HyperparameterTesting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7bbe790f",
   "metadata": {},
   "source": [
    "# HyperparameterTesting\n",
    "\n",
    "Choosing the right LR for each model by running it on all tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bf2e1e8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import DataLoader, Training, os\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "data_path = \"F:/Masterthesis/Data/\"\n",
    "df_path = os.getcwd()+\"\\\\HyperparameterTesting\\\\\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e2a06f",
   "metadata": {},
   "source": [
    "### EEGNet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d13fd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 1 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5199\u001b[0m        \u001b[32m0.8677\u001b[0m                     \u001b[35m0.5285\u001b[0m        \u001b[31m0.7018\u001b[0m  0.1000  2.1760\n",
      "      2                     \u001b[36m0.5472\u001b[0m        \u001b[32m0.6900\u001b[0m                     0.5284        0.7237  0.0996  1.5536\n",
      "      3                     \u001b[36m0.5480\u001b[0m        0.6964                     \u001b[35m0.5518\u001b[0m        \u001b[31m0.6889\u001b[0m  0.0983  1.6208\n",
      "      4                     \u001b[36m0.5583\u001b[0m        0.6962                     \u001b[35m0.5869\u001b[0m        \u001b[31m0.6689\u001b[0m  0.0962  1.5604\n",
      "      5                     \u001b[36m0.5766\u001b[0m        0.6922                     \u001b[35m0.6139\u001b[0m        \u001b[31m0.6592\u001b[0m  0.0933  1.6031\n",
      "      6                     \u001b[36m0.6124\u001b[0m        \u001b[32m0.6577\u001b[0m                     0.5355        0.6875  0.0897  1.5074\n",
      "      7                     \u001b[36m0.6326\u001b[0m        \u001b[32m0.6425\u001b[0m                     0.5559        0.6846  0.0854  1.4915\n",
      "      8                     0.6276        0.6485                     \u001b[35m0.6832\u001b[0m        \u001b[31m0.6109\u001b[0m  0.0804  1.4930\n",
      "      9                     \u001b[36m0.6455\u001b[0m        \u001b[32m0.6365\u001b[0m                     0.6790        0.6267  0.0750  1.5358\n",
      "     10                     \u001b[36m0.6563\u001b[0m        \u001b[32m0.6325\u001b[0m                     0.6682        0.6766  0.0691  1.4931\n",
      "     11                     \u001b[36m0.6695\u001b[0m        \u001b[32m0.6172\u001b[0m                     0.6685        0.6448  0.0629  1.5447\n",
      "     12                     0.6693        \u001b[32m0.6124\u001b[0m                     0.5810        0.6579  0.0565  1.5825\n",
      "     13                     \u001b[36m0.6783\u001b[0m        \u001b[32m0.6089\u001b[0m                     0.6711        \u001b[31m0.5924\u001b[0m  0.0500  1.4998\n",
      "     14                     0.6780        \u001b[32m0.6046\u001b[0m                     0.5980        0.6684  0.0435  1.6187\n",
      "     15                     \u001b[36m0.6799\u001b[0m        \u001b[32m0.6007\u001b[0m                     0.6444        0.6423  0.0371  1.5936\n",
      "     16                     \u001b[36m0.6864\u001b[0m        \u001b[32m0.5941\u001b[0m                     0.6804        0.6944  0.0309  1.5786\n",
      "     17                     \u001b[36m0.6931\u001b[0m        \u001b[32m0.5844\u001b[0m                     0.5333        0.6920  0.0250  1.6174\n",
      "     18                     \u001b[36m0.7029\u001b[0m        \u001b[32m0.5764\u001b[0m                     \u001b[35m0.6901\u001b[0m        0.6133  0.0196  1.6802\n",
      "     19                     \u001b[36m0.7084\u001b[0m        \u001b[32m0.5711\u001b[0m                     \u001b[35m0.7044\u001b[0m        \u001b[31m0.5812\u001b[0m  0.0146  1.5835\n",
      "     20                     \u001b[36m0.7176\u001b[0m        \u001b[32m0.5605\u001b[0m                     0.6596        0.6525  0.0103  1.5111\n",
      "     21                     \u001b[36m0.7304\u001b[0m        \u001b[32m0.5517\u001b[0m                     0.6872        0.6337  0.0067  1.6009\n",
      "     22                     \u001b[36m0.7319\u001b[0m        \u001b[32m0.5421\u001b[0m                     0.6958        \u001b[31m0.5660\u001b[0m  0.0038  1.7446\n",
      "     23                     \u001b[36m0.7332\u001b[0m        \u001b[32m0.5336\u001b[0m                     \u001b[35m0.7230\u001b[0m        \u001b[31m0.5387\u001b[0m  0.0017  1.6390\n",
      "     24                     \u001b[36m0.7381\u001b[0m        \u001b[32m0.5308\u001b[0m                     \u001b[35m0.7346\u001b[0m        \u001b[31m0.5350\u001b[0m  0.0004  1.5094\n",
      "     25                     0.7348        0.5333                     \u001b[35m0.7385\u001b[0m        \u001b[31m0.5299\u001b[0m  0.0000  1.5313\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 1 1 ... 1 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.6353\u001b[0m        \u001b[32m0.8548\u001b[0m                     \u001b[35m0.6654\u001b[0m        \u001b[31m1.0080\u001b[0m  0.1000  0.9476\n",
      "      2                     \u001b[36m0.6878\u001b[0m        \u001b[32m0.6001\u001b[0m                     0.5340        1.0605  0.0996  0.9361\n",
      "      3                     \u001b[36m0.7059\u001b[0m        \u001b[32m0.5745\u001b[0m                     0.5654        \u001b[31m0.7284\u001b[0m  0.0983  0.9599\n",
      "      4                     0.6797        0.6036                     0.5000        1.6098  0.0962  0.9324\n",
      "      5                     0.6856        0.6015                     0.5767        \u001b[31m0.6498\u001b[0m  0.0933  0.9467\n",
      "      6                     0.7002        0.5982                     0.6212        0.7021  0.0897  0.9484\n",
      "      7                     0.6797        0.6011                     \u001b[35m0.6909\u001b[0m        \u001b[31m0.5932\u001b[0m  0.0854  0.8932\n",
      "      8                     0.7037        0.5924                     0.5179        0.9979  0.0804  0.8944\n",
      "      9                     0.6853        0.6112                     0.5013        4.2067  0.0750  0.8939\n",
      "     10                     \u001b[36m0.7089\u001b[0m        \u001b[32m0.5710\u001b[0m                     0.5286        0.6608  0.0691  0.9477\n",
      "     11                     0.6993        0.5812                     0.5013        0.7769  0.0629  0.9421\n",
      "     12                     0.6638        0.6303                     0.4996        1.2057  0.0565  0.8972\n",
      "     13                     \u001b[36m0.7093\u001b[0m        \u001b[32m0.5681\u001b[0m                     0.6624        0.6411  0.0500  0.9056\n",
      "     14                     0.6894        0.5813                     0.5000        2.0262  0.0435  0.9633\n",
      "     15                     0.7027        0.5892                     0.5000        3.1679  0.0371  0.9397\n",
      "     16                     \u001b[36m0.7207\u001b[0m        \u001b[32m0.5557\u001b[0m                     0.5000        2.1791  0.0309  0.9146\n",
      "     17                     \u001b[36m0.7290\u001b[0m        \u001b[32m0.5469\u001b[0m                     0.5355        0.7804  0.0250  0.9357\n",
      "     18                     0.7192        \u001b[32m0.5428\u001b[0m                     0.6232        0.6950  0.0196  0.8998\n",
      "     19                     \u001b[36m0.7416\u001b[0m        \u001b[32m0.5235\u001b[0m                     0.5167        1.2235  0.0146  0.8952\n",
      "     20                     \u001b[36m0.7514\u001b[0m        \u001b[32m0.5112\u001b[0m                     \u001b[35m0.7245\u001b[0m        \u001b[31m0.5453\u001b[0m  0.0103  0.8941\n",
      "     21                     \u001b[36m0.7554\u001b[0m        \u001b[32m0.5064\u001b[0m                     0.5375        0.7844  0.0067  0.8952\n",
      "     22                     0.7531        0.5070                     0.6771        0.6410  0.0038  0.8939\n",
      "     23                     \u001b[36m0.7569\u001b[0m        \u001b[32m0.4975\u001b[0m                     0.6997        0.5896  0.0017  0.8938\n",
      "     24                     \u001b[36m0.7590\u001b[0m        \u001b[32m0.4936\u001b[0m                     \u001b[35m0.7304\u001b[0m        0.5655  0.0004  0.8952\n",
      "     25                     0.7534        \u001b[32m0.4921\u001b[0m                     \u001b[35m0.7329\u001b[0m        \u001b[31m0.5439\u001b[0m  0.0000  0.8937\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 0 0 ... 0 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5802\u001b[0m        \u001b[32m0.7778\u001b[0m                     \u001b[35m0.5250\u001b[0m        \u001b[31m0.8410\u001b[0m  0.1000  1.9682\n",
      "      2                     \u001b[36m0.6131\u001b[0m        \u001b[32m0.7008\u001b[0m                     0.4994        \u001b[31m0.7529\u001b[0m  0.0996  2.0312\n",
      "      3                     \u001b[36m0.6171\u001b[0m        \u001b[32m0.6671\u001b[0m                     \u001b[35m0.6263\u001b[0m        \u001b[31m0.6561\u001b[0m  0.0983  2.0817\n",
      "      4                     \u001b[36m0.6211\u001b[0m        0.6729                     0.6058        0.8168  0.0962  2.0706\n",
      "      5                     \u001b[36m0.6312\u001b[0m        0.6734                     0.5353        1.6066  0.0933  1.9873\n",
      "      6                     0.6140        0.6761                     0.6099        0.7020  0.0897  1.9481\n",
      "      7                     0.6216        \u001b[32m0.6605\u001b[0m                     \u001b[35m0.6378\u001b[0m        0.6562  0.0854  1.9468\n",
      "      8                     0.6148        0.6771                     0.5093        0.7534  0.0804  1.9480\n",
      "      9                     \u001b[36m0.6507\u001b[0m        \u001b[32m0.6367\u001b[0m                     0.5939        0.6784  0.0750  1.9644\n",
      "     10                     0.6454        \u001b[32m0.6339\u001b[0m                     0.5230        0.7604  0.0691  1.9451\n",
      "     11                     \u001b[36m0.6597\u001b[0m        \u001b[32m0.6300\u001b[0m                     0.5370        1.0478  0.0629  1.9458\n",
      "     12                     0.6573        \u001b[32m0.6238\u001b[0m                     0.5707        0.6704  0.0565  1.9467\n",
      "     13                     \u001b[36m0.6752\u001b[0m        \u001b[32m0.6074\u001b[0m                     0.4923        0.7738  0.0500  1.9470\n",
      "     14                     \u001b[36m0.6756\u001b[0m        0.6133                     0.6085        0.6669  0.0435  1.9474\n",
      "     15                     \u001b[36m0.6869\u001b[0m        \u001b[32m0.6018\u001b[0m                     0.5057        0.7482  0.0371  1.9470\n",
      "     16                     \u001b[36m0.6991\u001b[0m        \u001b[32m0.5911\u001b[0m                     0.5121        0.7419  0.0309  1.9568\n",
      "     17                     0.6818        0.5928                     0.5025        0.6993  0.0250  1.9942\n",
      "     18                     \u001b[36m0.6999\u001b[0m        \u001b[32m0.5815\u001b[0m                     \u001b[35m0.6433\u001b[0m        \u001b[31m0.6179\u001b[0m  0.0196  2.0454\n",
      "     19                     \u001b[36m0.7164\u001b[0m        \u001b[32m0.5617\u001b[0m                     \u001b[35m0.6506\u001b[0m        0.6274  0.0146  2.0492\n",
      "     20                     \u001b[36m0.7187\u001b[0m        \u001b[32m0.5568\u001b[0m                     0.5632        0.7275  0.0103  1.9523\n",
      "     21                     \u001b[36m0.7327\u001b[0m        \u001b[32m0.5441\u001b[0m                     0.5990        0.6596  0.0067  2.0667\n",
      "     22                     0.7320        \u001b[32m0.5388\u001b[0m                     0.5492        0.7105  0.0038  2.1056\n",
      "     23                     \u001b[36m0.7416\u001b[0m        \u001b[32m0.5297\u001b[0m                     \u001b[35m0.6551\u001b[0m        0.6285  0.0017  2.0862\n",
      "     24                     \u001b[36m0.7494\u001b[0m        \u001b[32m0.5233\u001b[0m                     \u001b[35m0.7210\u001b[0m        \u001b[31m0.5548\u001b[0m  0.0004  2.1941\n",
      "     25                     \u001b[36m0.7549\u001b[0m        \u001b[32m0.5142\u001b[0m                     \u001b[35m0.7351\u001b[0m        \u001b[31m0.5284\u001b[0m  0.0000  2.1155\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 0 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr      dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  -------\n",
      "      1                     \u001b[36m0.5067\u001b[0m        \u001b[32m0.7565\u001b[0m                     \u001b[35m0.4997\u001b[0m        \u001b[31m0.7668\u001b[0m  0.1000  10.1595\n",
      "      2                     \u001b[36m0.5222\u001b[0m        \u001b[32m0.7168\u001b[0m                     \u001b[35m0.5007\u001b[0m        \u001b[31m0.6935\u001b[0m  0.0996  10.1711\n",
      "      3                     0.5078        0.7209                     0.5000        0.7224  0.0983  10.1780\n",
      "      4                     0.5071        0.7257                     \u001b[35m0.5127\u001b[0m        0.6947  0.0962  10.1831\n",
      "      5                     0.5117        0.7171                     0.5001        0.7488  0.0933  10.1793\n",
      "      6                     0.5097        0.7218                     0.5000        0.7915  0.0897  10.1811\n",
      "      7                     0.5136        0.7279                     0.5005        0.8576  0.0854  10.1799\n",
      "      8                     0.5128        \u001b[32m0.7140\u001b[0m                     0.5000        0.8134  0.0804  10.1769\n",
      "      9                     0.5186        \u001b[32m0.7113\u001b[0m                     0.5019        0.7186  0.0750  10.1806\n",
      "     10                     \u001b[36m0.5226\u001b[0m        \u001b[32m0.7102\u001b[0m                     0.5001        2.0503  0.0691  10.1840\n",
      "     11                     \u001b[36m0.5253\u001b[0m        0.7116                     \u001b[35m0.5635\u001b[0m        \u001b[31m0.6818\u001b[0m  0.0629  10.2598\n",
      "     12                     \u001b[36m0.5400\u001b[0m        \u001b[32m0.6978\u001b[0m                     0.5011        0.7910  0.0565  10.1845\n",
      "     13                     0.5270        0.7066                     0.5002        1.0289  0.0500  10.1831\n",
      "     14                     0.5353        0.6984                     0.5015        0.7032  0.0435  10.3509\n",
      "     15                     \u001b[36m0.5436\u001b[0m        \u001b[32m0.6916\u001b[0m                     0.4996        0.8634  0.0371  10.2435\n",
      "     16                     \u001b[36m0.5504\u001b[0m        \u001b[32m0.6892\u001b[0m                     0.5000        1.5212  0.0309  10.1878\n",
      "     17                     \u001b[36m0.5549\u001b[0m        \u001b[32m0.6875\u001b[0m                     0.5013        0.7422  0.0250  10.1868\n",
      "     18                     \u001b[36m0.5658\u001b[0m        \u001b[32m0.6834\u001b[0m                     0.5000        0.7976  0.0196  10.1834\n",
      "     19                     0.5644        \u001b[32m0.6819\u001b[0m                     0.4999        0.7346  0.0146  10.1835\n",
      "     20                     \u001b[36m0.5735\u001b[0m        \u001b[32m0.6781\u001b[0m                     0.5206        0.6908  0.0103  10.1843\n",
      "     21                     \u001b[36m0.5824\u001b[0m        \u001b[32m0.6762\u001b[0m                     0.5131        0.6975  0.0067  10.1829\n",
      "     22                     \u001b[36m0.5863\u001b[0m        \u001b[32m0.6722\u001b[0m                     0.5011        0.7841  0.0038  10.2083\n",
      "     23                     \u001b[36m0.5887\u001b[0m        \u001b[32m0.6706\u001b[0m                     0.5494        0.6819  0.0017  10.2102\n",
      "     24                     \u001b[36m0.5924\u001b[0m        \u001b[32m0.6674\u001b[0m                     \u001b[35m0.5805\u001b[0m        \u001b[31m0.6790\u001b[0m  0.0004  10.1854\n",
      "     25                     \u001b[36m0.5970\u001b[0m        \u001b[32m0.6659\u001b[0m                     \u001b[35m0.5891\u001b[0m        \u001b[31m0.6686\u001b[0m  0.0000  10.1848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 1 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.4977\u001b[0m        \u001b[32m0.7656\u001b[0m                     \u001b[35m0.5018\u001b[0m        \u001b[31m1.5816\u001b[0m  0.1000  2.8405\n",
      "      2                     \u001b[36m0.5241\u001b[0m        \u001b[32m0.7216\u001b[0m                     \u001b[35m0.5293\u001b[0m        \u001b[31m0.6922\u001b[0m  0.0996  2.8085\n",
      "      3                     0.5195        \u001b[32m0.7214\u001b[0m                     0.5049        0.7568  0.0983  2.8094\n",
      "      4                     \u001b[36m0.5323\u001b[0m        \u001b[32m0.7169\u001b[0m                     0.4982        0.7181  0.0962  2.8084\n",
      "      5                     0.5236        \u001b[32m0.7046\u001b[0m                     0.4955        0.7361  0.0933  2.8094\n",
      "      6                     \u001b[36m0.5456\u001b[0m        \u001b[32m0.6987\u001b[0m                     0.4999        0.8200  0.0897  2.8112\n",
      "      7                     0.5446        0.7000                     0.4986        0.7137  0.0854  2.8087\n",
      "      8                     \u001b[36m0.5582\u001b[0m        \u001b[32m0.6916\u001b[0m                     0.4969        0.7187  0.0804  2.8138\n",
      "      9                     0.5565        0.6924                     0.4999        0.7644  0.0750  2.8083\n",
      "     10                     \u001b[36m0.5657\u001b[0m        \u001b[32m0.6894\u001b[0m                     0.4986        0.7523  0.0691  2.8086\n",
      "     11                     \u001b[36m0.5692\u001b[0m        \u001b[32m0.6849\u001b[0m                     0.5141        0.7003  0.0629  2.8094\n",
      "     12                     \u001b[36m0.5859\u001b[0m        \u001b[32m0.6764\u001b[0m                     0.4987        0.8029  0.0565  2.8086\n",
      "     13                     \u001b[36m0.5883\u001b[0m        \u001b[32m0.6740\u001b[0m                     0.4996        0.9128  0.0500  2.8100\n",
      "     14                     \u001b[36m0.5951\u001b[0m        \u001b[32m0.6728\u001b[0m                     0.4995        0.7188  0.0435  2.8100\n",
      "     15                     \u001b[36m0.6095\u001b[0m        \u001b[32m0.6635\u001b[0m                     \u001b[35m0.5921\u001b[0m        \u001b[31m0.6700\u001b[0m  0.0371  2.8092\n",
      "     16                     \u001b[36m0.6216\u001b[0m        \u001b[32m0.6570\u001b[0m                     \u001b[35m0.6130\u001b[0m        \u001b[31m0.6661\u001b[0m  0.0309  2.8085\n",
      "     17                     0.6171        \u001b[32m0.6563\u001b[0m                     0.5516        0.7034  0.0250  2.8091\n",
      "     18                     \u001b[36m0.6368\u001b[0m        \u001b[32m0.6484\u001b[0m                     0.5123        0.7563  0.0196  2.8102\n",
      "     19                     \u001b[36m0.6509\u001b[0m        \u001b[32m0.6330\u001b[0m                     0.5000        1.5048  0.0146  2.8090\n",
      "     20                     \u001b[36m0.6565\u001b[0m        \u001b[32m0.6297\u001b[0m                     0.5009        0.9463  0.0103  2.8097\n",
      "     21                     \u001b[36m0.6586\u001b[0m        \u001b[32m0.6258\u001b[0m                     0.5013        0.7979  0.0067  2.8093\n",
      "     22                     \u001b[36m0.6644\u001b[0m        \u001b[32m0.6199\u001b[0m                     0.5018        0.8392  0.0038  2.8093\n",
      "     23                     \u001b[36m0.6703\u001b[0m        \u001b[32m0.6158\u001b[0m                     0.5620        0.6736  0.0017  2.8102\n",
      "     24                     0.6701        \u001b[32m0.6119\u001b[0m                     0.5877        0.6727  0.0004  2.8087\n",
      "     25                     \u001b[36m0.6727\u001b[0m        \u001b[32m0.6083\u001b[0m                     \u001b[35m0.6532\u001b[0m        \u001b[31m0.6230\u001b[0m  0.0000  2.8094\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 1 0 ... 0 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.7787\u001b[0m        \u001b[32m0.5402\u001b[0m                     \u001b[35m0.7351\u001b[0m        \u001b[31m0.6179\u001b[0m  0.1000  4.1984\n",
      "      2                     \u001b[36m0.7984\u001b[0m        \u001b[32m0.4814\u001b[0m                     0.5076        1.5721  0.0996  4.1682\n",
      "      3                     0.7897        0.4962                     0.5000        3.3877  0.0983  4.1627\n",
      "      4                     0.7890        0.4853                     0.5000        1.5967  0.0962  4.2116\n",
      "      5                     0.7874        0.4816                     0.5051        1.0031  0.0933  4.1649\n",
      "      6                     \u001b[36m0.8038\u001b[0m        \u001b[32m0.4439\u001b[0m                     0.5102        0.8817  0.0897  4.1623\n",
      "      7                     \u001b[36m0.8112\u001b[0m        \u001b[32m0.4268\u001b[0m                     0.5018        0.9516  0.0854  4.1618\n",
      "      8                     \u001b[36m0.8130\u001b[0m        0.4310                     0.5000        1.5519  0.0804  4.1636\n",
      "      9                     0.8099        0.4460                     0.5007        0.9322  0.0750  4.1623\n",
      "     10                     \u001b[36m0.8235\u001b[0m        \u001b[32m0.4190\u001b[0m                     0.5002        1.5301  0.0691  4.1620\n",
      "     11                     \u001b[36m0.8247\u001b[0m        \u001b[32m0.4109\u001b[0m                     0.5000        4.0950  0.0629  4.1635\n",
      "     12                     0.8178        \u001b[32m0.4109\u001b[0m                     0.5002        0.9397  0.0565  4.1633\n",
      "     13                     \u001b[36m0.8281\u001b[0m        \u001b[32m0.3982\u001b[0m                     0.5422        0.7612  0.0500  4.1659\n",
      "     14                     \u001b[36m0.8371\u001b[0m        \u001b[32m0.3872\u001b[0m                     0.5000        2.5982  0.0435  4.1639\n",
      "     15                     \u001b[36m0.8406\u001b[0m        \u001b[32m0.3710\u001b[0m                     0.5000        6.6653  0.0371  4.1638\n",
      "     16                     \u001b[36m0.8475\u001b[0m        \u001b[32m0.3709\u001b[0m                     0.5000        1.4801  0.0309  4.1639\n",
      "     17                     \u001b[36m0.8569\u001b[0m        \u001b[32m0.3448\u001b[0m                     0.5000        3.0511  0.0250  4.1702\n",
      "     18                     0.8545        0.3475                     0.5000        3.0278  0.0196  4.1645\n",
      "     19                     \u001b[36m0.8578\u001b[0m        0.3506                     0.5894        0.7370  0.0146  4.1658\n",
      "     20                     \u001b[36m0.8646\u001b[0m        \u001b[32m0.3243\u001b[0m                     \u001b[35m0.7612\u001b[0m        \u001b[31m0.5060\u001b[0m  0.0103  4.2360\n",
      "     21                     \u001b[36m0.8669\u001b[0m        \u001b[32m0.3229\u001b[0m                     0.5012        1.2506  0.0067  4.3091\n",
      "     22                     \u001b[36m0.8762\u001b[0m        \u001b[32m0.3031\u001b[0m                     0.6219        0.7243  0.0038  4.2995\n",
      "     23                     \u001b[36m0.8868\u001b[0m        \u001b[32m0.2912\u001b[0m                     \u001b[35m0.8746\u001b[0m        \u001b[31m0.3310\u001b[0m  0.0017  4.2397\n",
      "     24                     \u001b[36m0.8875\u001b[0m        \u001b[32m0.2839\u001b[0m                     0.8074        0.3933  0.0004  4.2197\n",
      "     25                     \u001b[36m0.8880\u001b[0m        \u001b[32m0.2768\u001b[0m                     \u001b[35m0.8858\u001b[0m        \u001b[31m0.2872\u001b[0m  0.0000  4.2147\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 1 0 ... 0 1 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.6072\u001b[0m        \u001b[32m0.7045\u001b[0m                     \u001b[35m0.5955\u001b[0m        \u001b[31m0.8609\u001b[0m  0.1000  4.2177\n",
      "      2                     \u001b[36m0.6524\u001b[0m        \u001b[32m0.6353\u001b[0m                     0.5609        0.9680  0.0996  4.2127\n",
      "      3                     \u001b[36m0.6771\u001b[0m        \u001b[32m0.6050\u001b[0m                     0.5003        0.9645  0.0983  4.2147\n",
      "      4                     \u001b[36m0.6823\u001b[0m        0.6067                     0.5264        \u001b[31m0.7107\u001b[0m  0.0962  4.2048\n",
      "      5                     \u001b[36m0.6868\u001b[0m        \u001b[32m0.6003\u001b[0m                     \u001b[35m0.6227\u001b[0m        \u001b[31m0.6401\u001b[0m  0.0933  4.2085\n",
      "      6                     \u001b[36m0.6874\u001b[0m        \u001b[32m0.6000\u001b[0m                     0.5019        1.6869  0.0897  4.2048\n",
      "      7                     \u001b[36m0.6971\u001b[0m        \u001b[32m0.5900\u001b[0m                     0.5291        0.6860  0.0854  4.2454\n",
      "      8                     0.6946        0.5920                     0.6179        0.6607  0.0804  4.1906\n",
      "      9                     0.6953        \u001b[32m0.5891\u001b[0m                     0.5095        0.8460  0.0750  4.1674\n",
      "     10                     \u001b[36m0.7130\u001b[0m        \u001b[32m0.5718\u001b[0m                     0.5031        1.0037  0.0691  4.1653\n",
      "     11                     0.7124        \u001b[32m0.5701\u001b[0m                     \u001b[35m0.6723\u001b[0m        0.6474  0.0629  4.1668\n",
      "     12                     \u001b[36m0.7285\u001b[0m        \u001b[32m0.5425\u001b[0m                     0.5126        0.8400  0.0565  4.1681\n",
      "     13                     0.7238        0.5497                     0.5009        0.7807  0.0500  4.1668\n",
      "     14                     \u001b[36m0.7301\u001b[0m        \u001b[32m0.5423\u001b[0m                     0.6161        0.6497  0.0435  4.1667\n",
      "     15                     \u001b[36m0.7440\u001b[0m        \u001b[32m0.5273\u001b[0m                     0.5101        0.8618  0.0371  4.1656\n",
      "     16                     \u001b[36m0.7501\u001b[0m        \u001b[32m0.5158\u001b[0m                     \u001b[35m0.7361\u001b[0m        \u001b[31m0.5812\u001b[0m  0.0309  4.1651\n",
      "     17                     \u001b[36m0.7631\u001b[0m        \u001b[32m0.5012\u001b[0m                     0.5009        1.0311  0.0250  4.1668\n",
      "     18                     \u001b[36m0.7652\u001b[0m        \u001b[32m0.4902\u001b[0m                     0.6938        0.5824  0.0196  4.1670\n",
      "     19                     \u001b[36m0.7735\u001b[0m        \u001b[32m0.4790\u001b[0m                     0.5885        0.6426  0.0146  4.1670\n",
      "     20                     \u001b[36m0.7827\u001b[0m        \u001b[32m0.4623\u001b[0m                     0.7322        \u001b[31m0.5408\u001b[0m  0.0103  4.1719\n",
      "     21                     \u001b[36m0.7888\u001b[0m        \u001b[32m0.4540\u001b[0m                     \u001b[35m0.7834\u001b[0m        \u001b[31m0.5206\u001b[0m  0.0067  4.1662\n",
      "     22                     \u001b[36m0.8002\u001b[0m        \u001b[32m0.4379\u001b[0m                     0.6098        0.8280  0.0038  4.1714\n",
      "     23                     \u001b[36m0.8063\u001b[0m        \u001b[32m0.4264\u001b[0m                     \u001b[35m0.8067\u001b[0m        \u001b[31m0.4434\u001b[0m  0.0017  4.1679\n",
      "     24                     \u001b[36m0.8096\u001b[0m        \u001b[32m0.4229\u001b[0m                     0.7999        0.4457  0.0004  4.1672\n",
      "     25                     \u001b[36m0.8126\u001b[0m        \u001b[32m0.4200\u001b[0m                     0.8058        \u001b[31m0.4309\u001b[0m  0.0000  4.1668\n"
     ]
    }
   ],
   "source": [
    "## EEGNet LR testing\n",
    "preprocessing = \"medium\"\n",
    "model_name = \"eegnet\"\n",
    "model_folder = \"HyperparameterTesting\"\n",
    "n_epochs = 25\n",
    "n_splits = 1\n",
    "\n",
    "# run all tasks for different learning rates\n",
    "results = []\n",
    "for task in [\"N170\", \"N400\", \"P3\", \"MMN\", \"N2pc\", \"ERN\", \"LRP\"]:\n",
    "    df = DataLoader.load_df(data_path, task, preprocessing)\n",
    "    data, labels = DataLoader.create_data_labels(df)\n",
    "    for lr in [0.1]:\n",
    "        Training.run_exp(data, labels, task, preprocessing, model_folder, model_name, \n",
    "                lr, n_epochs, n_splits, batch_size=64, additional_save_param=\"_\"+str(lr))\n",
    "        accuracy = Training.load_exp(model_folder, model_name, task, \n",
    "                                     preprocessing, n_splits, \n",
    "                                     additional_save_param=\"_\"+str(lr))[\"valid_balanced_accuracy\"][n_epochs-1]\n",
    "        results.append((task, lr, accuracy))\n",
    "\n",
    "# save results        \n",
    "df = pd.DataFrame(results, columns=[\"Task\",\"LR\",\"valid_balanced_accuracy\"])\n",
    "df_path = os.getcwd()+\"\\\\HyperparameterTesting\\\\\"\n",
    "df.to_csv(df_path+\"eegnet_lr2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ce4df66d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valid_balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>0.746863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.003</th>\n",
       "      <td>0.757678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>0.756847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.030</th>\n",
       "      <td>0.749240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>0.734346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       valid_balanced_accuracy\n",
       "LR                            \n",
       "0.001                 0.746863\n",
       "0.003                 0.757678\n",
       "0.010                 0.756847\n",
       "0.030                 0.749240\n",
       "0.100                 0.734346"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(df_path+\"eegnet_lr.csv\")\n",
    "df.groupby([\"LR\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "59256c8b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3200x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "plt.figure(figsize=(8, 4), dpi=400)\n",
    "sns.barplot(x=\"Task\", y=\"valid_balanced_accuracy\", \n",
    "            hue=\"LR\", \n",
    "            data=df, ci=\"sd\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "18946234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task = \"N170\"\n",
    "preprocessing = \"medium\"\n",
    "model_name = \"eegnet\"\n",
    "model_folder = \"HyperparameterTesting\"\n",
    "lr = 0.003\n",
    "clf, model = Training.init_model(model_name, lr)\n",
    "model_path = os.getcwd()+\"\\\\\"+model_folder+\"\\\\\"+model_name+\"\\\\\"+task+\"\\\\\"+preprocessing+\"\\\\\"\n",
    "clf.load_params(f_params=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_model.pkl\",\n",
    "                       f_optimizer=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_optimizer.pkl\",\n",
    "                       f_history=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_history.json\")\n",
    "\n",
    "\n",
    "## modified from braindecode\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import pandas as pd\n",
    "\n",
    "# Extract loss and accuracy values for plotting from history object\n",
    "results_columns = [\"train_loss\", \"valid_loss\", \"train_balanced_accuracy\", \"valid_balanced_accuracy\"]\n",
    "df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n",
    "                  index=clf.history[:, 'epoch'])\n",
    "\n",
    "# get percent of misclass for better visual comparison to loss\n",
    "df = df.assign(train_misclass=100 - 100 * df.train_balanced_accuracy,\n",
    "               valid_misclass=100 - 100 * df.valid_balanced_accuracy)\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "fig, ax1 = plt.subplots(figsize=(16, 6))\n",
    "df.loc[:, ['train_loss', 'valid_loss']].plot(\n",
    "    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14, ms=4)\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\n",
    "ax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "df.loc[:, ['train_misclass', 'valid_misclass']].plot(\n",
    "    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False, ms=4)\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\n",
    "ax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\n",
    "ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\n",
    "ax1.set_xlabel(\"Epoch\", fontsize=14)\n",
    "\n",
    "# where some data has already been plotted to ax\n",
    "handles = []\n",
    "handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\n",
    "handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\n",
    "plt.legend(handles, [h.get_label() for h in handles], fontsize=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "024285da",
   "metadata": {},
   "source": [
    "### Shallow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "087d3cf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 1 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5048\u001b[0m        \u001b[32m6.6018\u001b[0m                     \u001b[35m0.4941\u001b[0m        \u001b[31m6.8093\u001b[0m  0.1000  1.0903\n",
      "      2                     0.5028        \u001b[32m3.6727\u001b[0m                     \u001b[35m0.5375\u001b[0m        \u001b[31m1.6068\u001b[0m  0.0996  0.4924\n",
      "      3                     \u001b[36m0.5174\u001b[0m        \u001b[32m2.0622\u001b[0m                     0.4954        1.7536  0.0983  0.4910\n",
      "      4                     \u001b[36m0.5232\u001b[0m        \u001b[32m1.6409\u001b[0m                     0.5096        \u001b[31m1.5626\u001b[0m  0.0962  0.5755\n",
      "      5                     \u001b[36m0.5418\u001b[0m        \u001b[32m1.0839\u001b[0m                     \u001b[35m0.5444\u001b[0m        \u001b[31m0.7349\u001b[0m  0.0933  0.5419\n",
      "      6                     0.5185        \u001b[32m0.8846\u001b[0m                     \u001b[35m0.5590\u001b[0m        \u001b[31m0.7285\u001b[0m  0.0897  0.5164\n",
      "      7                     0.5405        \u001b[32m0.7835\u001b[0m                     0.5546        \u001b[31m0.6803\u001b[0m  0.0854  0.5037\n",
      "      8                     \u001b[36m0.5431\u001b[0m        \u001b[32m0.7727\u001b[0m                     0.5351        0.7082  0.0804  0.4995\n",
      "      9                     0.5270        0.7770                     0.5573        0.7089  0.0750  0.5153\n",
      "     10                     \u001b[36m0.5543\u001b[0m        \u001b[32m0.7440\u001b[0m                     \u001b[35m0.5637\u001b[0m        0.6932  0.0691  0.4987\n",
      "     11                     \u001b[36m0.5579\u001b[0m        \u001b[32m0.7241\u001b[0m                     \u001b[35m0.5707\u001b[0m        \u001b[31m0.6755\u001b[0m  0.0629  0.5316\n",
      "     12                     \u001b[36m0.5589\u001b[0m        \u001b[32m0.7133\u001b[0m                     \u001b[35m0.5855\u001b[0m        0.6858  0.0565  0.5014\n",
      "     13                     0.5557        \u001b[32m0.7108\u001b[0m                     0.5661        0.6765  0.0500  0.5043\n",
      "     14                     \u001b[36m0.5742\u001b[0m        \u001b[32m0.6867\u001b[0m                     0.5751        \u001b[31m0.6727\u001b[0m  0.0435  0.4894\n",
      "     15                     \u001b[36m0.5809\u001b[0m        \u001b[32m0.6824\u001b[0m                     0.5736        0.6734  0.0371  0.5190\n",
      "     16                     \u001b[36m0.5842\u001b[0m        \u001b[32m0.6738\u001b[0m                     0.5709        0.6761  0.0309  0.5021\n",
      "     17                     0.5786        0.6779                     0.5709        0.6775  0.0250  0.5098\n",
      "     18                     \u001b[36m0.5990\u001b[0m        \u001b[32m0.6651\u001b[0m                     \u001b[35m0.6002\u001b[0m        \u001b[31m0.6573\u001b[0m  0.0196  0.5505\n",
      "     19                     \u001b[36m0.6044\u001b[0m        0.6658                     \u001b[35m0.6022\u001b[0m        \u001b[31m0.6541\u001b[0m  0.0146  0.6453\n",
      "     20                     \u001b[36m0.6046\u001b[0m        0.6661                     \u001b[35m0.6190\u001b[0m        0.6546  0.0103  0.5375\n",
      "     21                     \u001b[36m0.6075\u001b[0m        \u001b[32m0.6576\u001b[0m                     0.6176        \u001b[31m0.6519\u001b[0m  0.0067  0.5589\n",
      "     22                     \u001b[36m0.6117\u001b[0m        \u001b[32m0.6571\u001b[0m                     0.6139        \u001b[31m0.6506\u001b[0m  0.0038  0.5272\n",
      "     23                     \u001b[36m0.6148\u001b[0m        \u001b[32m0.6541\u001b[0m                     \u001b[35m0.6257\u001b[0m        \u001b[31m0.6500\u001b[0m  0.0017  0.5452\n",
      "     24                     \u001b[36m0.6206\u001b[0m        \u001b[32m0.6498\u001b[0m                     \u001b[35m0.6265\u001b[0m        \u001b[31m0.6496\u001b[0m  0.0004  0.5338\n",
      "     25                     0.6152        0.6521                     \u001b[35m0.6282\u001b[0m        0.6520  0.0000  0.5814\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 1 1 ... 1 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.6059\u001b[0m        \u001b[32m7.5208\u001b[0m                     \u001b[35m0.6583\u001b[0m        \u001b[31m2.1305\u001b[0m  0.1000  0.3968\n",
      "      2                     \u001b[36m0.6258\u001b[0m        \u001b[32m3.7116\u001b[0m                     0.5976        2.5549  0.0996  0.2929\n",
      "      3                     0.6167        \u001b[32m2.1346\u001b[0m                     0.6500        \u001b[31m1.7987\u001b[0m  0.0983  0.2934\n",
      "      4                     \u001b[36m0.6444\u001b[0m        \u001b[32m1.4529\u001b[0m                     0.6017        \u001b[31m1.1491\u001b[0m  0.0962  0.2932\n",
      "      5                     0.6064        2.1529                     \u001b[35m0.6902\u001b[0m        \u001b[31m0.8810\u001b[0m  0.0933  0.2931\n",
      "      6                     \u001b[36m0.6447\u001b[0m        \u001b[32m0.9281\u001b[0m                     0.6023        0.9747  0.0897  0.2936\n",
      "      7                     0.6357        0.9346                     0.6521        \u001b[31m0.6820\u001b[0m  0.0854  0.2931\n",
      "      8                     \u001b[36m0.6567\u001b[0m        \u001b[32m0.8059\u001b[0m                     0.6139        0.7801  0.0804  0.2932\n",
      "      9                     0.6503        \u001b[32m0.7740\u001b[0m                     \u001b[35m0.7215\u001b[0m        \u001b[31m0.5590\u001b[0m  0.0750  0.2942\n",
      "     10                     \u001b[36m0.6912\u001b[0m        \u001b[32m0.6188\u001b[0m                     0.6614        0.6230  0.0691  0.2932\n",
      "     11                     \u001b[36m0.6955\u001b[0m        \u001b[32m0.6055\u001b[0m                     \u001b[35m0.7280\u001b[0m        0.5627  0.0629  0.2934\n",
      "     12                     0.6782        0.6261                     0.7127        0.5626  0.0565  0.2944\n",
      "     13                     \u001b[36m0.7034\u001b[0m        \u001b[32m0.5772\u001b[0m                     0.7122        0.5660  0.0500  0.2942\n",
      "     14                     0.6966        0.5905                     0.7037        \u001b[31m0.5556\u001b[0m  0.0435  0.3037\n",
      "     15                     \u001b[36m0.7162\u001b[0m        \u001b[32m0.5679\u001b[0m                     0.7258        0.5566  0.0371  0.3012\n",
      "     16                     \u001b[36m0.7167\u001b[0m        \u001b[32m0.5573\u001b[0m                     \u001b[35m0.7345\u001b[0m        \u001b[31m0.5448\u001b[0m  0.0309  0.2922\n",
      "     17                     0.7142        0.5648                     \u001b[35m0.7396\u001b[0m        \u001b[31m0.5359\u001b[0m  0.0250  0.2942\n",
      "     18                     0.7128        0.5636                     0.7342        0.5457  0.0196  0.2923\n",
      "     19                     \u001b[36m0.7236\u001b[0m        \u001b[32m0.5473\u001b[0m                     \u001b[35m0.7402\u001b[0m        0.5369  0.0146  0.2926\n",
      "     20                     \u001b[36m0.7329\u001b[0m        \u001b[32m0.5436\u001b[0m                     \u001b[35m0.7439\u001b[0m        0.5366  0.0103  0.2933\n",
      "     21                     0.7309        \u001b[32m0.5300\u001b[0m                     \u001b[35m0.7468\u001b[0m        \u001b[31m0.5333\u001b[0m  0.0067  0.3142\n",
      "     22                     0.7241        0.5398                     \u001b[35m0.7470\u001b[0m        0.5338  0.0038  0.2918\n",
      "     23                     \u001b[36m0.7394\u001b[0m        0.5316                     \u001b[35m0.7489\u001b[0m        0.5341  0.0017  0.2922\n",
      "     24                     0.7368        \u001b[32m0.5285\u001b[0m                     0.7476        0.5351  0.0004  0.2932\n",
      "     25                     \u001b[36m0.7435\u001b[0m        0.5317                     0.7464        0.5341  0.0000  0.2931\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 0 0 ... 0 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5499\u001b[0m        \u001b[32m5.7287\u001b[0m                     \u001b[35m0.5534\u001b[0m        \u001b[31m3.0367\u001b[0m  0.1000  0.7212\n",
      "      2                     \u001b[36m0.5657\u001b[0m        \u001b[32m2.4092\u001b[0m                     \u001b[35m0.5706\u001b[0m        \u001b[31m1.1833\u001b[0m  0.0996  0.6324\n",
      "      3                     \u001b[36m0.5799\u001b[0m        \u001b[32m1.1816\u001b[0m                     \u001b[35m0.6016\u001b[0m        \u001b[31m0.7129\u001b[0m  0.0983  0.6323\n",
      "      4                     \u001b[36m0.5902\u001b[0m        \u001b[32m0.7862\u001b[0m                     0.5757        0.7273  0.0962  0.6328\n",
      "      5                     \u001b[36m0.6054\u001b[0m        \u001b[32m0.7157\u001b[0m                     \u001b[35m0.6731\u001b[0m        \u001b[31m0.6162\u001b[0m  0.0933  0.6333\n",
      "      6                     \u001b[36m0.6235\u001b[0m        \u001b[32m0.6846\u001b[0m                     0.6178        0.6833  0.0897  0.6342\n",
      "      7                     0.5861        0.7653                     0.6256        0.6933  0.0854  0.6334\n",
      "      8                     0.6051        0.7211                     \u001b[35m0.6783\u001b[0m        0.6330  0.0804  0.6326\n",
      "      9                     0.6143        0.7199                     \u001b[35m0.6797\u001b[0m        \u001b[31m0.6132\u001b[0m  0.0750  0.6330\n",
      "     10                     0.6227        0.6938                     0.6789        0.6175  0.0691  0.6345\n",
      "     11                     \u001b[36m0.6411\u001b[0m        \u001b[32m0.6835\u001b[0m                     \u001b[35m0.7120\u001b[0m        \u001b[31m0.5972\u001b[0m  0.0629  0.6328\n",
      "     12                     \u001b[36m0.6419\u001b[0m        \u001b[32m0.6622\u001b[0m                     0.6647        0.6252  0.0565  0.6325\n",
      "     13                     \u001b[36m0.6610\u001b[0m        \u001b[32m0.6498\u001b[0m                     0.5963        0.6823  0.0500  0.6326\n",
      "     14                     \u001b[36m0.6642\u001b[0m        \u001b[32m0.6401\u001b[0m                     0.6496        0.6365  0.0435  0.6325\n",
      "     15                     \u001b[36m0.6731\u001b[0m        \u001b[32m0.6247\u001b[0m                     0.7018        \u001b[31m0.5862\u001b[0m  0.0371  0.6336\n",
      "     16                     \u001b[36m0.6780\u001b[0m        \u001b[32m0.6088\u001b[0m                     0.7079        \u001b[31m0.5820\u001b[0m  0.0309  0.6333\n",
      "     17                     \u001b[36m0.6830\u001b[0m        \u001b[32m0.6087\u001b[0m                     0.7069        0.5853  0.0250  0.6337\n",
      "     18                     \u001b[36m0.6895\u001b[0m        \u001b[32m0.6026\u001b[0m                     \u001b[35m0.7161\u001b[0m        \u001b[31m0.5737\u001b[0m  0.0196  0.6333\n",
      "     19                     \u001b[36m0.6979\u001b[0m        \u001b[32m0.5873\u001b[0m                     \u001b[35m0.7169\u001b[0m        \u001b[31m0.5688\u001b[0m  0.0146  0.6336\n",
      "     20                     \u001b[36m0.7047\u001b[0m        \u001b[32m0.5773\u001b[0m                     0.7128        \u001b[31m0.5639\u001b[0m  0.0103  0.6346\n",
      "     21                     \u001b[36m0.7098\u001b[0m        \u001b[32m0.5733\u001b[0m                     \u001b[35m0.7236\u001b[0m        0.5675  0.0067  0.6340\n",
      "     22                     \u001b[36m0.7227\u001b[0m        \u001b[32m0.5645\u001b[0m                     0.7203        \u001b[31m0.5593\u001b[0m  0.0038  0.6332\n",
      "     23                     0.7127        \u001b[32m0.5639\u001b[0m                     \u001b[35m0.7247\u001b[0m        0.5606  0.0017  0.6328\n",
      "     24                     0.7140        0.5665                     0.7213        0.5611  0.0004  0.6338\n",
      "     25                     0.7185        0.5667                     0.7238        0.5603  0.0000  0.6346\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 0 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5074\u001b[0m        \u001b[32m2.0608\u001b[0m                     \u001b[35m0.4997\u001b[0m        \u001b[31m0.7759\u001b[0m  0.1000  3.5960\n",
      "      2                     0.5047        \u001b[32m0.7711\u001b[0m                     0.4986        \u001b[31m0.7628\u001b[0m  0.0996  3.2821\n",
      "      3                     0.5030        0.8910                     0.4980        0.7828  0.0983  3.2789\n",
      "      4                     0.4992        0.8932                     \u001b[35m0.5001\u001b[0m        1.6971  0.0962  3.2786\n",
      "      5                     0.5017        0.9265                     \u001b[35m0.5002\u001b[0m        1.1223  0.0933  3.2784\n",
      "      6                     0.5024        0.9901                     \u001b[35m0.5043\u001b[0m        \u001b[31m0.7372\u001b[0m  0.0897  3.2791\n",
      "      7                     0.5057        0.8314                     0.4991        1.0098  0.0854  3.2780\n",
      "      8                     \u001b[36m0.5125\u001b[0m        0.8875                     \u001b[35m0.5255\u001b[0m        \u001b[31m0.7071\u001b[0m  0.0804  3.2878\n",
      "      9                     0.5070        0.8478                     0.4994        0.8770  0.0750  3.2841\n",
      "     10                     0.5052        0.8063                     0.5018        0.7900  0.0691  3.2812\n",
      "     11                     0.5043        0.8021                     0.5000        0.8684  0.0629  3.2796\n",
      "     12                     0.5096        0.8027                     0.4999        0.8177  0.0565  3.2810\n",
      "     13                     0.5009        \u001b[32m0.7620\u001b[0m                     0.4999        0.7885  0.0500  3.2844\n",
      "     14                     \u001b[36m0.5168\u001b[0m        \u001b[32m0.7600\u001b[0m                     0.5012        \u001b[31m0.7001\u001b[0m  0.0435  3.2856\n",
      "     15                     0.5104        \u001b[32m0.7378\u001b[0m                     0.5015        0.7134  0.0371  3.2851\n",
      "     16                     0.5040        \u001b[32m0.7278\u001b[0m                     0.5111        0.7005  0.0309  3.2869\n",
      "     17                     \u001b[36m0.5213\u001b[0m        \u001b[32m0.7191\u001b[0m                     0.4991        0.7394  0.0250  3.2886\n",
      "     18                     \u001b[36m0.5271\u001b[0m        \u001b[32m0.7079\u001b[0m                     0.5040        0.7075  0.0196  3.2846\n",
      "     19                     \u001b[36m0.5275\u001b[0m        \u001b[32m0.7018\u001b[0m                     \u001b[35m0.5557\u001b[0m        \u001b[31m0.6843\u001b[0m  0.0146  3.2856\n",
      "     20                     \u001b[36m0.5341\u001b[0m        \u001b[32m0.6953\u001b[0m                     \u001b[35m0.5595\u001b[0m        \u001b[31m0.6828\u001b[0m  0.0103  3.2869\n",
      "     21                     \u001b[36m0.5480\u001b[0m        \u001b[32m0.6897\u001b[0m                     0.5387        0.6878  0.0067  3.2896\n",
      "     22                     \u001b[36m0.5576\u001b[0m        \u001b[32m0.6851\u001b[0m                     0.5594        \u001b[31m0.6822\u001b[0m  0.0038  3.2895\n",
      "     23                     \u001b[36m0.5666\u001b[0m        \u001b[32m0.6813\u001b[0m                     \u001b[35m0.5715\u001b[0m        \u001b[31m0.6795\u001b[0m  0.0017  3.2871\n",
      "     24                     \u001b[36m0.5730\u001b[0m        \u001b[32m0.6793\u001b[0m                     \u001b[35m0.5738\u001b[0m        \u001b[31m0.6794\u001b[0m  0.0004  3.2888\n",
      "     25                     0.5728        \u001b[32m0.6790\u001b[0m                     0.5736        0.6794  0.0000  3.2896\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 1 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5215\u001b[0m        \u001b[32m4.3985\u001b[0m                     \u001b[35m0.4948\u001b[0m        \u001b[31m1.0137\u001b[0m  0.1000  1.1199\n",
      "      2                     0.5175        \u001b[32m0.9985\u001b[0m                     \u001b[35m0.5199\u001b[0m        \u001b[31m0.7218\u001b[0m  0.0996  0.9170\n",
      "      3                     0.5116        \u001b[32m0.7988\u001b[0m                     0.5052        0.7630  0.0983  0.9112\n",
      "      4                     0.5049        \u001b[32m0.7747\u001b[0m                     0.5028        0.7724  0.0962  0.9077\n",
      "      5                     0.5130        0.7768                     0.5013        0.8025  0.0933  0.9086\n",
      "      6                     0.5158        \u001b[32m0.7734\u001b[0m                     \u001b[35m0.5489\u001b[0m        0.7236  0.0897  0.9085\n",
      "      7                     0.4924        0.7909                     0.5369        \u001b[31m0.7161\u001b[0m  0.0854  0.9082\n",
      "      8                     0.5147        \u001b[32m0.7566\u001b[0m                     0.5310        \u001b[31m0.7149\u001b[0m  0.0804  0.9078\n",
      "      9                     0.5157        \u001b[32m0.7320\u001b[0m                     0.5227        \u001b[31m0.7083\u001b[0m  0.0750  0.9438\n",
      "     10                     \u001b[36m0.5255\u001b[0m        \u001b[32m0.7268\u001b[0m                     0.5064        0.8207  0.0691  0.9104\n",
      "     11                     \u001b[36m0.5298\u001b[0m        0.7336                     0.5013        0.7670  0.0629  0.9186\n",
      "     12                     \u001b[36m0.5498\u001b[0m        \u001b[32m0.7095\u001b[0m                     \u001b[35m0.5962\u001b[0m        \u001b[31m0.6764\u001b[0m  0.0565  0.9094\n",
      "     13                     0.5477        \u001b[32m0.7061\u001b[0m                     0.5882        0.6798  0.0500  0.9088\n",
      "     14                     \u001b[36m0.5663\u001b[0m        \u001b[32m0.6954\u001b[0m                     0.5703        0.6942  0.0435  0.9084\n",
      "     15                     \u001b[36m0.5685\u001b[0m        \u001b[32m0.6929\u001b[0m                     \u001b[35m0.6094\u001b[0m        \u001b[31m0.6722\u001b[0m  0.0371  0.9090\n",
      "     16                     \u001b[36m0.5828\u001b[0m        \u001b[32m0.6793\u001b[0m                     \u001b[35m0.6225\u001b[0m        \u001b[31m0.6676\u001b[0m  0.0309  0.9090\n",
      "     17                     \u001b[36m0.5931\u001b[0m        \u001b[32m0.6758\u001b[0m                     0.5596        0.6902  0.0250  0.9098\n",
      "     18                     0.5897        0.6771                     0.6054        \u001b[31m0.6644\u001b[0m  0.0196  0.9088\n",
      "     19                     \u001b[36m0.6100\u001b[0m        \u001b[32m0.6631\u001b[0m                     \u001b[35m0.6314\u001b[0m        \u001b[31m0.6508\u001b[0m  0.0146  0.9111\n",
      "     20                     0.6068        0.6646                     0.6255        0.6627  0.0103  0.9092\n",
      "     21                     \u001b[36m0.6181\u001b[0m        \u001b[32m0.6591\u001b[0m                     0.6247        \u001b[31m0.6491\u001b[0m  0.0067  0.9087\n",
      "     22                     \u001b[36m0.6226\u001b[0m        \u001b[32m0.6525\u001b[0m                     \u001b[35m0.6317\u001b[0m        \u001b[31m0.6451\u001b[0m  0.0038  0.9091\n",
      "     23                     \u001b[36m0.6260\u001b[0m        \u001b[32m0.6510\u001b[0m                     0.6307        \u001b[31m0.6437\u001b[0m  0.0017  0.9083\n",
      "     24                     \u001b[36m0.6300\u001b[0m        \u001b[32m0.6503\u001b[0m                     0.6285        \u001b[31m0.6435\u001b[0m  0.0004  0.9089\n",
      "     25                     0.6278        \u001b[32m0.6500\u001b[0m                     0.6295        0.6440  0.0000  0.9084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 1 0 ... 0 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.6886\u001b[0m        \u001b[32m4.4766\u001b[0m                     \u001b[35m0.7910\u001b[0m        \u001b[31m1.3390\u001b[0m  0.1000  1.6062\n",
      "      2                     \u001b[36m0.7477\u001b[0m        \u001b[32m1.3520\u001b[0m                     0.7516        \u001b[31m0.7144\u001b[0m  0.0996  1.3442\n",
      "      3                     \u001b[36m0.7670\u001b[0m        \u001b[32m0.6767\u001b[0m                     \u001b[35m0.8007\u001b[0m        \u001b[31m0.4770\u001b[0m  0.0983  1.3432\n",
      "      4                     \u001b[36m0.7996\u001b[0m        \u001b[32m0.4968\u001b[0m                     \u001b[35m0.8321\u001b[0m        \u001b[31m0.4157\u001b[0m  0.0962  1.3432\n",
      "      5                     0.7880        0.5321                     \u001b[35m0.8453\u001b[0m        \u001b[31m0.3819\u001b[0m  0.0933  1.3442\n",
      "      6                     0.7790        0.6311                     0.7390        0.7382  0.0897  1.3434\n",
      "      7                     0.7908        0.5909                     0.6852        0.7286  0.0854  1.3438\n",
      "      8                     0.7846        0.5782                     0.8430        0.3956  0.0804  1.3438\n",
      "      9                     0.7880        0.5882                     \u001b[35m0.8656\u001b[0m        \u001b[31m0.3391\u001b[0m  0.0750  1.3448\n",
      "     10                     0.7912        0.5635                     0.7831        0.4525  0.0691  1.3455\n",
      "     11                     \u001b[36m0.8135\u001b[0m        \u001b[32m0.4819\u001b[0m                     0.8095        0.4909  0.0629  1.3449\n",
      "     12                     \u001b[36m0.8166\u001b[0m        \u001b[32m0.4509\u001b[0m                     \u001b[35m0.8685\u001b[0m        \u001b[31m0.3261\u001b[0m  0.0565  1.3444\n",
      "     13                     \u001b[36m0.8336\u001b[0m        \u001b[32m0.4189\u001b[0m                     0.8322        0.4152  0.0500  1.3442\n",
      "     14                     0.8308        \u001b[32m0.4140\u001b[0m                     \u001b[35m0.8743\u001b[0m        0.3311  0.0435  1.3439\n",
      "     15                     \u001b[36m0.8347\u001b[0m        \u001b[32m0.3940\u001b[0m                     0.8615        0.3365  0.0371  1.3451\n",
      "     16                     \u001b[36m0.8414\u001b[0m        \u001b[32m0.3848\u001b[0m                     0.8610        0.3539  0.0309  1.3440\n",
      "     17                     \u001b[36m0.8420\u001b[0m        \u001b[32m0.3678\u001b[0m                     0.8479        0.3386  0.0250  1.3460\n",
      "     18                     \u001b[36m0.8483\u001b[0m        \u001b[32m0.3539\u001b[0m                     \u001b[35m0.8765\u001b[0m        0.3310  0.0196  1.3454\n",
      "     19                     \u001b[36m0.8552\u001b[0m        \u001b[32m0.3437\u001b[0m                     \u001b[35m0.8803\u001b[0m        \u001b[31m0.3023\u001b[0m  0.0146  1.3455\n",
      "     20                     \u001b[36m0.8659\u001b[0m        \u001b[32m0.3309\u001b[0m                     \u001b[35m0.8834\u001b[0m        0.3032  0.0103  1.3450\n",
      "     21                     \u001b[36m0.8722\u001b[0m        \u001b[32m0.3243\u001b[0m                     \u001b[35m0.8860\u001b[0m        \u001b[31m0.3011\u001b[0m  0.0067  1.3456\n",
      "     22                     \u001b[36m0.8729\u001b[0m        \u001b[32m0.3124\u001b[0m                     0.8846        \u001b[31m0.2951\u001b[0m  0.0038  1.3430\n",
      "     23                     \u001b[36m0.8770\u001b[0m        \u001b[32m0.3093\u001b[0m                     0.8818        0.2962  0.0017  1.3466\n",
      "     24                     \u001b[36m0.8792\u001b[0m        \u001b[32m0.3026\u001b[0m                     0.8805        0.2970  0.0004  1.3465\n",
      "     25                     0.8744        0.3109                     0.8773        0.2971  0.0000  1.3451\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 1 0 ... 0 1 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5244\u001b[0m        \u001b[32m2.5368\u001b[0m                     \u001b[35m0.5186\u001b[0m        \u001b[31m0.8514\u001b[0m  0.1000  1.3415\n",
      "      2                     \u001b[36m0.5754\u001b[0m        \u001b[32m0.7113\u001b[0m                     \u001b[35m0.6491\u001b[0m        \u001b[31m0.6357\u001b[0m  0.0996  1.3433\n",
      "      3                     \u001b[36m0.6124\u001b[0m        \u001b[32m0.6788\u001b[0m                     \u001b[35m0.6697\u001b[0m        \u001b[31m0.6249\u001b[0m  0.0983  1.3455\n",
      "      4                     0.5985        0.7234                     0.6562        0.6306  0.0962  1.3433\n",
      "      5                     \u001b[36m0.6213\u001b[0m        0.6925                     0.5506        0.7348  0.0933  1.3433\n",
      "      6                     0.6118        0.6989                     \u001b[35m0.6739\u001b[0m        \u001b[31m0.6140\u001b[0m  0.0897  1.3436\n",
      "      7                     0.6171        0.7072                     0.6455        0.6498  0.0854  1.3438\n",
      "      8                     \u001b[36m0.6216\u001b[0m        0.7038                     0.5449        0.8212  0.0804  1.3454\n",
      "      9                     \u001b[36m0.6234\u001b[0m        0.6929                     \u001b[35m0.6851\u001b[0m        \u001b[31m0.6056\u001b[0m  0.0750  1.3444\n",
      "     10                     \u001b[36m0.6236\u001b[0m        0.6821                     \u001b[35m0.7066\u001b[0m        \u001b[31m0.5775\u001b[0m  0.0691  1.3452\n",
      "     11                     \u001b[36m0.6365\u001b[0m        \u001b[32m0.6601\u001b[0m                     0.6789        0.6063  0.0629  1.3445\n",
      "     12                     \u001b[36m0.6479\u001b[0m        \u001b[32m0.6505\u001b[0m                     0.6810        0.5982  0.0565  1.3473\n",
      "     13                     \u001b[36m0.6508\u001b[0m        \u001b[32m0.6458\u001b[0m                     0.5578        0.7273  0.0500  1.3452\n",
      "     14                     \u001b[36m0.6604\u001b[0m        \u001b[32m0.6335\u001b[0m                     \u001b[35m0.7081\u001b[0m        \u001b[31m0.5692\u001b[0m  0.0435  1.3459\n",
      "     15                     \u001b[36m0.6717\u001b[0m        \u001b[32m0.6188\u001b[0m                     0.6487        0.6205  0.0371  1.3454\n",
      "     16                     \u001b[36m0.6898\u001b[0m        \u001b[32m0.5997\u001b[0m                     \u001b[35m0.7372\u001b[0m        \u001b[31m0.5450\u001b[0m  0.0309  1.3457\n",
      "     17                     \u001b[36m0.6970\u001b[0m        \u001b[32m0.5901\u001b[0m                     0.7167        0.5540  0.0250  1.3446\n",
      "     18                     \u001b[36m0.7015\u001b[0m        \u001b[32m0.5791\u001b[0m                     0.7191        0.5505  0.0196  1.3439\n",
      "     19                     \u001b[36m0.7116\u001b[0m        \u001b[32m0.5676\u001b[0m                     0.7333        \u001b[31m0.5390\u001b[0m  0.0146  1.3441\n",
      "     20                     \u001b[36m0.7199\u001b[0m        \u001b[32m0.5605\u001b[0m                     \u001b[35m0.7425\u001b[0m        \u001b[31m0.5334\u001b[0m  0.0103  1.3451\n",
      "     21                     \u001b[36m0.7290\u001b[0m        \u001b[32m0.5494\u001b[0m                     0.7362        0.5364  0.0067  1.3441\n",
      "     22                     \u001b[36m0.7331\u001b[0m        \u001b[32m0.5443\u001b[0m                     \u001b[35m0.7434\u001b[0m        \u001b[31m0.5258\u001b[0m  0.0038  1.3458\n",
      "     23                     0.7320        \u001b[32m0.5427\u001b[0m                     0.7432        \u001b[31m0.5238\u001b[0m  0.0017  1.3460\n",
      "     24                     0.7324        \u001b[32m0.5411\u001b[0m                     \u001b[35m0.7476\u001b[0m        \u001b[31m0.5230\u001b[0m  0.0004  1.3452\n",
      "     25                     \u001b[36m0.7381\u001b[0m        \u001b[32m0.5371\u001b[0m                     0.7467        0.5237  0.0000  1.3459\n"
     ]
    }
   ],
   "source": [
    "## EEGNet LR testing\n",
    "preprocessing = \"medium\"\n",
    "model_name = \"shallow\"\n",
    "model_folder = \"HyperparameterTesting\"\n",
    "n_epochs = 25\n",
    "n_splits = 1\n",
    "\n",
    "# run all tasks for different learning rates\n",
    "results = []\n",
    "for task in [\"N170\", \"N400\", \"P3\", \"MMN\", \"N2pc\", \"ERN\", \"LRP\"]:\n",
    "    df = DataLoader.load_df(data_path, task, preprocessing)\n",
    "    data, labels = DataLoader.create_data_labels(df)\n",
    "    for lr in [0.1]:\n",
    "        Training.run_exp(data, labels, task, preprocessing, model_folder, model_name, \n",
    "                lr, n_epochs, n_splits, batch_size=64, additional_save_param=\"_\"+str(lr))\n",
    "        accuracy = Training.load_exp(model_folder, model_name, task, \n",
    "                                     preprocessing, n_splits, \n",
    "                                     additional_save_param=\"_\"+str(lr))[\"valid_balanced_accuracy\"][n_epochs-1]\n",
    "        results.append((task, lr, accuracy))\n",
    "\n",
    "# save results        \n",
    "df = pd.DataFrame(results, columns=[\"Task\",\"LR\",\"valid_balanced_accuracy\"])\n",
    "df_path = os.getcwd()+\"\\\\HyperparameterTesting\\\\\"\n",
    "df.to_csv(df_path+\"shallow_lr2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "64f92147",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valid_balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>0.733575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.003</th>\n",
       "      <td>0.736173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>0.739668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.030</th>\n",
       "      <td>0.731771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>0.703629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       valid_balanced_accuracy\n",
       "LR                            \n",
       "0.001                 0.733575\n",
       "0.003                 0.736173\n",
       "0.010                 0.739668\n",
       "0.030                 0.731771\n",
       "0.100                 0.703629"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(df_path+\"shallow_lr.csv\")\n",
    "df.groupby([\"LR\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e46c785d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3200x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "plt.figure(figsize=(8, 4), dpi=400)\n",
    "sns.barplot(x=\"Task\", y=\"valid_balanced_accuracy\", \n",
    "            hue=\"LR\", \n",
    "            data=df, ci=\"sd\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "458fd13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task = \"P3\"\n",
    "preprocessing = \"medium\"\n",
    "model_name = \"shallow\"\n",
    "model_folder = \"HyperparameterTesting\"\n",
    "lr = 0.01\n",
    "clf, model = Training.init_model(model_name, lr)\n",
    "model_path = os.getcwd()+\"\\\\\"+model_folder+\"\\\\\"+model_name+\"\\\\\"+task+\"\\\\\"+preprocessing+\"\\\\\"\n",
    "clf.load_params(f_params=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_model.pkl\",\n",
    "                       f_optimizer=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_optimizer.pkl\",\n",
    "                       f_history=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_history.json\")\n",
    "\n",
    "\n",
    "## modified from braindecode\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import pandas as pd\n",
    "\n",
    "# Extract loss and accuracy values for plotting from history object\n",
    "results_columns = [\"train_loss\", \"valid_loss\", \"train_balanced_accuracy\", \"valid_balanced_accuracy\"]\n",
    "df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n",
    "                  index=clf.history[:, 'epoch'])\n",
    "\n",
    "# get percent of misclass for better visual comparison to loss\n",
    "df = df.assign(train_misclass=100 - 100 * df.train_balanced_accuracy,\n",
    "               valid_misclass=100 - 100 * df.valid_balanced_accuracy)\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "fig, ax1 = plt.subplots(figsize=(16, 6))\n",
    "df.loc[:, ['train_loss', 'valid_loss']].plot(\n",
    "    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14, ms=4)\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\n",
    "ax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "df.loc[:, ['train_misclass', 'valid_misclass']].plot(\n",
    "    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False, ms=4)\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\n",
    "ax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\n",
    "ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\n",
    "ax1.set_xlabel(\"Epoch\", fontsize=14)\n",
    "\n",
    "# where some data has already been plotted to ax\n",
    "handles = []\n",
    "handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\n",
    "handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\n",
    "plt.legend(handles, [h.get_label() for h in handles], fontsize=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70262348",
   "metadata": {},
   "source": [
    "### Deep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3de5f177",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 1 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n",
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\torch\\nn\\functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ..\\c10/core/TensorImpl.h:1156.)\n",
      "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5020\u001b[0m        \u001b[32m4.2273\u001b[0m                     \u001b[35m0.5582\u001b[0m        \u001b[31m0.7261\u001b[0m  0.1000  0.9443\n",
      "      2                     \u001b[36m0.5430\u001b[0m        \u001b[32m0.8170\u001b[0m                     0.5424        \u001b[31m0.7255\u001b[0m  0.0996  0.4750\n",
      "      3                     \u001b[36m0.5813\u001b[0m        \u001b[32m0.7106\u001b[0m                     \u001b[35m0.6408\u001b[0m        \u001b[31m0.6537\u001b[0m  0.0983  0.4732\n",
      "      4                     \u001b[36m0.6032\u001b[0m        \u001b[32m0.6910\u001b[0m                     0.6383        0.6578  0.0962  0.4733\n",
      "      5                     \u001b[36m0.6204\u001b[0m        \u001b[32m0.6796\u001b[0m                     0.6374        \u001b[31m0.6423\u001b[0m  0.0933  0.4737\n",
      "      6                     \u001b[36m0.6412\u001b[0m        \u001b[32m0.6573\u001b[0m                     0.5977        0.6670  0.0897  0.4738\n",
      "      7                     \u001b[36m0.6549\u001b[0m        \u001b[32m0.6475\u001b[0m                     \u001b[35m0.6917\u001b[0m        \u001b[31m0.6014\u001b[0m  0.0854  0.4724\n",
      "      8                     \u001b[36m0.6679\u001b[0m        \u001b[32m0.6316\u001b[0m                     0.6626        \u001b[31m0.6005\u001b[0m  0.0804  0.4727\n",
      "      9                     \u001b[36m0.6744\u001b[0m        \u001b[32m0.6160\u001b[0m                     \u001b[35m0.7062\u001b[0m        \u001b[31m0.5725\u001b[0m  0.0750  0.4741\n",
      "     10                     \u001b[36m0.6790\u001b[0m        0.6181                     \u001b[35m0.7093\u001b[0m        0.5755  0.0691  0.4737\n",
      "     11                     \u001b[36m0.6812\u001b[0m        \u001b[32m0.6083\u001b[0m                     0.7063        \u001b[31m0.5661\u001b[0m  0.0629  0.4744\n",
      "     12                     \u001b[36m0.6964\u001b[0m        \u001b[32m0.5885\u001b[0m                     \u001b[35m0.7336\u001b[0m        \u001b[31m0.5457\u001b[0m  0.0565  0.4757\n",
      "     13                     \u001b[36m0.7042\u001b[0m        \u001b[32m0.5771\u001b[0m                     0.7170        \u001b[31m0.5456\u001b[0m  0.0500  0.4729\n",
      "     14                     \u001b[36m0.7192\u001b[0m        \u001b[32m0.5674\u001b[0m                     0.7164        \u001b[31m0.5423\u001b[0m  0.0435  0.4746\n",
      "     15                     0.7110        \u001b[32m0.5643\u001b[0m                     0.7278        \u001b[31m0.5296\u001b[0m  0.0371  0.4751\n",
      "     16                     0.7120        \u001b[32m0.5627\u001b[0m                     0.7336        0.5374  0.0309  0.4747\n",
      "     17                     0.7150        \u001b[32m0.5623\u001b[0m                     0.7190        0.5461  0.0250  0.4760\n",
      "     18                     \u001b[36m0.7311\u001b[0m        \u001b[32m0.5413\u001b[0m                     0.7175        0.5415  0.0196  0.4747\n",
      "     19                     \u001b[36m0.7415\u001b[0m        \u001b[32m0.5337\u001b[0m                     \u001b[35m0.7515\u001b[0m        \u001b[31m0.5115\u001b[0m  0.0146  0.4765\n",
      "     20                     0.7368        \u001b[32m0.5332\u001b[0m                     0.7174        0.5341  0.0103  0.4748\n",
      "     21                     \u001b[36m0.7516\u001b[0m        \u001b[32m0.5186\u001b[0m                     0.7407        0.5133  0.0067  0.4758\n",
      "     22                     0.7488        0.5239                     0.7357        0.5158  0.0038  0.4767\n",
      "     23                     0.7425        \u001b[32m0.5184\u001b[0m                     \u001b[35m0.7535\u001b[0m        \u001b[31m0.5017\u001b[0m  0.0017  0.4749\n",
      "     24                     0.7469        \u001b[32m0.5173\u001b[0m                     0.7458        0.5036  0.0004  0.4758\n",
      "     25                     0.7477        \u001b[32m0.5121\u001b[0m                     0.7501        0.5019  0.0000  0.4756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 1 1 ... 1 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5800\u001b[0m        \u001b[32m5.2289\u001b[0m                     \u001b[35m0.5714\u001b[0m        \u001b[31m1.1786\u001b[0m  0.1000  0.4105\n",
      "      2                     \u001b[36m0.6671\u001b[0m        \u001b[32m0.7321\u001b[0m                     \u001b[35m0.5893\u001b[0m        \u001b[31m0.8960\u001b[0m  0.0996  0.2883\n",
      "      3                     \u001b[36m0.6821\u001b[0m        \u001b[32m0.6566\u001b[0m                     \u001b[35m0.7304\u001b[0m        \u001b[31m0.6018\u001b[0m  0.0983  0.2842\n",
      "      4                     0.6814        0.7709                     0.6932        0.7239  0.0962  0.2843\n",
      "      5                     \u001b[36m0.6900\u001b[0m        \u001b[32m0.6199\u001b[0m                     0.5796        0.8782  0.0933  0.2849\n",
      "      6                     0.6354        0.8668                     0.4996        1.1386  0.0897  0.2843\n",
      "      7                     0.6244        0.8925                     0.6602        0.6782  0.0854  0.2852\n",
      "      8                     0.6537        0.8063                     0.6322        0.7884  0.0804  0.2853\n",
      "      9                     0.6861        0.6351                     0.6492        0.6418  0.0750  0.2852\n",
      "     10                     \u001b[36m0.6962\u001b[0m        \u001b[32m0.6069\u001b[0m                     0.6973        \u001b[31m0.5842\u001b[0m  0.0691  0.2842\n",
      "     11                     \u001b[36m0.7076\u001b[0m        \u001b[32m0.5796\u001b[0m                     0.7302        \u001b[31m0.5620\u001b[0m  0.0629  0.2857\n",
      "     12                     \u001b[36m0.7195\u001b[0m        0.5815                     0.6595        0.6005  0.0565  0.2852\n",
      "     13                     0.7193        \u001b[32m0.5711\u001b[0m                     0.6820        0.5842  0.0500  0.2852\n",
      "     14                     \u001b[36m0.7214\u001b[0m        \u001b[32m0.5624\u001b[0m                     \u001b[35m0.7408\u001b[0m        \u001b[31m0.5507\u001b[0m  0.0435  0.2853\n",
      "     15                     \u001b[36m0.7222\u001b[0m        \u001b[32m0.5542\u001b[0m                     0.7286        \u001b[31m0.5366\u001b[0m  0.0371  0.2863\n",
      "     16                     \u001b[36m0.7396\u001b[0m        \u001b[32m0.5266\u001b[0m                     0.7341        \u001b[31m0.5200\u001b[0m  0.0309  0.2861\n",
      "     17                     \u001b[36m0.7422\u001b[0m        \u001b[32m0.5190\u001b[0m                     \u001b[35m0.7439\u001b[0m        0.5246  0.0250  0.2852\n",
      "     18                     \u001b[36m0.7460\u001b[0m        \u001b[32m0.5138\u001b[0m                     \u001b[35m0.7527\u001b[0m        0.5339  0.0196  0.2852\n",
      "     19                     0.7386        0.5264                     0.7461        \u001b[31m0.5166\u001b[0m  0.0146  0.2853\n",
      "     20                     \u001b[36m0.7476\u001b[0m        \u001b[32m0.5062\u001b[0m                     0.7352        \u001b[31m0.5111\u001b[0m  0.0103  0.2863\n",
      "     21                     \u001b[36m0.7535\u001b[0m        0.5078                     0.7496        \u001b[31m0.5109\u001b[0m  0.0067  0.2862\n",
      "     22                     0.7485        0.5070                     0.7436        0.5182  0.0038  0.2861\n",
      "     23                     0.7437        0.5106                     0.7405        0.5116  0.0017  0.2852\n",
      "     24                     0.7475        0.5077                     0.7389        \u001b[31m0.5097\u001b[0m  0.0004  0.2862\n",
      "     25                     0.7458        \u001b[32m0.5042\u001b[0m                     0.7398        0.5135  0.0000  0.2862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 0 0 ... 0 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5507\u001b[0m        \u001b[32m3.1789\u001b[0m                     \u001b[35m0.5094\u001b[0m        \u001b[31m1.1047\u001b[0m  0.1000  0.7237\n",
      "      2                     \u001b[36m0.5993\u001b[0m        \u001b[32m0.7723\u001b[0m                     \u001b[35m0.5711\u001b[0m        \u001b[31m0.7048\u001b[0m  0.0996  0.6181\n",
      "      3                     \u001b[36m0.6270\u001b[0m        \u001b[32m0.6618\u001b[0m                     \u001b[35m0.6799\u001b[0m        \u001b[31m0.6192\u001b[0m  0.0983  0.6177\n",
      "      4                     \u001b[36m0.6560\u001b[0m        \u001b[32m0.6511\u001b[0m                     \u001b[35m0.6889\u001b[0m        \u001b[31m0.6024\u001b[0m  0.0962  0.6173\n",
      "      5                     0.6421        0.6746                     0.6143        0.6942  0.0933  0.6154\n",
      "      6                     0.6545        0.7338                     \u001b[35m0.7125\u001b[0m        \u001b[31m0.5744\u001b[0m  0.0897  0.6168\n",
      "      7                     \u001b[36m0.6649\u001b[0m        \u001b[32m0.6463\u001b[0m                     0.6469        0.7113  0.0854  0.6162\n",
      "      8                     0.6307        0.7127                     0.6455        0.7132  0.0804  0.6169\n",
      "      9                     0.6466        0.7508                     0.6014        0.6561  0.0750  0.6162\n",
      "     10                     0.6595        0.7298                     \u001b[35m0.7219\u001b[0m        0.6032  0.0691  0.6168\n",
      "     11                     \u001b[36m0.6866\u001b[0m        \u001b[32m0.6124\u001b[0m                     0.6802        0.5981  0.0629  0.6164\n",
      "     12                     \u001b[36m0.6903\u001b[0m        0.6149                     0.5264        0.9279  0.0565  0.6164\n",
      "     13                     0.6868        0.6244                     0.6666        0.6492  0.0500  0.6168\n",
      "     14                     \u001b[36m0.7004\u001b[0m        \u001b[32m0.6067\u001b[0m                     0.6561        0.6200  0.0435  0.6178\n",
      "     15                     \u001b[36m0.7048\u001b[0m        \u001b[32m0.5823\u001b[0m                     0.7172        \u001b[31m0.5622\u001b[0m  0.0371  0.6166\n",
      "     16                     \u001b[36m0.7178\u001b[0m        \u001b[32m0.5586\u001b[0m                     0.7071        0.5634  0.0309  0.6184\n",
      "     17                     \u001b[36m0.7221\u001b[0m        \u001b[32m0.5574\u001b[0m                     \u001b[35m0.7382\u001b[0m        \u001b[31m0.5396\u001b[0m  0.0250  0.6164\n",
      "     18                     \u001b[36m0.7268\u001b[0m        \u001b[32m0.5438\u001b[0m                     0.7330        \u001b[31m0.5378\u001b[0m  0.0196  0.6173\n",
      "     19                     0.7234        0.5494                     \u001b[35m0.7458\u001b[0m        \u001b[31m0.5344\u001b[0m  0.0146  0.6166\n",
      "     20                     \u001b[36m0.7392\u001b[0m        \u001b[32m0.5363\u001b[0m                     0.7340        \u001b[31m0.5335\u001b[0m  0.0103  0.6184\n",
      "     21                     \u001b[36m0.7403\u001b[0m        \u001b[32m0.5273\u001b[0m                     0.7234        0.5452  0.0067  0.6173\n",
      "     22                     0.7328        0.5331                     \u001b[35m0.7526\u001b[0m        \u001b[31m0.5285\u001b[0m  0.0038  0.6174\n",
      "     23                     \u001b[36m0.7445\u001b[0m        \u001b[32m0.5221\u001b[0m                     0.7411        \u001b[31m0.5266\u001b[0m  0.0017  0.6162\n",
      "     24                     \u001b[36m0.7499\u001b[0m        \u001b[32m0.5152\u001b[0m                     0.7494        0.5293  0.0004  0.6183\n",
      "     25                     0.7488        0.5157                     0.7405        0.5307  0.0000  0.6169\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 0 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.4966\u001b[0m        \u001b[32m1.5144\u001b[0m                     \u001b[35m0.4981\u001b[0m        \u001b[31m0.9003\u001b[0m  0.1000  3.4665\n",
      "      2                     \u001b[36m0.5051\u001b[0m        1.8299                     0.4975        1.2219  0.0996  3.1799\n",
      "      3                     0.4992        2.0308                     \u001b[35m0.4998\u001b[0m        0.9035  0.0983  3.2045\n",
      "      4                     0.4990        1.9340                     0.4984        1.6335  0.0962  3.1827\n",
      "      5                     0.5035        1.5708                     0.4988        1.4303  0.0933  3.1815\n",
      "      6                     \u001b[36m0.5055\u001b[0m        1.7377                     0.4974        1.0759  0.0897  3.1816\n",
      "      7                     0.4997        1.5717                     0.4990        \u001b[31m0.8517\u001b[0m  0.0854  3.2240\n",
      "      8                     0.4988        \u001b[32m1.2783\u001b[0m                     0.4978        1.2933  0.0804  3.1842\n",
      "      9                     0.5021        1.9793                     \u001b[35m0.5004\u001b[0m        1.6532  0.0750  3.1865\n",
      "     10                     0.5018        1.3038                     0.4992        5.4434  0.0691  3.1816\n",
      "     11                     0.4999        \u001b[32m1.2335\u001b[0m                     0.4985        0.9217  0.0629  3.1820\n",
      "     12                     0.5043        \u001b[32m1.1994\u001b[0m                     0.4985        1.0705  0.0565  3.1849\n",
      "     13                     0.5007        1.2477                     0.4996        0.9551  0.0500  3.1866\n",
      "     14                     \u001b[36m0.5066\u001b[0m        \u001b[32m0.9696\u001b[0m                     0.4992        \u001b[31m0.8169\u001b[0m  0.0435  3.1864\n",
      "     15                     \u001b[36m0.5077\u001b[0m        \u001b[32m0.8809\u001b[0m                     \u001b[35m0.5006\u001b[0m        \u001b[31m0.7337\u001b[0m  0.0371  3.1856\n",
      "     16                     \u001b[36m0.5115\u001b[0m        0.8827                     0.4989        0.7987  0.0309  3.1862\n",
      "     17                     \u001b[36m0.5292\u001b[0m        \u001b[32m0.7784\u001b[0m                     0.5005        0.7400  0.0250  3.1859\n",
      "     18                     \u001b[36m0.5374\u001b[0m        \u001b[32m0.7356\u001b[0m                     \u001b[35m0.5025\u001b[0m        \u001b[31m0.6995\u001b[0m  0.0196  3.1880\n",
      "     19                     \u001b[36m0.5446\u001b[0m        \u001b[32m0.7218\u001b[0m                     \u001b[35m0.5155\u001b[0m        \u001b[31m0.6926\u001b[0m  0.0146  3.1884\n",
      "     20                     \u001b[36m0.5520\u001b[0m        \u001b[32m0.7041\u001b[0m                     \u001b[35m0.5724\u001b[0m        \u001b[31m0.6867\u001b[0m  0.0103  3.1895\n",
      "     21                     \u001b[36m0.5617\u001b[0m        \u001b[32m0.6924\u001b[0m                     0.5460        \u001b[31m0.6848\u001b[0m  0.0067  3.1892\n",
      "     22                     \u001b[36m0.5656\u001b[0m        \u001b[32m0.6872\u001b[0m                     \u001b[35m0.5774\u001b[0m        \u001b[31m0.6843\u001b[0m  0.0038  3.1911\n",
      "     23                     \u001b[36m0.5731\u001b[0m        \u001b[32m0.6820\u001b[0m                     0.5766        \u001b[31m0.6822\u001b[0m  0.0017  3.1921\n",
      "     24                     0.5684        \u001b[32m0.6812\u001b[0m                     0.5761        \u001b[31m0.6803\u001b[0m  0.0004  3.1885\n",
      "     25                     0.5697        0.6833                     0.5756        0.6805  0.0000  3.1914\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[0 0 0 ... 1 0 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5028\u001b[0m        \u001b[32m2.5514\u001b[0m                     \u001b[35m0.5002\u001b[0m        \u001b[31m0.7444\u001b[0m  0.1000  1.2009\n",
      "      2                     0.4974        \u001b[32m0.7487\u001b[0m                     \u001b[35m0.5047\u001b[0m        0.8597  0.0996  0.8861\n",
      "      3                     \u001b[36m0.5069\u001b[0m        \u001b[32m0.7393\u001b[0m                     0.4961        0.7940  0.0983  0.8873\n",
      "      4                     0.5050        0.7649                     0.4990        \u001b[31m0.7281\u001b[0m  0.0962  0.8860\n",
      "      5                     0.4937        0.8522                     0.4975        0.8741  0.0933  0.8847\n",
      "      6                     0.5030        0.9184                     0.4970        0.8577  0.0897  0.8851\n",
      "      7                     0.5004        1.1947                     0.5021        1.1010  0.0854  0.8949\n",
      "      8                     0.4961        1.0214                     0.4961        0.8856  0.0804  0.8849\n",
      "      9                     0.4939        1.0441                     0.5000        1.2222  0.0750  0.8849\n",
      "     10                     0.4991        0.9543                     0.5044        0.7692  0.0691  0.8865\n",
      "     11                     0.5037        0.8526                     0.5006        0.7751  0.0629  0.8870\n",
      "     12                     0.5016        0.7519                     0.5004        \u001b[31m0.7068\u001b[0m  0.0565  0.8859\n",
      "     13                     0.4951        0.7485                     0.5000        0.7167  0.0500  0.8862\n",
      "     14                     \u001b[36m0.5080\u001b[0m        0.7837                     0.4992        0.7312  0.0435  0.8869\n",
      "     15                     0.4898        0.7743                     0.4970        0.7967  0.0371  0.8867\n",
      "     16                     0.5032        0.7622                     0.5022        0.7269  0.0309  0.8862\n",
      "     17                     0.4992        \u001b[32m0.7138\u001b[0m                     0.4978        0.7119  0.0250  0.8862\n",
      "     18                     0.4994        \u001b[32m0.7029\u001b[0m                     0.5000        \u001b[31m0.6962\u001b[0m  0.0196  0.8878\n",
      "     19                     \u001b[36m0.5089\u001b[0m        \u001b[32m0.6979\u001b[0m                     0.5008        0.6971  0.0146  0.8867\n",
      "     20                     0.5016        \u001b[32m0.6948\u001b[0m                     0.5003        0.6992  0.0103  0.8851\n",
      "     21                     0.5011        \u001b[32m0.6944\u001b[0m                     0.4978        \u001b[31m0.6946\u001b[0m  0.0067  0.8857\n",
      "     22                     0.4992        \u001b[32m0.6933\u001b[0m                     0.4995        \u001b[31m0.6939\u001b[0m  0.0038  0.8865\n",
      "     23                     0.4999        \u001b[32m0.6929\u001b[0m                     0.4999        0.6944  0.0017  0.8923\n",
      "     24                     0.5011        0.6932                     0.5013        0.6943  0.0004  0.8888\n",
      "     25                     0.5006        0.6932                     0.4999        \u001b[31m0.6939\u001b[0m  0.0000  0.8870\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 1 0 ... 0 0 0] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.7429\u001b[0m        \u001b[32m1.5665\u001b[0m                     \u001b[35m0.7887\u001b[0m        \u001b[31m0.5297\u001b[0m  0.1000  1.6578\n",
      "      2                     \u001b[36m0.7963\u001b[0m        \u001b[32m0.5007\u001b[0m                     0.7881        0.6679  0.0996  1.3080\n",
      "      3                     \u001b[36m0.8023\u001b[0m        0.5727                     0.7459        1.4956  0.0983  1.3045\n",
      "      4                     0.7509        1.4893                     \u001b[35m0.8197\u001b[0m        0.5920  0.0962  1.3029\n",
      "      5                     \u001b[36m0.8172\u001b[0m        0.5379                     0.7080        0.7325  0.0933  1.3056\n",
      "      6                     0.7906        0.8164                     0.7365        0.8827  0.0897  1.3050\n",
      "      7                     0.8141        0.6013                     \u001b[35m0.8735\u001b[0m        \u001b[31m0.3549\u001b[0m  0.0854  1.3036\n",
      "      8                     \u001b[36m0.8394\u001b[0m        \u001b[32m0.4226\u001b[0m                     0.8442        0.3704  0.0804  1.3075\n",
      "      9                     0.8162        0.6287                     0.8695        \u001b[31m0.3484\u001b[0m  0.0750  1.3046\n",
      "     10                     0.8295        0.4777                     0.7681        0.5097  0.0691  1.3046\n",
      "     11                     0.8227        0.5574                     0.8733        \u001b[31m0.3148\u001b[0m  0.0629  1.3063\n",
      "     12                     \u001b[36m0.8429\u001b[0m        0.4314                     0.8384        0.3869  0.0565  1.3043\n",
      "     13                     \u001b[36m0.8525\u001b[0m        \u001b[32m0.3644\u001b[0m                     \u001b[35m0.8809\u001b[0m        0.3269  0.0500  1.3056\n",
      "     14                     \u001b[36m0.8717\u001b[0m        \u001b[32m0.3206\u001b[0m                     \u001b[35m0.8949\u001b[0m        \u001b[31m0.2927\u001b[0m  0.0435  1.3058\n",
      "     15                     0.8712        0.3209                     0.8218        0.3756  0.0371  1.3053\n",
      "     16                     \u001b[36m0.8764\u001b[0m        \u001b[32m0.3069\u001b[0m                     0.8798        0.2995  0.0309  1.3066\n",
      "     17                     0.8738        \u001b[32m0.3043\u001b[0m                     0.8770        0.3080  0.0250  1.3056\n",
      "     18                     \u001b[36m0.8839\u001b[0m        \u001b[32m0.2846\u001b[0m                     0.8867        \u001b[31m0.2842\u001b[0m  0.0196  1.3062\n",
      "     19                     0.8838        \u001b[32m0.2812\u001b[0m                     \u001b[35m0.9007\u001b[0m        \u001b[31m0.2704\u001b[0m  0.0146  1.3080\n",
      "     20                     \u001b[36m0.8895\u001b[0m        \u001b[32m0.2752\u001b[0m                     0.8767        0.2981  0.0103  1.3054\n",
      "     21                     \u001b[36m0.8902\u001b[0m        \u001b[32m0.2669\u001b[0m                     0.8917        0.2754  0.0067  1.3056\n",
      "     22                     \u001b[36m0.8940\u001b[0m        \u001b[32m0.2644\u001b[0m                     0.8903        0.2725  0.0038  1.3063\n",
      "     23                     \u001b[36m0.8993\u001b[0m        \u001b[32m0.2590\u001b[0m                     0.8961        \u001b[31m0.2673\u001b[0m  0.0017  1.3077\n",
      "     24                     0.8982        \u001b[32m0.2563\u001b[0m                     0.8966        0.2726  0.0004  1.3297\n",
      "     25                     0.8964        \u001b[32m0.2563\u001b[0m                     0.8971        \u001b[31m0.2663\u001b[0m  0.0000  1.3283\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vapor\\anaconda3\\envs\\braindecode\\lib\\site-packages\\sklearn\\utils\\validation.py:67: FutureWarning: Pass classes=[0 1], y=[1 1 0 ... 0 1 1] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  warnings.warn(\"Pass {} as keyword args. From version 0.25 \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Re-initializing optimizer because the following parameters were re-set: lr, weight_decay.\n",
      "  epoch    train_balanced_accuracy    train_loss    valid_balanced_accuracy    valid_loss      lr     dur\n",
      "-------  -------------------------  ------------  -------------------------  ------------  ------  ------\n",
      "      1                     \u001b[36m0.5053\u001b[0m        \u001b[32m1.8845\u001b[0m                     \u001b[35m0.5000\u001b[0m        \u001b[31m0.7215\u001b[0m  0.1000  1.3064\n",
      "      2                     0.5042        \u001b[32m0.7089\u001b[0m                     0.4942        \u001b[31m0.7016\u001b[0m  0.0996  1.3063\n",
      "      3                     0.4981        0.7232                     \u001b[35m0.5000\u001b[0m        0.7122  0.0983  1.3055\n",
      "      4                     0.4990        0.8151                     \u001b[35m0.5007\u001b[0m        0.7314  0.0962  1.3069\n",
      "      5                     0.4996        0.8499                     0.5000        0.9866  0.0933  1.3050\n",
      "      6                     0.5045        1.2763                     0.4986        3.2199  0.0897  1.3053\n",
      "      7                     0.5019        1.4875                     \u001b[35m0.5022\u001b[0m        0.9647  0.0854  1.3055\n",
      "      8                     0.5032        0.8114                     0.4986        0.7482  0.0804  1.3055\n",
      "      9                     \u001b[36m0.5432\u001b[0m        0.7242                     \u001b[35m0.6658\u001b[0m        \u001b[31m0.6446\u001b[0m  0.0750  1.3046\n",
      "     10                     \u001b[36m0.6709\u001b[0m        \u001b[32m0.6479\u001b[0m                     \u001b[35m0.7297\u001b[0m        \u001b[31m0.5588\u001b[0m  0.0691  1.3060\n",
      "     11                     \u001b[36m0.7055\u001b[0m        \u001b[32m0.6396\u001b[0m                     0.6654        0.7080  0.0629  1.3047\n",
      "     12                     \u001b[36m0.7251\u001b[0m        \u001b[32m0.5752\u001b[0m                     \u001b[35m0.7462\u001b[0m        \u001b[31m0.5303\u001b[0m  0.0565  1.3047\n",
      "     13                     \u001b[36m0.7453\u001b[0m        \u001b[32m0.5399\u001b[0m                     \u001b[35m0.7780\u001b[0m        \u001b[31m0.4958\u001b[0m  0.0500  1.3046\n",
      "     14                     \u001b[36m0.7527\u001b[0m        \u001b[32m0.5273\u001b[0m                     0.7750        \u001b[31m0.4884\u001b[0m  0.0435  1.3039\n",
      "     15                     \u001b[36m0.7617\u001b[0m        \u001b[32m0.5097\u001b[0m                     0.7726        0.4904  0.0371  1.3056\n",
      "     16                     \u001b[36m0.7663\u001b[0m        \u001b[32m0.4924\u001b[0m                     0.7722        \u001b[31m0.4791\u001b[0m  0.0309  1.3071\n",
      "     17                     \u001b[36m0.7779\u001b[0m        \u001b[32m0.4767\u001b[0m                     \u001b[35m0.7859\u001b[0m        \u001b[31m0.4675\u001b[0m  0.0250  1.3052\n",
      "     18                     \u001b[36m0.7787\u001b[0m        \u001b[32m0.4674\u001b[0m                     0.7849        0.4680  0.0196  1.3036\n",
      "     19                     \u001b[36m0.7859\u001b[0m        \u001b[32m0.4589\u001b[0m                     \u001b[35m0.7900\u001b[0m        \u001b[31m0.4497\u001b[0m  0.0146  1.3057\n",
      "     20                     \u001b[36m0.7882\u001b[0m        \u001b[32m0.4550\u001b[0m                     \u001b[35m0.7932\u001b[0m        \u001b[31m0.4453\u001b[0m  0.0103  1.3053\n",
      "     21                     \u001b[36m0.7932\u001b[0m        \u001b[32m0.4521\u001b[0m                     \u001b[35m0.7954\u001b[0m        \u001b[31m0.4432\u001b[0m  0.0067  1.3053\n",
      "     22                     \u001b[36m0.7934\u001b[0m        \u001b[32m0.4446\u001b[0m                     \u001b[35m0.7978\u001b[0m        \u001b[31m0.4394\u001b[0m  0.0038  1.3064\n",
      "     23                     0.7931        0.4446                     \u001b[35m0.7981\u001b[0m        \u001b[31m0.4386\u001b[0m  0.0017  1.3074\n",
      "     24                     \u001b[36m0.7962\u001b[0m        \u001b[32m0.4409\u001b[0m                     \u001b[35m0.8001\u001b[0m        \u001b[31m0.4386\u001b[0m  0.0004  1.3063\n",
      "     25                     0.7940        0.4415                     0.7982        0.4407  0.0000  1.3088\n"
     ]
    }
   ],
   "source": [
    "## EEGNet LR testing\n",
    "preprocessing = \"medium\"\n",
    "model_name = \"deep\"\n",
    "model_folder = \"HyperparameterTesting\"\n",
    "n_epochs = 25\n",
    "n_splits = 1\n",
    "\n",
    "# run all tasks for different learning rates\n",
    "results = []\n",
    "for task in [\"N170\", \"N400\", \"P3\", \"MMN\", \"N2pc\", \"ERN\", \"LRP\"]:\n",
    "    df = DataLoader.load_df(data_path, task, preprocessing)\n",
    "    data, labels = DataLoader.create_data_labels(df)\n",
    "    for lr in [0.1]:\n",
    "        Training.run_exp(data, labels, task, preprocessing, model_folder, model_name, \n",
    "                lr, n_epochs, n_splits, batch_size=64, additional_save_param=\"_\"+str(lr))\n",
    "        accuracy = Training.load_exp(model_folder, model_name, task, \n",
    "                                     preprocessing, n_splits, \n",
    "                                     additional_save_param=\"_\"+str(lr))[\"valid_balanced_accuracy\"][n_epochs-1]\n",
    "        results.append((task, lr, accuracy))\n",
    "\n",
    "# save results        \n",
    "df = pd.DataFrame(results, columns=[\"Task\",\"LR\",\"valid_balanced_accuracy\"])\n",
    "df_path = os.getcwd()+\"\\\\HyperparameterTesting\\\\\"\n",
    "df.to_csv(df_path+\"deep_lr2.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d5587fad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valid_balanced_accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LR</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>0.752708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.003</th>\n",
       "      <td>0.761434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.010</th>\n",
       "      <td>0.762924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.030</th>\n",
       "      <td>0.757632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.100</th>\n",
       "      <td>0.714451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       valid_balanced_accuracy\n",
       "LR                            \n",
       "0.001                 0.752708\n",
       "0.003                 0.761434\n",
       "0.010                 0.762924\n",
       "0.030                 0.757632\n",
       "0.100                 0.714451"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(df_path+\"deep_lr.csv\")\n",
    "df.groupby([\"LR\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "795faf6d",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 3200x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"darkgrid\")\n",
    "plt.figure(figsize=(8, 4), dpi=400)\n",
    "sns.barplot(x=\"Task\", y=\"valid_balanced_accuracy\", \n",
    "            hue=\"LR\", \n",
    "            data=df, ci=\"sd\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "82c0cdb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task = \"N170\"\n",
    "lr = 0.01\n",
    "clf, model = Training.init_model(model_name, lr)\n",
    "model_path = os.getcwd()+\"\\\\\"+model_folder+\"\\\\\"+model_name+\"\\\\\"+task+\"\\\\\"+preprocessing+\"\\\\\"\n",
    "clf.load_params(f_params=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_model.pkl\",\n",
    "                       f_optimizer=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_optimizer.pkl\",\n",
    "                       f_history=model_path+\"split_\"+str(1)+\"_\"+str(lr)+\"_history.json\")\n",
    "\n",
    "\n",
    "## modified from braindecode\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "import pandas as pd\n",
    "\n",
    "# Extract loss and accuracy values for plotting from history object\n",
    "results_columns = [\"train_loss\", \"valid_loss\", \"train_balanced_accuracy\", \"valid_balanced_accuracy\"]\n",
    "df = pd.DataFrame(clf.history[:, results_columns], columns=results_columns,\n",
    "                  index=clf.history[:, 'epoch'])\n",
    "\n",
    "# get percent of misclass for better visual comparison to loss\n",
    "df = df.assign(train_misclass=100 - 100 * df.train_balanced_accuracy,\n",
    "               valid_misclass=100 - 100 * df.valid_balanced_accuracy)\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "fig, ax1 = plt.subplots(figsize=(16, 6))\n",
    "df.loc[:, ['train_loss', 'valid_loss']].plot(\n",
    "    ax=ax1, style=['-', ':'], marker='o', color='tab:blue', legend=False, fontsize=14, ms=4)\n",
    "\n",
    "ax1.tick_params(axis='y', labelcolor='tab:blue', labelsize=14)\n",
    "ax1.set_ylabel(\"Loss\", color='tab:blue', fontsize=14)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "df.loc[:, ['train_misclass', 'valid_misclass']].plot(\n",
    "    ax=ax2, style=['-', ':'], marker='o', color='tab:red', legend=False, ms=4)\n",
    "ax2.tick_params(axis='y', labelcolor='tab:red', labelsize=14)\n",
    "ax2.set_ylabel(\"Misclassification Rate [%]\", color='tab:red', fontsize=14)\n",
    "ax2.set_ylim(ax2.get_ylim()[0], 85)  # make some room for legend\n",
    "ax1.set_xlabel(\"Epoch\", fontsize=14)\n",
    "\n",
    "# where some data has already been plotted to ax\n",
    "handles = []\n",
    "handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle='-', label='Train'))\n",
    "handles.append(Line2D([0], [0], color='black', linewidth=1, linestyle=':', label='Valid'))\n",
    "plt.legend(handles, [h.get_label() for h in handles], fontsize=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d061be9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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