09-Task-Evaluate_Data/01c2_PRE_BEH_Trial_Exclusion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9a9ba0-1fa4-467c-be6d-de2c92c50a30",
   "metadata": {},
   "source": [
    "# BEH Trial Exclusion\n",
    "Status: Implemented\n",
    " -- Fully automatized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cace8cd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "from utils.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eaf57434",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'003': WindowsPath('../08-Data-Trial_Recordings/raw/E003'), '004': WindowsPath('../08-Data-Trial_Recordings/raw/E004'), '005': WindowsPath('../08-Data-Trial_Recordings/raw/E005'), '007': WindowsPath('../08-Data-Trial_Recordings/raw/E007'), '008': WindowsPath('../08-Data-Trial_Recordings/raw/E008'), '009': WindowsPath('../08-Data-Trial_Recordings/raw/E009'), '010': WindowsPath('../08-Data-Trial_Recordings/raw/E010'), '011': WindowsPath('../08-Data-Trial_Recordings/raw/E011'), '012': WindowsPath('../08-Data-Trial_Recordings/raw/E012'), '013': WindowsPath('../08-Data-Trial_Recordings/raw/E013'), '014': WindowsPath('../08-Data-Trial_Recordings/raw/E014'), '015': WindowsPath('../08-Data-Trial_Recordings/raw/E015'), '016': WindowsPath('../08-Data-Trial_Recordings/raw/E016'), '017': WindowsPath('../08-Data-Trial_Recordings/raw/E017'), '018': WindowsPath('../08-Data-Trial_Recordings/raw/E018'), '019': WindowsPath('../08-Data-Trial_Recordings/raw/E019'), '020': WindowsPath('../08-Data-Trial_Recordings/raw/E020'), '021': WindowsPath('../08-Data-Trial_Recordings/raw/E021'), '022': WindowsPath('../08-Data-Trial_Recordings/raw/E022'), '023': WindowsPath('../08-Data-Trial_Recordings/raw/E023'), '024': WindowsPath('../08-Data-Trial_Recordings/raw/E024'), '101': WindowsPath('../08-Data-Trial_Recordings/raw/E101'), '102': WindowsPath('../08-Data-Trial_Recordings/raw/E102'), '106': WindowsPath('../08-Data-Trial_Recordings/raw/E106')}\n"
     ]
    }
   ],
   "source": [
    "# get all files from participants\n",
    "folders = get_participant_folder_per_participant()\n",
    "print(folders)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56717332",
   "metadata": {},
   "source": [
    "### Check exclusion criteria for behavioral data \n",
    "* response time outside E+-3std\n",
    "* correctness per participant higher expected value using chance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d0303f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(01/05) Load behavioral data from all participants\n",
      "(02/05) Check participant correctness\n",
      "Correctness subjectwise mean: 79.33 % , std: 8.64 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(03/05) Mark as excluded\n",
      "----------------------------------------------\n",
      "Participant 003\n",
      "Correctness: 61 snippets, 84.72 %\n",
      "----------------------------------------------\n",
      "Participant 004\n",
      "Correctness: 55 snippets, 76.39 %\n",
      "----------------------------------------------\n",
      "Participant 005\n",
      "Correctness: 54 snippets, 75.0 %\n",
      "----------------------------------------------\n",
      "Participant 007\n",
      "Correctness: 63 snippets, 87.5 %\n",
      "----------------------------------------------\n",
      "Participant 008\n",
      "Correctness: 62 snippets, 86.11 %\n",
      "----------------------------------------------\n",
      "Participant 009\n",
      "Correctness: 63 snippets, 87.5 %\n",
      "----------------------------------------------\n",
      "Participant 010\n",
      "Correctness: 69 snippets, 95.83 %\n",
      "----------------------------------------------\n",
      "Participant 011\n",
      "Correctness: 61 snippets, 84.72 %\n",
      "----------------------------------------------\n",
      "Participant 012\n",
      "Correctness: 55 snippets, 76.39 %\n",
      "----------------------------------------------\n",
      "Participant 013\n",
      "Correctness: 52 snippets, 72.22 %\n",
      "----------------------------------------------\n",
      "Participant 014\n",
      "Correctness: 53 snippets, 74.65 %\n",
      "----------------------------------------------\n",
      "Participant 015\n",
      "Correctness: 59 snippets, 81.94 %\n",
      "----------------------------------------------\n",
      "Participant 016\n",
      "Correctness: 55 snippets, 76.39 %\n",
      "----------------------------------------------\n",
      "Participant 017\n",
      "Correctness: 56 snippets, 77.78 %\n",
      "----------------------------------------------\n",
      "Participant 018\n",
      "Correctness: 41 snippets, 56.94 %\n",
      "----------------------------------------------\n",
      "Participant 019\n",
      "Correctness: 64 snippets, 88.89 %\n",
      "----------------------------------------------\n",
      "Participant 020\n",
      "Correctness: 59 snippets, 81.94 %\n",
      "----------------------------------------------\n",
      "Participant 021\n",
      "Correctness: 57 snippets, 79.17 %\n",
      "----------------------------------------------\n",
      "Participant 022\n",
      "Correctness: 63 snippets, 87.5 %\n",
      "----------------------------------------------\n",
      "Participant 023\n",
      "Correctness: 59 snippets, 81.94 %\n",
      "----------------------------------------------\n",
      "Participant 024\n",
      "Correctness: 58 snippets, 80.56 %\n",
      "----------------------------------------------\n",
      "Participant 101\n",
      "Correctness: 44 snippets, 61.11 %\n",
      "----------------------------------------------\n",
      "Participant 102\n",
      "Correctness: 56 snippets, 77.78 %\n",
      "----------------------------------------------\n",
      "Participant 106\n",
      "Correctness: 51 snippets, 70.83 %\n",
      "(04/05) Calculate acceptance interval for duration according to Tukey (E+-3 std)\n",
      "Mean duration: 11.720871116299941 std duration 7.651136621039239\n",
      "Acceptance interval: -11.232538746817776 to 34.67428097941766\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Acceptance rate: 1.0\n",
      "(05/05) Mark as excluded\n"
     ]
    }
   ],
   "source": [
    "# Analyze data across all participants\n",
    "print('(01/05) Load behavioral data from all participants')\n",
    "behavioral_data = get_all_behavioral_data(\n",
    "    {participant: get_behavioral_data_path(participant, final_data_exclusion=False) for participant in folders})\n",
    "\n",
    "print('(02/05) Check participant correctness')\n",
    "correctness_data = check_correctness(behavioral_data)\n",
    "fig, axes = plt.subplots(1, 4)\n",
    "print('Correctness subjectwise mean:',\n",
    "      round(correctness_data['mean'].mean()*100, 2), '%',\n",
    "      ', std:', round(correctness_data['mean'].std()*100, 2), '%')\n",
    "correctness_data.boxplot(column=['mean'], ax=axes[0])\n",
    "correctness_data.boxplot(column=['sum'], ax=axes[1])\n",
    "sns.violinplot(correctness_data, y='sum', ax=axes[2])\n",
    "sns.violinplot(correctness_data, y='mean', ax=axes[3])\n",
    "fig.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('(03/05) Mark as excluded')\n",
    "for participant, row in correctness_data.iterrows():\n",
    "    print('----------------------------------------------')\n",
    "    print(PARTICIPANT, participant)\n",
    "    print(BEHAVIORAL_COLUMN_CORRECTNESS+':', row['sum'], 'snippets,',\n",
    "          round(row['mean']*100, 2), '%')\n",
    "    if not row[ACCEPTED]:\n",
    "        print('\\texcluded participant')\n",
    "        update_exclusions(participant, PARTICIPANT, BEHAVIORAL, {\n",
    "                          BEHAVIORAL_COLUMN_CORRECTNESS: '01c2_PRE_BEH_Trial_Exclusion: Correctness too low'})\n",
    "\n",
    "print('(04/05) Calculate acceptance interval for duration according to Tukey (E+-3 std)')\n",
    "behavioral_data = calculate_duration_acceptance(behavioral_data)\n",
    "\n",
    "print('(05/05) Mark as excluded')\n",
    "unaccepted = behavioral_data[~behavioral_data[ACCEPTED]]\n",
    "for participant in unaccepted[PARTICIPANT].unique():\n",
    "    print('----------------------------------------------')\n",
    "    print(PARTICIPANT, participant, type(participant))\n",
    "    snippets = unaccepted[unaccepted[PARTICIPANT]\n",
    "                          == participant][SNIPPET].to_list()\n",
    "    update_exclusions(participant, SNIPPET, BEHAVIORAL, {\n",
    "                      s: '01c2_PRE_BEH_Trial_Exclusion: Behavioral value outside tukey interval' for s in snippets})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ce044dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c4e4078126d495ca5fb4bdde257d9bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/24 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------\n",
      "Participant 003\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 004\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 005\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 007\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 008\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 009\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 010\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 011\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 012\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 013\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 014\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "\t1 snippets were excluded due to behavior for 1 different reasons.\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 015\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 016\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 017\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 018\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 019\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 020\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 021\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 022\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 023\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 024\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 101\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 102\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n",
      "----------------------------------------------\n",
      "Participant 106\n",
      "(01/04) Load behavioral data\n",
      "(02/04) Load exclusions\n",
      "(03/04) Remove excluded snippets\n",
      "(04/04) Save under new path\n"
     ]
    }
   ],
   "source": [
    "participant_data = []\n",
    "for participant in tqdm(folders):\n",
    "    print('----------------------------------------------')\n",
    "    print(PARTICIPANT, participant)\n",
    "\n",
    "    print('(01/04) Load behavioral data')\n",
    "    behavioral_data = pd.read_csv(get_behavioral_data_path(\n",
    "        participant, final_data_exclusion=False), sep=SEPARATOR, index_col=False, dtype={PARTICIPANT: str})\n",
    "\n",
    "    print('(02/04) Load exclusions')\n",
    "    exclusions = get_exclusions(\n",
    "        participant, [PARTICIPANT, SNIPPET], [BEHAVIORAL])\n",
    "    excluded_participant: list[str] = exclusions[PARTICIPANT][BEHAVIORAL]\n",
    "    excluded_snippets: dict[str, list[str]] = {snippet: exclusions[SNIPPET][snippet][BEHAVIORAL]\n",
    "                                               for snippet in exclusions[SNIPPET] if exclusions[SNIPPET][snippet][BEHAVIORAL]}\n",
    "\n",
    "    print('(03/04) Remove excluded snippets')\n",
    "    if excluded_participant:\n",
    "        print(f'\\tExclude participant')\n",
    "        behavioral_data = behavioral_data.drop(behavioral_data.index)\n",
    "    else:\n",
    "        if excluded_snippets:\n",
    "            possible_reasons = set(\n",
    "                r for reasons in excluded_snippets.values() for r in reasons)\n",
    "            print(\n",
    "                f'\\t{len(excluded_snippets)} snippets were excluded due to behavior for {len(possible_reasons)} different reasons.')\n",
    "            behavioral_data = behavioral_data[~behavioral_data[SNIPPET].isin(\n",
    "                excluded_snippets.keys())]\n",
    "\n",
    "    print('(04/04) Save under new path')\n",
    "    behavioral_data.to_csv(get_behavioral_data_path(\n",
    "        participant, final_data_exclusion=True), sep=SEPARATOR, index=False)\n",
    "    participant_data.append(behavioral_data)\n",
    "behavioral_data = pd.concat(participant_data)\n",
    "behavioral_data.to_csv(get_behavioral_data_path(\n",
    "    final_data_exclusion=True), sep=SEPARATOR, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf25b8ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "behavioral_data = pd.read_csv(get_behavioral_data_path(final_data_exclusion=True), sep=SEPARATOR, index_col=False, dtype={PARTICIPANT: str})\n",
    "behavioral_data[BEHAVIORAL_COLUMN_CORRECTNESS] = behavioral_data[BEHAVIORAL_COLUMN_CORRECTNESS]*1\n",
    "behavioral_data[BEHAVIORAL_COLUMN_RATING] = behavioral_data[BEHAVIORAL_COLUMN_RATING].apply(\n",
    "    lambda v: (v == RATING_EASY))*1\n",
    "behavioral_data[SNIPPET_COLUMN_NUMBER] = behavioral_data[SNIPPET_COLUMN_NUMBER].astype(\n",
    "    'int').astype('str').apply(lambda v: \"S\"+v)\n",
    "behavioral_data[PARTICIPANT] = behavioral_data[PARTICIPANT].apply(lambda v: \"P\"+str(v))\n",
    "behavioral_data['Trial_No'] = 0\n",
    "behavioral_data['Block_No'] = 0\n",
    "behavioral_data['In_Block_No'] = 0\n",
    "behavioral_data = behavioral_data.sort_values(\n",
    "    [PARTICIPANT, HDF_INDEX, BEHAVIORAL_COLUMN_START])\n",
    "for participant in behavioral_data[PARTICIPANT].unique():\n",
    "    participant_data = behavioral_data[behavioral_data[PARTICIPANT] == participant]\n",
    "    trial_no = 0\n",
    "    block_no = 0\n",
    "    in_block_no = 0\n",
    "    version = ''\n",
    "    for i, row in participant_data.iterrows():\n",
    "        trial_no += 1\n",
    "        behavioral_data.at[i, 'Trial_No'] = trial_no\n",
    "        if row[SNIPPET_COLUMN_VERSION] != version:\n",
    "            block_no += 1\n",
    "            version = row[SNIPPET_COLUMN_VERSION]\n",
    "            in_block_no = 0\n",
    "        in_block_no += 1\n",
    "        behavioral_data.at[i, 'Block_No'] = block_no\n",
    "        behavioral_data.at[i, 'In_Block_No'] = in_block_no\n",
    "behavioral_data[BEHAVIORAL_COLUMN_DURATION_LOG] = np.log(behavioral_data[BEHAVIORAL_COLUMN_DURATION])\n",
    "behavioral_data.to_csv(get_behavioral_data_path(final_data_exclusion=True, analysis_prep=True), sep=SEPARATOR, index=False)"
   ]
  }
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