNFWFAVyVsKeT8Gm2NvUQM/xEHMJ6ENAPiiLVqo11fLuzncpqC8AYEDwAG7udbMkW6dxOuF68cUXHa9vueUWYmJiWL9+PUlJSVx77bVtGpwQQgghhGgjNhscXg1ZP0Fdub3NDQgfBL2uAUOES8MTnU+jpZEPdn9AQX0Bfm5+XBZ5GUMjh6JWdb/CGL/mgkvLDBs2jGHDhrVFLEIIIYQQoq3YbFB8AArWQ0U+1BfByeVXauzrsvrcYK80KOuyhJOsNiufHvqU/Np8vDXe3N77dqIN0a4Oq0NyOuGKjo5m9OjRjBo1itGjR5OQkNAecQkhhBBCCGcoij3BqjkK1XlQsg8a65r3UQOp10P8KNDpQSPrs4Tz6s31zN0/lyxjFm4qNyanTpZk61c4nXA9//zzrF69mn/84x/ce++9REREMGrUKEcClpSU1B5xCiGEEEKI09lsUF8OxXuhdC+U74emplP7Zdmwvw5Nh/DB4B0A+iDwNLguZtHpldSVMHffXIoaivBy8+KWpFuI9493dVgdmtMTLCdNmsS7777LoUOHKCgo4KWXXgLgwQcfJDk52al7vfDCCwwePBgfHx+Cg4O5/vrrOXjwYLM+o0ePRqVSNftz//33N+uTn5/PhAkT8PLyIjg4mCeeeAKLxdKsz8qVKxkwYADu7u4kJiby0UcfOfvWhRBCCCFcy2aFgr2w43+w8BH46c+wYy4U7jiVbIX1g+SrYcgDcPXLMHwqxA6BoERJtsR5syk2Nh/bzJvb36SooQiDm4EpvafQN7Svq0Pr8M5rDVd9fT1r165l5cqVrFixgu3bt9OnTx9Gjx7t1H1WrVrF1KlTGTx4MBaLhT//+c+MGzeOffv24e3t7eh377338uyzzzqOvby8HK+tVisTJkwgNDSU9evXU1RUxOTJk9FqtTz//PMA5ObmMmHCBO6//37mzp3LsmXLuOeeewgLC2P8+PHn8yUQQgghhGhfNitUF0B5NtTkQ02FfRTrl8ut9EEQOQD8kyEo9lRSJeuyRBvZU7yHn4/97KhEGKOP4ebkmwnzCXNxZJ2D0wnX8OHD2b59OykpKYwePZo//elPjBw5En9/f6cf/tNPPzU7/uijjwgODmbr1q2MHDnS0e7l5UVoaOgZ77FkyRL27dvHzz//TEhICOnp6fztb3/jySef5JlnnkGn0/H2228TFxfHyy+/DEBKSgpr167llVdekYRLCCGEEK5nMUFZHtQcg9pjUFsJlQfAdGLGzunTBDXYKwsGpkL0IJCNZUU7sNqsrD+6nm1l2zhWZ98Q21PtyaCQQVwZfyU6jc7FEXYeTidcBw4cwNvbm+TkZJKTk0lJSTmvZOtMqqurAQgICGjWPnfuXP73v/8RGhrKtddey9NPP+0Y5crMzKRv376EhIQ4+o8fP54HHniAvXv30r9/fzIzM7n88sub3XP8+PE88sgjZ4yjqamJpqYmx7HRaGyLtyeEEEII0VxdGWSvgJyfwKy0PK8C/BIgJA68I8ArGAKiQetx0UMV3UNVYxWbCzazsWgjFeYKR/vA4IFcFXcVBneZluospxOu8vJydu/ezcqVK1m8eDFPPfUUOp2OUaNGcdlll3HvvfeeVyA2m41HHnmEESNG0KdPH0f7rbfeSkxMDOHh4ezatYsnn3ySgwcP8vXXXwNQXFzcLNkCHMfFxcW/2sdoNNLQ0ICnZ/PfDL3wwgvMnj37vN6HEEIIIUSrHM6E7e/ByWXnWhX4JoFfsD258omA4ATQ6E5VE5RpgqKN1Zpq2VG8g4PGg5TWlFJprQRAsSl4qb0YFTmK9JB0AjwDznEncTZOJ1wqlYq0tDTS0tKYNm0aW7du5fXXX2fu3Ll8/vnn551wTZ06lT179rB27dpm7ffdd5/jdd++fQkLC2Ps2LHk5OS0W0n6mTNnMn36dMex0WgkKiqqXZ4lhBBCiG6mvsK++XDOCvuxIRpihkHCGCnTLtqVxWqh1lxLnaWO2oZaiuqKWF+4nkpLpaOPSq0iwiuCoYFD6RfWD0+ZsnrBnE64tm3bxsqVK1m5ciVr166lpqaGvn37Mm3aNEaNGnVeQTz00EP8+OOPrF69msjIyF/tO3ToUACys7NJSEggNDSUTZs2NetTUlIC4Fj3FRoa6mg7vY+vr2+L0S0Ad3d33N3dz+u9CCGEEEKc1eH1sHOOvfCFGkgcBX1uBbXThaOFOCerzUqtuZajxqPkGnPZUbIDo6XlUplAXSADggcQ5xdHhCECvU6PVUZT24zTCdeQIUPo378/o0aN4t5772XkyJEYDOc3l1NRFKZNm8Y333zDypUriYuLO+c1O3bsACAszF4VJSMjg+eee47S0lKCg4MBWLp0Kb6+vqSmpjr6LFy4sNl9li5dSkZGxnnFLYQQQgjhFKsJdn8O2avtx74RkPIbiB0k0wRFm7DarJTUl1BaU8rRuqPk1+ZTUFOAWTGjUqsA+zRBAC+1FwZ3A55aT6L10YyNHYuHm31doEZGWduc0wlXRUUFvr6+bfLwqVOnMm/ePL777jt8fHwca64MBgOenp7k5OQwb948rr76anr06MGuXbt49NFHGTlyJGlpaQCMGzeO1NRUbr/9dv75z39SXFzMrFmzmDp1qmOU6v777+f1119nxowZ3HXXXSxfvpwvvviCBQsWtMn7EEIIIYQ4q4o82PEFVGTZj+OHQ79JoJPCF+LCKIrCofJDZJZkcrjiMPW2+jP2C/EIIdQnlCTvJAaEDcBNc147Q7lUg8nKriOVJCa6OhLnOf3VbqtkC+Ctt94CaLF/15w5c7jjjjvQ6XT8/PPPvPrqq9TV1REVFcVNN93ErFmzHH01Gg0//vgjDzzwABkZGXh7ezNlypRm+3bFxcWxYMECHn30UV577TUiIyN5//33pSS8EEIIIdqPxQR7v4SslfZjDdDvDogf4cKgRFdgsVnYUbyDjcc3kl2Z7WjXoCFcH06gVyBRnlFE+UUR6ROJu9Y+CNEZpwker2lkQ24pu4st2ILqSOiZRL8oP1eH5RSXpreKcobyp6eJiopi1apV57xPTExMiymDvzR69Gi2b9/uVHxCCCGEEE6zWe1TBw/Mh8YTIw5hadDnRjBEuDY20alZrBa2FGxhfcl6ipvsM8O0Ki3JPZIZFjyM+IB43NSdb/TqTCrrTazYX8CyAxWYLApaLy9iwzXYzpE/dERd4zsihBBCCNERVBfA5o+h/MSog04L6fdA9ADXxiU6tcLaQnaV7mJL8RaqLfZ9a9017gwIGUBGaAZh+jAXR3hhKupM7DpaTk55PdU1ZiobbZTVm1FsNgASAzwY2y+Gu24cSv/ottn/92KShEsIIYQQ4kIVH4K8ZVC8DWzYP2El3wCJl4ObztXRiU6o0dLI7uLdbC3fSk51jqO9h7YH6cHpDI8ejr+Hf6ecJnjSgWNVrMg+zu6CWmynDVypTlTtjPVzZ3RyIIPjgwjw98PgqXVRpBdGEi4hhBBCiPNlaoAdn0D+ZvuxGvCNhoFTwD/apaGJzkdRFPaX7Wdz6Wb2lu7FcmJXbBUqEvwS6O/fnwERA3BTu3W6aoI2m0KRsZGsogqyjtdxvLKJ3Komx/loX3f6xeqJ8PXG19uDYIMH3lo1KpXKhVG3DacTLqvVykcffcSyZcsoLS3FdmKo76Tly5e3WXBCCCGEEB2SzQoFe+HgD1CVZ28L7QtxoyGiL9D5PySK9mWxWWiyNtFgaeBA6QF2VuykoL4Ai2JxlG/30fiQHpLO4NDBnWraoNlqo7SmiWpjI4cqasguqueo0YTJqjimCZ50SU8/hscEEh/i42g7mUx25tG70zmdcP3xj3/ko48+YsKECfTp06dLZJ1CCCGEEK1WfQy2/A8qT0zzclPBkEcgPBVOjjp0kQ+Kom0oisLekr1sLt9MVX0VTZYmyk3lKDQvAKFSq9CqtCQFJjEkcAgpQSkd/rO2xWrjaHkdhZV17C4ykl/WyPE6c4t+KrUatQp6BnsSF+JFrEFPsL8noQZPF0R9cTmdcH322Wd88cUXXH311e0RjxBCCCFEx2S12ke0Diywr9MCiOgPva6GgFhXRiY6IEVRyKvOY3/Ffg5WHqSwrvCM/dSoCfIIYmDwQHoG9STQMxB3tftFjtY5WUVG1h8po9xo4Vh1E/VNlhZ9dG4qQr10BPvr6BWiJzHEn1CDByo6X5XBC+V0wqXT6UjsjDuOCSGEEEKcL2MRrH8HqvPtx4G9oN/Nsk5LNFPVWEVuRS67qnZRWF1IhbkCldo+QuWGG32D+5Lmn4aPhw9eOi/8df5o1BrHKFZHnkqnKArrs0rZkFNJVnkDcKq4hYdWRbheR2KYN0k9fIgM1GPw1KJWnxqd68jvrb05nXA99thjvPbaa7z++usdfohTCCGEEOKCWE2QtQoOfQ8NjfaiGP1uh/hLQT4HdWuNlkaOVB6huKaY4qZijlQdobixuEW/1B6ppPqm0iuwFwZ3gwsivTCKonCw0MiygyXsLqwDwE2tIi1Sz5CYHvj7eBBpcEejUbs40o7L6YRr7dq1rFixgkWLFtG7d2+02ublGb/++us2C04IIYQQwmUOLYX9X4L1xBQon1AY9gcwRLo2LnFRNVgaKDQWYrFZaDA1cNB4kGO1xyhpLHEUtzhdqEcokb6R9OvRjxi/GPQeeqBzjuwoisK8zUdYc6gSsCdal6cGMCopFH9vXbcetXKG0wmXn58fN9xwQ3vEIoQQQgjhemWH4dBCKNxpP/Zwg6RrIW4k6PSujU20qVpTLUaTkTpTHU1KExV1FdSb66k0V9JobaSyvpKihqIW152cJujv5k+wTzBBHkFEeEWQFJjUKUexziSryMiS/cWOUa3Bsb5ckRpGdICXiyPrfJxOuObMmdMecQghhBBCuI65AYoOwJHVULLnVHvqtZAywb55sfwWv9NpsDSQV57HsdpjNFoaMdlMVJorqWmqobKhklprLWBPoFRqFYpNOeOolY+bDwZ3A25qN4I8g0j2TybaPxo/nd9Ffkftz2pT+HpbHkv2lgOgUauYMiSCIYlBLo6s8zrvjY+PHz/OwYMHAejVqxdBQfJNEEIIIUQnYTVB/g4o2Q5VxVBz5FTlQYDQNOh1FQQlnir1LjqFRksjO4p2sK1iG0dqjrTYM/aXPNWe+Hn44e3hjYebB75qXzzdPPFz88PDzYNQ31BCvEOa1S7oqlPpjtc08t91R8iuaAQgPVLPlb3DiA2Skd0L4XTCVVdXx7Rp0/jvf//r+AHWaDRMnjyZ//znP3h5yTCjEEIIITqwvE2w6z2w0DzJcneHkDSIGwtBCa6KTjhBURTqzHWUNZRR21DL7qrd7C3bS5OtyTHtL0AbQA/vHoR6hqJVa/HUeBLgEYCflx9BnkF4aj3RaDRoNBqsVmuXS6Jaw2ZTWHOomG93ltJoBS+dmlv6hzMkQQZU2oLTCdf06dNZtWoVP/zwAyNGjADshTQefvhhHnvsMd566602D1IIIYQQ4oIpCuSshB3z7NUG3YDI4RDQG3yDwS8K1DKa1VHYFBtHjUc5UH6AclM5JpuJeks9FqsFi8WCTbFRa66lzlrX4tpAXSB9g/syMGwgIZ4hLoi+czBbbew7WsXP+0sdpd7jAjy5d3QCAV7ac1wtWsvphGv+/Pl89dVXjB492tF29dVX4+npyc033ywJlxBCCCE6HqsZts6B/M3248iBMOR+18YkHI43HCevLI9j9ceoMFXQYGrgeP1xaiw1zfqdHLX65Torg5sBvU5PmD6Mfj360SuwF25u9o+53XHE6lwURWF7XjlfbCukqsH+9fHQqrg8JYCr+kajc9PI160NOZ1w1dfXExLS8jcFwcHB1NfXt0lQQgghhBBtpuoobHwXak7skZQ6AXrfAGq1FMJwkTpzHftL91NrriWvIY995fvOWKxChYoEvwTi9HHo3fR46jzRu+tRKSo0ag0atYZAz0C8tLKk5VxsNoVDBdVsL6pkV34N5fUWALQaFSOS/BiXEk7AaaXeRdtxOuHKyMjgr3/9K//973/x8PAAoKGhgdmzZ5ORkdHmAQohhBBCnBerFXZ/Ctmr7MduKki/D2IH2ZMtcdEoioLZZmZ38W72Vu8lvyqfaks1cGrUKtI7kgh9BMHuwXhrvfH38ifKNwqt5tTUtq5arKK9FVQ18PnGPPYXn5p+qdOouCzZnwl9I9G5SZLVnpxOuF577TXGjx9PZGQk/fr1A2Dnzp14eHiwePHiNg9QCCGEEMJpNcWw5RMoP2Q/7tETBt8BeikC0J7MVjOVjZUYzUbK68rJrckltzKXKnMVNppXC+yh7UGUfxT+On/6BPchWh/toqi7ppLqRnYWlLM1p5r8GrOjvX+UDyNiA0gMN+ChlUTrYnA64erTpw9ZWVnMnTuXAwcOAPD73/+e2267DU9PzzYPUAghhBDCKWWHYf2/wGQGDdD/Hogd6uqouiSLzULm0Uz2V++nvK6cCnMFcPa1Vj4aHwaEDCBWH0tiYCIebh4yatWGrDaFTYdLWXuogpwTpd0BVGo1fcO9uT49gnA/+bx+sZ3XPlxeXl7ce++9bR2LEEIIIcT5M9VB1s+w7zt7uXfvABj6IATEuDqyLqeqsYodxTvYXbGb/Nr8ZufUqAn2CMbXw5cQ9xASDYmE+Yah0+jw0HigkUqQ7eJIWR3fbDvKgdIGR1u8vzuDEvwYHBeCn5dOkloXaVXC9f3333PVVVeh1Wr5/vvvf7Xvdddd1yaBCSGEEEK0WnkubH0Pao/bk62ARBj+AHj4ujqyLsVqs5KZn8mi/EU02Owf7D3VnlwafinxfvEE64PxcvNCp9XZ+8sH/HZXWWfi+93H2JBTjaLY12aNSQng0sQQeujdAaQQhou1KuG6/vrrKS4uJjg4mOuvv/6s/VQqlfzFEkIIIcTF01ANhxZA9gpQAR7e0OsmiB0O8iGzzRhNRvaU7mF90XpKGkoACPMMY0DQANJC0gjwDHBxhN2LzaaQXVzNT/uK2VN4qnR+Wrg316VHEhkgVRs7klYlXDab7YyvhRBCCCFcJm8T7HgPLCeOA3vB0HvAw8+VUXUpJXUl7D2+lzUFa6hT7BXufDQ+XBJxCSOjR+KmPq/VKeIC5FfU8eWmo2RXNDnaEgM8uKZfOMkRBhdGJs7G6b8l//3vf7nllltwd3dv1m4ymfjss8+YPHlymwUnhBBCCNGCzQZ7voRDP9uPPX0h9XcQnyF7a7WRw5WH+enIT+Qacx1tIZ4h9A/qz7CIYXhrvV0YXfekKApL9hXw3Y7j2BTwdNfQL8qX0YmBxPTwRqVSuTpEcRZOJ1x33nknV155JcHBwc3aa2pquPPOOyXhEkIIIUT7sVph5ydweJ39OH4E9LsNNFrZW6sN1Jhq+DbnW3Yf342CvcJgnG8cyYZkRseNRqvRyvKRi6zJbOXI8VoW7Sli/4mCGH3Dvbk1I55Avbt8PzoBpxMuRVHOmEEfO3YMg0GGMYUQQgjRThqqYcO7p/bW6vtb6DkO5Df7F6y6qZqtRVtZV7gOo8UIQHJAMlfGXEmETwQghRcutn35law6XMaB4jqaLPbkV+em4qb0EEYlh8n3oxNpdcLVv39/VCoVKpWKsWPH4uZ26lKr1Upubi5XXnlluwQphBBCiG6uqQZWvQS1JaAGBtwLsUNcHVWn1mBuYNvRbeyu3E1OVY6jPdg9mN8k/IaegT1dGF33lF9RR3aJkQNFRrYfPVUMw1unpmeIN9f2k320OqNWJ1wnqxPu2LGD8ePHo9frHed0Oh2xsbHcdNNNbR6gEEIIIbo5qwky37EnWx56yHgYesS5OqpOp6KhgiJjEVnVWZQ0llBgLKCRU5vjhniEMCR4CMOjhuOmkWIY7a28ton9hZUcqWrAWG+hqLKJ4w326YHKiSJ1GQkGLk0IJi5I1mh1Zq3+2/TXv/4VgNjYWG655RY8PDzaLSghhBBCCMC+ZmvDu1B20P6pRZKtVlMUhdyqXMrrytln3Mfesr2OdVkn+ev8GRQyiD5BfQjXh7so0u6joKqBvYUV7Mozkl3R2OK8RqMmJcSbyAAdfUL9SQz1cUGUoq05/euLKVOmtEccQgghhBDN1R6HLR9DyT77HluD/ijJVisV1Bbw9cGvm1UZBPt0wUhDJHFecQTqA4kPiJdCGO3MbLWx5XAZW/LK2VVY1+xcvL87iWHeBHq7E+ztSUyIL3oPN/l+dDFOJ1xWq5VXXnmFL774gvz8fEwmU7PzFRUVbRacEEIIIbohmw32zoecJWDjRLJ1P0T2cXVkHZ5NsZF1PIuvs76mrKkMFSrifOPw9/BnWNgwYg2xzfpL4YX2oSgKeWV17CmqYE9+PfnGJsc0wV5BnvSL9qV/dCD+3rpm18n3o2tyOuGaPXs277//Po899hizZs3iqaeeIi8vj2+//Za//OUv7RGjEEIIIbqL6gLYPR+Kd9uLY/hGQPpkCIx3dWQd2vH64+wu2c2W0i2UmcsACPcK59aUWwnxDnFxdN1DSXUjWSVVbMuvJut4A2arffqmSq3Gz1PDiAR/BsUEEWqQohfdjdMJ19y5c3nvvfeYMGECzzzzDL///e9JSEggLS2NDRs28PDDD7dHnEIIIYToyhQFDi6GPfNPtaX9HpLG8ItlR92ayWoivzqffGM+jZZGKi2VlNeVc7TuqKOPSq1iUPAgxkWPw8/Dz3XBdhMFlfX8sLOAHcdqm7Xr3FT0DtOTGubLoPggPN2k6EV35XTCVVxcTN++fQHQ6/VUV1cDcM011/D000+3bXRCCCGE6PqO7YED30LVEftxQBL0vh7CU+zHsp6FysZKFucuZv/x/dRYa87YJ943nr4BfRkYMRC9Ti/rgNqAxWqj0WLDarPRZLZhrDdRUdtErcVKVYOJrIJacquaHP17BnqSFO5Fv4gehBo80LlpHNME5fvRfTmdcEVGRlJUVER0dDQJCQksWbKEAQMGsHnzZtzd3dsjRiGEEEJ0VfnbYdOb9tcqoPdN0Gu8bGZ8wtGao2wu2sz2su002ZpQbAp6jZ4oQxSB7oF4aDwI8ggiyj+KQM9AQNYBtYbVaiP3eB1ltQ00WqwYTVYarVDbaKauzozFasNktnHUaMJiaznEqlKrgVPl23uHejMhLZz4YH2LvkI4nXDdcMMNLFu2jKFDhzJt2jQmTZrEBx98QH5+Po8++mh7xCiEEEKIrihvC2x/x/46PB3SbgZ9kEtD6ggURSG7IptVRas4WHEQsE8TjPCK4IqIK+gV1AuNWpIqZ1mtNnblV7I+t5zdv6gWqFKrUanVKDabI4n6JZ1Gha+7G0E+Wgw+7nhq1UT5upMUaiDIR7ZLEmfndML14osvOl7fcsstxMTEsH79epKSkrj22mvbNDghhBBCdFFZy2Hnp/bXIX1g6P0gIzPkG/P58fCPzcq5x/nGcVnkZaQEpch6NieVGhvJKarimLGerXlGyustjnMeWhVJQV54uGvwdHcjUO+BVqXgrdHgpdPgplbj4e5GeIAnWrW62cbDMk1QOMPphGv16tUMHz4cNzf7pcOGDWPYsGFYLBZWr17NyJEjW32vF154ga+//poDBw7g6enJ8OHD+cc//kGvXr0cfRobG3nsscf47LPPaGpqYvz48bz55puEhJyquJOfn88DDzzAihUr0Ov1TJkyhRdeeMERI8DKlSuZPn06e/fuJSoqilmzZnHHHXc4+/aFEEIIcaH2fQ+7v7W/jh8Babd2+2SrtK6UZUeXsa10GwAqVPQN6svoyNFE+UTJB/xWqm20UFRZT5Gxnn0ldew8VttsxMpTq2ZkTz+GxAYT6uuORmOfGqjR2NdaWa1W+RqLNud0wnXZZZdRVFREcHBws/bq6mouu+wyp35IV61axdSpUxk8eDAWi4U///nPjBs3jn379uHt7Q3Ao48+yoIFC/jyyy8xGAw89NBD3Hjjjaxbtw6w/49nwoQJhIaGsn79eoqKipg8eTJarZbnn38egNzcXCZMmMD999/P3LlzWbZsGffccw9hYWGMHz/e2S+BEEIIIc5XzhrY+639dcKlkH57t12vVVhbSF5FHrsrdzumDoK9+MVve/6WIC+ZXnk6RVFoMFkxW200mS3UNVloaDRjsSpUNzWx6XAl+0sbHP1PrrOK9XMnyE9HcrAP/aJ7oPdw+uOvEBfE6Z84RVGaDameVF5e7kiSWuunn35qdvzRRx8RHBzM1q1bGTlyJNXV1XzwwQfMmzePMWPGADBnzhxSUlLYsGEDw4YNY8mSJezbt4+ff/6ZkJAQ0tPT+dvf/saTTz7JM888g06n4+233yYuLo6XX34ZgJSUFNauXcsrr7wiCZcQQghxseTvgO1z7K97XQF9ftftkq0maxM55TmsL13PoapDACgnijJEekdyRdQVpAanujLEDkFRFOpMVvJLazlUbuTI8QaOVTVRa7Z/rc62zgog0MuNUIM7CaF6egUbiAv0OuNnVyEullYnXDfeeCMAKpWKO+64o1lFQqvVyq5duxg+fPgFBXOyxHxAQAAAW7duxWw2c/nllzv6JCcnEx0dTWZmJsOGDSMzM5O+ffs2m2I4fvx4HnjgAfbu3Uv//v3JzMxsdo+TfR555JEzxtHU1ERT06kSn0aj8YLelxBCCNHt5W2GLe/aNzOOHAS9f9ttkq3jDcfZVrSNg1UHOVrbfL+sWJ9Y4vXxJAcmE+Mb060TgwaTle1HythbXMOhknpqGlvOmjo5aqVRq9BpVAR5anHXqtFowN9XxxXJYYT72TcWlmmYoqNodcJlMBgA+28cfHx88PQ8tUu2Tqdj2LBh3HvvvecdiM1m45FHHmHEiBH06dMHsO/5pdPp8PPza9Y3JCSE4uJiR5/Tk62T50+e+7U+RqORhoaGZu8F7GvLZs+efd7vRQghhBCnKdgL2961v44cBEPuxZ55dV2VjZVsL9rOIeMhcqpzmp3zd/MnOiCasdFjifSJ7LYJQXF1A3sLK9hf1EB1vZmjVY0t+gR6aUkI9SImwINoPx+ig3zQualBOfsIlxAdTasTrjlz7FMAYmNjefzxx52ePnguU6dOZc+ePaxdu7ZN73s+Zs6cyfTp0x3HRqORqKgoF0YkhBBCdFLZq2HHJ/bXoX1g6B9ArelymxkbTUa2FG4hqyaL8rpyKk2Vzc5H66MZGDiQpMAkAj0DUalU3Wq/LKtNITO7hAMltdTUW6hqsFBSawZOjVoBBHtrGRTvS6K/D3Ghvnhoz1Yd8OLGL8SFcHoN11//+tc2D+Khhx7ixx9/ZPXq1URGRjraQ0NDMZlMVFVVNRvlKikpITQ01NFn06ZNze5XUlLiOHfyvyfbTu/j6+vbYnQLwN3dXTZxFkIIIS5U7oZTyVbMUOg/2Z5sdRGKonCs9hjrCtax8/hOLIoFlfpUchCtj6ZfQD8SAxMJ14e7MFLXqWkws/VIGRuyK8mrampxPjXEi9QoA/E9fHB3UxPu59Gtp1WKrsnphCsuLu5X/yIcPny41fdSFIVp06bxzTffsHLlSuLi4pqdHzhwIFqtlmXLlnHTTTcBcPDgQfLz88nIyAAgIyOD5557jtLSUkflxKVLl+Lr60tqaqqjz8KFC5vde+nSpY57CCGEEKINWc2QtRj2fmc/jh0GA+/qMmu2LDYL+0r3sbZ4bbP9siK8IhgYPJAovygCPQLR6/QujNJ1LFYbWcW1rD9SRmbWqZE+jVrFZb38SOrhg6fOjSCDJ/7eOllrJbo8pxOuXxaaMJvNbN++nZ9++oknnnjCqXtNnTqVefPm8d133+Hj4+NYc2UwGPD09MRgMHD33Xczffp0AgIC8PX1Zdq0aWRkZDBs2DAAxo0bR2pqKrfffjv//Oc/KS4uZtasWUydOtUxSnX//ffz+uuvM2PGDO666y6WL1/OF198wYIFC5x9+0IIIYT4NY1G2PQ+lO63H4f1gwF3dolkq9HSyPbC7awuXE2ZqczRnuiXyOiw0fQM7OnYA7S7JQ8mi5Xi6kb2FFaw5lAlVU2ndmiO9XMnOVLPsLggQg0tZxYJ0dU5nXD98Y9/PGP7G2+8wZYtW5y611tvvQXA6NGjm7XPmTPHsSnxK6+8glqt5qabbmq28fFJGo2GH3/8kQceeICMjAy8vb2ZMmUKzz77rKNPXFwcCxYs4NFHH+W1114jMjKS999/X0rCCyGEEG2pIg82vgV1FaABev8OEi4DdectkKEoCoerDrOpaBN7j++lzloHgKfak4EhAxkaPpRQ71AXR+kaVpvCriMVbDxSwa6CWmyncizctRrSo30YGulPSqRBpgmKbq3Ndn676qqrmDlzpqO4RmsoinLOPh4eHrzxxhu88cYbZ+0TExPTYsrgL40ePZrt27e3OjYhhBBCOKFoP6x9CWyAhycMfRiCEl0d1XlTFIW86jyW5C8hx2ivMqjYFPzc/BgYMpCR0SPx0nq5OMqLS1EUcsvq2JxXwqFj9RTXmzBZTn2WU6mgV5AnA2P9GJoQiqdO0+1G+oQ4kzZLuL766ivH/llCCCGE6EaK98P6E8lWQBIMvQu8A10d1Xmx2qzkVOawKG8Rx+qOAfb9spIDkhnSYwg9A3ui0+hcHOXFVVbbxMbDJWQerqG83tJs02Efdw2D4nzpH+ZPYpgv6hNFQ7pTBUYhzsXphKt///7NhoUVRaG4uJjjx483m+onhBBCiG6gMh8y/2VPtkLTYNh94Na5Kv0qisLRmqMcKjvE1tKtLdZnXR1/NbGG2G43WmO1KSzac5Ql+yowWRRUajU6jYrekT4MifYnyOBFkI8OnZskV0L8GqcTruuvv77ZsVqtJigoiNGjR5OcnNxWcQkhhBCio2uohHWvgEWBHr1g6L2dItkymoxkV2STV5NHaX0pFXUVVFqa75vVJ7AP18ZdS4BnQLcbrSmvbWJbfhlbD1c7SrnH+blzSXIQQ+ODcOu8S/KEcIkOsQ+XEEIIIToZqxk2fgiNteAbAhn3g9bD1VGdUWl9KTuKd5BVnUVpXSn1tnrHflnKiUoPbio3Yg2xpPimMDBiIN5ab1eG7BLVDSa+3XmMTYeNWE98XXRuKm5MD+HSnqHotN2zAqMQF+q813CVlpZSWlqK7bR5vABpaWkXHJQQQgghOjCbFTZ/AGUH7J8kMh4ETwN0kA/ideY69pXso7ChkMPVhymsL2zRJ8QjhCS/JELdQwnwDiDSNxIPt46ZMLa32kYLqw4W8NPeMhrN9kQrMcCD9FhfBkQHEaDvXmvWhGhrTidcW7duZcqUKezfv79FlUGVSiW/9RBCCCG6MkWBnXPh2FZQAQOngV+US0OqaKhgc9FmjpuP02RpIrcylyZbU7M+CYYEevv1JsY/hh6ePfD18AW672iNoigcKasj80gFa7IqsVntv0CP9XNnfO8Q0mMDpJS7EG3E6YTrrrvuomfPnnzwwQeEhITIX0YhhBCiu2iqhV2fwZGN9uOB90DUxZ/ZYlNs1JhqyK3IJbcml+2l22mwNTSbJthD24PEgESivaPpGdgTPw+/ix5nR5VVZOSbbcc4XNmE6sQeadG+7oxO6cGwhGBHpUEhRNtwOuE6fPgw8+fPJzGx8+6tIYQQQggnmRth/VtQfsh+nHYLxA5t88dUN1VzpPIIVY1VVFoqKW0spcHUgM1mw6bYMFvNVJoqsdJ8ZCrCK4L04HS83Lww6AwkBiSiUXevYhfnUllvYtHeQtZlVWG1KWjUKvpG6hmVEEhKhMHV4QnRZTmdcI0dO5adO3dKwiWEEEJ0F4oC2z+xJ1tuwIAHIbr/r17SZG2irL4Mk9VEXWMdTdYmrIoVVGBRLNSZ6miwNmBRLDTYGiivL6esvowGW4PjHr8sbPFLwe7BxBhiiNPH0T+8P+5ae4XE7jpN8NfkHa/l/TV5lNWbARgQ7cPNg6Lp4eMJyNdMiPbkdML1/vvvM2XKFPbs2UOfPn3QarXNzl933XVtFpwQQggh2peiKJTWl1JnqqPR3EhFYwUVTRVYVVbHiJKtughrdRaKrw5zjxQaK9ajlK1DQUFRFBSVgoKCzWofhTKajM0Sp9OdK4kCCPUIJUgfhL+7P8EewfjqfHFTu6FRa1Cr1HhpvfDz8EOr1srShlbYllvG/zYXUG+yEe3rzvXpYaRE+cnXToiLxOmEKzMzk3Xr1rFo0aIW56RohhBCCNExWWwWyhrLqDXXUl1XTY2phpzaHAqrC6kwV7To3yIx8jhRJr3uyLn7nuCh9sBX64vOTYeX1guNWoPGTYMKFRo0GNwMuKvd0ag1+Gp9CTeE46vzRa/TAzj2v5LPFufHZLGycE8BP+0pByAt3JvJw+PRe5x3kWohxHlw+m/ctGnTmDRpEk8//TQhISHtEZMQQgghzpPFZuFwxWEKaws5ZDxEWW0ZRosRK9azJkZq1AR5BKHVaPF29yZAF4BBZ0DTeBzl8HI0ioIqOB1VcD80Kg1eOi+0ai1qldqePGntI082qw21So2bxo0enj3wdPNErWq+S64kUe3vYEE1Sw4Uc7C4liaL/Xs9spc/twyMQSMFMYS46JxOuMrLy3n00Ucl2RJCCCE6CEVRyDfms+/4PvaU76GkseSM/dxww9/dHw+NB946b/x0fvQO6E2MX0yLPag0NQWw4gusJgWiB9srEqrVZ7yvJFEdQ1W9iR/3FJCZY8Sm2BPrAC83ruwTxMieoa4OT4huy+mE68Ybb2TFihUkJCS0RzxCCCGEaKW86jx2Hd/F7rLdVJmqHO1uuBHrF0u8Pp54v3j8Pf3RaXTo3fVo1JpzJ0YN1ZD5JlhsEJAEA6acNdkSHcOOvHLmbi6gpsmKSq1mcJwvI+N6EBukx00j3zshXMnphKtnz57MnDmTtWvX0rdv3xZFMx5++OE2C04IIYQQLWVXZLMobxH5tfmOaYJuuBHtG01vv94MCB/gWAd1ulaVSbeaYON7UFsKngbIuA/c3Nv6LYg20mi28vnWI2RmVwOQEODBNf3C6RMdICOOQnQQ51WlUK/Xs2rVKlatWtXsnEqlkoRLCCGEaAeKopBTmcPyY8vJqspytCcHJDMkaAg9A3qi0+gu7CFWK2x6H8oOgk4Fw/8Inn4Xdk/Rbmobzby7ModDZfaKkKN6+XPzgGh0Ou05rhRCXExOJ1y5ubntEYcQQgghzuB4/XG+O/wdeVV5NNmaHO29A3tzZcyVRPhGAG2wfspqgs0fQMF2UAFDHoWAWHsSJjqcqnoTH689zKGyBnw9NNw1LJrkSD9XhyWEOAOpCyqEEEJ0QLnVuSzMWUhOdY6jTYOGlMAUxkSNIconqu0eZrPB1o/g2Db78cD7IKJP291ftKmaBjPvLM8mt6oJL52aB0fFExvUcgqpEKJjkIRLCCGE6EB2F+8msyyTnKocbDYbAGGeYVyXcB2RvpEtqgleMJsNtn8E+Zvtx4P+ALGD2vYZos0cKqxm7qajlNSaCfLWcvelscQGSrIlREcmCZcQQgjRAdSZ61h0eBEbSzY6CmEk+iUyLnocMYaYFvtZtQlTPWS+Dcf3248H3SPJVgdltdpYtLeAH3aUYFMg2FvLXZJsCdEpSMIlhBBCuNjhysN8degrjjcdB2B46HCGhQ8j1Kud9k6yWiH7Z8hZCvXVoAb6TYbYoe3zPHFBiqsb+GzTUQ4eb0BRYEi8gZsHRKH3kOIYQnQGknAJIYQQLlBnriPzaCb7qvZxtPYoAD20Pbg+8Xp6h/YG2mkjYasVtn4I+ZvsxzodZDwKQYlt/yxxQWw2ha93HGHZ/kpQqfHUqrm5fxjDEoNdHZoQwglOJ1w//fQTer2eSy65BIA33niD9957j9TUVN544w38/f3bPEghhBCiq8g35rOqYBVZ5Vk02Boc7WmBadyQeMMZ989qM7XHYdsnULTnxEN/B7HDoT2fKc5LfnkdX246Sla5/WckqYcHky+JI0h/gaX/hRAXndMJ1xNPPME//vEPAHbv3s1jjz3G9OnTWbFiBdOnT2fOnDltHqQQQgjR2Z3cQ2t/2X5HW6hHKIOCB5ESlEKwdzuPWpQcgs2vgclkL/ve/y6Iz2jfZwqnNZmtLDtQyKI95ZitCl46NTelh3JpchgqlUo2MxaiEzqvfbhSU1MBmD9/Ptdccw3PP/8827Zt4+qrr27zAIUQQojOTFEUvs/+nnUl6xxtSX5JjIkcQ4J/AiqVqn0DsNkgZyXs+tSeaPlGQPpkCIxv3+cKp+0/WsW8Lcc4XmcGIDXUi9uGxdJD797+PydCiHbjdMKl0+mor68H4Oeff2by5MkABAQEYDQa2zY6IYQQohNTFIXFhxeztmgtKrWK1B6pjIkYQ7Rv9MUJwGq1l3zP22A/DukNGQ+Axv3iPF+0yr78StbklrP9aA0APu4arusXzPDEEDRqSbSE6OycTrguueQSpk+fzogRI9i0aROff/45AIcOHSIyMrLNAxRCCCE6I6vNyoKcBawpWgPAddHXMSpu1MWZEtZQDfvmw7GNYLbv5UXv30DKNaDR2BMx4VJ5x2spNjay/WgVO4/VONpHJPlxU/8ovHRS10yIrsLpv82vv/46Dz74IF999RVvvfUWERERACxatIgrr7yyzQMUQgghOptGSyOfHfqMvWV7Abg2+lpGxY1q/wfbbHBkE+z7AhpOfIjXaSHtLvv+WhpN+8cgzqqgqoEd+cfZdaSWI9VNqNSn9lbLSDAwMjGYuCApYCJEV+N0whUdHc2PP/7Yov2VV15pk4CEEEKIziyvOo/5B+dT3FiMBg2/S/gdAyMGtv+Da4/Dxg+gMsd+7KGHtNshog9opLKdK5isVg4eq2Z7URVHSxs4ajQ5zqlUkBzsSYi/B0OjAyTREqILO6/x6pycHObMmUNOTg6vvfYawcHBLFq0iOjoaHr37t3WMQohhBCdwq7iXXyZ/SWNtkZ83Xz5bdJvSQlKaf8HF+2Dja9DU6O9MEav8ZA4Djx82//ZogVjg5mf9x1ja66R0hMFME5KCfYkPdpAekwPAvSeQDvttyaE6DCcTrhWrVrFVVddxYgRI1i9ejXPPfccwcHB7Ny5kw8++ICvvvqqPeIUQgghOrR9pfv436H/oaAQ5xvHpNRJ+OraMeFRFCg5CAd/hPKDYAO8A2DoAxAQ237PFWdV3WDiu12FbMkzYjLbkyh3NxV9I/QMiPQjKlBPkI+Hi6MUQlxsTidcf/rTn/j73//O9OnT8fHxcbSPGTOG119/vU2DE0IIITqDQ+WHmHdoHgoKvQN7M7HnRDzc2uGDtakWjm6Do5uh/AAowMllQNGDIW2ijGpdRLWNZvYcq6S8vpHSWgv7C2upMSkARPu6M7ynP4PigtC7SwEMIbozp/8PsHv3bubNm9eiPTg4mLKysjYJSgghhOgsDpYd5MO9H2LFSk//nkzqNQk3TTt8wM5ZA7v/C5ZftEcMgL6/Ba+gtn+mcLBabRRXN1Ba2cARYy15pY0cKK3HalOa9Yv19+T69HB6hfvI3llCCOA8Ei4/Pz+KioqIi4tr1r59+3ZHxUIhhBCiO9h/fD/zDszDipVEv0RuS76t7ZMtYxHkroCsFfZjD2+IHAARI8AQDp4nii3IOqA2pSgKZbVNVNeZOFRWw9qsSip+sR4LINbPnahgT7x1GuL8vOkbG4hWo5Z1WUIIB6f/VZg4cSJPPvkkX375JSqVCpvNxrp163j88ccdmyALIYQQXZmiKGwp2MLXeV9jxUqMPoY7e9+Jri2qASqKPckqPwLFOyB/06lzKVdDynWglvLu7aWkupGfDxaz/1gNZfX24cTTy7fH+7sT6OdOQg8vogJ8iA30Rn3a5sQajbrFPYUQ3ZvTCdfzzz/P1KlTiYqKwmq1kpqaitVq5dZbb2XWrFntEaMQQgjRYRTWFrLg8AIOVR1CpVbRK6AXtyTecn7Jls0GxgIwlkPVfjAeh6psMDWcOH+in18sJIyFuGFt9TbEL9SZLMzfls/67Opm7eF6LQa9jsGx/gyO64FWEiohhJOcTrh0Oh3vvfceTz/9NHv27KG2tpb+/fuTlJTUHvEJIYQQHUZWeRbzDsyj1lqLVqVlTOQYLo+73F68whmmWji0GHJX2ku5/5Ia8Am3VxsMSofo/hccuzi74zWNfLIuj0Nl9kQ3OdiTEYk96BMZgKdOg+bEhtEyTVAIcT7Oe6J5dHQ00dHRF/Tw1atX89JLL7F161aKior45ptvuP766x3n77jjDj7++ONm14wfP56ffvrJcVxRUcG0adP44YcfUKvV3HTTTbz22mvo9ac2ENy1axdTp05l8+bNBAUFMW3aNGbMmHFBsQshhOg+LDYLCw8vZG3hWhQUoryjuCX5FsJ9w4EzfBC3WqAiD2pKoakc6krBVA/1ldBYAY11zfvrA6FHAhiiwTsSQhNA5yXrsi6CvLJaPlyTR2mdGT9PDZOHRJMa5efqsIQQXYjTCZeiKHz11VesWLGC0tJSbDZbs/Nff/11q+9VV1dHv379uOuuu7jxxhvP2OfKK69kzpw5jmN3d/dm52+77TaKiopYunQpZrOZO++8k/vuu89RSdFoNDJu3Dguv/xy3n77bXbv3s1dd92Fn58f9913X6tjFUII0T1VN1UzP2s++yv2A5ASkMItvW7BW+sNVjMU7IHGKrBZwFwL1cegdCeYm//76CjffrLZwxt6XgPRQ1qWctfIGq2L4WBhNe+sOUxNo5V4f3fuuDSeYF/ZJ0sI0bacTrgeeeQR3nnnHS677DJCQkIuqOTpVVddxVVXXfWrfdzd3QkNDT3juf379/PTTz+xefNmBg0aBMB//vMfrr76av71r38RHh7O3LlzMZlMfPjhh+h0Onr37s2OHTv497//LQmXEEKIX1VYW8jcvXMpbSpFp9JxbcRYhrqHospeDqVZUG5PwrCd4WI3wDcJ9P7g5Q9egeAeAFov8PYDrx4gZcNdoqS6kZ0FlXy/6zgWi5WegZ78YUwi3jrZL0sI0fac/j/LJ598wtdff83VV1/dHvG0sHLlSoKDg/H392fMmDH8/e9/p0ePHgBkZmbi5+fnSLYALr/8ctRqNRs3buSGG24gMzOTkSNHotOdWsw8fvx4/vGPf1BZWYm/v3+LZzY1NdHU1OQ4NhqN7fgOhRBCdDSKzcayrB/4uWgtFmwE2uC31aUklHzavKMa0ACBqaB1t1cP9PSDHn0htCecXiL+5KiVTBN0ibLaJvYXVrI1r4oDpQ2OyoPpkXruHBGPu1ZGFYUQ7cPphMtgMBAfH98esbRw5ZVXcuONNxIXF0dOTg5//vOfueqqq8jMzESj0VBcXExwcHCza9zc3AgICKC4uBiA4uLiFnuGhYSEOM6dKeF64YUXmD17dju9KyGEEBeVqQFK9kNDKdSX2hMe24k/TfVgMdmrBSoWsJk5ptSwUGvlgNY+hT3G3MQt1Q0EOaYC6sE3CgITIbQPBFycfxOF8yrrTGQVVbMup5yDxxsc7SqVvbx7/zg/xva6sNk6QghxLk4nXM888wyzZ8/mww8/xNPTsz1icpg4caLjdd++fUlLSyMhIYGVK1cyduzYdnvuzJkzmT59uuPYaDQSFRXVbs8TQghxDjYr1B2H2kpoqISK/WCqsydKVhsoNvtrmxVM5WCpB5vZPtXPRsv1U2dRqIF3ehhoUGtRW22Mt3owOuwS1IkREBAJ3kHN98CSUasOparexKpDxew/Wk1pnYWapubfl3h/dxLCvBkWF0x0oL24llQeFEK0N6cTrptvvplPP/2U4OBgYmNj0Wq1zc5v27atzYL7pfj4eAIDA8nOzmbs2LGEhoZSWlrarI/FYqGiosKx7is0NJSSkpJmfU4en21tmLu7e4viHEIIIdqJotiTI1MdVORDXaG9CEVdJdQboakMGqtbJkutTKIAcPeEwBR7cQqtrz1pUruBWgs6A6i1NCpWvihYSoPlOFGe4UyIvpL4oOS2fa+izTWZreSV1rK7uJI1OUZMFgXltIJesQZ3EsO9GJsSjr9XG2xMLYQQTnI64ZoyZQpbt25l0qRJF1w0w1nHjh2jvLycsLAwADIyMqiqqmLr1q0MHDgQgOXLl2Oz2Rg6dKijz1NPPYXZbHYkh0uXLqVXr15nnE4ohBDiIlAUOLoD9s2H2tN+KXauJMrLDzyCwBAIfvHg5mXfA0ultv9BA25a8NTbkyqN1p5Y6QPt88jOMpqhKApfH/yUAlMpQR5B3NnvHvRu+jP2Fa5X22ThUGEVeZV1bMqtpqrB/n1VqdVE+uoY1dOfmEBffD3dMHhKkiWEcC2nE64FCxawePFiLrnkkgt+eG1tLdnZ2Y7j3NxcduzYQUBAAAEBAcyePZubbrqJ0NBQcnJymDFjBomJiYwfPx6AlJQUrrzySu69917efvttzGYzDz30EBMnTiQ83L43yq233srs2bO5++67efLJJ9mzZw+vvfYar7zyygXHL4QQ4jxU5MHur+H4/pbnPPQQ2AvcfewV/TwCwNMfPLxAH9a8XLoz0/nO8cvBzcc2s710O2rU/LbXbzG4G2SqWQdjtSkcLa8jM6+MTYeraTit7L6nVk1quDcZMUH0jfFHUVoz7CmEEBeH0wlXVFQUvr6+5+7YClu2bOGyyy5zHJ9cNzVlyhTeeustdu3axccff0xVVRXh4eGMGzeOv/3tb82m+82dO5eHHnqIsWPHOjY+/r//+z/HeYPBwJIlS5g6dSoDBw4kMDCQv/zlL1ISXgghLiarBQ5nwqGFzUe0oodA8gT7VD+NDrQevzoS1R7yjfl8l/cdAOOjx5MUkHTRni1+XYPJys4jFWzMLeNAST0Wm+I4F2NwJzzIg8QAbwbHB6Jz06A5kYRLriyE6EicTrhefvllZsyYwdtvv01sbOwFPXz06NEoinLW84sXLz7nPQICAhybHJ9NWloaa9ascTo+IYQQ50FRoDwXmhrtGwEbD8HRTdBwqkocgcnQ+zcQlNj82otcLa7WVMuXB77EpJhICUhhdMzoi/p8cXbZxUY+2XiM0jqzY02Wu5uKpCAvLk3oQVpMgFQXFEJ0Ck4nXJMmTaK+vp6EhAS8vLxaFM2oqKhos+CEEEJ0EuWH4VgmVBZB5UE4fYRBfdp/E8dC0nj7NEEXs9qszD0wl+LGYoLdg7m5582oVepzXyjajaIoHDhWzeL9xY69svTuGobH+9EvPIDoQG/cNPI9EkJ0Lk4nXK+++mo7hCGEEKJTUBSoK4PqEjAWQmUOHD8ADWfYIN43Atw8wTcAgtMhPA3cOk4F2J8O/0R2VTaeak9uTr4ZvU6KZLhSdrGRb7cVkF3R6GgbEO3DpKGxeGolyRJCdF7nVaVQCCFEN9BohMpCqD4MDWXQUANVefaS7dCykmBIbwhLB99ICIgGtxPV4TrgXlWbj21mZeFKAH6b+FtiDDGuDaibMjaY2ZBbSk5pPTuP1QLgplYxON6X8SlhRATIXllCiM6vVQmX0Wh0FMowGs/wW8zTtFVBDSGEEBeRqQGylkDhXqgvtq+9OttnXDXg5Q9eoeAXBn4pEJJgryzYCeRW5fJt7rcAjI0YS1pommsD6kYsVhtZxbUcqaolt7SWA8V11JlOZe6DY325cUCU7JclhOhSWpVw+fv7U1RURHBwMH5+fmdcpKooCiqVSn4LJYQQnYmiQM5KOPQtNNa3HLXSAAEp4BcKnsH2Mu0RKaDTd6gRq9Y6VHaI/x34n71IRo8UxsWPc3VIXZ7VppBdZGT9kXIOFNZiNNmLZZ0shBHho2NAvC9JPXzpGW5wZahCCNEuWpVwLV++nICAAABWrFjRrgEJIYS4CGw2KM2G7J+geLd91MrdA5KuA/840HmAZwDovFpee/peWJ1IZn4m83PmY8NGtD6aiT0nSpGMdmKzKezIK2dVdhlHKhtpNJ+qSOzrqSUpxIsQHzcS/X1IDPdF59Y5f6aEEKI1WpVwjRo1yvE6Li6OqKioFqNciqJw9OjRto1OCCFE2yvYAzs+tK/JOilpLPS6xj5y1cVYbVZ+OvwTywuXo6DQO7A3v+/1e9w1HaeAR1fRaLayOquYjVlVFNSYHO1qFQyJMzAowo+U6AB0bhqZESOE6DacLpoRFxfnmF54uoqKCuLi4uR/oEII0ZFlr4Qdc+2v1UBIP+h1NYSc2Oy3i/0/PK86j2+yvqGosQiAsZFjGR83XvZvagNWm0KxsZFqYyNVJgs5FXXsPWqkqsH+M+StUzMs3sDgmEAiA7wc5dw1nXSEVAghzpfTCdfJtVq/VFtbi4eHR5sEJYQQoo0pCmQthV1f2o/D0mDw3WeeMthFFNQWMHfvXKosVfhqfbk69moGhg10dVidmsliZVd+BZvzq8gurXcUvFCp7cmUYrMR6OXG5amBDI0PxlMnyZUQQrQ64Zo+fToAKpWKp59+Gi+vU/9IW61WNm7cSHp6epsHKIQQ4gJZrbDjv5C73n6cOBL6TYIuPMqTb8xn3t55VFmqiNHHcHe/u9Hr9DILo5VMFivFlQ1UGBsprqkjv6KR/LJGyurNzfpp1CpiDDr03jrC/NyJ9/OiV4QfHlpJtIQQ4qRWJ1zbt28H7CNcu3fvRqc7VbJVp9PRr18/Hn/88baPUAghxPkzN8CGd6Fop/049VpIubbLJltWm5X1R9ezKH8RZsVMpHckU/pMkU2Nf4WiKBRWNXD4uJH9xbWUVDZRWGsC1alRq9P5eWoYGOPLgKgexAR646ZRO6YJSkIrhBAttTrhOlmd8M477+S1116T/baEEKKjMzfAqlegPNu+XmvAvRA7xNVRtZtaUy1fZX/F3rK9AETro7mzz52SbJ1FZZ2JZQcKOVBQxzGjqcV5T52KKIMH/no3wnx1RPt6ExLgRYC3TtbACSGEE5xewzVnzpz2iEMIIURbMtXBhrehMht0GhjyKIT2cnVU7cJitbCreBdLjy6lzFSGChVXRl/JqOhRaNQyte10ZquNzOxSdh2r4kBxHU0We7l2jVpFUqAH8SFeJPr7EOjnSaifl+yvKYQQbcDphEsIIUQHl7cJ9s+HhgpwU8HQ6RDS09VRtYt6cz1z9swhpzoHgB7aHvy2129JDEh0cWQdS3WDiQ2HS9mRV0tedZNjmmCvIE8u6xVEYqgveg9ts2tkFEsIIdqGJFxCCNFVVByBHZ9DRZb92F0Dw2dAj3jXxtUOFEVhzZE1rC5YTXlTOe5qd4aEDGFMzBiZQniamgYz6w+XsPpgJeX1FlRqNXp3DZcn9yApyI+4IG9JrIQQop1JwiWEEF1B3hbY9g6crG+QOBJSrgOvgC63t1atqZbvD3/PjrIdAATqApmYMpEYQ4xrA+tAFEVh79FKvthaSGmdvbJglK+OS5ODGBgbiLdO7eIIhRCi+2hVwjVgwACWLVuGv78/zz77LI8//nizsvBCCCFcpL4Cdn8JR7fYj/0TYOBt4BcFXXCD2S0FW1h0ZBFGixG1Ws2E6AkMixiGTqM798XdQE2DmX0FVazLLudQWQMAET46Lu0VwLCEYLw97F8nWZclhBAXT6sSrv3791NXV4e/vz+zZ8/m/vvvl4RLCCFcRVEgfwfkLYfS/afaY4bCgCmg0Z710s5KURR+zv2ZJceWABDqEcqEhAn0Du7d7ZOH2kYLKw8WsjOvmryqpmbnhsYb+N2AqBbrs4QQQlw8rUq40tPTufPOO7nkkktQFIV//etf6PVnniP/l7/8pU0DFEIIcZqi/ZC1CIr2nGrzT4DU6yEs2WVhtbfTk61Lwy7lqvir8NB5uDgq1zpe08iS/cXsPFZLTaPVUQgjwkdHYrg3o3uGEGbwdHGUQgghWpVwffTRR/z1r3/lxx9/RKVSsWjRItzcWl6qUqkk4RJCiLZWngslu6FwDxhzT7XHDoOYURDUdSvy1Zvr+S7nO7aVbgPg2uhruTTm0m5d6KHOZGHlgUIW763AZFVQqdXEGNwZ3SuAXuF++HvJ9EohhOhIWpVw9erVi88++wwAtVrNsmXLCA4ObtfAhBCi2ys+AIcWNp82qAGih0Hc5RDQtYtEFNQU8On+TylpLAFgXOQ4RsaOdHFUrlNVb2JtdjGrDlZR02SfRhnt68616RH0jfYDFJfGJ4QQ4sycrlJos9nO3UkIIcT5URQoy4HsJVCw/VR7YC+IGACR6aAP6nKVB09ntprZkL+BJUeXUGetI0AbwA2JN5Ac1HWnTJ5NWW0Tqw8Vs/9YLUeNJkd7uF7LqOQeXJIUgk5nX5/V3deyCSFER3VeZeFzcnJ49dVX2b/f/lvX1NRU/vjHP5KQkNCmwQkhRLehKFCwDw58D5U5p9rD0yF5AgTE2o+7YOXBkxosDWw/up31xespabCPakXro5mUOgl/D38XR3dx1TZaWLq/gOX7y2g0nxq56hnoybAEfwbFB6Lrwj8LQgjRlTidcC1evJjrrruO9PR0RowYAcC6devo3bs3P/zwA1dccUWbBymEEF1a8QHYOQ+qjp1qC+0LsZdBRB/oBuuV8qry+PzA55RbygHooe3B0LChXBp1KW6a7rVlZN7xWuZuPMoxownFppAU6MnonoEkhPriJ+uzhBCi03H6X7E//elPPProo7z44ost2p988klJuIQQwhn7f4T939lfq4DQftDnBjBEuDSsi2nzsc0sPLKQWmstwR7B9A/uz5DwIfjqfF0d2kWjKApZRUZ+2lfMvuJ6VGo1AV5uTOgdyND4IDQa2ahYCCE6K6cTrv379/PFF1+0aL/rrrt49dVX2yImIYTo+hQFdn8Oh5aBGogcCH1uBq8AV0d2UW0p2MIXh+3/psT7xnNn2p14a7271XqkQ4XVfLu9gMOVp/bQGhDtw00Dogjwkv2zhBCis3M64QoKCmLHjh0kJSU1a9+xY4dULhRCiNYwN8COuXBko/24zw2QfA10o6JExXXFLDmyhN1luwHICM3gN4m/QaftPlPmFEVh8+EyPt1SSIPZhptaxYAYH8anhhMdaN/rsjslnkII0VU5nXDde++93HfffRw+fJjhw4cD9jVc//jHP5g+fXqbByiEEF2KsRDWvwm19qIQpP0OUq5ybUwXkU2xsebIGpYfW069rR44lWxp1N2nCMSOvHJ+2l1CXrV9VKtfpJ5bh8Zg8Ow+CacQQnQXTidcTz/9ND4+Prz88svMnDkTgPDwcJ555hkefvjhNg9QCCG6jMp8WPcyNNaDTgcDHoDIPq6O6qJQFIVdxbtYfmw5BfUFAER6R3Jt/LXE+8e7OLqLx9hgZs6abPYU1QH2eihXpARwTb9IqToohBBdlNMJl0ql4tFHH+XRRx+lpqYGAB8fnzYPTAghupS8TbDjPbAA+hC49BHwDnR1VBdFo6WRr7K+YlvxNgB0Kh3jo8YzInpEtxrVyi2r5dP1R8itbABgRJKBK1PDCfLxcHFkQggh2tMF1dqVREsIIc7BaoWDP8C+BfZjQzRcMg08/Vwa1sVitpr5aP9HZFVlATA6fDQZERkEeHb94iA2m0Lu8Vp2F1VSXNXE/tIGTBaFEL2W24fFkBgq/4YKIUR30L02NxFCiIvJZoWtH0L+JvtxeDoMvb9Lb158OqvNytyDc8mqysJT7cnvEn9Hn9CuO4XSarVxtLKerNJqcssayCqpp6bpVNELlVpNnzBvJg+LwcdTqg8KIUR3IQmXEEK0h5Nl308mW2k3Q+IY6CZT6OrMdXx28DMOVBzAXePOpJRJJPknnfvCTqS2yUJWUSVHjXXkH6/nQEk9FpvSrI9Oo6JvpJ6kIG/C/XzoFeaLonSfapRCCCEk4RJCiLZns8KGN6Fwu/14wBSIv8S1MV1EtaZaPtrzEUdqj6BT6bit120kByZ3iRLn5bVN5BQb2V9Ww+bDRv6/vTuPj6us+///Omf2ySSTfU+apE3TfYdSChWkWharLLcsVm5Afy4ICqIi3IoI3go3cnMr3AhuLCqIcn8BAaFQ2spS2kJLS/emadImbbNvk8ns51y/PyaZduhCA0mT0M8T8iBzzjXnXGdyyMw712agAaD6pvR32jTGZrkZm++iMsvLmJwU7NZ4yLb0tWx+Al4GIYQQAzCgwBWNRjn33HN5+OGHD1uHSwghRJ+tz8TDlg5MveKkCludoU7+vPXPNPQ2kGpN5UsTvkRVTtVwV+sji8QM6pr91HX2sLslwOYDvUn7yzKclOa5yHZbqchMpTzHg8WiD1NthRBCjEQDClw2m41NmzYNVV2EEGL027UCdr4aD1uzvg6lpwx3jU6Ypt4m/rzlz7SEW8i0ZbJk8hJK00qHu1rHpJTCVGAoE2WCoRRgsqa2hW37fVS3BIgYyd0Ex2W5yM9ycGpRJhNKMtA07RPReieEEGJoDLhL4Ze//GX++Mc/cvfddw9FfYQQYvSqfx/e/2v8+0mLofy0T3z/sY5gB9Wt1ezw72BH2w4MDHIduVw5+UryPfnDXb0kSil27u9mTUM7LR1h2gJRukOH/3w0Pd5C1d9NMMWuU5WfQlGGg/HZXioL0g6W1bQTU3khhBCj1oADVywW45FHHuG1115j9uzZpKSkJO2/7777jvtYb7zxBr/85S9Zv349jY2NPPvss1x44YWJ/Uopbr/9dn7/+9/T1dXF/Pnzeeihh5K6M3Z0dPDtb3+bF154AV3XueSSS/j1r3+Nx+NJlNm0aRPXXXcd7777Ljk5OXz729/m5ptvHuilCyHE0dW/B+sein9fNg8mXTis1RlqwViQ56ufZ23z2qTtxSnFXDnpyhE17fvetl7W1bezfX8v+7pDx/WcFLvO6RXpTC/KpCzHg0WXYCWEEOKjGXDg2rJlC7NmzQKguro6ad9A/9LX29vL9OnT+cpXvsLFF1982P577rmH+++/n8cff5zy8nJuu+02Fi1axLZt23A64wtFLlmyhMbGRpYtW0Y0GuWaa67h61//Ok8++SQAPp+Pz372syxcuJCHH36YzZs385WvfIX09HS+/vWvD/TyhRAimWnAtmdhxyvxx3lTYOa/wye45WNV/SperX+VnmgPEA9ZVRlVjEsfR0VGBbo2vGOYDFPxenUj1U299ARi7O4IJVqtLLrG9GIP0wu95HpdeN12bLqGbtHQ0bDoYLda0XUN05TZBIUQQnx8Aw5cK1euHLSTn3feeZx33nlH3KeU4le/+hU//vGP+cIXvgDAn/70J/Ly8njuuee4/PLL2b59O0uXLuXdd99lzpw5ADzwwAOcf/753HvvvRQWFvLEE08QiUR45JFHsNvtTJ48mY0bN3LfffdJ4BJCfDwhH6x7DJo2xx9XnAHTrgDLJ28C2KgRZf2B9bzb+i4NgQYAMm2ZLC5fPKLW1tqyt4Ol21qoaQ8mbZ9WlMLpZdmMK/CQYj/2z0cmvRBCCDGYPvKngpqaGnbv3s2CBQtwuVwopQa1L3tdXR1NTU0sXLgwsc3r9TJ37lxWr17N5ZdfzurVq0lPT0+ELYCFCxei6zpr167loosuYvXq1SxYsAC73Z4os2jRIv7rv/6Lzs5OMjIyDjt3OBwmHA4nHvt8vkG7LiHEJ0RTNWx8DPytB2cjrPz0cNdqUIWNMPt9+6ntrGV9y3raIm0AWHUrZxedzadLP411hITLjt4IT72zh03747MIOm0aZ1VlUpaeQkFGCoWZ8e7vMrmFEEKIE23A75Tt7e1ceumlrFy5Ek3T2LVrFxUVFXz1q18lIyOD//7v/x6UijU1NQGQl5eXtD0vLy+xr6mpidzc3KT9VquVzMzMpDLl5eWHHaN/35EC11133cUdd9wxKNchhPiECflgx0vxmQgBXKlw6rcgZ9zw1muQtAfb2dW6i42dG9ndvTtpn12zMydvDgvGLCDXnTsiwkskZrBlbzvPv9/E/p4IAGeOT+czEwrITXMOc+2EEEKIjxC4vvvd72Kz2aivr2fixImJ7Zdddhk33XTToAWu4XTrrbdy0003JR77fD5KSkqGsUZCiGGnFNSthU2PQCga35Y3BeZcDS7vsFZtMNT76nll7ytsb9uetN1j8ZCbksuE9AmcUngKHrsnsYDvcOvojfC7lbvY3dd9MM9j46p5Y6jISx3mmgkhhBAHDThwvfrqq7zyyisUFxcnba+srGTv3r2DVrH8/Ph0ws3NzRQUFCS2Nzc3M2PGjESZlpaWpOfFYjE6OjoSz8/Pz6e5uTmpTP/j/jIf5HA4cDgcg3IdQohRzohC3SrYuwb6W3zcGTDh8/HZCPWRET6OV9gIU9tRywH/AfYG9tLZ20lnpJMIkUSZIncREzImMCt/FjnunBE19XnEMHhndysb6n3sbAthmAqv08KssjQ+O6mQDLf9ww8ihBBixDODQYJ79hCpb8AMBYmGI+zLfI6Sn/0njoryDz/ACDLgwNXb24vb7T5se0dHx6CGlPLycvLz81m+fHkiYPl8PtauXcu1114LwLx58+jq6mL9+vXMnj0bgBUrVmCaJnPnzk2U+dGPfkQ0GsVmswGwbNkyqqqqjtidUAghEnqaYO2j0FUbf6wDYxfAxEvAfvjvweEUjAbpjnTjC/nwh/w0h5oxMIioCJ2hTjoDnfREegiYgSM+X9d1xqWP49wx51KSOrJa9H3BKJsa2qnrDPBurS+xELGm64zNdPLleaXke13DXEshhBAfl1KK0K4aet94ndCOnZixWGKfZrMRSEvD9HUPYw0/mgEHrjPPPJM//elP/OxnPwPiU8Gbpsk999zD2WefPaBj+f1+ampqEo/r6urYuHEjmZmZlJaWcuONN/Kf//mfVFZWJqaFLywsTKzVNXHiRM4991y+9rWv8fDDDxONRrn++uu5/PLLKSwsBOBLX/oSd9xxB1/96lf54Q9/yJYtW/j1r3/N//zP/wz00oUQJ4v+Vq0tT0AMsGow/nwoORXSS074YsZKKaJmlGAsHqoafY10h7vxx/w0BZroCnTRGes87Hla39pRylRJ271WLwVpBRS5ihiTNgavy0uWOwu3zT0ixmUBmKbivT1t7Gjzs6Whh67gwXp5HBbOqEznlPJcSjLcMn27EEKMUkopwrt3E66rI1RbS7huD4RC6P09K1JTcY4ZgzU7G29mBtmTJmP7QC+70WDAgeuee+7hnHPOYd26dUQiEW6++Wa2bt1KR0cHq1atGtCx1q1blxTS+sdNXXXVVTz22GPcfPPN9Pb28vWvf52uri7OOOMMli5dmliDC+CJJ57g+uuv55xzzkksfHz//fcn9nu9Xl599VWuu+46Zs+eTXZ2Nj/5yU9kSnghxJHVroIdz0GgK/44cxzMuRLSCuEDY5dCsRAtgRZ8AR++iA8TExSYykTTNRSKmBlLbFMoDGXQa/QSM2OYff8oTaGUwjTjZZRSRI0owWiQcDRMe6Q9fmyOHqIAbJqNTEcmLrsLj91DgasAi2bBho3clFy8Li9um5s0e9ph3QRHyrgspRTv1rbxz40HOOCPJrYXemxMLPYwPjuVicXp2K2WEVNnIYQQx8+MRAjV1NC7bh3B6l3g9ycXsNlwVo0nfeFCLGPGJN6vvF4vmTNnYs3OHoZafzwDDlxTpkyhurqa//3f/yU1NRW/38/FF1/MddddlzTW6nicddZZKHX4h4Z+mqZx5513cueddx61TGZmZmKR46OZNm0ab7755oDqJoQ4yYR8sPX/Qd3b8cd2C4xdBOPPxbQ62NNZy4GeA3REOuiOdtPe287+wP6jHu5YwejjlPVavaQ708lx5pBiTSHLkUV+Wj7Zrmw8dk9S2f5AMlJarT5Mmz/M0+sb2LS/F2Wa2C0ac8emMzbTzfTSLFx2CVhCCDEaKcMgXF9P92uv0btjJ0QiSfvtVVU4y8twjB2Ls6wMa1/jymh5//owH2kBFa/Xy49+9KPBrosQQpx4pgl1a2DTYxDp6yteMR9jyhfZH2ynsWkTq5tXsz+w/4jByKW74mHH4cGqW9E0DU3T4t+jgQm6pqNpGjrxBXXtuh231Y3e94/FYon/Bc8kXkaLPyfVkYrD5sBj95BqS8VmseGwxcfKjvY3IX8ohj8cJWIomrt6WVPbydamXjRdx6prnDMpi7Or8vG6ZBIMIYQYzSL19bQ+9RSqMb5kE0qhpafjHFuBZ84cHGPHots/2b/rP1Lg6uzs5I9//CPbt8enD540aRLXXHMNmZmZg1o5IYQYUp318N6foXsPmGA6PdSPP5eteozN7z1Ae6Q9qfj49PEUugtx6S7S7ekUeYuOOovfQFqXRltL1PGKGiahiIGJgT8SpbnDz57OXrY2+BNrZn3QpDw3i2cUU5Ylk2AIIcRopKJRoi0thPfW0/veeiI18Rl+dU3DMaGKtM98Bnt5+YiaAXeoDThwvfHGGyxevBiv18ucOXMAuP/++7nzzjt54YUXWLBgwaBXUgghBl39e7DuIQBaLbCxeDrvqSDNTa8lilixUpxaTHlqObMKZlGUVgR88oLRYNjT6qeh009HIEJPxMAfiFHdEiAQMdH0eMue+sDkFi6bjsum47FaKMlzckZFLuPy42uayWsshBAjk+H3E9m3DzMUAr8fMxAgGggQ3L0bo7EJDplZsJ+9ajz5V16Jxes9KX+/DzhwXXfddVx22WU89NBDSX+V/da3vsV1113H5s2bB72SQggxqLY+Q2j7y2x0wrtpWTToJioUX0dQR6civYLJ3snMLpyNyyYtLccSMQxe3bqff25u52hDcq26ht2qUeC2k5PhoCrHw8SidOkuKIQQI4wRDGL09EA0SrClhVhrK7H2dlQoTMzfQ8znQ7W2Jcr3zyZofvANwG7Hmp+Ha3wVnjmzseXnn9QTHQ04cNXU1PB///d/SS+axWLhpptu4k9/+tOgVk4IIQZLMBakt2s/1dXPsyVQy77sNIK6jqbHW11KPaVMzZzKzIKZeB3eYa7tyKeUYn9ngGfW72NrUy8Ak/NTSE+1kZ1iw2m1UJjqZlx+KnZ7fA3Ek/GvmkIIMdIYgQCR+npULIYZCGB0dxNpbCTS1Ixx4MDRQ9QhtKwsbFmZ2OwOLBkZmDYb1nQvrvHj0VNT0Z1OtJM4YH3QgAPXrFmz2L59O1VVVUnbt2/fzvTp0wetYkII8XF1hbrY0bKDjZ0bqe2uRdH35mGPt1pl27OZnT+bmQUzyXJkDWNNRw+lFDXNPbywcR/b+oKWw6pxxZxCThuXO8y1E0IIAfHf1YFNmwhu2kTM50OLGSjTwIhEMRobj/1kmw093Ytms2PPzsKal4clNQ3d5cSakYEtNxfd40HTtE/sGOTBdlyBa9OmTYnvv/Od73DDDTdQU1PDaaedBsCaNWt48MEHufvuu4emlkIIcZxCsRC7Wnax27ebDU0bCJiBxD6bUnjRmZg1iSlFCyhLL8NmldaX47Wn1c8z6/exqyOc2DY5P4XF0wspy/Ec45lCCCE+KqUUmCbKMDD9vcQ6O4h1dUFvgFhLC+H2NpRhxPeHwhg+H/T0JB3jg61WWmYmtowMsFqxelKwZGTgqKjAlpWFs7AQkPfFwXRcgWvGjBlompa0ZtbNN998WLkvfelLXHbZZYNXOyGEOE69kR4271vLm03raDU7gPj07TlGjOnBXmZGFLnFp8HUy8Ah4eB49QSjvLe3jfcbfGxrjodXTdeZUexh8bRCCtNljJsQJyOlFPR/xTeAUonPipqug1IY/RMoHFr+kOeoSAQjFAKliH3wA/6hxwYUYNF0QB0WBuL16S8VZ+mvwyFlzUgEFQodPH/fsS2ajkXTMEwDwzQPXl/iBP31iG/TNQ0UGKaRfN7+55gmht+P4euBQC8YZvy4qENeq77nHfLYAmAYRLq7IRoFw4h/HcGHdv2z20mZPRtHeTn2dC/oOqamobvc2AryT6pZAofbcQWuurq6oa6HEEIMWMSIsLVpC2saVrAn3ITZ9+bhQTE1FKAiHGWKsmAtXQBVi8ElY7OORzBiUNfcwxu7W9m038+h6zFPLUzhkjljKMpwy18/hfgQyjAwe3sxg8H4B2pTgTLj6/8phaXvj9mGaaJiMYxgkPj/cIeEl74P5RZN6wsP5sEP/oeW6dumDBMtFEIZMYxYrK91pO+8inhLiTLBVBjd3ahgIH7cvjqp/mMdco5EGDBNzK4ujMiRl3U41PGMAxppZS2ahqHUsNZD9YegY5TVc3Kwpnuxu1Ow5OSgZ2Whu91oViuaRceSkoKekoLucqG74n8Uk65/w+u4AteYMWOGuh5CCDEgu/dt4B91z9KogvENmkZBLMr0mMbpjkI8+QUYBadB9liQv+J9qEjMYH9XkJfe38f7+/1J7/VlXgezxnqZUZxFbprzpJ5pSoweKhbD6OnB9PsPa4FQSiW1fiR2K7OvyMGgYwYC6IYJpkEsGkOZBpgmZiiE2RtAxWKoUIhYrx/D74fOLojFMKLReLA6hpESNI637FD9LtW9XrT+hW81DTQArf9fDvkm3sKlgaHUIds/+N/4fw6WJbFf00BzONAslvji9H3bLbqGRdMx6A9chx/z4PXHy4NG391zSD0PfukOB5aMdOypqegpKfE6a1q8bN9yGRz6vaZhtVhA01AWC3pKCuh6fPIJXQeLBc1qRbfFu8JLiBo9PtLCxwcOHOCtt96ipaUF8wO/TL7zne8MSsWEECe5kA9C3dDVDIF9oGIQC2D4OlkebmCZPf725jJNTg9HmF4wl/yqC9EcnoOBQN6Ejqk7GGFzQwfbmvxsbQoQianEWllep4XKPDefHp9Hea5Hup58QGJMRV/XIzMWiz+GROsFfWXiLRsmRiCAMozEh6Pk3k/J3ZeSuisdPGnyf/v0t3zE+n52KhLBDIaSuj/1d4+Kh4z+D2iH7uuvw8G6WPo+BCa1qPSdv78blhmIBw6L9sGuWwfLkDg+iVad+HEPqcMHX5BDW3Ug3vpyyEt18DUj6Rh6LEbM7yfS2BTvjnUMQx1gkn5OLhdYrfEPz5oGFgvoGhY9/t/453oNzeFIfJiOf7jvCx19rS/xD/gcEir6P9z3PeoPDzYbFm8ahqni59Q16Asfmq4f8r0Fe3Y2FrcrEQZAi5cH0PS+TfHzWHQ9HoxcroPBIlFX4qGg73trX0hI/Ow0LX7thz6nb9tQLRQ/0LIWiwXjkP9Hh6sex1tWjB4DDlyPPfYY3/jGN7Db7WRlZSW9CWuaJoFLCPHRRIPQUgftm2HfuxDoTt6vQ70V/u5J4UDfNONTTAefLz2XjDGngkXWdDpewYjB2toWXtzcij8cf1PXdB2rrjE228Xi6UWMzUsd5loOLzMYJNrRQcznwwyFiHV0EmttJdrehtHTg9HWDuHwiGh1kLKHl08KO6mp8ZBw6Af+vtYMLdH68YHWjP7goYFmsWBzu9F0C6ZGPKzoGug6Fm86utMRb3VwOrFmZmL3etEdDgxNi3fxstvRHY4j1nckfGgfqrJ6X1klwUGIgQeu2267jZ/85Cfceuut6Ic2hwohxEAEuyHcBa3VsPdd6DrCWFEr4MzGyCznZS3I6+H98b/YAufkz2PRuAvjf60Vh2nxhahvD3CgJ0Bzdwh/MIY/aBA2TNoCsUS5Ao+NmeVpTMrPZHxB2mG9FkYipRSmz4dpGKholIjPh+rrvqUMM9Hly+juJrJvPyoaRe8bNxOLRBLjZA5tpcI0wTBQpomlr4vY8X7I/1C6Hm9lcDjQvWloR+uudGj3pr4AYBxa9Cjdp6yJlo+DhfvXwPng8yx9LRcHPwIf0v/qA124LH1dm+ItHyRaJrREy4uGZrWgu1OwWOMfrhOvWVKL6KGtGhxsOet/HQ49f6J4/Pt4WS3eSpIIRf2HPbQ+9JW3YE33QloalrS05BajDxjqUKJJ0BBC9Blw4AoEAlx++eUStoQQxy/kgwOboGcv9LRBdz0EfdD/a6T/M77dChkToGg2FEwFl5fucDd/r/47u7r3AzApaxIXjr2QDGfGsFzKSNbZG2H93la27OthR0swEUbVEUJUttvGvEovn64qxGW3DMu4LGWaxNrbUdEYGPEueZpSGD09hJuaMHp7iba0Em1rQ0Ui8VAVi0E4Pi39kA9at9mw5GSju1xYXC6sObnYcnKwZmdhcbuxpKdjsdlA0+JBta871cFWFD05DIyAVodPctmPUl4IIU6EAQeur371qzz99NPccsstQ1EfIcQnSbAbtjwNDWsPhqpDWXXwVkJuFRTNAU9e0kDinW07eXLHkwTMAC6ri4vHXsyMvBknrPojnVKKTXs7eau2jZ0t8TFYh6rMcpGf5SAvxUqGw0Ga247dbsFu0clJdSZaOwaT4fcTa2vD8PuJtrRgdPvirUuGQSwUJtbVhREKomIGZlvbYePsBhKMNKcTHA50hwPd7Yp3Yeobn6LpOprVgq2gAGtGJlabFaxWTOKtP7rDkRyO+gISmo7VZo0PVne7P7QOMl5QCCHEhxlw4Lrrrrv43Oc+x9KlS5k6dSq2DzTV33fffYNWOSHEKBX2w6a/w563Dgat1ALIGQueIkgpgIwCSM2J7/vAh9WWQAtL9yxlc9tmAPKd+Xxx0hcp95bLX6777NjXxUtbmqhuCyZtL0mzM7k0lVPLcijJiq83NlivmRmJED1wAK2/K19XV7zlKRIh1tFJqK4Oo7HxsOd9aIhKS4uPdbFasVotfV3vvFizs9FTUrAXFWFJSYnP1GWxoFks6B4Ptr7pjoe7VUUIIYQ4lo8UuF555RWqqqoADps0QwhxEvM3Q83LsHcV9E8Q5i2FCV+A4qkfGNdxhKdH/Ly9723eOvAWQTMeJKbnTufSyktx2WWBXX84xsqd+1lV00lX4OAMbPMr01kwNpc8rxOnbfC7Bob37MG38l8Ed+6EUOhDA5SWmYnFk4I1PR1rVjY2lxPNasUANLsdW04OFqcTrFasOTno9oMTnkjYEUII8Ukz4MD13//93zzyyCNcffXVQ1AdIcSoZBhQ/RJsf/7gtrTCeNAqmfmhQcswDd7Z9w7L9i2jJ9YDQKmnlMUViylLLxvCio8eNU0+nlxTT2MgHkRsFo055WksmlhAvndowmjkQCMd/3iOyM7qgxvdbqzZWWgWK8pux+J2o9ls6Ckp2PJycY0fjzUzM+k4EqKEEEKczAYcuBwOB/Pnzx+KugghRptgN9S9DtUvQv/4oaxxMP7c+KQXHzK5zp7uPbx54E1q22rpjsWngc9z5jE/fz6nFp8aX6PmJBeJGby2/QAvbGpDKSj2Ojl3ch6Ti7247EPz+hjBIN2vvYb/7dUQjLc0OqdMJnXBApxjx2Lta5GSACWEEEJ8uAEHrhtuuIEHHniA+++/fyjqI4QYDYxIvDWr+pWDY7SswKRLoeozoFuOOYlAa7CVl+peYmvHVgCUqUixpHBW0VmcXnI6dllTiz2tft5raOe9PT209XUfnFaUwr/PH4vXZR/UsKOUItrUhBkMEvH76Xz5ZYyGfQDYysvIuuhi7KUlg3Y+IYQQ4mQy4MD1zjvvsGLFCl588UUmT5582KQZzzzzzKBVTggxwhgRaNgImx6FSN9aTqkFUHEWjJkHdlc8bB1FMBbkzfo3WbFvBQYGmq4xKWsSp2SeQllmGSm2lBNyGSOZUoq3djXz5LuNibVbc1JsnDclh3njcrFaB/xr+4hiHR1EmpoI79lDcOdOzPoG4OC4LD0nh/TzzyNl2rT4ZBVCCCGE+EgG/M6dnp7OxRdfPBR1EUKMVJEg1P4Ltj8Dkb6WFSsw7Woom/ehXQdNZbK5eTOv7n2VlnALACUpJSwet5hxmeOka1qfxu4g/9y0n3V74+PYqnJcnFGZxfQxmdg/YuhRSmH4/YR21xLcvo3w3nqMtjaIRpPK6ZqGJT8fzWLBlpFBxgXnY8vP/9jXJIQQQpzsBhy4Hn300aGohxBiJFIK6tfD1icg5I93H7QCxafB1EvBkXrUpxqmwdaWrWzq2kRtey09RjxEZNmzOKvwLOaWzB201prRLhIzWF/Xzv97bz/dIQNNg8XTszlvcvGAZ39VsRi9mzcTeG8DRnMzRns75lECraWwEHtONs6qCXgmT8aamSHhVwghhBhk8mlHCHFkYT+sfxwObAQdsDtg/IUw7uxjdhus99WzsXkjG5s20hXrSmx36k7m5M1hYdnCk7broD8co6M3gr83QiAaJRwz2ecLsnV/gNbeKMo0qchwcPHsYsblpx3XMZVpEmtpoeeddwnu2IHR1ARmfGCdfkhYsxTk4ywrxzV5Era8PCxpafHFf/v3S7dBIYQQYkgMOHCVl5cf8y+utbW1H6tCQohhZhrQvAvefxz8bfFtpafC9C/BUYJSdXs1m9s3s6drD82RZiA+EYZdszMzdyaTMiYxLmvcSTEZhmkqwjGTtt4Q7b4g/nCYA74Qe5oD1HaGj/gcTdfJdFs5rSKVhROKPnT2QaO7m57NWwhu20poz17o6Uku4PXinjiB1BkzsOfnYzoc6E6nrJUohBBCDIMBB64bb7wx6XE0GmXDhg0sXbqUH/zgB4NVLyHEiaYU7H0nPk6rtyO+zemBOd+Aosnxxx/obravZx8v73mZ6s6D6zTpuk6Ft4JJaZOYVTjrE9maFTVMGtoDHOjyU98dIhozicZM2roj7OmKhyqtb1ybMs2k57rtOrkuGykuK3abjtOhMzHXy6yyLKxHGAqnDINYayuh2lpCO3Zi+nuI1u05bNFhW3kZnrlzcVZUYM3IQLPZZP0rIYQQYgT4SNPCH8mDDz7IunXrPnaFhBDDIOyHd/4AzVsPbiucDjO/DK70pKIRI8Lujt281/4em1o2YfbNCz8hcwJzsuZQnl1OhvOTNxaoOxihozfGrpZuVmxvpSt47Otz2jSK05ykunQcdp2qbA/lOWnkeZ2HlT00GJmRCNEDB+ITXdTW4l+3Hny+RNn+boKW0hJcY8fhmjgBR3k5+gdmjBVCCCHEyDBoY7jOO+88br31VplUQ4jRpqka1v0vhIKgAZWfhsrzDgtajf5GdrXu4q0Db9EWaUtsr0yv5NyycylNKwU+GWOBDFNR0+hjW0sXTd0RWrrCNPqjSa1WHoeFknQHJVlOvC4rNt2Cx2alJDeVVIcVl92GrmvHDJ5mJEKsqQmjo5PwrmoC+w8Q27cPYrHDytrKy3GUl+MuL8eWk42elyddBIUQQohRYNAC1//93/+RmZk5WIcTQpwI+7fC6v8GA3ClwanXQs44IN6S1R3uZnfbbta2rWV/YD/KjHdjS7WkUpFVwSlZpzA+e/wn4oN/Z2+EnY1d7Gjxs2lfD4GIeViZnBQbual2ZpWkcmpFDlbL0afD1/WjvyZGTw++t96iZ9Xb4PcnWq0S3QRdLqz5eVg9qbinT8M9fXqiBUu6CQohhBCjy4AD18yZM5M+XCmlaGpqorW1ld/85jeDWjkhxBAxTdjyN6h+LT7Ve85EOP1bYHOyr2cfS/cspbqzGkU8AGh94aHEU0Klt5JPl30ah8VxjBOMfM3dIdq6AzT4Q2za46O2I5i032nTmFacytgsN3keN0VZbtI98e6AAw07ZjRKePdugrt2Ea6rI7pvP0Qi8Z1WK/bSUmy5udjGj8dekI8tP/8TEWKFEEII8REC14UXXpj0WNd1cnJyOOuss5gwYcJg1UsIMVSMGLz7BziwngYL7MufTKhgGr31K9nTs4da38GZRnV08lx5zMiZwSlFp5BqO/q6WyNVIBKjrslHbZefhrYQLb4IHaEYkVh/mDzYSjUu00lZvouqrDSqirzYrQPrHmmGw5g+HzGfDy0aJVSzm+CeOiK7a+OTkhzCUlKC9+yzSZk+Das9PnujtFoJIYQQnzwDDly33377UNRDCDGEIkaEtxvepranhu7OOnxmiN7cDABUbD807E8qX5FWwQXlF1CSVoKmaaOuG1urP0xNYzcbGjrZ2uhPhKsPKvM6yMpwUpXrYUphOpmegU9brwyDwJat9Lz1JpFdNYnth3UTdLtxVlTgmjQRR1ERttJSacUSQgghTgKy8LEQn3C1nbU8V/McjcHGgxt1HQ2wYqU4rZhURypO3Um6LZ2q7KrEBBijQcwwOdAdpKUrwPbWHuoaAzQG4sGwf0r2PI+NMTkuKrLcFKa6SfXYSXPZSLFbBxwmzVCI4J49RBsbCdftIVRbi+ruPlhA09DzcrG53FhTPdjGj8dRXIytqEhmEhRCCCFOQscduHRd/9C/xmqaRuwIs2sJIU68lkALz9U8y66ueKuL2zSZF+yhVDlIm3IlqflVuG1ubNroCgFN3UE27e9gT1uQbn+EvV0RYmZyC5am64zLdDIm18HskmzKslM+dmtSqKYG/8qVhHZWY37w95yuk3LaaaSeMR9bQcGobBUUQgghxNA47sD17LPPHnXf6tWruf/++zHNw2f1EkKcWB3BDtY3rmPNgbfwmfGJIKaGelnsj5JRfApMuxycaaMqEAQiMepbfby+u5UN9b7D9tutGmXpTvIynUzPT6Mkx0umx37c16aUwoxEMLq7MXt742thNTUTa2sj0tRIpLkFursT3QS1zEzsebnY8vJwlJXhqqpCd7kG9ZqFEEII8clw3IHrC1/4wmHbdu7cyS233MILL7zAkiVLuPPOOwe1ckKI42eYBq/VvcbK+uVEiLfAlEbDfKEnSGnpfJj7GfAWDXMtj093MML2/V00+UPUNQeobguCdnByi6ocFxOLPBR63GR5XeR7nVgOmYb9SGuBRVtbCWzdSmTvXmJd3SjTQBkmumFgtLdj9s8aeDRWK+7p00hbsACLjL8SQgghxHH6SGO4Dhw4wO23387jjz/OokWL2LhxI1OmTBnsugkhjoNhGmxq2sSKAytoDjWjUBTEosyJxJibOwfH3PPBkzvc1TwqpRR72vxUt3Szty1IfXuI1t7oYeWyUqxMLErhjPJsyrI9H37cWIzw3r1EGpsIbd9GcNPmI5c7NDjpOnpWFprdjsXlxJafjzUnJz4Gq7AQuyd+3tHQKiiEEEKIkWFAgau7u5tf/OIXPPDAA8yYMYPly5dz5plnDlXd+OlPf8odd9yRtK2qqoodO3YAEAqF+N73vsdTTz1FOBxm0aJF/OY3vyEvLy9Rvr6+nmuvvZaVK1fi8Xi46qqruOuuu7BaZb4QMboppdjduZuX6l6iobcBTdewm4oLfV3MzpqFNu8KcKUPdzWTRGIGO/Z10xYK0dAVot0Xo7EnQk/w8IBVnu6gONdFUZqTyvx0SrOOL+wow8C/bh1dy5cTbWxK2mctLcE1vgpbQT6W1FQ0XcdqtaK53JDqQXc40I7QOiaEEEII8VEdd+q45557+K//+i/y8/P561//esQuhkNh8uTJvPbaa4nHhwal7373u/zzn//k6aefxuv1cv3113PxxRezatUqIP7B7IILLiA/P5+3336bxsZG/v3f/x2bzcYvfvGLE1J/IQZbMBpkQ+MG1rWuo6G3AQCbUswL+FkQjuGdfA1UnH7C62Waiu5glM5AlEjEoMUfoKE7RGdPhMbuMMGYSSCSPM6zfw0sm0VjYr6bihw3Zemp5GW6SHfZBtRtTxkG/rVr6VqxEq29Pb7RZsNWWoJjTBmu8ZU4q6oOO+ZoGssmhBBCiNHnuAPXLbfcgsvlYty4cTz++OM8/vjjRyz3zDPPDFrlIB6w8vPzD9ve3d3NH//4R5588kk+/elPA/Doo48yceJE1qxZw2mnncarr77Ktm3beO2118jLy2PGjBn87Gc/44c//CE//elPsdsHvuaOEMOhJ9LDtuZt1PbWsqt9Fz1GDwBWBRPCAc7zRyjIHA8LvobhTB+yenQHI+xp9tMaCNITjtHRG6U3ZNDdG6M9FCUUVYkQpY4yiU6Gy0J5jpssj43KbC9pKQ4KvfYBLzIM8Va+yN69BHfswL9hI2ZzMwB6Sgrecz6Ne/58dIfjo1+wEEIIIcTHdNyB69///d+HZZD4rl27KCwsxOl0Mm/ePO666y5KS0tZv3490WiUhQsXJspOmDCB0tJSVq9ezWmnncbq1auZOnVqUhfDRYsWce2117J161Zmzpx5xHOGw2HC4XDisc93+KxoQpwI3eFu/rX3X7x94G2i6mC3uyxrOlMCnczzdZOlgMpzYMrFYHPBAFtqIjGDumY/rb1BQjGDtkCUhpYgPWGDmFLETIWJRsxUhCIffuxst5VMtw2HFfLTHeR6HOSkuPB64qEq3WVD75vg4nhbl5RSRA4cILx3L6EDjcTa2zF6/URbWsHvP1jQ4SDt7LNIP+ssLCkp0molhBBCiGF33IHrscceG8JqHNncuXN57LHHqKqqorGxkTvuuIMzzzyTLVu20NTUhN1uJz09Pek5eXl5NDXFx200NTUlha3+/f37juauu+46bOyYECdSR7CD5fXLWde6DoVCKUWBq4AKbzkVsRCTdv0La9QEtxdOux4yy+AoY4+UUvhDMXojMepb/ezq8NPljxKJQXsgSpv/Q2bn42DXP4iPrcr02slIseJ12kh32vE67aS4bOSkOXD1tRx/3LATbW3F9/rrRNvaiDTsQw8EADBV8ppbWCzYK8fhnjKFlOnTsaSmHnGWQiGEEEKI4TCiZ44477zzEt9PmzaNuXPnMmbMGP7+97/jGsI1b2699VZuuummxGOfz0dJScmQnU+IQ72z7x1e2PMCITOEpmsUugs5u+BspmVUoK35LbTtBEB5S4me+jWirmxiwQg9YZNI1CAaidEdilDX2Ys/FGNva4iWI8z6d2iISnNaKMt0kuK24rbqFHldFKSnYLXo6LqG3WrBomtYNIXXNTRdcZVSRBsa6N20idDuWqJ1dckFbDZsJcXYiouxZGRizczE6k3Dlpcna2AJIYQQYsQa0YHrg9LT0xk/fjw1NTV85jOfIRKJ0NXVldTK1dzcnBjzlZ+fzzvvvJN0jOa+MR5HGhfWz+Fw4JBxH2KItfnDbD/QSdQwiSmFP9bD3t63ORDdDoBT5eIJziLaU8Sb+7vIC/+UfHwYwJvM5NnmUwi/0Ag0Anzo2CmHVSPPbae8wE2p10WG24nTYSXdbcPrsiWtY/VBQzmxRLS1le41awhs2Ypx4EDSPmtpCZ5TTsWWk417/Hh0m026CQohhBBiVBlVgcvv97N7926uvPJKZs+ejc1mY/ny5VxyySVAfCHm+vp65s2bB8C8efP4+c9/TktLC7m58XWIli1bRlpaGpMmTRq26xAnj6hh0uIL09kTpCUQoqYlQCBs0hGI0dLTP05QYfduxp62DU2Lh6VITxU9nbPo0uEsyxrO1VeRokFAwW8jl7BT5R12LqdNI9dlx6KZWCw6+ZlOClLteB12JhVn4LInd7Mb7tn5Io2N+JcuJbBpc1I3QcekiaTMmIG9oABbcXFi7Kgu3QSFEEIIMQqN6MD1/e9/n8WLFzNmzJjEYssWi4UrrrgCr9fLV7/6VW666SYyMzNJS0vj29/+NvPmzeO0004D4LOf/SyTJk3iyiuv5J577qGpqYkf//jHXHfdddKCJYZMbyTGu7Wt1LT2sqMpgD+cHGgO7cpXmtlLzPMOPX2tVOl6IYXOUxifXUhe1lZKO97GFWrDMCHi9NI25ZtclJKPpmmkuW04rRYsFrBqOjZb/H/nkdwCpJQiuH073cuXE91di94XpqxjxpA67zTcU6Zg8Xz4osZCCCGEEKPFiA5c+/bt44orrqC9vZ2cnBzOOOMM1qxZQ05ODgD/8z//g67rXHLJJUkLH/ezWCy8+OKLXHvttcybN4+UlBSuuuoq7rzzzuG6JPEJFTVM1tW2saWpm00NPkLR5IkdKjIcpKXYKMxwkJ8GzdFa6v3b2eOvBcCqWflc6QWcbstE69gBNX+BiIFFB2w6jL0A+7iFlNrdw3B1H58RDOL717/oeXs19PQktjsqK0k/dxG2iophrJ0QQgghxNAZ0YHrqaeeOuZ+p9PJgw8+yIMPPnjUMmPGjOGll14a7KoJAUAwYrBix35er/HREzIS46cKPTaml6UxNtPD+CIvMTPMztadvN+1irf2bwNAmfFQNj5jPBekTaew+hXorj94cIcTyuZD5bkwhGtrDaXIvn0Etm2jd+P7B8dn2WykzJlN6oIFuIqLgZHdKieEEEII8XGM6MAlxEhkmoqaJh+bGjtZWxcPWpqu47BqnFqewYw8LxNL0tE0jSZ/E6/vXcGapjX4YvH13DRdI9+Zz6TMSUyyZlJavwZ2/v7gCQqmQ94UKDsdHH2z742iQBLZvx/fG28Q2LcPo74hsV3Lzib9nHNImTkD3ekcxhoKIYQQQpw4EriEOA7hqMHuph7qOn3sPBCgui2Y2FfgsXHO5Dxml2fjsmoYpkFddx0v73mZPb49iXIei4cpOVOYnT+bipiJufFp6Kg5eJKSOTD5YvDknMArGxyxjg6CO3bSu+l9Yjurgb71sjQNe+U4XJMm45k9C0tq6jDXVAghhBDixJLAJcRRdPRGeH5TAzv29dAeiCXts1lgUpFGVmYXuR5oiVbzxI4OugJdNIeak8pWplcyNX0qMwpn4LI4sex6Bbb+P+hvtCqcAWM/A3njT8yFDYJoUxOh2jpCu3YRbW8nVn+wK6SuadgrKnDPPx1nWRnWzMxhrKkQQgghxPCSwCXEIXqCUTbv62DN7k52dcSnbe8fl5XptlKRZ0Nz1eMztlMb3EdtB9AR7yYYL3twsoyKtAourryYvJS+Kdy798PWZ6Hp/fjj7CqYfilklJ6w6/s4Inv30rlyJcH6elRr22H7beVlOMrKSZ0+DefYsTIuSwghhBACCVxCAFDb3MP/rWugtjOc2KbpOhUZDs6pykZ3tFHXu53NrZvp8R+cZa/UU0qGOwOP1UO+K59Uayq5abmk29Ox6tb4GlKRAFS/BDWvQAzQgSkXw7hFcMgU8SNRrL2d3s1biO3eTXDLlqT1sqxlY3BWjMUxZgyOokKs2dnAwfW9hBBCCCGEBC5xkusMRFi69QCrdnUR62udKk1zMK7YSmGWjy6jgVVdy6j3H+wyl23PZmr2VOaXzMfr8ALHWES4rRbW/RH8LfHH6WNg+mWQP2FEToShDIPo/v1EWloIvL+J0LZtYBiJ9bKckyeTeuYZ2IuKZDyWEEIIIcRxkMAlTkp7Wv2sqG7mnTpfYtuMEg/Ty/xs7n6LdV01EIhvV6bCqlmZmDWRKelTmJQ7Caf1Q2bZ87fCzhegbnX8sU2DyZdD+QKwj7xFt6OtrfRu2ID/vfcwm5LHoNnHjSVl/HiclZXYysuHqYZCCCGEEKOTBC5xUmnxBXl24x7WN7Sh2zuxpfaS6wlRnOMmSgfP7N2VKJvnyGOMdwzZ9mym5E4hx30cswcqBdWvwJb/B/297wpnwLRLR9Tsg7HWVgJ1dYS2biPY0Y7RsO/gTrsdW3ExtsxMPKfPw1lRcfQWPCGEEEIIcUwSuMSoZCqTBl8DveFeuiPd9MR6aA+1EzEjhCIxQpEYMTNGIBYkZBiYysQghtKDaHoMT+HBsUg+YFtX/HsdnVPyT2FO3hzGZo4FBhAywj2w+vfQvCX+OK0IqhbDmNmDd+HHSRkGRlcXsc5OzEgEMxQm2tRIZN8+wg37oKcn0U2wf1yWfdw4UmbNwj1zBhaX64TXWQghhBDik0gClxg1TGWy37+f9Y3r2dK2he5YN3DkGQKTaH1fh5QFyLBmkO3JJsORQYolBZtuozKrkjJv2cArV78BNv0JeuN1YsoXoOoC0LRjP28AVCxGZN8+Qnv2EG1owAyFUaYRD1SBILphoGIxYpEI9PbGW9uOwVpagmv8eGxl5Vgz0rEXFg5aXYUQQgghRJwELjHiKaXY2baTV+tfpaG3IbHdoTvIduTSG7bQ6rdhxmwo04EFKwVuFw67DavuJD0lleJUN3bdQnaqk/LsAqz0zSD4cQU64L2/QMvm+GOnG067AbIrPvahlVKE6+oIbNlCb81uog0NEI0evXz/9RwStPScHCypHjSrFYvbjb2sHHt+Ho6yMmwpKYB0ExRCCCGEGEoSuMSIZSqT2s5aljUso6arJrG9Im0sKeY02trS2bk3DFp8avWxGQ4ml3k4rSKXDLf9iMcctLFIhgF7V8O2pyEUiE/1XjYPJl4CLu9HOqRSivDu3UQONBJrbydYXY1qagIOdvvD5cJRNgZ7SQnWzEw0ux3NakV3OrG73WCxYGoaWCzx/TJFuxBCCCHEsJLAJUacmBFjdcNq3mp8i9ZQKwBWrEzOmYYrPJN3aiJ0hwwgvmbWtKIUFk0qYGxuytBXrrMe6lbA/vUQDsW3eXLg1P8PciqPe6p3o7ub4IEDxNraCO2qIdrRQaytLd4V8BC6xYJr0iQcUyZjLyrCVlBw1BAlE1sIIYQQQow8ErjEiOGP+FnXtI439r+B3/ADoKFR7qnCEpzFe5t1ApEgANluK/PHZzCtKIvSbA8wxEEj5IMd/4CaNw5usxJfvHj8+eCKr0llBIOo3l5iPh8qEkHFYpjBEEZXJ5GmJqLtHWihEEZzc9IiwoeyV1Vhz8vDXlKCZ/IkLKmpEqKEEEIIIUYpCVxi2PVEelixewXLapcRIt5qlGnLZEzaNHbXFPJ+fX8wMSn02FgwIYvTx+Vit56A7nK+A1CzHHavBLOvFhlVxDKnEezQMGp6Md79G1o0Sqynh8i+/R86WUX/7ICWgnwsqWnYi4txlJdhcadgy87C4j3YJdEiXQKFEEIIIUY1CVzihPNH/LT6W2n2N7OhbQM1nTWEw2EM0yDPlU+Bazr76gt5Y3cUZZrYrRozSlI5fUwW4wrSsFr0oa9k2A87nkXteoNAB0RCEAqmEg2moLr2A/uTiuuHTlhhs6GlpmJJS0W3WsFiwZKSgjU7G1tBAc6seKjSvB9trJcQQgghhBg9JHCJIeWP+GnuaabL6CJsDbOtaRu7W3ejrMmtQIXuIly2cnbUlbA7AhCfjW9GsYfLTh1z1EkwBo1SEOrCaNhK75o1RFprUSGNSHcKRqR/NkMFxLs6YrFgKxuDo6QES1oa9owMdKcTS14e1szMY55KxloJIYQQQpw8JHCJIdHS28JLe17i/eb30awaFrsFq9OKETEwMMix55DuysCqctDC49hao+jqCaHp4HFYmFuextlVBWSl2IamgkpB517Y/y5mcx091fX0tkCsq/98joOtVk4HzrFjcVRU4Cgbgy07Gz01FU0/2NImIUoIIYQQQhyJBC4xqGJGjFdrXuWVfa+g+v7JseeQm5FLWU4ZbtNNhaeCLHcp/7tiJztbAgAYkQipTgsXTM3jU1X5iW6Dgx5ggt3QsA5q38Rsr6enDfyNTozeg8HOkuHENa4SV9U0LBkZWIuK0J3Owa2HEEIIIYQ4KUjgEoOioaeB5buXU91VTXekG4Cy1DIWlS5icv5kXC4XHo+H1s4eXt5Qx2s1m/CFDOxWjTMrsij32piU78ZhH6IWLUDt34Hx9n2EuhQ9XTrBFjdE+1qpUtykn3M27hmzsGRkoGmatFoJIYQQQoiPTQKX+FgO+A/w1+q/Ut1SjRkz0XSNNGsanyr6FJ8u/3QirBiGyZs1bTz51g72d/SiW62Upjm47pzxFGW4CQaDBIPBQa2bMgyCO3cSrq0ltGsn0YYGdBWfQr5/SnbN6yXtzDPwnHoqlrS0QT2/EEIIIYQQErjER6KU4tXdr/J66+tE9AgAlemVLChewMTMiVj0g9OZ7+8K8pe11ewJgBGJUpJm55zJxXxqQi4O2+BOe66UIrh1Kz1vryZcUwORyCF7NdDAkpuLa9w43JMn4Rg3Dt0+xBNyCCGEEEKIk5YELjFgSime3vE0q5pWYbFbKEsr49+m/ht5jjwsFktSF7y6Vj+/XbWXHsNCamoKnxqbx8IJWWR7UwatPrGODgJbtuLftpVwwz7w+Q7udLtwZfhwpoRx5BfgOP9m9BSvdBMUQgghhBAnhAQuMWCv1r7KqqZVaGhcMvYSzht/Hr29vUQOaU2KGAZv7GjmmQ0HiGKhKj+F7y+ejtfOoHQdVIZB78aN+N99l9jOauBgN0EcDjxzT8WdFcLRshJNAzLHwWlfQ0+Rta+EEEIIIcSJI4FLDMiyumW8uPdFAC4ZewkLyxei9U+fDoSiBm9XN7Fieyv7eyIoUzGtJIUbzp1Mvtf1scKWEQwS3L6dwHvvEapvSLRk6ZqGtagQ+7hKXBMn4CgvR695GbatBA3InQRzvwYOz8e6diGEEEIIIQZKApc4bv/a+y+eqX0GgM8UfYYzS89M2r9tXxfPbDjAns54qEpzWlg0MYdF08rwOAZ+q5nhMMGaGsL19YT37CW8YweY5sECbjeeObPxzpuHvbg43k0wEoCtf4Xdb8XLTLoAJn4BDgmFQgghhBBCnCgSuMRxWbF3BU/XPI2ma5xTdA6fG/e5xL4Of4SHV+xkXW0Hmq6T5rRwdlUWZ43Px23XsejHF3bMSIRIXR2RpiYitXUEa2owenuTyug5ObgnTcI1eRKO0lJ0pzM+fbtSsGctbP4zhMPxwhMWwaQLB+slEEIIIYQQYsAkcIljihgRXt79Mi/vfRmATxV8mrOKzmN/Z5BtBzrZ0hxkR3uYWCgEwNwKLxdPLyYzJT7z37Emp1BKYXR2EnjvPdrWrMFXtwc9Fksqo2VmYs/LwzGmFNeECTjKyj54EGh4H2pegZYd8W1OD0z6Nyg/fXBeBCGEEEIIIT4iCVziMEopmvxNvFH3Bv98/xXaY80AGL2VvLi2hOdj76P6uvZZ7HZ0q5VxmU4unFHIxKL0o4YsFYsROdBIz9YtxDZsINLUjNnZCcQn2cAw0DMycBQXYysqxDV+PNYxY9AsR5g6PhaB+nVQ/RIEmg9uH/cpmPJFsDoG90URQgghhBDiI5DAJQDoifSwrmEdT61/iuqmanrowYgYRGNRlOkk3D2HcGcZWl/3QJtFY4zXwdzxhcwem0uGzSQWiwJgRqMY7e3EOjoI7j+A1tqKvceH7UAjAZ+PoGFg1/XEua0lxaTPmQPFxTiLi9GsB29LI+SHjv3QvQ+CrdDbBgEfdNZA36SE2IDS+VD+aUgvPVEvmRBCCCGEEB9KAtdJTCnFm3vf5LGtj7HbvxszYhLuCKNMhW7VMaLZxHxFnJ5/GnOnlJJig3yvCzMWQ6l4C5fL5cKm67S9t4HuTe8T2VlNeP/+xDkMpbDrOqbFgrUvSNnKxuCdMQPnhInYiouwuN0Eu1oI7qtGa1gHXTuhqxn8DRDoPWLd4wfSYOxnoeo8cKaBrK0lhBBCCCFGGAlcJ6nqzmp+tupnvLPrHcyIicVlIdWWTizkxdc9kRRLKZ8tyWL+LDe5aU6UUoR7e6GtleCePUR9PsxwGH9zM+bOanzt7URME0vfbICa3Y4lOwtbWhoppWNIKywgvWIsqqSYUDSK3aJB81bY8RzUvwldLRAB9CNU1gqkV0FaDrgywZULKdmQOQYsVjhSl0MhhBBCCCFGAAlcJ5GeSA9/2PwHVu1fxc7OnShDoaFxXsUFGIH5PPtOL6HuVsakObn9wmm42w/QtOI1WnbVEN63j1B7OxZNI2KaGH2LDLssFuy6jmaz4aocR+qpc7GNKcWSmYmmaRiGgd1ux+Vy4eqtJ/j+36G1Bjo2gmEmV9DhhNRS8KRDZiV4CsGTDa4MmdZdCCGEEEKMShK4TgIxM8aftv2JP2z6Az3RHgA0NKZkzaHE/TmWvxOmKRxFKTilOJNvjLdh+c2vaFyzhu5oNPlgFgvW4iLsqalodgeenBw848aSMX48MV3HYrFgdO2H5u3g74BYAGJ+COyDrvXxVqwI8TvP5oScqZA3A3KnAk6wfuCWlG6CQgghhBBiFJPA9QkWiAZ4bOtj/G3n3+gIdQBQ5CniM4VX8PLaDN7eCtFAN46gjy+3buOsjs1072sgEA4nJrVwVFbimjIJe24auAxssU6i4R6MSA8EunGZLdgbV+LfHYBIKB6QDm24MonfZXbACRTOBXcJFE6E/Kmg93UHDAbjX0IIIYQQQnyCSOD6hFrTuIbbV93Ogd4DADitTr407uuE2+fx/IsbyIvu5NstW6msq8Gxr5EUNMKmiYpEAHAUWvGWBQlZ1oJvDfggEgNNB2IcDFV24ndRuG97/xgsd99YK7sbnE7ILIPxp4GnNB6s7PYT+GoIIYQQQggxPCRwfcKEjTCPbXmM/934vwB47RnMMeaSvSnMrGWPMbb551zWbSHcZSNqQFs0Gp/swg3ZpT3YUrvRXDHcTg1/RBEK9B3YYgVXIXjzwZ4OugOsKZBVCN7seDdBQ4HNAVYX2D3x5xlGPFy5XODxSCuWEEIIIYQ4qZxUgevBBx/kl7/8JU1NTUyfPp0HHniAU089dbirNSiaehp5cePD/GXvy7QbQexRxVXvmyyu34uxr5Fob/xH3YMr8RyLE7yVmTimZJM7ewphLIS6IsQc3ngLlbJDRIsHKGcaRCLxGQEjkYNjq1yueKDy+w/ul3FXQgghhBBCACdR4Prb3/7GTTfdxMMPP8zcuXP51a9+xaJFi9i5cye5ubnDXb2PRCnF+r0reXH1//BCuI6cdji1TjG22WRejYktqBPCnShvzXSScsocUj61CMeESVjGjaO9o4NQKITm8UAoBPv2QSwW7wbo90N39zBeoRBCCCGEEKPbSRO47rvvPr72ta9xzTXXAPDwww/zz3/+k0ceeYRbbrllmGs3MLFYlL+/+gs2vP0sRk+UsmbFT5sV4xoPLaVjpLhxzptN1vyzSTv/fCxe7weOEzuh9RZCCCGEEOJkc1IErkgkwvr167n11lsT23RdZ+HChaxevfqw8uFwmHA4nHjs8/lOSD2Px/O3XUnKynXMbIOZR9gfGjeBggWn4542Fc+CBehu9xFKCSGEEEIIIU6EkyJwtbW1YRgGeXl5Sdvz8vLYsWPHYeXvuusu7rjjjhNVvQHpDndR2Rb/PuTUCBSVkDdzHtmzpuOaMR1HRcVxH8tqtZKfn5+0rbi4eDCrK4QQQgghxEntpAhcA3Xrrbdy0003JR77fD5KSkqGsUYHnXPlz3jF+wvOueB7TJh+KpqmDXeVhBBCCCGEEEdxUgSu7OxsLBYLzc3NSdubm5sPa+EBcDgcOByOE1W9ASmcOoNrpv59uKshhBBCCCGEOA76hxcZ/ex2O7Nnz2b58uWJbaZpsnz5cubNmzeMNRNCCCGEEEJ8kp0ULVwAN910E1dddRVz5szh1FNP5Ve/+hW9vb2JWQuFEEIIIYQQYrCdNIHrsssuo7W1lZ/85Cc0NTUxY8YMli5dethEGkIIIYQQQggxWDSllBruSox0Pp8Pr9dLd3c3aWlpw10dIYQQQgghxDAZaDY4KcZwCSGEEEIIIcRwkMAlhBBCCCGEEENEApcQQgghhBBCDBEJXEIIIYQQQggxRCRwCSGEEEIIIcQQkcAlhBBCCCGEEENEApcQQgghhBBCDBEJXEIIIYQQQggxRCRwCSGEEEIIIcQQkcAlhBBCCCGEEEPEOtwVGA2UUgD4fL5hrokQQgghhBBiOPVngv6M8GEkcB2Hnp4eAEpKSoa5JkIIIYQQQoiRoKenB6/X+6HlNHW80ewkZpomBw4cIDU1FU3Thrs6+Hw+SkpKaGhoIC0tbbirI0YRuXfExyH3j/g45P4RH4fcP+LjGOz7RylFT08PhYWF6PqHj9CSFq7joOs6xcXFw12Nw6SlpckvHfGRyL0jPg65f8THIfeP+Djk/hEfx2DeP8fTstVPJs0QQgghhBBCiCEigUsIIYQQQgghhogErlHI4XBw++2343A4hrsqYpSRe0d8HHL/iI9D7h/xccj9Iz6O4b5/ZNIMIYQQQgghhBgi0sIlhBBCCCGEEENEApcQQgghhBBCDBEJXEIIIYQQQggxRCRwCSGEEEIIIcQQkcA1yjz44IOUlZXhdDqZO3cu77zzznBXSZxgd911F6eccgqpqank5uZy4YUXsnPnzqQyoVCI6667jqysLDweD5dccgnNzc1JZerr67ngggtwu93k5ubygx/8gFgsllTmX//6F7NmzcLhcDBu3Dgee+yxob48cQLdfffdaJrGjTfemNgm9444lv379/PlL3+ZrKwsXC4XU6dOZd26dYn9Sil+8pOfUFBQgMvlYuHChezatSvpGB0dHSxZsoS0tDTS09P56le/it/vTyqzadMmzjzzTJxOJyUlJdxzzz0n5PrE0DEMg9tuu43y8nJcLhdjx47lZz/7GYfO3Sb3j+j3xhtvsHjxYgoLC9E0jeeeey5p/4m8V55++mkmTJiA0+lk6tSpvPTSSwO/ICVGjaeeekrZ7Xb1yCOPqK1bt6qvfe1rKj09XTU3Nw931cQJtGjRIvXoo4+qLVu2qI0bN6rzzz9flZaWKr/fnyjzzW9+U5WUlKjly5erdevWqdNOO02dfvrpif2xWExNmTJFLVy4UG3YsEG99NJLKjs7W916662JMrW1tcrtdqubbrpJbdu2TT3wwAPKYrGopUuXntDrFUPjnXfeUWVlZWratGnqhhtuSGyXe0ccTUdHhxozZoy6+uqr1dq1a1Vtba165ZVXVE1NTaLM3Xffrbxer3ruuefU+++/rz7/+c+r8vJyFQwGE2XOPfdcNX36dLVmzRr15ptvqnHjxqkrrrgisb+7u1vl5eWpJUuWqC1btqi//vWvyuVyqd/+9rcn9HrF4Pr5z3+usrKy1Isvvqjq6urU008/rTwej/r1r3+dKCP3j+j30ksvqR/96EfqmWeeUYB69tlnk/afqHtl1apVymKxqHvuuUdt27ZN/fjHP1Y2m01t3rx5QNcjgWsUOfXUU9V1112XeGwYhiosLFR33XXXMNZKDLeWlhYFqNdff10ppVRXV5ey2Wzq6aefTpTZvn27AtTq1auVUvFfZLquq6ampkSZhx56SKWlpalwOKyUUurmm29WkydPTjrXZZddphYtWjTUlySGWE9Pj6qsrFTLli1Tn/rUpxKBS+4dcSw//OEP1RlnnHHU/aZpqvz8fPXLX/4ysa2rq0s5HA7117/+VSml1LZt2xSg3n333USZl19+WWmapvbv36+UUuo3v/mNysjISNxP/eeuqqoa7EsSJ9AFF1ygvvKVryRtu/jii9WSJUuUUnL/iKP7YOA6kffKpZdeqi644IKk+sydO1d94xvfGNA1SJfCUSISibB+/XoWLlyY2KbrOgsXLmT16tXDWDMx3Lq7uwHIzMwEYP369USj0aR7ZcKECZSWlibuldWrVzN16lTy8vISZRYtWoTP52Pr1q2JMoceo7+M3G+j33XXXccFF1xw2M9X7h1xLM8//zxz5szhi1/8Irm5ucycOZPf//73if11dXU0NTUl/ey9Xi9z585Nun/S09OZM2dOoszChQvRdZ21a9cmyixYsAC73Z4os2jRInbu3ElnZ+dQX6YYIqeffjrLly+nuroagPfff5+33nqL8847D5D7Rxy/E3mvDNb7mQSuUaKtrQ3DMJI+5ADk5eXR1NQ0TLUSw800TW688Ubmz5/PlClTAGhqasJut5Oenp5U9tB7pamp6Yj3Uv++Y5Xx+XwEg8GhuBxxAjz11FO899573HXXXYftk3tHHEttbS0PPfQQlZWVvPLKK1x77bV85zvf4fHHHwcO/vyP9T7V1NREbm5u0n6r1UpmZuaA7jEx+txyyy1cfvnlTJgwAZvNxsyZM7nxxhtZsmQJIPePOH4n8l45WpmB3kvWAZUWQowo1113HVu2bOGtt94a7qqIUaChoYEbbriBZcuW4XQ6h7s6YpQxTZM5c+bwi1/8AoCZM2eyZcsWHn74Ya666qphrp0Y6f7+97/zxBNP8OSTTzJ58mQ2btzIjTfeSGFhodw/4hNPWrhGiezsbCwWy2GzhTU3N5Ofnz9MtRLD6frrr+fFF19k5cqVFBcXJ7bn5+cTiUTo6upKKn/ovZKfn3/Ee6l/37HKpKWl4XK5BvtyxAmwfv16WlpamDVrFlarFavVyuuvv87999+P1WolLy9P7h1xVAUFBUyaNClp28SJE6mvrwcO/vyP9T6Vn59PS0tL0v5YLEZHR8eA7jEx+vzgBz9ItHJNnTqVK6+8ku9+97uJ1na5f8TxOpH3ytHKDPReksA1StjtdmbPns3y5csT20zTZPny5cybN28YayZONKUU119/Pc8++ywrVqygvLw8af/s2bOx2WxJ98rOnTupr69P3Cvz5s1j8+bNSb+Mli1bRlpaWuID1bx585KO0V9G7rfR65xzzmHz5s1s3Lgx8TVnzhyWLFmS+F7uHXE08+fPP2wJiurqasaMGQNAeXk5+fn5ST97n8/H2rVrk+6frq4u1q9fnyizYsUKTNNk7ty5iTJvvPEG0Wg0UWbZsmVUVVWRkZExZNcnhlYgEEDXkz92WiwWTNME5P4Rx+9E3iuD9n42oCk2xLB66qmnlMPhUI899pjatm2b+vrXv67S09OTZgsTn3zXXnut8nq96l//+pdqbGxMfAUCgUSZb37zm6q0tFStWLFCrVu3Ts2bN0/Nmzcvsb9/au/PfvazauPGjWrp0qUqJyfniFN7/+AHP1Dbt29XDz74oEzt/Ql06CyFSsm9I47unXfeUVarVf385z9Xu3btUk888YRyu93qL3/5S6LM3XffrdLT09U//vEPtWnTJvWFL3zhiFM1z5w5U61du1a99dZbqrKyMmmq5q6uLpWXl6euvPJKtWXLFvXUU08pt9st03qPcldddZUqKipKTAv/zDPPqOzsbHXzzTcnysj9I/r19PSoDRs2qA0bNihA3XfffWrDhg1q7969SqkTd6+sWrVKWa1Wde+996rt27er22+/XaaFPxk88MADqrS0VNntdnXqqaeqNWvWDHeVxAkGHPHr0UcfTZQJBoPqW9/6lsrIyFBut1tddNFFqrGxMek4e/bsUeedd55yuVwqOztbfe9731PRaDSpzMqVK9WMGTOU3W5XFRUVSecQnwwfDFxy74hjeeGFF9SUKVOUw+FQEyZMUL/73e+S9pumqW677TaVl5enHA6HOuecc9TOnTuTyrS3t6srrrhCeTwelZaWpq655hrV09OTVOb9999XZ5xxhnI4HKqoqEjdfffdQ35tYmj5fD51ww03qNLSUuV0OlVFRYX60Y9+lDQlt9w/ot/KlSuP+FnnqquuUkqd2Hvl73//uxo/fryy2+1q8uTJ6p///OeAr0dT6pAlvoUQQgghhBBCDBoZwyWEEEIIIYQQQ0QClxBCCCGEEEIMEQlcQgghhBBCCDFEJHAJIYQQQgghxBCRwCWEEEIIIYQQQ0QClxBCCCGEEEIMEQlcQgghhBBCCDFEJHAJIYQ46ezZswdN09i4ceOQn+uxxx4jPT19yM8jhBBiZJLAJYQQYsS5+uqr0TTtsK9zzz13uKt2TGVlZfzqV79K2nbZZZdRXV09PBUSQggx7KzDXQEhhBDiSM4991weffTRpG0Oh2OYavPRuVwuXC7XcFdDCCHEMJEWLiGEECOSw+EgPz8/6SsjI4MvfelLXHbZZUllo9Eo2dnZ/OlPfwJg6dKlnHHGGaSnp5OVlcXnPvc5du/efdRzHanb33PPPYemaYnHu3fv5gtf+AJ5eXl4PB5OOeUUXnvttcT+s846i7179/Ld73430SJ3tGM/9NBDjB07FrvdTlVVFX/+85+T9muaxh/+8Acuuugi3G43lZWVPP/884n9nZ2dLFmyhJycHFwuF5WVlYeFUyGEECODBC4hhBCjypIlS3jhhRfw+/2Jba+88gqBQICLLroIgN7eXm666SbWrVvH8uXL0XWdiy66CNM0P/J5/X4/559/PsuXL2fDhg2ce+65LF68mPr6egCeeeYZiouLufPOO2lsbKSxsfGIx3n22We54YYb+N73vseWLVv4xje+wTXXXMPKlSuTyt1xxx1ceumlbNq0ifPPP58lS5bQ0dEBwG233ca2bdt4+eWX2b59Ow899BDZ2dkf+dqEEEIMHelSKIQQYkR68cUX8Xg8Sdv+4z/+g5tvvpmUlBSeffZZrrzySgCefPJJPv/5z5OamgrAJZdckvS8Rx55hJycHLZt28aUKVM+Un2mT5/O9OnTE49/9rOf8eyzz/L8889z/fXXk5mZicViITU1lfz8/KMe59577+Xqq6/mW9/6FgA33XQTa9as4d577+Xss89OlLv66qu54oorAPjFL37B/fffzzvvvMO5555LfX09M2fOZM6cOUB87JgQQoiRSVq4hBBCjEhnn302GzduTPr65je/idVq5dJLL+WJJ54A4q1Z//jHP1iyZEniubt27eKKK66goqKCtLS0RCDpb436KPx+P9///veZOHEi6enpeDwetm/fPuBjbt++nfnz5ydtmz9/Ptu3b0/aNm3atMT3KSkppKWl0dLSAsC1117LU089xYwZM7j55pt5++23P+JVCSGEGGrSwiWEEGJESklJYdy4cUfct2TJEj71qU/R0tLCsmXLcLlcSTMYLl68mDFjxvD73/+ewsJCTNNkypQpRCKRIx5P13WUUknbotFo0uPvf//7LFu2jHvvvZdx48bhcrn4t3/7t6Me8+Oy2WxJjzVNS3SJPO+889i7dy8vvfQSy5Yt45xzzuG6667j3nvvHZK6CCGE+OikhUsIIcSoc/rpp1NSUsLf/vY3nnjiCb74xS8mAkp7ezs7d+7kxz/+Meeccw4TJ06ks7PzmMfLycmhp6eH3t7exLYPrtG1atUqrr76ai666CKmTp1Kfn4+e/bsSSpjt9sxDOOY55o4cSKrVq067NiTJk36kKs+vM5XXXUVf/nLX/jVr37F7373uwE9XwghxIkhLVxCCCFGpHA4TFNTU9I2q9WamBziS1/6Eg8//DDV1dVJE05kZGSQlZXF7373OwoKCqivr+eWW2455rnmzp2L2+3mP/7jP/jOd77D2rVreeyxx5LKVFZW8swzz7B48WI0TeO22247bBKOsrIy3njjDS6//HIcDscRJ7L4wQ9+wKWXXsrMmTNZuHAhL7zwAs8880zSjIcf5ic/+QmzZ89m8uTJhMNhXnzxRSZOnHjczxdCCHHiSAuXEEKIEWnp0qUUFBQkfZ1xxhmJ/UuWLGHbtm0UFRUljYnSdZ2nnnqK9evXM2XKFL773e/yy1/+8pjnyszM5C9/+QsvvfQSU6dO5a9//Ss//elPk8rcd999ZGRkcPrpp7N48WIWLVrErFmzksrceeed7Nmzh7Fjx5KTk3PEc1144YX8+te/5t5772Xy5Mn89re/5dFHH+Wss8467tfGbrdz6623Mm3aNBYsWIDFYuGpp5467ucLIYQ4cTT1wU7rQgghhBBCCCEGhbRwCSGEEEIIIcQQkcAlhBBCCCGEEENEApcQQgghhBBCDBEJXEIIIYQQQggxRCRwCSGEEEIIIcQQkcAlhBBCCCGEEENEApcQQgghhBBCDBEJXEIIIYQQQggxRCRwCSGEEEIIIcQQkcAlhBBCCCGEEENEApcQQgghhBBCDBEJXEIIIYQQQggxRP5/Zf1CwhV2be8AAAAASUVORK5CYII=", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def generate_plots():\n", " !pip install matplotlib > /dev/null\n", @@ -524,6 +1066,26 @@ " plt.legend()\n", " plt.show()\n", "\n", + " # average Brush weights for each generation --------------------------------\n", + " data = np.zeros( (kwargs['max_gen'], 4) )\n", + " for g in range(kwargs['max_gen']):\n", + " idx_start = g*(learner_log.shape[0]//kwargs['max_gen'])\n", + " idx_end = (g+1)*(learner_log.shape[0]//kwargs['max_gen'])\n", + "\n", + " df_in_range = learner_log.iloc[idx_start:idx_end]\n", + " df_in_range = df_in_range[[col for col in df_in_range.columns if col.startswith('weight')]]\n", + " data[g] = df_in_range.mean().values\n", + "\n", + " plt.figure(figsize=(10, 5))\n", + "\n", + " #plt.plot(data, label=est_mab.mutations_)\n", + " plt.stackplot(range(kwargs['max_gen']), data.T, labels=est_mab.mutations_)\n", + " plt.xlabel(\"Generations\")\n", + " plt.ylabel(\"Percentage of usage\")\n", + "\n", + " plt.legend()\n", + " plt.show()\n", + "\n", " # --------------------------------------------------------------------------\n", " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", " 'std m1', 'std m2', 'min m1', 'min m2'])\n", @@ -563,9 +1125,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0, " + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 41\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m, end\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m \u001b[39mif\u001b[39;00m (i\u001b[39m==\u001b[39m\u001b[39m29\u001b[39m) \u001b[39melse\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 40\u001b[0m est \u001b[39m=\u001b[39m BrushClassifier(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\u001b[39m.\u001b[39mfit(X,y)\n\u001b[0;32m---> 41\u001b[0m est_mab \u001b[39m=\u001b[39m BrushClassifierMod(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\u001b[39m.\u001b[39;49mfit(X,y)\n\u001b[1;32m 43\u001b[0m learner_log \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(est_mab\u001b[39m.\u001b[39mlearner_\u001b[39m.\u001b[39mpull_history)\u001b[39m.\u001b[39mset_index(\u001b[39m'\u001b[39m\u001b[39mt\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 45\u001b[0m total_rewards \u001b[39m=\u001b[39m learner_log\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39marm idx\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m'\u001b[39m\u001b[39mreward\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39msum()\u001b[39m.\u001b[39mto_dict()\n", + "Cell \u001b[0;32mIn[3], line 111\u001b[0m, in \u001b[0;36mBrushEstimatorMod.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msearch_space_ \u001b[39m=\u001b[39m _brush\u001b[39m.\u001b[39mSearchSpace(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunctions_)\n\u001b[1;32m 109\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtoolbox_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setup_toolbox(data\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_)\n\u001b[0;32m--> 111\u001b[0m archive, logbook \u001b[39m=\u001b[39m nsga2(\n\u001b[1;32m 112\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtoolbox_, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_gen, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpop_size, \u001b[39m0.9\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbosity)\n\u001b[1;32m 114\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39marchive_ \u001b[39m=\u001b[39m archive\n\u001b[1;32m 115\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogbook_ \u001b[39m=\u001b[39m logbook\n", + "File \u001b[0;32m~/Documents/github/brush/src/brush/deap_api/nsga2.py:51\u001b[0m, in \u001b[0;36mnsga2\u001b[0;34m(toolbox, NGEN, MU, CXPB, verbosity)\u001b[0m\n\u001b[1;32m 48\u001b[0m ind1, ind2 \u001b[39m=\u001b[39m toolbox\u001b[39m.\u001b[39mmate(ind1, ind2)\n\u001b[1;32m 50\u001b[0m off1 \u001b[39m=\u001b[39m toolbox\u001b[39m.\u001b[39mmutate(ind1)\n\u001b[0;32m---> 51\u001b[0m off2 \u001b[39m=\u001b[39m toolbox\u001b[39m.\u001b[39;49mmutate(ind2)\n\u001b[1;32m 53\u001b[0m \u001b[39m# avoid inserting empty solutions\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[39mif\u001b[39;00m off1: offspring\u001b[39m.\u001b[39mextend([off1])\n", + "Cell \u001b[0;32mIn[3], line 64\u001b[0m, in \u001b[0;36mBrushEstimatorMod._mutate\u001b[0;34m(self, ind1)\u001b[0m\n\u001b[1;32m 59\u001b[0m offspring \u001b[39m=\u001b[39m creator\u001b[39m.\u001b[39mIndividual(opt)\n\u001b[1;32m 60\u001b[0m \u001b[39m# print(\"mutation\")\u001b[39;00m\n\u001b[1;32m 61\u001b[0m \u001b[39m# print(ind1.prg.get_model())\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[39m# print(offspring.prg.get_model())\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m offspring\u001b[39m.\u001b[39mfitness\u001b[39m.\u001b[39mvalues \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtoolbox_\u001b[39m.\u001b[39;49mevaluate(offspring)\n\u001b[1;32m 66\u001b[0m \u001b[39m# We compare fitnesses using the deap overloaded operators\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[39m# from the docs: When comparing fitness values that are **minimized**,\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \u001b[39m# ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[39m# (this means that this comparison should work agnostic of min/max problems,\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[39m# or even a single-objective or multi-objective problem)\u001b[39;00m\n\u001b[1;32m 71\u001b[0m reward \u001b[39m=\u001b[39m \u001b[39m1.0\u001b[39m \u001b[39mif\u001b[39;00m offspring\u001b[39m.\u001b[39mfitness \u001b[39m>\u001b[39m ind1\u001b[39m.\u001b[39mfitness \u001b[39melse\u001b[39;00m \u001b[39m0.0\u001b[39m\n", + "Cell \u001b[0;32mIn[3], line 126\u001b[0m, in \u001b[0;36mBrushClassifierMod._fitness_function\u001b[0;34m(self, ind, data)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_fitness_function\u001b[39m(\u001b[39mself\u001b[39m, ind, data: _brush\u001b[39m.\u001b[39mDataset):\n\u001b[0;32m--> 126\u001b[0m ind\u001b[39m.\u001b[39;49mprg\u001b[39m.\u001b[39;49mfit(data)\n\u001b[1;32m 127\u001b[0m \u001b[39mreturn\u001b[39;00m (\n\u001b[1;32m 128\u001b[0m np\u001b[39m.\u001b[39mabs(data\u001b[39m.\u001b[39my\u001b[39m-\u001b[39mind\u001b[39m.\u001b[39mprg\u001b[39m.\u001b[39mpredict(data))\u001b[39m.\u001b[39msum(), \n\u001b[1;32m 129\u001b[0m ind\u001b[39m.\u001b[39mprg\u001b[39m.\u001b[39msize()\n\u001b[1;32m 130\u001b[0m )\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "if __name__ == '__main__':\n", " from brush import BrushClassifier\n", diff --git a/src/program/program.h b/src/program/program.h index 438acfa3..9247cdc5 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -87,10 +87,12 @@ template struct Program SSref = std::optional>{s}; } + /// @brief count the tree size of the program int size(){ return Tree.size(); } + /// @brief count the tree depth of the program int depth(){ return Tree.max_depth(); } diff --git a/tests/cpp/test_program.cpp b/tests/cpp/test_program.cpp index acd0f67b..07bdce78 100644 --- a/tests/cpp/test_program.cpp +++ b/tests/cpp/test_program.cpp @@ -204,18 +204,12 @@ TEST(Operators, ProgramSizeAndDepthPARAMS) PRG.get_model("compact", true), PRG.Tree.max_depth(), PRG.Tree.size() ); - // There are two ways of assessing the size of a program - // GUI TODO: which style are we going to use? i) implement - // PRG.max_depth() (or PRG.depth()); or ii) get rid of PRG.size() - // and use always PRG.Tree.<>) ASSERT_TRUE(PRG.Tree.size() > 0); ASSERT_TRUE(PRG.Tree.size() <= s+max_arity); ASSERT_TRUE(PRG.size() > 0); ASSERT_TRUE(PRG.size() <= s+max_arity); - // GUI TODO: max_depth() counts the number of edges (so a program with - // one node has max_depth()==0). Is this how it should behave? ASSERT_TRUE(PRG.Tree.max_depth() >= 0); ASSERT_TRUE(PRG.Tree.max_depth() <= d+1); } diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index ed508429..d48e6882 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -107,6 +107,8 @@ TEST(Operators, MutationSizeAndDepthLimit) for (int d = 5; d < 15; ++d) { + int successes = 0; + for (int s = 5; s < 15; ++s) { PARAMS["max_size"] = s; @@ -124,9 +126,6 @@ TEST(Operators, MutationSizeAndDepthLimit) auto opt = PRG.mutate(); - // TODO: count the number of fails and assert that it is not equal to - // the number of mutations applied (there is no point in having mutation - // if it doesn't work) if (!opt){ fmt::print( "=================================================\n" @@ -138,6 +137,8 @@ TEST(Operators, MutationSizeAndDepthLimit) ); } else { + successes += 1; + // Extracting the child from the std::optional and checking // if it is within size and depth restrictions. There is no // margin for having slightly bigger expressions. @@ -171,6 +172,7 @@ TEST(Operators, MutationSizeAndDepthLimit) ASSERT_TRUE(Child.Tree.max_depth() <= d); } } + ASSERT_TRUE(successes > 0); } } @@ -335,8 +337,6 @@ TEST(Operators, CrossoverSizeAndDepthLimit) } } -// TODO: make a test that will always choose one mutation and check for errors - TEST(Operators, CrossoverSizeAndDepthPARAMS) { MatrixXf X(10,2); From f5707035989afb4055e85bd955a6bcea18e2e08b Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 4 Jun 2023 17:20:15 -0400 Subject: [PATCH 025/102] Adds control to avoid weights in boolean nodes --- src/program/node.h | 37 ++++++++++++++++++++++++++++++++++++- 1 file changed, 36 insertions(+), 1 deletion(-) diff --git a/src/program/node.h b/src/program/node.h index 00e5a8fc..4e4e2be9 100644 --- a/src/program/node.h +++ b/src/program/node.h @@ -39,6 +39,28 @@ using Brush::Data::Dataset; namespace Brush{ +// should I move this declaration to another place? +template +inline auto Isnt(DataType dt) -> bool { return !((dt == T) || ...); } + +template +inline auto IsWeighable() noexcept -> bool { + return Isnt(DT); +} +inline auto IsWeighable(DataType dt) noexcept -> bool { + return Isnt(dt); +} + struct uint32_vector_hasher { std::size_t operator()(std::vector const& vec) const { std::size_t seed = vec.size(); @@ -137,6 +159,9 @@ struct Node { set_prob_change(1.0); fixed=false; + // cant weight an boolean terminal + if (!IsWeighable(this->ret_type)) + this->is_weighted = false; } /// @brief gets a string version of the node for printing. @@ -219,13 +244,22 @@ struct Node { inline void set_feature(string f){ feature = f; }; inline string get_feature() const { return feature; }; + // TODO: use this in every occurence of is_weighted + inline bool get_is_weighted() const {return this->is_weighted;}; + inline void set_is_weighted(bool is_weighted){ + + // cant change the weight of a boolean terminal + if (IsWeighable(this->ret_type)) + this->is_weighted = is_weighted; + }; + private: /// @brief feature name for terminals or splitting nodes string feature; }; -//TODO: add nt to template as first argument, make these constexpr +//TODO GUI: add nt to template as first argument, make these constexpr template inline auto Is(NodeType nt) -> bool { return ((nt == T) || ...); } @@ -278,6 +312,7 @@ inline auto IsWeighable(NodeType nt) noexcept -> bool { NodeType::SplitBest >(nt); } + ostream& operator<<(ostream& os, const Node& n); ostream& operator<<(ostream& os, const NodeType& nt); From d53f13ef648478c2a998f4f44c78ef009018a131 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 4 Jun 2023 17:21:46 -0400 Subject: [PATCH 026/102] Fix Issue #37 crash when fitting a program with bool terminal --- src/program/operator.h | 23 ++++++++++++++++++----- src/program/program.h | 10 +++++----- src/program/signatures.h | 5 +++-- src/types.h | 6 ++++++ src/variation.h | 2 +- 5 files changed, 33 insertions(+), 13 deletions(-) diff --git a/src/program/operator.h b/src/program/operator.h index 28061605..3c9e1031 100644 --- a/src/program/operator.h +++ b/src/program/operator.h @@ -16,6 +16,7 @@ namespace util{ /// @param weights option pointer to a weight array, used in place of node weight /// @return template + requires (!is_one_of_v) Scalar get_weight(const TreeNode& tn, const W** weights=nullptr) { Scalar w; @@ -31,7 +32,7 @@ namespace util{ w = **weights; // NLS case 2: a Jet/Dual weight is stored in weights, but this constant is a // integer type. We need to do some casting - else if constexpr (is_same_v && is_same_v) { + else if constexpr (is_same_v && is_same_v) { using WScalar = typename Scalar::Scalar; WScalar tmp = WScalar((**weights).a); w = Scalar(tmp); @@ -44,6 +45,16 @@ namespace util{ } return w; }; + template + requires (is_one_of_v) + Scalar get_weight(const TreeNode& tn, const W** weights=nullptr) + { + // we cannot weight a boolean feature. Nevertheless, we need to provide + // an implementation for get_weight behavior, so the metaprogramming + // doesn't fail to get a matching signature. + + return Scalar(true); + }; } //////////////////////////////////////////////////////////////////////////////// // Operator class @@ -199,7 +210,7 @@ struct Operator auto inputs = get_kids(d, tn, weights); if constexpr (is_one_of_v) { - if (tn.data.is_weighted) + if (tn.data.get_is_weighted()) { auto w = util::get_weight(tn, weights); return this->apply(inputs)*w; @@ -218,13 +229,14 @@ struct Operator using RetType = typename S::RetType; using W = typename S::WeightType; + // Standard C++ types template requires (is_one_of_v) RetType eval(const Dataset& d, const TreeNode& tn, const W** weights=nullptr) const { if constexpr (is_one_of_v) { - if (tn.data.is_weighted) + if (tn.data.get_is_weighted()) { auto w = util::get_weight(tn, weights); return this->get(d, tn.data.get_feature())*w; @@ -233,6 +245,7 @@ struct Operator return this->get(d,tn.data.get_feature()); }; + // Jet types template requires( is_one_of_v) RetType eval(const Dataset &d, const TreeNode &tn, const W **weights = nullptr) const @@ -240,7 +253,7 @@ struct Operator using nonJetType = UnJetify_t; if constexpr (is_one_of_v) { - if (tn.data.is_weighted) + if (tn.data.get_is_weighted()) { auto w = util::get_weight(tn, weights); return this->get(d, tn.data.get_feature()).template cast()*w; @@ -249,6 +262,7 @@ struct Operator return this->get(d, tn.data.get_feature()).template cast(); }; + // Accessing dataset directly template auto get(const Dataset& d, const string& feature) const { @@ -261,7 +275,6 @@ struct Operator )); return T(); - } }; diff --git a/src/program/program.h b/src/program/program.h index 9247cdc5..1f6c06ae 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -221,7 +221,7 @@ template struct Program for (PostIter i = Tree.begin_post(); i != Tree.end_post(); ++i) { const auto& node = i.node->data; - if (node.is_weighted) + if (node.get_is_weighted()) ++count; } return count; @@ -239,7 +239,7 @@ template struct Program for (PostIter t = Tree.begin_post(); t != Tree.end_post(); ++t) { const auto& node = t.node->data; - if (node.is_weighted) + if (node.get_is_weighted()) { weights(i) = node.W; ++i; @@ -264,7 +264,7 @@ template struct Program for (PostIter i = Tree.begin_post(); i != Tree.end_post(); ++i) { auto& node = i.node->data; - if (node.is_weighted) + if (node.get_is_weighted()) { node.W = weights(j); ++j; @@ -333,7 +333,7 @@ template struct Program // if the first node is weighted, make a dummy output node so that the // first node's weight can be shown - if (i==0 && parent->data.is_weighted) + if (i==0 && parent->data.get_is_weighted()) { out += "y [shape=box];\n"; out += fmt::format("y -> \"{}\" [label=\"{:.2f}\"];\n", @@ -366,7 +366,7 @@ template struct Program // string kid_id = fmt::format("{}",fmt::ptr(kid)); // kid_id = kid_id.substr(2); - if (kid->data.is_weighted && Isnt(kid->data.node_type)){ + if (kid->data.get_is_weighted() && Isnt(kid->data.node_type)){ edge_label = fmt::format("{:.2f}",kid->data.W); } diff --git a/src/program/signatures.h b/src/program/signatures.h index 1d9e13cc..a46e58a9 100644 --- a/src/program/signatures.h +++ b/src/program/signatures.h @@ -195,8 +195,9 @@ template struct Signatures; template struct Signatures>>{ using type = std::tuple< - Signature, - Signature + Signature, + Signature, + Signature >; }; diff --git a/src/types.h b/src/types.h index 31664384..5badc481 100644 --- a/src/types.h +++ b/src/types.h @@ -179,6 +179,12 @@ template<> struct DataEnumType{ using type = ArrayXXf; }; template<> struct DataEnumType{ using type = Data::TimeSeriesb; }; template<> struct DataEnumType{ using type = Data::TimeSeriesi; }; template<> struct DataEnumType{ using type = Data::TimeSeriesf; }; +template<> struct DataEnumType{ using type = ArrayXbJet; }; +template<> struct DataEnumType{ using type = ArrayXiJet; }; +template<> struct DataEnumType{ using type = ArrayXfJet; }; +template<> struct DataEnumType{ using type = ArrayXXbJet; }; +template<> struct DataEnumType{ using type = ArrayXXiJet; }; +template<> struct DataEnumType{ using type = ArrayXXfJet; }; template struct DataTypeEnum; template <> struct DataTypeEnum { static constexpr DT value = DT::ArrayB; }; diff --git a/src/variation.h b/src/variation.h index 5fa5d9df..8346e344 100644 --- a/src/variation.h +++ b/src/variation.h @@ -113,7 +113,7 @@ inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) /// @param SS the search space (unused) inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { - spot.node->data.is_weighted = !spot.node->data.is_weighted; + spot.node->data.set_is_weighted(!spot.node->data.get_is_weighted()); return true; } From 0a7d01cf77aabc2d17b5449d69a7ddb5ddd80856 Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 4 Jun 2023 17:22:49 -0400 Subject: [PATCH 027/102] Improve test log --- tests/cpp/test_data.cpp | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index d40866e7..e3d7c9cb 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -53,10 +53,12 @@ TEST(Data, MixedVariableTypes) // visualizing detailed information for the model std::for_each(PRG.Tree.begin(), PRG.Tree.end(), - [](const auto& n) { - fmt::print("Name {}, node {}, feature {}, sig_hash {}\n", - n.name, n.node_type, n.get_feature(), n.sig_hash); - }); + [](const auto& n) { + fmt::print("Name {}, node {}, feature {}\n" + " sig_hash {}\n ret_type {}\n ret_type type {}\n", + n.name, n.node_type, n.get_feature(), + n.sig_hash, n.ret_type, typeid(n.ret_type).name()); + }); std::cout << std::endl; From 25c86740dd9643220d031d502ee33bf01121720b Mon Sep 17 00:00:00 2001 From: gAldeia Date: Sun, 4 Jun 2023 17:23:11 -0400 Subject: [PATCH 028/102] Improve plots of learner history --- src/brush/D_TS_experiments.ipynb | 3626 +++++++++++++++++++++++++----- 1 file changed, 3007 insertions(+), 619 deletions(-) diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index 5703f0a8..0b9783ae 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -45,16 +45,12 @@ "\n", "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user.\n", "\n", - "My suggestion is that we use the expected value for each Beta distribution:\n", - "\n", - "$$\\mathop{\\mathbb{E}}[X] = \\int_{0}^{\\infty} x,$$\n", - "\n", - "which, in our case, would be calculated for each arm $k$, so we replace $f(x; \\cdot)$ by $p(\\theta^k; \\cdot)$ and we can get these weights. Since the C++ does not expect the weights to have their sum equals to 1, as long as the proportions are representative, we can work with this. Execution logs shows us some empirical evidence that the expected value is proportional to the average number of times each mutation was used (plots 3 and 4)." + "My suggestion is that, at the end of the evolution, we use the success ratio of each arm as the weights for each arm." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -143,24 +139,148 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_learner_history(learner, arm_labels=[]):\n", + " !pip install matplotlib > /dev/null\n", + " import matplotlib.pyplot as plt\n", + " import matplotlib.gridspec as gridspec\n", + "\n", + " # getting the labels to use in plots\n", + " if len(arm_labels) != learner.num_bandits:\n", + " arm_labels = [f'arm {i}' for i in range(learner.num_bandits)]\n", + "\n", + " # Setting up the figure layout\n", + " fig = plt.figure(figsize=(12, 10), tight_layout=True)\n", + " gs = gridspec.GridSpec(6, 6)\n", + "\n", + " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", + " \n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + "\n", + " data_total_pulls = np.array([total_pulls[k] for k in sorted(total_pulls)])\n", + " data_total_rewards = np.array([total_rewards[k] for k in sorted(total_rewards)])\n", + " data_total_failures = data_total_pulls-data_total_rewards\n", + "\n", + " axs = fig.add_subplot(gs[0:2, 4:])\n", + " axs.bar(arm_labels, data_total_failures, label=\"Null reward\")\n", + " axs.bar(arm_labels, data_total_rewards, bottom = data_total_failures, label=\"Positive reward\")\n", + " axs.set_xlabel(\"Arm\")\n", + " axs.legend()\n", + "\n", + " win_ratios = pd.DataFrame.from_dict({\n", + " 'arm' : arm_labels,\n", + " 'totpulls' : data_total_pulls,\n", + " '0 reward' : data_total_failures,\n", + " '+ reward' : data_total_rewards,\n", + " 'success%' : (data_total_rewards/(data_total_pulls)).round(2)\n", + " })\n", + "\n", + " axs = fig.add_subplot(gs[3:5, 4:])\n", + " axs.table(cellText=win_ratios.values, colLabels=win_ratios.columns, loc='center')\n", + " axs.axis('off')\n", + " axs.axis('tight')\n", + "\n", + " # Plotting rewards and pulls -----------------------------------------------\n", + " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", + " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", + " for i, row in learner_log.iterrows():\n", + " data[i+1, :] = data[i]\n", + " data[i+1, row['arm idx'].astype(int)] += 1\n", + "\n", + " axs = fig.add_subplot(gs[0:2, :4])\n", + " axs.plot(data, label=arm_labels)\n", + " axs.set_ylabel(\"Number of times mutation was used\")\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + "\n", + " # Plotting alphas and betas ------------------------------------------------\n", + " for i, col in enumerate(['alpha', 'beta']):\n", + " columns = learner_log.columns[learner_log.columns.str.startswith(f'{col} ')]\n", + " labels = [f\"{col} {arm_labels[i]}\" for i in range(4)] \n", + " data = learner_log.loc[:, columns]\n", + "\n", + " axs = fig.add_subplot(gs[(i+1)*2:(i+1)*2+2, :4])\n", + " axs.plot(data, label=labels)\n", + " axs.set_ylabel(f\"{col}s\")\n", + " axs.legend()\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " axs.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + " \n", + " axs.set_xlabel(\"Evaluations\") # Label only on last plot\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 361, 1: 366, 2: 338, 3: 497}\n", - "number of pulls for each arm: {3: 3077, 1: 2398, 0: 2370, 2: 2155}\n", + "------------------------ optimizing ------------------------\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "(it was expected: similar amount of pulls for each arm)\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 8293, 1: 11, 2: 21, 3: 0}\n", - "number of pulls for each arm: {0: 9933, 2: 39, 1: 23, 3: 5}\n", + "------------------------ optimizing ------------------------\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "(it was expected: more pulls for first arm, less pulls for last)\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 50, 1: 3940, 2: 1, 3: 4384}\n", - "number of pulls for each arm: {3: 5226, 1: 4694, 0: 74, 2: 6}\n", + "------------------------ optimizing ------------------------\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "(it was expected: 2nd approx 4th > 1st > 3rd)\n" ] } @@ -192,25 +312,19 @@ " print(\"------------------------ optimizing ------------------------\")\n", "\n", " learner = D_TS(4)\n", - " for i in range(10000):\n", + " for i in range(1000):\n", " arm_idx = learner.choose_arm()\n", " reward = bandits.pull(arm_idx)\n", "\n", " learner.update(arm_idx, reward) \n", "\n", - " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", - "\n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - "\n", - " print(\"cum. reward for each arm : \", total_rewards)\n", - " print(\"number of pulls for each arm: \", total_pulls)\n", + " plot_learner_history(learner)\n", " print(f\"(it was expected: {expec})\")" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -392,478 +506,2865 @@ "output_type": "stream", "text": [ "0, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.75]\t[ nan 0.92059763]\t[nan 20.]\n", - "1 \t94 \t94 \t[ nan 15.6] \t[ nan 6.08604962]\t[nan 1.]\n", - "2 \t97 \t97 \t[ nan 9.28] \t[ nan 5.50105444]\t[nan 1.]\n", - "3 \t99 \t99 \t[ nan 3.33] \t[ nan 2.34117492]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 1.59] \t[ nan 0.70844901]\t[nan 1.]\n", - "5 \t100 \t100 \t[0.61577302 1.18 ]\t[0.33362003 0.38418745]\t[0.26090035 1. ]\n", - "6 \t100 \t100 \t[0.44268739 1.15 ]\t[0.12072349 0.35707142]\t[0.26090035 1. ]\n", - "7 \t100 \t100 \t[0.38106325 1.09 ]\t[0.04378987 0.28618176]\t[0.26090035 1. ]\n", - "8 \t100 \t100 \t[0.38200725 1.07 ]\t[0.02594982 0.38091994]\t[0.24684934 1. ]\n", - "9 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", - "10 \t100 \t100 \t[0.38211246 1.07 ]\t[0.03103629 0.45287967]\t[0.13583826 1. ]\n", - "11 \t100 \t100 \t[0.37959786 1.15 ]\t[0.03953694 0.90967027]\t[0.13583824 1. ]\n", - "12 \t100 \t100 \t[0.37819053 1.17 ]\t[0.04168421 0.92795474]\t[0.13583824 1. ]\n", - "13 \t100 \t100 \t[0.37567592 1.25 ]\t[0.04814319 1.21140414]\t[0.13583824 1. ]\n", - "14 \t100 \t100 \t[0.37567592 1.25 ]\t[0.04814319 1.21140414]\t[0.13583824 1. ]\n", - "15 \t100 \t100 \t[0.37280568 1.3 ]\t[0.05723907 1.37477271]\t[0.06369215 1. ]\n", - "16 \t100 \t100 \t[0.37604174 1.2 ]\t[0.0480877 0.96953597]\t[0.06369214 1. ]\n", - "17 \t100 \t100 \t[0.37744907 1.18 ]\t[0.04630412 0.95268043]\t[0.06369214 1. ]\n", - "18 \t100 \t100 \t[0.37744907 1.18 ]\t[0.04630412 0.95268043]\t[0.06369214 1. ]\n", - "19 \t100 \t100 \t[0.37282535 1.27 ]\t[0.05713309 1.18198985]\t[0.06369214 1. ]\n", - "20 \t100 \t100 \t[0.37282535 1.27 ]\t[0.05713309 1.18198985]\t[0.06369214 1. ]\n", - "21 \t100 \t100 \t[0.36827331 1.39 ]\t[0.06597425 1.59307878]\t[0.05849276 1. ]\n", - "22 \t100 \t100 \t[0.36480056 1.45 ]\t[0.07359937 1.65151446]\t[0.03482345 1. ]\n", - "23 \t100 \t100 \t[0.36480056 1.45 ]\t[0.07359937 1.65151446]\t[0.03482345 1. ]\n", - "24 \t100 \t100 \t[0.36480056 1.45 ]\t[0.07359937 1.65151446]\t[0.03482345 1. ]\n", - "25 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "26 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "27 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", - "28 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", - "29 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", - "30 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656503 1. ]\n", - "31 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "32 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "33 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "34 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "35 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", - "36 \t100 \t100 \t[0.37866097 1.13 ]\t[0.03470452 0.57714816]\t[0.19034052 1. ]\n", - "37 \t100 \t100 \t[0.37866097 1.13 ]\t[0.03470452 0.57714816]\t[0.19034052 1. ]\n", - "38 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "39 \t100 \t100 \t[0.38189896 1.11 ]\t[0.02650796 0.64645185]\t[0.24240461 1. ]\n", - "40 \t100 \t100 \t[0.38189883 1.11 ]\t[0.02650865 0.64645185]\t[0.24239156 1. ]\n", - "41 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", - "42 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", - "43 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", - "44 \t100 \t100 \t[0.38137968 1.08 ]\t[0.02959637 0.4621688 ]\t[0.1889583 1. ] \n", - "45 \t100 \t100 \t[0.37997235 1.1 ]\t[0.03248637 0.5 ]\t[0.1889583 1. ] \n", - "46 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", - "47 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", - "48 \t100 \t100 \t[0.37998617 1.1 ]\t[0.03240528 0.5 ]\t[0.19034052 1. ]\n", - "49 \t100 \t100 \t[0.37998617 1.1 ]\t[0.03240528 0.5 ]\t[0.19034052 1. ]\n", - "50 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", - "51 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", - "52 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", - "53 \t100 \t100 \t[0.37758954 1.17 ]\t[0.0397096 0.84917607]\t[0.15831375 1. ]\n", - "54 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", - "55 \t100 \t100 \t[0.38027484 1.1 ]\t[0.0308036 0.5 ] \t[0.21920769 1. ]\n", - "56 \t100 \t100 \t[0.38027484 1.1 ]\t[0.0308036 0.5 ] \t[0.21920769 1. ]\n", - "57 \t100 \t100 \t[0.38005515 1.1 ]\t[0.03200642 0.5 ]\t[0.19723824 1. ]\n", - "58 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ]\n", - "59 \t100 \t100 \t[0.38022217 1.09 ]\t[0.03108221 0.42649736]\t[0.21394053 1. ]\n", - "60 \t100 \t100 \t[0.37835398 1.13 ]\t[0.03584952 0.57714816]\t[0.20047927 1. ]\n", - "61 \t100 \t100 \t[0.37835398 1.13 ]\t[0.03584952 0.57714816]\t[0.20047927 1. ]\n", - "62 \t100 \t100 \t[0.37835398 1.13 ]\t[0.03584952 0.57714816]\t[0.20047927 1. ]\n", - "63 \t100 \t100 \t[0.37475221 1.2 ]\t[0.04309672 0.74833148]\t[0.20047927 1. ]\n", - "64 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "65 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "66 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "67 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", - "68 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", - "69 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ]\n", - "70 \t100 \t100 \t[0.3817898 1.07 ] \t[0.02710657 0.38091994]\t[0.2299699 1. ] \n", - "71 \t100 \t100 \t[0.38021321 1.12 ]\t[0.03103986 0.62096699]\t[0.22963998 1. ]\n", - "72 \t100 \t100 \t[0.38021321 1.12 ]\t[0.03103986 0.62096699]\t[0.22963998 1. ]\n", - "73 \t100 \t100 \t[0.3768396 1.16 ] \t[0.03859054 0.64373908]\t[0.19034052 1. ]\n", - "74 \t100 \t100 \t[0.3768396 1.16 ] \t[0.03859054 0.64373908]\t[0.19034052 1. ]\n", - "75 \t100 \t100 \t[0.37442131 1.22 ]\t[0.04491781 0.86694867]\t[0.14546938 1. ]\n", - "76 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "77 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "78 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "79 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", - "80 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "81 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "82 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "83 \t100 \t100 \t[0.38341174 1.04 ]\t[0.02211388 0.24166092]\t[0.25143063 1. ]\n", - "84 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "85 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "86 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "87 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "88 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "89 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "90 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ]\n", - "91 \t100 \t100 \t[0.3833479 1.05 ] \t[0.02250061 0.32787193]\t[0.24504705 1. ]\n", - "92 \t100 \t100 \t[0.38001964 1.11 ]\t[0.03227117 0.54580216]\t[0.19034052 1. ]\n", - "93 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", - "94 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", - "95 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", - "96 \t100 \t100 \t[0.37954213 1.07 ]\t[0.04406084 0.38091994]\t[3.37517238e-04 1.00000000e+00]\n", - "97 \t100 \t100 \t[0.37818345 1.09 ]\t[0.04585887 0.42649736]\t[3.37517238e-04 1.00000000e+00]\n", - "98 \t100 \t100 \t[0.37818345 1.09 ]\t[0.04585887 0.42649736]\t[3.37517238e-04 1.00000000e+00]\n", - "99 \t100 \t100 \t[0.37818345 1.09 ]\t[0.04585887 0.42649736]\t[3.37517238e-04 1.00000000e+00]\n", - "Final population hypervolume is 49499.533985\n", - "best model: 2.04*Cos(Sub(1.12*x2,1.08*x1))\n", + "0 \t100 \t \t[ nan 20.73]\t[ nan 1.00851376]\t[nan 17.]\n", + "1 \t91 \t91 \t[ nan 15.62]\t[ nan 5.93258797]\t[nan 1.]\n", + "2 \t97 \t97 \t[ nan 8.75] \t[ nan 5.11541787]\t[nan 1.]\n", + "3 \t100 \t100 \t[ nan 3.67] \t[ nan 2.18657266]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 1.89] \t[ nan 0.96845237]\t[nan 1.]\n", + "5 \t100 \t100 \t[0.63582207 1.14 ]\t[0.26436657 0.34698703]\t[0.31053385 1. ]\n", + "6 \t100 \t100 \t[0.49312953 1.07 ]\t[0.12367445 0.25514702]\t[0.27511281 1. ]\n", + "7 \t100 \t100 \t[0.41630859 1.04 ]\t[0.08465657 0.19595918]\t[0.27511281 1. ]\n", + "8 \t100 \t100 \t[0.38476919 1.03 ]\t[0.01781903 0.2215852 ]\t[0.24656506 1. ]\n", + "9 \t100 \t100 \t[0.38476919 1.03 ]\t[0.01781903 0.2215852 ]\t[0.24656506 1. ]\n", + "10 \t100 \t100 \t[0.38279961 1.07 ]\t[0.02629828 0.45287967]\t[0.19034052 1. ]\n", + "11 \t100 \t100 \t[0.38225459 1.07 ]\t[0.03050896 0.45287967]\t[0.13583826 1. ]\n", + "12 \t100 \t100 \t[0.38084725 1.09 ]\t[0.03335683 0.49183331]\t[0.13583826 1. ]\n", + "13 \t100 \t100 \t[0.38336185 1.05 ]\t[0.02250505 0.29580399]\t[0.24656506 1. ]\n", + "14 \t100 \t100 \t[0.38012579 1.1 ]\t[0.03895815 0.57445626]\t[0.06369215 1. ]\n", + "15 \t100 \t100 \t[0.37761119 1.14 ]\t[0.0459093 0.69310894]\t[0.06369215 1. ]\n", + "16 \t100 \t100 \t[0.37509659 1.18 ]\t[0.05181644 0.79221209]\t[0.06369215 1. ]\n", + "17 \t100 \t100 \t[0.37509659 1.18 ]\t[0.05181644 0.79221209]\t[0.06369215 1. ]\n", + "18 \t100 \t100 \t[0.37509659 1.18 ]\t[0.05181644 0.79221209]\t[0.06369215 1. ]\n", + "19 \t100 \t100 \t[0.37508382 1.18 ]\t[0.05184123 0.79221209]\t[0.06369215 1. ]\n", + "20 \t100 \t100 \t[0.37184776 1.25 ]\t[0.06037546 1.04283268]\t[0.06369213 1. ]\n", + "21 \t100 \t100 \t[0.37184776 1.25 ]\t[0.06037546 1.04283268]\t[0.06369213 1. ]\n", + "22 \t100 \t100 \t[0.3817758 1.08 ] \t[0.02723744 0.41665333]\t[0.2299699 1. ] \n", + "23 \t100 \t100 \t[0.3817758 1.08 ] \t[0.02723744 0.41665333]\t[0.2299699 1. ] \n", + "24 \t100 \t100 \t[0.37853974 1.14 ]\t[0.04174773 0.72138755]\t[0.06369214 1. ]\n", + "25 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", + "26 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", + "27 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", + "28 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", + "29 \t100 \t100 \t[0.37373576 1.25 ]\t[0.04904115 0.94207218]\t[0.06369214 1. ]\n", + "30 \t100 \t100 \t[0.37373576 1.25 ]\t[0.04904115 0.94207218]\t[0.06369214 1. ]\n", + "31 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ]\n", + "32 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ]\n", + "33 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ]\n", + "34 \t100 \t100 \t[0.3794761 1.1 ] \t[0.04431328 0.57445626]\t[8.72925551e-11 1.00000000e+00]\n", + "35 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ] \n", + "36 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ] \n", + "37 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ] \n", + "38 \t100 \t100 \t[0.37837715 1.12 ]\t[0.04523826 0.66753277]\t[0.00356714 1. ] \n", + "39 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", + "40 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", + "41 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", + "42 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", + "43 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", + "44 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", + "45 \t100 \t100 \t[0.38260318 1.11 ]\t[0.02301651 0.64645185]\t[0.26415548 1. ] \n", + "46 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", + "47 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", + "48 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656506 1. ] \n", + "49 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656506 1. ] \n", + "50 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656506 1. ] \n", + "51 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ] \n", + "52 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ] \n", + "53 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ] \n", + "54 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", + "55 \t100 \t100 \t[0.38382032 1.04 ]\t[0.01978635 0.24166092]\t[0.26641718 1. ] \n", + "56 \t100 \t100 \t[0.38378047 1.04 ]\t[0.0200253 0.24166092]\t[0.26243275 1. ] \n", + "57 \t100 \t100 \t[0.38378047 1.04 ]\t[0.0200253 0.24166092]\t[0.26243275 1. ] \n", + "58 \t100 \t100 \t[0.38176123 1.08 ]\t[0.02798752 0.4621688 ]\t[0.19034052 1. ] \n", + "59 \t100 \t100 \t[0.38154701 1.08 ]\t[0.02920492 0.4621688 ]\t[0.17982 1. ] \n", + "60 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", + "61 \t100 \t100 \t[0.38264569 1.08 ]\t[0.02280061 0.4621688 ]\t[0.26784596 1. ] \n", + "62 \t100 \t100 \t[0.38026224 1.14 ]\t[0.02785768 0.63277168]\t[0.26784596 1. ] \n", + "63 \t100 \t100 \t[0.37906772 1.17 ]\t[0.03000837 0.69361373]\t[0.26784596 1. ] \n", + "64 \t100 \t100 \t[0.37705243 1.25 ]\t[0.03562874 1.04283268]\t[0.18576901 1. ] \n", + "65 \t100 \t100 \t[0.3726603 1.36 ] \t[0.05209291 1.54609185]\t[0.02559096 1. ] \n", + "66 \t100 \t100 \t[0.37034493 1.39 ]\t[0.06331501 1.78266654]\t[2.28585293e-08 1.00000000e+00]\n", + "67 \t100 \t100 \t[0.3719726 1.33 ] \t[0.06591118 1.63740649]\t[1.09988078e-11 1.00000000e+00]\n", + "68 \t100 \t100 \t[0.38245833 1.07 ]\t[0.02838402 0.45287967]\t[0.1699218 1. ] \n", + "69 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ] \n", + "70 \t100 \t100 \t[0.3860344 1.01 ] \t[0.01257644 0.09949874]\t[0.26090035 1. ] \n", + "71 \t100 \t100 \t[0.3860344 1.01 ] \t[0.01257644 0.09949874]\t[0.26090035 1. ] \n", + "72 \t100 \t100 \t[0.38265748 1.07 ]\t[0.02691114 0.45287967]\t[0.19034052 1. ] \n", + "73 \t100 \t100 \t[0.38125015 1.09 ]\t[0.03012017 0.49183331]\t[0.19034052 1. ] \n", + "74 \t100 \t100 \t[0.38125015 1.09 ]\t[0.03012017 0.49183331]\t[0.19034052 1. ] \n", + "75 \t100 \t100 \t[0.37828691 1.15 ]\t[0.04172145 0.76648549]\t[0.09097429 1. ] \n", + "76 \t100 \t100 \t[0.37384055 1.26 ]\t[0.05249719 1.12800709]\t[0.08539494 1. ] \n", + "77 \t100 \t100 \t[0.37384055 1.26 ]\t[0.05249719 1.12800709]\t[0.08539494 1. ] \n", + "78 \t100 \t100 \t[0.38603439 1.01 ]\t[0.01257645 0.09949874]\t[0.26090032 1. ] \n", + "79 \t100 \t100 \t[0.38467572 1.03 ]\t[0.01837081 0.2215852 ]\t[0.25143063 1. ] \n", + "80 \t100 \t100 \t[0.38462706 1.03 ]\t[0.01872665 0.2215852 ]\t[0.24656506 1. ] \n", + "81 \t100 \t100 \t[0.38321973 1.05 ]\t[0.02322169 0.29580399]\t[0.24656506 1. ] \n", + "82 \t100 \t100 \t[0.38462706 1.03 ]\t[0.01872665 0.2215852 ]\t[0.24656503 1. ] \n", + "83 \t100 \t100 \t[0.38321973 1.05 ]\t[0.02322169 0.29580399]\t[0.24656503 1. ] \n", + "84 \t100 \t100 \t[0.38321973 1.05 ]\t[0.02322169 0.29580399]\t[0.24656503 1. ] \n", + "85 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ] \n", + "86 \t100 \t100 \t[0.37953733 1.1 ]\t[0.03521773 0.5 ]\t[0.14545636 1. ] \n", + "87 \t100 \t100 \t[0.37953733 1.1 ]\t[0.03521773 0.5 ]\t[0.14545636 1. ] \n", + "88 \t100 \t100 \t[0.37953733 1.1 ]\t[0.03521773 0.5 ]\t[0.14545636 1. ] \n", + "89 \t100 \t100 \t[0.37711891 1.14 ]\t[0.04221109 0.63277168]\t[0.14545636 1. ] \n", + "90 \t100 \t100 \t[0.37711891 1.14 ]\t[0.04221109 0.63277168]\t[0.14545636 1. ] \n", + "91 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", + "92 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", + "93 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", + "94 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", + "95 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", + "96 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ] \n", + "97 \t100 \t100 \t[0.38149489 1.09 ]\t[0.02886038 0.54945427]\t[0.20047927 1. ] \n", + "98 \t100 \t100 \t[0.38149489 1.09 ]\t[0.02886038 0.54945427]\t[0.20047927 1. ] \n", + "99 \t100 \t100 \t[0.38149489 1.09 ]\t[0.02886038 0.54945427]\t[0.20047927 1. ] \n", + "Final population hypervolume is 49489.883527\n", + "best model: If(x1>0.91,1.61,Max(-0.64*x1,0.14*x2,0.17))\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.88]\t[ nan 0.96208108]\t[nan 20.]\n", + "1 \t0 \t85 \t[ nan 16.15]\t[ nan 5.8726059] \t[nan 1.]\n", + "2 \t0 \t96 \t[ nan 9.41] \t[ nan 5.60909084]\t[nan 1.]\n", + "3 \t0 \t100 \t[ nan 3.62] \t[ nan 2.33572259]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.92] \t[ nan 0.92390476]\t[nan 1.]\n", + "5 \t0 \t100 \t[ nan 1.34] \t[ nan 0.56956123]\t[nan 1.]\n", + "6 \t0 \t100 \t[5.09753385 1.08 ]\t[1.32846308 0.2712932 ]\t[2.61403799 1. ]\n", + "7 \t0 \t100 \t[4.77122647 1.05 ]\t[1.22736445 0.21794495]\t[2.61403799 1. ]\n", + "8 \t0 \t100 \t[4.31298023 1.06 ]\t[1.04917369 0.23748684]\t[2.61403799 1. ]\n", + "9 \t0 \t100 \t[3.76372997 1.25 ]\t[0.55135192 0.84113019]\t[2.02158666 1. ]\n", + "10 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "11 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "12 \t0 \t100 \t[3.83362872 1.05 ]\t[0.22415653 0.32787193]\t[2.45537734 1. ]\n", + "13 \t0 \t100 \t[3.80631677 1.07 ]\t[0.34959507 0.38091994]\t[1.13151622 1. ]\n", + "14 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "15 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352633 0.24166092]\t[2.46565032 1. ]\n", + "16 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352633 0.24166092]\t[2.46565032 1. ]\n", + "17 \t0 \t100 \t[3.80707055 1.09 ]\t[0.34386122 0.54945427]\t[1.2068944 1. ] \n", + "18 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352633 0.24166092]\t[2.46565032 1. ]\n", + "19 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", + "20 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221685 0.29580399]\t[2.46565032 1. ]\n", + "21 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "22 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "23 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "24 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "25 \t0 \t100 \t[3.84728087 1.04 ]\t[0.17994462 0.31368774]\t[2.56668186 1. ]\n", + "26 \t0 \t100 \t[3.79072391 1.15 ]\t[0.44842463 0.85293611]\t[0.1826939 1. ] \n", + "27 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "28 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "29 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "30 \t0 \t100 \t[3.82706133 1.07 ]\t[0.26668339 0.45287967]\t[1.90340519 1. ]\n", + "31 \t0 \t100 \t[3.82706133 1.07 ]\t[0.26668339 0.45287967]\t[1.90340519 1. ]\n", + "32 \t0 \t100 \t[3.83317034 1.05 ]\t[0.22652029 0.29580399]\t[2.51430631 1. ]\n", + "33 \t0 \t100 \t[3.78060498 1.12 ]\t[0.45488453 0.5706137 ]\t[0.13627219 1. ]\n", + "34 \t0 \t100 \t[3.78060498 1.12 ]\t[0.45488453 0.5706137 ]\t[0.13627219 1. ]\n", + "35 \t0 \t100 \t[3.78060498 1.12 ]\t[0.45488453 0.5706137 ]\t[0.13627219 1. ]\n", + "36 \t0 \t100 \t[3.75465206 1.16 ]\t[0.58438177 0.79649231]\t[0.00325558 1. ]\n", + "37 \t0 \t100 \t[3.74057753 1.18 ]\t[0.59815397 0.81706793]\t[0.00325558 1. ]\n", + "38 \t0 \t100 \t[3.74057753 1.18 ]\t[0.59815397 0.81706793]\t[0.00325558 1. ]\n", + "39 \t0 \t100 \t[3.77927481 1.12 ]\t[0.4656074 0.5706137] \t[0.00325559 1. ]\n", + "40 \t0 \t100 \t[3.77927481 1.12 ]\t[0.4656074 0.5706137] \t[0.00325559 1. ]\n", + "41 \t0 \t100 \t[3.77927481 1.12 ]\t[0.4656074 0.5706137] \t[0.00325559 1. ]\n", + "42 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980988 0.41665333]\t[2.45047021 1. ]\n", + "43 \t0 \t100 \t[3.80181861 1.12 ]\t[0.31121164 0.5706137 ]\t[2.25763535 1. ]\n", + "44 \t0 \t100 \t[3.76148092 1.22 ]\t[0.41721139 0.90088845]\t[1.45456362 1. ]\n", + "45 \t0 \t100 \t[3.74206228 1.26 ]\t[0.50930546 1.12800709]\t[0.63692153 1. ]\n", + "46 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980988 0.41665333]\t[2.45047021 1. ]\n", + "47 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221685 0.29580399]\t[2.46565032 1. ]\n", + "48 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980987 0.41665333]\t[2.45047045 1. ]\n", + "49 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980987 0.41665333]\t[2.45047045 1. ]\n", + "50 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "51 \t0 \t100 \t[3.84675712 1.03 ]\t[0.1837081 0.2215852] \t[2.51430631 1. ]\n", + "52 \t0 \t100 \t[3.83317034 1.05 ]\t[0.22652029 0.29580399]\t[2.51430631 1. ]\n", + "53 \t0 \t100 \t[3.83317034 1.05 ]\t[0.22652029 0.29580399]\t[2.51430631 1. ]\n", + "54 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "55 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "56 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014736 0.59824744]\t[1.90340519 1. ]\n", + "57 \t0 \t100 \t[3.90709746 1.02 ]\t[0.352289 0.14 ] \t[2.60900354 1. ]\n", + "58 \t0 \t100 \t[3.84611876 1.05 ]\t[0.18838836 0.40926764]\t[2.45047045 1. ]\n", + "59 \t0 \t100 \t[3.84611876 1.05 ]\t[0.18838839 0.40926764]\t[2.45046997 1. ]\n", + "60 \t0 \t100 \t[3.84611876 1.05 ]\t[0.18838839 0.40926764]\t[2.45046997 1. ]\n", + "61 \t0 \t100 \t[3.84611875 1.05 ]\t[0.18838842 0.40926764]\t[2.45046997 1. ]\n", + "62 \t0 \t100 \t[3.8280083 1.06 ] \t[0.26214986 0.42 ]\t[1.90340519 1. ]\n", + "63 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "64 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", + "65 \t0 \t100 \t[3.82004398 1.08 ]\t[0.25964636 0.4621688 ]\t[2.46565056 1. ]\n", + "66 \t0 \t100 \t[3.81442152 1.08 ]\t[0.29287514 0.4621688 ]\t[1.90340519 1. ]\n", + "67 \t0 \t100 \t[3.80083475 1.1 ]\t[0.32009366 0.5 ]\t[1.90340519 1. ]\n", + "68 \t0 \t100 \t[3.79318234 1.13 ]\t[0.35672129 0.67312703]\t[1.79772139 1. ]\n", + "69 \t0 \t100 \t[3.77157637 1.21 ]\t[0.41233082 1.03242433]\t[1.71238637 1. ]\n", + "70 \t0 \t100 \t[3.74970025 1.33 ]\t[0.4614735 1.562402 ] \t[1.68537199 1. ]\n", + "71 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "72 \t0 \t100 \t[3.80533228 1.09 ]\t[0.2953361 0.42649736]\t[2.45047045 1. ]\n", + "73 \t0 \t100 \t[3.80533228 1.09 ]\t[0.2953361 0.42649736]\t[2.45047045 1. ]\n", + "74 \t0 \t100 \t[3.79110715 1.12 ]\t[0.3245486 0.51536395]\t[2.45047045 1. ]\n", + "75 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "76 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "77 \t0 \t100 \t[3.8280083 1.06 ] \t[0.26214986 0.42 ]\t[1.90340519 1. ]\n", + "78 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "79 \t0 \t100 \t[3.83443739 1.05 ]\t[0.2192433 0.32787193]\t[2.54631495 1. ]\n", + "80 \t0 \t100 \t[3.83443719 1.05 ]\t[0.21924451 0.32787193]\t[2.54629421 1. ]\n", + "81 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", + "82 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "83 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "84 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "85 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "86 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "87 \t0 \t100 \t[3.79472574 1.12 ]\t[0.3491038 0.60464866]\t[1.90340519 1. ]\n", + "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "89 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "90 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "91 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ]\n", + "92 \t0 \t100 \t[3.80268167 1.1 ]\t[0.37695258 0.59160798]\t[0.79325575 1. ]\n", + "93 \t0 \t100 \t[3.76398439 1.17 ]\t[0.5337628 0.90614568]\t[0.0032556 1. ] \n", + "94 \t0 \t100 \t[3.74991106 1.19 ]\t[0.54903786 0.9240671 ]\t[0.0032556 1. ] \n", + "95 \t0 \t100 \t[3.70191681 1.28 ]\t[0.60511095 1.04957134]\t[0.0032556 1. ] \n", + "96 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "97 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "98 \t0 \t100 \t[3.81383966 1.09 ]\t[0.29568479 0.54945427]\t[1.89387512 1. ]\n", + "99 \t0 \t100 \t[3.77394115 1.2 ]\t[0.4012903 0.9591663] \t[1.75132275 1. ]\n", + "Final population hypervolume is 49410.836238\n", + "fit, 1, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.95]\t[ nan 1.00374299]\t[nan 20.]\n", + "1 \t86 \t86 \t[ nan 15.66]\t[ nan 6.39096237]\t[nan 1.]\n", + "2 \t94 \t94 \t[ nan 9.43] \t[ nan 5.59688306]\t[nan 1.]\n", + "3 \t99 \t99 \t[ nan 4.47] \t[ nan 2.15153434]\t[nan 1.]\n", + "4 \t100 \t100 \t[nan 2.9] \t[ nan 0.93273791]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 2.71] \t[ nan 0.79113842]\t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 2.53] \t[ nan 0.67014924]\t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.39] \t[ nan 0.61473572]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.31] \t[ nan 0.59489495]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.26] \t[ nan 0.57654141]\t[nan 1.]\n", + "10 \t100 \t100 \t[nan 2.2] \t[ nan 0.54772256]\t[nan 1.]\n", + "11 \t100 \t100 \t[nan 2.2] \t[ nan 0.54772256]\t[nan 1.]\n", + "12 \t100 \t100 \t[nan 2.2] \t[ nan 0.54772256]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", + "17 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.18] \t[ nan 0.53628351]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.23] \t[ nan 0.56311633]\t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.25] \t[ nan 0.57227616]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.25] \t[ nan 0.57227616]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.26] \t[ nan 0.57654141]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.27] \t[ nan 0.58060313]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.26] \t[ nan 0.57654141]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.24] \t[ nan 0.56780278]\t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.22] \t[ nan 0.55821143]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.17] \t[ nan 0.53018865]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", + "30 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.11] \t[ nan 0.48774994]\t[nan 1.]\n", + "33 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "34 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", + "38 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "39 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "40 \t100 \t100 \t[ nan 2.11] \t[ nan 0.48774994]\t[nan 1.]\n", + "41 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "45 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 2.08] \t[ nan 0.4621688] \t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 2.07] \t[ nan 0.45287967]\t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 2.07] \t[ nan 0.45287967]\t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 2.03] \t[ nan 0.4112177] \t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 1.99] \t[ nan 0.36041643]\t[nan 1.]\n", + "53 \t100 \t100 \t[ nan 1.95] \t[ nan 0.29580399]\t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 1.95] \t[ nan 0.29580399]\t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(-1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.79]\t[ nan 0.95178779]\t[nan 20.]\n", + "1 \t0 \t88 \t[ nan 15.14]\t[ nan 6.39846857]\t[nan 1.]\n", + "2 \t0 \t99 \t[ nan 7.78] \t[ nan 4.93878528]\t[nan 1.]\n", + "3 \t0 \t100 \t[ nan 2.76] \t[ nan 1.51736614]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.51] \t[ nan 0.68549252]\t[nan 1.]\n", + "5 \t0 \t100 \t[5.26673321 1.06 ]\t[1.26729378 0.23748684]\t[2.73836112 1. ]\n", + "6 \t0 \t100 \t[4.56688371 1.06 ]\t[1.15484383 0.23748684]\t[2.73836112 1. ]\n", + "7 \t0 \t100 \t[3.78914207 1.16 ]\t[0.60358184 0.62801274]\t[9.30948616e-08 1.00000000e+00]\n", + "8 \t0 \t100 \t[3.81101844 1.08 ]\t[0.41613617 0.54184869]\t[3.14239568e-09 1.00000000e+00]\n", + "9 \t0 \t100 \t[3.83567494 1.06 ]\t[0.21312651 0.36932371]\t[2.46565032 1. ] \n", + "10 \t0 \t100 \t[3.81990402 1.09 ]\t[0.26101346 0.47106263]\t[2.46565032 1. ] \n", + "11 \t0 \t100 \t[3.79972168 1.13 ]\t[0.32514054 0.61081912]\t[1.90340519 1. ] \n", + "12 \t0 \t100 \t[3.75328773 1.21 ]\t[0.47040772 0.86365502]\t[0.63692147 1. ] \n", + "13 \t0 \t100 \t[3.81931956 1.08 ]\t[0.26398485 0.41665333]\t[2.45585918 1. ] \n", + "14 \t0 \t100 \t[3.8136971 1.1 ] \t[0.29671447 0.53851648]\t[1.90340519 1. ] \n", + "15 \t0 \t100 \t[3.8136971 1.1 ] \t[0.29671447 0.53851648]\t[1.90340519 1. ] \n", + "16 \t0 \t100 \t[3.79400132 1.14 ]\t[0.35229044 0.6636264 ]\t[1.90340519 1. ] \n", + "17 \t0 \t100 \t[3.7608594 1.21 ] \t[0.42208268 0.88651001]\t[1.69341516 1. ] \n", + "18 \t0 \t100 \t[3.7608594 1.21 ] \t[0.42208268 0.88651001]\t[1.69341516 1. ] \n", + "19 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "20 \t0 \t100 \t[3.8382249 1.04 ] \t[0.19773434 0.24166092]\t[2.66634941 1. ] \n", + "21 \t0 \t100 \t[3.79797463 1.08 ]\t[0.43425924 0.4621688 ]\t[6.73430023e-09 1.00000000e+00]\n", + "22 \t0 \t100 \t[3.78438785 1.1 ]\t[0.45256852 0.5 ]\t[6.56295018e-09 1.00000000e+00]\n", + "23 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[6.56295018e-09 1.00000000e+00]\n", + "24 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067638 0.63277168]\t[6.56295018e-09 1.00000000e+00]\n", + "25 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[7.40513539e-10 1.00000000e+00]\n", + "26 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "27 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "28 \t0 \t100 \t[3.81456761 1.1 ]\t[0.25470453 0.5 ]\t[2.67845964 1. ] \n", + "29 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "30 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "31 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "32 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "33 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "34 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "35 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "36 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "37 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "38 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "39 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", + "40 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", + "41 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "42 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "43 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668955 0.24166092]\t[2.68406439 1. ] \n", + "44 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "45 \t0 \t100 \t[3.82651285 1.08 ]\t[0.22772444 0.44 ]\t[2.68406439 1. ] \n", + "46 \t0 \t100 \t[3.80267843 1.14 ]\t[0.278351 0.61676576]\t[2.67846012 1. ] \n", + "47 \t0 \t100 \t[3.81456761 1.1 ]\t[0.25470453 0.5 ]\t[2.67845964 1. ] \n", + "48 \t0 \t100 \t[3.80681707 1.1 ]\t[0.29737574 0.5 ]\t[1.90340519 1. ] \n", + "49 \t0 \t100 \t[3.81456762 1.09 ]\t[0.2547045 0.42649736]\t[2.67846036 1. ] \n", + "50 \t0 \t100 \t[3.82651285 1.06 ]\t[0.22772444 0.31048349]\t[2.68406439 1. ] \n", + "51 \t0 \t100 \t[3.8132428 1.09 ] \t[0.2608801 0.42649736]\t[2.54597807 1. ] \n", + "52 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", + "53 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "54 \t0 \t100 \t[3.7841615 1.14 ] \t[0.4256492 0.74859869]\t[0.63692147 1. ] \n", + "55 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "56 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ] \n", + "57 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "58 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "59 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "60 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "61 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "62 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "63 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "64 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "65 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "66 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "67 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "68 \t0 \t100 \t[3.817536 1.1 ] \t[0.27817292 0.64031242]\t[2.00479293 1. ] \n", + "69 \t0 \t100 \t[3.79885409 1.16 ]\t[0.33145254 0.86856203]\t[2.00479293 1. ] \n", + "70 \t0 \t100 \t[3.79885409 1.16 ]\t[0.33145254 0.86856203]\t[2.00479293 1. ] \n", + "71 \t0 \t100 \t[3.80086088 1.08 ]\t[0.4066491 0.4621688] \t[0.33728087 1. ] \n", + "72 \t0 \t100 \t[3.80086088 1.08 ]\t[0.4066491 0.4621688] \t[0.33728087 1. ] \n", + "73 \t0 \t100 \t[3.78185486 1.11 ]\t[0.44540273 0.54580216]\t[0.33728087 1. ] \n", + "74 \t0 \t100 \t[3.74312502 1.16 ]\t[0.58294494 0.73102668]\t[1.73793865e-07 1.00000000e+00]\n", + "75 \t0 \t100 \t[3.74312502 1.16 ]\t[0.58294494 0.73102668]\t[1.73793865e-07 1.00000000e+00]\n", + "76 \t0 \t100 \t[3.74312502 1.16 ]\t[0.58294495 0.73102668]\t[1.60990735e-10 1.00000000e+00]\n", + "77 \t0 \t100 \t[3.72905169 1.18 ]\t[0.5964709 0.75339233]\t[1.60990735e-10 1.00000000e+00]\n", + "78 \t0 \t100 \t[3.72905169 1.18 ]\t[0.5964709 0.75339233]\t[1.60990735e-10 1.00000000e+00]\n", + "79 \t0 \t100 \t[3.69032185 1.23 ]\t[0.70223119 0.89280457]\t[1.60990735e-10 1.00000000e+00]\n", + "80 \t0 \t100 \t[3.69032185 1.23 ]\t[0.70223119 0.89280457]\t[1.60990735e-10 1.00000000e+00]\n", + "81 \t0 \t100 \t[3.69032185 1.23 ]\t[0.70223119 0.89280457]\t[1.60990735e-10 1.00000000e+00]\n", + "82 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "83 \t0 \t100 \t[3.83746583 1.04 ]\t[0.20232391 0.24166092]\t[2.59044313 1. ] \n", + "84 \t0 \t100 \t[3.798736 1.07 ] \t[0.43206919 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "85 \t0 \t100 \t[3.798736 1.07 ] \t[0.43206919 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "86 \t0 \t100 \t[3.79843911 1.07 ]\t[0.43290873 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "87 \t0 \t100 \t[3.79843911 1.07 ]\t[0.43290873 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "88 \t0 \t100 \t[3.79843911 1.07 ]\t[0.43290873 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "89 \t0 \t100 \t[3.79797463 1.07 ]\t[0.43425924 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "90 \t0 \t100 \t[3.79797463 1.07 ]\t[0.43425924 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", + "91 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[4.34097291e-09 1.00000000e+00]\n", + "92 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "93 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "94 \t0 \t100 \t[3.8264568 1.08 ] \t[0.22800614 0.4621688 ]\t[2.67845941 1. ] \n", + "95 \t0 \t100 \t[3.80204619 1.16 ]\t[0.28093924 0.73102668]\t[2.62644625 1. ] \n", + "96 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "97 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "98 \t0 \t100 \t[3.80078927 1.11 ]\t[0.32231329 0.54580216]\t[1.90340519 1. ] \n", + "99 \t0 \t100 \t[3.77227907 1.19 ]\t[0.42454641 0.95598117]\t[1.02196312 1. ] \n", + "Final population hypervolume is 49444.005521\n", + "fit, 2, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.67]\t[ nan 1.00054985]\t[nan 20.]\n", + "1 \t91 \t91 \t[ nan 16.76]\t[ nan 5.55899271]\t[nan 1.]\n", + "2 \t96 \t96 \t[ nan 10.37]\t[ nan 5.54013538]\t[nan 1.]\n", + "3 \t98 \t98 \t[ nan 5.39] \t[ nan 2.32333812]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 3.94] \t[ nan 1.39871369]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 3.21] \t[ nan 0.99292497]\t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 2.83] \t[ nan 0.77530639]\t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.74] \t[ nan 0.71582121]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.49] \t[ nan 0.55668663]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.42] \t[ nan 0.55099909]\t[nan 1.]\n", + "10 \t100 \t100 \t[ nan 2.44] \t[ nan 0.5535341] \t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.41] \t[ nan 0.54945427]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.39] \t[ nan 0.54580216]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.36] \t[ nan 0.53888774]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.34] \t[ nan 0.53329167]\t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.32] \t[ nan 0.52687759]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.31] \t[ nan 0.52335456]\t[nan 1.]\n", + "17 \t100 \t100 \t[nan 2.3] \t[ nan 0.51961524]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.28] \t[ nan 0.51146847]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.25] \t[ nan 0.49749372]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.28] \t[ nan 0.51146847]\t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.28] \t[ nan 0.51146847]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.29] \t[ nan 0.51565492]\t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.32] \t[ nan 0.52687759]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.31] \t[ nan 0.52335456]\t[nan 1.]\n", + "30 \t100 \t100 \t[nan 2.3] \t[ nan 0.51961524]\t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.29] \t[ nan 0.51565492]\t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.26] \t[ nan 0.50239427]\t[nan 1.]\n", + "33 \t100 \t100 \t[ nan 2.25] \t[ nan 0.49749372]\t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.22] \t[ nan 0.48124838]\t[nan 1.]\n", + "35 \t100 \t100 \t[nan 2.2] \t[ nan 0.46904158]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.19] \t[ nan 0.46249324]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.16] \t[ nan 0.44090815]\t[nan 1.]\n", + "38 \t100 \t100 \t[ nan 2.16] \t[ nan 0.44090815]\t[nan 1.]\n", + "39 \t100 \t100 \t[ nan 2.14] \t[ nan 0.42473521]\t[nan 1.]\n", + "40 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", + "41 \t100 \t100 \t[ nan 2.12] \t[ nan 0.4069398] \t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 2.12] \t[ nan 0.4069398] \t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 2.11] \t[ nan 0.39736633]\t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 2.07] \t[ nan 0.35369478]\t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 2.03] \t[ nan 0.29849623]\t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 2.02] \t[ nan 0.28213472]\t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 1.99] \t[ nan 0.22338308]\t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 1.99] \t[ nan 0.22338308]\t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 1.99] \t[ nan 0.22338308]\t[nan 1.]\n", + "53 \t100 \t100 \t[ nan 1.98] \t[ nan 0.19899749]\t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", + "Final population hypervolume is 49486.871854\n", + "best model: Cos(-1.72*x2)\n", "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.84]\t[ nan 0.91345498]\t[nan 18.]\n", - "1 \t0 \t91 \t[ nan 14.52]\t[ nan 6.47993827]\t[nan 1.]\n", - "2 \t0 \t100 \t[ nan 6.36] \t[ nan 4.80524713]\t[nan 1.]\n", - "3 \t0 \t100 \t[ nan 2.04] \t[ nan 1.67284189]\t[nan 1.]\n", - "4 \t0 \t100 \t[5.27745839 1.04 ]\t[1.22288142 0.19595918]\t[2.73843455 1. ]\n", - "5 \t0 \t100 \t[4.41301567 1.02 ]\t[1.02084156 0.14 ]\t[2.73843455 1. ]\n", - "6 \t0 \t100 \t[3.8616382 1.01 ] \t[0.11288621 0.09949874]\t[2.73843455 1. ]\n", - "7 \t0 \t100 \t[3.83780377 1.07 ]\t[0.20009637 0.45287967]\t[2.67845964 1. ]\n", - "8 \t0 \t100 \t[3.82591384 1.09 ]\t[0.230646 0.49183331]\t[2.67845964 1. ]\n", - "9 \t0 \t100 \t[3.82591384 1.08 ]\t[0.230646 0.41665333]\t[2.67845964 1. ]\n", - "10 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ]\n", - "11 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ]\n", - "12 \t0 \t100 \t[3.80922492 1.1 ]\t[0.27911144 0.5 ]\t[2.48370647 1. ]\n", - "13 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", - "14 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", - "15 \t0 \t100 \t[3.80953091 1.11 ]\t[0.27767113 0.58129167]\t[2.51430607 1. ]\n", - "16 \t0 \t100 \t[3.82006443 1.11 ]\t[0.26238577 0.73341666]\t[2.25763559 1. ]\n", - "17 \t0 \t100 \t[3.82006443 1.08 ]\t[0.26238577 0.4621688 ]\t[2.25763559 1. ]\n", - "18 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[2.88191373e-08 1.00000000e+00]\n", - "19 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572203 0.4621688 ]\t[2.88191373e-08 1.00000000e+00]\n", - "20 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[2.74334333e-10 1.00000000e+00]\n", - "21 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[2.74334333e-10 1.00000000e+00]\n", - "22 \t0 \t100 \t[3.80883429 1.06 ]\t[0.42256887 0.36932371]\t[2.74334333e-10 1.00000000e+00]\n", - "23 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "24 \t0 \t100 \t[3.84805069 1.03 ]\t[0.17524863 0.2215852 ]\t[2.51430631 1. ] \n", - "25 \t0 \t100 \t[3.83446392 1.05 ]\t[0.21979537 0.29580399]\t[2.51430631 1. ] \n", - "26 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "27 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "28 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269327 0.2215852 ]\t[2.68406439 1. ] \n", - "29 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566732 0.29580399]\t[2.46565056 1. ] \n", - "30 \t0 \t100 \t[3.82786834 1.07 ]\t[0.26350819 0.45287967]\t[1.90340519 1. ] \n", - "31 \t0 \t100 \t[3.80817256 1.11 ]\t[0.32567449 0.59824744]\t[1.90340519 1. ] \n", - "32 \t0 \t100 \t[3.80642941 1.12 ]\t[0.33596219 0.66753277]\t[1.78690541 1. ] \n", - "33 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "34 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566732 0.29580399]\t[2.46565056 1. ] \n", - "35 \t0 \t100 \t[3.82241812 1.08 ]\t[0.30554029 0.54184869]\t[1.3583827 1. ] \n", - "36 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", - "37 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", - "38 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", - "39 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", - "40 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "41 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "42 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "43 \t0 \t100 \t[3.81775795 1.08 ]\t[0.27237438 0.41665333]\t[2.29969907 1. ] \n", - "44 \t0 \t100 \t[3.79806216 1.12 ]\t[0.3322903 0.5706137] \t[1.90340519 1. ] \n", - "45 \t0 \t100 \t[3.79701344 1.12 ]\t[0.33837801 0.5706137 ]\t[1.79853261 1. ] \n", - "46 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "47 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "48 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "49 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "50 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "51 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "52 \t0 \t100 \t[3.78128059 1.15 ]\t[0.36961324 0.63835727]\t[1.79853261 1. ] \n", - "53 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "54 \t0 \t100 \t[3.80865912 1.11 ]\t[0.32369874 0.59824744]\t[1.90340519 1. ] \n", - "55 \t0 \t100 \t[3.80817256 1.11 ]\t[0.32567448 0.59824744]\t[1.90340519 1. ] \n", - "56 \t0 \t100 \t[3.80817256 1.11 ]\t[0.32567448 0.59824744]\t[1.90340519 1. ] \n", - "57 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] 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0.53851648]\t[1.90340519 1. ] \n", - "70 \t0 \t100 \t[3.79079968 1.15 ]\t[0.3656629 0.72629195]\t[1.89969528 1. ] \n", - "71 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "72 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269326 0.2215852 ]\t[2.68406463 1. ] \n", - "73 \t0 \t100 \t[3.83780304 1.06 ]\t[0.20010039 0.36932371]\t[2.67846036 1. ] \n", - "74 \t0 \t100 \t[3.81357457 1.12 ]\t[0.25905493 0.5706137 ]\t[2.63905644 1. ] \n", - "75 \t0 \t100 \t[3.86163746 1.01 ]\t[0.11289357 0.09949874]\t[2.73836064 1. ] \n", - "76 \t0 \t100 \t[3.82596989 1.09 ]\t[0.23036767 0.47106263]\t[2.68406439 1. ] \n", - "77 \t0 \t100 \t[3.81913318 1.12 ]\t[0.2706184 0.66753277]\t[2.00599861 1. ] \n", - "78 \t0 \t100 \t[3.80756807 1.16 ]\t[0.33089355 0.95624265]\t[1.69170713 1. ] \n", - "79 \t0 \t100 \t[3.77665686 1.21 ]\t[0.39110684 1.01286722]\t[1.69170713 1. ] \n", - "80 \t0 \t100 \t[3.73059714 1.33 ]\t[0.49209474 1.28883668]\t[1.69170713 1. ] \n", - "81 \t0 \t100 \t[3.9575751 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]\t[2.51430607 1. ] \n", - "93 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897879 0.2215852 ]\t[2.46565056 1. ] \n", - "94 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897881 0.2215852 ]\t[2.46565032 1. ] \n", - "95 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "96 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "97 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "98 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", - "99 \t0 \t100 \t[3.8334908 1.05 ] \t[0.22566734 0.29580399]\t[2.46565032 1. ] \n", + "0 \t100 \t \t[ nan 20.68]\t[ nan 1.33326666]\t[nan 11.]\n", + "1 \t0 \t89 \t[ nan 16.53]\t[ nan 5.33376977]\t[nan 1.]\n", + "2 \t0 \t98 \t[ nan 9.77] \t[ nan 5.49700828]\t[nan 1.]\n", + "3 \t0 \t100 \t[nan 3.7] \t[ nan 2.39374184]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.44] \t[ nan 0.76576759]\t[nan 1.]\n", + "5 \t0 \t100 \t[4.8843533 1.1 ]\t[1.23236078 0.3 ]\t[2.73843527 1. ]\n", + "6 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "7 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", + "8 \t0 \t100 \t[3.83840205 1.04 ]\t[0.19668954 0.24166092]\t[2.68406463 1. ]\n", + "9 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668956 0.24166092]\t[2.68406415 1. ]\n", + "10 \t0 \t100 \t[3.82388065 1.09 ]\t[0.24198897 0.54945427]\t[2.42084408 1. ]\n", + "11 \t0 \t100 \t[3.8075711 1.13 ] \t[0.28690461 0.67312703]\t[2.42084408 1. ]\n", + "12 \t0 \t100 \t[3.79321028 1.15 ]\t[0.31760269 0.698212 ]\t[2.39755893 1. ]\n", + "13 \t0 \t100 \t[3.77845603 1.22 ]\t[0.34650913 0.97549987]\t[2.39755869 1. ]\n", + "14 \t0 \t100 \t[3.76427598 1.26 ]\t[0.37053214 1.04517941]\t[2.39755869 1. ]\n", + "15 \t0 \t100 \t[3.78841744 1.15 ]\t[0.34059017 0.698212 ]\t[1.97238231 1. ]\n", + "16 \t0 \t100 \t[3.78776964 1.17 ]\t[0.34315736 0.77530639]\t[1.97238231 1. ]\n", + "17 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", + "18 \t0 \t100 \t[3.81870626 1.09 ]\t[0.27518907 0.51176166]\t[1.90340519 1. ]\n", + "19 \t0 \t100 \t[3.80623891 1.12 ]\t[0.30110856 0.58787754]\t[1.84558952 1. ]\n", + "20 \t0 \t100 \t[3.79757999 1.16 ]\t[0.3426752 0.85697141]\t[1.82377696 1. ]\n", + "21 \t0 \t100 \t[3.79711064 1.14 ]\t[0.34539965 0.70738957]\t[1.77684164 1. ]\n", + "22 \t0 \t100 \t[3.79711064 1.14 ]\t[0.34539965 0.70738957]\t[1.77684164 1. ]\n", + "23 \t0 \t100 \t[3.78516539 1.17 ]\t[0.36278708 0.76229915]\t[1.77684164 1. ]\n", + "24 \t0 \t100 \t[3.78516539 1.17 ]\t[0.36278708 0.76229915]\t[1.77684164 1. ]\n", + "25 \t0 \t100 \t[3.81587694 1.08 ]\t[0.29038737 0.4621688 ]\t[1.79023099 1. ]\n", + "26 \t0 \t100 \t[3.81587694 1.08 ]\t[0.29038737 0.4621688 ]\t[1.79023099 1. ]\n", + "27 \t0 \t100 \t[3.80229016 1.1 ]\t[0.31788123 0.5 ]\t[1.79023099 1. ]\n", + "28 \t0 \t100 \t[3.80229016 1.1 ]\t[0.31788123 0.5 ]\t[1.79023099 1. ]\n", + "29 \t0 \t100 \t[3.80229016 1.1 ]\t[0.31788123 0.5 ]\t[1.79023099 1. ]\n", + "30 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0.42649736]\t[3.55161039e-11 1.00000000e+00]\n", + "66 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "67 \t0 \t100 \t[3.8264568 1.08 ] \t[0.22800613 0.4621688 ]\t[2.67845964 1. ] \n", + "68 \t0 \t100 \t[3.81451156 1.12 ]\t[0.25495379 0.60464866]\t[2.67845964 1. ] \n", + "69 \t0 \t100 \t[3.82635846 1.07 ]\t[0.22850284 0.38091994]\t[2.66862535 1. ] \n", + "70 \t0 \t100 \t[3.81446927 1.09 ]\t[0.25514469 0.42649736]\t[2.66862535 1. ] \n", + "71 \t0 \t100 \t[3.81432624 1.09 ]\t[0.25579018 0.42649736]\t[2.65432239 1. ] \n", + "72 \t0 \t100 \t[3.81432624 1.09 ]\t[0.25579018 0.42649736]\t[2.65432239 1. ] \n", + "73 \t0 \t100 \t[3.79357637 1.13 ]\t[0.32499155 0.57714816]\t[1.79799652 1. ] \n", + "74 \t0 \t100 \t[3.79168965 1.13 ]\t[0.33207051 0.57714816]\t[1.79799652 1. ] \n", + "75 \t0 \t100 \t[3.87366802 1.03 ]\t[0.28353893 0.17058722]\t[2.73836088 1. ] \n", + "76 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "77 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "78 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "79 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "80 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", + "81 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", + "82 \t0 \t100 \t[3.79515281 1.14 ]\t[0.35188929 0.74859869]\t[1.79386842 1. ] \n", + "83 \t0 \t100 \t[3.78107948 1.16 ]\t[0.37582336 0.77097341]\t[1.79386842 1. ] \n", + "84 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "85 \t0 \t100 \t[3.83750969 1.06 ]\t[0.20205385 0.42 ]\t[2.59482932 1. ] \n", + "86 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "87 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "88 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "89 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "90 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "91 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "92 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "93 \t0 \t100 \t[3.80599109 1.11 ]\t[0.2948976 0.58129167]\t[2.25763559 1. ] \n", + "94 \t0 \t100 \t[3.80341631 1.1 ]\t[0.30918326 0.5 ]\t[2.00015688 1. ] \n", + "95 \t0 \t100 \t[3.80341631 1.1 ]\t[0.30918326 0.5 ]\t[2.00015688 1. ] \n", + "96 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "97 \t0 \t100 \t[3.81451158 1.12 ]\t[0.25495373 0.60464866]\t[2.67846012 1. ] \n", + "98 \t0 \t100 \t[3.81451156 1.12 ]\t[0.2549538 0.60464866]\t[2.67845964 1. ] \n", + "99 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ] \n", + "Final population hypervolume is 49369.975647\n", + "fit, 3, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.86]\t[ nan 0.98]\t[nan 20.]\n", + "1 \t85 \t85 \t[ nan 16.87]\t[ nan 4.96518882]\t[nan 1.]\n", + "2 \t92 \t92 \t[ nan 11.08]\t[ nan 5.37341605]\t[nan 1.]\n", + "3 \t98 \t98 \t[ nan 6.77] \t[ nan 3.19954684]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 4.51] \t[ nan 1.53944795]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 3.66] \t[ nan 1.1934823] \t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 3.15] \t[ nan 0.9313968] \t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.84] \t[ nan 0.73102668]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.69] \t[ nan 0.65871086]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.62] \t[ nan 0.61286214]\t[nan 1.]\n", + "10 \t100 \t100 \t[ nan 2.49] \t[ nan 0.51951901]\t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.47] \t[ nan 0.51874849]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.42] \t[ nan 0.51341991]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.35] \t[ nan 0.49749372]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.44] \t[ nan 0.68293484]\t[nan 1.]\n", + "15 \t100 \t100 \t[nan 2.3] \t[ nan 0.47958315]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.29] \t[ nan 0.47528939]\t[nan 1.]\n", + "17 \t100 \t100 \t[ nan 2.28] \t[ nan 0.47074409]\t[nan 1.]\n", + "18 \t100 \t100 \t[nan 2.3] \t[ nan 0.47958315]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.28] \t[ nan 0.47074409]\t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.26] \t[ nan 0.46086874]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.24] \t[ nan 0.44988888]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.23] \t[ nan 0.44395946]\t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", + "30 \t100 \t100 \t[ nan 2.28] \t[ nan 0.47074409]\t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.29] \t[ nan 0.47528939]\t[nan 1.]\n", + "32 \t100 \t100 \t[nan 2.3] \t[ nan 0.47958315]\t[nan 1.]\n", + "33 \t100 \t100 \t[ nan 2.32] \t[ nan 0.4874423] \t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.35] \t[ nan 0.49749372]\t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.37] \t[ nan 0.50309045]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.37] \t[ nan 0.50309045]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.35] \t[ nan 0.49749372]\t[nan 1.]\n", + "38 \t100 \t100 \t[ nan 2.33] \t[ nan 0.49101935]\t[nan 1.]\n", + "39 \t100 \t100 \t[ nan 2.29] \t[ nan 0.47528939]\t[nan 1.]\n", + "40 \t100 \t100 \t[ nan 2.26] \t[ nan 0.46086874]\t[nan 1.]\n", + "41 \t100 \t100 \t[ nan 2.23] \t[ nan 0.44395946]\t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 2.23] \t[ nan 0.44395946]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", + "46 \t100 \t100 \t[nan 2.2] \t[ nan 0.42426407]\t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", + "52 \t100 \t100 \t[nan 2.2] \t[ nan 0.42426407]\t[nan 1.]\n", + "53 \t100 \t100 \t[nan 2.2] \t[ nan 0.42426407]\t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 2.18] \t[ nan 0.40938979]\t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 2.15] \t[ nan 0.38405729]\t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 2.15] \t[ nan 0.38405729]\t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 2.12] \t[ nan 0.3544009] \t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 2.09] \t[ nan 0.31921779]\t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 2.04] \t[ nan 0.24166092]\t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 2.02] \t[ nan 0.19899749]\t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 2.01] \t[ nan 0.17291616]\t[nan 1.]\n", + "72 \t100 \t100 \t[nan 2.] \t[ nan 0.14142136]\t[nan 1.]\n", + "73 \t100 \t100 \t[nan 2.] \t[ nan 0.14142136]\t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(-1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.7]\t[ nan 0.9539392]\t[nan 20.]\n", + "1 \t0 \t87 \t[ nan 16.12]\t[ nan 6.12744645]\t[nan 1.]\n", + "2 \t0 \t92 \t[ nan 10.36]\t[ nan 6.2330089] \t[nan 1.]\n", + "3 \t0 \t100 \t[ nan 3.96] \t[ nan 2.61120662]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.66] \t[ nan 0.72415468]\t[nan 1.]\n", + "5 \t0 \t100 \t[5.18937999 1.12 ]\t[1.34063908 0.32496154]\t[2.73836088 1. ]\n", + "6 \t0 \t100 \t[4.65234133 1.27 ]\t[1.30255619 0.91493169]\t[2.30348039 1. ]\n", + "7 \t0 \t100 \t[4.26154792 1.34 ]\t[1.13750098 1.0219589 ]\t[2.30348039 1. ]\n", + "8 \t0 \t100 \t[3.86186356 1.01 ]\t[0.11293867 0.09949874]\t[2.73836088 1. ]\n", + "9 \t0 \t100 \t[3.84969223 1.05 ]\t[0.16309529 0.40926764]\t[2.67845964 1. ]\n", + "10 \t0 \t100 \t[3.84969223 1.05 ]\t[0.16309529 0.40926764]\t[2.67845964 1. ]\n", + "11 \t0 \t100 \t[3.83780303 1.06 ]\t[0.20010042 0.36932371]\t[2.67845964 1. ]\n", + "12 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", + "13 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", + "14 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", + "15 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", + "16 \t0 \t100 \t[3.80262237 1.12 ]\t[0.27857674 0.51536395]\t[2.67845964 1. ]\n", + "17 \t0 \t100 \t[3.80262237 1.12 ]\t[0.27857674 0.51536395]\t[2.67845964 1. ]\n", + "18 \t0 \t100 \t[3.78780033 1.17 ]\t[0.31187945 0.70788417]\t[2.39077997 1. ]\n", + "19 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", + "20 \t0 \t100 \t[3.81721189 1.07 ]\t[0.28029557 0.38091994]\t[1.97238231 1. ]\n", + "21 \t0 \t100 \t[3.79820588 1.12 ]\t[0.33497349 0.62096699]\t[1.97238219 1. ]\n", + "22 \t0 \t100 \t[3.79751611 1.11 ]\t[0.33878185 0.54580216]\t[1.90340519 1. ]\n", + "23 \t0 \t100 \t[3.79751611 1.11 ]\t[0.33878185 0.54580216]\t[1.90340519 1. ]\n", + "24 \t0 \t100 \t[3.79751611 1.11 ]\t[0.33878185 0.54580216]\t[1.90340519 1. ]\n", + "25 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", + "26 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", + "27 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ]\n", + "28 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", + "29 \t0 \t100 \t[3.80569521 1.11 ]\t[0.30185255 0.54580216]\t[1.90340519 1. ]\n", + "30 \t0 \t100 \t[3.79967221 1.07 ]\t[0.42954409 0.38091994]\t[1.01975928e-11 1.00000000e+00]\n", + "31 \t0 \t100 \t[3.79797463 1.07 ]\t[0.43425924 0.38091994]\t[1.01975928e-11 1.00000000e+00]\n", + "32 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[1.19196319e-13 1.00000000e+00]\n", + "33 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", + "34 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", + "35 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", + "36 \t0 \t100 \t[3.75875823 1.1 ]\t[0.5766332 0.47958315]\t[1.19196319e-13 1.00000000e+00]\n", + "37 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", + "38 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", + "39 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", + "40 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", + "41 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.43614026e-14 1.00000000e+00]\n", + "42 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.43614026e-14 1.00000000e+00]\n", + "43 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.43614026e-14 1.00000000e+00]\n", + "44 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "45 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "46 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "47 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "48 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "49 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "50 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "51 \t0 \t100 \t[3.82645681 1.07 ]\t[0.22800609 0.38091994]\t[2.67846036 1. ] \n", + "52 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "53 \t0 \t100 \t[3.82214457 1.09 ]\t[0.2505484 0.54945427]\t[2.46565032 1. ] \n", + "54 \t0 \t100 \t[3.80346267 1.12 ]\t[0.30891311 0.62096699]\t[2.00479293 1. ] \n", + "55 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "56 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "57 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "58 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "59 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "60 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "61 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "62 \t0 \t100 \t[3.80807123 1.1 ]\t[0.28451955 0.5 ]\t[2.46565008 1. ] \n", + "63 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "64 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "65 \t0 \t100 \t[3.8264568 1.08 ] \t[0.22800614 0.4621688 ]\t[2.67845941 1. ] \n", + "66 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", + "67 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "68 \t0 \t100 \t[3.78817556 1.13 ]\t[0.34253698 0.57714816]\t[1.90340519 1. ] \n", + "69 \t0 \t100 \t[3.78817556 1.13 ]\t[0.34253698 0.57714816]\t[1.90340519 1. ] \n", + "70 \t0 \t100 \t[3.79679867 1.11 ]\t[0.35121757 0.54580216]\t[1.35838246 1. ] \n", + "71 \t0 \t100 \t[3.76443804 1.18 ]\t[0.47127144 0.87612784]\t[0.63692153 1. ] \n", + "72 \t0 \t100 \t[3.73207742 1.27 ]\t[0.56457536 1.23979837]\t[0.63692141 1. ] \n", + "73 \t0 \t100 \t[3.78252544 1.14 ]\t[0.37596243 0.61676576]\t[1.35838246 1. ] \n", + "74 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "75 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "76 \t0 \t100 \t[3.78438812 1.09 ]\t[0.4525663 0.42649736]\t[2.66079151e-05 1.00000000e+00]\n", + "77 \t0 \t100 \t[3.74565828 1.14 ]\t[0.58860317 0.6483826 ]\t[1.1639014e-07 1.0000000e+00] \n", + "78 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[6.69603262e-14 1.00000000e+00]\n", + "79 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[6.69603262e-14 1.00000000e+00]\n", + "80 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", + "81 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", + "82 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", + "83 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", + "84 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", + "85 \t0 \t100 \t[3.78341474 1.09 ]\t[0.45534222 0.42649736]\t[5.89805982e-14 1.00000000e+00]\n", + "86 \t0 \t100 \t[3.78341474 1.09 ]\t[0.45534222 0.42649736]\t[5.89805982e-14 1.00000000e+00]\n", + "87 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", + "88 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", + "89 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", + "90 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", + "91 \t0 \t100 \t[3.75875823 1.1 ]\t[0.5766332 0.47958315]\t[3.43614026e-14 1.00000000e+00]\n", + "92 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "93 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "94 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "95 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "96 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", + "97 \t0 \t100 \t[3.70595506 1.17 ]\t[0.69818381 0.70788417]\t[2.98372438e-14 1.00000000e+00]\n", + "98 \t0 \t100 \t[3.70595506 1.17 ]\t[0.69818381 0.70788417]\t[2.98372438e-14 1.00000000e+00]\n", + "99 \t0 \t100 \t[3.70595506 1.17 ]\t[0.69818381 0.70788417]\t[2.98372438e-14 1.00000000e+00]\n", + "Final population hypervolume is 49495.461503\n", + "fit, 4, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.87]\t[ nan 1.02620661]\t[nan 20.]\n", + "1 \t88 \t88 \t[ nan 17.46]\t[ nan 5.01481804]\t[nan 1.]\n", + "2 \t98 \t98 \t[ nan 12.37]\t[ nan 6.42130049]\t[nan 1.]\n", + "3 \t100 \t100 \t[ nan 5.64] \t[ nan 4.36238467]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 1.58] \t[ nan 0.75073298]\t[nan 1.]\n", + "5 \t100 \t100 \t[0.53064906 1.04 ]\t[0.12134526 0.19595918]\t[0.27383608 1. ]\n", + "6 \t100 \t100 \t[0.44851342 1.02 ]\t[0.10580728 0.14 ]\t[0.27383608 1. ]\n", + "7 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "8 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "9 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "10 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "11 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "12 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "13 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "14 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "15 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "16 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "17 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "18 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "19 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "20 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "21 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "22 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "23 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "24 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "25 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "26 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "27 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "28 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "29 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "30 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "31 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "32 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "33 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "34 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "35 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "36 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "37 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "38 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "39 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "40 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "41 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "42 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "43 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "44 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "45 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "46 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "47 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "48 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "49 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "50 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "51 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "52 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "53 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "54 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "55 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "56 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "57 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "58 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "59 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "60 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "61 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "62 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "63 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "64 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "65 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "66 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "67 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "68 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "69 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "70 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "71 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "72 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "73 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "74 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "75 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "76 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "77 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "78 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "79 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "80 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "81 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "82 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "83 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "84 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "85 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "86 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "87 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "88 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "89 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "90 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "91 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "92 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "93 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "94 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "95 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "96 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "97 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "98 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "99 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", + "Final population hypervolume is 49486.388383\n", + "best model: Square(0.96*x1)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.87]\t[ nan 1.36861244]\t[nan 12.]\n", + "1 \t0 \t87 \t[ nan 16.01]\t[ nan 5.88641657]\t[nan 1.]\n", + "2 \t0 \t100 \t[ nan 8.39] \t[ nan 4.88035859]\t[nan 1.]\n", + "3 \t0 \t100 \t[ nan 2.79] \t[ nan 1.7452507] \t[nan 1.]\n", + "4 \t0 \t100 \t[5.0944862 1.12 ]\t[1.21237069 0.32496154]\t[2.73843145 1. ]\n", + "5 \t0 \t100 \t[4.25595285 1.23 ]\t[0.9040586 0.58060313]\t[2.67845964 1. ]\n", + "6 \t0 \t100 \t[3.90590007 1.02 ]\t[0.37655996 0.14 ]\t[2.73836136 1. ]\n", + "7 \t0 \t100 \t[3.83785909 1.05 ]\t[0.19977615 0.29580399]\t[2.68406463 1. ]\n", + "8 \t0 \t100 \t[3.83666901 1.06 ]\t[0.20687419 0.36932371]\t[2.56505728 1. ]\n", + "9 \t0 \t100 \t[3.83785908 1.05 ]\t[0.19977618 0.29580399]\t[2.68406439 1. ]\n", + "10 \t0 \t100 \t[3.8480507 1.03 ] \t[0.17524858 0.2215852 ]\t[2.51430631 1. ]\n", + "11 \t0 \t100 \t[3.82914203 1.09 ]\t[0.25435182 0.63395583]\t[2.03077316 1. ]\n", + "12 \t0 \t100 \t[3.81520352 1.09 ]\t[0.36614475 0.63395583]\t[0.63692153 1. ]\n", + "13 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", + "14 \t0 \t100 \t[3.81102456 1.08 ]\t[0.41608011 0.54184869]\t[6.11990981e-04 1.00000000e+00]\n", + "15 \t0 \t100 \t[3.80884042 1.08 ]\t[0.42251369 0.54184869]\t[6.11990981e-04 1.00000000e+00]\n", + "16 \t0 \t100 \t[3.7804941 1.13 ] \t[0.46287745 0.64272856]\t[6.11990981e-04 1.00000000e+00]\n", + "17 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ] \n", + "18 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269326 0.2215852 ]\t[2.68406439 1. ] \n", + "19 \t0 \t100 \t[3.83785908 1.05 ]\t[0.19977621 0.29580399]\t[2.68406439 1. ] \n", + "20 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "21 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "22 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "23 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "24 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "25 \t0 \t100 \t[3.82214457 1.08 ]\t[0.2505484 0.4621688] \t[2.46565032 1. ] \n", + "26 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "27 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "28 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "29 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "30 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "31 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "32 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "33 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "34 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "35 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "36 \t0 \t100 \t[3.81870626 1.08 ]\t[0.27518907 0.4621688 ]\t[1.90340519 1. ] \n", + "37 \t0 \t100 \t[3.78611721 1.17 ]\t[0.35699166 0.8006872 ]\t[1.84281576 1. ] \n", + "38 \t0 \t100 \t[3.78574688 1.16 ]\t[0.35902081 0.73102668]\t[1.80578291 1. ] \n", + "39 \t0 \t100 \t[3.80543991 1.11 ]\t[0.30522623 0.54580216]\t[1.80550706 1. ] \n", + "40 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "41 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ] \n", + "42 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", + "43 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838258 1. ] \n", + "44 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "45 \t0 \t100 \t[3.79487183 1.13 ]\t[0.31776999 0.57714816]\t[1.90340519 1. ] \n", + "46 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", + "47 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "48 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "49 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "50 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", + "51 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "52 \t0 \t100 \t[3.7841615 1.14 ] \t[0.42564921 0.74859869]\t[0.63692141 1. ] \n", + "53 \t0 \t100 \t[3.7841615 1.14 ] \t[0.42564921 0.74859869]\t[0.63692141 1. ] \n", + "54 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "55 \t0 \t100 \t[3.78341474 1.11 ]\t[0.45534221 0.58129167]\t[1.07177982e-07 1.00000000e+00]\n", + "56 \t0 \t100 \t[3.78341474 1.11 ]\t[0.45534221 0.58129167]\t[1.07177982e-07 1.00000000e+00]\n", + "57 \t0 \t100 \t[3.77779228 1.13 ]\t[0.4746412 0.67312703]\t[2.02363126e-12 1.00000000e+00]\n", + "58 \t0 \t100 \t[3.7580965 1.17 ] \t[0.50984213 0.77530639]\t[2.02363126e-12 1.00000000e+00]\n", + "59 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "60 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "61 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "62 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "63 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "64 \t0 \t100 \t[3.80795929 1.09 ]\t[0.28504941 0.42649736]\t[2.45445561 1. ] \n", + "65 \t0 \t100 \t[3.79377401 1.12 ]\t[0.31516565 0.51536395]\t[2.45445561 1. ] \n", + "66 \t0 \t100 \t[3.76958981 1.16 ]\t[0.39166417 0.64373908]\t[1.45456362 1. ] \n", + "67 \t0 \t100 \t[3.76958981 1.16 ]\t[0.39166417 0.64373908]\t[1.45456362 1. ] \n", + "68 \t0 \t100 \t[3.73086577 1.21 ]\t[0.54207819 0.80367904]\t[5.79449348e-04 1.00000000e+00]\n", + "69 \t0 \t100 \t[3.72989831 1.21 ]\t[0.54622452 0.80367904]\t[1.44563719e-05 1.00000000e+00]\n", + "70 \t0 \t100 \t[3.72989831 1.21 ]\t[0.54622452 0.80367904]\t[1.44563719e-05 1.00000000e+00]\n", + "71 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "72 \t0 \t100 \t[3.79487183 1.13 ]\t[0.31776999 0.57714816]\t[1.90340519 1. ] \n", + "73 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", + "74 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", + "75 \t0 \t100 \t[3.78372612 1.14 ]\t[0.36371051 0.63277168]\t[1.90340519 1. ] \n", + "76 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", + "77 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "78 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "79 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "80 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "81 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488197 0.4621688 ]\t[1.90340519 1. ] \n", + "82 \t0 \t100 \t[3.79136331 1.15 ]\t[0.37547346 0.8291562 ]\t[1.35710287 1. ] \n", + "83 \t0 \t100 \t[3.79136331 1.15 ]\t[0.37547346 0.8291562 ]\t[1.35710287 1. ] \n", + "84 \t0 \t100 \t[3.75536723 1.22 ]\t[0.44880734 0.97549987]\t[1.35710239 1. ] \n", + "85 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "86 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572201 0.54945427]\t[1.10674193e-07 1.00000000e+00]\n", + "87 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", + "88 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", + "89 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", + "90 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", + "91 \t0 \t100 \t[3.78341474 1.11 ]\t[0.45534222 0.58129167]\t[3.72903097e-12 1.00000000e+00]\n", + "92 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "93 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "94 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "95 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", + "96 \t0 \t100 \t[3.79699856 1.1 ]\t[0.35045337 0.5 ]\t[1.35838246 1. ] \n", + "97 \t0 \t100 \t[3.7974887 1.08 ] \t[0.43571647 0.4621688 ]\t[6.37756893e-05 1.00000000e+00]\n", + "98 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "99 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", "Final population hypervolume is 49377.110287\n", - "fit, " + "fit, 5, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.76]\t[ nan 0.82607506]\t[nan 20.]\n", + "1 \t94 \t94 \t[ nan 14.53]\t[ nan 6.45051161]\t[nan 1.]\n", + "2 \t100 \t100 \t[ nan 5.52] \t[ nan 4.06073885]\t[nan 1.]\n", + "3 \t100 \t100 \t[0.52386678 1.5 ]\t[0.13434398 0.97467943]\t[0.26124257 1. ]\n", + "4 \t100 \t100 \t[0.48764257 1.2 ]\t[0.12952334 0.77459667]\t[0.24604428 1. ]\n", + "5 \t100 \t100 \t[0.4329244 1.07 ] \t[0.10314919 0.3241913 ]\t[0.24656506 1. ]\n", + "6 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ]\n", + "7 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ]\n", + "8 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "9 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "10 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "11 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "12 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "13 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "14 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "15 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", + "16 \t100 \t100 \t[0.38031386 1.09 ]\t[0.03108113 0.42649736]\t[0.19723824 1. ]\n", + "17 \t100 \t100 \t[0.38031386 1.09 ]\t[0.03108113 0.42649736]\t[0.19723824 1. ]\n", + "18 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "19 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "20 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "21 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "22 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "23 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "24 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "25 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "26 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "27 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "28 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "29 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "30 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "31 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "32 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "33 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "34 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "35 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "36 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "37 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "38 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "39 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "40 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "41 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "42 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "43 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "44 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "45 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "46 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "47 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "48 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "49 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "50 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "51 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "52 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "53 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "54 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "55 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "56 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "57 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "58 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "59 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "60 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "61 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "62 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "63 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "64 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "65 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "66 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "67 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "68 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "69 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "70 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "71 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "72 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "73 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "74 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "75 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "76 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "77 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "78 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "79 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "80 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "81 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", + "82 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "83 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "84 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "85 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "86 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "87 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "88 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "89 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "90 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "91 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", + "92 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "93 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "94 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "95 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "96 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "97 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "98 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "99 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", + "Final population hypervolume is 49490.270076\n", + "best model: Square(If(x1>0.91,3.94*x2,Square(0.97*x1)))\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.78]\t[ nan 0.94424573]\t[nan 20.]\n", + "1 \t0 \t89 \t[ nan 15.78]\t[ nan 5.8422256] \t[nan 1.]\n", + "2 \t0 \t96 \t[ nan 8.89] \t[ nan 4.99178325]\t[nan 1.]\n", + "3 \t0 \t99 \t[ nan 4.03] \t[ nan 2.597903] \t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 2.22] \t[ nan 1.00578328]\t[nan 1.]\n", + "5 \t0 \t100 \t[ nan 1.77] \t[ nan 0.75967098]\t[nan 1.]\n", + "6 \t0 \t100 \t[ nan 1.38] \t[ nan 0.52497619]\t[nan 1.]\n", + "7 \t0 \t100 \t[5.14505445 1.07 ]\t[1.28281345 0.25514702]\t[2.61403799 1. ]\n", + "8 \t0 \t100 \t[4.43905738 1.05 ]\t[1.0778302 0.21794495]\t[2.61403799 1. ]\n", + "9 \t0 \t100 \t[3.80069793 1.12 ]\t[0.44789478 0.66753277]\t[0.34041035 1. ]\n", + "10 \t0 \t100 \t[3.79178594 1.12 ]\t[0.43737804 0.66753277]\t[0.34041035 1. ]\n", + "11 \t0 \t100 \t[3.77904142 1.13 ]\t[0.45388862 0.67312703]\t[0.32490379 1. ]\n", + "12 \t0 \t100 \t[3.82063123 1.06 ]\t[0.25666747 0.31048349]\t[2.51430631 1. ]\n", + "13 \t0 \t100 \t[3.78620915 1.15 ]\t[0.41434717 0.80467385]\t[0.63692135 1. ]\n", + "14 \t0 \t100 \t[3.77213581 1.19 ]\t[0.43456757 0.89101066]\t[0.63692135 1. ]\n", + "15 \t0 \t100 \t[3.75806248 1.21 ]\t[0.45345149 0.9087904 ]\t[0.63692135 1. ]\n", + "16 \t0 \t100 \t[3.75989289 1.15 ]\t[0.48820415 0.72629195]\t[0.63692135 1. ]\n", + "17 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "18 \t0 \t100 \t[3.84631263 1.04 ]\t[0.1869565 0.31368774]\t[2.46985793 1. ]\n", + "19 \t0 \t100 \t[3.83134578 1.09 ]\t[0.23734163 0.58472216]\t[2.37629771 1. ]\n", + "20 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "21 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "22 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "23 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "24 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014738 0.59824744]\t[1.90340519 1. ]\n", + "25 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014738 0.59824744]\t[1.90340519 1. ]\n", + "26 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014738 0.59824744]\t[1.90340519 1. ]\n", + "27 \t0 \t100 \t[3.78619527 1.17 ]\t[0.3855081 0.83731714]\t[1.80461228 1. ]\n", + "28 \t0 \t100 \t[3.76309473 1.25 ]\t[0.44414527 1.14345966]\t[1.60617304 1. ]\n", + "29 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "30 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", + "31 \t0 \t100 \t[3.81812389 1.11 ]\t[0.26904363 0.66174013]\t[2.46565032 1. ]\n", + "32 \t0 \t100 \t[3.81250144 1.09 ]\t[0.30120175 0.49183331]\t[1.90340519 1. ]\n", + "33 \t0 \t100 \t[3.79822821 1.12 ]\t[0.33040084 0.5706137 ]\t[1.90340519 1. ]\n", + "34 \t0 \t100 \t[3.77850476 1.15 ]\t[0.38812256 0.63835727]\t[1.35838246 1. ]\n", + "35 \t0 \t100 \t[3.77850476 1.15 ]\t[0.38812256 0.63835727]\t[1.35838246 1. ]\n", + "36 \t0 \t100 \t[3.78971543 1.12 ]\t[0.3767489 0.5706137] \t[1.35838246 1. ]\n", + "37 \t0 \t100 \t[3.76456941 1.16 ]\t[0.44760684 0.68876701]\t[1.35838246 1. ]\n", + "38 \t0 \t100 \t[3.76456941 1.16 ]\t[0.44760684 0.68876701]\t[1.35838246 1. ]\n", + "39 \t0 \t100 \t[3.75192961 1.17 ]\t[0.4619826 0.69361373]\t[1.35838246 1. ]\n", + "40 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", + "41 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", + "42 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", + "43 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", + "44 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", + "45 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "46 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "47 \t0 \t100 \t[3.83368399 1.05 ]\t[0.22375156 0.32787193]\t[2.47097492 1. ]\n", + "48 \t0 \t100 \t[3.81398821 1.09 ]\t[0.29482394 0.51176166]\t[1.90340519 1. ]\n", + "49 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", + "50 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", + "51 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", + "52 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", + "53 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", + "54 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", + "55 \t0 \t100 \t[3.81393497 1.08 ]\t[0.29506685 0.4621688 ]\t[1.90340519 1. ]\n", + "56 \t0 \t100 \t[3.79986163 1.1 ]\t[0.32405282 0.5 ]\t[1.90340519 1. ]\n", + "57 \t0 \t100 \t[3.7791138 1.18 ] \t[0.38025358 0.93145048]\t[1.79820001 1. ]\n", + "58 \t0 \t100 \t[3.79880958 1.1 ]\t[0.33031825 0.5 ]\t[1.79820001 1. ]\n", + "59 \t0 \t100 \t[3.77745768 1.15 ]\t[0.38868636 0.698212 ]\t[1.7377938 1. ] \n", + "60 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ]\n", + "61 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "62 \t0 \t100 \t[3.80340393 1.1 ]\t[0.30465856 0.5 ]\t[2.25763559 1. ]\n", + "63 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", + "64 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", + "65 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", + "66 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", + "67 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", + "68 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", + "69 \t0 \t100 \t[3.74379506 1.25 ]\t[0.45020819 0.98361578]\t[1.45456362 1. ]\n", + "70 \t0 \t100 \t[3.74379506 1.25 ]\t[0.45020819 0.98361578]\t[1.45456362 1. ]\n", + "71 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "72 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "73 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "74 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ]\n", + "75 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "76 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "77 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "78 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "79 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "80 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "81 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "82 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "83 \t0 \t100 \t[3.8332147 1.06 ] \t[0.22663974 0.42 ]\t[2.42404604 1. ]\n", + "84 \t0 \t100 \t[3.8332147 1.06 ] \t[0.22663974 0.42 ]\t[2.42404604 1. ]\n", + "85 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "86 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "87 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "89 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "90 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", + "91 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "92 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "93 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "94 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "95 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "96 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "97 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", + "98 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "99 \t0 \t100 \t[3.83443739 1.05 ]\t[0.2192433 0.32787193]\t[2.54631495 1. ]\n", + "Final population hypervolume is 49373.231387\n", + "fit, 6, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.72]\t[ nan 0.92822411]\t[nan 20.]\n", + "1 \t91 \t91 \t[ nan 16.3] \t[ nan 5.52358579]\t[nan 1.]\n", + "2 \t94 \t94 \t[ nan 10.53]\t[ nan 5.17195321]\t[nan 1.]\n", + "3 \t99 \t99 \t[ nan 5.85] \t[ nan 2.77983812]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 3.63] \t[ nan 1.33157801]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 2.98] \t[ nan 0.9162969] \t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 2.62] \t[ nan 0.67498148]\t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.45] \t[ nan 0.57227616]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.55] \t[ nan 0.698212] \t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.36] \t[ nan 0.55713553]\t[nan 1.]\n", + "10 \t100 \t100 \t[ nan 2.29] \t[ nan 0.53469618]\t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.24] \t[ nan 0.51224994]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", + "17 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.19] \t[ nan 0.48363209]\t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.21] \t[ nan 0.49588305]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.24] \t[ nan 0.51224994]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.25] \t[ nan 0.51720402]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.26] \t[ nan 0.52191953]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.21] \t[ nan 0.49588305]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", + "30 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.13] \t[ nan 0.43943145]\t[nan 1.]\n", + "33 \t100 \t100 \t[ nan 2.13] \t[ nan 0.43943145]\t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.05] \t[ nan 0.35707142]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.01] \t[ nan 0.29983329]\t[nan 1.]\n", + "38 \t100 \t100 \t[ nan 1.99] \t[ nan 0.26438608]\t[nan 1.]\n", + "39 \t100 \t100 \t[ nan 1.97] \t[ nan 0.2215852] \t[nan 1.]\n", + "40 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "41 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "53 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(-1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.88]\t[ nan 0.96208108]\t[nan 20.]\n", + "1 \t0 \t93 \t[ nan 16.47]\t[ nan 5.20279732]\t[nan 1.]\n", + "2 \t0 \t98 \t[ nan 10.82]\t[ nan 5.73477114]\t[nan 1.]\n", + "3 \t0 \t99 \t[ nan 5.44] \t[ nan 3.63406109]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 2.01] \t[ nan 1.06296754]\t[nan 1.]\n", + "5 \t0 \t100 \t[7.48380712 1.15 ]\t[4.6706179 0.35707142]\t[2.61403799 1. ]\n", + "6 \t0 \t100 \t[5.07630429 1.1 ]\t[1.27410028 0.3 ]\t[2.61403799 1. ]\n", + "7 \t0 \t100 \t[4.49317648 1.08 ]\t[1.10128196 0.30594117]\t[2.61403799 1. ]\n", + "8 \t0 \t100 \t[3.79277136 1.22 ]\t[0.45888391 0.78204859]\t[2.50877285 1. ]\n", + "9 \t0 \t100 \t[3.81957858 1.09 ]\t[0.26177879 0.49183331]\t[2.50877285 1. ]\n", + "10 \t0 \t100 \t[3.81957858 1.09 ]\t[0.26177879 0.49183331]\t[2.50877285 1. ]\n", + "11 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", + "12 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", + "13 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", + "14 \t0 \t100 \t[3.80350464 1.11 ]\t[0.30426431 0.58129167]\t[2.25763559 1. ]\n", + "15 \t0 \t100 \t[3.80350464 1.11 ]\t[0.30426431 0.58129167]\t[2.25763559 1. ]\n", + "16 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "17 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", + "18 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", + "19 \t0 \t100 \t[3.82112455 1.07 ]\t[0.31036292 0.45287967]\t[1.35838258 1. ]\n", + "20 \t0 \t100 \t[3.76987834 1.18 ]\t[0.49635867 0.95268043]\t[0.15569621 1. ]\n", + "21 \t0 \t100 \t[3.71862019 1.31 ]\t[0.62550101 1.39064733]\t[0.15450163 1. ]\n", + "22 \t0 \t100 \t[3.69347316 1.35 ]\t[0.66790368 1.43788038]\t[0.15440011 1. ]\n", + "23 \t0 \t100 \t[3.76783187 1.17 ]\t[0.43680837 0.72187256]\t[1.35838246 1. ]\n", + "24 \t0 \t100 \t[3.72949114 1.24 ]\t[0.57294808 0.99116094]\t[0.03891063 1. ]\n", + "25 \t0 \t100 \t[3.72949114 1.24 ]\t[0.57294808 0.99116094]\t[0.03891063 1. ]\n", + "26 \t0 \t100 \t[3.72949114 1.24 ]\t[0.57294808 0.99116094]\t[0.03891063 1. ]\n", + "27 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "28 \t0 \t100 \t[3.82657478 1.07 ]\t[0.26911137 0.45287967]\t[1.90340519 1. ]\n", + "29 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "30 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "31 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "32 \t0 \t100 \t[3.84097907 1.06 ]\t[0.22902357 0.50635956]\t[1.9365015 1. ] \n", + "33 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "34 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014736 0.59824744]\t[1.90340519 1. ]\n", + "35 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014736 0.59824744]\t[1.90340519 1. ]\n", + "36 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014737 0.59824744]\t[1.90340519 1. ]\n", + "37 \t0 \t100 \t[3.80487381 1.13 ]\t[0.34180246 0.74370693]\t[1.79605389 1. ]\n", + "38 \t0 \t100 \t[3.90709747 1.01 ]\t[0.352289 0.09949874]\t[2.60900354 1. ]\n", + "39 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", + "40 \t0 \t100 \t[3.8181239 1.09 ] \t[0.26904361 0.49183331]\t[2.46565032 1. ]\n", + "41 \t0 \t100 \t[3.81250144 1.09 ]\t[0.30120173 0.49183331]\t[1.90340519 1. ]\n", + "42 \t0 \t100 \t[3.79280566 1.13 ]\t[0.35601188 0.62697687]\t[1.90340519 1. ]\n", + "43 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014737 0.59824744]\t[1.90340519 1. ]\n", + "44 \t0 \t100 \t[3.78651179 1.18 ]\t[0.38389063 0.90972523]\t[1.83626354 1. ]\n", + "45 \t0 \t100 \t[3.81152246 1.09 ]\t[0.30749848 0.49183331]\t[1.80550706 1. ]\n", + "46 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "47 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "48 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "49 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "50 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "51 \t0 \t100 \t[3.84675712 1.03 ]\t[0.1837081 0.2215852] \t[2.51430631 1. ]\n", + "52 \t0 \t100 \t[3.80757448 1.06 ]\t[0.42572604 0.36932371]\t[0.00337517 1. ]\n", + "53 \t0 \t100 \t[3.80757447 1.06 ]\t[0.42572605 0.36932371]\t[0.00337517 1. ]\n", + "54 \t0 \t100 \t[3.80757447 1.06 ]\t[0.42572605 0.36932371]\t[0.00337517 1. ]\n", + "55 \t0 \t100 \t[3.80757447 1.06 ]\t[0.42572605 0.36932371]\t[0.00337517 1. ]\n", + "56 \t0 \t100 \t[3.75480386 1.15 ]\t[0.58404485 0.80467385]\t[0.00325559 1. ]\n", + "57 \t0 \t100 \t[3.75480386 1.15 ]\t[0.58404486 0.80467385]\t[0.00325559 1. ]\n", + "58 \t0 \t100 \t[3.75480386 1.15 ]\t[0.58404486 0.80467385]\t[0.00325559 1. ]\n", + "59 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", + "60 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", + "61 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", + "62 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", + "63 \t0 \t100 \t[3.74214014 1.16 ]\t[0.59507683 0.80894994]\t[8.64391623e-04 1.00000000e+00]\n", + "64 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "65 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "66 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "67 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ] \n", + "68 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ] \n", + "69 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ] \n", + "70 \t0 \t100 \t[3.80518049 1.1 ]\t[0.29602772 0.47958315]\t[2.45047045 1. ] \n", + "71 \t0 \t100 \t[3.7854847 1.14 ] \t[0.35123498 0.61676576]\t[1.90340519 1. ] \n", + "72 \t0 \t100 \t[3.79820212 1.11 ]\t[0.33122676 0.54580216]\t[1.90340519 1. ] \n", + "73 \t0 \t100 \t[3.78246927 1.14 ]\t[0.36312923 0.61676576]\t[1.90340519 1. ] \n", + "74 \t0 \t100 \t[3.76277349 1.18 ]\t[0.40829132 0.72636079]\t[1.90340519 1. ] \n", + "75 \t0 \t100 \t[3.76277349 1.18 ]\t[0.40829132 0.72636079]\t[1.90340519 1. ] \n", + "76 \t0 \t100 \t[ nan 1.06] \t[ nan 0.23748684]\t[nan 1.] \n", + "77 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "78 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ] \n", + "79 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "80 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "81 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ] \n", + "82 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ] \n", + "83 \t0 \t100 \t[3.78072214 1.13 ]\t[0.46165267 0.67312703]\t[0.00463617 1. ] \n", + "84 \t0 \t100 \t[3.766635 1.15 ] \t[0.47983125 0.698212 ]\t[0.00325559 1. ] \n", + "85 \t0 \t100 \t[3.77625156 1.13 ]\t[0.47649872 0.67312703]\t[0.00325559 1. ] \n", + "86 \t0 \t100 \t[3.73754505 1.19 ]\t[0.60653432 0.89101066]\t[0.00233289 1. ] \n", + "87 \t0 \t100 \t[3.69849361 1.26 ]\t[0.71202729 1.15429632]\t[2.50209763e-04 1.00000000e+00]\n", + "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", + "89 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ] \n", + "90 \t0 \t100 \t[3.81494884 1.08 ]\t[0.28860377 0.4621688 ]\t[2.00479269 1. ] \n", + "91 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ] \n", + "92 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ] \n", + "93 \t0 \t100 \t[3.80354182 1.12 ]\t[0.30384662 0.62096699]\t[2.29141402 1. ] \n", + "94 \t0 \t100 \t[3.80309528 1.12 ]\t[0.30609307 0.62096699]\t[2.24675989 1. ] \n", + "95 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", + "96 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", + "97 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", + "98 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", + "99 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", + "Final population hypervolume is 49389.248764\n", + "fit, 7, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.83]\t[ nan 1.03975959]\t[nan 16.]\n", + "1 \t86 \t86 \t[ nan 15.36]\t[ nan 6.22979935]\t[nan 1.]\n", + "2 \t93 \t93 \t[ nan 9.21] \t[ nan 5.01057881]\t[nan 1.]\n", + "3 \t98 \t98 \t[ nan 4.88] \t[ nan 2.42602556]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 3.26] \t[ nan 1.14560028]\t[nan 1.]\n", + "5 \t100 \t100 \t[nan 2.8] \t[ nan 0.88317609]\t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 2.48] \t[ nan 0.68527367]\t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.36] \t[ nan 0.60860496]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.34] \t[ nan 0.60365553]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.32] \t[ nan 0.59799666]\t[nan 1.]\n", + "10 \t100 \t100 \t[ nan 2.22] \t[ nan 0.55821143]\t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.18] \t[ nan 0.6225753] \t[nan 1.]\n", + "17 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.13] \t[ nan 0.50309045]\t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.22] \t[ nan 0.62577951]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", + "30 \t100 \t100 \t[ nan 2.13] \t[ nan 0.50309045]\t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.13] \t[ nan 0.50309045]\t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", + "33 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.08] \t[ nan 0.4621688] \t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.07] \t[ nan 0.45287967]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "38 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "39 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", + "40 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", + "41 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 2.03] \t[ nan 0.4112177] \t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 2.01] \t[ nan 0.38716921]\t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 1.99] \t[ nan 0.36041643]\t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 1.95] \t[ nan 0.29580399]\t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 1.94] \t[ nan 0.2764055] \t[nan 1.]\n", + "53 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(-1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.94]\t[ nan 1.09380071]\t[nan 20.]\n", + "1 \t0 \t88 \t[ nan 17.04]\t[ nan 5.17091868]\t[nan 1.]\n", + "2 \t0 \t97 \t[ nan 11.25]\t[ nan 5.93864463]\t[nan 1.]\n", + "3 \t0 \t99 \t[ nan 5.25] \t[ nan 3.25384388]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 2.78] \t[ nan 1.41124059]\t[nan 1.]\n", + "5 \t0 \t100 \t[ nan 2.03] \t[ nan 0.84208076]\t[nan 1.]\n", + "6 \t0 \t100 \t[ nan 1.83] \t[ nan 0.72187256]\t[nan 1.]\n", + "7 \t0 \t100 \t[ nan 1.43] \t[ nan 0.51487863]\t[nan 1.]\n", + "8 \t0 \t100 \t[ nan 1.21] \t[ nan 0.40730824]\t[nan 1.]\n", + "9 \t0 \t100 \t[5.33986482 1.1 ]\t[1.65229309 0.3 ]\t[2.61403799 1. ]\n", + "10 \t0 \t100 \t[4.66403539 1.23 ]\t[1.2919498 0.81061705]\t[2.51780605 1. ]\n", + "11 \t0 \t100 \t[4.35897743 1.36 ]\t[1.26466267 1.0150862 ]\t[2.48370647 1. ]\n", + "12 \t0 \t100 \t[3.94386113 1.48 ]\t[1.04626317 1.26870012]\t[2.48370647 1. ]\n", + "13 \t0 \t100 \t[3.68975136 1.27 ]\t[0.61256303 0.59757845]\t[2.51430631 1. ]\n", + "14 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "15 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "16 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", + "17 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", + "18 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "19 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "20 \t0 \t100 \t[3.81504954 1.08 ]\t[0.28818354 0.4621688 ]\t[2.00479293 1. ]\n", + "21 \t0 \t100 \t[3.77550609 1.14 ]\t[0.4014521 0.6483826] \t[1.35838246 1. ]\n", + "22 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221682 0.29580399]\t[2.46565056 1. ]\n", + "23 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221682 0.29580399]\t[2.46565056 1. ]\n", + "24 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "25 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", + "26 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", + "27 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", + "28 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", + "29 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", + "30 \t0 \t100 \t[3.81538893 1.08 ]\t[0.28376563 0.41665333]\t[2.19215393 1. ]\n", + "31 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221685 0.29580399]\t[2.46565032 1. ]\n", + "32 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980987 0.41665333]\t[2.45047045 1. ]\n", + "33 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "34 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "35 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", + "36 \t0 \t100 \t[3.83209932 1.06 ]\t[0.23279432 0.36932371]\t[2.45585966 1. ]\n", + "37 \t0 \t100 \t[3.81802598 1.08 ]\t[0.26953713 0.41665333]\t[2.45585942 1. ]\n", + "38 \t0 \t100 \t[3.7983302 1.12 ] \t[0.32998464 0.5706137 ]\t[1.90340519 1. ]\n", + "39 \t0 \t100 \t[3.7983302 1.12 ] \t[0.32998464 0.5706137 ]\t[1.90340519 1. ]\n", + "40 \t0 \t100 \t[3.7983302 1.12 ] \t[0.32998464 0.5706137 ]\t[1.90340519 1. ]\n", + "41 \t0 \t100 \t[3.79822821 1.12 ]\t[0.33040085 0.5706137 ]\t[1.90340519 1. ]\n", + "42 \t0 \t100 \t[3.79822821 1.12 ]\t[0.33040085 0.5706137 ]\t[1.90340519 1. ]\n", + "43 \t0 \t100 \t[3.79676859 1.12 ]\t[0.33663646 0.5706137 ]\t[1.90340519 1. ]\n", + "44 \t0 \t100 \t[3.79676859 1.12 ]\t[0.33663646 0.5706137 ]\t[1.90340519 1. ]\n", + "45 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", + "46 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "47 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "48 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "49 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "50 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "51 \t0 \t100 \t[3.80618485 1.09 ]\t[0.35209812 0.54945427]\t[1.07973862 1. ]\n", + "52 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", + "53 \t0 \t100 \t[3.81442152 1.08 ]\t[0.29287515 0.4621688 ]\t[1.90340519 1. ]\n", + "54 \t0 \t100 \t[3.78026834 1.19 ]\t[0.37519254 0.95598117]\t[1.86215627 1. ]\n", + "55 \t0 \t100 \t[3.77734991 1.2 ]\t[0.39029443 1.03923048]\t[1.67518544 1. ]\n", + "56 \t0 \t100 \t[3.78870204 1.13 ]\t[0.38233028 0.67312703]\t[1.4545635 1. ] \n", + "57 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "58 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "59 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "60 \t0 \t100 \t[3.80548408 1.14 ]\t[0.29464275 0.73511904]\t[2.46565032 1. ]\n", + "61 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ]\n", + "62 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", + "63 \t0 \t100 \t[3.80542935 1.1 ]\t[0.29489206 0.5 ]\t[2.46017694 1. ]\n", + "64 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ]\n", + "65 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ]\n", + "66 \t0 \t100 \t[3.81265904 1.06 ]\t[0.29953566 0.31048349]\t[2.12073183 1. ]\n", + "67 \t0 \t100 \t[3.81265904 1.06 ]\t[0.29953566 0.31048349]\t[2.12073183 1. ]\n", + "68 \t0 \t100 \t[3.79296326 1.1 ]\t[0.35461218 0.5 ]\t[1.90340519 1. ]\n", + "69 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "70 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "71 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "72 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "73 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "74 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "75 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "76 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", + "77 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", + "78 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", + "79 \t0 \t100 \t[3.8005514 1.09 ] \t[0.3200642 0.42649736]\t[1.97238231 1. ]\n", + "80 \t0 \t100 \t[3.78154539 1.12 ]\t[0.36803454 0.51536395]\t[1.97238231 1. ]\n", + "81 \t0 \t100 \t[3.78154539 1.12 ]\t[0.36803454 0.51536395]\t[1.97238231 1. ]\n", + "82 \t0 \t100 \t[3.78154539 1.12 ]\t[0.36803454 0.51536395]\t[1.97238231 1. ]\n", + "83 \t0 \t100 \t[3.75639938 1.18 ]\t[0.43983027 0.77948701]\t[1.35838258 1. ]\n", + "84 \t0 \t100 \t[3.74384922 1.2 ]\t[0.49972018 0.84852814]\t[0.63692141 1. ]\n", + "85 \t0 \t100 \t[3.74384922 1.2 ]\t[0.49972018 0.84852814]\t[0.63692141 1. ]\n", + "86 \t0 \t100 \t[3.69231898 1.33 ]\t[0.62758593 1.37880383]\t[0.12729283 1. ]\n", + "87 \t0 \t100 \t[3.69125033 1.32 ]\t[0.63371656 1.3029198 ]\t[0.02042805 1. ]\n", + "88 \t0 \t100 \t[3.69096342 1.32 ]\t[0.63451892 1.3029198 ]\t[0.02042804 1. ]\n", + "89 \t0 \t100 \t[3.67025884 1.37 ]\t[0.6614501 1.38314858]\t[0.02042804 1. ]\n", + "90 \t0 \t100 \t[3.65056306 1.41 ]\t[0.68405779 1.42895066]\t[0.02042804 1. ]\n", + "91 \t0 \t100 \t[3.74215382 1.2 ]\t[0.45197205 0.74833148]\t[1.90340519 1. ]\n", + "92 \t0 \t100 \t[3.74215382 1.2 ]\t[0.45197205 0.74833148]\t[1.90340519 1. ]\n", + "93 \t0 \t100 \t[3.76115983 1.17 ]\t[0.41565341 0.69361373]\t[1.90340519 1. ]\n", + "94 \t0 \t100 \t[3.74052443 1.23 ]\t[0.45859443 0.90393584]\t[1.80944371 1. ]\n", + "95 \t0 \t100 \t[3.74041199 1.22 ]\t[0.45906901 0.84356387]\t[1.79820001 1. ]\n", + "96 \t0 \t100 \t[3.74041199 1.22 ]\t[0.45906901 0.84356387]\t[1.79820001 1. ]\n", + "97 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "98 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "99 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", + "Final population hypervolume is 49374.815151\n", + "fit, 8, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.89]\t[ nan 0.95807098]\t[nan 20.]\n", + "1 \t86 \t86 \t[ nan 14.95]\t[ nan 6.67139416]\t[nan 1.]\n", + "2 \t95 \t95 \t[ nan 8.51] \t[ nan 5.10195061]\t[nan 1.]\n", + "3 \t99 \t99 \t[ nan 4.52] \t[ nan 2.11886762]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 3.29] \t[ nan 1.25135926]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 2.81] \t[ nan 0.94546285]\t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 2.41] \t[ nan 0.69419018]\t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.28] \t[ nan 0.60133186]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.36] \t[ nan 0.62481997]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.24] \t[ nan 0.58514955]\t[nan 1.]\n", + "10 \t100 \t100 \t[ nan 2.18] \t[ nan 0.55461698]\t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.15] \t[ nan 0.53619026]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.13] \t[ nan 0.52258971]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.12] \t[ nan 0.51536395]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.14] \t[ nan 0.52952809]\t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.15] \t[ nan 0.53619026]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.17] \t[ nan 0.5487258] \t[nan 1.]\n", + "17 \t100 \t100 \t[ nan 2.18] \t[ nan 0.55461698]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.19] \t[ nan 0.56026779]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.22] \t[ nan 0.5758472] \t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.24] \t[ nan 0.58514955]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.25] \t[ nan 0.58949131]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.24] \t[ nan 0.58514955]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.26] \t[ nan 0.59363288]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.23] \t[ nan 0.58060313]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.22] \t[ nan 0.5758472] \t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.18] \t[ nan 0.55461698]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.16] \t[ nan 0.5425864] \t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.13] \t[ nan 0.52258971]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.14] \t[ nan 0.52952809]\t[nan 1.]\n", + "30 \t100 \t100 \t[ nan 2.12] \t[ nan 0.51536395]\t[nan 1.]\n", + "31 \t100 \t100 \t[nan 2.1] \t[nan 0.5] \t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.08] \t[ nan 0.48332184]\t[nan 1.]\n", + "33 \t100 \t100 \t[ nan 2.05] \t[ nan 0.45552168]\t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.05] \t[ nan 0.45552168]\t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.06] \t[ nan 0.46518813]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.01] \t[ nan 0.41218928]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.01] \t[ nan 0.41218928]\t[nan 1.]\n", + "38 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", + "39 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", + "40 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", + "41 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 1.96] \t[ nan 0.34409301]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 1.94] \t[ nan 0.31048349]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 1.93] \t[ nan 0.29171904]\t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "53 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(-1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.79]\t[ nan 0.93053748]\t[nan 20.]\n", + "1 \t0 \t82 \t[ nan 16.43]\t[ nan 5.57001795]\t[nan 1.]\n", + "2 \t0 \t97 \t[ nan 10.03]\t[ nan 5.57576004]\t[nan 1.]\n", + "3 \t0 \t99 \t[ nan 3.27] \t[ nan 2.74173303]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.41] \t[ nan 0.6495383] \t[nan 1.]\n", + "5 \t0 \t100 \t[5.51457515 1.1 ]\t[1.61918953 0.3 ]\t[2.73836112 1. ]\n", + "6 \t0 \t100 \t[4.81866309 1.06 ]\t[1.20779848 0.2764055 ]\t[2.73836112 1. ]\n", + "7 \t0 \t100 \t[3.82670571 1.14 ]\t[0.46479199 0.56603887]\t[2.60930681 1. ]\n", + "8 \t0 \t100 \t[3.81743176 1.08 ]\t[0.28068137 0.4621688 ]\t[1.90340519 1. ]\n", + "9 \t0 \t100 \t[3.81685361 1.08 ]\t[0.28465477 0.4621688 ]\t[1.84558952 1. ]\n", + "10 \t0 \t100 \t[3.81685361 1.08 ]\t[0.28465477 0.4621688 ]\t[1.84558952 1. ]\n", + "11 \t0 \t100 \t[3.81685361 1.08 ]\t[0.28465477 0.4621688 ]\t[1.84558952 1. ]\n", + "12 \t0 \t100 \t[3.81643052 1.08 ]\t[0.28655267 0.4621688 ]\t[1.84558952 1. ]\n", + "13 \t0 \t100 \t[3.80284375 1.1 ]\t[0.31440598 0.5 ]\t[1.84558952 1. ]\n", + "14 \t0 \t100 \t[3.80284375 1.1 ]\t[0.31440598 0.5 ]\t[1.84558952 1. ]\n", + "15 \t0 \t100 \t[3.80284375 1.1 ]\t[0.31440598 0.5 ]\t[1.84558952 1. ]\n", + "16 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", + "17 \t0 \t100 \t[3.78438785 1.11 ]\t[0.45256852 0.58129167]\t[4.87665464e-14 1.00000000e+00]\n", + "18 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[4.87665464e-14 1.00000000e+00]\n", + "19 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "20 \t0 \t100 \t[3.82214457 1.08 ]\t[0.2505484 0.4621688] \t[2.46565032 1. ] \n", + "21 \t0 \t100 \t[3.82006442 1.08 ]\t[0.26238578 0.4621688 ]\t[2.25763559 1. ] \n", + "22 \t0 \t100 \t[3.80599109 1.1 ]\t[0.29489762 0.5 ]\t[2.25763559 1. ] \n", + "23 \t0 \t100 \t[3.78983761 1.14 ]\t[0.33261515 0.63277168]\t[2.25763559 1. ] \n", + "24 \t0 \t100 \t[3.78983761 1.14 ]\t[0.33261515 0.63277168]\t[2.25763559 1. ] \n", + "25 \t0 \t100 \t[3.80346266 1.1 ]\t[0.30891314 0.5 ]\t[2.00479269 1. ] \n", + "26 \t0 \t100 \t[3.80346266 1.1 ]\t[0.30891314 0.5 ]\t[2.00479269 1. ] \n", + "27 \t0 \t100 \t[3.78248508 1.16 ]\t[0.36888532 0.77097341]\t[1.77522588 1. ] \n", + "28 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "29 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "30 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", + "31 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "32 \t0 \t100 \t[3.82443289 1.08 ]\t[0.2386721 0.4621688] \t[2.49596119 1. ] \n", + "33 \t0 \t100 \t[3.83712755 1.04 ]\t[0.20442599 0.24166092]\t[2.55661488 1. ] \n", + "34 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "35 \t0 \t100 \t[3.79731289 1.12 ]\t[0.34068481 0.60464866]\t[1.90340519 1. ] \n", + "36 \t0 \t100 \t[3.76300484 1.2 ]\t[0.41363517 0.86023253]\t[1.80085552 1. ] \n", + "37 \t0 \t100 \t[3.78212247 1.16 ]\t[0.37209155 0.77097341]\t[1.80085552 1. ] \n", + "38 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "39 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "40 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "41 \t0 \t100 \t[3.83840204 1.05 ]\t[0.19668955 0.32787193]\t[2.68406439 1. ] \n", + "42 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "43 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "44 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "45 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "46 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "47 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "48 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "49 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "50 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "51 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "52 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "53 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "54 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "55 \t0 \t100 \t[3.78445531 1.17 ]\t[0.36054725 0.82528783]\t[1.71940589 1. ] \n", + "56 \t0 \t100 \t[3.74474092 1.24 ]\t[0.52195148 1.09654001]\t[0.10877079 1. ] \n", + "57 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ] \n", + "58 \t0 \t100 \t[3.78612688 1.14 ]\t[0.35038454 0.6483826 ]\t[2.00479293 1. ] \n", + "59 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ] \n", + "60 \t0 \t100 \t[3.77244816 1.15 ]\t[0.43591932 0.72629195]\t[0.63692153 1. ] \n", + "61 \t0 \t100 \t[3.77244816 1.14 ]\t[0.43591934 0.6483826 ]\t[0.63692129 1. ] \n", + "62 \t0 \t100 \t[3.73955526 1.23 ]\t[0.53900296 1.09412065]\t[0.58369392 1. ] \n", + "63 \t0 \t100 \t[3.76607895 1.13 ]\t[0.48371838 0.57714816]\t[5.75518661e-11 1.00000000e+00]\n", + "64 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "65 \t0 \t100 \t[3.80256634 1.14 ]\t[0.27880219 0.61676576]\t[2.67845941 1. ] \n", + "66 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "67 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "68 \t0 \t100 \t[3.80262238 1.14 ]\t[0.2785767 0.63277168]\t[2.67846012 1. ] \n", + "69 \t0 \t100 \t[3.80054649 1.15 ]\t[0.28757411 0.698212 ]\t[2.47087169 1. ] \n", + "70 \t0 \t100 \t[3.80909657 1.11 ]\t[0.27972322 0.58129167]\t[2.47087169 1. ] \n", + "71 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "72 \t0 \t100 \t[3.80953091 1.09 ]\t[0.27767113 0.42649736]\t[2.51430607 1. ] \n", + "73 \t0 \t100 \t[3.79545758 1.14 ]\t[0.3080959 0.6483826] \t[2.46565032 1. ] \n", + "74 \t0 \t100 \t[3.82214457 1.09 ]\t[0.2505484 0.54945427]\t[2.46565032 1. ] \n", + "75 \t0 \t100 \t[3.82214457 1.09 ]\t[0.25054842 0.54945427]\t[2.46565008 1. ] \n", + "76 \t0 \t100 \t[3.82214457 1.09 ]\t[0.25054842 0.54945427]\t[2.46565008 1. ] \n", + "77 \t0 \t100 \t[3.82214457 1.09 ]\t[0.25054842 0.54945427]\t[2.46565008 1. ] \n", + "78 \t0 \t100 \t[3.80807123 1.11 ]\t[0.28451955 0.58129167]\t[2.46565008 1. ] \n", + "79 \t0 \t100 \t[3.80807123 1.11 ]\t[0.28451955 0.58129167]\t[2.46565008 1. ] \n", + "80 \t0 \t100 \t[3.80807123 1.11 ]\t[0.28451955 0.58129167]\t[2.46565008 1. ] \n", + "81 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "82 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "83 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "84 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488197 0.4621688 ]\t[1.90340519 1. ] \n", + "85 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", + "86 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "87 \t0 \t100 \t[3.78342053 1.11 ]\t[0.45529409 0.58129167]\t[5.79447194e-04 1.00000000e+00]\n", + "88 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "89 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "90 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572202 0.4621688 ]\t[1.24752386e-08 1.00000000e+00]\n", + "91 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[1.24752386e-08 1.00000000e+00]\n", + "92 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[1.24752386e-08 1.00000000e+00]\n", + "93 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572203 0.4621688 ]\t[3.60822483e-14 1.00000000e+00]\n", + "94 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572203 0.4621688 ]\t[3.60822483e-14 1.00000000e+00]\n", + "95 \t0 \t100 \t[3.75947604 1.14 ]\t[0.50489108 0.61676576]\t[3.60822483e-14 1.00000000e+00]\n", + "96 \t0 \t100 \t[3.74540271 1.16 ]\t[0.5208915 0.64373908]\t[3.60822483e-14 1.00000000e+00]\n", + "97 \t0 \t100 \t[3.76440872 1.13 ]\t[0.48958502 0.57714816]\t[3.60822483e-14 1.00000000e+00]\n", + "98 \t0 \t100 \t[3.76440872 1.13 ]\t[0.48958502 0.57714816]\t[3.60822483e-14 1.00000000e+00]\n", + "99 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081127 0.42649736]\t[1.97238207 1. ] \n", + "Final population hypervolume is 49400.787163\n", + "fit, 9, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.8]\t[ nan 0.96953597]\t[nan 20.]\n", + "1 \t85 \t85 \t[ nan 17.74]\t[ nan 4.85102051]\t[nan 1.]\n", + "2 \t100 \t100 \t[ nan 11.92]\t[ nan 6.46324996]\t[nan 1.]\n", + "3 \t100 \t100 \t[ nan 4.38] \t[ nan 3.22422084]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 1.89] \t[ nan 1.02854266]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 1.14] \t[ nan 0.34698703]\t[nan 1.]\n", + "6 \t100 \t100 \t[0.46558893 1.03 ]\t[0.11581515 0.17058722]\t[0.2614038 1. ]\n", + "7 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ]\n", + "8 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ]\n", + "9 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ]\n", + "10 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "11 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "12 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "13 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "14 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "15 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "16 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "17 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "18 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "19 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "20 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "21 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "22 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "23 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "24 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "25 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "26 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "27 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "28 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "29 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "30 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "31 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "32 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "33 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "34 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "35 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "36 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "37 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "38 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "39 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "40 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "41 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "42 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "43 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "44 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "45 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "46 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "47 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "48 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "49 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "50 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "51 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "52 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "53 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "54 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "55 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "56 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "57 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "58 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "59 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "60 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "61 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "62 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "63 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "64 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "65 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "66 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "67 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "68 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "69 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "70 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "71 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "72 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "73 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "74 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "75 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "76 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "77 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "78 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "79 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "80 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "81 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "82 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "83 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "84 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "85 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "86 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "87 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "88 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "89 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "90 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "91 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "92 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "93 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "94 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "95 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "96 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "97 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "98 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "99 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.64]\t[ nan 1.07256701]\t[nan 14.]\n", + "1 \t0 \t91 \t[ nan 16.18]\t[ nan 6.01395045]\t[nan 1.]\n", + "2 \t0 \t94 \t[ nan 9.77] \t[ nan 5.93271439]\t[nan 1.]\n", + "3 \t0 \t98 \t[ nan 3.82] \t[ nan 2.50351753]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.96] \t[ nan 1.02878569]\t[nan 1.]\n", + "5 \t0 \t100 \t[ nan 1.23] \t[ nan 0.44395946]\t[nan 1.]\n", + "6 \t0 \t100 \t[5.08870241 1.04 ]\t[1.23406672 0.19595918]\t[2.73843527 1. ]\n", + "7 \t0 \t100 \t[4.2706165 1.08 ] \t[0.96661207 0.4621688 ]\t[2.67845964 1. ]\n", + "8 \t0 \t100 \t[3.83780303 1.07 ]\t[0.20010044 0.45287967]\t[2.67845941 1. ]\n", + "9 \t0 \t100 \t[3.84805069 1.03 ]\t[0.1752486 0.2215852] \t[2.51430631 1. ]\n", + "10 \t0 \t100 \t[3.84805069 1.03 ]\t[0.1752486 0.2215852] \t[2.51430631 1. ]\n", + "11 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531785 0.31048349]\t[2.51430607 1. ]\n", + "12 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", + "13 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", + "14 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", + "15 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", + "16 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", + "17 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", + "18 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", + "19 \t0 \t100 \t[3.81581314 1.08 ]\t[0.28968991 0.4621688 ]\t[1.83250618 1. ]\n", + "20 \t0 \t100 \t[3.79699857 1.1 ]\t[0.35045335 0.5 ]\t[1.35838246 1. ]\n", + "21 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838246 1. ]\n", + "22 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838246 1. ]\n", + "23 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838246 1. ]\n", + "24 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "25 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "26 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "27 \t0 \t100 \t[3.83834601 1.05 ]\t[0.19701893 0.32787193]\t[2.67846036 1. ]\n", + "28 \t0 \t100 \t[3.81451158 1.12 ]\t[0.25495371 0.62096699]\t[2.67845988 1. ]\n", + "29 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "30 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", + "31 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897877 0.2215852 ]\t[2.46565056 1. ]\n", + "32 \t0 \t100 \t[3.77730278 1.15 ]\t[0.39777991 0.698212 ]\t[1.35838246 1. ]\n", + "33 \t0 \t100 \t[3.73772754 1.21 ]\t[0.54341445 0.9087904 ]\t[0.63692135 1. ]\n", + "34 \t0 \t100 \t[3.699419 1.3 ] \t[0.65591404 1.26095202]\t[0.04212945 1. ]\n", + "35 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", + "36 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", + "37 \t0 \t100 \t[3.81107189 1.09 ]\t[0.32396352 0.54945427]\t[1.3583827 1. ] \n", + "38 \t0 \t100 \t[3.80385728 1.09 ]\t[0.38143909 0.54945427]\t[0.63692147 1. ]\n", + "39 \t0 \t100 \t[3.81657607 1.08 ]\t[0.28451997 0.4621688 ]\t[1.90879989 1. ]\n", + "40 \t0 \t100 \t[3.77775108 1.16 ]\t[0.39513859 0.77097341]\t[1.39781797 1. ]\n", + "41 \t0 \t100 \t[3.77775108 1.16 ]\t[0.39513859 0.77097341]\t[1.39781797 1. ]\n", + "42 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", + "43 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", + "44 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", + "45 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", + "46 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", + "47 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ]\n", + "48 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ]\n", + "49 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", + "50 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", + "51 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ]\n", + "52 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", + "53 \t0 \t100 \t[3.80953091 1.09 ]\t[0.27767113 0.42649736]\t[2.51430607 1. ]\n", + "54 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[6.29218899e-14 1.00000000e+00]\n", + "55 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "56 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "57 \t0 \t100 \t[3.80797333 1.09 ]\t[0.28498275 0.42649736]\t[2.45585966 1. ] \n", + "58 \t0 \t100 \t[3.78282732 1.13 ]\t[0.37489405 0.57714816]\t[1.35838246 1. ] \n", + "59 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[1.19196319e-13 1.00000000e+00]\n", + "60 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "61 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "62 \t0 \t100 \t[3.80382372 1.09 ]\t[0.30682469 0.42649736]\t[2.04089832 1. ] \n", + "63 \t0 \t100 \t[3.78550286 1.12 ]\t[0.35332275 0.51536395]\t[2.04089832 1. ] \n", + "64 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ] \n", + "65 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ] \n", + "66 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ] \n", + "67 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "68 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "69 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[1.09446063e-10 1.00000000e+00]\n", + "70 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[1.09446063e-10 1.00000000e+00]\n", + "71 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "72 \t0 \t100 \t[3.80599109 1.11 ]\t[0.29489761 0.54580216]\t[2.25763559 1. ] \n", + "73 \t0 \t100 \t[3.80599109 1.11 ]\t[0.29489761 0.54580216]\t[2.25763559 1. ] \n", + "74 \t0 \t100 \t[3.79748832 1.07 ]\t[0.43571984 0.38091994]\t[2.50977228e-05 1.00000000e+00]\n", + "75 \t0 \t100 \t[3.74468515 1.14 ]\t[0.59067479 0.6483826 ]\t[1.53982521e-11 1.00000000e+00]\n", + "76 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "77 \t0 \t100 \t[3.81107189 1.1 ]\t[0.32396354 0.64031242]\t[1.35838246 1. ] \n", + "78 \t0 \t100 \t[3.74722408 1.23 ]\t[0.48074672 0.98848369]\t[1.35838246 1. ] \n", + "79 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "80 \t0 \t100 \t[3.82651285 1.06 ]\t[0.22772444 0.31048349]\t[2.68406439 1. ] \n", + "81 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "82 \t0 \t100 \t[3.82006442 1.08 ]\t[0.26238579 0.4621688 ]\t[2.25763535 1. ] \n", + "83 \t0 \t100 \t[3.80079323 1.13 ]\t[0.32182787 0.67312703]\t[1.94586444 1. ] \n", + "84 \t0 \t100 \t[3.78671989 1.15 ]\t[0.34806475 0.698212 ]\t[1.94586444 1. ] \n", + "85 \t0 \t100 \t[3.78671989 1.15 ]\t[0.34806475 0.698212 ]\t[1.94586444 1. ] \n", + "86 \t0 \t100 \t[3.77377617 1.14 ]\t[0.41532169 0.63277168]\t[1.45456362 1. ] \n", + "87 \t0 \t100 \t[3.77377617 1.14 ]\t[0.41532169 0.63277168]\t[1.45456362 1. ] \n", + "88 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "89 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ] \n", + "90 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "91 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "92 \t0 \t100 \t[3.79945568 1.07 ]\t[0.43011148 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", + "93 \t0 \t100 \t[3.79945568 1.07 ]\t[0.43011148 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", + "94 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "95 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "96 \t0 \t100 \t[3.77686267 1.18 ]\t[0.39211423 0.84118963]\t[1.87661743 1. ] \n", + "97 \t0 \t100 \t[3.87366802 1.03 ]\t[0.28353893 0.17058722]\t[2.73836088 1. ] \n", + "98 \t0 \t100 \t[3.89704481 1.02 ]\t[0.36669234 0.14 ]\t[2.73836088 1. ] \n", + "99 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "Final population hypervolume is 49363.883825\n", + "fit, 10, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.84]\t[ nan 1.00717426]\t[nan 19.]\n", + "1 \t89 \t89 \t[ nan 15.76]\t[ nan 6.33895891]\t[nan 1.]\n", + "2 \t96 \t96 \t[ nan 9.03] \t[ nan 5.38229505]\t[nan 1.]\n", + "3 \t100 \t100 \t[ nan 4.38] \t[ nan 1.81537875]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 3.32] \t[ nan 1.15654658]\t[nan 1.]\n", + "5 \t100 \t100 \t[nan 2.9] \t[ nan 0.91104336]\t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 2.58] \t[ nan 0.6508456] \t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.42] \t[ nan 0.56885851]\t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.42] \t[ nan 0.56885851]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.37] \t[ nan 0.55955339]\t[nan 1.]\n", + "10 \t100 \t100 \t[ nan 2.29] \t[ nan 0.53469618]\t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.24] \t[ nan 0.51224994]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.21] \t[ nan 0.49588305]\t[nan 1.]\n", + "17 \t100 \t100 \t[nan 2.2] \t[ nan 0.48989795]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.19] \t[ nan 0.48363209]\t[nan 1.]\n", + "19 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.28] \t[ nan 0.63371918]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", + "26 \t100 \t100 \t[nan 2.2] \t[ nan 0.48989795]\t[nan 1.]\n", + "27 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", + "29 \t100 \t100 \t[nan 2.2] \t[ nan 0.48989795]\t[nan 1.]\n", + "30 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.19] \t[ nan 0.48363209]\t[nan 1.]\n", + "33 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.12] \t[ nan 0.43081318]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.11] \t[ nan 0.42178193]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.12] \t[ nan 0.43081318]\t[nan 1.]\n", + "38 \t100 \t100 \t[ nan 2.11] \t[ nan 0.42178193]\t[nan 1.]\n", + "39 \t100 \t100 \t[ nan 2.11] \t[ nan 0.42178193]\t[nan 1.]\n", + "40 \t100 \t100 \t[nan 2.1] \t[ nan 0.41231056]\t[nan 1.]\n", + "41 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 2.08] \t[ nan 0.39191836]\t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 2.07] \t[ nan 0.38091994]\t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "53 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "54 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 2.05] \t[ nan 0.35707142]\t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 2.05] \t[ nan 0.35707142]\t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 2.04] \t[ nan 0.34409301]\t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 2.02] \t[ nan 0.31559468]\t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 2.01] \t[ nan 0.29983329]\t[nan 1.]\n", + "61 \t100 \t100 \t[nan 2.] \t[ nan 0.28284271]\t[nan 1.]\n", + "62 \t100 \t100 \t[nan 2.] \t[ nan 0.28284271]\t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.99] \t[ nan 0.26438608]\t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.98] \t[ nan 0.24413111]\t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.97] \t[ nan 0.2215852] \t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.7]\t[ nan 0.88881944]\t[nan 20.]\n", + "1 \t0 \t89 \t[ nan 15.98]\t[ nan 5.92449154]\t[nan 1.]\n", + "2 \t0 \t96 \t[nan 8.9] \t[ nan 5.5380502] \t[nan 1.]\n", + "3 \t0 \t98 \t[ nan 3.74] \t[ nan 1.85267374]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 2.41] \t[ nan 1.18401858]\t[nan 1.]\n", + "5 \t0 \t100 \t[ nan 1.85] \t[ nan 0.75332596]\t[nan 1.]\n", + "6 \t0 \t100 \t[ nan 1.73] \t[ nan 0.70505319]\t[nan 1.]\n", + "7 \t0 \t100 \t[nan 1.5] \t[ nan 0.59160798]\t[nan 1.]\n", + "8 \t0 \t100 \t[ nan 1.44] \t[ nan 0.5535341] \t[nan 1.]\n", + "9 \t0 \t100 \t[ nan 1.27] \t[ nan 0.48692915]\t[nan 1.]\n", + "10 \t0 \t100 \t[5.29680751 1.1 ]\t[1.3932257 0.3 ] \t[2.61403799 1. ]\n", + "11 \t0 \t100 \t[4.97166727 1.02 ]\t[1.20146216 0.14 ]\t[2.61403799 1. ]\n", + "12 \t0 \t100 \t[4.36341317 1.08 ]\t[1.03552625 0.4621688 ]\t[2.61403799 1. ]\n", + "13 \t0 \t100 \t[3.97585385 1.08 ]\t[0.66799793 0.36551334]\t[2.51430631 1. ]\n", + "14 \t0 \t100 \t[3.8326314 1.06 ] \t[0.22973302 0.36932371]\t[2.4553771 1. ] \n", + "15 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", + "16 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", + "17 \t0 \t100 \t[3.84660571 1.06 ]\t[0.18484048 0.50635956]\t[2.49413061 1. ]\n", + "18 \t0 \t100 \t[3.84632091 1.03 ]\t[0.18693422 0.2215852 ]\t[2.46565056 1. ]\n", + "19 \t0 \t100 \t[3.84632091 1.03 ]\t[0.18693422 0.2215852 ]\t[2.46565056 1. ]\n", + "20 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", + "21 \t0 \t100 \t[3.83322069 1.05 ]\t[0.2262486 0.29580399]\t[2.51430631 1. ]\n", + "22 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", + "23 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", + "24 \t0 \t100 \t[3.78869987 1.12 ]\t[0.38138147 0.5706137 ]\t[1.35838258 1. ]\n", + "25 \t0 \t100 \t[3.75117614 1.2 ]\t[0.5277514 0.96953597]\t[0.12061065 1. ]\n", + "26 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", + "27 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", + "28 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", + "29 \t0 \t100 \t[3.81255179 1.09 ]\t[0.30100092 0.49183331]\t[1.90340519 1. ]\n", + "30 \t0 \t100 \t[3.80483008 1.08 ]\t[0.35494812 0.41665333]\t[1.13123405 1. ]\n", + "31 \t0 \t100 \t[3.81890341 1.06 ]\t[0.32848523 0.36932371]\t[1.13123405 1. ]\n", + "32 \t0 \t100 \t[3.78654278 1.12 ]\t[0.45615368 0.69685006]\t[0.63692147 1. ]\n", + "33 \t0 \t100 \t[3.8463209 1.03 ] \t[0.18693424 0.2215852 ]\t[2.46565032 1. ]\n", + "34 \t0 \t100 \t[3.8463209 1.03 ] \t[0.18693424 0.2215852 ]\t[2.46565032 1. ]\n", + "35 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", + "36 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", + "37 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "38 \t0 \t100 \t[3.83421801 1.04 ]\t[0.22058139 0.24166092]\t[2.51430631 1. ]\n", + "39 \t0 \t100 \t[3.83421801 1.04 ]\t[0.22058139 0.24166092]\t[2.51430631 1. ]\n", + "40 \t0 \t100 \t[3.78785613 1.14 ]\t[0.40894588 0.74859869]\t[0.63692147 1. ]\n", + "41 \t0 \t100 \t[3.75880984 1.2 ]\t[0.45119195 0.87177979]\t[0.63692147 1. ]\n", + "42 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "43 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "44 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "45 \t0 \t100 \t[3.80558478 1.1 ]\t[0.2942344 0.47958315]\t[2.46565032 1. ]\n", + "46 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "47 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", + "48 \t0 \t100 \t[3.81403566 1.08 ]\t[0.2946562 0.4621688] \t[1.90340519 1. ]\n", + "49 \t0 \t100 \t[3.76349371 1.17 ]\t[0.40625135 0.69361373]\t[1.90340519 1. ]\n", + "50 \t0 \t100 \t[3.79223765 1.09 ]\t[0.37413753 0.42649736]\t[1.1309371 1. ] \n", + "51 \t0 \t100 \t[3.79223765 1.09 ]\t[0.37413753 0.42649736]\t[1.1309371 1. ] \n", + "52 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", + "53 \t0 \t100 \t[3.79996233 1.1 ]\t[0.32368332 0.5 ]\t[1.90340519 1. ]\n", + "54 \t0 \t100 \t[3.89632376 1.03 ]\t[0.38602402 0.17058722]\t[2.61403799 1. ]\n", + "55 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "56 \t0 \t100 \t[3.83401625 1.05 ]\t[0.22179446 0.32787193]\t[2.49413061 1. ]\n", + "57 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", + "58 \t0 \t100 \t[3.83103194 1.05 ]\t[0.2409808 0.32787193]\t[2.19569993 1. ]\n", + "59 \t0 \t100 \t[3.78833035 1.12 ]\t[0.38725121 0.5706137 ]\t[1.16357994 1. ]\n", + "60 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "61 \t0 \t100 \t[3.84675712 1.03 ]\t[0.1837081 0.2215852] \t[2.51430631 1. ]\n", + "62 \t0 \t100 \t[3.79314042 1.14 ]\t[0.35479823 0.6636264 ]\t[1.90340519 1. ]\n", + "63 \t0 \t100 \t[3.75930257 1.22 ]\t[0.42285219 0.90088845]\t[1.84787631 1. ]\n", + "64 \t0 \t100 \t[3.85831254 1.05 ]\t[0.34043252 0.29580399]\t[2.51430631 1. ]\n", + "65 \t0 \t100 \t[3.80080972 1.11 ]\t[0.39039524 0.66174013]\t[0.63692153 1. ]\n", + "66 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "67 \t0 \t100 \t[3.84064811 1.05 ]\t[0.23178279 0.40926764]\t[1.90340519 1. ]\n", + "68 \t0 \t100 \t[3.81992897 1.12 ]\t[0.30802973 0.80349238]\t[1.80106997 1. ]\n", + "69 \t0 \t100 \t[3.78347715 1.23 ]\t[0.40053311 1.20710397]\t[1.58647907 1. ]\n", + "70 \t0 \t100 \t[3.74084192 1.37 ]\t[0.49231131 1.653209 ]\t[1.58375895 1. ]\n", + "71 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "72 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "73 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", + "74 \t0 \t100 \t[3.82706133 1.07 ]\t[0.26668339 0.45287967]\t[1.90340519 1. ]\n", + "75 \t0 \t100 \t[3.80649308 1.15 ]\t[0.33333645 0.90967027]\t[1.81615853 1. ]\n", + "76 \t0 \t100 \t[3.79193319 1.17 ]\t[0.36071692 0.92795474]\t[1.81615853 1. ]\n", + "77 \t0 \t100 \t[3.81250144 1.09 ]\t[0.30120173 0.49183331]\t[1.90340519 1. ]\n", + "78 \t0 \t100 \t[3.79892996 1.1 ]\t[0.32959051 0.5 ]\t[1.8102386 1. ] \n", + "79 \t0 \t100 \t[3.79892996 1.1 ]\t[0.32959051 0.5 ]\t[1.8102386 1. ] \n", + "80 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", + "81 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", + "82 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", + "83 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", + "84 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", + "85 \t0 \t100 \t[3.77694002 1.15 ]\t[0.39138142 0.698212 ]\t[1.70537317 1. ]\n", + "86 \t0 \t100 \t[3.77694002 1.15 ]\t[0.39138142 0.698212 ]\t[1.70537317 1. ]\n", + "87 \t0 \t100 \t[3.74071384 1.21 ]\t[0.52549225 0.9087904 ]\t[0.25036559 1. ]\n", + "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "89 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "90 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "91 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "92 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "93 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", + "94 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "95 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", + "96 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "97 \t0 \t100 \t[3.79797792 1.08 ]\t[0.41622753 0.4621688 ]\t[0.30770087 1. ]\n", + "98 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", + "99 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", + "Final population hypervolume is 49377.174955\n", + "fit, 11, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.64]\t[ nan 0.91126286]\t[nan 20.]\n", + "1 \t90 \t90 \t[ nan 16.98]\t[ nan 5.15941857]\t[nan 1.]\n", + "2 \t95 \t95 \t[ nan 11.03]\t[ nan 5.10970645]\t[nan 1.]\n", + "3 \t97 \t97 \t[ nan 6.69] \t[ nan 2.9178588] \t[nan 1.]\n", + "4 \t99 \t99 \t[ nan 4.52] \t[ nan 1.65819179]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 3.56] \t[ nan 1.16034478]\t[nan 1.]\n", + "6 \t100 \t100 \t[ nan 3.02] \t[ nan 0.77433843]\t[nan 1.]\n", + "7 \t100 \t100 \t[ nan 2.83] \t[ nan 0.735595] \t[nan 1.]\n", + "8 \t100 \t100 \t[ nan 2.58] \t[ nan 0.58617404]\t[nan 1.]\n", + "9 \t100 \t100 \t[ nan 2.48] \t[ nan 0.53814496]\t[nan 1.]\n", + "10 \t100 \t100 \t[nan 2.5] \t[ nan 0.53851648]\t[nan 1.]\n", + "11 \t100 \t100 \t[ nan 2.46] \t[ nan 0.53702886]\t[nan 1.]\n", + "12 \t100 \t100 \t[ nan 2.42] \t[ nan 0.55099909]\t[nan 1.]\n", + "13 \t100 \t100 \t[ nan 2.35] \t[ nan 0.51720402]\t[nan 1.]\n", + "14 \t100 \t100 \t[ nan 2.36] \t[ nan 0.52] \t[nan 1.]\n", + "15 \t100 \t100 \t[ nan 2.36] \t[ nan 0.52] \t[nan 1.]\n", + "16 \t100 \t100 \t[ nan 2.34] \t[ nan 0.51419841]\t[nan 1.]\n", + "17 \t100 \t100 \t[ nan 2.33] \t[ nan 0.51097945]\t[nan 1.]\n", + "18 \t100 \t100 \t[ nan 2.32] \t[ nan 0.5075431] \t[nan 1.]\n", + "19 \t100 \t100 \t[nan 2.3] \t[nan 0.5] \t[nan 1.]\n", + "20 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", + "21 \t100 \t100 \t[ nan 2.27] \t[ nan 0.48692915]\t[nan 1.]\n", + "22 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", + "23 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", + "24 \t100 \t100 \t[ nan 2.28] \t[ nan 0.49152823]\t[nan 1.]\n", + "25 \t100 \t100 \t[ nan 2.26] \t[ nan 0.48207883]\t[nan 1.]\n", + "26 \t100 \t100 \t[ nan 2.28] \t[ nan 0.49152823]\t[nan 1.]\n", + "27 \t100 \t100 \t[nan 2.3] \t[nan 0.5] \t[nan 1.]\n", + "28 \t100 \t100 \t[ nan 2.33] \t[ nan 0.51097945]\t[nan 1.]\n", + "29 \t100 \t100 \t[ nan 2.32] \t[ nan 0.5075431] \t[nan 1.]\n", + "30 \t100 \t100 \t[nan 2.3] \t[nan 0.5] \t[nan 1.]\n", + "31 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", + "32 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", + "33 \t100 \t100 \t[ nan 2.27] \t[ nan 0.48692915]\t[nan 1.]\n", + "34 \t100 \t100 \t[ nan 2.25] \t[ nan 0.4769696] \t[nan 1.]\n", + "35 \t100 \t100 \t[ nan 2.17] \t[ nan 0.42555846]\t[nan 1.]\n", + "36 \t100 \t100 \t[ nan 2.15] \t[ nan 0.40926764]\t[nan 1.]\n", + "37 \t100 \t100 \t[ nan 2.14] \t[ nan 0.40049969]\t[nan 1.]\n", + "38 \t100 \t100 \t[ nan 2.12] \t[ nan 0.38157568]\t[nan 1.]\n", + "39 \t100 \t100 \t[ nan 2.11] \t[ nan 0.37134889]\t[nan 1.]\n", + "40 \t100 \t100 \t[nan 2.1] \t[ nan 0.36055513]\t[nan 1.]\n", + "41 \t100 \t100 \t[nan 2.1] \t[ nan 0.36055513]\t[nan 1.]\n", + "42 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", + "43 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", + "44 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", + "45 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", + "46 \t100 \t100 \t[ nan 2.08] \t[ nan 0.33704599]\t[nan 1.]\n", + "47 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", + "48 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", + "49 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", + "50 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", + "51 \t100 \t100 \t[ nan 2.05] \t[ nan 0.29580399]\t[nan 1.]\n", + "52 \t100 \t100 \t[ nan 2.03] \t[ nan 0.26286879]\t[nan 1.]\n", + "53 \t100 \t100 \t[nan 2.] \t[nan 0.2] \t[nan 1.]\n", + "54 \t100 \t100 \t[nan 2.] \t[nan 0.2] \t[nan 1.]\n", + "55 \t100 \t100 \t[ nan 1.99] \t[ nan 0.17291616]\t[nan 1.]\n", + "56 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "57 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "58 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "59 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "60 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "61 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "62 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "63 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "64 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "65 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "66 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "67 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "68 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "69 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "70 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "71 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "72 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "73 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "74 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "75 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "76 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "77 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "78 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "79 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "80 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "81 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "82 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "83 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "84 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "85 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "86 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "87 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "88 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "89 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "90 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "91 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "92 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "93 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "94 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "95 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "96 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "97 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "98 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "99 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(-1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.78]\t[ nan 0.99579114]\t[nan 20.]\n", + "1 \t0 \t93 \t[ nan 16.58]\t[ nan 5.39477525]\t[nan 1.]\n", + "2 \t0 \t100 \t[ nan 10.3] \t[ nan 5.62227712]\t[nan 1.]\n", + "3 \t0 \t99 \t[ nan 4.26] \t[ nan 3.19255384]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.56] \t[ nan 0.88679197]\t[nan 1.]\n", + "5 \t0 \t100 \t[4.7289087 1.06 ]\t[1.16429666 0.23748684]\t[2.73836112 1. ]\n", + "6 \t0 \t100 \t[3.92584497 1.05 ]\t[0.50894191 0.25980762]\t[2.73836088 1. ]\n", + "7 \t0 \t100 \t[3.82759879 1.06 ]\t[0.22233973 0.31048349]\t[2.73836088 1. ]\n", + "8 \t0 \t100 \t[3.82759879 1.06 ]\t[0.22233973 0.31048349]\t[2.73836088 1. ]\n", + "9 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "10 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ]\n", + "11 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ]\n", + "12 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ]\n", + "13 \t0 \t100 \t[3.78636326 1.19 ]\t[0.31712268 0.82091412]\t[2.44182491 1. ]\n", + "14 \t0 \t100 \t[3.79770948 1.18 ]\t[0.29921565 0.81706793]\t[2.44182491 1. ]\n", + "15 \t0 \t100 \t[3.79770948 1.18 ]\t[0.29921565 0.81706793]\t[2.44182491 1. ]\n", + "16 \t0 \t100 \t[3.79770948 1.18 ]\t[0.29921565 0.81706793]\t[2.44182491 1. ]\n", + "17 \t0 \t100 \t[3.79734043 1.18 ]\t[0.30061539 0.81706793]\t[2.44182491 1. ]\n", + "18 \t0 \t100 \t[3.7859942 1.19 ] \t[0.31843058 0.82091412]\t[2.44182491 1. ]\n", + "19 \t0 \t100 \t[3.82190632 1.1 ]\t[0.25184618 0.64031242]\t[2.44182491 1. ]\n", + "20 \t0 \t100 \t[3.80759473 1.16 ]\t[0.28677838 0.86856203]\t[2.44182491 1. ]\n", + "21 \t0 \t100 \t[3.80759473 1.16 ]\t[0.28677839 0.86856203]\t[2.44182491 1. ]\n", + "22 \t0 \t100 \t[3.79352139 1.18 ]\t[0.31624227 0.88746831]\t[2.44182491 1. ]\n", + "23 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", + "24 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", + "25 \t0 \t100 \t[3.78787961 1.13 ]\t[0.44333761 0.67312703]\t[8.52790061e-14 1.00000000e+00]\n", + "26 \t0 \t100 \t[3.78787961 1.13 ]\t[0.44333761 0.67312703]\t[8.52790061e-14 1.00000000e+00]\n", + "27 \t0 \t100 \t[3.75230863 1.22 ]\t[0.48104812 0.84356387]\t[8.52790061e-14 1.00000000e+00]\n", + "28 \t0 \t100 \t[3.75644727 1.18 ]\t[0.49272434 0.75339233]\t[8.52790061e-14 1.00000000e+00]\n", + "29 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "30 \t0 \t100 \t[3.81430285 1.09 ]\t[0.25589632 0.42649736]\t[2.65198374 1. ] \n", + "31 \t0 \t100 \t[3.78372612 1.14 ]\t[0.36371051 0.63277168]\t[1.90340519 1. ] \n", + "32 \t0 \t100 \t[3.76200517 1.2 ]\t[0.41674326 0.82462113]\t[1.79820001 1. ] \n", + "33 \t0 \t100 \t[3.74753596 1.23 ]\t[0.43785648 0.87011493]\t[1.75861263 1. ] \n", + "34 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "35 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "36 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "37 \t0 \t100 \t[3.82214457 1.08 ]\t[0.2505484 0.4621688] \t[2.46565032 1. ] \n", + "38 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[8.03782041e-11 1.00000000e+00]\n", + "39 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[8.03782041e-11 1.00000000e+00]\n", + "40 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[8.03782041e-11 1.00000000e+00]\n", + "41 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[8.03782041e-11 1.00000000e+00]\n", + "42 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "43 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "44 \t0 \t100 \t[3.82179834 1.08 ]\t[0.25243949 0.4621688 ]\t[2.43102694 1. ] \n", + "45 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "46 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "47 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "48 \t0 \t100 \t[3.80304065 1.11 ]\t[0.31123381 0.54580216]\t[1.97238231 1. ] \n", + "49 \t0 \t100 \t[3.78222212 1.16 ]\t[0.3699486 0.73102668]\t[1.79113078 1. ] \n", + "50 \t0 \t100 \t[3.82204666 1.07 ]\t[0.25107984 0.38091994]\t[2.45585918 1. ] \n", + "51 \t0 \t100 \t[3.82204666 1.07 ]\t[0.25107984 0.38091994]\t[2.45585918 1. ] \n", + "52 \t0 \t100 \t[3.81789705 1.07 ]\t[0.27583346 0.38091994]\t[2.04089785 1. ] \n", + "53 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "54 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "55 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "56 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "57 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "58 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "59 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "60 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "61 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "62 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "63 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "64 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", + "65 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.64071689e-11 1.00000000e+00]\n", + "66 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.64071689e-11 1.00000000e+00]\n", + "67 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.64071689e-11 1.00000000e+00]\n", + "68 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.57480712e-12 1.00000000e+00]\n", + "69 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.57480712e-12 1.00000000e+00]\n", + "70 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067639 0.6483826 ]\t[1.35780276e-13 1.00000000e+00]\n", + "71 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067639 0.6483826 ]\t[1.35780276e-13 1.00000000e+00]\n", + "72 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "73 \t0 \t100 \t[3.81456761 1.11 ]\t[0.25470452 0.54580216]\t[2.67845964 1. ] \n", + "74 \t0 \t100 \t[3.80262237 1.15 ]\t[0.27857673 0.66895441]\t[2.67845964 1. ] \n", + "75 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "76 \t0 \t100 \t[3.80807124 1.1 ]\t[0.28451952 0.5 ]\t[2.46565032 1. ] \n", + "77 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "78 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "79 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "80 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", + "81 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", + "82 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", + "83 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "84 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "85 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "86 \t0 \t100 \t[3.7809906 1.13 ] \t[0.44959071 0.67312703]\t[0.31983161 1. ] \n", + "87 \t0 \t100 \t[3.72965009 1.21 ]\t[0.59054222 0.9087904 ]\t[0.14626619 1. ] \n", + "88 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[9.73288894e-10 1.00000000e+00]\n", + "89 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067638 0.63277168]\t[9.73288894e-10 1.00000000e+00]\n", + "90 \t0 \t100 \t[3.72567889 1.17 ]\t[0.61626563 0.69361373]\t[9.73288894e-10 1.00000000e+00]\n", + "91 \t0 \t100 \t[3.78341474 1.09 ]\t[0.45534222 0.42649736]\t[3.68523129e-11 1.00000000e+00]\n", + "92 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.68523129e-11 1.00000000e+00]\n", + "93 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.68523129e-11 1.00000000e+00]\n", + "94 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[1.19793064e-13 1.00000000e+00]\n", + "95 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.61516372e-14 1.00000000e+00]\n", + "96 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.61516372e-14 1.00000000e+00]\n", + "97 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.61516372e-14 1.00000000e+00]\n", + "98 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "99 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", + "Final population hypervolume is 49377.110287\n", + "fit, 12, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.81]\t[ nan 0.94546285]\t[nan 20.]\n", + "1 \t92 \t92 \t[ nan 17.06]\t[ nan 5.12605111]\t[nan 1.]\n", + "2 \t97 \t97 \t[ nan 12.05]\t[ nan 5.6945149] \t[nan 1.]\n", + "3 \t100 \t100 \t[ nan 6.54] \t[ nan 4.15552644]\t[nan 1.]\n", + "4 \t100 \t100 \t[ nan 2.63] \t[ nan 1.79808231]\t[nan 1.]\n", + "5 \t100 \t100 \t[ nan 1.35] \t[ nan 0.66895441]\t[nan 1.]\n", + "6 \t100 \t100 \t[0.47949067 1.03 ]\t[0.11979875 0.17058722]\t[0.2614038 1. ]\n", + "7 \t100 \t100 \t[0.39641299 1.04 ]\t[0.06164404 0.19595918]\t[0.2614038 1. ]\n", + "8 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "9 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "10 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "11 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "12 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "13 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "14 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "15 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "16 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "17 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "18 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "19 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "20 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "21 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "22 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "23 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "24 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "25 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "26 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "27 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "28 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "29 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "30 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "31 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "32 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "33 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "34 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "35 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "36 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "37 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "38 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "39 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "40 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "41 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "42 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "43 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "44 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "45 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "46 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "47 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "48 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "49 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "50 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "51 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "52 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "53 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "54 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "55 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "56 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "57 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "58 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "59 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "60 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "61 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "62 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "63 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "64 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "65 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "66 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "67 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "68 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "69 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "70 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "71 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "72 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "73 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "74 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "75 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "76 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "77 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "78 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "79 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "80 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "81 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "82 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "83 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "84 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "85 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "86 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "87 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "88 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "89 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "90 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "91 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "92 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "93 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "94 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "95 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "96 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "97 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "98 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "99 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", + "Final population hypervolume is 49486.997565\n", + "best model: Cos(1.72*x2)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.8]\t[ nan 0.98994949]\t[nan 20.]\n", + "1 \t0 \t85 \t[ nan 16.88]\t[ nan 5.5933532] \t[nan 1.]\n", + "2 \t0 \t97 \t[ nan 9.68] \t[ nan 6.04132436]\t[nan 1.]\n", + "3 \t0 \t100 \t[ nan 3.07] \t[ nan 2.59327978]\t[nan 1.]\n", + "4 \t0 \t100 \t[5.34613959 1.05 ]\t[1.23600914 0.21794495]\t[2.73836112 1. ]\n", + "5 \t0 \t100 \t[4.58834292 1.03 ]\t[1.12811656 0.17058722]\t[2.73836112 1. ]\n", + "6 \t0 \t100 \t[3.87306902 1.05 ]\t[0.28598945 0.40926764]\t[2.67845988 1. ]\n", + "7 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", + "8 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", + "9 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", + "10 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", + "11 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", + "12 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", + "13 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "14 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", + "15 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", + "16 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", + "17 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", + "18 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ]\n", + "19 \t0 \t100 \t[3.75500628 1.16 ]\t[0.52432788 0.73102668]\t[9.3104461e-08 1.0000000e+00]\n", + "20 \t0 \t100 \t[3.75500628 1.16 ]\t[0.52432788 0.73102668]\t[9.3104461e-08 1.0000000e+00]\n", + "21 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ] \n", + "22 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054837 0.31048349]\t[2.46565056 1. ] \n", + "23 \t0 \t100 \t[3.817536 1.1 ] \t[0.2781729 0.64031242]\t[2.00479293 1. ] \n", + "24 \t0 \t100 \t[3.817536 1.1 ] \t[0.2781729 0.64031242]\t[2.00479293 1. ] \n", + "25 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ] \n", + "26 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492272 0.5 ]\t[1.90340519 1. ] \n", + "27 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492272 0.5 ]\t[1.90340519 1. ] \n", + "28 \t0 \t100 \t[3.76849715 1.19 ]\t[0.39043935 0.82091412]\t[1.89494383 1. ] \n", + "29 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054837 0.31048349]\t[2.46565056 1. ] \n", + "30 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ] \n", + "31 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "32 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", + "33 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "34 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "35 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "36 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "37 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "38 \t0 \t100 \t[3.80772501 1.1 ]\t[0.2861692 0.5 ] \t[2.43102694 1. ] \n", + "39 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[9.53261292e-08 1.00000000e+00]\n", + "40 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[9.53261292e-08 1.00000000e+00]\n", + "41 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[6.29947483e-12 1.00000000e+00]\n", + "42 \t0 \t100 \t[3.7446849 1.18 ] \t[0.59067638 0.93145048]\t[9.10937992e-14 1.00000000e+00]\n", + "43 \t0 \t100 \t[3.80787135 1.09 ]\t[0.28546804 0.42649736]\t[2.44566083 1. ] \n", + "44 \t0 \t100 \t[3.79009169 1.15 ]\t[0.3323735 0.72629195]\t[2.09501839 1. ] \n", + "45 \t0 \t100 \t[3.77231182 1.22 ]\t[0.37258663 0.99579114]\t[2.09499598 1. ] \n", + "46 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "47 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", + "48 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ] \n", + "49 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897877 0.2215852 ]\t[2.46565056 1. ] \n", + "50 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897877 0.2215852 ]\t[2.46565056 1. ] \n", + "51 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897879 0.2215852 ]\t[2.46565032 1. ] \n", + "52 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "53 \t0 \t100 \t[3.7975161 1.11 ] \t[0.33878186 0.54580216]\t[1.90340519 1. ] \n", + "54 \t0 \t100 \t[3.77782032 1.15 ]\t[0.38756268 0.66895441]\t[1.90340519 1. ] \n", + "55 \t0 \t100 \t[3.76374699 1.17 ]\t[0.40882039 0.69361373]\t[1.90340519 1. ] \n", + "56 \t0 \t100 \t[3.74299915 1.22 ]\t[0.45301001 0.84356387]\t[1.79820001 1. ] \n", + "57 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "58 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", + "59 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", + "60 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "61 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", + "62 \t0 \t100 \t[3.82048506 1.07 ]\t[0.25990265 0.38091994]\t[2.29969907 1. ] \n", + "63 \t0 \t100 \t[3.80471572 1.11 ]\t[0.30085244 0.54580216]\t[2.29604959 1. ] \n", + "64 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "65 \t0 \t100 \t[3.81231754 1.12 ]\t[0.26519494 0.62096699]\t[2.49596143 1. ] \n", + "66 \t0 \t100 \t[3.85959469 1.04 ]\t[0.31626159 0.24166092]\t[2.46565056 1. ] \n", + "67 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "68 \t0 \t100 \t[3.80346267 1.1 ]\t[0.30891311 0.5 ]\t[2.00479269 1. ] \n", + "69 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", + "70 \t0 \t100 \t[3.79585011 1.13 ]\t[0.34803627 0.67312703]\t[1.80578291 1. ] \n", + "71 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "72 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "73 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "74 \t0 \t100 \t[3.80797333 1.09 ]\t[0.28498275 0.42649736]\t[2.45585966 1. ] \n", + "75 \t0 \t100 \t[3.78344277 1.13 ]\t[0.36367127 0.57714816]\t[1.90340519 1. ] \n", + "76 \t0 \t100 \t[3.76346742 1.2 ]\t[0.41010004 0.89442719]\t[1.87544811 1. ] \n", + "77 \t0 \t100 \t[3.89704481 1.02 ]\t[0.36669234 0.14 ]\t[2.73836088 1. ] \n", + "78 \t0 \t100 \t[3.81451156 1.12 ]\t[0.25495379 0.60464866]\t[2.67845964 1. ] \n", + "79 \t0 \t100 \t[3.86184666 1.04 ]\t[0.32756127 0.24166092]\t[2.51430631 1. ] \n", + "80 \t0 \t100 \t[3.85959469 1.04 ]\t[0.31626159 0.24166092]\t[2.46565056 1. ] \n", + "81 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "82 \t0 \t100 \t[3.78778547 1.1 ]\t[0.4435541 0.5 ] \t[2.45394272e-04 1.00000000e+00]\n", + "83 \t0 \t100 \t[3.79797708 1.08 ]\t[0.43423778 0.4621688 ]\t[2.45394272e-04 1.00000000e+00]\n", + "84 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "85 \t0 \t100 \t[3.79784478 1.1 ]\t[0.43261651 0.64031242]\t[0.03567135 1. ] \n", + "86 \t0 \t100 \t[3.74551392 1.2 ]\t[0.52407409 0.87177979]\t[0.03567135 1. ] \n", + "87 \t0 \t100 \t[3.73417745 1.23 ]\t[0.62061031 1.1563304 ]\t[0.0208514 1. ] \n", + "88 \t0 \t100 \t[3.73417745 1.23 ]\t[0.62061031 1.1563304 ]\t[0.0208514 1. ] \n", + "89 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "90 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", + "91 \t0 \t100 \t[3.80599109 1.1 ]\t[0.29489762 0.5 ]\t[2.25763535 1. ] \n", + "92 \t0 \t100 \t[3.79025824 1.13 ]\t[0.33069832 0.57714816]\t[2.25763535 1. ] \n", + "93 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "94 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", + "95 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", + "96 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", + "97 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", + "98 \t0 \t100 \t[3.82660945 1.08 ]\t[0.22724511 0.4621688 ]\t[2.67845964 1. ] \n", + "99 \t0 \t100 \t[3.81466421 1.12 ]\t[0.2542806 0.60464866]\t[2.67845964 1. ] \n", + "Final population hypervolume is 49366.768166\n", + "fit, 13, est, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.96]\t[ nan 1.01901914]\t[nan 20.]\n", + "1 \t96 \t96 \t[ nan 13.79]\t[ nan 6.8735653] \t[nan 1.]\n", + "2 \t99 \t99 \t[ nan 4.28] \t[ nan 3.80021052]\t[nan 1.]\n", + "3 \t100 \t100 \t[nan 1.1] \t[nan 0.3] \t[nan 1.]\n", + "4 \t100 \t100 \t[0.46337418 1.03 ]\t[0.11117594 0.17058722]\t[0.28053975 1. ]\n", + "5 \t100 \t100 \t[0.3864333 1.03 ] \t[0.02979342 0.17058722]\t[0.28053975 1. ]\n", + "6 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "7 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "8 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "9 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "10 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "11 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "12 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "13 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "14 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "15 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "16 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "17 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "18 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "19 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "20 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "21 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "22 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "23 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "24 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "25 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "26 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "27 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "28 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "29 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "30 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "31 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "32 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "33 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "34 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "35 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "36 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "37 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "38 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "39 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "40 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "41 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "42 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "43 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "44 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "45 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "46 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "47 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "48 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "49 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "50 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "51 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "52 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "53 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "54 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "55 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "56 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "57 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "58 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "59 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "60 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "61 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "62 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "63 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "64 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "65 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "66 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "67 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "68 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "69 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "70 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "71 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "72 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "73 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "74 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "75 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "76 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "77 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "78 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "79 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "80 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "81 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "82 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "83 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "84 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "85 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "86 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "87 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "88 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "89 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "90 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "91 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "92 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "93 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "94 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "95 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "96 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "97 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "98 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "99 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", + "Final population hypervolume is 49486.059903\n", + "best model: Logabs(2.31*x1)\n", + "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", + "0 \t100 \t \t[ nan 20.82]\t[ nan 0.93145048]\t[nan 20.]\n", + "1 \t0 \t85 \t[ nan 15.97]\t[ nan 6.10156537]\t[nan 1.]\n", + "2 \t0 \t100 \t[ nan 8.97] \t[ nan 5.86933557]\t[nan 1.]\n", + "3 \t0 \t98 \t[ nan 2.92] \t[ nan 1.96814634]\t[nan 1.]\n", + "4 \t0 \t100 \t[ nan 1.42] \t[ nan 0.55099909]\t[nan 1.]\n", + "5 \t0 \t100 \t[5.23502642 1.12 ]\t[1.84065349 0.32496154]\t[2.73843455 1. ]\n", + "6 \t0 \t100 \t[4.7483042 1.04 ] \t[1.21124893 0.19595918]\t[2.73843455 1. ]\n", + "7 \t0 \t100 \t[4.26851731 1.12 ]\t[1.01155656 0.53441557]\t[2.51430631 1. ]\n", + "8 \t0 \t100 \t[3.80342191 1.1 ]\t[0.3108392 0.5 ] \t[1.90340519 1. ]\n", + "9 \t0 \t100 \t[3.80342191 1.1 ]\t[0.3108392 0.5 ] \t[1.90340519 1. ]\n", + "10 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260681 0.4621688 ]\t[1.90340519 1. ]\n", + "11 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", + "12 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", + "13 \t0 \t100 \t[3.78934857 1.13 ]\t[0.33803979 0.57714816]\t[1.90340519 1. ]\n", + "14 \t0 \t100 \t[3.78829985 1.13 ]\t[0.34399921 0.57714816]\t[1.79853261 1. ]\n", + "15 \t0 \t100 \t[3.80140007 1.11 ]\t[0.32135395 0.54580216]\t[1.79853261 1. ]\n", + "16 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", + "17 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", + "18 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ]\n", + "19 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "20 \t0 \t100 \t[3.81700868 1.08 ]\t[0.2826068 0.4621688] \t[1.90340519 1. ]\n", + "21 \t0 \t100 \t[3.80342191 1.1 ]\t[0.3108392 0.5 ] \t[1.90340519 1. ]\n", + "22 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", + "23 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", + "24 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", + "25 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", + "26 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ]\n", + "27 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ]\n", + "28 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "29 \t0 \t100 \t[3.83840205 1.05 ]\t[0.19668952 0.32787193]\t[2.68406439 1. ]\n", + "30 \t0 \t100 \t[3.81456762 1.11 ]\t[0.25470449 0.54580216]\t[2.67845988 1. ]\n", + "31 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", + "32 \t0 \t100 \t[3.83840205 1.04 ]\t[0.19668951 0.24166092]\t[2.68406463 1. ]\n", + "33 \t0 \t100 \t[3.82645681 1.08 ]\t[0.2280061 0.4621688] \t[2.67845964 1. ]\n", + "34 \t0 \t100 \t[3.81451157 1.12 ]\t[0.25495377 0.60464866]\t[2.67845964 1. ]\n", + "35 \t0 \t100 \t[3.82591384 1.08 ]\t[0.230646 0.41665333]\t[2.67845964 1. ]\n", + "36 \t0 \t100 \t[3.79487183 1.13 ]\t[0.31776999 0.57714816]\t[1.90340519 1. ]\n", + "37 \t0 \t100 \t[3.78097606 1.13 ]\t[0.44970266 0.67312703]\t[0.31837761 1. ]\n", + "38 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", + "39 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", + "40 \t0 \t100 \t[3.817536 1.08 ] \t[0.27817292 0.4621688 ]\t[2.00479269 1. ]\n", + "41 \t0 \t100 \t[3.80346267 1.1 ]\t[0.30891311 0.5 ]\t[2.00479269 1. ]\n", + "42 \t0 \t100 \t[3.81107189 1.08 ]\t[0.32396354 0.4621688 ]\t[1.35838246 1. ]\n", + "43 \t0 \t100 \t[3.77871127 1.14 ]\t[0.45234847 0.74859869]\t[0.63692153 1. ]\n", + "44 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", + "45 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", + "46 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", + "47 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", + "48 \t0 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]\t[1.89126921 1. ] \n", + "60 \t0 \t100 \t[3.78224801 1.15 ]\t[0.37156462 0.698212 ]\t[1.75559449 1. ] \n", + "61 \t0 \t100 \t[3.78224801 1.15 ]\t[0.37156462 0.698212 ]\t[1.75559449 1. ] \n", + "62 \t0 \t100 \t[3.78215505 1.15 ]\t[0.37207248 0.698212 ]\t[1.74629819 1. ] \n", + "63 \t0 \t100 \t[3.78118194 1.15 ]\t[0.37543555 0.698212 ]\t[1.74629819 1. ] \n", + "64 \t0 \t100 \t[3.78341499 1.09 ]\t[0.45534012 0.42649736]\t[2.53178478e-05 1.00000000e+00]\n", + "65 \t0 \t100 \t[3.74468515 1.15 ]\t[0.59067477 0.72629195]\t[1.02204356e-12 1.00000000e+00]\n", + "66 \t0 \t100 \t[3.89704481 1.02 ]\t[0.36669234 0.14 ]\t[2.73836088 1. ] \n", + "67 \t0 \t100 \t[3.79967221 1.08 ]\t[0.42954408 0.4621688 ]\t[8.50486543e-08 1.00000000e+00]\n", + "68 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668955 0.24166092]\t[2.68406439 1. ] \n", + "69 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "70 \t0 \t100 \t[3.76371897 1.15 ]\t[0.49215104 0.698212 ]\t[1.40610166e-06 1.00000000e+00]\n", + "71 \t0 \t100 \t[3.77779228 1.13 ]\t[0.4746412 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", + "72 \t0 \t100 \t[3.75875823 1.13 ]\t[0.5766332 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", + "73 \t0 \t100 \t[3.75875823 1.13 ]\t[0.5766332 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", + "74 \t0 \t100 \t[3.75875823 1.13 ]\t[0.5766332 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", + "75 \t0 \t100 \t[3.78341473 1.1 ]\t[0.45534223 0.5 ]\t[4.77323994e-08 1.00000000e+00]\n", + "76 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "77 \t0 \t100 \t[3.82645681 1.08 ]\t[0.22800611 0.4621688 ]\t[2.67846012 1. ] \n", + "78 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", + "79 \t0 \t100 \t[3.77614262 1.18 ]\t[0.39565319 0.84118963]\t[1.80461228 1. ] \n", + "80 \t0 \t100 \t[3.75593549 1.23 ]\t[0.43937701 0.96803926]\t[1.80461228 1. ] \n", + "81 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "82 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "83 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", + "84 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", + "85 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668955 0.24166092]\t[2.68406439 1. ] \n", + "86 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "87 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", + "88 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", + "89 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n" ] - }, - { - "data": { - "text/html": [ - "
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Brush versionOriginalModified
metricscorebest modelsizedepthscorebest modelsizedepthpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
run 00.9991292.04*Cos(Sub(1.12*x2,1.08*x1))420.363372If(x1>0.91,1.61,-0.52*x1)31364127562437966
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Brush versionOriginalModified
metricscoresizedepthscoresizedepthpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
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stdNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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50%0.9991294.02.00.3633723.01.03641.02756.02437.0966.0
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" - ], - "text/plain": [ - "Brush version Original Modified \n", - "metric score size depth score size depth point mutation calls \n", - "count 1.000000 1.0 1.0 1.000000 1.0 1.0 1.0 \\\n", - "mean 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", - "std NaN NaN NaN NaN NaN NaN NaN \n", - "min 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", - "25% 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", - "50% 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", - "75% 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", - "max 0.999129 4.0 2.0 0.363372 3.0 1.0 3641.0 \n", - "\n", - "Brush version \n", - "metric insert mutation calls delete mutation calls \n", - "count 1.0 1.0 \\\n", - "mean 2756.0 2437.0 \n", - "std NaN NaN \n", - "min 2756.0 2437.0 \n", - "25% 2756.0 2437.0 \n", - "50% 2756.0 2437.0 \n", - "75% 2756.0 2437.0 \n", - "max 2756.0 2437.0 \n", - "\n", - "Brush version \n", - "metric toggle_weight mutation calls \n", - "count 1.0 \n", - "mean 966.0 \n", - "std NaN \n", - "min 966.0 \n", - "25% 966.0 \n", - "50% 966.0 \n", - "75% 966.0 \n", - "max 966.0 " - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ @@ -874,9 +3375,9 @@ " import warnings\n", " warnings.filterwarnings(\"ignore\")\n", "\n", - " # data = pd.read_csv('../../docs/examples/datasets/d_enc.csv')\n", - " # X = data.drop(columns='label')\n", - " # y = data['label']\n", + " data = pd.read_csv('../../docs/examples/datasets/d_example_patients.csv')\n", + " X = data.drop(columns='target')\n", + " y = data['target']\n", "\n", " # data = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", " # X = data.drop(columns='target')\n", @@ -907,7 +3408,7 @@ " names=('Brush version', 'metric')))\n", " \n", " est_mab = None\n", - " for i in range(1):\n", + " for i in range(30):\n", " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", @@ -947,103 +3448,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def generate_plots():\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", - " \n", - " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", - " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", - " for i, row in learner_log.iterrows():\n", - " data[i+1, :] = data[i]\n", - " data[i+1, row['arm idx'].astype(int)] += 1\n", - "\n", - " plt.figure(figsize=(10, 5))\n", - "\n", - " plt.plot(data, label=est_mab.mutations_)\n", - " plt.xlabel(\"Evaluations\")\n", - " plt.ylabel(\"Number of times mutation was used\")\n", - "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - "\n", - " plt.legend()\n", - " plt.show()\n", - "\n", - " # --------------------------------------------------------------------------\n", - " fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", - "\n", - " for i, col in enumerate(['alpha', 'beta']):\n", - " columns = learner_log.columns[learner_log.columns.str.startswith(f'{col} ')]\n", - " labels = [columns[i].replace(str(i), est_mab.mutations_[i]) for i in range(4)] \n", - " data = learner_log.loc[:, columns]\n", - "\n", - " axs[i].plot(data, label=labels)\n", - " axs[i].set_xlabel(\"Evaluations\")\n", - " axs[i].set_ylabel(f\"{col}s\")\n", - " axs[i].legend()\n", - "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " axs[i].vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - "\n", - " plt.show()\n", - "\n", " # Approximating the percentage of usage for each generation ----------------\n", " # TODO: test if different batch sizes will produce different plots here\n", " data = np.zeros( (kwargs['max_gen'], 4) )\n", @@ -1112,6 +3521,7 @@ "\n", " plt.show()\n", "\n", + "plot_learner_history(est_mab.learner_)\n", "generate_plots()" ] }, @@ -1125,32 +3535,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0, " - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 41\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mi\u001b[39m}\u001b[39;00m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m, end\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m \u001b[39mif\u001b[39;00m (i\u001b[39m==\u001b[39m\u001b[39m29\u001b[39m) \u001b[39melse\u001b[39;00m \u001b[39m'\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 40\u001b[0m est \u001b[39m=\u001b[39m BrushClassifier(\u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\u001b[39m.\u001b[39mfit(X,y)\n\u001b[0;32m---> 41\u001b[0m est_mab \u001b[39m=\u001b[39m BrushClassifierMod(\u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\u001b[39m.\u001b[39;49mfit(X,y)\n\u001b[1;32m 43\u001b[0m learner_log \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(est_mab\u001b[39m.\u001b[39mlearner_\u001b[39m.\u001b[39mpull_history)\u001b[39m.\u001b[39mset_index(\u001b[39m'\u001b[39m\u001b[39mt\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m 45\u001b[0m total_rewards \u001b[39m=\u001b[39m learner_log\u001b[39m.\u001b[39mgroupby(\u001b[39m'\u001b[39m\u001b[39marm idx\u001b[39m\u001b[39m'\u001b[39m)[\u001b[39m'\u001b[39m\u001b[39mreward\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39msum()\u001b[39m.\u001b[39mto_dict()\n", - "Cell \u001b[0;32mIn[3], line 111\u001b[0m, in \u001b[0;36mBrushEstimatorMod.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msearch_space_ \u001b[39m=\u001b[39m _brush\u001b[39m.\u001b[39mSearchSpace(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfunctions_)\n\u001b[1;32m 109\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtoolbox_ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_setup_toolbox(data\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata_)\n\u001b[0;32m--> 111\u001b[0m archive, logbook \u001b[39m=\u001b[39m nsga2(\n\u001b[1;32m 112\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtoolbox_, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_gen, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpop_size, \u001b[39m0.9\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mverbosity)\n\u001b[1;32m 114\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39marchive_ \u001b[39m=\u001b[39m archive\n\u001b[1;32m 115\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlogbook_ \u001b[39m=\u001b[39m logbook\n", - "File \u001b[0;32m~/Documents/github/brush/src/brush/deap_api/nsga2.py:51\u001b[0m, in \u001b[0;36mnsga2\u001b[0;34m(toolbox, NGEN, MU, CXPB, verbosity)\u001b[0m\n\u001b[1;32m 48\u001b[0m ind1, ind2 \u001b[39m=\u001b[39m toolbox\u001b[39m.\u001b[39mmate(ind1, ind2)\n\u001b[1;32m 50\u001b[0m off1 \u001b[39m=\u001b[39m toolbox\u001b[39m.\u001b[39mmutate(ind1)\n\u001b[0;32m---> 51\u001b[0m off2 \u001b[39m=\u001b[39m toolbox\u001b[39m.\u001b[39;49mmutate(ind2)\n\u001b[1;32m 53\u001b[0m \u001b[39m# avoid inserting empty solutions\u001b[39;00m\n\u001b[1;32m 54\u001b[0m \u001b[39mif\u001b[39;00m off1: offspring\u001b[39m.\u001b[39mextend([off1])\n", - "Cell \u001b[0;32mIn[3], line 64\u001b[0m, in \u001b[0;36mBrushEstimatorMod._mutate\u001b[0;34m(self, ind1)\u001b[0m\n\u001b[1;32m 59\u001b[0m offspring \u001b[39m=\u001b[39m creator\u001b[39m.\u001b[39mIndividual(opt)\n\u001b[1;32m 60\u001b[0m \u001b[39m# print(\"mutation\")\u001b[39;00m\n\u001b[1;32m 61\u001b[0m \u001b[39m# print(ind1.prg.get_model())\u001b[39;00m\n\u001b[1;32m 62\u001b[0m \u001b[39m# print(offspring.prg.get_model())\u001b[39;00m\n\u001b[0;32m---> 64\u001b[0m offspring\u001b[39m.\u001b[39mfitness\u001b[39m.\u001b[39mvalues \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtoolbox_\u001b[39m.\u001b[39;49mevaluate(offspring)\n\u001b[1;32m 66\u001b[0m \u001b[39m# We compare fitnesses using the deap overloaded operators\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \u001b[39m# from the docs: When comparing fitness values that are **minimized**,\u001b[39;00m\n\u001b[1;32m 68\u001b[0m \u001b[39m# ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[39m# (this means that this comparison should work agnostic of min/max problems,\u001b[39;00m\n\u001b[1;32m 70\u001b[0m \u001b[39m# or even a single-objective or multi-objective problem)\u001b[39;00m\n\u001b[1;32m 71\u001b[0m reward \u001b[39m=\u001b[39m \u001b[39m1.0\u001b[39m \u001b[39mif\u001b[39;00m offspring\u001b[39m.\u001b[39mfitness \u001b[39m>\u001b[39m ind1\u001b[39m.\u001b[39mfitness \u001b[39melse\u001b[39;00m \u001b[39m0.0\u001b[39m\n", - "Cell \u001b[0;32mIn[3], line 126\u001b[0m, in \u001b[0;36mBrushClassifierMod._fitness_function\u001b[0;34m(self, ind, data)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_fitness_function\u001b[39m(\u001b[39mself\u001b[39m, ind, data: _brush\u001b[39m.\u001b[39mDataset):\n\u001b[0;32m--> 126\u001b[0m ind\u001b[39m.\u001b[39;49mprg\u001b[39m.\u001b[39;49mfit(data)\n\u001b[1;32m 127\u001b[0m \u001b[39mreturn\u001b[39;00m (\n\u001b[1;32m 128\u001b[0m np\u001b[39m.\u001b[39mabs(data\u001b[39m.\u001b[39my\u001b[39m-\u001b[39mind\u001b[39m.\u001b[39mprg\u001b[39m.\u001b[39mpredict(data))\u001b[39m.\u001b[39msum(), \n\u001b[1;32m 129\u001b[0m ind\u001b[39m.\u001b[39mprg\u001b[39m.\u001b[39msize()\n\u001b[1;32m 130\u001b[0m )\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "if __name__ == '__main__':\n", " from brush import BrushClassifier\n", @@ -1160,7 +3547,7 @@ "\n", " from pmlb import fetch_data\n", "\n", - " # X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", + " #X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", "\n", " data = pd.read_csv('../../docs/examples/datasets/d_analcatdata_aids.csv')\n", " X = data.drop(columns='target')\n", @@ -1224,6 +3611,7 @@ "metadata": {}, "outputs": [], "source": [ + "plot_learner_history(est_mab.learner_)\n", "generate_plots()" ] } From 4dc807770fd03f78cb146818d1ca2b3321afddeb Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 09:59:55 -0400 Subject: [PATCH 029/102] Adds an error message if it finds a weighted boolean terminal --- src/program/operator.h | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/program/operator.h b/src/program/operator.h index 3c9e1031..3f8b8927 100644 --- a/src/program/operator.h +++ b/src/program/operator.h @@ -51,7 +51,14 @@ namespace util{ { // we cannot weight a boolean feature. Nevertheless, we need to provide // an implementation for get_weight behavior, so the metaprogramming - // doesn't fail to get a matching signature. + // doesn't fail to get a matching signature. + + if (tn.data.get_is_weighted()) + // Node's init() function avoids the creation of weighted nodes, + // and the setter for `is_weighted` prevent enabling weight on + // boolean values. + HANDLE_ERROR_THROW(fmt::format("boolean terminal is weighted, but " + "it should not\n")); return Scalar(true); }; From a93da07edf913cb753e566f8d8f46c528e1ca7b5 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 13:52:40 -0400 Subject: [PATCH 030/102] Update D_MAB and D_TS to have similar results. Include time measure. --- src/brush/D_MAB_experiments.ipynb | 2203 +++----------------- src/brush/D_TS_experiments.ipynb | 3189 ++--------------------------- src/brush/D_TS_experiments.py | 12 - 3 files changed, 409 insertions(+), 4995 deletions(-) delete mode 100644 src/brush/D_TS_experiments.py diff --git a/src/brush/D_MAB_experiments.ipynb b/src/brush/D_MAB_experiments.ipynb index 486ba7c1..95f97235 100644 --- a/src/brush/D_MAB_experiments.ipynb +++ b/src/brush/D_MAB_experiments.ipynb @@ -48,13 +48,31 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "!pip install matplotlib > /dev/null\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "\n", "import numpy as np\n", + "import time\n", + "import pandas as pd\n", "\n", - "# TODO: update this to work with optional mutation\n", + "from brush.estimator import BrushEstimator\n", + "from sklearn.base import ClassifierMixin, RegressorMixin\n", + "from deap import creator\n", + "import _brush\n", + "from deap_api import nsga2 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "class D_MAB:\n", " def __init__(self, num_bandits, delta=0.15, lmbda=0.25):\n", " self.num_bandits = num_bandits\n", @@ -62,8 +80,8 @@ " # Store learner status when the update function is called\n", " self.pull_history = {\n", " c:[] for c in ['t', 'arm idx', 'reward', 'update'] + \n", - " [f'UCB1 {i}' for i in range(num_bandits)]} \n", - "\n", + " [f'UCB1 {i}' for i in range(num_bandits)] + \n", + " [f'weight {i}' for i in range(num_bandits)] } \n", "\n", " # This is the probability that should be used to update brush probs\n", " self._probabilities = np.ones(num_bandits)/num_bandits\n", @@ -115,20 +133,15 @@ " # Here we expect that the reward was already scaled to be in the \n", " # interval [0, 1] (in the original paper, they sugest using a scaling\n", " # factor as an hyperparameter).\n", - "\n", " self.pull_history['t'].append( len(self.pull_history['t']) )\n", " self.pull_history['arm idx'].append( arm_idx )\n", " self.pull_history['reward'].append( reward )\n", "\n", - " for i, UCB1 in enumerate(self._calculate_UCB1s()):\n", - " self.pull_history[f'UCB1 {i}'].append( UCB1 )\n", - "\n", - " if np.isfinite(reward):\n", - " self._avg_rewards[arm_idx] = \\\n", - " (self._num_pulls[arm_idx]*self._avg_rewards[arm_idx] + reward)/(self._num_pulls[arm_idx]+1)\n", - " self._avg_deviations[arm_idx] = \\\n", - " self._avg_deviations[arm_idx] + (self._avg_rewards[arm_idx] - reward + self.delta)\n", - " \n", + " # Updating counters\n", + " self._avg_rewards[arm_idx] = \\\n", + " (self._num_pulls[arm_idx]*self._avg_rewards[arm_idx] + reward)/(self._num_pulls[arm_idx]+1)\n", + " self._avg_deviations[arm_idx] = \\\n", + " self._avg_deviations[arm_idx] + (self._avg_rewards[arm_idx] - reward + self.delta) \n", " self._num_pulls[arm_idx] = self._num_pulls[arm_idx] +1\n", " self._max_deviations[arm_idx] = \\\n", " np.maximum(self._max_deviations[arm_idx], self._avg_deviations[arm_idx])\n", @@ -139,9 +152,100 @@ " else:\n", " self.pull_history['update'].append( 0 )\n", "\n", + " self._probabilities = self._calculate_UCB1s()\n", + "\n", + " for i, UCB1 in enumerate(self._calculate_UCB1s()):\n", + " self.pull_history[f'UCB1 {i}'].append( UCB1 )\n", + " self.pull_history[f'weight {i}'].append( self.probabilities[i] )\n", + "\n", " return self" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_learner_history(learner, arm_labels=[]):\n", + "\n", + " # getting the labels to use in plots\n", + " if len(arm_labels) != learner.num_bandits:\n", + " arm_labels = [f'arm {i}' for i in range(learner.num_bandits)]\n", + "\n", + " # Setting up the figure layout\n", + " fig = plt.figure(figsize=(15, 10), tight_layout=True)\n", + " gs = gridspec.GridSpec(7, 6)\n", + "\n", + " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", + " \n", + " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + "\n", + " data_total_pulls = np.array([total_pulls[k] for k in sorted(total_pulls)])\n", + " data_total_rewards = np.array([total_rewards[k] for k in sorted(total_rewards)])\n", + " data_total_failures = data_total_pulls-data_total_rewards\n", + "\n", + " ylim = np.maximum(data_total_rewards.max(), data_total_failures.max())\n", + "\n", + " axs = fig.add_subplot(gs[0:2, 4:])\n", + "\n", + " axs.bar(arm_labels, -1*data_total_failures, label=\"Null reward\")\n", + " axs.bar(arm_labels, data_total_rewards, label=\"Positive reward\")\n", + "\n", + " axs.set_xlabel(\"Arm\")\n", + " axs.set_ylim( (-1.05*ylim, 1.05*ylim) )\n", + " axs.legend()\n", + "\n", + " win_ratios = pd.DataFrame.from_dict({\n", + " 'arm' : arm_labels,\n", + " 'totpulls' : data_total_pulls,\n", + " '0 reward' : data_total_failures,\n", + " '+ reward' : data_total_rewards,\n", + " 'success%' : (data_total_rewards/(data_total_pulls)).round(2)\n", + " })\n", + "\n", + " axs = fig.add_subplot(gs[2:4, 4:])\n", + " axs.table(cellText=win_ratios.values, colLabels=win_ratios.columns, loc='center')\n", + " axs.axis('off')\n", + " axs.axis('tight')\n", + "\n", + " # Plotting rewards and pulls -----------------------------------------------\n", + " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", + " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", + " for i, row in learner_log.iterrows():\n", + " data[i+1, :] = data[i]\n", + " data[i+1, row['arm idx'].astype(int)] += 1\n", + "\n", + " axs = fig.add_subplot(gs[0:2, :4])\n", + " axs.plot(data, label=arm_labels)\n", + " axs.set_ylabel(\"Number of times mutation was used\")\n", + " axs.legend()\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + "\n", + " # Plotting alphas and betas ------------------------------------------------\n", + " for i, col in enumerate(['UCB1']):\n", + " columns = learner_log.columns[learner_log.columns.str.startswith(f'{col} ')]\n", + " labels = [f\"{col} {arm_labels[i]}\" for i in range(4)] \n", + " data = learner_log.loc[:, columns]\n", + "\n", + " axs = fig.add_subplot(gs[(i+1)*2:(i+1)*2+2, :4])\n", + " axs.plot(data, label=labels)\n", + " axs.set_ylabel(f\"{col}s\")\n", + " axs.legend()\n", + "\n", + " # multiple lines all full height showing when D-TS used the dynamic update rule\n", + " axs.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", + " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + " \n", + " axs.set_xlabel(\"Evaluations\") # Label only on last plot\n", + "\n", + " plt.show()" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -152,32 +256,11 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 40.0, 1: 41.0, 2: 37.0, 3: 33.0}\n", - "number of pulls for each arm: {0: 278, 1: 255, 2: 247, 3: 220}\n", - "(it was expected: similar amount of pulls for each arm)\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 441.0, 1: 73.0, 2: 103.0, 3: 16.0}\n", - "number of pulls for each arm: {0: 514, 2: 207, 1: 179, 3: 100}\n", - "(it was expected: more pulls for first arm, less pulls for last)\n", - "------------------------ optimizing ------------------------\n", - "cum. reward for each arm : {0: 105.0, 1: 266.0, 2: 61.0, 3: 295.0}\n", - "number of pulls for each arm: {3: 358, 1: 324, 0: 186, 2: 132}\n", - "(it was expected: 2nd approx 4th > 1st > 3rd)\n" - ] - } - ], + "outputs": [], "source": [ "# Sanity checks\n", - "import pandas as pd\n", - "\n", "class Bandits:\n", " def __init__(self, reward_prob):\n", " # Implementing simple bandits.\n", @@ -189,7 +272,7 @@ " result = np.random.randn()\n", " \n", " # return a positive or nullary reward (Bernoulli random variable).\n", - " return 1.0 if result > self.reward_prob[arm_idx] else 0.0\n", + " return 1 if result > self.reward_prob[arm_idx] else 0\n", "\n", "for probs, descr, expec in [\n", " (np.array([ 1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs' , 'similar amount of pulls for each arm' ),\n", @@ -207,13 +290,7 @@ "\n", " learner.update(arm_idx, reward) \n", "\n", - " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", - "\n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - "\n", - " print(\"cum. reward for each arm : \", total_rewards)\n", - " print(\"number of pulls for each arm: \", total_pulls)\n", + " plot_learner_history(learner)\n", " print(f\"(it was expected: {expec})\")" ] }, @@ -231,16 +308,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from brush.estimator import BrushEstimator\n", - "from sklearn.base import ClassifierMixin, RegressorMixin\n", - "from deap import creator\n", - "import _brush\n", - "from deap_api import nsga2 \n", - "\n", "class BrushEstimatorMod(BrushEstimator): # Modifying brush estimator\n", " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", @@ -255,7 +326,7 @@ " # store all rewards in gen_rewards_ (which is reseted at the beggining\n", " # of every generation) and do a batch of updates only after finishing\n", " # mutating the solutions.\n", - " self.batch_size_ = self.pop_size*2 #\n", + " self.batch_size_ = self.pop_size #\n", " self.batch_rewards_ = []\n", "\n", " def _mutate(self, ind1):\n", @@ -267,45 +338,43 @@ " \n", " params = self.get_params()\n", " \n", - " ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", - " or ind1.prg.depth()+1>=self.max_depth) else False\n", - "\n", - " # Insert Mutation will not work, even if we force it, when the expression\n", - " # is already at maximum size.\n", - " # In this case, we'll do the mutation without controlling the probabilities.\n", - " if ignore_this_time:\n", - " for i, m in enumerate(self.mutations_):\n", - " params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", - " else:\n", - " mutation_idx = self.learner_.choose_arm()\n", + " mutation_idx = self.learner_.choose_arm()\n", "\n", - " for i, m in enumerate(self.mutations_):\n", - " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", + " for i, m in enumerate(self.mutations_):\n", + " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", "\n", " _brush.set_params(params)\n", - " \n", - " # ind1.prg.mutate is a convenient interface that uses the current search \n", - " # space to sample mutations\n", - " offspring = creator.Individual(ind1.prg.mutate())\n", "\n", - " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", - " \n", - " # We compare fitnesses using the deap overloaded operators\n", - " # from the docs: When comparing fitness values that are **minimized**,\n", - " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", - " # (this means that this comparison should work agnostic of min/max problems,\n", - " # or even a single-objective or multi-objective problem)\n", - " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", - " \n", - " if not ignore_this_time:\n", + " opt = ind1.prg.mutate()\n", + "\n", + " if opt:\n", + " offspring = creator.Individual(opt)\n", + " # print(\"mutation\")\n", + " # print(ind1.prg.get_model())\n", + " # print(offspring.prg.get_model())\n", + "\n", + " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", + " \n", + " # We compare fitnesses using the deap overloaded operators\n", + " # from the docs: When comparing fitness values that are **minimized**,\n", + " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", + " # (this means that this comparison should work agnostic of min/max problems,\n", + " # or even a single-objective or multi-objective problem)\n", + " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", + " \n", + " # if not ignore_this_time:\n", + " # self.batch_rewards_.append( (mutation_idx, reward) )\n", + "\n", " self.batch_rewards_.append( (mutation_idx, reward) )\n", - " \n", - " if len(self.batch_rewards_) > self.batch_size_:\n", - " for (mutation_idx, reward) in self.batch_rewards_:\n", - " self.learner_.update(mutation_idx, reward)\n", - " self.batch_rewards_ = []\n", + "\n", + " if len(self.batch_rewards_) >= self.batch_size_:\n", + " for (mutation_idx, reward) in self.batch_rewards_:\n", + " self.learner_.update(mutation_idx, reward)\n", + " self.batch_rewards_ = []\n", " \n", - " return offspring\n", + " return offspring\n", + "\n", + " return None\n", " \n", " def fit(self, X, y):\n", "\n", @@ -391,834 +460,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" - ] - }, - { - "data": { - "text/html": [ - "
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Brush versionOriginalModified
metricscorebest modelsizedepthscorebest modelsizedepthpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
run 00.490733Square(If(x1>0.91,1.34*x1,-0.85*x1))420.3263581.04*Cos(1.73*x2)213417221122111809
run 10.325058Cos(-1.72*x2)210.3263581.04*Cos(1.73*x2)213819241220101407
run 20.325058Cos(1.72*x2)210.306978Sum(0.79,-0.70*x2)312814241222112211
run 30.325058Cos(-1.72*x2)210.508543Median(2.01,1.27,-1.94*x2,1.27*x1)513417321618091206
run 40.198205Abs(0.74*x1)210.3263581.04*Cos(1.73*x2)213618241220101608
run 50.3149720.51*Acos(1.10*x2)210.3263581.04*Cos(1.73*x2)213618201020102010
run 60.325058Cos(-1.72*x2)211.0000000.60*Square(Sum(0.63*x1,-0.00,0.65*x1,Median(1...933417241222111608
run 70.325058Cos(-1.72*x2)210.3263581.04*Cos(1.73*x2)213819281416081407
run 80.306978Sum(-0.70*x2,0.79)310.3263581.04*Cos(1.73*x2)213819241218091608
run 90.507152Logistic(243.34*Logabs(-1.13*x1))320.350809If(x1>0.91,1.61,0.38)313216241222111809
run 100.292958Square(0.96*x1)210.350809If(x1>0.91,1.61,0.38)313015261324121608
run 110.264208Atan(Square(1.02*x1))320.9930121.75*Cos(Square(Mean(2.60*x2,0.02,-2.76*x1)))633216261326131206
run 120.325058Cos(-1.72*x2)210.3263581.04*Cos(1.73*x2)214422201018091407
run 130.292958Square(0.96*x1)210.363372If(x1>0.91,1.61,-0.52*x1)313417261320101608
run 140.3263581.04*Cos(1.73*x2)210.3263581.04*Cos(1.73*x2)213015241222112010
run 150.325058Cos(1.72*x2)210.363372If(x1>0.91,1.61,-0.52*x1)312814241222112211
run 160.325058Cos(-1.72*x2)210.9648131.65*Cos(Mean(2.12*x2,1.39*x2,-3.00*x1))522814281422111809
run 170.325058Cos(-1.72*x2)210.828817Mean(If(x1>0.91,8.33,Sqrtabs(0.05*x1)),1.55,-2...833015241222112010
run 180.306978Median(Median(-2.81*x2,0.79),1.18)520.3263581.04*Cos(1.73*x2)212613241224122211
run 190.3263581.04*Cos(1.73*x2)210.500913Add(If(x1>0.91,1.12,0.20),0.61*Cos(1.99*x2))623618201020102010
run 200.3149720.51*Acos(1.10*x2)210.2929580.91*Square(x1)213015261324121608
run 210.292958Square(0.96*x1)210.921552Mean(If(x1>0.91,5.58,-2.98*x1),Max(-6.53*x2,0....1223216241220102010
run 220.325058Cos(-1.72*x2)210.363372If(x1>0.91,1.61,-0.52*x1)314221201020101407
run 230.198205Abs(0.74*x1)210.5730101.03*Median(1.10,1.17*x1,1.05,Add(-3.56*x2,0.85))723417221120102010
run 240.3263581.04*Cos(1.73*x2)210.508543Median(2.01,1.27,-1.94*x2,1.27*x1)513015241224121809
run 250.325058Cos(-1.72*x2)210.624433Sub(If(x1>0.91,1.82,0.65),0.67*x2)523618261320101407
run 260.317954Mul(0.72*x1,Add(1.36*x1,0.19))520.3263581.04*Cos(1.73*x2)213819201020101809
run 270.325058Cos(1.72*x2)210.624433Sum(If(x1>0.91,1.82,0.65),-0.33*x2,-0.33*x2)623618241222111407
run 280.325058Cos(-1.72*x2)210.363372If(x1>0.91,1.70*x1,-0.52*x1)312814261322112010
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local_cache_dir='./')\n", + "\n", + " data = pd.read_csv('../../docs/examples/datasets/d_example_patients.csv')\n", + " X = data.drop(columns='target')\n", + " y = data['target']\n", "\n", " # data = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", " # X = data.drop(columns='target')\n", " # y = data['target']\n", "\n", - " data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", - " X = data.drop(columns='target')\n", - " y = data['target']\n", + " # data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", + " # X = data.drop(columns='target')\n", + " # y = data['target']\n", "\n", " kwargs = {\n", " 'verbosity' : False,\n", - " 'pop_size' : 100,\n", - " 'max_gen' : 100,\n", + " 'pop_size' : 60,\n", + " 'max_gen' : 300,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", @@ -1250,9 +498,9 @@ "\n", " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), \n", + " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", " ('Modified', 'point mutation calls'),\n", " ('Modified', 'insert mutation calls'),\n", " ('Modified', 'delete mutation calls'),\n", @@ -1264,26 +512,28 @@ " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", + " est_start_time = time.time()\n", " est = BrushRegressor(**kwargs).fit(X,y)\n", + " est_end_time = time.time() - est_start_time\n", + "\n", + " est_mab_start_time = time.time()\n", " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", + " est_mab_end_time = time.time() - est_mab_start_time\n", "\n", " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " \n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", " \n", " results.loc[f'run {i}'] = [\n", " # Original implementation\n", " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", "\n", " # Implementation using Dynamic Thompson Sampling\n", " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", " \n", " # Mutation count\n", " *total_pulls.values()]\n", - " \n", " except Exception as e:\n", " print(e)\n", "\n", @@ -1294,112 +544,59 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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J2rSRGjWSiovN2KSq0JIEAABQf1nVXWATZfjj7+bokDRo0CDNnDlTn376qV544QXt3LlTv/nNb5SVlVXpbaZNm6aUlBTvlpaWFsSKT87lknr0MN+frMsdIQkAAKD+8Sy8WsgA9VrJzc2VJEVHR9f6Phzd3W7UqFHe73v37q1BgwapXbt2evfdd3XTTTdVeJvJkydr0qRJ3p8zMzMdF5R69pSWLJG2bZPy86UGDSrej5AEAABQ/0RFRSk+Pl5HjhxRdHS0IiIc3a7hGJZlKTc3V4cPH1Zqaqo3bNaGo0PSiVJTU9WlSxdt27at0n1iY2MVGxsbxKpqrlkzqWlTs7Ds5s1mnFJFCEkAAAD1j8vlUsuWLbVz507t3r3b7nJCTmpqap3nNAipkJSdna3t27fruuuus7uUOuvRQ1q40HS5qywksaAsAABA/RQTE6POnTvT5a6GoqOj69SC5OHokHTvvffqkksuUbt27bR//35NmTJFkZGRuuaaa+wurc569jQhaccOKTdXio8vv09RkZSdLSUmBr08AAAA2CwiIkINKhuXgYBydAfHffv26ZprrlHXrl115ZVXqnHjxlq+fLmaNm1qd2l11rix1KKF5HZLmzZVvh9d7gAAAIDgcnRL0qxZs+wuIaB69pQOHjRd7vr3r3if48clh807AQAAAIQ1R7ckhTvPVOC7dpludRVhXBIAAAAQXIQkG6WmmsVlLUvauLHifehuBwAAAAQXIclmntak9esrvp6QBAAAAAQXIclm3bubr3v3ShkZ5a8nJAEAAADBRUiyWXKy1K6d+X7DhvLXZ2ZKJSXBrQkAAACozwhJDuDpcldRSLKsiluYAAAAAAQGIckBuneXXC5p/37p2LHy19PlDgAAAAgeQpIDJCRIHTqY7ytqTSIkAQAAAMFDSHIIT5e7zZvLX0dIAgAAAIKHkOQQnTubr/v3S7m5Za9jQVkAAAAgeAhJDpGUJDVrZr7fubPsdbQkAQAAAMFDSHKQjh3N1+3by15OSAIAAACCh5DkIJ06ma/bt5upvz3y8qT8fHtqAgAAAOobQpKDtGsnRUaaBWSPHi17HeOSAAAAgOAgJDlIdLQJShJd7gAAAAC7EJIcxjMuaceOspcTkgAAAIDgICQ5jGdc0s6dUklJ6eWEJAAAACA4CEkO07y5lJAgFRVJe/eWXk5IAgAAAIKDkOQwLlfFU4EzcQMAAAAQHIQkB/J0ufMdl5SeXnZacAAAAACBQUhyIE9L0v79Um6u+b64WMrKsq8mAEDlSkqkI0ekTZukJUvMUg4AgNAVZXcBKC8pSWrWTDp82Ezg0KOHufzYMSk52d7aAKA+y84278XHjkm//GK2I0fMuFG3u3S/1aulCROk1FTbSgUA1AEhyaE6djQhafv20pC0cKH50HW5bC0NqHeKiqTCQrMVFJifndj9NTFRatTI7ipCx88/m1Z6yzIBx7JKv3e7Tev98eMmEB0/brbCwurd9/Hj0owZ5j2b5wQAQg8hyaE6dZKWLzchybJMMNq1y1w2eLDd1QHhq6RE+vprad260mDk20LgZJGR0vDh0hlncDKlKpYlffqptGJFYB8nI8MEpd/+VmraNLCPBQDwL8YkOVS7duaAJzNTOnq09PIFC0wLEwD/O3RIeukl6ZtvzP9efn7oBCTJBLzPPpPefrt0PCPKcrulefMCH5A8srKkmTPNawsAEDoISQ4VHW2CklR2KvDiYmn27LILzQKoG8syweill8LjYHbrVunFF6Xdu+2uxFlKSqQPPpDWrAnu4+bkmKC0f39wHxcAUHt0t3Owjh3NNOA7dkiDBpVefvCgGZ80bJhtpQWFp5sh4FFSYlp4MjLM15yc0rEkUumYEs/PTZqYkw2JiZXf5/Hj0pw50p49ga8/mDIzpVdflc45Rzr7bP6Xioqkd9+VfvrJnsfPy5Nee00aP15q08aeGuAfR45IP/xgurVW9d4CILQRkhysUyfpyy/NDHclJab7nce330pdukhpafbVF0iZmdL770sREdL550utW9tdEYLp+HEzqP7AATNo3hOMcnJqN2GCJyy1b2++emaJXLXKdE+r7mD8UON2m/FVu3ZJl19uZs6sjwoKTBfEXbvsrSM/3wSlq64qXQ8PoSEnR/rxRxOODhwwl/Xvb29NQG243ebYyh8KC83/hmdzu6WEhNKtQQP/PI5dQiokPfnkk5o8ebLuuusu/f3vf7e7nIBr3ty8yHJypL17zQGeh9ttzn7feqsUE1P7x8jKktavP/k4p9rO5BUZKZ1+utSiRfVvs2OH6RKTk2N+fvllqXt303LWuHHt6oBzZWebQLR/f+lXf4+n8UzVvGqV+blRIyk+Xtq3z7+P41Q7d5rud506lZ3F7cSWt/h4c2b8xC0pqW7vM3bKy5PeeMO8tpygsFB6/XUT1ocMkTp3tr+Vb88eMylQt25S79721uIkxcXSli0mGG3bFlrjE4ET7d5t/s+3bDHHlo0aSQ0bmq++m8tlPpd9t5ycst97tqKiqh8zMrI0MHXrZno2hJKQCUnff/+9/v3vf6t3PXoHd7lMl7sffzTBwTckSeYM+2efSZdcUrP7zcuTNm4097t7d+CnMl69Wurb14SchITK97Msswjj11+Xr2njRmnzZqlfP2noUHu7OLjdUnq62TIySr/3bCNGmFCHirndJvRv3Wq2I0eCX4NnnZv6JCfHzNhXW1WdeezQQbruutrfd6BkZZmA5MRxZrt3m61pUzNjae/eUlQQP5FLSswJsuXLS1tGunQJ7uPv2WPG3O7aZQ6mkpOllJTyX2NizHurZxr248dLf05PD1xLsO8JBNRccbF5jf/0k9lycqTYWPN8xsaW/b5Xr/BrXXXCkIHi4tL/84MHSy/PyjJboMeterrIZ2aG5ppxIRGSsrOzNW7cOL388st6/PHH7S4nqDwhaft26bzzyl+/apXUtevJP9wKC83ZA899BXPiB8syQWnDBnMWYdCgsl0HJRPc5swxB82VcbullSvNgd4ZZ5izsLGxgavb7TYH0ocPmwP5w4fNduxY1X8/zjaWl5dnzsRu3Wq+5uXZXRFqqqrXtd0HksXFpYu6ev5PDx82B9B213YyR46Y2fa++sq8Nw4YIMXFBe7xsrPN++jKleb7YDp61Pz/e4JRuHZzrc8yM00g2rrVtGCf+Bzn51d8uyZNQjckFRWVf+85csSc1LWr5SQ7W/r+e/N/7umVg5oLiZA0ceJEXXTRRRo+fPhJQ1JBQYEKCgq8P2dmZga6vIDyvGl4uiDFx5ffZ9486fbby16Xm2vO1u/ZY77u328OJOxUUCB9/rn5px0xwoQ7ydT27rvmgKY6CgulxYtNq1NUVNktMrL0+7r0uS0oMB/odv/NTvTmm6a2UFJcbM5gER5RkZwc6Z13an/73Fxz4iLUX1/Z2WaJhyVLatY9uSYsy7zfBuIk2bp15r29MpmZ1X+PR9VWrDAnHZ0mP9+ZS5R8+GHZpVT8KSsrMCdjtm+XFi2q3W0D+X9e3zg+JM2aNUurV6/W999/X639p02bpkceeSTAVQVPUpLUrJl549m5U+rRo/w+2dnmTaBr19JQFKg3BH84dswMou7UyXTTWbiwdmHEsswZnJP1iQ0n+/bRCoPwUlwcfjML1kVhYWj+PTIyQrPuUHTsGH/rmjhwoGxXs1CQk8Nz7ASOXidp7969uuuuu/Tmm2+qQTWnyJg8ebIyMjK82969ewNcZeB17Gi++q6XdKItW0yL0tq1zg5IvrZvN7P3Oa21BgAAAPWbo1uSVq1apcOHD6tfv37ey0pKSrR48WI999xzKigoUOQJg1tiY2MVG8iBKjbo1MkMutuxwxkDAQEAAIBw5uiQNGzYMP34449lLrvhhhvUrVs33X///eUCUrhq186MtcnIkObPl1q1Mn3WmzUL3Wl5AQAAAKdydEhKSkpSz549y1yWkJCgxo0bl7s8nEVHm9akrVulNWvM5tGokVlPqXlzM71iXFzZrUGD8jPJOYHbbdYtSU83C+KG4tSQAAAACE+ODkkodfnlZtzRoUOlW3Z26XovmzZVftuYGLM+UfPmUuvWpiWqVavgr4ScmWnGIW3bZroO+k4F2rixGXvVqZNZD6qiHpNZWWYA5v79Zvvll9LZW3zXs/DHLDMREaVBMz7e/K083/uGUN+fA73GSVpaaExpfCKXy6x10qiReZ59v4b6atyom23bpDPPrNviwZGR5f8vPVtCgnlfSUryX82B4Hab97SffjJ/k4yMwD2WZzIg3y05uW7duJ94QvrrX6ue0js6uvyilZ7FLH3fO91u89lQUGC2/HzzPt+gQekWGxvcNaWcZNIkacYMZ05YFBlpTnhW9F4fHV26n2WZscie5zk/X/ruO9ND5pRT/FvT4MFmJsBAzX4ZFVX5+09hofnsO+20mt3nG29Id91Vt5lsU1PNMUNamtS2rb1rS4aykHubWbhwod0l2CI2tvxK6Dk55UNTXl7p5gkhhYVmO37cLMjq0aRJaWhKTQ3MWKfiYjPb3vbt5acGbdDAfEAePGgmmzh61MzrHxEhtWlTOmGFJxhlZfm/vqrU9PGio80b46xZ0uOPS//zP/6t5/Dh0F1XJDvbtByeKCHBvAaSksybuO9XzxYX59/Xpttt/hcyM81jpaaW/QAPN5ZVOk2t50PX9+/p+d6yzHuG73tIXp4JMHl5VR+UxcRIX3whvf56zWorKfHPYsJVrfQQEWEWqjzjjMBNrV0bOTnmffGnn8zXYM1amZNTfqav2FgTltavl+64wyzYXdP7PH785Pulp5sTZCdKSjKvv4KC6h/8R0UFPjC5XKa21FSzNWxY+tWuEzxOn0o9M7PiWdmSkszz5AlGFYWWnBzpoYf8W8/Ro4E/dqjspMbnn5vftaYhKS+v7oud5+SYz9zly83PDRuWhqYWLczfv6Cg9PjQ93vJPFfR0aWb78+eE8Tx8ebnqj6fi4pKP0eKi82MxgMH1u13C6aQC0mhrlUr/73Bec6SesLEiTxn5PLyzJuEpwXG083tl1/M9sMP/qmnOlq3Nq1Fp5xivo+IMDXu2mUOFLZvNx+2e/aUf6N1uUyw87SENW9eenDrcpX+o574tTaKi8sfLJ540Oi7+U5HnplZtzPjlVm9WvrPf0JvnaSSEvOcHjtmPrA8X3NySreqxMRUfGayceOTB6icHBMuPScSPAv9nTijYny8OeOXmlr61XPfvq8n382JSkrM/3Z6uvmbe74PxnoZ27bV/Dbt20tLl9Y8XPnyfAhX9P959Kh5z/vhB7O1b2/CUufOdVtHrbaKi816QqtXlz9pEBtr3hs7d5ZatgzMa8yyzOvCd9HLo0fNe8revWY799yah6Tf/94saL5iReX75OWVvg/4bgUFFR/E+oagyMjSFgfP+19xsTn5EugFcSubOrpBA2nuXOnVV6U+fQJbg69HHpH69zcLwztNYWH55/foUfO8VfQcu1zm+Y2NNZ/tDRv6v6aPPjLvL4GY9dfzuV/ZsUFcnFSbkSFjx5rPuAULaleX223+tz1Lwhw+bP73jh837z/+5NuSHx9v3lc9f4fc3PInPX74wYytDxWEpCAbNsy05gRj4cOIiNK037ixOUDwyMkpDUw//1y3g/qqun+5XFLTpiYUdexY8WK4DRpI3bqZTTL/yJ4V2SMiSkNRixbOnKjCcwbU88bQv795nv2tRw/zNwjFdZJatix/mWfB3owM8wGanV32a1aW+ZsWFpoDlYoOVqKiKg/Fng+wikRFmTCUnV363OXmmlbLcOTp8hgXV3qZ7/+t53tPt9KKupRW9b/XsmXtWk5jY6Xu3U0rRqB4zqZu3GjeU3btMgdjgwZJffsG5z0lP19atcrU4XtQ7+le1LmzaT0PRnBr3rz0vVYyAfqXX8yBVGqqdPbZtbvPjh3NWn5Vadeu7M+WVRqeIiJKQ5EnGFXEcwbcE5o83fECwe02J718TzgcP27eK/LzzQFfRZ9pgdS6tfksP3QouI9bXW3alL/Md8Fn3y6TMTGl79vDh0tnneX/erp0MX8zO8Zmn3uudM45Nb9do0bmPaEuQbhFi9LeR/n5Zo1Fz4mQX34xJ5hjYkqfB9/N5TKfncXFpSd/PT8XFpYGoJISs3k+ryvjGb7gac0KJYSkIGvcWBowwPS/tVNCgvkn7NzZ3joq0rCh+RsNGGB3JdXjcpW+8Uvmjb51a3trCgWxsaUBuDLFxebAxNMd07c1KjOzemtsNWxoDuKaNSv92qhR6QFpfr55jIyM0gOhjIzSM9ae8W4nbk4UEVHaEubbPSglJbAH4B07OrcLRevW5szs+eeb991Vq8yB7qefmq1Bg7JjCjxjEOPjzVgdz98xObnmf8PMTNO6snJlaTeW5GQT0Hr1csZYqcjI0sl/xowxdQWLy1V6Iq+6fMeL2qWw0LxP/OY35YMfyqvpcwz/atDAnIzx53gvz0lI31aj3FwThD3Pt+d9NTbW/K+feqp01VX+qyEYCEk2OOcccwbKn92mmjUzL8jdu517AHciTyvTiWOVAI+oKNMNo0mT8tcVFZXvanNi60hiYsWTgPhq0MCcdXPSeBX4X3KyOVt99tnm/XfFitKuQL6TyFQmIsLchyd4Jiebs65RUaWbp+++y2UGi69bV9proGlTacgQ0/3GiTOOovpiYsxn7siRzuzdAASay1Xa8hTOsxMHPCQVFhZq586d6tSpk6Lq63Q0J0hIMB+WX33ln/tr3FiaMMHcb3q6OQBYty4wfXD9afhwqV8/6cUXAzubE8JTdHRg+rAjvMXESKefblqqPWc/KxpTkJtbtnXRd7xXTbRrZ2bw69zZuWPZAADlBSy15Obm6s4779Srr74qSdq6das6duyoO++8U61bt9YDDzwQqIcOCYMHmy4YVc3KVB3JydJ115mAJJlEf845Ztu71wSm9eurd6Y0mAYONEFRkq64wkxpGozB5QAgmcCSkFD63lkV3xkCPQOgs7JMd0/fzdNvv7jYtBwNHlzxGA0AgPMFLCRNnjxZP/zwgxYuXKiRI0d6Lx8+fLimTp1a70NSdLQZ1Pfhh7W/j/h4E5Aqa+r0TPc4cqQJS59+6oy1Fbp1k0aNKv25TRvpggukTz6p/n1ERUkjRpiWBN9pKn23unRpycoy61Jt2mRmiAnGRBvVce+9dldQc/n50tdfm7EgodIVFMGTklK3aX8968Rt3mwmowkEl8uckEpONmuOBFL//tKFFwbmvt1u6csvq56FrrbOOsu0mFVm714zJXKgnqP6ZMQI85npNMePS599Zqa1d5JbbgncZ8+hQ6XvP/5YzsCjVy8zWVNtuN3SvHnOnAEx1AQsJM2dO1fvvPOOzjjjDLl8+hj06NFD27dvD9TDhpTTTjOzHdVmlpqYGGncOHO28mSioswHb1qa9O67ZmYTu7RpYwZRn9jtZNAgM55q48aT30dMjHTNNWa+/UBJTTU1DRpkut1s3WreBLdvtzdohuJYhoQE6eKLzWvwk08qXkMD9VtdXtdNm5rt7LNN97jNm822e7dzTm5U12mnmf+VQHXLi4w0J6jatDEHUf58L3O5qn4e27eXbr7ZHLgtWEAX67qwY/r66mjSxByX/PSTCUt2Hmv4CuTfyzP50LBhZojD5s0mNFW0LmBNnOz/qSqRkdLll5uJYZYurVsd9V3AQtKRI0fUrIJ5XXNycsqEpvrM5TIzLr3xRs1uFxkpXX11zWdQa9bMnFH58EMzqDjYGjeWrr228oU7R482gbGqsVQNGpg34WBOIxkfbw5eTjvNHFRs327WhPGs6YTqadlSuvFGM17uiy+Cvzgwwl9KSunJjbw806W5tmuNBFuvXuY9MBgfj716mc+Dd96p+6KVNeFymWmJu3c3JwiXLKl6AqPERPNen5hoQi+T/ISGzp3NjJfffSctWuS87v6B0rixGUYwZIj5fLPz93a5TGtjcrIJrPTiqJ2AhaQBAwbo448/1p133ilJ3mD0n//8R4MHDw7Uw4Ycz/pBFa1CXpGICNMSU9kCsicTE2PGALVta7o+BGscUEKCCTdVTQMaGytdeaX08ssVT+2ckGC6F9o5C1l0dPk1nXbsMIFp587QXMMo2Hr3Nn+/xYulZcsYi4bAiIszUzRv3lz3s7qB1r27dNllwZ3YoXlzc9JszhzTtTiYoqJM97y+fc1B9MqV5iCuWbPSbuJt25afmCUnx6xztXOn+eqUlgqUFxlpxuT17m0mqVq9uvRAPSbGtDo1a2a2pk3Db9mMpCRnTPF/xhmmjjlzqrdkBspyWVZg8uU333yjUaNGafz48Zo5c6Z+97vfaePGjVq6dKkWLVqk/v37B+Jhy8nMzFRKSooyMjKUnJwclMesqYMHpX//u3pJ/9JLzYxw/rBvn/Tee4Hv9hAdLV1/ffXfBFevNl1BfKWkmIBU0VTQTmFZpr99fDyzrlXX0aMmLG3YwBu4E7lc5n+uXbuq//dSUswaGE60e7eZGMapunY1J4fs6kprWdI335gD2dGjTYt5sGVklC4wWhNZWSYs5eQEpCzl55sWr8paBO66i/f66jp40DzPzZqZ7ux0KAquXbukWbPsbd1y0jpJ1c0GAQtJkrRjxw5NmzZNP/zwg7Kzs9WvXz/df//96hXE1epCISRJJuX/8EPl18fHmxnrBg3y7+Pm5koffGBaQgIhIsL8U3TtWrPbzZ0rrV1rvm/USPrtb8N7Lv76LjfXPN+rVjl/6vpwFhFhukW2bWuCUdu24bEI5NtvB7+1pDpOOcWMr3TCWMMdO8yJii5d7K7EWbKzzaRH69eXv46QhFBy6JAZ3uHb1T0iwrQqe97v27Uzlx0+bCaiOHKk9PsT1yWsKULSr4qKivS73/1ODz30kDoEcnR9NYRKSMrIkP75z7Jn01u0MB9YnTubgbaBOvNiWWYq8pq+Eqqzf1RU7Zqci4pMtzvJBKTExJrfB0KPZZmuNCtXmoPaQHXF69bNvGF//LFUWBiYx3Ca3r3N5AaWZSY08Gyen10u82EZjotjHjkivfCCsyZy6NDBdEFm+cDQsG2beb/wHYdKSEKoycgwob9pUxOK0tKq34Kbm2sC0549Ztu7t+oxhZJ5f2vVyjxW167BHU9eFdtbklJSUrR27VpCUg0sXCgdOGBCUZcuZsBdfXbsmOmCEQ5nslFz2dmm6+Xq1TVfwLMyjRqZ2b06dzY/79snvflm+I8l6907+GNenGb+fNNS6QRt25ruw5VNYgNnKioyY6iWLjWBm5CE+sztNq1Te/aYbs179pj/kbS00pap1q2deSLI9pA0YcIEnXbaabrnnnsCcffVFkohCUB5lmWmlF250nytzTtWdLQZKD5kSPk37EOHpNdfr3tXAqciIBlZWaa13u6Ww4gIaeJEMxMWQtOhQ9JHH5lplglJQCnLCo3Pmupmg4Dlu86dO+vRRx/Vt99+q/79+yvhhGXNf//73wfqoQGEEZfLtKx26WK6Cnhal6o7hXjXrmZB5coOZpo3N1OTv/aa/1qsnKJPH2nMmND40Aq0pCQz29aiRfbW0bcvASnUed4zmFYZKCvcPmsC1pJUVTc7l8ulHdWd87qOaEkCwo/bbcYsbd1admyNZ3yN512tX7/qD0TPyjJByZ+rptuJgFReYaH0//5f4GZDO5moKOn3v6crNQDYyfbudk5BSAJQXbm5Zvaf/fvtrqRuCEiV+/57MwDfDmeeaRZ4BADYp7rZICIYxViWpTDPYgDCQHy8NGGC1L693ZXU3mmnEZCq0r+/Pd3dYmPNuDgAQGgI6JwTr732mp5++mn99NNPkqQuXbroj3/8o6677rpAPiwA1FpsrDR+vJneND+/8s2J532aNpWGDycgVSUiwvyN3nknuI975pnM1AkAoSRgIelvf/ubHnroId1xxx0aMmSIJOmbb77Rrbfeql9++cX2We8AoDJRUWYdG4SnU08109Tu3Rucx0tIMJNGAABCR0AnbnjkkUf029/+tszlr776qqZOnaqdO3cG4mHLYUwSAOBEe/dK06cH57FGjZIGDQrOYwEAqmb7FOAHDhzQmWeeWe7yM888UwcOHAjUwwIAcFJpaaZb5cGD0i+/lG75+f59nNRUacAA/94nACDwAhaSTjnlFL377rt68MEHy1z+zjvvqLNnuXsAAGxyyilm85WVZcLSkSPSxo3Srl11e4yhQ6XIyLrdBwAg+AIWkh555BFdddVVWrx4sXdM0rfffqsFCxbo3XffDdTDAgBQa0lJZuvQQRo4UDp61CxevHZtzddXatrUTMcOAAg9AV0nadWqVXr22We1adMmSdKpp56qP/zhD+rbt2+gHrIcxiQBAOqqpMQsYLxqlbRjR/VmN7zqKjNJBADAOVhM9leEJACAP6WnSz/+KO3ZI+3bJ+Xlld+nTRvpf/836KUBAE7C9okb/vvf/yoyMlIjRowoc/lnn30mt9utUaNGBeqhAQAImNRU6Te/Md9blumSt3ev2fbtM+OZhg2ztUQAQB1FBOqOH3jgAZWUlJS73LIsPfDAA9W6jxdeeEG9e/dWcnKykpOTNXjwYH3yySf+LhUAgFpxuaQmTaS+faVLL5Vuv1164AHW2QKAUBewkPTTTz+pe/fu5S7v1q2btm3bVq37aNOmjZ588kmtWrVKK1eu1HnnnafRo0drw4YN/i4XAAC/iI21uwIAQF0FLCSlpKRox44d5S7ftm2bEhISqnUfl1xyiS688EJ17txZXbp00Z///GclJiZq+fLl/i4XAAAAACQFMCSNHj1ad999t7Zv3+69bNu2bfrDH/6gSy+9tMb3V1JSolmzZiknJ0eDBw+udL+CggJlZmaW2QAAAACgugIWkp566iklJCSoW7du6tChgzp06KBu3bqpcePG+utf/1rt+/nxxx+VmJio2NhY3XrrrZozZ06F3fg8pk2bppSUFO+Wlpbmj18HAAAAQD0R0CnALcvSF198oR9++EFxcXHq06ePfuOZEqiaCgsLtWfPHmVkZOj999/Xf/7zHy1atKjSoFRQUKCCggLvz5mZmUpLS2MKcAAAAKCes22dpGXLluno0aO6+OKLvZe9+uqrmjJlinJzczVmzBj985//VGwtR7YOHz5cnTp10r///e9q7c86SQAAAACk6mcDv3e3e/TRR8vMPvfjjz/q5ptv1vnnn68HHnhA8+fP17Rp02p9/263u0xLEQAAAAD4k98Xk127dq0ee+wx78+zZs3SwIED9fLLL0uS0tLSNGXKFE2dOvWk9zV58mSNGjVKbdu2VVZWlt566y0tXLhQn332mb/LBgAAAABJAQhJx48fV/Pmzb0/L1q0SKNGjfL+fPrpp2vv3r3Vuq/Dhw/rt7/9rQ4cOKCUlBT17t1bn332mc4//3x/lw0AAAAAkgIQkpo3b66dO3cqLS1NhYWFWr16tR555BHv9VlZWYqOjq7WfU2fPt3f5QEAAABAlfw+JunCCy/UAw88oCVLlmjy5MmKj48vM6PdunXr1KlTJ38/LAAAAAD4hd9bkh577DFdfvnlOuecc5SYmKhXX31VMTEx3utfeeUVXXDBBf5+WAAAAADwi4Ctk5SRkaHExERFRkaWufzYsWNKTEwsE5wCiSnAAQAAAEjVzwZ+b0nySElJqfDyRo0aBeohAQAAAKDO/D4mCQAAAABCGSEJAAAAAHwQkgAAAADAR8DGJKG8QYMG6ejRowG7/4EDB+qhhx7SqaeeGrDH8JfCwkK98cYb2rp1q6ZOnaoGDRr47b6PHDmi559/Xhs3bqz1fbRp00Z/+tOf1LhxY7/VVVfXXnutiouL7S6jxuLj43XnnXeqf//+dpcCh9m/f7/uvvvuOt1Hz549dd999/n1PcQuJSUl+uc//6mlS5cG5P5dLpeuu+46XXzxxX6937fffltz5sypcp9u3bpp8uTJiouL8+tj1zcvvviivvrqK7vLqFDTpk31pz/9Sa1atbK7FK+77rpLBw4cCNj9DxkyRHfccUe5Scrq4ssvv9RLL71U69u7XC5dc801GjNmjN9qqresMJeRkWFJsjIyMuwuxWrWrJklKaBbRESENX78eOunn36y+9etUF5envX8889bbdu29db8j3/8wy/3vX//fusPf/iDFR8f75e/ZatWrayvvvrKL7X5Q0xMTMBfP4HaYmNjrddff93uPyEcZvPmzX55ffXp08favHmz3b9Onezbt886++yzA/6/2Lx5c6ukpMSvtT/44IPVeuwBAwZYe/fu9etj1zc33XST7e/nVW0tW7a0li9fbvefyatz584B/53PPfdc6+eff/ZbzS+99FKda0pMTLQOHz7st5rCTXWzQcCmAHcKJ00BvnLlShUVFQXkvnNycvSvf/3LezYvMjJSEyZM0EMPPaT27dsH5DFrIicnRy+99JKefvpp71mdqKgoFRcX66KLLtJHH31U6/veu3evnnrqKb388ssqKCiQJA0YMEDjxo1TdHR0je/P7Xbr+eef15YtW+RyuTR58mRNnTq1VvflTy+88ILcbretNdTGf//7X/33v/+VJN1777168skn/XrWDaHr+PHjeuutt2p9+7y8PD311FM6cuSIEhIS9K9//Uu//e1v/VhhcHz88ceaMGGCjh49qsTERD3wwANKTU31++Pcf//9ysnJ0dq1a9WnTx+/3e/y5cu1atWqSq/Pz8/XtGnTdPToUTVv3lyzZ8/WmWee6bfHr0+++uorbdq0ye4yyrEsSy+++KI2bNigmJgYvfTSS5owYYLdZemNN95QRkZGQO772LFj+stf/qKcnBw1bdpUr776qkaNGlXn+92wYYMWLlxY69u/+OKLWr9+ve699149/fTTda4nHFU7GwQjsdnJSS1JwbBy5Urrwgsv9J5NiI6Otm677TZr3759ttSTkZFhTZs2zWratKm3prS0NOu5556zli1bZkmy4uPjrfz8/Brf9/bt262bb77Zio6O9t73mWeeaX3yySeW2+2uU93Z2dnW//7v/3rvd9CgQdb27dvrdJ/1VUlJSZkzzSNGjLCOHTtmd1kIEz///LN17rnnel9f1113nZWVlWV3WdVSUFBg3XPPPd7a+/XrF9BeABdffLElyfrLX/4SsMeozI4dO6xevXpZkqyYmBhr+vTpQa8BgZWZmWmNGTPG+3q+5557rKKiIrvLCqjNmzdbffr08f7O9957r1VQUGBrTR9//LElyYqLi7MOHDhgay1OVd1sQEgKU0uXLrWGDRvm/cd1uVxWdHR00LeIiAhvDR07drRefvll7xtISUmJ1bx5c0tSjbu1ffjhh1ZkZGSZ5u4FCxbUORyd6N1337VSUlIsSVZSUpL15ptv+vX+65NZs2ZZcXFxliSrc+fO1qZNm+wuCWGiuLjYevTRR73vN126dLHWrFljd1lV2rZtmzVgwADve9hdd91Vq5NFNfGPf/zDkmSdd955AX2cymRlZVmXX36593f+/e9/H/YH0fVNSUmJ9fDDD3uf4/PPP986evSo3WUFVF5ennXHHXd4f+eBAwfaelLV7XZbZ5xxhvd/DOXR3e5XTupuZ4dFixbpoYce0pIlS2yroWvXrvrTn/6ka665RlFRZecKue666/TGG2/ogQce0LRp06p9n0OGDNHSpUt1zjnn6M9//rOGDBni77K9du/erXHjxunbb7+VZGqeNGmSoqOjFRkZqaioKO/m+TkpKSksBpL729q1azV69Gjt2bNHycnJeuutt3TRRRfZUotlWXK5XLY8th1KSkp07NixgN1/VFSUGjZsGLD7r44lS5bo2muv1b59+xQTE6NnnnlGEydOdNzzPGvWLN1yyy3KyspSo0aNNGPGDF166aUBf9ytW7eqa9euiomJ0bFjx5SQkBDwxzyR2+3W448/rilTpkiSzjvvPL377ruVTpKTn5+vrKysYJboFRsbWy+PG/zhgw8+0G9/+1vl5uaqU6dO+vDDD9WjRw+7ywqoOXPm6MYbb1R6erqSk5P1n//8R1dccYUttXz55Zc6//zzFRMTo23btiktLa1atzvZ50Rd3ucPHz6s3NxcRwwBobvdr+prS9KJDh06ZO3bty/o2/79+6scJPz6669bkqy+fftW+3fZv3+/5XK5LElB60ZYVFRkTZkypUzLWFXbf/7zn6DUFYoOHTpk/eY3v/G2cD799NNBr+G5556zYmNjraVLlwb9se1QUlJi9evXL6CDl8844wy7f03Lsizrl19+sS655BJvXX/729/sLqmM9957z1vbWWedZe3Zsydoj+12u6127dpZkqyPP/44aI9bkdmzZ1sJCQmWJKtDhw7W3XffbY0fP94aOXKkNWDAAKt9+/ZWYmJiQF+zJ9tcLpf18ssv2/p3CmU//PCD1b59e+9EAvPnz7e7pIDbtWuXdeaZZ3pfQ3fffbctdbjdbuucc86xJFm/+93vqnWb3bt3V2uii27dulmTJk2yvvzyyypbv0tKSqzvvvvOmjJlinX66adbLpfLmjBhgp9+w7qhu92vCEnOdvDgQe8/3qFDh6p1mxdeeME7TijYFi9ebPXv399q2bKl1bRpU6thw4ZWUlKSFR8fb8XExHhD1IwZM4JeWygpKCiwfve733mf+9WrVwftsUtKSqw2bdpYkqxHHnkkaI9rp8OHDwf8gNIpIcmyzAHCH/7wB0uSdcEFF9hdThme1/3VV19tS1ezW265xZJM9z67rVu3zurQoYOtQehk24UXXmj3nymkHTlyxBo6dKglmbFo3377rd0lBVxhYaE1efJky+Vy2XrCdPHixZYkKyoqytqxY0eV+2ZnZ5cZW1XdLTEx0RozZoz10ksvWXv37rWOHTtmzZo1y/rtb39bZiy6ZxsxYkSQfvuqEZJ+RUhyvtNOO82SZL3xxhvV2v+CCy6wJHsGH1eH2+32+xS74Wrs2LGWJOuOO+4I2mMuWbLE+4Y9ceLEoD2unTZu3GhJslJTU+0uJWg+//xzS5LVo0cPu0spw/Oaf+6552x5/Pfff997NtgJfvnlF2vKlCnW/fffbz399NPWjBkzrPnz51vLly+3tm3bZqWnp/t9rGl1eN4n0tLSgv7Y4aawsNA7Fq1p06bWrl277C4pKNauXWvLa9fX+eefb0mybrjhhkr3KSkp8b4vnez5SU9Pt95//33rhhtusFq0aFEuBJ3Y2yYpKckaO3asNX36dL9Ok15XhKRfEZKc7/7777ckMyvVyRw7dsyKioqyJFlbt24NQnUIpE8//dSSZDVq1Cjgg9Y9br/9du8b+JVXXhmUx7TbokWLLMlMmFFfbNiwwZHB0LMW0qxZs2x5/OPHj3sPZHbv3m1LDaHg2LFj3veJ48eP211OyMvOzrb69u1rSbJ69+4dMjNQhrrly5d7w8uWLVsq3Gfq1KmWZGZD/uabb6p93yUlJdaqVausxx57zBo8eLB3GET37t2te++91/rqq69sn+mvMtXNBhECbHbBBRdIkj7//HNZJ5lH5OOPP1ZxcbF69uypzp07B6M8BNDw4cPVpk0bHTt2TPPmzQv44xUXF+u9997z/nzkyJGAP6YT/PLLL5KkJk2a2FxJ8LRu3VqSlJ6ertzcXJurKeV5Lpo2bWrL46empmrQoEGSzHsuKtawYUO1adNGkrR+/Xqbqwl9CQkJ+vDDD9W8eXOtW7dO48ePD8l1/0LNoEGDdNFFF8ntduuRRx4pd/0HH3ygqVOnSjLrK9VkEqyIiAj169dP//d//6elS5fqyJEj2rdvnzZs2KCnn35a5557rmJiYvz1q9iCkATbDRkyRPHx8Tp06JDWrVtX5b6zZ8+WJF122WXBKA0BFhkZ6V38c8aMGQF/vK+++qpMMDp8+HDAH9MJ6mNISk5O9s7e9vPPP9tcTSknPBcjRoyQJH322We21RAKevXqJUn68ccfba4kPKSlpenDDz9UbGysPvzwQ/3pT3+yu6R64dFHH5Ukvf3229qwYYP38rVr13o/f++++27deOONdXqcxo0be09OhQtCEmwXGxuroUOHSqr6zGZubq4+/fRTSdLll18ejNIQBNdff70kc8AW6IPZWbNmSZLOPPNMSbQkhTOXy+VtCdi3b5/N1Rhut1tHjx6V5IyQ9OWXX6qkpMS2OpyOkOR/gwYN0iuvvCJJevLJJ/X666/bXFH469evny6//HJZluWdev/QoUO69NJLlZubqxEjRujpp5+2uUpnIiTBEapzZvOzzz5TXl6eOnTooD59+gSrNARY586dddZZZ8ntdgf0A7OgoMDbEvn73/9ekgkP9aHLh91dvOziOavplJakjIwMbyipbF2gYBgwYIBSU1OVnp6u77//3rY6nI6QFBjXXnutHnzwQUnS//7v/2rZsmU2VxT+HnnkEblcLn3wwQdasWKFLr/8cu3du1ddunTRrFmzyq1hCYOQBEfwjEtasmSJcnJyKtzHt6ud0xaHRN3ccMMNkkyXu5ONS6utTz/9VBkZGWrTpo3GjBkjyZzZD+QCq07haTGrTy1JkvNCkud5SEpKUmxsrG11REVFafjw4ZIYl1QV35AUqPel+uqxxx7TmDFjVFhYqDFjxmjPnj12lxTWevbsqauuukqSNGzYMC1dulQpKSmaN2+eUlNT7S3OwQhJcISuXbuqbdu2Kiws1OLFi8tdX1hYqI8++kgSXe3C0RVXXKH4+Hht3bpVy5cvD8hjvP3225Kkq666SrGxsd4PhvrQ5a4+dreTnBeSnNSi5zkxxbikynXr1k2RkZHKyMhwTJfNcBEREaHXX39dffr00eHDh3XppZcqOzvb7rLC2tSpUxUREaGcnBxFRETo3XffVdeuXe0uy9EISXAEl8tVZZe7hQsXKj09Xc2bN9fgwYODXR4CLCkpSVdccYWkwEzgkJ2d7Z097+qrr5ZUeqBaHyZvqO8hySkHuE56HjwhacWKFUpPT7e3GIeKjY31HkTS5c7/EhMTNW/ePDVr1kw//PCDfve739ldUljr2rWrbrnlFknSs88+630PQOUISXAM36nATzRnzhxJ0pgxYxQRwcs2HHm63M2aNcvvUzbPnz9feXl5OuWUU9S/f39JUrNmzSTRkhTOPBM3OK0lyQnPQ7t27dS1a1eVlJToq6++srscx2JcUmC1bdtWc+fOVadOnXTvvffaXU7Y++c//6m9e/d6x+WiahxtwjGGDRumiIgIbdq0SXv37vVe7na7NXfuXElM/R3Ozj77bHXs2FFZWVne8Wf+4ulqd/XVV3vHs3lakupDSPL8jk7o5hVMTu1u54SQJDEVeHUQkgJv8ODB2rRpk/r27Wt3KWEvKirKe/IIJ0dIgmM0bNhQAwcOlFS2NWn58uU6ePCgUlJSdO6559pVHgLM5XJ5pwP3Z5e748ePe6eOv+aaa7yX15fudnl5ed7JUJxycB4snpB08OBBFRcX21yN80KS77gkJiaoGCEpOKKjo+0uASiHkARHqejMpqdV4eKLLw751ZtRtQkTJsjlcumrr77Srl27/HKfs2fPVlFRkXr16qXu3bt7L68v3e086/JERkYqJSXF5mqCq3nz5oqMjFRJSYkOHTpkdzmOa9EbOnSoYmJitHv3bv300092l+NInpC0adMmFRUV2VwNgGBydEiaNm2aTj/9dCUlJalZs2YaM2aMtmzZYndZCCDPmU3PIoeWZXnHIzGrXfhr27athg0bJkl69dVX/XKfngVkfVuRpPrT3c639aK+TZ0fGRmpli1bSnJGlzuntSQlJCTorLPOksRU4JVp166dEhMTVVRUpK1bt9pdDoAgcnRIWrRokSZOnKjly5friy++UFFRkS644IJK19FB6Bs4cKBSUlJ0/PhxrVy5UuvWrdOOHTvUoEEDbysTwptnAoeZM2fWeaHXgwcPegele9aI8PC0JIV7dzsnTTttByeNS3JaSJKYCvxkIiIi1LNnT0l0uQPqG0eHpE8//VTXX3+9evTooT59+mjmzJnas2ePVq1aZXdpCJCoqChvS8Jnn33mbUUaOXKkEhIS7CwNQXLZZZcpJSVFu3bt0qJFi+p0X++//77cbrcGDRqkjh07lrmuvrQk1deFZD0ISVXznHz6+uuvVVhYaHM1zsS4JKB+cnRIOlFGRoYkqVGjRpXuU1BQoMzMzDIbQovnQ/vzzz/3jkdiVrv6Iy4uzruWUV0ncPCd1e5E9WXiBicemAcTIalqvXv3VrNmzZSTk6OlS5faXY4jEZKA+ilkQpLb7dbdd9+tIUOGeJu+KzJt2jSlpKR4t7S0tCBWCX/wdP9YunSpfvzxR0VFReniiy+2uSoEk2eWu/fff7/WJzp2796tpUuXyuVy6corryx3vae73dGjR+vcrc/JnHhgHkxOWVC2qKjIu2irk7o+RkREVLlGHQhJQH0VZXcB1TVx4kStX79e33zzTZX7TZ48WZMmTfL+nJmZSVAKMe3bt1eXLl28g2SHDh1aZeshws+gQYPUrVs3bd68Wbfddpt31fsTxcXFqXv37urZs6fatm1bZmKCd955R5J0zjnnqFWrVuVu6wkNbrdbx44dC9sQUd9DklMWlPXMMhgREaHU1FRbaznRBRdcoDfeeEOfffaZnnjiCbvLcRxPSNq1a5eysrKUlJRkc0UAgiEkQtIdd9yhjz76SIsXLz7pIlixsbGKjY0NUmUIlBEjRnhDErPa1T8ul0s33nij7rvvPr311lvVuk1SUpJ69uzp3Tyz4504q51HdHS0UlNTlZ6ersOHD4dtiHDatNPB5pTudp6w2qhRI0VGRtpay4nOP/98SdLq1at1+PBhbysrjMaNG6tly5Y6cOCANmzYoDPOOMPukgAEgaNDkmVZuvPOOzVnzhwtXLhQHTp0sLskBMkFF1ygf/7zn5Kk0aNH21wN7DBx4kSlp6fr2LFjle6Tnp6uDRs2aPPmzcrKytKyZcu0bNky7/VRUVEaO3Zspbdv1qyZ0tPTw3ryhvrekuQbkizLsm0adCc/Dy1atFCfPn30ww8/6Msvv9S1115rd0mO06tXLx04cEA//vgjIQmoJxwdkiZOnKi33npLH374oZKSknTw4EFJUkpKiuLi4myuDoF0/vnna/To0erSpUuFXaUQ/uLj4/XnP/+5Wvt61jBZv3691q9frx9//FFbtmzRFVdcocaNG1d6u6ZNm2rr1q1hPXmDkw/Og8ETknJycpSRkWFbVzenPw8jRozQoUOHWGKjEr169dLnn3/OuCSgHnF0SHrhhRckmTEpvmbMmOEd2I3wFBsbq7lz59pdBkJEdHS0evTooR49epRbD6kqnm5FtCSFr/j4eDVs2FDHjx/Xzz//bFtIcnq3x6lTp+rJJ5+sdwsOVxeTNwD1j6NDkmVZdpcAIIyF+1pJlmXV+8VkJdOa5AlJPXr0sKUGp4dVemdUzTck2dltE0DwhMwU4ADgb+G+VlJmZqaKiookqcpuh+HOCZM3OD0koWqnnnqqIiIidPToUW/XfwDhjZAEoN4K9+52ngPz+Ph4xcfH21yNfQhJqKu4uDh17txZEl3ugPqCkASg3gr37nYcmBuepSPsXFCW5yL0MS4JqF8ISQDqLU9LUrh2t+PA3HBCS5LTJ27AyRGSgPqFkASg3gr3liQOzA0nhCQCa+gjJAH1CyEJQL3lCQ+//PKLSkpKbK7G/zgwNwhJ8AdPSNq4cWNYvl8AKIuQBKDe8hywWpalY8eO2VyN/3FgbnhC0uHDh1VQUBD0x8/NzVVeXp4knotQ1rFjR8XHxys/P1/btm2zuxwAAUZIAlBvRUdHq2HDhpLCs8sdIclo0qSJYmJiJEkHDhwI+uN7noeYmBglJiYG/fHhHxEREd51tuhyB4Q/QhKAei2c10piIVnD5XLZ2uXOd2wYi5CGNsYlAfUHIQlAvRbOayV5fqf63pIk2TsuiRa98EFIAuoPQhKAei2cZ7jj4LyUJyTZsVYSz0P4ICQB9QchCUC9Fs5rJXFwXsqzoCwtSagLT0javn27cnJybK4GQCARkgDUa+HaklRcXKzjx49L4uBcorsd/KNZs2Zq1qyZLMvSxo0b7S4HQAARkgDUa+E6ccOxY8dkWZYkqXHjxjZXYz+nTNyA0NezZ09JdLkDwh0hCUC9Fq4TN3haLxo2bKioqCibq7EfLUnwF8YlAfUDIQlAvRau3e04MC/Ld0ySp4UtWHguwgshCagfCEkA6rVwnbiBA/OyWrZsKUkqLCz0/m2ChecivBCSgPqBkASgXvO0JB09elQlJSU2V+M/LCRbVkxMjDcQB7vLHSEpvPTo0UMul0uHDx8Ou5MrAEoRkgDUa55JDSzL0rFjx2yuxn9YSLY8O8Ylud1uAmuYSUhIUMeOHSWJGe6AMEZIAlCvRUdHq1GjRpLCq8sdrRfl2bGgbEZGhreFklkGw8fcuXN1+PBhDR061O5SAAQIUx4BqPeaNm2qY8eOhdXkDYSk8uxYUNbzPCQlJSk2NjZoj4vA8kwDDiB80ZIEoN4Lx7WSCEnl2dHdjucBAEITIQlAvReOayWxgGl5hCQAQHURkgDUe+G4VhIH5+XZMSaJsAoAoYmQBKDeC8e1kghJ5dk5JonnAQBCCyEJQL0Xbi1JeXl5ysnJkcTBuS9PS1J6erpyc3OD8piEJAAITYQkAPVeuIWko0ePSpKioqKUkpJiczXOkZycrISEBEnBa00iJAFAaCIkAaj3wq27ne9Csi6Xy+ZqnMPlcgV98gZCEgCEJseHpMWLF+uSSy5Rq1at5HK5NHfuXLtLAhBmwq0liQPzynnGJQVr8gbfwAoACB2OD0k5OTnq06ePnn/+ebtLARCmPC1JR48eVUlJic3V1B0hqXJ2tSQxux0AhJYouws4mVGjRmnUqFF2lwEgjDVu3FiSZFmWjh496g1NoYqQVDm62wEAqsPxLUk1VVBQoMzMzDIbAFQlKipKjRo1khQeXe5Ym6dywQxJRUVFSk9Pl0RIAoBQE3Yhadq0aUpJSfFuaWlpdpcEIASE0+QNtF5ULpgLyh47dkySmTCiYcOGAX88AID/hF1Imjx5sjIyMrzb3r177S4JQAgIp8kbCEmVC+aCsp7XUqNGjRQZGRnwxwMA+I/jxyTVVGxsrGJjY+0uA0CI8YQkWpLCm6cl6eDBgyopKQloeGHSBgAIXWHXkgQAteHpbhdOLUkcnJfXvHlzRUZGqqSkRIcOHQroYxFWASB0OT4kZWdna+3atVq7dq0kaefOnVq7dq327Nljb2EAwko4dbdjbZ7KRUZGqkWLFpICPy6JkAQAocvxIWnlypXq27ev+vbtK0maNGmS+vbtq4cfftjmygCEk3CZuMGyLA7OTyJY45J4HgAgdDl+TNLQoUNlWZbdZQAIc+HSkpSZmani4mJJpes/oaxgTQNOSAKA0OX4liQACIZwCUmeA/P4+HjFx8fbXI0zBSsksV4VAIQuQhIAKHy623FgfnK0JAEAToaQBAAqDRXHjh3zdlcLRRyYn1ywFpTluQCA0EVIAgCZ8Tsul0uWZeno0aN2l1NrHJifHBM3AABOhpAEAJKioqLUqFEjSaE9LokD85Pz7W4XyImBeC4AIHQRkgDgV+EweQMLyZ6cJyTl5OQoMzMzII+Rm5ur3NxcSTwXABCKHD8FOAAES7NmzbR58+aQnryBhWRPLj4+XqmpqUpPT1efPn0UHR1d4/to1KiR3nnnHbVv377C6z1hNSYmRomJiXUpFwBgA0ISAPwqnFqSCElVGzhwoD7//HPt3r271vfx73//W9OmTavwOt/nweVy1foxAAD2ICQBwK8ISfXH3LlztWbNmlqNSVq8eLEefPBBzZs3r1ohCQAQeghJAPCrcFgriYPz6omLi9OZZ55Zq9t2795dDz/8sDZu3Kht27bplFNOKbcPzwMAhDYmbgCAX4VDSxKLyQZew4YNdfbZZ0uS5s+fX+E+PA8AENoISQDwq1BvSSouLtbx48cl0YIRaJdeeqkk6cMPP6zwelqSACC0EZIA4Feh3pJ07Ngx7/eeNZ8QGJdccokk6Ztvvqlw8WFCEgCENkISAPwq1EOS58C8YcOGiopiyGkgdezYUT179lRJSYk++eSTctcTkgAgtBGSAOBXnu52R48eVXFxsc3V1BwLyQbX6NGjJUnz5s0rdx0hCQBCGyEJAH7VuHFj75o2FXWhcjoWkg0uz7ikTz/9VAUFBWWuY+IGAAhthCQA+FVkZKQaN24sKTS73NF6EVwDBgxQixYtlJWVpUWLFpW5jucCAEIbIQkAfHjO/IfiDHccmAdXRESEdwIH31nuLMviuQCAEEdIAgAfoTx5Awfmwefpcjdv3jxZliVJysjIUElJiSR5WyYBAKGFkAQAPkJ5rSTGwQTfsGHDFB8fr3379mnt2rWSSsNqYmKiGjRoYGN1AIDaIiQBgA9aklATcXFxuuCCCySVznJHWAWA0EdIAgAfhCTUlG+XO4nnAQDCASEJAHyEcnc7Ds7tcdFFF8nlcmn16tXat28fzwMAhAFCEgD4CIeWJLp5BVezZs00ePBgSaY1iZAEAKGPkAQAPjwtSaEWkvLy8pSTkyOJg3M7+Ha5IyQBQOgjJAGAj1BdJ8lzYB4VFaXk5GSbq6l/Ro8eLUn66quvtGPHDkm06AFAKCMkAYCPFi1aSJKOHTum++67T/n5+TZXVD2+rRcul8vmauqfrl27qnPnzioqKtJHH30kiZYkAAhlhCQA8NG4cWNNmjRJkvT0009rwIABWrNmjc1VnRxdvOzlcrm8Xe48wZrnAgBCV0iEpOeff17t27dXgwYNNGjQIH333Xd2lwQgjD3zzDOaO3eumjVrpg0bNmjgwIF6/PHHVVxcbHdplWJtHvt5QpIHIQkAQpfjQ9I777yjSZMmacqUKVq9erX69OmjESNGhNx4AQChZfTo0Vq/fr0uv/xyFRcX66GHHtKQIUO0ZcsWu0urEC1J9jvzzDPVqFEj7888FwAQuqLsLuBk/va3v+nmm2/WDTfcIEl68cUX9fHHH+uVV17RAw88YHN1AMJZ06ZN9f777+vNN9/UHXfcoe+++06nnXaapk2bpuHDh9tdXhmbN2+WxIG5naKionTxxRfrtddek0SrHgCEMkeHpMLCQq1atUqTJ0/2XhYREaHhw4dr2bJlFd6moKBABQUF3p8zMzMDXieA8OVyuTR+/HgNHTpUN910kz7//HPdc889dpdVKUKSvS699FK99tprcrlcatiwod3lAABqydEh6ZdfflFJSYmaN29e5vLmzZt7z5qeaNq0aXrkkUeCUR6AeqRNmzb69NNP9eKLL+qvf/2rsrOz7S6pnNTUVF122WV2l1GvjRw5Uv3791enTp0UGRlpdzkAgFpyWZZl2V1EZfbv36/WrVtr6dKl3tXMJem+++7TokWLtGLFinK3qaglKS0tTRkZGawdAgAAANRjmZmZSklJOWk2cHRLUpMmTRQZGalDhw6VufzQoUPetUxOFBsbq9jY2GCUBwAAACAMOXp2u5iYGPXv318LFizwXuZ2u7VgwYIyLUsAAAAA4C+ObkmSpEmTJmnChAkaMGCABg4cqL///e/KycnxznYHAAAAAP7k+JB01VVX6ciRI3r44Yd18OBBnXbaafr000/LTeYAAAAAAP7g6Ikb/KG6g7MAAAAAhLfqZgNHj0kCAAAAgGAjJAEAAACAD0ISAAAAAPggJAEAAACAD0ISAAAAAPhw/BTgdeWZvC8zM9PmSgAAAADYyZMJTjbBd9iHpKysLElSWlqazZUAAAAAcIKsrCylpKRUen3Yr5Pkdru1f/9+JSUlyeVy2V0OQkBmZqbS0tK0d+9e1taC3/H6QqDw2kIg8fpCoAT7tWVZlrKystSqVStFRFQ+8ijsW5IiIiLUpk0bu8tACEpOTuaDAAHD6wuBwmsLgcTrC4ESzNdWVS1IHkzcAAAAAAA+CEkAAAAA4IOQBJwgNjZWU6ZMUWxsrN2lIAzx+kKg8NpCIPH6QqA49bUV9hM3AAAAAEBN0JIEAAAAAD4ISQAAAADgg5AEAAAAAD4ISQAAAADgg5CEemnatGk6/fTTlZSUpGbNmmnMmDHasmVLmX3y8/M1ceJENW7cWImJiRo7dqwOHTpkU8UIZU8++aRcLpfuvvtu72W8vlBbP//8s8aPH6/GjRsrLi5OvXr10sqVK73XW5alhx9+WC1btlRcXJyGDx+un376ycaKESpKSkr00EMPqUOHDoqLi1OnTp302GOPyXeOL15fqK7FixfrkksuUatWreRyuTR37twy11fntXTs2DGNGzdOycnJSk1N1U033aTs7Oyg1E9IQr20aNEiTZw4UcuXL9cXX3yhoqIiXXDBBcrJyfHuc88992j+/Pl67733tGjRIu3fv1+XX365jVUjFH3//ff697//rd69e5e5nNcXauP48eMaMmSIoqOj9cknn2jjxo165pln1LBhQ+8+Tz31lP7xj3/oxRdf1IoVK5SQkKARI0YoPz/fxsoRCv7yl7/ohRde0HPPPadNmzbpL3/5i5566in985//9O7D6wvVlZOToz59+uj555+v8PrqvJbGjRunDRs26IsvvtBHH32kxYsX65ZbbgnOL2ABsA4fPmxJshYtWmRZlmWlp6db0dHR1nvvvefdZ9OmTZYka9myZXaViRCTlZVlde7c2friiy+sc845x7rrrrssy+L1hdq7//77rbPOOqvS691ut9WiRQvr6aef9l6Wnp5uxcbGWm+//XYwSkQIu+iii6wbb7yxzGWXX365NW7cOMuyeH2h9iRZc+bM8f5cndfSxo0bLUnW999/793nk08+sVwul/Xzzz8HvGZakgBJGRkZkqRGjRpJklatWqWioiINHz7cu0+3bt3Utm1bLVu2zJYaEXomTpyoiy66qMzrSOL1hdqbN2+eBgwYoCuuuELNmjVT37599fLLL3uv37lzpw4ePFjmtZWSkqJBgwbx2sJJnXnmmVqwYIG2bt0qSfrhhx/0zTffaNSoUZJ4fcF/qvNaWrZsmVJTUzVgwADvPsOHD1dERIRWrFgR8BqjAv4IgMO53W7dfffdGjJkiHr27ClJOnjwoGJiYpSamlpm3+bNm+vgwYM2VIlQM2vWLK1evVrff/99uet4faG2duzYoRdeeEGTJk3Sgw8+qO+//16///3vFRMTowkTJnhfP82bNy9zO15bqI4HHnhAmZmZ6tatmyIjI1VSUqI///nPGjdunCTx+oLfVOe1dPDgQTVr1qzM9VFRUWrUqFFQXm+EJNR7EydO1Pr16/XNN9/YXQrCxN69e3XXXXfpiy++UIMGDewuB2HE7XZrwIABeuKJJyRJffv21fr16/Xiiy9qwoQJNleHUPfuu+/qzTff1FtvvaUePXpo7dq1uvvuu9WqVSteX6h36G6Heu2OO+7QRx99pK+//lpt2rTxXt6iRQsVFhYqPT29zP6HDh1SixYtglwlQs2qVat0+PBh9evXT1FRUYqKitKiRYv0j3/8Q1FRUWrevDmvL9RKy5Yt1b179zKXnXrqqdqzZ48keV8/J86UyGsL1fHHP/5RDzzwgK6++mr16tVL1113ne655x5NmzZNEq8v+E91XkstWrTQ4cOHy1xfXFysY8eOBeX1RkhCvWRZlu644w7NmTNHX331lTp06FDm+v79+ys6OloLFizwXrZlyxbt2bNHgwcPDna5CDHDhg3Tjz/+qLVr13q3AQMGaNy4cd7veX2hNoYMGVJuuYKtW7eqXbt2kqQOHTqoRYsWZV5bmZmZWrFiBa8tnFRubq4iIsoeGkZGRsrtdkvi9QX/qc5rafDgwUpPT9eqVau8+3z11Vdyu90aNGhQ4IsM+NQQgAPddtttVkpKirVw4ULrwIED3i03N9e7z6233mq1bdvW+uqrr6yVK1dagwcPtgYPHmxj1QhlvrPbWRavL9TOd999Z0VFRVl//vOfrZ9++sl68803rfj4eOuNN97w7vPkk09aqamp1ocffmitW7fOGj16tNWhQwcrLy/PxsoRCiZMmGC1bt3a+uijj6ydO3das2fPtpo0aWLdd9993n14faG6srKyrDVr1lhr1qyxJFl/+9vfrDVr1li7d++2LKt6r6WRI0daffv2tVasWGF98803VufOna1rrrkmKPUTklAvSapwmzFjhnefvLw86/bbb7caNmxoxcfHW5dddpl14MAB+4pGSDsxJPH6Qm3Nnz/f6tmzpxUbG2t169bNeumll8pc73a7rYceeshq3ry5FRsbaw0bNszasmWLTdUilGRmZlp33XWX1bZtW6tBgwZWx44drT/96U9WQUGBdx9eX6iur7/+usJjrQkTJliWVb3X0tGjR61rrrnGSkxMtJKTk60bbrjBysrKCkr9LsvyWUYZAAAAAOo5xiQBAAAAgA9CEgAAAAD4ICQBAAAAgA9CEgAAAAD4ICQBAAAAgA9CEgAAAAD4ICQBAAAAgA9CEgAANTBz5kylpqbaXQYAIIAISQCAgDh48KDuuusunXLKKWrQoIGaN2+uIUOG6IUXXlBubq7d5VVL+/bt9fe//73MZVdddZW2bt1qT0EAgKCIsrsAAED42bFjh4YMGaLU1FQ98cQT6tWrl2JjY/Xjjz/qpZdeUuvWrXXppZfaUptlWSopKVFUVO0+AuPi4hQXF+fnqgAATkJLEgDA726//XZFRUVp5cqVuvLKK3XqqaeqY8eOGj16tD7++GNdcsklkqT09HT97//+r5o2bark5GSdd955+uGHH7z3M3XqVJ122ml6/fXX1b59e6WkpOjqq69WVlaWdx+3261p06apQ4cOiouLU58+ffT+++97r1+4cKFcLpc++eQT9e/fX7Gxsfrmm2+0fft2jR49Ws2bN1diYqJOP/10ffnll97bDR06VLt379Y999wjl8sll8slqeLudi+88II6deqkmJgYde3aVa+//nqZ610ul/7zn//osssuU3x8vDp37qx58+b57e8NAPAvQhIAwK+OHj2qzz//XBMnTlRCQkKF+3gCxxVXXKHDhw/rk08+0apVq9SvXz8NGzZMx44d8+67fft2zZ07Vx999JE++ugjLVq0SE8++aT3+mnTpum1117Tiy++qA0bNuiee+7R+PHjtWjRojKP+cADD+jJJ5/Upk2b1Lt3b2VnZ+vCCy/UggULtGbNGo0cOVKXXHKJ9uzZI0maPXu22rRpo0cffVQHDhzQgQMHKvxd5syZo7vuukt/+MMftH79ev3ud7/TDTfcoK+//rrMfo888oiuvPJKrVu3ThdeeKHGjRtX5vcEADiIBQCAHy1fvtySZM2ePbvM5Y0bN7YSEhKshIQE67777rOWLFliJScnW/n5+WX269Spk/Xvf//bsizLmjJlihUfH29lZmZ6r//jH/9oDRo0yLIsy8rPz7fi4+OtpUuXlrmPm266ybrmmmssy7Ksr7/+2pJkzZ0796S19+jRw/rnP//p/bldu3bWs88+W2afGTNmWCkpKd6fzzzzTOvmm28us88VV1xhXXjhhd6fJVn/93//5/05OzvbkmR98sknJ60JABB8jEkCAATFd999J7fbrXHjxqmgoEA//PCDsrOz1bhx4zL75eXlafv27d6f27dvr6SkJO/PLVu21OHDhyVJ27ZtU25urs4///wy91FYWKi+ffuWuWzAgAFlfs7OztbUqVP18ccf68CBAyouLlZeXp63Jam6Nm3apFtuuaXMZUOGDNH/+3//r8xlvXv39n6fkJCg5ORk7+8BAHAWQhIAwK9OOeUUuVwubdmypczlHTt2lCTvpAfZ2dlq2bKlFi5cWO4+fMf8REdHl7nO5XLJ7XZ770OSPv74Y7Vu3brMfrGxsWV+PrHr37333qsvvvhCf/3rX3XKKacoLi5O//M//6PCwsJq/qY1U9XvAQBwFkISAMCvGjdurPPPP1/PPfec7rzzzkrHJfXr108HDx5UVFSU2rdvX6vH6t69u2JjY7Vnzx6dc845Nbrtt99+q+uvv16XXXaZJBO4du3aVWafmJgYlZSUVHk/p556qr799ltNmDChzH137969RvUAAJyDkAQA8Lt//etfGjJkiAYMGKCpU6eqd+/eioiI0Pfff6/Nmzerf//+Gj58uAYPHqwxY8boqaeeUpcuXbR//359/PHHuuyyy8p1j6tIUlKS7r33Xt1zzz1yu90666yzlJGRoW+//VbJycllgsuJOnfurNmzZ+uSSy6Ry+XSQw89VK5lp3379lq8eLGuvvpqxcbGqkmTJuXu549//KOuvPJK9e3bV8OHD9f8+fM1e/bsMjPlAQBCCyEJAOB3nTp10po1a/TEE09o8uTJ2rdvn2JjY9W9e3fde++9uv322+VyufTf//5Xf/rTn3TDDTfoyJEjatGihc4++2w1b9682o/12GOPqWnTppo2bZp27Nih1NRU9evXTw8++GCVt/vb3/6mG2+8UWeeeaaaNGmi+++/X5mZmWX2efTRR/W73/1OnTp1UkFBgSzLKnc/Y8aM0f/7f/9Pf/3rX3XXXXepQ4cOmjFjhoYOHVrt3wEA4Cwuq6J3fAAAAACop1gnCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwAchCQAAAAB8EJIAAAAAwEeU3QUEmtvt1v79+5WUlCSXy2V3OQAAAABsYlmWsrKy1KpVK0VEVN5eFPYhaf/+/UpLS7O7DAAAAAAOsXfvXrVp06bS68M+JCUlJUkyf4jk5GSbqwEAAABgl8zMTKWlpXkzQmXCPiR5utglJycTkgAAAACcdBgOEzcAAAAAgA9CEgAAAAD4ICQBAAAAgI+wH5MEAAAAhKKSkhIVFRXZXUZIiYyMVFRUVJ2X/iEkAQAAAA6TnZ2tffv2ybIsu0sJOfHx8WrZsqViYmJqfR+EJAAAAMBBSkpKtG/fPsXHx6tp06Z1bhWpLyzLUmFhoY4cOaKdO3eqc+fOVS4YWxVCEgAAAOAgRUVFsixLTZs2VVxcnN3lhJS4uDhFR0dr9+7dKiwsVIMGDWp1P0zcAAAAADgQLUi1U9vWozL34Yc6AAAAACBsEJKCbNcuuysAAAAAUBVCUpDNnSu9+aZ0/LjdlQAAAAD+t2zZMkVGRuqiiy6yu5RaIyTZ4KefpH/9S1q8WCopsbsaAAAAwH+mT5+uO++8U4sXL9b+/fvtLqdWCEk2KSqSvvrKhKUdO+yuBgAAAKi77OxsvfPOO7rtttt00UUXaebMmZKka6+9VldddVWZfYuKitSkSRO99tprkqSsrCyNGzdOCQkJatmypZ599lkNHTpUd999d5B/C0KS7Y4elV57TfrgAykvz+5qAAAA4DSWJeXk2LPVdC3bd999V926dVPXrl01fvx4vfLKK7IsS+PGjdP8+fOVnZ3t3fezzz5Tbm6uLrvsMknSpEmT9O2332revHn64osvtGTJEq1evdqff8pqY50kh/jxR6lXL6lLF7srAQAAgJPk5kqJifY8dna2lJBQ/f2nT5+u8ePHS5JGjhypjIwMLVq0SCNGjFBCQoLmzJmj6667TpL01ltv6dJLL1VSUpKysrL06quv6q233tKwYcMkSTNmzFCrVq38/jtVBy1JDkJLEgAAAELVli1b9N133+maa66RJEVFRemqq67S9OnTFRUVpSuvvFJvvvmmJCknJ0cffvihxo0bJ0nasWOHioqKNHDgQO/9paSkqGvXrsH/RURLkqMQkgAAAHCi+HjTomPXY1fX9OnTVVxcXKb1x7IsxcbG6rnnntO4ceN0zjnn6PDhw/riiy8UFxenkSNHBqDquiMkOQghCQAAACdyuWrW5c0OxcXFeu211/TMM8/oggsuKHPdmDFj9Pbbb+vWW29VWlqa3nnnHX3yySe64oorFB0dLUnq2LGjoqOj9f3336tt27aSpIyMDG3dulVnn3120H8fQlKQWJa0apUZe9SypRRVwV+ekAQAAIBQ9NFHH+n48eO66aablJKSUua6sWPHavr06br11lt17bXX6sUXX9TWrVv19ddfe/dJSkrShAkT9Mc//lGNGjVSs2bNNGXKFEVERMjlcnn3mzx5sn7++WfvjHiBwpikIDr3XOmVV6SMjIqvJyQBAAAgFE2fPl3Dhw8vF5AkE5JWrlypdevWady4cdq4caNat26tIUOGlNnvb3/7mwYPHqyLL75Yw4cP15AhQ3TqqaeqQYMG3n0OHDigPXv2BPz3oSUpSFwuqU0bafNmE5IaNy6/DyEJAAAAoWj+/PmVXjdw4EBZPnOJW5XMK56UlOSd2EEykzs88sgjuuWWW7yXedZdCjRCUhClpZmQlJlZ8fWEJAAAANRXa9as0ebNmzVw4EBlZGTo0UcflSSNHj066LUQkoIoLc18JSQBAAAA5f31r3/Vli1bFBMTo/79+2vJkiVq0qRJ0OsgJAVRmzbmK2OSAAAAgLL69u2rVatW2V2GJJsnbpg2bZpOP/10JSUlqVmzZhozZoy2bNlSZp/8/HxNnDhRjRs3VmJiosaOHatDhw7ZVHHdeFqSsrIqvj4/38yCBwAAAMA+toakRYsWaeLEiVq+fLm++OILFRUV6YILLlBOTo53n3vuuUfz58/Xe++9p0WLFmn//v26/PLLbay69jwhqbKWJMsyQQkAAACAfWztbvfpp5+W+XnmzJlq1qyZVq1apbPPPlsZGRmaPn263nrrLZ133nmSpBkzZujUU0/V8uXLdcYZZ9hRdq1lZKySlKuMjP6SKl6+OC9PiosLalkAAAAAfDhqnaSMX5tYGjVqJElatWqVioqKNHz4cO8+3bp1U9u2bbVs2bIK76OgoECZmZllNqe49dYLJJ2tgoJdKiyseB/GJQEAAAD2ckxIcrvduvvuuzVkyBD17NlTknTw4EHFxMQoNTW1zL7NmzfXwYMHK7yfadOmKSUlxbulefq4OUBcnGchrHxmuAMAAAAcyjEhaeLEiVq/fr1mzZpVp/uZPHmyMjIyvNvevXv9VGHdla4WnM8MdwAAAIBDOSIk3XHHHfroo4/09ddfq41nnmxJLVq0UGFhodLT08vsf+jQIbVo0aLC+4qNjVVycnKZzSl8QxItSQAAAEDFpk6dqtNOO822x7d14gbLsnTnnXdqzpw5WrhwoTp06FDm+v79+ys6OloLFizQ2LFjJUlbtmzRnj17NHjwYDtKrhNCEgAAAGpr6lTnP96RI0f08MMP6+OPP9ahQ4fUsGFD9enTRw8//LCGDBkil8ulOXPmaMyYMf4u169sDUkTJ07UW2+9pQ8//FBJSUnecUYpKSmKi4tTSkqKbrrpJk2aNEmNGjVScnKy7rzzTg0ePDjkZraT6G4HAACA8DZ27FgVFhbq1VdfVceOHXXo0CEtWLBAR48etbu0GrE1JL3wwguSpKFDh5a5fMaMGbr++uslSc8++6wiIiI0duxYFRQUaMSIEfrXv/4V5Er9g5YkAAAAhKv09HQtWbJECxcu1DnnnCNJateunQYOHChJat++vSTpsssu8163a9cuSdKTTz6pZ599Vrm5ubryyivVtGnToNfvy9YxSZZlVbh5ApJkgsXzzz+vY8eOKScnR7Nnz650PJLTlYakAkISAAAAwkpiYqISExM1d+5cFRQUlLv++++/l2QaRA4cOOD9+d1339XUqVP1xBNPaOXKlWrZsqXtjSKOmLihvqAlCQAAAOEqKipKM2fO1KuvvqrU1FQNGTJEDz74oNatWydJ3tah1NRUtWjRwvvz3//+d91000266aab1LVrVz3++OPq3r27bb+HREgKKt+QVFAgVRCwCUkAAAAIWWPHjtX+/fs1b948jRw5UgsXLlS/fv00c+bMSm+zadMmDRo0qMxldk/SRkgKIk9IiorKl6QKJ28gJAEAACCUNWjQQOeff74eeughLV26VNdff72mTJlid1k1QkgKIk9IiokxIamiLnf5+cGsCAAAAAis7t27KycnR5IUHR2tkpKSMtefeuqpWrFiRZnLli9fHrT6KkJICiJPSIqNrTwklZRU3A0PAAAAcLKjR4/qvPPO0xtvvKF169Zp586deu+99/TUU09p9OjRkswMdwsWLNDBgwd1/PhxSdJdd92lV155RTNmzNDWrVs1ZcoUbdiwocx9z5kzR926dQva72LrFOD1TXW620mmy11sbLCqAgAAAOouMTFRgwYN0rPPPqvt27erqKhIaWlpuvnmm/Xggw9Kkp555hlNmjRJL7/8slq3bq1du3bpqquu0vbt23XfffcpPz9fY8eO1W233abPPvvMe98ZGRnasmVL0H4Xl2VZVtAezQaZmZlKSUlRRkaGkpOTba3lscce08MPP6xWrW7R/v3/1mmnSb+G6jJ+9zupZcuglwcAAAAHyM/P186dO9WhQwefib9QXVX9/aqbDehuF0SeJykysvLudhKTNwAAAAB2IiQFkSckRUQQkgAAAACnIiQF0YkhKSNDqqizIyEJAAAAsA8hKYg8IcnlMiGpqIgFZQEAAACnISQFkSckWVa+4uLMZSwoCwAAADgLISmIPCGppCRfnsk0KhqXREgCAAAA7ENICiLfkJSSYi4jJAEAAADOQkgKoopakuhuBwAAADgLISmIPCGpuJjudgAAAIBTEZKCyBOSiooISQAAAKjfhg4dqrvvvtvuMipESAoi35DEmCQAAACEm+uvv14ul0u33npruesmTpwol8ul66+/XpI0e/ZsPfbYY0GusHoISUFUUUtSRQvKFhebNZQAAACAUJOWlqZZs2Ypz+fMf35+vt566y21bdvWe1mjRo2UlJRkR4knRUgKotKQVKikJLckE4gqajmiNQkAAAChqF+/fkpLS9Ps2bO9l82ePVtt27ZV3759vZed2N2uffv2euKJJ3TjjTcqKSlJbdu21UsvvRTM0r0ISUHkCUlGgeLjzXd0uQMAAEBlLMtSTk6OLZt1Ypenarrxxhs1Y8YM78+vvPKKbrjhhpPe7plnntGAAQO0Zs0a3X777brtttu0ZcuWWtVQF1FBf8R6zDckFRfnKyUlTrm5JiS1aFF2X0ISAAAAJCk3N1eJiYm2PHZ2drYSEhJqfLvx48dr8uTJ2r17tyTp22+/1axZs7Rw4cIqb3fhhRfq9ttvlyTdf//9evbZZ/X111+ra9euNa6hLghJQRQVFaWIiAi53W7vNOAHDrBWEgAAAMJL06ZNddFFF2nmzJmyLEsXXXSRmjRpctLb9e7d2/u9y+VSixYtdPjw4UCWWiFCUhC5XC41aNBAubm5rJUEAACAaomPj1d2drZtj11bN954o+644w5J0vPPP1+t20RHR5f52eVyye1217qG2iIkBRkhCQAAADXhcrlq1eXNbiNHjlRhYaFcLpdGjBhhdzk1wsQNQeYZl0RIAgAAQDiLjIzUpk2btHHjRkVGRvrlPocNG6bnnnvOL/dVFVqSgsw3JLGgLAAAAMJZsqdVwE+2b9+uX375xa/3WRFCUpB5QlJJSdmQZFmSy1W6HyEJAAAAoWbmzJlVXj937lzv9yfOdLdr165y+69du/ak+wQC3e2CzBOSXK58eRYYLi6WcnPL7kdIAgAAAOxBSAoy35AUFSV5xuCd2OWOkAQAAADYg5AUZKULyuZLUqXjkghJAAAAgD0ISUF2YkjyjGU7cUFZQhIAAABgD0JSkFUWkk5sSSoslEpKglcXAAAAAIOQFGSekGRZVYckidYkAACA+syyLLtLCEn++LsRkoLME5Lc7qrHJEmEJAAAgPrIs/BqYWGhzZWEptxfp42Ojo6u9X2wTlKQnRiSKhuTJBGSAAAA6qOoqCjFx8fryJEjio6OVkQE7RrVYVmWcnNzdfjwYaWmpnrDZm0QkoKsspCUlcWCsgAAAJBcLpdatmypnTt3avfu3XaXE3JSU1PVokWLOt0HISnIPCGppMSEJM+CsiUlUk6OlJhYui8hCQAAoH6KiYlR586d6XJXQ9HR0XVqQfIgJAXZiSEpMtIEo+xsMy6JkAQAAABJioiI8JkZGcFEB8cg87zQi4vzvZd5Jm9grSQAAADAfoSkIKsoJFU2DTghCQAAAAg+QlKQeUJSUREhCQAAAHAiQlKQEZIAAAAAZyMkBZknJBUWlh+TREgCAAAA7EdICjLfkORZE6myBWUJSQAAAEDwEZKCzBOS8vPzFRNjLvNdUNbtLt2XkAQAAAAEHyEpyHxDUmysuSwpSXK5TEDKySndt6CgbGgCAAAAEHiEpCDzDUmetcEiIkxQksqOS7IsKT9fAAAAAIKIkBRkFbUkSaUhKSur7P50uQMAAACCy9aQtHjxYl1yySVq1aqVXC6X5s6dW+b666+/Xi6Xq8w2cuRIe4r1k8pCUkKC+erb3U4iJAEAAADBZmtIysnJUZ8+ffT8889Xus/IkSN14MAB7/b2228HsUL/q6i7nSTFx5uvubll9yckAQAAAMEVZeeDjxo1SqNGjapyn9jYWLVo0SJIFQVe6RTghYqOdsuTUwlJAAAAgDM4fkzSwoUL1axZM3Xt2lW33Xabjh49WuX+BQUFyszMLLM5SQOf5qPIyALv94QkAAAAwBkcHZJGjhyp1157TQsWLNBf/vIXLVq0SKNGjVJJSUmlt5k2bZpSUlK8W1paWhArPjnfkORylU5dR0gCAAAAnMHW7nYnc/XVV3u/79Wrl3r37q1OnTpp4cKFGjZsWIW3mTx5siZNmuT9OTMz01FBKSoqShEREXK73WVCkmfiBkISAAAAYC9HtySdqGPHjmrSpIm2bdtW6T6xsbFKTk4uszmJy+XyaU0q35LE7HYAAACAvUIqJO3bt09Hjx5Vy5Yt7S6lTjwhybLobgcAAAA4ja3d7bKzs8u0Cu3cuVNr165Vo0aN1KhRIz3yyCMaO3asWrRooe3bt+u+++7TKaecohEjRthYdd1V1ZJUVGS26GjzMyEJAAAACC5bW5JWrlypvn37qm/fvpKkSZMmqW/fvnr44YcVGRmpdevW6dJLL1WXLl100003qX///lqyZIlifVdhDUGekOR2l4ak2Fgp4tdnw7c1iZAEAAAABJetLUlDhw6VZVmVXv/ZZ58FsZrgqai7nctlWpOys01ISkkxlxOSAAAAgOAKqTFJ4cITkkpK8stcXtEMd/n5UhU5EgAAAICfEZJsUFF3O6niGe7cbqmgQAAAAACChJBkA09IKizM907QIDHDHQAAAOAEhCQbeEJSfn6+vBPdiZAEAAAAOAEhyQa+Icl3oj4WlAUAAADsR0iyQWUhyTNxw4mhiJAEAAAABA8hyQYn625HSxIAAABgH0KSDU7W3Y4xSQAAAIB9CEk2YOIGAAAAwLkISTaozpgkt7v0ckISAAAAEDyEJBtU1pIUF2e+WpaU77POLCEJAAAACB5Ckg0qa0mKjJQ3NPl2uSMkAQAAAMFDSLJBZSFJqniGO0ISAAAAEDyEJBtU1t1OqnjyhhMncgAAAAAQOIQkG1TVkuSZvME3GPmOTwIAAAAQWIQkG1TVkuSZvMG3u11JiVRQEKTiAAAAgHqOkGSDmrYkSYxLAgAAAIKFkGSDmo5JkghJAAAAQLAQkmxQndntCEkAAACAPQhJNvANSVFRZn0kD0ISAAAAYC9Ckg18Q5L5ufQ6xiQBAAAA9iIk2eDEkOTb5a6ixWQl1koCAAAAgoWQZIOqWpI8Iam4WCoqKr2ckAQAAAAEByHJBp6QVFhYKLfbXaYlKSamdIySb2vSiS1LAAAAAAKDkGSDBj5NRwUFBWVaklyuiidvoCUJAAAACA5Ckg18Q1J1F5QlJAEAAADBQUiyQVRUlCIizJ++qrWSfLvYEZIAAACA4CAk2cDlcpWZvMG3u51EdzsAAADAToQkm/iGpMpaknyDUVFR2dnuAAAAAAQGIckmNW1JquhnAAAAAP5X65C0ZMkSjR8/XoMHD9bPP/8sSXr99df1zTff+K24cFbTliSJacABAACAYKhVSPrggw80YsQIxcXFac2aNSooKJAkZWRk6IknnvBrgeGqqpakima3q+hnAAAAAP5Xq5D0+OOP68UXX9TLL7+s6Oho7+VDhgzR6tWr/VZcOKtOS9KJLUeEJAAAACDwahWStmzZorPPPrvc5SkpKUpPT69rTfVCbbrbEZIAAACAwKtVSGrRooW2bdtW7vJvvvlGHTt2rHNR9UF1utvl5Ulud+nlhCQAAAAg8GoVkm6++WbdddddWrFihVwul/bv368333xT9957r2677TZ/1xiWqmpJiosr/T4vr/R7QhIAAAAQeFG1udEDDzwgt9utYcOGKTc3V2effbZiY2N177336s477/R3jWHJNyTFxEgREaWtRhERJijl5ZlgVNlEDgAAAAD8r1YhyeVy6U9/+pP++Mc/atu2bcrOzlb37t2VmJjo7/rClm9IkqTY2LKtRvHxpSHJgynAAQAAgMCrVXe71157TZs2bVJMTIy6d++ugQMHKjExUfn5+Xrttdf8XWNYOjEkVbagrG8woiUJAAAACLxahaTrr79eAwcO1AcffFDm8oyMDN1www1+KSzcVdSS5KuiGe4ISQAAAEDg1SokSdIjjzyi6667TlOnTvVjOfVHbUJSXp5kWcGoDgAAAKi/ah2Sxo8fr6+++kr//ve/9T//8z/K8x1Qg5OqTXc7t1v6dXcAAAAAAVKrkORyuSRJZ5xxhlasWKFt27bpzDPP1K5du/xZW1g7WUuS71pJvuhyBwAAAARWrUKS5dPnq23btlq6dKnat2+v888/32+FhbvatCRJhCQAAAAg0GoVkqZMmVJmuu/4+HjNmTNH99xzj84++2y/FRfOajMmSWIacAAAACDQarVO0pQpUyq8/JFHHqlTMfXJyVqSKltAlpYkAAAAILCqHZLmzZunUaNGKTo6WvPmzat0P5fLpUsuucQvxYWzmrQkWZb06zAwQhIAAAAQYNUOSWPGjNHBgwfVrFkzjRkzptL9XC6XSkpK/FFbWKvumKTiYqmoSIqJMT8TkgAAAIDAqnZIcrvdFX6P2jlZS1J0tBQVZUJSbi4hCQAAAAiWGk3csGzZMn300UdlLnvttdfUoUMHNWvWTLfccosKCgqqfX+LFy/WJZdcolatWsnlcmnu3LllrrcsSw8//LBatmypuLg4DR8+XD/99FNNSnas2F9TUWUhyeWqeIY7QhIAAAAQWDUKSY8++qg2bNjg/fnHH3/UTTfdpOHDh+uBBx7Q/PnzNW3atGrfX05Ojvr06aPnn3++wuufeuop/eMf/9CLL76oFStWKCEhQSNGjPAGi1B2su52UsUz3BGSAAAAgMCq0ex2a9eu1WOPPeb9edasWRo0aJBefvllSVJaWpqmTJmiqVOnVuv+Ro0apVGjRlV4nWVZ+vvf/67/+7//0+jRoyWZVqvmzZtr7ty5uvrqq2tSuuOcrLudVPEMd0wBDgAAAARWjVqSjh8/rubNm3t/XrRoUZmQc/rpp2vv3r1+KWznzp06ePCghg8f7r0sJSVFgwYN0rJlyyq9XUFBgTIzM8tsTlSTliS62wEAAADBU6OQ1Lx5c+3cuVOSVFhYqNWrV+uMM87wXp+VlaXo6Gi/FHbw4EHvY55Yg+e6ikybNk0pKSneLS0tzS/1+NuJISkmpnSab4+KutsVFEhMHggAAAAETo1C0oUXXqgHHnhAS5Ys0eTJkxUfH6/f/OY33uvXrVunTp06+b3Impg8ebIyMjK8m79atvztxJDkclW9VpIvWpMAAACAwKnRmKTHHntMl19+uc455xwlJibq1VdfVYxnbmpJr7zyii644AK/FNaiRQtJ0qFDh9SyZUvv5YcOHdJpp51W6e1iY2O9M8c5mSckFRYWyrIsuVwuxcZKvnNSVBWSkpKCVCgAAABQz9QoJDVp0kSLFy9WRkaGEhMTFRkZWeb69957T4mJiX4prEOHDmrRooUWLFjgDUWZmZlasWKFbrvtNr88hp0a+AxCKigoUIMGDcq1JFU0cUNFPwMAAADwnxqFJI+UlJQKL2/UqFGN7ic7O1vbtm3z/rxz506tXbtWjRo1Utu2bXX33Xfr8ccfV+fOndWhQwc99NBDatWqlcaMGVObsh3FNyTl5+erQYMG5SZvoLsdAAAAEHy1Ckn+snLlSp177rnenydNmiRJmjBhgmbOnKn77rtPOTk5uuWWW5Senq6zzjpLn376aZmAEaqio6PlcrlkWZZ3XJKn5cijotntKvoZAAAAgP/YGpKGDh0qy7Iqvd7lcunRRx/Vo48+GsSqgsPlcqlBgwbKy8vzhqQTeyp6QlJ+vuR2SxG/TrNBSxIAAAAQODWa3Q7+deIMdyeGpLi40u99gxEhCQAAAAgcQpKNThaSIiJKgxIhCQAAAAgOQpKNThaSpIpnuCMkAQAAAIFDSLJRdUJSRTPcEZIAAACAwCEk2ejEkHTi7HZSxTPcEZIAAACAwCEk2YiWJAAAAMB5CEk2OjEkRUWpWgvKFhdLBQXBqBAAAACofwhJNjoxJEnlW5Mqmrihop8BAAAA+AchyUbVCUkVtSRV9DMAAAAA/yAk2agmIcl34gaJkAQAAAAECiHJRrQkAQAAAM5DSLJRTcckWVbp5YQkAAAAIDAISTaqSUtSSYlUWFh6+Ynd7wAAAAD4ByHJRtUJSdHRZpNYKwkAAAAIBkKSjaoTkiQWlAUAAACCiZBko5qGJN8udoQkAAAAIDAISTaqKCTFx0suV9n9aEkCAAAAgoeQZKOKQlJEROmMdh5JSeZrRkbpZYQkAAAAIDAISTaqKCRJ5bvcNWxovqanl16Wl1d2SnAAAAAA/kFIslF1Q1JqqvnqG5Isi9YkAAAAIBAISTaqaUvS8eNlLyckAQAAAP5HSLJRTUNSZqZZVNaDkAQAAAD4HyHJRtUNSQkJUlSU6WLH5A0AAABAYBGSbFTdkORyVTwuiZAEAAAA+B8hyUbVDUlSxeOSCEkAAACA/xGSbFSTkORpSfINSTk5ASoMAAAAqMcISTaqLCSduJisVPFaSbQkAQAAAP5HSLKRJyQVFBTI8lkZNi7OTNTgizFJAAAAQHAQkmzkCUmSCUq+TmxNYkwSAAAAEByEJBv5hqSTjUvytCTl5kqFhaXfAwAAAPAvQpKNoqOj5XK5JJ08JDVoYLrhSaWtSYQkAAAAwP8ISTZyuVy1muHOMy6psFAqLg5cfQAAAEB9REiyWV3XSmIacAAAAMC/CEk2q+taSXS5AwAAAPyLkGSz2rQkMQ04AAAAEDiEJJvVZUySREgCAAAA/I2QZLPajknyrD1LSAIAAAD8i5Bks5qEpJQU87WoqDQcEZIAAAAA/yIk2ayykBQdLcXGlt03KkpKSjLfs1YSAAAAEBiEJJtVFpKk6k3ewBTgAAAAgH8RkmxW25BESxIAAAAQGIQkm9U0JJ24VhIhCQAAAPAvQpLNahuSPN3tCEkAAACAfxGSbFbXMUmEJAAAAMC/CEk2q21IysiQ3G6zVXBTAAAAALVESLJZTUNSUpIUGWnCUWamuYzWJAAAAMB/CEk2q2lIcrlKF5X1TN7ANOAAAACA/xCSbFbTkCQxLgkAAAAIJEKSzaoKSQkJpuXoREwDDgAAAASOo0PS1KlT5XK5ymzdunWzuyy/qiokRURIcXHlb3NiSxLd7QAAAAD/ibK7gJPp0aOHvvzyS+/PUVGOL7lGqgpJkulyd2JL0YktSRs3SmedFaACAQAAgHrG8YkjKipKLVq0sLuMgKlOSDp8uOxlJ7Yk7d8v/fyz1Lp1gIoEAAAA6hFHd7eTpJ9++kmtWrVSx44dNW7cOO3Zs6fK/QsKCpSZmVlmc7LqhKQTeVqSsrOloiLz/cqVASgOAAAAqIccHZIGDRqkmTNn6tNPP9ULL7ygnTt36je/+Y2ysrIqvc20adOUkpLi3dLS0oJYcc3VJiTFxUmxseZ7T2vS+vVSXl4ACgQAAADqGUeHpFGjRumKK65Q7969NWLECP33v/9Venq63n333UpvM3nyZGVkZHi3vXv3BrHimqtNSHK5yo9LKiqS1q71f30AAABAfePokHSi1NRUdenSRdu2bat0n9jYWCUnJ5fZnKw2IUkqPy5JMl3uLMuPxQEAAAD1UEiFpOzsbG3fvl0tW7a0uxS/qW1IOrElSZKOHpV27PBjcQAAAEA95OiQdO+992rRokXatWuXli5dqssuu0yRkZG65ppr7C7Nb/zZkiRJ33/vp8IAAACAesrRU4Dv27dP11xzjY4ePaqmTZvqrLPO0vLly9W0aVO7S/OburYknRiStm6VMjKklBT/1AcAAADUN44OSbNmzbK7hIDzhKSCggJZliWXy1Xm+rg4KTJSKikpeztPS9Lx42Yckudmbre0apV03nmBrhwAAAAIT47ublcfeEKSZILSiVwuKSGh/O08LUkFBdKJjVCrV5cPVQAAAACqh5BkM9+QVJMud9HRpeHJd/IGySwyu2mTvyoEAAAA6hdCks2io6O9Xez8NXmDxAQOAAAAQG0RkmzmcrnqPMPdiS1JkrR7t3T4sD8qBAAAAOoXQpID+HOtJF+0JgEAAAA1R0hyAH9PA+6xbp1UWFi32gAAAID6hpDkAP5eUNajoED64Yc6FgcAAADUM4QkB/BHSLKsivdZupTpwAEAAICaICQ5QG1DUnKyWUeppETKyqp4n+PHpe++80eVAAAAQP1ASHKA2oakiAgpJcV8X9nkDZK0eLGUl1eXCgEAAID6g5DkACcLSTExZqvIycYlSSYgLVlShwIBAACAeoSQ5AAnC0mSlJBQ8eWeGe62b6/6Mb77ruogBQAAAMAgJDlAdUJSZV3u+vQxX3/8UVqzpvLHKC6WFiyobYUAAABA/UFIcoDqhKSkpIovb9dOOvdc8/1//ysdPFj546xfL+3fX9sqAQAAgPqBkOQA1QlJ7dtXfvvf/Ebq3Nm0Fr37rlTZ3ViW9PnndSgUAAAAqAcISQ5QnZDUs6eZza4iLpd02WVmprvjx6UPP6x83aRdu6StW+tYMAAAABDGCEkOUJ2QFB8vnXJK5fcRFyddeaUUGSlt3mwWka3MF19IbndtqwUAAADCGyHJAaoTkiSpd++q76dVK2nkSPP9ggWm1agiR45UPckDAAAAUJ8RkhyguiGpa1cpNrbq++rf34Qpy5Lef1/Kyqp4v6+/lgoLa1MtAAAAEN4ISQ5Q3ZAUHS2demrV9+VySRdfLDVrJuXkSB98UHHXuuzsqrvkAQAAAPUVIckBqhuSJKlXr5PfX3S0GZ8UEyPt3m3GIFVkxQqpqKgmlQIAAADhj5DkADUJSR07Vr5mkq/GjaXRo833y5dLP/xQfp+8PGnt2hoUCgAAANQDhCQHqElIcrnMdODV0b27WUNJkubPl37+ufw+K1ZUPl04AAAAUB8RkhygJiFJOvksd77OPVfq0kUqKZHeeaf8RA6//CJt3179+wMAAADCHSHJAWoaklq2lJo2rd59u1zS5ZdLTZqYgPTuu1Jxcdl9VqyoSbUAAABAeCMkOUBNQ5JUs9ak2Fjp6qulBg2kffuk//63bBe7bdtMixIAAAAAQpIj1CYk9eplWomqq3FjaexYc5s1a6Tvvy+9zrJoTQKAYMvLM92dv/lGmjNH+vRTszTDhg3S3r1SRkbFSzgAAAIvyu4CULuQlJoqpaVJe/ZU/3FOOUUaPtxMCf7pp6bLXocO5roffpCGDTOtTQAA/ykpkdLTpePHpQMHzLZ/v7nsZCIizEmuq64y3aYBAMFBSHKA2oQkyXS5q0lIkqTBg6WDB6Uff5Tee0+6+WapYUOpsFBavVo688ya3R8AoNSmTdKhQyYQeYJRVlbtZxF1u6UjR6SZM6XrrpOaN/dntQCAytDdzgFqG5J69JAiI2v2WC6XdMklZvKHvDwTlEpKzHXffUfXDgCorTVrzCyiCxea1vndu6XMTP8ss5CdbYLS/v11vy8AwMkRkhygtiEpLk7q3LnmjxcdbbpuxMWZbh9ffWUuT0+Xtmyp+f0BQH23b5/08ceBfYy8POnVV2vegwAAUHN0t3MAT0gqKCiQZVly1WBGht69pc2ba/6YKSmmRendd81A4VNOMeOTli+XTj215vcXaHv3Srm5/r3PqCipUyf/3ifCk9ttzuDv2mW2Y8cqbx1o1sysT9aiRTArhJ2yskwL0onLKwRCQYH0xhtmxtKOHQP/eABQXxGSHKCBz2wJBQUFZX4+mS5dzGQLNWyEkmTCUL9+ZizSnDnSrbea7iEHDzrnAM+yTEvXkiWBuf9zz5XOOadmt9m50wS2Hj0CUxPs53ZLP/9s/h927TJn7gsLq3fb48elrVvNDJTnnmvG/CF8FRdLs2aVX6g7kAoLpbfekq680nwG1Gd79khJSfyfAXbbvduMd+/YUerWzUw6E+oISQ7gG4ry8/NrFJKioqSBA82U3nl5NX/sESPMC/voUWn+fPOhu3y5NGZMze/L34qKpLlzzXS4gfL111J8vHT66dXbf+dOc3BSVGRat84/v+bjwuBMlmWe0/XrzWsuJ6du97Vunbmf/v1NEE9IqHjfggJzYuKXX0rHBzpdUpLUvr3pslvfzZ9vAnWwFReb1quxY6Xu3YP/+E6wfr05wRcZaU5InHFGzZbGCKb8fPNZe8opfGZUZPduM3tjZe+T8K/CQnMCsGNHcxxZWyUl5nNu+fLS8ZIrV5rPiL59zedfSopfSraFy7L8MaTUuTIzM5WSkqKMjAwlJyfbXU6FLMtSZGSkLMvSgQMH1KKWzTiZmWZWpcOHzddDh6p34HXggPSf/5iz5xdfLA0aJN1zj71vVtnZ0ttvB+fgw+WS/ud/Tt4ytGuX9OabJiB5pKVJV1whOfSlhWo4cMCc/dqwwaxLEwgxMWZmyT59zAmJAwdMMDp40LQ8heK7sMtlWpw7dDBbu3bm96xPli2TPvvM3hpcLhMOhg2r28GOPxw8aHomDBwY+OnKly83f3vf/53WraVLL3XWDIC7dpkJPTZuNJ8djRubk2vdutldmTOUlEhffmn+l6KjpQEDpCFDpMREuysLXwcPSu+/b44PGzQwr8WePU1gqm7rT26utGqVmfCrqlZ0l8uMnR8wwHx1ykmM6mYDQpJDxMfHKy8vTzt37lT79u39dr/5+eYNaNWqqg/Evv3W7BcdLd1yiznwr2k3NH85fNi01lRnDRF/iYyUrr228jFKu3ebgFRRl6uEBHM2l/EBoaOw0JztWrXKhBbUXWSkOUht0aLyD8KoKHNCoWFDs9ZbamroBqvt2817glNmBG3WTLrsMjNzabAVF5sZ/ZYuNX+PiAgzXnboUPMc+5Nlmc+qb7+t+PrISHOQffbZ9oXGrCxp7VoTjo4dq3if9u2lCy6QWrUKZmXOkp5uZtg98WRodLQZCnDWWaZFAv7z/ffm5EJF4ycTEkyrdK9e5gRwYaHpUeG7ZWebE3sbNpQ9YVwdffqY9ygnICT9KlRCUqNGjXT8+HFt2rRJ3QJwimn3bmnevMoPCC1Lev11052sZUvp9ttNi1KXLuafJVh9S7dtM2+aBQXBeTxfMTHShAnmQM/Xnj1moHRVY1JcLtPd4ze/Cd6ZkqIisybLhg2mNpfLPE8REaXfR0aag6e0NPN7heoBqb8UFJgzX8uW+X8iENROfLw5kE5Jqfx9JiLCtA54Xsd2t5gcOya9/HLtujhblvnfzcszW36++VpQUPZ/2HeLjDQtEMnJVb+/REaak1tnnRW89+xdu0yXw4o+WyIjTZebs8/2T2t7SYn04YemK+vJNG1qJidKSzMHdllZ5bdAfM7k5Zm/SXXCs8tlwuSwYfWvN8KmTea5rGo8dVSUef2cdVZod9lygvx8cwy4cWP19o+I8P8JoFNPNTMrOwEh6VehEpJatWqlAwcOaM2aNTrttNMC8hjFxdKiReYMXEUv/sxM6cUXzZv8mWeaLgGSGXdwyikmMJ1ySmDGIeTnmzNvn39u75nZ+HjpxhtLu4rs3WsCUnU/TLt0MR94jRsH5kDOsswH8A8/mDe76k4mIJU90PRslT2XLpc5m+eUpvG6ys+XVqwwXXRqc2Armb+958xaVWfQIiNNV5HY2PD5+zlJZKRprfJ9HQfjrb2gwPS537/fdCk7MRRkZJiTTIcOmX0LC83m+d7zNS+v9u9x8fGm5cF3q+hMe5s25oxt48a1e5zqyM+XvvjC/C1OdhQRFWXGfdalG1VhoRmDtX179W/jcpnNKa19lYmONn+f2ryOXS5zu+bNTQut099ziovN5/x331X/NhER5ndLSSltgfZsDRtWffIvOtreCQQsy3Rr27/fvDfExZmaGzUyX4MxrvPnn033uuPHA/9YVSEkOVCohKSOHTtq586dWrZsmc4444yAPtbBg+aMQkWLEm7ebD6IJHMWrnVr8yEcF1d6ljMtzZyli4sr3Ro0KP0+Pt5sVQ1Ozc42rVt79pivhw6VftAWF5sDkMOHzXbkiGmWD8QrNTW19GCjZUvzAZ6SIt10kzno8QQk35qOHDG1NGxYuiUn///27j26yfr+A/g7aZr03kILKfeWwuReWiqcUn/TSR0ODwPcQDh1p0MnbtStwEBRhzA9UkRlKjLQ7Qw35yZzK0zK6hkrUFYupRQEgVIQkGvTCrVN75fk+/vjuyd5kl4JbZKW9+uc5+T2JPkm+eR5ns/39jhuiDUa+ToREXIJD5eXHdUEt6WhQSZFJ09237gZZzqdfaek7h7lrg17V7Ba5Xd25IhjjaUQ9pp85xp95XptrVzUXQ1uZ2IFX1/531GWoCDZncHbD2I6Q6OR8aHTyc+pvt7e/179POfn3slgdr2+9Ti9k+58NTXy4EJJjG7dctwGmc2ywuLSJXl5u92DtVrHbaefn3x9q7Xl0tTU8v0VQUFy22U0yuQxMlJ+doNBdufqjtMcmExAdrbjWITGRrldr6uTn0X5XM4HqErLofNvFRradgw0NclWB2Wf1dwsD/iURauV/63AQPn6gYH2fZa7CSG31+ptinLZ2Ci/F6WMytJVFVJ6vew5EBkp48Fo9K6xPXV1QFaWHJMJyO+qpkbu08xm+fs7H1Pc6SQXPj5yv6uOOeWyO8ZdWyzyeEbZdpSUtF+ZqSRNffrY49b5mOpOvofiYtk91WKR33dVlfzPlJfL30Ovl9sK50tf35bbIYtFXjY3t9w/Ktfr6uRz1Z9D+SwxMcCCBbKyxNM6mxtwdjsvocxod/DgQdTcybRanRQdLTfkJ086tpLo9bK16MsvZRcKhbJDV5IgZaOu7hKi1Nop1/V6+2IwyMVqlQcT1dXyD6cszc1yQ1JRITeW7lJWJqdrVgQEyIRm2zb5vZSXd65MGo3cGQUFyddQfw/O348rO0Nlw9TcLA8Ymprs15ub7RsxIewbQ+W20l3Hx8d+MKos7ZVF/Tzn57v6OdxNaf2pr5e/Z329fXGVj4+M/7ZYLPbfqLy87TEJ1FJn4tE5hluLT/Xiai2yclCg3kYp15XuW86UShHlIENJAvV6e1KoHIh09P9z1twst0U3b8qYunXLvi09f14uCh8fedD1xz92T4VGc7O9QqG2Vl621bqq09m/D4Oh/d+vre/DapXvoXST62xrsJ+ffF1lW+h82R0Vb668plYry+pqrCqxr8SZEmtKK4q3bKuV31Fd8dRRK5+Pj4wbJT6cu5R3tC9SthtabcvY647vRtkHt7U4Hye19nnaunRFQ4N9e1Vd7flW1YKCvsjPj/NsIW4DW5K8REJCAgoLCz1dDCIiIiKiLhcdPR0XL37m6WKwJamnWbJkCd58801YPHSiFCHszaet1TSom1qda+HUaXZ796k5t6oo15VaME/0IRbCsXUGcOwa1F6Z1DVFHX03rlJ/Z63VojlfKtedy9NW+VrT3nN7irYGw/eGE931Bl0Rix3ddlVb2ymt1rvH7Cmtmc3N3fd/VWri1TX1zt9Hay04gOPv09nf37llu6P/r3pf5bxNVF92h9vZtqi7WLrK+ftUFk+3GrRG/Tsq19vSWqtfW5ftcfd+zHl70VrMdfa4qSu2Yc4ttp7i5wdMnx7tuQK4gC1J5KCxUY7fOHhQJkzKIHTnxc/PcUyB+lLdpUTpZqFWU2Mfb6Qed3QnXaC60qBBctBzZaUcu+XKGCBlbJMya5dy3dU+0E1NcvzWxYvAtWt3dtJRjUZ2xYmIaHu8hpKwKv2JDQb7deW37wmEkN9bUZHsI967t3Z3F61WDn5WTkDpHKNK3Lp6UKB0ty0vt/fh/+ab25/21hP0enlOktGju34KbkB288vPlxPbdJavrxyTEBzc9m/VVuJisQB5eY5dCnubsDDZVdsVBoN9LLB6XJbSNd5bKMcXZ8/e2ev4+trHeAYFtb8/Uu/HnPdl3fHdWK3ymMF521FdbV9Ho5FlDw6W46WUcatKd1Tn46o7qTguKZHHc56etAHgxA1eiUmSa5RaSHcO0C8pkefaKC5233uqtTZ9bkMD8Nln8nwXHenTR051O25c9+6YlDNlX7wol7Iy+2M+PvZxYEqyqiRE/frZJ5HoKUlOVzKbZbJUVCQTJ2+sZb3b+PraD1wMhrbXU2a3Usdx377urxUVQh7snDkjZ3Zr7VwjnhIQIGfXHD1aJiPu+I9fuyZnjDxzpu3/06BBchrncePkb30nTp+WE0aoDzjb4u8vT2rbv3/LiVmU201NXV9x0tRkn9ynI76+8rw08fHyZMx3ixs3gL17O056NRo5UdT48fZJKJQKkZ6msVEmTwaD/Bzu7Mlgtcr/Tl6enFSiI8r2tqnJPitnV/xPmCR5ISZJPc/168CePbc31eud6uhEjOfPy4ksWpvAQUmOYmM904VLGcSsDAanjtXUyGT80iUZb909uUL//vJgaNAgOfC+pETOEHbzZs9P1vz95f8mMlJe9u3b9rparb0m905aebxBaSnwj384VlK4m0Yjkw/lINtTXUgrK2XL0rFjMvkIDJTbw7g4mdR2pY5OkB4UBCQmAgkJnjuYrquTs7Z+9ZVc1LO3AvJ/Eh8vD/7vNHHsya5dk8mS876+Xz95/qjx47unJfRuJYQ8lsnLkxWFCuVE4MOGyZMcDxni2MtEObdbQ4NcampkXF+4IH/D9vZhGo3cN8TEyCTJ+TyUnsIk6X+YJPVcly/LZOny5e57D41G7lAffLDjmtf6elmLeeKEvO3p5Ii6Tl2drN28ft2+dKa2uj39+wNjx8rkqK0DxeZmeQBlMsna556SMKkTo7v5IKapSZ69/uhR9793VJSc4nvgQPe/d1saG+V/xx0J29WrsuJKSVL79JFTC0+c6H0t5coJZktLgXvuabsy7m515Yo8cI+IkIkRv5/upyTxQ4fK86q52vuloUFWNl64IJfycjm8YPhwmRhFR3fPVOt3iknS/zBJ6vkuXJD9mG/nxKmd4eMD/N//3X43h+JiOV6LyVHvppw4Vn0+COWyvVgMDpaJkXJCYur9iork+EVXT1R8OyIi5Im+77mn+9/L21kssrtfcLBsUeP2mMizamtdH1vnTkyS/odJEhERdTezGcjMlLWz3SEwEPjOd2Q3LSYDRESu4xTgREREbhISAqSmypm76usdT0CrvlSf0FiZQKC+XrZOGgz2WcnUS0iIHKPREwesExH1VD0iSdq0aRNef/11mEwmxMbGYuPGjZg8ebKni0VERGSj0cjByURE1PN5faP9tm3bsGzZMqxevRrHjh1DbGwspk+fjjJPTilERERERES9ltcnSRs2bMBTTz2FhQsXYsyYMdiyZQsCAgLwhz/8wdNFIyIiIiKiXsirk6TGxkYUFhYiOTnZdp9Wq0VycjIOHTrU6nMaGhpgNpsdFiIiIiIios7y6iTp5s2bsFgsMBqNDvcbjUaYTKZWn5ORkYHQ0FDbMmTIEHcUlYiIiIiIegmvTpJc8fzzz6OystK2XL161dNFIiIiIiKiHsSrZ7eLiIiAj48PSktLHe4vLS1FZGRkq88xGAwwcJ5UIiIiIiJykVe3JOn1ekyaNAk5OTm2+6xWK3JycpCYmOjBkhERERERUW/l1S1JALBs2TKkpqYiISEBkydPxltvvYWamhosXLjQ00UjIiIiIqJeyOuTpMceewxff/01XnrpJZhMJkycOBGfffZZi8kciIiIiIiIuoJGCCE8XYjuZDabERoaisrKSoSEhHi6OERERERE5CGdzQ28ekwSERERERGRu3l9d7s7pTSU8aSyRERERER3NyUn6KgzXa9PkqqqqgCAJ5UlIiIiIiIAMkcIDQ1t8/FePybJarXixo0bCA4Ohkaj8WhZzGYzhgwZgqtXr3J8FHUa44ZcxdghVzBuyBWMG3KVu2NHCIGqqioMHDgQWm3bI496fUuSVqvF4MGDPV0MByEhIdyA0G1j3JCrGDvkCsYNuYJxQ65yZ+y014Kk4MQNREREREREKkySiIiIiIiIVJgkuZHBYMDq1athMBg8XRTqQRg35CrGDrmCcUOuYNyQq7w1dnr9xA1ERERERES3gy1JREREREREKkySiIiIiIiIVJgkERERERERqTBJIiIiIiIiUmGS5EabNm1CVFQU/Pz8MGXKFBw5csTTRSIvkpGRgXvvvRfBwcHo378/Zs+ejeLiYod16uvrkZaWhvDwcAQFBeEHP/gBSktLPVRi8kbr1q2DRqPBkiVLbPcxbqg1169fx+OPP47w8HD4+/tj/PjxOHr0qO1xIQReeuklDBgwAP7+/khOTsb58+c9WGLyBhaLBatWrUJ0dDT8/f0RExODV155Bep5wBg7tH//fsycORMDBw6ERqPBjh07HB7vTIyUl5cjJSUFISEhCAsLw5NPPonq6mq3fQYmSW6ybds2LFu2DKtXr8axY8cQGxuL6dOno6yszNNFIy+Rm5uLtLQ0HD58GLt370ZTUxO++93voqamxrbO0qVLsXPnTnzyySfIzc3FjRs38Oijj3qw1ORNCgoK8N5772HChAkO9zNuyNk333yDpKQk+Pr6Ijs7G2fOnMGbb76JPn362NZZv3493nnnHWzZsgX5+fkIDAzE9OnTUV9f78GSk6e99tpr2Lx5M959910UFRXhtddew/r167Fx40bbOowdqqmpQWxsLDZt2tTq452JkZSUFJw+fRq7d+9GVlYW9u/fj0WLFrnrIwCC3GLy5MkiLS3NdttisYiBAweKjIwMD5aKvFlZWZkAIHJzc4UQQlRUVAhfX1/xySef2NYpKioSAMShQ4c8VUzyElVVVWLkyJFi9+7d4v777xfp6elCCMYNte65554T9913X5uPW61WERkZKV5//XXbfRUVFcJgMIi//vWv7igiealHHnlEPPHEEw73PfrooyIlJUUIwdihlgCI7du32253JkbOnDkjAIiCggLbOtnZ2UKj0Yjr16+7pdxsSXKDxsZGFBYWIjk52XafVqtFcnIyDh065MGSkTerrKwEAPTt2xcAUFhYiKamJoc4GjVqFIYOHco4IqSlpeGRRx5xiA+AcUOt+/TTT5GQkIC5c+eif//+iIuLw+9+9zvb45cuXYLJZHKIm9DQUEyZMoVxc5ebOnUqcnJycO7cOQDAiRMnkJeXh+9973sAGDvUsc7EyKFDhxAWFoaEhATbOsnJydBqtcjPz3dLOXVueZe73M2bN2GxWGA0Gh3uNxqNOHv2rIdKRd7MarViyZIlSEpKwrhx4wAAJpMJer0eYWFhDusajUaYTCYPlJK8xccff4xjx46hoKCgxWOMG2rNxYsXsXnzZixbtgwvvPACCgoK8Itf/AJ6vR6pqam22Ghtv8W4ubutXLkSZrMZo0aNgo+PDywWC1599VWkpKQAAGOHOtSZGDGZTOjfv7/D4zqdDn379nVbHDFJIvJCaWlpOHXqFPLy8jxdFPJyV69eRXp6Onbv3g0/Pz9PF4d6CKvVioSEBKxduxYAEBcXh1OnTmHLli1ITU31cOnIm/3tb3/DRx99hL/85S8YO3YsPv/8cyxZsgQDBw5k7FCvwu52bhAREQEfH58Ws0mVlpYiMjLSQ6Uib/XMM88gKysLe/fuxeDBg233R0ZGorGxERUVFQ7rM47uboWFhSgrK0N8fDx0Oh10Oh1yc3PxzjvvQKfTwWg0Mm6ohQEDBmDMmDEO940ePRpXrlwBAFtscL9FzlasWIGVK1di/vz5GD9+PH70ox9h6dKlyMjIAMDYoY51JkYiIyNbTG7W3NyM8vJyt8URkyQ30Ov1mDRpEnJycmz3Wa1W5OTkIDEx0YMlI28ihMAzzzyD7du3Y8+ePYiOjnZ4fNKkSfD19XWIo+LiYly5coVxdBebNm0avvjiC3z++ee2JSEhASkpKbbrjBtylpSU1OIUA+fOncOwYcMAANHR0YiMjHSIG7PZjPz8fMbNXa62thZarePho4+PD6xWKwDGDnWsMzGSmJiIiooKFBYW2tbZs2cPrFYrpkyZ4p6CumV6CBIff/yxMBgM4oMPPhBnzpwRixYtEmFhYcJkMnm6aOQlfvazn4nQ0FCxb98+UVJSYltqa2tt6/z0pz8VQ4cOFXv27BFHjx4ViYmJIjEx0YOlJm+knt1OCMYNtXTkyBGh0+nEq6++Ks6fPy8++ugjERAQIP785z/b1lm3bp0ICwsT//znP8XJkyfFrFmzRHR0tKirq/NgycnTUlNTxaBBg0RWVpa4dOmSyMzMFBEREeLZZ5+1rcPYoaqqKnH8+HFx/PhxAUBs2LBBHD9+XFy+fFkI0bkYefjhh0VcXJzIz88XeXl5YuTIkWLBggVu+wxMktxo48aNYujQoUKv14vJkyeLw4cPe7pI5EUAtLps3brVtk5dXZ1YvHix6NOnjwgICBBz5swRJSUlnis0eSXnJIlxQ63ZuXOnGDdunDAYDGLUqFHi/fffd3jcarWKVatWCaPRKAwGg5g2bZooLi72UGnJW5jNZpGeni6GDh0q/Pz8xPDhw8WLL74oGhoabOswdmjv3r2tHtOkpqYKIToXI7du3RILFiwQQUFBIiQkRCxcuFBUVVW57TNohFCdIpmIiIiIiOguxzFJREREREREKkySiIiIiIiIVJgkERERERERqTBJIiIiIiIiUmGSREREREREpMIkiYiIiIiISIVJEhERERERkQqTJCIiotvwwQcfICwszNPFICKibsQkiYiIuoXJZEJ6ejpGjBgBPz8/GI1GJCUlYfPmzaitrfV08TolKioKb731lsN9jz32GM6dO+eZAhERkVvoPF0AIiLqfS5evIikpCSEhYVh7dq1GD9+PAwGA7744gu8//77GDRoEL7//e97pGxCCFgsFuh0ru0C/f394e/v38WlIiIib8KWJCIi6nKLFy+GTqfD0aNHMW/ePIwePRrDhw/HrFmzsGvXLsycORMAUFFRgZ/85Cfo168fQkJC8OCDD+LEiRO211mzZg0mTpyIDz/8EFFRUQgNDcX8+fNRVVVlW8dqtSIjIwPR0dHw9/dHbGws/v73v9se37dvHzQaDbKzszFp0iQYDAbk5eXhwoULmDVrFoxGI4KCgnDvvffiP//5j+15DzzwAC5fvoylS5dCo9FAo9EAaL273ebNmxETEwO9Xo977rkHH374ocPjGo0Gv//97zFnzhwEBARg5MiR+PTTT7vs+yYioq7FJImIiLrUrVu38O9//xtpaWkIDAxsdR0l4Zg7dy7KysqQnZ2NwsJCxMfHY9q0aSgvL7ete+HCBezYsQNZWVnIyspCbm4u1q1bZ3s8IyMDf/rTn7BlyxacPn0aS5cuxeOPP47c3FyH91y5ciXWrVuHoqIiTJgwAdXV1ZgxYwZycnJw/PhxPPzww5g5cyauXLkCAMjMzMTgwYPx8ssvo6SkBCUlJa1+lu3btyM9PR2//OUvcerUKTz99NNYuHAh9u7d67Der3/9a8ybNw8nT57EjBkzkJKS4vA5iYjIiwgiIqIudPjwYQFAZGZmOtwfHh4uAgMDRWBgoHj22WfFf//7XxESEiLq6+sd1ouJiRHvvfeeEEKI1atXi4CAAGE2m22Pr1ixQkyZMkUIIUR9fb0ICAgQBw8edHiNJ598UixYsEAIIcTevXsFALFjx44Oyz527FixceNG2+1hw4aJ3/zmNw7rbN26VYSGhtpuT506VTz11FMO68ydO1fMmDHDdhuA+NWvfmW7XV1dLQCI7OzsDstERETuxzFJRETkFkeOHIHVakVKSgoaGhpw4sQJVFdXIzw83GG9uro6XLhwwXY7KioKwcHBttsDBgxAWVkZAODLL79EbW0tHnroIYfXaGxsRFxcnMN9CQkJDrerq6uxZs0a7Nq1CyUlJWhubkZdXZ2tJamzioqKsGjRIof7kpKS8PbbbzvcN2HCBNv1wMBAhISE2D4HERF5FyZJRETUpUaMGAGNRoPi4mKH+4cPHw4AtkkPqqurMWDAAOzbt6/Fa6jH/Pj6+jo8ptFoYLVaba8BALt27cKgQYMc1jMYDA63nbv+LV++HLt378Ybb7yBESNGwN/fHz/84Q/R2NjYyU96e9r7HERE5F2YJBERUZcKDw/HQw89hHfffRc///nP2xyXFB8fD5PJBJ1Oh6ioKJfea8yYMTAYDLhy5Qruv//+23rugQMH8OMf/xhz5swBIBOur776ymEdvV4Pi8XS7uuMHj0aBw4cQGpqqsNrjxkz5rbKQ0RE3oNJEhERdbnf/va3SEpKQkJCAtasWYMJEyZAq9WioKAAZ8+exaRJk5CcnIzExETMnj0b69evx7e+9S3cuHEDu3btwpw5c1p0j2tNcHAwli9fjqVLl8JqteK+++5DZWUlDhw4gJCQEIfExdnIkSORmZmJmTNnQqPRYNWqVS1adqKiorB//37Mnz8fBoMBERERLV5nxYoVmDdvHuLi4pCcnIydO3ciMzPTYaY8IiLqWZgkERFRl4uJicHx48exdu1aPP/887h27RoMBgPGjBmD5cuXY/HixdBoNPjXv/6FF198EQsXLsTXX3+NyMhIfPvb34bRaOz0e73yyivo168fMjIycPHiRYSFhSE+Ph4vvPBCu8/bsGEDnnjiCUydOhURERF47rnnYDabHdZ5+eWX8fTTTyMmJgYNDQ0QQrR4ndmzZ+Ptt9/GG2+8gfT0dERHR2Pr1q144IEHOv0ZiIjIu2hEa1t8IiIiIiKiuxTPk0RERERERKTCJImIiIiIiEiFSRIREREREZEKkyQiIiIiIiIVJklEREREREQqTJKIiIiIiIhUmCQRERERERGpMEkiIiIiIiJSYZJERERERESkwiSJiIiIiIhIhUkSERERERGRCpMkIiIiIiIilf8H2LtGLzDl33kAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "def generate_plots():\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", - " \n", - " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", - " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", - " for i, row in learner_log.iterrows():\n", - " data[i+1, :] = data[i]\n", - " data[i+1, row['arm idx'].astype(int)] += 1\n", + "def generate_plots(est_mab):\n", "\n", - " plt.figure(figsize=(10, 5))\n", + " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", "\n", - " plt.plot(data, label=est_mab.mutations_)\n", - " plt.xlabel(\"Evaluations\")\n", - " plt.ylabel(\"Number of times mutation was used\")\n", + " # Setting up the figure layout\n", + " fig = plt.figure(figsize=(12, 6), tight_layout=True)\n", + " gs = gridspec.GridSpec(6, 6)\n", "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - "\n", - " plt.legend()\n", - " plt.show()\n", + " # Approximating the percentage of usage for each generation ----------------\n", + " data = np.zeros( (est_mab.max_gen, 4) )\n", + " for g in range(est_mab.max_gen):\n", + " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", + " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", "\n", - " # --------------------------------------------------------------------------\n", - " fig, axs = plt.subplots(1, 1, figsize=(10, 4))\n", + " df_in_range = learner_log.iloc[idx_start:idx_end]\n", + " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", + " for k, v in g_data.items():\n", + " data[g, k] = v\n", "\n", - " columns = learner_log.columns[learner_log.columns.str.startswith('UCB1 ')]\n", - " labels = [columns[i].replace(str(i), est_mab.mutations_[i]) for i in range(4)] \n", - " data = learner_log.loc[:, columns]\n", + " axs = fig.add_subplot(gs[0:3, :3])\n", + " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", "\n", - " axs.plot(data, label=labels)\n", - " axs.set_xlabel(\"Evaluations\")\n", - " axs.set_ylabel(f\"UCB1s\")\n", + " axs.set_ylabel(\"Percentage of usage\")\n", " axs.legend()\n", "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " axs.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", + " # average Brush weights for each generation --------------------------------\n", + " data = np.zeros( (est_mab.max_gen, 4) )\n", + " for g in range(est_mab.max_gen):\n", + " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", + " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", "\n", - " plt.show()\n", + " learner_log_in_range = learner_log.iloc[idx_start:idx_end]\n", "\n", - " # Approximating the percentage of usage for each generation ----------------\n", - " data = np.zeros( (kwargs['max_gen'], 4) )\n", - " for g in range(kwargs['max_gen']):\n", - " idx_start = g*(learner_log.shape[0]%kwargs['max_gen'])\n", - " idx_end = (g+1)*(learner_log.shape[0]%kwargs['max_gen'])\n", + " total_rewards = learner_log_in_range.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log_in_range['arm idx'].value_counts().to_dict()\n", "\n", - " df_in_range = learner_log.iloc[idx_start:idx_end]\n", - " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", - " for k, v in g_data.items():\n", - " data[g, k] = v\n", + " keys = total_pulls.keys()\n", + " data_total_pulls = np.array([total_pulls[k] for k in sorted(keys)])\n", + " data_total_rewards = np.array([total_rewards[k] for k in sorted(keys)])\n", "\n", - " plt.figure(figsize=(10, 5))\n", + " # Success rate\n", + " data[g, [int(i) for i in keys]] = data_total_rewards/data_total_pulls\n", "\n", - " #plt.plot(data, label=est_mab.mutations_)\n", - " plt.stackplot(range(kwargs['max_gen']), data.T, labels=est_mab.mutations_)\n", - " plt.xlabel(\"Generations\")\n", - " plt.ylabel(\"Percentage of usage\")\n", + " axs = fig.add_subplot(gs[3:6, :3])\n", + " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", "\n", - " plt.legend()\n", - " plt.show()\n", + " axs.set_xlabel(\"Generations\")\n", + " axs.set_ylabel(\"brush Weights conversion\")\n", + " axs.legend()\n", "\n", " # --------------------------------------------------------------------------\n", " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", @@ -1410,24 +607,34 @@ " item['gen'], item['evals'], *item['ave'], *item['std'], *item['min']\n", " )\n", "\n", - " fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", " x = logbook['gen']\n", " for i, metric in enumerate(['m1', 'm2']):\n", + " axs = fig.add_subplot(gs[(3*i):(3*i + 3), 3:])\n", + "\n", " y = logbook[f'ave {metric}']\n", " y_err = logbook[f'std {metric}']\n", " y_min = logbook[f'min {metric}']\n", "\n", - " axs[i].plot(x, y, 'b', label='Avg.')\n", - " axs[i].fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", - " axs[i].plot(x, y_min, 'k', label='Min.')\n", + " axs.plot(x, y, 'b', label='Avg.')\n", + " axs.fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", + " axs.plot(x, y_min, 'k', label='Min.')\n", "\n", - " axs[i].set_xlabel(\"Generation\")\n", - " axs[i].set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", - " axs[i].legend()\n", + " axs.set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", + " axs.legend()\n", "\n", - " plt.show()\n", + " axs.set_xlabel(\"Generations\")\n", "\n", - "generate_plots()" + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", + "generate_plots(est_mab)" ] }, { @@ -1440,867 +647,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, \n" - 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Brush versionOriginalModified
metricscorebest modelsizedepthscorebest modelsizedepthpoint mutation callsinsert mutation callsdelete mutation callstoggle_weight mutation calls
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run 200.78Logistic(Sum(9477.80*AIDS,1.00,-6.76*Total,351...620.78Logistic(Mean(-0.00*Total,0.48,0.02*AIDS))523819221118091809
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run 290.78Logistic(Sub(Abs(-2.09*AIDS),0.00*Total))530.68Tanh(0.00*AIDS)213819221120101608
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4020.00000 3015.000000 \n", - "\n", - "Brush version \n", - "metric delete mutation calls toggle_weight mutation calls \n", - "count 30.000000 30.000000 \n", - "mean 2070.300000 1701.800000 \n", - "std 183.992532 240.351381 \n", - "min 1608.000000 1206.000000 \n", - "25% 2010.000000 1608.000000 \n", - "50% 2010.000000 1809.000000 \n", - "75% 2211.000000 1809.000000 \n", - "max 2412.000000 2010.000000 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "if __name__ == '__main__':\n", " from brush import BrushClassifier\n", @@ -2318,8 +667,8 @@ "\n", " kwargs = {\n", " 'verbosity' : False,\n", - " 'pop_size' : 100,\n", - " 'max_gen' : 100,\n", + " 'pop_size' : 60,\n", + " 'max_gen' : 300,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", @@ -2327,9 +676,9 @@ "\n", " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), \n", + " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", " ('Modified', 'point mutation calls'),\n", " ('Modified', 'insert mutation calls'),\n", " ('Modified', 'delete mutation calls'),\n", @@ -2341,22 +690,25 @@ " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", + " est_start_time = time.time()\n", " est = BrushClassifier(**kwargs).fit(X,y)\n", + " est_end_time = time.time() - est_start_time\n", + "\n", + " est_mab_start_time = time.time()\n", " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", + " est_mab_end_time = time.time() - est_mab_start_time\n", "\n", " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " \n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", " \n", " results.loc[f'run {i}'] = [\n", " # Original implementation\n", " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", "\n", " # Implementation using Dynamic Thompson Sampling\n", " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", " \n", " # Mutation count\n", " *total_pulls.values()]\n", @@ -2371,58 +723,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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18M2VUkjkr1NopQ6GF0s1pHVQcSfyanNvVA0rstVSapX/u5pynDiEU1oPjlO2NgG/MWESThyhrgNRD8LJo29F0FXDhLNYlP+bAgZbHNTBzGZtU21tCOpHONX4nK4DR9oU9SCcnHq42TTTeN+4QYjrwHEKaxROYBBOhVHyJyTGQvUS+gpoaUb+742m2gRIR8LjYraHQira1YE4dl164dQZVT++FUryf1PA3GJMPXQxeKfGojCltD7OcaGGiqMAkNaBfTjQ9XbUSWfjC5d/q7bt1vRpGpw6OD+6qY87z1Ix9RCqV3ZrXGlnaPLY3QRsT77XCjEWqscgnHxX/MMjzdR2Ha4PeYTT9PxGlv1uRT04TqrbVOGsQdlYi3AKXIyGQkp90BXiG8qbrIccp+VuC1DDkFqsE8cpX2OoXjqICm7+xWE1bbdSiic9XtH777/t2/BrXBRSNV7GZ5xwBmbtNQsXfu3C2ja0DZQyU4xKMo0nFRuEuA5C9cpObcIpqTV2ugo6mxkKJFSIqQUgnyGQOTFUIVAnSY05TmsjnpC59g3vsOx3K+rgHLvYSL7Pzqj68S1NFZo9nhDQSjDmONWBcHo1ru38FBmef9WQi0e+4zTQqsqE8WMxurXGdIQqPxcGdG0ebKieZcQQK/mruEVV20p7EtM/ONZn5fedMReqR58gn/jyJ9VJjY7TmoCnyeMOG95CkyugwWQdOE5e0kG+z/U1VsZraOFU61K9aTwPy2p8ftUaAkdFrsbjTOoh7nKIUL3pc47Htd/5If5t4ZUYvdvh2Omg43DrT+7rfW8QhFhwydex/f7vR9Muh2Dng4/Ddf9xO9RmQdbV2YXLv3A5jph1BOa0z8G/ffjf8NLzL/V+/uZv3oyPHPUR/OKuX+DY2cfigB0PwCULLsGTf34SP7n1J9hr0l7Ya9JeWLF8hf6vXhfCVi/1cedZKqYeHKdCjcIpriGUpVrWZQLyfVaKqdVWnyHHKfbkD8qxX5v4WFnmES8KKXZumYKXu5l7Ogl3nJzWUVCgd+c6ahROTbWGQhNgbEwRLpzUhO0Q17huXS/CqbsG5xSoE+E0DK7//k/w1Qs+i4v//d/wiwcX47MXXYf3HjIbu8+Yju/c/l944H/+Fz+/5evYaeoUvPXOarz1zupePbbwrIVoamrC9+7+HkaPHo2f//jn+NRHPoUH//Igxo7fVJ1x+RvL8fvf/B6LfrQIjutgh2k7YNnryzBz1kws+MoCAMD4ieMNfDP5z2jd1MedZ6mYqA6EUz6tTThFDI7T6kyBfJ+VYi5ULyYfI+M6cJyiGoXTinKWbSSe7rXiZZ5d95K6ss+xO4an6fX6oLbfJVtjKDQFjRqqF0+YWPM28nUinLqiGh2nupiYD329Hfe+w/C5Mz4GAPjKuWfgxtt+ij/++UnsPmM6lq9YhZnt03D4wftDKYWdd9wBALAewNN/eRrPP/08/vfF/0Umu2mx+IKrLsAffvsH/M9//w8+Ov+jAIAwDHHtzddiwsR3+4P5vo+m5iZMnFz79TYYKq2H86OX+rjzLBVTD8Ipl9aW2xGGnrlGIIPwjpuj3WEVmBJOXhLRCydP/mpj6NfmfOYjF2lTE1REX7mtPeF/6EkvAOKOoRcgqdeEuFTbufGllJvfBn5sxnFSSnYuamlM7Sv/9SKcuqPaFgDqwXEazhHus+fMd9+vFKZM2g5r1m8KAT7jYx/EP53yOex+xIfxgaMPxb/MOwLvf+9cqBR4+YWXUcgXcNhu/YthlEtlvLXsrd6/d9hxh36iiQ7+Zwg19XHnWSomqoNT25XU9mAPQp9cOK10uzf1lUjkPpiNherFEfmIUQ+heqFX+0WYZEbD5RBOpTz5PrckdR3ZwqmVXoCkfo2tGgB4NYZCU2CqUqf0HKfu0RqEU41ODhVdNRaHiEWPDj0Mfb35W+RyKqWQbJ5HHLD3HnjjL/+N3/3hcfz+T0vwsXO+gnmHz8Ftd92CQr6ASZMn4Ue/+tFW2xw99l03vLmF535P03o4P3qpjzvPUjFRHRSH6KzRcQpC+u8YqQRqdCvSzi7yfQ8bQ5MGU6vD2yKqB8fJqz3XLs6MgVtYq+FoKqO9cw3AbExID9XzWujHmcSrfRLkoQ6Ek7G8SSE9ygZhfdPYmreRi2XfNz101ficrgvhpOEQx4xuxcknHIuTTzgWJx1/DD7wiQX41oaN2GOfPbBuzTq4noupO02taJt+xkcSm13kTRswVK/xvnGDENaBJt4Y15YbUqoxB6BqmHIehoup1VYvoa8oWA/CqezWLpxCn+ea2nn9m1DME5NUuKvoNtH/PrGGinhOKqBi4hCYEk7Sc5zWZGu/3+tBOKXKQT6u7f6OZJ/KzdR2kDd8/yf4r189hJdefQOvvPYm7v3N7zGlbSLGjRmDue+di30P3Befn/95PP7Hx7Fi+QosXbIUN33tJjz/zPPb3O7UaVPx7NPPYsXyFdiwfkOvw6WTRnScZD+xLFVTD6F6tTR4BIBSmec7xgw5D5VgLlSPXjiF8ucGKGsI1Su7PMKpJchjcrO5xOHhkEgXTln6sNxIRynxGh19Cho1x2mFW3soZnc9hOppcE4TKHOJu0IY3dqCb373Thz4z5/EQcefhmVvvYPf3vUduI6CUgrfu/t7mD13Ni77/GU4/pDjccGnL8A7b7+D7SZtt83tnnHuGXBdFyccfgKOmHUEVr69UvuxN6JwqoM7z1IN9eA4rQtrWxEtlB3qFCcAQDC6iTu6aZuYcpz8OAL1kFFjXjEJJad2x6notmo4kupoz07AqiJ9mGAPqSt7UuT69P3LIg2FHdKEvl1DpfiGesMp4aF6b0CHcHIB4eNj6ml6UrousIU7WTzpcT3b1oAawHF69Be39f7/sr8+uNW/P/PI3b3/f/Yn/hVnf+Jft3pP9+btjmodhYuvuxgXX3fxgPs/98vn4twvn7vV69N3nY6f/u6nQ3+BGkiSxhNOsp9YlqqpB+G0PqzRcQo8OAwrUaVW2RMSU6ut1nEamKJTu3zPO4zCSfGGdCWGGjbrwvXoi3YEGnowpbHk5Z1NmBpTlJItnP4R1X5uumssukBBqqvBtiN9qmqorH4dhCkmAiqzUtN437hBKLN4MZWxLqhdgDS59JOD/CjZt42pUD0vpm/+G7jynxwlt/Z7LQe+8M/pAW9T50R6cQi3SL7PsgbHKY5lL/AAQMZYcQi5oXqquRlr0trHjDBRSB3Z4inR9HxOxQsnMwzkZEkjto6TZaQQpLInIwCwOqj94ZFlEE7dzbIHClOrrRyOU+DJf3AUNFSw7NIQulMt7fkNbPsGgFi646ToK2iWNQQDR5H8xTPf0GKMaMdpu0n6tuXJLgASa3KcEuHhvI3qOCkoJMKP0QTSr0ZLlQSp7JWo1M0gryGBpUlDSEulbGwS/FAGYKoUrx8xCCfh1bEAIK9BOG1M+Byn9o632fYN1EGoXrqRfJ8lVbtwCmoMhaZgU96kfiTnOAXjt53QXwnaQuEMoUs4pdKTuUwJJ+GOE0eqhAQa81s3AIHwgSbN6MnpyGgIaamUjib6ZPFKMFZVj6EceT2E6uVU7Sv7GxK+njuTO1dilI4qblUi2nFSCm5C78gVdQgnDY6+aYw5TpA7RhfH1N78tgfpwklHkRMASMSH6hl6TqWyn39WOFlGFGXhjlPi6wlN8h36B8c6nz5ZvBJMrrZ6xDH1JUfuynEPeQ2LFB0aksVrYefmNrZ9S3acnNZWlrCvgoYeTKV6EE6RKeEkd9zoHDVO27YS8cJJz/HFSu4YAZhzhqQ7TsoKJ8tIQnqoXuTpEU6ehpXZSlnt0yeLV4LJiZ6voYJcJZRduUnePXRruNfWMgundo+vql+N/TGN4o7l+V3yOoRTSfYzADCYN6nkOk7rmsZo21bCsHBYCaHSEy4ai5+gN6ZwcoRHNplC+tVoqZJyIvuCrmfhtMLrJt9nJRh1nBTtdVV26kA4aXh4rC7zToDaGRfoY8FDlTeaJ4Qxn9R+PeRKgn/YzRhznFK5wmmVr084xa7sPLZQQ487AIjET1UbtDiEeEFrhsb81g1AWXhVvbKGzukA4ID+wbHazW1qyCcUs46TDdXbki4N99rKgNlxKuXY9h0JDtVzW3kEbbeG5rWFUh2E6pnq4yQ4x+ktR58Yj8U7TppC9bRsxSSmhJP0hcPGlBCN+a0bgJJwx6ms6eHhgP7BESOFGjOafL/Dx9ykgV44yZ0A9dClIVTvnRLvyvH0javZ9i3ZcXJbeMLdujQ4TnGiWNo1DBdPeQZzQ+SOG6+nOoWTbMeprGlhM4DcxRUAFRVxOOqks/GFy781rPf++J5fYe6uc6s9KuOotDElhPwgaEtVlIQ7TiWlp4qY0pALUBVjWoENG3n2PQQjKVSvWA+Ok4bO6V2RhzSbhYrLGo6ocqavWwZn5+2RMKxwRoLnRG4Tz8F1xXrcohavBeVYZjEbX0Pj6MFQCCF1evOPWJ+YjTTlEJlCl3AKB5igH/zUqVq2PVyWzP6vQf/NVO0KVUVVvb0m7YWb7rwJxxx3jIEj2hLBg7dBGlMuNgBF4Y5TQZNwgoaQlmqIxvCVbx4Kk6utPrFwEu84eR4CTcNomuVzMTNxGTswVdbT0M7NGG6WJ1SmU1PzWo4+d8PF19D/bFCE5jipceNR0LhKryuHyBRlTREhKQBILUlusuKf8ByntEEdp8b81g1AKZG52tZDHnoe6CmTcApGyw2BUQYrShmd7AxAUXB1LABQTfr6L0UZfUnj1dCe0ddfphIkh+p5Ps/1tyHSM65lJQsng2G/mxwneSQT9DW/BfRVrTNFSafrJzSvWG1DOOULRcz//GVonXkYtt///bj+lrv6/Xu5HOBLV9+IqbOPxagZh2LOv8zHo39+8t1tD6Cc/vC7P+Cj7/soDtjxAHzgwA/gu9/6LqJo0zj1/gPeDwA47/TzsNekvXr/Hupz1dKowkn27NpSNSUN4UMmyUNTY7yY58FRGuWDr2XptlGpuUmDR1xFp6BkToB6adIXKhp6oxlKnbzLdHj4fwz7DQWXjnI9njC3DZocp4wuZ98ARoUTQ7Pu4RCM1yycWEeMoSmlGo/PcQGJgngbwumCry7CY395Cr++/Qa0TZyAi7/+n3j6uZew3567AQAWXPoN/N8rr+Pu716HHSZPwi8f+iM+8MkFeO73P8fMXXbaKhDuqSeewsXnXoyLrr0IBxxyAN5a9hau+uJVAIDPXfA53P0/d+PIPY7ENd+5Boe/73A4rjOsz1VLmtpQPcsIoiB5GRdAd6JHOMWacgEqJd8i99YxG6pnhVM/svqcx7LHW3CkPTBTGnooIlewcHLyLPvt0NS81nPkOuMmw36Vis2GUFVJrlWvqxsq2ZUTi9B3fKlQx2mw6yyXL+CHd/8K377sfBxzxBzsvcdM3LnoakTRprzd5StW4kf3PIB7v/9NHDHnAOw6fRq+dM58HH7QfvjRPb8ecJvf+/b3cNbnz8IJp5yAadOn4dCjDsWCCxfg3jvvBQBMmDgBADB67GhMnDyx9++hPlctjSqcrOM0QikKd5w6NVSNAviEU3ez3AHDZJgKdY5TwZEtnFKNwqnk8jWhBYD27g5w9DMMHcHCSXWz5BmsD/U8ml1Nzr4JTI8lyvOQhrLGj40t47RuT1fxBVMUEn3P58Rx66rd6mvL3kYQhJhzwF69r00YPxa777ozAOC5F19FHMfY7YgT+32uHITYbvzYAbf58gsvY+mSpbj1xlt7X0uSBOVSGcVCEc0tAzvM1X5uKJJE7jzIJFY4jVCKwh2nTk2OUxj6LJO9Dc2Cq70ZTIz2iKvo5MHjggyXJKMvVK/g6OltVi3tHcuBSfShXaKFU7qBfJ+pctEd6RJOkkP1DA/cngcIE05rMnpd5UCjo2OCgsYc5MRxZAqnKp3NXL4A13Xx1O9+Ctftv9DdOmrg3MRCvoBzv3wu5h0/b6t/y24jbLzazw1FYh0ny0hCunDSlfwcRTzCqSMr64HcF5WaExvUoXqpwqYJUI1JrKaINQqnHHiF03a5tRgz9T3oCrpJ9xspoU0eHQduupF+v5kWoKhnU0rTApUJSBwno3uonJV+q9Y2e2Xhwimv0XGKlSPz2w6iHXadviN838Nfn34eO03dHgCwYWMXXnn9Tbz3kAOw/16zEMcx1qzvwBFzDhjWrvbYew+88eob2GmXnQZ9j+d7SOL+Y+pwPlcNifDIJlNY4TRCKQi/oDsiPRPOcuiCoQcu1mZ4+u0MB7OhevTXlcpkkEoVTr6+FdUuZuEEANOb2vAssXAKhOY4uaNHQ4Fe1CWexkp4XH3uhoFvOMVaefIWD99UeqscllKRUqKXvMa2KDHDs2c4qEGUU+uoFpx1yom44JpF2G78WLRNnIBLvnEznM1l1XfbdWd84l//GfPPuxzXX34+9t9rFtau34DFf1qCffaYiePnHbHVNj/7pc/i3E+ci+133B7v/+D7oRyFl194Ga+++Co+f/HnAQBTp03FX/7fX7D/wfvDz/oYO27ssD5XDbHQNS/TWOE0QsnHLosTM1zWh3omnOWA5xJe6fMkjQ+J4xid7FGH6gEAshmgUKDf7zCIfH0T040pf1jVOIa+MKFQx8kdy5NzplM4cVUdHQ4Z446TvAfgq4lm4STTg+klrzEHORIqnLb1SPzWZV9ALl/AB8/4Aka3jsIXP/NJdHbnev/9RzdciWtu+gG+ePWNWLFqDSZOGIdDDtgb/zKAaAKAw953GG7+6c343re/h9v/43Z4nof2me34yCc/0vueC66+AN+87Ju476770LZ9G/7n6f8Z1ucqxVEuBCcsGMUKpxFKPnJEC6e1gZ4HeqnMcwm/49Guyg8X5Zn9PXwO4eTLHaZCjY7Thpi/547PUIkscIQKp9E85yPSKpzkOk6mWxsoV9i44Xl4Q7OQLQt3nHIa+0lGWziUS2b/l7Zt18Q2xszWUS246z+uQd/uTRd89vTe//d9H1d96bO46kufHfDzZ5z8IRx+xqkox+9GkRz2vsNw2PsOG3SfRx17FI469qitXh/qc5XiSBWyBDTuNx/hRKmDVPCFvVZTud1iwKMO16r8ptwbYSjf7IOURzjJnRyEnr6J0PqYPx+Fw1EUK5xG8bg1kauxqbKmXFITGB9LPFnPP7XdRKSav3MplfcM6ku3pn5kABByPHsEMFgoIDdWOFlGJq7Mh2aqHKzXJJxKZR7hlCpAjeHtuzMQpsNTWB7Tgh2nQKNwWhvyCycOYSw2VK+FZ2wJNfZeCkO5iw6m8yWVsL4/sebmtwBQ0OjomKBLU3VIAAikTlcNu/QyZRMgtMYhCUKvRIsWXKEPzYy+JPgwduA7PN8zZcqB2CaGJws+Qx5/Klg4lTUuTqwOGlM4BUpmpLzXxDNlCTQ6ToFk4WT4WpOW41QcO0H/NjVWrTOBTuEUSi19bfiwpDpOyjpOlpFIypDoPRwSX69T06RxolEJ8Rj+nJQtUYZFBsfEOskIFk6evonL6oA/H4VjGlaWGqqX5RF0ZaVvPCuVZT4DAPPXmnJlTW+6R43Tvk3pjlN3pE+8JgDgyDqngHlHSKZsAhpZPjTuN28AUiYnZihiX2/Z5azLs1JfbuV3CLbEdHgKR2eUVNjKcV9KGu+xlSX+Sa7H4ChKdZxcn6cEfknpE9Alpqqjw8G44yRMOHW0jNW+zbxw4aTTcQJgPKKiKkyH6jEU7BkWUh1AAmSNLBatpEJD9UJPt3DicZyKrfIeWqbDUzhC9RJf4MNyMzqFU0fosy92cOw9kJrj5JZY9ltS+hZkiiV5Y1QPxscSYcJpjeZICwDIx3LPb6oc5GPN50CkcKrrzdeArPuLksb95g1AIjRUL3T1hrhlNCZTV0KeKXl8W5h2nPyUXjnFwqpj9aXo6J24pNkxWrdXKSyhepDZ3Nh1c0O/yQCFVN94livJG6N6yDSY4/S2pz8nVrJwgqd/QTN15F7PphArnFJZ9xcljfvNG4BEaKheydHrOPlMwqmbv1/pVph3nBiEk2DHKa/03mNxhlc4cYTqlR2ZoXoeeHq1FaBvPAtCF55mca8L02OJcmRNOd8w0OA6F8sdG1NP/3M5kSicGrQ4RGpD9SwjEanCqezofYB4GkNbKmEjU/L4NjG8yupZx6kfeaV3UhoaCOepBA5hXBbaf95NN7DsN5/qjRRo0RwarQvjwkmY4/Rqor+YUE6w45QYEU6yzmkjk1rHyTISSTSvhuuiqLFqFAC44AlJXNcUsOx3W5ieLPgpfT5K4sld2SpoFk5lrwGFkxIYque6cJJOll3nYr3jWZPm0Ghd+KYLzbhyxg3V0oI1Bgo56KxapxsTqQKxwBLYEqvq7TVpLyz+7WLtx9KXah2nsz76L/jmlRdV9BmlFH71q19VtT8TyF2usNRMLNRxykOvcHI0hrZUwlqfJ3l8WyjD7gzHxDoULJy6NQ+hJZe3NxiHo1gSKJzcMaOhFEPcIoCuRG9Zeq7iOUPRUKF62000stnuyBW7/J0YqHbbVzgtO+mj2re/Lab/4t6B/2GQy+yok87GfnvuhkVXX1DTfgVdxf1IkuqO7IZb74KnuW3Ko48+iqOPPhobNmzAuHHjtG57IKxwGsEkmlfDdZHTLZxSnv43q/w8y363hWnHyWNwnCLJwinVu+Kbd3iFE4ejKNFxcsfwnYfuRO9KfdYR6jgljROqF4zbzsh2uyMXTAEXQxK7+p/LkUDHyTRSn35JlY7T2PHjNR8JPY13FTYQsdBQvZzGqlEAgJTne67weKpubQtlODzFT6xw6otuxykP3kkuh3AKVGy8F0qluKP5XJrOSO945msOjdaF6WtN0hy7OGaCke2WEhepkhmuFxko2hRKnLIOMHSd8YUr8NgTT+GmH/4X1NQDoKYegGVvvYPHnngKBx9/GrLtc7D9/u/Hhdd+B1H07sJRdy6PTyy4BKNmHIrt938/brz1J/jIv3wcX7/k673vWbtqLT576mcxe9psHDv7WDx434N4/wHvx1233DXoIa5csRJfPOuLmLvrXBw681D8+2n/jhXLVwz51f7x4j+wd9ve6FjXAQDo3NCJvdv2xpfO/lKv43TrTd/G6f/6gXc/89L/4XOnnYRDdt8RR++/Gy4+7zPY0LG+99+3DNVbuXIljj/+eDQ3N6O9vR0/+9nPMH36dCxatKjfsaxbtw4f/vCH0dLSgpkzZ+KBBx4AACxbtgxHH300AGD8+PFQSuGMM84Y8rvVgsCr0KKLSKhw6ko0D6iaQ1uGy3qnAPiyfuORmOMkOIwfXZodpy7wJvJ7hl2AwXcsyx33RvEt42+M9I5nXMVzhqKRQvU6R+lvftuLx/P8G4rI0X9coVj/pT83Xf0lzJ29D87+xIexcun/YOXS/4HveTjutH/HQfvuib8/cje+d91F+OF//QrX3PSD3s8tvOoGPP63Z/DAj27EI//1Xfy/JUvx3N9f6LftixZchLWr1uJHv/oRbrz9Rtz743t7hc1AhGGIz3zsM2hpbcGd/30n7nrwLrSMasE5J5+DMAi3+T1mzJqBcRPG4ck/PwkAeOovT/X+HW8WTk/99XEcdMjhAICuzk6cfcoJmPWeffBfD/4B373rF1i/di0u+OyZg+7jjNNPxzvvvINHH30U9913H2699VasWbNmq/ddddVV+NjHPoZnn30Wxx13HD7xiU+go6MD06ZNw3333QcAePnll7Fy5UrcdNNN2/xetWKF0wgmFhqq1xnrHVATzaEtlaDG8paP3hLTkwUvoa+AFgoWTp2J3oPrNFCyuBL8lKfCnRImnFzGHm0bY72LMVw5oENhfCwRNG6sazInnFIDIXE6CA0UhxDpOA3A2DGjkcn4aGlqwpS2iZjSNhHfvfPnmLbDFPzn1y7ErBntOPEDR+OqL56D67//EyRJgu5cHnfe+9/49mXn45gj5mCvWTPwoxuuRNznPnn9H6/jL4/9BVfeeCX2mb0P9tx3T1x949UoFQfPt37oVw8hTVJcvehq7Lbnbth1t11xzXeuwcoVK7Hk8SXb/B5KKcw+ZDb+9vjfAAB/e/xvOPHUExEEAd549RWEYYhnnlyC2YccBgC4+47bMOs9++DzF16O9hm7YY+99sHV1/8H/vbn/4dlr7+61fbfePUVLF78e9x2222YM2cODjjgAPzgBz9AsVjc6r1nnHEGTj31VMyYMQPXXnstcrkclixZAtd1MWHCJke3ra0NU6ZMwdixBhcqYHOcRjRSHacNsd4HeaJ5olEJ6dhWYN36od9IhfFQPfqJdeAyuSDDoFOz47QhZhZODKGYAMQ5Tm4T38p2R6D3t3B0h0ZrImM8VE+OO7HKb4WpqvtihZPSL5wCgb2Dhhtl/OKrb2Du7L2h+nzgsIP2Qy5fwNsrV2PDxm6EYYSD939P77+PHTMaM2a09/697NVl8DwPe+6zZ+9rO+2yE8aMG3wB9+UXXsbyN5bj4OkH93u9XCrjrWVvDXncBx56IH5x1y8AAE/++Umcd8l5WP7acvztiT+hc+MGRFGI/Q6aAwB45cXn8bcn/h8O2X3Hrbbz9ptvYPouM/q9tuy1V+F5Hg444IDe12bMmIHxA+RB7bPPPr3/P2rUKIwZM2ZAZ4oCWU8ri1ZCocKpI9I7oMaMwika3SzqJjId188hnEJJP3AfVDaLVHPoSEfMm+PE4Shu2rEgewCAm2ESkAA6Qs3jGVMo81CYHkuUI2fBZbljTjglblaSudZLqPRfdykAOC7ANU7VIYV8AXvuuye+8b1vbPVv4ycOXajhoMMOwjcu/QbefO1NvPbKazhgzgFY/tpyPPnEn9DduRHv2Wc/NDe3bN5XDu+d9wF84aIrt9rOxMmTa/oe/hZpEUopJEwLffXhe1qqIhI1pX+XjlCvcIo0J1NXQtAqa1JiujgEx8Q6EDQB6keT/pX8tSHv9cQhjDftWNZY5fp8PdrWaxZOacMKJ6Obr4jXE3NOciLUcSqbKvfnSpOJAz+fMr6PuM/Efo8Z7XjiqeeQ9snte/xvz2B06yjsuP1k7LLzVPi+h78983+9/97Z1Y3XX1vW+/f0GdMRRRFefO7F3teWv74cXRu7Bj26PffZE2++/iYmTJqAnXbZqd9/o8cM3Tdwtz13w5hxY/D9G76PWXvNQktrC+YcPhdP/fXP+NsTj+PAzflNALDHXvvitVdewg7TdsJO7bv0+6+lZev83em7zkAURVi6dGnva6+++io2bKis+Xgms+lai2Oa55egocWim1CocFqnWTiFEd/3LLbKcvVMh6f4CX3p6LLLt/q/TbL6hdOakDesiivHSVyonsfToy11swir7I8yGLHmhrq6MD2WiBFOSuFV3QWR+pC4Ms9vwwinQW7X6dO2x1+XPo9lb72DdR0b8LnTP4a33lmFf7/0G3jp1Tfw64cfxRXX34KFn/4EHMfB6NZROP2jH8QF1yzCHx//G154+TWc9cWroZTqDe/bZeYuOOS9h+CqhVfhuaefw4vPvogrv3glmpqbBj2O4z9yPMZPGI9/P+3f8dQTT+HtN9/GkseX4NqLrsWqd1YN/fWUwuy5s/HgfQ/ioEMPAgDssdd7EJTLWPL4Y735TQBw8umfQufGDbhwwafw/DNP461lb+DxRxfjsoXnDihq2mfshmOOmYdPf/rTWLJkCZYuXYpPf/rTaG5u7hfSOBQ777wzlFL4zW9+g7Vr1yKXM1vxWMrQYjGAVMdpTaBZOOkObamAXIusW8h0eIpPtKLTl8ARKpwy+ld6V5Z5V489hvMLQNxkyHN4Wg2kGf1VFRPNVfp0YXwsYWpgvCVq7DgUUnPPidhALpEOyjBz3SWOrLFiML70mflwHQd7HnUSJu19DMIowm/v+g8seeYF7PtPp+CcC6/FWaeeiEvP+1TvZ264YiHmzt4H/3L6eZh3ymdx2EH7YrfddkUm++45vu4/r8N2k7bD6R86HeedcR5OOu0ktLS2INs08O/d3NKMOx+4E9tP3R5fOPML+NBhH8LlX7gcQTlA6+jh9as7aO5BiOMYBx22STi5jofZcw6FUgr7b85vAoC2Kdvjzl8+hDiOcc4n/xUn/dNh+NZVF2PMmLFwnIHvgTvuvBOTJ0/GkUceiQ9/+MM4++yzMXr0aDRVENExdepUXHXVVbjwwgsxefJkLFiwYNifrQaZM2uLFsQ6ToFeoVMOPGjuqTtsupplPJx7ML3K6sX0jlNJaHGIJGtIODFqcT+JWKqRpb6syZCLwUNfTJJ4+geyUHNOqS4axXFKJ5hpfttDbKB6nQ6KMLOgmW6egE//xb1Gtl85Az+fdtt1Zzzx33f2e236tB2w5MHB+y2Nbh2Fn/7n13r/zheKuPLG23DCaR/pfW3SlEn43t3f6/171Tur0LG2Azu179T72vNrn++33YmTJ+Lam68d3tcZgNPOOQ2nnXPauy+kCot++NMB37tz+6648bbBv+MP7/1Nv7+33357/Pa3v+39++2338aaNWswY8a7hSTSAVoXbNy4sd/fl112GS677LJtfQ1tsA8tN998M6ZPn46mpibMmTMHS5ZsuzziokWLsPvuu6O5uRnTpk3D+eefj1KJJ6xCOhKFU+q3aA9FKWmuQlUJG5qEJakaLw6x7b4PJig7wn7jzSQGHKd1gY/U4buefQZhDECc4+SmlcXY6yL29DtOgeaFKl2YvtaUEMcpbjJb8MVEvyQdlAw1po+FOU46ZzNLn38J//Wrh/Dasrfw9HMv4hMLLgEAvO+f39f7nr/+v7/ijw/9EW+/+TaWLlmKC86+AFN3morZc2drPJKh0DfR+MMf/oAHHngAb7zxBv785z/jlFNOwfTp03HkkUdq24duWGfW99xzDxYuXIhbbrkFc+bMwaJFi3Dsscfi5ZdfRltb21bv/9nPfoYLL7wQt99+Ow499FC88sorOOOMM6CUwg033MDwDWQTCKy1k/j6JwblgO97rs/yJZEPhPFQPYYcp5IrUzjFBoQTAKTZMVDFwRsamoSrql7qOXJaW3oe3JTHcYpd/Y5TOfBhKt2kFsw7TjKEU2I4f89E2W8dlAw5TrEUK9EQ377lx3j5tTeRyfiYvfceeOg3P8OY7d6tfheFEW762k14+8230dLagv0O2g/fuOUbW1WdGy4H7XzQoP92y923DCzINIaehmGIiy++GK+//jpGjx6NQw89FD/96U+r/j4UsAqnG264AWeffTbOPHNTV+FbbrkFDz74IG6//XZceOGFW73/z3/+Mw477DB8/OMfBwBMnz4dp556Kv7617+SHne9EBha8amFxB+6ikulFEoe24W8JiPL7TRejjyOQB3LVVYyc5xi34xwijNj4DAJJz+mdxQBIPVcMcLJZWxqHbr6iwiUhDpOGcPXmhIybiSu2adTJDRUr5A0iHDS6Gzuv9csPPXQz/q91p1txfL03QXaw953GA5732FbfrRq7vvjfYP+W9v2WxsYAACN/bSOPfZYHPfPH9C2PQrYhFMQBHjqqadw0UUX9b7mOA7mzZuHJ554YsDPHHroofjJT36CJUuW4OCDD8brr7+O3/72tzjttNMGfD8AlMtllMvl3r+7unhWEjkoC3ScIl9/2EIpdDAaCukgscYmWe0WyPe5LUyHp3hRAOqEsqLDM5kfisg3M2GJvFZDa7VDw+EoAkDiuvxx45vxxuh3xYdL4OofH4slec8BAPAjw/e1kFA908IpZBsttk0hMTQ+VlBtjQaz15kaIL9HJzvtstPQb9qCxGCxk3qATTitW7cOcRxj8hZNsSZPnoyXXnppwM98/OMfx7p163D44YcjTVNEUYRzzjkHF1988aD7ue6663DVVVdpPfZ6IUjk5TiFrv5JSZoqNHlNKEZF7dseirc9WULcfFW9EPTCSWaoXmjIcSr7o7lqncCLQ4BhIpYa7j9WCW4r168PBEq/41QoeWJEaV/82GyYs5QcJ9PCKRAaqpc35DhFIq9mcyiGBeGhSDU6TvVIXV2Bjz76KK699lp897vfxdNPP437778fDz74IL761a8O+pmLLroInZ2dvf+99dZbhEfMS5DKW2ksO2YSZZsM5AYMh41OCcpAdbVqMR2ewhHKVVQyHafQMzNhKTnDKxFrAj/iydlLXDmPIncU30S0bEI4BS4caeFNMH+tKSFtDGLDhU8CoY5TXsPCbZICQAr0cV1CcdNWw8JGnm5CUqfCaaDqfNXAZklMnDgRruti9erV/V5fvXo1pkyZMuBnLrvsMpx22mn41Kc21b3fe++9kc/n8elPfxqXXHLJgHXis9kssoImtpSUU3mOU8lAKAoAZJmEEwBg7GhgTXno9xFgepWVY2JdUjIdp8AzM2EpcAonpqp6iSdnMuS28C04lQwIpzRVaPZakA95elMNhulFGAUpwsmssAkkVv4AkItrn39sLCUI4xRpFEBtdvgDYZN200cj0XFKNFdGpiIINs1f3BoXM9hm1plMBrNnz8bixYtx4oknAgCSJMHixYsHbV5VKBS2Ekc9P4AuJTmSKAt0nErKjHDKOOY6sw9FOrYVWLOObf/9MLzKylGOPO/IqlzYQ9k1M2HJKb4cG89w+NRgiHKcGNfZijAzjjW7AoWTacdJSHGIyLDjVB7BjlMxSrH49Rz+JeNi/ARAeRkESqGcyDi3AJBGMRyD4iZAjETItdxDEoZINYmnUqkE1zEvxJIkwdq1a9HS0gKvxkqXrJbEwoULcfrpp+PAAw/EwQcfjEWLFiGfz/dW2Zs/fz6mTp2K6667DgDwwQ9+EDfccAP2339/zJkzB6+++iouu+wyfPCDH6xZQY5EJDpOBQMrqgDgMwqncHSzmEcXxSqrpzxEKZ0zkRcaqlcytJLcDT7hxBeqJ2cF08vyOZwFmFFtrI78IBhfhBHiVMeG+7KZKvtdK92RnuO6/8U8AOCYXWL4rgKggFInkMoQE15eQcHctRy6PtYKydfrIQ0DpJq8Nq/QBIeo4IfjONhpp52gatwf68z65JNPxtq1a3H55Zdj1apV2G+//fDQQw/1FoxYvnx5P4fp0ksvhVIKl156KVasWIFJkybhgx/8IL72ta8NtouGpiSw8kk+NfMA9w0JsuEQtGbFPLoo4vp9x0NEGNIlNcfJlHDqNHSPDAcvjeEoBwnxpERUqJ7P53DmU0PCyVBuabV4BE2epThOseHiEGWBbUcAoCvS871TAPe9mMeD/yhgfJMDRwG3/t9PgHfe0bL9Wtnxgz4yyQpj218xbkd8aZSMaxkAmv0WrHv5HG3be+DfD8eoDI0UyWQyA6b0VAq7JbFgwYJBQ/MeffTRfn97nocrrrgCV1xxBcGR1T+lWJ4L121oUugqvviaQqvP6BH0h2Ky4DkeQLiYG6gYcBxAUHgGABSVmQnLhoR3kuspD0FKKx5iglCN4eJ6JbaE7FxiZhzLOLIcJ98xP9lXlIPUNggds8/h4ggXTj2UohQrc5vOaVoM4a5cqXX71dKcc+En5oqONfvASl/O+NjmTsGKbn33VjbbhKYsuxSpCDnLfBbtFAWG6nUnZpwh11CIy3DIN8sZ1BTMO0EUk54tURl5CdBFQ47T+oh3kkvhBmyJpDUeV/HlAnUbcpw8JUs4ZSiEk5BQvciwcCoJbDsCAF2Rue8djOIroLMVqdmICK6m5IPR5Ar67ZmwwmkEU0wEzUY202loYuAwCqfOZjnxxySOk2K4rgQKp4Ihw34ds3DyGYSTpBwnF51s+84ZahrqGio6US2+IgjVE+I4mRZOch0nc9+72Dza2LYrRRkOJfeY8k4Ho8mREl/DhxVOIxiJoXobIzMCR6V8E+sNjMnkW0KxyuobnggMhCKKga6EgqHJ39qQVyRSTGq3JJYknNINbPvujMyce2VowapaaMYQntL6WxIZvp8KAh2nVDkoGJx/5JrkuB7KcKEkaY6TLyxfkgMrnEYwpUTe6e0wJJxgKDdgOKzLyujhtAkC4cQwsU59eauqOUO/w6oy7yTXYxDGsZChSmUycFO+UL2NsSnh1IiOk4ycyMDw/VQQGJIPz6xr3pWR43qYrKgHyBNOLqxwEvK4spjA5IpPtXQYWlFNDYW4DIc1mRLbvrfE9OoXAHiKYdjw5U0OcoYmLO8wCyefIRQzEvIkcsfyhgB1airhvBWMC0sDQeE4KSXDcTJdHKIQy1tUSj2zQr3DlzN5V4YL6XD11hsMh7HqqxSEPK4sJigKdJzWGwpDShK+h8dKT05jSZJQPYaJdSpQOHXDzO+wJvCRcuSRbYYjx8lgOkRFuGN4Q4A6AjPjWBILE04E1zfFItJwCAyNEz0UBOYyJ4aF0zpBVSJNF2Ry0wQOx2LlIKhEzm/PhZyzYdGORMdpbdnUxIDPcXrHFSScKKrqcQgngTlOXYaKQ6SpQprlcz48hseClFA9t5U3pG2DIccpMuT0VwuJcBKS4xQa/q65WN7YmLhmhfpqxob3/fBofnuOSraDkcRCfntGhDyuLCYoChROawIzA2psKsRlGHQ7ZahmGYMJxWTBI+ry3ZeE6AFVCV2pufsryYwxtu2h4Cj+ETkyKlO6o3gnKOtDM9d5LE44UUw9ZAinwLBwkrhAmjhmhdNKIcVOFNFziaWS7SDEkYy5DidWOI1g8lKWcTeTuhljxxRqbrZXMWP5Jrp9IXGcGIaN1Jfz4Oih02AobOgzCieG8xsKOb1eM9+BpMpBpyHhFDIuLA0ExTWmDPfXGS5lwyKxW6DjFBt2nFZIEU5ERYs4wqcHI2Su+ioBWTNri1bECaeMufyBMOSdGCRjpFT5oQjVYwjl8mRdy1AK3QarWQUeX64NR/EPMY4T53zMYMJ72VDuVLVQjCFSQvVMO045KQmCfYgMh9KtjD3A5f/eyqM5BknCqcxcvEgCwmYjFp3EqcOaZL4liW9OXASGVmqHSzRaRsKk6Qo/AOCBIVRPmOOksmYnBkWXL8eJQxiHUoRThm+ynRgUTkVDuaXVQuM4SRFOhh0n7miLAYgMh+qlUFBj+KM8qISTpFC9Utk6TlY4jXRcORd5ZHAVvcQc61NulbEKQ1McwjpOaDItnPgcTJZQPTHCiS+8K/HMCadSSdbk2ifIkzTdX2e4lA3mQgJAl0DHKXTMzzvSMeOM72MoqHKcOHonDkbBCicrnEY8rpyVxtAzNxksM6+oRhkZtxJFXL/P4DjFvozft5cms0I5Bz7hxFH8Q0qonsPY+yc2KJzyZTkTL4BoDBGS41QyvNCUj12kDGPytgiV+YXENMu/WEkXqidHHOeKssYSDoTNRiy6SQlWfoZL4JqbGBTKvJdy5Ml4cFGssnIMm5Er4/ftIc2YdZy6GYUThzAOlQzhxNk0NXLNhfsWpDlOBNeYUqmIPJiS4T5OAACPX0T0JYD5eUfiCVgUJrq+WJrOD0KuIOB3Z0bO2bAYIRXkOJUdg6EozCuqIib2ShFV1WNwJIQI0x5Mr3Z2MnZnZxFOjvnGzcOBUzgFBpt6xolC1pVTRpjqqUQVSrUtSinBNIu1qsnWBIpAOLn851a5NFNojt6JA9HitSBOZD2LObDCaYSTCmqcVjIonMLYgcdYeUaCcCKLt7bCCUnG7ERlQ2LuXhkKjuIfgZBQPaX4BJxJ4QQALQZDpSvFJzrdIoQTwTQrEeY4lRvEcSIL1RMjnPiqvUrCCqcRTiJIOBVgdmLQZDDUZShEFDaiasaX0k9yRfy+fYh9sxODjojTcaIndBKGvW4NZ++fwHAJZ87xcUvoHCf+CSeFcEqFOU5lmD+eRnKcOBazBqLZCicAVjiNeBJBOU5508LJYxRO/M9nQseJHikNUnuIfLMTg7WM3dk5HMWA0enpC6fjVFJmz3nWYI5ppWSo1l4EOE5FglC9xHD570opEjwlEgHnli5UT8ZUvcmR41pzIuNsWIwhyXHKwfDEwPCK7baQMLFXPpFwYoiqCoWNVJFhx2lNyHctcziKgZjiEHyOU8nw+JhRkhwnmvOtRBSHMD94xcIcp1Jqft4RC8jfVkRtMjhaRAyEr6xwAqxwGvFIEk7dqdmJgc8pnATkaJA14+OYWHv8v29fQsOO0+qAbyLEMWIEUopDEDSQHoyC4fAm33AOVSX4RGOIhFC9IkEyfSwosgQAigTCKRIQqocGC9XzlBzXmhMrnEY4saDGaV3xyF1RFeE4Ea2uUk16+hK4soRT4JmdqKwsMQonhp+6LCVUj6Aq5WAUDDtOruHtVwKZcBLgOBUNN8AFGlM4xQKEk3JoBA1Fw+jh4DBWe5WEFU4jnFjJcZw6E7OTQY8xzjtw+ZPbR3KOkzzhZPZXWBX4SJni2lkcRSmOE8ps+86nZscvR5BworrGqEKptkWRwC2IhOU4FRLzQk6C46SIqulKCdVTiRVOgBVOIx5JwqkjNDuYugSVfAYjEFAVjGqS4KX037Us4PftS2B4hTdNFZAZbXQfg8HhKJaVjPOrEr5QvW7Dk01leOGqEvyE6HxzO06eh5RCOBH0TaqEfEIQqufwu4l0VfVkkBqOGqoXrHAa4UgK1euITa+ocjpObLt+F6pQPapJTx/KAhy9vpQIEpPjhhJOfCFyfVHgE04508LGcI5pJfhEiy9UE9tB9+/TLFyGwkL18on5eUfI2LexB6qgACnL3zFjtVdJWOE0wokEOU7rQ8PCyXCoy7aQEGpEVhqVY2ItTDgVCYRTmBljfB8DQTWp7UsA/vsH4C0O0RWbnfwmhheuKoFq8YVbOIFIOEViptabyMUN4jhRCadURo5TGMkZQzip6rS/9tpruPTSS3HqqadizZo1AIDf/e53eOGFF7QenKV2IkGO07rA8GCa8q26SQglI6uql9BPciX8vn0pElSrDDwex4kjFLPo8JUB78XzoBjLoncanmwmhoVZJfgpzRjCLpwM50L2EAgL1cvF5p9FgQTHiSjHySMq3z8U5UDWdcZFxaPKY489hr333ht//etfcf/99yOXywEA/v73v+OKK67QfoCW2pDkOK0pGz4WgoTUwZAwsSdLVGUI1SsJqbrWQ5Hgviq5PF3a/YTBURTgODkZ3knBxsjs/kPD26+EjHWctBI0oOMkIVQPVFX1ZOgmlBirvUqi4lHlwgsvxDXXXINHHnkEmT4Pmve97334y1/+ovXgLLUTCUkrTJWLjtDwiiqjcCo5/DkadKF69JPcooDfty95god20WESTgyOU4mx8WwPVA2kB2NjZHZ8jAwX56kEPya6n7mFE1GlU2nCqSsy/70DJSFUj0bRcITHD0TB9OJ3nVDxqPLcc8/hwx/+8Favt7W1Yd26dVoOyqKPUIhwQsZ8x2nOUBQJVcHIhBOH4yQgh6wveYL7KsfUpZ0lFFOAo0iVyD8YpheWAsPbrwQ/oRFOVC78YKREoXplyBHFAI1wChspx4lmN0OSKwiZTzJT8WkfN24cVq5cudXrS5cuxdSpU7UclEUfUoRT4ptfPU8IwgMGQ8TEniremmjS0xdpjlOOIHewGzzCyWcQTgDIVucHg9txMi2cSoJWi/2YKMeJKJRqMFKia7osZmq9ia7IvKgpi3CcaPbDkXc6ELmCrOuMi4pP+ymnnIKvfOUrWLVqFZRSSJIEjz/+OL70pS9h/vz5Jo7RUgOhkAE18s1PAkPDoS7boiSgnDJZF3OGiXVRQChXX3IECxKdaYvxfQwER6geQNfAWer+OwKz+y+K6JmwiYZxnIhczFIq4znfQyeBcLKherQ0e82IEluIG6hCOF177bWYNWsWpk2bhlwuhz333BNHHnkkDj30UFx66aUmjtFSAyH4BxcAiDzzwiniFE4CHBG6UD0O4cT/+/Yll5q/rzYmPMKJw1EEADA7PsrnGytTN4uy4UlJsSRnct0ojlPi0lzTkoRTqlwUCarqlQVUDG4k4TSKqcqrRCq+8jKZDG677TZcdtlleP7555HL5bD//vtj5syZJo7PUiOBkFC9wDUvnILQY2uxXQS/I0K1AOfHEfnvnJdQrroPXQQLEh1MXdr9OAbLegu748QonHzzIjlf8hhbhPfHT4juZ27HqQGFEzyacassoA0pVfsCjyGveEuaCRa/64Wq7+qddtoJO+20k85jsRggSGUIp7JjfmIQhB7QbHw3AyIhB4dqddWjqojVhwL4GpMORFdifpK9jqlLu5/wCCfluqzdSjiFU0IgnILQwSjHQ8TlKPbBj2mEE1UOymAkRIsBxUTGcx4AUjLhJCCahkg4cYVP9yXrWOHUQ8V328KFCwd8XSmFpqYmzJgxAyeccAImTJhQ88FZaieQMLgAKDnmFU3JcI7AtpAhnGj246cMxSGE5Th1EkxU1oY8wsmLQ5YyTimjcAF4Q/USjyYss8Ubha6gk2Rf24KqHHmjhOoVhCyQAkBCJJyKEnKciKrpShBOGSuceqn4blu6dCmefvppxHGM3XffHQDwyiuvwHVdzJo1C9/97nfxxS9+EX/605+w5557aj9gS2UEiQwLv6jMTwxKZb6HhwRHhKwBLtFqcV9ShU3NJEMBAsp1USAIE+kmKOk7EGxV9bhznBh7/kQujVXe7LagCwKEU0QzXlLloAxGTCWchDznASBxaQJCRYTqEV1fHC0itsQVUmhMAhVfeSeccALmzZuHd955B0899RSeeuopvP322/inf/onnHrqqVixYgWOPPJInH/++SaO11IhIuxsAAWCGLpimW8gDVUCOMwDOVWiKkOoHgCorIxeJSpLMzHIEyRYDwRVxbOtcJkdJ49x/CASTlmXp+DIlvhxmWQ/7KF6VMKJaawYiMSlcZxKAuY2ZI6TgBwnZYVTLxUPK9/61rfw1a9+FWPGjOl9bezYsbjyyivxzW9+Ey0tLbj88svx1FNPaT1QS3WUCKp/DYcczA+mxcCFAl9ohsrwTuzJElUjJteHuUFpL000E4NczOPAeAyOIiAgVI+xkACVcMoQhEwPBzLXmlk4RQ7NPZwX5DjFDs1zsMh9cgEoosbdbFEAfXCscOql4iuvs7MTa9as2er1tWvXoqurC8CmJrlBwB+6ZAHKQpJGu1Pzk800VcgShQkMCHeoEVUfp5jp3s4IGbizNMIpH/NMDDhCMYHGFk6BormmfCVEOFGF6vGmOCEmclHzTIssAxE5NNdyQUBPIbqqevw51GwliwVSVajev/3bv+GXv/wl3n77bbz99tv45S9/ibPOOgsnnngiAGDJkiXYbbfddB+rpQrKBNW/hkNXQjOYNnmMEwNmR4SspwTTxJr79+0hHemhekyOYsqYYwTwhuqViYSTJ0A4KSi6BrjsOU40Y1ZOUKhe5NCMjxIcJ1A5TkR9z7aFElSAhJuKf4nvf//7OP/883HKKacgijYNfp7n4fTTT8eNN94IAJg1axZ+8IMf6D1SS1WUhYTqdRL1pGlymwFsINnXVrA7TkTx1lyOBGPVs74kGaJQvcgFGKI/+UL1mIUT4+6LRILGJQiZHgqPKHwNoHMEBiNyaMasHFMhmYEIGylUD0TCKeUXTrDCqZeKf4nW1lbcdtttuPHGG/H6668DAHbZZRe0trb2vme//fbTdoCW2igKcZw2xjSrUD5RmMDAO2d2nIjCUtgm1hmfMYPtXZIMUdWoxEGqXCjihyZXKGbiuazp3pyhekWi1rQq5W+B6zuE42TD5DjJeM4DQKiIHCcRoXoJKJrPeXHE0iKiH0IW4SVQ9V3d2tqKffbZR+exWAwgxXHqiGhWoViTn5kdJ6qqegopPOUhIu7nlHquCOEUUxYB8bJAWKDbHxgdReZQPcflcycKVE5QIkE4UTpOvNXIqByn7ohfRPQQEAmnPLcqBqHjJCDHKU25lZscqhrBnnzySfz85z/H8uXLtyoCcf/992s5MIsepDhO60Mix4lo0B4I9uR2wkmC73iIiMuSJ74n4FEJRD7dNZa6GShi4eQlERQUUoql1D4kzMKJ8+IqEDlBKVGu6bbINFCoXkgmnDwxufsBkTVSSp1NYRYp3zlWKiJxnLhagPRDyFxSAhU/Ku6++24ceuihePHFF/HLX/4SYRjihRdewB/+8AeMHTvWxDFaaqAoJGl0XUgzmLqMwilhzsGhFE6UeQo9JL4E2QREPp3jlLo8Je5Zzi9jqBzA6zjlUprznMT8vdB8RSiciPI+ByNSRMJJyHMeAMqEiZmKOzweNILGEyCcUpvj1EvFM5Frr70WN954I/77v/8bmUwGN910E1566SV87GMfw0477WTiGC01IMVxWhvQDKacyc/cxQsoK0hRhtv0kDA7ej2EHqHjRJRovSWkeSib4XacOCuwdRM5QTFRyPS28IlcGKBxHKc0VWyLLFtSJsrXAwBw906kCtXjagHSh0SQOOem4ifVa6+9huOPPx4AkMlkkM/noZTC+eefj1tvvVX7AVpqoxDJuNjXlGkm2g5HGbLNcE/sFQgdJ8JV4x5iIY5T4NGJioSpL5lHtFLel8TjdZw4J9ndRE5QGPHnKfiU1xZzjlNAKBJBuKCzLYqUVQwIx+IBUTROEFsLkD4k1nHqpeKZyPjx49Hd3Q0AmDp1Kp5//nkAwMaNG1Eo0MbiW4ZGQuWZ1B+FOCU6jqSBhRNRTwkA8B366ypmLlfdQ0C4spuwOU4MwpiogfNgcIZ1dcU0E8CAyPnfFpTCibs4REi4wJRyNn/vQ4myiAB3qF5KI2i4Ktn2xTpO71LxXX3kkUfikUcewd57742PfvSjOO+88/CHP/wBjzzyCI455hgTx2ipgWLMP9lMMqPI9sVZbpd/xZywOASH48T8+/ZQJmpqCTSYcGLOceKcZG8kcpzKgc/SG6wvjSScAuWQFA8ANrnT/E97oECUrweAXzg1UKheLCTtQwIVPx3/8z//E6VSCQBwySWXwPd9/PnPf8ZHPvIRXHrppdoP0FIbhcQFdw3nxGsd+k2aSCkH7S2IuXNwVEz2kOYI5YqYJ9Y9lAiFU8yQawQwhepxCydGx6mTKPeoFPCH6nmEnYapJraDERDeR1xhvVtSSghdNuZQPQUaJ8iP+B2n2DpOvVR8hU+YMKH3/x3HwYUXXqj1gCx6yccOe5nSyGsh21dKFPIyENyOCOUkwSec/PQQCclxKhIKp8jhmQyR5qFshtscVw7fJHtDSDNIF0r8kx+f0BehDF8eiDJhS+eEaazYEkrHKfV5m6KrlMYJctMYjnKQpHyLOxH3AC2Iin+Jp59+Gs8991zv37/+9a9x4okn4uKLL96qp5OFHwmhegGh4xQzltvlDzWiFE4cjhP5LgekpEa+48Ryfhto4WFLOojaNRRK/I4T7aKLgFA9ImKmsN4tySd011jq8a4KK8Im8BwFmfpiHad3qfiu/sxnPoNXXnkFAPD666/j5JNPRktLC+699158+ctf1n6AltqIUwcpQ75CXwKXznGKWR0n5nLKhBM/ynCbHogW5YekQChmIkKR1hfKktE9cK/xcLoTZMKp7MJhuHf7kqEM1WsgxykWEqqXt6F6RuDIO+1LJGXlUgAVj2CvvPIK9ttvPwDAvffei/e+97342c9+hjvuuAP33Xef7uOz6IC5v0PJaSbbV8g4u+ZfMadb/aIMt+khZGxQ2pc84cpfpJga4DKc30YVTqlysJFwUtJMGDo9EJTBVfw5TnQXNddYsSU5wsXLhLscOVFVPYCnKXlfbKjeu1T8S6RpiiTZZH///ve/x3HHHQcAmDZtGtatW6f36Cx6YAr36aGo6B7UIdHK7UBE3BM/QuHE4TgFQopD5AmTBkPKnih9YMlhYxbGlKvH/fCbkaZ013YzYQTAQNAKJ7oxcSBKhI5TJCRUrzumGx8Tl1FMuC5p7zeOSrZ9CbgnOIKo+Jc48MADcc011+Cuu+7CY4891tsM94033sDkyZO1H6CldlLCZPaBKIDOcQo4hRNzKBmt40QvYqQ4TjnCB1jIFarHIZy4+zgRNbPckoTYAWpqKOHEHKpH1b8QQCjFcSJ0T2NGx0kRl0LnCJ/uSxjaUL0eKr6rFy1ahKeffhoLFizAJZdcghkzZgAAfvGLX+DQQw/VfoCW2kmZV6LyhMKpzHhzh+wLMoTCicVxEiKcCFeRuRwnj0EYRw6348QknHy6PncAkHHpxuOBIL2imcRwDyXCcVKKcOomDNWLGR0nRdx+hNtxCm2oXi8Vn4l99tmnX1W9Hr71rW/Bda0ilUji+IRTva3JEzalLZf5Bhfu4gWkoXoME+syY7novnSldCc6aCDHKeQWToonVC8mFjIZxSycCE8zZdWzgSgS5gpKEU5dhKEXrMKJeN8c4fF9CflXhsWg7cw3NTXp2pRFMylzjhNlX4dCwCicuCd+hImqHFdUWYjjRCqcUibhxOE4Ma+7OYT3T18iYuHkMLmYPVDunT3HKaGbbAbM57WHLspQPU7h5NPumztUL7DCqZeKz7zjOFBq8IdqHMtYFba8S8IsnEqEk79SyQVXUVbuHBzS0qgcjpPL25Olh66U7gFWbqBQPe6FB4CnD2FILZyYXMweSB0nRODsAF8kjPUIIMNx6iQUThFncQjyUD0+4aSgENhQvV4qvup++ctf9vs7DEMsXboUd955J6666iptB2bRB7dwKhL2dQhiB6OUh4ghRIM7B4fSceJ4XJUdGcKpk3QVmWdiwFL8Q/GeX4dJOAWE7RoACY4T3Ti5aTGJUTgRVkuUIJxS5aJI2Cg1YizRrYhTUzhaRPSQYW5pI42Kr7oTTjhhq9dOOukkvOc978E999yDs846S8uBWfTB3VG8QBjaBABNXhNyYY50nwAQcIfqUTpODF+1xNzMEgDgeQgIH2Bl4nunB46pNbtjm/AIp7JDG+aumMI/eyAdO5hznEqEVfXKjAKxF4/2WuZ0nOiLQ/AJJ5958V0a2s7EIYccgsWLF1f8uZtvvhnTp09HU1MT5syZgyVLlmzz/Rs3bsS5556L7bffHtlsFrvttht++9vfVnvYDUHCXI2lkNDedFzldrmLF9CG6tFTcngnQQCgiHM5uYSTx6BhQsKeKAOhUGbZb5m4WIPidpwSOmeRrTfXZoqEzi1lSPxgpMTCKWTM+2ks4WQdp75oeSoXi0V85zvfwdSpUyv63D333IOFCxfilltuwZw5c7Bo0SIce+yxePnll9HW1rbV+4MgwD/90z+hra0Nv/jFLzB16lS8+eabGDdunI6vMWKJmWPaC4TWPQBkXJ5CJayheh5xhR+Gr1p0BThOWdprq0QY5toXFseJMxRTKbZCAiVFm5WpmMR4D35KKJwSZuFEmA9ZEuA4JdSOE2uoHq2Q4QzV86zj1I+Kr7rx48f3Kw6Rpim6u7vR0tKCn/zkJxVt64YbbsDZZ5+NM888EwBwyy234MEHH8Ttt9+OCy+8cKv333777ejo6MCf//xn+Jubj02fPr3Sr9BwxMwXfZ7YccoQh770wOk4ka9+pfTKqcTckwUAkKWd5BaZ3AGOvQaMOU7UzSz7UkiJxyv2UD3CHCcVA0oBDOMV9WJWifg5OxCJSzs+hpwFE4iFE0feae++rePUj4rv7EWLFvX723EcTJo0CXPmzMH48eOHvZ0gCPDUU0/hoosu6retefPm4YknnhjwMw888ADmzp2Lc889F7/+9a8xadIkfPzjH8dXvvIV20NqG0TMoXr5mLhsp2ISTpwTP+IO6hyP6KKAPk4pseNEWVilLyyheoyOk8rwTTrzxHVAU27HiTBUDwCU5yEN6Z0nlaGdbBYFhOolxNEeYUPlOPEJJ485akkaFV91p59+upYdr1u3DnEcY/Lkyf1enzx5Ml566aUBP/P666/jD3/4Az7xiU/gt7/9LV599VV87nOfQxiGuOKKKwb8TLlcRrn8bux6V1eXluOvJ7hD9XLEoXoecehLD2XGUDJFvLpJGW7TQ0HxJO/3JcnQXltsoXoMK/SN6zgRj1dM11QPfko8TnoewCCcQHxNcS2y9CVyaK/lMueisEsrZDhaRPTgWuHUj7oqzJ4kCdra2nDrrbdi9uzZOPnkk3HJJZfglltuGfQz1113HcaOHdv737Rp0wiPWAYRc+xzjrCTOAC4TMKJM5SMevXLY5hYFxRvvgIAxMTCqZDwOOk8wqlxFh76kktor6mkAR0nFoijAKir1w4EdQXfgLFggnIaJ1TPCqf+sF11EydOhOu6WL16db/XV69ejSlTpgz4me233x677bZbv7C8PfbYA6tWrUIQDLwafdFFF6Gzs7P3v7feekvfl6gT2EP1iCd/LnhC9UqsOU7EjhPx5AcAigKq6tELJ65QPXrhxJoj6PONkd0J7WQz5XacEtrzTL2o1AvxNVWI+Se3EXF+ccA4t1EetXDiwwqn/rAJp0wmg9mzZ/crYZ4kCRYvXoy5c+cO+JnDDjsMr776KpI+k7ZXXnkF22+/PTKDxBNns1mMGTOm33+NRshcfrY7or3MVMqTyFjkLF5APYgzTKzzTA1K+xL5xHkLbI4TvTBmdZwYhVNXTHtNJUzXVA+ZRhFOxI4T9QLlQITEjlOZsziEQxyqx1i01wqn/gxrtvXAAw8gNBAjvHDhQtx2222488478eKLL+Kzn/0s8vl8b5W9+fPn9yse8dnPfhYdHR0477zz8Morr+DBBx/Etddei3PPPVf7sY0kQuZQvW7iUD1FnTOwGdZQPeou5sSTHwCIVAIwF4GJfGLHibiwSg8cwrjcQKGufemMaa+pmDjndEvIHSemAgIpcRQAdRGmgQiJw+TLnKF6xDlOnNLFYV58l8aw7rQPf/jDWLVqFSZNmgTXdbFy5coB+yxVysknn4y1a9fi8ssvx6pVq7DffvvhoYce6i0YsXz5cjh94kinTZuGhx9+GOeffz722WcfTJ06Feeddx6+8pWv1HwsIxl24UT9oGZynHhD9ajLkfMk8quMj7TI9zsHHu21lWea5HoMoZisVSkZHadO4glvHHtgTJeAHxMLZGI3voeU2HHKCRBOAblw4ixH3jjCSQnoESaJYf0akyZNwl/+8hd88IMfRJqm/fo41cqCBQuwYMGCAf/t0Ucf3eq1uXPn4i9/+Yu2/TcCnMIpdTNIU9oBJmGK9S4ydqknF04ME2sAQCYDFEs8+wYQEgsn6oqUPZBXPgOz40Tck6UvG0Pa8SqO3Srq6erDT2jPM7Ub30NK7HRxjRV9KYN2fCyB8Ts3UKieY4VTP4b1a5xzzjk44YQToJSCUmrQ4g0AEMf8vVYs/WF1nDz6sLmEuEpVD0WHUThRN+NjCNUDNpWNZnx+oOwS5y1EjZPjVGbNceITTh0h7WQz4hZOxI4TlyhOiMuRU1evHYiA2BcpMxaGpo4S5Aif7kHZUL1+DOtOu/LKK3HKKafg1VdfxYc+9CH86Ec/wrhx4wwfmkUXAavjxCCcmBwnzuIF1JMDjhwnAEgZG5UCQMllCNVjCKtiCdVDY+Y4dYS043MUuSDuudsPP6FdYOJynBJix6mbaZGlL2XiC6vI6Dg1lHASUOpeEsP+NWbNmoVZs2bhiiuuwEc/+lG0tLSYPC6LRoKUb3BJGIRTSDwR6SFV2NRsMaKfALI4ThyLfYy5KAC949TN5A5wOIq8xVX4kn6ohVPIPMH2Y2Jnnslxog7V645dcEauAUCR2JkopZzFIWj3x9Eios/eGfctj4p/jSuuuAIAsHbtWrz88ssAgN133x2TJk3Se2QWbQSMqwUcwimOfbYHiPJ9pAzCiXpy4CcRi3BKM7wDeMkhrpQVOQ0jnMqMfboctsl1BsUS7WAVEreH2JJGCdWLORwnZuFUSomFE2sDXNr9cRVkAgBYx6kfFZ/6QqGAf/u3f8MOO+yAI488EkceeSR22GEHnHXWWSgUCiaO0VIjrI6TQy+cAuJk634whZKRV/jhCtUjLvG7JUXyfhaKJdzVY8hVjZECDs9ESHk8jlPq00duBCHv7NqLaEOaqRuV9kAdqhenDlKHN5S5QFzRtsA4t9FYI21YsAonAT3CJFHxiHL++efjsccewwMPPICNGzdi48aN+PWvf43HHnsMX/ziF00co6VGyoyJfTFxQzwAKAeMNzlTKBm1cPKoSwpvJvF5B/A8seMEACCu5AfQVz7rQREn1Pful2nhOvUYhBNzqF4mJhZOxNXPeqB2nACwFGPqS5HYcSoSV+zti3JpQ+e8hC9ULyU+r9Kp+M6+77778Itf/AJHHXVU72vHHXccmpub8bGPfQzf+973dB6fRQNlxtWCmGG1vMyU4wQAYJv4UTtOPBUEE99ljUYpMHRQT50sFLpJ9+nHIU/jEM8DymXy3XI5TjGHcAoUYy0ywKd2nBokVA8AUrcJCjny/fZQSGi/c4ExxwmKVshwtIjoIbWOUz+qCtXraVDbl7a2NhuqJ5QSo50dKfrV8lKZs/x6YzhO5E0sN8PuODEkHKXElfwAvlBMMFW343KcIq+ZfJ9l5lA9auEEpsIfsUP/O3OMFX0pJNSheow5TtTCiat3IoDE5jj1o+Krbu7cubjiiitQKr3bhLJYLOKqq67C3LlztR6cRQ9lTuHEEqrHN5imXKF61M34mIRTzJSv0ENO0Z9fjgIrHluoHtfCA8tuETPkgEaJA5fpCzvKgUe8cs4VqhcxOE4cY0Vf8sSOE285cupQPT7HKRHQXFkSFV/lN910E4499ljsuOOO2HfffQEAf//739HU1ISHH35Y+wFaaqfEaLOGDI5TOXDZ2pSkvsvRdoe8mpIfBwCaaHcKfuHUzfCgThgWH8hLRm+GuoRzD2yOE4NwAgDf8Vma1XsMCw+NFKoXuxnWVqU54h6KYaoA1wUYrmV6x4lROBELYulU/Gvstdde+Mc//oGf/vSneOmllwAAp556Kj7xiU+guZk+7MAyNGXGi55DOAWxg2blIGGoQpN6PMKJPMcpakzh1MVwLyXEvaOAnlBMjuR2rlA9njCY0KG/hwAg42ZRiktDv1EzPkNxFS5RHDK4ehxVbPvSHTOcX99HyiGciMcMP4lYmqEDQJLwPnelUdVV3tLSgrPPPlv3sVgMUWwwxwkAMk6GZWKQ+B5L4jX96hdXqB5fFSWAx3GKGe6hTSWjG0k4sewWIUOxEQDwGVzMTfvlEE5MoXoM35Wjim1fuiOOpnM+UKJ/1itFLZwStj5dsQ3V64eVkQ1AiXG1IGAKHMgyxXonPlMfGuou5tQJ3puJmIVTJ8MiRMzQm4UtVK/RHCfFFarXOMKJa7IZMhSH4Ar97IFHOPFcy9TCiSvvFLDCaUuscGoAioyhegGYHtBcwokpnp68iznTxDpkqpDVQydDFSeOyRBXuXk24UQ8CeohYHLkPSbhlGFYBGBznBhC9Tiq2PaFI1SPqwUIqB0npoJMABBZ4dQPK5wagELMd5q5mu9mmFbeEraJH22onkLKkujNsaDZg8pkEDMMmTFDOBeXo5iyLTzwNJcsMy0seVwhggxiguvc8jhOvMKpi6O5MlfvRFCH6vEVh7COU3+scGoAOHOcuISTzxQCw5WDQ93FHAA8hokBawuaJp5E/ohhkusnERRDJjKf48QzKSkzjVMel9PFIpzIdwkACBi+K1dOcQ+dDMIp5eqdSDxmeExRHgAQRVYq9KWqX2Pjxo34wQ9+gIsuuggdHR0AgKeffhorVqzQenAWPRQYVwvKKY9w4gpFYav6xqDXfIawm4CzKmqWRzhxTYY8hnwUPseJRziVmMZHt6EcJ/JdAuBxnLhCPwEgVS6KDHON1GNynIiFE2eoXmgdp35U/GR89tlnMW/ePIwdOxbLli3D2WefjQkTJuD+++/H8uXL8eMf/9jEcVpqoMgYqlfiqMwFwGVyutgcJ4YcDY5E75Ap7AYAkOVxB7gqr3mOh5A41ylhWnjgcpzYhBNbJADD+SUOY+4hUC5AvOuQs4uT3wQU6Xeb+j5PCxAQC6ckBJjOb2gdp35U/GssXLgQZ5xxBv7xj3+gqU/oynHHHYf//d//1XpwFj1w5jiVEq4VVZ6Vt4ipeAFHHD9H2E3o8QmnJMMknNgmuQ3kOCme1dwSU46TYx0n43CE6nFVsQWA1OVx5Dn63AEMoXpMeacAELDGyMuj4iHlb3/7Gz7zmc9s9frUqVOxatUqLQdl0QtnqF6RLRSFK1SvkRwn+uuqzOg4JVyOE5dwYnAUuapSOkyOU4FpfHTYxDj9+EhdOKeHgEGxcRUbAYDEYxJOTDlOAO1iC1clWwAIIt5qttKo+M7OZrPo6ura6vVXXnkFkyZN0nJQFr0UGPs4cZVCd5geIFwLMxyTAw5HoszUbwcAEp9HOAVM4a4cxT/YyvkTT4J6KCRMPWi4hBNDPSq+iokMC0uMjlPC1QKEK8eJuqoeo3CyoXr9qfjX+NCHPoSrr74aYbjpJCqlsHz5cnzlK1/BRz7yEe0HaKmdNFVIORoPAiikXMKJZzDlqMYK8ORoeBwrqh6fcIqYQvW4VpE5hHHMFeoKnklJgWlhSTGNyxw5Tlw9usoMIpErZw4AEqZQvdhluoeIxwzeUD3rOPWl4jv7+uuvRy6XQ1tbG4rFIt773vdixowZGD16NL72ta+ZOEaLDphWgwox02pQ2liOE3UzPoAnX4HTcYqYHKcyk+PEcX4TtlBXHscpz5QDyuU4ZTjS+JlC9TgcJ07hxNGoG2AUTsRjhpfGcBgWHjzHQ8LQ+F0yFV9xY8eOxSOPPII//elPePbZZ5HL5XDAAQdg3rx5Jo7PoonU9aEYFlXzXD2kmFZU+UL1GkU48TUBDD0eMR4wXcscjmLsMAmnlMdxysVM+RlME2yO+mdcOU5lhvunxDRWAEDMJpy4FmfpF1s85SFIaZ2nDHNTZYlUfZcdfvjhOPzww3Uei8UgKUPPHQDIN9jEgKtcNkeOBsfEuugw9rLweR4gXL3QOIQxVx0bBZ4wGC7HCUwhgizCicmlLlnHiWa/LlPTbIbFFs/xECS0YxVHv0bpVDx6fuc73xnwdaUUmpqaMGPGDBx55JFwmS5my8CkLs+kj2tFNeUSTi6TcFIJec8QjkTvEmOoXsB0D3GtIvMIp8bKceIbH5muKYbhkTqJv4cyg0gsMjpOIVfTebZy5CH9M5chV92zjtNWVHwWbrzxRqxduxaFQgHjx48HAGzYsAEtLS1obW3FmjVrsMsuu+CPf/wjpk2bpv2ALdXB5TjlmJaQU6YV1cDleUgrFZMP4hyOU4kpFwUAykzVm7hK+jdWqB6P49QdMQknNseJHq7iEEUGx6nI5WACCBWT48RQ/RMAwBCqx1Gwx7OO01ZU/GS89tprcdBBB+Ef//gH1q9fj/Xr1+OVV17BnDlzcNNNN2H58uWYMmUKzj//fBPHa6mShEs4MU0MEqYHSNBAoXocYTecoXplppXNElOeIEfPHbZQPSbh1MU2PjaOcAJTj64iQ0I9W04xgIBLOHEVh2AYMzhaRPhMPTElU/EVd+mll+K+++7Drrvu2vvajBkz8O1vfxsf+chH8Prrr+Ob3/ymLU0uDC7h1M1UnzthCoEpczlOoJ8ccEysCxwVTjZTYgpZYAvVY1gxZyvnjzLLfju5xkcm4eRxhOoxOU6llGF85MopBl/bhJCp1QpHeC9H+LR1nLam4iWRlStXIoq2XvWNogirVq0CAOywww7o7u6u/egs2kgUk3BiWkJOmPYbMD2kOVZVPYYcpwJjqF6Rqxca0yqyx+AoxhxVb12XLQ+mi21hicnFTOmVE8eiEsDlOPFNcstMzkTIICbgOCxjBkeOk8s0d5RMxXf20Ucfjc985jNYunRp72tLly7FZz/7Wbzvfe8DADz33HNob2/Xd5SWmmk0xylmWlFlc5w44q3J9wgUHT7HqcjkOBW5wqoYHEWOBvXK55sYdDKF6sVc4Z8cwqmRQvW4Yl3BV/0zZAjVUx6TY8uwWOky9XyTTMVn4Yc//CEmTJiA2bNnI5vNIpvN4sADD8SECRPwwx/+EADQ2tqK66+/XvvBWqonZhBOqeMjZmqcFjFNSEpMD2mWsAEGRyKv+LqnF7gcJ6bwG47zGzHkCKoMz8QgdbNIGcK5ACBiuqYy1nEyCltfMAAlplC9gKFgApdw8hlynKzjtDUVn/0pU6bgkUcewUsvvYRXXnkFALD77rtj9913733P0Ucfre8ILVqIOS5+jydZFADCyK2hS1n1lJmKF7D0cSLf4+aqekoBDBOwAss35gzVo4fDoOaaBMFr4tkvgDhywZDCBj/lcOQZxmSlUGZwB7giPAC+HlIBQ/VP5XMtZtF/V8cKp62o+uzPmjULs2bN0nksFoPEDKsyqcs3MQiZHiAlh8txYgjV4ykgCOX7SAN65ynXYMLJOk5mSRiFUxi5AMO6FodwYslfYxLjOUbhVEiZHCeOSACuUD0GkegwPfckU9Uv8vbbb+OBBx7A8uXLEWwxgbnhhhu0HJhFLxyOU8LqODHlOHGF6jGURmVbh8pmAA7hxJGEDCAXNk6z0lAxCCePqUAD48JSxFUcIuEI1aNfVOLKm+uOvSoSMPTA1W+uzOE4uVwtIhi+qxVOW1HxL7J48WJ86EMfwi677IKXXnoJe+21F5YtW4Y0TXHAAQeYOEaLBiIGxylh7DgdhDxPj2IDOU4cpYUBAFyTEqYHCFdvFg7HKXTpLyqHKewmcZlDmRnwU/rxUXFU4vR5nn1dkQumVCMUuHonMsScci22cITqWeG0NRWfhYsuughf+tKX8Nxzz6GpqQn33Xcf3nrrLbz3ve/FRz/6URPHaNFAxOAPcE4MgpBnYCty9RlKOYpDMMElnJgEDFf4jceQRxZyhOoxhd3EnMKJaWHJTxiEE4NYA5MYDxIHKZMzziWcygzfl004MVQ6dZicRMlUPHq++OKLmD9/PgDA8zwUi0W0trbi6quvxje+8Q3tB2jRQ6M5TuWAyXFiEk4sVfVYEr0BZHgmJV0pz8OSq8Qwx+MyZOiDpjyesSJ2GBeWOOq+A/CTBikO4TFONply5/JMbRNKHI4TU6geR289njJBsql49Bw1alRvXtP222+P1157rfff1q1bp+/ILFqJGC7+mFU4cTlODA9ppgaeHD1ZACBlcpw2MgmnXAM1Kw0Y+qBxrR5HnMKJaWHJTxjyjRjceE7hlDLlFudi6ziZhiN8GqkVTltS8S9yyCGH4E9/+hP22GMPHHfccfjiF7+I5557Dvfffz8OOeQQE8do0UDIsH7MOjGIHTQrBwmxK1Jg6DPE1oyPTTi59I8PpdDNJJzCRCF1PCjiSSdLqF4DOU6hw1ccohy5HEX1kGFwnDa58bSLeClXiXts6g/GQTdTD6kSRzUMl2nhgUE4pUxOomQq/kVuuOEG5HI5AMBVV12FXC6He+65BzNnzrQV9QQTMjhOkeJznAAg62ZRjIqk+yyrmLzPEFszPpawGyD16QMWVDaLlCVMYjNuFiAWTiyOk9NAwolxfAwiB01QSEF7jv2Y3v1RKb3LxSmcEjfL0aIL3UyVbDkaDSsm4cS0RMqyV8lU/Ivssssuvf8/atQo3HLLLVoPyGKGgEM4MYbqAUDGyaAIWuEE0PcZYgsbYHKcEt+lX2PM8rkDAJC6GagwT7pPFuHEUM6faxLEKZwAIONmUI7LpPv0Y4ZQPYb8z5QxVC9hivTo4nKcOEL1uBwnhkeudZy2puKzv8suu2D9+vVbvb5x48Z+osoiCw7HiXti4HNVraLOwWEL1eNxnBKfQSg28QsnajjOL4vj5PI4iQH3+MiwsOUxCCeOiqOJyzfZjBnGCoCv+ieL48TkUvOE6vE1VZZKxWd/2bJliOOtVwXL5TJWrFih5aAs+gkY8jO4JwYZrhyrDK1wYnOcGEoLA0DM8NBKs3z5egBP3gJHKGbAkuPEI5zKXA13NpNhEE5+whCqp1KAuApawlTABuCr1tjFFarHUlWPZ8zwiENrAes4DcSwf5EHHnig9/8ffvhhjB07tvfvOI6xePFiTJ8+XevBWfTB4jjxdfoBAPiKy3Gi/a3Zupgz5TglDEIxzfAKJ47S/hzl5nlC9ZiEE9f4tBkOx4kjxwnYlAeaDrDgawpWx8nhee52MjlOBQ7HyeEZMzhC9eKEx12TzLDv7hNPPBEAoJTC6aef3u/ffN/H9OnTcf3112s9OIs+ygwlJQNm4eRx5VgRh84ppmaLbI6TT//QSjK8oXoJS6ge/fktcwgnpnlBibmxJMf4yJHjBGwWTmW6fC5O4cRRlClVLopMbRMaqTgER95pYh2nrRj2L5JsXl1ub2/H3/72N0ycONHYQVn0wxGqxx2K4nIJN+IGrWwVfpiEU8TgEMQZ3muZoycaT6geh+NEvksAAoQTwwTbj+jbNQD04cxxgwkn+E1gqMMEAMgzCCdwheqxCCeb47QlFd/db7zxhonjsBimzCKceCcGLlOOVUoeqsfVzDKuIkuydiKffqeRzxtWFSv6e4nDUSwxNJBmE07MC0s8jhNPqB51FEDCFD4NACHDeU3dZvJ99lBM6UUMl0vN4ThFTE6iZKoaTRYvXozFixdjzZo1vU5UD7fffruWA7Popcywusm9osomnDzaBq2sxSE4hBPDal/kN57jxOEo8oTq8ZTVL7CPj/T7z8RMjhOxkIkdPscpZBDkice3sJRCbRLGEd2iC1deJEfeaRTZHKctqfjuvuqqq3D11VfjwAMPxPbbbw+lGJtCWoZNicFu5RZODtOKbuJ5tHV+2EL1ePIVOIo3RR6vcOLoiebHMagLVjWUcEoaL5TZj2j7RvVAvbgUMYbqcVSzTbhaf2xGZTJIKYUTU3EIjhYRMVN/LslU/IvccsstuOOOO3DaaaeZOB6LIThC9UoMBSn64jCFCqbE5bLZQvWYEr1DBoMtYBZOLKF6Kf35LYMhVI9NODGPj8QTbEc5cJl6v9E7TnzhTRxFmRKXt3gOde9ErjHDTzhC9azjtCUV/yJBEODQQw81cSwWg3A4TkVmx0mlTI4TcYNWvhwnK5yoCBmEE0ez0pJi6PPDNAnKJ9yOPK1w85nKZAP0DUsjxlA9DuEUcfVM7IFcOJHurheWUD2b47QVFZ/+T33qU/jZz35m4lgsBikxOE5F5okBmBwv6gatbFX1mBK9Q4bTWmYoB94XjrwFDkcxVAlAHP7tMDTdBYAccwgMtSPvM4oJ6ga4nKF6HNVsuZru9kKdg8oknDjyTkOb47QVFd/dpVIJt956K37/+99jn332gb+F0r/hhhu0HZxFHxyOE3coCpgcr4RcODElqsYROJ4gAYNDUHZ5FwE4HCcuRxGeB4R0olw5PGX1uR0nRTw++orveUC9uBRxlWoEUGpAxyml7p3I5TgxCKcgtHUMtqTiq+3ZZ5/FfvvtBwB4/vnn+/2bLRQhF47mdNzCKWUSTuSOE9cgHocA6B+YgUvvEBS5hRPDZIjLUVSeh5RSODWo40TtyHM6TtTCKWTMceJodl92+MqRAyB3nLjGDI5QvTCyoXpbUvEd9sc//tHEcVgMw+E45bmFE9P+Y492AYEvVC8Ah3AqMzhORccHeFJhAAABcT4K0OMoMtxDvg8U6bppKoZKfgDQzVEesg+KPFSPbwLWSMKJI7e47PAWh0g8nzT2gWuxkqOSbRjaUL0tqfoXefXVV/Hwww+juPkBlzI05rIMn2JCf/FzO04JUygMeZ8htlA9Hkei7NGvuhU48zMABCw5TlzNSomLqzA03QWALmbhRB3K3EihepzCiaMNSAm8wok+VI+pql7MEKpnc5y2ouJfZP369TjmmGOw22674bjjjsPKlSsBAGeddRa++MUvaj9Aix6KDCKmwFyNJWEKhYmJhQxbqF7EJJwcBuHEVNq+hzKD8+MxNSulF048jlMncwhMShyFwCmcqHvdhYw5ThwLlkWGyIO+JB5xVT2uUD3iHCdHOQhtOfKtqPgXOf/88+H7PpYvX46Wlpbe108++WQ89NBDWg/Ooo88w6oBdwx/wiTcqOdDXMUhvIRLONFPdPOckz4AAcMqMp/jRLx6zNA7CgC6uIUTdY4T1woP6MfIgFE4cVSzLTA7TglxFUOleBwn6rxTn6Hxej1Q8dX2P//zP3j44Yex44479nt95syZePPNN7UdmEUvBYZVg27miUHMFCoYUec4MQ3iTprAVS7ilFbIlBiEU45ZOHE4Tn7UQI4Twy3UyRyqlxCPjz6jmFAO7ZgcKpctJ5LDccqnjeU4gSHqAaB3nDh7r0mm4tl0Pp/v5zT10NHRgWyWuZa/ZVCKDMUhcszCKWKamFA3aGVcyGWplFXkEE4MfdD6UmKYDHGVI0+phRPonbXUzSJNeavQUocyW8eJBo6iTNzCKaZ2nMAlnGjHZOs4DUzFI9kRRxyBH//4x71/K6WQJAm++c1v4uijj9Z6cBZ9sDhOzKF6XGU0yaMiGYWTxyCcSg79hL6bo7pcHzh6swA855e6WSmHcILHG9oEAAl5jhOjcCJ2nMqcOU4Mz91c0mDCiSkvkj5UzzpOA1Hx1fbNb34TxxxzDJ588kkEQYAvf/nLeOGFF9DR0YHHH3/cxDFaNFCIXVDPvbhj+LmEU+jRxmhwVfgBeBK+SwwPrS40nuMEbHpwRsSrnKnngnKayyGcEgHCKaZ2nDhXeIgdpxLjdy0wRJd0xbzOBLlwYquqF4JyIucxNF6vByq+u/faay+88sorOPzww3HCCScgn8/jX//1X7F06VLsuuuuVR3EzTffjOnTp6OpqQlz5szBkiVLhvW5u+++G0opnHjiiVXtt9FICVcPUuUiYCiB3peQKVQvcKmFE+nu+sERqldQ9BPdLobJSF84qmICPOc3pW4gzSGcXAnCidhxIpXD/aEeIwNG4cRRlKkr4RVOEXVVPfA4TtR5p54N1RuQqu6wsWPH4pJLLtFyAPfccw8WLlyIW265BXPmzMGiRYtw7LHH4uWXX0ZbW9ugn1u2bBm+9KUv4YgjjtByHA2BmwGoKqEJWFENmBq3BcSrUVzFIQAm4eTQT3Q7iSuQbQlHjiIAeAwhR4nr0DazTMuEe9tE4vLnA0exC0oj1VeMwol415yhehy5xZ3MjlNE3DeLTTjFtO6/dZwGpuLn049+9CPce++9W71+77334s4776z4AG644QacffbZOPPMM7HnnnvilltuQUtLC26//fZBPxPHMT7xiU/gqquuwi677FLxPhuV1KUb3FKPf2IQUFdp6NkvuePEJ5w8BruLXDg5Dgopr3vK1Uyax3Ea+TlOsQThRDzBzjC6MNTFIcqM37WbIdJiY8g7wY6IxynFUKAIoO+t51rhNCAV393XXXcdJk6cuNXrbW1tuPbaayvaVhAEeOqppzBv3rx3D8hxMG/ePDzxxBODfu7qq69GW1sbzjrrrCH3US6X0dXV1e+/RiV1CUP1BEwMygHPwyskbo7H1YwP4CkxXCTuu6MEVAstMjleHDlsCXGzUg7HKXb4r6mQPFSPEeLFpRLjQks3Q//CDRG3cKL+zo0Rqucwt+GQSsV39/Lly9He3r7V6zvvvDOWL19e0bbWrVuHOI4xefLkfq9PnjwZq1atGvAzf/rTn/DDH/4Qt91227D2cd1112Hs2LG9/02bNq2iYxxJUOY4SQhFKQc8jlOZ2nFqMOFUcIj7CzXxh50WmJo5cziKCbE7oFJ6xymSIJyIy39yTsGoQ/U4i0Nw9E/cwOw4hdTFIVKeVg1eGkMR5gq6yuY4DUTFd3dbWxueffbZrV7/+9//ju22207LQQ1Gd3c3TjvtNNx2220Dul4DcdFFF6Gzs7P3v7feesvoMUomJUz0SwRMDILYgcMw8SsT2/i8oXr0D+kYKeARPiizjSucOIRxQlgcQvk+S45g6PBfUyFxKLPPN0yRF4coMVbhLMQuUuIvvD5gbj1C7IxwlSMHaFtEOMxtOKRS8a9y6qmn4vOf/zxGjx6NI488EgDw2GOP4bzzzsMpp5xS0bYmTpwI13WxevXqfq+vXr0aU6ZM2er9r732GpYtW4YPfvCDva8lyabVds/z8PLLL29V2S+bzdrGvJtJCB2nmDCfaltk3SyKUZF0n9TCCayOE8/KqspkkEY0q36pAOGUbyThROg4KZ9npTwUsJJLnQPK6UlQLy5xOk4AADcLED33UihsYKpg20NAXhyCx3ECNrWICImKfDm8AbZiqfhq/+pXv4ply5bhmGOOgbd51TdJEsyfP7/iHKdMJoPZs2dj8eLFvSXFkyTB4sWLsWDBgq3eP2vWLDz33HP9Xrv00kvR3d2Nm266qaHD8IYDqXAS4DgBQMbJoAhq4dRAOU5cE4SMDxRodpUIWHjJMzWT5gnVI3ScMg0snIhD9XgdJ9qdF5mLyaReFopqwdBvQVriq5gIACG1cFKcwonuWaCs4zQgFf0qaZpi1apVuOOOO3DNNdfgmWeeQXNzM/bee2/svPPOVR3AwoULcfrpp+PAAw/EwQcfjEWLFiGfz+PMM88EAMyfPx9Tp07Fddddh6amJuy11179Pj9u3DgA2Op1y9YkhHZ2LKT+v8+Qa1WiDtVjSlQFeCbWAABCpyDxJQinxnGcYsJLSvlMvd4ECKdy6IDyyvZTTuVEu7tCyiskKIszpX4z2b4GI6AO1WPKcQJow+OtcBqYioXTjBkz8MILL2DmzJmYOXNmzQdw8sknY+3atbj88suxatUq7LfffnjooYd6C0YsX74cjsNse48QKB2nSMDEAAAyDM5XiXg1SjkJwDQnYWtqSTjhjTP8wikXu+STP4AnFDOmzHGizJXrQxn842PQQMKJ2pVnd5wIhVPsjSLb12AE5ONUgzhOqQ3VG4iKzoDjOJg5cybWr1+vRTT1sGDBggFD8wDg0Ucf3eZn77jjDm3HMdKJCYVTKMVxUtZxMglXjhMIQ6xin/9azkUOS5KIx6DWkgZwnMoM49LWKGScDIKEpkolr3BqrFC92M2SeQWxx+84lakdJ4bebz2QRgGkfEVOJFPx3f31r38dF1xwAZ5//nkTx2MxSEzoAkmI4QcAj0HAlRTtoMoaqsfkOKWEE95QQKheIXaRMvzWHDlslNWUFXGz3R5KQlZyM4RFfDIpXy6mosw7dRyEzMUhKKvaRm4L2b4Gg9px4mhh0ANpJVum/oHSqfhXmT9/PgqFAvbdd19kMhk0N/dfbejo6NB2cBa9RISrMqGAUBSApw9BkTpUT8WMoXo8JL5LNjUJPRnXMrwsEJVId+lTN8ABEJNW1eOZGJSEjI8+4cKSnzAKJ0LHiSv8sy+UOcahy+84lchznBrDcUqtcBqQin+VRYsWGTgMCwWxIgzVI9zXtnAZpvYFh3pQZYy3ZnOc6B4egYBQPQCAmyEXThyOImlxCCbHqZjIGB99QmfCZ3ScSFs2MJW470vk0rVQCAT0JKMu/84aqkdZQdAKpwGp+Fc5/fTTTRyHhQBKxykQUv+fw3EqEQsZzp4SHpPTlXguWYvJQEhPstTNkMsYjruYskq2Iix93peikPHRI8x75XWcCPft8Z/bskMXPhc4/I5TmVI4KcX7zCX8rmlihdNAVHUGXnvtNVx66aU49dRTsWbNGgDA7373O7zwwgtaD86il5iwtKQU4eQwhMQUqHOcGLuYc4bqUVF2ZVzLCUNpfQ5HkTbHiUc4FRIZYtwjXFjyE75xilQ4MfUG60uJUDiVlATHibDSHHMoJmVBpiSxxSEGouIz8Nhjj2HvvffGX//6V9x///3I5XIAgL///e+44oortB+gRR+U4XMSyu0CPJ2vI5UALmGvBcaeElxThJgwxKokRDilDKX1ORxFUseJTTjJWMn1CJ8JrMKJsoCOAMepqOhcoBJpUfuBKVI6TlY4NTwVn4ELL7wQ11xzDR555BFkMu9Ojt/3vvfhL3/5i9aDs+glIlyVKQlxnFTKI+AUYZx7I4bqxT6dEyJFOCUMx8ETqkeYyE9YiKIveSE5To3jONHtOxVQHKJAKJwK4A/Vo8xx4nacKPNOEyELPNKo+Gp77rnn8OEPf3ir19va2rBu3TotB2UxQ0gonMpSkgq5joM0QZgxUZWpnB9lk9QiccWmwaCslNXDiBdOTI5TLpYhnJwGcZyAxspxyqWUwonfcSoQ9s3iKijTA6njFFvHaSAqPgPjxo3DypUrt3p96dKlmDp1qpaDspiB0nEqCulTAq7jICxzzFoalclxijy6VbciYQL9tkgYhBOHoxiSOk5ku+pHXsiExCUVTnzOeKM5TrmULu8ozxTV0RfKhsNcLQx6oMw7jW2o3oBUfLWdcsop+MpXvoJVq1ZBKYUkSfD444/jS1/6EubPn2/iGC2aoCzYIKXBY8omnBolVI+nUlZEGGKVF+I4RRyOU0qvnGiFE0+oXk5IqB5lDqgfMwonwhynRIBw6krohFOOcF+DQZnjpAjzlweCMlQvpuwNUUdU/Ktce+21mDVrFqZNm4ZcLoc999wTRx55JA499FBceumlJo7RoomArICznORntnKaGUrHKSDb15b4CY/lROk45Qmd2m0RM5TW5xFOdGLcYRJO3ZGMa4pWOPE545SLS6mAnMiNMV34XE5AhUjSUD1m4UTpOEVCnHFpVDx6ZzIZ3Hbbbbj88svx3HPPIZfLYf/998fMmTNNHJ9FIwFhvo+UBo8J03GkhHHQKg3B1IeWLccpJBzPc0KaOUcMx+GNdMeJcF996RIinBSlcOIM1Wswx6kzoRNOXQKe9YWEMlSPWzjREVH2hqgjhn2HJ0mCb33rW3jggQcQBAGOOeYYXHHFFWhu5q+oYhkeYQM6TknMVRyCaL9KsfZx8pgSvkPC05oTUuiEQzj5DKGYjZDj1CllQkIYyszpOEHRibbE5R8vOiI64dRJuK/BKMMBlAIoFnq4Q/UI13qs4zQww5bpX/va13DxxRejtbUVU6dOxU033YRzzz3X5LFZNENZ6S4vRjjx3PgJkXDiLo3KEcoF0DpOXYQLDtsibJBQvYBwIYDPcZJxTSnCZwJrqB5hrzuOtgFbsj6gGys2CqkQqTI031m5vHk/pI6TzXEakGH/Kj/+8Y/x3e9+Fw8//DB+9atf4b//+7/x05/+FEnCkxxuqRzKUL08l9OzBTGTgEuJyhxT9osaCJ/p/g9cuglvVypjkkvZwLqHEV8cgjCfqi+dQkL1KNs1+BFfLiZljlPM7EgAwLqQUDhFMoQTVUEm9nLkhPsKQiucBmLYv8ry5ctx3HHH9f49b948KKXwzjvvGDkwi37KhBNAKcIpYpqgJFSDK/MgzhaqRymchJRkDRm6KnGc30DRiRnClii9pG4WacqUlLgFlFVHG6U4RCwgVG9tQHdeN4QNJpyYCsr0QBmqF0ZWOA3EsH+VKIrQ1NS/7KTv+whDxrhlS0VQCqeckNjYkCkkhko4sYfqMQmnsks3ue4U4zjRn2s/pT+/IaVwItxXLx5/+eYeKKuO+jGn40Q3T5EgnNYQhuqtD/i/LwCyxsPsoXqEUQDWcRqYYV/xaZrijDPOQDb7biJgqVTCOeecg1GjRvW+dv/99+s9Qos2SoTVb3JCHCc+4UQUqsddGpVLOFGFWHnepsRjAQQMPck4ys2XHTp3gCNULxEknBJCNzXDGKoHwhyn2OFfaAkThdRrhoqKxvfV0WCOExpJOFnHaUCGPbs9/fTTt3rtk5/8pNaDsZilRLhy3i0k+TlkCtWLyXKcuItDJCyl0MtEoXqqSc4kt8wSqkdfQpo2VI9BOLlyrikqx8lVLhymZtkAreMUCXCcACDNtBoXTqnjoygkuiT1qYpD8IbqWeHEz7Dv8B/96Ecmj8NCQJlwdVGKcOKymmOiBq3cjpMXR1V0g6udskPkdGXlTHIDBuHE4SgGVOcWtLkvPSQuf/nmHmKiyADf4RUTKiEM1RPgOAFA7LfCwVqzO/FbzG6/AlLPI1nDUw53jhPNAoTneGJyMaVh5WQDUSJsEidHOPEcB5njRLSfweBqalkiE05yJrmU7QR64BBOZcJmpRw5TrEo4UQzPvoObziXUvGmPj8ERMwisYfIGzX0m2ok8c3vY7ikDVIcgqq3XsaRM05JwwqnBqJE5DilyiXb11CUA55LPCJynLir6vkxT45TiSjEKhXkOJUYhBNHqF6ZtI8T/fUbC5qQNIrjBNAV0gmFOE6hZ94Nir1m4/sYLglZcQhm4USUd8q92CEZK5waiCJVBSVPzsSgHDA5TkR3FnuoHmEITF9KRAUEEkGOU4mjOETMIJxGeKheJEo40YwfHkNFyK0gGiulCKeya94Nil1Bwokot4w/VI9mfPQd+obr9YIVTg1EkShUL3Xl3HBB7MBhaNZC5Tixr34x9WYpUgknooTj4cDhOHE4iiXCRH6l6IVT6MhxMSOiFR5fgJggc5wkiEQAZce840Thag0XMseJedZM1XTes47ToFjh1ECQheoJiuEHgCzD8VClVrF3MWcqR14kmvDGvpxruUTYc6cHDkeR1nGi/36hkiPGI6JcVF81kHASIBIBoKjMu0Gh04COE/diJVGOk3WcBscKpwaiSLS6KE04ZRgGAKraGOxhA0xNLbucMsl+IkHCqcjQiJfDUaR1nDga/MqZkIRUxSFECCeiUD0B3xUACjAvagJB7ilZ42GHvrddXzyixUpXWcdpMKxwaiDyRInAkqpGAYA/kh0n7mZ8TE0t1zp5kv2EgkL1CoQNrHtgqapHKGaclP76DQQJJ6o+LRkBxSGoCukE3LFcm8kTCKcSgas1XGKqUD3FK5yoKtla4TQ4Mu5wCwn5mMadSAQlPwM8ZTUDoqpv7MKJoXgAAOSdECpjfgIaenImuRyNJj0mYQyisCqOUL0y5FxTIVHxHF/AVIOqkE4gJMcpl5p3g8pKzrOeqn8Wd5SHH9PMLVwh17FE+EczCxlUE69YWGyszzC4R1RjDnO8NdvEGgBGtxrfRSBJODHkOPlMoZjKH8HCSdBks0yW48Q/1aAL1eP/rgDQTSCcKMIBh0vkUhWH4A7Vo1msdBgartcLMu5wCwkFohwnacLJYzieMtHgyr76xVSOHADQar6ikyThVGDojcblKJI5TilNrlxfOMrKD0YQ0owfIoQTkTtfhowcp42JeYFehJxFgIisHDlzqB5R3qkN1Rsc/tHMQkYpcZHC/INSUp8SAHAZcgroQvVIdjMoTprAZTqIeJT5FdUS0SrmcMgzhOpx5bDRCSf671cSFKoXhETlyAVMNahC9crcg/JmNsbmx8d8KudZT1XNkD/HiSYH1DpOg8M/mlloIZgISqoaBQAuwwAQNIjjBAAeU+J3NMr8Q1uWcGII1eNyFMlynOiFU5GhyMdgJKlDcv/6in+cApnjJGNatTEy/xzOCRJOEdVzqGFC9WyO02DIuMMtdBBMBCNhoXocjlPZpVkVkrC46TMJp7DZ/HktCWoCmKOqcb8FLMKYIh/FcaBAH4pYFLaSS1E8R4bjRCScBIQlAsD60Px5zSdynvV0jhNNNMlgkLWIYGi4Xi/IuMMtZKQEoiYUNjFwGEJjyEL1BNzBPlP1nVKz+S9fFBTnnSfKUdwSlvNLIJyUz3NuC4ImmwCQcc0fj4S7SHmNleO0LjT/q3cJupZDogUefuFki0NwI2DaZaEkJXCcJPUpAXgGgHIDCSePaKVvS0pN5r98XkL/mc10N5TjZH6ffMJJzjUFAD7BYppPkFs7FFRhzSUh06p1gXnHqSuWE6pH5jhxh+qR5Z3KGqckIeMOt5BB4TgFwlYqVEov5EqKKARIwB3M5Tjls+YnQgVBjlM3Q3EIAPAZwhVTAneAquT5luQF5TgBgE+w0CVCOBGF6kkRTqsD89dZZyznWg6I4tYVuB0nG6rHjYw73EJGQjAJCgRVjQLAMgCQOU7MFX4AvhynXNb8d88LWnVLU4WUQcR4DIl0KUEFNC7HKSdosgnQtGvwU/5xiqrnXSmVMa3KR67x8aIzknMtUzUeVoomf3kwyAr2WOE0KDLucAsZFI5TWZjjBIa+KUWHxnESEarHdBBdGfMPsJy07ukMfaV8hlDMlCTHiclxYnIOB8OjcJwE6CayUD0hwgkA0ozZJuEdoZxF0nKDFIegCtVLGfoG1gty7nALCRSOU0mYcEoZhFNJ0awKccdbA4DPVNpvY8b8AyQnJNG7h9SlzyngCMVMCcKqFEXlvgHICQvVo2h0KeEbU4XqFQVNqxLDwmlDKGdhiawMPLvjFEERhL6m1nEaFDl3uIWEhCCsqswgVLZFypCMXXSIypFLCNVjEk4bfPPCqTsVJpwYSv1zOIrJCM5x6o5kTUhIhJOAUD2qy7iQyJlWRd4oo9tfT1C5b7iQheox5zgBNAV7rOM0OHLucAsJMcFDsihMOCUMK7wlqv4wzGEDAOAxJX53eGXj++gSJpwSFseJ/jdISBwnphBTccKpMXKcqEL1igIKYfRgUjilUOhoQMeJo/fbllAIp0RY9U9JWOHUYJCE6gm74ZKYw3GiynHin5BwOU5r3YLxfXQKW3VLCXrubInP4DiN5FC9Tqay8oPhEBRAkSCcqKJui4JynAKnxdzG/WakqRyRWKKqqsccqgfQhE8nwp59kpBzh1tIaEjHiUE4FUCTwCkjVI9nGFnrmBdOXYKq6gFAzBCqN1IdJ4dNOMm6phRBFVQ/5XfGSRwnx0EkaFpVcg06Tr5BUVYFJSJlLMFxoqhka4XT4Mi5wy0kRATCqSAs+TliqGJVpCoOIWD1iytUr9spAyZLSvs+QkErqgCNY7wlHOc3oSgdzRaqJ2tCQuE4ZRIJwsn8IhNXifvBKKlmY9tOPGHCicjpkyCcKJrOx8Kqf0rCCqcGIyKweIvCVipiBscpVQA88/tVRP2itoWv+MSFajW3oqqamoxtu1pihyPHif4xEXvmrymHqMpaX1I3Kyq8CQBJvxYZjhPBToQJp6JB4RR55rZdDSWicUqCcLKherxY4dRgUITq5YXlOIVMK7wqYz4ERkKFH59zGDEonCBQOFE4xlvCIZwoHCdFIM62wpN3TSmC0Go/4XfGSS5jT5ZwysOgcHJlOU4FstYREoST+e8aCXPGJWGFU4MREYRlFKxw2gRBuWMJoXo+YxWppNXgRDQrb5LLkePEEaoXE+SjKIpwwC1IBAonEsdJQKgeKPJBmUrcD0bOqHCS5ThRFeWQ4TiZn9OENlRvUKxwajBCAos3xxAaty1CpmRsinh3CcKJ82xHLeZC15IMfVjcULA4ThzCicJx4hBOrjzhRNHosmEcJ59+YWNbdCfmrrfAkXUtl6jKkac0+cvbgqK3nnWcBscKpwaDxHEStlIRhDyXeUqy+si/kuszFvYLWswJiSQrTziFBD13tmTECieGYUGkcCKIEPAS/lV6igqkKUFeayV0JebGsLIjy3HKJzTjlAK/cCIJ1YuF5WIKwgqnBoNCOHULc5zKgQ3VMwlncYhys7nfOPHlCScOx4njbqZYe+FoPxYz9OEaipSgCqoMx4lghUdYjtPG2KBwUrIWAYpkjhP/IgBF3ilbikMdYIVTgxESTINywm64kew4SYi39hibWxabzZ3bSGCoXgiGUD2G0xsT3LIswomhKuJQUFTP8kU4Tubd+USY47TBoHAqQpZwyidUwommR+O28AgWK0OmeVM9YH+ZBiMgmHh1C2vwyOU4pQQNNhX4V3I5Q/UKWXMPkEig4xQw+D8coXoUay8kDsQWRCKFE0GOU8wvnCiKQ6SurGdfR9Q4wimGA7gEz1yiHo3bgqKSbSAs5UISVjg1GBQTL2kNHoPYgcOQ0EDiOCn+CQlncEoua24yFHrywqooFj62xAOHwDC/D44cp1BYQj1A0+gyI0A4NWKOU0dobgwrQN4igDJdnEPI+SUJ1bOO06DYX6bBCAxXUEqhkBe4UpF16Qf5xCdY/ZJQ4YexuWV3xpzjFgqrkAUAARiKQzA4ihGBG6Rc+i/GUdxjKCiEkx/zj1MUzcITYY7TmsDc9ZZP5QknZMwuLCmCKJLhQNEiIghtcYjBsMKpwQhMN4nzBA6mADIM/W8Sz/ztJSNUjy9Wr9M3NyELBCbylzlC9RjOL4njRJDzsiUyhVNjhOpRNAuXJpzWBuaERM5gxb6qMVycQwkp/mE6fNpRDiKinLF6xP4yDYZpxwkMzs5w8DkcJ5LVKf4JCWeO0wbfXKJuIDFUj6DnzpZwFP+ICNwgCgdiSwKBwikicZz4E+pBURxCmHBaH/hIDYV15RJ517J5x0nG+TUtnHyGheZ6wgqnBsP0xCsR6zgxCCeKXjQSKvwwhuqt98rGtl12ZDwk+1JqEMcpJMhH4XCcygyhlkNBUXbYj/jHKYrzHQtxJPqRGWVks10ShZNxx0lKqJ5ZOCJ06gkrnBqMcmr2xk+lOk6K/rhinyJUT4DjxNijZZ1bNLbtkitvElRKGcqRMxSHCEeo41RmGIeGgkQ4SchxIghrjoU5TgCQ+K1GttsZyZtcp36DOE6Gh0ffkffsk4QVTg2GaeGUCCy3CwAewwpKTJLjxD8h4cxxWuPmjW27KPDhUSYoHb0lXkIvMEiKQzA0j+YQvkMREiSUyXCcCISTI8OR6EtsSjjFAoWTaceP4Jk+HEyH6nHMl+oJGVeBhYyy4WaHsVDHyWXILYhJQvX4hRPHxLqHTqdkrESsROFUYshx8hlCMcORKpwEhupRNAj3E/5xiqI4RCTQcQo9M6F6G0J546PpBsSKoE/UcDCdd+oJfPZJwgqnBsP0xCsWulLhMvS/iSiEkwTHiTFUDwDUaDMrqnklbxJUZHCc/IQjx8n8JNdh6IFWTORNSALDoXqe8uAw5kH20KiOU+i2GNnuxkjetWzacZKS42T6l/cFFrGRhBVODUbRtOMkVTgxDASRZ1g4iYm3Zp4UtZqZGBSUvIlBkaOqHoMwDgjyjzhK+RdZ20UPTBCYfSb4YoqsmBfKkZjv+i5lx8z4uD6Q912TRhFOhh0nV+CzTxIihNPNN9+M6dOno6mpCXPmzMGSJUsGfe9tt92GI444AuPHj8f48eMxb968bb7f0p+SYeEUCc1xchhCZEznXEtJVPUS3gIVyahmI9vNMVSwG4oCQc+dLfFTeoFBE6pH79YWBFYiCwznOHlCxASFUI4EOk4lQ8KpI5R3LZsuzqFcEVNm46F6rsBoC0mwXwX33HMPFi5ciCuuuAJPP/009t13Xxx77LFYs2bNgO9/9NFHceqpp+KPf/wjnnjiCUybNg3vf//7sWLFCuIjr0+MCyehFq/DEqpndvtShJPPmOMEANEoM2I9p+RNggqG79+B4Di/AUFYFUdFygJDqOVQxImCZ3CiJMVxojjfEoVTUelfWEodD/mYffq4FYnhSqhShJN1nHhhvwpuuOEGnH322TjzzDOx55574pZbbkFLSwtuv/32Ad//05/+FJ/73Oew3377YdasWfjBD36AJEmwePFi4iOvT0yH6kVCbziV0gu60LRw8mVMSPyY13EKm81cc90CHacig+PE4SjShOrRO055gTlOAOAbnHCKEU4EOW2BkO/al4IB4QTfjItVK7Hh4g2NIpwc6zhtE9arIAgCPPXUU5g3b17va47jYN68eXjiiSeGtY1CoYAwDDFhwoQB/71cLqOrq6vff41MyfAqUSjUcQJDbohx4SQk3po7VK/cbObcdjO4O0PBscrLUfwjICgOweE45WKZwslkw0tfyCRMEYSchgIdp1yqXzglvplKfbUSm+69J0Q4mW46z5HaUE+wXgXr1q1DHMeYPHlyv9cnT56MVatWDWsbX/nKV7DDDjv0E199ue666zB27Nje/6ZNm1bzcdczph2nQKxwop+wmG7iKaU0qh/zVtUrNZkZxroM9zyrhjxLVT0Gx4kkVI++t1A+lndNAYBvMDfVFxPyav46DsV813fpTpu0bzP2zOSV1kpkvBy5+Uq5w8F0+LQjMNpCEjLkc5V8/etfx913341f/vKXaGoaeHC46KKL0NnZ2fvfW2+9RXyUsjAunARWjQKAlEE4mQ43EhOqx+w45ZvMPMw2ShRODBNvDmFcphBOKb1wykkN1TPqOMm4jyh63kkUTl2JCeEkM1TPdFVDMcLJsOOkrHDaJqy/zsSJE+G6LlavXt3v9dWrV2PKlCnb/Oy3v/1tfP3rX8fvf/977LPPPoO+L5vNIpuVWemNg6LhUB+xwolhpT4w7DhBiOPkMTe3zGUN/M5KoVuicDJdcWQAPIYctjJBPgpHjlN3JHNCYrLhpS8kfG3T+TYbEREqF6Bve7ZNOmP985/Qkek4Ga+q58gQTp7hsFPFkNpQT7A6TplMBrNnz+5X2KGn0MPcuXMH/dw3v/lNfPWrX8VDDz2EAw88kOJQRwymV6zLQmNjE4aVXuOOk5B4a+7iEN1Z/Q8Rlc0ihYyHZF9yHI4TgzCmyXEqG9/HlnRJFU4GQ6wzQlwYlZofp8pCvmtfNpoQTq5M4RQ2iuNkOFRPCV0AlwL7KL5w4UKcfvrpOPDAA3HwwQdj0aJFyOfzOPPMMwEA8+fPx9SpU3HdddcBAL7xjW/g8ssvx89+9jNMnz69NxeqtbUVra2tbN+jXsjFLmBwbC8LveEShmpkgeFeNMqTIpwCAHyubqdvYEKU1R/eooPuyDW9aL4VHKGYJQrHiSFUr5PBMRwOJhuE+0IyAigcxkDJ+K59WR/pH5sDoY5TZFq4Cjm9xgv2WMdpm7D/OieffDLWrl2Lyy+/HKtWrcJ+++2Hhx56qLdgxPLly+E4716t3/ve9xAEAU466aR+27niiitw5ZVXUh56XdIZekgzWajYzGpriSGXaDhwCKeya9pxkjEJ86MQnMJpg29gAjxIziQ35cRBqhwowzHufdnkKNJea8ZD9TwPCvT9qTqFOk4m+7aMMRgGWBEEOU6BQMfJRKPaspI5PgbGG+BKcZyscOJExK+zYMECLFiwYMB/e/TRR/v9vWzZMvMHNMKJR7XB6zJTJEOqcIpiF9SRV2XHcByykFA9L+bNcerw9C8CpBmZIacAADcLREWy3XlRAIB2hdl0cQiu5tFdUh0ng5ECk1IZ45RS6aa8UIPFTgLiBYbhsN6AcCoJFU6mi3OIyXGKY6NrWanA/F5JyBjRLKQUmyYP/aYqkSqcYg7HyXCehhTh5Ef0IU99WefqFxGp0FA9AIBLK+p8BmFcMhxWpTL041TqZpGmMiZeW+IYdJzaEjnVEkwL5pJAx2lNoP/cFqUKJ9M5TkrGtWw8fJqhmFY9IWPmZSGl259obNtFoTdcyLDSW3JNhxvJuH05igf0Za2T177NJCO3EmfSCMLJcKie8hkWeDyZk00AcAw6Tm1Bydi2K8W4cBI4pVob6B8viqnM8dF0qKQUXWxaOKU2VG+byLvLLcbpcLYztu2C0BuORTgZbrgoJWzASRM4jEnRHW5Re2n2WLBwSl3aY+NwFFMFo+X2OXqgJYKFkzIYKdBWyhnbdqUoz3RVWXlTqjBRSH29fZfyUoWT4dL3Ump/mG4RkRju91nvCLkMLJSsxnhj2y4IbfAYMiRll42XI5chnADAZ04AV62jtG4v9uXmOJE7TkyOokl3wOEQTq5g4WTSccp3GNt2xRh2nMpCc0NSX+/4mGcsBrQtTJeDb5RQvURo5JAUrHBqQFZE44xtu8DQZ2Y4BCH9pV40HW4kJMcJAHzDseVDolk4hb7MiQEAJA69qPMUw/k1KG44ikOIFk4GIwXautYY23almK5EWhLY+w0AYl9vq5ZcInN8NC6cTDe1Hyamw6dj6zhtEzkzLwsZb5RHG9t2XqjjVA7oB4KiYzjBXdDd6zEHfyetequ+RZ5gx4lBOLEIY4PihqMHWkzsFFaEoVC9Fq8FraUuI9uuBtOhehJznAAg0uw4dcUyr2XjoZJCdLHxUD2hC+BSkHmXW4zyj9IYY9vOM1SvGw4j0nESkuME8DtOcYveFdBAsHCKORwnhvNrcpLLkeMUOzJX6QFzyeBt2XFGtlstph2notApVejqFU7diczx0XyoHn3vt4EwnXcaW+G0TWTe5RajvJjTmyjal5zQG47FcVKGc0ME3b0+RyhXH4IWvQ9yK5z6wyGMU4PNLDnCXCPBwslU+eFJnt4Je62YdhpLQnpWbUnZ1fvM3xjJjCwpGc4xEyOcDOedWuG0bWTe5RajrA98pFkzrlOOoQjDcAhih7zym2nhJKk4BHeoXtCi97orCw6rig323BmMkZfjRP/oCx25OU6JKcdJmlg07TglMqdUZUe3cJI5PpYNP+OVIyPHyXTT+SiWeR1Lwf46DUrQYqYJrlTHCQCyxGWcyyoGlDlxI2UQBwDfcBnYoSg26R3Kyq7MFVUACBWH48Rwfg2KG9O5LgPBcd6GS2ooxLpNWMNf005jQeiUqqgaw3EqoEGq6hkO1YsEz+MkIPMutxinkG0zsl3JwinDsPppstGmpOIQPrPjVGjSO0ErChZOEUeoHsP5TU32cWJwawMlzH3pgzHHKYqNbLdaTAunojCh2ENB6XU7OwKZ46Pp4hxKybieTZcjt8Jp2wiaelko6fTMNMHtZmg0O1x8hgknMo0hnDzmg8ln9K4EFpn7Um2L0GDPncHgCMVMTRaHYAjVCxhCLIdLbCg0Z1JQMLLdajEqnFwXsdApVT7VW3W0I5R5LRsvziEkxwkwW7AnimRex1Kwv06Dsl4ZEk6CVyoyxKF6AACjjpOMsAEA8JmHku6M3gdagbsv1TaIGCbgLI6TSeHEMEwFkBuqFxsK1Ztc6Day3WoxKZxMRhfUSneq2XEKZT7niw1SHAIw23Q+ZKhCXE/YX6dBWZmO077N1M0gFRqqAAA+R6iMyQR3IfHWAH+oXmdGb+hCgcHVGS4h6EWdz+AopiYnuQyhemXBoXqmqmhNyq03st2qMek4eXLHjO5En+OUes2IhVYPLCZm72sFsyFylWCy0mlgHadtYn+dBuWtcJz+jXpyJwYA4HGE6jWI4+QZLIIxHDb4Za3bk+w4BSyhegzCyajjRH+9lgw1mdWBiZwGBYW2rlXat1sLRi9jht5gw6Uz1fdsTjU309WJ+eIQchwnk+HToVBHUQpWODUor5dHa99myhEKVwEuR1Urk9W7JDlOzEPJes3CKW/4AVwLZYaQLw7HKTHpODEsOpQEh+pFBnJTx2XGwI/NVv+qFKM5ToIdp42xvmdz4uvNl9JJwXA5eCnFIQCzjlPZliPfJvbXaVBeLrRq32YiXTgxrNSnGYOheoIcJ46JdV/Wu0Wt2+sWHKoXMITqeQyPitSgK8QRWVpM5F5ToQHh1JYdp32btaIcg06j4BynDaE+0R5rbqarkyJMh+oJEk4Ge+uFkdyUCwlY4dSgvJxrQap5siteODE4TqlnMsdJUNgA8/7XuHqrd+UEO04coXq+4QnJQMQjzHEqChbjoYGchkmuQGfCoBhPBTtO6yN9z+bIE3heN5NCGRawcnKcTIVP+44vOlddAlY4NSjlxEHSMlHrNhNpXeK3wGEIlUlMNvEUJJw4JtZ96VAFQGPfn65ErnAqG64cNRAsoXom7x0G4VRI5IbqBQZyGiYLLL9u8jI2uUhWK+s1Ok6hREHcB6O9EyWF6hmyzVmqD9cZVjg1MEGT3ia4MUfxhQpwOEL1jFbVs8Kph1QBapS+pOVOBnEyXDiKDHgM5zcx6A44LMJJ7sQ6MBCqN0nO8NSLyVA9yY7TukCjcHJkCyf45uYhKpXjOJkSTibLnI8UrHBqYHJZzcJJ+EqFSumFXWyy0aYo4SSAVn2x97KFE0M5cvI9mhVOyqG/d/KCc5yCQP9v3RbKKgwBmM1tk+w4rS7ru/bK0oWTyabzCI1tu1JMCSfPCqchscKpgdng6m2CG3FUrasEhgmn2VA9OWEDk2L+QhXpKE0PdMdB3gqnfvDkOBkUTgyLDrlY7oSkbCBUr62U177NWjHpOCWuXOGUj12krp7nc0npbaarHYPOn6Q+Tm2GFqp96fM4AVjh1MCsxQSt24uEh+qBIcQpMdmLRpDj1F7KcR8ColF6HiQqK9s5LTGEfI044cQQqpc31GRWB1HiwNW8gt1W2Kh1e1oweN4TwY4TAKQZPZV0S5AtnFKTOU6ChNN0Q3m4rnWchsQKpwbmnXis1u2FwlcqUgbhFHsGJ3+CSqNO37ia+xAQtmi6/ppkTwyKDMLJS+mFhslWIkrRT4BygkP1AP25DZO612rdng5M9umW7DgBQKKpcW0RsheWjDpOqZzw0/ay3hYcPfgCi7pIwwqnBubNcJzW7YkXTgwTzoYRTuuWwWHu5VRu1nR+s7KFE0eRAY5H6UgL1euOZE+sdVbT8hwP2+XWadueLkwOUdKFU+TpEU4F4cIpaZBQvfYuMwsTrhVOQ2KFUwPzWklvE1zpwilhWPGNjOY4yRnEM3EZOzTrLTZSKaVmPaELqXDhxOE4+QwpbCYj25RDv+jQJVw4+RpDrSdmx0OBP+9xS0yGaMbChVPo6Xne51PZ46PR6oapnOIQ7euWGdmuY4XTkFjh1MC8mNMrnDgac1ZCEjM4TibDjQQ5TgDQnhnPuv9Ckx6HIhGe41Rg6DHFEapnoCdrL4phAtRpoOS3TnQKpzZ/jLZt6aSRhVPg6qk6mmOoTlsJJnPNJFXVG1XuRluT3gJfAOAKn8dJwAqnBmZZsQmpxvCMgKHBbCVwCKfIYKiepC7mADAdvBOHfFbPpCjxhQsnhiID/kgTTgyhep3CHSdPp3CS2prC4HAcO7LPb9nRI5y6BTdyBgwKJ8eBgpyCTAAwPatfOFnHaWiscGpw4lGTtW2rLHylImKYcJpcZJYUqgcA7QFv4mx3Rs9DLc4InfRtJs+wAMBxZ5u9d+hXjruEO06exglTG/MiymCYFMzSHaeS0tOuoTuWPT7GrpnRShms1lct7Ur/uXCE3ruSsMKpwSk26ctLkS6cYoYJp4H2KL1I6mIOAO3dHaz735jRMxmODXae1wFHWWsvpV9pjQ2GVVGH3KRuFmlKX9K9EjyNOaqTIllhxD0oZe6aihzZwrig9DhOXYL7kQHminQogeXm20P9cwBH+DxOAlY4NTjd/kRt2yozlPuuhJBhxbehhFPHctb9d/p6fo9IeKgeh3DyE3rhFBoVTsT3jic7oR7QW01rcljWti2dKMfcdRy5soVTHnocp42R7IWl2JDAUQZ7MlZLe6FT+zaVdZyGxAqnBme9oy9GtiT8huMRTgYnfwzhRttiu9xajMmMZtt/h1fSsp3QZFUmDeQYruMRl+ME2rDSpB6Ek8aV5kmlbm3b0olRx0nJfv7lNTWuFS+cTOWaCXScpm9YoX+jqbzvKQ0rnBqcNZigbVsl4Q0eQ4bkbKPCSVBp1B6mN01i2/d6V49wCjzZE4MujlC9hD70aiQ5TokrXzjpTApvy2/Qti2tGMxxkh6q15XoEk6yn/ORoYUviY7T9hveRrPmsUUJjxySgBVODc7b0Vht2yoKv+GCkP5yD0xO/gQKp3ZNJW+rYY2b17KdsnDhVDBZ434QeEL1zO1TpbShZHUhnDQ6Tm2dq7VtSycmi0OEDSKc1geyHYnIkOOkBIZiKqTYuUVfga9NyPue0rDCqcF5o6yv3wZHY85KKAf0A4JR4SSop0QP7RFf08t1Th5QtSfgB4aqMukiTRVSl1bc+QzFIcw6TrShejHx+aoGpUk4tXgtaC1LDdVrXOHUmejJ3VwXyh4fTTl/ypc5v2n3NIfH21C9IbHCqcH5R1FfE9yC8BuOxXFyDU44BTpO04t8E6ZEAap1VM3bKWnsZ2MMauE0wkL1nJRYODmyC44AgNI0frdlx2nZjglMNg0Phfdx2qAhNyl1POSFl9WPTFXVc2VOl6drvqRThgbr9YbMK8FCxv/lNAon4WVKORynsinh5LpGE52rpX3jSt4D0CCcisIdJwBaG1cPBy+hr+BoLFRPKXLHKaoH4aTJcWrz9D1TdKOUOeEUCJ9OrY80XIM+Xyj2cDElYCWG6gFAe6mgdXup8AVwCci+0y3G2RB6SLN6wvXywlcqgtiBo2gv+bKhB7XEnhIAMG39m/AYq0ulrbWX3C0KXzkG6IUTS44TzOyT494JHfk5TtCUozpJtGNrMlRP9rixPqx9zEi8OhBOytA8RKjj1N65Ruv2UuEpFxKQeSVYSAlapmjZTp6hwWylZIknnKYcJ6nx1n4SYUftyarDJ26pfYJadOQ7Tgl5qB6941Q25DipDP3EPtTYXNYYmiZMbYIb/Zp0nMrEi3KVsi6ofVyLPT29oEwSmMpx8mSe3507lkFB3z2XCF8Al4DMK8FCSiGrp4R0rg6EU4Y4ZCYwlIws1XECgOm+voIjlRK01D5BzQvvxwIACfGqvh+PnFA9jnsnUPJD9XSF6LRFshpz98VkjlMgvBrZWi3CqfZQaNMYE05CHafmoIApzRO1bc8Kp6GReSVYSOn09Nx0OYb+MpXiE084S46hB7XQeGsAaGdshBy01L7vvPBGzgC9cOLo42QszDXDIZzku5i6QnTagqKW7ZjAZP+uQLjjtD7wkdZ4jFEdlNUPDS18SRVOANCe1dePM66DeRw3cq8ECxnrlZ6bLsfQYLZSMsSheiVl5kEtNVQPANrLehrRVkOpqfZBP1cHk9yY2nFK6Cs4BoYWHZRPf34DyA/VS3QJp0KXlu2YoJEdJwBAprbCHaEjP1SvbCjHSbRw0ji+6BoHRjJyrwQLGSvT8Vq20y28TCkA+MQhM0XHkHCS7Dh1r2fbd7Gp9ljvXB1UFSIXTjGDcDIW5spQXbMOQvW0Cadch5btGMHQQhYAlEwVJdBI4tcmnAJXvnAKjAknubl77aG+8dk6TkNjhZMFy8OxWrbTXQc3nEcdqmfKcWKY/A2X9vVvsu07l629RHtXHawcx8QFLPyYPlSvqMyINQ63tqSpYp1JEg3jt4LCpK5VGo7GDCo1J5zKdTCdiv3acpTKSn6onjnHSa5wmq5xscIKp6GRf6dbjPN6WU8yfz04Ti5xdauiIeHUPF1fTLNuxhY2YAJTE8zuTO0uRVcq/zqOiK9jL6btewQA/5dZC7Wd/uucY9GhVAeherGGpPDx2bHwGa6V4WIyx6lUB9Op0K2tnHipDoRTydDCV/MkeX0Te2jveFvbtsJI/nXMjf2FLHg5X3ulnNTxEafyLyePXDiZWTUfPaXbyHZ1Mb1JX5WfSujM1D4x6qqDqkIRcR6WH9FPhmOk6Jjdrn27HGWFi6l84RRpqIo6KcNXUXM4mBRO9eA4BTU2Jy5CvnAydR5Gt7xoZLs6aOtahdYa3cQeIus4DYn8O91inFfyLTVX24EnP4YfABzQTjhNOE6qqQmj1NPat6uTdqYk4o2Z2oVqPYTqUfcF4ujjBAB/2lX/wgOH41Ssg4TrWEPEQJsrvFx1ai5Xz5TToZOyU5vjVA/CyUSuWXbXneEnb2nfrk6mN7dp2U4YW1kwFPYXsqCcOEhaanMI0jooUwoADnHITEHpX6kftc+ucCC35C8AtEf0OTEA0OGVa9uA56FUB84ptePkMThOAPDLsa9CtdQ22dsSjupYhTpwnEIdwkl4DzSTVfWKifxxo1zjgla+Dq5jEyGTo2eN075N3UzXtGgR2VC9IbG/kAUAUG6eXNPnE+s4DUisUu09l0bvxCNKKqE9z1OSeL1bm6BU2fq4jkPiXlMcVfUAIKcC5A+YqXWbHKF6hTpwnHSE6LSZKYSoDWXUcZI/nSqqWoWT/AVSE87f6LHLtG9TN+2apgVhKP865sb+QhYAQD4zqabPUzfkrBbFsGKmMhrFmuuiNfN3fdszRPvGFSz7XePma9tAVv7EAAAC4gUAhRQek5vw9G56J0IcwimfyK+qp8NxmhTKLQwBAAoGhVMqt+paD3nU5t7m6sBxKmoWTv6UyWhK5OY39dBe1JP3XK6DIl/cWOFkAQBscLer6fMJcWPZquHo0ePpmzS17LkrvISvT9JwmdrxFjIMYnqdWwBUDROYpjoRTsQ5TgDgOzzC6ecTXwc8ffvmKCuci+tAOGlYaZ5cqnHhwjAmi0PonrCbIFdjjlJXLP85X9QsYFv3mqJ1e6Zo71yjZTvWcRoa+wtZAABrUVvZ33pxnMCxYqbRcWrdpT5+ZydNsFOLnmTVSoiR1pQTk2bkTwwAIGBYAPCYhNMqN4dob33hejzCSX6oXqAht2FSYWPtB2IajSK8L4U6cJxyNYbaddWBc6pbwI6etFbr9kyx07plcDUUxtAxDox07C9kAQCsSMbV9Pm4ToRTyuE4aWy4Obr1ZW3bMk27z1SauLX6JNmkTnKcysShegCf4wQAL75ntLZtOQzGQF0Ip6D26UBbt/xJpjImnORPpzqT2oRTZyT/Oa/zPDhjRqMlfUbb9kySicvYobm2lAtXuYgT+QsA3Mi/0y0kvBmMrenzkVMfE86UI0lbk3DKtu+ETPymlm1RMJ2pH1LaWn0CdFInjlOZw3FirJh2/2R9DR4NVCseknoQTrXmNniOhwm5dZqOxhxGhJPnIYX8CWdnXJtw2lgPwknjxL9133aj4Z26ac+Mr+nzviPfUZSAFU4WAMBrpdpWdCOGnItqSBhCDVJNwql1j9oGRWrayzwl0+NR1U8OYr9ehBOH48T3uHghswaYqacZbq0t66qhK5IvnILIgaph8j8pOx4KqcYjMoMJ4aQ05rGaZGONOUqdkfzvqdNxGr19Ttu2KGivMRIhUy+56sxY4WQBALyYq60HQFQnoXoJw8pvqqnh5uixy7Vsh4r2Lp6wnbCl+msxqhPhVGJwnHzmHj1v7FNbGEoPHMKps04qVWXc6u+dSb6+cEqTGGmArDEc2yQdNTpG60P5wimAA2hY5FGZDFrdZ2o/IEKmB6WaPm8dp+FhhZMFAPBmsQmpV/1KfVgvjhOHcNLwUPXaJqE5fUHD0dDRvm4Zy36DluoH/9Cvj+uYJ1SPd/L/u2l6qkkql94V6awDxwkA/BoWwCa7tfUIIsOIcKqPCef6sLbxraMOhBMAKA3no2WfGXDSOnOcujtq+rxH3Fi9XrHCydJLXEMVNI7yyNWgo8ljpehwnEbvvYOGI6FlVLkbbU21lbmvhlJz9b936NXHdczjOPE+Lh5tfhNq+9oadQOAcuiFU1e9OE41CKdJdVAcAQCU5obkALS2nDDJuhqF0/qgPq5jaFgAG72z/LDTLWlfX1sOtGcdp2FRHyOdhYRiU/XCKWSo8lUNMYPjlGhouNk6SX7vpoFoz9ILp0JT9XkaQZ0IpyJDkROf2XECgNUH7FTzNpRKNBzJ8EndLNI6KFUN1OY4tcW0v2u1GAnVM1SpTzdrg+rPb+o1Ia4TcVxz6KTjYHT2OT3HQsiE/HqMzVRfzdatkwVwburkLrBQ0OVPrPqzQZ0Ip5Bh5Tfxa9un09qKUViq6Whoma7oc4by2epXCutFOBUYhJPH7DgBwKPTCzVvg9xxqiEEmhqvFuEUljUeiTmUq/86TuvFcQqqP87Ury0PmpQaHafmWbvAS/Q0lKVmelP1uaDWcRoe/E9Cixg6nOrdgTLqY8LJIZziGh2nTSVRQ01HQ0t7SF/KtTtT/cp3ya2PBweL4yTgcfHA6FehxtTWH0w5tM5IUk/CqYYV57ZSt8YjMYeJUL20TnKcwkQh9atrEJ54dZLDBtScc9Y6o46+6xa0u9U3gHfrZAGcG/4noUUMq9Pqy11zNOSshpAhSTupcYVz9FSest46aM9vJN9nVyau+rP1IpwKDD2yJDhOgYrROXtGTdtQqvrroxoSt46EUy05TrnaEtPJMOI41UeoHgAkmdaqPhd79eM41eoAjm59VdOR0NMeVb8w5NriEMOC/0kI4Oabb8b06dPR1NSEOXPmYMmSJdt8/7333otZs2ahqakJe++9N377298SHenI5u24euFUYugrUw1BSH/Jx371+1S+j1F1VhK1L+0bVpDvs9Or3p0r1UmoQp6hyIkExwkAlsyozTEid5zqSDjVMnGa3FUfoU1GQvXqZMEFABKvOuEU1ZHjVIsDmNlpKrLJaxqPhpb2QlfVn3VQPwsAnLA/Ce+55x4sXLgQV1xxBZ5++mnsu+++OPbYY7FmzcCD8J///GeceuqpOOuss7B06VKceOKJOPHEE/H8888TH/nI441y9X046kU4lRmqAtUSHdiy9wy4afUDITdTNq5AM/HEcb1ffa5FsU6EE4fj5Aupb3DvhNegMtU7Iwq0jlNcQ28kaqoN1WnxWjCqXCehehqK9WxJUkeOU1RlqF7k1M8CQOpWfz5G71l9rrcE2jtXVf1Z6zgND3bhdMMNN+Dss8/GmWeeiT333BO33HILWlpacPvttw/4/ptuugkf+MAHcMEFF2CPPfbAV7/6VRxwwAH4z//8T+IjH3m8WqxFONXHg4PFcarhQT16upDZapUopNi5pfYS0pWw3q0+tLHI3OR1uOQZQk49/scFAGCDU0Rpv92q/rxyiIWTUx9NlQHAqTLHqS1bfbQCNcrRP6YmNUzUqQnc6kLughpyZ6hJanCcWifQR0noZMf1b8JzqrseVZ2kXHDD+iQMggBPPfUU5s2b1/ua4ziYN28ennjiiQE/88QTT/R7PwAce+yxg77fMnz+L1d9DHPROk6DErlVPqiVQmtTfTW9HYh2r3pBXg1rveorr+XrRTixhOrJEfHP7lH96rcCbcGSqJ6EU5WhOm11lP9iIlSvEYRTuY4cp6TKHCd3uwlojuuvDHlfvCTCtObqFiuVDdUbFqy/0rp16xDHMSZP7n+SJ0+ejJdeemnAz6xatWrA969aNbA9WS6XUS6/G7rT2dkJAOjqkhH+1JZNMGdHObHDG5reDzepfMVejWoT9T0GQymgZexBSEFXkjicOgXJQQdV/Dl/u9EoTnkTRdSWDM/N/u6OWDuW7qHrpQ6SgyMgrfwcj5o8AXOU/Ot4u4yLLhxBus+dvR1x4FgZIuDlllGYXcU9BQCFtibEhKvna1v3rIuxEQB28qYjOzZf8ef2y05E1w6012O1hMnOSJReoVecvgvmTK6Pc7x69HuQ3aHyPNB1o3avm+u46M6AV0VscdOMSejevn7c08E4omUWtvO2r/hzU7xp5Oe4kOtGUubvEdijCdJhzBtGvLy87rrrcNVVV231+rRp0xiORj4/r/qTD2o8ipHFHdwHwA79tfGDaj/4k7t0HoZRvke+R1n3+LXVfvAnOo9iODwI4FvUOyXlDgCf5z6IYSPrOqamEZ7x1X/HkUIdnat/5z6C/nR3d2Ps2LHbfA+rcJo4cSJc18Xq1av7vb569WpMmTJlwM9MmTKlovdfdNFFWLhwYe/fSZKgo6MD2223HZTiDz3p6urCtGnT8NZbb2FMjf1JLI2DvW4s1WCvG0u12GvHUg32urFUA/V1k6Ypuru7scMOOwz5XlbhlMlkMHv2bCxevBgnnngigE3CZvHixViwYMGAn5k7dy4WL16ML3zhC72vPfLII5g7d+6A789ms8hm+4eXjBs3Tsfha2XMmDF2ULFUjL1uLNVgrxtLtdhrx1IN9rqxVAPldTOU09QDe6jewoULcfrpp+PAAw/EwQcfjEWLFiGfz+PMM88EAMyfPx9Tp07FddddBwA477zz8N73vhfXX389jj/+eNx999148sknceutt3J+DYvFYrFYLBaLxTKCYRdOJ598MtauXYvLL78cq1atwn777YeHHnqotwDE8uXL4TjvVsE59NBD8bOf/QyXXnopLr74YsycORO/+tWvsNdee3F9BYvFYrFYLBaLxTLCYRdOALBgwYJBQ/MeffTRrV776Ec/io9+9KOGj4qGbDaLK664YqtwQotlW9jrxlIN9rqxVIu9dizVYK8bSzVIvm5UOpzaexaLxWKxWCwWi8Xy/9u796Aoy/YP4N/VhWVBYRVwQR0EkRJUjIMygKYTeB4SNE1nbQgtTbEQE4+hpqOgmeUp1JrBySzLBk1BZiJEDFNExCOI5olSkFIRkZOy1++Pd96dNrHF96fsvq/fz8zOcN/39Tx73XDNLNfs7vM8xyzjVvBEREREREQWjI0TERERERGRCWyciIiIiIiITGDjREREREREZAIbJzPatGkT3N3dYWNjg6CgIBw7dszcKZEFSUpKQr9+/dC+fXt06tQJkZGRKC0tNYqpr69HbGwsHB0d0a5dO4wdOxY3b940U8ZkiZKTk6FQKIxuGs66oce5fv06Jk2aBEdHR6jVavTp0wfHjx83rIsIFi9eDFdXV6jVaoSHh+PixYtmzJjMrampCYmJifDw8IBarYanpyeWL1+Ov157jHVDAHDo0CFERESgc+fOUCgU2LNnj9F6S+rk9u3b0Ol0sLe3h0ajwZQpU1BTU9Nqe2DjZCbffvstZs+ejSVLluDEiRPo27cvhg0bhsrKSnOnRhYiNzcXsbGxOHr0KLKysvDgwQMMHToU9+/fN8TEx8dj37592LVrF3Jzc3Hjxg2MGTPGjFmTJSkoKMCWLVvg6+trNM+6oebcuXMHoaGhsLKyQmZmJoqLi/Hxxx+jQ4cOhpjVq1dj/fr12Lx5M/Lz82FnZ4dhw4ahvr7ejJmTOa1atQopKSnYuHEjSkpKsGrVKqxevRobNmwwxLBuCADu37+Pvn37YtOmTc2ut6ROdDodzp07h6ysLKSnp+PQoUOYOnVqa20BEDKL/v37S2xsrGHc1NQknTt3lqSkJDNmRZassrJSAEhubq6IiFRVVYmVlZXs2rXLEFNSUiIA5MiRI+ZKkyzEvXv3xMvLS7KysmTQoEESFxcnIqwberx58+bJgAEDHruu1+vFxcVFPvroI8NcVVWVqFQq+eabb1ojRbJAo0aNksmTJxvNjRkzRnQ6nYiwbqh5AGT37t2GcUvqpLi4WABIQUGBISYzM1MUCoVcv369VfLmO05m0NjYiMLCQoSHhxvm2rRpg/DwcBw5csSMmZElu3v3LgCgY8eOAIDCwkI8ePDAqI569uwJNzc31hEhNjYWo0aNMqoPgHVDj7d3714EBgZi3Lhx6NSpE/z8/PD5558b1q9cuYKKigqj2nFwcEBQUBBr5zkWEhKC7OxsXLhwAQBw6tQp5OXlYcSIEQBYN9QyLamTI0eOQKPRIDAw0BATHh6ONm3aID8/v1XyVLbKs5CRP//8E01NTdBqtUbzWq0W58+fN1NWZMn0ej1mzZqF0NBQ9O7dGwBQUVEBa2traDQao1itVouKigozZEmWYufOnThx4gQKCgoeWWPd0ONcvnwZKSkpmD17NhYuXIiCggK89957sLa2RnR0tKE+mnvtYu08v+bPn4/q6mr07NkTbdu2RVNTE1asWAGdTgcArBtqkZbUSUVFBTp16mS0rlQq0bFjx1arJTZORP8FYmNjcfbsWeTl5Zk7FbJwv/32G+Li4pCVlQUbGxtzp0P/RfR6PQIDA7Fy5UoAgJ+fH86ePYvNmzcjOjrazNmRpfruu++wY8cOfP311+jVqxdOnjyJWbNmoXPnzqwb+p/Dj+qZgZOTE9q2bfvIVaxu3rwJFxcXM2VFlmrmzJlIT09HTk4Ounbtaph3cXFBY2MjqqqqjOJZR8+3wsJCVFZWwt/fH0qlEkqlErm5uVi/fj2USiW0Wi3rhprl6uoKHx8fozlvb2+UlZUBgKE++NpFf5WQkID58+djwoQJ6NOnD9544w3Ex8cjKSkJAOuGWqYldeLi4vLIRdQePnyI27dvt1otsXEyA2trawQEBCA7O9swp9frkZ2djeDgYDNmRpZERDBz5kzs3r0bBw4cgIeHh9F6QEAArKysjOqotLQUZWVlrKPnWFhYGM6cOYOTJ08aHoGBgdDpdIafWTfUnNDQ0EdueXDhwgV069YNAODh4QEXFxej2qmurkZ+fj5r5zlWW1uLNm2M/51s27Yt9Ho9ANYNtUxL6iQ4OBhVVVUoLCw0xBw4cAB6vR5BQUGtk2irXIKCHrFz505RqVSybds2KS4ulqlTp4pGo5GKigpzp0YWYvr06eLg4CAHDx6U8vJyw6O2ttYQ884774ibm5scOHBAjh8/LsHBwRIcHGzGrMkS/fWqeiKsG2resWPHRKlUyooVK+TixYuyY8cOsbW1la+++soQk5ycLBqNRn744Qc5ffq0jB49Wjw8PKSurs6MmZM5RUdHS5cuXSQ9PV2uXLkiaWlp4uTkJHPnzjXEsG5I5F9Xey0qKpKioiIBIGvXrpWioiK5du2aiLSsToYPHy5+fn6Sn58veXl54uXlJRMnTmy1PbBxMqMNGzaIm5ubWFtbS//+/eXo0aPmToksCIBmH6mpqYaYuro6mTFjhnTo0EFsbW0lKipKysvLzZc0WaS/N06sG3qcffv2Se/evUWlUknPnj1l69atRut6vV4SExNFq9WKSqWSsLAwKS0tNVO2ZAmqq6slLi5O3NzcxMbGRrp37y6LFi2ShoYGQwzrhkREcnJymv2/Jjo6WkRaVie3bt2SiRMnSrt27cTe3l5iYmLk3r17rbYHhchfbu1MREREREREj+B3nIiIiIiIiExg40RERERERGQCGyciIiIiIiIT2DgRERERERGZwMaJiIiIiIjIBDZOREREREREJrBxIiIiIiIiMoGNExER0RPatm0bNBqNudMgIqJWxMaJiIiemYqKCsTFxaFHjx6wsbGBVqtFaGgoUlJSUFtba+70WsTd3R2ffvqp0dzrr7+OCxcumCchIiIyC6W5EyAiov9Nly9fRmhoKDQaDVauXIk+ffpApVLhzJkz2Lp1K7p06YJXX33VLLmJCJqamqBU/mcvg2q1Gmq1+ilnRURElozvOBER0TMxY8YMKJVKHD9+HOPHj4e3tze6d++O0aNHIyMjAxEREQCAqqoqvPXWW3B2doa9vT1eeeUVnDp1ynCepUuX4qWXXsL27dvh7u4OBwcHTJgwAffu3TPE6PV6JCUlwcPDA2q1Gn379sX3339vWD948CAUCgUyMzMREBAAlUqFvLw8XLp0CaNHj4ZWq0W7du3Qr18//PTTT4bjBg8ejGvXriE+Ph4KhQIKhQJA8x/VS0lJgaenJ6ytrfHiiy9i+/btRusKhQJffPEFoqKiYGtrCy8vL+zdu9ewfufOHeh0Ojg7O0OtVsPLywupqan//z8EERE9FWyciIjoqbt16xZ+/PFHxMbGws7OrtmYfzch48aNQ2VlJTIzM1FYWAh/f3+EhYXh9u3bhthLly5hz549SE9PR3p6OnJzc5GcnGxYT0pKwpdffonNmzfj3LlziI+Px6RJk5Cbm2v0nPPnz0dycjJKSkrg6+uLmpoajBw5EtnZ2SgqKsLw4cMRERGBsrIyAEBaWhq6du2KZcuWoby8HOXl5c3uZffu3YiLi8P777+Ps2fPYtq0aYiJiUFOTo5R3Icffojx48fj9OnTGDlyJHQ6nWGfiYmJKC4uRmZmJkpKSpCSkgInJ6cn/M0TEdEzI0RERE/Z0aNHBYCkpaUZzTs6OoqdnZ3Y2dnJ3Llz5eeffxZ7e3upr683ivP09JQtW7aIiMiSJUvE1tZWqqurDesJCQkSFBQkIiL19fVia2srv/zyi9E5pkyZIhMnThQRkZycHAEge/bsMZl7r169ZMOGDYZxt27d5JNPPjGKSU1NFQcHB8M4JCRE3n77baOYcePGyciRIw1jAPLBBx8YxjU1NQJAMjMzRUQkIiJCYmJiTOZHRETmwe84ERFRqzl27Bj0ej10Oh0aGhpw6tQp1NTUwNHR0Siurq4Oly5dMozd3d3Rvn17w9jV1RWVlZUAgF9//RW1tbUYMmSI0TkaGxvh5+dnNBcYGGg0rqmpwdKlS5GRkYHy8nI8fPgQdXV1hnecWqqkpARTp041mgsNDcW6deuM5nx9fQ0/29nZwd7e3rCP6dOnY+zYsThx4gSGDh2KyMhIhISEPFEeRET07LBxIiKip65Hjx5QKBQoLS01mu/evTsAGC6sUFNTA1dXVxw8ePCRc/z1O0RWVlZGawqFAnq93nAOAMjIyECXLl2M4lQqldH47x8bnDNnDrKysrBmzRr06NEDarUar732GhobG1u40yfzT/sYMWIErl27hv379yMrKwthYWGIjY3FmjVrnkkuRET0ZNg4ERHRU+fo6IghQ4Zg48aNePfddx/7PSd/f39UVFRAqVTC3d39P3ouHx8fqFQqlJWVYdCgQU907OHDh/Hmm28iKioKwL+asKtXrxrFWFtbo6mp6R/P4+3tjcOHDyM6Otro3D4+Pk+Uj7OzM6KjoxEdHY2BAwciISGBjRMRkYVg40RERM/EZ599htDQUAQGBmLp0qXw9fVFmzZtUFBQgPPnzyMgIADh4eEIDg5GZGQkVq9ejRdeeAE3btxARkYGoqKiHvloXXPat2+POXPmID4+Hnq9HgMGDMDdu3dx+PBh2NvbGzUzf+fl5YW0tDRERERAoVAgMTHR8A7Qv7m7u+PQoUOYMGECVCpVsxdsSEhIwPjx4+Hn54fw8HDs27cPaWlpRlfoM2Xx4sUICAhAr1690NDQgPT0dHh7e7f4eCIierbYOBER0TPh6emJoqIirFy5EgsWLMDvv/8OlUoFHx8fzJkzBzNmzIBCocD+/fuxaNEixMTE4I8//oCLiwtefvllaLXaFj/X8uXL4ezsjKSkJFy+fBkajQb+/v5YuHDhPx63du1aTJ48GSEhIXBycsK8efNQXV1tFLNs2TJMmzYNnp6eaGhogIg8cp7IyEisW7cOa9asQVxcHDw8PJCamorBgwe3eA/W1tZYsGABrl69CrVajYEDB2Lnzp0tPp6IiJ4thTT3CkBEREREREQGvI8TERERERGRCWyciIiIiIiITGDjREREREREZAIbJyIiIiIiIhPYOBEREREREZnAxomIiIiIiMgENk5EREREREQmsHEiIiIiIiIygY0TERERERGRCWyciIiIiIiITGDjREREREREZAIbJyIiIiIiIhP+D5ozCPZ1B2UuAAAAAElFTkSuQmCC", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "generate_plots()" + "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", + "generate_plots(est_mab)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "brush", "language": "python", "name": "python3" }, @@ -2436,7 +748,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.11.3" + }, + "vscode": { + "interpreter": { + "hash": "dccdbee601866cd4c45494445ca79bf9b696b8bf13c00622eb9e8a421ade3c36" + } } }, "nbformat": 4, diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index 0b9783ae..f666b8bf 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -43,19 +43,36 @@ "\n", "> In our work, the mutations would be the arms, and this update would be used during the evolution to adjust the mutation probabilities.\n", "\n", - "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user.\n", - "\n", - "My suggestion is that, at the end of the evolution, we use the success ratio of each arm as the weights for each arm." + "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "!pip install matplotlib > /dev/null\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.gridspec as gridspec\n", + "\n", "import numpy as np\n", + "import time\n", + "import pandas as pd\n", "\n", + "from brush.estimator import BrushEstimator\n", + "from sklearn.base import ClassifierMixin, RegressorMixin\n", + "from deap import creator\n", + "import _brush\n", + "from deap_api import nsga2 " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "class D_TS:\n", " def __init__(self, num_bandits, C=100):\n", " self.num_bandits = num_bandits\n", @@ -139,22 +156,19 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def plot_learner_history(learner, arm_labels=[]):\n", - " !pip install matplotlib > /dev/null\n", - " import matplotlib.pyplot as plt\n", - " import matplotlib.gridspec as gridspec\n", "\n", " # getting the labels to use in plots\n", " if len(arm_labels) != learner.num_bandits:\n", " arm_labels = [f'arm {i}' for i in range(learner.num_bandits)]\n", "\n", " # Setting up the figure layout\n", - " fig = plt.figure(figsize=(12, 10), tight_layout=True)\n", - " gs = gridspec.GridSpec(6, 6)\n", + " fig = plt.figure(figsize=(15, 10), tight_layout=True)\n", + " gs = gridspec.GridSpec(7, 6)\n", "\n", " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", " \n", @@ -165,10 +179,15 @@ " data_total_rewards = np.array([total_rewards[k] for k in sorted(total_rewards)])\n", " data_total_failures = data_total_pulls-data_total_rewards\n", "\n", + " ylim = np.maximum(data_total_rewards.max(), data_total_failures.max())\n", + "\n", " axs = fig.add_subplot(gs[0:2, 4:])\n", - " axs.bar(arm_labels, data_total_failures, label=\"Null reward\")\n", - " axs.bar(arm_labels, data_total_rewards, bottom = data_total_failures, label=\"Positive reward\")\n", + "\n", + " axs.bar(arm_labels, -1*data_total_failures, label=\"Null reward\")\n", + " axs.bar(arm_labels, data_total_rewards, label=\"Positive reward\")\n", + "\n", " axs.set_xlabel(\"Arm\")\n", + " axs.set_ylim( (-1.05*ylim, 1.05*ylim) )\n", " axs.legend()\n", "\n", " win_ratios = pd.DataFrame.from_dict({\n", @@ -179,7 +198,7 @@ " 'success%' : (data_total_rewards/(data_total_pulls)).round(2)\n", " })\n", "\n", - " axs = fig.add_subplot(gs[3:5, 4:])\n", + " axs = fig.add_subplot(gs[2:4, 4:])\n", " axs.table(cellText=win_ratios.values, colLabels=win_ratios.columns, loc='center')\n", " axs.axis('off')\n", " axs.axis('tight')\n", @@ -194,6 +213,7 @@ " axs = fig.add_subplot(gs[0:2, :4])\n", " axs.plot(data, label=arm_labels)\n", " axs.set_ylabel(\"Number of times mutation was used\")\n", + " axs.legend()\n", "\n", " # multiple lines all full height showing when D-TS used the dynamic update rule\n", " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", @@ -221,74 +241,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------------------ optimizing ------------------------\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(it was expected: similar amount of pulls for each arm)\n", - "------------------------ optimizing ------------------------\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(it was expected: more pulls for first arm, less pulls for last)\n", - "------------------------ optimizing ------------------------\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(it was expected: 2nd approx 4th > 1st > 3rd)\n" - ] - } - ], + "outputs": [], "source": [ "# Sanity checks\n", - "import pandas as pd\n", - "\n", "class Bandits:\n", " def __init__(self, reward_prob):\n", " # Implementing simple bandits.\n", @@ -324,16 +281,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from brush.estimator import BrushEstimator\n", - "from sklearn.base import ClassifierMixin, RegressorMixin\n", - "from deap import creator\n", - "import _brush\n", - "from deap_api import nsga2 \n", - "\n", "class BrushEstimatorMod(BrushEstimator): # Modifying brush estimator\n", " def __init__(self, **kwargs):\n", " super().__init__(**kwargs)\n", @@ -360,22 +311,6 @@ " \n", " params = self.get_params()\n", " \n", - " # if the mutation returns an invalid expression, this should count as reward=0\n", - " # ignore_this_time = True if (ind1.prg.size()+1>=self.max_size\n", - " # or ind1.prg.depth()+1>=self.max_depth) else False\n", - "\n", - " # Insert Mutation will not work, even if we force it, when the expression\n", - " # is already at maximum size.\n", - " # In this case, we'll do the mutation without controlling the probabilities.\n", - " # if ignore_this_time:\n", - " # for i, m in enumerate(self.mutations_):\n", - " # params['mutation_options'][m] = 0.25 # let cpp do the mutation \n", - " # else:\n", - " # mutation_idx = self.learner_.choose_arm()\n", - "\n", - " # for i, m in enumerate(self.mutations_):\n", - " # params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", - "\n", " mutation_idx = self.learner_.choose_arm()\n", "\n", " for i, m in enumerate(self.mutations_):\n", @@ -498,2875 +433,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.73]\t[ nan 1.00851376]\t[nan 17.]\n", - "1 \t91 \t91 \t[ nan 15.62]\t[ nan 5.93258797]\t[nan 1.]\n", - "2 \t97 \t97 \t[ nan 8.75] \t[ nan 5.11541787]\t[nan 1.]\n", - "3 \t100 \t100 \t[ nan 3.67] \t[ nan 2.18657266]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 1.89] \t[ nan 0.96845237]\t[nan 1.]\n", - "5 \t100 \t100 \t[0.63582207 1.14 ]\t[0.26436657 0.34698703]\t[0.31053385 1. ]\n", - "6 \t100 \t100 \t[0.49312953 1.07 ]\t[0.12367445 0.25514702]\t[0.27511281 1. ]\n", - "7 \t100 \t100 \t[0.41630859 1.04 ]\t[0.08465657 0.19595918]\t[0.27511281 1. ]\n", - "8 \t100 \t100 \t[0.38476919 1.03 ]\t[0.01781903 0.2215852 ]\t[0.24656506 1. ]\n", - "9 \t100 \t100 \t[0.38476919 1.03 ]\t[0.01781903 0.2215852 ]\t[0.24656506 1. ]\n", - "10 \t100 \t100 \t[0.38279961 1.07 ]\t[0.02629828 0.45287967]\t[0.19034052 1. ]\n", - "11 \t100 \t100 \t[0.38225459 1.07 ]\t[0.03050896 0.45287967]\t[0.13583826 1. ]\n", - "12 \t100 \t100 \t[0.38084725 1.09 ]\t[0.03335683 0.49183331]\t[0.13583826 1. ]\n", - "13 \t100 \t100 \t[0.38336185 1.05 ]\t[0.02250505 0.29580399]\t[0.24656506 1. ]\n", - "14 \t100 \t100 \t[0.38012579 1.1 ]\t[0.03895815 0.57445626]\t[0.06369215 1. ]\n", - "15 \t100 \t100 \t[0.37761119 1.14 ]\t[0.0459093 0.69310894]\t[0.06369215 1. ]\n", - "16 \t100 \t100 \t[0.37509659 1.18 ]\t[0.05181644 0.79221209]\t[0.06369215 1. ]\n", - "17 \t100 \t100 \t[0.37509659 1.18 ]\t[0.05181644 0.79221209]\t[0.06369215 1. ]\n", - "18 \t100 \t100 \t[0.37509659 1.18 ]\t[0.05181644 0.79221209]\t[0.06369215 1. ]\n", - "19 \t100 \t100 \t[0.37508382 1.18 ]\t[0.05184123 0.79221209]\t[0.06369215 1. ]\n", - "20 \t100 \t100 \t[0.37184776 1.25 ]\t[0.06037546 1.04283268]\t[0.06369213 1. ]\n", - "21 \t100 \t100 \t[0.37184776 1.25 ]\t[0.06037546 1.04283268]\t[0.06369213 1. ]\n", - "22 \t100 \t100 \t[0.3817758 1.08 ] \t[0.02723744 0.41665333]\t[0.2299699 1. ] \n", - "23 \t100 \t100 \t[0.3817758 1.08 ] \t[0.02723744 0.41665333]\t[0.2299699 1. ] \n", - "24 \t100 \t100 \t[0.37853974 1.14 ]\t[0.04174773 0.72138755]\t[0.06369214 1. ]\n", - "25 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", - "26 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", - "27 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", - "28 \t100 \t100 \t[0.37692439 1.18 ]\t[0.04441736 0.81706793]\t[0.06369214 1. ]\n", - "29 \t100 \t100 \t[0.37373576 1.25 ]\t[0.04904115 0.94207218]\t[0.06369214 1. ]\n", - "30 \t100 \t100 \t[0.37373576 1.25 ]\t[0.04904115 0.94207218]\t[0.06369214 1. ]\n", - "31 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ]\n", - "32 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ]\n", - "33 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ]\n", - "34 \t100 \t100 \t[0.3794761 1.1 ] \t[0.04431328 0.57445626]\t[8.72925551e-11 1.00000000e+00]\n", - "35 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ] \n", - "36 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ] \n", - "37 \t100 \t100 \t[0.38334909 1.05 ]\t[0.02256674 0.29580399]\t[0.24656503 1. ] \n", - "38 \t100 \t100 \t[0.37837715 1.12 ]\t[0.04523826 0.66753277]\t[0.00356714 1. ] \n", - "39 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", - "40 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", - "41 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", - "42 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", - "43 \t100 \t100 \t[0.37257026 1.22 ]\t[0.061397 0.96519428]\t[0.00356714 1. ] \n", - "44 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", - "45 \t100 \t100 \t[0.38260318 1.11 ]\t[0.02301651 0.64645185]\t[0.26415548 1. ] \n", - "46 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", - "47 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", - "48 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656506 1. ] \n", - "49 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656506 1. ] \n", - "50 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656506 1. ] \n", - "51 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ] \n", - "52 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ] \n", - "53 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ] \n", - "54 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", - "55 \t100 \t100 \t[0.38382032 1.04 ]\t[0.01978635 0.24166092]\t[0.26641718 1. ] \n", - "56 \t100 \t100 \t[0.38378047 1.04 ]\t[0.0200253 0.24166092]\t[0.26243275 1. ] \n", - "57 \t100 \t100 \t[0.38378047 1.04 ]\t[0.0200253 0.24166092]\t[0.26243275 1. ] \n", - "58 \t100 \t100 \t[0.38176123 1.08 ]\t[0.02798752 0.4621688 ]\t[0.19034052 1. ] \n", - "59 \t100 \t100 \t[0.38154701 1.08 ]\t[0.02920492 0.4621688 ]\t[0.17982 1. ] \n", - "60 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ] \n", - "61 \t100 \t100 \t[0.38264569 1.08 ]\t[0.02280061 0.4621688 ]\t[0.26784596 1. ] \n", - "62 \t100 \t100 \t[0.38026224 1.14 ]\t[0.02785768 0.63277168]\t[0.26784596 1. ] \n", - "63 \t100 \t100 \t[0.37906772 1.17 ]\t[0.03000837 0.69361373]\t[0.26784596 1. ] \n", - "64 \t100 \t100 \t[0.37705243 1.25 ]\t[0.03562874 1.04283268]\t[0.18576901 1. ] \n", - "65 \t100 \t100 \t[0.3726603 1.36 ] \t[0.05209291 1.54609185]\t[0.02559096 1. ] \n", - "66 \t100 \t100 \t[0.37034493 1.39 ]\t[0.06331501 1.78266654]\t[2.28585293e-08 1.00000000e+00]\n", - "67 \t100 \t100 \t[0.3719726 1.33 ] \t[0.06591118 1.63740649]\t[1.09988078e-11 1.00000000e+00]\n", - "68 \t100 \t100 \t[0.38245833 1.07 ]\t[0.02838402 0.45287967]\t[0.1699218 1. ] \n", - "69 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ] \n", - "70 \t100 \t100 \t[0.3860344 1.01 ] \t[0.01257644 0.09949874]\t[0.26090035 1. ] \n", - "71 \t100 \t100 \t[0.3860344 1.01 ] \t[0.01257644 0.09949874]\t[0.26090035 1. ] \n", - "72 \t100 \t100 \t[0.38265748 1.07 ]\t[0.02691114 0.45287967]\t[0.19034052 1. ] \n", - "73 \t100 \t100 \t[0.38125015 1.09 ]\t[0.03012017 0.49183331]\t[0.19034052 1. ] \n", - "74 \t100 \t100 \t[0.38125015 1.09 ]\t[0.03012017 0.49183331]\t[0.19034052 1. ] \n", - "75 \t100 \t100 \t[0.37828691 1.15 ]\t[0.04172145 0.76648549]\t[0.09097429 1. ] \n", - "76 \t100 \t100 \t[0.37384055 1.26 ]\t[0.05249719 1.12800709]\t[0.08539494 1. ] \n", - "77 \t100 \t100 \t[0.37384055 1.26 ]\t[0.05249719 1.12800709]\t[0.08539494 1. ] \n", - "78 \t100 \t100 \t[0.38603439 1.01 ]\t[0.01257645 0.09949874]\t[0.26090032 1. ] \n", - "79 \t100 \t100 \t[0.38467572 1.03 ]\t[0.01837081 0.2215852 ]\t[0.25143063 1. ] \n", - "80 \t100 \t100 \t[0.38462706 1.03 ]\t[0.01872665 0.2215852 ]\t[0.24656506 1. ] \n", - "81 \t100 \t100 \t[0.38321973 1.05 ]\t[0.02322169 0.29580399]\t[0.24656506 1. ] \n", - "82 \t100 \t100 \t[0.38462706 1.03 ]\t[0.01872665 0.2215852 ]\t[0.24656503 1. ] \n", - "83 \t100 \t100 \t[0.38321973 1.05 ]\t[0.02322169 0.29580399]\t[0.24656503 1. ] \n", - "84 \t100 \t100 \t[0.38321973 1.05 ]\t[0.02322169 0.29580399]\t[0.24656503 1. ] \n", - "85 \t100 \t100 \t[0.38195575 1.06 ]\t[0.02621265 0.31048349]\t[0.24656503 1. ] \n", - "86 \t100 \t100 \t[0.37953733 1.1 ]\t[0.03521773 0.5 ]\t[0.14545636 1. ] \n", - "87 \t100 \t100 \t[0.37953733 1.1 ]\t[0.03521773 0.5 ]\t[0.14545636 1. ] \n", - "88 \t100 \t100 \t[0.37953733 1.1 ]\t[0.03521773 0.5 ]\t[0.14545636 1. ] \n", - "89 \t100 \t100 \t[0.37711891 1.14 ]\t[0.04221109 0.63277168]\t[0.14545636 1. ] \n", - "90 \t100 \t100 \t[0.37711891 1.14 ]\t[0.04221109 0.63277168]\t[0.14545636 1. ] \n", - "91 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", - "92 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", - "93 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", - "94 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", - "95 \t100 \t100 \t[0.38477041 1.02 ]\t[0.01769573 0.14 ]\t[0.26090032 1. ] \n", - "96 \t100 \t100 \t[0.38336308 1.04 ]\t[0.02240762 0.24166092]\t[0.24656506 1. ] \n", - "97 \t100 \t100 \t[0.38149489 1.09 ]\t[0.02886038 0.54945427]\t[0.20047927 1. ] \n", - "98 \t100 \t100 \t[0.38149489 1.09 ]\t[0.02886038 0.54945427]\t[0.20047927 1. ] \n", - "99 \t100 \t100 \t[0.38149489 1.09 ]\t[0.02886038 0.54945427]\t[0.20047927 1. ] \n", - "Final population hypervolume is 49489.883527\n", - "best model: If(x1>0.91,1.61,Max(-0.64*x1,0.14*x2,0.17))\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.88]\t[ nan 0.96208108]\t[nan 20.]\n", - "1 \t0 \t85 \t[ nan 16.15]\t[ nan 5.8726059] \t[nan 1.]\n", - "2 \t0 \t96 \t[ nan 9.41] \t[ nan 5.60909084]\t[nan 1.]\n", - "3 \t0 \t100 \t[ nan 3.62] \t[ nan 2.33572259]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.92] \t[ nan 0.92390476]\t[nan 1.]\n", - "5 \t0 \t100 \t[ nan 1.34] \t[ nan 0.56956123]\t[nan 1.]\n", - "6 \t0 \t100 \t[5.09753385 1.08 ]\t[1.32846308 0.2712932 ]\t[2.61403799 1. ]\n", - "7 \t0 \t100 \t[4.77122647 1.05 ]\t[1.22736445 0.21794495]\t[2.61403799 1. ]\n", - "8 \t0 \t100 \t[4.31298023 1.06 ]\t[1.04917369 0.23748684]\t[2.61403799 1. ]\n", - "9 \t0 \t100 \t[3.76372997 1.25 ]\t[0.55135192 0.84113019]\t[2.02158666 1. ]\n", - "10 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "11 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "12 \t0 \t100 \t[3.83362872 1.05 ]\t[0.22415653 0.32787193]\t[2.45537734 1. ]\n", - "13 \t0 \t100 \t[3.80631677 1.07 ]\t[0.34959507 0.38091994]\t[1.13151622 1. ]\n", - "14 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "15 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352633 0.24166092]\t[2.46565032 1. ]\n", - "16 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352633 0.24166092]\t[2.46565032 1. ]\n", - "17 \t0 \t100 \t[3.80707055 1.09 ]\t[0.34386122 0.54945427]\t[1.2068944 1. ] \n", - "18 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352633 0.24166092]\t[2.46565032 1. ]\n", - "19 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", - "20 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221685 0.29580399]\t[2.46565032 1. ]\n", - "21 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "22 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "23 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "24 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "25 \t0 \t100 \t[3.84728087 1.04 ]\t[0.17994462 0.31368774]\t[2.56668186 1. ]\n", - "26 \t0 \t100 \t[3.79072391 1.15 ]\t[0.44842463 0.85293611]\t[0.1826939 1. ] \n", - "27 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "28 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "29 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "30 \t0 \t100 \t[3.82706133 1.07 ]\t[0.26668339 0.45287967]\t[1.90340519 1. ]\n", - "31 \t0 \t100 \t[3.82706133 1.07 ]\t[0.26668339 0.45287967]\t[1.90340519 1. ]\n", - "32 \t0 \t100 \t[3.83317034 1.05 ]\t[0.22652029 0.29580399]\t[2.51430631 1. ]\n", - "33 \t0 \t100 \t[3.78060498 1.12 ]\t[0.45488453 0.5706137 ]\t[0.13627219 1. ]\n", - "34 \t0 \t100 \t[3.78060498 1.12 ]\t[0.45488453 0.5706137 ]\t[0.13627219 1. ]\n", - "35 \t0 \t100 \t[3.78060498 1.12 ]\t[0.45488453 0.5706137 ]\t[0.13627219 1. ]\n", - "36 \t0 \t100 \t[3.75465206 1.16 ]\t[0.58438177 0.79649231]\t[0.00325558 1. ]\n", - "37 \t0 \t100 \t[3.74057753 1.18 ]\t[0.59815397 0.81706793]\t[0.00325558 1. ]\n", - "38 \t0 \t100 \t[3.74057753 1.18 ]\t[0.59815397 0.81706793]\t[0.00325558 1. ]\n", - "39 \t0 \t100 \t[3.77927481 1.12 ]\t[0.4656074 0.5706137] \t[0.00325559 1. ]\n", - "40 \t0 \t100 \t[3.77927481 1.12 ]\t[0.4656074 0.5706137] \t[0.00325559 1. ]\n", - "41 \t0 \t100 \t[3.77927481 1.12 ]\t[0.4656074 0.5706137] \t[0.00325559 1. ]\n", - "42 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980988 0.41665333]\t[2.45047021 1. ]\n", - "43 \t0 \t100 \t[3.80181861 1.12 ]\t[0.31121164 0.5706137 ]\t[2.25763535 1. ]\n", - "44 \t0 \t100 \t[3.76148092 1.22 ]\t[0.41721139 0.90088845]\t[1.45456362 1. ]\n", - "45 \t0 \t100 \t[3.74206228 1.26 ]\t[0.50930546 1.12800709]\t[0.63692153 1. ]\n", - "46 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980988 0.41665333]\t[2.45047021 1. ]\n", - "47 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221685 0.29580399]\t[2.46565032 1. ]\n", - "48 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980987 0.41665333]\t[2.45047045 1. ]\n", - "49 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980987 0.41665333]\t[2.45047045 1. ]\n", - "50 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "51 \t0 \t100 \t[3.84675712 1.03 ]\t[0.1837081 0.2215852] \t[2.51430631 1. ]\n", - "52 \t0 \t100 \t[3.83317034 1.05 ]\t[0.22652029 0.29580399]\t[2.51430631 1. ]\n", - "53 \t0 \t100 \t[3.83317034 1.05 ]\t[0.22652029 0.29580399]\t[2.51430631 1. ]\n", - "54 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "55 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "56 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014736 0.59824744]\t[1.90340519 1. ]\n", - "57 \t0 \t100 \t[3.90709746 1.02 ]\t[0.352289 0.14 ] \t[2.60900354 1. ]\n", - "58 \t0 \t100 \t[3.84611876 1.05 ]\t[0.18838836 0.40926764]\t[2.45047045 1. ]\n", - "59 \t0 \t100 \t[3.84611876 1.05 ]\t[0.18838839 0.40926764]\t[2.45046997 1. ]\n", - "60 \t0 \t100 \t[3.84611876 1.05 ]\t[0.18838839 0.40926764]\t[2.45046997 1. ]\n", - "61 \t0 \t100 \t[3.84611875 1.05 ]\t[0.18838842 0.40926764]\t[2.45046997 1. ]\n", - "62 \t0 \t100 \t[3.8280083 1.06 ] \t[0.26214986 0.42 ]\t[1.90340519 1. ]\n", - "63 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "64 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", - "65 \t0 \t100 \t[3.82004398 1.08 ]\t[0.25964636 0.4621688 ]\t[2.46565056 1. ]\n", - "66 \t0 \t100 \t[3.81442152 1.08 ]\t[0.29287514 0.4621688 ]\t[1.90340519 1. ]\n", - "67 \t0 \t100 \t[3.80083475 1.1 ]\t[0.32009366 0.5 ]\t[1.90340519 1. ]\n", - "68 \t0 \t100 \t[3.79318234 1.13 ]\t[0.35672129 0.67312703]\t[1.79772139 1. ]\n", - "69 \t0 \t100 \t[3.77157637 1.21 ]\t[0.41233082 1.03242433]\t[1.71238637 1. ]\n", - "70 \t0 \t100 \t[3.74970025 1.33 ]\t[0.4614735 1.562402 ] \t[1.68537199 1. ]\n", - "71 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "72 \t0 \t100 \t[3.80533228 1.09 ]\t[0.2953361 0.42649736]\t[2.45047045 1. ]\n", - "73 \t0 \t100 \t[3.80533228 1.09 ]\t[0.2953361 0.42649736]\t[2.45047045 1. ]\n", - "74 \t0 \t100 \t[3.79110715 1.12 ]\t[0.3245486 0.51536395]\t[2.45047045 1. ]\n", - "75 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "76 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "77 \t0 \t100 \t[3.8280083 1.06 ] \t[0.26214986 0.42 ]\t[1.90340519 1. ]\n", - "78 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "79 \t0 \t100 \t[3.83443739 1.05 ]\t[0.2192433 0.32787193]\t[2.54631495 1. ]\n", - "80 \t0 \t100 \t[3.83443719 1.05 ]\t[0.21924451 0.32787193]\t[2.54629421 1. ]\n", - "81 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", - "82 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "83 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "84 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "85 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "86 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "87 \t0 \t100 \t[3.79472574 1.12 ]\t[0.3491038 0.60464866]\t[1.90340519 1. ]\n", - "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "89 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "90 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "91 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ]\n", - "92 \t0 \t100 \t[3.80268167 1.1 ]\t[0.37695258 0.59160798]\t[0.79325575 1. ]\n", - "93 \t0 \t100 \t[3.76398439 1.17 ]\t[0.5337628 0.90614568]\t[0.0032556 1. ] \n", - "94 \t0 \t100 \t[3.74991106 1.19 ]\t[0.54903786 0.9240671 ]\t[0.0032556 1. ] \n", - "95 \t0 \t100 \t[3.70191681 1.28 ]\t[0.60511095 1.04957134]\t[0.0032556 1. ] \n", - "96 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "97 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "98 \t0 \t100 \t[3.81383966 1.09 ]\t[0.29568479 0.54945427]\t[1.89387512 1. ]\n", - "99 \t0 \t100 \t[3.77394115 1.2 ]\t[0.4012903 0.9591663] \t[1.75132275 1. ]\n", - "Final population hypervolume is 49410.836238\n", - "fit, 1, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.95]\t[ nan 1.00374299]\t[nan 20.]\n", - "1 \t86 \t86 \t[ nan 15.66]\t[ nan 6.39096237]\t[nan 1.]\n", - "2 \t94 \t94 \t[ nan 9.43] \t[ nan 5.59688306]\t[nan 1.]\n", - "3 \t99 \t99 \t[ nan 4.47] \t[ nan 2.15153434]\t[nan 1.]\n", - "4 \t100 \t100 \t[nan 2.9] \t[ nan 0.93273791]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 2.71] \t[ nan 0.79113842]\t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 2.53] \t[ nan 0.67014924]\t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.39] \t[ nan 0.61473572]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.31] \t[ nan 0.59489495]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.26] \t[ nan 0.57654141]\t[nan 1.]\n", - "10 \t100 \t100 \t[nan 2.2] \t[ nan 0.54772256]\t[nan 1.]\n", - "11 \t100 \t100 \t[nan 2.2] \t[ nan 0.54772256]\t[nan 1.]\n", - "12 \t100 \t100 \t[nan 2.2] \t[ nan 0.54772256]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", - "17 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.18] \t[ nan 0.53628351]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.23] \t[ nan 0.56311633]\t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.25] \t[ nan 0.57227616]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.25] \t[ nan 0.57227616]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.26] \t[ nan 0.57654141]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.27] \t[ nan 0.58060313]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.26] \t[ nan 0.57654141]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.24] \t[ nan 0.56780278]\t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.22] \t[ nan 0.55821143]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.17] \t[ nan 0.53018865]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", - "30 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.11] \t[ nan 0.48774994]\t[nan 1.]\n", - "33 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "34 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", - "38 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "39 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "40 \t100 \t100 \t[ nan 2.11] \t[ nan 0.48774994]\t[nan 1.]\n", - "41 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "45 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 2.08] \t[ nan 0.4621688] \t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 2.07] \t[ nan 0.45287967]\t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 2.07] \t[ nan 0.45287967]\t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 2.03] \t[ nan 0.4112177] \t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 1.99] \t[ nan 0.36041643]\t[nan 1.]\n", - "53 \t100 \t100 \t[ nan 1.95] \t[ nan 0.29580399]\t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 1.95] \t[ nan 0.29580399]\t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.79]\t[ nan 0.95178779]\t[nan 20.]\n", - "1 \t0 \t88 \t[ nan 15.14]\t[ nan 6.39846857]\t[nan 1.]\n", - "2 \t0 \t99 \t[ nan 7.78] \t[ nan 4.93878528]\t[nan 1.]\n", - "3 \t0 \t100 \t[ nan 2.76] \t[ nan 1.51736614]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.51] \t[ nan 0.68549252]\t[nan 1.]\n", - "5 \t0 \t100 \t[5.26673321 1.06 ]\t[1.26729378 0.23748684]\t[2.73836112 1. ]\n", - "6 \t0 \t100 \t[4.56688371 1.06 ]\t[1.15484383 0.23748684]\t[2.73836112 1. ]\n", - "7 \t0 \t100 \t[3.78914207 1.16 ]\t[0.60358184 0.62801274]\t[9.30948616e-08 1.00000000e+00]\n", - "8 \t0 \t100 \t[3.81101844 1.08 ]\t[0.41613617 0.54184869]\t[3.14239568e-09 1.00000000e+00]\n", - "9 \t0 \t100 \t[3.83567494 1.06 ]\t[0.21312651 0.36932371]\t[2.46565032 1. ] \n", - "10 \t0 \t100 \t[3.81990402 1.09 ]\t[0.26101346 0.47106263]\t[2.46565032 1. ] \n", - "11 \t0 \t100 \t[3.79972168 1.13 ]\t[0.32514054 0.61081912]\t[1.90340519 1. ] \n", - "12 \t0 \t100 \t[3.75328773 1.21 ]\t[0.47040772 0.86365502]\t[0.63692147 1. ] \n", - "13 \t0 \t100 \t[3.81931956 1.08 ]\t[0.26398485 0.41665333]\t[2.45585918 1. ] \n", - "14 \t0 \t100 \t[3.8136971 1.1 ] \t[0.29671447 0.53851648]\t[1.90340519 1. ] \n", - "15 \t0 \t100 \t[3.8136971 1.1 ] \t[0.29671447 0.53851648]\t[1.90340519 1. ] \n", - "16 \t0 \t100 \t[3.79400132 1.14 ]\t[0.35229044 0.6636264 ]\t[1.90340519 1. ] \n", - "17 \t0 \t100 \t[3.7608594 1.21 ] \t[0.42208268 0.88651001]\t[1.69341516 1. ] \n", - "18 \t0 \t100 \t[3.7608594 1.21 ] \t[0.42208268 0.88651001]\t[1.69341516 1. ] \n", - "19 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "20 \t0 \t100 \t[3.8382249 1.04 ] \t[0.19773434 0.24166092]\t[2.66634941 1. ] \n", - "21 \t0 \t100 \t[3.79797463 1.08 ]\t[0.43425924 0.4621688 ]\t[6.73430023e-09 1.00000000e+00]\n", - "22 \t0 \t100 \t[3.78438785 1.1 ]\t[0.45256852 0.5 ]\t[6.56295018e-09 1.00000000e+00]\n", - "23 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[6.56295018e-09 1.00000000e+00]\n", - "24 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067638 0.63277168]\t[6.56295018e-09 1.00000000e+00]\n", - "25 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[7.40513539e-10 1.00000000e+00]\n", - "26 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "27 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "28 \t0 \t100 \t[3.81456761 1.1 ]\t[0.25470453 0.5 ]\t[2.67845964 1. ] \n", - "29 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "30 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "31 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "32 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "33 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "34 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "35 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "36 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "37 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "38 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "39 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", - "40 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", - "41 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "42 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "43 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668955 0.24166092]\t[2.68406439 1. ] \n", - "44 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "45 \t0 \t100 \t[3.82651285 1.08 ]\t[0.22772444 0.44 ]\t[2.68406439 1. ] \n", - "46 \t0 \t100 \t[3.80267843 1.14 ]\t[0.278351 0.61676576]\t[2.67846012 1. ] \n", - "47 \t0 \t100 \t[3.81456761 1.1 ]\t[0.25470453 0.5 ]\t[2.67845964 1. ] \n", - "48 \t0 \t100 \t[3.80681707 1.1 ]\t[0.29737574 0.5 ]\t[1.90340519 1. ] \n", - "49 \t0 \t100 \t[3.81456762 1.09 ]\t[0.2547045 0.42649736]\t[2.67846036 1. ] \n", - "50 \t0 \t100 \t[3.82651285 1.06 ]\t[0.22772444 0.31048349]\t[2.68406439 1. ] \n", - "51 \t0 \t100 \t[3.8132428 1.09 ] \t[0.2608801 0.42649736]\t[2.54597807 1. ] \n", - "52 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", - "53 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "54 \t0 \t100 \t[3.7841615 1.14 ] \t[0.4256492 0.74859869]\t[0.63692147 1. ] \n", - "55 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "56 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ] \n", - "57 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "58 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "59 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "60 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "61 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "62 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "63 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "64 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "65 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "66 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "67 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "68 \t0 \t100 \t[3.817536 1.1 ] \t[0.27817292 0.64031242]\t[2.00479293 1. ] \n", - "69 \t0 \t100 \t[3.79885409 1.16 ]\t[0.33145254 0.86856203]\t[2.00479293 1. ] \n", - "70 \t0 \t100 \t[3.79885409 1.16 ]\t[0.33145254 0.86856203]\t[2.00479293 1. ] \n", - "71 \t0 \t100 \t[3.80086088 1.08 ]\t[0.4066491 0.4621688] \t[0.33728087 1. ] \n", - "72 \t0 \t100 \t[3.80086088 1.08 ]\t[0.4066491 0.4621688] \t[0.33728087 1. ] \n", - "73 \t0 \t100 \t[3.78185486 1.11 ]\t[0.44540273 0.54580216]\t[0.33728087 1. ] \n", - "74 \t0 \t100 \t[3.74312502 1.16 ]\t[0.58294494 0.73102668]\t[1.73793865e-07 1.00000000e+00]\n", - "75 \t0 \t100 \t[3.74312502 1.16 ]\t[0.58294494 0.73102668]\t[1.73793865e-07 1.00000000e+00]\n", - "76 \t0 \t100 \t[3.74312502 1.16 ]\t[0.58294495 0.73102668]\t[1.60990735e-10 1.00000000e+00]\n", - "77 \t0 \t100 \t[3.72905169 1.18 ]\t[0.5964709 0.75339233]\t[1.60990735e-10 1.00000000e+00]\n", - "78 \t0 \t100 \t[3.72905169 1.18 ]\t[0.5964709 0.75339233]\t[1.60990735e-10 1.00000000e+00]\n", - "79 \t0 \t100 \t[3.69032185 1.23 ]\t[0.70223119 0.89280457]\t[1.60990735e-10 1.00000000e+00]\n", - "80 \t0 \t100 \t[3.69032185 1.23 ]\t[0.70223119 0.89280457]\t[1.60990735e-10 1.00000000e+00]\n", - "81 \t0 \t100 \t[3.69032185 1.23 ]\t[0.70223119 0.89280457]\t[1.60990735e-10 1.00000000e+00]\n", - "82 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "83 \t0 \t100 \t[3.83746583 1.04 ]\t[0.20232391 0.24166092]\t[2.59044313 1. ] \n", - "84 \t0 \t100 \t[3.798736 1.07 ] \t[0.43206919 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "85 \t0 \t100 \t[3.798736 1.07 ] \t[0.43206919 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "86 \t0 \t100 \t[3.79843911 1.07 ]\t[0.43290873 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "87 \t0 \t100 \t[3.79843911 1.07 ]\t[0.43290873 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "88 \t0 \t100 \t[3.79843911 1.07 ]\t[0.43290873 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "89 \t0 \t100 \t[3.79797463 1.07 ]\t[0.43425924 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "90 \t0 \t100 \t[3.79797463 1.07 ]\t[0.43425924 0.38091994]\t[4.34097291e-09 1.00000000e+00]\n", - "91 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[4.34097291e-09 1.00000000e+00]\n", - "92 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "93 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "94 \t0 \t100 \t[3.8264568 1.08 ] \t[0.22800614 0.4621688 ]\t[2.67845941 1. ] \n", - "95 \t0 \t100 \t[3.80204619 1.16 ]\t[0.28093924 0.73102668]\t[2.62644625 1. ] \n", - "96 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "97 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "98 \t0 \t100 \t[3.80078927 1.11 ]\t[0.32231329 0.54580216]\t[1.90340519 1. ] \n", - "99 \t0 \t100 \t[3.77227907 1.19 ]\t[0.42454641 0.95598117]\t[1.02196312 1. ] \n", - "Final population hypervolume is 49444.005521\n", - "fit, 2, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.67]\t[ nan 1.00054985]\t[nan 20.]\n", - "1 \t91 \t91 \t[ nan 16.76]\t[ nan 5.55899271]\t[nan 1.]\n", - "2 \t96 \t96 \t[ nan 10.37]\t[ nan 5.54013538]\t[nan 1.]\n", - "3 \t98 \t98 \t[ nan 5.39] \t[ nan 2.32333812]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 3.94] \t[ nan 1.39871369]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 3.21] \t[ nan 0.99292497]\t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 2.83] \t[ nan 0.77530639]\t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.74] \t[ nan 0.71582121]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.49] \t[ nan 0.55668663]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.42] \t[ nan 0.55099909]\t[nan 1.]\n", - "10 \t100 \t100 \t[ nan 2.44] \t[ nan 0.5535341] \t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.41] \t[ nan 0.54945427]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.39] \t[ nan 0.54580216]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.36] \t[ nan 0.53888774]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.34] \t[ nan 0.53329167]\t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.32] \t[ nan 0.52687759]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.31] \t[ nan 0.52335456]\t[nan 1.]\n", - "17 \t100 \t100 \t[nan 2.3] \t[ nan 0.51961524]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.28] \t[ nan 0.51146847]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.25] \t[ nan 0.49749372]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.27] \t[ nan 0.50705029]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.28] \t[ nan 0.51146847]\t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.28] \t[ nan 0.51146847]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.29] \t[ nan 0.51565492]\t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.32] \t[ nan 0.52687759]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.31] \t[ nan 0.52335456]\t[nan 1.]\n", - "30 \t100 \t100 \t[nan 2.3] \t[ nan 0.51961524]\t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.29] \t[ nan 0.51565492]\t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.26] \t[ nan 0.50239427]\t[nan 1.]\n", - "33 \t100 \t100 \t[ nan 2.25] \t[ nan 0.49749372]\t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.22] \t[ nan 0.48124838]\t[nan 1.]\n", - "35 \t100 \t100 \t[nan 2.2] \t[ nan 0.46904158]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.19] \t[ nan 0.46249324]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.16] \t[ nan 0.44090815]\t[nan 1.]\n", - "38 \t100 \t100 \t[ nan 2.16] \t[ nan 0.44090815]\t[nan 1.]\n", - "39 \t100 \t100 \t[ nan 2.14] \t[ nan 0.42473521]\t[nan 1.]\n", - "40 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", - "41 \t100 \t100 \t[ nan 2.12] \t[ nan 0.4069398] \t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 2.13] \t[ nan 0.41605288]\t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 2.12] \t[ nan 0.4069398] \t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 2.11] \t[ nan 0.39736633]\t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 2.07] \t[ nan 0.35369478]\t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 2.03] \t[ nan 0.29849623]\t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 2.02] \t[ nan 0.28213472]\t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 1.99] \t[ nan 0.22338308]\t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 1.99] \t[ nan 0.22338308]\t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 1.99] \t[ nan 0.22338308]\t[nan 1.]\n", - "53 \t100 \t100 \t[ nan 1.98] \t[ nan 0.19899749]\t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.97] \t[ nan 0.17058722]\t[nan 1.]\n", - "Final population hypervolume is 49486.871854\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.68]\t[ nan 1.33326666]\t[nan 11.]\n", - "1 \t0 \t89 \t[ nan 16.53]\t[ nan 5.33376977]\t[nan 1.]\n", - "2 \t0 \t98 \t[ nan 9.77] \t[ nan 5.49700828]\t[nan 1.]\n", - "3 \t0 \t100 \t[nan 3.7] \t[ nan 2.39374184]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.44] \t[ nan 0.76576759]\t[nan 1.]\n", - "5 \t0 \t100 \t[4.8843533 1.1 ]\t[1.23236078 0.3 ]\t[2.73843527 1. ]\n", - "6 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "7 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ]\n", - "8 \t0 \t100 \t[3.83840205 1.04 ]\t[0.19668954 0.24166092]\t[2.68406463 1. ]\n", - "9 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668956 0.24166092]\t[2.68406415 1. ]\n", - "10 \t0 \t100 \t[3.82388065 1.09 ]\t[0.24198897 0.54945427]\t[2.42084408 1. ]\n", - "11 \t0 \t100 \t[3.8075711 1.13 ] \t[0.28690461 0.67312703]\t[2.42084408 1. ]\n", - "12 \t0 \t100 \t[3.79321028 1.15 ]\t[0.31760269 0.698212 ]\t[2.39755893 1. ]\n", - "13 \t0 \t100 \t[3.77845603 1.22 ]\t[0.34650913 0.97549987]\t[2.39755869 1. ]\n", - "14 \t0 \t100 \t[3.76427598 1.26 ]\t[0.37053214 1.04517941]\t[2.39755869 1. ]\n", - "15 \t0 \t100 \t[3.78841744 1.15 ]\t[0.34059017 0.698212 ]\t[1.97238231 1. ]\n", - "16 \t0 \t100 \t[3.78776964 1.17 ]\t[0.34315736 0.77530639]\t[1.97238231 1. ]\n", - "17 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "18 \t0 \t100 \t[3.81870626 1.09 ]\t[0.27518907 0.51176166]\t[1.90340519 1. ]\n", - "19 \t0 \t100 \t[3.80623891 1.12 ]\t[0.30110856 0.58787754]\t[1.84558952 1. ]\n", - "20 \t0 \t100 \t[3.79757999 1.16 ]\t[0.3426752 0.85697141]\t[1.82377696 1. ]\n", - "21 \t0 \t100 \t[3.79711064 1.14 ]\t[0.34539965 0.70738957]\t[1.77684164 1. ]\n", - "22 \t0 \t100 \t[3.79711064 1.14 ]\t[0.34539965 0.70738957]\t[1.77684164 1. ]\n", - "23 \t0 \t100 \t[3.78516539 1.17 ]\t[0.36278708 0.76229915]\t[1.77684164 1. ]\n", - "24 \t0 \t100 \t[3.78516539 1.17 ]\t[0.36278708 0.76229915]\t[1.77684164 1. ]\n", - "25 \t0 \t100 \t[3.81587694 1.08 ]\t[0.29038737 0.4621688 ]\t[1.79023099 1. ]\n", - "26 \t0 \t100 \t[3.81587694 1.08 ]\t[0.29038737 0.4621688 ]\t[1.79023099 1. ]\n", - "27 \t0 \t100 \t[3.80229016 1.1 ]\t[0.31788123 0.5 ]\t[1.79023099 1. ]\n", - "28 \t0 \t100 \t[3.80229016 1.1 ]\t[0.31788123 0.5 ]\t[1.79023099 1. ]\n", - "29 \t0 \t100 \t[3.80229016 1.1 ]\t[0.31788123 0.5 ]\t[1.79023099 1. ]\n", - "30 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "31 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", - "32 \t0 \t100 \t[3.80807124 1.09 ]\t[0.28451952 0.42649736]\t[2.46565032 1. ]\n", - "33 \t0 \t100 \t[3.76867967 1.17 ]\t[0.38970558 0.69361373]\t[1.90340519 1. ]\n", - "34 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "35 \t0 \t100 \t[3.81870626 1.08 ]\t[0.27518907 0.4621688 ]\t[1.90340519 1. ]\n", - "36 \t0 \t100 \t[3.81870626 1.08 ]\t[0.27518907 0.4621688 ]\t[1.90340519 1. ]\n", - "37 \t0 \t100 \t[3.80506416 1.11 ]\t[0.30442191 0.54580216]\t[1.90340519 1. ]\n", - "38 \t0 \t100 \t[3.80506416 1.11 ]\t[0.30442191 0.54580216]\t[1.90340519 1. ]\n", - "39 \t0 \t100 \t[3.81547007 1.08 ]\t[0.29204914 0.4621688 ]\t[1.79820001 1. ]\n", - "40 \t0 \t100 \t[3.81547007 1.08 ]\t[0.29204914 0.4621688 ]\t[1.79820001 1. ]\n", - "41 \t0 \t100 \t[3.81547007 1.08 ]\t[0.29204914 0.4621688 ]\t[1.79820001 1. ]\n", - "42 \t0 \t100 \t[3.81547007 1.08 ]\t[0.29204914 0.4621688 ]\t[1.79820001 1. ]\n", - "43 \t0 \t100 \t[3.81533288 1.08 ]\t[0.29299838 0.4621688 ]\t[1.78448129 1. ]\n", - "44 \t0 \t100 \t[3.81533288 1.08 ]\t[0.29299839 0.4621688 ]\t[1.78448129 1. ]\n", - "45 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "46 \t0 \t100 \t[3.82162314 1.08 ]\t[0.25340885 0.4621688 ]\t[2.4135077 1. ] \n", - "47 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "48 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "49 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "50 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "51 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "52 \t0 \t100 \t[3.81721189 1.09 ]\t[0.2802956 0.54945427]\t[1.97238207 1. ]\n", - "53 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ]\n", - "54 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ]\n", - "55 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ]\n", - "56 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ]\n", - "57 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ]\n", - "58 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "59 \t0 \t100 \t[3.81825618 1.09 ]\t[0.27707478 0.51176166]\t[1.90340519 1. ]\n", - "60 \t0 \t100 \t[3.81825618 1.09 ]\t[0.27707478 0.51176166]\t[1.90340519 1. ]\n", - "61 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ]\n", - "62 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ]\n", - "63 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ]\n", - "64 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.55161039e-11 1.00000000e+00]\n", - "65 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.55161039e-11 1.00000000e+00]\n", - "66 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "67 \t0 \t100 \t[3.8264568 1.08 ] \t[0.22800613 0.4621688 ]\t[2.67845964 1. ] \n", - "68 \t0 \t100 \t[3.81451156 1.12 ]\t[0.25495379 0.60464866]\t[2.67845964 1. ] \n", - "69 \t0 \t100 \t[3.82635846 1.07 ]\t[0.22850284 0.38091994]\t[2.66862535 1. ] \n", - "70 \t0 \t100 \t[3.81446927 1.09 ]\t[0.25514469 0.42649736]\t[2.66862535 1. ] \n", - "71 \t0 \t100 \t[3.81432624 1.09 ]\t[0.25579018 0.42649736]\t[2.65432239 1. ] \n", - "72 \t0 \t100 \t[3.81432624 1.09 ]\t[0.25579018 0.42649736]\t[2.65432239 1. ] \n", - "73 \t0 \t100 \t[3.79357637 1.13 ]\t[0.32499155 0.57714816]\t[1.79799652 1. ] \n", - "74 \t0 \t100 \t[3.79168965 1.13 ]\t[0.33207051 0.57714816]\t[1.79799652 1. ] \n", - "75 \t0 \t100 \t[3.87366802 1.03 ]\t[0.28353893 0.17058722]\t[2.73836088 1. ] \n", - "76 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "77 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "78 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "79 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "80 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", - "81 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", - "82 \t0 \t100 \t[3.79515281 1.14 ]\t[0.35188929 0.74859869]\t[1.79386842 1. ] \n", - "83 \t0 \t100 \t[3.78107948 1.16 ]\t[0.37582336 0.77097341]\t[1.79386842 1. ] \n", - "84 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "85 \t0 \t100 \t[3.83750969 1.06 ]\t[0.20205385 0.42 ]\t[2.59482932 1. ] \n", - "86 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "87 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "88 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "89 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "90 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "91 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "92 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "93 \t0 \t100 \t[3.80599109 1.11 ]\t[0.2948976 0.58129167]\t[2.25763559 1. ] \n", - "94 \t0 \t100 \t[3.80341631 1.1 ]\t[0.30918326 0.5 ]\t[2.00015688 1. ] \n", - "95 \t0 \t100 \t[3.80341631 1.1 ]\t[0.30918326 0.5 ]\t[2.00015688 1. ] \n", - "96 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "97 \t0 \t100 \t[3.81451158 1.12 ]\t[0.25495373 0.60464866]\t[2.67846012 1. ] \n", - "98 \t0 \t100 \t[3.81451156 1.12 ]\t[0.2549538 0.60464866]\t[2.67845964 1. ] \n", - "99 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ] \n", - "Final population hypervolume is 49369.975647\n", - "fit, 3, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.86]\t[ nan 0.98]\t[nan 20.]\n", - "1 \t85 \t85 \t[ nan 16.87]\t[ nan 4.96518882]\t[nan 1.]\n", - "2 \t92 \t92 \t[ nan 11.08]\t[ nan 5.37341605]\t[nan 1.]\n", - "3 \t98 \t98 \t[ nan 6.77] \t[ nan 3.19954684]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 4.51] \t[ nan 1.53944795]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 3.66] \t[ nan 1.1934823] \t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 3.15] \t[ nan 0.9313968] \t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.84] \t[ nan 0.73102668]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.69] \t[ nan 0.65871086]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.62] \t[ nan 0.61286214]\t[nan 1.]\n", - "10 \t100 \t100 \t[ nan 2.49] \t[ nan 0.51951901]\t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.47] \t[ nan 0.51874849]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.42] \t[ nan 0.51341991]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.35] \t[ nan 0.49749372]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.44] \t[ nan 0.68293484]\t[nan 1.]\n", - "15 \t100 \t100 \t[nan 2.3] \t[ nan 0.47958315]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.29] \t[ nan 0.47528939]\t[nan 1.]\n", - "17 \t100 \t100 \t[ nan 2.28] \t[ nan 0.47074409]\t[nan 1.]\n", - "18 \t100 \t100 \t[nan 2.3] \t[ nan 0.47958315]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.28] \t[ nan 0.47074409]\t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.26] \t[ nan 0.46086874]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.24] \t[ nan 0.44988888]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.23] \t[ nan 0.44395946]\t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.27] \t[ nan 0.46593991]\t[nan 1.]\n", - "30 \t100 \t100 \t[ nan 2.28] \t[ nan 0.47074409]\t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.29] \t[ nan 0.47528939]\t[nan 1.]\n", - "32 \t100 \t100 \t[nan 2.3] \t[ nan 0.47958315]\t[nan 1.]\n", - "33 \t100 \t100 \t[ nan 2.32] \t[ nan 0.4874423] \t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.35] \t[ nan 0.49749372]\t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.37] \t[ nan 0.50309045]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.37] \t[ nan 0.50309045]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.35] \t[ nan 0.49749372]\t[nan 1.]\n", - "38 \t100 \t100 \t[ nan 2.33] \t[ nan 0.49101935]\t[nan 1.]\n", - "39 \t100 \t100 \t[ nan 2.29] \t[ nan 0.47528939]\t[nan 1.]\n", - "40 \t100 \t100 \t[ nan 2.26] \t[ nan 0.46086874]\t[nan 1.]\n", - "41 \t100 \t100 \t[ nan 2.23] \t[ nan 0.44395946]\t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 2.23] \t[ nan 0.44395946]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", - "46 \t100 \t100 \t[nan 2.2] \t[ nan 0.42426407]\t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 2.22] \t[ nan 0.43772137]\t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 2.21] \t[ nan 0.43116122]\t[nan 1.]\n", - "52 \t100 \t100 \t[nan 2.2] \t[ nan 0.42426407]\t[nan 1.]\n", - "53 \t100 \t100 \t[nan 2.2] \t[ nan 0.42426407]\t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 2.18] \t[ nan 0.40938979]\t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 2.17] \t[ nan 0.40137264]\t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 2.15] \t[ nan 0.38405729]\t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 2.15] \t[ nan 0.38405729]\t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 2.13] \t[ nan 0.36482873]\t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 2.12] \t[ nan 0.3544009] \t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 2.09] \t[ nan 0.31921779]\t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 2.04] \t[ nan 0.24166092]\t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 2.02] \t[ nan 0.19899749]\t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 2.01] \t[ nan 0.17291616]\t[nan 1.]\n", - "72 \t100 \t100 \t[nan 2.] \t[ nan 0.14142136]\t[nan 1.]\n", - "73 \t100 \t100 \t[nan 2.] \t[ nan 0.14142136]\t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.99] \t[ nan 0.09949874]\t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.7]\t[ nan 0.9539392]\t[nan 20.]\n", - "1 \t0 \t87 \t[ nan 16.12]\t[ nan 6.12744645]\t[nan 1.]\n", - "2 \t0 \t92 \t[ nan 10.36]\t[ nan 6.2330089] \t[nan 1.]\n", - "3 \t0 \t100 \t[ nan 3.96] \t[ nan 2.61120662]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.66] \t[ nan 0.72415468]\t[nan 1.]\n", - "5 \t0 \t100 \t[5.18937999 1.12 ]\t[1.34063908 0.32496154]\t[2.73836088 1. ]\n", - "6 \t0 \t100 \t[4.65234133 1.27 ]\t[1.30255619 0.91493169]\t[2.30348039 1. ]\n", - "7 \t0 \t100 \t[4.26154792 1.34 ]\t[1.13750098 1.0219589 ]\t[2.30348039 1. ]\n", - "8 \t0 \t100 \t[3.86186356 1.01 ]\t[0.11293867 0.09949874]\t[2.73836088 1. ]\n", - "9 \t0 \t100 \t[3.84969223 1.05 ]\t[0.16309529 0.40926764]\t[2.67845964 1. ]\n", - "10 \t0 \t100 \t[3.84969223 1.05 ]\t[0.16309529 0.40926764]\t[2.67845964 1. ]\n", - "11 \t0 \t100 \t[3.83780303 1.06 ]\t[0.20010042 0.36932371]\t[2.67845964 1. ]\n", - "12 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", - "13 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", - "14 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", - "15 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ]\n", - "16 \t0 \t100 \t[3.80262237 1.12 ]\t[0.27857674 0.51536395]\t[2.67845964 1. ]\n", - "17 \t0 \t100 \t[3.80262237 1.12 ]\t[0.27857674 0.51536395]\t[2.67845964 1. ]\n", - "18 \t0 \t100 \t[3.78780033 1.17 ]\t[0.31187945 0.70788417]\t[2.39077997 1. ]\n", - "19 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", - "20 \t0 \t100 \t[3.81721189 1.07 ]\t[0.28029557 0.38091994]\t[1.97238231 1. ]\n", - "21 \t0 \t100 \t[3.79820588 1.12 ]\t[0.33497349 0.62096699]\t[1.97238219 1. ]\n", - "22 \t0 \t100 \t[3.79751611 1.11 ]\t[0.33878185 0.54580216]\t[1.90340519 1. ]\n", - "23 \t0 \t100 \t[3.79751611 1.11 ]\t[0.33878185 0.54580216]\t[1.90340519 1. ]\n", - "24 \t0 \t100 \t[3.79751611 1.11 ]\t[0.33878185 0.54580216]\t[1.90340519 1. ]\n", - "25 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "26 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", - "27 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ]\n", - "28 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "29 \t0 \t100 \t[3.80569521 1.11 ]\t[0.30185255 0.54580216]\t[1.90340519 1. ]\n", - "30 \t0 \t100 \t[3.79967221 1.07 ]\t[0.42954409 0.38091994]\t[1.01975928e-11 1.00000000e+00]\n", - "31 \t0 \t100 \t[3.79797463 1.07 ]\t[0.43425924 0.38091994]\t[1.01975928e-11 1.00000000e+00]\n", - "32 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[1.19196319e-13 1.00000000e+00]\n", - "33 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", - "34 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", - "35 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", - "36 \t0 \t100 \t[3.75875823 1.1 ]\t[0.5766332 0.47958315]\t[1.19196319e-13 1.00000000e+00]\n", - "37 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", - "38 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", - "39 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", - "40 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[1.19196319e-13 1.00000000e+00]\n", - "41 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.43614026e-14 1.00000000e+00]\n", - "42 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.43614026e-14 1.00000000e+00]\n", - "43 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.43614026e-14 1.00000000e+00]\n", - "44 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "45 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "46 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "47 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "48 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "49 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "50 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "51 \t0 \t100 \t[3.82645681 1.07 ]\t[0.22800609 0.38091994]\t[2.67846036 1. ] \n", - "52 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "53 \t0 \t100 \t[3.82214457 1.09 ]\t[0.2505484 0.54945427]\t[2.46565032 1. ] \n", - "54 \t0 \t100 \t[3.80346267 1.12 ]\t[0.30891311 0.62096699]\t[2.00479293 1. ] \n", - "55 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "56 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "57 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "58 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "59 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "60 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "61 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "62 \t0 \t100 \t[3.80807123 1.1 ]\t[0.28451955 0.5 ]\t[2.46565008 1. ] \n", - "63 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "64 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "65 \t0 \t100 \t[3.8264568 1.08 ] \t[0.22800614 0.4621688 ]\t[2.67845941 1. ] \n", - "66 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", - "67 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "68 \t0 \t100 \t[3.78817556 1.13 ]\t[0.34253698 0.57714816]\t[1.90340519 1. ] \n", - "69 \t0 \t100 \t[3.78817556 1.13 ]\t[0.34253698 0.57714816]\t[1.90340519 1. ] \n", - "70 \t0 \t100 \t[3.79679867 1.11 ]\t[0.35121757 0.54580216]\t[1.35838246 1. ] \n", - "71 \t0 \t100 \t[3.76443804 1.18 ]\t[0.47127144 0.87612784]\t[0.63692153 1. ] \n", - "72 \t0 \t100 \t[3.73207742 1.27 ]\t[0.56457536 1.23979837]\t[0.63692141 1. ] \n", - "73 \t0 \t100 \t[3.78252544 1.14 ]\t[0.37596243 0.61676576]\t[1.35838246 1. ] \n", - "74 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "75 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "76 \t0 \t100 \t[3.78438812 1.09 ]\t[0.4525663 0.42649736]\t[2.66079151e-05 1.00000000e+00]\n", - "77 \t0 \t100 \t[3.74565828 1.14 ]\t[0.58860317 0.6483826 ]\t[1.1639014e-07 1.0000000e+00] \n", - "78 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[6.69603262e-14 1.00000000e+00]\n", - "79 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[6.69603262e-14 1.00000000e+00]\n", - "80 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", - "81 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", - "82 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", - "83 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", - "84 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572202 0.38091994]\t[5.89805982e-14 1.00000000e+00]\n", - "85 \t0 \t100 \t[3.78341474 1.09 ]\t[0.45534222 0.42649736]\t[5.89805982e-14 1.00000000e+00]\n", - "86 \t0 \t100 \t[3.78341474 1.09 ]\t[0.45534222 0.42649736]\t[5.89805982e-14 1.00000000e+00]\n", - "87 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", - "88 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", - "89 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", - "90 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.43614026e-14 1.00000000e+00]\n", - "91 \t0 \t100 \t[3.75875823 1.1 ]\t[0.5766332 0.47958315]\t[3.43614026e-14 1.00000000e+00]\n", - "92 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "93 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "94 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "95 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "96 \t0 \t100 \t[3.7446849 1.12 ] \t[0.59067639 0.51536395]\t[3.43614026e-14 1.00000000e+00]\n", - "97 \t0 \t100 \t[3.70595506 1.17 ]\t[0.69818381 0.70788417]\t[2.98372438e-14 1.00000000e+00]\n", - "98 \t0 \t100 \t[3.70595506 1.17 ]\t[0.69818381 0.70788417]\t[2.98372438e-14 1.00000000e+00]\n", - "99 \t0 \t100 \t[3.70595506 1.17 ]\t[0.69818381 0.70788417]\t[2.98372438e-14 1.00000000e+00]\n", - "Final population hypervolume is 49495.461503\n", - "fit, 4, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.87]\t[ nan 1.02620661]\t[nan 20.]\n", - "1 \t88 \t88 \t[ nan 17.46]\t[ nan 5.01481804]\t[nan 1.]\n", - "2 \t98 \t98 \t[ nan 12.37]\t[ nan 6.42130049]\t[nan 1.]\n", - "3 \t100 \t100 \t[ nan 5.64] \t[ nan 4.36238467]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 1.58] \t[ nan 0.75073298]\t[nan 1.]\n", - "5 \t100 \t100 \t[0.53064906 1.04 ]\t[0.12134526 0.19595918]\t[0.27383608 1. ]\n", - "6 \t100 \t100 \t[0.44851342 1.02 ]\t[0.10580728 0.14 ]\t[0.27383608 1. ]\n", - "7 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "8 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "9 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "10 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "11 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "12 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "13 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "14 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "15 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "16 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "17 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "18 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "19 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "20 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "21 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "22 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "23 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "24 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "25 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "26 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "27 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "28 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "29 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "30 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "31 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "32 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "33 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "34 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "35 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "36 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "37 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "38 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "39 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "40 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "41 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "42 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "43 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "44 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "45 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "46 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "47 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "48 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "49 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "50 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "51 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "52 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "53 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "54 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "55 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "56 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "57 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "58 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "59 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "60 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "61 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "62 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "63 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "64 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "65 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "66 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "67 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "68 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "69 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "70 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "71 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "72 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "73 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "74 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "75 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "76 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "77 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "78 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "79 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "80 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "81 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "82 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "83 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "84 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "85 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "86 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "87 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "88 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "89 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "90 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "91 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "92 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "93 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "94 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "95 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "96 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "97 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "98 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "99 \t100 \t100 \t[0.38502913 1.02 ]\t[0.01588472 0.14 ]\t[0.27383608 1. ]\n", - "Final population hypervolume is 49486.388383\n", - "best model: Square(0.96*x1)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.87]\t[ nan 1.36861244]\t[nan 12.]\n", - "1 \t0 \t87 \t[ nan 16.01]\t[ nan 5.88641657]\t[nan 1.]\n", - "2 \t0 \t100 \t[ nan 8.39] \t[ nan 4.88035859]\t[nan 1.]\n", - "3 \t0 \t100 \t[ nan 2.79] \t[ nan 1.7452507] \t[nan 1.]\n", - "4 \t0 \t100 \t[5.0944862 1.12 ]\t[1.21237069 0.32496154]\t[2.73843145 1. ]\n", - "5 \t0 \t100 \t[4.25595285 1.23 ]\t[0.9040586 0.58060313]\t[2.67845964 1. ]\n", - "6 \t0 \t100 \t[3.90590007 1.02 ]\t[0.37655996 0.14 ]\t[2.73836136 1. ]\n", - "7 \t0 \t100 \t[3.83785909 1.05 ]\t[0.19977615 0.29580399]\t[2.68406463 1. ]\n", - "8 \t0 \t100 \t[3.83666901 1.06 ]\t[0.20687419 0.36932371]\t[2.56505728 1. ]\n", - "9 \t0 \t100 \t[3.83785908 1.05 ]\t[0.19977618 0.29580399]\t[2.68406439 1. ]\n", - "10 \t0 \t100 \t[3.8480507 1.03 ] \t[0.17524858 0.2215852 ]\t[2.51430631 1. ]\n", - "11 \t0 \t100 \t[3.82914203 1.09 ]\t[0.25435182 0.63395583]\t[2.03077316 1. ]\n", - "12 \t0 \t100 \t[3.81520352 1.09 ]\t[0.36614475 0.63395583]\t[0.63692153 1. ]\n", - "13 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289352 0.09949874]\t[2.73836112 1. ]\n", - "14 \t0 \t100 \t[3.81102456 1.08 ]\t[0.41608011 0.54184869]\t[6.11990981e-04 1.00000000e+00]\n", - "15 \t0 \t100 \t[3.80884042 1.08 ]\t[0.42251369 0.54184869]\t[6.11990981e-04 1.00000000e+00]\n", - "16 \t0 \t100 \t[3.7804941 1.13 ] \t[0.46287745 0.64272856]\t[6.11990981e-04 1.00000000e+00]\n", - "17 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ] \n", - "18 \t0 \t100 \t[3.84974827 1.03 ]\t[0.16269326 0.2215852 ]\t[2.68406439 1. ] \n", - "19 \t0 \t100 \t[3.83785908 1.05 ]\t[0.19977621 0.29580399]\t[2.68406439 1. ] \n", - "20 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "21 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "22 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "23 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "24 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "25 \t0 \t100 \t[3.82214457 1.08 ]\t[0.2505484 0.4621688] \t[2.46565032 1. ] \n", - "26 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "27 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "28 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "29 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "30 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "31 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "32 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "33 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "34 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "35 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "36 \t0 \t100 \t[3.81870626 1.08 ]\t[0.27518907 0.4621688 ]\t[1.90340519 1. ] \n", - "37 \t0 \t100 \t[3.78611721 1.17 ]\t[0.35699166 0.8006872 ]\t[1.84281576 1. ] \n", - "38 \t0 \t100 \t[3.78574688 1.16 ]\t[0.35902081 0.73102668]\t[1.80578291 1. ] \n", - "39 \t0 \t100 \t[3.80543991 1.11 ]\t[0.30522623 0.54580216]\t[1.80550706 1. ] \n", - "40 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "41 \t0 \t100 \t[3.8264568 1.07 ] \t[0.22800613 0.38091994]\t[2.67845964 1. ] \n", - "42 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", - "43 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838258 1. ] \n", - "44 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "45 \t0 \t100 \t[3.79487183 1.13 ]\t[0.31776999 0.57714816]\t[1.90340519 1. ] \n", - "46 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", - "47 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "48 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "49 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "50 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", - "51 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "52 \t0 \t100 \t[3.7841615 1.14 ] \t[0.42564921 0.74859869]\t[0.63692141 1. ] \n", - "53 \t0 \t100 \t[3.7841615 1.14 ] \t[0.42564921 0.74859869]\t[0.63692141 1. ] \n", - "54 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "55 \t0 \t100 \t[3.78341474 1.11 ]\t[0.45534221 0.58129167]\t[1.07177982e-07 1.00000000e+00]\n", - "56 \t0 \t100 \t[3.78341474 1.11 ]\t[0.45534221 0.58129167]\t[1.07177982e-07 1.00000000e+00]\n", - "57 \t0 \t100 \t[3.77779228 1.13 ]\t[0.4746412 0.67312703]\t[2.02363126e-12 1.00000000e+00]\n", - "58 \t0 \t100 \t[3.7580965 1.17 ] \t[0.50984213 0.77530639]\t[2.02363126e-12 1.00000000e+00]\n", - "59 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "60 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "61 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "62 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "63 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "64 \t0 \t100 \t[3.80795929 1.09 ]\t[0.28504941 0.42649736]\t[2.45445561 1. ] \n", - "65 \t0 \t100 \t[3.79377401 1.12 ]\t[0.31516565 0.51536395]\t[2.45445561 1. ] \n", - "66 \t0 \t100 \t[3.76958981 1.16 ]\t[0.39166417 0.64373908]\t[1.45456362 1. ] \n", - "67 \t0 \t100 \t[3.76958981 1.16 ]\t[0.39166417 0.64373908]\t[1.45456362 1. ] \n", - "68 \t0 \t100 \t[3.73086577 1.21 ]\t[0.54207819 0.80367904]\t[5.79449348e-04 1.00000000e+00]\n", - "69 \t0 \t100 \t[3.72989831 1.21 ]\t[0.54622452 0.80367904]\t[1.44563719e-05 1.00000000e+00]\n", - "70 \t0 \t100 \t[3.72989831 1.21 ]\t[0.54622452 0.80367904]\t[1.44563719e-05 1.00000000e+00]\n", - "71 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "72 \t0 \t100 \t[3.79487183 1.13 ]\t[0.31776999 0.57714816]\t[1.90340519 1. ] \n", - "73 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260682 0.4621688 ]\t[1.90340519 1. ] \n", - "74 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", - "75 \t0 \t100 \t[3.78372612 1.14 ]\t[0.36371051 0.63277168]\t[1.90340519 1. ] \n", - "76 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", - "77 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "78 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "79 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "80 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "81 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488197 0.4621688 ]\t[1.90340519 1. ] \n", - "82 \t0 \t100 \t[3.79136331 1.15 ]\t[0.37547346 0.8291562 ]\t[1.35710287 1. ] \n", - "83 \t0 \t100 \t[3.79136331 1.15 ]\t[0.37547346 0.8291562 ]\t[1.35710287 1. ] \n", - "84 \t0 \t100 \t[3.75536723 1.22 ]\t[0.44880734 0.97549987]\t[1.35710239 1. ] \n", - "85 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "86 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572201 0.54945427]\t[1.10674193e-07 1.00000000e+00]\n", - "87 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", - "88 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", - "89 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", - "90 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[3.72903097e-12 1.00000000e+00]\n", - "91 \t0 \t100 \t[3.78341474 1.11 ]\t[0.45534222 0.58129167]\t[3.72903097e-12 1.00000000e+00]\n", - "92 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "93 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "94 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "95 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", - "96 \t0 \t100 \t[3.79699856 1.1 ]\t[0.35045337 0.5 ]\t[1.35838246 1. ] \n", - "97 \t0 \t100 \t[3.7974887 1.08 ] \t[0.43571647 0.4621688 ]\t[6.37756893e-05 1.00000000e+00]\n", - "98 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "99 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "Final population hypervolume is 49377.110287\n", - "fit, 5, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.76]\t[ nan 0.82607506]\t[nan 20.]\n", - "1 \t94 \t94 \t[ nan 14.53]\t[ nan 6.45051161]\t[nan 1.]\n", - "2 \t100 \t100 \t[ nan 5.52] \t[ nan 4.06073885]\t[nan 1.]\n", - "3 \t100 \t100 \t[0.52386678 1.5 ]\t[0.13434398 0.97467943]\t[0.26124257 1. ]\n", - "4 \t100 \t100 \t[0.48764257 1.2 ]\t[0.12952334 0.77459667]\t[0.24604428 1. ]\n", - "5 \t100 \t100 \t[0.4329244 1.07 ] \t[0.10314919 0.3241913 ]\t[0.24656506 1. ]\n", - "6 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ]\n", - "7 \t100 \t100 \t[0.3836218 1.04 ] \t[0.02102416 0.24166092]\t[0.24656503 1. ]\n", - "8 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "9 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "10 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "11 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "12 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "13 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "14 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "15 \t100 \t100 \t[0.38221446 1.06 ]\t[0.02505484 0.31048349]\t[0.24656503 1. ]\n", - "16 \t100 \t100 \t[0.38031386 1.09 ]\t[0.03108113 0.42649736]\t[0.19723824 1. ]\n", - "17 \t100 \t100 \t[0.38031386 1.09 ]\t[0.03108113 0.42649736]\t[0.19723824 1. ]\n", - "18 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "19 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "20 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "21 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "22 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "23 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "24 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "25 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "26 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "27 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "28 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "29 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "30 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "31 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "32 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "33 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "34 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "35 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "36 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "37 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "38 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "39 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "40 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "41 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "42 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "43 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "44 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "45 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "46 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "47 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "48 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "49 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "50 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "51 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "52 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "53 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "54 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "55 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "56 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "57 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "58 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "59 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "60 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "61 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "62 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "63 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "64 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "65 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "66 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "67 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "68 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "69 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "70 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "71 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "72 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "73 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "74 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "75 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "76 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "77 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "78 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "79 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "80 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "81 \t100 \t100 \t[0.37841326 1.12 ]\t[0.03601532 0.51536395]\t[0.19723824 1. ]\n", - "82 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "83 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "84 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "85 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "86 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "87 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "88 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "89 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "90 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "91 \t100 \t100 \t[0.37647247 1.16 ]\t[0.04044151 0.64373908]\t[0.19321902 1. ]\n", - "92 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "93 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "94 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "95 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "96 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "97 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "98 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "99 \t100 \t100 \t[0.37647237 1.16 ]\t[0.04044194 0.64373908]\t[0.19320959 1. ]\n", - "Final population hypervolume is 49490.270076\n", - "best model: Square(If(x1>0.91,3.94*x2,Square(0.97*x1)))\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.78]\t[ nan 0.94424573]\t[nan 20.]\n", - "1 \t0 \t89 \t[ nan 15.78]\t[ nan 5.8422256] \t[nan 1.]\n", - "2 \t0 \t96 \t[ nan 8.89] \t[ nan 4.99178325]\t[nan 1.]\n", - "3 \t0 \t99 \t[ nan 4.03] \t[ nan 2.597903] \t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 2.22] \t[ nan 1.00578328]\t[nan 1.]\n", - "5 \t0 \t100 \t[ nan 1.77] \t[ nan 0.75967098]\t[nan 1.]\n", - "6 \t0 \t100 \t[ nan 1.38] \t[ nan 0.52497619]\t[nan 1.]\n", - "7 \t0 \t100 \t[5.14505445 1.07 ]\t[1.28281345 0.25514702]\t[2.61403799 1. ]\n", - "8 \t0 \t100 \t[4.43905738 1.05 ]\t[1.0778302 0.21794495]\t[2.61403799 1. ]\n", - "9 \t0 \t100 \t[3.80069793 1.12 ]\t[0.44789478 0.66753277]\t[0.34041035 1. ]\n", - "10 \t0 \t100 \t[3.79178594 1.12 ]\t[0.43737804 0.66753277]\t[0.34041035 1. ]\n", - "11 \t0 \t100 \t[3.77904142 1.13 ]\t[0.45388862 0.67312703]\t[0.32490379 1. ]\n", - "12 \t0 \t100 \t[3.82063123 1.06 ]\t[0.25666747 0.31048349]\t[2.51430631 1. ]\n", - "13 \t0 \t100 \t[3.78620915 1.15 ]\t[0.41434717 0.80467385]\t[0.63692135 1. ]\n", - "14 \t0 \t100 \t[3.77213581 1.19 ]\t[0.43456757 0.89101066]\t[0.63692135 1. ]\n", - "15 \t0 \t100 \t[3.75806248 1.21 ]\t[0.45345149 0.9087904 ]\t[0.63692135 1. ]\n", - "16 \t0 \t100 \t[3.75989289 1.15 ]\t[0.48820415 0.72629195]\t[0.63692135 1. ]\n", - "17 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "18 \t0 \t100 \t[3.84631263 1.04 ]\t[0.1869565 0.31368774]\t[2.46985793 1. ]\n", - "19 \t0 \t100 \t[3.83134578 1.09 ]\t[0.23734163 0.58472216]\t[2.37629771 1. ]\n", - "20 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "21 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "22 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "23 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "24 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014738 0.59824744]\t[1.90340519 1. ]\n", - "25 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014738 0.59824744]\t[1.90340519 1. ]\n", - "26 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014738 0.59824744]\t[1.90340519 1. ]\n", - "27 \t0 \t100 \t[3.78619527 1.17 ]\t[0.3855081 0.83731714]\t[1.80461228 1. ]\n", - "28 \t0 \t100 \t[3.76309473 1.25 ]\t[0.44414527 1.14345966]\t[1.60617304 1. ]\n", - "29 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "30 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726651 0.2215852 ]\t[2.46565056 1. ]\n", - "31 \t0 \t100 \t[3.81812389 1.11 ]\t[0.26904363 0.66174013]\t[2.46565032 1. ]\n", - "32 \t0 \t100 \t[3.81250144 1.09 ]\t[0.30120175 0.49183331]\t[1.90340519 1. ]\n", - "33 \t0 \t100 \t[3.79822821 1.12 ]\t[0.33040084 0.5706137 ]\t[1.90340519 1. ]\n", - "34 \t0 \t100 \t[3.77850476 1.15 ]\t[0.38812256 0.63835727]\t[1.35838246 1. ]\n", - "35 \t0 \t100 \t[3.77850476 1.15 ]\t[0.38812256 0.63835727]\t[1.35838246 1. ]\n", - "36 \t0 \t100 \t[3.78971543 1.12 ]\t[0.3767489 0.5706137] \t[1.35838246 1. ]\n", - "37 \t0 \t100 \t[3.76456941 1.16 ]\t[0.44760684 0.68876701]\t[1.35838246 1. ]\n", - "38 \t0 \t100 \t[3.76456941 1.16 ]\t[0.44760684 0.68876701]\t[1.35838246 1. ]\n", - "39 \t0 \t100 \t[3.75192961 1.17 ]\t[0.4619826 0.69361373]\t[1.35838246 1. ]\n", - "40 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", - "41 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", - "42 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", - "43 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", - "44 \t0 \t100 \t[3.73459382 1.2 ]\t[0.48885908 0.74833148]\t[1.35838246 1. ]\n", - "45 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "46 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "47 \t0 \t100 \t[3.83368399 1.05 ]\t[0.22375156 0.32787193]\t[2.47097492 1. ]\n", - "48 \t0 \t100 \t[3.81398821 1.09 ]\t[0.29482394 0.51176166]\t[1.90340519 1. ]\n", - "49 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", - "50 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", - "51 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", - "52 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", - "53 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", - "54 \t0 \t100 \t[3.79996812 1.12 ]\t[0.32361495 0.58787754]\t[1.90340519 1. ]\n", - "55 \t0 \t100 \t[3.81393497 1.08 ]\t[0.29506685 0.4621688 ]\t[1.90340519 1. ]\n", - "56 \t0 \t100 \t[3.79986163 1.1 ]\t[0.32405282 0.5 ]\t[1.90340519 1. ]\n", - "57 \t0 \t100 \t[3.7791138 1.18 ] \t[0.38025358 0.93145048]\t[1.79820001 1. ]\n", - "58 \t0 \t100 \t[3.79880958 1.1 ]\t[0.33031825 0.5 ]\t[1.79820001 1. ]\n", - "59 \t0 \t100 \t[3.77745768 1.15 ]\t[0.38868636 0.698212 ]\t[1.7377938 1. ] \n", - "60 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ]\n", - "61 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "62 \t0 \t100 \t[3.80340393 1.1 ]\t[0.30465856 0.5 ]\t[2.25763559 1. ]\n", - "63 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", - "64 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", - "65 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", - "66 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", - "67 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", - "68 \t0 \t100 \t[3.78725045 1.14 ]\t[0.34117662 0.63277168]\t[2.25763559 1. ]\n", - "69 \t0 \t100 \t[3.74379506 1.25 ]\t[0.45020819 0.98361578]\t[1.45456362 1. ]\n", - "70 \t0 \t100 \t[3.74379506 1.25 ]\t[0.45020819 0.98361578]\t[1.45456362 1. ]\n", - "71 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "72 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "73 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "74 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ]\n", - "75 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "76 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "77 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "78 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "79 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "80 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "81 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "82 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "83 \t0 \t100 \t[3.8332147 1.06 ] \t[0.22663974 0.42 ]\t[2.42404604 1. ]\n", - "84 \t0 \t100 \t[3.8332147 1.06 ] \t[0.22663974 0.42 ]\t[2.42404604 1. ]\n", - "85 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "86 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "87 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "89 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "90 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", - "91 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "92 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "93 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "94 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "95 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "96 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "97 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", - "98 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "99 \t0 \t100 \t[3.83443739 1.05 ]\t[0.2192433 0.32787193]\t[2.54631495 1. ]\n", - "Final population hypervolume is 49373.231387\n", - "fit, 6, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.72]\t[ nan 0.92822411]\t[nan 20.]\n", - "1 \t91 \t91 \t[ nan 16.3] \t[ nan 5.52358579]\t[nan 1.]\n", - "2 \t94 \t94 \t[ nan 10.53]\t[ nan 5.17195321]\t[nan 1.]\n", - "3 \t99 \t99 \t[ nan 5.85] \t[ nan 2.77983812]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 3.63] \t[ nan 1.33157801]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 2.98] \t[ nan 0.9162969] \t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 2.62] \t[ nan 0.67498148]\t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.45] \t[ nan 0.57227616]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.55] \t[ nan 0.698212] \t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.36] \t[ nan 0.55713553]\t[nan 1.]\n", - "10 \t100 \t100 \t[ nan 2.29] \t[ nan 0.53469618]\t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.24] \t[ nan 0.51224994]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", - "17 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.19] \t[ nan 0.48363209]\t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.21] \t[ nan 0.49588305]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.24] \t[ nan 0.51224994]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.25] \t[ nan 0.51720402]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.26] \t[ nan 0.52191953]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.21] \t[ nan 0.49588305]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", - "30 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.13] \t[ nan 0.43943145]\t[nan 1.]\n", - "33 \t100 \t100 \t[ nan 2.13] \t[ nan 0.43943145]\t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.05] \t[ nan 0.35707142]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.01] \t[ nan 0.29983329]\t[nan 1.]\n", - "38 \t100 \t100 \t[ nan 1.99] \t[ nan 0.26438608]\t[nan 1.]\n", - "39 \t100 \t100 \t[ nan 1.97] \t[ nan 0.2215852] \t[nan 1.]\n", - "40 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "41 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "53 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.88]\t[ nan 0.96208108]\t[nan 20.]\n", - "1 \t0 \t93 \t[ nan 16.47]\t[ nan 5.20279732]\t[nan 1.]\n", - "2 \t0 \t98 \t[ nan 10.82]\t[ nan 5.73477114]\t[nan 1.]\n", - "3 \t0 \t99 \t[ nan 5.44] \t[ nan 3.63406109]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 2.01] \t[ nan 1.06296754]\t[nan 1.]\n", - "5 \t0 \t100 \t[7.48380712 1.15 ]\t[4.6706179 0.35707142]\t[2.61403799 1. ]\n", - "6 \t0 \t100 \t[5.07630429 1.1 ]\t[1.27410028 0.3 ]\t[2.61403799 1. ]\n", - "7 \t0 \t100 \t[4.49317648 1.08 ]\t[1.10128196 0.30594117]\t[2.61403799 1. ]\n", - "8 \t0 \t100 \t[3.79277136 1.22 ]\t[0.45888391 0.78204859]\t[2.50877285 1. ]\n", - "9 \t0 \t100 \t[3.81957858 1.09 ]\t[0.26177879 0.49183331]\t[2.50877285 1. ]\n", - "10 \t0 \t100 \t[3.81957858 1.09 ]\t[0.26177879 0.49183331]\t[2.50877285 1. ]\n", - "11 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", - "12 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", - "13 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", - "14 \t0 \t100 \t[3.80350464 1.11 ]\t[0.30426431 0.58129167]\t[2.25763559 1. ]\n", - "15 \t0 \t100 \t[3.80350464 1.11 ]\t[0.30426431 0.58129167]\t[2.25763559 1. ]\n", - "16 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "17 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", - "18 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", - "19 \t0 \t100 \t[3.82112455 1.07 ]\t[0.31036292 0.45287967]\t[1.35838258 1. ]\n", - "20 \t0 \t100 \t[3.76987834 1.18 ]\t[0.49635867 0.95268043]\t[0.15569621 1. ]\n", - "21 \t0 \t100 \t[3.71862019 1.31 ]\t[0.62550101 1.39064733]\t[0.15450163 1. ]\n", - "22 \t0 \t100 \t[3.69347316 1.35 ]\t[0.66790368 1.43788038]\t[0.15440011 1. ]\n", - "23 \t0 \t100 \t[3.76783187 1.17 ]\t[0.43680837 0.72187256]\t[1.35838246 1. ]\n", - "24 \t0 \t100 \t[3.72949114 1.24 ]\t[0.57294808 0.99116094]\t[0.03891063 1. ]\n", - "25 \t0 \t100 \t[3.72949114 1.24 ]\t[0.57294808 0.99116094]\t[0.03891063 1. ]\n", - "26 \t0 \t100 \t[3.72949114 1.24 ]\t[0.57294808 0.99116094]\t[0.03891063 1. ]\n", - "27 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "28 \t0 \t100 \t[3.82657478 1.07 ]\t[0.26911137 0.45287967]\t[1.90340519 1. ]\n", - "29 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "30 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "31 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "32 \t0 \t100 \t[3.84097907 1.06 ]\t[0.22902357 0.50635956]\t[1.9365015 1. ] \n", - "33 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "34 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014736 0.59824744]\t[1.90340519 1. ]\n", - "35 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014736 0.59824744]\t[1.90340519 1. ]\n", - "36 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014737 0.59824744]\t[1.90340519 1. ]\n", - "37 \t0 \t100 \t[3.80487381 1.13 ]\t[0.34180246 0.74370693]\t[1.79605389 1. ]\n", - "38 \t0 \t100 \t[3.90709747 1.01 ]\t[0.352289 0.09949874]\t[2.60900354 1. ]\n", - "39 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", - "40 \t0 \t100 \t[3.8181239 1.09 ] \t[0.26904361 0.49183331]\t[2.46565032 1. ]\n", - "41 \t0 \t100 \t[3.81250144 1.09 ]\t[0.30120173 0.49183331]\t[1.90340519 1. ]\n", - "42 \t0 \t100 \t[3.79280566 1.13 ]\t[0.35601188 0.62697687]\t[1.90340519 1. ]\n", - "43 \t0 \t100 \t[3.80687899 1.11 ]\t[0.33014737 0.59824744]\t[1.90340519 1. ]\n", - "44 \t0 \t100 \t[3.78651179 1.18 ]\t[0.38389063 0.90972523]\t[1.83626354 1. ]\n", - "45 \t0 \t100 \t[3.81152246 1.09 ]\t[0.30749848 0.49183331]\t[1.80550706 1. ]\n", - "46 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "47 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "48 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "49 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "50 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "51 \t0 \t100 \t[3.84675712 1.03 ]\t[0.1837081 0.2215852] \t[2.51430631 1. ]\n", - "52 \t0 \t100 \t[3.80757448 1.06 ]\t[0.42572604 0.36932371]\t[0.00337517 1. ]\n", - "53 \t0 \t100 \t[3.80757447 1.06 ]\t[0.42572605 0.36932371]\t[0.00337517 1. ]\n", - "54 \t0 \t100 \t[3.80757447 1.06 ]\t[0.42572605 0.36932371]\t[0.00337517 1. ]\n", - "55 \t0 \t100 \t[3.80757447 1.06 ]\t[0.42572605 0.36932371]\t[0.00337517 1. ]\n", - "56 \t0 \t100 \t[3.75480386 1.15 ]\t[0.58404485 0.80467385]\t[0.00325559 1. ]\n", - "57 \t0 \t100 \t[3.75480386 1.15 ]\t[0.58404486 0.80467385]\t[0.00325559 1. ]\n", - "58 \t0 \t100 \t[3.75480386 1.15 ]\t[0.58404486 0.80467385]\t[0.00325559 1. ]\n", - "59 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", - "60 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", - "61 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", - "62 \t0 \t100 \t[3.74216405 1.16 ]\t[0.59492652 0.80894994]\t[0.00325559 1. ]\n", - "63 \t0 \t100 \t[3.74214014 1.16 ]\t[0.59507683 0.80894994]\t[8.64391623e-04 1.00000000e+00]\n", - "64 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "65 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "66 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "67 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ] \n", - "68 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ] \n", - "69 \t0 \t100 \t[3.83347895 1.05 ]\t[0.22500612 0.32787193]\t[2.45047045 1. ] \n", - "70 \t0 \t100 \t[3.80518049 1.1 ]\t[0.29602772 0.47958315]\t[2.45047045 1. ] \n", - "71 \t0 \t100 \t[3.7854847 1.14 ] \t[0.35123498 0.61676576]\t[1.90340519 1. ] \n", - "72 \t0 \t100 \t[3.79820212 1.11 ]\t[0.33122676 0.54580216]\t[1.90340519 1. ] \n", - "73 \t0 \t100 \t[3.78246927 1.14 ]\t[0.36312923 0.61676576]\t[1.90340519 1. ] \n", - "74 \t0 \t100 \t[3.76277349 1.18 ]\t[0.40829132 0.72636079]\t[1.90340519 1. ] \n", - "75 \t0 \t100 \t[3.76277349 1.18 ]\t[0.40829132 0.72636079]\t[1.90340519 1. ] \n", - "76 \t0 \t100 \t[ nan 1.06] \t[ nan 0.23748684]\t[nan 1.] \n", - "77 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "78 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ] \n", - "79 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "80 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "81 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ] \n", - "82 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ] \n", - "83 \t0 \t100 \t[3.78072214 1.13 ]\t[0.46165267 0.67312703]\t[0.00463617 1. ] \n", - "84 \t0 \t100 \t[3.766635 1.15 ] \t[0.47983125 0.698212 ]\t[0.00325559 1. ] \n", - "85 \t0 \t100 \t[3.77625156 1.13 ]\t[0.47649872 0.67312703]\t[0.00325559 1. ] \n", - "86 \t0 \t100 \t[3.73754505 1.19 ]\t[0.60653432 0.89101066]\t[0.00233289 1. ] \n", - "87 \t0 \t100 \t[3.69849361 1.26 ]\t[0.71202729 1.15429632]\t[2.50209763e-04 1.00000000e+00]\n", - "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ] \n", - "89 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ] \n", - "90 \t0 \t100 \t[3.81494884 1.08 ]\t[0.28860377 0.4621688 ]\t[2.00479269 1. ] \n", - "91 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ] \n", - "92 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ] \n", - "93 \t0 \t100 \t[3.80354182 1.12 ]\t[0.30384662 0.62096699]\t[2.29141402 1. ] \n", - "94 \t0 \t100 \t[3.80309528 1.12 ]\t[0.30609307 0.62096699]\t[2.24675989 1. ] \n", - "95 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", - "96 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", - "97 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", - "98 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", - "99 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ] \n", - "Final population hypervolume is 49389.248764\n", - "fit, 7, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.83]\t[ nan 1.03975959]\t[nan 16.]\n", - "1 \t86 \t86 \t[ nan 15.36]\t[ nan 6.22979935]\t[nan 1.]\n", - "2 \t93 \t93 \t[ nan 9.21] \t[ nan 5.01057881]\t[nan 1.]\n", - "3 \t98 \t98 \t[ nan 4.88] \t[ nan 2.42602556]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 3.26] \t[ nan 1.14560028]\t[nan 1.]\n", - "5 \t100 \t100 \t[nan 2.8] \t[ nan 0.88317609]\t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 2.48] \t[ nan 0.68527367]\t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.36] \t[ nan 0.60860496]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.34] \t[ nan 0.60365553]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.32] \t[ nan 0.59799666]\t[nan 1.]\n", - "10 \t100 \t100 \t[ nan 2.22] \t[ nan 0.55821143]\t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.18] \t[ nan 0.6225753] \t[nan 1.]\n", - "17 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.13] \t[ nan 0.50309045]\t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.19] \t[ nan 0.54212545]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.15] \t[ nan 0.51720402]\t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.22] \t[ nan 0.62577951]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.16] \t[ nan 0.52383203]\t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.14] \t[ nan 0.51029403]\t[nan 1.]\n", - "30 \t100 \t100 \t[ nan 2.13] \t[ nan 0.50309045]\t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.13] \t[ nan 0.50309045]\t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.12] \t[ nan 0.49558047]\t[nan 1.]\n", - "33 \t100 \t100 \t[nan 2.1] \t[ nan 0.47958315]\t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.09] \t[ nan 0.47106263]\t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.08] \t[ nan 0.4621688] \t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.07] \t[ nan 0.45287967]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "38 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "39 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", - "40 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", - "41 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 2.06] \t[ nan 0.4431704] \t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 2.05] \t[ nan 0.4330127] \t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 2.03] \t[ nan 0.4112177] \t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 2.01] \t[ nan 0.38716921]\t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 1.99] \t[ nan 0.36041643]\t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 1.95] \t[ nan 0.29580399]\t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 1.94] \t[ nan 0.2764055] \t[nan 1.]\n", - "53 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.93] \t[ nan 0.25514702]\t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.94]\t[ nan 1.09380071]\t[nan 20.]\n", - "1 \t0 \t88 \t[ nan 17.04]\t[ nan 5.17091868]\t[nan 1.]\n", - "2 \t0 \t97 \t[ nan 11.25]\t[ nan 5.93864463]\t[nan 1.]\n", - "3 \t0 \t99 \t[ nan 5.25] \t[ nan 3.25384388]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 2.78] \t[ nan 1.41124059]\t[nan 1.]\n", - "5 \t0 \t100 \t[ nan 2.03] \t[ nan 0.84208076]\t[nan 1.]\n", - "6 \t0 \t100 \t[ nan 1.83] \t[ nan 0.72187256]\t[nan 1.]\n", - "7 \t0 \t100 \t[ nan 1.43] \t[ nan 0.51487863]\t[nan 1.]\n", - "8 \t0 \t100 \t[ nan 1.21] \t[ nan 0.40730824]\t[nan 1.]\n", - "9 \t0 \t100 \t[5.33986482 1.1 ]\t[1.65229309 0.3 ]\t[2.61403799 1. ]\n", - "10 \t0 \t100 \t[4.66403539 1.23 ]\t[1.2919498 0.81061705]\t[2.51780605 1. ]\n", - "11 \t0 \t100 \t[4.35897743 1.36 ]\t[1.26466267 1.0150862 ]\t[2.48370647 1. ]\n", - "12 \t0 \t100 \t[3.94386113 1.48 ]\t[1.04626317 1.26870012]\t[2.48370647 1. ]\n", - "13 \t0 \t100 \t[3.68975136 1.27 ]\t[0.61256303 0.59757845]\t[2.51430631 1. ]\n", - "14 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "15 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "16 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", - "17 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", - "18 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "19 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "20 \t0 \t100 \t[3.81504954 1.08 ]\t[0.28818354 0.4621688 ]\t[2.00479293 1. ]\n", - "21 \t0 \t100 \t[3.77550609 1.14 ]\t[0.4014521 0.6483826] \t[1.35838246 1. ]\n", - "22 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221682 0.29580399]\t[2.46565056 1. ]\n", - "23 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221682 0.29580399]\t[2.46565056 1. ]\n", - "24 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "25 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", - "26 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", - "27 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", - "28 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", - "29 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726649 0.2215852 ]\t[2.46565032 1. ]\n", - "30 \t0 \t100 \t[3.81538893 1.08 ]\t[0.28376563 0.41665333]\t[2.19215393 1. ]\n", - "31 \t0 \t100 \t[3.83219723 1.05 ]\t[0.23221685 0.29580399]\t[2.46565032 1. ]\n", - "32 \t0 \t100 \t[3.81797209 1.08 ]\t[0.26980987 0.41665333]\t[2.45047045 1. ]\n", - "33 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "34 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "35 \t0 \t100 \t[3.84627056 1.03 ]\t[0.18726647 0.2215852 ]\t[2.46565056 1. ]\n", - "36 \t0 \t100 \t[3.83209932 1.06 ]\t[0.23279432 0.36932371]\t[2.45585966 1. ]\n", - "37 \t0 \t100 \t[3.81802598 1.08 ]\t[0.26953713 0.41665333]\t[2.45585942 1. ]\n", - "38 \t0 \t100 \t[3.7983302 1.12 ] \t[0.32998464 0.5706137 ]\t[1.90340519 1. ]\n", - "39 \t0 \t100 \t[3.7983302 1.12 ] \t[0.32998464 0.5706137 ]\t[1.90340519 1. ]\n", - "40 \t0 \t100 \t[3.7983302 1.12 ] \t[0.32998464 0.5706137 ]\t[1.90340519 1. ]\n", - "41 \t0 \t100 \t[3.79822821 1.12 ]\t[0.33040085 0.5706137 ]\t[1.90340519 1. ]\n", - "42 \t0 \t100 \t[3.79822821 1.12 ]\t[0.33040085 0.5706137 ]\t[1.90340519 1. ]\n", - "43 \t0 \t100 \t[3.79676859 1.12 ]\t[0.33663646 0.5706137 ]\t[1.90340519 1. ]\n", - "44 \t0 \t100 \t[3.79676859 1.12 ]\t[0.33663646 0.5706137 ]\t[1.90340519 1. ]\n", - "45 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576448 0.09949874]\t[2.60900307 1. ]\n", - "46 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "47 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "48 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "49 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "50 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "51 \t0 \t100 \t[3.80618485 1.09 ]\t[0.35209812 0.54945427]\t[1.07973862 1. ]\n", - "52 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113886 0.24166092]\t[2.51430607 1. ]\n", - "53 \t0 \t100 \t[3.81442152 1.08 ]\t[0.29287515 0.4621688 ]\t[1.90340519 1. ]\n", - "54 \t0 \t100 \t[3.78026834 1.19 ]\t[0.37519254 0.95598117]\t[1.86215627 1. ]\n", - "55 \t0 \t100 \t[3.77734991 1.2 ]\t[0.39029443 1.03923048]\t[1.67518544 1. ]\n", - "56 \t0 \t100 \t[3.78870204 1.13 ]\t[0.38233028 0.67312703]\t[1.4545635 1. ] \n", - "57 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "58 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "59 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "60 \t0 \t100 \t[3.80548408 1.14 ]\t[0.29464275 0.73511904]\t[2.46565032 1. ]\n", - "61 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407623 0.24166092]\t[2.46565032 1. ]\n", - "62 \t0 \t100 \t[3.81955741 1.06 ]\t[0.26212653 0.31048349]\t[2.46565032 1. ]\n", - "63 \t0 \t100 \t[3.80542935 1.1 ]\t[0.29489206 0.5 ]\t[2.46017694 1. ]\n", - "64 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ]\n", - "65 \t0 \t100 \t[3.8311413 1.04 ] \t[0.24007231 0.24166092]\t[2.21670604 1. ]\n", - "66 \t0 \t100 \t[3.81265904 1.06 ]\t[0.29953566 0.31048349]\t[2.12073183 1. ]\n", - "67 \t0 \t100 \t[3.81265904 1.06 ]\t[0.29953566 0.31048349]\t[2.12073183 1. ]\n", - "68 \t0 \t100 \t[3.79296326 1.1 ]\t[0.35461218 0.5 ]\t[1.90340519 1. ]\n", - "69 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "70 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "71 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "72 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "73 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "74 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "75 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "76 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", - "77 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", - "78 \t0 \t100 \t[3.81955742 1.06 ]\t[0.26212651 0.31048349]\t[2.46565056 1. ]\n", - "79 \t0 \t100 \t[3.8005514 1.09 ] \t[0.3200642 0.42649736]\t[1.97238231 1. ]\n", - "80 \t0 \t100 \t[3.78154539 1.12 ]\t[0.36803454 0.51536395]\t[1.97238231 1. ]\n", - "81 \t0 \t100 \t[3.78154539 1.12 ]\t[0.36803454 0.51536395]\t[1.97238231 1. ]\n", - "82 \t0 \t100 \t[3.78154539 1.12 ]\t[0.36803454 0.51536395]\t[1.97238231 1. ]\n", - "83 \t0 \t100 \t[3.75639938 1.18 ]\t[0.43983027 0.77948701]\t[1.35838258 1. ]\n", - "84 \t0 \t100 \t[3.74384922 1.2 ]\t[0.49972018 0.84852814]\t[0.63692141 1. ]\n", - "85 \t0 \t100 \t[3.74384922 1.2 ]\t[0.49972018 0.84852814]\t[0.63692141 1. ]\n", - "86 \t0 \t100 \t[3.69231898 1.33 ]\t[0.62758593 1.37880383]\t[0.12729283 1. ]\n", - "87 \t0 \t100 \t[3.69125033 1.32 ]\t[0.63371656 1.3029198 ]\t[0.02042805 1. ]\n", - "88 \t0 \t100 \t[3.69096342 1.32 ]\t[0.63451892 1.3029198 ]\t[0.02042804 1. ]\n", - "89 \t0 \t100 \t[3.67025884 1.37 ]\t[0.6614501 1.38314858]\t[0.02042804 1. ]\n", - "90 \t0 \t100 \t[3.65056306 1.41 ]\t[0.68405779 1.42895066]\t[0.02042804 1. ]\n", - "91 \t0 \t100 \t[3.74215382 1.2 ]\t[0.45197205 0.74833148]\t[1.90340519 1. ]\n", - "92 \t0 \t100 \t[3.74215382 1.2 ]\t[0.45197205 0.74833148]\t[1.90340519 1. ]\n", - "93 \t0 \t100 \t[3.76115983 1.17 ]\t[0.41565341 0.69361373]\t[1.90340519 1. ]\n", - "94 \t0 \t100 \t[3.74052443 1.23 ]\t[0.45859443 0.90393584]\t[1.80944371 1. ]\n", - "95 \t0 \t100 \t[3.74041199 1.22 ]\t[0.45906901 0.84356387]\t[1.79820001 1. ]\n", - "96 \t0 \t100 \t[3.74041199 1.22 ]\t[0.45906901 0.84356387]\t[1.79820001 1. ]\n", - "97 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "98 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "99 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", - "Final population hypervolume is 49374.815151\n", - "fit, 8, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.89]\t[ nan 0.95807098]\t[nan 20.]\n", - "1 \t86 \t86 \t[ nan 14.95]\t[ nan 6.67139416]\t[nan 1.]\n", - "2 \t95 \t95 \t[ nan 8.51] \t[ nan 5.10195061]\t[nan 1.]\n", - "3 \t99 \t99 \t[ nan 4.52] \t[ nan 2.11886762]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 3.29] \t[ nan 1.25135926]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 2.81] \t[ nan 0.94546285]\t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 2.41] \t[ nan 0.69419018]\t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.28] \t[ nan 0.60133186]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.36] \t[ nan 0.62481997]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.24] \t[ nan 0.58514955]\t[nan 1.]\n", - "10 \t100 \t100 \t[ nan 2.18] \t[ nan 0.55461698]\t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.15] \t[ nan 0.53619026]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.13] \t[ nan 0.52258971]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.12] \t[ nan 0.51536395]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.14] \t[ nan 0.52952809]\t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.15] \t[ nan 0.53619026]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.17] \t[ nan 0.5487258] \t[nan 1.]\n", - "17 \t100 \t100 \t[ nan 2.18] \t[ nan 0.55461698]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.19] \t[ nan 0.56026779]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.22] \t[ nan 0.5758472] \t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.24] \t[ nan 0.58514955]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.25] \t[ nan 0.58949131]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.24] \t[ nan 0.58514955]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.26] \t[ nan 0.59363288]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.23] \t[ nan 0.58060313]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.22] \t[ nan 0.5758472] \t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.18] \t[ nan 0.55461698]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.16] \t[ nan 0.5425864] \t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.13] \t[ nan 0.52258971]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.14] \t[ nan 0.52952809]\t[nan 1.]\n", - "30 \t100 \t100 \t[ nan 2.12] \t[ nan 0.51536395]\t[nan 1.]\n", - "31 \t100 \t100 \t[nan 2.1] \t[nan 0.5] \t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.08] \t[ nan 0.48332184]\t[nan 1.]\n", - "33 \t100 \t100 \t[ nan 2.05] \t[ nan 0.45552168]\t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.05] \t[ nan 0.45552168]\t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.06] \t[ nan 0.46518813]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.01] \t[ nan 0.41218928]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.01] \t[ nan 0.41218928]\t[nan 1.]\n", - "38 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", - "39 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", - "40 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", - "41 \t100 \t100 \t[nan 2.] \t[nan 0.4] \t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 1.96] \t[ nan 0.34409301]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 1.94] \t[ nan 0.31048349]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 1.93] \t[ nan 0.29171904]\t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "53 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.92] \t[ nan 0.2712932] \t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.79]\t[ nan 0.93053748]\t[nan 20.]\n", - "1 \t0 \t82 \t[ nan 16.43]\t[ nan 5.57001795]\t[nan 1.]\n", - "2 \t0 \t97 \t[ nan 10.03]\t[ nan 5.57576004]\t[nan 1.]\n", - "3 \t0 \t99 \t[ nan 3.27] \t[ nan 2.74173303]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.41] \t[ nan 0.6495383] \t[nan 1.]\n", - "5 \t0 \t100 \t[5.51457515 1.1 ]\t[1.61918953 0.3 ]\t[2.73836112 1. ]\n", - "6 \t0 \t100 \t[4.81866309 1.06 ]\t[1.20779848 0.2764055 ]\t[2.73836112 1. ]\n", - "7 \t0 \t100 \t[3.82670571 1.14 ]\t[0.46479199 0.56603887]\t[2.60930681 1. ]\n", - "8 \t0 \t100 \t[3.81743176 1.08 ]\t[0.28068137 0.4621688 ]\t[1.90340519 1. ]\n", - "9 \t0 \t100 \t[3.81685361 1.08 ]\t[0.28465477 0.4621688 ]\t[1.84558952 1. ]\n", - "10 \t0 \t100 \t[3.81685361 1.08 ]\t[0.28465477 0.4621688 ]\t[1.84558952 1. ]\n", - "11 \t0 \t100 \t[3.81685361 1.08 ]\t[0.28465477 0.4621688 ]\t[1.84558952 1. ]\n", - "12 \t0 \t100 \t[3.81643052 1.08 ]\t[0.28655267 0.4621688 ]\t[1.84558952 1. ]\n", - "13 \t0 \t100 \t[3.80284375 1.1 ]\t[0.31440598 0.5 ]\t[1.84558952 1. ]\n", - "14 \t0 \t100 \t[3.80284375 1.1 ]\t[0.31440598 0.5 ]\t[1.84558952 1. ]\n", - "15 \t0 \t100 \t[3.80284375 1.1 ]\t[0.31440598 0.5 ]\t[1.84558952 1. ]\n", - "16 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", - "17 \t0 \t100 \t[3.78438785 1.11 ]\t[0.45256852 0.58129167]\t[4.87665464e-14 1.00000000e+00]\n", - "18 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[4.87665464e-14 1.00000000e+00]\n", - "19 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "20 \t0 \t100 \t[3.82214457 1.08 ]\t[0.2505484 0.4621688] \t[2.46565032 1. ] \n", - "21 \t0 \t100 \t[3.82006442 1.08 ]\t[0.26238578 0.4621688 ]\t[2.25763559 1. ] \n", - "22 \t0 \t100 \t[3.80599109 1.1 ]\t[0.29489762 0.5 ]\t[2.25763559 1. ] \n", - "23 \t0 \t100 \t[3.78983761 1.14 ]\t[0.33261515 0.63277168]\t[2.25763559 1. ] \n", - "24 \t0 \t100 \t[3.78983761 1.14 ]\t[0.33261515 0.63277168]\t[2.25763559 1. ] \n", - "25 \t0 \t100 \t[3.80346266 1.1 ]\t[0.30891314 0.5 ]\t[2.00479269 1. ] \n", - "26 \t0 \t100 \t[3.80346266 1.1 ]\t[0.30891314 0.5 ]\t[2.00479269 1. ] \n", - "27 \t0 \t100 \t[3.78248508 1.16 ]\t[0.36888532 0.77097341]\t[1.77522588 1. ] \n", - "28 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "29 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "30 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", - "31 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "32 \t0 \t100 \t[3.82443289 1.08 ]\t[0.2386721 0.4621688] \t[2.49596119 1. ] \n", - "33 \t0 \t100 \t[3.83712755 1.04 ]\t[0.20442599 0.24166092]\t[2.55661488 1. ] \n", - "34 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "35 \t0 \t100 \t[3.79731289 1.12 ]\t[0.34068481 0.60464866]\t[1.90340519 1. ] \n", - "36 \t0 \t100 \t[3.76300484 1.2 ]\t[0.41363517 0.86023253]\t[1.80085552 1. ] \n", - "37 \t0 \t100 \t[3.78212247 1.16 ]\t[0.37209155 0.77097341]\t[1.80085552 1. ] \n", - "38 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "39 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "40 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "41 \t0 \t100 \t[3.83840204 1.05 ]\t[0.19668955 0.32787193]\t[2.68406439 1. ] \n", - "42 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "43 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "44 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "45 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "46 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "47 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "48 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "49 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "50 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "51 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "52 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "53 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "54 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "55 \t0 \t100 \t[3.78445531 1.17 ]\t[0.36054725 0.82528783]\t[1.71940589 1. ] \n", - "56 \t0 \t100 \t[3.74474092 1.24 ]\t[0.52195148 1.09654001]\t[0.10877079 1. ] \n", - "57 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ] \n", - "58 \t0 \t100 \t[3.78612688 1.14 ]\t[0.35038454 0.6483826 ]\t[2.00479293 1. ] \n", - "59 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ] \n", - "60 \t0 \t100 \t[3.77244816 1.15 ]\t[0.43591932 0.72629195]\t[0.63692153 1. ] \n", - "61 \t0 \t100 \t[3.77244816 1.14 ]\t[0.43591934 0.6483826 ]\t[0.63692129 1. ] \n", - "62 \t0 \t100 \t[3.73955526 1.23 ]\t[0.53900296 1.09412065]\t[0.58369392 1. ] \n", - "63 \t0 \t100 \t[3.76607895 1.13 ]\t[0.48371838 0.57714816]\t[5.75518661e-11 1.00000000e+00]\n", - "64 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "65 \t0 \t100 \t[3.80256634 1.14 ]\t[0.27880219 0.61676576]\t[2.67845941 1. ] \n", - "66 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "67 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "68 \t0 \t100 \t[3.80262238 1.14 ]\t[0.2785767 0.63277168]\t[2.67846012 1. ] \n", - "69 \t0 \t100 \t[3.80054649 1.15 ]\t[0.28757411 0.698212 ]\t[2.47087169 1. ] \n", - "70 \t0 \t100 \t[3.80909657 1.11 ]\t[0.27972322 0.58129167]\t[2.47087169 1. ] \n", - "71 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "72 \t0 \t100 \t[3.80953091 1.09 ]\t[0.27767113 0.42649736]\t[2.51430607 1. ] \n", - "73 \t0 \t100 \t[3.79545758 1.14 ]\t[0.3080959 0.6483826] \t[2.46565032 1. ] \n", - "74 \t0 \t100 \t[3.82214457 1.09 ]\t[0.2505484 0.54945427]\t[2.46565032 1. ] \n", - "75 \t0 \t100 \t[3.82214457 1.09 ]\t[0.25054842 0.54945427]\t[2.46565008 1. ] \n", - "76 \t0 \t100 \t[3.82214457 1.09 ]\t[0.25054842 0.54945427]\t[2.46565008 1. ] \n", - "77 \t0 \t100 \t[3.82214457 1.09 ]\t[0.25054842 0.54945427]\t[2.46565008 1. ] \n", - "78 \t0 \t100 \t[3.80807123 1.11 ]\t[0.28451955 0.58129167]\t[2.46565008 1. ] \n", - "79 \t0 \t100 \t[3.80807123 1.11 ]\t[0.28451955 0.58129167]\t[2.46565008 1. ] \n", - "80 \t0 \t100 \t[3.80807123 1.11 ]\t[0.28451955 0.58129167]\t[2.46565008 1. ] \n", - "81 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "82 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "83 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "84 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488197 0.4621688 ]\t[1.90340519 1. ] \n", - "85 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", - "86 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "87 \t0 \t100 \t[3.78342053 1.11 ]\t[0.45529409 0.58129167]\t[5.79447194e-04 1.00000000e+00]\n", - "88 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "89 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "90 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572202 0.4621688 ]\t[1.24752386e-08 1.00000000e+00]\n", - "91 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[1.24752386e-08 1.00000000e+00]\n", - "92 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[1.24752386e-08 1.00000000e+00]\n", - "93 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572203 0.4621688 ]\t[3.60822483e-14 1.00000000e+00]\n", - "94 \t0 \t100 \t[3.79748807 1.08 ]\t[0.43572203 0.4621688 ]\t[3.60822483e-14 1.00000000e+00]\n", - "95 \t0 \t100 \t[3.75947604 1.14 ]\t[0.50489108 0.61676576]\t[3.60822483e-14 1.00000000e+00]\n", - "96 \t0 \t100 \t[3.74540271 1.16 ]\t[0.5208915 0.64373908]\t[3.60822483e-14 1.00000000e+00]\n", - "97 \t0 \t100 \t[3.76440872 1.13 ]\t[0.48958502 0.57714816]\t[3.60822483e-14 1.00000000e+00]\n", - "98 \t0 \t100 \t[3.76440872 1.13 ]\t[0.48958502 0.57714816]\t[3.60822483e-14 1.00000000e+00]\n", - "99 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081127 0.42649736]\t[1.97238207 1. ] \n", - "Final population hypervolume is 49400.787163\n", - "fit, 9, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.8]\t[ nan 0.96953597]\t[nan 20.]\n", - "1 \t85 \t85 \t[ nan 17.74]\t[ nan 4.85102051]\t[nan 1.]\n", - "2 \t100 \t100 \t[ nan 11.92]\t[ nan 6.46324996]\t[nan 1.]\n", - "3 \t100 \t100 \t[ nan 4.38] \t[ nan 3.22422084]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 1.89] \t[ nan 1.02854266]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 1.14] \t[ nan 0.34698703]\t[nan 1.]\n", - "6 \t100 \t100 \t[0.46558893 1.03 ]\t[0.11581515 0.17058722]\t[0.2614038 1. ]\n", - "7 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ]\n", - "8 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ]\n", - "9 \t100 \t100 \t[0.38603943 1.01 ]\t[0.01252635 0.09949874]\t[0.2614038 1. ]\n", - "10 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "11 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "12 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "13 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "14 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "15 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "16 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "17 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "18 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "19 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "20 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "21 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "22 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "23 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "24 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "25 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "26 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "27 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "28 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "29 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "30 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "31 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "32 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "33 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "34 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "35 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "36 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "37 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "38 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "39 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "40 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "41 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "42 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "43 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "44 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "45 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "46 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "47 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "48 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "49 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "50 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "51 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "52 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "53 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "54 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "55 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "56 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "57 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "58 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "59 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "60 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "61 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "62 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "63 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "64 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "65 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "66 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "67 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "68 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "69 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "70 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "71 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "72 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "73 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "74 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "75 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "76 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "77 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "78 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "79 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "80 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "81 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "82 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "83 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "84 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "85 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "86 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "87 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "88 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "89 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "90 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "91 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "92 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "93 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "94 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "95 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "96 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "97 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "98 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "99 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.64]\t[ nan 1.07256701]\t[nan 14.]\n", - "1 \t0 \t91 \t[ nan 16.18]\t[ nan 6.01395045]\t[nan 1.]\n", - "2 \t0 \t94 \t[ nan 9.77] \t[ nan 5.93271439]\t[nan 1.]\n", - "3 \t0 \t98 \t[ nan 3.82] \t[ nan 2.50351753]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.96] \t[ nan 1.02878569]\t[nan 1.]\n", - "5 \t0 \t100 \t[ nan 1.23] \t[ nan 0.44395946]\t[nan 1.]\n", - "6 \t0 \t100 \t[5.08870241 1.04 ]\t[1.23406672 0.19595918]\t[2.73843527 1. ]\n", - "7 \t0 \t100 \t[4.2706165 1.08 ] \t[0.96661207 0.4621688 ]\t[2.67845964 1. ]\n", - "8 \t0 \t100 \t[3.83780303 1.07 ]\t[0.20010044 0.45287967]\t[2.67845941 1. ]\n", - "9 \t0 \t100 \t[3.84805069 1.03 ]\t[0.1752486 0.2215852] \t[2.51430631 1. ]\n", - "10 \t0 \t100 \t[3.84805069 1.03 ]\t[0.1752486 0.2215852] \t[2.51430631 1. ]\n", - "11 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531785 0.31048349]\t[2.51430607 1. ]\n", - "12 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", - "13 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", - "14 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", - "15 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", - "16 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", - "17 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", - "18 \t0 \t100 \t[3.80271291 1.1 ]\t[0.31522209 0.5 ]\t[1.83250618 1. ]\n", - "19 \t0 \t100 \t[3.81581314 1.08 ]\t[0.28968991 0.4621688 ]\t[1.83250618 1. ]\n", - "20 \t0 \t100 \t[3.79699857 1.1 ]\t[0.35045335 0.5 ]\t[1.35838246 1. ]\n", - "21 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838246 1. ]\n", - "22 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838246 1. ]\n", - "23 \t0 \t100 \t[3.8110719 1.08 ] \t[0.32396352 0.4621688 ]\t[1.35838246 1. ]\n", - "24 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "25 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "26 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "27 \t0 \t100 \t[3.83834601 1.05 ]\t[0.19701893 0.32787193]\t[2.67846036 1. ]\n", - "28 \t0 \t100 \t[3.81451158 1.12 ]\t[0.25495371 0.62096699]\t[2.67845988 1. ]\n", - "29 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "30 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", - "31 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897877 0.2215852 ]\t[2.46565056 1. ]\n", - "32 \t0 \t100 \t[3.77730278 1.15 ]\t[0.39777991 0.698212 ]\t[1.35838246 1. ]\n", - "33 \t0 \t100 \t[3.73772754 1.21 ]\t[0.54341445 0.9087904 ]\t[0.63692135 1. ]\n", - "34 \t0 \t100 \t[3.699419 1.3 ] \t[0.65591404 1.26095202]\t[0.04212945 1. ]\n", - "35 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "36 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ]\n", - "37 \t0 \t100 \t[3.81107189 1.09 ]\t[0.32396352 0.54945427]\t[1.3583827 1. ] \n", - "38 \t0 \t100 \t[3.80385728 1.09 ]\t[0.38143909 0.54945427]\t[0.63692147 1. ]\n", - "39 \t0 \t100 \t[3.81657607 1.08 ]\t[0.28451997 0.4621688 ]\t[1.90879989 1. ]\n", - "40 \t0 \t100 \t[3.77775108 1.16 ]\t[0.39513859 0.77097341]\t[1.39781797 1. ]\n", - "41 \t0 \t100 \t[3.77775108 1.16 ]\t[0.39513859 0.77097341]\t[1.39781797 1. ]\n", - "42 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", - "43 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", - "44 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", - "45 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", - "46 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", - "47 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ]\n", - "48 \t0 \t100 \t[3.80480879 1.09 ]\t[0.30127105 0.42649736]\t[2.13940525 1. ]\n", - "49 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "50 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "51 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ]\n", - "52 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ]\n", - "53 \t0 \t100 \t[3.80953091 1.09 ]\t[0.27767113 0.42649736]\t[2.51430607 1. ]\n", - "54 \t0 \t100 \t[3.78438785 1.09 ]\t[0.45256852 0.42649736]\t[6.29218899e-14 1.00000000e+00]\n", - "55 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "56 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "57 \t0 \t100 \t[3.80797333 1.09 ]\t[0.28498275 0.42649736]\t[2.45585966 1. ] \n", - "58 \t0 \t100 \t[3.78282732 1.13 ]\t[0.37489405 0.57714816]\t[1.35838246 1. ] \n", - "59 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[1.19196319e-13 1.00000000e+00]\n", - "60 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "61 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "62 \t0 \t100 \t[3.80382372 1.09 ]\t[0.30682469 0.42649736]\t[2.04089832 1. ] \n", - "63 \t0 \t100 \t[3.78550286 1.12 ]\t[0.35332275 0.51536395]\t[2.04089832 1. ] \n", - "64 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ] \n", - "65 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ] \n", - "66 \t0 \t100 \t[3.80313855 1.09 ]\t[0.31081128 0.42649736]\t[1.97238195 1. ] \n", - "67 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "68 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "69 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[1.09446063e-10 1.00000000e+00]\n", - "70 \t0 \t100 \t[3.79748807 1.09 ]\t[0.43572202 0.54945427]\t[1.09446063e-10 1.00000000e+00]\n", - "71 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "72 \t0 \t100 \t[3.80599109 1.11 ]\t[0.29489761 0.54580216]\t[2.25763559 1. ] \n", - "73 \t0 \t100 \t[3.80599109 1.11 ]\t[0.29489761 0.54580216]\t[2.25763559 1. ] \n", - "74 \t0 \t100 \t[3.79748832 1.07 ]\t[0.43571984 0.38091994]\t[2.50977228e-05 1.00000000e+00]\n", - "75 \t0 \t100 \t[3.74468515 1.14 ]\t[0.59067479 0.6483826 ]\t[1.53982521e-11 1.00000000e+00]\n", - "76 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "77 \t0 \t100 \t[3.81107189 1.1 ]\t[0.32396354 0.64031242]\t[1.35838246 1. ] \n", - "78 \t0 \t100 \t[3.74722408 1.23 ]\t[0.48074672 0.98848369]\t[1.35838246 1. ] \n", - "79 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "80 \t0 \t100 \t[3.82651285 1.06 ]\t[0.22772444 0.31048349]\t[2.68406439 1. ] \n", - "81 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "82 \t0 \t100 \t[3.82006442 1.08 ]\t[0.26238579 0.4621688 ]\t[2.25763535 1. ] \n", - "83 \t0 \t100 \t[3.80079323 1.13 ]\t[0.32182787 0.67312703]\t[1.94586444 1. ] \n", - "84 \t0 \t100 \t[3.78671989 1.15 ]\t[0.34806475 0.698212 ]\t[1.94586444 1. ] \n", - "85 \t0 \t100 \t[3.78671989 1.15 ]\t[0.34806475 0.698212 ]\t[1.94586444 1. ] \n", - "86 \t0 \t100 \t[3.77377617 1.14 ]\t[0.41532169 0.63277168]\t[1.45456362 1. ] \n", - "87 \t0 \t100 \t[3.77377617 1.14 ]\t[0.41532169 0.63277168]\t[1.45456362 1. ] \n", - "88 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "89 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ] \n", - "90 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "91 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "92 \t0 \t100 \t[3.79945568 1.07 ]\t[0.43011148 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", - "93 \t0 \t100 \t[3.79945568 1.07 ]\t[0.43011148 0.38091994]\t[1.19196319e-13 1.00000000e+00]\n", - "94 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "95 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "96 \t0 \t100 \t[3.77686267 1.18 ]\t[0.39211423 0.84118963]\t[1.87661743 1. ] \n", - "97 \t0 \t100 \t[3.87366802 1.03 ]\t[0.28353893 0.17058722]\t[2.73836088 1. ] \n", - "98 \t0 \t100 \t[3.89704481 1.02 ]\t[0.36669234 0.14 ]\t[2.73836088 1. ] \n", - "99 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "Final population hypervolume is 49363.883825\n", - "fit, 10, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.84]\t[ nan 1.00717426]\t[nan 19.]\n", - "1 \t89 \t89 \t[ nan 15.76]\t[ nan 6.33895891]\t[nan 1.]\n", - "2 \t96 \t96 \t[ nan 9.03] \t[ nan 5.38229505]\t[nan 1.]\n", - "3 \t100 \t100 \t[ nan 4.38] \t[ nan 1.81537875]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 3.32] \t[ nan 1.15654658]\t[nan 1.]\n", - "5 \t100 \t100 \t[nan 2.9] \t[ nan 0.91104336]\t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 2.58] \t[ nan 0.6508456] \t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.42] \t[ nan 0.56885851]\t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.42] \t[ nan 0.56885851]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.37] \t[ nan 0.55955339]\t[nan 1.]\n", - "10 \t100 \t100 \t[ nan 2.29] \t[ nan 0.53469618]\t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.24] \t[ nan 0.51224994]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.21] \t[ nan 0.49588305]\t[nan 1.]\n", - "17 \t100 \t100 \t[nan 2.2] \t[ nan 0.48989795]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.19] \t[ nan 0.48363209]\t[nan 1.]\n", - "19 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.28] \t[ nan 0.63371918]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.15] \t[ nan 0.45552168]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.17] \t[ nan 0.47021272]\t[nan 1.]\n", - "26 \t100 \t100 \t[nan 2.2] \t[ nan 0.48989795]\t[nan 1.]\n", - "27 \t100 \t100 \t[ nan 2.22] \t[ nan 0.50159745]\t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.23] \t[ nan 0.50705029]\t[nan 1.]\n", - "29 \t100 \t100 \t[nan 2.2] \t[ nan 0.48989795]\t[nan 1.]\n", - "30 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.18] \t[ nan 0.47707442]\t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.19] \t[ nan 0.48363209]\t[nan 1.]\n", - "33 \t100 \t100 \t[ nan 2.16] \t[ nan 0.46303348]\t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.14] \t[ nan 0.44766059]\t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.12] \t[ nan 0.43081318]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.11] \t[ nan 0.42178193]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.12] \t[ nan 0.43081318]\t[nan 1.]\n", - "38 \t100 \t100 \t[ nan 2.11] \t[ nan 0.42178193]\t[nan 1.]\n", - "39 \t100 \t100 \t[ nan 2.11] \t[ nan 0.42178193]\t[nan 1.]\n", - "40 \t100 \t100 \t[nan 2.1] \t[ nan 0.41231056]\t[nan 1.]\n", - "41 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 2.09] \t[ nan 0.40236799]\t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 2.08] \t[ nan 0.39191836]\t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 2.07] \t[ nan 0.38091994]\t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "53 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "54 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 2.06] \t[ nan 0.36932371]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 2.05] \t[ nan 0.35707142]\t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 2.05] \t[ nan 0.35707142]\t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 2.04] \t[ nan 0.34409301]\t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 2.02] \t[ nan 0.31559468]\t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 2.01] \t[ nan 0.29983329]\t[nan 1.]\n", - "61 \t100 \t100 \t[nan 2.] \t[ nan 0.28284271]\t[nan 1.]\n", - "62 \t100 \t100 \t[nan 2.] \t[ nan 0.28284271]\t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.99] \t[ nan 0.26438608]\t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.98] \t[ nan 0.24413111]\t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.97] \t[ nan 0.2215852] \t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.96] \t[ nan 0.19595918]\t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.7]\t[ nan 0.88881944]\t[nan 20.]\n", - "1 \t0 \t89 \t[ nan 15.98]\t[ nan 5.92449154]\t[nan 1.]\n", - "2 \t0 \t96 \t[nan 8.9] \t[ nan 5.5380502] \t[nan 1.]\n", - "3 \t0 \t98 \t[ nan 3.74] \t[ nan 1.85267374]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 2.41] \t[ nan 1.18401858]\t[nan 1.]\n", - "5 \t0 \t100 \t[ nan 1.85] \t[ nan 0.75332596]\t[nan 1.]\n", - "6 \t0 \t100 \t[ nan 1.73] \t[ nan 0.70505319]\t[nan 1.]\n", - "7 \t0 \t100 \t[nan 1.5] \t[ nan 0.59160798]\t[nan 1.]\n", - "8 \t0 \t100 \t[ nan 1.44] \t[ nan 0.5535341] \t[nan 1.]\n", - "9 \t0 \t100 \t[ nan 1.27] \t[ nan 0.48692915]\t[nan 1.]\n", - "10 \t0 \t100 \t[5.29680751 1.1 ]\t[1.3932257 0.3 ] \t[2.61403799 1. ]\n", - "11 \t0 \t100 \t[4.97166727 1.02 ]\t[1.20146216 0.14 ]\t[2.61403799 1. ]\n", - "12 \t0 \t100 \t[4.36341317 1.08 ]\t[1.03552625 0.4621688 ]\t[2.61403799 1. ]\n", - "13 \t0 \t100 \t[3.97585385 1.08 ]\t[0.66799793 0.36551334]\t[2.51430631 1. ]\n", - "14 \t0 \t100 \t[3.8326314 1.06 ] \t[0.22973302 0.36932371]\t[2.4553771 1. ] \n", - "15 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", - "16 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", - "17 \t0 \t100 \t[3.84660571 1.06 ]\t[0.18484048 0.50635956]\t[2.49413061 1. ]\n", - "18 \t0 \t100 \t[3.84632091 1.03 ]\t[0.18693422 0.2215852 ]\t[2.46565056 1. ]\n", - "19 \t0 \t100 \t[3.84632091 1.03 ]\t[0.18693422 0.2215852 ]\t[2.46565056 1. ]\n", - "20 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", - "21 \t0 \t100 \t[3.83322069 1.05 ]\t[0.2262486 0.29580399]\t[2.51430631 1. ]\n", - "22 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", - "23 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", - "24 \t0 \t100 \t[3.78869987 1.12 ]\t[0.38138147 0.5706137 ]\t[1.35838258 1. ]\n", - "25 \t0 \t100 \t[3.75117614 1.2 ]\t[0.5277514 0.96953597]\t[0.12061065 1. ]\n", - "26 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", - "27 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", - "28 \t0 \t100 \t[3.83224757 1.05 ]\t[0.23195203 0.29580399]\t[2.46565056 1. ]\n", - "29 \t0 \t100 \t[3.81255179 1.09 ]\t[0.30100092 0.49183331]\t[1.90340519 1. ]\n", - "30 \t0 \t100 \t[3.80483008 1.08 ]\t[0.35494812 0.41665333]\t[1.13123405 1. ]\n", - "31 \t0 \t100 \t[3.81890341 1.06 ]\t[0.32848523 0.36932371]\t[1.13123405 1. ]\n", - "32 \t0 \t100 \t[3.78654278 1.12 ]\t[0.45615368 0.69685006]\t[0.63692147 1. ]\n", - "33 \t0 \t100 \t[3.8463209 1.03 ] \t[0.18693424 0.2215852 ]\t[2.46565032 1. ]\n", - "34 \t0 \t100 \t[3.8463209 1.03 ] \t[0.18693424 0.2215852 ]\t[2.46565032 1. ]\n", - "35 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", - "36 \t0 \t100 \t[3.86039424 1.01 ]\t[0.12526352 0.09949874]\t[2.61403799 1. ]\n", - "37 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "38 \t0 \t100 \t[3.83421801 1.04 ]\t[0.22058139 0.24166092]\t[2.51430631 1. ]\n", - "39 \t0 \t100 \t[3.83421801 1.04 ]\t[0.22058139 0.24166092]\t[2.51430631 1. ]\n", - "40 \t0 \t100 \t[3.78785613 1.14 ]\t[0.40894588 0.74859869]\t[0.63692147 1. ]\n", - "41 \t0 \t100 \t[3.75880984 1.2 ]\t[0.45119195 0.87177979]\t[0.63692147 1. ]\n", - "42 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "43 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "44 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "45 \t0 \t100 \t[3.80558478 1.1 ]\t[0.2942344 0.47958315]\t[2.46565032 1. ]\n", - "46 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "47 \t0 \t100 \t[3.83373145 1.04 ]\t[0.22352631 0.24166092]\t[2.46565056 1. ]\n", - "48 \t0 \t100 \t[3.81403566 1.08 ]\t[0.2946562 0.4621688] \t[1.90340519 1. ]\n", - "49 \t0 \t100 \t[3.76349371 1.17 ]\t[0.40625135 0.69361373]\t[1.90340519 1. ]\n", - "50 \t0 \t100 \t[3.79223765 1.09 ]\t[0.37413753 0.42649736]\t[1.1309371 1. ] \n", - "51 \t0 \t100 \t[3.79223765 1.09 ]\t[0.37413753 0.42649736]\t[1.1309371 1. ] \n", - "52 \t0 \t100 \t[3.81965812 1.06 ]\t[0.261662 0.31048349]\t[2.46565056 1. ]\n", - "53 \t0 \t100 \t[3.79996233 1.1 ]\t[0.32368332 0.5 ]\t[1.90340519 1. ]\n", - "54 \t0 \t100 \t[3.89632376 1.03 ]\t[0.38602402 0.17058722]\t[2.61403799 1. ]\n", - "55 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "56 \t0 \t100 \t[3.83401625 1.05 ]\t[0.22179446 0.32787193]\t[2.49413061 1. ]\n", - "57 \t0 \t100 \t[3.84780478 1.02 ]\t[0.1762524 0.14 ] \t[2.61403799 1. ]\n", - "58 \t0 \t100 \t[3.83103194 1.05 ]\t[0.2409808 0.32787193]\t[2.19569993 1. ]\n", - "59 \t0 \t100 \t[3.78833035 1.12 ]\t[0.38725121 0.5706137 ]\t[1.16357994 1. ]\n", - "60 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "61 \t0 \t100 \t[3.84675712 1.03 ]\t[0.1837081 0.2215852] \t[2.51430631 1. ]\n", - "62 \t0 \t100 \t[3.79314042 1.14 ]\t[0.35479823 0.6636264 ]\t[1.90340519 1. ]\n", - "63 \t0 \t100 \t[3.75930257 1.22 ]\t[0.42285219 0.90088845]\t[1.84787631 1. ]\n", - "64 \t0 \t100 \t[3.85831254 1.05 ]\t[0.34043252 0.29580399]\t[2.51430631 1. ]\n", - "65 \t0 \t100 \t[3.80080972 1.11 ]\t[0.39039524 0.66174013]\t[0.63692153 1. ]\n", - "66 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "67 \t0 \t100 \t[3.84064811 1.05 ]\t[0.23178279 0.40926764]\t[1.90340519 1. ]\n", - "68 \t0 \t100 \t[3.81992897 1.12 ]\t[0.30802973 0.80349238]\t[1.80106997 1. ]\n", - "69 \t0 \t100 \t[3.78347715 1.23 ]\t[0.40053311 1.20710397]\t[1.58647907 1. ]\n", - "70 \t0 \t100 \t[3.74084192 1.37 ]\t[0.49231131 1.653209 ]\t[1.58375895 1. ]\n", - "71 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "72 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "73 \t0 \t100 \t[3.86034389 1.01 ]\t[0.12576444 0.09949874]\t[2.60900354 1. ]\n", - "74 \t0 \t100 \t[3.82706133 1.07 ]\t[0.26668339 0.45287967]\t[1.90340519 1. ]\n", - "75 \t0 \t100 \t[3.80649308 1.15 ]\t[0.33333645 0.90967027]\t[1.81615853 1. ]\n", - "76 \t0 \t100 \t[3.79193319 1.17 ]\t[0.36071692 0.92795474]\t[1.81615853 1. ]\n", - "77 \t0 \t100 \t[3.81250144 1.09 ]\t[0.30120173 0.49183331]\t[1.90340519 1. ]\n", - "78 \t0 \t100 \t[3.79892996 1.1 ]\t[0.32959051 0.5 ]\t[1.8102386 1. ] \n", - "79 \t0 \t100 \t[3.79892996 1.1 ]\t[0.32959051 0.5 ]\t[1.8102386 1. ] \n", - "80 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", - "81 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", - "82 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", - "83 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", - "84 \t0 \t100 \t[3.79861612 1.1 ]\t[0.33149346 0.5 ]\t[1.77885473 1. ]\n", - "85 \t0 \t100 \t[3.77694002 1.15 ]\t[0.39138142 0.698212 ]\t[1.70537317 1. ]\n", - "86 \t0 \t100 \t[3.77694002 1.15 ]\t[0.39138142 0.698212 ]\t[1.70537317 1. ]\n", - "87 \t0 \t100 \t[3.74071384 1.21 ]\t[0.52549225 0.9087904 ]\t[0.25036559 1. ]\n", - "88 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "89 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "90 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "91 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "92 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "93 \t0 \t100 \t[3.82053053 1.06 ]\t[0.25714138 0.31048349]\t[2.51430631 1. ]\n", - "94 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "95 \t0 \t100 \t[3.83411731 1.04 ]\t[0.22113884 0.24166092]\t[2.51430631 1. ]\n", - "96 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "97 \t0 \t100 \t[3.79797792 1.08 ]\t[0.41622753 0.4621688 ]\t[0.30770087 1. ]\n", - "98 \t0 \t100 \t[3.84770408 1.02 ]\t[0.17695729 0.14 ]\t[2.60900307 1. ]\n", - "99 \t0 \t100 \t[3.83363075 1.04 ]\t[0.22407622 0.24166092]\t[2.46565056 1. ]\n", - "Final population hypervolume is 49377.174955\n", - "fit, 11, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.64]\t[ nan 0.91126286]\t[nan 20.]\n", - "1 \t90 \t90 \t[ nan 16.98]\t[ nan 5.15941857]\t[nan 1.]\n", - "2 \t95 \t95 \t[ nan 11.03]\t[ nan 5.10970645]\t[nan 1.]\n", - "3 \t97 \t97 \t[ nan 6.69] \t[ nan 2.9178588] \t[nan 1.]\n", - "4 \t99 \t99 \t[ nan 4.52] \t[ nan 1.65819179]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 3.56] \t[ nan 1.16034478]\t[nan 1.]\n", - "6 \t100 \t100 \t[ nan 3.02] \t[ nan 0.77433843]\t[nan 1.]\n", - "7 \t100 \t100 \t[ nan 2.83] \t[ nan 0.735595] \t[nan 1.]\n", - "8 \t100 \t100 \t[ nan 2.58] \t[ nan 0.58617404]\t[nan 1.]\n", - "9 \t100 \t100 \t[ nan 2.48] \t[ nan 0.53814496]\t[nan 1.]\n", - "10 \t100 \t100 \t[nan 2.5] \t[ nan 0.53851648]\t[nan 1.]\n", - "11 \t100 \t100 \t[ nan 2.46] \t[ nan 0.53702886]\t[nan 1.]\n", - "12 \t100 \t100 \t[ nan 2.42] \t[ nan 0.55099909]\t[nan 1.]\n", - "13 \t100 \t100 \t[ nan 2.35] \t[ nan 0.51720402]\t[nan 1.]\n", - "14 \t100 \t100 \t[ nan 2.36] \t[ nan 0.52] \t[nan 1.]\n", - "15 \t100 \t100 \t[ nan 2.36] \t[ nan 0.52] \t[nan 1.]\n", - "16 \t100 \t100 \t[ nan 2.34] \t[ nan 0.51419841]\t[nan 1.]\n", - "17 \t100 \t100 \t[ nan 2.33] \t[ nan 0.51097945]\t[nan 1.]\n", - "18 \t100 \t100 \t[ nan 2.32] \t[ nan 0.5075431] \t[nan 1.]\n", - "19 \t100 \t100 \t[nan 2.3] \t[nan 0.5] \t[nan 1.]\n", - "20 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", - "21 \t100 \t100 \t[ nan 2.27] \t[ nan 0.48692915]\t[nan 1.]\n", - "22 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", - "23 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", - "24 \t100 \t100 \t[ nan 2.28] \t[ nan 0.49152823]\t[nan 1.]\n", - "25 \t100 \t100 \t[ nan 2.26] \t[ nan 0.48207883]\t[nan 1.]\n", - "26 \t100 \t100 \t[ nan 2.28] \t[ nan 0.49152823]\t[nan 1.]\n", - "27 \t100 \t100 \t[nan 2.3] \t[nan 0.5] \t[nan 1.]\n", - "28 \t100 \t100 \t[ nan 2.33] \t[ nan 0.51097945]\t[nan 1.]\n", - "29 \t100 \t100 \t[ nan 2.32] \t[ nan 0.5075431] \t[nan 1.]\n", - "30 \t100 \t100 \t[nan 2.3] \t[nan 0.5] \t[nan 1.]\n", - "31 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", - "32 \t100 \t100 \t[ nan 2.29] \t[ nan 0.49588305]\t[nan 1.]\n", - "33 \t100 \t100 \t[ nan 2.27] \t[ nan 0.48692915]\t[nan 1.]\n", - "34 \t100 \t100 \t[ nan 2.25] \t[ nan 0.4769696] \t[nan 1.]\n", - "35 \t100 \t100 \t[ nan 2.17] \t[ nan 0.42555846]\t[nan 1.]\n", - "36 \t100 \t100 \t[ nan 2.15] \t[ nan 0.40926764]\t[nan 1.]\n", - "37 \t100 \t100 \t[ nan 2.14] \t[ nan 0.40049969]\t[nan 1.]\n", - "38 \t100 \t100 \t[ nan 2.12] \t[ nan 0.38157568]\t[nan 1.]\n", - "39 \t100 \t100 \t[ nan 2.11] \t[ nan 0.37134889]\t[nan 1.]\n", - "40 \t100 \t100 \t[nan 2.1] \t[ nan 0.36055513]\t[nan 1.]\n", - "41 \t100 \t100 \t[nan 2.1] \t[ nan 0.36055513]\t[nan 1.]\n", - "42 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", - "43 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", - "44 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", - "45 \t100 \t100 \t[ nan 2.09] \t[ nan 0.34914181]\t[nan 1.]\n", - "46 \t100 \t100 \t[ nan 2.08] \t[ nan 0.33704599]\t[nan 1.]\n", - "47 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", - "48 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", - "49 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", - "50 \t100 \t100 \t[ nan 2.07] \t[ nan 0.3241913] \t[nan 1.]\n", - "51 \t100 \t100 \t[ nan 2.05] \t[ nan 0.29580399]\t[nan 1.]\n", - "52 \t100 \t100 \t[ nan 2.03] \t[ nan 0.26286879]\t[nan 1.]\n", - "53 \t100 \t100 \t[nan 2.] \t[nan 0.2] \t[nan 1.]\n", - "54 \t100 \t100 \t[nan 2.] \t[nan 0.2] \t[nan 1.]\n", - "55 \t100 \t100 \t[ nan 1.99] \t[ nan 0.17291616]\t[nan 1.]\n", - "56 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "57 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "58 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "59 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "60 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "61 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "62 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "63 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "64 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "65 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "66 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "67 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "68 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "69 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "70 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "71 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "72 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "73 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "74 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "75 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "76 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "77 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "78 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "79 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "80 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "81 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "82 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "83 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "84 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "85 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "86 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "87 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "88 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "89 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "90 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "91 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "92 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "93 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "94 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "95 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "96 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "97 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "98 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "99 \t100 \t100 \t[ nan 1.98] \t[ nan 0.14] \t[nan 1.]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(-1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.78]\t[ nan 0.99579114]\t[nan 20.]\n", - "1 \t0 \t93 \t[ nan 16.58]\t[ nan 5.39477525]\t[nan 1.]\n", - "2 \t0 \t100 \t[ nan 10.3] \t[ nan 5.62227712]\t[nan 1.]\n", - "3 \t0 \t99 \t[ nan 4.26] \t[ nan 3.19255384]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.56] \t[ nan 0.88679197]\t[nan 1.]\n", - "5 \t0 \t100 \t[4.7289087 1.06 ]\t[1.16429666 0.23748684]\t[2.73836112 1. ]\n", - "6 \t0 \t100 \t[3.92584497 1.05 ]\t[0.50894191 0.25980762]\t[2.73836088 1. ]\n", - "7 \t0 \t100 \t[3.82759879 1.06 ]\t[0.22233973 0.31048349]\t[2.73836088 1. ]\n", - "8 \t0 \t100 \t[3.82759879 1.06 ]\t[0.22233973 0.31048349]\t[2.73836088 1. ]\n", - "9 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "10 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ]\n", - "11 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ]\n", - "12 \t0 \t100 \t[3.81456762 1.09 ]\t[0.25470451 0.42649736]\t[2.67845964 1. ]\n", - "13 \t0 \t100 \t[3.78636326 1.19 ]\t[0.31712268 0.82091412]\t[2.44182491 1. ]\n", - "14 \t0 \t100 \t[3.79770948 1.18 ]\t[0.29921565 0.81706793]\t[2.44182491 1. ]\n", - "15 \t0 \t100 \t[3.79770948 1.18 ]\t[0.29921565 0.81706793]\t[2.44182491 1. ]\n", - "16 \t0 \t100 \t[3.79770948 1.18 ]\t[0.29921565 0.81706793]\t[2.44182491 1. ]\n", - "17 \t0 \t100 \t[3.79734043 1.18 ]\t[0.30061539 0.81706793]\t[2.44182491 1. ]\n", - "18 \t0 \t100 \t[3.7859942 1.19 ] \t[0.31843058 0.82091412]\t[2.44182491 1. ]\n", - "19 \t0 \t100 \t[3.82190632 1.1 ]\t[0.25184618 0.64031242]\t[2.44182491 1. ]\n", - "20 \t0 \t100 \t[3.80759473 1.16 ]\t[0.28677838 0.86856203]\t[2.44182491 1. ]\n", - "21 \t0 \t100 \t[3.80759473 1.16 ]\t[0.28677839 0.86856203]\t[2.44182491 1. ]\n", - "22 \t0 \t100 \t[3.79352139 1.18 ]\t[0.31624227 0.88746831]\t[2.44182491 1. ]\n", - "23 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ]\n", - "24 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ]\n", - "25 \t0 \t100 \t[3.78787961 1.13 ]\t[0.44333761 0.67312703]\t[8.52790061e-14 1.00000000e+00]\n", - "26 \t0 \t100 \t[3.78787961 1.13 ]\t[0.44333761 0.67312703]\t[8.52790061e-14 1.00000000e+00]\n", - "27 \t0 \t100 \t[3.75230863 1.22 ]\t[0.48104812 0.84356387]\t[8.52790061e-14 1.00000000e+00]\n", - "28 \t0 \t100 \t[3.75644727 1.18 ]\t[0.49272434 0.75339233]\t[8.52790061e-14 1.00000000e+00]\n", - "29 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "30 \t0 \t100 \t[3.81430285 1.09 ]\t[0.25589632 0.42649736]\t[2.65198374 1. ] \n", - "31 \t0 \t100 \t[3.78372612 1.14 ]\t[0.36371051 0.63277168]\t[1.90340519 1. ] \n", - "32 \t0 \t100 \t[3.76200517 1.2 ]\t[0.41674326 0.82462113]\t[1.79820001 1. ] \n", - "33 \t0 \t100 \t[3.74753596 1.23 ]\t[0.43785648 0.87011493]\t[1.75861263 1. ] \n", - "34 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "35 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "36 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "37 \t0 \t100 \t[3.82214457 1.08 ]\t[0.2505484 0.4621688] \t[2.46565032 1. ] \n", - "38 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[8.03782041e-11 1.00000000e+00]\n", - "39 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[8.03782041e-11 1.00000000e+00]\n", - "40 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[8.03782041e-11 1.00000000e+00]\n", - "41 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[8.03782041e-11 1.00000000e+00]\n", - "42 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "43 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "44 \t0 \t100 \t[3.82179834 1.08 ]\t[0.25243949 0.4621688 ]\t[2.43102694 1. ] \n", - "45 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "46 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "47 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "48 \t0 \t100 \t[3.80304065 1.11 ]\t[0.31123381 0.54580216]\t[1.97238231 1. ] \n", - "49 \t0 \t100 \t[3.78222212 1.16 ]\t[0.3699486 0.73102668]\t[1.79113078 1. ] \n", - "50 \t0 \t100 \t[3.82204666 1.07 ]\t[0.25107984 0.38091994]\t[2.45585918 1. ] \n", - "51 \t0 \t100 \t[3.82204666 1.07 ]\t[0.25107984 0.38091994]\t[2.45585918 1. ] \n", - "52 \t0 \t100 \t[3.81789705 1.07 ]\t[0.27583346 0.38091994]\t[2.04089785 1. ] \n", - "53 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "54 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "55 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "56 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "57 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "58 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "59 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "60 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "61 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "62 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "63 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "64 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.64071689e-11 1.00000000e+00]\n", - "65 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.64071689e-11 1.00000000e+00]\n", - "66 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.64071689e-11 1.00000000e+00]\n", - "67 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.64071689e-11 1.00000000e+00]\n", - "68 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.57480712e-12 1.00000000e+00]\n", - "69 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.57480712e-12 1.00000000e+00]\n", - "70 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067639 0.6483826 ]\t[1.35780276e-13 1.00000000e+00]\n", - "71 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067639 0.6483826 ]\t[1.35780276e-13 1.00000000e+00]\n", - "72 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "73 \t0 \t100 \t[3.81456761 1.11 ]\t[0.25470452 0.54580216]\t[2.67845964 1. ] \n", - "74 \t0 \t100 \t[3.80262237 1.15 ]\t[0.27857673 0.66895441]\t[2.67845964 1. ] \n", - "75 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "76 \t0 \t100 \t[3.80807124 1.1 ]\t[0.28451952 0.5 ]\t[2.46565032 1. ] \n", - "77 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "78 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "79 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "80 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", - "81 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", - "82 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492276 0.5 ]\t[1.90340519 1. ] \n", - "83 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "84 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "85 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "86 \t0 \t100 \t[3.7809906 1.13 ] \t[0.44959071 0.67312703]\t[0.31983161 1. ] \n", - "87 \t0 \t100 \t[3.72965009 1.21 ]\t[0.59054222 0.9087904 ]\t[0.14626619 1. ] \n", - "88 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[9.73288894e-10 1.00000000e+00]\n", - "89 \t0 \t100 \t[3.7446849 1.14 ] \t[0.59067638 0.63277168]\t[9.73288894e-10 1.00000000e+00]\n", - "90 \t0 \t100 \t[3.72567889 1.17 ]\t[0.61626563 0.69361373]\t[9.73288894e-10 1.00000000e+00]\n", - "91 \t0 \t100 \t[3.78341474 1.09 ]\t[0.45534222 0.42649736]\t[3.68523129e-11 1.00000000e+00]\n", - "92 \t0 \t100 \t[3.79748807 1.07 ]\t[0.43572203 0.38091994]\t[3.68523129e-11 1.00000000e+00]\n", - "93 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.68523129e-11 1.00000000e+00]\n", - "94 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[1.19793064e-13 1.00000000e+00]\n", - "95 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.61516372e-14 1.00000000e+00]\n", - "96 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.61516372e-14 1.00000000e+00]\n", - "97 \t0 \t100 \t[3.78341473 1.09 ]\t[0.45534224 0.42649736]\t[3.61516372e-14 1.00000000e+00]\n", - "98 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "99 \t0 \t100 \t[3.82214457 1.06 ]\t[0.25054841 0.31048349]\t[2.46565032 1. ] \n", - "Final population hypervolume is 49377.110287\n", - "fit, 12, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.81]\t[ nan 0.94546285]\t[nan 20.]\n", - "1 \t92 \t92 \t[ nan 17.06]\t[ nan 5.12605111]\t[nan 1.]\n", - "2 \t97 \t97 \t[ nan 12.05]\t[ nan 5.6945149] \t[nan 1.]\n", - "3 \t100 \t100 \t[ nan 6.54] \t[ nan 4.15552644]\t[nan 1.]\n", - "4 \t100 \t100 \t[ nan 2.63] \t[ nan 1.79808231]\t[nan 1.]\n", - "5 \t100 \t100 \t[ nan 1.35] \t[ nan 0.66895441]\t[nan 1.]\n", - "6 \t100 \t100 \t[0.47949067 1.03 ]\t[0.11979875 0.17058722]\t[0.2614038 1. ]\n", - "7 \t100 \t100 \t[0.39641299 1.04 ]\t[0.06164404 0.19595918]\t[0.2614038 1. ]\n", - "8 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "9 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "10 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "11 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "12 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "13 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "14 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "15 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "16 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "17 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "18 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "19 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "20 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "21 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "22 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "23 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "24 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "25 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "26 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "27 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "28 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "29 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "30 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "31 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "32 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "33 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "34 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "35 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "36 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "37 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "38 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "39 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "40 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "41 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "42 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "43 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "44 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "45 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "46 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "47 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "48 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "49 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "50 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "51 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "52 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "53 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "54 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "55 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "56 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "57 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "58 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "59 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "60 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "61 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "62 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "63 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "64 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "65 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "66 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "67 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "68 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "69 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "70 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "71 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "72 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "73 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "74 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "75 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "76 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "77 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "78 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "79 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "80 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "81 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "82 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "83 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "84 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "85 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "86 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "87 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "88 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "89 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "90 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "91 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "92 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "93 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "94 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "95 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "96 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "97 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "98 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "99 \t100 \t100 \t[0.38478048 1.02 ]\t[0.01762524 0.14 ]\t[0.2614038 1. ]\n", - "Final population hypervolume is 49486.997565\n", - "best model: Cos(1.72*x2)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.8]\t[ nan 0.98994949]\t[nan 20.]\n", - "1 \t0 \t85 \t[ nan 16.88]\t[ nan 5.5933532] \t[nan 1.]\n", - "2 \t0 \t97 \t[ nan 9.68] \t[ nan 6.04132436]\t[nan 1.]\n", - "3 \t0 \t100 \t[ nan 3.07] \t[ nan 2.59327978]\t[nan 1.]\n", - "4 \t0 \t100 \t[5.34613959 1.05 ]\t[1.23600914 0.21794495]\t[2.73836112 1. ]\n", - "5 \t0 \t100 \t[4.58834292 1.03 ]\t[1.12811656 0.17058722]\t[2.73836112 1. ]\n", - "6 \t0 \t100 \t[3.87306902 1.05 ]\t[0.28598945 0.40926764]\t[2.67845988 1. ]\n", - "7 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", - "8 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", - "9 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", - "10 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", - "11 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", - "12 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710199 0.24166092]\t[2.51430607 1. ]\n", - "13 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "14 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", - "15 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", - "16 \t0 \t100 \t[3.83670447 1.04 ]\t[0.20710197 0.24166092]\t[2.51430631 1. ]\n", - "17 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", - "18 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ]\n", - "19 \t0 \t100 \t[3.75500628 1.16 ]\t[0.52432788 0.73102668]\t[9.3104461e-08 1.0000000e+00]\n", - "20 \t0 \t100 \t[3.75500628 1.16 ]\t[0.52432788 0.73102668]\t[9.3104461e-08 1.0000000e+00]\n", - "21 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ] \n", - "22 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054837 0.31048349]\t[2.46565056 1. ] \n", - "23 \t0 \t100 \t[3.817536 1.1 ] \t[0.2781729 0.64031242]\t[2.00479293 1. ] \n", - "24 \t0 \t100 \t[3.817536 1.1 ] \t[0.2781729 0.64031242]\t[2.00479293 1. ] \n", - "25 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ] \n", - "26 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492272 0.5 ]\t[1.90340519 1. ] \n", - "27 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492272 0.5 ]\t[1.90340519 1. ] \n", - "28 \t0 \t100 \t[3.76849715 1.19 ]\t[0.39043935 0.82091412]\t[1.89494383 1. ] \n", - "29 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054837 0.31048349]\t[2.46565056 1. ] \n", - "30 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ] \n", - "31 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "32 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", - "33 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "34 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "35 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "36 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "37 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "38 \t0 \t100 \t[3.80772501 1.1 ]\t[0.2861692 0.5 ] \t[2.43102694 1. ] \n", - "39 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[9.53261292e-08 1.00000000e+00]\n", - "40 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[9.53261292e-08 1.00000000e+00]\n", - "41 \t0 \t100 \t[3.78341474 1.1 ]\t[0.45534222 0.5 ]\t[6.29947483e-12 1.00000000e+00]\n", - "42 \t0 \t100 \t[3.7446849 1.18 ] \t[0.59067638 0.93145048]\t[9.10937992e-14 1.00000000e+00]\n", - "43 \t0 \t100 \t[3.80787135 1.09 ]\t[0.28546804 0.42649736]\t[2.44566083 1. ] \n", - "44 \t0 \t100 \t[3.79009169 1.15 ]\t[0.3323735 0.72629195]\t[2.09501839 1. ] \n", - "45 \t0 \t100 \t[3.77231182 1.22 ]\t[0.37258663 0.99579114]\t[2.09499598 1. ] \n", - "46 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "47 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ] \n", - "48 \t0 \t100 \t[3.86163747 1.01 ]\t[0.11289354 0.09949874]\t[2.73836088 1. ] \n", - "49 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897877 0.2215852 ]\t[2.46565056 1. ] \n", - "50 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897877 0.2215852 ]\t[2.46565056 1. ] \n", - "51 \t0 \t100 \t[3.84756413 1.03 ]\t[0.17897879 0.2215852 ]\t[2.46565032 1. ] \n", - "52 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "53 \t0 \t100 \t[3.7975161 1.11 ] \t[0.33878186 0.54580216]\t[1.90340519 1. ] \n", - "54 \t0 \t100 \t[3.77782032 1.15 ]\t[0.38756268 0.66895441]\t[1.90340519 1. ] \n", - "55 \t0 \t100 \t[3.76374699 1.17 ]\t[0.40882039 0.69361373]\t[1.90340519 1. ] \n", - "56 \t0 \t100 \t[3.74299915 1.22 ]\t[0.45301001 0.84356387]\t[1.79820001 1. ] \n", - "57 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "58 \t0 \t100 \t[3.81652212 1.08 ]\t[0.28488196 0.4621688 ]\t[1.90340519 1. ] \n", - "59 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", - "60 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "61 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n", - "62 \t0 \t100 \t[3.82048506 1.07 ]\t[0.25990265 0.38091994]\t[2.29969907 1. ] \n", - "63 \t0 \t100 \t[3.80471572 1.11 ]\t[0.30085244 0.54580216]\t[2.29604959 1. ] \n", - "64 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "65 \t0 \t100 \t[3.81231754 1.12 ]\t[0.26519494 0.62096699]\t[2.49596143 1. ] \n", - "66 \t0 \t100 \t[3.85959469 1.04 ]\t[0.31626159 0.24166092]\t[2.46565056 1. ] \n", - "67 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "68 \t0 \t100 \t[3.80346267 1.1 ]\t[0.30891311 0.5 ]\t[2.00479269 1. ] \n", - "69 \t0 \t100 \t[3.80244879 1.1 ]\t[0.31492274 0.5 ]\t[1.90340519 1. ] \n", - "70 \t0 \t100 \t[3.79585011 1.13 ]\t[0.34803627 0.67312703]\t[1.80578291 1. ] \n", - "71 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "72 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "73 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "74 \t0 \t100 \t[3.80797333 1.09 ]\t[0.28498275 0.42649736]\t[2.45585966 1. ] \n", - "75 \t0 \t100 \t[3.78344277 1.13 ]\t[0.36367127 0.57714816]\t[1.90340519 1. ] \n", - "76 \t0 \t100 \t[3.76346742 1.2 ]\t[0.41010004 0.89442719]\t[1.87544811 1. ] \n", - "77 \t0 \t100 \t[3.89704481 1.02 ]\t[0.36669234 0.14 ]\t[2.73836088 1. ] \n", - "78 \t0 \t100 \t[3.81451156 1.12 ]\t[0.25495379 0.60464866]\t[2.67845964 1. ] \n", - "79 \t0 \t100 \t[3.86184666 1.04 ]\t[0.32756127 0.24166092]\t[2.51430631 1. ] \n", - "80 \t0 \t100 \t[3.85959469 1.04 ]\t[0.31626159 0.24166092]\t[2.46565056 1. ] \n", - "81 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "82 \t0 \t100 \t[3.78778547 1.1 ]\t[0.4435541 0.5 ] \t[2.45394272e-04 1.00000000e+00]\n", - "83 \t0 \t100 \t[3.79797708 1.08 ]\t[0.43423778 0.4621688 ]\t[2.45394272e-04 1.00000000e+00]\n", - "84 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "85 \t0 \t100 \t[3.79784478 1.1 ]\t[0.43261651 0.64031242]\t[0.03567135 1. ] \n", - "86 \t0 \t100 \t[3.74551392 1.2 ]\t[0.52407409 0.87177979]\t[0.03567135 1. ] \n", - "87 \t0 \t100 \t[3.73417745 1.23 ]\t[0.62061031 1.1563304 ]\t[0.0208514 1. ] \n", - "88 \t0 \t100 \t[3.73417745 1.23 ]\t[0.62061031 1.1563304 ]\t[0.0208514 1. ] \n", - "89 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "90 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531784 0.31048349]\t[2.51430631 1. ] \n", - "91 \t0 \t100 \t[3.80599109 1.1 ]\t[0.29489762 0.5 ]\t[2.25763535 1. ] \n", - "92 \t0 \t100 \t[3.79025824 1.13 ]\t[0.33069832 0.57714816]\t[2.25763535 1. ] \n", - "93 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "94 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", - "95 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", - "96 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", - "97 \t0 \t100 \t[3.838346 1.06 ] \t[0.197019 0.42 ] \t[2.67845964 1. ] \n", - "98 \t0 \t100 \t[3.82660945 1.08 ]\t[0.22724511 0.4621688 ]\t[2.67845964 1. ] \n", - "99 \t0 \t100 \t[3.81466421 1.12 ]\t[0.2542806 0.60464866]\t[2.67845964 1. ] \n", - "Final population hypervolume is 49366.768166\n", - "fit, 13, est, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.96]\t[ nan 1.01901914]\t[nan 20.]\n", - "1 \t96 \t96 \t[ nan 13.79]\t[ nan 6.8735653] \t[nan 1.]\n", - "2 \t99 \t99 \t[ nan 4.28] \t[ nan 3.80021052]\t[nan 1.]\n", - "3 \t100 \t100 \t[nan 1.1] \t[nan 0.3] \t[nan 1.]\n", - "4 \t100 \t100 \t[0.46337418 1.03 ]\t[0.11117594 0.17058722]\t[0.28053975 1. ]\n", - "5 \t100 \t100 \t[0.3864333 1.03 ] \t[0.02979342 0.17058722]\t[0.28053975 1. ]\n", - "6 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "7 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "8 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "9 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "10 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "11 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "12 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "13 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "14 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "15 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "16 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "17 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "18 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "19 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "20 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "21 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "22 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "23 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "24 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "25 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "26 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "27 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "28 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "29 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "30 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "31 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "32 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "33 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "34 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "35 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "36 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "37 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "38 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "39 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "40 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "41 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "42 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "43 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "44 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "45 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "46 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "47 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "48 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "49 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "50 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "51 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "52 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "53 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "54 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "55 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "56 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "57 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "58 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "59 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "60 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "61 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "62 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "63 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "64 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "65 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "66 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "67 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "68 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "69 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "70 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "71 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "72 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "73 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "74 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "75 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "76 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "77 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "78 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "79 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "80 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "81 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "82 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "83 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "84 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "85 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "86 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "87 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "88 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "89 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "90 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "91 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "92 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "93 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "94 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "95 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "96 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "97 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "98 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "99 \t100 \t100 \t[0.3851632 1.02 ] \t[0.01494621 0.14 ]\t[0.28053975 1. ]\n", - "Final population hypervolume is 49486.059903\n", - "best model: Logabs(2.31*x1)\n", - "fit, est_mab, gen\tevals\toffspring\tave \tstd \tmin \n", - "0 \t100 \t \t[ nan 20.82]\t[ nan 0.93145048]\t[nan 20.]\n", - "1 \t0 \t85 \t[ nan 15.97]\t[ nan 6.10156537]\t[nan 1.]\n", - "2 \t0 \t100 \t[ nan 8.97] \t[ nan 5.86933557]\t[nan 1.]\n", - "3 \t0 \t98 \t[ nan 2.92] \t[ nan 1.96814634]\t[nan 1.]\n", - "4 \t0 \t100 \t[ nan 1.42] \t[ nan 0.55099909]\t[nan 1.]\n", - "5 \t0 \t100 \t[5.23502642 1.12 ]\t[1.84065349 0.32496154]\t[2.73843455 1. ]\n", - "6 \t0 \t100 \t[4.7483042 1.04 ] \t[1.21124893 0.19595918]\t[2.73843455 1. ]\n", - "7 \t0 \t100 \t[4.26851731 1.12 ]\t[1.01155656 0.53441557]\t[2.51430631 1. ]\n", - "8 \t0 \t100 \t[3.80342191 1.1 ]\t[0.3108392 0.5 ] \t[1.90340519 1. ]\n", - "9 \t0 \t100 \t[3.80342191 1.1 ]\t[0.3108392 0.5 ] \t[1.90340519 1. ]\n", - "10 \t0 \t100 \t[3.81700868 1.08 ]\t[0.28260681 0.4621688 ]\t[1.90340519 1. ]\n", - "11 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", - "12 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083922 0.5 ]\t[1.90340519 1. ]\n", - "13 \t0 \t100 \t[3.78934857 1.13 ]\t[0.33803979 0.57714816]\t[1.90340519 1. ]\n", - "14 \t0 \t100 \t[3.78829985 1.13 ]\t[0.34399921 0.57714816]\t[1.79853261 1. ]\n", - "15 \t0 \t100 \t[3.80140007 1.11 ]\t[0.32135395 0.54580216]\t[1.79853261 1. ]\n", - "16 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", - "17 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", - "18 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ]\n", - "19 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "20 \t0 \t100 \t[3.81700868 1.08 ]\t[0.2826068 0.4621688] \t[1.90340519 1. ]\n", - "21 \t0 \t100 \t[3.80342191 1.1 ]\t[0.3108392 0.5 ] \t[1.90340519 1. ]\n", - "22 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", - "23 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", - "24 \t0 \t100 \t[3.82311769 1.06 ]\t[0.24531782 0.31048349]\t[2.51430631 1. ]\n", - "25 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024155 0.24166092]\t[2.46565056 1. ]\n", - "26 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024157 0.24166092]\t[2.46565032 1. ]\n", - "27 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565032 1. ]\n", - "28 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "29 \t0 \t100 \t[3.83840205 1.05 ]\t[0.19668952 0.32787193]\t[2.68406439 1. ]\n", - "30 \t0 \t100 \t[3.81456762 1.11 ]\t[0.25470449 0.54580216]\t[2.67845988 1. ]\n", - "31 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884716 0.14 ]\t[2.73836112 1. ]\n", - "32 \t0 \t100 \t[3.83840205 1.04 ]\t[0.19668951 0.24166092]\t[2.68406463 1. ]\n", - "33 \t0 \t100 \t[3.82645681 1.08 ]\t[0.2280061 0.4621688] \t[2.67845964 1. ]\n", - "34 \t0 \t100 \t[3.81451157 1.12 ]\t[0.25495377 0.60464866]\t[2.67845964 1. ]\n", - "35 \t0 \t100 \t[3.82591384 1.08 ]\t[0.230646 0.41665333]\t[2.67845964 1. ]\n", - "36 \t0 \t100 \t[3.79487183 1.13 ]\t[0.31776999 0.57714816]\t[1.90340519 1. ]\n", - "37 \t0 \t100 \t[3.78097606 1.13 ]\t[0.44970266 0.67312703]\t[0.31837761 1. ]\n", - "38 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", - "39 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ]\n", - "40 \t0 \t100 \t[3.817536 1.08 ] \t[0.27817292 0.4621688 ]\t[2.00479269 1. ]\n", - "41 \t0 \t100 \t[3.80346267 1.1 ]\t[0.30891311 0.5 ]\t[2.00479269 1. ]\n", - "42 \t0 \t100 \t[3.81107189 1.08 ]\t[0.32396354 0.4621688 ]\t[1.35838246 1. ]\n", - "43 \t0 \t100 \t[3.77871127 1.14 ]\t[0.45234847 0.74859869]\t[0.63692153 1. ]\n", - "44 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", - "45 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", - "46 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", - "47 \t0 \t100 \t[3.76463794 1.16 ]\t[0.47071594 0.77097341]\t[0.63692153 1. ]\n", - "48 \t0 \t100 \t[3.7071313 1.26 ] \t[0.61135788 1.03556748]\t[0.63692153 1. ]\n", - "49 \t0 \t100 \t[3.78341715 1.1 ]\t[0.45532215 0.5 ]\t[2.41753834e-04 1.00000000e+00]\n", - "50 \t0 \t100 \t[3.78341715 1.1 ]\t[0.45532215 0.5 ]\t[2.41753834e-04 1.00000000e+00]\n", - "51 \t0 \t100 \t[3.78341715 1.1 ]\t[0.45532215 0.5 ]\t[2.41753834e-04 1.00000000e+00]\n", - "52 \t0 \t100 \t[3.78341473 1.1 ]\t[0.45534224 0.5 ]\t[2.72803269e-09 1.00000000e+00]\n", - "53 \t0 \t100 \t[3.78341473 1.1 ]\t[0.45534224 0.5 ]\t[2.72803269e-09 1.00000000e+00]\n", - "54 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "55 \t0 \t100 \t[3.81870626 1.08 ]\t[0.27518907 0.4621688 ]\t[1.90340519 1. ] \n", - "56 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "57 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", - "58 \t0 \t100 \t[3.80342191 1.1 ]\t[0.31083921 0.5 ]\t[1.90340519 1. ] \n", - "59 \t0 \t100 \t[3.78360476 1.15 ]\t[0.36433939 0.698212 ]\t[1.89126921 1. ] \n", - "60 \t0 \t100 \t[3.78224801 1.15 ]\t[0.37156462 0.698212 ]\t[1.75559449 1. ] \n", - "61 \t0 \t100 \t[3.78224801 1.15 ]\t[0.37156462 0.698212 ]\t[1.75559449 1. ] \n", - "62 \t0 \t100 \t[3.78215505 1.15 ]\t[0.37207248 0.698212 ]\t[1.74629819 1. ] \n", - "63 \t0 \t100 \t[3.78118194 1.15 ]\t[0.37543555 0.698212 ]\t[1.74629819 1. ] \n", - "64 \t0 \t100 \t[3.78341499 1.09 ]\t[0.45534012 0.42649736]\t[2.53178478e-05 1.00000000e+00]\n", - "65 \t0 \t100 \t[3.74468515 1.15 ]\t[0.59067477 0.72629195]\t[1.02204356e-12 1.00000000e+00]\n", - "66 \t0 \t100 \t[3.89704481 1.02 ]\t[0.36669234 0.14 ]\t[2.73836088 1. ] \n", - "67 \t0 \t100 \t[3.79967221 1.08 ]\t[0.42954408 0.4621688 ]\t[8.50486543e-08 1.00000000e+00]\n", - "68 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668955 0.24166092]\t[2.68406439 1. ] \n", - "69 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "70 \t0 \t100 \t[3.76371897 1.15 ]\t[0.49215104 0.698212 ]\t[1.40610166e-06 1.00000000e+00]\n", - "71 \t0 \t100 \t[3.77779228 1.13 ]\t[0.4746412 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", - "72 \t0 \t100 \t[3.75875823 1.13 ]\t[0.5766332 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", - "73 \t0 \t100 \t[3.75875823 1.13 ]\t[0.5766332 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", - "74 \t0 \t100 \t[3.75875823 1.13 ]\t[0.5766332 0.67312703]\t[1.22614026e-08 1.00000000e+00]\n", - "75 \t0 \t100 \t[3.78341473 1.1 ]\t[0.45534223 0.5 ]\t[4.77323994e-08 1.00000000e+00]\n", - "76 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "77 \t0 \t100 \t[3.82645681 1.08 ]\t[0.22800611 0.4621688 ]\t[2.67846012 1. ] \n", - "78 \t0 \t100 \t[3.79682634 1.12 ]\t[0.34254648 0.60464866]\t[1.90340519 1. ] \n", - "79 \t0 \t100 \t[3.77614262 1.18 ]\t[0.39565319 0.84118963]\t[1.80461228 1. ] \n", - "80 \t0 \t100 \t[3.75593549 1.23 ]\t[0.43937701 0.96803926]\t[1.80461228 1. ] \n", - "81 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "82 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "83 \t0 \t100 \t[3.82214458 1.06 ]\t[0.25054839 0.31048349]\t[2.46565056 1. ] \n", - "84 \t0 \t100 \t[3.85029124 1.02 ]\t[0.15884719 0.14 ]\t[2.73836088 1. ] \n", - "85 \t0 \t100 \t[3.83840204 1.04 ]\t[0.19668955 0.24166092]\t[2.68406439 1. ] \n", - "86 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "87 \t0 \t100 \t[3.83670446 1.04 ]\t[0.207102 0.24166092]\t[2.51430631 1. ] \n", - "88 \t0 \t100 \t[3.83621791 1.04 ]\t[0.21024158 0.24166092]\t[2.46565056 1. ] \n", - "89 \t0 \t100 \t[3.8362179 1.04 ] \t[0.21024159 0.24166092]\t[2.46565032 1. ] \n" - ] - } - ], + "outputs": [], "source": [ "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", "if __name__ == '__main__':\n", @@ -3375,6 +444,10 @@ " import warnings\n", " warnings.filterwarnings(\"ignore\")\n", "\n", + " from pmlb import fetch_data\n", + "\n", + " # X, y = fetch_data('537_houses', return_X_y=True, local_cache_dir='./')\n", + "\n", " data = pd.read_csv('../../docs/examples/datasets/d_example_patients.csv')\n", " X = data.drop(columns='target')\n", " y = data['target']\n", @@ -3383,14 +456,14 @@ " # X = data.drop(columns='target')\n", " # y = data['target']\n", "\n", - " data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", - " X = data.drop(columns='target')\n", - " y = data['target']\n", + " # data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", + " # X = data.drop(columns='target')\n", + " # y = data['target']\n", "\n", " kwargs = {\n", - " 'verbosity' : True,\n", - " 'pop_size' : 100,\n", - " 'max_gen' : 100,\n", + " 'verbosity' : False,\n", + " 'pop_size' : 60,\n", + " 'max_gen' : 300,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", @@ -3398,9 +471,9 @@ "\n", " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), \n", + " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", " ('Modified', 'point mutation calls'),\n", " ('Modified', 'insert mutation calls'),\n", " ('Modified', 'delete mutation calls'),\n", @@ -3412,29 +485,25 @@ " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", - " print(f\"est, \", end='\\n' if (i==29) else '')\n", + " est_start_time = time.time()\n", " est = BrushRegressor(**kwargs).fit(X,y)\n", - " print(f\"fit, \", end='\\n' if (i==29) else '')\n", - " est.score(X,y)\n", + " est_end_time = time.time() - est_start_time\n", "\n", - " print(f\"est_mab, \", end='\\n' if (i==29) else '')\n", + " est_mab_start_time = time.time()\n", " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", - " print(f\"fit, \", end='\\n' if (i==29) else '')\n", - " est_mab.score(X,y)\n", + " est_mab_end_time = time.time() - est_mab_start_time\n", "\n", " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " \n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", " \n", " results.loc[f'run {i}'] = [\n", " # Original implementation\n", " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", "\n", " # Implementation using Dynamic Thompson Sampling\n", " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", " \n", " # Mutation count\n", " *total_pulls.values()]\n", @@ -3446,54 +515,69 @@ " display(results.describe())" ] }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspecting the last execution" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "def generate_plots():\n", + "def generate_plots(est_mab):\n", + "\n", + " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", + "\n", + " # Setting up the figure layout\n", + " fig = plt.figure(figsize=(12, 6), tight_layout=True)\n", + " gs = gridspec.GridSpec(6, 6)\n", + "\n", " # Approximating the percentage of usage for each generation ----------------\n", - " # TODO: test if different batch sizes will produce different plots here\n", - " data = np.zeros( (kwargs['max_gen'], 4) )\n", - " for g in range(kwargs['max_gen']):\n", - " idx_start = g*(learner_log.shape[0]//kwargs['max_gen'])\n", - " idx_end = (g+1)*(learner_log.shape[0]//kwargs['max_gen'])\n", + " data = np.zeros( (est_mab.max_gen, 4) )\n", + " for g in range(est_mab.max_gen):\n", + " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", + " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", "\n", " df_in_range = learner_log.iloc[idx_start:idx_end]\n", " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", " for k, v in g_data.items():\n", " data[g, k] = v\n", "\n", - " plt.figure(figsize=(10, 5))\n", - "\n", - " #plt.plot(data, label=est_mab.mutations_)\n", - " plt.stackplot(range(kwargs['max_gen']), data.T, labels=est_mab.mutations_)\n", - " plt.xlabel(\"Generations\")\n", - " plt.ylabel(\"Percentage of usage\")\n", + " axs = fig.add_subplot(gs[0:3, :3])\n", + " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", "\n", - " plt.legend()\n", - " plt.show()\n", + " axs.set_ylabel(\"Percentage of usage\")\n", + " axs.legend()\n", "\n", " # average Brush weights for each generation --------------------------------\n", - " data = np.zeros( (kwargs['max_gen'], 4) )\n", - " for g in range(kwargs['max_gen']):\n", - " idx_start = g*(learner_log.shape[0]//kwargs['max_gen'])\n", - " idx_end = (g+1)*(learner_log.shape[0]//kwargs['max_gen'])\n", + " data = np.zeros( (est_mab.max_gen, 4) )\n", + " for g in range(est_mab.max_gen):\n", + " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", + " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", "\n", - " df_in_range = learner_log.iloc[idx_start:idx_end]\n", - " df_in_range = df_in_range[[col for col in df_in_range.columns if col.startswith('weight')]]\n", - " data[g] = df_in_range.mean().values\n", + " learner_log_in_range = learner_log.iloc[idx_start:idx_end]\n", + "\n", + " total_rewards = learner_log_in_range.groupby('arm idx')['reward'].sum().to_dict()\n", + " total_pulls = learner_log_in_range['arm idx'].value_counts().to_dict()\n", "\n", - " plt.figure(figsize=(10, 5))\n", + " keys = total_pulls.keys()\n", + " data_total_pulls = np.array([total_pulls[k] for k in sorted(keys)])\n", + " data_total_rewards = np.array([total_rewards[k] for k in sorted(keys)])\n", "\n", - " #plt.plot(data, label=est_mab.mutations_)\n", - " plt.stackplot(range(kwargs['max_gen']), data.T, labels=est_mab.mutations_)\n", - " plt.xlabel(\"Generations\")\n", - " plt.ylabel(\"Percentage of usage\")\n", + " # Success rate\n", + " data[g, [int(i) for i in keys]] = data_total_rewards/data_total_pulls\n", "\n", - " plt.legend()\n", - " plt.show()\n", + " axs = fig.add_subplot(gs[3:6, :3])\n", + " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", + "\n", + " axs.set_xlabel(\"Generations\")\n", + " axs.set_ylabel(\"brush Weights conversion\")\n", + " axs.legend()\n", "\n", " # --------------------------------------------------------------------------\n", " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", @@ -3504,25 +588,34 @@ " item['gen'], item['evals'], *item['ave'], *item['std'], *item['min']\n", " )\n", "\n", - " fig, axs = plt.subplots(2, 1, figsize=(10, 8))\n", " x = logbook['gen']\n", " for i, metric in enumerate(['m1', 'm2']):\n", + " axs = fig.add_subplot(gs[(3*i):(3*i + 3), 3:])\n", + "\n", " y = logbook[f'ave {metric}']\n", " y_err = logbook[f'std {metric}']\n", " y_min = logbook[f'min {metric}']\n", "\n", - " axs[i].plot(x, y, 'b', label='Avg.')\n", - " axs[i].fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", - " axs[i].plot(x, y_min, 'k', label='Min.')\n", + " axs.plot(x, y, 'b', label='Avg.')\n", + " axs.fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", + " axs.plot(x, y_min, 'k', label='Min.')\n", "\n", - " axs[i].set_xlabel(\"Generation\")\n", - " axs[i].set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", - " axs[i].legend()\n", + " axs.set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", + " axs.legend()\n", "\n", - " plt.show()\n", + " axs.set_xlabel(\"Generations\")\n", "\n", - "plot_learner_history(est_mab.learner_)\n", - "generate_plots()" + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", + "generate_plots(est_mab)" ] }, { @@ -3547,7 +640,7 @@ "\n", " from pmlb import fetch_data\n", "\n", - " #X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", + " # X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", "\n", " data = pd.read_csv('../../docs/examples/datasets/d_analcatdata_aids.csv')\n", " X = data.drop(columns='target')\n", @@ -3555,8 +648,8 @@ "\n", " kwargs = {\n", " 'verbosity' : False,\n", - " 'pop_size' : 100,\n", - " 'max_gen' : 100,\n", + " 'pop_size' : 60,\n", + " 'max_gen' : 300,\n", " 'max_depth' : 10,\n", " 'max_size' : 20,\n", " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", @@ -3564,9 +657,9 @@ "\n", " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), \n", + " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), \n", + " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", " ('Modified', 'point mutation calls'),\n", " ('Modified', 'insert mutation calls'),\n", " ('Modified', 'delete mutation calls'),\n", @@ -3578,25 +671,29 @@ " try:\n", " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", "\n", + " est_start_time = time.time()\n", " est = BrushClassifier(**kwargs).fit(X,y)\n", + " est_end_time = time.time() - est_start_time\n", + "\n", + " est_mab_start_time = time.time()\n", " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", + " est_mab_end_time = time.time() - est_mab_start_time\n", "\n", " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " \n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", + " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", " \n", " results.loc[f'run {i}'] = [\n", " # Original implementation\n", " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(),\n", + " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", "\n", " # Implementation using Dynamic Thompson Sampling\n", " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(),\n", + " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", " \n", " # Mutation count\n", " *total_pulls.values()]\n", + " \n", " except Exception as e:\n", " print(e)\n", "\n", @@ -3605,20 +702,27 @@ " display(results.describe())" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspecting the last execution" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "plot_learner_history(est_mab.learner_)\n", - "generate_plots()" + "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", + "generate_plots(est_mab)" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "brush", "language": "python", "name": "python3" }, @@ -3632,7 +736,12 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.11.3" + }, + "vscode": { + "interpreter": { + "hash": "dccdbee601866cd4c45494445ca79bf9b696b8bf13c00622eb9e8a421ade3c36" + } } }, "nbformat": 4, diff --git a/src/brush/D_TS_experiments.py b/src/brush/D_TS_experiments.py deleted file mode 100644 index 899aeaa7..00000000 --- a/src/brush/D_TS_experiments.py +++ /dev/null @@ -1,12 +0,0 @@ - -from brush import BrushRegressor -import pandas as pd - -if __name__ == '__main__': - - data = pd.read_csv('docs/examples/datasets/d_example_patients.csv') - X = data.drop(columns='target') - y = data['target'] - - est = BrushRegressor().fit(X,y) - \ No newline at end of file From a4e63ff76a714f630de0bb5e39f5d8cf43d098d9 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 15:07:46 -0400 Subject: [PATCH 031/102] Mutation returns std::optional Old behavior (perform a fixed number of attempts to find a crossover point and successfully swap subtrees) was removed, replaced by only 1 attempt that returns `std::nullopt` if fails, otherwise returns the offspring --- src/program/program.h | 4 +- src/variation.h | 101 +++++++++++++++++++----------------------- 2 files changed, 48 insertions(+), 57 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index 1f6c06ae..3a9951ca 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -429,7 +429,7 @@ template struct Program * @param other another program to cross with this one. * @return a new version of this and the other program */ - Program cross(Program other) const; + std::optional> cross(Program other) const; /// @brief turns program tree into a linear program. /// @return a vector of nodes encoding the program in reverse polish notation @@ -468,7 +468,7 @@ std::optional> Program::mutate() const /// swaps subtrees between this and other (note the pass by copy) template -Program Program::cross(Program other) const +std::optional> Program::cross(Program other) const { return variation::cross(*this, other); }; diff --git a/src/variation.h b/src/variation.h index 8346e344..51920373 100644 --- a/src/variation.h +++ b/src/variation.h @@ -219,7 +219,7 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS /// @param other the donating parent /// @return new program of type `T` template -Program cross(const Program& root, const Program& other) +std::optional> cross(const Program& root, const Program& other) { /* subtree crossover between this and other, producing new Program */ // choose location by weighted sampling of program @@ -233,60 +233,53 @@ Program cross(const Program& root, const Program& other) [](const auto& n){ return n.get_prob_change(); } ); - bool matching_spots_found = false; - for (int tries = 0; tries < 3; ++tries) - { - auto child_spot = r.select_randomly(child.Tree.begin(), - child.Tree.end(), - child_weights.begin(), - child_weights.end() - ); - - auto child_ret_type = child_spot.node->data.ret_type; - - auto allowed_size = PARAMS["max_size"].get() - - ( child.Tree.size() - child.Tree.size(child_spot) ); - auto allowed_depth = PARAMS["max_depth"].get() - - ( child.Tree.depth(child_spot) ); - - // pick a subtree to insert. Selection is based on other_weights - vector other_weights(other.Tree.size()); - - // iterator to get the size of subtrees inside transform - auto other_iter = other.Tree.begin(); - - // lambda function to check feasibility of solution and increment the iterator - const auto check_and_incrm = [other, &other_iter, allowed_size, allowed_depth]() -> bool { - int s = other.Tree.size(other_iter); - int d = other.Tree.max_depth(other_iter); - - std::advance(other_iter, 1); - return (s <= allowed_size) && (d <= allowed_depth); - }; - - std::transform(other.Tree.begin(), other.Tree.end(), - other_weights.begin(), - [child_ret_type, check_and_incrm](const auto& n){ - // need to pick a node that has a matching output type to the child_spot. - // also need to check if swaping this node wouldn't exceed max_size - if (check_and_incrm() && (n.ret_type == child_ret_type)) - return n.get_prob_change(); - else - // setting the weight to zero to indicate a non-feasible crossover point - return float(0.0); - } - ); + auto child_spot = r.select_randomly(child.Tree.begin(), + child.Tree.end(), + child_weights.begin(), + child_weights.end() + ); - for (const auto& w: other_weights) - { - matching_spots_found = w > 0.0; + auto child_ret_type = child_spot.node->data.ret_type; - if (matching_spots_found) - break; - } + auto allowed_size = PARAMS["max_size"].get() - + ( child.Tree.size() - child.Tree.size(child_spot) ); + auto allowed_depth = PARAMS["max_depth"].get() - + ( child.Tree.depth(child_spot) ); - if (matching_spots_found) - { + // pick a subtree to insert. Selection is based on other_weights + vector other_weights(other.Tree.size()); + + // iterator to get the size of subtrees inside transform + auto other_iter = other.Tree.begin(); + + // lambda function to check feasibility of solution and increment the iterator + const auto check_and_incrm = [other, &other_iter, allowed_size, allowed_depth]() -> bool { + int s = other.Tree.size(other_iter); + int d = other.Tree.max_depth(other_iter); + + std::advance(other_iter, 1); + return (s <= allowed_size) && (d <= allowed_depth); + }; + + std::transform(other.Tree.begin(), other.Tree.end(), + other_weights.begin(), + [child_ret_type, check_and_incrm](const auto& n){ + // need to pick a node that has a matching output type to the child_spot. + // also need to check if swaping this node wouldn't exceed max_size + if (check_and_incrm() && (n.ret_type == child_ret_type)) + return n.get_prob_change(); + else + // setting the weight to zero to indicate a non-feasible crossover point + return float(0.0); + } + ); + + bool matching_spots_found = false; + for (const auto& w: other_weights) + { + matching_spots_found = w > 0.0; + + if (matching_spots_found) { auto other_spot = r.select_randomly( other.Tree.begin(), other.Tree.end(), @@ -299,10 +292,8 @@ Program cross(const Program& root, const Program& other) child.Tree.move_ontop(child_spot, other_spot); return child; } - // fmt::print("try {} failed\n",tries); } - - return child; + return std::nullopt; }; } //namespace variation #endif \ No newline at end of file From 123e0504051d3e348c47b14487d02691f245fd3a Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 15:09:54 -0400 Subject: [PATCH 032/102] Update python wrapper to work with mutation's `std::optional` --- src/bindings/bind_programs.h | 3 ++- src/brush/deap_api/nsga2.py | 10 ++++++---- src/brush/estimator.py | 9 ++++++--- 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/src/bindings/bind_programs.h b/src/bindings/bind_programs.h index 41592b0d..e9874402 100644 --- a/src/bindings/bind_programs.h +++ b/src/bindings/bind_programs.h @@ -47,7 +47,8 @@ void bind_program(py::module& m, string name) .def("get_weights", &T::get_weights) .def("size", &T::size) .def("depth", &T::depth) - .def("cross", &T::cross) + .def("cross", &T::cross, py::return_value_policy::automatic, + "Performs one attempt to stochastically swap subtrees between two programs and generate a child") .def("mutate", &T::mutate, py::return_value_policy::automatic, "Performs one attempt to stochastically mutate the program and generate a child") .def("set_search_space", &T::set_search_space) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 9c6aeed6..e1e41638 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -45,13 +45,15 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): for ind1, ind2 in zip(parents[::2], parents[1::2]): if random.random() <= CXPB: - ind1, ind2 = toolbox.mate(ind1, ind2) + off1, off2 = toolbox.mate(ind1, ind2) + else: + off1, off2 = ind1, ind2 - off1 = toolbox.mutate(ind1) - off2 = toolbox.mutate(ind2) - # avoid inserting empty solutions + if off1: off1 = toolbox.mutate(off1) if off1: offspring.extend([off1]) + + if off2: off2 = toolbox.mutate(off2) if off2: offspring.extend([off2]) # archive.update(offspring) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 16bb3014..b3565f34 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -115,9 +115,12 @@ def _crossover(self, ind1, ind2): offspring = [] for i,j in [(ind1,ind2),(ind2,ind1)]: - off = creator.Individual(i.prg.cross(j.prg)) - # off.fitness.valid = False - offspring.append(off) + child = i.prg.cross(j.prg) + if child: + off = creator.Individual(child) + offspring.append(off) + else: # so we'll always have two elements in `offspring` + offspring.append(None) return offspring[0], offspring[1] From a8902807f71672a4414cbbf5b6f05d384157d7f3 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 15:10:23 -0400 Subject: [PATCH 033/102] Update tests to work with mutation's `std::optional` --- tests/cpp/test_variation.cpp | 200 +++++++++++++---------------------- 1 file changed, 76 insertions(+), 124 deletions(-) diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index d48e6882..b3cad650 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -31,6 +31,7 @@ TEST(Operators, Mutation) for (int d = 1; d < 10; ++d) { + int successes = 0; for (int s = 1; s < 10; ++s) { fmt::print("d={},s={}\n",d,s); @@ -60,6 +61,7 @@ TEST(Operators, Mutation) ); } else { + successes += 1; auto Child = opt.value(); fmt::print( "=================================================\n" @@ -76,6 +78,8 @@ TEST(Operators, Mutation) y_pred = Child.predict(data); } } + // since x1 and x2 have same type, we shoudn't get fails + ASSERT_TRUE(successes > 0); } } @@ -108,7 +112,6 @@ TEST(Operators, MutationSizeAndDepthLimit) for (int d = 5; d < 15; ++d) { int successes = 0; - for (int s = 5; s < 15; ++s) { PARAMS["max_size"] = s; @@ -196,6 +199,7 @@ TEST(Operators, Crossover) for (int d = 1; d < 10; ++d) { + int successes = 0; for (int s = 1; s < 10; ++s) { RegressorProgram PRG1 = SS.make_regressor(d, s); @@ -212,31 +216,42 @@ TEST(Operators, Crossover) PRG1.get_model("compact", true), PRG2.get_model("compact", true) ); + ArrayXf y_pred = PRG1.predict(data); fmt::print("cross one\n"); - auto Child1 = PRG1.cross(PRG2); - fmt::print( - "Model 1 after cross: {}\n" - "Model 2 after cross: {}\n", - PRG1.get_model("compact", true), - PRG2.get_model("compact", true) - ); - fmt::print("cross two\n"); - auto Child2 = PRG2.cross(PRG1); - - fmt::print( - "Crossed Model 1: {}\n" - "Crossed Model 2: {}\n" - "=================================================\n", - Child1.get_model("compact", true), - Child2.get_model("compact", true) - ); - Child1.fit(data); - Child2.fit(data); - auto child_pred1 = Child1.predict(data); - auto child_pred2 = Child2.predict(data); + auto opt = PRG1.cross(PRG2); + if (!opt){ + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Original model 1: {}\n" + "Original model 2: {}\n", + "Crossover failed to create a child", + d, s, + PRG1.get_model("compact", true), + PRG2.get_model("compact", true) + ); + } + else { + successes += 1; + auto Child = opt.value(); + fmt::print( + "Original model 1 after cross: {}\n" + "Original model 2 after cross: {}\n", + PRG1.get_model("compact", true), + PRG2.get_model("compact", true) + ); + fmt::print( + "Crossed Model: {}\n" + "=================================================\n", + Child.get_model("compact", true) + ); + Child.fit(data); + auto child_pred1 = Child.predict(data); + } } + ASSERT_TRUE(successes > 0); } } @@ -264,6 +279,7 @@ TEST(Operators, CrossoverSizeAndDepthLimit) for (int d = 5; d < 15; ++d) { + int successes = 0; for (int s = 5; s < 15; ++s) { PARAMS["max_size"] = s; @@ -287,111 +303,47 @@ TEST(Operators, CrossoverSizeAndDepthLimit) PRG2.get_model("compact", true) ); - fmt::print("cross one\n"); - auto Child1 = PRG1.cross(PRG2); - fmt::print( - "Model 1 after cross: {}\n" - "Model 2 after cross: {}\n", - PRG1.get_model("compact", true), - PRG2.get_model("compact", true) - ); - - fmt::print("cross two\n"); - auto Child2 = PRG2.cross(PRG1); - fmt::print( - "Crossed Model 1 : {}\n" - "Crossed Model 1 depth: {}\n" - "Crossed Model 1 size : {}\n" - "Crossed Model 2 : {}\n" - "Crossed Model 2 depth: {}\n" - "Crossed Model 2 size : {}\n" - "=================================================\n", - Child1.get_model("compact", true), - Child1.Tree.max_depth(), Child1.Tree.size(), - Child2.get_model("compact", true), - Child2.Tree.max_depth(), Child2.Tree.size() - ); - - // Original didn't change - ASSERT_TRUE(PRG1_model == PRG1.get_model("compact", true)); - ASSERT_TRUE(PRG2_model == PRG2.get_model("compact", true)); - - // Child1 is within restrictions - ASSERT_TRUE(Child1.size() > 0); - ASSERT_TRUE(Child1.size() <= s); - ASSERT_TRUE(Child1.Tree.size() > 0); - ASSERT_TRUE(Child1.Tree.size() <= s); - - ASSERT_TRUE(Child1.Tree.max_depth() >= 0); - ASSERT_TRUE(Child1.Tree.max_depth() <= d); - - // Child2 is within restrictions - ASSERT_TRUE(Child2.size() > 0); - ASSERT_TRUE(Child2.size() <= s); - ASSERT_TRUE(Child2.Tree.size() > 0); - ASSERT_TRUE(Child2.Tree.size() <= s); - - ASSERT_TRUE(Child2.Tree.max_depth() >= 0); - ASSERT_TRUE(Child2.Tree.max_depth() <= d); - } - } -} - -TEST(Operators, CrossoverSizeAndDepthPARAMS) -{ - MatrixXf X(10,2); - ArrayXf y(10); - X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, - 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, - - 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, - 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; - - y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, - 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; - - Dataset data(X,y); - - SearchSpace SS; - SS.init(data); - - // split operator --> arity 3 - // prod operator --> arity 4 - int max_arity = 4; - - for (int d = 1; d < 10; ++d) - { - for (int s = 1; s < 10; ++s) - { - PARAMS["max_size"] = s; - PARAMS["max_depth"] = d; - - RegressorProgram PRG1 = SS.make_regressor(0, 0); - RegressorProgram PRG2 = SS.make_regressor(0, 0); - - auto PRG1_model = PRG1.get_model("compact", true); - auto PRG2_model = PRG2.get_model("compact", true); + fmt::print("cross\n"); + auto opt = PRG1.cross(PRG2); - auto Child1 = PRG1.cross(PRG2); - auto Child2 = PRG2.cross(PRG1); - - // Child1 is within restrictions - ASSERT_TRUE(Child1.size() > 0); - ASSERT_TRUE(Child1.size() <= s+max_arity); - ASSERT_TRUE(Child1.Tree.size() > 0); - ASSERT_TRUE(Child1.Tree.size() <= s+max_arity); + if (!opt){ + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Original model 1: {}\n" + "Original model 2: {}\n", + "Crossover failed to create a child", + d, s, + PRG1.get_model("compact", true), + PRG2.get_model("compact", true) + ); + } + else { + successes += 1; + auto Child = opt.value(); + fmt::print( + "Child Model : {}\n" + "Child Model depth: {}\n" + "Child Model size : {}\n" + "=================================================\n", + Child.get_model("compact", true), + Child.Tree.max_depth(), Child.Tree.size() + ); - ASSERT_TRUE(Child1.Tree.max_depth() >= 0); - ASSERT_TRUE(Child1.Tree.max_depth() <= d+1); + // Original didn't change + ASSERT_TRUE(PRG1_model == PRG1.get_model("compact", true)); + ASSERT_TRUE(PRG2_model == PRG2.get_model("compact", true)); - // Child2 is within restrictions - ASSERT_TRUE(Child2.size() > 0); - ASSERT_TRUE(Child2.size() <= s+max_arity); - ASSERT_TRUE(Child2.Tree.size() > 0); - ASSERT_TRUE(Child2.Tree.size() <= s+max_arity); + // Child is within restrictions + ASSERT_TRUE(Child.size() > 0); + ASSERT_TRUE(Child.size() <= s); + ASSERT_TRUE(Child.Tree.size() > 0); + ASSERT_TRUE(Child.Tree.size() <= s); - ASSERT_TRUE(Child2.Tree.max_depth() >= 0); - ASSERT_TRUE(Child2.Tree.max_depth() <= d+1); + ASSERT_TRUE(Child.Tree.max_depth() >= 0); + ASSERT_TRUE(Child.Tree.max_depth() <= d); + } } + ASSERT_TRUE(successes > 0); } -} +} \ No newline at end of file From e03a6fc84acfea6eda70daae5dd22238a1e96134 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 15:13:44 -0400 Subject: [PATCH 034/102] Updated MAB notebooks --- src/brush/D_MAB_experiments.ipynb | 7 +++++++ src/brush/D_TS_experiments.ipynb | 7 +++++++ 2 files changed, 14 insertions(+) diff --git a/src/brush/D_MAB_experiments.ipynb b/src/brush/D_MAB_experiments.ipynb index 95f97235..bae0dc58 100644 --- a/src/brush/D_MAB_experiments.ipynb +++ b/src/brush/D_MAB_experiments.ipynb @@ -730,6 +730,13 @@ "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", "generate_plots(est_mab)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb index f666b8bf..e10d95c3 100644 --- a/src/brush/D_TS_experiments.ipynb +++ b/src/brush/D_TS_experiments.ipynb @@ -718,6 +718,13 @@ "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", "generate_plots(est_mab)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From cb0c580040bdb679aaf5b2bdc8ea36e37b4d349b Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 5 Jun 2023 16:53:07 -0400 Subject: [PATCH 035/102] Rewrite to have same style as other mutations Other mutations use the optional value as an early stop, and have return true at the end of the function. --- src/variation.h | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/src/variation.h b/src/variation.h index 51920373..2a0523e7 100644 --- a/src/variation.h +++ b/src/variation.h @@ -38,14 +38,13 @@ inline bool point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) // terminal_weights, and maybe will return a Node. std::optional newNode = SS.get_node_like(spot.node->data); + if (!newNode) // newNode == std::nullopt + return false; + // if optional contains a Node, we access its contained value - if (newNode) { - Tree.replace(spot, *newNode); - return true; - } + Tree.replace(spot, *newNode); - // in case mutation fails - return false; + return true; } /// insert a node with spot as a child From 366d1cc284035199118f9e38be5103110e4657e3 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 6 Jun 2023 10:04:51 -0400 Subject: [PATCH 036/102] Update documentation --- src/program/node.h | 3 -- src/search_space.h | 4 +-- src/variation.h | 69 ++++++++++++++++++++++++++++++++++++---------- 3 files changed, 56 insertions(+), 20 deletions(-) diff --git a/src/program/node.h b/src/program/node.h index 4e4e2be9..0af3b3d6 100644 --- a/src/program/node.h +++ b/src/program/node.h @@ -244,10 +244,8 @@ struct Node { inline void set_feature(string f){ feature = f; }; inline string get_feature() const { return feature; }; - // TODO: use this in every occurence of is_weighted inline bool get_is_weighted() const {return this->is_weighted;}; inline void set_is_weighted(bool is_weighted){ - // cant change the weight of a boolean terminal if (IsWeighable(this->ret_type)) this->is_weighted = is_weighted; @@ -259,7 +257,6 @@ struct Node { string feature; }; -//TODO GUI: add nt to template as first argument, make these constexpr template inline auto Is(NodeType nt) -> bool { return ((nt == T) || ...); } diff --git a/src/search_space.h b/src/search_space.h index 9e655f4e..6c84609b 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -383,7 +383,7 @@ struct SearchSpace /// @param arg argument type to match /// @param terminal_compatible if true, the other args the returned operator takes must exist in the terminal types. /// @param max_args if zero, there is no limit on number of arguments of the operator. If not, the operator can have at most `max_args` arguments. - /// @return a matching operator. + /// @return `std::optional` that may contain a matching operator. std::optional get_op_with_arg(DataType ret, DataType arg, bool terminal_compatible=true, int max_arg=0) const @@ -451,7 +451,7 @@ struct SearchSpace /// @brief get a node with a signature matching `node` /// @param node the node to match - /// @return a Node + /// @return `std::optional` that may contain a Node std::optional get_node_like(Node node) const { if (Is(node.node_type)){ diff --git a/src/variation.h b/src/variation.h index 2a0523e7..58787c84 100644 --- a/src/variation.h +++ b/src/variation.h @@ -29,7 +29,11 @@ namespace variation { typedef tree::pre_order_iterator Iter; -/// point mutation: replace node with same typed node +/// @brief replace node with same typed node +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to sample a node like `spot` +/// @return boolean indicating the success (true) or fail (false) of the operation inline bool point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "point mutation\n"; @@ -47,7 +51,11 @@ inline bool point_mutation(tree& Tree, Iter spot, const SearchSpace& SS) return true; } -/// insert a node with spot as a child +/// @brief insert a node with spot as a child +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to sample a node like `spot` +/// @return boolean indicating the success (true) or fail (false) of the operation inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "insert mutation\n"; @@ -90,17 +98,21 @@ inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) return true; } -/// delete subtree and replace it with a terminal of the same return type +/// @brief delete subtree and replace it with a terminal of the same return type +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to sample a node like `spot` +/// @return boolean indicating the success (true) or fail (false) of the operation inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "delete mutation\n"; // get_terminal will sample based on terminal_weights + // TODO: this may fail. I need to return optional here as well auto terminal = SS.get_terminal(spot.node->data.ret_type); Tree.erase_children(spot); - // TODO: this may fail. I need to return optional here as well Tree.replace(spot, terminal); return true; @@ -110,6 +122,7 @@ inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) /// @param Tree the program tree /// @param spot an iterator to the node that is being mutated /// @param SS the search space (unused) +/// @return boolean indicating the success (true) or fail (false) of the operation inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { spot.node->data.set_is_weighted(!spot.node->data.get_is_weighted()); @@ -118,7 +131,7 @@ inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpac } /** - * @brief Mutate a program. + * @brief Stochastically mutate a program. * * Types of mutation: * @@ -127,18 +140,26 @@ inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpac * - deletion mutation deletes a node * - toggle_weight mutation turns a node's weight on or off. * - * Every mutation has a probability of occur based on global parameters. The - * place where the mutation will take place is sampled based on attribute + * Every mutation has a probability (weight) based on global parameters. The + * spot where the mutation will take place is sampled based on attribute * `get_prob_change` of each node in the tree. Inside each type of mutation, * when a new node is inserted, it is sampled based on `terminal_weights`. * - * By default, all probability distributions are uniform, but they can be - * dynamically optimized based on a Multi-Armed Bandit. + * Due to the stochastic behavior, and the several sampling steps, it may come to + * a case where the search space does not hold any possible modification to do in + * the program. In this case, the method returns `std::nullopt` (and has overloads + * so it can be used in a boolean context). + * + * If the mutation succeeds, the mutated program can be accessed through the + * `.value()` attribute of the `std::optional`. + * + * This means that, if you use the mutation as `auto opt = mutate(parent, SS)`, + * either `opt==false` or `opt.value()` contains the child program. * * @tparam T program type * @param parent the program to be mutated * @param SS a search space - * @return `child`, the mutated program + * @return `std::optional` that may contain the child program of type `T` */ template std::optional> mutate(const Program& parent, const SearchSpace& SS) @@ -212,11 +233,29 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS } }; -/// @brief swaps subtrees between root and other, returning new program -/// @tparam T the program type -/// @param root the root parent -/// @param other the donating parent -/// @return new program of type `T` +/** + * @brief Stochastically swaps subtrees between root and other, returning a new program. + * + * The spot where the cross will take place in the `root` parent is sampled + * based on attribute `get_prob_change` of each node in the tree. After selecting + * the cross spot, the program will iterate through the `other` parent searching + * for all compatible sub-trees to replace. + * + * Due to the stochastic behavior, it may come to a case where there is no + * candidate to replace the spot node. In this case, the method returns + * `std::nullopt` (and has overloads so it can be used in a boolean context). + * + * If the cross succeeds, the child program can be accessed through the + * `.value()` attribute of the `std::optional`. + * + * This means that, if you use the cross as `auto opt = mutate(parent, SS)`, + * either `opt==false` or `opt.value()` contains the child. + * + * @tparam T the program type + * @param root the root parent + * @param other the donating parent + * @return `std::optional` that may contain the child program of type `T` + */ template std::optional> cross(const Program& root, const Program& other) { From 5e81e8e1dd301b210ed8921bffbaa25ab533996a Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 6 Jun 2023 15:21:24 -0400 Subject: [PATCH 037/102] Fixed crash caused by wrong number of values passed to y --- tests/cpp/test_data.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index e3d7c9cb..f997b2a6 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -20,7 +20,7 @@ TEST(Data, MixedVariableTypes) X.transposeInPlace(); - ArrayXf y(5); + ArrayXf y(3); y << 6.1, 7.7, -4.2; // y = x_0 + x_1 + x_2 From 7beb03ae1118ac0571dd3e0244c713e2d5aaba30 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 7 Jun 2023 10:43:27 -0400 Subject: [PATCH 038/102] Rename get_X to sample_X and make them return std::optional --- src/search_space.h | 279 ++++++++++++++++++++++++++++++--------------- src/variation.h | 33 ++++-- 2 files changed, 208 insertions(+), 104 deletions(-) diff --git a/src/search_space.h b/src/search_space.h index 6c84609b..9a2d5edf 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -68,7 +68,8 @@ extern std::unordered_map ArgsName; * - assertion check to make sure there is at least one operator that * returns the output type of the model. * - * When sampling in the search space, some methods can fail to return a + * When sampling in the search space (using any of the sampling functions + * `sample_op` or `sample_terminal`), some methods can fail to return a * value --- given a specific set of parameters to a function, the candidate * solutions set may be empty --- and, for these methods, the return type is * either a valid value, or a `std::nullopt`. This is controlled wrapping @@ -249,60 +250,6 @@ struct SearchSpace template Node get(NodeType type, DataType R, S sig){ return get(type, R, sig.hash()); }; - /// @brief Get a specific node type that matches a return value. - /// @param type the node type - /// @param R the return type - /// @return A Node of type `type` with return type `R`. - Node get(NodeType type, DataType R) - { - check(R); - auto ret_match = node_map.at(R); - vector matches; - vector weights; - for (const auto& kv: ret_match) - { - auto arg_hash = kv.first; - auto node_type_map = kv.second; - if (node_type_map.find(type) != node_type_map.end()) - { - matches.push_back(node_type_map.at(type)); - weights.push_back(node_map_weights.at(R).at(arg_hash).at(type)); - } - } - - return (*r.select_randomly(matches.begin(), - matches.end(), - weights.begin(), - weights.end())); - }; - - /// get a random terminal - Node get_terminal() const - { - //TODO: match terminal args_type (probably '{}' or something?) - // make a separate terminal_map - auto match = *r.select_randomly(terminal_map.begin(), terminal_map.end()); - return *r.select_randomly( - match.second.begin(), match.second.end(), - terminal_weights.at(match.first).begin(), - terminal_weights.at(match.first).end() - ); - }; - - /// get a terminal with return type `R` - Node get_terminal(DataType R) const - { - if (terminal_map.find(R) == terminal_map.end()){ - auto msg = fmt::format("{} not in terminal_map\n",R); - HANDLE_ERROR_THROW(msg); - } - auto rval = *r.select_randomly(terminal_map.at(R).begin(), - terminal_map.at(R).end(), - terminal_weights.at(R).begin(), - terminal_weights.at(R).end()); - return rval; - }; - /// @brief get weights of the return types /// @return a weight vector, each element corresponding to a return type. vector get_weights() const @@ -354,12 +301,79 @@ struct SearchSpace return v; }; + /// @brief Get a random terminal + /// @return `std::optional` that may contain a terminal Node. + std::optional sample_terminal() const + { + //TODO: match terminal args_type (probably '{}' or something?) + // make a separate terminal_map + + // We'll make terminal types to have its weights proportional to the + // DataTypes Weights they hold + vector data_type_weights(terminal_weights.size()); + std::transform( + terminal_weights.begin(), + terminal_weights.end(), + data_type_weights.begin(), + [](const auto& tw){ + return std::reduce(tw.second.begin(), tw.second.end()); } + ); + + // empty solution space, or candidates have weight zero + if (std::all_of(data_type_weights.begin(), + data_type_weights.end(), + [](const auto& weight) { return weight<=0.0; })) + { + return std::nullopt; + } + + // If we got this far, then it is garanteed that we'll return something + // The match take into account datatypes with non-zero weights + auto match = *r.select_randomly( + terminal_map.begin(), + terminal_map.end(), + data_type_weights.begin(), + data_type_weights.end() + ); + + return *r.select_randomly( + match.second.begin(), match.second.end(), + terminal_weights.at(match.first).begin(), + terminal_weights.at(match.first).end() + ); + }; + + /// @brief Get a random terminal with return type `R` + /// @return `std::optional` that may contain a terminal Node of type `R`. + std::optional sample_terminal(DataType R) const + { + // should I keep doing this check? + // if (terminal_map.find(R) == terminal_map.end()){ + // auto msg = fmt::format("{} not in terminal_map\n",R); + // HANDLE_ERROR_THROW(msg); + // } + + if ( (terminal_map.find(R) == terminal_map.end()) + || (std::all_of(terminal_weights.at(R).begin(), + terminal_weights.at(R).end(), + [](int i) { return i==0.0; })) ) + { + return std::nullopt; + } + + return *r.select_randomly(terminal_map.at(R).begin(), + terminal_map.at(R).end(), + terminal_weights.at(R).begin(), + terminal_weights.at(R).end()); + }; + /// @brief get an operator matching return type `ret`. /// @param ret return type - /// @return a randomly chosen operator - Node get_op(DataType ret) const + /// @return `std::optional` that may contain a randomly chosen operator matching return type `ret` + Node sample_op(DataType ret) const { - check(ret); + // check(ret); + //TODO: match terminal args_type (probably '{}' or something?) auto ret_match = node_map.at(ret); @@ -377,35 +391,74 @@ struct SearchSpace name_w.end())).second; }; - + /// @brief Get a specific node type that matches a return value. + /// @param type the node type + /// @param R the return type + /// @return `std::optional` that may contain a Node of type `type` with return type `R`. + std::optional sample_op(NodeType type, DataType R) + { + // check(R); + + auto ret_match = node_map.at(R); + + vector matches; + vector weights; + for (const auto& kv: ret_match) + { + auto arg_hash = kv.first; + auto node_type_map = kv.second; + if (node_type_map.find(type) != node_type_map.end()) + { + matches.push_back(node_type_map.at(type)); + weights.push_back(node_map_weights.at(R).at(arg_hash).at(type)); + } + } + + // empty solution space, or candidates have weight zero + if ( (weights.size()==0) + || (std::all_of(weights.begin(), + weights.end(), + [](float i) { return i<=0.0; })) ) + { + return std::nullopt; + } + + return (*r.select_randomly(matches.begin(), + matches.end(), + weights.begin(), + weights.end())); + }; + /// @brief get operator with at least one argument matching arg /// @param ret return type /// @param arg argument type to match /// @param terminal_compatible if true, the other args the returned operator takes must exist in the terminal types. /// @param max_args if zero, there is no limit on number of arguments of the operator. If not, the operator can have at most `max_args` arguments. - /// @return `std::optional` that may contain a matching operator. - std::optional get_op_with_arg(DataType ret, DataType arg, + /// @return `std::optional` that may contain a matching operator respecting all restrictions. + std::optional sample_op_with_arg(DataType ret, DataType arg, bool terminal_compatible=true, - int max_arg=0) const + int max_args=0) const { - // TODO: take out the size limit here and add the return std::nullopt when it fails // thoughts (TODO): // this could be templated by return type and arg. although the lookup in the map should be // fairly fast. //TODO: these needs to be overhauled - // fmt::print("get_op_with_arg"); + // fmt::print("sample_op_with_arg"); check(ret); auto args_map = node_map.at(ret); vector matches; - vector weights; - vector invalids; + vector weights; for (const auto& [args_type, name_map]: args_map) { for (const auto& [name, node]: name_map) { auto node_arg_types = node.get_arg_types(); + + // has no size limit (max_arg_count==0) or the number of + // arguments woudn't exceed the maximum number of arguments + auto within_size_limit = !(max_args) || (node.get_arg_count() <= max_args); - if ( in(node_arg_types, arg) ) { + if ( in(node_arg_types, arg) && within_size_limit) { // if checking terminal compatibility, make sure there's // a compatible terminal for the node's other arguments if (terminal_compatible) { @@ -424,26 +477,18 @@ struct SearchSpace // if we made it this far, include the node as a match! matches.push_back(node); weights.push_back(node_map_weights.at(ret).at(args_type).at(name)); - - // saving for future checking - // has no size limit (max_arg is 0) or the number of - // arguments woudn't exceed the maximum number of arguments - invalids.push_back(!(max_arg) || (node.get_arg_count() <= max_arg)); } } } - // If one or more nodes are respecting the size limit, we'll focus only - // on them. We do that by setting the probabilities of invalid nodes - // to zero. If all of them are invalid, we relax size restriction ( - // we ignore it) to avoid selecting over an empty collection. - if (std::find(invalids.begin(), invalids.end(), false) != invalids.end()) { - std::transform(weights.begin(), weights.end(), - invalids.begin(), weights.begin(), - [](int weight, bool invalid) { - return invalid ? 0.0 : weight; - }); - } + // empty solution space, or candidates have weight zero + if ( (weights.size()==0) + || (std::all_of(weights.begin(), + weights.end(), + [](float i) { return i<=0.0; })) ) + { + return std::nullopt; + } return (*r.select_randomly(matches.begin(), matches.end(), weights.begin(), weights.end())); @@ -455,11 +500,21 @@ struct SearchSpace std::optional get_node_like(Node node) const { if (Is(node.node_type)){ - return get_terminal(node.ret_type); + return sample_terminal(node.ret_type); } auto matches = node_map.at(node.ret_type).at(node.args_type()); auto match_weights = get_weights(node.ret_type, node.args_type()); + + // empty solution space, or candidates have weight zero + if ( (match_weights.size()==0) + || (std::all_of(match_weights.begin(), + match_weights.end(), + [](float i) { return i<=0.0; })) ) + { + return std::nullopt; + } + return (*r.select_randomly(matches.begin(), matches.end(), match_weights.begin(), @@ -594,10 +649,23 @@ P SearchSpace::make_program(int max_d, int max_size) if (max_size == 1) { - auto root = Tree.insert(Tree.begin(), get_terminal(root_type)); + // auto root = Tree.insert(Tree.begin(), sample_terminal(root_type)); + + // We can only have a terminal here, but the terminal must be compatible + auto opt = sample_terminal(root_type); + + if (!opt){ + auto msg = fmt::format("Program with size=1 could not be created. " + "The search space does not contain any terminal with data type {}./n", + root_type); + HANDLE_ERROR_THROW(msg); + } + + auto root = Tree.insert(Tree.begin(), opt.value()); } - else + else // Our program can (and will) be grater than 1 node { + // building the root node for each program case Node n; if (P::program_type == ProgramType::BinaryClassifier) { @@ -607,12 +675,12 @@ P SearchSpace::make_program(int max_d, int max_size) } else if (P::program_type == ProgramType::MulticlassClassifier) { - n = get(NodeType::Softmax, DataType::MatrixF); + n = get(NodeType::Softmax, DataType::MatrixF, Signature()); n.set_prob_change(0.0); n.fixed=true; } else - n = get_op(root_type); + n = sample_op(root_type); /* cout << "chose " << n.name << endl; */ // auto spot = Tree.set_head(n); @@ -630,6 +698,7 @@ P SearchSpace::make_program(int max_d, int max_size) queue.push_back(make_tuple(child_spot, a, d)); } + // Now we actually start the PTC2 procedure to create the program tree /* cout << "queue size: " << queue.size() << endl; */ /* cout << "entering first while loop...\n"; */ while (queue.size() + s < max_size && queue.size() > 0) @@ -642,15 +711,26 @@ P SearchSpace::make_program(int max_d, int max_size) { // choose terminal of matching type /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ - // qspot = get_terminal(t); - Tree.replace(qspot, get_terminal(t)); - // Tree.append_child(qspot, get_terminal(t)); + // qspot = sample_terminal(t); + // Tree.replace(qspot, sample_terminal(t)); + // Tree.append_child(qspot, sample_terminal(t)); + + auto opt = sample_terminal(t); + + // TODO: we can get an infinite loop here. Maybe I should put a constant (or a neutral element of the node type) + if (!opt) { // lets push back and try again later + queue.push_back(make_tuple(qspot, t, d)); + continue; + } + + // If we successfully get a terminal, use it + Tree.replace(qspot, opt.value()); } else { //choose a nonterminal of matching type /* cout << "getting op of type " << DataTypeName[t] << endl; */ - auto n = get_op(t); + auto n = sample_op(t); /* cout << "chose " << n.name << endl; */ // TreeIter new_spot = Tree.append_child(qspot, n); // qspot = n; @@ -661,6 +741,7 @@ P SearchSpace::make_program(int max_d, int max_size) /* cout << "queing a node of type " << DataTypeName[a] << endl; */ // queue.push_back(make_tuple(new_spot, a, d+1)); auto child_spot = Tree.append_child(newspot); + queue.push_back(make_tuple(child_spot, a, d+1)); } } @@ -678,10 +759,18 @@ P SearchSpace::make_program(int max_d, int max_size) auto [qspot, t, d] = RandomDequeue(queue); /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ - // Tree.append_child(qspot, get_terminal(t)); - // qspot = get_terminal(t); - auto newspot = Tree.replace(qspot, get_terminal(t)); + // Tree.append_child(qspot, sample_terminal(t)); + // qspot = sample_terminal(t); + // auto newspot = Tree.replace(qspot, sample_terminal(t)); + + auto opt = sample_terminal(t); + + if (!opt) { // set push back and try again later + queue.push_back(make_tuple(qspot, t, d)); + continue; + } + auto newspot = Tree.replace(qspot, opt.value()); } } /* cout << "final tree:\n" */ diff --git a/src/variation.h b/src/variation.h index 58787c84..5e235411 100644 --- a/src/variation.h +++ b/src/variation.h @@ -68,7 +68,7 @@ inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) // size restriction, which will be relaxed here (just as it is in the PTC2 // algorithm). This mutation can create a new expression that exceeds the // maximum size by the highest arity among the operators. - std::optional n = SS.get_op_with_arg(spot_type, spot_type, true, + std::optional n = SS.sample_op_with_arg(spot_type, spot_type, true, PARAMS["max_size"].get()-Tree.size()-1); if (!n) // there is no operator with compatible arguments @@ -84,15 +84,27 @@ inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) if (spot_filled) { // if spot is in its child position, append children. - // reminding that get_terminal may fail as well - Tree.append_child(parent_node, SS.get_terminal(a)); + // TODO: reminding that sample_terminal may fail as well + auto opt = SS.sample_terminal(a); + + if (!opt) + return false; + + Tree.append_child(parent_node, opt.value()); } // if types match, treat this spot as filled by the spot node else if (a == spot_type) spot_filled = true; // otherwise, add siblings before spot node - else - Tree.insert(spot, SS.get_terminal(a)); + else { + auto opt = SS.sample_terminal(a); + + if (!opt) + return false; + + Tree.insert(spot, opt.value()); + } + } return true; @@ -107,13 +119,16 @@ inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { // cout << "delete mutation\n"; - // get_terminal will sample based on terminal_weights - // TODO: this may fail. I need to return optional here as well - auto terminal = SS.get_terminal(spot.node->data.ret_type); + // sample_terminal will sample based on terminal_weights. If it succeeds, + // then the new terminal will be in `opt.value()` + auto opt = SS.sample_terminal(spot.node->data.ret_type); + if (!opt) // there is no terminal with compatible arguments + return false; + Tree.erase_children(spot); - Tree.replace(spot, terminal); + Tree.replace(spot, opt.value()); return true; }; From da1b96dba0774a21e7b995f569ac5bb59332501e Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 7 Jun 2023 11:16:25 -0400 Subject: [PATCH 039/102] Moved experimental notebooks to a dedicated repo --- src/brush/D_MAB_experiments.ipynb | 761 ------------------------------ src/brush/D_TS_experiments.ipynb | 749 ----------------------------- 2 files changed, 1510 deletions(-) delete mode 100644 src/brush/D_MAB_experiments.ipynb delete mode 100644 src/brush/D_TS_experiments.ipynb diff --git a/src/brush/D_MAB_experiments.ipynb b/src/brush/D_MAB_experiments.ipynb deleted file mode 100644 index 95f97235..00000000 --- a/src/brush/D_MAB_experiments.ipynb +++ /dev/null @@ -1,761 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Implementing D-MAB, as described in DaCosta et al. - 2008 - Adaptive operator selection with dynamic multi-arm**\n", - "\n", - "> (hybrid between UCB1 and Page-Hinkley (PH) test)\n", - "\n", - "D-MAB maintains four indicators for each arm $i$:\n", - "1. number $n_{i, t}$ of times $i$-th arm has been played up to time $t$;\n", - "2. the average empirical reward $\\widehat{p}_{j, t}$ at time $t$;\n", - "3. the average and maximum deviation $m_i$ and $M_i$ involved in the PH test, initialized to $0$ and updated as detailed below. At each time step $t$:\n", - "\n", - "D-MAB selects the arm $i$ that maximizes equation 1:\n", - "\n", - "$$\\widehat{p}_{i, t} + \\sqrt{\\frac{2 \\log \\sum_{k}n_{k, t}}{n_{i, t}}}$$\n", - "\n", - "> Notice that the sum of the number of times each arm was pulled is equal to the time $\\sum_{k}n_{k, t} = t$, but since their algorithm resets the number of picks, we need to go with the summation. \n", - "\n", - "and receives some reward $r_t$, drawn after reward distribution $p_{i, t}$.\n", - "\n", - "> I think there is a typo in the eq. 1 on the paper. I replaced $j$ with $i$ in the lower indexes.\n", - "\n", - "The four indicators are updated accordingly:\n", - "\n", - "- $\\widehat{p}_{i, t} :=\\frac{1}{n_{i, t} + 1}(n_{i, t}\\widehat{p}_{i, t} + r_t)$\n", - "- $n_{i, t} := n_{i, t}+1$\n", - "- $m_i := m_i + (\\widehat{p}_{i, t} - r_t + \\delta)$\n", - "- $M_i:= \\text{max}(M_i, m_i)$\n", - "\n", - "And if the PH test is triggered ($M_i - m_i > \\lambda$), the bandit is restarted, i.e., for all arms, all indicators are set to zero (the authors argue that, empirically, resetting the values is more robust than decreasing them with some mechanism such as probability matching).\n", - "\n", - "> I will reset to 1 instead of 0 (as the original paper does) to avoid divide by zero when calculating UCB1.\n", - "\n", - "The PH test is a standard test for the change hypothesis. It works by monitoring the difference between $M_i$ and $m_i$, and when the difference is greater than some uuser-specified threshold $\\lambda$, the PH test is triggered, i.e., it is considered that the Change hypothesis holds.\n", - "\n", - "Parameter $\\lambda$ controls the trade-off between false alarms and un-noticed changes. Parameter $\\delta$ enforces the robustness of the test when dealing with slowly varying environments.\n", - "\n", - "We also need a scaling mechanism to control the Exploration _versus_ Exploitation balance. They proposed two, from which I will focus on the first: Multiplicative Scaling (cUCB). **It consists on multiplying all rewards by a fixed user-defined parameter $C_{M-\\text{scale}}$.\n", - "\n", - "This way, we need to give to our D-MAB 3 parameters: $\\lambda$, $\\delta$, and $C_{M - \\text{scale}}$. In the paper they did a sensitivity analysis of the parameters, but I think they should be fine tuned for each specific data set.\n", - "\n", - "> Brush originally sample the mutations using an uniform distribution. This algorithm chooses the arms using an deterministic approach --- the one that maximizes the UCB1 score. Somehow we need to convert them to have a transparent implementation to the user." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install matplotlib > /dev/null\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "\n", - "import numpy as np\n", - "import time\n", - "import pandas as pd\n", - "\n", - "from brush.estimator import BrushEstimator\n", - "from sklearn.base import ClassifierMixin, RegressorMixin\n", - "from deap import creator\n", - "import _brush\n", - "from deap_api import nsga2 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class D_MAB:\n", - " def __init__(self, num_bandits, delta=0.15, lmbda=0.25):\n", - " self.num_bandits = num_bandits\n", - "\n", - " # Store learner status when the update function is called\n", - " self.pull_history = {\n", - " c:[] for c in ['t', 'arm idx', 'reward', 'update'] + \n", - " [f'UCB1 {i}' for i in range(num_bandits)] + \n", - " [f'weight {i}' for i in range(num_bandits)] } \n", - "\n", - " # This is the probability that should be used to update brush probs\n", - " self._probabilities = np.ones(num_bandits)/num_bandits\n", - "\n", - " self.delta = delta # how to define these values???\n", - " self.lmbda = lmbda\n", - "\n", - " self._reset_indicators() # Creating the indicators \n", - "\n", - " def _reset_indicators(self):\n", - " self._avg_rewards = np.zeros(self.num_bandits)\n", - " self._num_pulls = np.zeros(self.num_bandits)\n", - " self._avg_deviations = np.zeros(self.num_bandits)\n", - " self._max_deviations = np.zeros(self.num_bandits)\n", - "\n", - " def _calculate_UCB1s(self):\n", - " # We need that the reward is in [0, 1] (not avg_reward, as it seems to\n", - " # render worse results). It looks like normalizing the rewards is a\n", - " # problem: reward should be [0, 1], but not necessarely avg_rewards too\n", - " rs = self._avg_rewards\n", - " ns = self._num_pulls\n", - " \n", - " UCB1s = rs + np.sqrt(2*np.log1p(sum(ns))/(ns+1))\n", - "\n", - " return UCB1s\n", - "\n", - " @property\n", - " def probabilities(self):\n", - " # How to transform our UCB1 scores into node probabilities?\n", - " return self._probabilities\n", - " \n", - " @probabilities.setter\n", - " def probabilities(self, new_probabilities):\n", - " if len(self._probabilities)==len(new_probabilities):\n", - " self._probabilities = new_probabilities\n", - " else:\n", - " print(f\"New probabilities must have size {self.num_bandits}\")\n", - "\n", - " def choose_arm(self):\n", - " \"\"\"Uses previous recordings of rewards to pick the arm that maximizes\n", - " the UCB1 function. The choice is made in a deterministic way.\n", - " \"\"\"\n", - "\n", - " UCB1s = self._calculate_UCB1s()\n", - "\n", - " return np.nanargmax(UCB1s)\n", - "\n", - " def update(self, arm_idx, reward):\n", - " # Here we expect that the reward was already scaled to be in the \n", - " # interval [0, 1] (in the original paper, they sugest using a scaling\n", - " # factor as an hyperparameter).\n", - " self.pull_history['t'].append( len(self.pull_history['t']) )\n", - " self.pull_history['arm idx'].append( arm_idx )\n", - " self.pull_history['reward'].append( reward )\n", - "\n", - " # Updating counters\n", - " self._avg_rewards[arm_idx] = \\\n", - " (self._num_pulls[arm_idx]*self._avg_rewards[arm_idx] + reward)/(self._num_pulls[arm_idx]+1)\n", - " self._avg_deviations[arm_idx] = \\\n", - " self._avg_deviations[arm_idx] + (self._avg_rewards[arm_idx] - reward + self.delta) \n", - " self._num_pulls[arm_idx] = self._num_pulls[arm_idx] +1\n", - " self._max_deviations[arm_idx] = \\\n", - " np.maximum(self._max_deviations[arm_idx], self._avg_deviations[arm_idx])\n", - "\n", - " if (self._max_deviations[arm_idx] - self._avg_deviations[arm_idx] > self.lmbda):\n", - " self._reset_indicators()\n", - " self.pull_history['update'].append( 1 )\n", - " else:\n", - " self.pull_history['update'].append( 0 )\n", - "\n", - " self._probabilities = self._calculate_UCB1s()\n", - "\n", - " for i, UCB1 in enumerate(self._calculate_UCB1s()):\n", - " self.pull_history[f'UCB1 {i}'].append( UCB1 )\n", - " self.pull_history[f'weight {i}'].append( self.probabilities[i] )\n", - "\n", - " return self" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_learner_history(learner, arm_labels=[]):\n", - "\n", - " # getting the labels to use in plots\n", - " if len(arm_labels) != learner.num_bandits:\n", - " arm_labels = [f'arm {i}' for i in range(learner.num_bandits)]\n", - "\n", - " # Setting up the figure layout\n", - " fig = plt.figure(figsize=(15, 10), tight_layout=True)\n", - " gs = gridspec.GridSpec(7, 6)\n", - "\n", - " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", - " \n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - "\n", - " data_total_pulls = np.array([total_pulls[k] for k in sorted(total_pulls)])\n", - " data_total_rewards = np.array([total_rewards[k] for k in sorted(total_rewards)])\n", - " data_total_failures = data_total_pulls-data_total_rewards\n", - "\n", - " ylim = np.maximum(data_total_rewards.max(), data_total_failures.max())\n", - "\n", - " axs = fig.add_subplot(gs[0:2, 4:])\n", - "\n", - " axs.bar(arm_labels, -1*data_total_failures, label=\"Null reward\")\n", - " axs.bar(arm_labels, data_total_rewards, label=\"Positive reward\")\n", - "\n", - " axs.set_xlabel(\"Arm\")\n", - " axs.set_ylim( (-1.05*ylim, 1.05*ylim) )\n", - " axs.legend()\n", - "\n", - " win_ratios = pd.DataFrame.from_dict({\n", - " 'arm' : arm_labels,\n", - " 'totpulls' : data_total_pulls,\n", - " '0 reward' : data_total_failures,\n", - " '+ reward' : data_total_rewards,\n", - " 'success%' : (data_total_rewards/(data_total_pulls)).round(2)\n", - " })\n", - "\n", - " axs = fig.add_subplot(gs[2:4, 4:])\n", - " axs.table(cellText=win_ratios.values, colLabels=win_ratios.columns, loc='center')\n", - " axs.axis('off')\n", - " axs.axis('tight')\n", - "\n", - " # Plotting rewards and pulls -----------------------------------------------\n", - " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", - " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", - " for i, row in learner_log.iterrows():\n", - " data[i+1, :] = data[i]\n", - " data[i+1, row['arm idx'].astype(int)] += 1\n", - "\n", - " axs = fig.add_subplot(gs[0:2, :4])\n", - " axs.plot(data, label=arm_labels)\n", - " axs.set_ylabel(\"Number of times mutation was used\")\n", - " axs.legend()\n", - "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - "\n", - " # Plotting alphas and betas ------------------------------------------------\n", - " for i, col in enumerate(['UCB1']):\n", - " columns = learner_log.columns[learner_log.columns.str.startswith(f'{col} ')]\n", - " labels = [f\"{col} {arm_labels[i]}\" for i in range(4)] \n", - " data = learner_log.loc[:, columns]\n", - "\n", - " axs = fig.add_subplot(gs[(i+1)*2:(i+1)*2+2, :4])\n", - " axs.plot(data, label=labels)\n", - " axs.set_ylabel(f\"{col}s\")\n", - " axs.legend()\n", - "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " axs.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - " \n", - " axs.set_xlabel(\"Evaluations\") # Label only on last plot\n", - "\n", - " plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below I'll create a simple bandit configuration so we can do a sanity check of our `D_MAB` implementation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity checks\n", - "class Bandits:\n", - " def __init__(self, reward_prob):\n", - " # Implementing simple bandits.\n", - " self.reward_prob = reward_prob # True reward prob., which learner shoudn't know\n", - " self.n_bandits = len(reward_prob) \n", - "\n", - " def pull(self, arm_idx):\n", - " # Sampling over a normal distr. with mu=0 and var=1\n", - " result = np.random.randn()\n", - " \n", - " # return a positive or nullary reward (Bernoulli random variable).\n", - " return 1 if result > self.reward_prob[arm_idx] else 0\n", - "\n", - "for probs, descr, expec in [\n", - " (np.array([ 1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs' , 'similar amount of pulls for each arm' ),\n", - " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob' , 'more pulls for first arm, less pulls for last'),\n", - " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd approx 4th > 1st > 3rd' ),\n", - "]:\n", - " bandits = Bandits(probs)\n", - "\n", - " print(\"------------------------ optimizing ------------------------\")\n", - "\n", - " learner = D_MAB(4)\n", - " for i in range(1000):\n", - " arm_idx = learner.choose_arm()\n", - " reward = bandits.pull(arm_idx)\n", - "\n", - " learner.update(arm_idx, reward) \n", - "\n", - " plot_learner_history(learner)\n", - " print(f\"(it was expected: {expec})\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ok, so the D-MAB seems to work. Now let's add this MAB inside mutation to update PARAMS option and control dinamically the mutaiton probabilities during evolution.\n", - "\n", - "We can import the brush estimator and replace the `_mutation` by a custom function. Ideally, to use this python MAB optimizer, we need to have an object created to keep track of the variables, and the object needs to wrap the _pull_ action, as well as evaluating the reward based on the result.\n", - "\n", - "> we'll need to do a _gambiarra_ to know which mutation is used so we can correctly update `D_MAB`. All MAB logic is implemented in python, and we chose the mutation in python as well. To make sure a specific mutation was used, we force it to happen by setting others' weights to zero. this way we know exactly what happened in the C++ code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class BrushEstimatorMod(BrushEstimator): # Modifying brush estimator\n", - " def __init__(self, **kwargs):\n", - " super().__init__(**kwargs)\n", - "\n", - " # mutations optimized by the learner. Learner arms correspond to\n", - " # these mutations in the order they appear here\n", - " self.mutations_ = ['point', 'insert', 'delete', 'toggle_weight']\n", - "\n", - " # Whether the learner should update after each mutation, or if it should\n", - " # update only after a certain number of evaluations.\n", - " # Otherwise, it will\n", - " # store all rewards in gen_rewards_ (which is reseted at the beggining\n", - " # of every generation) and do a batch of updates only after finishing\n", - " # mutating the solutions.\n", - " self.batch_size_ = self.pop_size #\n", - " self.batch_rewards_ = []\n", - "\n", - " def _mutate(self, ind1):\n", - " # Overriding the mutation so it updates our sampling method. Doing the\n", - " # logic on the python-side for now.\n", - "\n", - " # Creating a wrapper for mutation to be able to control what is happening\n", - " # in the C++ code (this should be prettier in a future implementation)\n", - " \n", - " params = self.get_params()\n", - " \n", - " mutation_idx = self.learner_.choose_arm()\n", - "\n", - " for i, m in enumerate(self.mutations_):\n", - " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", - "\n", - " _brush.set_params(params)\n", - "\n", - " opt = ind1.prg.mutate()\n", - "\n", - " if opt:\n", - " offspring = creator.Individual(opt)\n", - " # print(\"mutation\")\n", - " # print(ind1.prg.get_model())\n", - " # print(offspring.prg.get_model())\n", - "\n", - " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", - " \n", - " # We compare fitnesses using the deap overloaded operators\n", - " # from the docs: When comparing fitness values that are **minimized**,\n", - " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", - " # (this means that this comparison should work agnostic of min/max problems,\n", - " # or even a single-objective or multi-objective problem)\n", - " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", - " \n", - " # if not ignore_this_time:\n", - " # self.batch_rewards_.append( (mutation_idx, reward) )\n", - "\n", - " self.batch_rewards_.append( (mutation_idx, reward) )\n", - "\n", - " if len(self.batch_rewards_) >= self.batch_size_:\n", - " for (mutation_idx, reward) in self.batch_rewards_:\n", - " self.learner_.update(mutation_idx, reward)\n", - " self.batch_rewards_ = []\n", - " \n", - " return offspring\n", - "\n", - " return None\n", - " \n", - " def fit(self, X, y):\n", - "\n", - " _brush.set_params(self.get_params())\n", - "\n", - " self.data_ = self._make_data(X,y)\n", - " # self.data_.print()\n", - "\n", - " # set n classes if relevant\n", - " if self.mode==\"classification\":\n", - " self.n_classes_ = len(np.unique(y))\n", - "\n", - " # We have 4 different mutations, and the learner will learn to choose\n", - " # between these options by maximizing the reward when using each one\n", - " self.learner_ = D_MAB(4)\n", - "\n", - " if isinstance(self.functions, list):\n", - " self.functions_ = {k:1.0 for k in self.functions}\n", - " else:\n", - " self.functions_ = self.functions\n", - "\n", - " self.search_space_ = _brush.SearchSpace(self.data_, self.functions_)\n", - "\n", - " self.toolbox_ = self._setup_toolbox(data=self.data_)\n", - "\n", - " archive, logbook = nsga2(\n", - " self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", - "\n", - " self.archive_ = archive\n", - " self.logbook_ = logbook\n", - " self.best_estimator_ = self.archive_[0].prg\n", - "\n", - " return self\n", - " \n", - "\n", - "class BrushClassifierMod(BrushEstimatorMod,ClassifierMixin):\n", - " def __init__( self, **kwargs):\n", - " super().__init__(mode='classification',**kwargs)\n", - "\n", - " def _fitness_function(self, ind, data: _brush.Dataset):\n", - " ind.prg.fit(data)\n", - " return (\n", - " np.abs(data.y-ind.prg.predict(data)).sum(), \n", - " ind.prg.size()\n", - " )\n", - " \n", - " def _make_individual(self):\n", - " return creator.Individual(\n", - " self.search_space_.make_classifier(self.max_depth, self.max_size)\n", - " if self.n_classes_ == 2 else\n", - " self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size)\n", - " )\n", - "\n", - " def predict_proba(self, X):\n", - " data = self._make_data(X)\n", - " return self.best_estimator_.predict_proba(data)\n", - "\n", - "\n", - "class BrushRegressorMod(BrushEstimatorMod, RegressorMixin):\n", - " def __init__(self, **kwargs):\n", - " super().__init__(mode='regressor',**kwargs)\n", - "\n", - " def _fitness_function(self, ind, data: _brush.Dataset):\n", - " ind.prg.fit(data)\n", - " return (\n", - " np.sum((data.y- ind.prg.predict(data))**2),\n", - " ind.prg.size()\n", - " )\n", - "\n", - " def _make_individual(self):\n", - " return creator.Individual(\n", - " self.search_space_.make_regressor(self.max_depth, self.max_size)\n", - " )" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regression problem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", - "if __name__ == '__main__':\n", - " from brush import BrushRegressor\n", - " \n", - " import warnings\n", - " warnings.filterwarnings(\"ignore\")\n", - "\n", - " from pmlb import fetch_data\n", - "\n", - " # X, y = fetch_data('537_houses', return_X_y=True, local_cache_dir='./')\n", - "\n", - " data = pd.read_csv('../../docs/examples/datasets/d_example_patients.csv')\n", - " X = data.drop(columns='target')\n", - " y = data['target']\n", - "\n", - " # data = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", - " # X = data.drop(columns='target')\n", - " # y = data['target']\n", - "\n", - " # data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", - " # X = data.drop(columns='target')\n", - " # y = data['target']\n", - "\n", - " kwargs = {\n", - " 'verbosity' : False,\n", - " 'pop_size' : 60,\n", - " 'max_gen' : 300,\n", - " 'max_depth' : 10,\n", - " 'max_size' : 20,\n", - " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", - " }\n", - "\n", - " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", - " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", - " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", - " ('Modified', 'point mutation calls'),\n", - " ('Modified', 'insert mutation calls'),\n", - " ('Modified', 'delete mutation calls'),\n", - " ('Modified', 'toggle_weight mutation calls')],\n", - " names=('Brush version', 'metric')))\n", - " \n", - " est_mab = None\n", - " for i in range(30):\n", - " try:\n", - " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - "\n", - " est_start_time = time.time()\n", - " est = BrushRegressor(**kwargs).fit(X,y)\n", - " est_end_time = time.time() - est_start_time\n", - "\n", - " est_mab_start_time = time.time()\n", - " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", - " est_mab_end_time = time.time() - est_mab_start_time\n", - "\n", - " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - " \n", - " results.loc[f'run {i}'] = [\n", - " # Original implementation\n", - " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", - "\n", - " # Implementation using Dynamic Thompson Sampling\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", - " \n", - " # Mutation count\n", - " *total_pulls.values()]\n", - " except Exception as e:\n", - " print(e)\n", - "\n", - " # Showing results and statistics\n", - " display(results)\n", - " display(results.describe())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_plots(est_mab):\n", - "\n", - " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - "\n", - " # Setting up the figure layout\n", - " fig = plt.figure(figsize=(12, 6), tight_layout=True)\n", - " gs = gridspec.GridSpec(6, 6)\n", - "\n", - " # Approximating the percentage of usage for each generation ----------------\n", - " data = np.zeros( (est_mab.max_gen, 4) )\n", - " for g in range(est_mab.max_gen):\n", - " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", - " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", - "\n", - " df_in_range = learner_log.iloc[idx_start:idx_end]\n", - " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", - " for k, v in g_data.items():\n", - " data[g, k] = v\n", - "\n", - " axs = fig.add_subplot(gs[0:3, :3])\n", - " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", - "\n", - " axs.set_ylabel(\"Percentage of usage\")\n", - " axs.legend()\n", - "\n", - " # average Brush weights for each generation --------------------------------\n", - " data = np.zeros( (est_mab.max_gen, 4) )\n", - " for g in range(est_mab.max_gen):\n", - " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", - " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", - "\n", - " learner_log_in_range = learner_log.iloc[idx_start:idx_end]\n", - "\n", - " total_rewards = learner_log_in_range.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log_in_range['arm idx'].value_counts().to_dict()\n", - "\n", - " keys = total_pulls.keys()\n", - " data_total_pulls = np.array([total_pulls[k] for k in sorted(keys)])\n", - " data_total_rewards = np.array([total_rewards[k] for k in sorted(keys)])\n", - "\n", - " # Success rate\n", - " data[g, [int(i) for i in keys]] = data_total_rewards/data_total_pulls\n", - "\n", - " axs = fig.add_subplot(gs[3:6, :3])\n", - " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", - "\n", - " axs.set_xlabel(\"Generations\")\n", - " axs.set_ylabel(\"brush Weights conversion\")\n", - " axs.legend()\n", - "\n", - " # --------------------------------------------------------------------------\n", - " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", - " 'std m1', 'std m2', 'min m1', 'min m2'])\n", - " for item in est_mab.logbook_:\n", - " # I'll store the calculate\n", - " logbook.loc[item['gen']] = (\n", - " item['gen'], item['evals'], *item['ave'], *item['std'], *item['min']\n", - " )\n", - "\n", - " x = logbook['gen']\n", - " for i, metric in enumerate(['m1', 'm2']):\n", - " axs = fig.add_subplot(gs[(3*i):(3*i + 3), 3:])\n", - "\n", - " y = logbook[f'ave {metric}']\n", - " y_err = logbook[f'std {metric}']\n", - " y_min = logbook[f'min {metric}']\n", - "\n", - " axs.plot(x, y, 'b', label='Avg.')\n", - " axs.fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", - " axs.plot(x, y_min, 'k', label='Min.')\n", - "\n", - " axs.set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", - " axs.legend()\n", - "\n", - " axs.set_xlabel(\"Generations\")\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", - "generate_plots(est_mab)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Classification problem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if __name__ == '__main__':\n", - " from brush import BrushClassifier\n", - " \n", - " import warnings\n", - " warnings.filterwarnings(\"ignore\")\n", - "\n", - " from pmlb import fetch_data\n", - "\n", - " # X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", - "\n", - " data = pd.read_csv('../../docs/examples/datasets/d_analcatdata_aids.csv')\n", - " X = data.drop(columns='target')\n", - " y = data['target']\n", - "\n", - " kwargs = {\n", - " 'verbosity' : False,\n", - " 'pop_size' : 60,\n", - " 'max_gen' : 300,\n", - " 'max_depth' : 10,\n", - " 'max_size' : 20,\n", - " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", - " }\n", - "\n", - " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", - " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", - " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", - " ('Modified', 'point mutation calls'),\n", - " ('Modified', 'insert mutation calls'),\n", - " ('Modified', 'delete mutation calls'),\n", - " ('Modified', 'toggle_weight mutation calls')],\n", - " names=('Brush version', 'metric')))\n", - " \n", - " est_mab = None\n", - " for i in range(30):\n", - " try:\n", - " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - "\n", - " est_start_time = time.time()\n", - " est = BrushClassifier(**kwargs).fit(X,y)\n", - " est_end_time = time.time() - est_start_time\n", - "\n", - " est_mab_start_time = time.time()\n", - " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", - " est_mab_end_time = time.time() - est_mab_start_time\n", - "\n", - " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - " \n", - " results.loc[f'run {i}'] = [\n", - " # Original implementation\n", - " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", - "\n", - " # Implementation using Dynamic Thompson Sampling\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", - " \n", - " # Mutation count\n", - " *total_pulls.values()]\n", - " \n", - " except Exception as e:\n", - " print(e)\n", - "\n", - " # Showing results and statistics\n", - " display(results)\n", - " display(results.describe())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", - "generate_plots(est_mab)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brush", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - }, - "vscode": { - "interpreter": { - "hash": "dccdbee601866cd4c45494445ca79bf9b696b8bf13c00622eb9e8a421ade3c36" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/src/brush/D_TS_experiments.ipynb b/src/brush/D_TS_experiments.ipynb deleted file mode 100644 index f666b8bf..00000000 --- a/src/brush/D_TS_experiments.ipynb +++ /dev/null @@ -1,749 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**(Dynamic) Thompson Sampling, as described in Gupta et al. - 2011 - Thompson sampling for dynamic multi-armed bandits**\n", - "\n", - "> Thompson sampling is a probabilistic approach to solve the Multi-Armed Bandit. This paper modifies the original algorithm to make it handle distribution changes during the execution\n", - "\n", - "The Thompson Sampling in this paper considers that each arm is a Bernoulli trial, having the output set ${0, 1}$, with $\\theta^k$ denoting the probability of success for arm $k$.\n", - "\n", - "The probability distribution of successes $S$ obtained in $n^k$ trials is a Binomial distribution:\n", - "\n", - "$$p(S = s|\\theta^k) = \\binom{n^k}{s} (1-\\theta^k)^{n-s}(\\theta^k)^s.$$\n", - "\n", - "The Beta distribution is a conjugate prior (is of the same probability distribution family as the prior probability, which is the Binomial distribution), parameterized by $\\alpha_0$ and $\\beta_0$:\n", - "\n", - "$$p(\\widehat{\\theta}^k; \\alpha_0, \\beta_0) = \\frac{x^{\\alpha_0-1}(1-x)^{\\beta_0-1}}{B(\\alpha_0, \\beta_0)},$$\n", - "\n", - "with $B$ being a binonial distribution.\n", - "\n", - "> We use conjugate prior to derive a closed-form expression for the posterior distribution, usually easier to interpret, manipulate and update. In Bayesian statistics, we adjust the hyperparameters of the posterior distribution to optimize the likelihood with the prior distribution.\n", - "\n", - "The **original Thompson sampling** updates $\\alpha_n$ and $\\beta_n$ for the $n$-th trial, with reward $r_n$ as:\n", - "\n", - "$$\\alpha^k_ n = \\alpha^k_{n-1} + r_n,$$\n", - "$$\\beta^k_ n = \\beta^k_{n-1} + (1-r_n).$$\n", - "\n", - "The proposed method extends the original algorithm by inserting a new update rule based on an hyperparameter $C$. $C$ is a threshold that provides exponential weighting of the outcomes of the trials, making more recent rewards getting more weight. This way, if prior distributions change during the execution, the learned posterior distributions would respond to it.\n", - "\n", - "We update $\\alpha_n$ and $\\beta_n$ conditionally based on $C$:\n", - "\n", - "If $\\alpha_{n-1}+\\beta_{n-1} The paper suggest initializing all $\\alpha$ and $\\beta$ with the value $2$ for all arms.\n", - "\n", - "The remaining of the paper performs an sensitivity analysis and some experiments to check how well the Dynamic Thompson Sampling performs.\n", - "\n", - "> In our work, the mutations would be the arms, and this update would be used during the evolution to adjust the mutation probabilities.\n", - "\n", - "> Brush originally sample the mutations using an uniform distribution. This algorithm learns hyperparameters to Beta distributions. Somehow we need to convert them to have a transparent implementation to the user." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install matplotlib > /dev/null\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.gridspec as gridspec\n", - "\n", - "import numpy as np\n", - "import time\n", - "import pandas as pd\n", - "\n", - "from brush.estimator import BrushEstimator\n", - "from sklearn.base import ClassifierMixin, RegressorMixin\n", - "from deap import creator\n", - "import _brush\n", - "from deap_api import nsga2 " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class D_TS:\n", - " def __init__(self, num_bandits, C=100):\n", - " self.num_bandits = num_bandits\n", - "\n", - " # Store learner status when the update function is called\n", - " self.pull_history = {\n", - " c:[] for c in ['t', 'arm idx', 'reward', 'update'] + \n", - " [f'alpha {i}' for i in range(num_bandits)] + \n", - " [f'beta {i}' for i in range(num_bandits)] + \n", - " [f'weight {i}' for i in range(num_bandits)] } \n", - "\n", - " # This is the probability that should be used to update brush probs\n", - " self._probabilities = np.ones(num_bandits)/num_bandits\n", - "\n", - " self._alphas = 2*np.ones(num_bandits) # Paper suggests starting with 2's\n", - " self._betas = 2*np.ones(num_bandits)\n", - " self.C = C # how to define this value???\n", - "\n", - " @property\n", - " def probabilities(self):\n", - " return self._probabilities\n", - " \n", - " @probabilities.setter\n", - " def probabilities(self, new_probabilities):\n", - " if len(self._probabilities)==len(new_probabilities):\n", - " self._probabilities = new_probabilities\n", - " else:\n", - " print(f\"New probabilities must have size {self.num_bandits}\")\n", - "\n", - " def choose_arm(self):\n", - " \"\"\"Uses the learned distributions to randomly choose an arm to pull. \n", - " \n", - " Returns the index of the arm that was choosen based on the Beta\n", - " probabilities of previous successes and fails.\n", - " \"\"\"\n", - " \n", - " # probability estimates from the beta distribution\n", - " thetas = np.random.beta(self._alphas, self._betas)\n", - " \n", - " arm_idx = np.argmax(thetas)\n", - " \n", - " return arm_idx\n", - " \n", - " def update(self, arm_idx, reward):\n", - " # There are informations about state. we'll save the pull history of\n", - " # other stuff after updating their values\n", - " self.pull_history['t'].append( len(self.pull_history['t']) )\n", - " self.pull_history['arm idx'].append( arm_idx )\n", - " self.pull_history['reward'].append( reward )\n", - " \n", - " if self._alphas[arm_idx] + self._betas[arm_idx] < self.C:\n", - " # This is the pure thompson scheme\n", - " self._alphas[arm_idx] = self._alphas[arm_idx]+reward\n", - " self._betas[arm_idx] = self._betas[arm_idx]+(1-reward)\n", - "\n", - " self.pull_history['update'].append( 0 )\n", - " else:\n", - " # This is the dynamic adjust\n", - " self._alphas[arm_idx] = (self._alphas[arm_idx]+reward)*(self.C/(self.C+1))\n", - " self._betas[arm_idx] = (self._betas[arm_idx]+(1-reward))*(self.C/(self.C+1))\n", - "\n", - " self.pull_history['update'].append( 1 )\n", - "\n", - " # How to transform our Beta distributions into node probabilities?\n", - " # onde idea is to return the expected value of this distribution as\n", - " # the weight that will be given to each arm. In the case of our prior\n", - " # (which is a beta distribution), the expected value is given by\n", - " # 1 / (1 + beta/alpha)\n", - " #self._probabilities = 1 / (1 + (self._betas/self._alphas))\n", - " self._probabilities = (self._alphas-1)/(self._alphas+self._betas-2)\n", - "\n", - " # Now that we finished updating the values we save them to the logs\n", - " for i in range(self.num_bandits):\n", - " self.pull_history[f'alpha {i}'].append( self._alphas[i] )\n", - " self.pull_history[f'beta {i}'].append( self._betas[i] )\n", - " self.pull_history[f'weight {i}'].append( self.probabilities[i] )\n", - "\n", - "\n", - " return self" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def plot_learner_history(learner, arm_labels=[]):\n", - "\n", - " # getting the labels to use in plots\n", - " if len(arm_labels) != learner.num_bandits:\n", - " arm_labels = [f'arm {i}' for i in range(learner.num_bandits)]\n", - "\n", - " # Setting up the figure layout\n", - " fig = plt.figure(figsize=(15, 10), tight_layout=True)\n", - " gs = gridspec.GridSpec(7, 6)\n", - "\n", - " learner_log = pd.DataFrame(learner.pull_history).set_index('t')\n", - " \n", - " total_rewards = learner_log.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - "\n", - " data_total_pulls = np.array([total_pulls[k] for k in sorted(total_pulls)])\n", - " data_total_rewards = np.array([total_rewards[k] for k in sorted(total_rewards)])\n", - " data_total_failures = data_total_pulls-data_total_rewards\n", - "\n", - " ylim = np.maximum(data_total_rewards.max(), data_total_failures.max())\n", - "\n", - " axs = fig.add_subplot(gs[0:2, 4:])\n", - "\n", - " axs.bar(arm_labels, -1*data_total_failures, label=\"Null reward\")\n", - " axs.bar(arm_labels, data_total_rewards, label=\"Positive reward\")\n", - "\n", - " axs.set_xlabel(\"Arm\")\n", - " axs.set_ylim( (-1.05*ylim, 1.05*ylim) )\n", - " axs.legend()\n", - "\n", - " win_ratios = pd.DataFrame.from_dict({\n", - " 'arm' : arm_labels,\n", - " 'totpulls' : data_total_pulls,\n", - " '0 reward' : data_total_failures,\n", - " '+ reward' : data_total_rewards,\n", - " 'success%' : (data_total_rewards/(data_total_pulls)).round(2)\n", - " })\n", - "\n", - " axs = fig.add_subplot(gs[2:4, 4:])\n", - " axs.table(cellText=win_ratios.values, colLabels=win_ratios.columns, loc='center')\n", - " axs.axis('off')\n", - " axs.axis('tight')\n", - "\n", - " # Plotting rewards and pulls -----------------------------------------------\n", - " # plot the cumulative number of pulls (for evaluations, not generations) ---\n", - " data = np.zeros( (learner_log.shape[0]+1, 4) )\n", - " for i, row in learner_log.iterrows():\n", - " data[i+1, :] = data[i]\n", - " data[i+1, row['arm idx'].astype(int)] += 1\n", - "\n", - " axs = fig.add_subplot(gs[0:2, :4])\n", - " axs.plot(data, label=arm_labels)\n", - " axs.set_ylabel(\"Number of times mutation was used\")\n", - " axs.legend()\n", - "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " plt.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - "\n", - " # Plotting alphas and betas ------------------------------------------------\n", - " for i, col in enumerate(['alpha', 'beta']):\n", - " columns = learner_log.columns[learner_log.columns.str.startswith(f'{col} ')]\n", - " labels = [f\"{col} {arm_labels[i]}\" for i in range(4)] \n", - " data = learner_log.loc[:, columns]\n", - "\n", - " axs = fig.add_subplot(gs[(i+1)*2:(i+1)*2+2, :4])\n", - " axs.plot(data, label=labels)\n", - " axs.set_ylabel(f\"{col}s\")\n", - " axs.legend()\n", - "\n", - " # multiple lines all full height showing when D-TS used the dynamic update rule\n", - " axs.vlines(x=[i for i, e in enumerate(learner_log['update']) if e != 0],\n", - " ymin=0, ymax=np.max(data), colors='k', ls='-', lw=0.025)\n", - " \n", - " axs.set_xlabel(\"Evaluations\") # Label only on last plot\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Sanity checks\n", - "class Bandits:\n", - " def __init__(self, reward_prob):\n", - " # Implementing simple bandits.\n", - " self.reward_prob = reward_prob # True reward prob., which learner shoudn't know\n", - " self.n_bandits = len(reward_prob) \n", - "\n", - " def pull(self, arm_idx):\n", - " # Sampling over a normal distr. with mu=0 and var=1\n", - " result = np.random.randn()\n", - " \n", - " # return a positive or nullary reward (Bernoulli random variable).\n", - " return 1 if result > self.reward_prob[arm_idx] else 0\n", - "\n", - "for probs, descr, expec in [\n", - " (np.array([ 1.0, 1.0, 1.0, 1.0]), 'All bandits with same probs' , 'similar amount of pulls for each arm' ),\n", - " (np.array([-1.0, 0.2, 0.0, 1.0]), 'One bandit with higher prob' , 'more pulls for first arm, less pulls for last'),\n", - " (np.array([-0.2, -1.0, 0.0, -1.0]), 'Two bandits with higher probs', '2nd approx 4th > 1st > 3rd' ),\n", - "]:\n", - " bandits = Bandits(probs)\n", - "\n", - " print(\"------------------------ optimizing ------------------------\")\n", - "\n", - " learner = D_TS(4)\n", - " for i in range(1000):\n", - " arm_idx = learner.choose_arm()\n", - " reward = bandits.pull(arm_idx)\n", - "\n", - " learner.update(arm_idx, reward) \n", - "\n", - " plot_learner_history(learner)\n", - " print(f\"(it was expected: {expec})\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class BrushEstimatorMod(BrushEstimator): # Modifying brush estimator\n", - " def __init__(self, **kwargs):\n", - " super().__init__(**kwargs)\n", - "\n", - " # mutations optimized by the learner. Learner arms correspond to\n", - " # these mutations in the order they appear here\n", - " self.mutations_ = ['point', 'insert', 'delete', 'toggle_weight']\n", - "\n", - " # Whether the learner should update after each mutation, or if it should\n", - " # update only after a certain number of evaluations.\n", - " # Otherwise, it will\n", - " # store all rewards in gen_rewards_ (which is reseted at the beggining\n", - " # of every generation) and do a batch of updates only after finishing\n", - " # mutating the solutions.\n", - " self.batch_size_ = self.pop_size #\n", - " self.batch_rewards_ = []\n", - "\n", - " def _mutate(self, ind1):\n", - " # Overriding the mutation so it updates our sampling method. Doing the\n", - " # logic on the python-side for now.\n", - "\n", - " # Creating a wrapper for mutation to be able to control what is happening\n", - " # in the C++ code (this should be prettier in a future implementation)\n", - " \n", - " params = self.get_params()\n", - " \n", - " mutation_idx = self.learner_.choose_arm()\n", - "\n", - " for i, m in enumerate(self.mutations_):\n", - " params['mutation_options'][m] = 0 if i != mutation_idx else 1.0\n", - "\n", - " _brush.set_params(params)\n", - " \n", - " opt = ind1.prg.mutate()\n", - "\n", - " if opt:\n", - " offspring = creator.Individual(opt)\n", - " # print(\"mutation\")\n", - " # print(ind1.prg.get_model())\n", - " # print(offspring.prg.get_model())\n", - "\n", - " offspring.fitness.values = self.toolbox_.evaluate(offspring)\n", - " \n", - " # We compare fitnesses using the deap overloaded operators\n", - " # from the docs: When comparing fitness values that are **minimized**,\n", - " # ``a > b`` will return :data:`True` if *a* is **smaller** than *b*.\n", - " # (this means that this comparison should work agnostic of min/max problems,\n", - " # or even a single-objective or multi-objective problem)\n", - " reward = 1.0 if offspring.fitness > ind1.fitness else 0.0\n", - " \n", - " # if not ignore_this_time:\n", - " # self.batch_rewards_.append( (mutation_idx, reward) )\n", - "\n", - " self.batch_rewards_.append( (mutation_idx, reward) )\n", - "\n", - " if len(self.batch_rewards_) >= self.batch_size_:\n", - " for (mutation_idx, reward) in self.batch_rewards_:\n", - " self.learner_.update(mutation_idx, reward)\n", - " self.batch_rewards_ = []\n", - " \n", - " return offspring\n", - "\n", - " return None\n", - "\n", - " def fit(self, X, y):\n", - "\n", - " _brush.set_params(self.get_params())\n", - "\n", - " self.data_ = self._make_data(X,y)\n", - " # self.data_.print()\n", - "\n", - " # set n classes if relevant\n", - " if self.mode==\"classification\":\n", - " self.n_classes_ = len(np.unique(y))\n", - "\n", - " # We have 4 different mutations, and the learner will learn to choose\n", - " # between these options by maximizing the reward when using each one\n", - " self.learner_ = D_TS(4, C=self.pop_size) # C=self.pop_size\n", - "\n", - " if isinstance(self.functions, list):\n", - " self.functions_ = {k:1.0 for k in self.functions}\n", - " else:\n", - " self.functions_ = self.functions\n", - "\n", - " self.search_space_ = _brush.SearchSpace(self.data_, self.functions_)\n", - "\n", - " self.toolbox_ = self._setup_toolbox(data=self.data_)\n", - "\n", - " archive, logbook = nsga2(\n", - " self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity)\n", - "\n", - " self.archive_ = archive\n", - " self.logbook_ = logbook\n", - " self.best_estimator_ = self.archive_[0].prg\n", - "\n", - " return self\n", - " \n", - "\n", - "class BrushClassifierMod(BrushEstimatorMod,ClassifierMixin):\n", - " def __init__( self, **kwargs):\n", - " super().__init__(mode='classification',**kwargs)\n", - "\n", - " def _fitness_function(self, ind, data: _brush.Dataset):\n", - " ind.prg.fit(data)\n", - " return (\n", - " np.abs(data.y-ind.prg.predict(data)).sum(), \n", - " ind.prg.size()\n", - " )\n", - " \n", - " def _make_individual(self):\n", - " return creator.Individual(\n", - " self.search_space_.make_classifier(self.max_depth, self.max_size)\n", - " if self.n_classes_ == 2 else\n", - " self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size)\n", - " )\n", - "\n", - " def predict_proba(self, X):\n", - " data = self._make_data(X)\n", - " return self.best_estimator_.predict_proba(data)\n", - "\n", - "\n", - "class BrushRegressorMod(BrushEstimatorMod, RegressorMixin):\n", - " def __init__(self, **kwargs):\n", - " super().__init__(mode='regressor',**kwargs)\n", - "\n", - " def _fitness_function(self, ind, data: _brush.Dataset):\n", - " ind.prg.fit(data)\n", - " return (\n", - " np.sum((data.y- ind.prg.predict(data))**2),\n", - " ind.prg.size()\n", - " )\n", - "\n", - " def _make_individual(self):\n", - " return creator.Individual(\n", - " self.search_space_.make_regressor(self.max_depth, self.max_size)\n", - " )" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regression problem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# This is needed to avoid racing conditions (https://deap.readthedocs.io/en/master/tutorials/basic/part4.html)\n", - "if __name__ == '__main__':\n", - " from brush import BrushRegressor\n", - " \n", - " import warnings\n", - " warnings.filterwarnings(\"ignore\")\n", - "\n", - " from pmlb import fetch_data\n", - "\n", - " # X, y = fetch_data('537_houses', return_X_y=True, local_cache_dir='./')\n", - "\n", - " data = pd.read_csv('../../docs/examples/datasets/d_example_patients.csv')\n", - " X = data.drop(columns='target')\n", - " y = data['target']\n", - "\n", - " # data = pd.read_csv('../../docs/examples/datasets/d_2x1_subtract_3x2.csv')\n", - " # X = data.drop(columns='target')\n", - " # y = data['target']\n", - "\n", - " # data = pd.read_csv('../../docs/examples/datasets/d_square_x1_plus_2_x1_x2_plus_square_x2.csv')\n", - " # X = data.drop(columns='target')\n", - " # y = data['target']\n", - "\n", - " kwargs = {\n", - " 'verbosity' : False,\n", - " 'pop_size' : 60,\n", - " 'max_gen' : 300,\n", - " 'max_depth' : 10,\n", - " 'max_size' : 20,\n", - " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", - " }\n", - "\n", - " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", - " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", - " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", - " ('Modified', 'point mutation calls'),\n", - " ('Modified', 'insert mutation calls'),\n", - " ('Modified', 'delete mutation calls'),\n", - " ('Modified', 'toggle_weight mutation calls')],\n", - " names=('Brush version', 'metric')))\n", - " \n", - " est_mab = None\n", - " for i in range(30):\n", - " try:\n", - " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - "\n", - " est_start_time = time.time()\n", - " est = BrushRegressor(**kwargs).fit(X,y)\n", - " est_end_time = time.time() - est_start_time\n", - "\n", - " est_mab_start_time = time.time()\n", - " est_mab = BrushRegressorMod(**kwargs).fit(X,y)\n", - " est_mab_end_time = time.time() - est_mab_start_time\n", - "\n", - " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - " \n", - " results.loc[f'run {i}'] = [\n", - " # Original implementation\n", - " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", - "\n", - " # Implementation using Dynamic Thompson Sampling\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", - " \n", - " # Mutation count\n", - " *total_pulls.values()]\n", - " except Exception as e:\n", - " print(e)\n", - "\n", - " # Showing results and statistics\n", - " display(results)\n", - " display(results.describe())" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inspecting the last execution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def generate_plots(est_mab):\n", - "\n", - " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - "\n", - " # Setting up the figure layout\n", - " fig = plt.figure(figsize=(12, 6), tight_layout=True)\n", - " gs = gridspec.GridSpec(6, 6)\n", - "\n", - " # Approximating the percentage of usage for each generation ----------------\n", - " data = np.zeros( (est_mab.max_gen, 4) )\n", - " for g in range(est_mab.max_gen):\n", - " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", - " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", - "\n", - " df_in_range = learner_log.iloc[idx_start:idx_end]\n", - " g_data = df_in_range['arm idx'].value_counts(normalize=True).to_dict()\n", - " for k, v in g_data.items():\n", - " data[g, k] = v\n", - "\n", - " axs = fig.add_subplot(gs[0:3, :3])\n", - " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", - "\n", - " axs.set_ylabel(\"Percentage of usage\")\n", - " axs.legend()\n", - "\n", - " # average Brush weights for each generation --------------------------------\n", - " data = np.zeros( (est_mab.max_gen, 4) )\n", - " for g in range(est_mab.max_gen):\n", - " idx_start = g*(learner_log.shape[0]//est_mab.max_gen)\n", - " idx_end = (g+1)*(learner_log.shape[0]//est_mab.max_gen)\n", - "\n", - " learner_log_in_range = learner_log.iloc[idx_start:idx_end]\n", - "\n", - " total_rewards = learner_log_in_range.groupby('arm idx')['reward'].sum().to_dict()\n", - " total_pulls = learner_log_in_range['arm idx'].value_counts().to_dict()\n", - "\n", - " keys = total_pulls.keys()\n", - " data_total_pulls = np.array([total_pulls[k] for k in sorted(keys)])\n", - " data_total_rewards = np.array([total_rewards[k] for k in sorted(keys)])\n", - "\n", - " # Success rate\n", - " data[g, [int(i) for i in keys]] = data_total_rewards/data_total_pulls\n", - "\n", - " axs = fig.add_subplot(gs[3:6, :3])\n", - " axs.stackplot(range(est_mab.max_gen), data.T, labels=est_mab.mutations_)\n", - "\n", - " axs.set_xlabel(\"Generations\")\n", - " axs.set_ylabel(\"brush Weights conversion\")\n", - " axs.legend()\n", - "\n", - " # --------------------------------------------------------------------------\n", - " logbook = pd.DataFrame(columns=['gen', 'evals', 'ave m1', 'ave m2',\n", - " 'std m1', 'std m2', 'min m1', 'min m2'])\n", - " for item in est_mab.logbook_:\n", - " # I'll store the calculate\n", - " logbook.loc[item['gen']] = (\n", - " item['gen'], item['evals'], *item['ave'], *item['std'], *item['min']\n", - " )\n", - "\n", - " x = logbook['gen']\n", - " for i, metric in enumerate(['m1', 'm2']):\n", - " axs = fig.add_subplot(gs[(3*i):(3*i + 3), 3:])\n", - "\n", - " y = logbook[f'ave {metric}']\n", - " y_err = logbook[f'std {metric}']\n", - " y_min = logbook[f'min {metric}']\n", - "\n", - " axs.plot(x, y, 'b', label='Avg.')\n", - " axs.fill_between(x, y-y_err, y+y_err, fc='b', alpha=0.5, label=\"Std.\")\n", - " axs.plot(x, y_min, 'k', label='Min.')\n", - "\n", - " axs.set_ylabel(\"Score\" if metric=='m1' else \"Size\")\n", - " axs.legend()\n", - "\n", - " axs.set_xlabel(\"Generations\")\n", - "\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", - "generate_plots(est_mab)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Classification problem" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "if __name__ == '__main__':\n", - " from brush import BrushClassifier\n", - " \n", - " import warnings\n", - " warnings.filterwarnings(\"ignore\")\n", - "\n", - " from pmlb import fetch_data\n", - "\n", - " # X, y = fetch_data('adult', return_X_y=True, local_cache_dir='./')\n", - "\n", - " data = pd.read_csv('../../docs/examples/datasets/d_analcatdata_aids.csv')\n", - " X = data.drop(columns='target')\n", - " y = data['target']\n", - "\n", - " kwargs = {\n", - " 'verbosity' : False,\n", - " 'pop_size' : 60,\n", - " 'max_gen' : 300,\n", - " 'max_depth' : 10,\n", - " 'max_size' : 20,\n", - " 'mutation_options' : {\"point\":0.25, \"insert\": 0.25, \"delete\": 0.25, \"toggle_weight\": 0.25}\n", - " }\n", - "\n", - " results = pd.DataFrame(columns=pd.MultiIndex.from_tuples(\n", - " [('Original', 'score'), ('Original', 'best model'), \n", - " ('Original', 'size'), ('Original', 'depth'), ('Original', 'Time'), \n", - " ('Modified', 'score'), ('Modified', 'best model'), \n", - " ('Modified', 'size'), ('Modified', 'depth'), ('Modified', 'Time'), \n", - " ('Modified', 'point mutation calls'),\n", - " ('Modified', 'insert mutation calls'),\n", - " ('Modified', 'delete mutation calls'),\n", - " ('Modified', 'toggle_weight mutation calls')],\n", - " names=('Brush version', 'metric')))\n", - " \n", - " est_mab = None\n", - " for i in range(30):\n", - " try:\n", - " print(f\"{i}, \", end='\\n' if (i==29) else '')\n", - "\n", - " est_start_time = time.time()\n", - " est = BrushClassifier(**kwargs).fit(X,y)\n", - " est_end_time = time.time() - est_start_time\n", - "\n", - " est_mab_start_time = time.time()\n", - " est_mab = BrushClassifierMod(**kwargs).fit(X,y)\n", - " est_mab_end_time = time.time() - est_mab_start_time\n", - "\n", - " learner_log = pd.DataFrame(est_mab.learner_.pull_history).set_index('t')\n", - " total_pulls = learner_log['arm idx'].value_counts().to_dict()\n", - " \n", - " results.loc[f'run {i}'] = [\n", - " # Original implementation\n", - " est.score(X,y), est.best_estimator_.get_model(),\n", - " est.best_estimator_.size(), est.best_estimator_.depth(), est_end_time,\n", - "\n", - " # Implementation using Dynamic Thompson Sampling\n", - " est_mab.score(X,y), est_mab.best_estimator_.get_model(), \n", - " est_mab.best_estimator_.size(), est_mab.best_estimator_.depth(), est_mab_end_time,\n", - " \n", - " # Mutation count\n", - " *total_pulls.values()]\n", - " \n", - " except Exception as e:\n", - " print(e)\n", - "\n", - " # Showing results and statistics\n", - " display(results)\n", - " display(results.describe())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Inspecting the last execution" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_learner_history(est_mab.learner_, arm_labels=est_mab.mutations_)\n", - "generate_plots(est_mab)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "brush", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.3" - }, - "vscode": { - "interpreter": { - "hash": "dccdbee601866cd4c45494445ca79bf9b696b8bf13c00622eb9e8a421ade3c36" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 042c847228094e64f8daeb3745acf6ae0844b02a Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 7 Jun 2023 11:43:26 -0400 Subject: [PATCH 040/102] Fixed `sample_op` method not returning `std::optional` I also added some TODOs regarding the possibility of having an infinite loop in the PTC2 method, due the way I am handling the `std::optional`. Cases where I can see this happening are like: the user has only binary features in the dataset, and specified only ArrayF operators (i.e. exponentiation). --- src/search_space.h | 45 +++++++++++++++++++++++++++++++++++++++------ 1 file changed, 39 insertions(+), 6 deletions(-) diff --git a/src/search_space.h b/src/search_space.h index 9a2d5edf..a606b4f6 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -356,7 +356,7 @@ struct SearchSpace if ( (terminal_map.find(R) == terminal_map.end()) || (std::all_of(terminal_weights.at(R).begin(), terminal_weights.at(R).end(), - [](int i) { return i==0.0; })) ) + [](int i) { return i<=0.0; })) ) { return std::nullopt; } @@ -370,7 +370,7 @@ struct SearchSpace /// @brief get an operator matching return type `ret`. /// @param ret return type /// @return `std::optional` that may contain a randomly chosen operator matching return type `ret` - Node sample_op(DataType ret) const + std::optional sample_op(DataType ret) const { // check(ret); @@ -379,12 +379,28 @@ struct SearchSpace vector args_w = get_weights(ret); + if (std::all_of(args_w.begin(), + args_w.end(), + [](int i) { return i<=0.0; })) + { + return std::nullopt; + } + auto arg_match = *r.select_randomly(ret_match.begin(), ret_match.end(), args_w.begin(), args_w.end()); vector name_w = get_weights(ret, arg_match.first); + + // TODO: This could be a function check_weights + if (std::all_of(name_w.begin(), + name_w.end(), + [](int i) { return i<=0.0; })) + { + return std::nullopt; + } + return (*r.select_randomly(arg_match.second.begin(), arg_match.second.end(), name_w.begin(), @@ -679,8 +695,15 @@ P SearchSpace::make_program(int max_d, int max_size) n.set_prob_change(0.0); n.fixed=true; } - else - n = sample_op(root_type); + else // TODO: how to avoid infinite loop here? (may be impossible to have one though, + // only if user gives really bad input. Maybe SS should do a check on constructor?) + { + auto opt = sample_op(root_type); + while (!opt) { + opt = sample_op(root_type); + } + n = opt.value(); + } /* cout << "chose " << n.name << endl; */ // auto spot = Tree.set_head(n); @@ -703,6 +726,9 @@ P SearchSpace::make_program(int max_d, int max_size) /* cout << "entering first while loop...\n"; */ while (queue.size() + s < max_size && queue.size() > 0) { + // TODO: we can get an infinite loop here (due to the way we handle + // optional). Maybe I should put a constant (or a neutral element of the node type) + /* cout << "queue size: " << queue.size() << endl; */ auto [qspot, t, d] = RandomDequeue(queue); @@ -717,7 +743,6 @@ P SearchSpace::make_program(int max_d, int max_size) auto opt = sample_terminal(t); - // TODO: we can get an infinite loop here. Maybe I should put a constant (or a neutral element of the node type) if (!opt) { // lets push back and try again later queue.push_back(make_tuple(qspot, t, d)); continue; @@ -730,10 +755,18 @@ P SearchSpace::make_program(int max_d, int max_size) { //choose a nonterminal of matching type /* cout << "getting op of type " << DataTypeName[t] << endl; */ - auto n = sample_op(t); + auto opt = sample_op(t); /* cout << "chose " << n.name << endl; */ // TreeIter new_spot = Tree.append_child(qspot, n); // qspot = n; + + if (!opt) { // lets push back and try again later + queue.push_back(make_tuple(qspot, t, d)); + continue; + } + + n = opt.value(); + auto newspot = Tree.replace(qspot, n); // For each arg of n, add to queue for (auto a : n.arg_types) From f65be352b2a36a673232c715ce0d86c29ec21fee Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 8 Jun 2023 14:10:58 -0400 Subject: [PATCH 041/102] Adds check for empty dataset --- src/data/data.cpp | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/data/data.cpp b/src/data/data.cpp index 610293e9..449f2d00 100644 --- a/src/data/data.cpp +++ b/src/data/data.cpp @@ -153,6 +153,13 @@ void Dataset::init() // note this will have to change in unsupervised settings // n_samples = this->y.size(); + if (this->features.size() == 0){ + HANDLE_ERROR_THROW( + fmt::format("Error during the initialization of the dataset. It " + "does not contain any data\n") + ); + } + // fmt::print("Dataset::init()\n"); for (const auto& [name, value]: this->features) { From f127a2513f2cbb518d7f65bf8786755a13e15949 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 8 Jun 2023 14:11:47 -0400 Subject: [PATCH 042/102] Modified Brush's error so it can be captured by GTEST. new test_data --- src/util/error.cpp | 7 ++++++- tests/cpp/test_data.cpp | 22 ++++++++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/src/util/error.cpp b/src/util/error.cpp index a36cc220..e1fb8f55 100644 --- a/src/util/error.cpp +++ b/src/util/error.cpp @@ -13,7 +13,12 @@ namespace Brush{ namespace Util{ void HandleErrorThrow(string err, const char *file, int line ) { fmt::print(stderr, "FATAL ERROR {}:{}: {}\n", file, line, err); - throw; + + // when called with no arguments, will call terminate(), which + // throws a std::terminate_handler (and can't be handled in GTEST). + // Here we throw a runtime_error with same information printed on + // screen. + throw std::runtime_error(fmt::format("FATAL ERROR {}:{}: {}\n", file, line, err)); } ///prints error to stderr and returns diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index f997b2a6..bd1b3208 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -3,6 +3,28 @@ #include "../../src/program/program.h" #include "../../src/program/dispatch_table.h" +TEST(Data, ErrorHandling) +{ + // Creating an empty dataset throws error + EXPECT_THROW({ + MatrixXf X(0,0); + ArrayXf y(0); + + try + { + Dataset dt(X, y); + } + catch( const std::runtime_error& err ) + { + const string msg = err.what(); + ASSERT_NE( + msg.find("Error during the initialization of the dataset"), + std::string::npos); + throw; + } + }, std::runtime_error); +} + TEST(Data, MixedVariableTypes) { // We need to set at least the mutation options (and respective From 71e814c6aac02fbb0b38e8817dfae97b30431989 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 8 Jun 2023 14:50:11 -0400 Subject: [PATCH 043/102] Add function to check if a set of weights define a empty solution set --- src/search_space.h | 67 +++++++++++++++++----------------------------- 1 file changed, 25 insertions(+), 42 deletions(-) diff --git a/src/search_space.h b/src/search_space.h index a606b4f6..034c75b5 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -228,6 +228,18 @@ struct SearchSpace return true; } + /// @brief Takes iterators to weight vectors and checks if they have a + /// non-empty solution space. An empty solution space is defined as + /// having no non-zero, positive values + /// @tparam T type of iterator. + /// @param start Start iterator + /// @param end End iterator + /// @return true if at least one weight is positive + template + bool has_solution_space(Iter start, Iter end) const { + return !std::all_of(start, end, [](const auto& w) { return w<=0.0; }); + } + template Node get(const string& name); /// @brief get a typed node @@ -264,7 +276,6 @@ struct SearchSpace { v.back() += w; } - } } return v; @@ -319,13 +330,9 @@ struct SearchSpace return std::reduce(tw.second.begin(), tw.second.end()); } ); - // empty solution space, or candidates have weight zero - if (std::all_of(data_type_weights.begin(), - data_type_weights.end(), - [](const auto& weight) { return weight<=0.0; })) - { + if (!has_solution_space(data_type_weights.begin(), + data_type_weights.end())) return std::nullopt; - } // If we got this far, then it is garanteed that we'll return something // The match take into account datatypes with non-zero weights @@ -354,13 +361,10 @@ struct SearchSpace // } if ( (terminal_map.find(R) == terminal_map.end()) - || (std::all_of(terminal_weights.at(R).begin(), - terminal_weights.at(R).end(), - [](int i) { return i<=0.0; })) ) - { + || (!has_solution_space(terminal_weights.at(R).begin(), + terminal_weights.at(R).end())) ) return std::nullopt; - } - + return *r.select_randomly(terminal_map.at(R).begin(), terminal_map.at(R).end(), terminal_weights.at(R).begin(), @@ -379,12 +383,8 @@ struct SearchSpace vector args_w = get_weights(ret); - if (std::all_of(args_w.begin(), - args_w.end(), - [](int i) { return i<=0.0; })) - { + if (!has_solution_space(args_w.begin(), args_w.end())) return std::nullopt; - } auto arg_match = *r.select_randomly(ret_match.begin(), ret_match.end(), @@ -393,13 +393,8 @@ struct SearchSpace vector name_w = get_weights(ret, arg_match.first); - // TODO: This could be a function check_weights - if (std::all_of(name_w.begin(), - name_w.end(), - [](int i) { return i<=0.0; })) - { + if (!has_solution_space(name_w.begin(), name_w.end())) return std::nullopt; - } return (*r.select_randomly(arg_match.second.begin(), arg_match.second.end(), @@ -430,14 +425,10 @@ struct SearchSpace } } - // empty solution space, or candidates have weight zero if ( (weights.size()==0) - || (std::all_of(weights.begin(), - weights.end(), - [](float i) { return i<=0.0; })) ) - { + || (!has_solution_space(weights.begin(), + weights.end())) ) return std::nullopt; - } return (*r.select_randomly(matches.begin(), matches.end(), @@ -497,14 +488,10 @@ struct SearchSpace } } - // empty solution space, or candidates have weight zero if ( (weights.size()==0) - || (std::all_of(weights.begin(), - weights.end(), - [](float i) { return i<=0.0; })) ) - { + || (!has_solution_space(weights.begin(), + weights.end())) ) return std::nullopt; - } return (*r.select_randomly(matches.begin(), matches.end(), weights.begin(), weights.end())); @@ -522,14 +509,10 @@ struct SearchSpace auto matches = node_map.at(node.ret_type).at(node.args_type()); auto match_weights = get_weights(node.ret_type, node.args_type()); - // empty solution space, or candidates have weight zero if ( (match_weights.size()==0) - || (std::all_of(match_weights.begin(), - match_weights.end(), - [](float i) { return i<=0.0; })) ) - { + || (!has_solution_space(match_weights.begin(), + match_weights.end())) ) return std::nullopt; - } return (*r.select_randomly(matches.begin(), matches.end(), From f014eebb7f94b05c5a6e2a04d69583c11c12d849 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 8 Jun 2023 14:59:37 -0400 Subject: [PATCH 044/102] Fixed compiler warning caused due `sig_hash` declarations --- tests/cpp/test_optimization.cpp | 6 +++--- tests/cpp/test_program.cpp | 2 -- tests/cpp/test_search_space.cpp | 2 ++ 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/cpp/test_optimization.cpp b/tests/cpp/test_optimization.cpp index e087cec0..857ea47d 100644 --- a/tests/cpp/test_optimization.cpp +++ b/tests/cpp/test_optimization.cpp @@ -7,9 +7,9 @@ using testing::TestWithParam; // Hashes corresponding to a 3-ary Prod operator -const std::size_t sig_hash = 5617655905677279916; -const std::size_t sig_dual_hash = 10188582206427064428; -const std::size_t complete_hash = 1786662244046809282; +const std::size_t sig_hash = 5617655905677279916u; +const std::size_t sig_dual_hash = 10188582206427064428u; +const std::size_t complete_hash = 1786662244046809282u; class OptimizerTest : public TestWithParam< std::tuple> > { diff --git a/tests/cpp/test_program.cpp b/tests/cpp/test_program.cpp index 07bdce78..4d334aef 100644 --- a/tests/cpp/test_program.cpp +++ b/tests/cpp/test_program.cpp @@ -9,8 +9,6 @@ TEST(Program, MakeRegressor) Dataset data = Data::read_csv("docs/examples/datasets/d_enc.csv","label"); - - SearchSpace SS; SS.init(data); diff --git a/tests/cpp/test_search_space.cpp b/tests/cpp/test_search_space.cpp index 9904684b..7a5fd625 100644 --- a/tests/cpp/test_search_space.cpp +++ b/tests/cpp/test_search_space.cpp @@ -42,3 +42,5 @@ TEST(SearchSpace, Initialization) /* dtable_fit.print(); */ /* dtable_predict.print(); */ } + +// TODO: check if searchspace recognizes when PTC2 is not going to work (avoid infinite loops) \ No newline at end of file From 5b2b4911b64c480356ae947e39486301081b08eb Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 9 Jun 2023 11:18:11 -0400 Subject: [PATCH 045/102] Stop using `PRG.Tree.size`, as it corresponds to tree.hh size Our program nodes can be weighted, thus the number of nodes used in the tree storing the program is not going to be the same as the number of actual operators in the program. Test cases should catch this. --- tests/cpp/test_variation.cpp | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index b3cad650..795a67df 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -158,8 +158,8 @@ TEST(Operators, MutationSizeAndDepthLimit) d, s, PRG.get_model("compact", true), Child.get_model("compact", true), - Child.Tree.max_depth(), - Child.Tree.size() + Child.max_depth(), + Child.size() ); // Original didn't change @@ -168,11 +168,11 @@ TEST(Operators, MutationSizeAndDepthLimit) ASSERT_TRUE(Child.size() > 0); ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.Tree.size() > 0); - ASSERT_TRUE(Child.Tree.size() <= s); + ASSERT_TRUE(Child.size() > 0); + ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.Tree.max_depth() >= 0); - ASSERT_TRUE(Child.Tree.max_depth() <= d); + ASSERT_TRUE(Child.max_depth() >= 0); + ASSERT_TRUE(Child.max_depth() <= d); } } ASSERT_TRUE(successes > 0); @@ -327,7 +327,7 @@ TEST(Operators, CrossoverSizeAndDepthLimit) "Child Model size : {}\n" "=================================================\n", Child.get_model("compact", true), - Child.Tree.max_depth(), Child.Tree.size() + Child.max_depth(), Child.size() ); // Original didn't change @@ -337,11 +337,11 @@ TEST(Operators, CrossoverSizeAndDepthLimit) // Child is within restrictions ASSERT_TRUE(Child.size() > 0); ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.Tree.size() > 0); - ASSERT_TRUE(Child.Tree.size() <= s); + ASSERT_TRUE(Child.size() > 0); + ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.Tree.max_depth() >= 0); - ASSERT_TRUE(Child.Tree.max_depth() <= d); + ASSERT_TRUE(Child.max_depth() >= 0); + ASSERT_TRUE(Child.max_depth() <= d); } } ASSERT_TRUE(successes > 0); From 57f970cc338048e2b31e94d596e9fd3ca10b015f Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 9 Jun 2023 11:36:48 -0400 Subject: [PATCH 046/102] Program size now takes into account the weights This change requires modifications in how we create expressions, since the wheight now has influence on how we calculate size. I am thinking in either start every node with `is_weighted` off, or making PTC2 be aware of weights as well. --- src/program/program.h | 14 ++++++++++++-- src/search_space.cpp | 4 +--- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index 3a9951ca..aad66ddf 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -87,9 +87,19 @@ template struct Program SSref = std::optional>{s}; } - /// @brief count the tree size of the program + /// @brief count the tree size of the program, including the weights in weighted nodes int size(){ - return Tree.size(); + int acc = 0; + + // iterate through each node to calculate tree size + std::for_each(Tree.begin(), Tree.end(), + [&acc](auto& node){ + acc += 1; // the node operator or terminal + if (node.get_is_weighted()==true) + acc += 2; // weight and multiplication, if it exists + }); + + return acc; } /// @brief count the tree depth of the program diff --git a/src/search_space.cpp b/src/search_space.cpp index e0ec7619..8a3721ae 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -56,7 +56,6 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user for (const auto& [op, weight] : user_ops) op_names.push_back(op); - // create nodes based on data types terminal_types = d.unique_data_types; @@ -72,12 +71,11 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user /* fmt::print("adding {} to search space...\n", term.get_name()); */ if (terminal_map.find(term.ret_type) == terminal_map.end()) terminal_map[term.ret_type] = vector(); + /* fmt::print("terminal ret_type: {}\n", DataTypeName[term.ret_type]); */ terminal_map[term.ret_type].push_back(term); terminal_weights[term.ret_type].push_back(1.0); } - - }; RegressorProgram SearchSpace::make_regressor(int max_d, int max_size) From 7efbedde462c2db5138f02e881e8c6e603816615 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 9 Jun 2023 11:54:18 -0400 Subject: [PATCH 047/102] Added more information in test_variation Currently, this test fails. This is due to the changes on how to calculate the size of the programs. --- tests/cpp/test_variation.cpp | 34 +++++++++++++++------------------- 1 file changed, 15 insertions(+), 19 deletions(-) diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index 795a67df..5d1686ea 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -158,7 +158,7 @@ TEST(Operators, MutationSizeAndDepthLimit) d, s, PRG.get_model("compact", true), Child.get_model("compact", true), - Child.max_depth(), + Child.depth(), Child.size() ); @@ -171,8 +171,8 @@ TEST(Operators, MutationSizeAndDepthLimit) ASSERT_TRUE(Child.size() > 0); ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.max_depth() >= 0); - ASSERT_TRUE(Child.max_depth() <= d); + ASSERT_TRUE(Child.depth() >= 0); + ASSERT_TRUE(Child.depth() <= d); } } ASSERT_TRUE(successes > 0); @@ -295,28 +295,24 @@ TEST(Operators, CrossoverSizeAndDepthLimit) fmt::print( "=================================================\n" + "settings: depth = {}, size= {}\n" + "Original model 1: {}\n" "depth = {}, size= {}\n" - "Initial Model 1: {}\n" - "Initial Model 2: {}\n", + "Original model 2: {}\n" + "depth = {}, size= {}\n", d, s, PRG1.get_model("compact", true), - PRG2.get_model("compact", true) + PRG1.depth(), PRG1.size(), + PRG2.get_model("compact", true), + PRG2.depth(), PRG2.size() ); fmt::print("cross\n"); auto opt = PRG1.cross(PRG2); if (!opt){ - fmt::print( - "=================================================\n" - "depth = {}, size= {}\n" - "Original model 1: {}\n" - "Original model 2: {}\n", - "Crossover failed to create a child", - d, s, - PRG1.get_model("compact", true), - PRG2.get_model("compact", true) - ); + fmt::print("Crossover failed to create a child" + "=================================================\n"); } else { successes += 1; @@ -327,7 +323,7 @@ TEST(Operators, CrossoverSizeAndDepthLimit) "Child Model size : {}\n" "=================================================\n", Child.get_model("compact", true), - Child.max_depth(), Child.size() + Child.depth(), Child.size() ); // Original didn't change @@ -340,8 +336,8 @@ TEST(Operators, CrossoverSizeAndDepthLimit) ASSERT_TRUE(Child.size() > 0); ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.max_depth() >= 0); - ASSERT_TRUE(Child.max_depth() <= d); + ASSERT_TRUE(Child.depth() >= 0); + ASSERT_TRUE(Child.depth() <= d); } } ASSERT_TRUE(successes > 0); From 6049a93fe47a95d41fcb1225a484542761cb9c2b Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 10:03:24 -0400 Subject: [PATCH 048/102] Added option to include weights or not in `size()` Default value is set to false. This way, this new version of size behaves like the old one (and does not break any test), but the new behavior can also be used if needed. --- src/program/program.h | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index aad66ddf..2a242f8d 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -87,15 +87,17 @@ template struct Program SSref = std::optional>{s}; } - /// @brief count the tree size of the program, including the weights in weighted nodes - int size(){ + /// @brief count the tree size of the program, including the weights in weighted nodes. + /// @param include_weight whether to include the node's weight in the count. + /// @return int number of nodes. + int size(bool include_weight=false){ int acc = 0; // iterate through each node to calculate tree size std::for_each(Tree.begin(), Tree.end(), [&acc](auto& node){ acc += 1; // the node operator or terminal - if (node.get_is_weighted()==true) + if (include_weights && node.get_is_weighted()==true) acc += 2; // weight and multiplication, if it exists }); @@ -104,7 +106,7 @@ template struct Program /// @brief count the tree depth of the program int depth(){ - return Tree.max_depth(); + return 1+Tree.max_depth(); } Program& fit(const Dataset& d) From a84705d346dd2ff6c1e148746726a81e3ec6badf Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 10:11:56 -0400 Subject: [PATCH 049/102] Fix errors in previous commit --- src/program/program.h | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index 2a242f8d..4a34165e 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -95,10 +95,11 @@ template struct Program // iterate through each node to calculate tree size std::for_each(Tree.begin(), Tree.end(), - [&acc](auto& node){ + [include_weight, &acc](auto& node){ acc += 1; // the node operator or terminal - if (include_weights && node.get_is_weighted()==true) - acc += 2; // weight and multiplication, if it exists + + if (include_weight && node.get_is_weighted()==true) + acc += 2; // weight and multiplication, if enabled }); return acc; From 39371c51ad4be2e5c44efe8feb6fcf0e4b7d88bc Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 11:51:46 -0400 Subject: [PATCH 050/102] Size and depth defined for subtrees This is intended to be used inside mutation and crossover. Current implementation uses the tree.hh size and depth, which does not take into account the weights. --- src/program/program.h | 51 ++++++++++++++++++++++++++++++++++++++----- 1 file changed, 46 insertions(+), 5 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index 4a34165e..f06e3b08 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -90,13 +90,12 @@ template struct Program /// @brief count the tree size of the program, including the weights in weighted nodes. /// @param include_weight whether to include the node's weight in the count. /// @return int number of nodes. - int size(bool include_weight=false){ + int size(bool include_weight=false) const{ int acc = 0; - // iterate through each node to calculate tree size std::for_each(Tree.begin(), Tree.end(), [include_weight, &acc](auto& node){ - acc += 1; // the node operator or terminal + ++acc; // the node operator or terminal if (include_weight && node.get_is_weighted()==true) acc += 2; // weight and multiplication, if enabled @@ -105,11 +104,53 @@ template struct Program return acc; } - /// @brief count the tree depth of the program - int depth(){ + /// @brief count the subtree size, including the weights in weighted nodes. + /// this function is not exposed to the python wrapper. + /// @param top root node of the subtree. + /// @param include_weight whether to include the node's weight in the count. + /// @return int number of nodes. + int size_at(const tree_node_& top, bool include_weight=false) const{ + + int acc = 0; + + // inspired in tree.hh size. First create two identical iterators + Iter it=top, eit=top; + + // Then make the second one point to the next sibling + eit.skip_children(); + ++eit; + + // calculate tree size for each node until reach next sibling + while(it!=eit) { + ++acc; // counting the node operator/terminal + + if (include_weight && it.node->data.get_is_weighted()==true) + acc += 2; // weight and multiplication, if enabled + + ++it; + } + + return acc; + } + + /// @brief count the tree depth of the program. The depth is not influenced by weighted nodes. + /// @return int tree depth. + int depth() const{ + //tree.hh count the number of edges. We need to ensure that a single-node + //tree has depth>0 return 1+Tree.max_depth(); } + /// @brief count the subtree depth. The depth is not influenced by weighted nodes. + /// this function is not exposed to the python wrapper. + /// @param top root node of the subtree. + /// @return int tree depth. + int depth_at(const tree_node_& top) const{ + Iter it = top; + + return 1+Tree.max_depth(it); + } + Program& fit(const Dataset& d) { TreeType out = Tree.begin().node->fit(d); From 160998deb0ee3c8c4235508248f3e72eb3575711 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 11:53:15 -0400 Subject: [PATCH 051/102] Fix python bindings to handle the new bool argument in `size()` --- src/bindings/bind_programs.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/bindings/bind_programs.h b/src/bindings/bind_programs.h index e9874402..bb377d6f 100644 --- a/src/bindings/bind_programs.h +++ b/src/bindings/bind_programs.h @@ -45,7 +45,7 @@ void bind_program(py::module& m, string name) ) .def("get_dot_model", &T::get_dot_model, py::arg("extras")="") .def("get_weights", &T::get_weights) - .def("size", &T::size) + .def("size", &T::size, py::arg("include_weight")=false) .def("depth", &T::depth) .def("cross", &T::cross, py::return_value_policy::automatic, "Performs one attempt to stochastically swap subtrees between two programs and generate a child") From b8a400bde96aab55203d46dc5aa9b8203d3b3709 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 14:04:16 -0400 Subject: [PATCH 052/102] Changed variation to work properly with program's size and depth Previous version was using the size and depth of the underlying tree structure managed by `tree.hh`. --- src/program/program.h | 22 ++++++++++++++-------- src/variation.h | 30 ++++++++++++++++-------------- tests/cpp/test_variation.cpp | 2 -- 3 files changed, 30 insertions(+), 24 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index f06e3b08..9a8784d4 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -104,12 +104,12 @@ template struct Program return acc; } - /// @brief count the subtree size, including the weights in weighted nodes. - /// this function is not exposed to the python wrapper. + /// @brief count the size of a given subtree, optionally including the + /// weights in weighted nodes. This function is not exposed to the python wrapper. /// @param top root node of the subtree. /// @param include_weight whether to include the node's weight in the count. /// @return int number of nodes. - int size_at(const tree_node_& top, bool include_weight=false) const{ + int size_at(Iter& top, bool include_weight=false) const{ int acc = 0; @@ -141,14 +141,20 @@ template struct Program return 1+Tree.max_depth(); } - /// @brief count the subtree depth. The depth is not influenced by weighted nodes. - /// this function is not exposed to the python wrapper. + /// @brief count the depth of a given subtree. The depth is not influenced by + /// weighted nodes. This function is not exposed to the python wrapper. /// @param top root node of the subtree. /// @return int tree depth. - int depth_at(const tree_node_& top) const{ - Iter it = top; + int depth_at(Iter& top) const{ + return 1+Tree.max_depth(top); + } - return 1+Tree.max_depth(it); + /// @brief count the depth until reaching the given subtree. The depth is + /// not influenced by weighted nodes. This function is not exposed to the python wrapper. + /// @param top root node of the subtree. + /// @return int tree depth. + int depth_to_reach(Iter& top) const{ + return 1+Tree.depth(top); } Program& fit(const Dataset& d) diff --git a/src/variation.h b/src/variation.h index 5e235411..07b8d00b 100644 --- a/src/variation.h +++ b/src/variation.h @@ -179,10 +179,11 @@ inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpac template std::optional> mutate(const Program& parent, const SearchSpace& SS) { + // all mutation validation and setup should be done here. Specific mutaiton + // functions are intended to work on the program tree thus cannot access + // program functions and attributes. Program child(parent); - // TODO: update documentation - // choose location by weighted sampling of program vector weights(child.Tree.size()); std::transform(child.Tree.begin(), child.Tree.end(), @@ -200,16 +201,17 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // don't increase an expression already at its maximum size!! // Setting to zero the weight of variations that increase the expression // if the expression is already at the maximum size or depth - if (child.Tree.size()+1 >= PARAMS["max_size"].get() - || child.Tree.max_depth()+1 >= PARAMS["max_depth"].get()) + if (child.size()+1 >= PARAMS["max_size"].get() + || child.depth()+1 >= PARAMS["max_depth"].get()) { // avoid using mutations that increase size/depth. New mutations that // has similar behavior should be listed here. options["insert"] = 0.0; + options["toggle_weight"] = 0.0; } // don't shrink an expression already at its minimum size - if (child.Tree.size() <= 1 || child.Tree.max_depth() <= 1) + if (child.size() <= 1 || child.depth() <= 1) { // avoid using mutations that decrease size/depth. New mutations that // has similar behavior should be listed here. @@ -240,8 +242,8 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS bool success = it->second(child.Tree, spot, SS); if (success - && ((child.Tree.size() <= PARAMS["max_size"].get()) - && (child.Tree.max_depth() <= PARAMS["max_depth"].get())) ){ + && ((child.size() <= PARAMS["max_size"].get()) + && (child.depth() <= PARAMS["max_depth"].get())) ){ return child; } else { return std::nullopt; @@ -282,9 +284,9 @@ std::optional> cross(const Program& root, const Program& other) // pick a subtree to replace vector child_weights(child.Tree.size()); std::transform(child.Tree.begin(), child.Tree.end(), - child_weights.begin(), - [](const auto& n){ return n.get_prob_change(); } - ); + child_weights.begin(), + [](const auto& n){ return n.get_prob_change(); } + ); auto child_spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), @@ -295,9 +297,9 @@ std::optional> cross(const Program& root, const Program& other) auto child_ret_type = child_spot.node->data.ret_type; auto allowed_size = PARAMS["max_size"].get() - - ( child.Tree.size() - child.Tree.size(child_spot) ); + ( child.size() - child.size_at(child_spot) ); auto allowed_depth = PARAMS["max_depth"].get() - - ( child.Tree.depth(child_spot) ); + ( child.depth_to_reach(child_spot) ); // pick a subtree to insert. Selection is based on other_weights vector other_weights(other.Tree.size()); @@ -307,8 +309,8 @@ std::optional> cross(const Program& root, const Program& other) // lambda function to check feasibility of solution and increment the iterator const auto check_and_incrm = [other, &other_iter, allowed_size, allowed_depth]() -> bool { - int s = other.Tree.size(other_iter); - int d = other.Tree.max_depth(other_iter); + int s = other.size_at( other_iter ); + int d = other.depth_at( other_iter ); std::advance(other_iter, 1); return (s <= allowed_size) && (d <= allowed_depth); diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index 5d1686ea..a591d0e7 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -333,8 +333,6 @@ TEST(Operators, CrossoverSizeAndDepthLimit) // Child is within restrictions ASSERT_TRUE(Child.size() > 0); ASSERT_TRUE(Child.size() <= s); - ASSERT_TRUE(Child.size() > 0); - ASSERT_TRUE(Child.size() <= s); ASSERT_TRUE(Child.depth() >= 0); ASSERT_TRUE(Child.depth() <= d); From 3118ffb987c931cffde1aa5a474a0456174814f9 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 15:17:15 -0400 Subject: [PATCH 053/102] Size count weights by default, and make_program can handle it --- src/program/program.h | 4 ++-- src/search_space.h | 20 +++++++++++++++++--- 2 files changed, 19 insertions(+), 5 deletions(-) diff --git a/src/program/program.h b/src/program/program.h index 9a8784d4..d1330cc2 100644 --- a/src/program/program.h +++ b/src/program/program.h @@ -90,7 +90,7 @@ template struct Program /// @brief count the tree size of the program, including the weights in weighted nodes. /// @param include_weight whether to include the node's weight in the count. /// @return int number of nodes. - int size(bool include_weight=false) const{ + int size(bool include_weight=true) const{ int acc = 0; std::for_each(Tree.begin(), Tree.end(), @@ -109,7 +109,7 @@ template struct Program /// @param top root node of the subtree. /// @param include_weight whether to include the node's weight in the count. /// @return int number of nodes. - int size_at(Iter& top, bool include_weight=false) const{ + int size_at(Iter& top, bool include_weight=true) const{ int acc = 0; diff --git a/src/search_space.h b/src/search_space.h index 034c75b5..d297e503 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -715,6 +715,8 @@ P SearchSpace::make_program(int max_d, int max_size) /* cout << "queue size: " << queue.size() << endl; */ auto [qspot, t, d] = RandomDequeue(queue); + int extra_size = 0; + /* cout << "current depth: " << d << endl; */ if (d == max_d) { @@ -732,7 +734,11 @@ P SearchSpace::make_program(int max_d, int max_size) } // If we successfully get a terminal, use it - Tree.replace(qspot, opt.value()); + n = opt.value(); + if (n.get_is_weighted()) + extra_size = 2; + + Tree.replace(qspot, n); } else { @@ -749,8 +755,11 @@ P SearchSpace::make_program(int max_d, int max_size) } n = opt.value(); + if (n.get_is_weighted()) + extra_size = 2; auto newspot = Tree.replace(qspot, n); + // For each arg of n, add to queue for (auto a : n.arg_types) { @@ -761,7 +770,7 @@ P SearchSpace::make_program(int max_d, int max_size) queue.push_back(make_tuple(child_spot, a, d+1)); } } - ++s; + ++s += extra_size; /* cout << "current tree size: " << s << endl; */ } /* cout << "entering second while loop...\n"; */ @@ -786,7 +795,12 @@ P SearchSpace::make_program(int max_d, int max_size) continue; } - auto newspot = Tree.replace(qspot, opt.value()); + // here we are already at maximum size, but there are some random + // leafs without any content. We'll fill them with terminals, and + // force them to have its weights turned off + n = opt.value(); + n.set_is_weighted(false); + auto newspot = Tree.replace(qspot, n); } } /* cout << "final tree:\n" */ From 4369d4f16907ba05af648c7d1645b4da881b1d4d Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 13 Jun 2023 16:11:53 -0400 Subject: [PATCH 054/102] Constants of all data types to avoid inifite loops in `make_program` --- src/search_space.cpp | 3 +++ tests/cpp/test_search_space.cpp | 4 +++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/src/search_space.cpp b/src/search_space.cpp index 8a3721ae..5949b5a6 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -33,6 +33,9 @@ vector generate_terminals(const Dataset& d) // add a constant terminals.push_back( Node(NodeType::Constant, Signature{}, true, "C")); + terminals.push_back( Node(NodeType::Constant, Signature{}, true, "C")); + terminals.push_back( Node(NodeType::Constant, Signature{}, false, "C")); + return terminals; }; diff --git a/tests/cpp/test_search_space.cpp b/tests/cpp/test_search_space.cpp index 7a5fd625..18e1fa8c 100644 --- a/tests/cpp/test_search_space.cpp +++ b/tests/cpp/test_search_space.cpp @@ -43,4 +43,6 @@ TEST(SearchSpace, Initialization) /* dtable_predict.print(); */ } -// TODO: check if searchspace recognizes when PTC2 is not going to work (avoid infinite loops) \ No newline at end of file +// TODO: check if searchspace recognizes when PTC2 is not going to work (avoid infinite loops) + +// TODO: test search space when I set only incompatible functions and terminals (should work) \ No newline at end of file From c761c353f36cdddae920c04f2854a16049e78e56 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 14 Jun 2023 09:27:37 -0400 Subject: [PATCH 055/102] Change python wrapper to count weights by default --- src/bindings/bind_programs.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/bindings/bind_programs.h b/src/bindings/bind_programs.h index bb377d6f..96a36b71 100644 --- a/src/bindings/bind_programs.h +++ b/src/bindings/bind_programs.h @@ -45,7 +45,7 @@ void bind_program(py::module& m, string name) ) .def("get_dot_model", &T::get_dot_model, py::arg("extras")="") .def("get_weights", &T::get_weights) - .def("size", &T::size, py::arg("include_weight")=false) + .def("size", &T::size, py::arg("include_weight")=true) .def("depth", &T::depth) .def("cross", &T::cross, py::return_value_policy::automatic, "Performs one attempt to stochastically swap subtrees between two programs and generate a child") From a1401171bbf5692c87c89001a431694ade2fad9a Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 15 Jun 2023 15:13:10 -0400 Subject: [PATCH 056/102] Number of evaluations should be the size of the offspring --- src/brush/deap_api/nsga2.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index e1e41638..3539c4da 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -15,7 +15,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # stats.register("max", np.max, axis=0) logbook = tools.Logbook() - logbook.header = "gen", "evals", "offspring", "ave", "std", "min" + logbook.header = "gen", "evals", "ave", "std", "min" pop = toolbox.population(n=MU) @@ -66,7 +66,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # Select the next generation population pop = toolbox.survive(pop + offspring, MU) record = stats.compile(pop) - logbook.record(gen=gen, evals=len(invalid_ind), offspring=len(offspring), **record) + logbook.record(gen=gen, evals=len(offspring), **record) if verbosity > 0: print(logbook.stream) From 1b2133f154a4110bfcb40bb0401d95130e649e7a Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 16 Jun 2023 14:47:26 -0400 Subject: [PATCH 057/102] Rewrite to get rid of a compiler error --- src/data/data.h | 5 ++-- src/search_space.h | 73 ++++++++++++++++++++-------------------------- src/util/rnd.h | 4 +-- 3 files changed, 36 insertions(+), 46 deletions(-) diff --git a/src/data/data.h b/src/data/data.h index 505a20a7..79814fe5 100644 --- a/src/data/data.h +++ b/src/data/data.h @@ -133,10 +133,9 @@ class Dataset }; auto get_X() const { - if (Xref.has_value()) - return this->Xref.value().get(); - else + if (!Xref.has_value()) HANDLE_ERROR_THROW("Dataset does not hold a reference to X."); + return this->Xref.value().get(); } void set_validation(bool v=true); inline int get_n_samples() const { diff --git a/src/search_space.h b/src/search_space.h index d297e503..e5dd5aca 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -16,7 +16,6 @@ license: GNU/GPL v3 #include #include - /* Defines the search space of Brush. * The search spaces consists of nodes and their accompanying probability * distribution. @@ -53,32 +52,32 @@ vector generate_terminals(const Dataset& d); extern std::unordered_map ArgsName; /*! @brief Holds a search space, consisting of operations and terminals - * and functions, and methods to sample that space to create programs. - * - * The set of operators is a user controlled parameter; however, we can - * automate, to some extent, the set of possible operators based on the - * data types in the problem. - * Constraints on operators based on data types: - * - only user specified operators are included. - * - operators whose arguments are covered by terminal types are included - * first. Then, a second pass includes any operators whose arguments - * are covered by terminal_types + return types of the current set of - * operators. One could imagine this continuing ad infinitum, but we - * just do two passes for simplicity. - * - assertion check to make sure there is at least one operator that - * returns the output type of the model. - * - * When sampling in the search space (using any of the sampling functions - * `sample_op` or `sample_terminal`), some methods can fail to return a - * value --- given a specific set of parameters to a function, the candidate - * solutions set may be empty --- and, for these methods, the return type is - * either a valid value, or a `std::nullopt`. This is controlled wrapping - * the return type with `std::optional`. - * - * Parameters - * ---------- - * -*/ + * and functions, and methods to sample that space to create programs. + * + * The set of operators is a user controlled parameter; however, we can + * automate, to some extent, the set of possible operators based on the + * data types in the problem. + * Constraints on operators based on data types: + * - only user specified operators are included. + * - operators whose arguments are covered by terminal types are included + * first. Then, a second pass includes any operators whose arguments + * are covered by terminal_types + return types of the current set of + * operators. One could imagine this continuing ad infinitum, but we + * just do two passes for simplicity. + * - assertion check to make sure there is at least one operator that + * returns the output type of the model. + * + * When sampling in the search space (using any of the sampling functions + * `sample_op` or `sample_terminal`), some methods can fail to return a + * value --- given a specific set of parameters to a function, the candidate + * solutions set may be empty --- and, for these methods, the return type is + * either a valid value, or a `std::nullopt`. This is controlled wrapping + * the return type with `std::optional`. + * + * Parameters + * ---------- + * + */ struct SearchSpace { using ArgsHash = std::size_t; @@ -360,6 +359,7 @@ struct SearchSpace // HANDLE_ERROR_THROW(msg); // } + // TODO: try to combine with above function if ( (terminal_map.find(R) == terminal_map.end()) || (!has_solution_space(terminal_weights.at(R).begin(), terminal_weights.at(R).end())) ) @@ -678,8 +678,7 @@ P SearchSpace::make_program(int max_d, int max_size) n.set_prob_change(0.0); n.fixed=true; } - else // TODO: how to avoid infinite loop here? (may be impossible to have one though, - // only if user gives really bad input. Maybe SS should do a check on constructor?) + else { auto opt = sample_op(root_type); while (!opt) { @@ -708,15 +707,10 @@ P SearchSpace::make_program(int max_d, int max_size) /* cout << "queue size: " << queue.size() << endl; */ /* cout << "entering first while loop...\n"; */ while (queue.size() + s < max_size && queue.size() > 0) - { - // TODO: we can get an infinite loop here (due to the way we handle - // optional). Maybe I should put a constant (or a neutral element of the node type) - + { /* cout << "queue size: " << queue.size() << endl; */ auto [qspot, t, d] = RandomDequeue(queue); - int extra_size = 0; - /* cout << "current depth: " << d << endl; */ if (d == max_d) { @@ -735,8 +729,6 @@ P SearchSpace::make_program(int max_d, int max_size) // If we successfully get a terminal, use it n = opt.value(); - if (n.get_is_weighted()) - extra_size = 2; Tree.replace(qspot, n); } @@ -755,9 +747,7 @@ P SearchSpace::make_program(int max_d, int max_size) } n = opt.value(); - if (n.get_is_weighted()) - extra_size = 2; - + auto newspot = Tree.replace(qspot, n); // For each arg of n, add to queue @@ -770,7 +760,8 @@ P SearchSpace::make_program(int max_d, int max_size) queue.push_back(make_tuple(child_spot, a, d+1)); } } - ++s += extra_size; + + ++s /* cout << "current tree size: " << s << endl; */ } /* cout << "entering second while loop...\n"; */ diff --git a/src/util/rnd.h b/src/util/rnd.h index d4b61f84..f8b2f772 100644 --- a/src/util/rnd.h +++ b/src/util/rnd.h @@ -12,6 +12,8 @@ license: GNU/GPL v3 #include "../init.h" +// Defines a multi-core random number generator and its operators. + using namespace std; using std::swap; @@ -123,7 +125,6 @@ namespace Brush { namespace Util{ && " attemping to return random choice from empty vector"); return *select_randomly(v.begin(),v.end()); } - template class C, class T> T random_choice(const C>& v, const vector& w ) @@ -164,7 +165,6 @@ namespace Brush { namespace Util{ vector rg; static Rnd* instance; - }; static Rnd &r = *Rnd::initRand(); From 0ed8060d19b31c5976b9bff4dbb83baf6f0312a7 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 16 Jun 2023 14:52:09 -0400 Subject: [PATCH 058/102] Makes PTC2 work as initial (original) version --- src/search_space.h | 8 ++------ tests/cpp/test_program.cpp | 20 ++++++++------------ 2 files changed, 10 insertions(+), 18 deletions(-) diff --git a/src/search_space.h b/src/search_space.h index e5dd5aca..38081360 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -761,7 +761,7 @@ P SearchSpace::make_program(int max_d, int max_size) } } - ++s + ++s; /* cout << "current tree size: " << s << endl; */ } /* cout << "entering second while loop...\n"; */ @@ -785,12 +785,8 @@ P SearchSpace::make_program(int max_d, int max_size) queue.push_back(make_tuple(qspot, t, d)); continue; } - - // here we are already at maximum size, but there are some random - // leafs without any content. We'll fill them with terminals, and - // force them to have its weights turned off n = opt.value(); - n.set_is_weighted(false); + auto newspot = Tree.replace(qspot, n); } } diff --git a/tests/cpp/test_program.cpp b/tests/cpp/test_program.cpp index 4d334aef..a8da41b8 100644 --- a/tests/cpp/test_program.cpp +++ b/tests/cpp/test_program.cpp @@ -177,10 +177,6 @@ TEST(Operators, ProgramSizeAndDepthPARAMS) SearchSpace SS; SS.init(data); - // split operator --> arity 3 - // prod operator --> arity 4 - int max_arity = 4; - for (int d = 1; d < 10; ++d) { for (int s = 1; s < 10; ++s) @@ -199,17 +195,17 @@ TEST(Operators, ProgramSizeAndDepthPARAMS) "Model size : {}\n" "=================================================\n", d, s, - PRG.get_model("compact", true), PRG.Tree.max_depth(), PRG.Tree.size() + PRG.get_model("compact", true), PRG.depth(), PRG.size() ); - ASSERT_TRUE(PRG.Tree.size() > 0); - ASSERT_TRUE(PRG.Tree.size() <= s+max_arity); - - ASSERT_TRUE(PRG.size() > 0); - ASSERT_TRUE(PRG.size() <= s+max_arity); + // Terminals are weighted by default, while operators not. Since we + // include the weights in the calculation of the size of the program, + // and PTC2 uses the tree size (not the program size), it is not + // expected that initial trees will strictly respect `max_size`. + ASSERT_TRUE(PRG.size() > 0); // size is always positive - ASSERT_TRUE(PRG.Tree.max_depth() >= 0); - ASSERT_TRUE(PRG.Tree.max_depth() <= d+1); + ASSERT_TRUE(PRG.depth() <= d+1); + ASSERT_TRUE(PRG.depth() > 0); // depth is always positive } } } \ No newline at end of file From 801a74bbd4e4afb2166133e4ba0619ccfd67d36f Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 20 Jun 2023 16:29:56 -0400 Subject: [PATCH 059/102] Added condition to avoid mutation in invalid cases random.choice returns nonsense if all weights are zero. added a check to avoid that, so there's no chance of using an invalid mutation when all mutations are invalid --- src/variation.h | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/variation.h b/src/variation.h index 07b8d00b..34d50ce5 100644 --- a/src/variation.h +++ b/src/variation.h @@ -104,7 +104,6 @@ inline bool insert_mutation(tree& Tree, Iter spot, const SearchSpace& SS) Tree.insert(spot, opt.value()); } - } return true; @@ -218,6 +217,12 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS options["delete"] = 0.0; } + // No mutation can be successfully applied to this solution + if (std::all_of(options.begin(), options.end(), [](const auto& kv) { + return kv.second<=0.0; + })) + return std::nullopt; + // choose a valid mutation option string choice = r.random_choice(options); @@ -242,7 +247,7 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS bool success = it->second(child.Tree, spot, SS); if (success - && ((child.size() <= PARAMS["max_size"].get()) + && ((child.size() <= PARAMS["max_size"].get() ) && (child.depth() <= PARAMS["max_depth"].get())) ){ return child; } else { From d10d619d722bf1b3834c956ed6fe7ae7f495d799 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 20 Jun 2023 16:31:44 -0400 Subject: [PATCH 060/102] Adds test to check overall behavior of insert mutation This test also allows us to see how well we can control the mutations by setting their weights to zero --- tests/cpp/test_variation.cpp | 107 +++++++++++++++++++++++++++++++++++ 1 file changed, 107 insertions(+) diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index a591d0e7..41c60389 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -4,6 +4,113 @@ #include "../../src/program/dispatch_table.h" #include "../../src/data/io.h" +TEST(Operators, InsertMutationWorks) +{ + // TODO: this tests could be parameterized. + // To understand design implementation of this test, check Mutation test + + PARAMS["mutation_options"] = { + {"point", 0.0}, {"insert", 1.0}, {"delete", 0.0}, {"toggle_weight", 0.0} + }; + + // retrieving the options to check if everything was set right + std::cout << "Initial mutation configuration" << std::endl; + auto options = PARAMS["mutation_options"].get>(); + for (const auto& [k, v] : options) + std::cout << k << " : " << v << std::endl; + + MatrixXf X(10,2); + ArrayXf y(10); + X << 0.85595296, 0.55417453, 0.8641915 , 0.99481109, 0.99123376, + 0.9742618 , 0.70894019, 0.94940306, 0.99748867, 0.54205151, + + 0.5170537 , 0.8324005 , 0.50316305, 0.10173936, 0.13211973, + 0.2254195 , 0.70526861, 0.31406024, 0.07082619, 0.84034526; + + y << 3.55634251, 3.13854087, 3.55887523, 3.29462895, 3.33443517, + 3.4378868 , 3.41092345, 3.5087468 , 3.25110243, 3.11382179; + + Dataset data(X,y); + + SearchSpace SS; + SS.init(data); + + int successes = 0; + for (int attempt = 0; attempt < 100; ++attempt) + { + // we need to have big values here so the mutation will work + // (when the xmen child exceeds the maximum limits, mutation returns + // std::nullopt) + PARAMS["max_size"] = 20; + PARAMS["max_depth"] = 10; + + fmt::print("d={},s={}\n", PARAMS["max_depth"].get(), PARAMS["max_size"].get()); + fmt::print("make_regressor\n"); + + // creating a "small" program (with a plenty amount of space to insert stuff) + RegressorProgram PRG = SS.make_regressor(5, 5); + + fmt::print("PRG.fit(data);\n"); + PRG.fit(data); + ArrayXf y_pred = PRG.predict(data); + + // applying mutation and checking if the optional result is non-empty + fmt::print("auto Child = PRG.mutate();\n"); + auto opt = PRG.mutate(); // We should assume that it will be always the insert mutation + + if (opt){ + successes += 1; + auto Child = opt.value(); + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model: {}\n" + "Mutated Model: {}\n", + PARAMS["max_depth"].get(), PARAMS["max_size"].get(), + PRG.get_model("compact", true), + Child.get_model("compact", true) + ); + + fmt::print("child fit\n"); + Child.fit(data); + y_pred = Child.predict(data); + + // since we successfully inserted a node, this should be always true + ASSERT_TRUE(Child.size() > PRG.size()); + + // maybe the insertion spot was a shorter branch than the maximum + // depth. At least, xmen depth should be equal to its parent + ASSERT_TRUE(Child.depth() >= PRG.depth()); + } + + // lets also see if it always fails when the child exceeds the maximum limits + PARAMS["max_size"] = PRG.size(); + PARAMS["max_depth"] = PRG.depth(); + + auto opt2 = PRG.mutate(); + if (opt2){ // This shoudl't happen. We'll print then error + auto Child2 = opt2.value(); + + std::cout << "Fail failed. Mutation weights:" << std::endl; + auto options2 = PARAMS["mutation_options"].get>(); + for (const auto& [k, v] : options2) + std::cout << k << " : " << v << std::endl; + + fmt::print( + "=================================================\n" + "depth = {}, size= {}\n" + "Initial Model: {}\n" + "Mutated Model: {}\n", + PARAMS["max_depth"].get(), PARAMS["max_size"].get(), + PRG.get_model("compact", true), + Child2.get_model("compact", true) + ); + ASSERT_TRUE(opt2==std::nullopt); + } + } + ASSERT_TRUE(successes > 0); +} + TEST(Operators, Mutation) { // test mutation From 1dff38f8506582e4c146f9e08ec6abf6ebee8eee Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 20 Jun 2023 16:33:13 -0400 Subject: [PATCH 061/102] Transforms regression into a maximization problem --- src/brush/estimator.py | 47 +++++++++++++++++++++++++++--------------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index b3565f34..623fcea1 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -23,7 +23,6 @@ class BrushEstimator(BaseEstimator): This class shouldn't be called directly; instead, call a child class like :py:class:`BrushRegressor ` or :py:class:`BrushClassifier `. All of the shared parameters are documented here. - Parameters ---------- @@ -91,10 +90,10 @@ def _setup_toolbox(self, data): # creator.create is used to "create new functions", and takes at least # 2 arguments: the name of the newly created class and a base class - # minimize MAE, minimize size. When solving multi-objective problems, - # selection and survival must support this feature. This means that - # these selection operators must accept a tuple of fitnesses as argument) - creator.create("FitnessMulti", base.Fitness, weights=(-1.0,-0.5)) + # Minimizing/maximizing problem: negative/positive weight, respectively. + # Our classification is using the error as a metric + # Comparing fitnesses: https://deap.readthedocs.io/en/master/api/base.html#deap.base.Fitness + creator.create("FitnessMulti", base.Fitness, weights=(+1.0,-1.0)) # create Individual class, inheriting from self.Individual with a fitness attribute creator.create("Individual", DeapIndividual, fitness=creator.FitnessMulti) @@ -102,6 +101,9 @@ def _setup_toolbox(self, data): toolbox.register("mate", self._crossover) toolbox.register("mutate", self._mutate) + # When solving multi-objective problems, selection and survival must + # support this feature. This means that these selection operators must + # accept a tuple of fitnesses as argument) toolbox.register("select", tools.selTournamentDCD) toolbox.register("survive", tools.selNSGA2) @@ -117,9 +119,8 @@ def _crossover(self, ind1, ind2): for i,j in [(ind1,ind2),(ind2,ind1)]: child = i.prg.cross(j.prg) if child: - off = creator.Individual(child) - offspring.append(off) - else: # so we'll always have two elements in `offspring` + offspring.append(creator.Individual(child)) + else: # so we'll always have two elements to unpack in `offspring` offspring.append(None) return offspring[0], offspring[1] @@ -164,8 +165,11 @@ def fit(self, X, y): self.logbook_ = logbook self.best_estimator_ = self.archive_[0].prg - if self.verbosity > 0: - print('best model:',self.best_estimator_.get_model()) + if self.verbosity > 0: + print(f'best model {self.best_estimator_.get_model()}'+ + f' with size {self.best_estimator_.size()}, ' + + f' depth {self.best_estimator_.depth()}, ' + + f' and fitness {self.archive_[0].fitness}' ) return self @@ -231,16 +235,21 @@ def __init__( self, **kwargs): def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) - return ( - np.abs(data.y-ind.prg.predict(data)).sum(), + return ( # (accuracy, size) + (data.y==ind.prg.predict(data)).sum() / data.y.shape[0], ind.prg.size() ) def _make_individual(self): + # C++'s PTC2-based `make_individual` will create a tree of at least + # the given size. By uniformly sampling the size, we can instantiate a + # population with more diversity + s = np.random.randint(1, self.max_size) + return creator.Individual( - self.search_space_.make_classifier(self.max_depth, self.max_size) + self.search_space_.make_classifier(self.max_depth, s) if self.n_classes_ == 2 else - self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size) + self.search_space_.make_multiclass_classifier(self.max_depth, s) ) def predict_proba(self, X): @@ -283,14 +292,18 @@ def __init__(self, **kwargs): def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) - return ( - np.mean((data.y- ind.prg.predict(data))**2), + + # We are squash the error and making it a maximization problem + return ( # (1/(1+MSE), size) + 1/(1+np.mean( (data.y-ind.prg.predict(data))**2 )), ind.prg.size() ) def _make_individual(self): + s = np.random.randint(1, self.max_size) + return creator.Individual( - self.search_space_.make_regressor(self.max_depth, self.max_size) + self.search_space_.make_regressor(self.max_depth, s) ) # Under development From 68c0e72d152d454641176d43dfcd80aaa4e7ae7c Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 28 Jun 2023 09:47:15 -0400 Subject: [PATCH 062/102] Test to check PARAMS consistency in multiple threads In addition, I also created the python binding to `get_params`, so we can retrieve what is being used by brush as the global parameters. The test is pretty simple, but if the `PARAMS` attribute was shared between threads, I would expect it to fail. --- src/bindings/bind_params.cpp | 1 + tests/python/test_params.py | 52 ++++++++++++++++++++++++++++++++++++ 2 files changed, 53 insertions(+) create mode 100644 tests/python/test_params.py diff --git a/src/bindings/bind_params.cpp b/src/bindings/bind_params.cpp index 8c054f0f..889eb85d 100644 --- a/src/bindings/bind_params.cpp +++ b/src/bindings/bind_params.cpp @@ -10,4 +10,5 @@ void bind_params(py::module& m) // .def(py::init<>()) m.def("set_params", &Brush::set_params); + m.def("get_params", &Brush::get_params); } \ No newline at end of file diff --git a/tests/python/test_params.py b/tests/python/test_params.py new file mode 100644 index 00000000..b9e941b5 --- /dev/null +++ b/tests/python/test_params.py @@ -0,0 +1,52 @@ +import pytest + +import _brush +import time +from pathos.multiprocessing import Pool + +def _change_and_wait(config): + "Will change the mutation weights to set only the `index` to 1, then wait " + "`seconts` to retrieve the _brush PARAMS and print weight values" + index, seconds = config + + # Sample configuration + params = { + 'verbosity': False, + 'pop_size' : 100, + 'max_gen' : 100, + 'max_depth': 5, + 'max_size' : 50, + 'mutation_options': {'point' : 0.0, + 'insert' : 0.0, + 'delete' : 0.0, + 'toggle_weight': 0.0} + } + + # We need to guarantee order to use the index correctly + mutations = ['point', 'insert', 'delete', 'toggle_weight'] + + for i, m in enumerate(mutations): + params['mutation_options'][m] = 0 if i != index else 1.0 + + print(f"(Thread id {index}{seconds}) Setting mutation {mutations[index]} to 1 and wait {seconds} seconds") + + _brush.set_params(params) + time.sleep(seconds) + + print(f"(Thread id {index}{seconds}) Retrieving PARAMS: {_brush.get_params()['mutation_options']}") + + assert params['mutation_options']==_brush.get_params()['mutation_options'], \ + f"(Thread id {index}{seconds}) BRUSH FAILED TO KEEP SEPARATE INSTANCES OF `PARAMS` BETWEEN MULTIPLE THREADS" + + +def test_global_PARAMS_sharing(): + print("By default, all threads starts with all mutations having weight zero.") + + scale = 1 # Scale the time of each thread (for human manual checking) + + # Checking if brush's PARAMS can be modified inside a pool without colateral effects. + # Each configuration will start in the same order as they are listed, but they + # will finish in different times. They are all modifying the brush's PARAMS. + Pool(processes=3).map(_change_and_wait, [(0, 3*scale), + (1, 1*scale), + (2, 2*scale)]) \ No newline at end of file From 2c243b085aa714d0d835ced2d9a8cc585321cbe2 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 28 Jun 2023 09:49:41 -0400 Subject: [PATCH 063/102] Not all times the `toggle_weight` should be ignored --- src/variation.h | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/src/variation.h b/src/variation.h index 34d50ce5..7838f684 100644 --- a/src/variation.h +++ b/src/variation.h @@ -200,13 +200,12 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // don't increase an expression already at its maximum size!! // Setting to zero the weight of variations that increase the expression // if the expression is already at the maximum size or depth - if (child.size()+1 >= PARAMS["max_size"].get() - || child.depth()+1 >= PARAMS["max_depth"].get()) + if (child.size() >= PARAMS["max_size"].get() + || child.depth() >= PARAMS["max_depth"].get()) { // avoid using mutations that increase size/depth. New mutations that // has similar behavior should be listed here. options["insert"] = 0.0; - options["toggle_weight"] = 0.0; } // don't shrink an expression already at its minimum size @@ -217,6 +216,12 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS options["delete"] = 0.0; } + // This one can increase and decrease, depending on the spot. However, if + // decreasing the program size, it will always be >= 1. We need to avoid this + // one only when the node is not weighted yet + if (!spot.node->data.get_is_weighted() && child.size()>=PARAMS["max_size"].get()) + options["toggle_weight"] = 0.0; + // No mutation can be successfully applied to this solution if (std::all_of(options.begin(), options.end(), [](const auto& kv) { return kv.second<=0.0; @@ -226,6 +231,10 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // choose a valid mutation option string choice = r.random_choice(options); + // std::cout << "mutation configuration (choice was " << choice << "):" << std::endl; + // for (const auto& [k, v] : options) + // std::cout << " - " << k << " : " << v << std::endl; + // Every mutation here works inplace, so they return bool instead of // std::optional to indicare the result of their manipulation over the // program tree. Here we call the mutation function and return the result @@ -249,6 +258,7 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS if (success && ((child.size() <= PARAMS["max_size"].get() ) && (child.depth() <= PARAMS["max_depth"].get())) ){ + // std::cout << "mutation success: " << success << std::endl; return child; } else { return std::nullopt; From 3445e1d20ced03a85faed25dbab2f1324a78c75d Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 28 Jun 2023 09:50:26 -0400 Subject: [PATCH 064/102] Explicitly checking if mutation and crossover returned `None` --- src/brush/deap_api/nsga2.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 3539c4da..28dcb323 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -19,7 +19,6 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): pop = toolbox.population(n=MU) - # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in pop if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) @@ -50,11 +49,11 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): off1, off2 = ind1, ind2 # avoid inserting empty solutions - if off1: off1 = toolbox.mutate(off1) - if off1: offspring.extend([off1]) + if off1 != None: off1 = toolbox.mutate(off1) + if off1 != None: offspring.extend([off1]) - if off2: off2 = toolbox.mutate(off2) - if off2: offspring.extend([off2]) + if off2 != None: off2 = toolbox.mutate(off2) + if off2 != None: offspring.extend([off2]) # archive.update(offspring) # Evaluate the individuals with an invalid fitness From d2ed4c0046f0e05191c090bec6b2efbf1e9a00c1 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 28 Jun 2023 10:10:41 -0400 Subject: [PATCH 065/102] Included pathos in `test_requires` --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 0dd66c13..d0da01bb 100644 --- a/setup.py +++ b/setup.py @@ -126,7 +126,7 @@ def build_extension(self, ext): 'scikit-learn', 'sphinx' ], - tests_require=['pytest', 'pmlb'], + tests_require=['pytest', 'pmlb', 'pathos'], extras_require={ 'docs': [ 'sphinx_rtd_theme', From 722a363aa84bf876f01a1d64376ba8bc0a13643e Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 28 Jun 2023 10:22:56 -0400 Subject: [PATCH 066/102] Replaced pathos with standard multiprocessing module --- setup.py | 2 +- tests/python/test_params.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index d0da01bb..0dd66c13 100644 --- a/setup.py +++ b/setup.py @@ -126,7 +126,7 @@ def build_extension(self, ext): 'scikit-learn', 'sphinx' ], - tests_require=['pytest', 'pmlb', 'pathos'], + tests_require=['pytest', 'pmlb'], extras_require={ 'docs': [ 'sphinx_rtd_theme', diff --git a/tests/python/test_params.py b/tests/python/test_params.py index b9e941b5..21956efd 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -2,7 +2,7 @@ import _brush import time -from pathos.multiprocessing import Pool +from multiprocessing import Pool def _change_and_wait(config): "Will change the mutation weights to set only the `index` to 1, then wait " From b3a580fbb9ca8742e417d43d604875a6d996fe1c Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 29 Jun 2023 15:52:38 -0400 Subject: [PATCH 067/102] Less restrictive when avoiding the insert mutation --- src/variation.h | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/variation.h b/src/variation.h index 7838f684..129f03cd 100644 --- a/src/variation.h +++ b/src/variation.h @@ -197,11 +197,11 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // these restrictions below increase the performance - // don't increase an expression already at its maximum size!! + // don't increase an expression already at its maximum size! // Setting to zero the weight of variations that increase the expression // if the expression is already at the maximum size or depth - if (child.size() >= PARAMS["max_size"].get() - || child.depth() >= PARAMS["max_depth"].get()) + if (child.size() >= PARAMS["max_size"].get() + || child.depth_to_reach(spot) >= PARAMS["max_depth"].get()) { // avoid using mutations that increase size/depth. New mutations that // has similar behavior should be listed here. @@ -341,8 +341,8 @@ std::optional> cross(const Program& root, const Program& other) else // setting the weight to zero to indicate a non-feasible crossover point return float(0.0); - } - ); + } + ); bool matching_spots_found = false; for (const auto& w: other_weights) From d8adb1c4ef97da0e220cff1cc652868392bea4fb Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 6 Jul 2023 10:47:41 -0400 Subject: [PATCH 068/102] Added `cx_prob` argument to estimators with default to original value This allow the user to specify the crossover probability, which previously was fixed as the original value from nsga2 paper's experiments. Default value is 0.9, which means that there's no practical effect on the results obtained before this change. Documentation was updated to include description of this new argument. --- src/brush/estimator.py | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 623fcea1..5bbb35dc 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -38,6 +38,8 @@ class BrushEstimator(BaseEstimator): Maximum depth of GP trees in the GP program. Use 0 for no limit. max_size : int, default 0 Maximum number of nodes in a tree. Use 0 for no limit. + cx_prob : float, default 0.9 + Probability of applying the crossover variation when generating the offspring mutation_options : dict, default {"point":0.5, "insert": 0.25, "delete": 0.25} A dictionary with keys naming the types of mutation and floating point values specifying the fraction of total mutations to do with that method. @@ -68,6 +70,7 @@ def __init__( verbosity=0, max_depth=3, max_size=20, + cx_prob=0.9, mutation_options = {"point":0.4, "insert": 0.25, "delete": 0.25, "toggle_weight": 0.1}, functions: list[str]|dict[str,float] = {}, batch_size: int = 0 @@ -78,6 +81,7 @@ def __init__( self.mode=mode self.max_depth=max_depth self.max_size=max_size + self.cx_prob=cx_prob self.mutation_options=mutation_options self.functions=functions self.batch_size=batch_size @@ -160,7 +164,7 @@ def fit(self, X, y): self.search_space_ = _brush.SearchSpace(self.data_, self.functions_) self.toolbox_ = self._setup_toolbox(data=self.data_) - archive, logbook = nsga2(self.toolbox_, self.max_gen, self.pop_size, 0.9, self.verbosity) + archive, logbook = nsga2(self.toolbox_, self.max_gen, self.pop_size, self.cx_prob, self.verbosity) self.archive_ = archive self.logbook_ = logbook self.best_estimator_ = self.archive_[0].prg @@ -293,11 +297,12 @@ def __init__(self, **kwargs): def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) + MSE = np.mean( (data.y-ind.prg.predict(data))**2 ) + if not np.isfinite(MSE): # numeric erros, np.nan, +-np.inf + MSE = np.inf + # We are squash the error and making it a maximization problem - return ( # (1/(1+MSE), size) - 1/(1+np.mean( (data.y-ind.prg.predict(data))**2 )), - ind.prg.size() - ) + return ( 1/(1+MSE), ind.prg.size() ) def _make_individual(self): s = np.random.randint(1, self.max_size) From 8463fd2dcf0631481701814a7e88002489720dee Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 10 Jul 2023 10:43:29 -0400 Subject: [PATCH 069/102] Update documentation --- src/brush/estimator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 5bbb35dc..8d809eb1 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -40,7 +40,7 @@ class BrushEstimator(BaseEstimator): Maximum number of nodes in a tree. Use 0 for no limit. cx_prob : float, default 0.9 Probability of applying the crossover variation when generating the offspring - mutation_options : dict, default {"point":0.5, "insert": 0.25, "delete": 0.25} + mutation_options : dict, default {"point":0.4, "insert":0.25, "delete":0.25, "toggle_weight":0.1} A dictionary with keys naming the types of mutation and floating point values specifying the fraction of total mutations to do with that method. functions: dict[str,float] or list[str], default {} @@ -71,7 +71,7 @@ def __init__( max_depth=3, max_size=20, cx_prob=0.9, - mutation_options = {"point":0.4, "insert": 0.25, "delete": 0.25, "toggle_weight": 0.1}, + mutation_options = {"point":0.4, "insert":0.25, "delete":0.25, "toggle_weight":0.1}, functions: list[str]|dict[str,float] = {}, batch_size: int = 0 ): From d9c82c26fcb6d599fbb65a3bdce2d0c4a17044cc Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 14 Jul 2023 15:58:59 -0400 Subject: [PATCH 070/102] NSGA-II creates a population with full-size programs --- src/brush/deap_api/nsga2.py | 2 +- src/brush/estimator.py | 13 +++++-------- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index 28dcb323..b189fead 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -43,7 +43,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): offspring = [] for ind1, ind2 in zip(parents[::2], parents[1::2]): - if random.random() <= CXPB: + if random.random() < CXPB: off1, off2 = toolbox.mate(ind1, ind2) else: off1, off2 = ind1, ind2 diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 8d809eb1..1a367d9c 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -71,7 +71,7 @@ def __init__( max_depth=3, max_size=20, cx_prob=0.9, - mutation_options = {"point":0.4, "insert":0.25, "delete":0.25, "toggle_weight":0.1}, + mutation_options = {"point":0.25, "insert":0.25, "delete":0.25, "toggle_weight":0.25}, functions: list[str]|dict[str,float] = {}, batch_size: int = 0 ): @@ -248,12 +248,11 @@ def _make_individual(self): # C++'s PTC2-based `make_individual` will create a tree of at least # the given size. By uniformly sampling the size, we can instantiate a # population with more diversity - s = np.random.randint(1, self.max_size) return creator.Individual( - self.search_space_.make_classifier(self.max_depth, s) + self.search_space_.make_classifier(self.max_depth, self.max_size) if self.n_classes_ == 2 else - self.search_space_.make_multiclass_classifier(self.max_depth, s) + self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size) ) def predict_proba(self, X): @@ -304,11 +303,9 @@ def _fitness_function(self, ind, data: _brush.Dataset): # We are squash the error and making it a maximization problem return ( 1/(1+MSE), ind.prg.size() ) - def _make_individual(self): - s = np.random.randint(1, self.max_size) - + def _make_individual(self): return creator.Individual( - self.search_space_.make_regressor(self.max_depth, s) + self.search_space_.make_regressor(self.max_depth, self.max_size) ) # Under development From 5a4ace7ecc0d10fa6caff861a1a4ec4860f54128 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 14 Jul 2023 16:00:28 -0400 Subject: [PATCH 071/102] Fixed bug where some terminal nodes had `prob_change` unintentionally modified --- src/program/node.cpp | 8 ++++++++ src/program/node.h | 2 +- src/search_space.h | 1 - 3 files changed, 9 insertions(+), 2 deletions(-) diff --git a/src/program/node.cpp b/src/program/node.cpp index 68becad2..2c632249 100644 --- a/src/program/node.cpp +++ b/src/program/node.cpp @@ -183,8 +183,10 @@ void from_json(const json &j, Node& p) if (j.contains("center_op")) j.at("center_op").get_to(p.center_op); + if (j.contains("fixed")) j.at("fixed").get_to(p.fixed); + if (j.contains("feature")) { // j.at("feature").get_to(p.feature); @@ -195,6 +197,12 @@ void from_json(const json &j, Node& p) else p.is_weighted=false; + if (j.contains("prob_change")) + j.at("prob_change").get_to(p.prob_change); + else + p.prob_change=1.0; + + // if node has a ret_type and arg_types, get them. if not we need to make // a signature bool make_signature=false; diff --git a/src/program/node.h b/src/program/node.h index 0af3b3d6..8742fdc7 100644 --- a/src/program/node.h +++ b/src/program/node.h @@ -156,8 +156,8 @@ struct Node { W = 1.0; // set_node_hash(); - set_prob_change(1.0); fixed=false; + set_prob_change(1.0); // cant weight an boolean terminal if (!IsWeighable(this->ret_type)) diff --git a/src/search_space.h b/src/search_space.h index 38081360..396817a0 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -107,7 +107,6 @@ struct SearchSpace * * { return_type : vector of Nodes } * - * */ unordered_map> terminal_map; From daae47defa4c1f272b03307118f62d250ff544d6 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 17 Jul 2023 10:59:35 -0400 Subject: [PATCH 072/102] Removed mutation size checks and added weight verification in mut and cx Previously, in mutation, I was checking the size and depth of the parent expression to avoid using mutations that would certainly produce an offspring that exceeded the max_size or max_depth. Some tests showed that this was decreasing overall performance, so I decided to remove this feature. Additionally, I made some tests and found that the random library functions will sample and return a value even if all the weights passed as arguments are smaller or equal to zero. To avoid this, I added a check in cx and mutation to check if I'm not sampling from a fully negative weight vector. --- src/variation.h | 58 +++++++++++++++++++------------------------------ 1 file changed, 22 insertions(+), 36 deletions(-) diff --git a/src/variation.h b/src/variation.h index 129f03cd..1a9f7650 100644 --- a/src/variation.h +++ b/src/variation.h @@ -190,51 +190,28 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS [](const auto& n){ return n.get_prob_change(); } ); - auto spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), - weights.begin(), weights.end()); - auto options = PARAMS["mutation_options"].get>(); - // these restrictions below increase the performance - - // don't increase an expression already at its maximum size! - // Setting to zero the weight of variations that increase the expression - // if the expression is already at the maximum size or depth - if (child.size() >= PARAMS["max_size"].get() - || child.depth_to_reach(spot) >= PARAMS["max_depth"].get()) - { - // avoid using mutations that increase size/depth. New mutations that - // has similar behavior should be listed here. - options["insert"] = 0.0; - } - - // don't shrink an expression already at its minimum size - if (child.size() <= 1 || child.depth() <= 1) - { - // avoid using mutations that decrease size/depth. New mutations that - // has similar behavior should be listed here. - options["delete"] = 0.0; + if (std::all_of(weights.begin(), weights.end(), [](const auto& w) { + return w<=0.0; + })) + { // There is no spot that has a probability to be selected + return std::nullopt; } - // This one can increase and decrease, depending on the spot. However, if - // decreasing the program size, it will always be >= 1. We need to avoid this - // one only when the node is not weighted yet - if (!spot.node->data.get_is_weighted() && child.size()>=PARAMS["max_size"].get()) - options["toggle_weight"] = 0.0; + auto spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), + weights.begin(), weights.end()); - // No mutation can be successfully applied to this solution if (std::all_of(options.begin(), options.end(), [](const auto& kv) { return kv.second<=0.0; })) + { // No mutation can be successfully applied to this solution return std::nullopt; - + } + // choose a valid mutation option string choice = r.random_choice(options); - // std::cout << "mutation configuration (choice was " << choice << "):" << std::endl; - // for (const auto& [k, v] : options) - // std::cout << " - " << k << " : " << v << std::endl; - // Every mutation here works inplace, so they return bool instead of // std::optional to indicare the result of their manipulation over the // program tree. Here we call the mutation function and return the result @@ -254,11 +231,12 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS HANDLE_ERROR_THROW(msg); } + // apply the mutation and check if it succeeded bool success = it->second(child.Tree, spot, SS); + if (success - && ((child.size() <= PARAMS["max_size"].get() ) - && (child.depth() <= PARAMS["max_depth"].get())) ){ - // std::cout << "mutation success: " << success << std::endl; + && ( (child.size() <= PARAMS["max_size"].get() ) + && (child.depth() <= PARAMS["max_depth"].get()) )){ return child; } else { return std::nullopt; @@ -303,6 +281,13 @@ std::optional> cross(const Program& root, const Program& other) [](const auto& n){ return n.get_prob_change(); } ); + if (std::all_of(child_weights.begin(), child_weights.end(), [](const auto& w) { + return w<=0.0; + })) + { // There is no spot that has a probability to be selected + return std::nullopt; + } + auto child_spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), child_weights.begin(), @@ -363,6 +348,7 @@ std::optional> cross(const Program& root, const Program& other) return child; } } + return std::nullopt; }; } //namespace variation From 2e4ccaf71346efbdfff576763e9ea554a691d2bf Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 17 Jul 2023 11:09:35 -0400 Subject: [PATCH 073/102] Mutation can write everything it did in `PARAMS["mutation_trace"]` Everytime a mutation is called, if `PARAMS["write_mutation_trace"]` is true, then a json object in `PARAMS["mutation_trace"]` will be updated with information about what happened inside the mutation call. This is usefull for debuging purposes, and also to get additional information in cases where mutation failed and returned `nullopt`. The json can be accessed in python without modifying the wrapper. --- src/variation.h | 43 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 43 insertions(+) diff --git a/src/variation.h b/src/variation.h index 1a9f7650..6682a561 100644 --- a/src/variation.h +++ b/src/variation.h @@ -192,6 +192,23 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS auto options = PARAMS["mutation_options"].get>(); + // whether we should write everything that happened inside the method + if (PARAMS.value("write_mutation_trace", false)==true) { + // Default fields of the trace. Initialize with default values, which are + // gradually changed throughout the execution of the method. + PARAMS["mutation_trace"] = json({ + {"parent", child.get_model("compact", true)}, + {"spot_weights", weights}, + {"mutation_weights", options}, + // default values, to be changed in case mutation works + {"spot", "not selected"}, + {"mutation", "not selected"}, + {"child", "failed to generate"}, + {"status", "initialized weight vectors"}, + {"success", "false"} + }); + } + if (std::all_of(weights.begin(), weights.end(), [](const auto& w) { return w<=0.0; })) @@ -202,6 +219,12 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS auto spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), weights.begin(), weights.end()); + // whether we should write everything that happened inside the method + if (PARAMS.value("write_mutation_trace", false)==true) { + PARAMS["mutation_trace"]["spot"] = spot.node->get_model(false); + PARAMS["mutation_trace"]["status"] = "sampled the mutation spot"; + } + if (std::all_of(options.begin(), options.end(), [](const auto& kv) { return kv.second<=0.0; })) @@ -212,6 +235,10 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // choose a valid mutation option string choice = r.random_choice(options); + // std::cout << "mutation configuration (choice was " << choice << "):" << std::endl; + // for (const auto& [k, v] : options) + // std::cout << " - " << k << " : " << v << std::endl; + // Every mutation here works inplace, so they return bool instead of // std::optional to indicare the result of their manipulation over the // program tree. Here we call the mutation function and return the result @@ -234,11 +261,27 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // apply the mutation and check if it succeeded bool success = it->second(child.Tree, spot, SS); + if (PARAMS.value("write_mutation_trace", false)==true) { + PARAMS["mutation_trace"]["mutation"] = choice; + PARAMS["mutation_trace"]["status"] = "sampled and aplied the mutation"; + if (success) + PARAMS["mutation_trace"]["child"] = child.get_model("compact", true); + } + if (success && ( (child.size() <= PARAMS["max_size"].get() ) && (child.depth() <= PARAMS["max_depth"].get()) )){ + // success is true only if mutation returned a valid program + if (PARAMS.value("write_mutation_trace", false)==true) + PARAMS["mutation_trace"]["success"] = true; + return child; } else { + // here we have a string in PARAMS["mutation_trace"]["child"], + // but success is false since it didnt return an valid program + if (PARAMS.value("write_mutation_trace", false)==true) + PARAMS["mutation_trace"]["status"] = "children exceeds max_size or max_depth"; + return std::nullopt; } }; From edcf72f7b43eeba8af2dfc2ed8f82373da94b745 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 20 Jul 2023 11:28:16 -0400 Subject: [PATCH 074/102] Improved random generator and added argument `random_state` --- src/bindings/bind_params.cpp | 10 ++++++++-- src/brush/estimator.py | 7 ++++++- src/util/rnd.cpp | 38 ++++++++++++++++++++---------------- src/util/rnd.h | 8 +++++++- tests/python/test_params.py | 36 +++++++++++++++++++++++++++++++--- 5 files changed, 75 insertions(+), 24 deletions(-) diff --git a/src/bindings/bind_params.cpp b/src/bindings/bind_params.cpp index 889eb85d..7ad12b5d 100644 --- a/src/bindings/bind_params.cpp +++ b/src/bindings/bind_params.cpp @@ -1,5 +1,8 @@ #include "module.h" #include "../params.h" +#include "../util/rnd.h" + +namespace br = Brush; void bind_params(py::module& m) { @@ -9,6 +12,9 @@ void bind_params(py::module& m) // py::class_(m, "Params", py::dynamic_attr()) // .def(py::init<>()) - m.def("set_params", &Brush::set_params); - m.def("get_params", &Brush::get_params); + m.def("set_params", &br::set_params); + m.def("get_params", &br::get_params); + m.def("set_random_state", [](unsigned int seed) + { br::Util::r = *br::Util::Rnd::initRand(); + br::Util::r.set_seed(seed); }); } \ No newline at end of file diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 1a367d9c..32e012e6 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -40,7 +40,7 @@ class BrushEstimator(BaseEstimator): Maximum number of nodes in a tree. Use 0 for no limit. cx_prob : float, default 0.9 Probability of applying the crossover variation when generating the offspring - mutation_options : dict, default {"point":0.4, "insert":0.25, "delete":0.25, "toggle_weight":0.1} + mutation_options : dict, default {"point":0.25, "insert":0.25, "delete":0.25, "toggle_weight":0.25} A dictionary with keys naming the types of mutation and floating point values specifying the fraction of total mutations to do with that method. functions: dict[str,float] or list[str], default {} @@ -73,6 +73,7 @@ def __init__( cx_prob=0.9, mutation_options = {"point":0.25, "insert":0.25, "delete":0.25, "toggle_weight":0.25}, functions: list[str]|dict[str,float] = {}, + random_state=None, batch_size: int = 0 ): self.pop_size=pop_size @@ -84,6 +85,7 @@ def __init__( self.cx_prob=cx_prob self.mutation_options=mutation_options self.functions=functions + self.random_state=random_state self.batch_size=batch_size @@ -152,6 +154,9 @@ def fit(self, X, y): _brush.set_params(self.get_params()) self.data_ = self._make_data(X,y) + if self.random_state != None: + _brush.set_random_state(self.random_state) + # set n classes if relevant if self.mode=="classification": self.n_classes_ = len(np.unique(y)) diff --git a/src/util/rnd.cpp b/src/util/rnd.cpp index aedb2c32..ac95b699 100644 --- a/src/util/rnd.cpp +++ b/src/util/rnd.cpp @@ -12,17 +12,24 @@ namespace Brush { namespace Util{ Rnd::Rnd() { /*! - * need a random generator for each core to do multiprocessing + * need a random generator for each core to do multiprocessing. + * The constructor will resize the random generators based on + * the number of available cores. */ + //cout << "Max threads are " <set_seed(0); // setting a random initial state } return instance; @@ -36,28 +43,25 @@ namespace Brush { namespace Util{ instance = NULL; } - void Rnd::set_seed(int seed) + void Rnd::set_seed(unsigned int seed) { /*! - * set seeds for each core's random number generator + * set seeds for each core's random number generator. */ - if (seed == 0) - { + if (seed == 0) { + // use a non-deterministic random generator to seed the deterministics std::random_device rd; - - for (auto& r : rg) - r.seed(rd()); + seed = rd(); } - else // seed first rg with seed, then seed rest with random ints from rg[0]. - { - rg[0].seed(seed); - - int imax = std::numeric_limits::max(); - - std::uniform_int_distribution<> dist(0, imax); - for (size_t i = 1; i < rg.size(); ++i) - rg[i].seed(dist(rg[0])); + // generating a seed sequence + std::seed_seq seq{seed}; + + std::vector seeds(rg.size()); + seq.generate(seeds.begin(), seeds.end()); + + for (size_t i = 0; i < rg.size(); ++i) { + rg[i].seed(seeds[i]); } } diff --git a/src/util/rnd.h b/src/util/rnd.h index f8b2f772..0a682e54 100644 --- a/src/util/rnd.h +++ b/src/util/rnd.h @@ -34,7 +34,7 @@ namespace Brush { namespace Util{ static void destroy(); - void set_seed(int seed); + void set_seed(unsigned int seed); int rnd_int( int lowerLimit, int upperLimit ); @@ -164,9 +164,15 @@ namespace Brush { namespace Util{ // Vector of pseudo-random number generators, one for each thread vector rg; + // private static attribute used by every instance of the class. + // All threads share common static members of the class static Rnd* instance; }; + // `Brush.Util` static attribute holding an singleton instance of Rnd. + // the instance is created by calling `initRand`, which creates + // an instance of the private static attribute `instance`. `r` will contain + // one generator for each thread (since it called the constructor) static Rnd &r = *Rnd::initRand(); } // Util } // Brush diff --git a/tests/python/test_params.py b/tests/python/test_params.py index 21956efd..0b91b7d5 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -3,6 +3,36 @@ import _brush import time from multiprocessing import Pool +import numpy as np + + +def test_random_state(): + test_y = np.array([1.,0.,1.4,1.,0.,1.,1.,0.,0.,0.]) + test_X = np.array([[1.1,2.0,3.0,4.0,5.0,6.5,7.0,8.0,9.0,10.0], + [2.0,1.2,6.0,4.0,5.0,8.0,7.0,5.0,9.0,10.0]]) + + data = _brush.Dataset(test_X, test_y) + SS = _brush.SearchSpace(data) + + _brush.set_random_state(123) + + first_run = [] + for d in range(1,4): + for s in range(1,20): + prg = SS.make_regressor(d, s) + first_run.append(prg.get_model()) + + _brush.set_random_state(123) + + second_run = [] + for d in range(1,4): + for s in range(1,20): + prg = SS.make_regressor(d, s) + second_run.append(prg.get_model()) + + for fr, sr in zip(first_run, second_run): + assert fr==sr, "random state failed to generate same expressions" + def _change_and_wait(config): "Will change the mutation weights to set only the `index` to 1, then wait " @@ -38,15 +68,15 @@ def _change_and_wait(config): assert params['mutation_options']==_brush.get_params()['mutation_options'], \ f"(Thread id {index}{seconds}) BRUSH FAILED TO KEEP SEPARATE INSTANCES OF `PARAMS` BETWEEN MULTIPLE THREADS" - def test_global_PARAMS_sharing(): print("By default, all threads starts with all mutations having weight zero.") - scale = 1 # Scale the time of each thread (for human manual checking) + scale = 0.25 # Scale the time of each thread (for human manual checking) # Checking if brush's PARAMS can be modified inside a pool without colateral effects. # Each configuration will start in the same order as they are listed, but they # will finish in different times. They are all modifying the brush's PARAMS. Pool(processes=3).map(_change_and_wait, [(0, 3*scale), (1, 1*scale), - (2, 2*scale)]) \ No newline at end of file + (2, 2*scale)]) + \ No newline at end of file From 17bf5eecca7b250084e6119d71ea4cb195ad32f7 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Thu, 20 Jul 2023 11:38:00 -0400 Subject: [PATCH 075/102] Added docstring for parameter `random_state` --- src/brush/estimator.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 32e012e6..3fe983bb 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -46,6 +46,9 @@ class BrushEstimator(BaseEstimator): functions: dict[str,float] or list[str], default {} A dictionary with keys naming the function set and values giving the probability of sampling them, or a list of functions which will be weighted uniformly. If empty, all available functions are included in the search space. + random_state: int or None, default None + If int, then the value is used to seed the c++ random generator; if None, + then a seed will be generated using a non-deterministic generator. Attributes ---------- From 3328d77751d46d148ca594058ade1eb454be9306 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 25 Jul 2023 10:16:24 -0400 Subject: [PATCH 076/102] `get_prob_change` returns 0 if node is fixed avoid choosing fixed nodes in variation operations --- src/program/node.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/program/node.h b/src/program/node.h index 8742fdc7..42791173 100644 --- a/src/program/node.h +++ b/src/program/node.h @@ -237,7 +237,7 @@ struct Node { //////////////////////////////////////////////////////////////////////////////// // getters and setters //TODO revisit - float get_prob_change() const { return this->prob_change;}; + float get_prob_change() const { return fixed ? 0.0 : this->prob_change;}; void set_prob_change(float w){ if (!fixed) this->prob_change = w;}; float get_prob_keep() const { return 1-this->prob_change;}; From e36b205cae342e0e189f5f450bd5b85727e9608e Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 26 Jul 2023 14:11:25 -0400 Subject: [PATCH 077/102] Added comment about `random_state` with multithreads --- src/brush/estimator.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 3fe983bb..e62971de 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -48,7 +48,12 @@ class BrushEstimator(BaseEstimator): If empty, all available functions are included in the search space. random_state: int or None, default None If int, then the value is used to seed the c++ random generator; if None, - then a seed will be generated using a non-deterministic generator. + then a seed will be generated using a non-deterministic generator. It is + important to notice that, even if the random state is fixed, it is + unlikely that running brush using multiple threads will have the same + results. This happens because the Operating System's scheduler is + responsible to choose which thread will run at any given time, thus + reproductibility is not guaranteed. Attributes ---------- From 39cf94f6ce64c7b300f5ee220b180b6868e49d25 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 26 Jul 2023 14:12:46 -0400 Subject: [PATCH 078/102] fixed test cases that causes core dump Although there core dumps were not occuring, the different size between X and y would crash the tests if we attempted to do some operation that relies on these values. I fixed this, as I'm starting to make search space initialize weights (the tests were crashing for this exact reason) --- src/program/node.h | 2 +- src/program/operator.h | 4 ++-- tests/python/test_params.py | 6 +++--- tests/python/test_program.py | 2 +- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/program/node.h b/src/program/node.h index 42791173..cf4541ef 100644 --- a/src/program/node.h +++ b/src/program/node.h @@ -39,7 +39,7 @@ using Brush::Data::Dataset; namespace Brush{ -// should I move this declaration to another place? +// TODO: should I move this declaration to another place? template inline auto Isnt(DataType dt) -> bool { return !((dt == T) || ...); } diff --git a/src/program/operator.h b/src/program/operator.h index 3f8b8927..195afc6a 100644 --- a/src/program/operator.h +++ b/src/program/operator.h @@ -16,7 +16,7 @@ namespace util{ /// @param weights option pointer to a weight array, used in place of node weight /// @return template - requires (!is_one_of_v) + requires (!is_one_of_v) Scalar get_weight(const TreeNode& tn, const W** weights=nullptr) { Scalar w; @@ -46,7 +46,7 @@ namespace util{ return w; }; template - requires (is_one_of_v) + requires (is_one_of_v) Scalar get_weight(const TreeNode& tn, const W** weights=nullptr) { // we cannot weight a boolean feature. Nevertheless, we need to provide diff --git a/tests/python/test_params.py b/tests/python/test_params.py index 0b91b7d5..a24a2a5c 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -7,9 +7,9 @@ def test_random_state(): - test_y = np.array([1.,0.,1.4,1.,0.,1.,1.,0.,0.,0.]) - test_X = np.array([[1.1,2.0,3.0,4.0,5.0,6.5,7.0,8.0,9.0,10.0], - [2.0,1.2,6.0,4.0,5.0,8.0,7.0,5.0,9.0,10.0]]) + test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) + test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], + [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T data = _brush.Dataset(test_X, test_y) SS = _brush.SearchSpace(data) diff --git a/tests/python/test_program.py b/tests/python/test_program.py index 5c7e51b7..78356bee 100644 --- a/tests/python/test_program.py +++ b/tests/python/test_program.py @@ -13,7 +13,7 @@ def test_data(): test_y = np.array([1.,0.,1.4,1.,0.,1.,1.,0.,0.,0.]) test_X = np.array([[1.1,2.0,3.0,4.0,5.0,6.5,7.0,8.0,9.0,10.0], - [2.0,1.2,6.0,4.0,5.0,8.0,7.0,5.0,9.0,10.0]]) + [2.0,1.2,6.0,4.0,5.0,8.0,7.0,5.0,9.0,10.0]]).T return (test_X, test_y) From 3bd7063b525af3357721ff2dcef4bb6d9b530df3 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 26 Jul 2023 14:14:20 -0400 Subject: [PATCH 079/102] Fix wrong axis used in normalize --- src/util/utils.cpp | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/util/utils.cpp b/src/util/utils.cpp index 05243649..9f469618 100644 --- a/src/util/utils.cpp +++ b/src/util/utils.cpp @@ -105,10 +105,10 @@ void Normalizer::fit(MatrixXf& X, const vector& dt) for (unsigned int i=0; iscale_all || dtypes.at(i)=='f') { - X.row(i) = X.row(i).array() - offset.at(i); + X.col(i) = X.col(i).array() - offset.at(i); if (scale.at(i) > NEAR_ZERO) - X.row(i) = X.row(i).array()/scale.at(i); + X.col(i) = X.col(i).array()/scale.at(i); } } } From fb925a168d6a62b02a3e8652228e775b585c8db6 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 26 Jul 2023 14:16:22 -0400 Subject: [PATCH 080/102] Initializing weights of floating number terminals --- src/search_space.cpp | 60 +++++++++++++++++++++++++++++++++++++++----- 1 file changed, 54 insertions(+), 6 deletions(-) diff --git a/src/search_space.cpp b/src/search_space.cpp index 5949b5a6..2b6d6d9f 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -4,6 +4,35 @@ namespace Brush{ + +float calc_initial_weight(const ArrayXf& value, const ArrayXf& y) +{ + // weights are initialized as the slope of the z-score of x and y. + + // If y has different length from X, we get a core dump here. + // TODO: need to make SS (or Datasaet) check for this when loading the data + + vector dtypes = {'f', 'f'}; + + MatrixXf data(value.size(), 2); + + data.col(0) << value; + data.col(1) << y; + + Normalizer n(true); + n.fit_normalize(data, dtypes); // normalize works row-wise + + // In slope function, argument order matters (if not normalized with z-score) + // The feature should be the first value, and the true value the second + // (it will divide covar(arg1, arg2) by var(arg2)). + // Since z-score normalizes so mean=0 and std=1, then order doesnt matter + float prob_change = std::abs(slope(data.col(0).array(), // x + data.col(1).array())); // y + + return prob_change; +} + + /// @brief generate terminals from the dataset features and random constants. /// @param d a dataset /// @return a vector of nodes @@ -19,21 +48,40 @@ vector generate_terminals(const Dataset& d) using Scalar = typename T::Scalar; constexpr bool weighted = std::is_same_v; // if constexpr (T::Scalar == float) - terminals.push_back(Node( + auto n = Node( NodeType::Terminal, Signature{}, weighted, feature_name - )); + ); + + float prob_change = 1.0; + + // if the value can be casted to float array, we can calculate slope + if (std::holds_alternative(value)) { + prob_change = calc_initial_weight(std::get(value), d.y); + } + + n.set_prob_change( prob_change ); + + terminals.push_back(n); }, value ); ++i; }; - // add a constant - terminals.push_back( Node(NodeType::Constant, Signature{}, true, "C")); - terminals.push_back( Node(NodeType::Constant, Signature{}, true, "C")); + // add constants + float num_const_prob_change = calc_initial_weight(VectorXf::Ones(d.y.size()), d.y); + + auto cXf = Node(NodeType::Constant, Signature{}, true, "C"); + // cXf.set_prob_change(num_const_prob_change); + terminals.push_back(cXf); + + auto cXi = Node(NodeType::Constant, Signature{}, true, "C"); + // cXi.set_prob_change(num_const_prob_change); + terminals.push_back(cXi); + terminals.push_back( Node(NodeType::Constant, Signature{}, false, "C")); return terminals; @@ -77,7 +125,7 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user /* fmt::print("terminal ret_type: {}\n", DataTypeName[term.ret_type]); */ terminal_map[term.ret_type].push_back(term); - terminal_weights[term.ret_type].push_back(1.0); + terminal_weights[term.ret_type].push_back(term.get_prob_change()); } }; From 174d9a55e967d300b18c3f6500fcf6106cb88e96 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Wed, 26 Jul 2023 14:16:43 -0400 Subject: [PATCH 081/102] Improved test with comments explaining expected behavior --- tests/cpp/test_search_space.cpp | 57 +++++++++++++++------------------ 1 file changed, 25 insertions(+), 32 deletions(-) diff --git a/tests/cpp/test_search_space.cpp b/tests/cpp/test_search_space.cpp index 18e1fa8c..84dd9bf3 100644 --- a/tests/cpp/test_search_space.cpp +++ b/tests/cpp/test_search_space.cpp @@ -5,44 +5,37 @@ TEST(SearchSpace, Initialization) { - - MatrixXf X(4,2); - MatrixXf X_v(3,2); - X << 0,1, - 0.47942554,0.87758256, - 0.84147098, 0.54030231, - 0.99749499, 0.0707372; - X_v << 0.90929743, -0.41614684, - 0.59847214, -0.80114362, - 0.14112001,-0.9899925; + ArrayXf y(4); + y << 3.00000, 3.59876, 7.18622, 15.19294; - X.transposeInPlace(); - X_v.transposeInPlace(); + // variables have different pairwise correlations with y. The idea is to + // see the mutation weights for each floating variable. The slope were + // calculated using python np.cov(xprime, yprime)[0][1]/np.var(xprime), + // where xprime and yprime are the z-score normalized arrays obtained + // from x and y. + MatrixXf X(5,4); + X << 0, 0, 1, 1, // x0, binary, expected weight=1 + 2, 0, 1, 2, // x1, categorical, expected weight=1 + 0.05699, 0.62737, 0.72406, 0.99294, // x2, slope ~= 1.069 + 0.03993, 0.36558, 0.01393, 0.25878, // x3, slope ~= 0.25 + 5.17539, 7.63579,-2.82560, 0.24645; // x4, slope ~= -0.799 + X.transposeInPlace(); // 4 rows x 5 variables - ArrayXf y(4); - ArrayXf y_v(3); - // y = 2*x1 + 3.x2 - y << 3.0, 3.59159876, 3.30384889, 2.20720158; - y_v << 0.57015434, -1.20648656, -2.68773747; - Dataset dt(X, y); - Dataset dv(X_v, y_v); - + + // different weights to check if searchspace is initialized correctnly unordered_map user_ops = { - {"Add", 1}, - {"Sub", 1}, - {"Div", .5}, - {"Times", 0.5} + {"Add", 1}, + {"Sub", 1}, + {"Div", .5}, + {"Mul", 0.5} }; - // SearchSpace SS; SearchSpace SS; SS.init(dt, user_ops); -/* dtable_fit.print(); */ -/* dtable_predict.print(); */ -} - -// TODO: check if searchspace recognizes when PTC2 is not going to work (avoid infinite loops) - -// TODO: test search space when I set only incompatible functions and terminals (should work) \ No newline at end of file + dt.print(); + SS.print(); + // dtable_fit.print(); + // dtable_predict.print(); +} \ No newline at end of file From 542727b40e43ec0deeaf5e858f3887bd364cb8c1 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 4 Aug 2023 15:23:11 -0400 Subject: [PATCH 082/102] Test to check if random_state works (currently not) --- tests/python/test_brush.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tests/python/test_brush.py b/tests/python/test_brush.py index 2fadd1e4..38c381dc 100644 --- a/tests/python/test_brush.py +++ b/tests/python/test_brush.py @@ -60,3 +60,14 @@ def test_fit(setup, brush_args, request): pytest.fail(f"Unexpected Exception caught: {e}") logging.error(traceback.format_exc()) + +# def test_random_state(): +# test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) +# test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], +# [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T + +# est1 = brush.BrushRegressor(random_state=42).fit(test_X, test_y) +# est2 = brush.BrushRegressor(random_state=42).fit(test_X, test_y) + +# assert est1.best_estimator_.get_model() == est2.best_estimator_.get_model(), \ +# "random state failed to generate same results" \ No newline at end of file From 3a150f8f8ca8503ed4da8ec6b2f6e562d147ce56 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 4 Aug 2023 15:23:40 -0400 Subject: [PATCH 083/102] Test to check if brush's random_state works (yes!) --- tests/python/test_params.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/tests/python/test_params.py b/tests/python/test_params.py index a24a2a5c..e6166c4e 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -6,7 +6,8 @@ import numpy as np -def test_random_state(): +def test_param_random_state(): + # Check if make_regressor, mutation and crossover will create the same expressions test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T @@ -20,16 +21,26 @@ def test_random_state(): for d in range(1,4): for s in range(1,20): prg = SS.make_regressor(d, s) - first_run.append(prg.get_model()) + prg = prg.mutate() + + if prg != None: prg = prg.cross(prg) + if prg != None: first_run.append(prg.get_model()) + assert len(first_run) > 0, "either mutation or crossover is always failing" + _brush.set_random_state(123) second_run = [] for d in range(1,4): for s in range(1,20): prg = SS.make_regressor(d, s) - second_run.append(prg.get_model()) + prg = prg.mutate() + + if prg != None: prg = prg.cross(prg) + if prg != None: second_run.append(prg.get_model()) + assert len(second_run) > 0, "either mutation or crossover is always failing" + for fr, sr in zip(first_run, second_run): assert fr==sr, "random state failed to generate same expressions" From d450c219b0b8e63ec4f0ae6b6a5c15857f614c37 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 4 Aug 2023 15:24:16 -0400 Subject: [PATCH 084/102] Implemented logic to train and validation partitions, and batch learning --- src/bindings/bind_dataset.cpp | 87 +++++++++++++++++++++++++++++------ src/data/data.cpp | 52 +++++++++++++++++++-- src/data/data.h | 57 +++++++++++++++++++---- 3 files changed, 171 insertions(+), 25 deletions(-) diff --git a/src/bindings/bind_dataset.cpp b/src/bindings/bind_dataset.cpp index 3c405f8f..6ff01ffb 100644 --- a/src/bindings/bind_dataset.cpp +++ b/src/bindings/bind_dataset.cpp @@ -9,29 +9,90 @@ namespace nl = nlohmann; void bind_dataset(py::module & m) { py::class_(m, "Dataset") + // construct from X - .def(py::init &>()) + // .def(py::init &>()) + // construct from X (and optional validation and batch sizes) with constructor 3. + .def(py::init([](const Ref& X, + const float validation_size=0.0, + const float batch_size=1.0){ + return br::Data::Dataset( + X, {}, validation_size, batch_size); + }), + py::arg("X"), + py::arg("validation_size") = 0.0, + py::arg("batch_size") = 1.0 + ) // construct from X, feature names - .def(py::init< - const Ref&, - const vector& - >() + // .def(py::init< + // const Ref&, + // const vector& + // >() + // ) + // construct from X, feature names (and optional validation and batch sizes) with constructor 3. + .def(py::init([](const Ref& X, + const vector& vn, + const float validation_size=0.0, + const float batch_size=1.0){ + return br::Data::Dataset( + X, vn, validation_size, batch_size); + }), + py::arg("X"), + py::arg("vn"), + py::arg("validation_size") = 0.0, + py::arg("batch_size") = 1.0 ) - // construct from X,y arrays - .def(py::init &, Ref &>()) + + // construct from X, y arrays + // .def(py::init &, Ref &>()) + // construct from X, y arrays (and optional validation and batch sizes) with constructor 2. + .def(py::init([](const Ref& X, + const Ref& y, + const float validation_size=0.0, + const float batch_size=1.0){ + return br::Data::Dataset( + X, y, {}, {}, false, validation_size, batch_size); + }), + py::arg("X"), + py::arg("y"), + py::arg("validation_size") = 0.0, + py::arg("batch_size") = 1.0 + ) + // construct from X, y, feature names - .def(py::init< - const Ref&, - const Ref&, - const vector& - >() + // .def(py::init< + // const Ref&, + // const Ref&, + // const vector& + // >() + // ) + // construct from X, y, feature names (and optional validation and batch sizes) with constructor 2. + .def(py::init([](const Ref& X, + const Ref& y, + const vector& vn, + const float validation_size=0.0, + const float batch_size=1.0){ + return br::Data::Dataset( + X, y, vn, {}, false, validation_size, batch_size); + }), + py::arg("X"), + py::arg("y"), + py::arg("vn"), + py::arg("validation_size") = 0.0, + py::arg("batch_size") = 1.0 ) + .def_readwrite("y", &br::Data::Dataset::y) - // .def_readwrite("features", &br::Data::Dataset::features) + // .def_readwrite("features", &br::Data::Dataset::features) .def("get_n_samples", &br::Data::Dataset::get_n_samples) .def("get_n_features", &br::Data::Dataset::get_n_features) .def("print", &br::Data::Dataset::print) .def("get_batch", &br::Data::Dataset::get_batch) + .def("get_training_data", &br::Data::Dataset::get_training_data) + .def("get_validation_data", &br::Data::Dataset::get_validation_data) + .def("get_batch_size", &br::Data::Dataset::get_batch_size) + .def("set_batch_size", &br::Data::Dataset::set_batch_size) + .def("split", &br::Data::Dataset::split) .def("get_X", &br::Data::Dataset::get_X) ; diff --git a/src/data/data.cpp b/src/data/data.cpp index 449f2d00..c2fdaafc 100644 --- a/src/data/data.cpp +++ b/src/data/data.cpp @@ -130,20 +130,36 @@ Dataset Dataset::operator()(const vector& idx) const return Dataset(new_features, new_y, this->classification); } -Dataset Dataset::get_batch(int batch_size) const +Dataset Dataset::get_batch() const { + // will always return a new dataset, even when use_batch is false (this case, returns itself) - batch_size = std::min(batch_size,int(this->get_n_samples())); - return (*this)(r.shuffled_index(get_n_samples())); + if (!use_batch) + return (*this); + + auto n_samples = int(this->get_n_samples()); + // garantee that at least one sample is going to be returned, since + // use_batch is true only if batch_size is (0, 1), and ceil will round + // up + n_samples = int(ceil(n_samples*batch_size)); + + return (*this)(r.shuffled_index(n_samples)); } array Dataset::split(const ArrayXb& mask) const { + // TODO: assert that mask is not filled with zeros or ones (would create + // one empty partition) + // split data into two based on mask. auto idx1 = Util::mask_to_index(mask); auto idx2 = Util::mask_to_index((!mask)); return std::array{ (*this)(idx1), (*this)(idx2) }; } + +Dataset Dataset::get_training_data() const { return (*this)(training_data_idx); } +Dataset Dataset::get_validation_data() const { return (*this)(validation_data_idx); } + /// call init at the end of constructors /// to define metafeatures of the data. void Dataset::init() @@ -172,6 +188,36 @@ void Dataset::init() // add feature to appropriate map list this->features_of_type[feature_type].push_back(name); } + + // setting the training and validation data indexes + auto n_samples = int(this->get_n_samples()); + auto idx = r.shuffled_index(n_samples); + + // garantee that at least one sample is going to be returned, since + // use_batch is true only if batch_size is (0, 1), and ceil will round + // up + auto n_train_samples = int(ceil(n_samples*(1-validation_size))); + + training_data_idx.resize(0); + std::transform(idx.begin(), idx.begin() + n_train_samples, + back_inserter(training_data_idx), + [&](int element) { return element; }); + + if ( use_validation && (n_samples - n_train_samples != 0) ) { + validation_data_idx.resize(0); + std::transform(idx.begin() + n_train_samples, idx.end(), + back_inserter(validation_data_idx), + [&](int element) { return element; }); + } + else { + validation_data_idx = training_data_idx; + } +} + +float Dataset::get_batch_size() { return batch_size; } +void Dataset::set_batch_size(float new_size) { + batch_size = new_size; + use_batch = batch_size > 0.0 && batch_size < 1.0; } /// turns input data into a feature map diff --git a/src/data/data.h b/src/data/data.h index 79814fe5..629c02b5 100644 --- a/src/data/data.h +++ b/src/data/data.h @@ -50,27 +50,39 @@ class Dataset //Dataset(ArrayXXf& X, ArrayXf& y, std::map, vector>>& Z): X(X), y(y), Z(Z){} private: + vector training_data_idx; + vector validation_data_idx; + public: /// @brief keeps track of the unique data types in the dataset. std::vector unique_data_types; + /// @brief types of data in the features. std::vector feature_types; + /// @brief map from data types to features having that type. std::unordered_map> features_of_type; - /// @brief dataset features, as key value pairs std::map features; // TODO: this should probably be a more complex type to include feature type // and potentially other info, like arbitrary relations between features - /// @brief length N array, the target label ArrayXf y; + /// @brief whether this is a classification problem bool classification; std::optional> Xref; + /// @brief percentage of original data used for train. if 0.0, then all data is used for train and validation + float validation_size; + bool use_validation; + + /// @brief percentage of training data size to use in each batch. if 1.0, then all data is used + float batch_size; + bool use_batch; + Dataset operator()(const vector& idx) const; /// call init at the end of constructors /// to define metafeatures of the data. @@ -82,34 +94,53 @@ class Dataset const vector& vn = {} ); - /// initialize data from a map. + /// 1. initialize data from a map. Dataset(std::map& d, const Ref& y_ = ArrayXf(), - bool c = false + bool c = false, + float validation_size = 0.0, + float batch_size = 1.0 ) : features(d) , y(y_) , classification(c) + , validation_size(validation_size) + , use_validation(validation_size > 0.0 && validation_size < 1.0) + , batch_size(batch_size) + , use_batch(batch_size > 0.0 && batch_size < 1.0) {init();}; - /// initialize data from a matrix with feature columns. + /// 2. initialize data from a matrix with feature columns. Dataset(const ArrayXXf& X, const Ref& y_ = ArrayXf(), const vector& vn = {}, const map& Z = {}, - bool c = false + bool c = false, + float validation_size = 0.0, + float batch_size = 1.0 ) : features(make_features(X,Z,vn)) , y(y_) , classification(c) + , validation_size(validation_size) + , use_validation(validation_size > 0.0 && validation_size < 1.0) + , batch_size(batch_size) + , use_batch(batch_size > 0.0 && batch_size < 1.0) { init(); Xref = optional>{X}; } - Dataset(const ArrayXXf& X, const vector& vn) + /// 3. initialize data from X and feature names + Dataset(const ArrayXXf& X, const vector& vn, + float validation_size = 0.0, + float batch_size = 1.0) : classification(false) , features(make_features(X,map{},vn)) + , validation_size(validation_size) + , use_validation(validation_size > 0.0 && validation_size < 1.0) + , batch_size(batch_size) + , use_batch(batch_size > 0.0 && batch_size < 1.0) { init(); Xref = optional>{X}; @@ -137,7 +168,12 @@ class Dataset HANDLE_ERROR_THROW("Dataset does not hold a reference to X."); return this->Xref.value().get(); } - void set_validation(bool v=true); + + // inner partition of original dataset for train and validation. + // if split is not set, then training = validation. + Dataset get_training_data() const; + Dataset get_validation_data() const; + inline int get_n_samples() const { return std::visit( [&](auto&& arg) -> int { return int(arg.size());}, @@ -146,7 +182,10 @@ class Dataset }; inline int get_n_features() const { return this->features.size(); }; /// select random subset of data for training weights. - Dataset get_batch(int batch_size) const; + Dataset get_batch() const; + + float get_batch_size(); + void set_batch_size(float new_size); std::array split(const ArrayXb& mask) const; From 665057bfd0831fef881c7f7e1271205304365c99 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 4 Aug 2023 15:28:08 -0400 Subject: [PATCH 085/102] Using validation partition and batch learning in nsga2 --- src/brush/deap_api/nsga2.py | 56 ++++++++++++----- src/brush/estimator.py | 116 ++++++++++++++++++++++++++++++------ 2 files changed, 140 insertions(+), 32 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index b189fead..ccb0ab59 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -1,28 +1,45 @@ from deap import tools from deap.benchmarks.tools import diversity, convergence, hypervolume import numpy as np -import random +import functools -def nsga2(toolbox, NGEN, MU, CXPB, verbosity): + +def nsga2(toolbox, NGEN, MU, CXPB, use_batch, verbosity, rng): # NGEN = 250 # MU = 100 # CXPB = 0.9 - stats = tools.Statistics(lambda ind: ind.fitness.values) - stats.register("ave", np.mean, axis=0) - stats.register("std", np.std, axis=0) - stats.register("min", np.min, axis=0) + def calculate_statistics(ind): + on_train = ind.fitness.values + on_val = toolbox.evaluateValidation(ind) + + return (*on_train, *on_val) + + stats = tools.Statistics(calculate_statistics) + + stats.register("ave train", np.mean, axis=0) + stats.register("std train", np.std, axis=0) + stats.register("min train", np.min, axis=0) + + stats.register("ave val", np.mean, axis=0) + stats.register("std val", np.std, axis=0) + stats.register("min val", np.min, axis=0) + # stats.register("max", np.max, axis=0) logbook = tools.Logbook() - logbook.header = "gen", "evals", "ave", "std", "min" + logbook.header = "gen", "evals", "ave train", "std train", "min train", \ + "ave val", "std val", "min val" pop = toolbox.population(n=MU) - # Evaluate the individuals with an invalid fitness - invalid_ind = [ind for ind in pop if not ind.fitness.valid] - fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) - for ind, fit in zip(invalid_ind, fitnesses): + batch = toolbox.getBatch() # everytime this function is called, a new random batch is generated + + # OBS: evaluate calls fit in the individual. It is different from using it to predict. The + # function evaluateValidation don't call the fit + fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), pop) + + for ind, fit in zip(pop, fitnesses): ind.fitness.values = fit # This is just to assign the crowding distance to the individuals @@ -30,12 +47,20 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): pop = toolbox.survive(pop, len(pop)) record = stats.compile(pop) - logbook.record(gen=0, evals=len(invalid_ind), **record) + logbook.record(gen=0, evals=len(pop), **record) + if verbosity > 0: print(logbook.stream) # Begin the generational process for gen in range(1, NGEN): + if (use_batch): #batch will be random only if it is not the size of the entire train set. In this case, we dont need to reevaluate the whole pop + batch = toolbox.getBatch() + fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), pop) + + for ind, fit in zip(pop, fitnesses): + ind.fitness.values = fit + # Vary the population # offspring = tools.selTournamentDCD(pop, len(pop)) parents = toolbox.select(pop, len(pop)) @@ -43,7 +68,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): offspring = [] for ind1, ind2 in zip(parents[::2], parents[1::2]): - if random.random() < CXPB: + if rng.random() < CXPB: off1, off2 = toolbox.mate(ind1, ind2) else: off1, off2 = ind1, ind2 @@ -58,14 +83,15 @@ def nsga2(toolbox, NGEN, MU, CXPB, verbosity): # archive.update(offspring) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] - fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) + fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Select the next generation population pop = toolbox.survive(pop + offspring, MU) record = stats.compile(pop) - logbook.record(gen=gen, evals=len(offspring), **record) + logbook.record(gen=gen, evals=len(offspring)+(len(pop) if use_batch else 0), **record) + if verbosity > 0: print(logbook.stream) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index e62971de..04b5ee20 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -5,6 +5,7 @@ control of the underlying GP objects. """ from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin +from sklearn.utils import check_random_state # from sklearn.metrics import mean_squared_error import numpy as np import pandas as pd @@ -44,8 +45,21 @@ class BrushEstimator(BaseEstimator): A dictionary with keys naming the types of mutation and floating point values specifying the fraction of total mutations to do with that method. functions: dict[str,float] or list[str], default {} - A dictionary with keys naming the function set and values giving the probability of sampling them, or a list of functions which will be weighted uniformly. + A dictionary with keys naming the function set and values giving the probability + of sampling them, or a list of functions which will be weighted uniformly. If empty, all available functions are included in the search space. + initialization : {"grow", "full"}, default "grow" + Strategy to create the initial population. If `full`, then every expression is created + with `max_size` nodes. If `grow`, size will be uniformly distributed. + validation_size : float, default 0.0 + Percentage of samples to use as a hold-out partition. These samples are used + to calculate statistics during evolution, but not used to train the models. + The `best_estimator_` will be selected using this partition. If zero, then + the same data used for training is used for validation. + batch_size : float, default 1.0 + Percentage of training data to sample every generation. If `1.0`, then + all data is used. Very small values can improve execution time, but + also lead to underfit. random_state: int or None, default None If int, then the value is used to seed the c++ random generator; if None, then a seed will be generated using a non-deterministic generator. It is @@ -62,7 +76,11 @@ class BrushEstimator(BaseEstimator): archive_ : list[deap_api.DeapIndividual] The final population from training. data_ : _brush.Dataset - The training data in Brush format. + The complete data in Brush format. + train_ : _brush.Dataset + Partition of `data_` containing `(1-validation_size)`% of the data, in Brush format. + validation_ : _brush.Dataset + Partition of `data_` containing `(validation_size)`% of the data, in Brush format. search_space_ : a Brush `SearchSpace` object. Holds the operators and terminals and sampling utilities to update programs. toolbox_ : deap.Toolbox @@ -81,8 +99,10 @@ def __init__( cx_prob=0.9, mutation_options = {"point":0.25, "insert":0.25, "delete":0.25, "toggle_weight":0.25}, functions: list[str]|dict[str,float] = {}, + initialization="grow", random_state=None, - batch_size: int = 0 + validation_size: float = 0.0, + batch_size: float = 1.0 ): self.pop_size=pop_size self.max_gen=max_gen @@ -93,11 +113,13 @@ def __init__( self.cx_prob=cx_prob self.mutation_options=mutation_options self.functions=functions + self.initialization=initialization self.random_state=random_state self.batch_size=batch_size + self.validation_size=validation_size - def _setup_toolbox(self, data): + def _setup_toolbox(self, data_train, data_validation, rng): """Setup the deap toolbox""" toolbox: base.Toolbox = base.Toolbox() @@ -122,11 +144,16 @@ def _setup_toolbox(self, data): toolbox.register("survive", tools.selNSGA2) # toolbox.population will return a list of elements by calling toolbox.individual - toolbox.register("population", tools.initRepeat, list, self._make_individual) - toolbox.register( "evaluate", self._fitness_function, data=data) + toolbox.register("createRandom", self._make_individual, rng=rng) + toolbox.register("population", tools.initRepeat, list, toolbox.createRandom) + + toolbox.register("getBatch", data_train.get_batch) + toolbox.register("evaluate", self._fitness_function) + toolbox.register("evaluateValidation", self._fitness_validation, data=data_validation) return toolbox + def _crossover(self, ind1, ind2): offspring = [] @@ -138,6 +165,7 @@ def _crossover(self, ind1, ind2): offspring.append(None) return offspring[0], offspring[1] + def _mutate(self, ind1): # offspring = (creator.Individual(ind1.prg.mutate(self.search_space_)),) @@ -148,6 +176,7 @@ def _mutate(self, ind1): return None + def fit(self, X, y): """ Fit an estimator to X,y. @@ -160,26 +189,39 @@ def fit(self, X, y): 1-d array of (boolean) target values. """ _brush.set_params(self.get_params()) - self.data_ = self._make_data(X,y) - + + rng = check_random_state(self.random_state) if self.random_state != None: _brush.set_random_state(self.random_state) + self.data_ = self._make_data(X,y) + # set n classes if relevant if self.mode=="classification": self.n_classes_ = len(np.unique(y)) + # These have a default behavior to return something meaningfull if + # no values are set + self.train_ = self.data_.get_training_data() + self.train_.set_batch_size(self.batch_size) + self.validation_ = self.data_.get_validation_data() + if isinstance(self.functions, list): self.functions_ = {k:1.0 for k in self.functions} else: self.functions_ = self.functions - self.search_space_ = _brush.SearchSpace(self.data_, self.functions_) - self.toolbox_ = self._setup_toolbox(data=self.data_) + self.search_space_ = _brush.SearchSpace(self.train_, self.functions_) + self.toolbox_ = self._setup_toolbox(data_train=self.train_, data_validation=self.validation_, rng=rng) - archive, logbook = nsga2(self.toolbox_, self.max_gen, self.pop_size, self.cx_prob, self.verbosity) + archive, logbook = nsga2( + self.toolbox_, self.max_gen, self.pop_size, self.cx_prob, + (0.0 0: @@ -191,6 +233,9 @@ def fit(self, X, y): return self def _make_data(self, X, y=None): + # This function should not partition data (as it is used in predict). + # partitioning is done in fit(). + if isinstance(y, pd.Series): y = y.values if isinstance(X, pd.DataFrame): @@ -203,11 +248,13 @@ def _make_data(self, X, y=None): return _brush.Dataset(X, y, feature_names) assert isinstance(X, np.ndarray) + # if there is no label, don't include it in library call to Dataset if isinstance(y, NoneType): return _brush.Dataset(X) - return _brush.Dataset(X,y) + return _brush.Dataset(X, y) + def predict(self, X): """Predict using the best estimator in the archive. """ @@ -250,6 +297,13 @@ class BrushClassifier(BrushEstimator,ClassifierMixin): def __init__( self, **kwargs): super().__init__(mode='classification',**kwargs) + def _fitness_validation(self, ind, data: _brush.Dataset): + return ( # (accuracy, size) + (data.y==ind.prg.predict(data)).sum() / data.y.shape[0], + ind.prg.size() + ) + + def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) return ( # (accuracy, size) @@ -257,15 +311,23 @@ def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.size() ) - def _make_individual(self): + def _make_individual(self, rng): # C++'s PTC2-based `make_individual` will create a tree of at least # the given size. By uniformly sampling the size, we can instantiate a # population with more diversity + s = 0 + if self.initialization=="grow": + s = rng.randint(1, self.max_size) + elif self.initialization=="full": + s = self.max_size + else: + raise ValueError(f"Invalid argument value for `initialization`. " + f"expected 'full' or 'grow'. got {self.initialization}") return creator.Individual( - self.search_space_.make_classifier(self.max_depth, self.max_size) + self.search_space_.make_classifier(self.max_depth, s) if self.n_classes_ == 2 else - self.search_space_.make_multiclass_classifier(self.max_depth, self.max_size) + self.search_space_.make_multiclass_classifier(self.max_depth, s) ) def predict_proba(self, X): @@ -306,6 +368,16 @@ class BrushRegressor(BrushEstimator, RegressorMixin): def __init__(self, **kwargs): super().__init__(mode='regressor',**kwargs) + + def _fitness_validation(self, ind, data: _brush.Dataset): + MSE = np.mean( (data.y-ind.prg.predict(data))**2 ) + if not np.isfinite(MSE): # numeric erros, np.nan, +-np.inf + MSE = np.inf + + # We are squash the error and making it a maximization problem + return ( 1/(1+MSE), ind.prg.size() ) + + def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) @@ -316,9 +388,19 @@ def _fitness_function(self, ind, data: _brush.Dataset): # We are squash the error and making it a maximization problem return ( 1/(1+MSE), ind.prg.size() ) - def _make_individual(self): + + def _make_individual(self, rng): + s = 0 + if self.initialization=="grow": + s = rng.randint(1, self.max_size) + elif self.initialization=="full": + s = self.max_size + else: + raise ValueError(f"Invalid argument value for `initialization`. " + f"expected 'full' or 'grow'. got {self.initialization}") + return creator.Individual( - self.search_space_.make_regressor(self.max_depth, self.max_size) + self.search_space_.make_regressor(self.max_depth, s) ) # Under development From dea6776ef5b4984bd53309738475f0f801f89163 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 4 Aug 2023 17:25:58 -0400 Subject: [PATCH 086/102] Implemented MCDM to select final solution --- src/bindings/bind_params.cpp | 1 + src/brush/deap_api/nsga2.py | 23 ++++++------- src/brush/estimator.py | 63 ++++++++++++++++++++---------------- src/search_space.h | 24 ++++++-------- 4 files changed, 56 insertions(+), 55 deletions(-) diff --git a/src/bindings/bind_params.cpp b/src/bindings/bind_params.cpp index 7ad12b5d..75521ab3 100644 --- a/src/bindings/bind_params.cpp +++ b/src/bindings/bind_params.cpp @@ -17,4 +17,5 @@ void bind_params(py::module& m) m.def("set_random_state", [](unsigned int seed) { br::Util::r = *br::Util::Rnd::initRand(); br::Util::r.set_seed(seed); }); + m.def("rnd_flt", [](){ return br::Util::r.rnd_flt(); }); } \ No newline at end of file diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index ccb0ab59..a7d44f4d 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -4,10 +4,11 @@ import functools -def nsga2(toolbox, NGEN, MU, CXPB, use_batch, verbosity, rng): +def nsga2(toolbox, NGEN, MU, CXPB, use_batch, verbosity, rnd_flt): # NGEN = 250 # MU = 100 # CXPB = 0.9 + # rnd_flt: random number generator to sample crossover prob def calculate_statistics(ind): on_train = ind.fitness.values @@ -17,19 +18,15 @@ def calculate_statistics(ind): stats = tools.Statistics(calculate_statistics) - stats.register("ave train", np.mean, axis=0) - stats.register("std train", np.std, axis=0) - stats.register("min train", np.min, axis=0) - - stats.register("ave val", np.mean, axis=0) - stats.register("std val", np.std, axis=0) - stats.register("min val", np.min, axis=0) - - # stats.register("max", np.max, axis=0) + stats.register("ave", np.mean, axis=0) + stats.register("std", np.std, axis=0) + stats.register("min", np.min, axis=0) + stats.register("max", np.max, axis=0) logbook = tools.Logbook() - logbook.header = "gen", "evals", "ave train", "std train", "min train", \ - "ave val", "std val", "min val" + logbook.header = "gen", "evals", "ave (O1 train, O2 train, O1 val, O2 val)", \ + "std (O1 train, O2 train, O1 val, O2 val)", \ + "min (O1 train, O2 train, O1 val, O2 val)" pop = toolbox.population(n=MU) @@ -68,7 +65,7 @@ def calculate_statistics(ind): offspring = [] for ind1, ind2 in zip(parents[::2], parents[1::2]): - if rng.random() < CXPB: + if rnd_flt() < CXPB: off1, off2 = toolbox.mate(ind1, ind2) else: off1, off2 = ind1, ind2 diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 04b5ee20..e16689f8 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -5,7 +5,6 @@ control of the underlying GP objects. """ from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin, TransformerMixin -from sklearn.utils import check_random_state # from sklearn.metrics import mean_squared_error import numpy as np import pandas as pd @@ -119,7 +118,7 @@ def __init__( self.validation_size=validation_size - def _setup_toolbox(self, data_train, data_validation, rng): + def _setup_toolbox(self, data_train, data_validation): """Setup the deap toolbox""" toolbox: base.Toolbox = base.Toolbox() @@ -131,6 +130,8 @@ def _setup_toolbox(self, data_train, data_validation, rng): # Comparing fitnesses: https://deap.readthedocs.io/en/master/api/base.html#deap.base.Fitness creator.create("FitnessMulti", base.Fitness, weights=(+1.0,-1.0)) + # TODO: make this weights attributes of each derivate class (creator is global) + # create Individual class, inheriting from self.Individual with a fitness attribute creator.create("Individual", DeapIndividual, fitness=creator.FitnessMulti) @@ -144,7 +145,7 @@ def _setup_toolbox(self, data_train, data_validation, rng): toolbox.register("survive", tools.selNSGA2) # toolbox.population will return a list of elements by calling toolbox.individual - toolbox.register("createRandom", self._make_individual, rng=rng) + toolbox.register("createRandom", self._make_individual) toolbox.register("population", tools.initRepeat, list, toolbox.createRandom) toolbox.register("getBatch", data_train.get_batch) @@ -190,7 +191,6 @@ def fit(self, X, y): """ _brush.set_params(self.get_params()) - rng = check_random_state(self.random_state) if self.random_state != None: _brush.set_random_state(self.random_state) @@ -212,17 +212,28 @@ def fit(self, X, y): self.functions_ = self.functions self.search_space_ = _brush.SearchSpace(self.train_, self.functions_) - self.toolbox_ = self._setup_toolbox(data_train=self.train_, data_validation=self.validation_, rng=rng) + self.toolbox_ = self._setup_toolbox(data_train=self.train_, data_validation=self.validation_) archive, logbook = nsga2( self.toolbox_, self.max_gen, self.pop_size, self.cx_prob, - (0.0 0: print(f'best model {self.best_estimator_.get_model()}'+ @@ -311,24 +322,22 @@ def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.size() ) - def _make_individual(self, rng): + def _make_individual(self): # C++'s PTC2-based `make_individual` will create a tree of at least # the given size. By uniformly sampling the size, we can instantiate a # population with more diversity - s = 0 - if self.initialization=="grow": - s = rng.randint(1, self.max_size) - elif self.initialization=="full": - s = self.max_size - else: + + if self.initialization not in ["grow", "full"]: raise ValueError(f"Invalid argument value for `initialization`. " f"expected 'full' or 'grow'. got {self.initialization}") return creator.Individual( - self.search_space_.make_classifier(self.max_depth, s) - if self.n_classes_ == 2 else - self.search_space_.make_multiclass_classifier(self.max_depth, s) - ) + self.search_space_.make_classifier( + self.max_depth,(0 if self.initialization=='grow' else self.max_size)) + if self.n_classes_ == 2 else + self.search_space_.make_multiclass_classifier( + self.max_depth, (0 if self.initialization=='grow' else self.max_size)) + ) def predict_proba(self, X): """Predict class probabilities for X. @@ -389,20 +398,18 @@ def _fitness_function(self, ind, data: _brush.Dataset): return ( 1/(1+MSE), ind.prg.size() ) - def _make_individual(self, rng): - s = 0 - if self.initialization=="grow": - s = rng.randint(1, self.max_size) - elif self.initialization=="full": - s = self.max_size - else: + def _make_individual(self): + if self.initialization not in ["grow", "full"]: raise ValueError(f"Invalid argument value for `initialization`. " f"expected 'full' or 'grow'. got {self.initialization}") - - return creator.Individual( - self.search_space_.make_regressor(self.max_depth, s) + + return creator.Individual( # No arguments (or zero): brush will use PARAMS passed in set_params. max_size is sampled between 1 and params['max_size'] if zero is provided + self.search_space_.make_regressor( + self.max_depth, (0 if self.initialization=='grow' else self.max_size)) ) + + # Under development # class BrushRepresenter(BrushEstimator, TransformerMixin): # """Brush for representation learning. diff --git a/src/search_space.h b/src/search_space.h index 396817a0..69d6eb50 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -630,7 +630,7 @@ P SearchSpace::make_program(int max_d, int max_size) // highest operator arity, and the real maximum depth is `max_depth` plus one. if (max_d == 0) - max_d = r.rnd_int(1, PARAMS["max_depth"].get()); + max_d = PARAMS["max_depth"].get(); if (max_size == 0) max_size = r.rnd_int(1, PARAMS["max_size"].get()); @@ -678,7 +678,7 @@ P SearchSpace::make_program(int max_d, int max_size) n.fixed=true; } else - { + { // we start with a non-terminal auto opt = sample_op(root_type); while (!opt) { opt = sample_op(root_type); @@ -699,7 +699,7 @@ P SearchSpace::make_program(int max_d, int max_size) { /* cout << "queing a node of type " << DataTypeName[a] << endl; */ auto child_spot = Tree.append_child(spot); - queue.push_back(make_tuple(child_spot, a, d)); + queue.push_back(make_tuple(child_spot, a, d+1)); } // Now we actually start the PTC2 procedure to create the program tree @@ -720,10 +720,8 @@ P SearchSpace::make_program(int max_d, int max_size) // Tree.append_child(qspot, sample_terminal(t)); auto opt = sample_terminal(t); - - if (!opt) { // lets push back and try again later - queue.push_back(make_tuple(qspot, t, d)); - continue; + while (!opt) { + opt = sample_terminal(t); } // If we successfully get a terminal, use it @@ -740,9 +738,8 @@ P SearchSpace::make_program(int max_d, int max_size) // TreeIter new_spot = Tree.append_child(qspot, n); // qspot = n; - if (!opt) { // lets push back and try again later - queue.push_back(make_tuple(qspot, t, d)); - continue; + while (!opt) { + opt = sample_op(t); } n = opt.value(); @@ -779,11 +776,10 @@ P SearchSpace::make_program(int max_d, int max_size) // auto newspot = Tree.replace(qspot, sample_terminal(t)); auto opt = sample_terminal(t); - - if (!opt) { // set push back and try again later - queue.push_back(make_tuple(qspot, t, d)); - continue; + while (!opt) { + opt = sample_terminal(t); } + n = opt.value(); auto newspot = Tree.replace(qspot, n); From 55e630b9e636bea7449ac2e66dd890a3bf845b3d Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Sat, 5 Aug 2023 15:28:17 -0400 Subject: [PATCH 087/102] Python wrapper lets user specify batch size and validation partition --- src/bindings/bind_dataset.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/bindings/bind_dataset.cpp b/src/bindings/bind_dataset.cpp index 6ff01ffb..872750d5 100644 --- a/src/bindings/bind_dataset.cpp +++ b/src/bindings/bind_dataset.cpp @@ -31,14 +31,14 @@ void bind_dataset(py::module & m) // ) // construct from X, feature names (and optional validation and batch sizes) with constructor 3. .def(py::init([](const Ref& X, - const vector& vn, + const vector& feature_names, const float validation_size=0.0, const float batch_size=1.0){ return br::Data::Dataset( - X, vn, validation_size, batch_size); + X, feature_names, validation_size, batch_size); }), py::arg("X"), - py::arg("vn"), + py::arg("feature_names"), py::arg("validation_size") = 0.0, py::arg("batch_size") = 1.0 ) @@ -69,15 +69,15 @@ void bind_dataset(py::module & m) // construct from X, y, feature names (and optional validation and batch sizes) with constructor 2. .def(py::init([](const Ref& X, const Ref& y, - const vector& vn, + const vector& feature_names, const float validation_size=0.0, const float batch_size=1.0){ return br::Data::Dataset( - X, y, vn, {}, false, validation_size, batch_size); + X, y, feature_names, {}, false, validation_size, batch_size); }), py::arg("X"), py::arg("y"), - py::arg("vn"), + py::arg("feature_names"), py::arg("validation_size") = 0.0, py::arg("batch_size") = 1.0 ) From ac3b67a0c28b4d88c991a42e1fee86782d89651d Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Sat, 5 Aug 2023 15:29:48 -0400 Subject: [PATCH 088/102] New subtree mutation Also fixed PTC2 generating programs twice as big as it should. Now PTC2 takes into account that terminal nodes are weighted by default, and discounts these nodes while expanding the tree. --- src/brush/estimator.py | 20 +++--- src/search_space.cpp | 158 +++++++++++++++++++++++++++++++++++++++++ src/search_space.h | 158 ++++++++--------------------------------- src/variation.h | 26 +++++++ 4 files changed, 223 insertions(+), 139 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index e16689f8..78f94d27 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -40,7 +40,7 @@ class BrushEstimator(BaseEstimator): Maximum number of nodes in a tree. Use 0 for no limit. cx_prob : float, default 0.9 Probability of applying the crossover variation when generating the offspring - mutation_options : dict, default {"point":0.25, "insert":0.25, "delete":0.25, "toggle_weight":0.25} + mutation_options : dict, default {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight":0.2} A dictionary with keys naming the types of mutation and floating point values specifying the fraction of total mutations to do with that method. functions: dict[str,float] or list[str], default {} @@ -96,7 +96,7 @@ def __init__( max_depth=3, max_size=20, cx_prob=0.9, - mutation_options = {"point":0.25, "insert":0.25, "delete":0.25, "toggle_weight":0.25}, + mutation_options = {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight":0.2}, functions: list[str]|dict[str,float] = {}, initialization="grow", random_state=None, @@ -149,7 +149,7 @@ def _setup_toolbox(self, data_train, data_validation): toolbox.register("population", tools.initRepeat, list, toolbox.createRandom) toolbox.register("getBatch", data_train.get_batch) - toolbox.register("evaluate", self._fitness_function) + toolbox.register("evaluate", self._fitness_function, data=data_train) toolbox.register("evaluateValidation", self._fitness_validation, data=data_validation) return toolbox @@ -194,7 +194,7 @@ def fit(self, X, y): if self.random_state != None: _brush.set_random_state(self.random_state) - self.data_ = self._make_data(X,y) + self.data_ = self._make_data(X,y, validation_size=self.validation_size) # set n classes if relevant if self.mode=="classification": @@ -243,7 +243,7 @@ def fit(self, X, y): return self - def _make_data(self, X, y=None): + def _make_data(self, X, y=None, validation_size=0.0): # This function should not partition data (as it is used in predict). # partitioning is done in fit(). @@ -254,17 +254,19 @@ def _make_data(self, X, y=None): feature_names = X.columns.to_list() X = X.values if isinstance(y, NoneType): - return _brush.Dataset(X, feature_names) + return _brush.Dataset(X, + feature_names=feature_names, validation_size=validation_size) else: - return _brush.Dataset(X, y, feature_names) + return _brush.Dataset(X, y, + feature_names=feature_names, validation_size=validation_size) assert isinstance(X, np.ndarray) # if there is no label, don't include it in library call to Dataset if isinstance(y, NoneType): - return _brush.Dataset(X) + return _brush.Dataset(X, validation_size=validation_size) - return _brush.Dataset(X, y) + return _brush.Dataset(X, y, validation_size=validation_size) def predict(self, X): diff --git a/src/search_space.cpp b/src/search_space.cpp index 2b6d6d9f..436f074f 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -129,6 +129,164 @@ void SearchSpace::init(const Dataset& d, const unordered_map& user } }; +std::optional> SearchSpace::sample_subtree(Node root, int max_d, int max_size) const +{ + // public interface to use PTC2 algorithm + + // PTC is designed to not fail (it will persistently try to find nodes with + // sampling functions). In pop initialization, this shoudnt be a problem, but + // during evolution, due to dynamic changes in node weights by the learners, + // it now may fail. We need to check, before calling it, that it has elements + // in search space to sample + auto ret_match = node_map.at(root.ret_type); + + vector args_w = get_weights(root.ret_type); + + // at least one operator that matches the weight must have positive probability + if (!has_solution_space(args_w.begin(), args_w.end())) + return std::nullopt; + + if ( (terminal_map.find(root.ret_type) == terminal_map.end()) + || (!has_solution_space(terminal_weights.at(root.ret_type).begin(), + terminal_weights.at(root.ret_type).end())) ) + return std::nullopt; + + // we should notice the difference between size of a PROGRAM and a TREE. + // program count weights in its size, while the TREE structure dont. Wenever + // using size of a program/tree, make sure you use the function from the correct class + return PTC2(root, max_d, max_size); +}; + +tree SearchSpace::PTC2(Node root, int max_d, int max_size) const +{ + // PTC2 is agnostic of program type + + // A comment about PTC2 method: + // PTC2 can work with depth or size restriction, but it does not strictly + // satisfies these conditions all time. Given a `max_size` and `max_depth` + // parameters, the real maximum size that can occur is `max_size` plus the + // highest operator arity, and the real maximum depth is `max_depth` plus one. + + auto Tree = tree(); + + /* fmt::print("building program with max size {}, max depth {}",max_size,max_d); */ + + // Queue of nodes that need children + vector> queue; + + /* cout << "chose " << n.name << endl; */ + // auto spot = Tree.set_head(n); + /* cout << "inserting...\n"; */ + auto spot = Tree.insert(Tree.begin(), root); + // node depth + int d = 1; + // current tree size + int s = 1; + //For each argument position a of n, Enqueue(a; g) + for (auto a : root.arg_types) + { + /* cout << "queing a node of type " << DataTypeName[a] << endl; */ + auto child_spot = Tree.append_child(spot); + queue.push_back(make_tuple(child_spot, a, d)); + } + + Node n; + // Now we actually start the PTC2 procedure to create the program tree + /* cout << "queue size: " << queue.size() << endl; */ + /* cout << "entering first while loop...\n"; */ + while ( 3*(queue.size()-1) + s < max_size && queue.size() > 0) + { + // by default, terminals are weighted (counts as 3 nodes in program size). + // since every spot in queue has potential to be a terminal, we multiply + // its size by 3. Subtracting one due to the fact that this loop will + // always insert a non terminal (which by default has weights off). + // this way, we can have PTC2 working properly. + + /* cout << "queue size: " << queue.size() << endl; */ + auto [qspot, t, d] = RandomDequeue(queue); + + /* cout << "current depth: " << d << endl; */ + if (d == max_d) + { + // choose terminal of matching type + /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ + // qspot = sample_terminal(t); + // Tree.replace(qspot, sample_terminal(t)); + // Tree.append_child(qspot, sample_terminal(t)); + + auto opt = sample_terminal(t); + while (!opt) + opt = sample_terminal(t); + + // If we successfully get a terminal, use it + n = opt.value(); + + Tree.replace(qspot, n); + } + else + { + //choose a nonterminal of matching type + /* cout << "getting op of type " << DataTypeName[t] << endl; */ + auto opt = sample_op(t); + /* cout << "chose " << n.name << endl; */ + // TreeIter new_spot = Tree.append_child(qspot, n); + // qspot = n; + + while (!opt) + opt = sample_op(t); + + n = opt.value(); + + auto newspot = Tree.replace(qspot, n); + + // For each arg of n, add to queue + for (auto a : n.arg_types) + { + /* cout << "queing a node of type " << DataTypeName[a] << endl; */ + // queue.push_back(make_tuple(new_spot, a, d+1)); + auto child_spot = Tree.append_child(newspot); + + queue.push_back(make_tuple(child_spot, a, d+1)); + } + } + + ++s; + /* cout << "current tree size: " << s << endl; */ + } + /* cout << "entering second while loop...\n"; */ + while (queue.size() > 0) + { + if (queue.size() == 0) + break; + + /* cout << "queue size: " << queue.size() << endl; */ + + auto [qspot, t, d] = RandomDequeue(queue); + + /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ + // Tree.append_child(qspot, sample_terminal(t)); + // qspot = sample_terminal(t); + // auto newspot = Tree.replace(qspot, sample_terminal(t)); + + auto opt = sample_terminal(t); + while (!opt) { + opt = sample_terminal(t); + } + + n = opt.value(); + + auto newspot = Tree.replace(qspot, n); + } + + /* cout << "final tree:\n" */ + /* << Tree.begin().node->get_model() << "\n" */ + /* << Tree.begin().node->get_tree_model(true) << endl; */ + /* << Tree.get_model() << "\n" */ + /* << Tree.get_model(true) << endl; // pretty */ + + return Tree; +}; + RegressorProgram SearchSpace::make_regressor(int max_d, int max_size) { return make_program(max_d, max_size); diff --git a/src/search_space.h b/src/search_space.h index 69d6eb50..ac751a65 100644 --- a/src/search_space.h +++ b/src/search_space.h @@ -520,10 +520,18 @@ struct SearchSpace ).second; }; + /// @brief create a subtree with maximum size and depth restrictions and root of type `root_type` + /// @param root_type return type + /// @param max_d the maximum depth + /// @param max_size the maximum size of the tree (will be sampled between [1, max_size]) + /// @return `std::optional` that may contain a tree + std::optional> sample_subtree(Node root, int max_d, int max_size) const; + /// @brief prints the search space map. void print() const; private: + tree PTC2(Node root, int max_d, int max_size) const; template requires (!is_in_v) @@ -623,12 +631,6 @@ T RandomDequeue(std::vector& Q) template P SearchSpace::make_program(int max_d, int max_size) { - // A comment about PTC2 method: - // PTC2 can work with depth or size restriction, but it does not strictly - // satisfies these conditions all time. Given a `max_size` and `max_depth` - // parameters, the real maximum size that can occur is `max_size` plus the - // highest operator arity, and the real maximum depth is `max_depth` plus one. - if (max_d == 0) max_d = PARAMS["max_depth"].get(); if (max_size == 0) @@ -637,14 +639,8 @@ P SearchSpace::make_program(int max_d, int max_size) DataType root_type = DataTypeEnum::value; ProgramType program_type = P::program_type; // ProgramType program_type = ProgramTypeEnum::value; - + auto Tree = tree(); - - /* fmt::print("building program with max size {}, max depth {}",max_size,max_d); */ - - // Queue of nodes that need children - vector> queue; - if (max_size == 1) { // auto root = Tree.insert(Tree.begin(), sample_terminal(root_type)); @@ -659,137 +655,39 @@ P SearchSpace::make_program(int max_d, int max_size) HANDLE_ERROR_THROW(msg); } - auto root = Tree.insert(Tree.begin(), opt.value()); + Tree.insert(Tree.begin(), opt.value()); } - else // Our program can (and will) be grater than 1 node - { + else {// Our program can (and will) be grater than 1 node + + // building the root node for each program case. We give the root, and it + // fills the rest of the tree + Node root; + // building the root node for each program case - Node n; if (P::program_type == ProgramType::BinaryClassifier) { - n = get(NodeType::Logistic, DataType::ArrayF, Signature()); - n.set_prob_change(0.0); - n.fixed=true; + root = get(NodeType::Logistic, DataType::ArrayF, Signature()); + root.set_prob_change(0.0); + root.fixed=true; + } else if (P::program_type == ProgramType::MulticlassClassifier) { - n = get(NodeType::Softmax, DataType::MatrixF, Signature()); - n.set_prob_change(0.0); - n.fixed=true; + root = get(NodeType::Softmax, DataType::MatrixF, Signature()); + root.set_prob_change(0.0); + root.fixed=true; } - else - { // we start with a non-terminal + else { + // we start with a non-terminal (can be replaced inside PTC2 though, if max_size==1) auto opt = sample_op(root_type); while (!opt) { opt = sample_op(root_type); } - n = opt.value(); - } - - /* cout << "chose " << n.name << endl; */ - // auto spot = Tree.set_head(n); - /* cout << "inserting...\n"; */ - auto spot = Tree.insert(Tree.begin(), n); - // node depth - int d = 1; - // current tree size - int s = 1; - //For each argument position a of n, Enqueue(a; g) - for (auto a : n.arg_types) - { - /* cout << "queing a node of type " << DataTypeName[a] << endl; */ - auto child_spot = Tree.append_child(spot); - queue.push_back(make_tuple(child_spot, a, d+1)); - } - - // Now we actually start the PTC2 procedure to create the program tree - /* cout << "queue size: " << queue.size() << endl; */ - /* cout << "entering first while loop...\n"; */ - while (queue.size() + s < max_size && queue.size() > 0) - { - /* cout << "queue size: " << queue.size() << endl; */ - auto [qspot, t, d] = RandomDequeue(queue); - - /* cout << "current depth: " << d << endl; */ - if (d == max_d) - { - // choose terminal of matching type - /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ - // qspot = sample_terminal(t); - // Tree.replace(qspot, sample_terminal(t)); - // Tree.append_child(qspot, sample_terminal(t)); - - auto opt = sample_terminal(t); - while (!opt) { - opt = sample_terminal(t); - } - - // If we successfully get a terminal, use it - n = opt.value(); - - Tree.replace(qspot, n); - } - else - { - //choose a nonterminal of matching type - /* cout << "getting op of type " << DataTypeName[t] << endl; */ - auto opt = sample_op(t); - /* cout << "chose " << n.name << endl; */ - // TreeIter new_spot = Tree.append_child(qspot, n); - // qspot = n; - - while (!opt) { - opt = sample_op(t); - } - - n = opt.value(); - - auto newspot = Tree.replace(qspot, n); - - // For each arg of n, add to queue - for (auto a : n.arg_types) - { - /* cout << "queing a node of type " << DataTypeName[a] << endl; */ - // queue.push_back(make_tuple(new_spot, a, d+1)); - auto child_spot = Tree.append_child(newspot); - - queue.push_back(make_tuple(child_spot, a, d+1)); - } - } - - ++s; - /* cout << "current tree size: " << s << endl; */ - } - /* cout << "entering second while loop...\n"; */ - while (queue.size() > 0) - { - if (queue.size() == 0) - break; - - /* cout << "queue size: " << queue.size() << endl; */ - - auto [qspot, t, d] = RandomDequeue(queue); - - /* cout << "getting " << DataTypeName[t] << " terminal\n"; */ - // Tree.append_child(qspot, sample_terminal(t)); - // qspot = sample_terminal(t); - // auto newspot = Tree.replace(qspot, sample_terminal(t)); - - auto opt = sample_terminal(t); - while (!opt) { - opt = sample_terminal(t); - } - - n = opt.value(); - - auto newspot = Tree.replace(qspot, n); + root = opt.value(); } + + Tree = PTC2(root, max_d, max_size); } - /* cout << "final tree:\n" */ - /* << Tree.begin().node->get_model() << "\n" */ - /* << Tree.begin().node->get_tree_model(true) << endl; */ - /* << Tree.get_model() << "\n" */ - /* << Tree.get_model(true) << endl; // pretty */ return P(*this,Tree); }; diff --git a/src/variation.h b/src/variation.h index 6682a561..8f1717cd 100644 --- a/src/variation.h +++ b/src/variation.h @@ -144,6 +144,31 @@ inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpac return true; } +/// @brief replaces the subtree rooted in `spot` +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space to generate a compatible subtree +/// @return boolean indicating the success (true) or fail (false) of the operation +inline bool subtree_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +{ + auto spot_type = spot.node->data.ret_type; + auto max_size = PARAMS["max_size"].get() - (Tree.size() - Tree.size(spot)); + auto max_depth = PARAMS["max_depth"].get() - (Tree.depth(spot)); + + // sample subtree uses PTC2, which operates on depth and size of the tree + // (and not on the program!). we shoudn't care for weights here + auto subtree = SS.sample_subtree(spot.node->data, max_depth, max_size); + + if (!subtree) // there is no terminal with compatible arguments + return false; + + // if optional contains a Node, we access its contained value + Tree.erase_children(spot); + Tree.replace(spot, subtree.value().begin()); + + return true; +} + /** * @brief Stochastically mutate a program. * @@ -248,6 +273,7 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS {"insert", insert_mutation}, {"delete", delete_mutation}, {"point", point_mutation}, + {"subtree", subtree_mutation}, {"toggle_weight", toggle_weight_mutation} }; From a564896995748226344015de14e53f6dfa7506b5 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Sat, 5 Aug 2023 15:30:50 -0400 Subject: [PATCH 089/102] Updated tests after new mutation --- tests/cpp/test_program.cpp | 4 ++++ tests/cpp/test_variation.cpp | 6 +++--- tests/python/test_brush.py | 2 +- tests/python/test_params.py | 3 ++- 4 files changed, 10 insertions(+), 5 deletions(-) diff --git a/tests/cpp/test_program.cpp b/tests/cpp/test_program.cpp index a8da41b8..3fb2de8b 100644 --- a/tests/cpp/test_program.cpp +++ b/tests/cpp/test_program.cpp @@ -203,6 +203,10 @@ TEST(Operators, ProgramSizeAndDepthPARAMS) // and PTC2 uses the tree size (not the program size), it is not // expected that initial trees will strictly respect `max_size`. ASSERT_TRUE(PRG.size() > 0); // size is always positive + + // PTC2: maximum size is s+max(arity). Since in Brush terminals are + // weighted by default, we set it to 3*max(arity) + ASSERT_TRUE(PRG.size() <= s+3*4); ASSERT_TRUE(PRG.depth() <= d+1); ASSERT_TRUE(PRG.depth() > 0); // depth is always positive diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index 41c60389..d7058714 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -10,7 +10,7 @@ TEST(Operators, InsertMutationWorks) // To understand design implementation of this test, check Mutation test PARAMS["mutation_options"] = { - {"point", 0.0}, {"insert", 1.0}, {"delete", 0.0}, {"toggle_weight", 0.0} + {"point", 0.0}, {"insert", 1.0}, {"delete", 0.0}, {"subtree", 0.0}, {"toggle_weight", 0.0} }; // retrieving the options to check if everything was set right @@ -117,7 +117,7 @@ TEST(Operators, Mutation) // TODO: set random seed PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight", 0.25} }; MatrixXf X(10,2); @@ -193,7 +193,7 @@ TEST(Operators, Mutation) TEST(Operators, MutationSizeAndDepthLimit) { PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight", 0.25} }; MatrixXf X(10,2); diff --git a/tests/python/test_brush.py b/tests/python/test_brush.py index 38c381dc..5e38898f 100644 --- a/tests/python/test_brush.py +++ b/tests/python/test_brush.py @@ -61,7 +61,7 @@ def test_fit(setup, brush_args, request): logging.error(traceback.format_exc()) -# def test_random_state(): +# def test_random_state(): # TODO: make it work # test_y = np.array( [1. , 0. , 1.4, 1. , 0. , 1. , 1. , 0. , 0. , 0. ]) # test_X = np.array([[1.1, 2.0, 3.0, 4.0, 5.0, 6.5, 7.0, 8.0, 9.0, 10.0], # [2.0, 1.2, 6.0, 4.0, 5.0, 8.0, 7.0, 5.0, 9.0, 10.0]]).T diff --git a/tests/python/test_params.py b/tests/python/test_params.py index e6166c4e..069a8987 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -60,11 +60,12 @@ def _change_and_wait(config): 'mutation_options': {'point' : 0.0, 'insert' : 0.0, 'delete' : 0.0, + 'subtree' : 0.0, 'toggle_weight': 0.0} } # We need to guarantee order to use the index correctly - mutations = ['point', 'insert', 'delete', 'toggle_weight'] + mutations = ['point', 'insert', 'delete', 'subtree', 'toggle_weight'] for i, m in enumerate(mutations): params['mutation_options'][m] = 0 if i != index else 1.0 From 1a4c968732ccb610cb74116ce8137290392291fe Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Mon, 7 Aug 2023 09:39:01 -0400 Subject: [PATCH 090/102] Improved how PTC2 deals with weighted terminals --- src/search_space.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/search_space.cpp b/src/search_space.cpp index 436f074f..625685c9 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -222,6 +222,9 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const n = opt.value(); Tree.replace(qspot, n); + + s=s+2; // (*) and (weight) nodes of the terminal. terminal itself is + // incremented at the end of the while loop } else { From 9b1d26eac70d79d266922e90b361df3d1d708f18 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 8 Aug 2023 13:47:22 -0400 Subject: [PATCH 091/102] Fixed bug of different array types :D --- src/data/data.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/data/data.cpp b/src/data/data.cpp index c2fdaafc..b80668df 100644 --- a/src/data/data.cpp +++ b/src/data/data.cpp @@ -90,7 +90,7 @@ State check_type(const ArrayXf& x) } else { - if(isCategorical && uniqueMap.size() < 10) + if(isCategorical && uniqueMap.size() <= 10) { tmp = ArrayXi(x.cast()); } From 1ac652cc22bb505719312ce8f462d6f9867789d0 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 8 Aug 2023 13:48:20 -0400 Subject: [PATCH 092/102] Implemented node weight initialization --- src/search_space.cpp | 71 +++++++++++++++++++++++++++++++++++++------- 1 file changed, 60 insertions(+), 11 deletions(-) diff --git a/src/search_space.cpp b/src/search_space.cpp index 625685c9..94a93c0f 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -55,13 +55,47 @@ vector generate_terminals(const Dataset& d) feature_name ); - float prob_change = 1.0; + float prob_change = 1.0; // default value // if the value can be casted to float array, we can calculate slope - if (std::holds_alternative(value)) { + if (std::holds_alternative(value)) + { prob_change = calc_initial_weight(std::get(value), d.y); } - + else if (std::holds_alternative(value)) + { + // for each variable we create a one-vs-all binary variable, then + // calculate slope. Final value will be the average of slopes + + auto tmp = std::get(value); + + //get number of unique values + std::map uniqueMap; + for(int i = 0; i < tmp.size(); i++) + uniqueMap[(float)tmp(i)] = true; + + ArrayXf slopes = ArrayXf::Ones(uniqueMap.size()); + int slopesIterator = 0; + for (const auto& pair : uniqueMap) + { + auto one_vs_all = ArrayXf::Ones(tmp.size()).array() * (tmp.array()==pair.first).cast(); + + slopes[slopesIterator++] = calc_initial_weight(one_vs_all, d.y); + } + + prob_change = slopes.mean(); + } + else if (std::holds_alternative(value)) + { + auto tmp = std::get(value).template cast(); + prob_change = calc_initial_weight(tmp, d.y); + } + else + { + auto msg = fmt::format("Brush coudn't calculate the initial weight of variable {}\n",feature_name); + HANDLE_ERROR_THROW(msg); + } + n.set_prob_change( prob_change ); terminals.push_back(n); @@ -71,18 +105,32 @@ vector generate_terminals(const Dataset& d) ++i; }; - // add constants - float num_const_prob_change = calc_initial_weight(VectorXf::Ones(d.y.size()), d.y); + // iterate through terminals and take the average of values of same signature + auto signature_avg = [terminals](DataType ret_type){ + float sum = 0.0; + int count = 0; + + for (const auto& n : terminals) { + if (n.ret_type == ret_type) { + sum += n.get_prob_change(); + count++; + } + } + + return sum / count; + }; auto cXf = Node(NodeType::Constant, Signature{}, true, "C"); - // cXf.set_prob_change(num_const_prob_change); + cXf.set_prob_change(signature_avg(cXf.ret_type)); terminals.push_back(cXf); auto cXi = Node(NodeType::Constant, Signature{}, true, "C"); - // cXi.set_prob_change(num_const_prob_change); + cXi.set_prob_change(signature_avg(cXi.ret_type)); terminals.push_back(cXi); - terminals.push_back( Node(NodeType::Constant, Signature{}, false, "C")); + auto cXb = Node(NodeType::Constant, Signature{}, false, "C"); + cXb.set_prob_change(signature_avg(cXb.ret_type)); + terminals.push_back(cXb); return terminals; }; @@ -222,9 +270,6 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const n = opt.value(); Tree.replace(qspot, n); - - s=s+2; // (*) and (weight) nodes of the terminal. terminal itself is - // incremented at the end of the while loop } else { @@ -253,7 +298,11 @@ tree SearchSpace::PTC2(Node root, int max_d, int max_size) const } } + // increment is different based on node weights ++s; + if (n.get_is_weighted()) + s += 2; + /* cout << "current tree size: " << s << endl; */ } /* cout << "entering second while loop...\n"; */ From 70208814ffc41e49d2dfcedef84fa8de7fcf3d18 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 8 Aug 2023 13:51:56 -0400 Subject: [PATCH 093/102] Removed mutation trace --- src/variation.h | 41 ++--------------------------------------- 1 file changed, 2 insertions(+), 39 deletions(-) diff --git a/src/variation.h b/src/variation.h index 8f1717cd..23c13ab7 100644 --- a/src/variation.h +++ b/src/variation.h @@ -217,23 +217,6 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS auto options = PARAMS["mutation_options"].get>(); - // whether we should write everything that happened inside the method - if (PARAMS.value("write_mutation_trace", false)==true) { - // Default fields of the trace. Initialize with default values, which are - // gradually changed throughout the execution of the method. - PARAMS["mutation_trace"] = json({ - {"parent", child.get_model("compact", true)}, - {"spot_weights", weights}, - {"mutation_weights", options}, - // default values, to be changed in case mutation works - {"spot", "not selected"}, - {"mutation", "not selected"}, - {"child", "failed to generate"}, - {"status", "initialized weight vectors"}, - {"success", "false"} - }); - } - if (std::all_of(weights.begin(), weights.end(), [](const auto& w) { return w<=0.0; })) @@ -244,12 +227,6 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS auto spot = r.select_randomly(child.Tree.begin(), child.Tree.end(), weights.begin(), weights.end()); - // whether we should write everything that happened inside the method - if (PARAMS.value("write_mutation_trace", false)==true) { - PARAMS["mutation_trace"]["spot"] = spot.node->get_model(false); - PARAMS["mutation_trace"]["status"] = "sampled the mutation spot"; - } - if (std::all_of(options.begin(), options.end(), [](const auto& kv) { return kv.second<=0.0; })) @@ -287,27 +264,13 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS // apply the mutation and check if it succeeded bool success = it->second(child.Tree, spot, SS); - if (PARAMS.value("write_mutation_trace", false)==true) { - PARAMS["mutation_trace"]["mutation"] = choice; - PARAMS["mutation_trace"]["status"] = "sampled and aplied the mutation"; - if (success) - PARAMS["mutation_trace"]["child"] = child.get_model("compact", true); - } - if (success && ( (child.size() <= PARAMS["max_size"].get() ) && (child.depth() <= PARAMS["max_depth"].get()) )){ - // success is true only if mutation returned a valid program - if (PARAMS.value("write_mutation_trace", false)==true) - PARAMS["mutation_trace"]["success"] = true; - + return child; } else { - // here we have a string in PARAMS["mutation_trace"]["child"], - // but success is false since it didnt return an valid program - if (PARAMS.value("write_mutation_trace", false)==true) - PARAMS["mutation_trace"]["status"] = "children exceeds max_size or max_depth"; - + return std::nullopt; } }; From 0b718a311e957d54300017a474b185533c1f5826 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 8 Aug 2023 16:53:59 -0400 Subject: [PATCH 094/102] Hardcoded weight initialization test --- tests/cpp/test_search_space.cpp | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/tests/cpp/test_search_space.cpp b/tests/cpp/test_search_space.cpp index 84dd9bf3..02eaaf19 100644 --- a/tests/cpp/test_search_space.cpp +++ b/tests/cpp/test_search_space.cpp @@ -38,4 +38,31 @@ TEST(SearchSpace, Initialization) SS.print(); // dtable_fit.print(); // dtable_predict.print(); + + // manually calculated. last value is the avg of prev values + ArrayXf expected_weights_Xf(4); // 4 elements (x3, x4, x5 and c) + expected_weights_Xf << 0.80240685, 0.19270448, 0.5994426, 0.531518; + + auto actual_weights_f = SS.terminal_weights.at(DataType::ArrayF); + Eigen::Map actual_weights_Xf(actual_weights_f.data(), actual_weights_f.size()); + + ASSERT_TRUE(expected_weights_Xf.isApprox(actual_weights_Xf)); + + + ArrayXf expected_weights_Xi(2); // 2 elements (x2 and c) + expected_weights_Xi << 0.2736814, 0.2736814; + + auto actual_weights_i = SS.terminal_weights.at(DataType::ArrayI); + Eigen::Map actual_weights_Xi(actual_weights_i.data(), actual_weights_i.size()); + + ASSERT_TRUE(expected_weights_Xi.isApprox(actual_weights_Xi)); + + + ArrayXf expected_weights_Xb(2); // 2 elements (x0 and c) + expected_weights_Xb << 0.8117065, 0.8117065; + + auto actual_weights_b = SS.terminal_weights.at(DataType::ArrayB); + Eigen::Map actual_weights_Xb(actual_weights_b.data(), actual_weights_b.size()); + + ASSERT_TRUE(expected_weights_Xb.isApprox(actual_weights_Xb)); } \ No newline at end of file From 351e195f55559a7d1169fb09ef5a704bd4696072 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 8 Aug 2023 16:54:33 -0400 Subject: [PATCH 095/102] spacing --- src/search_space.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/search_space.cpp b/src/search_space.cpp index 94a93c0f..4ea0b518 100644 --- a/src/search_space.cpp +++ b/src/search_space.cpp @@ -26,8 +26,8 @@ float calc_initial_weight(const ArrayXf& value, const ArrayXf& y) // The feature should be the first value, and the true value the second // (it will divide covar(arg1, arg2) by var(arg2)). // Since z-score normalizes so mean=0 and std=1, then order doesnt matter - float prob_change = std::abs(slope(data.col(0).array(), // x - data.col(1).array())); // y + float prob_change = std::abs(slope(data.col(0).array() , // x=variable + data.col(1).array() )); // y=target return prob_change; } From bcd320061264a5c2d59b99dc4f4ab305e964956a Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 11 Aug 2023 14:21:50 -0400 Subject: [PATCH 096/102] Regressor now uses MSE (instead of squashed version of the metric) --- src/brush/estimator.py | 50 +++++++++++++++++++++++------------------- 1 file changed, 28 insertions(+), 22 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 78f94d27..cf0b1d7b 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -128,9 +128,7 @@ def _setup_toolbox(self, data_train, data_validation): # Minimizing/maximizing problem: negative/positive weight, respectively. # Our classification is using the error as a metric # Comparing fitnesses: https://deap.readthedocs.io/en/master/api/base.html#deap.base.Fitness - creator.create("FitnessMulti", base.Fitness, weights=(+1.0,-1.0)) - - # TODO: make this weights attributes of each derivate class (creator is global) + creator.create("FitnessMulti", base.Fitness, weights=self.weights) # create Individual class, inheriting from self.Individual with a fitness attribute creator.create("Individual", DeapIndividual, fitness=creator.FitnessMulti) @@ -221,17 +219,23 @@ def fit(self, X, y): self.archive_ = archive self.logbook_ = logbook - # Selecting the best estimator using validation data and multi-criteria decision making - points = np.array([self.toolbox_.evaluateValidation(ind) for ind in self.archive_]) - points = points*np.array([+1.0,-1.0]) #Multiply by the weights TODO: use weights here instead of hardcoded + closest_idx = 0 + if self.validation_size==0.0: + # Selecting the best estimator using training data + # (train data==val data if validation_size is set to 0.0) + # and multi-criteria decision making + points = np.array([self.toolbox_.evaluateValidation(ind) for ind in self.archive_]) - # Normalizing - min_vals = np.min(points, axis=0) - max_vals = np.max(points, axis=0) - points = (points - min_vals) / (max_vals - min_vals) - - reference = np.array([0, 0]) - closest_idx = np.argmin( np.linalg.norm(points - reference, axis=1) ) + #Multiply by the weights so reference can be agnostic of min/max problems + points = points*np.array(self.weights) + + # Normalizing + min_vals = np.min(points, axis=0) + max_vals = np.max(points, axis=0) + points = (points - min_vals) / (max_vals - min_vals) + + reference = np.array([0, 0]) + closest_idx = np.argmin( np.linalg.norm(points - reference, axis=1) ) self.best_estimator_ = self.archive_[closest_idx].prg @@ -290,6 +294,7 @@ def predict(self, X): def get_params(self): return {k:v for k,v in self.__dict__.items() if not k.endswith('_')} + class BrushClassifier(BrushEstimator,ClassifierMixin): """Brush for classification. @@ -310,13 +315,16 @@ class BrushClassifier(BrushEstimator,ClassifierMixin): def __init__( self, **kwargs): super().__init__(mode='classification',**kwargs) + # Weight of each objective (+ for maximization, - for minimization) + self.weights = (+1.0,-1.0) + def _fitness_validation(self, ind, data: _brush.Dataset): + # Fitness without fitting the expression, used with validation data return ( # (accuracy, size) (data.y==ind.prg.predict(data)).sum() / data.y.shape[0], ind.prg.size() ) - def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) return ( # (accuracy, size) @@ -379,15 +387,17 @@ class BrushRegressor(BrushEstimator, RegressorMixin): def __init__(self, **kwargs): super().__init__(mode='regressor',**kwargs) + # Weight of each objective (+ for maximization, - for minimization) + self.weights = (-1.0,-1.0) def _fitness_validation(self, ind, data: _brush.Dataset): + # Fitness without fitting the expression, used with validation data + MSE = np.mean( (data.y-ind.prg.predict(data))**2 ) if not np.isfinite(MSE): # numeric erros, np.nan, +-np.inf MSE = np.inf - # We are squash the error and making it a maximization problem - return ( 1/(1+MSE), ind.prg.size() ) - + return ( MSE, ind.prg.size() ) def _fitness_function(self, ind, data: _brush.Dataset): ind.prg.fit(data) @@ -396,9 +406,7 @@ def _fitness_function(self, ind, data: _brush.Dataset): if not np.isfinite(MSE): # numeric erros, np.nan, +-np.inf MSE = np.inf - # We are squash the error and making it a maximization problem - return ( 1/(1+MSE), ind.prg.size() ) - + return ( MSE, ind.prg.size() ) def _make_individual(self): if self.initialization not in ["grow", "full"]: @@ -410,8 +418,6 @@ def _make_individual(self): self.max_depth, (0 if self.initialization=='grow' else self.max_size)) ) - - # Under development # class BrushRepresenter(BrushEstimator, TransformerMixin): # """Brush for representation learning. From 84fabf445138a87a65aaabf1f6514b9b1217d376 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Fri, 11 Aug 2023 16:09:30 -0400 Subject: [PATCH 097/102] Fixed wrong use of validation partition and use of MDCM --- src/brush/estimator.py | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index cf0b1d7b..df9326bd 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -219,25 +219,32 @@ def fit(self, X, y): self.archive_ = archive self.logbook_ = logbook - closest_idx = 0 - if self.validation_size==0.0: + final_ind_idx = 0 + + # Each individual is a point in the Multi-Objective space. We multiply + # the fitness by the weights so greater numbers are always better + points = np.array([self.toolbox_.evaluateValidation(ind) for ind in self.archive_]) + points = points*np.array(self.weights) + + if self.validation_size==0.0: # Using the multi-criteria decision making on training data # Selecting the best estimator using training data # (train data==val data if validation_size is set to 0.0) # and multi-criteria decision making - points = np.array([self.toolbox_.evaluateValidation(ind) for ind in self.archive_]) - - #Multiply by the weights so reference can be agnostic of min/max problems - points = points*np.array(self.weights) # Normalizing min_vals = np.min(points, axis=0) max_vals = np.max(points, axis=0) points = (points - min_vals) / (max_vals - min_vals) - reference = np.array([0, 0]) - closest_idx = np.argmin( np.linalg.norm(points - reference, axis=1) ) + # Reference should be best value each obj. can have (after normalization) + reference = np.array([1, 1]) + + # closest to the reference + final_ind_idx = np.argmin( np.linalg.norm(points - reference, axis=1) ) + else: # Best in obj.1 (loss) in validation data + final_ind_idx = np.argmax( points[:, 0] ) - self.best_estimator_ = self.archive_[closest_idx].prg + self.best_estimator_ = self.archive_[final_ind_idx].prg if self.verbosity > 0: print(f'best model {self.best_estimator_.get_model()}'+ From 0fb030f8456050e3e206110bc4c811304c03c4d6 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 15 Aug 2023 14:20:19 -0400 Subject: [PATCH 098/102] Mutation `toggle_weight` splitted in two different mutations Because sometimes it works as an insert mutation, and sometimes as a delete mutation. Since we want to use learners to optimize mutation choice, the less ambiguity the better --- src/brush/estimator.py | 4 ++-- src/variation.h | 41 ++++++++++++++++++++++++++---------- tests/cpp/test_data.cpp | 2 +- tests/cpp/test_variation.cpp | 6 +++--- tests/python/test_params.py | 13 ++++++------ 5 files changed, 43 insertions(+), 23 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index df9326bd..ab988bbe 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -40,7 +40,7 @@ class BrushEstimator(BaseEstimator): Maximum number of nodes in a tree. Use 0 for no limit. cx_prob : float, default 0.9 Probability of applying the crossover variation when generating the offspring - mutation_options : dict, default {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight":0.2} + mutation_options : dict, default {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight_on":0.1, "toggle_weight_off":0.1} A dictionary with keys naming the types of mutation and floating point values specifying the fraction of total mutations to do with that method. functions: dict[str,float] or list[str], default {} @@ -96,7 +96,7 @@ def __init__( max_depth=3, max_size=20, cx_prob=0.9, - mutation_options = {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight":0.2}, + mutation_options = {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight_on":0.1, "toggle_weight_off":0.1}, functions: list[str]|dict[str,float] = {}, initialization="grow", random_state=None, diff --git a/src/variation.h b/src/variation.h index 23c13ab7..dcfd8288 100644 --- a/src/variation.h +++ b/src/variation.h @@ -132,15 +132,32 @@ inline bool delete_mutation(tree& Tree, Iter spot, const SearchSpace& SS) return true; }; -/// @brief toggle the node's weight on or off. +/// @brief toggle the node's weight ON. /// @param Tree the program tree /// @param spot an iterator to the node that is being mutated /// @param SS the search space (unused) /// @return boolean indicating the success (true) or fail (false) of the operation -inline bool toggle_weight_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +inline bool toggle_weight_on_mutation(tree& Tree, Iter spot, const SearchSpace& SS) { - spot.node->data.set_is_weighted(!spot.node->data.get_is_weighted()); + if (spot.node->data.get_is_weighted()==true // cant turn on whats already on + || !IsWeighable(spot.node->data.ret_type)) // does not accept weights (e.g. boolean) + return false; // false indicates that mutation failed and should return std::nullopt + spot.node->data.set_is_weighted(true); + return true; +} + +/// @brief toggle the node's weight OFF. +/// @param Tree the program tree +/// @param spot an iterator to the node that is being mutated +/// @param SS the search space (unused) +/// @return boolean indicating the success (true) or fail (false) of the operation +inline bool toggle_weight_off_mutation(tree& Tree, Iter spot, const SearchSpace& SS) +{ + if (spot.node->data.get_is_weighted()==false) + return false; + + spot.node->data.set_is_weighted(false); return true; } @@ -176,8 +193,10 @@ inline bool subtree_mutation(tree& Tree, Iter spot, const SearchSpace& SS) * * - point mutation changes a single node. * - insertion mutation inserts a node as the parent of an existing node, and fills in the other arguments. - * - deletion mutation deletes a node - * - toggle_weight mutation turns a node's weight on or off. + * - deletion mutation deletes a node. + * - subtree mutation inserts a new subtree into the program. + * - toggle_weight_on mutation turns a node's weight ON. + * - toggle_weight_off mutation turns a node's weight OFF. * * Every mutation has a probability (weight) based on global parameters. The * spot where the mutation will take place is sampled based on attribute @@ -247,11 +266,12 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS using MutationFunc = std::function&, Iter, const SearchSpace&)>; std::map mutations{ - {"insert", insert_mutation}, - {"delete", delete_mutation}, - {"point", point_mutation}, - {"subtree", subtree_mutation}, - {"toggle_weight", toggle_weight_mutation} + {"insert", insert_mutation}, + {"delete", delete_mutation}, + {"point", point_mutation}, + {"subtree", subtree_mutation}, + {"toggle_weight_on", toggle_weight_on_mutation}, + {"toggle_weight_off", toggle_weight_off_mutation} }; // Try to find the mutation function based on the choice @@ -270,7 +290,6 @@ std::optional> mutate(const Program& parent, const SearchSpace& SS return child; } else { - return std::nullopt; } }; diff --git a/tests/cpp/test_data.cpp b/tests/cpp/test_data.cpp index bd1b3208..09893c2c 100644 --- a/tests/cpp/test_data.cpp +++ b/tests/cpp/test_data.cpp @@ -30,7 +30,7 @@ TEST(Data, MixedVariableTypes) // We need to set at least the mutation options (and respective // probabilities) in order to call PRG.predict() PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight", 0.25} + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"toggle_weight_on", 0.125}, {"toggle_weight_off", 0.125} }; MatrixXf X(5,3); diff --git a/tests/cpp/test_variation.cpp b/tests/cpp/test_variation.cpp index d7058714..d0eb9bcf 100644 --- a/tests/cpp/test_variation.cpp +++ b/tests/cpp/test_variation.cpp @@ -10,7 +10,7 @@ TEST(Operators, InsertMutationWorks) // To understand design implementation of this test, check Mutation test PARAMS["mutation_options"] = { - {"point", 0.0}, {"insert", 1.0}, {"delete", 0.0}, {"subtree", 0.0}, {"toggle_weight", 0.0} + {"point", 0.0}, {"insert", 1.0}, {"delete", 0.0}, {"subtree", 0.0}, {"toggle_weight_on", 0.0}, {"toggle_weight_off", 0.0} }; // retrieving the options to check if everything was set right @@ -117,7 +117,7 @@ TEST(Operators, Mutation) // TODO: set random seed PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight", 0.25} + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight_on", 0.125}, {"toggle_weight_off", 0.125} }; MatrixXf X(10,2); @@ -193,7 +193,7 @@ TEST(Operators, Mutation) TEST(Operators, MutationSizeAndDepthLimit) { PARAMS["mutation_options"] = { - {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight", 0.25} + {"point",0.25}, {"insert", 0.25}, {"delete", 0.25}, {"subtree", 0.0}, {"toggle_weight_on", 0.125}, {"toggle_weight_off", 0.125} }; MatrixXf X(10,2); diff --git a/tests/python/test_params.py b/tests/python/test_params.py index 069a8987..03d08bc4 100644 --- a/tests/python/test_params.py +++ b/tests/python/test_params.py @@ -57,15 +57,16 @@ def _change_and_wait(config): 'max_gen' : 100, 'max_depth': 5, 'max_size' : 50, - 'mutation_options': {'point' : 0.0, - 'insert' : 0.0, - 'delete' : 0.0, - 'subtree' : 0.0, - 'toggle_weight': 0.0} + 'mutation_options': {'point' : 0.0, + 'insert' : 0.0, + 'delete' : 0.0, + 'subtree' : 0.0, + 'toggle_weight_on' : 0.0, + 'toggle_weight_off': 0.0} } # We need to guarantee order to use the index correctly - mutations = ['point', 'insert', 'delete', 'subtree', 'toggle_weight'] + mutations = ['point', 'insert', 'delete', 'subtree', 'toggle_weight_on', 'toggle_weight_off'] for i, m in enumerate(mutations): params['mutation_options'][m] = 0 if i != index else 1.0 From 7ea04d3459736d1c7261b5bbcc22192e1bf67d66 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 15 Aug 2023 14:32:18 -0400 Subject: [PATCH 099/102] NSGA uses either cx or mutation (but never both) --- src/brush/deap_api/nsga2.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index a7d44f4d..a1a8c8b2 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -6,7 +6,7 @@ def nsga2(toolbox, NGEN, MU, CXPB, use_batch, verbosity, rnd_flt): # NGEN = 250 - # MU = 100 + # MU = 100 # CXPB = 0.9 # rnd_flt: random number generator to sample crossover prob @@ -18,15 +18,18 @@ def calculate_statistics(ind): stats = tools.Statistics(calculate_statistics) - stats.register("ave", np.mean, axis=0) + stats.register("avg", np.mean, axis=0) + stats.register("med", np.median, axis=0) stats.register("std", np.std, axis=0) stats.register("min", np.min, axis=0) stats.register("max", np.max, axis=0) logbook = tools.Logbook() - logbook.header = "gen", "evals", "ave (O1 train, O2 train, O1 val, O2 val)", \ + logbook.header = "gen", "evals", "avg (O1 train, O2 train, O1 val, O2 val)", \ + "med (O1 train, O2 train, O1 val, O2 val)", \ "std (O1 train, O2 train, O1 val, O2 val)", \ - "min (O1 train, O2 train, O1 val, O2 val)" + "min (O1 train, O2 train, O1 val, O2 val)", \ + "max (O1 train, O2 train, O1 val, O2 val)" pop = toolbox.population(n=MU) @@ -68,14 +71,12 @@ def calculate_statistics(ind): if rnd_flt() < CXPB: off1, off2 = toolbox.mate(ind1, ind2) else: - off1, off2 = ind1, ind2 - + off1 = toolbox.mutate(off1) + off2 = toolbox.mutate(off2) + # avoid inserting empty solutions - if off1 != None: off1 = toolbox.mutate(off1) - if off1 != None: offspring.extend([off1]) - - if off2 != None: off2 = toolbox.mutate(off2) - if off2 != None: offspring.extend([off2]) + if off1 is not None: offspring.extend([off1]) + if off2 is not None: offspring.extend([off2]) # archive.update(offspring) # Evaluate the individuals with an invalid fitness From efe419eb968d7f25e487f1d22078ef36f2f042e5 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 15 Aug 2023 14:41:59 -0400 Subject: [PATCH 100/102] Fixed incorrect variable being used in mutation --- src/brush/deap_api/nsga2.py | 5 +++-- src/brush/estimator.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index a1a8c8b2..b569ad47 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -68,11 +68,12 @@ def calculate_statistics(ind): offspring = [] for ind1, ind2 in zip(parents[::2], parents[1::2]): + off1, off2 = None, None if rnd_flt() < CXPB: off1, off2 = toolbox.mate(ind1, ind2) else: - off1 = toolbox.mutate(off1) - off2 = toolbox.mutate(off2) + off1 = toolbox.mutate(ind1) + off2 = toolbox.mutate(ind2) # avoid inserting empty solutions if off1 is not None: offspring.extend([off1]) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index ab988bbe..2039577d 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -189,7 +189,7 @@ def fit(self, X, y): """ _brush.set_params(self.get_params()) - if self.random_state != None: + if self.random_state is not None: _brush.set_random_state(self.random_state) self.data_ = self._make_data(X,y, validation_size=self.validation_size) From 8f930bf807d99bbba05d1bff105b18f2ffea0c56 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 15 Aug 2023 17:11:35 -0400 Subject: [PATCH 101/102] Uniform weight initialization between mutation options and cx --- src/brush/estimator.py | 22 ++++++++++++++++------ 1 file changed, 16 insertions(+), 6 deletions(-) diff --git a/src/brush/estimator.py b/src/brush/estimator.py index 2039577d..fd4913af 100644 --- a/src/brush/estimator.py +++ b/src/brush/estimator.py @@ -38,11 +38,20 @@ class BrushEstimator(BaseEstimator): Maximum depth of GP trees in the GP program. Use 0 for no limit. max_size : int, default 0 Maximum number of nodes in a tree. Use 0 for no limit. - cx_prob : float, default 0.9 - Probability of applying the crossover variation when generating the offspring - mutation_options : dict, default {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight_on":0.1, "toggle_weight_off":0.1} + cx_prob : float, default 1/7 + Probability of applying the crossover variation when generating the offspring, + must be between 0 and 1. + Given that there are `n` mutations, and either crossover or mutation is + used to generate each individual in the offspring (but not both at the + same time), we want to have by default an uniform probability between + crossover and every possible mutation. By setting `cx_prob=1/(n+1)`, and + `1/n` for each mutation, we can achieve an uniform distribution. + mutation_options : dict, default {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, "toggle_weight_on":1/6, "toggle_weight_off":1/6} A dictionary with keys naming the types of mutation and floating point - values specifying the fraction of total mutations to do with that method. + values specifying the fraction of total mutations to do with that method. + The probability of having a mutation is `(1-cx_prob)` and, in case the mutation + is applied, then each mutation option is sampled based on the probabilities + defined in `mutation_options`. The set of probabilities should add up to 1.0. functions: dict[str,float] or list[str], default {} A dictionary with keys naming the function set and values giving the probability of sampling them, or a list of functions which will be weighted uniformly. @@ -95,8 +104,9 @@ def __init__( verbosity=0, max_depth=3, max_size=20, - cx_prob=0.9, - mutation_options = {"point":0.2, "insert":0.2, "delete":0.2, "subtree":0.2, "toggle_weight_on":0.1, "toggle_weight_off":0.1}, + cx_prob= 1/7, + mutation_options = {"point":1/6, "insert":1/6, "delete":1/6, "subtree":1/6, + "toggle_weight_on":1/6, "toggle_weight_off":1/6}, functions: list[str]|dict[str,float] = {}, initialization="grow", random_state=None, From 3a9ebc7d7c4278483cb42d90159287341f3d8e04 Mon Sep 17 00:00:00 2001 From: Guilherme Aldeia Date: Tue, 22 Aug 2023 10:23:27 -0400 Subject: [PATCH 102/102] Spacing --- src/brush/deap_api/nsga2.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/brush/deap_api/nsga2.py b/src/brush/deap_api/nsga2.py index b569ad47..e45d011b 100644 --- a/src/brush/deap_api/nsga2.py +++ b/src/brush/deap_api/nsga2.py @@ -54,7 +54,9 @@ def calculate_statistics(ind): # Begin the generational process for gen in range(1, NGEN): - if (use_batch): #batch will be random only if it is not the size of the entire train set. In this case, we dont need to reevaluate the whole pop + # The batch will be random only if it is not the size of the entire train set. + # In this case, we dont need to reevaluate the whole pop + if (use_batch): batch = toolbox.getBatch() fitnesses = toolbox.map(functools.partial(toolbox.evaluate, data=batch), pop)