notebooks/visualize_performance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "innovative-cambodia",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "import os\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "several-louis",
   "metadata": {},
   "outputs": [],
   "source": [
    "project_dir = '../'\n",
    "with open(os.path.join(project_dir, 'reports/data/simon_36/EEGNet_SSVEP.pkl'), 'rb') as f:\n",
    "    eegnet_history = pickle.load(f)\n",
    "with open(os.path.join(project_dir, 'reports/data/simon_36/EEGNet_SSVEP_Spectral.pkl'), 'rb') as f:\n",
    "    eegnet_spectral_history = pickle.load(f)\n",
    "with open(os.path.join(project_dir, 'reports/data/simon_36/EEGNet_SSVEP_PCA.pkl'), 'rb') as f:\n",
    "    eegnet_pca_history = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "apparent-natural",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curve(history, epoch_start=0):\n",
    "    plt.figure()\n",
    "    capsize= 5\n",
    "    mean_acc = np.array(history['acc']).mean(axis=0)\n",
    "    std_acc = np.array(history['acc']).std(axis=0)\n",
    "    val_mean_acc = np.array(history['val_acc']).mean(axis=0)\n",
    "    val_std_acc = np.array(history['val_acc']).std(axis=0)\n",
    "    xticks = np.arange(epoch_start, len(mean_acc), 1)\n",
    "    plt.errorbar(xticks, mean_acc[epoch_start:], label='acc', capsize=capsize)\n",
    "    plt.errorbar(xticks, val_mean_acc[epoch_start:], label='val_acc', capsize=capsize)\n",
    "    # plt.xticks(xticks)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.figure()\n",
    "    capsize= 5\n",
    "    mean_loss = np.array(history['loss']).mean(axis=0)\n",
    "    std_loss = np.array(history['loss']).std(axis=0)\n",
    "    val_mean_loss = np.array(history['val_loss']).mean(axis=0)\n",
    "    val_std_loss = np.array(history['val_loss']).std(axis=0)\n",
    "    xticks = np.arange(epoch_start, len(mean_loss), 1)\n",
    "    plt.errorbar(xticks, mean_loss[epoch_start:], label='loss', capsize=capsize)\n",
    "    plt.errorbar(xticks, val_mean_loss[epoch_start:], label='val_loss', capsize=capsize)\n",
    "    # plt.xticks(xticks)\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend();\n",
    "\n",
    "plot_learning_curve(eegnet_history)\n",
    "plot_learning_curve(eegnet_spectral_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imperial-teaching",
   "metadata": {},
   "source": [
    "Use the maximum accuracy from all epochs as final accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "respected-manhattan",
   "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>EEGNet</th>\n",
       "      <th>EEGNet+Spectral-Filter</th>\n",
       "      <th>EEGNet+PCA-Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.651389</td>\n",
       "      <td>0.790278</td>\n",
       "      <td>0.634722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.108070</td>\n",
       "      <td>0.096990</td>\n",
       "      <td>0.069614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.770833</td>\n",
       "      <td>0.600694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.805556</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.718750</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.736111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EEGNet  EEGNet+Spectral-Filter  EEGNet+PCA-Filter\n",
       "count  10.000000               10.000000          10.000000\n",
       "mean    0.651389                0.790278           0.634722\n",
       "std     0.108070                0.096990           0.069614\n",
       "min     0.416667                0.555556           0.500000\n",
       "25%     0.625000                0.770833           0.600694\n",
       "50%     0.687500                0.805556           0.625000\n",
       "75%     0.718750                0.854167           0.687500\n",
       "max     0.763889                0.888889           0.736111"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eegnet_val_accs = np.max(eegnet_history['val_acc'], axis=1)\n",
    "eegnet_val_spectral_accs = np.max(eegnet_spectral_history['val_acc'], axis=1)\n",
    "eegnet_val_pca_accs = np.max(eegnet_pca_history['val_acc'], axis=1)\n",
    "f, ax = plt.subplots()\n",
    "ax.boxplot([eegnet_val_accs, eegnet_val_spectral_accs, eegnet_val_pca_accs])\n",
    "ax.set_xticklabels(['EEGNet', 'EEGNet+Spectral-Filter', 'EEGNet+PCA-Filter'])\n",
    "ax.set_ylabel('Validation Accuracy');\n",
    "\n",
    "df = pd.DataFrame(np.stack([eegnet_val_accs, eegnet_val_spectral_accs, eegnet_val_pca_accs], axis=1))\n",
    "df.columns = ['EEGNet', 'EEGNet+Spectral-Filter', 'EEGNet+PCA-Filter']\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "physical-tourism",
   "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>EEGNet</th>\n",
       "      <th>EEGNet+Spectral-Filter</th>\n",
       "      <th>EEGNet+PCA-Filter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.898611</td>\n",
       "      <td>0.939043</td>\n",
       "      <td>0.868981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.009869</td>\n",
       "      <td>0.003353</td>\n",
       "      <td>0.011763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.884259</td>\n",
       "      <td>0.933642</td>\n",
       "      <td>0.844136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.893519</td>\n",
       "      <td>0.937114</td>\n",
       "      <td>0.864583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.897377</td>\n",
       "      <td>0.938272</td>\n",
       "      <td>0.867284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.906636</td>\n",
       "      <td>0.939815</td>\n",
       "      <td>0.879630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.915123</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.882716</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EEGNet  EEGNet+Spectral-Filter  EEGNet+PCA-Filter\n",
       "count  10.000000               10.000000          10.000000\n",
       "mean    0.898611                0.939043           0.868981\n",
       "std     0.009869                0.003353           0.011763\n",
       "min     0.884259                0.933642           0.844136\n",
       "25%     0.893519                0.937114           0.864583\n",
       "50%     0.897377                0.938272           0.867284\n",
       "75%     0.906636                0.939815           0.879630\n",
       "max     0.915123                0.944444           0.882716"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eegnet_val_accs = np.max(eegnet_history['acc'], axis=1)\n",
    "eegnet_val_spectral_accs = np.max(eegnet_spectral_history['acc'], axis=1)\n",
    "eegnet_val_pca_accs = np.max(eegnet_pca_history['acc'], axis=1)\n",
    "f, ax = plt.subplots()\n",
    "ax.boxplot([eegnet_val_accs, eegnet_val_spectral_accs, eegnet_val_pca_accs])\n",
    "ax.set_xticklabels(['EEGNet', 'EEGNet+Spectral-Filter', 'EEGNet+PCA-Filter'])\n",
    "ax.set_ylabel('Training Accuracy');\n",
    "\n",
    "df = pd.DataFrame(np.stack([eegnet_val_accs, eegnet_val_spectral_accs, eegnet_val_pca_accs], axis=1))\n",
    "df.columns = ['EEGNet', 'EEGNet+Spectral-Filter', 'EEGNet+PCA-Filter']\n",
    "df.describe()"
   ]
  }
 ],
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