notebooks/MASS-SS3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset: **Montreal Archive of Sleep Studies** (MASS) \n",
    "http://ceams-carsm.ca/mass/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# !pip install mne pyedflib seglearn tsfel tsfresh antropy"
   ]
  },
  {
   "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": [
    "In order to access the data you should submit a request as is described here; http://ceams-carsm.ca/mass/ "
   ]
  },
  {
   "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": [
       "['SS3_EDF_part2',\n",
       " 'SS3_EDF_part1',\n",
       " 'SS5_EDF.zip',\n",
       " 'SS1_Annotations',\n",
       " 'SS1_EDF_1_30.zip',\n",
       " 'SS1_EDF_part2.zip',\n",
       " 'SS5_Annotations',\n",
       " 'SS4_Annotations',\n",
       " 'SS2_Annotations',\n",
       " 'SS3_Annotations',\n",
       " 'SS2_EDF_part1.zip',\n",
       " 'SS4_EDF_part1.zip']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_folder = \"../../../../data/sleep/MASSDB/\"\n",
    "\n",
    "os.listdir(data_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01-03-0021 PSG.edf',\n",
       " '01-03-0006 Base.edf',\n",
       " '01-03-0006 PSG.edf',\n",
       " '01-03-0025 Base.edf',\n",
       " '01-03-0009 PSG.edf',\n",
       " '01-03-0007 PSG.edf',\n",
       " '01-03-0024 PSG.edf',\n",
       " '01-03-0030 PSG.edf',\n",
       " '01-03-0032 Base.edf',\n",
       " '01-03-0024 Base.edf']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_folder + \"SS3_EDF_part1\")[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(data_folder + \"SS3_EDF_part1\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13G\t../../../../data/sleep/MASSDB/SS3_EDF_part1\n"
     ]
    }
   ],
   "source": [
    "!du -hs ../../../../data/sleep/MASSDB/SS3_EDF_part1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> In total 70 files (35 PSG files - 35 hypnogram files) => 13 GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['01-03-0047 Base.edf',\n",
       " '01-03-0062 PSG.edf',\n",
       " '01-03-0039 Base.edf',\n",
       " '01-03-0048 PSG.edf',\n",
       " '01-03-0036 Base.edf',\n",
       " '01-03-0053 Base.edf',\n",
       " '01-03-0037 Base.edf',\n",
       " '01-03-0053 PSG.edf',\n",
       " '01-03-0061 Base.edf',\n",
       " '01-03-0064 PSG.edf']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(data_folder + \"SS3_EDF_part2\")[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(os.listdir(data_folder + \"SS3_EDF_part2\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12G\t../../../../data/sleep/MASSDB/SS3_EDF_part2\n"
     ]
    }
   ],
   "source": [
    "!du -hs ../../../../data/sleep/MASSDB/SS3_EDF_part2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "=> In total 54 files (27 PSG files - 27 hypnogram files) => 12 GB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>01-03-0001 PSG.edf</td>\n",
       "      <td>01-03-0001 Base.edf</td>\n",
       "      <td>SS3_EDF_part1</td>\n",
       "      <td>01-03-0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-03-0002 PSG.edf</td>\n",
       "      <td>01-03-0002 Base.edf</td>\n",
       "      <td>SS3_EDF_part1</td>\n",
       "      <td>01-03-0002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-0003 PSG.edf</td>\n",
       "      <td>01-03-0003 Base.edf</td>\n",
       "      <td>SS3_EDF_part1</td>\n",
       "      <td>01-03-0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-03-0004 PSG.edf</td>\n",
       "      <td>01-03-0004 Base.edf</td>\n",
       "      <td>SS3_EDF_part1</td>\n",
       "      <td>01-03-0004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-03-0005 PSG.edf</td>\n",
       "      <td>01-03-0005 Base.edf</td>\n",
       "      <td>SS3_EDF_part1</td>\n",
       "      <td>01-03-0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>01-03-0060 PSG.edf</td>\n",
       "      <td>01-03-0060 Base.edf</td>\n",
       "      <td>SS3_EDF_part2</td>\n",
       "      <td>01-03-0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>01-03-0061 PSG.edf</td>\n",
       "      <td>01-03-0061 Base.edf</td>\n",
       "      <td>SS3_EDF_part2</td>\n",
       "      <td>01-03-0061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>01-03-0062 PSG.edf</td>\n",
       "      <td>01-03-0062 Base.edf</td>\n",
       "      <td>SS3_EDF_part2</td>\n",
       "      <td>01-03-0062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>01-03-0063 PSG.edf</td>\n",
       "      <td>01-03-0063 Base.edf</td>\n",
       "      <td>SS3_EDF_part2</td>\n",
       "      <td>01-03-0063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>01-03-0064 PSG.edf</td>\n",
       "      <td>01-03-0064 Base.edf</td>\n",
       "      <td>SS3_EDF_part2</td>\n",
       "      <td>01-03-0064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              psg_file           label_file      subfolder  patient_id\n",
       "0   01-03-0001 PSG.edf  01-03-0001 Base.edf  SS3_EDF_part1  01-03-0001\n",
       "1   01-03-0002 PSG.edf  01-03-0002 Base.edf  SS3_EDF_part1  01-03-0002\n",
       "2   01-03-0003 PSG.edf  01-03-0003 Base.edf  SS3_EDF_part1  01-03-0003\n",
       "3   01-03-0004 PSG.edf  01-03-0004 Base.edf  SS3_EDF_part1  01-03-0004\n",
       "4   01-03-0005 PSG.edf  01-03-0005 Base.edf  SS3_EDF_part1  01-03-0005\n",
       "..                 ...                  ...            ...         ...\n",
       "22  01-03-0060 PSG.edf  01-03-0060 Base.edf  SS3_EDF_part2  01-03-0060\n",
       "23  01-03-0061 PSG.edf  01-03-0061 Base.edf  SS3_EDF_part2  01-03-0061\n",
       "24  01-03-0062 PSG.edf  01-03-0062 Base.edf  SS3_EDF_part2  01-03-0062\n",
       "25  01-03-0063 PSG.edf  01-03-0063 Base.edf  SS3_EDF_part2  01-03-0063\n",
       "26  01-03-0064 PSG.edf  01-03-0064 Base.edf  SS3_EDF_part2  01-03-0064\n",
       "\n",
       "[62 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = []\n",
    "sub_folders = [\"SS3_EDF_part1\", \"SS3_EDF_part2\"]\n",
    "for sub_folder in sub_folders:\n",
    "    sorted_files = sorted(os.listdir(data_folder + sub_folder))\n",
    "    psg_hypnogram_files = [(h, p) 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",
    "    dfs += [df_files]\n",
    "    \n",
    "df_files = pd.concat(dfs)\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.split(\" \")[0])\n",
    "df_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_files.psg_file.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_files.patient_id.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": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('ECG ECGI', 256), 43),\n",
       " (('ECG ECGI', 512), 19),\n",
       " (('EEG A2-LER', 256), 43),\n",
       " (('EEG C3-LER', 256), 62),\n",
       " (('EEG C4-LER', 256), 62),\n",
       " (('EEG Cz-LER', 256), 62),\n",
       " (('EEG F3-LER', 256), 62),\n",
       " (('EEG F4-LER', 256), 62),\n",
       " (('EEG F7-LER', 256), 62),\n",
       " (('EEG F8-LER', 256), 62),\n",
       " (('EEG Fp1-LER', 256), 62),\n",
       " (('EEG Fp2-LER', 256), 62),\n",
       " (('EEG Fz-LER', 256), 62),\n",
       " (('EEG O1-LER', 256), 62),\n",
       " (('EEG O2-LER', 256), 62),\n",
       " (('EEG Oz-LER', 256), 62),\n",
       " (('EEG P3-LER', 256), 62),\n",
       " (('EEG P4-LER', 256), 62),\n",
       " (('EEG Pz-LER', 256), 62),\n",
       " (('EEG T3-LER', 256), 62),\n",
       " (('EEG T4-LER', 256), 62),\n",
       " (('EEG T5-LER', 256), 62),\n",
       " (('EEG T6-LER', 256), 62),\n",
       " (('EMG Chin1', 128), 43),\n",
       " (('EMG Chin1', 256), 19),\n",
       " (('EMG Chin2', 128), 43),\n",
       " (('EMG Chin2', 256), 19),\n",
       " (('EMG Chin3', 128), 43),\n",
       " (('EMG Chin3', 256), 19),\n",
       " (('EOG Left Horiz', 256), 62),\n",
       " (('EOG Right Horiz', 256), 62),\n",
       " (('Resp Belt Abdo', 64), 19),\n",
       " (('Resp Belt Thor', 64), 19)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_dict = {}\n",
    "\n",
    "for idx in range(len(df_files)):\n",
    "    sig, freq = load_signals(data_folder + df_files.iloc[idx].subfolder + \"/\" + df_files.iloc[idx].psg_file, only_info=True)\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",
    "sorted([(k, len(v)) for k, v in signal_dict.items()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_signals = [\n",
    "    'EOG Left Horiz', 'EOG Right Horiz',  # All have the same sampling rate (100 Hz)\n",
    "    'EEG C3-LER', 'EEG A2-LER', 'EEG F4-LER', 'EEG F8-LER', 'EEG Cz-LER', #'EEG P4-LER',\n",
    "    # 'EEG Cz-LER', 'EEG Pz-LER', 'EEG Oz-LER',\n",
    "    'EMG Chin1', 'EMG Chin2', #'EMG Chin3',  # Different sampling rate: 100 Hz for telemetry & 1 Hz for cassette\n",
    "]\n",
    "\n",
    "common_signals = [\n",
    "    'EOG Left Horiz', 'EOG Right Horiz',  # All have the same sampling rate (100 Hz)\n",
    "    'EEG C4-LER', 'EEG O1-LER', 'EEG F4-LER', 'EEG F8-LER', 'EEG Cz-LER', #'EEG P4-LER',\n",
    "    # 'EEG Cz-LER', 'EEG Pz-LER', 'EEG Oz-LER',\n",
    "    'EMG Chin1', 'EMG Chin2', #'EMG Chin3',  # Different sampling rate: 100 Hz for telemetry & 1 Hz for cassette\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_signals(data_folder + df_files.iloc[0].subfolder + \"/\" + df_files.iloc[0].psg_file, retrieve_signals=common_signals + ['ECG ECGI'])\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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": {
    "tags": []
   },
   "source": [
    "# EDA + pipeline (for 1 patient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8042/\n"
     ]
    }
   ],
   "source": [
    "idxs = [\"EOG\", \"EEG\", \"EMG\"]\n",
    "\n",
    "fig = FigureResampler(\n",
    "    make_subplots(\n",
    "        rows=len(idxs) + 1 + 1,\n",
    "        cols=1,\n",
    "        shared_xaxes=True,\n",
    "        subplot_titles=idxs + [\"ECG\"] + [\"Hypnogram\"],\n",
    "        vertical_spacing=0.05,\n",
    "    ),\n",
    "    default_n_shown_samples=2_000,\n",
    ")\n",
    "\n",
    "\n",
    "for s in data:\n",
    "    if \"ECG\" in s.name:\n",
    "        fig.add_trace(\n",
    "            go.Scattergl(\n",
    "                x=[], y=[], name=\"ECG\", line_width=1\n",
    "            ),\n",
    "            hf_x=s.index,\n",
    "            hf_y=s.values,\n",
    "            row=len(idxs) + 1,\n",
    "            col=1,\n",
    "        )\n",
    "\n",
    "    try:\n",
    "        idx = idxs.index(s.name.split(\" \")[0]) + 1\n",
    "    except:\n",
    "        continue\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\"].values,\n",
    "    row=len(idxs) + 2,\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=8042)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Process the signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\n",
       "\tbutter_bandpass_filter {'lowcut': 0.4, 'highcut': 30, 'fs': 256} :  ('EEG C4-LER',) ('EEG O1-LER',) ('EEG F4-LER',) ('EEG F8-LER',) ('EEG Cz-LER',) ('EOG Left Horiz',) ('EOG Right Horiz',)\n",
       "\tbutter_bandpass_filter {'lowcut': 0.5, 'highcut': 10} :  ('EMG Chin1',) ('EMG Chin2',)\n",
       "\taverage {} :  ('EOG Left Horiz', 'EOG Right Horiz') ('EMG Chin1', 'EMG Chin2')\n",
       "\tcreate_montage {} :  ('EEG C4-LER', 'EEG O1-LER') ('EEG F4-LER', 'EOG Left Horiz')\n",
       "\tresample {'fs': 100} :  ('EEG C4-LER - EEG O1-LER',) ('EEG F4-LER - EOG Left Horiz',) ('EOG Left Horiz_EOG Right Horiz_[AVG]',) ('EMG Chin1_EMG Chin2_[AVG]',)\n",
       "]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Processing\n",
    "from scipy.signal import butter, lfilter, iirnotch\n",
    "\n",
    "from tsflex.processing import SeriesPipeline, SeriesProcessor\n",
    "\n",
    "def determine_fs(sig):\n",
    "    return int(10**9 / data[0].index.to_series().diff().median().to_timedelta64().astype(float))\n",
    "\n",
    "def butter_bandpass_filter(sig, lowcut, highcut, fs=None, order=5):\n",
    "    if fs is None:\n",
    "        fs = determine_fs(sig)\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",
    "def resample(sig, fs):\n",
    "    return sig.resample(pd.Timedelta(10**3/fs, unit=\"ms\")).mean().interpolate()\n",
    "\n",
    "def create_montage(sig1, sig2):\n",
    "    return (sig1 - sig2).rename(sig1.name + \" - \" + sig2.name)\n",
    "\n",
    "def average(sig1, sig2):\n",
    "    return (sig1 + sig2).rename(sig1.name + \"_\" + sig2.name + \"_[AVG]\") / 2\n",
    "\n",
    "eeg_signals = [s for s in common_signals if s.startswith(\"EEG\")]\n",
    "eog_signals = [s for s in common_signals if s.startswith(\"EOG\")]\n",
    "emg_signals = [s for s in common_signals if s.startswith(\"EMG\")]\n",
    "\n",
    "eeg_bandpass = SeriesProcessor(\n",
    "    function=butter_bandpass_filter,\n",
    "    series_names=eeg_signals + eog_signals,\n",
    "    lowcut=0.4,\n",
    "    highcut=30,\n",
    "    fs=256,\n",
    ")\n",
    "\n",
    "emg_bandpass = SeriesProcessor(\n",
    "    function=butter_bandpass_filter,\n",
    "    series_names=emg_signals,\n",
    "    lowcut=0.5,\n",
    "    highcut=10,\n",
    ")\n",
    "\n",
    "## OG pipeline\n",
    "process_pipe = SeriesPipeline(\n",
    "    [\n",
    "        eeg_bandpass,\n",
    "        emg_bandpass,\n",
    "        SeriesProcessor(resample, series_names=common_signals, fs=100),\n",
    "        SeriesProcessor(\n",
    "            create_montage, \n",
    "            series_names=[(\"EEG C4-LER\", \"EEG O1-LER\"), (\"EEG F4-LER\", \"EOG Left Horiz\")],#, (\"EEG F8-LER\", \"EEG Cz-LER\")]\n",
    "        ),\n",
    "        SeriesProcessor(\n",
    "            average,\n",
    "            series_names=[(\"EOG Left Horiz\", \"EOG Right Horiz\"), (\"EMG Chin1\", \"EMG Chin2\")]\n",
    "        ),\n",
    "        # emg_bandpass,\n",
    "    ]\n",
    ")\n",
    "\n",
    "out_signals = ['EEG C4-LER - EEG O1-LER',\n",
    "#  'EEG F8-LER - EEG Cz-LER',\n",
    " 'EEG F4-LER - EOG Left Horiz',\n",
    " 'EOG Left Horiz_EOG Right Horiz_[AVG]',\n",
    " 'EMG Chin1_EMG Chin2_[AVG]']\n",
    "\n",
    "process_pipe = SeriesPipeline(\n",
    "    [        \n",
    "        eeg_bandpass,\n",
    "        emg_bandpass,\n",
    "        SeriesProcessor(\n",
    "            average,\n",
    "            series_names=[(\"EOG Left Horiz\", \"EOG Right Horiz\"), (\"EMG Chin1\", \"EMG Chin2\")]\n",
    "        ),\n",
    "        SeriesProcessor(\n",
    "            create_montage, \n",
    "            series_names=[(\"EEG C4-LER\", \"EEG O1-LER\"), (\"EEG F4-LER\", \"EOG Left Horiz\")]#, (\"EEG F8-LER\", \"EEG Cz-LER\")]\n",
    "        ),\n",
    "        SeriesProcessor(resample, series_names=out_signals, fs=100),\n",
    "    ]\n",
    ")\n",
    "\n",
    "process_pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.28 s, sys: 674 ms, total: 2.95 s\n",
      "Wall time: 2.92 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_processed = process_pipe.process(data, return_all_series=False, drop_keys=eeg_signals + eog_signals + emg_signals)\n",
    "# data_processed = process_pipe.process(data, return_all_series=False)#, drop_keys=eeg_signals + eog_signals + emg_signals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8042/\n"
     ]
    }
   ],
   "source": [
    "for s in data_processed:\n",
    "    idx = idxs.index(s.name.split(\" \")[0]) + 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=8042)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Extract the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['EEG F4-LER - EOG Left Horiz', 'EEG C4-LER - EEG O1-LER']\n",
      "['EOG Left Horiz_EOG Right Horiz_[AVG]']\n",
      "['EMG Chin1_EMG Chin2_[AVG]']\n"
     ]
    }
   ],
   "source": [
    "eeg_cols = [s.name for s in data_processed if s.name.startswith(\"EEG\")]\n",
    "eog_cols = [s.name for s in data_processed if s.name.startswith(\"EOG\")]\n",
    "emg_cols = [s.name for s in data_processed if s.name.startswith(\"EMG\")]\n",
    "print(eeg_cols)\n",
    "print(eog_cols)\n",
    "print(emg_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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, eeg_cols + eog_cols + emg_cols,\n",
    "    windows=[\"30s\", \"60s\", \"90s\"], strides=\"30s\",\n",
    ")\n",
    "freq_feats = MultipleFeatureDescriptors(\n",
    "    freq_funcs, eeg_cols + eog_cols,\n",
    "    windows=[\"30s\", \"60s\", \"90s\"], strides=\"30s\",\n",
    ")\n",
    "\n",
    "feature_collection = FeatureCollection([time_feats, freq_feats])\n",
    "# fc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging \n",
    "\n",
    "from tsflex.features.logger import logger\n",
    "logger.setLevel(level=logging.ERROR)\n",
    "\n",
    "from tsflex.processing.logger import logger\n",
    "logger.setLevel(level=logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7fb38c82ab9b410ebf6aa7020e8e7c10",
       "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 C4-LER - EEG O1-LER__TotalAbsPow__w=1m30s_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__TotalAbsPow__w=1m_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__TotalAbsPow__w=30s_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__alpha__w=1m30s_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__alpha__w=1m_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__alpha__w=30s_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__beta__w=1m30s_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__beta__w=1m_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__beta__w=30s_s=30s</th>\n",
       "      <th>EEG C4-LER - EEG O1-LER__binned_entropy_{'max_bins': 10}__w=1m30s_s=30s</th>\n",
       "      <th>...</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__skew__w=30s_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__std__w=1m30s_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__std__w=1m_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__std__w=30s_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__theta__w=1m30s_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__theta__w=1m_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__theta__w=30s_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__wrapped_higuchi_fd__w=1m30s_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__wrapped_higuchi_fd__w=1m_s=30s</th>\n",
       "      <th>EOG Left Horiz_EOG Right Horiz_[AVG]__wrapped_higuchi_fd__w=30s_s=30s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01 21:28:23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.457673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.147711</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.060797</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.300548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.632114</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.089987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.622883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 21:28:53</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.621956</td>\n",
       "      <td>38.987855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.109785</td>\n",
       "      <td>0.074727</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.05605</td>\n",
       "      <td>0.04344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.236141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.814378</td>\n",
       "      <td>15.328005</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.073491</td>\n",
       "      <td>0.045014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537349</td>\n",
       "      <td>1.450732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 21:29:23</th>\n",
       "      <td>35.66086</td>\n",
       "      <td>32.425519</td>\n",
       "      <td>44.962163</td>\n",
       "      <td>0.168682</td>\n",
       "      <td>0.18713</td>\n",
       "      <td>0.458264</td>\n",
       "      <td>0.05987</td>\n",
       "      <td>0.057253</td>\n",
       "      <td>0.05073</td>\n",
       "      <td>1.076488</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119529</td>\n",
       "      <td>14.119242</td>\n",
       "      <td>15.891333</td>\n",
       "      <td>16.412610</td>\n",
       "      <td>0.063796</td>\n",
       "      <td>0.051673</td>\n",
       "      <td>0.067629</td>\n",
       "      <td>1.513740</td>\n",
       "      <td>1.457032</td>\n",
       "      <td>1.467876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 21:29:53</th>\n",
       "      <td>29.378379</td>\n",
       "      <td>32.836996</td>\n",
       "      <td>27.899303</td>\n",
       "      <td>0.218926</td>\n",
       "      <td>0.316914</td>\n",
       "      <td>0.221476</td>\n",
       "      <td>0.065742</td>\n",
       "      <td>0.065687</td>\n",
       "      <td>0.072345</td>\n",
       "      <td>1.055687</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.123054</td>\n",
       "      <td>13.155476</td>\n",
       "      <td>11.906914</td>\n",
       "      <td>3.754816</td>\n",
       "      <td>0.076116</td>\n",
       "      <td>0.075971</td>\n",
       "      <td>0.090717</td>\n",
       "      <td>1.513196</td>\n",
       "      <td>1.553211</td>\n",
       "      <td>1.676340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01 21:30:23</th>\n",
       "      <td>27.176844</td>\n",
       "      <td>23.646118</td>\n",
       "      <td>18.956875</td>\n",
       "      <td>0.237448</td>\n",
       "      <td>0.17418</td>\n",
       "      <td>0.147383</td>\n",
       "      <td>0.068801</td>\n",
       "      <td>0.06996</td>\n",
       "      <td>0.072987</td>\n",
       "      <td>1.690932</td>\n",
       "      <td>...</td>\n",
       "      <td>0.440301</td>\n",
       "      <td>10.440067</td>\n",
       "      <td>5.362053</td>\n",
       "      <td>6.588197</td>\n",
       "      <td>0.089699</td>\n",
       "      <td>0.107171</td>\n",
       "      <td>0.102644</td>\n",
       "      <td>1.547175</td>\n",
       "      <td>1.605256</td>\n",
       "      <td>1.532670</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>2000-01-02 05:59:53</th>\n",
       "      <td>67.457257</td>\n",
       "      <td>65.833353</td>\n",
       "      <td>55.544329</td>\n",
       "      <td>0.070965</td>\n",
       "      <td>0.080932</td>\n",
       "      <td>0.088443</td>\n",
       "      <td>0.019352</td>\n",
       "      <td>0.019928</td>\n",
       "      <td>0.029542</td>\n",
       "      <td>1.642027</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.057074</td>\n",
       "      <td>6.756273</td>\n",
       "      <td>6.074860</td>\n",
       "      <td>6.215825</td>\n",
       "      <td>0.139421</td>\n",
       "      <td>0.152136</td>\n",
       "      <td>0.187128</td>\n",
       "      <td>1.410516</td>\n",
       "      <td>1.433275</td>\n",
       "      <td>1.476446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 06:00:23</th>\n",
       "      <td>78.736117</td>\n",
       "      <td>83.661787</td>\n",
       "      <td>151.995921</td>\n",
       "      <td>0.089132</td>\n",
       "      <td>0.089835</td>\n",
       "      <td>0.084587</td>\n",
       "      <td>0.022012</td>\n",
       "      <td>0.028249</td>\n",
       "      <td>0.061087</td>\n",
       "      <td>0.507468</td>\n",
       "      <td>...</td>\n",
       "      <td>0.278932</td>\n",
       "      <td>61.811473</td>\n",
       "      <td>75.587059</td>\n",
       "      <td>106.715272</td>\n",
       "      <td>0.136022</td>\n",
       "      <td>0.134824</td>\n",
       "      <td>0.032963</td>\n",
       "      <td>1.270499</td>\n",
       "      <td>1.259598</td>\n",
       "      <td>1.234685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 06:00:53</th>\n",
       "      <td>72.088155</td>\n",
       "      <td>92.1134</td>\n",
       "      <td>63.986004</td>\n",
       "      <td>0.110996</td>\n",
       "      <td>0.11289</td>\n",
       "      <td>0.135875</td>\n",
       "      <td>0.045194</td>\n",
       "      <td>0.099761</td>\n",
       "      <td>0.147312</td>\n",
       "      <td>0.517686</td>\n",
       "      <td>...</td>\n",
       "      <td>0.861418</td>\n",
       "      <td>67.744800</td>\n",
       "      <td>82.853550</td>\n",
       "      <td>48.386638</td>\n",
       "      <td>0.070451</td>\n",
       "      <td>0.057948</td>\n",
       "      <td>0.068542</td>\n",
       "      <td>1.297602</td>\n",
       "      <td>1.284611</td>\n",
       "      <td>1.376458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 06:01:23</th>\n",
       "      <td>116.554523</td>\n",
       "      <td>94.892555</td>\n",
       "      <td>285.597411</td>\n",
       "      <td>0.077191</td>\n",
       "      <td>0.072359</td>\n",
       "      <td>0.020969</td>\n",
       "      <td>0.11665</td>\n",
       "      <td>0.146382</td>\n",
       "      <td>0.07232</td>\n",
       "      <td>0.710774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614399</td>\n",
       "      <td>80.366705</td>\n",
       "      <td>63.199168</td>\n",
       "      <td>75.146153</td>\n",
       "      <td>0.085664</td>\n",
       "      <td>0.092577</td>\n",
       "      <td>0.083237</td>\n",
       "      <td>1.290983</td>\n",
       "      <td>1.331246</td>\n",
       "      <td>1.304121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02 06:01:53</th>\n",
       "      <td>144.475238</td>\n",
       "      <td>313.554024</td>\n",
       "      <td>306.732188</td>\n",
       "      <td>0.050367</td>\n",
       "      <td>0.020928</td>\n",
       "      <td>0.02435</td>\n",
       "      <td>0.11211</td>\n",
       "      <td>0.065843</td>\n",
       "      <td>0.066542</td>\n",
       "      <td>0.883048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.280129</td>\n",
       "      <td>72.457972</td>\n",
       "      <td>81.881639</td>\n",
       "      <td>88.102048</td>\n",
       "      <td>0.054511</td>\n",
       "      <td>0.04811</td>\n",
       "      <td>0.022119</td>\n",
       "      <td>1.305338</td>\n",
       "      <td>1.282856</td>\n",
       "      <td>1.261031</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1028 rows × 363 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    EEG C4-LER - EEG O1-LER__TotalAbsPow__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN    \n",
       "2000-01-01 21:28:53                                                NaN    \n",
       "2000-01-01 21:29:23                                           35.66086    \n",
       "2000-01-01 21:29:53                                          29.378379    \n",
       "2000-01-01 21:30:23                                          27.176844    \n",
       "...                                                                ...    \n",
       "2000-01-02 05:59:53                                          67.457257    \n",
       "2000-01-02 06:00:23                                          78.736117    \n",
       "2000-01-02 06:00:53                                          72.088155    \n",
       "2000-01-02 06:01:23                                         116.554523    \n",
       "2000-01-02 06:01:53                                         144.475238    \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__TotalAbsPow__w=1m_s=30s  \\\n",
       "2000-01-01 21:28:23                                              NaN   \n",
       "2000-01-01 21:28:53                                        38.621956   \n",
       "2000-01-01 21:29:23                                        32.425519   \n",
       "2000-01-01 21:29:53                                        32.836996   \n",
       "2000-01-01 21:30:23                                        23.646118   \n",
       "...                                                              ...   \n",
       "2000-01-02 05:59:53                                        65.833353   \n",
       "2000-01-02 06:00:23                                        83.661787   \n",
       "2000-01-02 06:00:53                                          92.1134   \n",
       "2000-01-02 06:01:23                                        94.892555   \n",
       "2000-01-02 06:01:53                                       313.554024   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__TotalAbsPow__w=30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                         42.457673   \n",
       "2000-01-01 21:28:53                                         38.987855   \n",
       "2000-01-01 21:29:23                                         44.962163   \n",
       "2000-01-01 21:29:53                                         27.899303   \n",
       "2000-01-01 21:30:23                                         18.956875   \n",
       "...                                                               ...   \n",
       "2000-01-02 05:59:53                                         55.544329   \n",
       "2000-01-02 06:00:23                                        151.995921   \n",
       "2000-01-02 06:00:53                                         63.986004   \n",
       "2000-01-02 06:01:23                                        285.597411   \n",
       "2000-01-02 06:01:53                                        306.732188   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__alpha__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                           NaN   \n",
       "2000-01-01 21:28:53                                           NaN   \n",
       "2000-01-01 21:29:23                                      0.168682   \n",
       "2000-01-01 21:29:53                                      0.218926   \n",
       "2000-01-01 21:30:23                                      0.237448   \n",
       "...                                                           ...   \n",
       "2000-01-02 05:59:53                                      0.070965   \n",
       "2000-01-02 06:00:23                                      0.089132   \n",
       "2000-01-02 06:00:53                                      0.110996   \n",
       "2000-01-02 06:01:23                                      0.077191   \n",
       "2000-01-02 06:01:53                                      0.050367   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__alpha__w=1m_s=30s  \\\n",
       "2000-01-01 21:28:23                                        NaN   \n",
       "2000-01-01 21:28:53                                   0.109785   \n",
       "2000-01-01 21:29:23                                    0.18713   \n",
       "2000-01-01 21:29:53                                   0.316914   \n",
       "2000-01-01 21:30:23                                    0.17418   \n",
       "...                                                        ...   \n",
       "2000-01-02 05:59:53                                   0.080932   \n",
       "2000-01-02 06:00:23                                   0.089835   \n",
       "2000-01-02 06:00:53                                    0.11289   \n",
       "2000-01-02 06:01:23                                   0.072359   \n",
       "2000-01-02 06:01:53                                   0.020928   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__alpha__w=30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                    0.147711   \n",
       "2000-01-01 21:28:53                                    0.074727   \n",
       "2000-01-01 21:29:23                                    0.458264   \n",
       "2000-01-01 21:29:53                                    0.221476   \n",
       "2000-01-01 21:30:23                                    0.147383   \n",
       "...                                                         ...   \n",
       "2000-01-02 05:59:53                                    0.088443   \n",
       "2000-01-02 06:00:23                                    0.084587   \n",
       "2000-01-02 06:00:53                                    0.135875   \n",
       "2000-01-02 06:01:23                                    0.020969   \n",
       "2000-01-02 06:01:53                                     0.02435   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__beta__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                          NaN   \n",
       "2000-01-01 21:28:53                                          NaN   \n",
       "2000-01-01 21:29:23                                      0.05987   \n",
       "2000-01-01 21:29:53                                     0.065742   \n",
       "2000-01-01 21:30:23                                     0.068801   \n",
       "...                                                          ...   \n",
       "2000-01-02 05:59:53                                     0.019352   \n",
       "2000-01-02 06:00:23                                     0.022012   \n",
       "2000-01-02 06:00:53                                     0.045194   \n",
       "2000-01-02 06:01:23                                      0.11665   \n",
       "2000-01-02 06:01:53                                      0.11211   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__beta__w=1m_s=30s  \\\n",
       "2000-01-01 21:28:23                                       NaN   \n",
       "2000-01-01 21:28:53                                   0.05605   \n",
       "2000-01-01 21:29:23                                  0.057253   \n",
       "2000-01-01 21:29:53                                  0.065687   \n",
       "2000-01-01 21:30:23                                   0.06996   \n",
       "...                                                       ...   \n",
       "2000-01-02 05:59:53                                  0.019928   \n",
       "2000-01-02 06:00:23                                  0.028249   \n",
       "2000-01-02 06:00:53                                  0.099761   \n",
       "2000-01-02 06:01:23                                  0.146382   \n",
       "2000-01-02 06:01:53                                  0.065843   \n",
       "\n",
       "                    EEG C4-LER - EEG O1-LER__beta__w=30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                   0.060797   \n",
       "2000-01-01 21:28:53                                    0.04344   \n",
       "2000-01-01 21:29:23                                    0.05073   \n",
       "2000-01-01 21:29:53                                   0.072345   \n",
       "2000-01-01 21:30:23                                   0.072987   \n",
       "...                                                        ...   \n",
       "2000-01-02 05:59:53                                   0.029542   \n",
       "2000-01-02 06:00:23                                   0.061087   \n",
       "2000-01-02 06:00:53                                   0.147312   \n",
       "2000-01-02 06:01:23                                    0.07232   \n",
       "2000-01-02 06:01:53                                   0.066542   \n",
       "\n",
       "                     EEG C4-LER - EEG O1-LER__binned_entropy_{'max_bins': 10}__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN                         \n",
       "2000-01-01 21:28:53                                                NaN                         \n",
       "2000-01-01 21:29:23                                           1.076488                         \n",
       "2000-01-01 21:29:53                                           1.055687                         \n",
       "2000-01-01 21:30:23                                           1.690932                         \n",
       "...                                                                ...                         \n",
       "2000-01-02 05:59:53                                           1.642027                         \n",
       "2000-01-02 06:00:23                                           0.507468                         \n",
       "2000-01-02 06:00:53                                           0.517686                         \n",
       "2000-01-02 06:01:23                                           0.710774                         \n",
       "2000-01-02 06:01:53                                           0.883048                         \n",
       "\n",
       "                     ...  \\\n",
       "2000-01-01 21:28:23  ...   \n",
       "2000-01-01 21:28:53  ...   \n",
       "2000-01-01 21:29:23  ...   \n",
       "2000-01-01 21:29:53  ...   \n",
       "2000-01-01 21:30:23  ...   \n",
       "...                  ...   \n",
       "2000-01-02 05:59:53  ...   \n",
       "2000-01-02 06:00:23  ...   \n",
       "2000-01-02 06:00:53  ...   \n",
       "2000-01-02 06:01:23  ...   \n",
       "2000-01-02 06:01:53  ...   \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__skew__w=30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                          -1.300548         \n",
       "2000-01-01 21:28:53                                          -0.236141         \n",
       "2000-01-01 21:29:23                                           0.119529         \n",
       "2000-01-01 21:29:53                                          -0.123054         \n",
       "2000-01-01 21:30:23                                           0.440301         \n",
       "...                                                                ...         \n",
       "2000-01-02 05:59:53                                          -0.057074         \n",
       "2000-01-02 06:00:23                                           0.278932         \n",
       "2000-01-02 06:00:53                                           0.861418         \n",
       "2000-01-02 06:01:23                                           0.614399         \n",
       "2000-01-02 06:01:53                                           0.280129         \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__std__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN          \n",
       "2000-01-01 21:28:53                                                NaN          \n",
       "2000-01-01 21:29:23                                          14.119242          \n",
       "2000-01-01 21:29:53                                          13.155476          \n",
       "2000-01-01 21:30:23                                          10.440067          \n",
       "...                                                                ...          \n",
       "2000-01-02 05:59:53                                           6.756273          \n",
       "2000-01-02 06:00:23                                          61.811473          \n",
       "2000-01-02 06:00:53                                          67.744800          \n",
       "2000-01-02 06:01:23                                          80.366705          \n",
       "2000-01-02 06:01:53                                          72.457972          \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__std__w=1m_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN       \n",
       "2000-01-01 21:28:53                                          12.814378       \n",
       "2000-01-01 21:29:23                                          15.891333       \n",
       "2000-01-01 21:29:53                                          11.906914       \n",
       "2000-01-01 21:30:23                                           5.362053       \n",
       "...                                                                ...       \n",
       "2000-01-02 05:59:53                                           6.074860       \n",
       "2000-01-02 06:00:23                                          75.587059       \n",
       "2000-01-02 06:00:53                                          82.853550       \n",
       "2000-01-02 06:01:23                                          63.199168       \n",
       "2000-01-02 06:01:53                                          81.881639       \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__std__w=30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                           9.632114        \n",
       "2000-01-01 21:28:53                                          15.328005        \n",
       "2000-01-01 21:29:23                                          16.412610        \n",
       "2000-01-01 21:29:53                                           3.754816        \n",
       "2000-01-01 21:30:23                                           6.588197        \n",
       "...                                                                ...        \n",
       "2000-01-02 05:59:53                                           6.215825        \n",
       "2000-01-02 06:00:23                                         106.715272        \n",
       "2000-01-02 06:00:53                                          48.386638        \n",
       "2000-01-02 06:01:23                                          75.146153        \n",
       "2000-01-02 06:01:53                                          88.102048        \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__theta__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN            \n",
       "2000-01-01 21:28:53                                                NaN            \n",
       "2000-01-01 21:29:23                                           0.063796            \n",
       "2000-01-01 21:29:53                                           0.076116            \n",
       "2000-01-01 21:30:23                                           0.089699            \n",
       "...                                                                ...            \n",
       "2000-01-02 05:59:53                                           0.139421            \n",
       "2000-01-02 06:00:23                                           0.136022            \n",
       "2000-01-02 06:00:53                                           0.070451            \n",
       "2000-01-02 06:01:23                                           0.085664            \n",
       "2000-01-02 06:01:53                                           0.054511            \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__theta__w=1m_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN         \n",
       "2000-01-01 21:28:53                                           0.073491         \n",
       "2000-01-01 21:29:23                                           0.051673         \n",
       "2000-01-01 21:29:53                                           0.075971         \n",
       "2000-01-01 21:30:23                                           0.107171         \n",
       "...                                                                ...         \n",
       "2000-01-02 05:59:53                                           0.152136         \n",
       "2000-01-02 06:00:23                                           0.134824         \n",
       "2000-01-02 06:00:53                                           0.057948         \n",
       "2000-01-02 06:01:23                                           0.092577         \n",
       "2000-01-02 06:01:53                                            0.04811         \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__theta__w=30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                           0.089987          \n",
       "2000-01-01 21:28:53                                           0.045014          \n",
       "2000-01-01 21:29:23                                           0.067629          \n",
       "2000-01-01 21:29:53                                           0.090717          \n",
       "2000-01-01 21:30:23                                           0.102644          \n",
       "...                                                                ...          \n",
       "2000-01-02 05:59:53                                           0.187128          \n",
       "2000-01-02 06:00:23                                           0.032963          \n",
       "2000-01-02 06:00:53                                           0.068542          \n",
       "2000-01-02 06:01:23                                           0.083237          \n",
       "2000-01-02 06:01:53                                           0.022119          \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__wrapped_higuchi_fd__w=1m30s_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN                         \n",
       "2000-01-01 21:28:53                                                NaN                         \n",
       "2000-01-01 21:29:23                                           1.513740                         \n",
       "2000-01-01 21:29:53                                           1.513196                         \n",
       "2000-01-01 21:30:23                                           1.547175                         \n",
       "...                                                                ...                         \n",
       "2000-01-02 05:59:53                                           1.410516                         \n",
       "2000-01-02 06:00:23                                           1.270499                         \n",
       "2000-01-02 06:00:53                                           1.297602                         \n",
       "2000-01-02 06:01:23                                           1.290983                         \n",
       "2000-01-02 06:01:53                                           1.305338                         \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__wrapped_higuchi_fd__w=1m_s=30s  \\\n",
       "2000-01-01 21:28:23                                                NaN                      \n",
       "2000-01-01 21:28:53                                           1.537349                      \n",
       "2000-01-01 21:29:23                                           1.457032                      \n",
       "2000-01-01 21:29:53                                           1.553211                      \n",
       "2000-01-01 21:30:23                                           1.605256                      \n",
       "...                                                                ...                      \n",
       "2000-01-02 05:59:53                                           1.433275                      \n",
       "2000-01-02 06:00:23                                           1.259598                      \n",
       "2000-01-02 06:00:53                                           1.284611                      \n",
       "2000-01-02 06:01:23                                           1.331246                      \n",
       "2000-01-02 06:01:53                                           1.282856                      \n",
       "\n",
       "                     EOG Left Horiz_EOG Right Horiz_[AVG]__wrapped_higuchi_fd__w=30s_s=30s  \n",
       "2000-01-01 21:28:23                                           1.622883                      \n",
       "2000-01-01 21:28:53                                           1.450732                      \n",
       "2000-01-01 21:29:23                                           1.467876                      \n",
       "2000-01-01 21:29:53                                           1.676340                      \n",
       "2000-01-01 21:30:23                                           1.532670                      \n",
       "...                                                                ...                      \n",
       "2000-01-02 05:59:53                                           1.476446                      \n",
       "2000-01-02 06:00:23                                           1.234685                      \n",
       "2000-01-02 06:00:53                                           1.376458                      \n",
       "2000-01-02 06:01:23                                           1.304121                      \n",
       "2000-01-02 06:01:53                                           1.261031                      \n",
       "\n",
       "[1028 rows x 363 columns]"
      ]
     },
     "execution_count": 28,
     "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": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42c15702f3dc4a4b832a8ff48b5587eb",
       "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",
    "def slice_data(series_list, start):\n",
    "    return [s[s.index >= start] for s in series_list]\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 annotations\n",
    "    annotations = load_annotations(file_folder + hypnogram_file, file_folder + psg_file)\n",
    "    annotations = annotation_to_30s_labels(annotations)\n",
    "    start_idx = annotations.index.values[0]\n",
    "    # Load the data, process the data and extract features\n",
    "    data = load_signals(file_folder + psg_file, retrieve_signals=common_signals)\n",
    "    data_processed = process_pipe.process(data, drop_keys=eeg_signals + eog_signals + emg_signals)\n",
    "    df_feat = feature_collection.calculate(\n",
    "        slice_data(data_processed, start_idx), 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",
    "    df_feat = pd.merge_asof(df_feat, annotations, left_index=True, right_index=True, tolerance=pd.Timedelta(0.05, unit=\"s\"), direction=\"nearest\")\n",
    "    # Add the file name & folder\n",
    "    df_feat[\"psg_file\"] = psg_file\n",
    "    df_feat[\"patient_id\"] = psg_file.split(\" \")[0]\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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feats.to_parquet(\"../features/mass_SS3_features_ALL__ext.parquet\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62767, 366)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats = pd.read_parquet(\"../features/mass_SS3_features_ALL__ext.parquet\")\n",
    "df_feats.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sleep stage 2    29802\n",
       "Sleep stage R    10581\n",
       "Sleep stage 3     7653\n",
       "Sleep stage W     6442\n",
       "Sleep stage 1     4839\n",
       "Sleep stage ?     3448\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats[\"label\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 62767 entries, 2000-01-01 21:28:00.010000 to 2000-01-02 07:14:30.010000\n",
      "Columns: 366 entries, EEG C4-LER - EEG O1-LER__TotalAbsPow__w=1m30s_s=30s to patient_id\n",
      "dtypes: float32(363), object(3)\n",
      "memory usage: 88.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_feats.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62767, 396)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bands = [\"alpha\", \"beta\", \"sdelta\", \"fdelta\", \"sigma\", \"theta\"]\n",
    "for eeg_sig in eeg_cols:\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": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "131 131 131\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(62767, 1051)"
      ]
     },
     "execution_count": 34,
     "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(\n",
    "        sub_df[feats_30s].shift(2).add_suffix(\"_shift=1m\"),\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "    sub_df = sub_df.merge(\n",
    "        sub_df[feats_30s].shift(1).add_suffix(\"_shift=30s\"),\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "    sub_df = sub_df.merge(\n",
    "        sub_df[feats_30s].shift(-1).add_suffix(\"_shift=-30s\"),\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "    sub_df = sub_df.merge(\n",
    "        sub_df[feats_30s].shift(-2).add_suffix(\"_shift=-1m\"),\n",
    "        left_index=True,\n",
    "        right_index=True,\n",
    "    )\n",
    "\n",
    "    # sub_df = sub_df.merge(sub_df[feats_2m].shift(8).add_suffix(\"_shift=4m\"), left_index=True, right_index=True)\n",
    "    # sub_df = sub_df.merge(sub_df[feats_1m].shift(4).add_suffix(\"_shift=2m\"), 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",
    "    # 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",
    "    # sub_df = sub_df.merge(sub_df[feats_1m].shift(-4).add_suffix(\"_shift=-2m\"), left_index=True, right_index=True)\n",
    "    # sub_df = sub_df.merge(sub_df[feats_2m].shift(-8).add_suffix(\"_shift=-4m\"), 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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 62767 entries, 2000-01-01 21:28:00.010000 to 2000-01-02 07:14:30.010000\n",
      "Columns: 1051 entries, EEG C4-LER - EEG O1-LER__TotalAbsPow__w=1m_s=30s to EEG C4-LER - EEG O1-LER__delta/theta__w=30s_s=30s_shift=-1m\n",
      "dtypes: float32(1048), object(3)\n",
      "memory usage: 252.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_feats.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Postprocess labels\n",
    "df_feats.loc[df_feats[\"label\"] == \"Sleep stage 4\", \"label\"] = \"Sleep stage 3\"\n",
    "df_feats.loc[df_feats[\"label\"] == \"Movement time\", \"label\"] = \"Sleep stage W\"\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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59317, 1051)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feats.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look into nan features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "265"
      ]
     },
     "execution_count": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EEG C4-LER - EEG O1-LER__delta/theta__w=1m_s=30s                                     15\n",
       "EEG F4-LER - EOG Left Horiz__TotalAbsPow__w=1m_s=30s                                 15\n",
       "EEG F4-LER - EOG Left Horiz__beta__w=1m_s=30s                                        15\n",
       "EEG F4-LER - EOG Left Horiz__binned_entropy_{'max_bins': 10}__w=1m_s=30s             15\n",
       "EEG F4-LER - EOG Left Horiz__binned_entropy_{'max_bins': 30}__w=1m_s=30s             15\n",
       "                                                                                     ..\n",
       "EOG Left Horiz_EOG Right Horiz_[AVG]__binned_entropy_{'max_bins': 10}__w=1m_s=30s    15\n",
       "EOG Left Horiz_EOG Right Horiz_[AVG]__binned_entropy_{'max_bins': 30}__w=1m_s=30s    15\n",
       "EOG Left Horiz_EOG Right Horiz_[AVG]__binned_entropy_{'max_bins': 5}__w=1m_s=30s     15\n",
       "EOG Left Horiz_EOG Right Horiz_[AVG]__binned_entropy_{'max_bins': 60}__w=1m_s=30s    15\n",
       "EEG C4-LER - EEG O1-LER__TotalAbsPow__w=1m_s=30s                                     15\n",
       "Length: 131, dtype: int64"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00025287860141274843"
      ]
     },
     "execution_count": 41,
     "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": [
    "=> 0.025% 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 0.025% 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": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 42,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sleep stage 2    29802\n",
       "Sleep stage R    10581\n",
       "Sleep stage 3     7653\n",
       "Sleep stage W     6442\n",
       "Sleep stage 1     4839\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "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": 45,
   "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": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    balanced_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": 47,
   "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": 48,
   "metadata": {
    "tags": []
   },
   "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": {
    "tags": []
   },
   "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.psg_file)\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": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20-FOLD: TRAIN\n",
      "  MACRO F1:           0.83\n",
      "  Balanced accuracy:  0.8393\n",
      "  Accuracy:           0.8712\n",
      "  Log loss:           0.5598\n"
     ]
    }
   ],
   "source": [
    "print(\"20-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": [
      "20-FOLD: TEST\n",
      "  MACRO F1:           0.8047\n",
      "  Balanced accuracy:  0.8143\n",
      "  Accuracy:           0.8525\n",
      "  Log loss:           0.706\n"
     ]
    }
   ],
   "source": [
    "print(\"20-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))\n"
   ]
  },
  {
   "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: 0.8066796509778678\n",
      "ACC: 0.8527066439637878\n",
      "KAPPA: 0.7857701838079729\n",
      "LOG LOSS: 0.7064492700020433\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       0.55      0.56      0.56      4745\n",
      "Sleep stage 2       0.88      0.91      0.89     28683\n",
      "Sleep stage 3       0.89      0.80      0.84      8481\n",
      "Sleep stage R       0.87      0.86      0.87     10762\n",
      "Sleep stage W       0.89      0.86      0.87      6646\n",
      "\n",
      "     accuracy                           0.85     59317\n",
      "    macro avg       0.82      0.80      0.81     59317\n",
      " weighted avg       0.85      0.85      0.85     59317\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, y_trues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 74,
   "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.psg_file)\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=20,\n",
    "    train_sizes=[0.05] + list(np.linspace(0.1,1,10))\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Grouped X-fold results for all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Total of 31 folds\n",
    "gkfold = StratifiedGroupKFold(n_splits=31)\n",
    "cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.psg_file)\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": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31-FOLD: TRAIN\n",
      "  MACRO F1:           0.8289\n",
      "  Balanced accuracy:  0.839\n",
      "  Accuracy:           0.8701\n",
      "  Log loss:           0.5431\n"
     ]
    }
   ],
   "source": [
    "print(\"31-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": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31-FOLD: TEST\n",
      "  MACRO F1:           0.7983\n",
      "  Balanced accuracy:  0.8111\n",
      "  Accuracy:           0.8522\n",
      "  Log loss:           0.6777\n"
     ]
    }
   ],
   "source": [
    "print(\"31-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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "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": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.8050360109673728\n",
      "ACC: 0.8522177453343898\n",
      "KAPPA: 0.7852814363896486\n",
      "LOG LOSS: 0.6779914875181943\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": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Sleep stage 1       0.55      0.55      0.55      4807\n",
      "Sleep stage 2       0.88      0.91      0.90     28564\n",
      "Sleep stage 3       0.88      0.80      0.84      8421\n",
      "Sleep stage R       0.88      0.86      0.87     10870\n",
      "Sleep stage W       0.88      0.86      0.87      6655\n",
      "\n",
      "     accuracy                           0.85     59317\n",
      "    macro avg       0.81      0.80      0.81     59317\n",
      " weighted avg       0.85      0.85      0.85     59317\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(np.hstack(preds), np.hstack(y_trues)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Normalized confusion matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 66,
     "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": 50,
   "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.psg_file)\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=20,\n",
    "    train_sizes=[0.05] + list(np.linspace(0.1,1,10))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## The simple, catboost pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": {
    "tags": []
   },
   "source": [
    "### Grouped 10-fold results for all the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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.psg_file)\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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20-FOLD: TRAIN\n",
      "  MACRO F1:           0.9063\n",
      "  Balanced accuracy:  0.8938\n",
      "  Accuracy:           0.9305\n",
      "  Log loss:           0.1998\n"
     ]
    }
   ],
   "source": [
    "print(\"20-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": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20-FOLD: TEST\n",
      "  MACRO F1:           0.815\n",
      "  Balanced accuracy:  0.8069\n",
      "  Accuracy:           0.8669\n",
      "  Log loss:           0.3442\n"
     ]
    }
   ],
   "source": [
    "print(\"20-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))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1: 0.8172823869281292\n",
      "ACC: 0.8671544413911695\n",
      "KAPPA: 0.8026215468560806\n",
      "LOG LOSS: 0.34376646960860113\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": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               precision    recall  f1-score   support\n",
      "\n",
      "Sleep stage 1       0.51      0.62      0.56      3930\n",
      "Sleep stage 2       0.92      0.89      0.91     30902\n",
      "Sleep stage 3       0.84      0.88      0.86      7324\n",
      "Sleep stage R       0.87      0.87      0.87     10586\n",
      "Sleep stage W       0.91      0.89      0.90      6575\n",
      "\n",
      "     accuracy                           0.87     59317\n",
      "    macro avg       0.81      0.83      0.82     59317\n",
      " weighted avg       0.87      0.87      0.87     59317\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(preds, y_trues))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Normalized confusion matrix'}, xlabel='Predicted label', ylabel='True label'>"
      ]
     },
     "execution_count": 53,
     "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": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cv = gkfold.split(df_feats, df_feats[\"label\"], groups=df_feats.psg_file)\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=20,\n",
    "#     train_sizes=[0.05] + list(np.linspace(0.1,1,10))\n",
    "# )\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
}