run_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch \n",
    "from collections import OrderedDict\n",
    "\n",
    "from utils.sample_parameters import ConsecutiveGenerator, HiddenAndKernelGenerator, SingleGenerator\n",
    "from utils.sample_parameters import ParamGenerators\n",
    "from models.CNN_1D import CNN_1D\n",
    "from models.MLP import MLP\n",
    "from utils.train_n_test import TrainTest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, val, test = torch.load('data/in-hospital-mortality/tensors.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating class weights\n",
    "perc_mort = np.concatenate((train[1], val[1], test[1])).mean()\n",
    "class_weights = torch.Tensor([perc_mort, 1-perc_mort]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1D-CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "generators_dict = {\n",
    "    # HYPERMARAMETER RANGES\n",
    "    'BATCH_SIZE': SingleGenerator([128, 256, 512]),\n",
    "    'EPOCHS': SingleGenerator([20, 40, 50]),\n",
    "    'OPTIMIZER': SingleGenerator([torch.optim.Adam]),\n",
    "    'LEARNING_RATE': SingleGenerator([1e-5, 5e-5, 1e-4, 5e-4]),\n",
    "    'LOSS': SingleGenerator([torch.nn.CrossEntropyLoss(weight= class_weights)]),\n",
    "    'EARLY_STOPPING': SingleGenerator([True]),\n",
    "    'PATIENCE': SingleGenerator(list(range(2,4))),\n",
    "    'MIN_DELTA': SingleGenerator([5e-5, 1e-4, 5e-4, 1e-3, 5e-3]), \n",
    "\n",
    "    # NETWORK PARAMETER RANGES\n",
    "    'HIDDEN_AND_KERNEL_SIZES': HiddenAndKernelGenerator(range(1,4), [64, 128, 256, 512], [3, 5, 7, 9], \n",
    "                               ascending=(True, False)),\n",
    "    'MAXPOOL': SingleGenerator([2]),\n",
    "    'FC_SIZES': ConsecutiveGenerator(range(1,3), [64, 128, 256, 512], ascending=False),\n",
    "    'DROPOUT': SingleGenerator(np.arange(8)/10)\n",
    "} # each of these params are sampled INDEPENDENTLY of one another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 30\n",
    "gen = ParamGenerators(generators_dict)\n",
    "param_samples = gen.sample(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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     "text": [
      "{'BATCH_SIZE': 512, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 1e-05, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'MAXPOOL': 2, 'FC_SIZES': array([512, 256]), 'DROPOUT': 0.4, 'HIDDEN_SIZES': array([256]), 'KERNEL_SIZES': array([7])}\n"
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     "text": [
      "Epoch: 0, train_loss: 0.6916839480400085, valid_loss: 0.6888604164123535\n"
     ]
    },
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     "text": [
      "Epoch: 1, train_loss: 0.6863657832145691, valid_loss: 0.6817464232444763\n"
     ]
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     "text": [
      "Epoch: 2, train_loss: 0.6777191758155823, valid_loss: 0.6717565059661865\n"
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      "Epoch: 3, train_loss: 0.6650317311286926, valid_loss: 0.6582186222076416\n"
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      "Epoch: 4, train_loss: 0.6488810777664185, valid_loss: 0.6452900171279907\n"
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      "Epoch: 5, train_loss: 0.6329199075698853, valid_loss: 0.6331968307495117\n"
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      "Epoch: 6, train_loss: 0.6174927949905396, valid_loss: 0.6219401359558105\n"
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      "Epoch: 7, train_loss: 0.6041408777236938, valid_loss: 0.6143920421600342\n"
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      "Epoch: 9, train_loss: 0.5869659781455994, valid_loss: 0.6010593175888062\n"
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      "Epoch: 10, train_loss: 0.5810566544532776, valid_loss: 0.5975388288497925\n"
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      "Epoch: 11, train_loss: 0.5774818062782288, valid_loss: 0.5920562744140625\n"
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      "val loss increased, patience count:  1\n",
      "Epoch: 12, train_loss: 0.5726965069770813, valid_loss: 0.5939213633537292\n"
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      "Epoch: 13, train_loss: 0.5699938535690308, valid_loss: 0.5860025882720947\n"
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      "Epoch: 14, train_loss: 0.5670726895332336, valid_loss: 0.5864145755767822\n"
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      "Epoch: 17, train_loss: 0.5599079728126526, valid_loss: 0.5846047401428223\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 50, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 5e-05, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 3, 'MIN_DELTA': 0.0001, 'MAXPOOL': 2, 'FC_SIZES': array([512, 256]), 'DROPOUT': 0.5, 'HIDDEN_SIZES': array([256]), 'KERNEL_SIZES': array([7])}\n"
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      "Epoch: 5, train_loss: 0.5482040643692017, valid_loss: 0.5792378187179565\n"
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      "Epoch: 10, train_loss: 0.52598637342453, valid_loss: 0.5588953495025635\n"
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      "Early stopped at Epoch:  12\n"
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      "Epoch: 19, train_loss: 0.5248295068740845, valid_loss: 0.5606446266174316\n"
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      "Epoch: 22, train_loss: 0.519450843334198, valid_loss: 0.5617954730987549\n"
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      "Early stopped at Epoch:  23\n"
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      "Epoch: 0, train_loss: 0.6627111434936523, valid_loss: 0.6310381293296814\n"
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      "Epoch: 10, train_loss: 0.5411604642868042, valid_loss: 0.5749251842498779\n"
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      "Early stopped at Epoch:  11\n"
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      "Epoch: 4, train_loss: 0.5216279625892639, valid_loss: 0.5604459047317505\n"
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      "Epoch: 4, train_loss: 0.5225187540054321, valid_loss: 0.5695176124572754\n"
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      "Early stopped at Epoch:  15\n"
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      "Epoch: 6, train_loss: 0.536829948425293, valid_loss: 0.5734195709228516\n"
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      "Epoch: 16, train_loss: 0.5561894774436951, valid_loss: 0.5770394206047058\n"
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      "Epoch: 4, train_loss: 0.5348036289215088, valid_loss: 0.5516189336776733\n"
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      "Epoch: 5, train_loss: 0.5289623737335205, valid_loss: 0.5518186688423157\n"
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      "Epoch: 6, train_loss: 0.5271323919296265, valid_loss: 0.5555201172828674\n"
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      "Early stopped at Epoch:  7\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 1e-05, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 3, 'MIN_DELTA': 0.0005, 'MAXPOOL': 2, 'FC_SIZES': array([64]), 'DROPOUT': 0.2, 'HIDDEN_SIZES': array([128, 256]), 'KERNEL_SIZES': array([3, 3])}\n"
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      "Epoch: 0, train_loss: 0.6914392709732056, valid_loss: 0.6904069781303406\n"
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      "Epoch: 1, train_loss: 0.6872761845588684, valid_loss: 0.685512900352478\n"
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      "Epoch: 2, train_loss: 0.6800056099891663, valid_loss: 0.6778411865234375\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch: 11, train_loss: 0.6431798934936523, valid_loss: 0.6470140218734741\n"
     ]
    },
    {
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      "Epoch: 12, train_loss: 0.6344438791275024, valid_loss: 0.6376574039459229\n"
     ]
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     "text": [
      "Epoch: 13, train_loss: 0.6235916018486023, valid_loss: 0.6319113373756409\n"
     ]
    },
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      "Epoch: 14, train_loss: 0.6164785027503967, valid_loss: 0.6250197887420654\n"
     ]
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      "Epoch: 15, train_loss: 0.6082441210746765, valid_loss: 0.61868816614151\n"
     ]
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      "Epoch: 16, train_loss: 0.6012573838233948, valid_loss: 0.6146048903465271\n"
     ]
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      "Epoch: 17, train_loss: 0.5970991849899292, valid_loss: 0.6101255416870117\n"
     ]
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      "Epoch: 18, train_loss: 0.5916188359260559, valid_loss: 0.6084335446357727\n"
     ]
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      "Epoch: 19, train_loss: 0.5881952047348022, valid_loss: 0.6057900786399841\n"
     ]
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      "{'BATCH_SIZE': 256, 'EPOCHS': 40, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0005, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 0.001, 'MAXPOOL': 2, 'FC_SIZES': array([64]), 'DROPOUT': 0.0, 'HIDDEN_SIZES': array([256, 512]), 'KERNEL_SIZES': array([9, 7])}\n"
     ]
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     "text": [
      "Epoch: 0, train_loss: 0.5906172394752502, valid_loss: 0.5786661505699158\n"
     ]
    },
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     "text": [
      "Epoch: 1, train_loss: 0.5533871650695801, valid_loss: 0.5749732255935669\n"
     ]
    },
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      "Epoch: 2, train_loss: 0.5323877930641174, valid_loss: 0.5491000413894653\n"
     ]
    },
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      "Epoch: 3, train_loss: 0.5267359018325806, valid_loss: 0.5475482940673828\n"
     ]
    },
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      "val loss increased, patience count:  1\n",
      "Epoch: 4, train_loss: 0.5179404020309448, valid_loss: 0.5506276488304138\n"
     ]
    },
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      "val loss increased, patience count:  2\n",
      "Epoch: 5, train_loss: 0.5038540363311768, valid_loss: 0.5652737617492676\n"
     ]
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     "text": [
      "Early stopped at Epoch:  6\n"
     ]
    },
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      "{'BATCH_SIZE': 512, 'EPOCHS': 40, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0005, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 0.001, 'MAXPOOL': 2, 'FC_SIZES': array([64]), 'DROPOUT': 0.1, 'HIDDEN_SIZES': array([256, 512]), 'KERNEL_SIZES': array([7, 7])}\n"
     ]
    },
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     "text": [
      "Epoch: 0, train_loss: 0.6048876643180847, valid_loss: 0.5964502692222595\n"
     ]
    },
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      "Epoch: 1, train_loss: 0.5558274388313293, valid_loss: 0.5802351236343384\n"
     ]
    },
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      "Epoch: 2, train_loss: 0.5550176501274109, valid_loss: 0.571073055267334\n"
     ]
    },
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      "Epoch: 3, train_loss: 0.5394884347915649, valid_loss: 0.5593241453170776\n"
     ]
    },
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      "val loss increased, patience count:  1\n",
      "Epoch: 4, train_loss: 0.5289837121963501, valid_loss: 0.5611085295677185\n"
     ]
    },
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      "val loss increased, patience count:  2\n",
      "Epoch: 5, train_loss: 0.5223955512046814, valid_loss: 0.5597814321517944\n"
     ]
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      "Epoch: 6, train_loss: 0.5144340395927429, valid_loss: 0.5531696081161499\n"
     ]
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      "Epoch: 7, train_loss: 0.5094866156578064, valid_loss: 0.5428001284599304\n"
     ]
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      "val loss increased, patience count:  1\n",
      "Epoch: 8, train_loss: 0.49025893211364746, valid_loss: 0.5576475858688354\n"
     ]
    },
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      "val loss increased, patience count:  2\n",
      "Epoch: 9, train_loss: 0.4859932065010071, valid_loss: 0.5491513609886169\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "Early stopped at Epoch:  10\n"
     ]
    }
   ],
   "source": [
    "stats = []\n",
    "for i in np.arange(N): \n",
    "    print(param_samples[i])\n",
    "    run = TrainTest(CNN_1D, (train, val, test), param_samples[i])\n",
    "    run.train()\n",
    "    stats.append(run.test())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in np.arange(N):\n",
    "    for key,val in stats[i].items():\n",
    "        param_samples[i][key] = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(param_samples, 'results/CNN1D_experiment_N'+str(N)+'.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "results = pd.DataFrame(param_samples).sort_values(by='auroc', ascending=False)\n",
    "#torch.save(results, 'results/CNN1D_experiment_N'+str(N)+'.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MLP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "generators_dict = {\n",
    "    # HYPERMARAMETER RANGES\n",
    "    'BATCH_SIZE': SingleGenerator([256]),\n",
    "    'EPOCHS': SingleGenerator([20]),\n",
    "    'OPTIMIZER': SingleGenerator([torch.optim.Adam]),\n",
    "    'LEARNING_RATE': SingleGenerator([1e-4]),\n",
    "    'LOSS': SingleGenerator([torch.nn.CrossEntropyLoss(weight= class_weights)]),\n",
    "    'EARLY_STOPPING': SingleGenerator([True]),\n",
    "    'PATIENCE': SingleGenerator(list(range(2,3))),\n",
    "    'MIN_DELTA': SingleGenerator([5e-5]), \n",
    "\n",
    "    # NETWORK PARAMETER RANGES\n",
    "    'FC_SIZES': ConsecutiveGenerator(range(3,5), [64, 128, 256, 512, 1024], ascending=False),\n",
    "    'DROPOUT': SingleGenerator(np.arange(8)/10)\n",
    "} # each of these params are sampled INDEPENDENTLY of one another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 10\n",
    "gen = ParamGenerators(generators_dict)\n",
    "param_samples = gen.sample(N, hidden_present=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([256, 128,  64]), 'DROPOUT': 0.2}\n"
     ]
    },
    {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, train_loss: 0.6759189367294312, valid_loss: 0.6434214115142822\n"
     ]
    },
    {
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     "text": [
      "Epoch: 1, train_loss: 0.6013957262039185, valid_loss: 0.5892404317855835\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "Epoch: 2, train_loss: 0.5674644708633423, valid_loss: 0.5802661180496216\n"
     ]
    },
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      "Epoch: 5, train_loss: 0.536687970161438, valid_loss: 0.5784708261489868\n"
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      "Epoch: 7, train_loss: 0.5270950794219971, valid_loss: 0.5814118385314941\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([512, 256, 128]), 'DROPOUT': 0.4}\n"
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      "Epoch: 4, train_loss: 0.5504885315895081, valid_loss: 0.6032788157463074\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([256, 128,  64]), 'DROPOUT': 0.4}\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([512, 256, 128]), 'DROPOUT': 0.3}\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([1024,  512,  256]), 'DROPOUT': 0.5}\n"
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      "Epoch: 0, train_loss: 0.6776481866836548, valid_loss: 0.6407449245452881\n"
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      "Epoch: 2, train_loss: 0.5696360468864441, valid_loss: 0.6052706241607666\n"
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      "Epoch: 5, train_loss: 0.5505721569061279, valid_loss: 0.5901687145233154\n"
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      "Epoch: 2, train_loss: 0.5428588390350342, valid_loss: 0.5833613872528076\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([1024,  512,  256,  128]), 'DROPOUT': 0.2}\n"
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      "Epoch: 7, train_loss: 0.51323002576828, valid_loss: 0.5617157220840454\n"
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      "Early stopped at Epoch:  8\n"
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      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([512, 256, 128,  64]), 'DROPOUT': 0.0}\n"
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      "Epoch: 0, train_loss: 0.6624584794044495, valid_loss: 0.6128974556922913\n"
     ]
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      "Epoch: 1, train_loss: 0.5667227506637573, valid_loss: 0.5817747116088867\n"
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      "Epoch: 2, train_loss: 0.5410277843475342, valid_loss: 0.5744630098342896\n"
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      "Epoch: 3, train_loss: 0.5265920162200928, valid_loss: 0.5688707828521729\n"
     ]
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      "Epoch: 4, train_loss: 0.5159434676170349, valid_loss: 0.5625243186950684\n"
     ]
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      "val loss increased, patience count:  1\n",
      "Epoch: 5, train_loss: 0.5052130818367004, valid_loss: 0.5667052865028381\n"
     ]
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     ]
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      "val loss increased, patience count:  1\n",
      "Epoch: 7, train_loss: 0.4991138279438019, valid_loss: 0.5673024654388428\n"
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      "Epoch: 8, train_loss: 0.48064500093460083, valid_loss: 0.5641711950302124\n"
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      "Early stopped at Epoch:  9\n",
      "{'BATCH_SIZE': 256, 'EPOCHS': 20, 'OPTIMIZER': <class 'torch.optim.adam.Adam'>, 'LEARNING_RATE': 0.0001, 'LOSS': CrossEntropyLoss(), 'EARLY_STOPPING': True, 'PATIENCE': 2, 'MIN_DELTA': 5e-05, 'FC_SIZES': array([1024,  512,  256,  128]), 'DROPOUT': 0.2}\n"
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      "Epoch: 0, train_loss: 0.6554171442985535, valid_loss: 0.619066596031189\n"
     ]
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     ]
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      "Epoch: 2, train_loss: 0.5480050444602966, valid_loss: 0.573277473449707\n"
     ]
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      "val loss increased, patience count:  1\n",
      "Epoch: 4, train_loss: 0.5258565545082092, valid_loss: 0.5717192888259888\n"
     ]
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      "val loss increased, patience count:  2\n",
      "Epoch: 5, train_loss: 0.5234124660491943, valid_loss: 0.5764535665512085\n"
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      "val loss increased, patience count:  1\n",
      "Epoch: 9, train_loss: 0.5033400058746338, valid_loss: 0.5905455946922302\n"
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      "Epoch: 10, train_loss: 0.5002127289772034, valid_loss: 0.5578606724739075\n"
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      "val loss increased, patience count:  1\n",
      "Epoch: 11, train_loss: 0.49900394678115845, valid_loss: 0.5658320188522339\n"
     ]
    },
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      "val loss increased, patience count:  2\n",
      "Epoch: 12, train_loss: 0.49110695719718933, valid_loss: 0.57680344581604\n"
     ]
    },
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    {
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     "text": [
      "Early stopped at Epoch:  13\n"
     ]
    }
   ],
   "source": [
    "stats = []\n",
    "for i in np.arange(N): \n",
    "    print(param_samples[i])\n",
    "    run = TrainTest(MLP, (train, val, test), param_samples[i], [])\n",
    "    run.train()\n",
    "    stats.append(run.test())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in np.arange(N):\n",
    "    for key,val in stats[i].items():\n",
    "        param_samples[i][key] = val\n",
    "\n",
    "torch.save(param_samples, 'results/MLP_experiment_N'+str(N)+'.pkl')\n",
    "\n",
    "import pandas as pd\n",
    "results = pd.DataFrame(param_samples).sort_values(by='auroc', ascending=False)\n",
    "#torch.save(results, 'results/CNN1D_experiment_N'+str(N)+'.pkl')"
   ]
  }
 ],
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