05 - ERD + Motor Imagery.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Event-related desynchronization on motor imagery dataset\n",
    "\n",
    "Here we shall look at the motor imagery task.  Look at the dataset, this belongs to one participant performing motor imagery task where they were asked to (1) squeeze the ball using their left and right hand (executed condition) and (2) to imagine squeezing the ball using their left and right hand (imagined).\n",
    "\n",
    "Runs 1 and 2 are fixations, odd runs are 3, 5, 7, 9 are executed, while even runs are 4, 6, 8, 10 are imagined.\n",
    "\n",
    "In this tutorial, we shall visit (1) event related desynchronization, and (2) convolution for decoding motor imagery."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import mne\n",
    "from mne import create_info\n",
    "from mne.io import RawArray\n",
    "from mne import Epochs, find_events\n",
    "import os\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import TensorDataset, WeightedRandomSampler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this will set the plotting size\n",
    "mne.set_config('MNE_BROWSE_RAW_SIZE','10,5')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NVIDIA RTX A6000'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 0. Config\n",
    "\n",
    "Below is a one place for all configurable parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_url = '../data/S020'\n",
    "runs = ['04', '06', '08']\n",
    "\n",
    "in_channels  = 2  #C3, C4\n",
    "out_channels = 5  #five is logical because we have freq= 8, 9, 10, 11, 12 that we want to capture\n",
    "out_size     = 2  #left or right\n",
    "kernel_size  = 250 #1 second\n",
    "\n",
    "BATCH_SIZE   = 9999 #basically use whole batch size...\n",
    "lr           = 1e-6\n",
    "weight_decay = 1e-1\n",
    "\n",
    "num_epochs = 1000\n",
    "\n",
    "modelpath  = '../models/motorCNN1D.pth.tar'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 1. Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(164135, 10)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>Fp1</th>\n",
       "      <th>Fp2</th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>P7</th>\n",
       "      <th>P8</th>\n",
       "      <th>O1</th>\n",
       "      <th>O2</th>\n",
       "      <th>STIM MARKERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000</td>\n",
       "      <td>4540.332127</td>\n",
       "      <td>9255.253710</td>\n",
       "      <td>8237.534203</td>\n",
       "      <td>4396.565724</td>\n",
       "      <td>9441.242553</td>\n",
       "      <td>10069.550015</td>\n",
       "      <td>15487.277880</td>\n",
       "      <td>8926.370181</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004</td>\n",
       "      <td>4542.366136</td>\n",
       "      <td>9257.377125</td>\n",
       "      <td>8261.405863</td>\n",
       "      <td>4408.680368</td>\n",
       "      <td>9452.306665</td>\n",
       "      <td>10094.583966</td>\n",
       "      <td>15499.794856</td>\n",
       "      <td>8948.833682</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.008</td>\n",
       "      <td>4533.716012</td>\n",
       "      <td>9246.648289</td>\n",
       "      <td>8278.572001</td>\n",
       "      <td>4407.518078</td>\n",
       "      <td>9457.984008</td>\n",
       "      <td>10106.430389</td>\n",
       "      <td>15505.427495</td>\n",
       "      <td>8962.021209</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.012</td>\n",
       "      <td>4525.803495</td>\n",
       "      <td>9238.489904</td>\n",
       "      <td>8261.987008</td>\n",
       "      <td>4389.949609</td>\n",
       "      <td>9444.796480</td>\n",
       "      <td>10086.291470</td>\n",
       "      <td>15496.285632</td>\n",
       "      <td>8946.442045</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.016</td>\n",
       "      <td>4527.412821</td>\n",
       "      <td>9239.629842</td>\n",
       "      <td>8239.031769</td>\n",
       "      <td>4382.618237</td>\n",
       "      <td>9435.006417</td>\n",
       "      <td>10066.621937</td>\n",
       "      <td>15489.893034</td>\n",
       "      <td>8927.487768</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time          Fp1          Fp2           C3           C4           P7  \\\n",
       "0  0.000  4540.332127  9255.253710  8237.534203  4396.565724  9441.242553   \n",
       "1  0.004  4542.366136  9257.377125  8261.405863  4408.680368  9452.306665   \n",
       "2  0.008  4533.716012  9246.648289  8278.572001  4407.518078  9457.984008   \n",
       "3  0.012  4525.803495  9238.489904  8261.987008  4389.949609  9444.796480   \n",
       "4  0.016  4527.412821  9239.629842  8239.031769  4382.618237  9435.006417   \n",
       "\n",
       "             P8            O1           O2  STIM MARKERS  \n",
       "0  10069.550015  15487.277880  8926.370181           0.0  \n",
       "1  10094.583966  15499.794856  8948.833682           0.0  \n",
       "2  10106.430389  15505.427495  8962.021209           0.0  \n",
       "3  10086.291470  15496.285632  8946.442045           0.0  \n",
       "4  10066.621937  15489.893034  8927.487768           0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raws = []\n",
    "\n",
    "for run in runs:\n",
    "    filename = f'S020R{run}_raw'\n",
    "    path_file = os.path.join(base_url, filename + '.fif')\n",
    "    raw = mne.io.read_raw_fif(path_file , preload=True, verbose='Warning')\n",
    "    raws.append(raw)\n",
    "\n",
    "eeg = mne.io.concatenate_raws(raws)\n",
    "df = eeg.to_data_frame()\n",
    "print(df.shape)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since motor imagery mostly lies on C3 and C4, let's drop all other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C3</th>\n",
       "      <th>C4</th>\n",
       "      <th>STIM MARKERS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8237.534203</td>\n",
       "      <td>4396.565724</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8261.405863</td>\n",
       "      <td>4408.680368</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8278.572001</td>\n",
       "      <td>4407.518078</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8261.987008</td>\n",
       "      <td>4389.949609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8239.031769</td>\n",
       "      <td>4382.618237</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C3           C4  STIM MARKERS\n",
       "0  8237.534203  4396.565724           0.0\n",
       "1  8261.405863  4408.680368           0.0\n",
       "2  8278.572001  4407.518078           0.0\n",
       "3  8261.987008  4389.949609           0.0\n",
       "4  8239.031769  4382.618237           0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['C3', 'C4', 'STIM MARKERS']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shall also drop from the raw file, so the meta data of raw will be updated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C3', 'C4', 'STIM MARKERS']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg = eeg.drop_channels(['Fp1', 'Fp2', 'P7', 'P8', 'O1', 'O2'])\n",
    "eeg.ch_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One run (file) contains 15 of left, 15 of right, where label 1 stands for left, 2 for right, and 4 for fixation.  We have total of three files, thus a total of 60 trials.   (I did not use run 10 because when I EDA, it does not look good)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0., 1., 2., 4.]), array([163865,     45,     45,    180]))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df['STIM MARKERS'], return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 2. Artifact Removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up band-stop filter from 49 - 51 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandstop filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 49.38\n",
      "- Lower transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 49.12 Hz)\n",
      "- Upper passband edge: 50.62 Hz\n",
      "- Upper transition bandwidth: 0.50 Hz (-6 dB cutoff frequency: 50.88 Hz)\n",
      "- Filter length: 1651 samples (6.604 sec)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>11 points</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG, 1 Stimulus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>0.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>125.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>S020R04_raw.fif&lt;br&gt;S020R06_raw.fif&lt;br&gt;S020R08_raw.fif</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:10:57 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Raw | S020R04_raw.fif, 3 x 164135 (656.5 s), ~3.8 MB, data loaded>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg.notch_filter(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filtering raw data in 3 contiguous segments\n",
      "Setting up band-pass filter from 7 - 14 Hz\n",
      "\n",
      "FIR filter parameters\n",
      "---------------------\n",
      "Designing a one-pass, zero-phase, non-causal bandpass filter:\n",
      "- Windowed time-domain design (firwin) method\n",
      "- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation\n",
      "- Lower passband edge: 7.00\n",
      "- Lower transition bandwidth: 2.00 Hz (-6 dB cutoff frequency: 6.00 Hz)\n",
      "- Upper passband edge: 14.00 Hz\n",
      "- Upper transition bandwidth: 3.50 Hz (-6 dB cutoff frequency: 15.75 Hz)\n",
      "- Filter length: 413 samples (1.652 sec)\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s finished\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Measurement date</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Experimenter</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "        <th>Participant</th>\n",
       "        \n",
       "        <td>Unknown</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Digitized points</th>\n",
       "        \n",
       "        <td>11 points</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Good channels</th>\n",
       "        <td>2 EEG, 1 Stimulus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Bad channels</th>\n",
       "        <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>EOG channels</th>\n",
       "        <td>Not available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>ECG channels</th>\n",
       "        <td>Not available</td>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Sampling frequency</th>\n",
       "        <td>250.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Highpass</th>\n",
       "        <td>7.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Lowpass</th>\n",
       "        <td>14.00 Hz</td>\n",
       "    </tr>\n",
       "    \n",
       "    \n",
       "    \n",
       "    <tr>\n",
       "        <th>Filenames</th>\n",
       "        <td>S020R04_raw.fif&lt;br&gt;S020R06_raw.fif&lt;br&gt;S020R08_raw.fif</td>\n",
       "    </tr>\n",
       "    \n",
       "    <tr>\n",
       "        <th>Duration</th>\n",
       "        <td>00:10:57 (HH:MM:SS)</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Raw | S020R04_raw.fif, 3 x 164135 (656.5 s), ~3.8 MB, data loaded>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#motor imagery falls in this range\n",
    "eeg.filter(7, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Effective window size : 1.024 (s)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<MNELineFigure size 1000x350 with 2 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<MNELineFigure size 1000x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eeg.compute_psd().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 3. Epoching\n",
    "\n",
    "To understand how we gonna epoch this, let's look at our experiment setup.\n",
    "\n",
    "<img src = \"figures/exp.png\" width = 400>\n",
    "\n",
    "Each trial is 7s long, where the real imagined happens at 2 to 5s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getEpochs(raw, events, event_id, tmin, tmax, picks, baseline):\n",
    "\n",
    "    epochs = Epochs(raw, events=events, event_id=event_id, \n",
    "                    tmin=tmin, tmax=tmax, \n",
    "                    baseline=baseline, preload=True,picks=picks)  #2 channels\n",
    "    print('sample drop %: ', (1 - len(epochs.events)/len(events)) * 100)\n",
    "\n",
    "    return epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270 events found\n",
      "Event IDs: [1 2 4]\n",
      "Not setting metadata\n",
      "90 matching events found\n",
      "Applying baseline correction (mode: mean)\n",
      "0 projection items activated\n",
      "Using data from preloaded Raw for 90 events and 1751 original time points ...\n",
      "3 bad epochs dropped\n",
      "sample drop %:  67.77777777777779\n"
     ]
    }
   ],
   "source": [
    "#this one requires expertise to specify the right tmin, tmax\n",
    "event_id = {'Left': 1, 'Right' : 2}\n",
    "events = find_events(eeg)\n",
    "events_no_fixation = mne.pick_events(events, exclude=4) #ignore fixation\n",
    "tmin=0\n",
    "tmax=7\n",
    "baseline=(0, 2) #baseline is from 0 to 2s which is 2s before the real event starts.  We subtract to enlarge our signal\n",
    "eeg_channels = mne.pick_types(eeg.info, eeg=True)\n",
    "picks= eeg_channels\n",
    "epochs = getEpochs(eeg, events, event_id, tmin, tmax, picks=picks, baseline=baseline)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"table table-hover table-striped table-sm table-responsive small\">\n",
       "    <tr>\n",
       "        <th>Number of events</th>\n",
       "        <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Events</th>\n",
       "        \n",
       "        <td>Left: 45<br/>Right: 42</td>\n",
       "        \n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Time range</th>\n",
       "        <td>0.000 – 7.000 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <th>Baseline</th>\n",
       "        <td>0.000 – 2.000 sec</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Epochs |  87 events (all good), 0 - 7 sec, baseline 0 – 2 sec, ~2.3 MB, data loaded,\n",
       " 'Left': 45\n",
       " 'Right': 42>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "epochs #all the fixation is dropped while 4 bad epochs on the right side also got dropped..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 4. EDA - Event Related Desynchronization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we shall use event-related desynchronization.  Event-related desynchronization/synchronization (ERD/ERS) is a relative power decrease/increase of electroencephalogram (EEG) in a specific frequency band during physical motor execution and mental motor imagery.\n",
    "\n",
    "So first, we shall extract the power in different frequency band.  \n",
    "\n",
    "Note that we cannot use `compute_psd` since it will just ignore the time element.  We shall instead use window-based psd using the multitaper technique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not setting metadata\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.1s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.2s finished\n"
     ]
    }
   ],
   "source": [
    "from mne.time_frequency import tfr_multitaper\n",
    "\n",
    "freqs = np.arange(7, 14)  # range of motor imagery\n",
    "\n",
    "tfr = tfr_multitaper(epochs, freqs=freqs, n_cycles=freqs, use_fft=True,\n",
    "                     return_itc=False, average=False, decim=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For plotting, we need to make baseline correction, and make it into percent, so it can plot around the zero line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying baseline correction (mode: percent)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<EpochsTFR | time : [0.000000, 7.000000], freq : [7.000000, 13.000000], epochs : 87, channels : 2, ~16.3 MB>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfr.crop(tmin, tmax).apply_baseline(baseline, mode=\"percent\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert to dataframe to understand what is happening."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting \"condition\" to \"category\"...\n",
      "Converting \"epoch\" to \"category\"...\n",
      "Converting \"channel\" to \"category\"...\n",
      "Converting \"ch_type\" to \"category\"...\n"
     ]
    }
   ],
   "source": [
    "df = tfr.to_data_frame(time_format=None, long_format=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So this basically list the power of each freq across all epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>condition</th>\n",
       "      <th>epoch</th>\n",
       "      <th>freq</th>\n",
       "      <th>time</th>\n",
       "      <th>channel</th>\n",
       "      <th>ch_type</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.702342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C4</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.398629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.701238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>C4</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.381013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.699695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  condition epoch  freq   time channel ch_type     value\n",
       "0      Left     1   7.0  0.000      C3     eeg -0.702342\n",
       "1      Left     1   7.0  0.000      C4     eeg -0.398629\n",
       "2      Left     1   7.0  0.004      C3     eeg -0.701238\n",
       "3      Left     1   7.0  0.004      C4     eeg -0.381013\n",
       "4      Left     1   7.0  0.008      C3     eeg -0.699695"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the unique frequencies we have, and how many of them.  As you can see, we have equal amounts of each frequencies, since we analyze each epoch across all frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 7.,  8.,  9., 10., 11., 12., 13.]),\n",
       " array([304674, 304674, 304674, 304674, 304674, 304674, 304674]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df.freq, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assign whether is theta, beta, etc. based on the freq bounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2132718, 8)\n",
      "(array(['alpha', 'theta'], dtype=object), array([1828044,  304674]))\n"
     ]
    },
    {
     "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>condition</th>\n",
       "      <th>epoch</th>\n",
       "      <th>freq</th>\n",
       "      <th>time</th>\n",
       "      <th>channel</th>\n",
       "      <th>ch_type</th>\n",
       "      <th>value</th>\n",
       "      <th>band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.702342</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>C4</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.398629</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.701238</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>C4</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.381013</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.699695</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  condition epoch  freq   time channel ch_type     value   band\n",
       "0      Left     1   7.0  0.000      C3     eeg -0.702342  theta\n",
       "1      Left     1   7.0  0.000      C4     eeg -0.398629  theta\n",
       "2      Left     1   7.0  0.004      C3     eeg -0.701238  theta\n",
       "3      Left     1   7.0  0.004      C4     eeg -0.381013  theta\n",
       "4      Left     1   7.0  0.008      C3     eeg -0.699695  theta"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_bounds = {'_': 0,\n",
    "               'delta': 3,\n",
    "               'theta': 7,\n",
    "               'alpha': 13,\n",
    "               'beta': 35,\n",
    "               'gamma': 140}\n",
    "\n",
    "df['band'] = pd.cut(df['freq'], list(freq_bounds.values()),\n",
    "                labels=list(freq_bounds)[1:])\n",
    "\n",
    "print(df.shape)\n",
    "print(np.unique(df.band, return_counts=True))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#it seems only 3-7 belongs to theta, thus only 7 is here, since we\n",
    "#cut from 7 to 14\n",
    "df[df.band=='theta'].freq.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.,  9., 10., 11., 12., 13.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8 ranges from 8 til 13\n",
    "df[df.band=='alpha'].freq.unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean some categories that have no rows, in case later we need to use `len()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['theta', 'alpha'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['band'] = df['band'].cat.remove_unused_categories() \n",
    "df['band'].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 753.667x600 with 4 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "#gives us a easy band x left/right plots\n",
    "g = sns.FacetGrid(df, row='band', col='condition')\n",
    "\n",
    "#plot the lineplot on each grid using these df columns\n",
    "#basically snslineplot(x, y, hue)\n",
    "g.map(sns.lineplot, 'time', 'value', 'channel')\n",
    "\n",
    "#trivial styles\n",
    "axline_kw = dict(color='black', linestyle='dashed', linewidth=0.5, alpha=0.5)\n",
    "g.map(plt.axhline, y=0, **axline_kw)  #just for easy spot\n",
    "g.map(plt.axvline, x=0, **axline_kw)  #just for easy spot\n",
    "g.set(ylim=(-1, None))\n",
    "g.set_axis_labels(\"Time (s)\", \"ERDS (%)\")\n",
    "g.set_titles(col_template=\"{col_name}\", row_template=\"{row_name}\")\n",
    "g.add_legend(ncol=2, loc='lower center')\n",
    "g.fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, whether the imagined movements was left or right can be seen clearly between the interaction of C3 and C4.  This is a common phenemonon reported in all literatures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 5. Machine Learning\n",
    "\n",
    "Before we tried end-to-end neural network without feature engineering, we can try machine learning.  Machine learning with the right features are usually very competitive!\n",
    "\n",
    "You can skip this part because it takes a long time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>condition</th>\n",
       "      <th>epoch</th>\n",
       "      <th>freq</th>\n",
       "      <th>time</th>\n",
       "      <th>channel</th>\n",
       "      <th>ch_type</th>\n",
       "      <th>value</th>\n",
       "      <th>band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>C3</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.702342</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  condition epoch  freq  time channel ch_type     value   band\n",
       "0      Left     1   7.0   0.0      C3     eeg -0.702342  theta"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>condition</th>\n",
       "      <th>epoch</th>\n",
       "      <th>freq</th>\n",
       "      <th>time</th>\n",
       "      <th>channel</th>\n",
       "      <th>ch_type</th>\n",
       "      <th>value</th>\n",
       "      <th>band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.702342</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.398629</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.701238</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.381013</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0</td>\n",
       "      <td>eeg</td>\n",
       "      <td>-0.699695</td>\n",
       "      <td>theta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  condition epoch  freq   time  channel ch_type     value   band\n",
       "0      Left     1   7.0  0.000        0     eeg -0.702342  theta\n",
       "1      Left     1   7.0  0.000        1     eeg -0.398629  theta\n",
       "2      Left     1   7.0  0.004        0     eeg -0.701238  theta\n",
       "3      Left     1   7.0  0.004        1     eeg -0.381013  theta\n",
       "4      Left     1   7.0  0.008        0     eeg -0.699695  theta"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "labelencoder = LabelEncoder()\n",
    "df['channel'] = labelencoder.fit_transform(df['channel'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['value', 'freq', 'channel', 'time']]\n",
    "y = df['condition']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1492902, 4), (639816, 4), (1492902,), (639816,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 Simple fitting\n",
    "\n",
    "To save time, let's just run super simple fitting to check accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test acc:  0.7057466521624968\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "score = clf.fit(X_train, y_train)\n",
    "\n",
    "y_hat = clf.predict(X_test)\n",
    "print(\"Test acc: \", accuracy_score(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 5.2 Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf.feature_importances_ \n",
    "feature_importance = pd.DataFrame(['value', 'freq', 'channel', 'time'], \n",
    "                                  columns=['features'])\n",
    "\n",
    "feature_importance['importance'] = clf.feature_importances_ \n",
    "feature_importance = feature_importance.sort_values(by = ['importance'], ascending=True)\n",
    "\n",
    "feature_importance.plot.barh(x='features')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's very clear the power is the one that determines left or right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Cross-validation\n",
    "\n",
    "If you want, you can try cross-validation, but it will take long time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV acc:  0.7020427321388698\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "cls = RandomForestClassifier()\n",
    "score = cross_val_score(cls, X_train, y_train)\n",
    "\n",
    "print(\"CV acc: \", score.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Grid search\n",
    "\n",
    "You can also try grid-search, but will take very long time.  I have skipped for sanity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# param_grid = {\n",
    "#     'max_depth'   : [5, 10, None],\n",
    "#     'n_estimators': [5, 6, 7, 8]\n",
    "# }\n",
    "\n",
    "# #3. define the base model\n",
    "# rf = RandomForestClassifier()\n",
    "\n",
    "# #4. create the gridsearch object\n",
    "# grid = GridSearchCV(estimator=rf,param_grid=param_grid,\n",
    "#                     n_jobs=-1,return_train_score=True,refit=True)\n",
    "\n",
    "# #5. run grid search\n",
    "# grid.fit(X_train, y_train)\n",
    "\n",
    "# #6. inference\n",
    "# y_hat = grid.predict(X_test)\n",
    "# print(\"Test acc: \", accuracy_score(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 6. Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we try the machine learning.  Let's try create an end-to-end model without any feature engineering, i.e., no extraction of frequency.  We let the neural network figure it out by itself.\n",
    "\n",
    "Let's get our X and y in numpy form. Here X should have shape of `(batch, channels, length)` and y should have shape of `(batch)`.\n",
    "\n",
    "For calculate of samples, since we get 0 to 7 seconds, and our sampling rate is 250, thus the total sample is 7 * 250 = 1750.  We got 1751, probably just signal jittering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(87, 2, 1751)\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X = epochs.get_data()\n",
    "y = epochs.events[:, -1]\n",
    "\n",
    "print(X.shape)\n",
    "# print(y.shape)\n",
    "print(type(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1751)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.3539e-06,  1.6512e-06,  1.8493e-06, ..., -2.3419e-07,\n",
       "         1.6556e-07,  5.3335e-07],\n",
       "       [ 7.6302e-07,  8.0512e-07,  8.4050e-07, ...,  2.2971e-06,\n",
       "         1.5442e-06,  7.7565e-07]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important**: It's sometimes very important to shuffle your dataset, to avoid any possible order effects of your experiment.   As for MI experiment, I thought it was very important thus I shuffle it to be sure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87, 2, 1751)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = shuffle(X, random_state=42)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.7170e-06,  2.8551e-06,  2.8384e-06, ..., -1.8331e-06,\n",
       "        -1.0298e-06, -1.7742e-07],\n",
       "       [ 1.5892e-06,  1.8722e-06,  2.0080e-06, ...,  3.6022e-07,\n",
       "         1.8155e-07,  5.6890e-08]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0][:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_size  = int(X.shape[0] * 0.7)\n",
    "val_size    = int(X.shape[0] * 0.2)\n",
    "test_size   = X.shape[0] - train_size - val_size\n",
    "\n",
    "#use our numpy indexing technique\n",
    "X_train   = X[:train_size]\n",
    "X_val     = X[train_size:val_size+train_size]\n",
    "X_test    = X[-test_size:]\n",
    "\n",
    "y_train   = y[:train_size]\n",
    "y_val     = y[train_size:val_size+train_size]\n",
    "y_test    = y[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60, 2, 1751), (17, 2, 1751), (10, 2, 1751))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_val.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important**:  We need to do some scaling, since EEG values are either very small or very big.  If not, the training won't go well due to vanishing/exploding gradients.  Minmax normalization is a common tool for applying on signals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#scale transform each channel independently\n",
    "scalers = {}\n",
    "for i in range(X_train.shape[1]):\n",
    "    scalers[i] = MinMaxScaler(feature_range=(0, 1))\n",
    "    X_train[:, i, :] = scalers[i].fit_transform(X_train[:, i, :]) \n",
    "\n",
    "for i in range(X_val.shape[1]):\n",
    "    X_val[:, i, :]   = scalers[i].transform(X_val[:, i, :])     \n",
    "    \n",
    "for i in range(X_test.shape[1]):\n",
    "    X_test[:, i, :]  = scalers[i].transform(X_test[:, i, :]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5000046139248385 0.49981424958261594 0.4992292592749264\n",
      "0.0 -0.6349846630827395 -0.7835945438820104\n",
      "1.0000000000000002 1.693128309680588 1.6338817754402974\n"
     ]
    }
   ],
   "source": [
    "print(X_train.mean(), X_val.mean(), X_test.mean())\n",
    "print(X_train.min(), X_val.min(), X_test.min())\n",
    "print(X_train.max(), X_val.max(), X_test.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert to torch tensor for pyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = torch.FloatTensor(X_train)\n",
    "X_val   = torch.FloatTensor(X_val)\n",
    "X_test  = torch.FloatTensor(X_test)\n",
    "\n",
    "y_train = torch.LongTensor(y_train) - 1 #minus 1 so it becomes 0 or 1\n",
    "y_val   = torch.LongTensor(y_val)   - 1 #minus 1 so it becomes 0 or 1\n",
    "y_test  = torch.LongTensor(y_test)  - 1 #minus 1 so it becomes 0 or 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the distribution of the class, to see whether there is class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31 29\n",
      "9 8\n",
      "5 5\n"
     ]
    }
   ],
   "source": [
    "print(len(y_train[y_train == 0]), len(y_train[y_train == 1]))\n",
    "print(len(y_val[y_val == 0]),     len(y_val[y_val == 1]))\n",
    "print(len(y_test[y_test == 0]),   len(y_test[y_test == 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Important**: Let's utilize the `Dataset` and `WeightRandomSampler` since our class is not exactly balanced.  What we can do is to force every batch to have the same amount of each class.\n",
    "\n",
    "The idea is to assign bigger weight to minority class, thus penaltizing if it is wrong."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2]), array([45, 42]))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_unique, counts = np.unique(y, return_counts=True)\n",
    "labels_unique, counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.9333333333333333, 2.0714285714285716]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_weights = [sum(counts) / c for c in counts]\n",
    "class_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are ready to create the `WeightRandomSampler`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.9333333333333333,\n",
       " 2.0714285714285716,\n",
       " 1.9333333333333333,\n",
       " 2.0714285714285716,\n",
       " 1.9333333333333333,\n",
       " 2.0714285714285716,\n",
       " 1.9333333333333333,\n",
       " 2.0714285714285716,\n",
       " 1.9333333333333333,\n",
       " 2.0714285714285716]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_weights_train = [class_weights[i] for i in y_train]\n",
    "sample_weights_val   = [class_weights[i] for i in y_val  ]\n",
    "sample_weights_test  = [class_weights[i] for i in y_test ]\n",
    "\n",
    "sample_weights_train[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sampler = WeightedRandomSampler(sample_weights_train, len(y_train), replacement=True)\n",
    "val_sampler   = WeightedRandomSampler(sample_weights_val,   len(y_val),   replacement=True)\n",
    "test_sampler  = WeightedRandomSampler(sample_weights_test,  len(y_test),  replacement=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define dataset\n",
    "ds_train = TensorDataset(X_train, y_train)\n",
    "ds_val   = TensorDataset(X_val, y_val)\n",
    "ds_test  = TensorDataset(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train set loader\n",
    "train_loader = torch.utils.data.DataLoader(dataset=ds_train, \n",
    "                                           batch_size=BATCH_SIZE, \n",
    "                                           sampler = train_sampler)\n",
    "#Validation set loader\n",
    "val_loader   = torch.utils.data.DataLoader(dataset=ds_val, \n",
    "                                           batch_size=BATCH_SIZE, \n",
    "                                           sampler = val_sampler)\n",
    "\n",
    "#Test set loader\n",
    "test_loader  = torch.utils.data.DataLoader(dataset=ds_test, \n",
    "                                          batch_size=BATCH_SIZE, \n",
    "                                          sampler = test_sampler)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## 6.1 Testing how train loader works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([0.5027, 0.5004, 0.4981, 0.5007, 0.4986, 0.5010, 0.5004, 0.4978, 0.4987,\n",
      "        0.5018, 0.4988, 0.4983, 0.5005, 0.5010, 0.4988, 0.4986, 0.4985, 0.5007,\n",
      "        0.4991, 0.5006, 0.5006, 0.4981, 0.4985, 0.4978, 0.4998, 0.5030, 0.4963,\n",
      "        0.4985, 0.5019, 0.5027, 0.5010, 0.4992, 0.5004, 0.4989, 0.5003, 0.4985,\n",
      "        0.4992, 0.4982, 0.4998, 0.4989, 0.5005, 0.5006, 0.4990, 0.5010, 0.4997,\n",
      "        0.5018, 0.5011, 0.5007, 0.5006, 0.5022, 0.5002, 0.4989, 0.5018, 0.4981,\n",
      "        0.4995, 0.5007, 0.4982, 0.4989, 0.4998, 0.4988])\n",
      "tensor([0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
      "        1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0,\n",
      "        0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0])\n"
     ]
    }
   ],
   "source": [
    "torch.set_printoptions(sci_mode=False)\n",
    "for X_train, y_train in train_loader:\n",
    "    print(torch.mean(X_train, axis=[1,2])) #mean across all channels and sequences\n",
    "    print(y_train)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 7. Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.1 Let's see how each layer works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "fake_eeg_data = torch.randn_like(X_train) #(batch_size, channel, length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "conv1d = nn.Sequential(\n",
    "        nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size),\n",
    "        nn.ReLU(inplace=True),\n",
    "        nn.MaxPool1d(10, stride=2),\n",
    "        nn.BatchNorm1d(out_channels),\n",
    "        nn.Dropout(0.50)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([60, 5, 747])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = conv1d(fake_eeg_data)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3735"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = out.reshape(out.size(0), -1)\n",
    "linear_shape = out.shape[1]\n",
    "linear_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([60, 2])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fc = nn.Linear(linear_shape, out_size)\n",
    "out = fc(out)\n",
    "out.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7.2 Defining the model\n",
    "\n",
    "The model can be much complex, but let me just try one conv1d.  I did not use LSTM since LSTM is not so good for very long sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "class motorCNN1D(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size, out_size):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.conv1d = nn.Sequential(\n",
    "            nn.Conv1d(in_channels, out_channels, kernel_size=kernel_size),\n",
    "            nn.ELU(inplace=True),\n",
    "            nn.AvgPool1d(10, stride=2),\n",
    "            nn.BatchNorm1d(out_channels),\n",
    "            nn.Dropout(0.50)\n",
    "        )\n",
    "                \n",
    "        self.fc = nn.Linear(linear_shape, out_size)\n",
    "        \n",
    "    def forward(self, seq):\n",
    "        out = self.conv1d(seq)\n",
    "        out = out.reshape(out.size(0), -1)\n",
    "        out = self.fc(out)\n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = motorCNN1D(in_channels, out_channels, kernel_size, out_size).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 8. Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = lr, weight_decay = lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2500\n",
      "     5\n",
      "     5\n",
      "     5\n",
      "  7470\n",
      "     2\n",
      "______\n",
      "  9987\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    params = [p.numel() for p in model.parameters() if p.requires_grad]\n",
    "    for item in params:\n",
    "        print(f'{item:>6}')\n",
    "    print(f'______\\n{sum(params):>6}')\n",
    "    \n",
    "count_parameters(model) #why 400?, why 10000????"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch:    0 | Train acc:  45.00 | loss: 0.80 | Val acc:  41.18 | loss: 1.24 | Time: 0m 1s\n",
      "Epoch:  100 | Train acc:  63.33 | loss: 0.73 | Val acc:  41.18 | loss: 0.93 | Time: 0m 0s\n",
      "Epoch:  200 | Train acc:  61.67 | loss: 0.68 | Val acc:  35.29 | loss: 0.84 | Time: 0m 0s\n",
      "Epoch:  300 | Train acc:  50.00 | loss: 0.83 | Val acc:  64.71 | loss: 0.71 | Time: 0m 0s\n",
      "Epoch:  400 | Train acc:  60.00 | loss: 0.68 | Val acc:  58.82 | loss: 0.74 | Time: 0m 0s\n",
      "Epoch:  500 | Train acc:  56.67 | loss: 0.72 | Val acc:  58.82 | loss: 0.82 | Time: 0m 0s\n",
      "Epoch:  600 | Train acc:  56.67 | loss: 0.67 | Val acc:  70.59 | loss: 0.66 | Time: 0m 0s\n",
      "Epoch:  700 | Train acc:  63.33 | loss: 0.63 | Val acc:  41.18 | loss: 0.87 | Time: 0m 0s\n",
      "Epoch:  800 | Train acc:  70.00 | loss: 0.57 | Val acc:  29.41 | loss: 1.08 | Time: 0m 0s\n",
      "Epoch:  900 | Train acc:  65.00 | loss: 0.56 | Val acc:  64.71 | loss: 0.70 | Time: 0m 0s\n"
     ]
    }
   ],
   "source": [
    "best_valid_loss = float('inf')\n",
    "model.train()\n",
    "\n",
    "#print(f\"Training {type(model).__name__}\")\n",
    "\n",
    "for i in range(num_epochs):\n",
    "    train_total = 0\n",
    "    train_correct = 0\n",
    "    val_total   = 0  \n",
    "    val_correct = 0\n",
    "    train_acc   = 0\n",
    "    val_acc     = 0\n",
    "    \n",
    "    for X_train, y_train in train_loader:\n",
    "    \n",
    "        start_time = time.time()\n",
    "        \n",
    "        X_train = X_train.to(device)\n",
    "        y_train = y_train.to(device)\n",
    "\n",
    "        yhat_train = model(X_train)\n",
    "                \n",
    "        #train acc\n",
    "        _, predicted = torch.max(yhat_train.data, 1)  #returns max value, indices\n",
    "        \n",
    "        train_total += y_train.size(0)  #keep track of total\n",
    "        train_correct += (predicted == y_train).sum().item()  #.item() give the raw number\n",
    "        train_acc = 100 * (train_correct / train_total)\n",
    "        \n",
    "        train_loss = criterion(yhat_train, y_train)\n",
    "        optimizer.zero_grad()\n",
    "        train_loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        end_time = time.time()\n",
    "\n",
    "        #val accuracy\n",
    "        for X_val, y_val in val_loader:\n",
    "            X_val = X_val.to(device)\n",
    "            y_val = y_val.to(device)\n",
    "            yhat_val  = model(X_val)\n",
    "            val_loss     = criterion(yhat_val, y_val)\n",
    "            _, predicted = torch.max(yhat_val.data, 1)  #returns max value, indices\n",
    "            val_total += y_val.size(0)  #keep track of total\n",
    "            val_correct += (predicted == y_val).sum().item()  #.item() give the raw number\n",
    "            val_acc = 100 * (val_correct / val_total)\n",
    "\n",
    "        #save the best model\n",
    "        if val_loss < best_valid_loss:\n",
    "            best_valid_loss = val_loss\n",
    "            #print(\"Model:{} saved.\".format(type(model).__name__))\n",
    "            torch.save(model.state_dict(), modelpath)\n",
    "            best_model_index = i\n",
    "\n",
    "        epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "        \n",
    "    if i%100 == 0:\n",
    "        print(f\"Epoch: {i:4.0f} | Train acc: {train_acc: 3.2f} | \" +\n",
    "          f\"loss: {train_loss:3.2f} | Val acc: {val_acc: 3.2f} | \" +\n",
    "          f\"loss: {val_loss:3.2f} | Time: {epoch_mins}m {epoch_secs}s\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 9. Inference / Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 70.000\n"
     ]
    }
   ],
   "source": [
    "model = motorCNN1D(in_channels, out_channels, kernel_size, out_size).to(device)\n",
    "model.load_state_dict(torch.load(modelpath))\n",
    "\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    total   = 0\n",
    "    correct = 0\n",
    "    for X_test, y_test in test_loader:\n",
    "        X_test = X_test.to(device)\n",
    "        y_test = y_test.to(device)\n",
    "        predictions   = model(X_test)\n",
    "        test_loss     = criterion(predictions, y_test)\n",
    "        _, predicted = torch.max(predictions.data, 1)  #returns max value, indices\n",
    "        total += y_test.size(0)\n",
    "        correct += (predicted == y_test).sum().item()  #.item() give the raw number\n",
    "        acc = 100 * (correct / total)\n",
    "    \n",
    "print(f\"Accuracy: {acc:2.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not really impressive accuracy.  The accuracy fluctuates a lot...  Here if we can design a good neural network, it should perform on-par with machine learning.   Lessons learned:\n",
    "\n",
    "1. Always EDA, if not, you won't know what you are modeling\n",
    "2. Try machine learning + feature engineering, to know that it can be predicted"
   ]
  }
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