sample-usage.ipynb

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      "   participant_decision participant_certaintity model_class  model_probability\n",
      "57              trujący           średnio pewny     jadalny               0.54\n",
      "58              jadalny           średnio pewny     jadalny               0.95\n",
      "59              jadalny      zdecydowanie pewny     trujący               1.00\n"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speaker_id</th>\n",
       "      <th>problem_id</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
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       "      <th>180</th>\n",
       "      <td>MW_SSH_05</td>\n",
       "      <td>__P1__</td>\n",
       "      <td>Ok. Średnica 17,30... Mogę po całej kartce?</td>\n",
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       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>MW</td>\n",
       "      <td>__P1__</td>\n",
       "      <td>Jasne, proszę sobie mazać spokojnie.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>MW_SSH_05</td>\n",
       "      <td>__P1__</td>\n",
       "      <td>Ok to wróćmy do… To była wysokość, średnica ka...</td>\n",
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       "    speaker_id problem_id                                               text\n",
       "180  MW_SSH_05     __P1__        Ok. Średnica 17,30... Mogę po całej kartce?\n",
       "181         MW     __P1__               Jasne, proszę sobie mazać spokojnie.\n",
       "182  MW_SSH_05     __P1__  Ok to wróćmy do… To była wysokość, średnica ka..."
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   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "#select participant which ouy would like to investigate\n",
    "participant_id = 'MW_SSH_05'\n",
    "\n",
    "problems = pd.read_csv('./zenodo/PROBLEMS.csv')\n",
    "responses = pd.read_csv('./zenodo/PROBLEMS_RESPONSES.csv')\n",
    "merged_problems = pd.merge(responses,problems, on='problem_id')\n",
    "\n",
    "prediction_features = ['participant_decision','participant_certaintity',\n",
    "                       'model_class','model_probability']\n",
    "print(merged_problems[merged_problems['participant_id']==participant_id][prediction_features])\n",
    "\n",
    "#retrieve related text that correspond to the problem P1 of a particiapnt MZ_SSH_04\n",
    "transcripts = pd.read_csv(f'./zenodo/transcripts/{participant_id}.csv')\n",
    "transcripts['problem_id'] = transcripts['problem_id'].ffill().dropna()\n",
    "transcripts[transcripts['problem_id'] == '__P1__'].dropna(axis=1)"
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