diff --git a/docs/Example.ipynb b/docs/Example.ipynb index a99e74a5..0368f777 100644 --- a/docs/Example.ipynb +++ b/docs/Example.ipynb @@ -65,7 +65,7 @@ " Examples:\n", " >>> experiment = weber_fechner_law()\n", " \n", - " # We can run the runner with numpy arrays or DataFrames. Ther return value will\n", + " # The runner can accept numpy arrays or pandas DataFrames, but the return value will\n", " # always be a pandas DataFrame.\n", " >>> experiment.run(np.array([[.1,.2]]), random_state=42)\n", " S1 S2 difference_detected\n", @@ -110,7 +110,7 @@ " Examples:\n", " >>> experiment = weber_fechner_law()\n", "\n", - " # We can run the runner with numpy arrays or DataFrames. Ther return value will\n", + " # The runner can accept numpy arrays or pandas DataFrames, but the return value will\n", " # always be a pandas DataFrame.\n", " >>> experiment.run(np.array([[.1,.2]]), random_state=42)\n", " S1 S2 difference_detected\n", @@ -260,7 +260,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "... the experiment_runner runner which can be called to generate experimental results:" + "... the experiment_runner which can be called to generate experimental results:" ] }, { @@ -299,31 +299,31 @@ " 0\n", " 0.010000\n", " 0.010000\n", - " -0.000829\n", + " -0.013734\n", " \n", " \n", " 1\n", " 0.010000\n", " 0.060404\n", - " 1.806354\n", + " 1.788438\n", " \n", " \n", " 2\n", " 0.010000\n", " 0.110808\n", - " 2.406270\n", + " 2.403593\n", " \n", " \n", " 3\n", " 0.010000\n", " 0.161212\n", - " 2.774411\n", + " 2.781474\n", " \n", " \n", " 4\n", " 0.010000\n", " 0.211616\n", - " 3.056933\n", + " 3.055966\n", " \n", " \n", " ...\n", @@ -335,31 +335,31 @@ " 5045\n", " 4.899192\n", " 4.949596\n", - " -0.000753\n", + " 0.025322\n", " \n", " \n", " 5046\n", " 4.899192\n", " 5.000000\n", - " 0.037958\n", + " 0.022726\n", " \n", " \n", " 5047\n", " 4.949596\n", " 4.949596\n", - " -0.013647\n", + " 0.000098\n", " \n", " \n", " 5048\n", " 4.949596\n", " 5.000000\n", - " 0.020839\n", + " 0.002932\n", " \n", " \n", " 5049\n", " 5.000000\n", " 5.000000\n", - " -0.021462\n", + " -0.010160\n", " \n", " \n", "\n", @@ -368,17 +368,17 @@ ], "text/plain": [ " S1 S2 difference_detected\n", - "0 0.010000 0.010000 -0.000829\n", - "1 0.010000 0.060404 1.806354\n", - "2 0.010000 0.110808 2.406270\n", - "3 0.010000 0.161212 2.774411\n", - "4 0.010000 0.211616 3.056933\n", + "0 0.010000 0.010000 -0.013734\n", + "1 0.010000 0.060404 1.788438\n", + "2 0.010000 0.110808 2.403593\n", + "3 0.010000 0.161212 2.781474\n", + "4 0.010000 0.211616 3.055966\n", "... ... ... ...\n", - "5045 4.899192 4.949596 -0.000753\n", - "5046 4.899192 5.000000 0.037958\n", - "5047 4.949596 4.949596 -0.013647\n", - "5048 4.949596 5.000000 0.020839\n", - "5049 5.000000 5.000000 -0.021462\n", + "5045 4.899192 4.949596 0.025322\n", + "5046 4.899192 5.000000 0.022726\n", + "5047 4.949596 4.949596 0.000098\n", + "5048 4.949596 5.000000 0.002932\n", + "5049 5.000000 5.000000 -0.010160\n", "\n", "[5050 rows x 3 columns]" ] @@ -435,7 +435,7 @@ "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -454,7 +454,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "These can be used to run a full experimental cycle" + "We can wrap this functions to use with the state logic of AutoRA:\n", + "First, we create the state with the variables:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from autora.state import StandardState, on_state, experiment_runner_on_state, estimator_on_state\n", + "from autora.experimentalist.grid import grid_pool\n", + "from autora.experimentalist.random import random_sample\n", + "from functools import partial\n", + "import random\n", + "\n", + "# We can get the variables from the runner\n", + "variables = s.variables\n", + "\n", + "# With the variables, we initialize a StandardState\n", + "state = StandardState(variables)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrap the experimentalists in `on_state` function to use them on state:" ] }, { @@ -466,49 +493,275 @@ "name": "stdout", "output_type": "stream", "text": [ - "finished cycle 1\n", - "finished cycle 2\n", - "finished cycle 3\n", - "I = -0.49 S0 +0.58 S1 -0.21\n" + " S1 S2\n", + "1484 0.715657 4.243939\n", + "5456 2.731818 2.832626\n", + "5539 2.782222 1.975758\n", + "6062 3.034242 3.135051\n", + "1622 0.816465 1.118889\n", + "5901 2.983838 0.060404\n", + "1936 0.967677 1.824545\n", + "6844 3.437475 2.227778\n", + "7073 3.538283 3.689495\n", + "2196 1.068485 4.848788\n", + "1710 0.866869 0.514040\n", + "3251 1.622929 2.580606\n", + "4298 2.126970 4.949596\n", + "7055 3.538283 2.782222\n", + "406 0.211616 0.312424\n", + "3787 1.874949 4.395152\n", + "4728 2.378990 1.421313\n", + "5214 2.631010 0.715657\n", + "1227 0.614848 1.370909\n", + "8482 4.243939 4.143131\n" ] } ], "source": [ - "from autora.workflow.protocol import ResultKind\n", - "from autora.experimentalist.pipeline import make_pipeline\n", - "from autora.experimentalist.pooler.grid import grid_pool\n", - "from autora.experimentalist.sampler.random_sampler import random_sample\n", - "from functools import partial\n", - "import random\n", - "variables = s.variables\n", - "pool = partial(grid_pool, ivs=variables.independent_variables)\n", - "random.seed(181) # set the seed for the random sampler\n", - "sampler = partial(random_sample, n=20)\n", - "experimentalist_pipeline = make_pipeline([pool, sampler])\n", + "# Wrap the functions to use on state\n", + "# Experimentalists:\n", + "pool_on_state = on_state(grid_pool, output=['conditions'])\n", + "sample_on_state = on_state(random_sample, output=['conditions'])\n", "\n", - "from autora.workflow import Controller\n", - "theorist = LinearRegression()\n", - "\n", - "cycle = Controller(\n", - " variables=variables, experimentalist=experimentalist_pipeline,\n", - " experiment_runner=s.experiment_runner, theorist=theorist,\n", - " monitor=lambda s: (s.history[-1].kind == ResultKind.MODEL) and\n", - " print(f\"finished cycle {len(s.models)}\"))\n", + "state = pool_on_state(state)\n", + "state = sample_on_state(state, num_samples=20)\n", + "print(state.conditions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrap the runner with the `experiment_runner_on_state` wrapper to use it on state:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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84824.2439394.143131-0.029045
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" + ], + "text/plain": [ + " S1 S2 difference_detected\n", + "1484 0.715657 4.243939 1.777697\n", + "5456 2.731818 2.832626 0.039988\n", + "5539 2.782222 1.975758 -0.344299\n", + "6062 3.034242 3.135051 0.036409\n", + "1622 0.816465 1.118889 0.287000\n", + "5901 2.983838 0.060404 -3.903989\n", + "1936 0.967677 1.824545 0.618795\n", + "6844 3.437475 2.227778 -0.432813\n", + "7073 3.538283 3.689495 0.035054\n", + "2196 1.068485 4.848788 1.514635\n", + "1710 0.866869 0.514040 -0.527993\n", + "3251 1.622929 2.580606 0.469571\n", + "4298 2.126970 4.949596 0.815710\n", + "7055 3.538283 2.782222 -0.237467\n", + "406 0.211616 0.312424 0.373714\n", + "3787 1.874949 4.395152 0.830938\n", + "4728 2.378990 1.421313 -0.503627\n", + "5214 2.631010 0.715657 -1.289548\n", + "1227 0.614848 1.370909 0.803107\n", + "8482 4.243939 4.143131 -0.029045" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Runner:\n", + "run_on_state = experiment_runner_on_state(s.run)\n", + "state = run_on_state(state)\n", "\n", - "c = cycle.run(10)\n", - "best_model = c.state.models[-1]\n", - "print(f\"I = \"\n", - " f\"{best_model.coef_[0]:.2f} S0 \"\n", - " f\"{best_model.coef_[1]:+.2f} S1 \"\n", - " f\"{best_model.intercept_:+.2f}\")\n" + "state.experiment_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrap the regressor with the `estimator_on_state` wrapper:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I = -0.62*S0 +0.57*S1 -0.09 \n" + ] + } + ], + "source": [ + "theorist = LinearRegression()\n", + "theorist_on_state = estimator_on_state(theorist)\n", + "\n", + "state = theorist_on_state(state)\n", + "# Access the last model:\n", + "model = state.models[-1]\n", + "\n", + "\n", + "print(f\"I = \"\n", + " f\"{model.coef_[0][0]:.2f}*S0 \"\n", + " f\"{model.coef_[0][1]:+.2f}*S1 \"\n", + " f\"{model.intercept_[0]:+.2f} \")" + ] } ], "metadata": { diff --git a/src/autora/experiment_runner/synthetic/abstract/lmm.py b/src/autora/experiment_runner/synthetic/abstract/lmm.py index 1fc7c926..a2c869c6 100644 --- a/src/autora/experiment_runner/synthetic/abstract/lmm.py +++ b/src/autora/experiment_runner/synthetic/abstract/lmm.py @@ -37,7 +37,8 @@ >>> formula_2 = 'rt ~ x1' >>> fixed_effects_2 = {'x1': 2.} >>> experiment_2 = lmm_experiment(formula=formula_2,fixed_effects=fixed_effects_2) - >>> experiment_1.ground_truth(conditions=conditions) == experiment_2.ground_truth(conditions=conditions) + >>> experiment_1.ground_truth(conditions=conditions) ==\ +experiment_2.ground_truth(conditions=conditions) x1 rt 0 True True 1 True True @@ -48,7 +49,9 @@ >>> formula = 'rt ~ 1 + (1|subject) + x1' >>> fixed_effects = {'Intercept': 1, 'x1': 2} >>> random_effects = {'subject': {'Intercept': .1}} - >>> experiment = lmm_experiment(formula=formula,fixed_effects=fixed_effects,random_effects=random_effects) + >>> experiment = lmm_experiment(formula=formula, + ... fixed_effects=fixed_effects, + ... random_effects=random_effects) >>> conditions_1 = pd.DataFrame({ ... 'x1':np.linspace(0, 1, 3), ... 'subject': np.repeat(1, 3) @@ -90,7 +93,9 @@ >>> formula = 'rt ~ (x1|subject) + x1' >>> fixed_effects = {'x1': 1.} >>> random_effects = {'subject': {'x1': .01}} - >>> experiment = lmm_experiment(formula=formula,fixed_effects=fixed_effects,random_effects=random_effects) + >>> experiment = lmm_experiment(formula=formula, + ... fixed_effects=fixed_effects, + ... random_effects=random_effects) >>> experiment.ground_truth(conditions=conditions,random_state=42) x1 subject rt 0 0.0 1 0.000000 @@ -106,7 +111,9 @@ ... 'subject': {'1': 0.5, 'x1': 0.3}, ... 'group': {'x2': 0.4} ... } - >>> experiment = lmm_experiment(formula=formula, fixed_effects=fixed_effects,random_effects=random_effects) + >>> experiment = lmm_experiment(formula=formula, + ... fixed_effects=fixed_effects, + ... random_effects=random_effects) >>> n_samples = 10 >>> rng = np.random.default_rng(0) >>> conditions = pd.DataFrame({ @@ -143,10 +150,9 @@ """ - -from functools import partial -from typing import Optional, List import re +from functools import partial +from typing import List, Optional import numpy as np import pandas as pd @@ -173,7 +179,7 @@ def lmm_experiment( fixed_effects: dictionary describing the fixed effects (Intercept and slopes) random_effects: nested dictionary describing the random effects of slopes and intercept. These are standard deviasions in a normal distribution with a mean of zero. - X: Independent variable descriptions. Used to add allowed values + X: Independent variable descriptions. Used to add allowed values """ if not fixed_effects: @@ -186,17 +192,17 @@ def lmm_experiment( name=name, formula=formula, fixed_effects=fixed_effects, - random_effects=random_effects + random_effects=random_effects, ) dependent, fixed_variables, random_variables = _extract_variable_names(formula) dependent = DV(name=dependent) - x = [IV(name=f) for f in fixed_variables] + [IV(name=r) for r in random_variables] + # x = [IV(name=f) for f in fixed_variables] + [IV(name=r) for r in random_variables] + # + # if X: + # x = X - if X: - x = X - variables = VariableCollection( independent_variables=[X], dependent_variables=[dependent], @@ -216,28 +222,32 @@ def run( rng_ = np.random.default_rng(random_state) else: rng_ = rng # use the RNG from the outer scope - - - dependent_var, rhs = formula.split('~') + + dependent_var, rhs = formula.split("~") dependent_var = dependent_var.strip() fixed_vars = fixed_variables - # Check for the presence of an intercept in the formula - has_intercept = True if '1' in fixed_effects or re.search(r'\b0\b', rhs) is None else False + has_intercept = ( + True if "1" in fixed_effects or re.search(r"\b0\b", rhs) is None else False + ) experiment_data = conditions.copy() # Initialize the dependent variable - experiment_data[dependent_var] = fixed_effects.get('Intercept', 0) if has_intercept else 0 + experiment_data[dependent_var] = ( + fixed_effects.get("Intercept", 0) if has_intercept else 0 + ) # Add fixed effects for var in fixed_vars: if var in experiment_data.columns: - experiment_data[dependent_var] += fixed_effects.get(var, 0) * experiment_data[var] + experiment_data[dependent_var] += ( + fixed_effects.get(var, 0) * experiment_data[var] + ) # Process each random effect term - random_effect_terms = re.findall(r'\((.+?)\|(.+?)\)', formula) + random_effect_terms = re.findall(r"\((.+?)\|(.+?)\)", formula) for term in random_effect_terms: random_effects_, group_var = term group_var = group_var.strip() @@ -247,25 +257,36 @@ def run( raise ValueError(f"Group variable '{group_var}' not found in the data") # Process each part of the random effect (intercept and slopes) - for part in random_effects_.split('+'): - part = 'Intercept' if part == '1' else part + for part in random_effects_.split("+"): + part = "Intercept" if part == "1" else part part = part.strip() std_dev = random_effects[group_var].get(part, 0.5) - random_effect_values = {group: rng_.normal(0, std_dev) for group in experiment_data[group_var].unique()} - if part == 'Intercept': # Random intercept + random_effect_values = { + group: rng_.normal(0, std_dev) + for group in experiment_data[group_var].unique() + } + if part == "Intercept": # Random intercept if has_intercept: - experiment_data[dependent_var] += experiment_data[group_var].map(random_effect_values) + experiment_data[dependent_var] += experiment_data[ + group_var + ].map(random_effect_values) else: # Random slopes if part in experiment_data.columns: - experiment_data[dependent_var] += experiment_data[group_var].map(random_effect_values) * experiment_data[part] + experiment_data[dependent_var] += ( + experiment_data[group_var].map(random_effect_values) + * experiment_data[part] + ) # Add noise - experiment_data[dependent_var] += rng_.normal(0, added_noise, len(experiment_data)) + experiment_data[dependent_var] += rng_.normal( + 0, added_noise, len(experiment_data) + ) return experiment_data ground_truth = partial(run, added_noise=0.0) - """A function which simulates perfect observations. This still uses random values for random effects.""" + """A function which simulates perfect observations. + This still uses random values for random effects.""" def domain(): """A function which returns all possible independent variable values as a 2D array.""" @@ -279,9 +300,9 @@ def plotter(model=None): plt.figure() dom = domain() data = ground_truth(dom) - - y = data[depedent] - x = data.drop(depenent, axis=1) + + y = data[dependent] + x = data.drop(dependent, axis=1) if x.shape[1] > 2: Exception( @@ -332,7 +353,8 @@ def _extract_variable_names(formula): formula (str): Formula specifying the model, e.g., 'y ~ x1 + x2 + (1 + x1|group) + (x2|subject)' Returns: - tuple of (list, list): A tuple containing two lists - one for fixed effects and another for random effects. + tuple of (list, list): A tuple containing two lists - one for fixed effects and another for + random effects. Examples: >>> formula_1 = 'y ~ x1 + x2 + (1 + x1|group) + (x2|subject)' >>> _extract_variable_names(formula_1) @@ -349,19 +371,24 @@ def _extract_variable_names(formula): """ # Extract the right-hand side of the formula - dependent, rhs = formula.split('~') + dependent, rhs = formula.split("~") dependent = dependent.strip() - fixed_effects = re.findall(r'[a-z]\w*(?![^\(]*\))', rhs) # Matches variables outside parentheses - random_effects = re.findall(r'\(([^\|]+)\|([^\)]+)\)', rhs) # Matches random effects groups + fixed_effects = re.findall( + r"[a-z]\w*(?![^\(]*\))", rhs + ) # Matches variables outside parentheses + random_effects = re.findall( + r"\(([^\|]+)\|([^\)]+)\)", rhs + ) # Matches random effects groups # Include variables from random effects in fixed effects and make unique for reffect in random_effects: - fixed_effects.extend(reffect[0].replace('1 + ', '').split('+')) - + fixed_effects.extend(reffect[0].replace("1 + ", "").split("+")) + # Removing duplicates and stripping whitespaces fixed_effects = sorted(list(set([effect.strip() for effect in fixed_effects]))) - random_groups = sorted(list(set([reffect[1].strip() for reffect in random_effects]))) - + random_groups = sorted( + list(set([reffect[1].strip() for reffect in random_effects])) + ) return dependent, fixed_effects, random_groups diff --git a/src/autora/experiment_runner/synthetic/psychology/q_learning.py b/src/autora/experiment_runner/synthetic/psychology/q_learning.py index c07650f2..ded28d3f 100644 --- a/src/autora/experiment_runner/synthetic/psychology/q_learning.py +++ b/src/autora/experiment_runner/synthetic/psychology/q_learning.py @@ -7,10 +7,13 @@ from autora.experiment_runner.synthetic.utilities import SyntheticExperimentCollection from autora.variable import DV, IV, ValueType, VariableCollection + def _check_in_0_1_range(x, name): - if not (0 <= x <= 1): - raise ValueError( - f'Value of {name} must be in [0, 1] range. Found value of {x}.') + if not (0 <= x <= 1): + raise ValueError( + f"Value of {name} must be in [0, 1] range. Found value of {x}." + ) + class AgentQ: """An agent that runs simple Q-learning for an n-armed bandits tasks. @@ -22,13 +25,13 @@ class AgentQ: """ def __init__( - self, - alpha: float = 0.2, - beta: float = 3., - n_actions: int = 2, - forget_rate: float = 0., - perseverance_bias: float = 0., - correlated_reward: bool = False, + self, + alpha: float = 0.2, + beta: float = 3.0, + n_actions: int = 2, + forget_rate: float = 0.0, + perseverance_bias: float = 0.0, + correlated_reward: bool = False, ): """Update the agent after one step of the task. @@ -49,8 +52,8 @@ def __init__( self._q_init = 0.5 self.new_sess() - _check_in_0_1_range(alpha, 'alpha') - _check_in_0_1_range(forget_rate, 'forget_rate') + _check_in_0_1_range(alpha, "alpha") + _check_in_0_1_range(forget_rate, "forget_rate") def new_sess(self): """Reset the agent for the beginning of a new session.""" @@ -69,9 +72,7 @@ def get_choice(self) -> int: choice = np.random.choice(self._n_actions, p=choice_probs) return choice - def update(self, - choice: int, - reward: float): + def update(self, choice: int, reward: float): """Update the agent after one step of the task. Args: @@ -82,15 +83,17 @@ def update(self, # Forgetting - restore q-values of non-chosen actions towards the initial value non_chosen_action = np.arange(self._n_actions) != choice self._q[non_chosen_action] = (1 - self._forget_rate) * self._q[ - non_chosen_action] + self._forget_rate * self._q_init + non_chosen_action + ] + self._forget_rate * self._q_init # Reward-based update - Update chosen q for chosen action with observed reward - q_reward_update = - self._alpha * self._q[choice] + self._alpha * reward + q_reward_update = -self._alpha * self._q[choice] + self._alpha * reward # Correlated update - Update non-chosen q for non-chosen action with observed reward if self._correlated_reward: # index_correlated_update = self._n_actions - choice - 1 - # self._q[index_correlated_update] = (1 - self._alpha) * self._q[index_correlated_update] + self._alpha * (1 - reward) + # self._q[index_correlated_update] = + # (1 - self._alpha) * self._q[index_correlated_update] + self._alpha * (1 - reward) # alternative implementation - not dependent on reward but on reward-based update index_correlated_update = self._n_actions - 1 - choice self._q[index_correlated_update] -= 0.5 * q_reward_update @@ -111,10 +114,10 @@ def q(self): def q_learning( name="Q-Learning", learning_rate: float = 0.2, - decision_noise: float = 3., + decision_noise: float = 3.0, n_actions: int = 2, - forget_rate: float = 0., - perseverance_bias: float = 0., + forget_rate: float = 0.0, + perseverance_bias: float = 0.0, correlated_reward: bool = False, ): """ @@ -136,7 +139,8 @@ def q_learning( # The runner can accept numpy arrays or pandas DataFrames, but the return value will # always be a list of numpy arrays. Each array corresponds to the choices made by the agent # for each trial in the input. Thus, arrays have shape (n_trials, n_actions). - >>> experiment.run(np.array([[0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0]]), random_state=42) + >>> experiment.run(np.array([[0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0]]), + ... random_state=42) [array([[1., 0.], [0., 1.], [0., 1.], @@ -147,7 +151,10 @@ def q_learning( # The runner can accept pandas DataFrames. Each cell of the DataFrame should contain a # numpy array with shape (n_trials, n_actions). The return value will be a list of numpy # arrays, each corresponding to the choices made by the agent for each trial in the input. - >>> experiment.run(pd.DataFrame({'reward array': [np.array([[0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0]])]}), random_state = 42) + >>> experiment.run( + ... pd.DataFrame( + ... {'reward array': [np.array([[0, 1], [0, 1], [0, 1], [1, 0], [1, 0], [1, 0]])]}), + ... random_state = 42) [array([[1., 0.], [0., 1.], [0., 1.], @@ -159,12 +166,12 @@ def q_learning( params = dict( name=name, trials=100, - learning_rate = learning_rate, - decision_noise = decision_noise, - n_actions = n_actions, - forget_rate = forget_rate, - perseverance_bias = perseverance_bias, - correlated_reward = correlated_reward, + learning_rate=learning_rate, + decision_noise=decision_noise, + n_actions=n_actions, + forget_rate=forget_rate, + perseverance_bias=perseverance_bias, + correlated_reward=correlated_reward, ) iv1 = IV( @@ -187,9 +194,11 @@ def q_learning( ) def run_AgentQ(rewards): - if (rewards.shape[1] != n_actions): - Warning("Number of actions in rewards does not match n_actions. Will use " + str(rewards.shape[1] - + " actions.")) + if rewards.shape[1] != n_actions: + Warning( + "Number of actions in rewards does not match n_actions. Will use " + + str(rewards.shape[1] + " actions.") + ) num_trials = rewards.shape[0] y = np.zeros(rewards.shape) @@ -216,9 +225,8 @@ def run_AgentQ(rewards): def run( conditions: Union[pd.DataFrame, np.ndarray, np.recarray], random_state: Optional[int] = None, - return_choice_probabilities = False, + return_choice_probabilities=False, ): - if random_state is not None: np.random.seed(random_state) @@ -256,5 +264,3 @@ def domain(): factory_function=q_learning, ) return collection - -