notebooks/SleepEDF-ST.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: **Sleep-EDF Expanded**  \n",
    "https://physionet.org/content/sleep-edfx/1.0.0/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install mne pyedflib seglearn tsfel tsfresh antropy plotly-resampler tsflex powershap --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append(\"../src/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.auto import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "from plotly_resampler import FigureResampler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Commands to download and extract the data;\n",
    "```sh\n",
    "mkdir data\n",
    "wget https://physionet.org/static/published-projects/sleep-edfx/sleep-edf-database-expanded-1.0.0.zip -P data\n",
    "# Extrac all data\n",
    "unzip data/sleep-edf-database-expanded-1.0.0.zip -d data\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create dataloader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Some EDF loading funcs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data import load_signals, load_annotations, annotation_to_30s_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sleep-telemetry',\n",
       " 'RECORDS-v1',\n",
       " 'SHA256SUMS.txt',\n",
       " 'SC-subjects.xls',\n",
       " 'sleep-cassette',\n",
       " 'ST-subjects.xls',\n",
       " 'RECORDS']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_folder = \"../../../../data/sleep/sleep-edf-database-expanded-1.0.0/\"\n",
    "\n",
    "os.listdir(data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ST7111J0-PSG.edf',\n",
       " 'ST7061J0-PSG.edf',\n",
       " 'ST7051J0-PSG.edf',\n",
       " 'ST7121JE-Hypnogram.edf',\n",
       " 'ST7201J0-PSG.edf',\n",
       " 'ST7241J0-PSG.edf',\n",
       " 'ST7041J0-PSG.edf',\n",
       " 'ST7132JR-Hypnogram.edf',\n",
       " 'ST7022J0-PSG.edf',\n",
       " 'ST7142JE-Hypnogram.edf']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_folder + \"sleep-telemetry\")[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(data_folder + \"sleep-telemetry\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.1G\t../../../../data/sleep/sleep-edf-database-expanded-1.0.0/sleep-telemetry\n"
     ]
    }
   ],
   "source": [
    "!du -hs ../../../../data/sleep/sleep-edf-database-expanded-1.0.0/sleep-telemetry"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> In total 88 files (44 PSG files - 153 hypnogram files) => 1.1 GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "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>psg_file</th>\n",
       "      <th>label_file</th>\n",
       "      <th>subfolder</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ST7011J0-PSG.edf</td>\n",
       "      <td>ST7011JP-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ST7012J0-PSG.edf</td>\n",
       "      <td>ST7012JP-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ST7021J0-PSG.edf</td>\n",
       "      <td>ST7021JM-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ST7022J0-PSG.edf</td>\n",
       "      <td>ST7022JM-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ST7041J0-PSG.edf</td>\n",
       "      <td>ST7041JO-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ST7042J0-PSG.edf</td>\n",
       "      <td>ST7042JO-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>ST7051J0-PSG.edf</td>\n",
       "      <td>ST7051JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>ST7052J0-PSG.edf</td>\n",
       "      <td>ST7052JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ST7061J0-PSG.edf</td>\n",
       "      <td>ST7061JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ST7062J0-PSG.edf</td>\n",
       "      <td>ST7062JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ST7071J0-PSG.edf</td>\n",
       "      <td>ST7071JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ST7072J0-PSG.edf</td>\n",
       "      <td>ST7072JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>ST7081J0-PSG.edf</td>\n",
       "      <td>ST7081JW-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ST7082J0-PSG.edf</td>\n",
       "      <td>ST7082JW-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ST7091J0-PSG.edf</td>\n",
       "      <td>ST7091JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ST7092J0-PSG.edf</td>\n",
       "      <td>ST7092JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ST7101J0-PSG.edf</td>\n",
       "      <td>ST7101JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>ST7102J0-PSG.edf</td>\n",
       "      <td>ST7102JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ST7111J0-PSG.edf</td>\n",
       "      <td>ST7111JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ST7112J0-PSG.edf</td>\n",
       "      <td>ST7112JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ST7121J0-PSG.edf</td>\n",
       "      <td>ST7121JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ST7122J0-PSG.edf</td>\n",
       "      <td>ST7122JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>ST7131J0-PSG.edf</td>\n",
       "      <td>ST7131JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ST7132J0-PSG.edf</td>\n",
       "      <td>ST7132JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ST7141J0-PSG.edf</td>\n",
       "      <td>ST7141JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>ST7142J0-PSG.edf</td>\n",
       "      <td>ST7142JE-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ST7151J0-PSG.edf</td>\n",
       "      <td>ST7151JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>ST7152J0-PSG.edf</td>\n",
       "      <td>ST7152JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ST7161J0-PSG.edf</td>\n",
       "      <td>ST7161JM-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ST7162J0-PSG.edf</td>\n",
       "      <td>ST7162JM-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ST7171J0-PSG.edf</td>\n",
       "      <td>ST7171JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ST7172J0-PSG.edf</td>\n",
       "      <td>ST7172JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ST7181J0-PSG.edf</td>\n",
       "      <td>ST7181JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ST7182J0-PSG.edf</td>\n",
       "      <td>ST7182JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ST7191J0-PSG.edf</td>\n",
       "      <td>ST7191JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>ST7192J0-PSG.edf</td>\n",
       "      <td>ST7192JR-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>ST7201J0-PSG.edf</td>\n",
       "      <td>ST7201JO-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ST7202J0-PSG.edf</td>\n",
       "      <td>ST7202JO-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>ST7211J0-PSG.edf</td>\n",
       "      <td>ST7211JJ-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>ST7212J0-PSG.edf</td>\n",
       "      <td>ST7212JJ-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>ST7221J0-PSG.edf</td>\n",
       "      <td>ST7221JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>ST7222J0-PSG.edf</td>\n",
       "      <td>ST7222JA-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>ST7241J0-PSG.edf</td>\n",
       "      <td>ST7241JO-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>ST7242J0-PSG.edf</td>\n",
       "      <td>ST7242JO-Hypnogram.edf</td>\n",
       "      <td>sleep-telemetry</td>\n",
       "      <td>ST724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            psg_file              label_file        subfolder patient_id\n",
       "0   ST7011J0-PSG.edf  ST7011JP-Hypnogram.edf  sleep-telemetry      ST701\n",
       "1   ST7012J0-PSG.edf  ST7012JP-Hypnogram.edf  sleep-telemetry      ST701\n",
       "2   ST7021J0-PSG.edf  ST7021JM-Hypnogram.edf  sleep-telemetry      ST702\n",
       "3   ST7022J0-PSG.edf  ST7022JM-Hypnogram.edf  sleep-telemetry      ST702\n",
       "4   ST7041J0-PSG.edf  ST7041JO-Hypnogram.edf  sleep-telemetry      ST704\n",
       "5   ST7042J0-PSG.edf  ST7042JO-Hypnogram.edf  sleep-telemetry      ST704\n",
       "6   ST7051J0-PSG.edf  ST7051JA-Hypnogram.edf  sleep-telemetry      ST705\n",
       "7   ST7052J0-PSG.edf  ST7052JA-Hypnogram.edf  sleep-telemetry      ST705\n",
       "8   ST7061J0-PSG.edf  ST7061JR-Hypnogram.edf  sleep-telemetry      ST706\n",
       "9   ST7062J0-PSG.edf  ST7062JR-Hypnogram.edf  sleep-telemetry      ST706\n",
       "10  ST7071J0-PSG.edf  ST7071JA-Hypnogram.edf  sleep-telemetry      ST707\n",
       "11  ST7072J0-PSG.edf  ST7072JA-Hypnogram.edf  sleep-telemetry      ST707\n",
       "12  ST7081J0-PSG.edf  ST7081JW-Hypnogram.edf  sleep-telemetry      ST708\n",
       "13  ST7082J0-PSG.edf  ST7082JW-Hypnogram.edf  sleep-telemetry      ST708\n",
       "14  ST7091J0-PSG.edf  ST7091JE-Hypnogram.edf  sleep-telemetry      ST709\n",
       "15  ST7092J0-PSG.edf  ST7092JE-Hypnogram.edf  sleep-telemetry      ST709\n",
       "16  ST7101J0-PSG.edf  ST7101JE-Hypnogram.edf  sleep-telemetry      ST710\n",
       "17  ST7102J0-PSG.edf  ST7102JE-Hypnogram.edf  sleep-telemetry      ST710\n",
       "18  ST7111J0-PSG.edf  ST7111JE-Hypnogram.edf  sleep-telemetry      ST711\n",
       "19  ST7112J0-PSG.edf  ST7112JE-Hypnogram.edf  sleep-telemetry      ST711\n",
       "20  ST7121J0-PSG.edf  ST7121JE-Hypnogram.edf  sleep-telemetry      ST712\n",
       "21  ST7122J0-PSG.edf  ST7122JE-Hypnogram.edf  sleep-telemetry      ST712\n",
       "22  ST7131J0-PSG.edf  ST7131JR-Hypnogram.edf  sleep-telemetry      ST713\n",
       "23  ST7132J0-PSG.edf  ST7132JR-Hypnogram.edf  sleep-telemetry      ST713\n",
       "24  ST7141J0-PSG.edf  ST7141JE-Hypnogram.edf  sleep-telemetry      ST714\n",
       "25  ST7142J0-PSG.edf  ST7142JE-Hypnogram.edf  sleep-telemetry      ST714\n",
       "26  ST7151J0-PSG.edf  ST7151JA-Hypnogram.edf  sleep-telemetry      ST715\n",
       "27  ST7152J0-PSG.edf  ST7152JA-Hypnogram.edf  sleep-telemetry      ST715\n",
       "28  ST7161J0-PSG.edf  ST7161JM-Hypnogram.edf  sleep-telemetry      ST716\n",
       "29  ST7162J0-PSG.edf  ST7162JM-Hypnogram.edf  sleep-telemetry      ST716\n",
       "30  ST7171J0-PSG.edf  ST7171JA-Hypnogram.edf  sleep-telemetry      ST717\n",
       "31  ST7172J0-PSG.edf  ST7172JA-Hypnogram.edf  sleep-telemetry      ST717\n",
       "32  ST7181J0-PSG.edf  ST7181JR-Hypnogram.edf  sleep-telemetry      ST718\n",
       "33  ST7182J0-PSG.edf  ST7182JR-Hypnogram.edf  sleep-telemetry      ST718\n",
       "34  ST7191J0-PSG.edf  ST7191JR-Hypnogram.edf  sleep-telemetry      ST719\n",
       "35  ST7192J0-PSG.edf  ST7192JR-Hypnogram.edf  sleep-telemetry      ST719\n",
       "36  ST7201J0-PSG.edf  ST7201JO-Hypnogram.edf  sleep-telemetry      ST720\n",
       "37  ST7202J0-PSG.edf  ST7202JO-Hypnogram.edf  sleep-telemetry      ST720\n",
       "38  ST7211J0-PSG.edf  ST7211JJ-Hypnogram.edf  sleep-telemetry      ST721\n",
       "39  ST7212J0-PSG.edf  ST7212JJ-Hypnogram.edf  sleep-telemetry      ST721\n",
       "40  ST7221J0-PSG.edf  ST7221JA-Hypnogram.edf  sleep-telemetry      ST722\n",
       "41  ST7222J0-PSG.edf  ST7222JA-Hypnogram.edf  sleep-telemetry      ST722\n",
       "42  ST7241J0-PSG.edf  ST7241JO-Hypnogram.edf  sleep-telemetry      ST724\n",
       "43  ST7242J0-PSG.edf  ST7242JO-Hypnogram.edf  sleep-telemetry      ST724"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = []\n",
    "sub_folder = \"sleep-telemetry\"\n",
    "sorted_files = sorted(os.listdir(data_folder + sub_folder))\n",
    "psg_hypnogram_files = [(p, h) for p, h in zip(sorted_files[::2], sorted_files[1:][::2])]\n",
    "df_files = pd.DataFrame(psg_hypnogram_files, columns=[\"psg_file\", \"label_file\"])\n",
    "df_files[\"subfolder\"] = sub_folder\n",
    "    \n",
    "# Because for the SC study patients were monitored for 2 consecutive nights\n",
    "df_files[\"patient_id\"] = df_files.psg_file.apply(lambda f: f[:5])\n",
    "df_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_files.patient_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_files.psg_file.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> psg file can be used as recordingid   \n",
    "=> patient id can be used as patient id "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('EEG Fpz-Cz', 100), 44),\n",
       " (('EEG Pz-Oz', 100), 44),\n",
       " (('EOG horizontal', 100), 44),\n",
       " (('EMG submental', 100), 44),\n",
       " (('Marker', 10), 44)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_dict = {}\n",
    "\n",
    "for idx in range(len(df_files)):\n",
    "    sig, freq = load_signals(\n",
    "        data_folder + df_files.iloc[idx].subfolder + \"/\" + df_files.iloc[idx].psg_file,\n",
    "        only_info=True,\n",
    "    )\n",
    "    for s, f in zip(sig, freq):\n",
    "        if (s, f) in signal_dict.keys():\n",
    "            signal_dict[(s, f)].append(idx)\n",
    "        else:\n",
    "            signal_dict[(s, f)] = [idx]\n",
    "\n",
    "[(k, len(v)) for k, v in signal_dict.items()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_signals = [\n",
    "    'EEG Fpz-Cz', 'EEG Pz-Oz', 'EOG horizontal',  # All have the same sampling rate (100 Hz)\n",
    "    'EMG submental',  # Also 100 Hz\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_signals(\n",
    "    data_folder + df_files.iloc[0].subfolder + \"/\" + df_files.iloc[0].psg_file,\n",
    "    retrieve_signals=common_signals,\n",
    ")\n",
    "annotations = load_annotations(\n",
    "    data_folder + df_files.iloc[0].subfolder + \"/\" + df_files.iloc[0].label_file,\n",
    "    data_folder + df_files.iloc[0].subfolder + \"/\" + df_files.iloc[0].psg_file,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Used for plotting\n",
    "cats = [\n",
    "    \"Sleep stage ?\",\n",
    "    \"Movement time\",\n",
    "    \"Sleep stage W\",\n",
    "    \"Sleep stage 1\",\n",
    "    \"Sleep stage 2\",\n",
    "    \"Sleep stage 3\",\n",
    "    \"Sleep stage 4\",\n",
    "    \"Sleep stage R\",\n",
    "][::-1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA + pipeline (for 1 patient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8043/\n"
     ]
    }
   ],
   "source": [
    "idxs = [s.name for s in data]\n",
    "\n",
    "fig = FigureResampler(\n",
    "    make_subplots(\n",
    "        rows=len(idxs) + 1,\n",
    "        cols=1,\n",
    "        shared_xaxes=True,\n",
    "        subplot_titles=idxs + [\"Hypnogram\"],\n",
    "        vertical_spacing=0.05,\n",
    "    ),\n",
    "    default_n_shown_samples=3_000,\n",
    ")\n",
    "\n",
    "for idx, s in enumerate(data, 1):\n",
    "    fig.add_trace(\n",
    "        go.Scattergl(\n",
    "            x=[], y=[], name=s.name, line_width=1,\n",
    "        ),\n",
    "        hf_x=s.index,\n",
    "        hf_y=s.values,\n",
    "        row=idx,\n",
    "        col=1,\n",
    "    )\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scattergl(\n",
    "        x=[], y=[], name=\"Hypnogram\", line_width=1.5, line_shape=\"hv\",\n",
    "    ),\n",
    "    hf_x=annotation_to_30s_labels(annotations).index,\n",
    "    hf_y=annotation_to_30s_labels(annotations)[\"label\"].astype(\"category\"),\n",
    "    row=len(idxs) + 1,\n",
    "    col=1,\n",
    ")\n",
    "\n",
    "fig.update_layout(height=900)\n",
    "fig.update_yaxes(categoryorder=\"array\", categoryarray=cats)\n",
    "\n",
    "fig.update_layout(\n",
    "    legend_traceorder=\"normal\",\n",
    "    template=\"plotly_white\",\n",
    "    #     legend=dict(orientation=\"h\", y=1.07, xanchor=\"right\", x=1),\n",
    ")\n",
    "\n",
    "fig.show_dash(port=8043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Process the signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\n",
       "\tbutter_bandpass_filter {'lowcut': 0.4, 'highcut': 30, 'fs': 100} :  ('EEG Fpz-Cz',) ('EEG Pz-Oz',) ('EOG horizontal',)\n",
       "\tbutter_bandpass_filter {'lowcut': 0.5, 'highcut': 10, 'fs': 100} :  ('EMG submental',)\n",
       "]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Processing\n",
    "from scipy.signal import butter, lfilter\n",
    "\n",
    "from tsflex.processing import SeriesPipeline, SeriesProcessor\n",
    "\n",
    "\n",
    "def butter_bandpass_filter(sig, lowcut, highcut, fs, order=5):\n",
    "    nyq = 0.5 * fs\n",
    "    low = lowcut / nyq\n",
    "    high = highcut / nyq\n",
    "    b, a = butter(order, [low, high], btype=\"band\")\n",
    "    y = lfilter(b, a, sig)\n",
    "    return y\n",
    "\n",
    "eeg_bandpass = SeriesProcessor(\n",
    "    function=butter_bandpass_filter,\n",
    "    series_names=[\"EEG Fpz-Cz\", \"EEG Pz-Oz\", \"EOG horizontal\"],\n",
    "    lowcut=0.4,\n",
    "    highcut=30,\n",
    "    fs=100,\n",
    ")\n",
    "\n",
    "emg_bandpass = SeriesProcessor(\n",
    "    function=butter_bandpass_filter,\n",
    "    series_names=[\"EMG submental\"],\n",
    "    lowcut=0.5,\n",
    "    highcut=10,\n",
    "    fs=100,\n",
    ")\n",
    "\n",
    "process_pipe = SeriesPipeline(\n",
    "    [\n",
    "        eeg_bandpass,\n",
    "        emg_bandpass,\n",
    "    ]\n",
    ")\n",
    "\n",
    "process_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 255 ms, sys: 10.9 ms, total: 266 ms\n",
      "Wall time: 265 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_processed = process_pipe.process(data, return_all_series=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8043/\n"
     ]
    }
   ],
   "source": [
    "for s in data_processed:\n",
    "    idx = idxs.index(s.name.split(\"_\")[-1]) + 1\n",
    "    fig.add_trace(\n",
    "        go.Scatter(x=[], y=[], name=s.name + \" processed\", line_width=1),\n",
    "        hf_x=s.index,\n",
    "        hf_y=s.values,\n",
    "        row=idx,\n",
    "        col=1,\n",
    "    )\n",
    "    \n",
    "fig.show_dash(port=8043)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Extract the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fft_aggregated': [{'aggtype': 'centroid'},\n",
       "  {'aggtype': 'variance'},\n",
       "  {'aggtype': 'skew'},\n",
       "  {'aggtype': 'kurtosis'}],\n",
       " 'fourier_entropy': [{'bins': 2},\n",
       "  {'bins': 3},\n",
       "  {'bins': 5},\n",
       "  {'bins': 10},\n",
       "  {'bins': 30},\n",
       "  {'bins': 60},\n",
       "  {'bins': 100}],\n",
       " 'binned_entropy': [{'max_bins': 5},\n",
       "  {'max_bins': 10},\n",
       "  {'max_bins': 30},\n",
       "  {'max_bins': 60}]}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the collection of tsfresh features to extract\n",
    "\n",
    "tsfresh_settings = {\n",
    "    \"fft_aggregated\": [\n",
    "        {\"aggtype\": \"centroid\"},\n",
    "        {\"aggtype\": \"variance\"},\n",
    "        {\"aggtype\": \"skew\"},\n",
    "        {\"aggtype\": \"kurtosis\"},\n",
    "    ],\n",
    "    \"fourier_entropy\": [\n",
    "        {\"bins\": 2},\n",
    "        {\"bins\": 3},\n",
    "        {\"bins\": 5},\n",
    "        {\"bins\": 10},\n",
    "        {\"bins\": 30},\n",
    "        {\"bins\": 60},\n",
    "        {\"bins\": 100},\n",
    "    ],\n",
    "    \"binned_entropy\": [\n",
    "        {\"max_bins\": 5},\n",
    "        {\"max_bins\": 10},\n",
    "        {\"max_bins\": 30},\n",
    "        {\"max_bins\": 60},\n",
    "    ],\n",
    "}\n",
    "\n",
    "tsfresh_settings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import antropy as ant\n",
    "import scipy.stats as ss\n",
    "from yasa import bandpower\n",
    "\n",
    "import scipy.stats as ss\n",
    "from tsflex.features import (\n",
    "    FeatureCollection,\n",
    "    FuncWrapper,\n",
    "    MultipleFeatureDescriptors,\n",
    "    FuncWrapper,\n",
    ")\n",
    "from tsflex.features.integrations import tsfresh_settings_wrapper\n",
    "\n",
    "\n",
    "def wrapped_higuchi_fd(x):\n",
    "    x = np.array(x, dtype=\"float64\")\n",
    "    return ant.higuchi_fd(x)\n",
    "\n",
    "\n",
    "bands = [\n",
    "    (0.4, 1, \"sdelta\"),\n",
    "    (1, 4, \"fdelta\"),\n",
    "    (4, 8, \"theta\"),\n",
    "    (8, 12, \"alpha\"),\n",
    "    (12, 16, \"sigma\"),\n",
    "    (16, 30, \"beta\"),\n",
    "]\n",
    "bandpowers_ouputs = [b[2] for b in bands] + [\"TotalAbsPow\"]\n",
    "\n",
    "\n",
    "def wrapped_bandpowers(x, sf, bands):\n",
    "    return bandpower(x, sf=sf, bands=bands).values[0][:-2]\n",
    "\n",
    "\n",
    "time_funcs = [\n",
    "    np.std,\n",
    "    ss.iqr,\n",
    "    ss.skew,\n",
    "    ss.kurtosis,\n",
    "    ant.num_zerocross,\n",
    "    FuncWrapper(\n",
    "        ant.hjorth_params, output_names=[\"horth_mobility\", \"hjorth_complexity\"]\n",
    "    ),\n",
    "    wrapped_higuchi_fd,\n",
    "    ant.petrosian_fd,\n",
    "    ant.perm_entropy,\n",
    "] + tsfresh_settings_wrapper(tsfresh_settings)\n",
    "\n",
    "freq_funcs = [\n",
    "    FuncWrapper(wrapped_bandpowers, sf=100, bands=bands, output_names=bandpowers_ouputs)\n",
    "]\n",
    "\n",
    "time_feats = MultipleFeatureDescriptors(\n",
    "    time_funcs,\n",
    "    [\"EEG Fpz-Cz\", \"EEG Pz-Oz\", \"EOG horizontal\", \"EMG submental\"],\n",
    "    windows=[\"30s\", \"60s\", \"90s\"],\n",
    "    strides=\"30s\",\n",
    ")\n",
    "freq_feats = MultipleFeatureDescriptors(\n",
    "    freq_funcs,\n",
    "    [\"EEG Fpz-Cz\", \"EEG Pz-Oz\", \"EOG horizontal\"],\n",
    "    windows=[\"30s\", \"60s\", \"90s\"],\n",
    "    strides=\"30s\",\n",
    ")\n",
    "\n",
    "feature_collection = FeatureCollection([time_feats, freq_feats])\n",
    "# fc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from tsflex.features.logger import logger\n",
    "\n",
    "logger.setLevel(level=logging.ERROR)\n",
    "\n",
    "from tsflex.processing.logger import logger\n",
    "\n",
    "logger.setLevel(level=logging.ERROR)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "300f18dc0fda443bb0f33b9c5a370581",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/261 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>EEG Fpz-Cz__TotalAbsPow__w=1m30s_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__TotalAbsPow__w=1m_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__TotalAbsPow__w=30s_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__alpha__w=1m30s_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__alpha__w=1m_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__alpha__w=30s_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__beta__w=1m30s_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__beta__w=1m_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__beta__w=30s_s=30s</th>\n",
       "      <th>EEG Fpz-Cz__binned_entropy_{'max_bins': 10}__w=1m30s_s=30s</th>\n",
       "      <th>...</th>\n",
       "      <th>EOG horizontal__skew__w=30s_s=30s</th>\n",
       "      <th>EOG horizontal__std__w=1m30s_s=30s</th>\n",
       "      <th>EOG horizontal__std__w=1m_s=30s</th>\n",
       "      <th>EOG horizontal__std__w=30s_s=30s</th>\n",
       "      <th>EOG horizontal__theta__w=1m30s_s=30s</th>\n",
       "      <th>EOG horizontal__theta__w=1m_s=30s</th>\n",
       "      <th>EOG horizontal__theta__w=30s_s=30s</th>\n",
       "      <th>EOG horizontal__wrapped_higuchi_fd__w=1m30s_s=30s</th>\n",
       "      <th>EOG horizontal__wrapped_higuchi_fd__w=1m_s=30s</th>\n",
       "      <th>EOG horizontal__wrapped_higuchi_fd__w=30s_s=30s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1994-07-12 23:00:30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-16.375955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.131014e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.668797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-12 23:01:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59.160905</td>\n",
       "      <td>430.740613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047708</td>\n",
       "      <td>0.012491</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.139694</td>\n",
       "      <td>0.027748</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.420953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.912056e+01</td>\n",
       "      <td>6.467190e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087044</td>\n",
       "      <td>0.027942</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.559840</td>\n",
       "      <td>1.490229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-12 23:01:30</th>\n",
       "      <td>81.785157</td>\n",
       "      <td>275.008346</td>\n",
       "      <td>163.929489</td>\n",
       "      <td>0.058184</td>\n",
       "      <td>0.023462</td>\n",
       "      <td>0.045098</td>\n",
       "      <td>0.086314</td>\n",
       "      <td>0.029236</td>\n",
       "      <td>0.034775</td>\n",
       "      <td>0.141902</td>\n",
       "      <td>...</td>\n",
       "      <td>0.516501</td>\n",
       "      <td>7.267354e+01</td>\n",
       "      <td>6.126265e+01</td>\n",
       "      <td>5.764924e+01</td>\n",
       "      <td>0.068868</td>\n",
       "      <td>0.066995</td>\n",
       "      <td>0.056915</td>\n",
       "      <td>1.531905</td>\n",
       "      <td>1.478861</td>\n",
       "      <td>1.461830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-12 23:02:00</th>\n",
       "      <td>85.391217</td>\n",
       "      <td>60.26556</td>\n",
       "      <td>48.112712</td>\n",
       "      <td>0.080072</td>\n",
       "      <td>0.123613</td>\n",
       "      <td>0.140403</td>\n",
       "      <td>0.085049</td>\n",
       "      <td>0.100283</td>\n",
       "      <td>0.137473</td>\n",
       "      <td>0.901174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.474760</td>\n",
       "      <td>5.614282e+01</td>\n",
       "      <td>5.134973e+01</td>\n",
       "      <td>4.414832e+01</td>\n",
       "      <td>0.068749</td>\n",
       "      <td>0.069228</td>\n",
       "      <td>0.075371</td>\n",
       "      <td>1.475525</td>\n",
       "      <td>1.463777</td>\n",
       "      <td>1.467424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-12 23:02:30</th>\n",
       "      <td>64.441105</td>\n",
       "      <td>62.164828</td>\n",
       "      <td>69.751903</td>\n",
       "      <td>0.124938</td>\n",
       "      <td>0.128999</td>\n",
       "      <td>0.154207</td>\n",
       "      <td>0.092693</td>\n",
       "      <td>0.09944</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.828177</td>\n",
       "      <td>...</td>\n",
       "      <td>0.941957</td>\n",
       "      <td>4.420045e+01</td>\n",
       "      <td>3.561747e+01</td>\n",
       "      <td>2.423127e+01</td>\n",
       "      <td>0.082505</td>\n",
       "      <td>0.090551</td>\n",
       "      <td>0.100799</td>\n",
       "      <td>1.499889</td>\n",
       "      <td>1.524624</td>\n",
       "      <td>1.598236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-13 08:56:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.00015</td>\n",
       "      <td>0.000322</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>1.927917</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011193</td>\n",
       "      <td>3.790348e-07</td>\n",
       "      <td>3.468444e-07</td>\n",
       "      <td>3.681260e-07</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>1.001196</td>\n",
       "      <td>1.001069</td>\n",
       "      <td>1.000949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-13 08:56:30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.00044</td>\n",
       "      <td>0.000376</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.000301</td>\n",
       "      <td>1.959032</td>\n",
       "      <td>...</td>\n",
       "      <td>0.067448</td>\n",
       "      <td>3.570080e-07</td>\n",
       "      <td>3.664674e-07</td>\n",
       "      <td>3.233459e-07</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.000456</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>1.001277</td>\n",
       "      <td>1.001320</td>\n",
       "      <td>1.001806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-13 08:57:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000331</td>\n",
       "      <td>0.000271</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>1.993882</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.509429</td>\n",
       "      <td>3.667196e-07</td>\n",
       "      <td>3.599661e-07</td>\n",
       "      <td>3.518555e-07</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>1.001044</td>\n",
       "      <td>1.001208</td>\n",
       "      <td>1.001589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-13 08:57:30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.00019</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>2.080425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.659906</td>\n",
       "      <td>3.468297e-07</td>\n",
       "      <td>3.353580e-07</td>\n",
       "      <td>3.149321e-07</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.00054</td>\n",
       "      <td>0.000613</td>\n",
       "      <td>1.001521</td>\n",
       "      <td>1.001866</td>\n",
       "      <td>1.002720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-07-13 08:58:00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000169</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>2.068010</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.185616</td>\n",
       "      <td>3.406136e-07</td>\n",
       "      <td>3.331369e-07</td>\n",
       "      <td>3.503567e-07</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.00068</td>\n",
       "      <td>0.00085</td>\n",
       "      <td>1.001416</td>\n",
       "      <td>1.001621</td>\n",
       "      <td>1.002232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1196 rows × 363 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    EEG Fpz-Cz__TotalAbsPow__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                                    NaN   \n",
       "1994-07-12 23:01:00                                    NaN   \n",
       "1994-07-12 23:01:30                              81.785157   \n",
       "1994-07-12 23:02:00                              85.391217   \n",
       "1994-07-12 23:02:30                              64.441105   \n",
       "...                                                    ...   \n",
       "1994-07-13 08:56:00                                    0.0   \n",
       "1994-07-13 08:56:30                                    0.0   \n",
       "1994-07-13 08:57:00                                    0.0   \n",
       "1994-07-13 08:57:30                                    0.0   \n",
       "1994-07-13 08:58:00                                    0.0   \n",
       "\n",
       "                    EEG Fpz-Cz__TotalAbsPow__w=1m_s=30s  \\\n",
       "1994-07-12 23:00:30                                 NaN   \n",
       "1994-07-12 23:01:00                           59.160905   \n",
       "1994-07-12 23:01:30                          275.008346   \n",
       "1994-07-12 23:02:00                            60.26556   \n",
       "1994-07-12 23:02:30                           62.164828   \n",
       "...                                                 ...   \n",
       "1994-07-13 08:56:00                                 0.0   \n",
       "1994-07-13 08:56:30                                 0.0   \n",
       "1994-07-13 08:57:00                                 0.0   \n",
       "1994-07-13 08:57:30                                 0.0   \n",
       "1994-07-13 08:58:00                                 0.0   \n",
       "\n",
       "                    EEG Fpz-Cz__TotalAbsPow__w=30s_s=30s  \\\n",
       "1994-07-12 23:00:30                             0.000129   \n",
       "1994-07-12 23:01:00                           430.740613   \n",
       "1994-07-12 23:01:30                           163.929489   \n",
       "1994-07-12 23:02:00                            48.112712   \n",
       "1994-07-12 23:02:30                            69.751903   \n",
       "...                                                  ...   \n",
       "1994-07-13 08:56:00                                  0.0   \n",
       "1994-07-13 08:56:30                                  0.0   \n",
       "1994-07-13 08:57:00                                  0.0   \n",
       "1994-07-13 08:57:30                                  0.0   \n",
       "1994-07-13 08:58:00                                  0.0   \n",
       "\n",
       "                    EEG Fpz-Cz__alpha__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                              NaN   \n",
       "1994-07-12 23:01:00                              NaN   \n",
       "1994-07-12 23:01:30                         0.058184   \n",
       "1994-07-12 23:02:00                         0.080072   \n",
       "1994-07-12 23:02:30                         0.124938   \n",
       "...                                              ...   \n",
       "1994-07-13 08:56:00                         0.000187   \n",
       "1994-07-13 08:56:30                         0.000398   \n",
       "1994-07-13 08:57:00                         0.000331   \n",
       "1994-07-13 08:57:30                         0.000237   \n",
       "1994-07-13 08:58:00                         0.000193   \n",
       "\n",
       "                    EEG Fpz-Cz__alpha__w=1m_s=30s  \\\n",
       "1994-07-12 23:00:30                           NaN   \n",
       "1994-07-12 23:01:00                      0.047708   \n",
       "1994-07-12 23:01:30                      0.023462   \n",
       "1994-07-12 23:02:00                      0.123613   \n",
       "1994-07-12 23:02:30                      0.128999   \n",
       "...                                           ...   \n",
       "1994-07-13 08:56:00                      0.000402   \n",
       "1994-07-13 08:56:30                       0.00044   \n",
       "1994-07-13 08:57:00                      0.000271   \n",
       "1994-07-13 08:57:30                      0.000188   \n",
       "1994-07-13 08:58:00                      0.000198   \n",
       "\n",
       "                    EEG Fpz-Cz__alpha__w=30s_s=30s  \\\n",
       "1994-07-12 23:00:30                       0.001455   \n",
       "1994-07-12 23:01:00                       0.012491   \n",
       "1994-07-12 23:01:30                       0.045098   \n",
       "1994-07-12 23:02:00                       0.140403   \n",
       "1994-07-12 23:02:30                       0.154207   \n",
       "...                                            ...   \n",
       "1994-07-13 08:56:00                       0.000454   \n",
       "1994-07-13 08:56:30                       0.000376   \n",
       "1994-07-13 08:57:00                       0.000168   \n",
       "1994-07-13 08:57:30                       0.000236   \n",
       "1994-07-13 08:58:00                       0.000169   \n",
       "\n",
       "                    EEG Fpz-Cz__beta__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                             NaN   \n",
       "1994-07-12 23:01:00                             NaN   \n",
       "1994-07-12 23:01:30                        0.086314   \n",
       "1994-07-12 23:02:00                        0.085049   \n",
       "1994-07-12 23:02:30                        0.092693   \n",
       "...                                             ...   \n",
       "1994-07-13 08:56:00                         0.00015   \n",
       "1994-07-13 08:56:30                        0.000319   \n",
       "1994-07-13 08:57:00                        0.000265   \n",
       "1994-07-13 08:57:30                         0.00019   \n",
       "1994-07-13 08:58:00                        0.000154   \n",
       "\n",
       "                    EEG Fpz-Cz__beta__w=1m_s=30s  \\\n",
       "1994-07-12 23:00:30                          NaN   \n",
       "1994-07-12 23:01:00                     0.139694   \n",
       "1994-07-12 23:01:30                     0.029236   \n",
       "1994-07-12 23:02:00                     0.100283   \n",
       "1994-07-12 23:02:30                      0.09944   \n",
       "...                                          ...   \n",
       "1994-07-13 08:56:00                     0.000322   \n",
       "1994-07-13 08:56:30                     0.000352   \n",
       "1994-07-13 08:57:00                     0.000217   \n",
       "1994-07-13 08:57:30                     0.000151   \n",
       "1994-07-13 08:58:00                     0.000158   \n",
       "\n",
       "                    EEG Fpz-Cz__beta__w=30s_s=30s  \\\n",
       "1994-07-12 23:00:30                       0.00123   \n",
       "1994-07-12 23:01:00                      0.027748   \n",
       "1994-07-12 23:01:30                      0.034775   \n",
       "1994-07-12 23:02:00                      0.137473   \n",
       "1994-07-12 23:02:30                      0.081213   \n",
       "...                                           ...   \n",
       "1994-07-13 08:56:00                      0.000364   \n",
       "1994-07-13 08:56:30                      0.000301   \n",
       "1994-07-13 08:57:00                      0.000134   \n",
       "1994-07-13 08:57:30                      0.000189   \n",
       "1994-07-13 08:58:00                      0.000136   \n",
       "\n",
       "                     EEG Fpz-Cz__binned_entropy_{'max_bins': 10}__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                                                NaN            \n",
       "1994-07-12 23:01:00                                                NaN            \n",
       "1994-07-12 23:01:30                                           0.141902            \n",
       "1994-07-12 23:02:00                                           0.901174            \n",
       "1994-07-12 23:02:30                                           0.828177            \n",
       "...                                                                ...            \n",
       "1994-07-13 08:56:00                                           1.927917            \n",
       "1994-07-13 08:56:30                                           1.959032            \n",
       "1994-07-13 08:57:00                                           1.993882            \n",
       "1994-07-13 08:57:30                                           2.080425            \n",
       "1994-07-13 08:58:00                                           2.068010            \n",
       "\n",
       "                     ...  EOG horizontal__skew__w=30s_s=30s  \\\n",
       "1994-07-12 23:00:30  ...                         -16.375955   \n",
       "1994-07-12 23:01:00  ...                          -0.420953   \n",
       "1994-07-12 23:01:30  ...                           0.516501   \n",
       "1994-07-12 23:02:00  ...                          -0.474760   \n",
       "1994-07-12 23:02:30  ...                           0.941957   \n",
       "...                  ...                                ...   \n",
       "1994-07-13 08:56:00  ...                          -0.011193   \n",
       "1994-07-13 08:56:30  ...                           0.067448   \n",
       "1994-07-13 08:57:00  ...                          -0.509429   \n",
       "1994-07-13 08:57:30  ...                           0.659906   \n",
       "1994-07-13 08:58:00  ...                          -0.185616   \n",
       "\n",
       "                     EOG horizontal__std__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                                 NaN   \n",
       "1994-07-12 23:01:00                                 NaN   \n",
       "1994-07-12 23:01:30                        7.267354e+01   \n",
       "1994-07-12 23:02:00                        5.614282e+01   \n",
       "1994-07-12 23:02:30                        4.420045e+01   \n",
       "...                                                 ...   \n",
       "1994-07-13 08:56:00                        3.790348e-07   \n",
       "1994-07-13 08:56:30                        3.570080e-07   \n",
       "1994-07-13 08:57:00                        3.667196e-07   \n",
       "1994-07-13 08:57:30                        3.468297e-07   \n",
       "1994-07-13 08:58:00                        3.406136e-07   \n",
       "\n",
       "                     EOG horizontal__std__w=1m_s=30s  \\\n",
       "1994-07-12 23:00:30                              NaN   \n",
       "1994-07-12 23:01:00                     7.912056e+01   \n",
       "1994-07-12 23:01:30                     6.126265e+01   \n",
       "1994-07-12 23:02:00                     5.134973e+01   \n",
       "1994-07-12 23:02:30                     3.561747e+01   \n",
       "...                                              ...   \n",
       "1994-07-13 08:56:00                     3.468444e-07   \n",
       "1994-07-13 08:56:30                     3.664674e-07   \n",
       "1994-07-13 08:57:00                     3.599661e-07   \n",
       "1994-07-13 08:57:30                     3.353580e-07   \n",
       "1994-07-13 08:58:00                     3.331369e-07   \n",
       "\n",
       "                     EOG horizontal__std__w=30s_s=30s  \\\n",
       "1994-07-12 23:00:30                      9.131014e+01   \n",
       "1994-07-12 23:01:00                      6.467190e+01   \n",
       "1994-07-12 23:01:30                      5.764924e+01   \n",
       "1994-07-12 23:02:00                      4.414832e+01   \n",
       "1994-07-12 23:02:30                      2.423127e+01   \n",
       "...                                               ...   \n",
       "1994-07-13 08:56:00                      3.681260e-07   \n",
       "1994-07-13 08:56:30                      3.233459e-07   \n",
       "1994-07-13 08:57:00                      3.518555e-07   \n",
       "1994-07-13 08:57:30                      3.149321e-07   \n",
       "1994-07-13 08:58:00                      3.503567e-07   \n",
       "\n",
       "                     EOG horizontal__theta__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                                   NaN   \n",
       "1994-07-12 23:01:00                                   NaN   \n",
       "1994-07-12 23:01:30                              0.068868   \n",
       "1994-07-12 23:02:00                              0.068749   \n",
       "1994-07-12 23:02:30                              0.082505   \n",
       "...                                                   ...   \n",
       "1994-07-13 08:56:00                              0.000509   \n",
       "1994-07-13 08:56:30                              0.000392   \n",
       "1994-07-13 08:57:00                              0.000319   \n",
       "1994-07-13 08:57:30                              0.000367   \n",
       "1994-07-13 08:58:00                              0.000631   \n",
       "\n",
       "                     EOG horizontal__theta__w=1m_s=30s  \\\n",
       "1994-07-12 23:00:30                                NaN   \n",
       "1994-07-12 23:01:00                           0.087044   \n",
       "1994-07-12 23:01:30                           0.066995   \n",
       "1994-07-12 23:02:00                           0.069228   \n",
       "1994-07-12 23:02:30                           0.090551   \n",
       "...                                                ...   \n",
       "1994-07-13 08:56:00                           0.000564   \n",
       "1994-07-13 08:56:30                           0.000456   \n",
       "1994-07-13 08:57:00                           0.000207   \n",
       "1994-07-13 08:57:30                            0.00054   \n",
       "1994-07-13 08:58:00                            0.00068   \n",
       "\n",
       "                     EOG horizontal__theta__w=30s_s=30s  \\\n",
       "1994-07-12 23:00:30                            0.004138   \n",
       "1994-07-12 23:01:00                            0.027942   \n",
       "1994-07-12 23:01:30                            0.056915   \n",
       "1994-07-12 23:02:00                            0.075371   \n",
       "1994-07-12 23:02:30                            0.100799   \n",
       "...                                                 ...   \n",
       "1994-07-13 08:56:00                            0.000852   \n",
       "1994-07-13 08:56:30                            0.000201   \n",
       "1994-07-13 08:57:00                            0.000294   \n",
       "1994-07-13 08:57:30                            0.000613   \n",
       "1994-07-13 08:58:00                             0.00085   \n",
       "\n",
       "                     EOG horizontal__wrapped_higuchi_fd__w=1m30s_s=30s  \\\n",
       "1994-07-12 23:00:30                                                NaN   \n",
       "1994-07-12 23:01:00                                                NaN   \n",
       "1994-07-12 23:01:30                                           1.531905   \n",
       "1994-07-12 23:02:00                                           1.475525   \n",
       "1994-07-12 23:02:30                                           1.499889   \n",
       "...                                                                ...   \n",
       "1994-07-13 08:56:00                                           1.001196   \n",
       "1994-07-13 08:56:30                                           1.001277   \n",
       "1994-07-13 08:57:00                                           1.001044   \n",
       "1994-07-13 08:57:30                                           1.001521   \n",
       "1994-07-13 08:58:00                                           1.001416   \n",
       "\n",
       "                     EOG horizontal__wrapped_higuchi_fd__w=1m_s=30s  \\\n",
       "1994-07-12 23:00:30                                             NaN   \n",
       "1994-07-12 23:01:00                                        1.559840   \n",
       "1994-07-12 23:01:30                                        1.478861   \n",
       "1994-07-12 23:02:00                                        1.463777   \n",
       "1994-07-12 23:02:30                                        1.524624   \n",
       "...                                                             ...   \n",
       "1994-07-13 08:56:00                                        1.001069   \n",
       "1994-07-13 08:56:30                                        1.001320   \n",
       "1994-07-13 08:57:00                                        1.001208   \n",
       "1994-07-13 08:57:30                                        1.001866   \n",
       "1994-07-13 08:58:00                                        1.001621   \n",
       "\n",
       "                     EOG horizontal__wrapped_higuchi_fd__w=30s_s=30s  \n",
       "1994-07-12 23:00:30                                         1.668797  \n",
       "1994-07-12 23:01:00                                         1.490229  \n",
       "1994-07-12 23:01:30                                         1.461830  \n",
       "1994-07-12 23:02:00                                         1.467424  \n",
       "1994-07-12 23:02:30                                         1.598236  \n",
       "...                                                              ...  \n",
       "1994-07-13 08:56:00                                         1.000949  \n",
       "1994-07-13 08:56:30                                         1.001806  \n",
       "1994-07-13 08:57:00                                         1.001589  \n",
       "1994-07-13 08:57:30                                         1.002720  \n",
       "1994-07-13 08:58:00                                         1.002232  \n",
       "\n",
       "[1196 rows x 363 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time\n",
    "feats = feature_collection.calculate(data_processed, return_df=True, show_progress=True)\n",
    "feats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Pipeline for all patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebe6c4db21ba4725803a1533eed92e9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tqdm.auto import tqdm\n",
    "\n",
    "df_feats = []\n",
    "for sub_folder, psg_file, hypnogram_file in tqdm(zip(df_files.subfolder, df_files.psg_file, df_files.label_file)):\n",
    "    file_folder = data_folder + sub_folder + \"/\"\n",
    "    # Load the data, process the data and extract features\n",
    "    data = load_signals(file_folder + psg_file)\n",
    "    data_processed = process_pipe.process(data)\n",
    "    df_feat = feature_collection.calculate(\n",
    "        data_processed, return_df=True, window_idx=\"begin\"\n",
    "    ).astype(\"float32\")\n",
    "    # Add the labels (and reduce features to only data for which we have labels)\n",
    "    annotations = load_annotations(file_folder + hypnogram_file, file_folder + psg_file)\n",
    "    annotations = annotation_to_30s_labels(annotations)\n",
    "    df_feat = df_feat.merge(annotations, left_index=True, right_index=True)\n",
    "    # Add the file name & folder\n",
    "    df_feat[\"psg_file\"] = psg_file\n",
    "    df_feat[\"patient_id\"] = psg_file[:5]\n",
    "    # Collect the dataframes\n",
    "    df_feats += [df_feat]\n",
    "\n",
    "df_feats = pd.concat(df_feats)\n",
    "df_feats.rename(columns={\"description\": \"label\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feats.to_parquet(\"../features/sleep-edf__telemetry_features_ALL__90s.parquet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42839, 366)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats = pd.read_parquet(\"../features/sleep-edf__telemetry_features_ALL__90s.parquet\")\n",
    "df_feats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sleep stage 2    19851\n",
       "Sleep stage R     8349\n",
       "Sleep stage W     4488\n",
       "Sleep stage 1     3653\n",
       "Sleep stage 3     3398\n",
       "Sleep stage 4     3017\n",
       "Movement time       83\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats[~df_feats.psg_file.isin(df_files.psg_file.unique())].psg_file.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 42839 entries, 1994-07-12 23:00:00 to 1994-09-16 07:44:00\n",
      "Columns: 366 entries, EEG Fpz-Cz__TotalAbsPow__w=1m30s_s=30s to patient_id\n",
      "dtypes: float32(363), object(3)\n",
      "memory usage: 60.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_feats.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42839, 396)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eeg_signals = [d.name for d in data_processed if \"EEG\" in d.name]\n",
    "bands = [\"alpha\", \"beta\", \"sdelta\", \"fdelta\", \"sigma\", \"theta\"]\n",
    "for eeg_sig in eeg_signals:\n",
    "    eeg_bands = [c for c in df_feats.columns if c.startswith(eeg_sig) and c.split(\"__\")[1] in bands]\n",
    "    windows = sorted(set(b.split(\"__\")[-1] for b in eeg_bands))\n",
    "    for window in windows:\n",
    "        # Select the spectral powers\n",
    "        delta = df_feats[\"__\".join([eeg_sig, \"sdelta\", window])] + df_feats[\"__\".join([eeg_sig, \"fdelta\", window])]\n",
    "        fdelta_theta = df_feats[\"__\".join([eeg_sig, \"fdelta\", window])] + df_feats[\"__\".join([eeg_sig, \"theta\", window])]\n",
    "        alpha = df_feats[\"__\".join([eeg_sig, \"alpha\", window])]\n",
    "        beta = df_feats[\"__\".join([eeg_sig, \"beta\", window])]\n",
    "        theta = df_feats[\"__\".join([eeg_sig, \"theta\", window])]\n",
    "        sigma = df_feats[\"__\".join([eeg_sig, \"sigma\", window])]\n",
    "        # Calculate the ratios\n",
    "        df_feats[\"__\".join([eeg_sig, \"fdelta+theta\", window])] = fdelta_theta.astype(\"float32\")        \n",
    "\n",
    "        df_feats[\"__\".join([eeg_sig, \"alpha/theta\", window])] = (alpha / theta).astype(\"float32\")\n",
    "        df_feats[\"__\".join([eeg_sig, \"delta/beta\", window])] = (delta / beta).astype(\"float32\")\n",
    "        df_feats[\"__\".join([eeg_sig, \"delta/sigma\", window])] = (delta / sigma).astype(\"float32\")\n",
    "        df_feats[\"__\".join([eeg_sig, \"delta/theta\", window])] = (delta / theta).astype(\"float32\")\n",
    "df_feats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_cols = [\"psg_file\", \"label\", \"patient_id\"]\n",
    "feat_cols = [c for c in df_feats.columns if c not in skip_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131 131 131\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(42839, 1051)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the 30s features from the minute before and the minute after the epoch (= current sample of 30s) to the epoch\n",
    "feats_30s = [f for f in df_feats.columns if \"w=30s\" in f]\n",
    "feats_60s = [f for f in df_feats.columns if \"w=1m_\" in f]\n",
    "feats_90s = [f for f in df_feats.columns if \"w=1m30s\" in f]\n",
    "print(len(feats_30s), len(feats_60s), len(feats_90s))\n",
    "dfs = []\n",
    "for psg_file in df_feats.psg_file.unique():\n",
    "    sub_df = df_feats[df_feats.psg_file == psg_file]\n",
    "\n",
    "    sub_df = sub_df.merge(\n",
    "        sub_df[feats_90s].shift(1).add_suffix(\"_shift=30s\"),\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "    sub_df = sub_df.drop(columns=feats_90s)\n",
    "\n",
    "    sub_df = sub_df.merge(\n",
    "        sub_df[feats_60s].shift(1).add_suffix(\"_shift=30s\"),\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "\n",
    "    sub_df = sub_df.merge(sub_df[feats_30s].shift(2).add_suffix(\"_shift=1m\"), left_index=True, right_index=True)\n",
    "    sub_df = sub_df.merge(sub_df[feats_30s].shift(1).add_suffix(\"_shift=30s\"), left_index=True, right_index=True)\n",
    "    sub_df = sub_df.merge(sub_df[feats_30s].shift(-1).add_suffix(\"_shift=-30s\"), left_index=True, right_index=True)\n",
    "    sub_df = sub_df.merge(sub_df[feats_30s].shift(-2).add_suffix(\"_shift=-1m\"), left_index=True, right_index=True)\n",
    "    dfs += [sub_df]\n",
    "df_feats = pd.concat(dfs)\n",
    "df_feats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 42839 entries, 1994-07-12 23:00:00 to 1994-09-16 07:44:00\n",
      "Columns: 1051 entries, EEG Fpz-Cz__TotalAbsPow__w=1m_s=30s to EEG Pz-Oz__delta/theta__w=30s_s=30s_shift=-1m\n",
      "dtypes: float32(1048), object(3)\n",
      "memory usage: 172.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_feats.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Postprocess labels\n",
    "df_feats.loc[df_feats[\"label\"] == \"Sleep stage 4\", \"label\"] = \"Sleep stage 3\"\n",
    "df_feats = df_feats[df_feats[\"label\"] != \"Movement time\"]\n",
    "df_feats = df_feats[df_feats[\"label\"] != \"Sleep stage ?\"]  # Label that is given when recording is done / end of the night\n",
    "\n",
    "df_feats = df_feats[~df_feats[\"label\"].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42756, 1051)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look into nan features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_shift_cols = [c for c in df_feats.columns if not \"shift=\" in c]# or \"w=1m\" in c or \"w=1m30s\" in c]\n",
    "len(no_shift_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EEG Pz-Oz__delta/theta__w=1m_s=30s                             7\n",
       "EEG Pz-Oz__TotalAbsPow__w=1m_s=30s                             7\n",
       "EEG Pz-Oz__beta__w=1m_s=30s                                    7\n",
       "EEG Pz-Oz__binned_entropy_{'max_bins': 10}__w=1m_s=30s         7\n",
       "EEG Pz-Oz__binned_entropy_{'max_bins': 30}__w=1m_s=30s         7\n",
       "                                                              ..\n",
       "EOG horizontal__binned_entropy_{'max_bins': 10}__w=1m_s=30s    7\n",
       "EOG horizontal__binned_entropy_{'max_bins': 30}__w=1m_s=30s    7\n",
       "EOG horizontal__binned_entropy_{'max_bins': 5}__w=1m_s=30s     7\n",
       "EOG horizontal__binned_entropy_{'max_bins': 60}__w=1m_s=30s    7\n",
       "EEG Fpz-Cz__TotalAbsPow__w=1m_s=30s                            7\n",
       "Length: 131, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normal_nan_mask = (df_feats[no_shift_cols].isna().sum() / df_feats.psg_file.nunique()).isin([0, 1, 2])\n",
    "df_feats[np.array(no_shift_cols)[~normal_nan_mask]].isna().sum().sort_values()[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for psg_file in df_feats.psg_file.unique():\n",
    "    assert not df_feats[df_feats.psg_file == psg_file].isna().any(axis=1)[2:-2].any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00016371971185330714"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats[np.array(no_shift_cols)[~normal_nan_mask]].isna().any(axis=1).sum() / len(df_feats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> 0.016% of the feature vectors contain some nan value\n",
    "\n",
    "*Further findings*: more in depth analysis revealed that the nan values are only present at the start and end of the file (in the first two and last two epochs -> when there is not enough data to calculate a larger window features (90s, 60s)).\n",
    "\n",
    "**What to do with this?**  \n",
    "For the sake of simplicity we will keep those feature vectors and just (simply) impute the nan values as\n",
    "* only 0.016% of the feature vectors (i.e., samples) is affected -> will have minimal / no impact on results\n",
    "* these samples ara at the start & end of the recording and are most likely thus wake (and rather easy to predict from the other samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## EDA on labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_feats[\"label\"].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sleep stage 2    19851\n",
       "Sleep stage R     8349\n",
       "Sleep stage 3     6415\n",
       "Sleep stage W     4488\n",
       "Sleep stage 1     3653\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> There is quite some label imbalance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA on features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip_cols = [\"psg_file\", \"label\", \"patient_id\"]\n",
    "feat_cols = [c for c in df_feats.columns if c not in skip_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plot_cols = [c for c in feat_cols if not \"_shift=\" in c]\n",
    "\n",
    "# n_cols = 10\n",
    "# n_rows = math.ceil(len(plot_cols) / n_cols)\n",
    "# plt.figure(figsize=(n_cols * 4.5, n_rows * 4.5))\n",
    "\n",
    "# for idx, col in enumerate(plot_cols):\n",
    "#     plt.subplot(n_rows, n_cols, idx + 1)\n",
    "#     plt.boxplot(df_feats[col].dropna(), vert=True)\n",
    "#     plt.xlabel(col)\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    classification_report,\n",
    "    f1_score,\n",
    "    cohen_kappa_score,\n",
    "    log_loss,\n",
    ")\n",
    "from sklearn.model_selection import GroupKFold, StratifiedGroupKFold, cross_validate\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import QuantileTransformer\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from visualizations import plot_linear_classification_coefs\n",
    "from visualizations import plot_learning_curve\n",
    "from visualizations import plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## The simple, linear pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create pipeline\n",
    "pipe = Pipeline(\n",
    "    [\n",
    "        (\"impute\", SimpleImputer()),\n",
    "        (\"scale\", QuantileTransformer(n_quantiles=100, subsample=200_000, random_state=0)),\n",
    "        (\n",
    "            \"linear_model\",\n",
    "            SGDClassifier(\n",
    "                loss=\"log\",\n",
    "                average=True,\n",
    "                class_weight=\"balanced\",\n",
    "                n_jobs=5,\n",
    "                random_state=0,\n",
    "            ),\n",
    "        ),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped 10-fold results for all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Total of 10 folds\n",
    "gkfold = StratifiedGroupKFold(n_splits=10)\n",
    "cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.patient_id)\n",
    "\n",
    "res = cross_validate(\n",
    "    pipe,\n",
    "    df_feats[feat_cols],\n",
    "    df_feats[\"label\"],\n",
    "    scoring=[\"f1_macro\", \"balanced_accuracy\", \"accuracy\", \"neg_log_loss\"],\n",
    "    cv=cv,\n",
    "    n_jobs=25,\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-FOLD: TRAIN\n",
      "  MACRO F1:           0.8444\n",
      "  Balanced accuracy:  0.8501\n",
      "  Accuracy:           0.8725\n",
      "  Log loss:           0.6266\n"
     ]
    }
   ],
   "source": [
    "print(\"10-FOLD: TRAIN\")\n",
    "print(\"  MACRO F1:          \", round(np.mean(res[\"train_f1_macro\"]), 4))\n",
    "print(\"  Balanced accuracy: \", round(np.mean(res[\"train_balanced_accuracy\"]), 4))\n",
    "print(\"  Accuracy:          \", round(np.mean(res[\"train_accuracy\"]), 4))\n",
    "print(\"  Log loss:          \", round(np.mean(-1 * res[\"train_neg_log_loss\"]), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10-FOLD: TEST\n",
      "  MACRO F1:           0.791\n",
      "  Balanced accuracy:  0.803\n",
      "  Accuracy:           0.828\n",
      "  Log loss:           1.0598\n"
     ]
    }
   ],
   "source": [
    "print(\"10-FOLD: TEST\")\n",
    "print(\"  MACRO F1:          \", round(np.mean(res[\"test_f1_macro\"]), 4))\n",
    "print(\"  Balanced accuracy: \", round(np.mean(res[\"test_balanced_accuracy\"]), 4))\n",
    "print(\"  Accuracy:          \", round(np.mean(res[\"test_accuracy\"]), 4))\n",
    "print(\"  Log loss:          \", round(np.mean(-1 * res[\"test_neg_log_loss\"]), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "preds = []\n",
    "preds_proba = []\n",
    "y_trues = []\n",
    "cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.patient_id)\n",
    "for fold_ix, (train_idx, test_idx) in enumerate(cv):\n",
    "    X_train = df_feats[feat_cols].iloc[train_idx]\n",
    "    y_train = df_feats[\"label\"].iloc[train_idx]\n",
    "    X_test = df_feats[feat_cols].iloc[test_idx]\n",
    "    y_test = df_feats[\"label\"].iloc[test_idx]\n",
    "    \n",
    "    estimator = res[\"estimator\"][fold_ix]\n",
    "    preds += [estimator.predict(X_test)]\n",
    "    preds_proba += [estimator.predict_proba(X_test)]\n",
    "    y_trues += [y_test]\n",
    "\n",
    "preds = np.hstack(preds)\n",
    "y_trues = np.hstack(y_trues)\n",
    "preds_proba = np.vstack(preds_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.7901036047003469\n",
      "ACC: 0.8272055384039667\n",
      "KAPPA: 0.75653760978059\n",
      "LOG LOSS: 1.060249989560196\n"
     ]
    }
   ],
   "source": [
    "print(\"F1:\", f1_score(preds, y_trues, average=\"macro\"))\n",
    "print(\"ACC:\", accuracy_score(preds, y_trues))\n",
    "print(\"KAPPA:\", cohen_kappa_score(preds, y_trues))\n",
    "print(\"LOG LOSS:\", log_loss(y_trues, preds_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Sleep stage 1       0.58      0.55      0.57      3802\n",
      "Sleep stage 2       0.85      0.87      0.86     19365\n",
      "Sleep stage 3       0.84      0.78      0.81      6893\n",
      "Sleep stage R       0.86      0.89      0.87      8060\n",
      "Sleep stage W       0.86      0.83      0.84      4636\n",
      "\n",
      "     accuracy                           0.83     42756\n",
      "    macro avg       0.80      0.78      0.79     42756\n",
      " weighted avg       0.83      0.83      0.83     42756\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, y_trues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Normalized confusion matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = [\n",
    "    'Sleep stage W',\n",
    "    'Sleep stage 1',\n",
    "    'Sleep stage 2',\n",
    "    'Sleep stage 3',\n",
    "    'Sleep stage R'\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(25,12))\n",
    "ax = plt.subplot(1,2,1)\n",
    "plot_confusion_matrix(y_pred=preds, y_true=y_trues, classes=classes, normalize=False, ax=ax)\n",
    "ax = plt.subplot(1,2,2)\n",
    "plot_confusion_matrix(y_pred=preds, y_true=y_trues, classes=classes, normalize=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.patient_id)\n",
    "\n",
    "_ = plot_learning_curve(\n",
    "    pipe,\n",
    "    title=\"Learning curve - F1 Macro\",\n",
    "    X=df_feats[feat_cols],\n",
    "    y=df_feats[\"label\"],\n",
    "    cv=cv,\n",
    "    scoring=\"f1_macro\",\n",
    "    n_jobs=25,\n",
    "    train_sizes=[0.05] + list(np.linspace(0.1,1,10))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped 22-fold for all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats.patient_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Total of 10 folds\n",
    "gkfold = GroupKFold(n_splits=22)\n",
    "cv = gkfold.split(df_feats, groups=df_feats.patient_id)\n",
    "\n",
    "res = cross_validate(\n",
    "    pipe,\n",
    "    df_feats[feat_cols],\n",
    "    df_feats[\"label\"],\n",
    "    scoring=[\"f1_macro\", \"balanced_accuracy\", \"accuracy\", \"neg_log_loss\"],\n",
    "    cv=cv,\n",
    "    n_jobs=30,\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22-FOLD: TRAIN\n",
      "  MACRO F1:           0.8428\n",
      "  Balanced accuracy:  0.8482\n",
      "  Accuracy:           0.8713\n",
      "  Log loss:           0.6261\n"
     ]
    }
   ],
   "source": [
    "print(\"22-FOLD: TRAIN\")\n",
    "print(\"  MACRO F1:          \", round(np.mean(res[\"train_f1_macro\"]), 4))\n",
    "print(\"  Balanced accuracy: \", round(np.mean(res[\"train_balanced_accuracy\"]), 4))\n",
    "print(\"  Accuracy:          \", round(np.mean(res[\"train_accuracy\"]), 4))\n",
    "print(\"  Log loss:          \", round(np.mean(-1 * res[\"train_neg_log_loss\"]), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22-FOLD: TEST\n",
      "  MACRO F1:           0.7768\n",
      "  Balanced accuracy:  0.8009\n",
      "  Accuracy:           0.8291\n",
      "  Log loss:           1.0376\n"
     ]
    }
   ],
   "source": [
    "print(\"22-FOLD: TEST\")\n",
    "print(\"  MACRO F1:          \", round(np.mean(res[\"test_f1_macro\"]), 4))\n",
    "print(\"  Balanced accuracy: \", round(np.mean(res[\"test_balanced_accuracy\"]), 4))\n",
    "print(\"  Accuracy:          \", round(np.mean(res[\"test_accuracy\"]), 4))\n",
    "print(\"  Log loss:          \", round(np.mean(-1 * res[\"test_neg_log_loss\"]), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "preds = []\n",
    "preds_proba = []\n",
    "y_trues = []\n",
    "cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.patient_id)\n",
    "for fold_ix, (train_idx, test_idx) in enumerate(cv):\n",
    "    X_train = df_feats[feat_cols].iloc[train_idx]\n",
    "    y_train = df_feats[\"label\"].iloc[train_idx]\n",
    "    X_test = df_feats[feat_cols].iloc[test_idx]\n",
    "    y_test = df_feats[\"label\"].iloc[test_idx]\n",
    "    \n",
    "    estimator = res[\"estimator\"][fold_ix]\n",
    "    preds += [estimator.predict(X_test)]\n",
    "    preds_proba += [estimator.predict_proba(X_test)]\n",
    "    y_trues += [y_test]\n",
    "\n",
    "preds = np.hstack(preds)\n",
    "y_trues = np.hstack(y_trues)\n",
    "preds_proba = np.vstack(preds_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.7915322051335323\n",
      "ACC: 0.8291000093554121\n",
      "KAPPA: 0.7589262302228097\n",
      "LOG LOSS: 1.0396094973586787\n"
     ]
    }
   ],
   "source": [
    "print(\"F1:\", f1_score(preds, y_trues, average=\"macro\"))\n",
    "print(\"ACC:\", accuracy_score(preds, y_trues))\n",
    "print(\"KAPPA:\", cohen_kappa_score(preds, y_trues))\n",
    "print(\"LOG LOSS:\", log_loss(y_trues, preds_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Sleep stage 1       0.58      0.56      0.57      3755\n",
      "Sleep stage 2       0.85      0.87      0.86     19462\n",
      "Sleep stage 3       0.83      0.78      0.81      6831\n",
      "Sleep stage R       0.86      0.89      0.88      8086\n",
      "Sleep stage W       0.86      0.83      0.84      4622\n",
      "\n",
      "     accuracy                           0.83     42756\n",
      "    macro avg       0.80      0.79      0.79     42756\n",
      " weighted avg       0.83      0.83      0.83     42756\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, y_trues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Normalized confusion matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = [\n",
    "    'Sleep stage W',\n",
    "    'Sleep stage 1',\n",
    "    'Sleep stage 2',\n",
    "    'Sleep stage 3',\n",
    "    'Sleep stage R'\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(25,12))\n",
    "ax = plt.subplot(1,2,1)\n",
    "plot_confusion_matrix(y_pred=preds, y_true=y_trues, classes=classes, normalize=False, ax=ax)\n",
    "ax = plt.subplot(1,2,2)\n",
    "plot_confusion_matrix(y_pred=preds, y_true=y_trues, classes=classes, normalize=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cv = gkfold.split(df_feats, groups=df_feats.patient_id)\n",
    "\n",
    "_ = plot_learning_curve(\n",
    "    pipe,\n",
    "    title=\"Learning curve - F1 Macro\",\n",
    "    X=df_feats[feat_cols],\n",
    "    y=df_feats[\"label\"],\n",
    "    cv=cv,\n",
    "    scoring=\"f1_macro\",\n",
    "    n_jobs=25,\n",
    "    train_sizes=[0.05] + list(np.linspace(0.1,1,10))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The simple, catboost pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Create pipeline\n",
    "from catboost import CatBoostClassifier\n",
    "pipe = CatBoostClassifier(verbose=0, depth=5, n_estimators=2_000, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouped 22-fold for all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats.patient_id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Total of 10 folds\n",
    "gkfold = GroupKFold(n_splits=22)\n",
    "cv = gkfold.split(df_feats, groups=df_feats.patient_id)\n",
    "\n",
    "res = cross_validate(\n",
    "    pipe,\n",
    "    df_feats[feat_cols],\n",
    "    df_feats[\"label\"],\n",
    "    scoring=[\"f1_macro\", \"balanced_accuracy\", \"accuracy\", \"neg_log_loss\"],\n",
    "    cv=cv,\n",
    "    # n_jobs=30,\n",
    "    return_train_score=True,\n",
    "    return_estimator=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22-FOLD: TRAIN\n",
      "  MACRO F1:           0.9355\n",
      "  Balanced accuracy:  0.927\n",
      "  Accuracy:           0.9462\n",
      "  Log loss:           0.1699\n"
     ]
    }
   ],
   "source": [
    "print(\"22-FOLD: TRAIN\")\n",
    "print(\"  MACRO F1:          \", round(np.mean(res[\"train_f1_macro\"]), 4))\n",
    "print(\"  Balanced accuracy: \", round(np.mean(res[\"train_balanced_accuracy\"]), 4))\n",
    "print(\"  Accuracy:          \", round(np.mean(res[\"train_accuracy\"]), 4))\n",
    "print(\"  Log loss:          \", round(np.mean(-1 * res[\"train_neg_log_loss\"]), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22-FOLD: TEST\n",
      "  MACRO F1:           0.779\n",
      "  Balanced accuracy:  0.7898\n",
      "  Accuracy:           0.8365\n",
      "  Log loss:           0.4348\n"
     ]
    }
   ],
   "source": [
    "print(\"22-FOLD: TEST\")\n",
    "print(\"  MACRO F1:          \", round(np.mean(res[\"test_f1_macro\"]), 4))\n",
    "print(\"  Balanced accuracy: \", round(np.mean(res[\"test_balanced_accuracy\"]), 4))\n",
    "print(\"  Accuracy:          \", round(np.mean(res[\"test_accuracy\"]), 4))\n",
    "print(\"  Log loss:          \", round(np.mean(-1 * res[\"test_neg_log_loss\"]), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "preds = []\n",
    "preds_proba = []\n",
    "y_trues = []\n",
    "cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.patient_id)\n",
    "for fold_ix, (train_idx, test_idx) in enumerate(cv):\n",
    "    X_train = df_feats[feat_cols].iloc[train_idx]\n",
    "    y_train = df_feats[\"label\"].iloc[train_idx]\n",
    "    X_test = df_feats[feat_cols].iloc[test_idx]\n",
    "    y_test = df_feats[\"label\"].iloc[test_idx]\n",
    "    \n",
    "    estimator = res[\"estimator\"][fold_ix]\n",
    "    preds += [estimator.predict(X_test).ravel()]\n",
    "    preds_proba += [estimator.predict_proba(X_test)]\n",
    "    y_trues += [y_test]\n",
    "\n",
    "preds = np.hstack(preds)\n",
    "y_trues = np.hstack(y_trues)\n",
    "preds_proba = np.vstack(preds_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.795091260707293\n",
      "ACC: 0.8362802881466929\n",
      "KAPPA: 0.7650660701989241\n",
      "LOG LOSS: 0.4361518421826315\n"
     ]
    }
   ],
   "source": [
    "print(\"F1:\", f1_score(preds, y_trues, average=\"macro\"))\n",
    "print(\"ACC:\", accuracy_score(preds, y_trues))\n",
    "print(\"KAPPA:\", cohen_kappa_score(preds, y_trues))\n",
    "print(\"LOG LOSS:\", log_loss(y_trues, preds_proba))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Sleep stage 1       0.52      0.63      0.57      3019\n",
      "Sleep stage 2       0.89      0.85      0.87     20945\n",
      "Sleep stage 3       0.77      0.81      0.79      6091\n",
      "Sleep stage R       0.86      0.89      0.88      8094\n",
      "Sleep stage W       0.88      0.85      0.86      4607\n",
      "\n",
      "     accuracy                           0.84     42756\n",
      "    macro avg       0.79      0.81      0.80     42756\n",
      " weighted avg       0.84      0.84      0.84     42756\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, y_trues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Normalized confusion matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = [\n",
    "    'Sleep stage W',\n",
    "    'Sleep stage 1',\n",
    "    'Sleep stage 2',\n",
    "    'Sleep stage 3',\n",
    "    'Sleep stage R'\n",
    "]\n",
    "\n",
    "plt.figure(figsize=(25,12))\n",
    "ax = plt.subplot(1,2,1)\n",
    "plot_confusion_matrix(y_pred=preds, y_true=y_trues, classes=classes, normalize=False, ax=ax)\n",
    "ax = plt.subplot(1,2,2)\n",
    "plot_confusion_matrix(y_pred=preds, y_true=y_trues, classes=classes, normalize=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv = gkfold.split(df_feats, groups=df_feats.patient_id)\n",
    "\n",
    "# _ = plot_learning_curve(\n",
    "#     pipe,\n",
    "#     title=\"Learning curve - F1 Macro\",\n",
    "#     X=df_feats[feat_cols],\n",
    "#     y=df_feats[\"label\"],\n",
    "#     cv=cv,\n",
    "#     scoring=\"f1_macro\",\n",
    "#     n_jobs=25,\n",
    "#     train_sizes=[0.05] + list(np.linspace(0.1,1,10))\n",
    "# )"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "8041eec424124ca4bc46361e33fda6cb471e587a5eddb1974ae044e5340fbf70"
  },
  "kernelspec": {
   "display_name": "Python 3.8.10 ('venv')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}