updrsTapping_own_group_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UPDRS-Tapping Assessment - Execution Notebook\n",
    "\n",
    "This notebooks investigates optimal hand- and fingertapping algorithms as part of the \n",
    "ReTune-Dyskinesia project.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Loading packages and functions, defining paths\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Python and external packages\n",
    "import os\n",
    "import sys\n",
    "import importlib\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn as sk\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "# import matplotlib.gridspec as gridspec\n",
    "\n",
    "import seaborn as sns\n",
    "from scipy import signal\n",
    "from scipy import stats\n",
    "from array import array\n",
    "import datetime as dt\n",
    "import h5py\n",
    "from dataclasses import  dataclass, field\n",
    "from itertools import compress\n",
    "from typing import Any"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python sys 3.9.12 (main, Jun  1 2022, 06:36:29) \n",
      "[Clang 12.0.0 ]\n",
      "pandas 1.4.3\n",
      "numpy 1.21.5\n",
      "sci-py 1.7.3\n",
      "sci-kit learn 1.1.1\n"
     ]
    }
   ],
   "source": [
    "# check some package versions for documentation and reproducability\n",
    "print('Python sys', sys.version)\n",
    "print('pandas', pd.__version__)\n",
    "print('numpy', np.__version__)\n",
    "# print('mne_bids', mne_bids.__version__)\n",
    "# print('mne', mne.__version__)\n",
    "print('sci-py', scipy.__version__)\n",
    "print('sci-kit learn', sk.__version__)\n",
    "\n",
    "\n",
    "## developed with:\n",
    "# Python sys 3.9.7 (default, Sep 16 2021, 08:50:36) \n",
    "# [Clang 10.0.0 ]\n",
    "# pandas 1.3.4\n",
    "# numpy 1.20.3\n",
    "# mne_bids 0.9\n",
    "# mne 0.24.1\n",
    "# sci-py 1.7.1\n",
    "# sci-kit learn 1.0.1\n",
    "\n",
    "## Currently (own env) since 31.08.22\n",
    "# Python sys 3.9.12 (main, Jun  1 2022, 06:36:29) \n",
    "# [Clang 12.0.0 ]\n",
    "# pandas 1.4.3\n",
    "# numpy 1.21.5\n",
    "# sci-py 1.7.3\n",
    "# sci-kit learn 1.1.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import run_finding_10sec_blocks as run_find_blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "# own data preprocessing functions\n",
    "import tap_load_data.updrsTapping_import as tap_import\n",
    "import tap_load_data.tapping_preprocess as tap_preproc\n",
    "import tap_load_data.tapping_find_blocks as find_blocks\n",
    "import tap_load_data.tapping_impact_finder as tap_impact\n",
    "import tap_load_data.tapping_time_detect as tap_times\n",
    "\n",
    "import tapping_run as tap_run\n",
    "\n",
    "# ft extraction\n",
    "import tap_extract_fts.tapping_featureset as tap_fts_set\n",
    "import tap_extract_fts.tapping_extract_features as tap_ft_extr\n",
    "import tap_extract_fts.tapping_feat_calc as ft_calc\n",
    "\n",
    "# own data exploration functions\n",
    "import tap_extract_fts.tapping_feat_boxplots as fts_boxplot\n",
    "\n",
    "# own helper functions\n",
    "from retap_utils import utils_dataManagement, tmsi_poly5reader, utils_preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import run_finding_10sec_blocks as run_blocks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Varvara's ACC Data - Minute-runs containing multiple 10 second-blocks\n",
    "\n",
    "\n",
    "First data is imported, then 10 sec active tapping blocks are detected, then features are extracted from these blocks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1a. Data Import, Extracting and Checking active Tapping-Blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "013\n",
      "014\n",
      "007\n",
      "015\n",
      "Sub-scores for full stim-Ampltiude range extracted\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(tap_import)\n",
    "importlib.reload(find_blocks)\n",
    "\n",
    "# define folder-directory of tap-data\n",
    "\n",
    "taptask = 'taps_rampStim_3x10s'\n",
    "verbose=False\n",
    "toPlot=True\n",
    "subs_excl = []  #['014', '015']\n",
    "\n",
    "data_dir = os.path.join(proj_dir, 'data', taptask)\n",
    "fig_dir = os.path.join(proj_dir, 'figures', taptask)\n",
    "\n",
    "sub_acc = {}\n",
    "run_IDs, run_ID_files = tap_import.find_run_IDs(data_dir)\n",
    "if verbose: print(f'Included subjects/sides to import: {run_IDs}')\n",
    "\n",
    "subscores = tap_import.tap3x10_updrs_scores(\n",
    "    file_dir=os.path.join(data_dir, 'updrs_scores'),\n",
    "    file_name='3x10s_updrs_subscores_all.csv',\n",
    "    incl_stimRange=True,\n",
    ")\n",
    "# Manual correction of error in CSV\n",
    "del(subscores['007_R_Off']['25mA'])\n",
    "\n",
    "sub_acc, block_acc, block_ind = {}, {}, {}\n",
    "for runID in run_IDs:\n",
    "        \n",
    "        key = runID[3:]\n",
    "        if verbose: print('\\nSTART', key)\n",
    "        if key[:3] in subs_excl:\n",
    "                if verbose: print(f'Skip sub {key}')\n",
    "                continue\n",
    "\n",
    "        sub_acc[key] = tap_import.accData(\n",
    "                OnFile=run_ID_files[runID]['on'],\n",
    "                OffFile=run_ID_files[runID]['off'],\n",
    "                orig_fs=250, wanted_fs=250,\n",
    "                to_resample=False,\n",
    "                to_check_polarity=True,\n",
    "                to_check_magnOrder=True,\n",
    "                verbose=False\n",
    "        )\n",
    "\n",
    "        if verbose: print('Acc imported and preprocessed')\n",
    "\n",
    "        for med in ['Off', 'On']:\n",
    "\n",
    "                temp_acc, temp_ind = find_blocks.find_active_blocks(\n",
    "                        acc_arr=getattr(sub_acc[key], med),\n",
    "                        fs=250, verbose=False, to_plot=False,\n",
    "                        figsave_dir=fig_dir,\n",
    "                        figsave_name=f'{key}_{med}_blocks_detected'\n",
    "                )\n",
    "\n",
    "                block_acc[f'{key}_{med}'] = temp_acc\n",
    "                block_ind[f'{key}_{med}'] = temp_ind\n",
    "                \n",
    "                if verbose: print(\n",
    "                        f'\\tFor {key}, Med-{med} '\n",
    "                        f'block-arrays extracted'\n",
    "                )\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manual block-index controle - 3 x 10 sec (only StimOff - stimOn)\n",
    "\n",
    "<b> If already done -> import json file with Info!! </b>\n",
    "\n",
    "Compare the detected blocks in the created figure [SUB_SIDE_MED_blocks_detected] with the blocks which should be rated and included. Adjust the block-number selection if necessary, resulting in corresponding selected block-numbers and block_scores "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### Standard tap-block selection\n",
    "# sel_blocks = {}\n",
    "# for key in block_acc:\n",
    "#     sel_blocks[key] = {'stimOff': [0, 1, 2], 'stimOn': [-9, -8, -7]}\n",
    "    \n",
    "# ### Manual adjustment of tap-block selection based on VISUALISATIONS\n",
    "# sel_blocks['007_L_Off']['stimOff'] = [1, 2, 3]\n",
    "# sel_blocks['007_L_Off']['stimOn'] = [-10, -9, -8]\n",
    "# sel_blocks['013_L_On']['stimOn'] = [-10, -9, -8]\n",
    "# sel_blocks['014_L_Off']['stimOn'] = [-10, -9, -8]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "### To save tap-block selection to JSON\n",
    "# # f = os.path.join(data_dir, 'prep_jsons', 'manual_block_selection_stimOffOnOnly.json')\n",
    "\n",
    "# with open (f, 'w') as jsonfile:\n",
    "\n",
    "#     json.dump(sel_blocks, jsonfile, indent=4)\n",
    "\n",
    "#     jsonfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Manual block-index controle - 3 x 10 sec (full stim-Amplitude range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### Standard tap-block selection\n",
    "\n",
    "# std_indx = {\n",
    "#     '0mA': [0, 1, 2],\n",
    "#     '05mA': [3, 4, 5],\n",
    "#     '1mA': [6, 7, 8],\n",
    "#     '15mA': [9, 10, 11],\n",
    "#     '2mA': [12, 13, 14],\n",
    "#     '25mA': [15, 16, 17],\n",
    "#     '3mA': [18, 19, 20],\n",
    "# }\n",
    "\n",
    "# sel_blocks_amps = {}\n",
    "\n",
    "# for key in subscores:\n",
    "\n",
    "#     sel_blocks_amps[key] = {}\n",
    "\n",
    "#     for amp in subscores[key].keys():\n",
    "\n",
    "#         sel_blocks_amps[key][amp] = std_indx[amp]\n",
    "    \n",
    "# ### Manual adjustment of tap-block selection based on VISUALISATIONS\n",
    "# sel_blocks_amps['007_L_Off'] = {\n",
    "#     '0mA': [1, 2, 3],\n",
    "#     '05mA': [4, 5, 6],\n",
    "#     '1mA': [7, 8, 9],\n",
    "#     '15mA': [11, 12, 13],\n",
    "#     '2mA': [14, 15, 16],\n",
    "#     '25mA': [18, 19, 20],\n",
    "# }\n",
    "\n",
    "# sel_blocks_amps['013_L_Off'] = {\n",
    "#     '0mA': [0, 1, 2],\n",
    "#     '05mA': [3, 4, 5],\n",
    "#     '1mA': [7, 8, 9],\n",
    "#     '15mA': [10, 11, 12],\n",
    "#     '2mA': [13, 14, 15],\n",
    "#     '25mA': [16, 17, 18],\n",
    "# }\n",
    "\n",
    "# sel_blocks_amps['013_L_On'] = {\n",
    "#     '0mA': [0, 1, 2],\n",
    "#     '05mA': [3, 4, 5],\n",
    "#     '1mA': [6, 7, 8],\n",
    "#     '15mA': [9, 10, 11],\n",
    "#     '2mA': [12, 13, 14],\n",
    "#     '25mA': [16, 17, 18],\n",
    "# }\n",
    "\n",
    "# sel_blocks_amps['014_L_Off'] = {\n",
    "#     '0mA': [0, 1, 2],\n",
    "#     '05mA': [6, 7, 8],\n",
    "#     '1mA': [9, 10, 11],\n",
    "#     '15mA': [12, 13, 14],\n",
    "#     '2mA': [15, 16, 17],\n",
    "#     '25mA': [18, 19, 20],\n",
    "#     '3mA': [21, 22, 23]\n",
    "# }\n",
    "\n",
    "# # del(sel_blocks_amps['007_R_Off']['25mA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # f = os.path.join(data_dir, 'prep_jsons', 'manual_block_selection_stimAmpRange.json')\n",
    "\n",
    "# with open (f, 'w') as jsonfile:\n",
    "\n",
    "#     json.dump(sel_blocks_amps, jsonfile, indent=4)\n",
    "\n",
    "#     jsonfile.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1b. Feature Extraction per 10-sec block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### VISUALISE FEATURES FOR DEVELOPMENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "013_L_Off\n",
      "Small slope changes 013_L_Off: 2468\n",
      "\t slopeChanges * nTaps = 29616\n",
      "013_R_Off\n",
      "Small slope changes 013_R_Off: 2844\n",
      "\t slopeChanges * nTaps = 93852\n",
      "007_L_Off\n",
      "Small slope changes 007_L_Off: 2347\n",
      "\t slopeChanges * nTaps = 35205\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x1152 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 1, figsize=(24, 16))\n",
    "\n",
    "for i, sub in enumerate([\n",
    "    '013_L_Off',\n",
    "    '013_R_Off',\n",
    "    '007_L_Off'\n",
    "]):\n",
    "\n",
    "    print(sub)\n",
    "\n",
    "    tempacc = block_acc[sub][-8]\n",
    "    temptap, _, _ = tap_run.run_updrs_tap_finder(\n",
    "        acc_arr=tempacc, fs=250, already_preprocd=True,\n",
    "    )\n",
    "\n",
    "    axes[i].plot(tempacc.T)\n",
    "    axes[i].scatter(\n",
    "        [t[0] for t in temptap],\n",
    "        [1] * len(temptap),\n",
    "        color='r',\n",
    "    )\n",
    "    axes[i].scatter(\n",
    "        [t[-1] for t in temptap],\n",
    "        [1] * len(temptap),\n",
    "        color='k',\n",
    "    )\n",
    "    axes[i].set_xlim(500, 2000)\n",
    "\n",
    "    slopeChanges = smallSlopeChanges(tempacc)\n",
    "    tempscore = getattr(FEATS[f'{sub}_S1_b2'], 'updrsSubScore')\n",
    "    axes[i].set_title(\n",
    "        f'{sub} UPDRS sub {tempscore}',\n",
    "        fontsize=18,\n",
    "    )\n",
    "    \n",
    "    print(f'Small slope changes {sub}: {slopeChanges}\\n'\n",
    "            f'\\t slopeChanges * nTaps = {slopeChanges * len(temptap)}')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EXTRACT FEATURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['007_L_Off', '007_L_On', '007_R_Off', '007_R_On', '014_L_Off', '014_L_On', '014_R_Off', '014_R_On', '015_L_Off', '015_L_On', '015_R_Off', '015_R_On', '013_L_Off', '013_L_On', '013_R_Off', '013_R_On'])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check which sessions present\n",
    "\n",
    "block_acc.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start 007_L_Off\n",
      "No taps detected for 007_L_Off 2mA_block 1\n",
      "start 007_L_On\n",
      "start 007_R_Off\n",
      "No taps detected for 007_R_Off 05mA_block 1\n",
      "No taps detected for 007_R_Off 05mA_block 3\n",
      "No taps detected for 007_R_Off 1mA_block 1\n",
      "No taps detected for 007_R_Off 1mA_block 2\n",
      "No taps detected for 007_R_Off 1mA_block 3\n",
      "No taps detected for 007_R_Off 15mA_block 1\n",
      "No taps detected for 007_R_Off 15mA_block 3\n",
      "No taps detected for 007_R_Off 2mA_block 1\n",
      "No taps detected for 007_R_Off 2mA_block 2\n",
      "No taps detected for 007_R_Off 2mA_block 3\n",
      "start 007_R_On\n",
      "start 014_L_Off\n",
      "start 014_L_On\n",
      "start 014_R_Off\n",
      "start 014_R_On\n",
      "start 015_L_Off\n",
      "start 015_L_On\n",
      "start 015_R_Off\n",
      "start 015_R_On\n",
      "start 013_L_Off\n",
      "start 013_L_On\n",
      "start 013_R_Off\n",
      "start 013_R_On\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(tap_ft_extr)\n",
    "importlib.reload(tap_ft_extr)\n",
    "importlib.reload(tap_fts_set)\n",
    "\n",
    "\n",
    "# Read in priorly Block-Selection\n",
    "block_indx_json = os.path.join(\n",
    "    data_dir,\n",
    "    'prep_jsons',\n",
    "    'manual_block_selection_stimAmpRange.json'\n",
    ")\n",
    " \n",
    "with open(block_indx_json) as f:\n",
    "    block_indx = json.load(f, )\n",
    "\n",
    "\n",
    "FEATS = {}\n",
    "fs=250\n",
    "for run in block_acc.keys():\n",
    "    print(f'start {run}')\n",
    "\n",
    "    # to select subjects/runs to extract\n",
    "    splits = run.split('_')\n",
    "    # if splits[0] != '007': continue\n",
    "    # if splits[2] != 'Off': continue\n",
    "\n",
    "    for S, stim in enumerate(subscores[run].keys()):\n",
    "\n",
    "        for b, block in enumerate([1, 2, 3]):\n",
    "\n",
    "            tapscore = subscores[run][stim][b]\n",
    "            block_n = block_indx[run][stim][b]\n",
    "            tap_acc = block_acc[run][block_n]\n",
    "\n",
    "            tap_i, imp_i, acc_temp = tap_run.run_updrs_tap_finder(\n",
    "                acc_arr=tap_acc, fs=fs, already_preprocd=True,\n",
    "            )\n",
    "            \n",
    "            if len(tap_i) < 1:\n",
    "                # print(f'No taps detected for {run}_Stim{S}_block {block}')  # for OnOff\n",
    "                print(f'No taps detected for {run} {stim}_block {block}')  # for Range\n",
    "                # continue\n",
    "\n",
    "            FEATS[f'{run}_{stim}_b{block}'] = tap_ft_extr.tapFeatures(\n",
    "                triax_arr=acc_temp,\n",
    "                fs=fs,\n",
    "                impacts=imp_i,\n",
    "                tapDict=tap_i,  # result of continTap\n",
    "                updrsSubScore=tapscore,\n",
    "            )         \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1c. Visualise Features as Group with Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "artificial 0 added\n",
      "artificial 0 added\n",
      "artificial 0 added\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n",
      "skipped 007_L_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b1, no taps available\n",
      "skipped 007_R_Off_05mA_b3, no taps available\n",
      "skipped 007_R_Off_1mA_b1, no taps available\n",
      "skipped 007_R_Off_1mA_b2, no taps available\n",
      "skipped 007_R_Off_1mA_b3, no taps available\n",
      "skipped 007_R_Off_15mA_b1, no taps available\n",
      "skipped 007_R_Off_15mA_b3, no taps available\n",
      "skipped 007_R_Off_2mA_b1, no taps available\n",
      "skipped 007_R_Off_2mA_b2, no taps available\n",
      "skipped 007_R_Off_2mA_b3, no taps available\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(fts_boxplot)\n",
    "importlib.reload(ft_calc)\n",
    "\n",
    "### PM, TODO: include runs without detected taps in boxplots\n",
    "# or indicate the number of them incl updrs subscore\n",
    "\n",
    "ftAx = 'svm'\n",
    "fts_include = [\n",
    "    'freq',\n",
    "    'nTaps',\n",
    "    'ampDecrement',\n",
    "    'dirChange_run',\n",
    "    'dirChange_taps',\n",
    "    f'runRMS_{ftAx}',\n",
    "    f'tapRMS_{ftAx}',\n",
    "    f'impactRMS_{ftAx}',\n",
    "    f'upVelo_{ftAx}',\n",
    "]\n",
    "merge_meth = 'coefVar'\n",
    "# for merge_meth in ['mean', 'sum', 'stddev', 'trend_slope']:\n",
    "tempScoreDict = fts_boxplot.combineFeatsPerScore(\n",
    "    ftDict=FEATS, fts_include=fts_include,\n",
    "    merge_method=merge_meth,\n",
    ")\n",
    "fts_boxplot.plot_boxplot_feats_per_subscore(\n",
    "    fts_include=fts_include,\n",
    "    featDict=tempScoreDict,\n",
    "    merge_method=merge_meth,\n",
    "    plot_title='Stim-Ramp 3x10sec Tapping',\n",
    "    figsave_name=f'stimRamp_3x10_4pts_{ftAx}Fts_{merge_meth}_v2',\n",
    "    figsave_dir=os.path.join(\n",
    "        fig_dir, 'fts_boxplots', 'results_fullRange'\n",
    "    ),\n",
    "    show=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1d. Visualise single Features over Tap-course"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/7d/4ptht2m910d1y872jrgp9cq40000gp/T/ipykernel_32352/1711045685.py:63: RuntimeWarning: Mean of empty slice\n",
      "  meanFtPlot = np.nanmean(meanFtArr, axis=0)\n"
     ]
    }
   ],
   "source": [
    "ft = 'tapRMS_svm'\n",
    "\n",
    "sub = '007'\n",
    "side='L'\n",
    "\n",
    "mA_range = ['0', '05', '1', '15', '2', '25']\n",
    "\n",
    "colors = {'Off': 'blue', 'On': 'green'}\n",
    "fs=14\n",
    "\n",
    "for ft in [\n",
    "    'tapRMS_svm',\n",
    "    # 'upVelo_svm',\n",
    "    # 'impactRMS_svm',\n",
    "    # 'dirChange_taps',\n",
    "    # 'tap_durations',\n",
    "]:\n",
    "\n",
    "    plt.figure(figsize=(12, 6))\n",
    "\n",
    "    # get mean feature-course over 3 tapping-blocks of 10 sec\n",
    "    for med in ['Off', 'On']:\n",
    "\n",
    "        for a, mA in enumerate(mA_range):\n",
    "\n",
    "            try:\n",
    "                meanFt = [\n",
    "                    getattr(\n",
    "                        FEATS[f'{sub}_{side}_{med}_{mA}mA_b{bN}'],\n",
    "                        ft\n",
    "                    ) for bN in [1,2,3]\n",
    "                ]\n",
    "\n",
    "            except KeyError:  # tap-run not existing\n",
    "                continue  # dont calculate and plot line\n",
    "\n",
    "            except AttributeError:  # no taps detected -> no features extracted\n",
    "                meanFt = []\n",
    "                emptyCount = ' '\n",
    "\n",
    "                for bN in [1,2,3]:\n",
    "                    key = f'{sub}_{side}_{med}_{mA}mA_b{bN}'\n",
    "                    \n",
    "                    if ft in vars(FEATS[key]).keys():\n",
    "                        meanFt.append(getattr(FEATS[key], ft))\n",
    "                    \n",
    "                    else:\n",
    "                        emptyCount += '*'\n",
    "                \n",
    "                if meanFt == []:  # all runs without taps/features\n",
    "                    meanFt = [[0]]\n",
    "\n",
    "            # transform ft-lists per run into mean array\n",
    "            maxLen = max(\n",
    "                [len(meanFt[n]) for n in range(len(meanFt))]\n",
    "            )\n",
    "            meanFtArr = np.array(\n",
    "                [[np.nan] * maxLen] * len(meanFt))\n",
    "            \n",
    "            for n, l in enumerate(meanFt):\n",
    "                meanFtArr[n, :len(l)] = l\n",
    "            \n",
    "            meanFtPlot = np.nanmean(meanFtArr, axis=0)\n",
    "\n",
    "            # average updrsSubScore over 3 runs\n",
    "            meanTapScore = np.round(np.mean([\n",
    "                getattr(\n",
    "                    FEATS[f'{sub}_{side}_{med}_{mA}mA_b{bN}'],\n",
    "                    'updrsSubScore'\n",
    "                ) for bN in [1,2,3]\n",
    "            ]), 1)\n",
    "\n",
    "            tapLabel = f'Med{med} {mA} mA  (updrs {meanTapScore})'\n",
    "            \n",
    "            try:\n",
    "                tapLabel += emptyCount\n",
    "                del(emptyCount)\n",
    "\n",
    "            except:\n",
    "                tapLabel = tapLabel\n",
    "\n",
    "            plt.plot(\n",
    "                meanFtPlot, c=colors[med],\n",
    "                alpha=.1 + (.15 * a),\n",
    "                label=tapLabel,\n",
    "            )\n",
    "            \n",
    "\n",
    "            \n",
    "\n",
    "    plt.ylabel(f'{ft} (a.u.)', size=fs,)\n",
    "    plt.xlabel('Taps over time (# of tap)', size=fs,)\n",
    "\n",
    "    plt.tick_params(axis='both', size=fs, labelsize=fs)\n",
    "\n",
    "    plt.legend(\n",
    "        frameon=False, ncol=1,\n",
    "        bbox_to_anchor=(1, .9), loc='upper left',\n",
    "        fontsize=fs,\n",
    "    )\n",
    "\n",
    "    plt.title(\n",
    "        f'Feature course of \"{ft}\"    -     '\n",
    "        f'Sub-{sub}, {side}-side'\n",
    "        f'\\n(mean over 3 10-sec blocks in same condition)',\n",
    "        fontsize=fs+6,\n",
    "    )\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(\n",
    "        os.path.join(\n",
    "            fig_dir,\n",
    "            'ft_course_tapping',\n",
    "            f'TEST____{sub}_{side}_ftCourse_{ft}'\n",
    "        ),\n",
    "        dpi=150, facecolor='w',\n",
    "    )\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Johannes ACC Data - Individual 30 second-runs, only tapping\n",
    "\n",
    "\n",
    "Compared to workflow for \"3 x 10s tapping blocks\", here there is no detection of active tapping blocks. Full time series of 30 seconds are used for single-tap-detection and feature extraction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2a. Feature Extraction and Visualisation of sub <b>with</b> UPDRS-subscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jeroenhabets/Research/CHARITE/projects/tapping/data/JB_30s/tapsMat.mat imported with h5py\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'mat' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_own_group_exploration.ipynb Cell 30\u001b[0m in \u001b[0;36m<cell line: 38>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_own_group_exploration.ipynb#X46sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m scores545 \u001b[39m=\u001b[39m arr_scores218[\u001b[39m-\u001b[39m\u001b[39m28\u001b[39m:,:]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_own_group_exploration.ipynb#X46sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m fs\u001b[39m=\u001b[39m\u001b[39m250\u001b[39m\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_own_group_exploration.ipynb#X46sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m acc545 \u001b[39m=\u001b[39m mat[:, :, \u001b[39m-\u001b[39m\u001b[39m28\u001b[39m:]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_own_group_exploration.ipynb#X46sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m fts545 \u001b[39m=\u001b[39m {}\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_own_group_exploration.ipynb#X46sZmlsZQ%3D%3D?line=40'>41</a>\u001b[0m \u001b[39m# ### check tap detection\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'mat' is not defined"
     ]
    }
   ],
   "source": [
    "importlib.reload(tap_run)\n",
    "importlib.reload(tap_times)\n",
    "importlib.reload(tap_ft_extr)\n",
    "importlib.reload(tap_fts_set)\n",
    "importlib.reload(tap_import)\n",
    "importlib.reload(tap_impact)\n",
    "importlib.reload(tap_preproc)\n",
    "\n",
    "# define directory and filename\n",
    "jb_dir = '/Users/jeroenhabets/Research/CHARITE/projects/tapping/data/JB_30s'\n",
    "jb_fig_dir = '/Users/jeroenhabets/Research/CHARITE/projects/tapping/figures/JB_30s'\n",
    "\n",
    "mat_dat_f = 'tapsMat.mat'\n",
    "csv_meta_f = 'tapsMeta.csv'\n",
    "\n",
    "# Import acc-traces\n",
    "mat_dat = tap_import.matlab_import(os.path.join(\n",
    "    jb_dir, mat_dat_f))\n",
    "fname = mat_dat.filename.split(sep='/')[-1]\n",
    "mat_dat = mat_dat[fname[:-4]]\n",
    "\n",
    "# import meta-data\n",
    "meta = tap_import.import_meta_aDBS(\n",
    "    os.path.join(jb_dir, csv_meta_f)\n",
    ")\n",
    "\n",
    "# Import subscores\n",
    "scores218 = np.genfromtxt(\n",
    "    os.path.join(jb_dir, 'updrs_218.txt'),\n",
    "    delimiter=',', dtype=str,\n",
    ")\n",
    "df_scores218 = pd.DataFrame(data=scores218[1:, :], columns=scores218[0, :])\n",
    "arr_scores218 = scores218[1:, 2:]\n",
    "scores545 = arr_scores218[-28:,:]\n",
    "\n",
    "fs=250\n",
    "\n",
    "acc545 = mat_dat[:, :, -28:]  # check mat_dat here\n",
    "fts545 = {}\n",
    "\n",
    "# ### check tap detection\n",
    "tap545 = {}\n",
    "imp545 = {}\n",
    "accproc545 = {}\n",
    "\n",
    "updrs_times = {'t1': (2 * fs, 12 * fs), 't2': (20 * fs, 30 * fs)}\n",
    "\n",
    "accDat = acc545\n",
    "\n",
    "for run_id in np.arange(acc545.shape[2]):\n",
    "    \n",
    "    raw_triax = acc545[:, :, run_id].T\n",
    "\n",
    "    for t_i, t in enumerate(updrs_times.keys()):\n",
    "\n",
    "        # print(f'\\nStart run_id {run_id}, timepoint {t_i}')\n",
    "\n",
    "        sig = raw_triax[\n",
    "            :, updrs_times[t][0]:updrs_times[t][1]\n",
    "        ]\n",
    "        subscore = scores545[run_id, t_i]\n",
    "\n",
    "        tap_inds, imp_inds, proc_triax = tap_run.run_updrs_tap_finder(\n",
    "            acc_arr=sig, fs=fs, already_preprocd=False, #orig_fs=4000,\n",
    "        )\n",
    "        # ## check tap detect\n",
    "        tap545[f'run{run_id}_t{t_i}'] = tap_inds\n",
    "        imp545[f'run{run_id}_t{t_i}'] = imp_inds\n",
    "        accproc545[f'run{run_id}_t{t_i}'] = proc_triax\n",
    "\n",
    "        fts545[f'run{run_id}_t{t_i}'] = tap_ft_extr.tapFeatures(\n",
    "            triax_arr=proc_triax,\n",
    "            fs=fs,\n",
    "            impacts=imp_inds,\n",
    "            tapDict=tap_inds,\n",
    "            updrsSubScore=subscore,\n",
    "        )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = '545'\n",
    "\n",
    "ax_in = 'svm'  # 'ax' or 'svm'\n",
    "\n",
    "fts_include = [\n",
    "    'freq',\n",
    "    'nTaps',\n",
    "    f'tapRMS_{ax_in}',\n",
    "    f'upVelo_{ax_in}',\n",
    "    f'impactRMS_{ax_in}',\n",
    "    'dirChange_taps',\n",
    "]\n",
    "merge_meth = 'coefVar'\n",
    "\n",
    "tempScoreDict = fts_boxplot.combineFeatsPerScore(\n",
    "    ftDict=fts545, fts_include=fts_include,\n",
    "    merge_method=merge_meth,\n",
    ")\n",
    "fts_boxplot.plot_boxplot_feats_per_subscore(\n",
    "    fts_include=fts_include,\n",
    "    featDict=tempScoreDict,\n",
    "    merge_method=merge_meth,\n",
    "    plot_title=f'Sub {sub}: Feature distribution vs UPDRS subscores (28 aDBS traces)',\n",
    "    figsave_name=f'sub{sub}_aDBS_box_{ax_in}fts_{merge_meth}',\n",
    "    figsave_dir=os.path.join(jb_fig_dir, 'fts_boxplot'),\n",
    "    show=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2b. Extract Features and Store them for Subs <b>without</b> UPDRS-subscores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8001, 3, 218)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat_dat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/jeroenhabets/Research/CHARITE/projects/tapping/data/JB_30s/tapsMat.mat imported with h5py\n"
     ]
    }
   ],
   "source": [
    "importlib.reload(tap_run)\n",
    "importlib.reload(tap_times)\n",
    "importlib.reload(tap_ft_extr)\n",
    "importlib.reload(tap_fts_set)\n",
    "importlib.reload(tap_import)\n",
    "importlib.reload(tap_impact)\n",
    "importlib.reload(tap_preproc)\n",
    "\n",
    "# define directory and filename\n",
    "jb_dir = '/Users/jeroenhabets/Research/CHARITE/projects/tapping/data/JB_30s'\n",
    "jb_fig_dir = '/Users/jeroenhabets/Research/CHARITE/projects/tapping/figures/JB_30s'\n",
    "\n",
    "mat_dat_f = 'tapsMat.mat'\n",
    "csv_meta_f = 'tapsMeta.csv'\n",
    "\n",
    "# Import acc-traces\n",
    "mat_dat = tap_import.matlab_import(os.path.join(\n",
    "    jb_dir, mat_dat_f))\n",
    "fname = mat_dat.filename.split(sep='/')[-1]\n",
    "mat_dat = mat_dat[fname[:-4]]\n",
    "\n",
    "# import meta-data\n",
    "meta = tap_import.import_meta_aDBS(\n",
    "    os.path.join(jb_dir, csv_meta_f)\n",
    ")\n",
    "# subs = meta['subject'].unique()\n",
    "\n",
    "fs=250\n",
    "\n",
    "accAll = mat_dat\n",
    "ftsAll = {}\n",
    "\n",
    "# fts529 = {}\n",
    "\n",
    "for run_id in np.arange(accAll.shape[2]):\n",
    "    \n",
    "    sig = accAll[:, :, run_id].T  # tri-axial acc sig\n",
    "\n",
    "    tap_inds, imp_inds, proc_triax = tap_run.run_updrs_tap_finder(\n",
    "        acc_arr=sig, fs=fs, already_preprocd=False,\n",
    "    )\n",
    "\n",
    "    ftsAll[f'run{run_id}'] = tap_ft_extr.tapFeatures(\n",
    "        triax_arr=proc_triax,\n",
    "        fs=fs,\n",
    "        impacts=imp_inds,\n",
    "        tapDict=tap_inds,  # result of continTap\n",
    "        # updrsSubScore=subscore,\n",
    "    )\n",
    "    \n",
    "    # if meta.iloc[run_id]['subject'] == '529':\n",
    "    #     fts529[f'run{run_id}'] = tap_ft_extr.tapFeatures(\n",
    "    #         triax_arr=proc_triax,\n",
    "    #         fs=fs,\n",
    "    #         impacts=imp_inds,\n",
    "    #         tapDict=tap_inds,  # result of continTap\n",
    "    #         # updrsSubScore=subscore,\n",
    "    #     )\n",
    "    # else:\n",
    "    #     continue\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualise all aDBS runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "import retap_utils.utils_aDBS_JB as utils_aDBS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "aDBSfts = utils_aDBS.aDBS_subjects(meta=meta, ftsAll=ftsAll)\n",
    "# submeta = meta[meta['subject'] == '529']\n",
    "# subFts = utils_aDBS.aDBS_subjects(meta=submeta, ftsAll=fts529)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['sub', 'run_name', 'stim', 'trial', 'fts'])\n"
     ]
    }
   ],
   "source": [
    "print(vars(aDBSfts.sub533.cOn).keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Change sub / side for run_ids\n",
    "change stim_amps for stim states\n",
    "\"\"\"\n",
    "\n",
    "colors = {\n",
    "    'cOff': 'red',\n",
    "    'cOn': 'green',\n",
    "    'a4': 'purple',  # largest B-supression (best)\n",
    "    'a9': 'orange',  # smallest B-supression (worst)\n",
    "    'a': 'blue'\n",
    "}\n",
    "lws = {'c': 2, 'a': .5}\n",
    "fs=14\n",
    "\n",
    "fts_incl = [\n",
    "    'tapRMS_svm',\n",
    "    'upVelo_svm',\n",
    "    'impactRMS_svm',\n",
    "    'dirChange_taps',\n",
    "    'tap_durations',\n",
    "]\n",
    "\n",
    "fts_name = [\n",
    "    'total Tap RMS',\n",
    "    'upwards Movement',\n",
    "    'closing-impact RMS',\n",
    "    'Hesitations',\n",
    "    'Tap durations'\n",
    "]\n",
    "\n",
    "for sub in aDBSfts.subs:\n",
    "\n",
    "    fig, axes = plt.subplots(\n",
    "        len(fts_incl), 1, figsize=(12, 12)\n",
    "    )\n",
    "\n",
    "    temp_sub = getattr(aDBSfts, f'sub{sub}')\n",
    "\n",
    "    for ax, ft in enumerate(fts_incl):\n",
    "\n",
    "        axes[ax].plot(\n",
    "            getattr(temp_sub.cOff.fts, ft),\n",
    "            c=colors['cOff'], lw=lws['c'],\n",
    "            label='cOff',\n",
    "        )\n",
    "        axes[ax].plot(\n",
    "            getattr(temp_sub.cOn.fts, ft),\n",
    "            c=colors['cOn'], lw=lws['c'],\n",
    "            label='cOn',\n",
    "        )\n",
    "\n",
    "        for aRun in temp_sub.run_names:\n",
    "\n",
    "            if aRun[0] != 'a': continue\n",
    "\n",
    "            aN = int(aRun[1])\n",
    "\n",
    "            aRun = getattr(temp_sub, aRun)\n",
    "\n",
    "            if aN in [4, 9]:\n",
    "\n",
    "                axes[ax].plot(\n",
    "                    getattr(aRun.fts, ft),\n",
    "                    c=colors[f'a{aN}'], lw=lws['c'],\n",
    "                    label=f'aDBS_preset{aN}',\n",
    "                    # alpha=.11 * aN,\n",
    "                )\n",
    "\n",
    "            else:\n",
    "\n",
    "                axes[ax].plot(\n",
    "                    getattr(aRun.fts, ft),\n",
    "                    c=colors['a'], lw=lws['a'],\n",
    "                    # label=f'aDBS_{aN}',\n",
    "                    alpha=.3,\n",
    "                )\n",
    "\n",
    "\n",
    "        axes[ax].set_title(\n",
    "            fts_name[ax], size=fs + 2)\n",
    "\n",
    "    \n",
    "        # ADD aDBS \n",
    "    \n",
    "    axes[-1].legend(\n",
    "        frameon=False, fontsize=fs, ncol=4,\n",
    "        bbox_to_anchor=[.5, -.35], loc='center'\n",
    "    )\n",
    "        \n",
    "\n",
    "            \n",
    "\n",
    "    # plt.ylabel(f'{ft} (a.u.)', size=fs,)\n",
    "    # plt.xlabel('Taps over time (# of tap)', size=fs,)\n",
    "\n",
    "    plt.tick_params(axis='both', size=fs, labelsize=fs)\n",
    "\n",
    "    # plt.legend(\n",
    "    #     frameon=False, ncol=1,\n",
    "    #     bbox_to_anchor=(1, .9), loc='upper left',\n",
    "    #     fontsize=fs,\n",
    "    # )\n",
    "\n",
    "    plt.suptitle(\n",
    "        f'aDBS-sub {sub}: Tapping features',\n",
    "        fontsize=fs+6, x=.2, ha='center', y=.98\n",
    "    )\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.savefig(\n",
    "        os.path.join(\n",
    "            jb_fig_dir,\n",
    "            'ft_course',\n",
    "            f'{sub}_aDBS_tapFeatures'\n",
    "        ),\n",
    "        dpi=150, facecolor='w',\n",
    "    )\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/jeroenhabets/Research/CHARITE/projects/tapping/figures/JB_30s'"
      ]
     },
     "execution_count": 401,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jb_fig_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Change sub / side for run_ids\n",
    "change stim_amps for stim states\n",
    "\"\"\"\n",
    "\n",
    "colors = {\n",
    "    'cOff': 'orange',\n",
    "    'cOn': 'green',\n",
    "    'a': 'blue'\n",
    "}\n",
    "lws = {'c': 3, 'a': 1}\n",
    "fs=14\n",
    "\n",
    "fts_incl = [\n",
    "    'tapRMS_svm',\n",
    "    'upVelo_svm',\n",
    "    'impactRMS_svm',\n",
    "    'dirChange_taps',\n",
    "    'tap_durations',\n",
    "]\n",
    "\n",
    "for sub in aDBSfts.subs:\n",
    "\n",
    "    fig, axes = plt.subplots(\n",
    "        len(fts_incl), 1, figsize=(12, 8)\n",
    "    )\n",
    "\n",
    "    temp_sub = getattr(aDBSfts, f'sub{sub}')\n",
    "\n",
    "    for ax, ft in enumerate(fts_incl):\n",
    "\n",
    "        axes[ax].plot(\n",
    "            getattr(temp_sub.cOff.fts, ft),\n",
    "            c=colors['cOff'], lw=lws['c'],\n",
    "            label='cOff',\n",
    "        )\n",
    "        axes[ax].plot(\n",
    "            getattr(temp_sub.cOn.fts, ft),\n",
    "            c=colors['cOn'], lw=lws['c'],\n",
    "            label='cOn',\n",
    "        )\n",
    "\n",
    "        # for aRun in temp_sub.run_names:\n",
    "\n",
    "        #     if aRun[0] != 'a': continue\n",
    "\n",
    "        #     aN = int(aRun[1])\n",
    "\n",
    "        #     aRun = getattr(temp_sub, aRun)\n",
    "\n",
    "        #     axes[ax].plot(\n",
    "        #         getattr(aRun.fts, ft),\n",
    "        #         c=colors['a'], lw=lws['a'],\n",
    "        #         label=f'aDBS_{aN}',\n",
    "        #         alpha=.11 * aN,\n",
    "        #     )\n",
    "\n",
    "\n",
    "        axes[ax].set_title(ft, size=fs + 2)\n",
    "\n",
    "    \n",
    "        # ADD aDBS \n",
    "    \n",
    "    # axes[0].legend(\n",
    "    #     frameon=False, fontsize=fs, ncol=2,\n",
    "    #     bbox_to_anchor=[.75, 2], loc='upper left'\n",
    "    # )\n",
    "        \n",
    "\n",
    "            \n",
    "\n",
    "    # plt.ylabel(f'{ft} (a.u.)', size=fs,)\n",
    "    # plt.xlabel('Taps over time (# of tap)', size=fs,)\n",
    "\n",
    "    # plt.tick_params(axis='both', size=fs, labelsize=fs)\n",
    "\n",
    "    # plt.legend(\n",
    "    #     frameon=False, ncol=1,\n",
    "    #     bbox_to_anchor=(1, .9), loc='upper left',\n",
    "    #     fontsize=fs,\n",
    "    # )\n",
    "\n",
    "    plt.suptitle(\n",
    "        f'aDBS-sub {sub}: Tapping features',\n",
    "        fontsize=fs+6, x=.5, ha='center', y=.9\n",
    "    )\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # plt.savefig(\n",
    "    #     os.path.join(\n",
    "    #         fig_dir,\n",
    "    #         'ft_course_tapping',\n",
    "    #         f'TEST____{sub}_{side}_ftCourse_{ft}'\n",
    "    #     ),\n",
    "    #     dpi=150, facecolor='w',\n",
    "    # )\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeroenhabets/Research/CHARITE/projects/dyskinesia_neurophys/code/lfpecog_features/tapping_feat_calc.py:87: RuntimeWarning: invalid value encountered in true_divide\n",
      "  print(values)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "artificial 0 added\n",
      "artificial 0 added\n",
      "artificial 0 added\n",
      "artificial 0 added\n"
     ]
    }
   ],
   "source": [
    "# ### Code to export features\n",
    "\n",
    "# importlib.reload(ft_calc)\n",
    "\n",
    "# ft_sel = [\n",
    "#     'nTaps',\n",
    "#     'freq',\n",
    "#     'tapRMS_ax',\n",
    "#     'impactRMS_ax',\n",
    "#     'upVelo_svm',\n",
    "#     'dirChange_taps',\n",
    "# ]\n",
    "\n",
    "# ft_colNames = [\n",
    "#     'nTaps',\n",
    "#     'freq',\n",
    "#     'tapRMS_ax_mean',\n",
    "#     'impactRMS_ax_mean',\n",
    "#     'upVelo_svm_mean',\n",
    "#     'dirChange_taps_coefVar',\n",
    "# ]\n",
    "\n",
    "# fts_df = pd.DataFrame(\n",
    "#     data=ft_calc.nan_array([\n",
    "#         len(ftsAll.keys()), len(ft_sel)\n",
    "#     ]),\n",
    "#     columns=ft_sel,\n",
    "#     index=ftsAll.keys(),\n",
    "# )\n",
    "\n",
    "\n",
    "# for run_id in ftsAll.keys():\n",
    "\n",
    "#     for ft in ft_sel:\n",
    "        \n",
    "#         value = getattr(ftsAll[run_id], ft)\n",
    "\n",
    "#         if ft in ['nTaps', 'freq']:\n",
    "#             value = value\n",
    "        \n",
    "#         elif ft == 'dirChange_taps':\n",
    "#             value = ft_calc.aggregate_arr_fts(\n",
    "#                 'coefVar', value\n",
    "#             )\n",
    "        \n",
    "#         else:\n",
    "#             value = ft_calc.aggregate_arr_fts(\n",
    "#                 'mean', value\n",
    "#             )\n",
    "        \n",
    "#         fts_df.loc[run_id][ft] = value\n",
    "\n",
    "\n",
    "# fts_df.to_csv(\n",
    "#     os.path.join(\n",
    "#         '/Users/jeroenhabets/Research/CHARITE/projects/tapping/results/JB_30s',\n",
    "#         'table_218subs_6fts.csv'\n",
    "#     ),\n",
    "#     header=True, index=True,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Clustering & Classifying"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Candidate vetors based on descriptives and concept\n",
    "    - nTaps\n",
    "    - freq\n",
    "    - upVelo sum [std-dev + coefVar]\n",
    "    - impact RMS [coefVar + stddev]\n",
    "    - tapRMS and impactRMS [sum]\n",
    "    - \n",
    "- include per run (array tap-features): sum, mean, stddev, trend_slope\n",
    "\n",
    "- Cluster on UPDRS 4?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3a. ML-Vector Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(tap_fts_set)\n",
    "\n",
    "FEATS['007_L_Off_1mA_b1'].impactRMS_svm\n",
    "\n",
    "tap_fts_set.amplitudeDecrement(\n",
    "    [\n",
    "        FEATS['007_L_Off_1mA_b1'].tapRMS_svm,\n",
    "        FEATS['007_L_Off_1mA_b1'].impactRMS_svm,\n",
    "        FEATS['007_L_Off_1mA_b1'].upVelo_svm\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeroenhabets/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/numpy/core/fromnumeric.py:84: FutureWarning: In a future version, DataFrame.max(axis=None) will return a scalar max over the entire DataFrame. To retain the old behavior, use 'frame.max(axis=0)' or just 'frame.max()'\n",
      "  return reduction(axis=axis, out=out, **passkwargs)\n",
      "/var/folders/7d/4ptht2m910d1y872jrgp9cq40000gp/T/ipykernel_32352/1716791283.py:91: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  Xdf.iloc[s][f'{ft_name}_{m}'] = value\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "artificial 0 added\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jeroenhabets/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/scipy/stats/_stats_mstats_common.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  slope = ssxym / ssxm\n",
      "/Users/jeroenhabets/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/scipy/stats/_stats_mstats_common.py:184: RuntimeWarning: invalid value encountered in sqrt\n",
      "  t = r * np.sqrt(df / ((1.0 - r + TINY)*(1.0 + r + TINY)))\n",
      "/Users/jeroenhabets/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/scipy/stats/_stats_mstats_common.py:187: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  slope_stderr = np.sqrt((1 - r**2) * ssym / ssxm / df)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "artificial 0 added\n",
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      "artificial 0 added\n",
      "X array contains missing values:\n",
      " nTaps                         False\n",
      "freq                          False\n",
      "ampDecrement                   True\n",
      "tapRMS_svm_mean               False\n",
      "tapRMS_svm_coefVar            False\n",
      "tapRMS_svm_variance           False\n",
      "tapRMS_svm_sum                False\n",
      "tapRMS_svm_trend_slope        False\n",
      "upVelo_svm_mean               False\n",
      "upVelo_svm_coefVar            False\n",
      "upVelo_svm_variance           False\n",
      "upVelo_svm_sum                False\n",
      "upVelo_svm_trend_slope        False\n",
      "impactRMS_svm_mean            False\n",
      "impactRMS_svm_coefVar         False\n",
      "impactRMS_svm_variance        False\n",
      "impactRMS_svm_sum             False\n",
      "impactRMS_svm_trend_slope     False\n",
      "dirChange_taps_mean           False\n",
      "dirChange_taps_coefVar        False\n",
      "dirChange_taps_variance       False\n",
      "dirChange_taps_sum            False\n",
      "dirChange_taps_trend_slope    False\n",
      "dtype: bool\n"
     ]
    },
    {
     "ename": "AssertionError",
     "evalue": "None",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb Cell 44'\u001b[0m in \u001b[0;36m<cell line: 96>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000037?line=92'>93</a>\u001b[0m \u001b[39m### assert np.sum(np.isnan(Xdf.values)) == 0\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000037?line=93'>94</a>\u001b[0m X \u001b[39m=\u001b[39m Xdf\u001b[39m.\u001b[39mvalues\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000037?line=95'>96</a>\u001b[0m \u001b[39massert\u001b[39;00m np\u001b[39m.\u001b[39misnan(X)\u001b[39m.\u001b[39many() \u001b[39m==\u001b[39m \u001b[39mFalse\u001b[39;00m, \u001b[39mprint\u001b[39m(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000037?line=96'>97</a>\u001b[0m     \u001b[39m'\u001b[39m\u001b[39mX array contains missing values:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m'\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000037?line=97'>98</a>\u001b[0m     np\u001b[39m.\u001b[39misnan(Xdf)\u001b[39m.\u001b[39many()\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000037?line=98'>99</a>\u001b[0m )\n",
      "\u001b[0;31mAssertionError\u001b[0m: None"
     ]
    }
   ],
   "source": [
    "importlib.reload(ft_calc)\n",
    "\n",
    "ft_axis = 'svm'\n",
    "single_ft_names = [\n",
    "    'nTaps',\n",
    "    'freq',\n",
    "    'ampDecrement'\n",
    "]\n",
    "arr_ft_names = [\n",
    "    f'tapRMS_{ft_axis}',\n",
    "    f'upVelo_{ft_axis}',\n",
    "    f'impactRMS_{ft_axis}',\n",
    "    'dirChange_taps',\n",
    "]\n",
    "ft_aggr_to_add = [\n",
    "    'mean', 'coefVar', 'variance', 'sum', 'trend_slope'\n",
    "]\n",
    "\n",
    "\n",
    "valid_sel = [\n",
    "    len(FEATS[s].tapDict) > 0 for s in FEATS.keys()\n",
    "]\n",
    "valid_samples = list(compress(FEATS.keys(), valid_sel))\n",
    "n_samples = len(valid_samples)\n",
    "\n",
    "Xdf = pd.DataFrame(\n",
    "    data=np.zeros((\n",
    "        n_samples,\n",
    "        len(single_ft_names) + (\n",
    "            len(arr_ft_names) * len(ft_aggr_to_add)\n",
    "        )\n",
    "    )), columns=[single_ft_names + [\n",
    "        f'{f}_{m}' for f in arr_ft_names for m in ft_aggr_to_add\n",
    "    ]]\n",
    ")\n",
    "y = ft_calc.nan_array([Xdf.shape[0], 1])\n",
    "for s in range(Xdf.shape[0]):\n",
    "    y[s] = getattr(\n",
    "        FEATS[valid_samples[s]],\n",
    "        'updrsSubScore'\n",
    "    )\n",
    "\n",
    "for ft in single_ft_names:\n",
    "    for s, sam in enumerate(valid_samples):\n",
    "        try:\n",
    "            ft_value = getattr(FEATS[sam], ft)\n",
    "        except AttributeError:\n",
    "            print(sam)\n",
    "\n",
    "        Xdf.iloc[s][ft] = ft_value\n",
    "\n",
    "\n",
    "max_nTaps = np.max([FEATS[k].nTaps for k in valid_samples])\n",
    "\n",
    "arr_feats = ft_calc.nan_array([\n",
    "    len(arr_ft_names),\n",
    "    max_nTaps,\n",
    "    n_samples\n",
    "])\n",
    "\n",
    "for f, ft in enumerate(arr_ft_names):\n",
    "\n",
    "    for s, sam in enumerate(valid_samples):\n",
    "\n",
    "        ft_values = getattr(FEATS[sam], ft)\n",
    "        arr_feats[f, :len(ft_values), s] = ft_values\n",
    "\n",
    "# Normalise vector per array-feature over all samples\n",
    "for ft_row in range(len(arr_ft_names)):\n",
    "\n",
    "    vec_max = np.nanmax(arr_feats[ft_row, :, :])\n",
    "    arr_feats[ft_row, :, :] = arr_feats[ft_row, :, :] / vec_max\n",
    "\n",
    "# Normalise single feature over all samples\n",
    "for ft in single_ft_names:\n",
    "\n",
    "    fmax = np.max(Xdf[ft])\n",
    "    Xdf[ft] = Xdf[ft] / fmax\n",
    "\n",
    "# Aggregate array features\n",
    "for ft, ft_name in enumerate(arr_ft_names):\n",
    "\n",
    "    for m in ft_aggr_to_add:\n",
    "\n",
    "        for s in np.arange(Xdf.shape[0]):\n",
    "            \n",
    "            value = ft_calc.aggregate_arr_fts(\n",
    "                method=m, arr=arr_feats[ft, :, s]\n",
    "            )\n",
    "            \n",
    "            Xdf.iloc[s][f'{ft_name}_{m}'] = value\n",
    "\n",
    "### assert np.sum(np.isnan(Xdf.values)) == 0\n",
    "X = Xdf.values\n",
    "\n",
    "assert np.isnan(X).any() == False, print(\n",
    "    'X array contains missing values:\\n',\n",
    "    np.isnan(Xdf).any()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3b. Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# DBSCAN\n",
    "# Laio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input X contains NaN.\nKMeans does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb Cell 46'\u001b[0m in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=8'>9</a>\u001b[0m \u001b[39m# X = pca.fit_transform(X)\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=10'>11</a>\u001b[0m kmeans \u001b[39m=\u001b[39m KMeans(\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=11'>12</a>\u001b[0m     n_clusters\u001b[39m=\u001b[39mn_clusters,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=12'>13</a>\u001b[0m     random_state\u001b[39m=\u001b[39m\u001b[39m27\u001b[39m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=13'>14</a>\u001b[0m )\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=14'>15</a>\u001b[0m y_clust_labels \u001b[39m=\u001b[39m kmeans\u001b[39m.\u001b[39;49mfit_predict(X)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=15'>16</a>\u001b[0m centroids \u001b[39m=\u001b[39m kmeans\u001b[39m.\u001b[39mcluster_centers_\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jeroenhabets/Research/CHARITE/projects/tapping/code/updrsTapping_repo/updrsTapping_main_run.ipynb#ch0000040?line=17'>18</a>\u001b[0m y_cluster0 \u001b[39m=\u001b[39m y[y_clust_labels \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m]\n",
      "File \u001b[0;32m~/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:996\u001b[0m, in \u001b[0;36m_BaseKMeans.fit_predict\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    973\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit_predict\u001b[39m(\u001b[39mself\u001b[39m, X, y\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    974\u001b[0m     \u001b[39m\"\"\"Compute cluster centers and predict cluster index for each sample.\u001b[39;00m\n\u001b[1;32m    975\u001b[0m \n\u001b[1;32m    976\u001b[0m \u001b[39m    Convenience method; equivalent to calling fit(X) followed by\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    994\u001b[0m \u001b[39m        Index of the cluster each sample belongs to.\u001b[39;00m\n\u001b[1;32m    995\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 996\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfit(X, sample_weight\u001b[39m=\u001b[39;49msample_weight)\u001b[39m.\u001b[39mlabels_\n",
      "File \u001b[0;32m~/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:1365\u001b[0m, in \u001b[0;36mKMeans.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1339\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfit\u001b[39m(\u001b[39mself\u001b[39m, X, y\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, sample_weight\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m   1340\u001b[0m     \u001b[39m\"\"\"Compute k-means clustering.\u001b[39;00m\n\u001b[1;32m   1341\u001b[0m \n\u001b[1;32m   1342\u001b[0m \u001b[39m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1363\u001b[0m \u001b[39m        Fitted estimator.\u001b[39;00m\n\u001b[1;32m   1364\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1365\u001b[0m     X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m   1366\u001b[0m         X,\n\u001b[1;32m   1367\u001b[0m         accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsr\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1368\u001b[0m         dtype\u001b[39m=\u001b[39;49m[np\u001b[39m.\u001b[39;49mfloat64, np\u001b[39m.\u001b[39;49mfloat32],\n\u001b[1;32m   1369\u001b[0m         order\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mC\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m   1370\u001b[0m         copy\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcopy_x,\n\u001b[1;32m   1371\u001b[0m         accept_large_sparse\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m   1372\u001b[0m     )\n\u001b[1;32m   1374\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_params(X)\n\u001b[1;32m   1375\u001b[0m     random_state \u001b[39m=\u001b[39m check_random_state(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrandom_state)\n",
      "File \u001b[0;32m~/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/sklearn/base.py:577\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    575\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mValidation should be done on X, y or both.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    576\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 577\u001b[0m     X \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_params)\n\u001b[1;32m    578\u001b[0m     out \u001b[39m=\u001b[39m X\n\u001b[1;32m    579\u001b[0m \u001b[39melif\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_y:\n",
      "File \u001b[0;32m~/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/sklearn/utils/validation.py:899\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    893\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    894\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mFound array with dim \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m expected <= 2.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    895\u001b[0m             \u001b[39m%\u001b[39m (array\u001b[39m.\u001b[39mndim, estimator_name)\n\u001b[1;32m    896\u001b[0m         )\n\u001b[1;32m    898\u001b[0m     \u001b[39mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 899\u001b[0m         _assert_all_finite(\n\u001b[1;32m    900\u001b[0m             array,\n\u001b[1;32m    901\u001b[0m             input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[1;32m    902\u001b[0m             estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[1;32m    903\u001b[0m             allow_nan\u001b[39m=\u001b[39;49mforce_all_finite \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    904\u001b[0m         )\n\u001b[1;32m    906\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_samples \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m    907\u001b[0m     n_samples \u001b[39m=\u001b[39m _num_samples(array)\n",
      "File \u001b[0;32m~/anaconda3/envs/updrsTapping/lib/python3.9/site-packages/sklearn/utils/validation.py:146\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    124\u001b[0m         \u001b[39mif\u001b[39;00m (\n\u001b[1;32m    125\u001b[0m             \u001b[39mnot\u001b[39;00m allow_nan\n\u001b[1;32m    126\u001b[0m             \u001b[39mand\u001b[39;00m estimator_name\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    130\u001b[0m             \u001b[39m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    131\u001b[0m             \u001b[39m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    132\u001b[0m             msg_err \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m    133\u001b[0m                 \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m does not accept missing values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    134\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    144\u001b[0m                 \u001b[39m\"\u001b[39m\u001b[39m#estimators-that-handle-nan-values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    145\u001b[0m             )\n\u001b[0;32m--> 146\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg_err)\n\u001b[1;32m    148\u001b[0m \u001b[39m# for object dtype data, we only check for NaNs (GH-13254)\u001b[39;00m\n\u001b[1;32m    149\u001b[0m \u001b[39melif\u001b[39;00m X\u001b[39m.\u001b[39mdtype \u001b[39m==\u001b[39m np\u001b[39m.\u001b[39mdtype(\u001b[39m\"\u001b[39m\u001b[39mobject\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m allow_nan:\n",
      "\u001b[0;31mValueError\u001b[0m: Input X contains NaN.\nKMeans does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values"
     ]
    }
   ],
   "source": [
    "### Cluster Settings\n",
    "## test with full features and PCA1-2;\n",
    "## test first PCA cluster and full-fts classification within clusters\n",
    "n_clusters = 4\n",
    "\n",
    "\n",
    "X = Xdf.values\n",
    "pca = PCA(2)\n",
    "# X = pca.fit_transform(X)\n",
    "\n",
    "kmeans = KMeans(\n",
    "    n_clusters=n_clusters,\n",
    "    random_state=27\n",
    ")\n",
    "y_clust_labels = kmeans.fit_predict(X)\n",
    "centroids = kmeans.cluster_centers_\n",
    "\n",
    "y_cluster0 = y[y_clust_labels == 0]\n",
    "y_cluster1 = y[y_clust_labels == 1]\n",
    "\n",
    "score_cols = {\n",
    "    0: 'green',\n",
    "    1: 'blue',\n",
    "    2: 'orange',\n",
    "    3: 'red',\n",
    "    4: 'purple'\n",
    "}\n",
    "\n",
    "clMarkers = ['o', 'x', '*', '+', '^']\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12))\n",
    "s = 75\n",
    "for n, X_pca_row in enumerate(X):\n",
    "\n",
    "    score = int(y[n])\n",
    "    col = score_cols[score]\n",
    "\n",
    "    for clN in range(n_clusters):\n",
    "\n",
    "        if y_clust_labels[n] == clN:\n",
    "            \n",
    "            ax.scatter(\n",
    "                X[n, 0], X[n, 1],\n",
    "                label=f'Cluster-{clN}, Tap-Score {score}',\n",
    "                s=s, color=col, alpha=.8,\n",
    "                marker=clMarkers[clN],\n",
    "            )\n",
    "\n",
    "    # if y_clust_labels[n] == 0:\n",
    "    #     ax.scatter(\n",
    "    #         X[n, 0], X[n, 1],\n",
    "    #         label=f'Cluster-0, Tap-Score {score}',\n",
    "    #         # edgecolor=col, facecolor='w',\n",
    "    #         s=s, color=col, marker='*',\n",
    "    #     )\n",
    "    \n",
    "    # elif y_clust_labels[n] == 1:\n",
    "    #     ax.scatter(\n",
    "    #         X[n, 0], X[n, 1], marker='+',\n",
    "    #         label=f'Cluster-1, Tap-Score {score}',\n",
    "    #         color=col, s=s, alpha=.7,\n",
    "    #     )\n",
    "\n",
    "    # elif y_clust_labels[n] == 2:\n",
    "    #     ax.scatter(\n",
    "    #         X[n, 0], X[n, 1],\n",
    "    #         label=f'Cluster-2, Tap-Score {score}',\n",
    "    #         color=col, s=s, alpha=.7,\n",
    "    #     )\n",
    "\n",
    "for c in range(centroids.shape[0]):\n",
    "    ax.scatter(\n",
    "        centroids[c, 0], centroids[c, 1],\n",
    "        edgecolor='k', s=s + 50, fc='w',\n",
    "        label='Cluster centers'\n",
    "    )\n",
    "\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "by_label = dict(zip(labels, handles))\n",
    "ax.legend(\n",
    "    by_label.values(), by_label.keys(),\n",
    "    frameon=False, fontsize=16,\n",
    "    ncol=1,\n",
    "    loc='upper left',\n",
    "    bbox_to_anchor=[1.01, .95]\n",
    ")\n",
    "\n",
    "ax.set_xlabel('PCA-1', fontsize=18)\n",
    "ax.set_ylabel('PCA-2', fontsize=18)\n",
    "ax.set_title(\n",
    "    'kMeans Clustering 10-seconds of Finger Tapping',\n",
    "    fontsize=20\n",
    ")\n",
    "\n",
    "# plt.savefig(\n",
    "#     os.path.join(\n",
    "#         fig_dir,\n",
    "#         f'kMeans_{n_clusters}clusts_1AX_20220727'),\n",
    "#     dpi=150, facecolor='w',\n",
    "# )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# plt.hist(labels0, color='purple')\n",
    "# plt.hist(labels1, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3c. Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import multilabel_confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classifying UPDRS 0 - 1 - 2 - 3 - 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Support Vector, fold #0\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: 'continuous'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/7d/4ptht2m910d1y872jrgp9cq40000gp/T/ipykernel_10037/4282525040.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'l2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m     \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;31m# for own scoring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ecog_dysk/lib/python3.9/site-packages/sklearn/svm/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         )\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ecog_dysk/lib/python3.9/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;34m\"multilabel-sequences\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     ]:\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
     ]
    }
   ],
   "source": [
    "# CLassification Settings\n",
    "nFolds = 4\n",
    "\n",
    "# Shuffle order\n",
    "X = Xdf.values\n",
    "\n",
    "allData = np.hstack((X, y))\n",
    "X_shf = allData[:, :14]\n",
    "y_shf = allData[:, 14]\n",
    "np.random.seed(27)\n",
    "np.random.shuffle(allData)\n",
    "\n",
    "skf = StratifiedKFold(n_splits=nFolds,)\n",
    "skf.get_n_splits(X_shf, y_shf)\n",
    "\n",
    "y_pred, y_true = {}, {}\n",
    "\n",
    "for F, (train_index, test_index) in enumerate(\n",
    "    skf.split(X, y)\n",
    "):\n",
    "    print(f'Linear Support Vector, fold #{F}')\n",
    "\n",
    "    X_train, X_test = X_shf[train_index], X_shf[test_index]\n",
    "    y_train, y_test = y_shf[train_index], y_shf[test_index]\n",
    "\n",
    "    clf = LinearSVC(penalty='l2', C=1.0,)\n",
    "    clf.fit(X=X_train, y=y_train)\n",
    "    print(clf.score(X=X_test, y=y_test))\n",
    "    # for own scoring\n",
    "    y_pred[F] = clf.predict(X=X_test)\n",
    "    y_true[F] = y_test\n",
    "    # print(multilabel_confusion_matrix(y_true[F], y_pred[F]))\n",
    "\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Boolean Classifying (UPDRS 0 or 4 vs The Rest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification of UPDRS subscore 0 versus thre rest\n",
      "\n",
      "Linear Support Vector, fold #0\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Unknown label type: 'continuous'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/7d/4ptht2m910d1y872jrgp9cq40000gp/T/ipykernel_10037/2107056001.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m     \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'l2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m     \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'Accuracy: {clf.score(X=X_test, y=y_test)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;31m# for own scoring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ecog_dysk/lib/python3.9/site-packages/sklearn/svm/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0maccept_large_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         )\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/ecog_dysk/lib/python3.9/site-packages/sklearn/utils/multiclass.py\u001b[0m in \u001b[0;36mcheck_classification_targets\u001b[0;34m(y)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0;34m\"multilabel-sequences\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m     ]:\n\u001b[0;32m--> 198\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unknown label type: %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Unknown label type: 'continuous'"
     ]
    }
   ],
   "source": [
    "# CLassification Settings\n",
    "nFolds = 4\n",
    "score_to_predict = 0\n",
    "\n",
    "# Shuffle order\n",
    "X = Xdf.values\n",
    "y_bool = y == score_to_predict\n",
    "\n",
    "allData = np.hstack((X, y_bool))\n",
    "\n",
    "np.random.seed(27)\n",
    "np.random.shuffle(allData)\n",
    "\n",
    "X_shf = allData[:, :14]\n",
    "y_shf = allData[:, 14]\n",
    "\n",
    "skf = StratifiedKFold(n_splits=nFolds,)\n",
    "skf.get_n_splits(X_shf, y_shf)\n",
    "\n",
    "y_pred, y_true = {}, {}\n",
    "print(\n",
    "    'Classification of UPDRS subscore '\n",
    "    f'{score_to_predict} versus thre rest')\n",
    "for F, (train_index, test_index) in enumerate(\n",
    "    skf.split(X, y)\n",
    "):\n",
    "    print(f'\\nLinear Support Vector, fold #{F}')\n",
    "\n",
    "    X_train, X_test = X_shf[train_index], X_shf[test_index]\n",
    "    y_train, y_test = y_shf[train_index], y_shf[test_index]\n",
    "\n",
    "    clf = LinearSVC(penalty='l2', C=1.0,)\n",
    "    clf.fit(X=X_train, y=y_train)\n",
    "    print(f'Accuracy: {clf.score(X=X_test, y=y_test)}')\n",
    "    # for own scoring\n",
    "    y_pred[F] = clf.predict(X=X_test)\n",
    "    y_true[F] = y_test\n",
    "    print(classification_report(y_true[F], y_pred[F]))\n",
    "    \n",
    "\n"
   ]
  }
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