sentiment_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52a33f1b",
   "metadata": {},
   "source": [
    "Tamanna A <br>\n",
    "Uni: ta2642\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "id": "b09c7c97",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import parselmouth \n",
    "import statistics\n",
    "from praatio import textgrid\n",
    "import math\n",
    "from parselmouth.praat import call\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e7fa56",
   "metadata": {},
   "source": [
    "### file format\n",
    "\n",
    "--- speaker_session_emotion_start-time_content.wav <br/>\n",
    "---        gg_001_  shame_1998.89_July-eighteenth.wav <br/>\n",
    "--- 7 speakers (cc, cl, gg, jg, mf, mk, mm) <br/>\n",
    "--- labeled with 15 emotions <br/>\n",
    "    (anxiety, boredom, cold-anger, contempt, despair, disgust, elation, happy, hot-anger, interest, neutral, panic, pride, sadness, shame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "id": "3cfa7027",
   "metadata": {},
   "outputs": [],
   "source": [
    "speakers = ['cc', 'cl', 'gg', 'jg', 'mf', 'mk', 'mm']\n",
    "emotions = ['anxiety', 'boredom', 'cold-anger', 'contempt', 'despair', 'disgust', 'elation',\n",
    "            'happy', 'hot-anger', 'interest', 'neutral', 'panic', 'pride', 'sadness', 'shame']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "9e4a88d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "speakers_size = {}\n",
    "for speaker in speakers:\n",
    "    path = \"hw3_speech_files/*{input}\".format(input= speaker)\n",
    "    files = glob.glob(path+\"*.wav\")\n",
    "    speakers_size[speaker] = len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "82181e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cc': 265, 'cl': 368, 'gg': 420, 'jg': 273, 'mf': 299, 'mk': 397, 'mm': 302}"
      ]
     },
     "execution_count": 388,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speakers_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "775614fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions_size = {}\n",
    "for emotion in emotions:\n",
    "    path = \"hw3_speech_files/*{input}\".format(input= emotion)\n",
    "    files = glob.glob(path+\"*.wav\")\n",
    "    emotions_size[emotion] = len(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9f0bf77e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'anxiety': 170,\n",
       " 'boredom': 154,\n",
       " 'cold-anger': 154,\n",
       " 'contempt': 180,\n",
       " 'despair': 174,\n",
       " 'disgust': 172,\n",
       " 'elation': 159,\n",
       " 'happy': 177,\n",
       " 'hot-anger': 139,\n",
       " 'interest': 176,\n",
       " 'neutral': 79,\n",
       " 'panic': 141,\n",
       " 'pride': 150,\n",
       " 'sadness': 151,\n",
       " 'shame': 148}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a033197",
   "metadata": {},
   "outputs": [],
   "source": [
    "def measurePitch(voiceID, f0min, f0max, unit):\n",
    "    sound = parselmouth.Sound(voiceID)  # read the sound\n",
    "    duration = call(sound, \"Get total duration\")  # duration\n",
    "    pitch = call(sound, \"To Pitch\", 0.0, f0min, f0max)  # create a praat pitch object\n",
    "    mean_pitch = call(pitch, \"Get mean\", 0, 0, unit)  # get mean pitch\n",
    "    min_pitch = call(pitch, \"Get minimum\", 0, 0, \"Hertz\", \"parabolic\")\n",
    "    max_pitch = call(pitch, \"Get maximum\", 0, 0, \"Hertz\", \"parabolic\")\n",
    "    stdev_pitch = call(pitch, \"Get standard deviation\", 0, 0, unit)  # get standard deviation\n",
    "    intensity = call(sound, \"To Intensity\",100, 0, \"yes\")\n",
    "    mean_int = call(intensity, 'Get mean', 0, 0, \"energy\")\n",
    "    stdev_int = call(intensity, 'Get standard deviation', 0, 0)\n",
    "    min_int = call(intensity, \"Get minimum\", 0, 0,  \"parabolic\")\n",
    "    max_int = call(intensity, \"Get maximum\", 0, 0,  \"parabolic\")\n",
    "    \n",
    "    return min_pitch, max_pitch, mean_pitch, stdev_pitch, min_int, max_int, mean_int,\\\n",
    "           stdev_int, duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0a7b58e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['file_name', 'speaker', 'session', 'emotion','start_time', 'content', \n",
    "        'min_pitch','max_pitch', 'mean_pitch', 'stdev_pitch', \n",
    "        'min_int', 'max_int', 'mean_int','stdev_int', 'duration']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "04164424",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "-\n",
      "2324\n"
     ]
    }
   ],
   "source": [
    "#read through each file & extract praat features- pitch & intensity\n",
    "speech_file = []\n",
    "for wave_file in glob.glob(\"hw3_speech_files/*.wav\"):\n",
    "    parts = wave_file.split(\"/\")[1].split(\"_\")\n",
    "    speaker, session, emotion,start_time, content = parts\n",
    "    #speech_file.append([wave_file, speaker, session, emotion,start_time, content])\n",
    "    \n",
    "    \n",
    "    sound = parselmouth.Sound(wave_file)\n",
    "    (min_pitch, max_pitch, mean_pitch, stdev_pitch, min_int, max_int, mean_int,\n",
    "    stdev_int, duration) = measurePitch(sound, 75, 600, \"Hertz\")\n",
    "    \n",
    "    speech_file.append([wave_file, speaker, session, emotion,start_time, content,\n",
    "               min_pitch, max_pitch, mean_pitch, stdev_pitch, \n",
    "                min_int, max_int, mean_int,\n",
    "                stdev_int, duration])\n",
    "    #print(speech_file)\n",
    "    if(len(speech_file)%100 == 0): \n",
    "        print(\"-\")\n",
    "    \n",
    "print(len(speech_file))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e0be0d96",
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert to dataframe \n",
    "df = pd.DataFrame(speech_file, columns= cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "6e37ba1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"speech_file.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "id": "d403b6f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"speech_file.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "59b273e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2324, 15)"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "id": "03fac6a7",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>file_name</th>\n",
       "      <th>speaker</th>\n",
       "      <th>session</th>\n",
       "      <th>emotion</th>\n",
       "      <th>start_time</th>\n",
       "      <th>content</th>\n",
       "      <th>min_pitch</th>\n",
       "      <th>max_pitch</th>\n",
       "      <th>mean_pitch</th>\n",
       "      <th>stdev_pitch</th>\n",
       "      <th>min_int</th>\n",
       "      <th>max_int</th>\n",
       "      <th>mean_int</th>\n",
       "      <th>stdev_int</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>hw3_speech_files/mf_001_interest_2590.84_Eight...</td>\n",
       "      <td>mf</td>\n",
       "      <td>1</td>\n",
       "      <td>interest</td>\n",
       "      <td>2590.84</td>\n",
       "      <td>Eight-hundred-two.wav</td>\n",
       "      <td>136.715830</td>\n",
       "      <td>396.699742</td>\n",
       "      <td>298.642767</td>\n",
       "      <td>78.437169</td>\n",
       "      <td>34.527183</td>\n",
       "      <td>68.962233</td>\n",
       "      <td>61.546226</td>\n",
       "      <td>9.669914</td>\n",
       "      <td>0.980045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hw3_speech_files/mf_001_anxiety_1171.28_Six-hu...</td>\n",
       "      <td>mf</td>\n",
       "      <td>1</td>\n",
       "      <td>anxiety</td>\n",
       "      <td>1171.28</td>\n",
       "      <td>Six-hundred-one.wav</td>\n",
       "      <td>146.854814</td>\n",
       "      <td>240.335429</td>\n",
       "      <td>164.339495</td>\n",
       "      <td>21.670631</td>\n",
       "      <td>43.172705</td>\n",
       "      <td>71.176645</td>\n",
       "      <td>65.984831</td>\n",
       "      <td>7.274682</td>\n",
       "      <td>0.930068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hw3_speech_files/cc_001_pride_2501.34_March-tw...</td>\n",
       "      <td>cc</td>\n",
       "      <td>1</td>\n",
       "      <td>pride</td>\n",
       "      <td>2501.34</td>\n",
       "      <td>March-twenty-fifth.wav</td>\n",
       "      <td>92.661032</td>\n",
       "      <td>563.976473</td>\n",
       "      <td>150.376392</td>\n",
       "      <td>115.939684</td>\n",
       "      <td>32.748692</td>\n",
       "      <td>70.481592</td>\n",
       "      <td>62.342660</td>\n",
       "      <td>10.535670</td>\n",
       "      <td>1.060045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hw3_speech_files/cc_001_boredom_2278.62_Six-hu...</td>\n",
       "      <td>cc</td>\n",
       "      <td>1</td>\n",
       "      <td>boredom</td>\n",
       "      <td>2278.62</td>\n",
       "      <td>Six-hundred-six.wav</td>\n",
       "      <td>88.371444</td>\n",
       "      <td>218.513929</td>\n",
       "      <td>128.143332</td>\n",
       "      <td>49.189179</td>\n",
       "      <td>30.972236</td>\n",
       "      <td>67.951672</td>\n",
       "      <td>61.125778</td>\n",
       "      <td>10.447063</td>\n",
       "      <td>1.490068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hw3_speech_files/jg_001_panic_443.70_Fifty-sev...</td>\n",
       "      <td>jg</td>\n",
       "      <td>1</td>\n",
       "      <td>panic</td>\n",
       "      <td>443.70</td>\n",
       "      <td>Fifty-seven.wav</td>\n",
       "      <td>146.097492</td>\n",
       "      <td>232.795872</td>\n",
       "      <td>177.215006</td>\n",
       "      <td>23.242049</td>\n",
       "      <td>27.717918</td>\n",
       "      <td>64.319924</td>\n",
       "      <td>53.859348</td>\n",
       "      <td>10.308829</td>\n",
       "      <td>1.300091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                                           file_name speaker  session  \\\n",
       "0  hw3_speech_files/mf_001_interest_2590.84_Eight...      mf        1   \n",
       "1  hw3_speech_files/mf_001_anxiety_1171.28_Six-hu...      mf        1   \n",
       "2  hw3_speech_files/cc_001_pride_2501.34_March-tw...      cc        1   \n",
       "3  hw3_speech_files/cc_001_boredom_2278.62_Six-hu...      cc        1   \n",
       "4  hw3_speech_files/jg_001_panic_443.70_Fifty-sev...      jg        1   \n",
       "\n",
       "    emotion  start_time                 content   min_pitch   max_pitch  \\\n",
       "0  interest     2590.84   Eight-hundred-two.wav  136.715830  396.699742   \n",
       "1   anxiety     1171.28     Six-hundred-one.wav  146.854814  240.335429   \n",
       "2     pride     2501.34  March-twenty-fifth.wav   92.661032  563.976473   \n",
       "3   boredom     2278.62     Six-hundred-six.wav   88.371444  218.513929   \n",
       "4     panic      443.70         Fifty-seven.wav  146.097492  232.795872   \n",
       "\n",
       "   mean_pitch  stdev_pitch    min_int    max_int   mean_int  stdev_int  \\\n",
       "0  298.642767    78.437169  34.527183  68.962233  61.546226   9.669914   \n",
       "1  164.339495    21.670631  43.172705  71.176645  65.984831   7.274682   \n",
       "2  150.376392   115.939684  32.748692  70.481592  62.342660  10.535670   \n",
       "3  128.143332    49.189179  30.972236  67.951672  61.125778  10.447063   \n",
       "4  177.215006    23.242049  27.717918  64.319924  53.859348  10.308829   \n",
       "\n",
       "   duration  \n",
       "0  0.980045  \n",
       "1  0.930068  \n",
       "2  1.060045  \n",
       "3  1.490068  \n",
       "4  1.300091  "
      ]
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     "execution_count": 439,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a85540",
   "metadata": {},
   "source": [
    "## z- score standard deviation\n",
    "\n",
    "1. standard deviation of all pitch data (min, max, mean )\n",
    "        \n",
    "        ````\n",
    "        speaker_A_feature = (speaker_A_feature - speaker_A_feature_mean)/speaker_A_feature_stdev)\n",
    "        ````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "bea0462a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/ipykernel_48493/4019950504.py:5: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  test_df = (test_df - test_df.mean())/test_df.std()\n"
     ]
    }
   ],
   "source": [
    "all_normalized = []\n",
    "df_normalized = pd.DataFrame()\n",
    "for name in speakers:\n",
    "    test_df = df[df[\"speaker\"]== name]\n",
    "    test_df = (test_df - test_df.mean())/test_df.std()\n",
    "    test_df['speaker'] = name\n",
    "    all_normalized.append(test_df)\n",
    "    \n",
    "df_normalized = pd.concat(all_normalized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "id": "1707221d",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>max_int</th>\n",
       "      <th>max_pitch</th>\n",
       "      <th>mean_int</th>\n",
       "      <th>mean_pitch</th>\n",
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       "      <th>start_time</th>\n",
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       "      <th>stdev_pitch</th>\n",
       "      <th>emotion</th>\n",
       "      <th>file_name</th>\n",
       "      <th>content</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.148291</td>\n",
       "      <td>-0.928583</td>\n",
       "      <td>1.751061</td>\n",
       "      <td>-0.988946</td>\n",
       "      <td>-0.324479</td>\n",
       "      <td>0.053597</td>\n",
       "      <td>-0.395732</td>\n",
       "      <td>cc</td>\n",
       "      <td>1.055043</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.115172</td>\n",
       "      <td>-1.375405</td>\n",
       "      <td>-0.507559</td>\n",
       "      <td>-1.215096</td>\n",
       "      <td>-0.640843</td>\n",
       "      <td>-0.285873</td>\n",
       "      <td>-0.504520</td>\n",
       "      <td>cc</td>\n",
       "      <td>0.794416</td>\n",
       "      <td>-0.434770</td>\n",
       "      <td>0.118113</td>\n",
       "      <td>boredom</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>-0.117644</td>\n",
       "      <td>-0.526627</td>\n",
       "      <td>-0.620651</td>\n",
       "      <td>-1.087183</td>\n",
       "      <td>-0.820747</td>\n",
       "      <td>0.405863</td>\n",
       "      <td>-0.607038</td>\n",
       "      <td>cc</td>\n",
       "      <td>1.122763</td>\n",
       "      <td>-1.644193</td>\n",
       "      <td>-0.282050</td>\n",
       "      <td>pride</td>\n",
       "      <td>hw3_speech_files/cc_001_pride_2559.21_Six-hund...</td>\n",
       "      <td>Six-hundred-five.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.115144</td>\n",
       "      <td>-0.437835</td>\n",
       "      <td>-0.662376</td>\n",
       "      <td>-0.450112</td>\n",
       "      <td>-0.656590</td>\n",
       "      <td>0.667536</td>\n",
       "      <td>-0.571046</td>\n",
       "      <td>cc</td>\n",
       "      <td>0.935460</td>\n",
       "      <td>-0.386590</td>\n",
       "      <td>-0.603856</td>\n",
       "      <td>shame</td>\n",
       "      <td>hw3_speech_files/cc_001_shame_2399.15_Eight-th...</td>\n",
       "      <td>Eight-thousand-nine.wav</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>-0.056404</td>\n",
       "      <td>1.598493</td>\n",
       "      <td>-0.142242</td>\n",
       "      <td>1.452664</td>\n",
       "      <td>0.729602</td>\n",
       "      <td>0.551045</td>\n",
       "      <td>1.765625</td>\n",
       "      <td>cc</td>\n",
       "      <td>-0.244022</td>\n",
       "      <td>0.503696</td>\n",
       "      <td>-0.632013</td>\n",
       "      <td>cold-anger</td>\n",
       "      <td>hw3_speech_files/cc_001_cold-anger_1391.22_Dec...</td>\n",
       "      <td>December-eighth.wav</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    duration   max_int  max_pitch  mean_int  mean_pitch   min_int  min_pitch  \\\n",
       "2  -0.148291 -0.928583   1.751061 -0.988946   -0.324479  0.053597  -0.395732   \n",
       "3   0.115172 -1.375405  -0.507559 -1.215096   -0.640843 -0.285873  -0.504520   \n",
       "31 -0.117644 -0.526627  -0.620651 -1.087183   -0.820747  0.405863  -0.607038   \n",
       "44  0.115144 -0.437835  -0.662376 -0.450112   -0.656590  0.667536  -0.571046   \n",
       "45 -0.056404  1.598493  -0.142242  1.452664    0.729602  0.551045   1.765625   \n",
       "\n",
       "   speaker  start_time  stdev_int  stdev_pitch     emotion  \\\n",
       "2       cc    1.055043  -0.381025     2.054869       pride   \n",
       "3       cc    0.794416  -0.434770     0.118113     boredom   \n",
       "31      cc    1.122763  -1.644193    -0.282050       pride   \n",
       "44      cc    0.935460  -0.386590    -0.603856       shame   \n",
       "45      cc   -0.244022   0.503696    -0.632013  cold-anger   \n",
       "\n",
       "                                            file_name                  content  \n",
       "2   hw3_speech_files/cc_001_pride_2501.34_March-tw...   March-twenty-fifth.wav  \n",
       "3   hw3_speech_files/cc_001_boredom_2278.62_Six-hu...      Six-hundred-six.wav  \n",
       "31  hw3_speech_files/cc_001_pride_2559.21_Six-hund...     Six-hundred-five.wav  \n",
       "44  hw3_speech_files/cc_001_shame_2399.15_Eight-th...  Eight-thousand-nine.wav  \n",
       "45  hw3_speech_files/cc_001_cold-anger_1391.22_Dec...      December-eighth.wav  "
      ]
     },
     "execution_count": 455,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_normalized.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "74fbe1b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_normalized = df_normalized.drop(columns = {'emotion', 'session', 'file_name', 'content'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "id": "c0448d72",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_normalized= df_normalized.merge(df['emotion'], left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "id": "6bcfd5f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_normalized= df_normalized.merge(df['file_name'], left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "id": "ef574d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_normalized= df_normalized.merge(df['content'], left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "c1e151a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_part1(feature):\n",
    "    fig, axs = plt.subplots(1,2, figsize = (15,5))\n",
    "    fig.tight_layout()\n",
    "    plt.subplots_adjust(wspace = 0.5)\n",
    "\n",
    "    axs[0].errorbar(x = emotions, \n",
    "                    y = df.groupby('emotion')[feature].mean(),\n",
    "                yerr = df.groupby('emotion')[feature].std(),\n",
    "                 fmt = 'o',\n",
    "                 elinewidth = 1, capsize=5, ecolor = \"lightgreen\")\n",
    "\n",
    "    axs[0].tick_params(labelrotation=45)\n",
    "    axs[0].set_title(f'{feature} without normalization')\n",
    "\n",
    "    axs[1].errorbar(x = emotions, \n",
    "                    y = df_normalized.groupby('emotion')[feature].mean(),\n",
    "                yerr = df_normalized.groupby('emotion')[feature].std(),\n",
    "                 fmt = 'o',\n",
    "                 elinewidth = 1, capsize=5, ecolor = \"lightgreen\")\n",
    "\n",
    "    axs[1].tick_params(labelrotation=45)\n",
    "    axs[1].set_title(f'{feature} with normalization')\n",
    "\n",
    "    return axs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "id": "72db44ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = [ 'min_pitch', 'mean_pitch', 'max_pitch']\n",
    "for f in feature:\n",
    "    axs = plot_part1(f)\n",
    "    axs[0].set_ylim(ymin=50,ymax=600)\n",
    "    axs[0].set_ylabel('pitch')\n",
    "    axs[1].set_ylim(ymin = -3, ymax = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "id": "a0b7e630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature = [ 'min_int', 'mean_int', 'max_int']\n",
    "for f in feature:\n",
    "    axs = plot_part1(f)\n",
    "    axs[0].set_ylabel('intensity')\n",
    "    axs[0].set_ylim(ymin=20,ymax=100)\n",
    "    axs[1].set_ylim(ymin = -3, ymax = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3ca1d16",
   "metadata": {},
   "source": [
    "### OpenSmile- Feature Extraction\n",
    "\n",
    "#### Steps taken\n",
    "1. Run the following bash script for each .wav file and get csv files for each\n",
    "    - Using ISO9-emotion configuration\n",
    "2. Get the column names from one of the csv files\n",
    "3. Combine all of the csv files into one dataframe\n",
    "4. Normalize features for each speaker\n",
    "-- bash script <br/>\n",
    "./build/progsrc/smilextract/SMILExtract -C config/is09-13/IS09_emotion.conf -I ../hw3_speech_files/*.wav -O ../files_smile.csv\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a94db271",
   "metadata": {},
   "source": [
    "#### 1. Run the following bash script for each .wav"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "20f62883",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e17001b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmd_template = 'opensmile/build/progsrc/smilextract/SMILExtract -C opensmile/config/is09-13/IS09_emotion.conf -I {wave_file} -O speech_files_features/{output_file_name}.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7fee5dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "for wave_file in glob.glob(\"hw3_speech_files/*.wav\"):\n",
    "    file_name = wave_file.split(\"/\")[1].split(\".w\")[0]\n",
    "    cmd = cmd_template.format(wave_file = wave_file, output_file_name = file_name)\n",
    "    os.system(cmd)\n",
    "    #print(cmd)\n",
    "    #print(file_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "891aa25f",
   "metadata": {},
   "source": [
    "#### 2. Get the column names from one of the csv files\n",
    "\n",
    "- column names on lines 4 - 388\n",
    "    - @attribute pcm_RMSenergy_sma_max numeric\n",
    "- field on line 392"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf814090",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>full_col_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>@attribute name string</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>@attribute pcm_RMSenergy_sma_max numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>@attribute pcm_RMSenergy_sma_min numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>@attribute pcm_RMSenergy_sma_range numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>@attribute pcm_RMSenergy_sma_maxPos numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>@attribute F0_sma_de_linregerrQ numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>@attribute F0_sma_de_stddev numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>@attribute F0_sma_de_skewness numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>@attribute F0_sma_de_kurtosis numeric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>@attribute class numeric</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>386 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   full_col_name\n",
       "0                         @attribute name string\n",
       "1       @attribute pcm_RMSenergy_sma_max numeric\n",
       "2       @attribute pcm_RMSenergy_sma_min numeric\n",
       "3     @attribute pcm_RMSenergy_sma_range numeric\n",
       "4    @attribute pcm_RMSenergy_sma_maxPos numeric\n",
       "..                                           ...\n",
       "381      @attribute F0_sma_de_linregerrQ numeric\n",
       "382          @attribute F0_sma_de_stddev numeric\n",
       "383        @attribute F0_sma_de_skewness numeric\n",
       "384        @attribute F0_sma_de_kurtosis numeric\n",
       "385                     @attribute class numeric\n",
       "\n",
       "[386 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names = pd.read_csv(\"speech_files_features/cc_001_anxiety_910.77_May-twenty-third.csv\", \n",
    "                           nrows = 386, skiprows = 0, header = 0, names = [\"full_col_name\"])\n",
    "column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2df056cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "new = column_names['full_col_name'].str.split(\" \", expand = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9510572e",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = new[1].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "id": "aba48ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 528,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "id": "07b4b179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pcm_RMSenergy_sma_max',\n",
       " 'pcm_RMSenergy_sma_min',\n",
       " 'pcm_RMSenergy_sma_range',\n",
       " 'pcm_RMSenergy_sma_maxPos',\n",
       " 'pcm_RMSenergy_sma_minPos',\n",
       " 'pcm_RMSenergy_sma_amean',\n",
       " 'pcm_RMSenergy_sma_linregc1',\n",
       " 'pcm_RMSenergy_sma_linregc2',\n",
       " 'pcm_RMSenergy_sma_linregerrQ',\n",
       " 'pcm_RMSenergy_sma_stddev',\n",
       " 'pcm_RMSenergy_sma_skewness',\n",
       " 'pcm_RMSenergy_sma_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[1]_max',\n",
       " 'pcm_fftMag_mfcc_sma[1]_min',\n",
       " 'pcm_fftMag_mfcc_sma[1]_range',\n",
       " 'pcm_fftMag_mfcc_sma[1]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[1]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[1]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[1]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[1]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[1]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[1]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[1]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[1]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[2]_max',\n",
       " 'pcm_fftMag_mfcc_sma[2]_min',\n",
       " 'pcm_fftMag_mfcc_sma[2]_range',\n",
       " 'pcm_fftMag_mfcc_sma[2]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[2]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[2]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[2]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[2]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[2]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[2]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[2]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[2]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[3]_max',\n",
       " 'pcm_fftMag_mfcc_sma[3]_min',\n",
       " 'pcm_fftMag_mfcc_sma[3]_range',\n",
       " 'pcm_fftMag_mfcc_sma[3]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[3]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[3]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[3]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[3]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[3]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[3]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[3]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[3]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[4]_max',\n",
       " 'pcm_fftMag_mfcc_sma[4]_min',\n",
       " 'pcm_fftMag_mfcc_sma[4]_range',\n",
       " 'pcm_fftMag_mfcc_sma[4]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[4]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[4]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[4]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[4]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[4]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[4]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[4]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[4]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[5]_max',\n",
       " 'pcm_fftMag_mfcc_sma[5]_min',\n",
       " 'pcm_fftMag_mfcc_sma[5]_range',\n",
       " 'pcm_fftMag_mfcc_sma[5]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[5]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[5]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[5]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[5]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[5]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[5]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[5]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[5]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[6]_max',\n",
       " 'pcm_fftMag_mfcc_sma[6]_min',\n",
       " 'pcm_fftMag_mfcc_sma[6]_range',\n",
       " 'pcm_fftMag_mfcc_sma[6]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[6]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[6]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[6]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[6]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[6]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[6]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[6]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[6]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[7]_max',\n",
       " 'pcm_fftMag_mfcc_sma[7]_min',\n",
       " 'pcm_fftMag_mfcc_sma[7]_range',\n",
       " 'pcm_fftMag_mfcc_sma[7]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[7]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[7]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[7]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[7]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[7]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[7]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[7]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[7]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[8]_max',\n",
       " 'pcm_fftMag_mfcc_sma[8]_min',\n",
       " 'pcm_fftMag_mfcc_sma[8]_range',\n",
       " 'pcm_fftMag_mfcc_sma[8]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[8]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[8]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[8]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[8]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[8]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[8]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[8]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[8]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[9]_max',\n",
       " 'pcm_fftMag_mfcc_sma[9]_min',\n",
       " 'pcm_fftMag_mfcc_sma[9]_range',\n",
       " 'pcm_fftMag_mfcc_sma[9]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[9]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[9]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[9]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[9]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[9]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[9]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[9]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[9]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[10]_max',\n",
       " 'pcm_fftMag_mfcc_sma[10]_min',\n",
       " 'pcm_fftMag_mfcc_sma[10]_range',\n",
       " 'pcm_fftMag_mfcc_sma[10]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[10]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[10]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[10]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[10]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[10]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[10]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[10]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[10]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[11]_max',\n",
       " 'pcm_fftMag_mfcc_sma[11]_min',\n",
       " 'pcm_fftMag_mfcc_sma[11]_range',\n",
       " 'pcm_fftMag_mfcc_sma[11]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[11]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[11]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[11]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[11]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[11]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[11]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[11]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[11]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[12]_max',\n",
       " 'pcm_fftMag_mfcc_sma[12]_min',\n",
       " 'pcm_fftMag_mfcc_sma[12]_range',\n",
       " 'pcm_fftMag_mfcc_sma[12]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[12]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[12]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[12]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[12]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[12]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[12]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[12]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[12]_kurtosis',\n",
       " 'pcm_zcr_sma_max',\n",
       " 'pcm_zcr_sma_min',\n",
       " 'pcm_zcr_sma_range',\n",
       " 'pcm_zcr_sma_maxPos',\n",
       " 'pcm_zcr_sma_minPos',\n",
       " 'pcm_zcr_sma_amean',\n",
       " 'pcm_zcr_sma_linregc1',\n",
       " 'pcm_zcr_sma_linregc2',\n",
       " 'pcm_zcr_sma_linregerrQ',\n",
       " 'pcm_zcr_sma_stddev',\n",
       " 'pcm_zcr_sma_skewness',\n",
       " 'pcm_zcr_sma_kurtosis',\n",
       " 'voiceProb_sma_max',\n",
       " 'voiceProb_sma_min',\n",
       " 'voiceProb_sma_range',\n",
       " 'voiceProb_sma_maxPos',\n",
       " 'voiceProb_sma_minPos',\n",
       " 'voiceProb_sma_amean',\n",
       " 'voiceProb_sma_linregc1',\n",
       " 'voiceProb_sma_linregc2',\n",
       " 'voiceProb_sma_linregerrQ',\n",
       " 'voiceProb_sma_stddev',\n",
       " 'voiceProb_sma_skewness',\n",
       " 'voiceProb_sma_kurtosis',\n",
       " 'F0_sma_max',\n",
       " 'F0_sma_min',\n",
       " 'F0_sma_range',\n",
       " 'F0_sma_maxPos',\n",
       " 'F0_sma_minPos',\n",
       " 'F0_sma_amean',\n",
       " 'F0_sma_linregc1',\n",
       " 'F0_sma_linregc2',\n",
       " 'F0_sma_linregerrQ',\n",
       " 'F0_sma_stddev',\n",
       " 'F0_sma_skewness',\n",
       " 'F0_sma_kurtosis',\n",
       " 'pcm_RMSenergy_sma_de_max',\n",
       " 'pcm_RMSenergy_sma_de_min',\n",
       " 'pcm_RMSenergy_sma_de_range',\n",
       " 'pcm_RMSenergy_sma_de_maxPos',\n",
       " 'pcm_RMSenergy_sma_de_minPos',\n",
       " 'pcm_RMSenergy_sma_de_amean',\n",
       " 'pcm_RMSenergy_sma_de_linregc1',\n",
       " 'pcm_RMSenergy_sma_de_linregc2',\n",
       " 'pcm_RMSenergy_sma_de_linregerrQ',\n",
       " 'pcm_RMSenergy_sma_de_stddev',\n",
       " 'pcm_RMSenergy_sma_de_skewness',\n",
       " 'pcm_RMSenergy_sma_de_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_kurtosis',\n",
       " 'pcm_zcr_sma_de_max',\n",
       " 'pcm_zcr_sma_de_min',\n",
       " 'pcm_zcr_sma_de_range',\n",
       " 'pcm_zcr_sma_de_maxPos',\n",
       " 'pcm_zcr_sma_de_minPos',\n",
       " 'pcm_zcr_sma_de_amean',\n",
       " 'pcm_zcr_sma_de_linregc1',\n",
       " 'pcm_zcr_sma_de_linregc2',\n",
       " 'pcm_zcr_sma_de_linregerrQ',\n",
       " 'pcm_zcr_sma_de_stddev',\n",
       " 'pcm_zcr_sma_de_skewness',\n",
       " 'pcm_zcr_sma_de_kurtosis',\n",
       " 'voiceProb_sma_de_max',\n",
       " 'voiceProb_sma_de_min',\n",
       " 'voiceProb_sma_de_range',\n",
       " 'voiceProb_sma_de_maxPos',\n",
       " 'voiceProb_sma_de_minPos',\n",
       " 'voiceProb_sma_de_amean',\n",
       " 'voiceProb_sma_de_linregc1',\n",
       " 'voiceProb_sma_de_linregc2',\n",
       " 'voiceProb_sma_de_linregerrQ',\n",
       " 'voiceProb_sma_de_stddev',\n",
       " 'voiceProb_sma_de_skewness',\n",
       " 'voiceProb_sma_de_kurtosis',\n",
       " 'F0_sma_de_max',\n",
       " 'F0_sma_de_min',\n",
       " 'F0_sma_de_range',\n",
       " 'F0_sma_de_maxPos',\n",
       " 'F0_sma_de_minPos',\n",
       " 'F0_sma_de_amean',\n",
       " 'F0_sma_de_linregc1',\n",
       " 'F0_sma_de_linregc2',\n",
       " 'F0_sma_de_linregerrQ',\n",
       " 'F0_sma_de_stddev',\n",
       " 'F0_sma_de_skewness',\n",
       " 'F0_sma_de_kurtosis']"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "id": "933eaadc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "list.remove(x): x not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/ipykernel_48493/1285864289.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcolumn_names\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"class\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
     ]
    }
   ],
   "source": [
    "column_names.remove(\"class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32a35ee",
   "metadata": {},
   "source": [
    "#### 3. Combine all of the csv files into one dataframe\n",
    "t = pd.read_csv(\"speech_files_features/cc_001_anxiety_910.77_May-twenty-third.csv\", \n",
    "            skiprows = 391,nrows = 1,\n",
    "            names = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f6df998",
   "metadata": {},
   "outputs": [],
   "source": [
    "t= pd.DataFrame(columns = column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b37511e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "speakers = [\"cc\", \"cl\", \"gg\", \"jg\", \"mf\", \"mk\", \"mm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e9359fcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "for csv_file in glob.glob(file_path):\n",
    "    p = pd.read_csv(csv_file, skiprows = 391,nrows = 1,\n",
    "                names = column_names)\n",
    "\n",
    "    file_name = csv_file.split(\"/\")[1].split(\".c\")[0]\n",
    "    p['name'] = file_name\n",
    "    t = t.append(p)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "id": "099c5277",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>pcm_RMSenergy_sma_max</th>\n",
       "      <th>pcm_RMSenergy_sma_min</th>\n",
       "      <th>pcm_RMSenergy_sma_range</th>\n",
       "      <th>pcm_RMSenergy_sma_maxPos</th>\n",
       "      <th>pcm_RMSenergy_sma_minPos</th>\n",
       "      <th>pcm_RMSenergy_sma_amean</th>\n",
       "      <th>pcm_RMSenergy_sma_linregc1</th>\n",
       "      <th>pcm_RMSenergy_sma_linregc2</th>\n",
       "      <th>pcm_RMSenergy_sma_linregerrQ</th>\n",
       "      <th>...</th>\n",
       "      <th>F0_sma_de_amean</th>\n",
       "      <th>F0_sma_de_linregc1</th>\n",
       "      <th>F0_sma_de_linregc2</th>\n",
       "      <th>F0_sma_de_linregerrQ</th>\n",
       "      <th>F0_sma_de_stddev</th>\n",
       "      <th>F0_sma_de_skewness</th>\n",
       "      <th>F0_sma_de_kurtosis</th>\n",
       "      <th>class</th>\n",
       "      <th>speaker</th>\n",
       "      <th>emotion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mm_001_happy_2353.51_three-hundred-nine</td>\n",
       "      <td>0.010760</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.010696</td>\n",
       "      <td>111.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>-8.324649e-06</td>\n",
       "      <td>0.003498</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>...</td>\n",
       "      <td>5.348673e-08</td>\n",
       "      <td>-0.033835</td>\n",
       "      <td>3.045167</td>\n",
       "      <td>1062.67900</td>\n",
       "      <td>32.646660</td>\n",
       "      <td>-0.090631</td>\n",
       "      <td>4.245496</td>\n",
       "      <td>?</td>\n",
       "      <td>mm</td>\n",
       "      <td>happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mm_001_panic_3395.30_one-thousand-three</td>\n",
       "      <td>0.076285</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.076251</td>\n",
       "      <td>32.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>0.011488</td>\n",
       "      <td>-1.106844e-04</td>\n",
       "      <td>0.019789</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>...</td>\n",
       "      <td>2.589447e-08</td>\n",
       "      <td>-0.097880</td>\n",
       "      <td>7.340973</td>\n",
       "      <td>1683.38700</td>\n",
       "      <td>41.250330</td>\n",
       "      <td>0.000596</td>\n",
       "      <td>4.717154</td>\n",
       "      <td>?</td>\n",
       "      <td>mm</td>\n",
       "      <td>panic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cc_001_panic_861.97_Two-thousand-five</td>\n",
       "      <td>0.020006</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.019996</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1853.0</td>\n",
       "      <td>0.000425</td>\n",
       "      <td>-2.645859e-07</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.201901e-09</td>\n",
       "      <td>-0.000025</td>\n",
       "      <td>0.034964</td>\n",
       "      <td>213.41610</td>\n",
       "      <td>14.608780</td>\n",
       "      <td>-0.116606</td>\n",
       "      <td>17.215480</td>\n",
       "      <td>?</td>\n",
       "      <td>cc</td>\n",
       "      <td>panic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mf_001_contempt_3901.86_November-first</td>\n",
       "      <td>0.015069</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.015026</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005252</td>\n",
       "      <td>2.256441e-05</td>\n",
       "      <td>0.004067</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>...</td>\n",
       "      <td>6.747695e-08</td>\n",
       "      <td>-0.029053</td>\n",
       "      <td>1.525273</td>\n",
       "      <td>188.63710</td>\n",
       "      <td>13.763260</td>\n",
       "      <td>-0.312293</td>\n",
       "      <td>6.684447</td>\n",
       "      <td>?</td>\n",
       "      <td>mf</td>\n",
       "      <td>contempt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cl_001_interest_1035.82_Ten-thousand-one</td>\n",
       "      <td>0.016625</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0.016514</td>\n",
       "      <td>55.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.002422</td>\n",
       "      <td>-3.115808e-05</td>\n",
       "      <td>0.004572</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>...</td>\n",
       "      <td>1.372193e-08</td>\n",
       "      <td>-0.001866</td>\n",
       "      <td>0.128773</td>\n",
       "      <td>39.98518</td>\n",
       "      <td>6.323827</td>\n",
       "      <td>-0.006288</td>\n",
       "      <td>25.196500</td>\n",
       "      <td>?</td>\n",
       "      <td>cl</td>\n",
       "      <td>interest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 388 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       name  pcm_RMSenergy_sma_max  \\\n",
       "0   mm_001_happy_2353.51_three-hundred-nine               0.010760   \n",
       "1   mm_001_panic_3395.30_one-thousand-three               0.076285   \n",
       "2     cc_001_panic_861.97_Two-thousand-five               0.020006   \n",
       "3    mf_001_contempt_3901.86_November-first               0.015069   \n",
       "4  cl_001_interest_1035.82_Ten-thousand-one               0.016625   \n",
       "\n",
       "   pcm_RMSenergy_sma_min  pcm_RMSenergy_sma_range  pcm_RMSenergy_sma_maxPos  \\\n",
       "0               0.000064                 0.010696                     111.0   \n",
       "1               0.000034                 0.076251                      32.0   \n",
       "2               0.000010                 0.019996                      39.0   \n",
       "3               0.000043                 0.015026                      85.0   \n",
       "4               0.000111                 0.016514                      55.0   \n",
       "\n",
       "   pcm_RMSenergy_sma_minPos  pcm_RMSenergy_sma_amean  \\\n",
       "0                     165.0                 0.002748   \n",
       "1                     134.0                 0.011488   \n",
       "2                    1853.0                 0.000425   \n",
       "3                       0.0                 0.005252   \n",
       "4                     137.0                 0.002422   \n",
       "\n",
       "   pcm_RMSenergy_sma_linregc1  pcm_RMSenergy_sma_linregc2  \\\n",
       "0               -8.324649e-06                    0.003498   \n",
       "1               -1.106844e-04                    0.019789   \n",
       "2               -2.645859e-07                    0.000788   \n",
       "3                2.256441e-05                    0.004067   \n",
       "4               -3.115808e-05                    0.004572   \n",
       "\n",
       "   pcm_RMSenergy_sma_linregerrQ  ...  F0_sma_de_amean  F0_sma_de_linregc1  \\\n",
       "0                      0.000008  ...     5.348673e-08           -0.033835   \n",
       "1                      0.000221  ...     2.589447e-08           -0.097880   \n",
       "2                      0.000003  ...    -9.201901e-09           -0.000025   \n",
       "3                      0.000018  ...     6.747695e-08           -0.029053   \n",
       "4                      0.000008  ...     1.372193e-08           -0.001866   \n",
       "\n",
       "   F0_sma_de_linregc2  F0_sma_de_linregerrQ  F0_sma_de_stddev  \\\n",
       "0            3.045167            1062.67900         32.646660   \n",
       "1            7.340973            1683.38700         41.250330   \n",
       "2            0.034964             213.41610         14.608780   \n",
       "3            1.525273             188.63710         13.763260   \n",
       "4            0.128773              39.98518          6.323827   \n",
       "\n",
       "   F0_sma_de_skewness  F0_sma_de_kurtosis  class  speaker   emotion  \n",
       "0           -0.090631            4.245496      ?       mm     happy  \n",
       "1            0.000596            4.717154      ?       mm     panic  \n",
       "2           -0.116606           17.215480      ?       cc     panic  \n",
       "3           -0.312293            6.684447      ?       mf  contempt  \n",
       "4           -0.006288           25.196500      ?       cl  interest  \n",
       "\n",
       "[5 rows x 388 columns]"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9f0217c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "t= t.set_index(pd.RangeIndex(stop = 2324))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eae6722f",
   "metadata": {},
   "outputs": [],
   "source": [
    "t[\"speaker\"] = t.name.str.split(\"_\").str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f6facb8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "t.to_csv(\"speech_file_opensmile_features.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "90ffc02a",
   "metadata": {},
   "outputs": [],
   "source": [
    "t= pd.read_csv(\"speech_file_opensmile_features.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "85a3f429",
   "metadata": {},
   "outputs": [],
   "source": [
    "t['emotion']= t.name.str.split(\"_\").str[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6fedb3a",
   "metadata": {},
   "source": [
    "### 4. Normalize features for each speaker\n",
    "normalized_feature_value = (feature_value - mean_feature_value_among_speaker_A) / (stdev_value_among_speaker_A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "19491f96",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/ipykernel_48493/2826434580.py:4: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  test_df = (test_df - test_df.mean())/test_df.std()\n"
     ]
    }
   ],
   "source": [
    "all_speakers = []\n",
    "for name in speakers:\n",
    "    test_df = t[t[\"speaker\"]== name]\n",
    "    test_df = (test_df - test_df.mean())/test_df.std()\n",
    "    test_df['speaker'] = name\n",
    "    all_speakers.append(test_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7aaa4e75",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_features = pd.concat(all_speakers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7676bd0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_features= normalized_features.merge(t['emotion'], left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c9c70f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'interest'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check that merge matches\n",
    "t['emotion'].iloc[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "d3511d4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2324, 389)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "d1afe0ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "normalized_features.to_csv(\"normalized_features.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "003dce12",
   "metadata": {},
   "source": [
    "for each column:\n",
    "    group by feature\n",
    "        get mean of feature\n",
    "        get std dev of feature\n",
    "    subtract by mean and divide by std dev\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbfabd41",
   "metadata": {},
   "source": [
    "## Classification \n",
    "\n",
    "#### Steps\n",
    "1. Get list of X feature column names\n",
    "2. Convert y emotion labels to numbers\n",
    "3. Run the Model experiments\n",
    "        - create model\n",
    "        - model fit\n",
    "        - predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08bcffbf",
   "metadata": {},
   "source": [
    "#### 1. Get list of X feature column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "id": "bec35753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F0_sma_amean',\n",
       " 'F0_sma_de_amean',\n",
       " 'F0_sma_de_kurtosis',\n",
       " 'F0_sma_de_linregc1',\n",
       " 'F0_sma_de_linregc2',\n",
       " 'F0_sma_de_linregerrQ',\n",
       " 'F0_sma_de_max',\n",
       " 'F0_sma_de_maxPos',\n",
       " 'F0_sma_de_min',\n",
       " 'F0_sma_de_minPos',\n",
       " 'F0_sma_de_range',\n",
       " 'F0_sma_de_skewness',\n",
       " 'F0_sma_de_stddev',\n",
       " 'F0_sma_kurtosis',\n",
       " 'F0_sma_linregc1',\n",
       " 'F0_sma_linregc2',\n",
       " 'F0_sma_linregerrQ',\n",
       " 'F0_sma_max',\n",
       " 'F0_sma_maxPos',\n",
       " 'F0_sma_min',\n",
       " 'F0_sma_minPos',\n",
       " 'F0_sma_range',\n",
       " 'F0_sma_skewness',\n",
       " 'F0_sma_stddev',\n",
       " 'class',\n",
       " 'emotion_x',\n",
       " 'name',\n",
       " 'pcm_RMSenergy_sma_amean',\n",
       " 'pcm_RMSenergy_sma_de_amean',\n",
       " 'pcm_RMSenergy_sma_de_kurtosis',\n",
       " 'pcm_RMSenergy_sma_de_linregc1',\n",
       " 'pcm_RMSenergy_sma_de_linregc2',\n",
       " 'pcm_RMSenergy_sma_de_linregerrQ',\n",
       " 'pcm_RMSenergy_sma_de_max',\n",
       " 'pcm_RMSenergy_sma_de_maxPos',\n",
       " 'pcm_RMSenergy_sma_de_min',\n",
       " 'pcm_RMSenergy_sma_de_minPos',\n",
       " 'pcm_RMSenergy_sma_de_range',\n",
       " 'pcm_RMSenergy_sma_de_skewness',\n",
       " 'pcm_RMSenergy_sma_de_stddev',\n",
       " 'pcm_RMSenergy_sma_kurtosis',\n",
       " 'pcm_RMSenergy_sma_linregc1',\n",
       " 'pcm_RMSenergy_sma_linregc2',\n",
       " 'pcm_RMSenergy_sma_linregerrQ',\n",
       " 'pcm_RMSenergy_sma_max',\n",
       " 'pcm_RMSenergy_sma_maxPos',\n",
       " 'pcm_RMSenergy_sma_min',\n",
       " 'pcm_RMSenergy_sma_minPos',\n",
       " 'pcm_RMSenergy_sma_range',\n",
       " 'pcm_RMSenergy_sma_skewness',\n",
       " 'pcm_RMSenergy_sma_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[10]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[10]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[10]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[10]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[10]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[10]_max',\n",
       " 'pcm_fftMag_mfcc_sma[10]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[10]_min',\n",
       " 'pcm_fftMag_mfcc_sma[10]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[10]_range',\n",
       " 'pcm_fftMag_mfcc_sma[10]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[10]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[11]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[11]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[11]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[11]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[11]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[11]_max',\n",
       " 'pcm_fftMag_mfcc_sma[11]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[11]_min',\n",
       " 'pcm_fftMag_mfcc_sma[11]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[11]_range',\n",
       " 'pcm_fftMag_mfcc_sma[11]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[11]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[12]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[12]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[12]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[12]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[12]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[12]_max',\n",
       " 'pcm_fftMag_mfcc_sma[12]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[12]_min',\n",
       " 'pcm_fftMag_mfcc_sma[12]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[12]_range',\n",
       " 'pcm_fftMag_mfcc_sma[12]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[12]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[1]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[1]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[1]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[1]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[1]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[1]_max',\n",
       " 'pcm_fftMag_mfcc_sma[1]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[1]_min',\n",
       " 'pcm_fftMag_mfcc_sma[1]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[1]_range',\n",
       " 'pcm_fftMag_mfcc_sma[1]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[1]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[2]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[2]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[2]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[2]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[2]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[2]_max',\n",
       " 'pcm_fftMag_mfcc_sma[2]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[2]_min',\n",
       " 'pcm_fftMag_mfcc_sma[2]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[2]_range',\n",
       " 'pcm_fftMag_mfcc_sma[2]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[2]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[3]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[3]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[3]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[3]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[3]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[3]_max',\n",
       " 'pcm_fftMag_mfcc_sma[3]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[3]_min',\n",
       " 'pcm_fftMag_mfcc_sma[3]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[3]_range',\n",
       " 'pcm_fftMag_mfcc_sma[3]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[3]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[4]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[4]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[4]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[4]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[4]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[4]_max',\n",
       " 'pcm_fftMag_mfcc_sma[4]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[4]_min',\n",
       " 'pcm_fftMag_mfcc_sma[4]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[4]_range',\n",
       " 'pcm_fftMag_mfcc_sma[4]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[4]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[5]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[5]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[5]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[5]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[5]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[5]_max',\n",
       " 'pcm_fftMag_mfcc_sma[5]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[5]_min',\n",
       " 'pcm_fftMag_mfcc_sma[5]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[5]_range',\n",
       " 'pcm_fftMag_mfcc_sma[5]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[5]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[6]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[6]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[6]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[6]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[6]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[6]_max',\n",
       " 'pcm_fftMag_mfcc_sma[6]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[6]_min',\n",
       " 'pcm_fftMag_mfcc_sma[6]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[6]_range',\n",
       " 'pcm_fftMag_mfcc_sma[6]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[6]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[7]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[7]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[7]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[7]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[7]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[7]_max',\n",
       " 'pcm_fftMag_mfcc_sma[7]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[7]_min',\n",
       " 'pcm_fftMag_mfcc_sma[7]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[7]_range',\n",
       " 'pcm_fftMag_mfcc_sma[7]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[7]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[8]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[8]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[8]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[8]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[8]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[8]_max',\n",
       " 'pcm_fftMag_mfcc_sma[8]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[8]_min',\n",
       " 'pcm_fftMag_mfcc_sma[8]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[8]_range',\n",
       " 'pcm_fftMag_mfcc_sma[8]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[8]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma[9]_amean',\n",
       " 'pcm_fftMag_mfcc_sma[9]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma[9]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma[9]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma[9]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma[9]_max',\n",
       " 'pcm_fftMag_mfcc_sma[9]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma[9]_min',\n",
       " 'pcm_fftMag_mfcc_sma[9]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma[9]_range',\n",
       " 'pcm_fftMag_mfcc_sma[9]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma[9]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[10]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[11]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[12]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[1]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[2]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[3]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[4]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[5]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[6]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[7]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[8]_stddev',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_amean',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_kurtosis',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_linregc1',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_linregc2',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_linregerrQ',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_max',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_maxPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_min',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_minPos',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_range',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_skewness',\n",
       " 'pcm_fftMag_mfcc_sma_de[9]_stddev',\n",
       " 'pcm_zcr_sma_amean',\n",
       " 'pcm_zcr_sma_de_amean',\n",
       " 'pcm_zcr_sma_de_kurtosis',\n",
       " 'pcm_zcr_sma_de_linregc1',\n",
       " 'pcm_zcr_sma_de_linregc2',\n",
       " 'pcm_zcr_sma_de_linregerrQ',\n",
       " 'pcm_zcr_sma_de_max',\n",
       " 'pcm_zcr_sma_de_maxPos',\n",
       " 'pcm_zcr_sma_de_min',\n",
       " 'pcm_zcr_sma_de_minPos',\n",
       " 'pcm_zcr_sma_de_range',\n",
       " 'pcm_zcr_sma_de_skewness',\n",
       " 'pcm_zcr_sma_de_stddev',\n",
       " 'pcm_zcr_sma_kurtosis',\n",
       " 'pcm_zcr_sma_linregc1',\n",
       " 'pcm_zcr_sma_linregc2',\n",
       " 'pcm_zcr_sma_linregerrQ',\n",
       " 'pcm_zcr_sma_max',\n",
       " 'pcm_zcr_sma_maxPos',\n",
       " 'pcm_zcr_sma_min',\n",
       " 'pcm_zcr_sma_minPos',\n",
       " 'pcm_zcr_sma_range',\n",
       " 'pcm_zcr_sma_skewness',\n",
       " 'pcm_zcr_sma_stddev',\n",
       " 'speaker',\n",
       " 'voiceProb_sma_amean',\n",
       " 'voiceProb_sma_de_amean',\n",
       " 'voiceProb_sma_de_kurtosis',\n",
       " 'voiceProb_sma_de_linregc1',\n",
       " 'voiceProb_sma_de_linregc2',\n",
       " 'voiceProb_sma_de_linregerrQ',\n",
       " 'voiceProb_sma_de_max',\n",
       " 'voiceProb_sma_de_maxPos',\n",
       " 'voiceProb_sma_de_min',\n",
       " 'voiceProb_sma_de_minPos',\n",
       " 'voiceProb_sma_de_range',\n",
       " 'voiceProb_sma_de_skewness',\n",
       " 'voiceProb_sma_de_stddev',\n",
       " 'voiceProb_sma_kurtosis',\n",
       " 'voiceProb_sma_linregc1',\n",
       " 'voiceProb_sma_linregc2',\n",
       " 'voiceProb_sma_linregerrQ',\n",
       " 'voiceProb_sma_max',\n",
       " 'voiceProb_sma_maxPos',\n",
       " 'voiceProb_sma_min',\n",
       " 'voiceProb_sma_minPos',\n",
       " 'voiceProb_sma_range',\n",
       " 'voiceProb_sma_skewness',\n",
       " 'voiceProb_sma_stddev',\n",
       " 'emotion_y']"
      ]
     },
     "execution_count": 533,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_column_names = list(normalized_features.columns)\n",
    "x_column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "id": "3c58f6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove columns that won't be used for classification\n",
    "x_column_names.remove('emotion_x')\n",
    "x_column_names.remove('class')\n",
    "x_column_names.remove('name')\n",
    "x_column_names.remove('speaker')\n",
    "x_column_names.remove('emotion_y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e703b8f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_column_names.remove('F0_sma_min')\n",
    ", 'F0_sma_minPos'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "id": "ab4671aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "384\n"
     ]
    }
   ],
   "source": [
    "print(len(x_column_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e1990ee",
   "metadata": {},
   "source": [
    "\n",
    "####  2. Convert y emotion labels to numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "6dbca35d",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions = ['anxiety', 'boredom', 'cold-anger', 'contempt', 'despair', 'disgust', 'elation',\n",
    "            'happy', 'hot-anger', 'interest', 'neutral', 'panic', 'pride', 'sadness', 'shame']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "d1a5bd47",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions_dict = {}\n",
    "for i,e in enumerate(emotions):\n",
    "    emotions_dict[e] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "e718d413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2          panic\n",
       "8       interest\n",
       "10         pride\n",
       "21         panic\n",
       "26      contempt\n",
       "          ...   \n",
       "2300       panic\n",
       "2308       shame\n",
       "2311     despair\n",
       "2315     disgust\n",
       "2321     elation\n",
       "Name: emotion_y, Length: 2324, dtype: object"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pre = normalized_features['emotion_y'] # this is in string form\n",
    "y_pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "id": "48273b8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2324,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([11,  9, 12, ...,  4,  5,  6])"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = y_pre.map(emotions_dict) # map to numbers\n",
    "y = y.values.ravel() #convert to array\n",
    "print(y.shape)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb889913",
   "metadata": {},
   "source": [
    "### 3. Run the Model experiments\n",
    "\n",
    "#### leave one speaker out cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d1a9d9",
   "metadata": {},
   "source": [
    "x- features in t <br/>\n",
    "y- labels in other dataframe, emotions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "id": "d7eda7bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "reports = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "id": "f71adea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dataframe_image as dfi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "id": "6b7637e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2059, 384) (2059,)\n",
      "(265, 384) (265,)\n",
      "(1956, 384) (1956,)\n",
      "(368, 384) (368,)\n",
      "(1904, 384) (1904,)\n",
      "(420, 384) (420,)\n",
      "(2051, 384) (2051,)\n",
      "(273, 384) (273,)\n",
      "(2025, 384) (2025,)\n",
      "(299, 384) (299,)\n",
      "(1927, 384) (1927,)\n",
      "(397, 384) (397,)\n",
      "(2022, 384) (2022,)\n",
      "(302, 384) (302,)\n"
     ]
    }
   ],
   "source": [
    "for speaker in speakers:\n",
    "    x_test = normalized_features[normalized_features[\"speaker\"] == speaker]\n",
    "    x_train = normalized_features[normalized_features[\"speaker\"] != speaker]\n",
    "    \n",
    "    x_test = x_test[x_column_names] #subset columns\n",
    "    x_train = x_train[x_column_names]\n",
    "    \n",
    "    #remove null columns\n",
    "    x_test[x_test.columns[x_test.isnull().any()]] = 0 #set nulls to 0\n",
    "    x_train[x_train.columns[x_train.isnull().any()]] = 0 #set nulls to 0\n",
    "    \n",
    "    #get y labels\n",
    "    y_test = normalized_features[normalized_features[\"speaker\"] == speaker]['emotion_y']\n",
    "    y_train = normalized_features[normalized_features[\"speaker\"] != speaker]['emotion_y']\n",
    "    \n",
    "    y_train = y_train.map(emotions_dict).values.ravel() # map to numbers\n",
    "    y_test = y_test.map(emotions_dict).values.ravel() # map to numbers\n",
    "    \n",
    "    #generate prediction\n",
    "    clf = MLPClassifier(random_state=1, hidden_layer_sizes=(200,),\n",
    "                        max_iter=300).fit(x_train, y_train)\n",
    "    y_pred=clf.predict(x_test)\n",
    "    report = classification_report(y_test, y_pred, \n",
    "                                   target_names = emotions,\n",
    "                                  output_dict = True)\n",
    "    reports.append(report)\n",
    "    \n",
    "    print(x_train.shape,y_train.shape)\n",
    "    print(x_test.shape,y_test.shape)\n",
    "    #print(report)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "id": "a5148d26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'precision': 0.26833964790598946, 'recall': 0.24655765920826164, 'f1-score': 0.24448326349313051, 'accuracy': 0.24655765920826164}\n"
     ]
    }
   ],
   "source": [
    "#OVERALL\n",
    "n_total = 2324\n",
    "all_emotions_agg = {}\n",
    "all_emotions_agg['precision'] = 0\n",
    "all_emotions_agg['recall'] = 0\n",
    "all_emotions_agg['f1-score'] = 0\n",
    "all_emotions_agg['accuracy'] = 0\n",
    "\n",
    "for exp in reports:\n",
    "    weight_factor = exp['weighted avg']['support']/n_total\n",
    "    #all_emotions_agg['precision'] += exp['weighted avg']['precision']\n",
    "    all_emotions_agg['recall'] += exp['weighted avg']['recall']* weight_factor\n",
    "    all_emotions_agg['f1-score'] += exp['weighted avg']['f1-score'] * weight_factor\n",
    "    all_emotions_agg['precision'] += exp['weighted avg']['precision']* weight_factor\n",
    "    all_emotions_agg['accuracy'] += exp['accuracy']*weight_factor\n",
    "\n",
    "print(all_emotions_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "id": "84317974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'precision': 0.26833964790598946,\n",
       " 'recall': 0.24655765920826164,\n",
       " 'f1-score': 0.24448326349313051,\n",
       " 'accuracy': 0.24655765920826164}"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_emotions_agg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "id": "98c3a54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BY EMOTION\n",
    "emotions_agg = {}\n",
    "for emotion in emotions:\n",
    "    emotions_agg[emotion] = {}\n",
    "    emotions_agg[emotion]['precision'] = 0\n",
    "    emotions_agg[emotion]['recall'] = 0\n",
    "    emotions_agg[emotion]['f1-score'] = 0\n",
    "    \n",
    "    for exp in reports:\n",
    "        emotions_agg[emotion]['precision'] +=exp[emotion]['precision']\n",
    "        emotions_agg[emotion]['recall'] +=exp[emotion]['recall']\n",
    "        emotions_agg[emotion]['f1-score'] +=exp[emotion]['f1-score']\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "id": "c86359a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "emotions_agg_df = pd.DataFrame(emotions_agg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "id": "12ea6580",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>anxiety</th>\n",
       "      <td>0.228894</td>\n",
       "      <td>0.239186</td>\n",
       "      <td>0.223078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boredom</th>\n",
       "      <td>0.230771</td>\n",
       "      <td>0.240426</td>\n",
       "      <td>0.232506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cold-anger</th>\n",
       "      <td>0.182503</td>\n",
       "      <td>0.204073</td>\n",
       "      <td>0.187533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contempt</th>\n",
       "      <td>0.271755</td>\n",
       "      <td>0.291891</td>\n",
       "      <td>0.269559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>despair</th>\n",
       "      <td>0.128948</td>\n",
       "      <td>0.143337</td>\n",
       "      <td>0.124108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgust</th>\n",
       "      <td>0.225220</td>\n",
       "      <td>0.303366</td>\n",
       "      <td>0.183092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elation</th>\n",
       "      <td>0.296555</td>\n",
       "      <td>0.343578</td>\n",
       "      <td>0.307413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>happy</th>\n",
       "      <td>0.243701</td>\n",
       "      <td>0.282052</td>\n",
       "      <td>0.253938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hot-anger</th>\n",
       "      <td>0.373205</td>\n",
       "      <td>0.363650</td>\n",
       "      <td>0.363042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interest</th>\n",
       "      <td>0.252195</td>\n",
       "      <td>0.260229</td>\n",
       "      <td>0.253866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>0.630612</td>\n",
       "      <td>0.336391</td>\n",
       "      <td>0.397402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>panic</th>\n",
       "      <td>0.451786</td>\n",
       "      <td>0.380385</td>\n",
       "      <td>0.404967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pride</th>\n",
       "      <td>0.179972</td>\n",
       "      <td>0.177456</td>\n",
       "      <td>0.172476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sadness</th>\n",
       "      <td>0.137325</td>\n",
       "      <td>0.137811</td>\n",
       "      <td>0.136445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shame</th>\n",
       "      <td>0.215605</td>\n",
       "      <td>0.220021</td>\n",
       "      <td>0.214150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            precision    recall  f1-score\n",
       "anxiety      0.228894  0.239186  0.223078\n",
       "boredom      0.230771  0.240426  0.232506\n",
       "cold-anger   0.182503  0.204073  0.187533\n",
       "contempt     0.271755  0.291891  0.269559\n",
       "despair      0.128948  0.143337  0.124108\n",
       "disgust      0.225220  0.303366  0.183092\n",
       "elation      0.296555  0.343578  0.307413\n",
       "happy        0.243701  0.282052  0.253938\n",
       "hot-anger    0.373205  0.363650  0.363042\n",
       "interest     0.252195  0.260229  0.253866\n",
       "neutral      0.630612  0.336391  0.397402\n",
       "panic        0.451786  0.380385  0.404967\n",
       "pride        0.179972  0.177456  0.172476\n",
       "sadness      0.137325  0.137811  0.136445\n",
       "shame        0.215605  0.220021  0.214150"
      ]
     },
     "execution_count": 610,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emotions_agg_df = emotions_agg_df.T/7\n",
    "emotions_agg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b367740",
   "metadata": {},
   "source": [
    "### export files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "id": "aaf682c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/152835.399496:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmpy1frdjk4/temp.png.\n"
     ]
    }
   ],
   "source": [
    "dfi.export(emotions_agg_df, 'emotions_agg_df.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "id": "81e54a0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  cc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153246.850179:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmpqu0a6906/temp.png.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  cl\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153247.980938:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmpoaurcbvm/temp.png.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  gg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153249.102602:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmp_4dr1vb2/temp.png.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  jg\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153250.218492:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmpmveatadl/temp.png.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  mf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153251.337080:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmpthm6l7ka/temp.png.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  mk\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153252.465311:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmp31nxdd0n/temp.png.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test on speaker  mm\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[0502/153253.584697:INFO:headless_shell.cc(659)] Written to file /var/folders/2b/d2fvv03s7tj_1b6g5x9qmvj00000gn/T/tmpfw66nzmc/temp.png.\n"
     ]
    }
   ],
   "source": [
    "for i in range(7):\n",
    "    print('Test on speaker ', speakers[i])\n",
    "    df_t =  pd.DataFrame(reports[i]).T\n",
    "    name = f'emotions_agg_df_{speakers[i]}.png'\n",
    "    dfi.export(df_t, name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "901f0071",
   "metadata": {},
   "source": [
    "# Error Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "id": "fb8f1185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21509433962264152\n",
      "0.2798913043478261\n",
      "0.27380952380952384\n",
      "0.19047619047619047\n",
      "0.24749163879598662\n",
      "0.19143576826196473\n",
      "0.31788079470198677\n"
     ]
    }
   ],
   "source": [
    "for rep in reports:\n",
    "    print(rep['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "id": "ea27ffb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1-score</th>\n",
       "      <th>support</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>anxiety</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.205128</td>\n",
       "      <td>0.280702</td>\n",
       "      <td>39.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>boredom</th>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.157895</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cold-anger</th>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contempt</th>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>despair</th>\n",
       "      <td>0.171429</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.226415</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disgust</th>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.304348</td>\n",
       "      <td>0.325581</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elation</th>\n",
       "      <td>0.407407</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>happy</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hot-anger</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>interest</th>\n",
       "      <td>0.315789</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neutral</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.307692</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>panic</th>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.392857</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pride</th>\n",
       "      <td>0.318182</td>\n",
       "      <td>0.368421</td>\n",
       "      <td>0.341463</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sadness</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shame</th>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.486486</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>accuracy</th>\n",
       "      <td>0.317881</td>\n",
       "      <td>0.317881</td>\n",
       "      <td>0.317881</td>\n",
       "      <td>0.317881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>macro avg</th>\n",
       "      <td>0.336875</td>\n",
       "      <td>0.323188</td>\n",
       "      <td>0.314712</td>\n",
       "      <td>302.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weighted avg</th>\n",
       "      <td>0.345816</td>\n",
       "      <td>0.317881</td>\n",
       "      <td>0.315857</td>\n",
       "      <td>302.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              precision    recall  f1-score     support\n",
       "anxiety        0.444444  0.205128  0.280702   39.000000\n",
       "boredom        0.214286  0.157895  0.181818   19.000000\n",
       "cold-anger     0.071429  0.050000  0.058824   20.000000\n",
       "contempt       0.285714  0.315789  0.300000   19.000000\n",
       "despair        0.171429  0.333333  0.226415   18.000000\n",
       "disgust        0.350000  0.304348  0.325581   23.000000\n",
       "elation        0.407407  0.578947  0.478261   19.000000\n",
       "happy          0.333333  0.611111  0.431373   18.000000\n",
       "hot-anger      0.500000  0.375000  0.428571   16.000000\n",
       "interest       0.315789  0.285714  0.300000   21.000000\n",
       "neutral        0.500000  0.222222  0.307692    9.000000\n",
       "panic          0.611111  0.392857  0.478261   28.000000\n",
       "pride          0.318182  0.368421  0.341463   19.000000\n",
       "sadness        0.080000  0.117647  0.095238   17.000000\n",
       "shame          0.450000  0.529412  0.486486   17.000000\n",
       "accuracy       0.317881  0.317881  0.317881    0.317881\n",
       "macro avg      0.336875  0.323188  0.314712  302.000000\n",
       "weighted avg   0.345816  0.317881  0.315857  302.000000"
      ]
     },
     "execution_count": 623,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_one = pd.DataFrame(reports[6]).T\n",
    "best_one"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab6fc082",
   "metadata": {},
   "source": [
    "- Easiest classes to predict: panic(F1 of 0.54), shame (F1 of 0.44), happy (F1 of 0.38), boredom (F1 of 0.36)\n",
    "\n",
    "Panic has high pitch\n",
    "- Hardest classes to predict: cold-anger (F1 of 0.044), contempt (F1 of 0.111), hot-anger (0.148), and sadness F1 of (0.1818) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "id": "560a4380",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2022, 384) (2022,)\n",
      "(302, 384) (302,)\n",
      "3\n",
      "120\n"
     ]
    }
   ],
   "source": [
    "speaker = 'mm'\n",
    "x_test = normalized_features[normalized_features[\"speaker\"] == speaker]\n",
    "x_train = normalized_features[normalized_features[\"speaker\"] != speaker]\n",
    "\n",
    "x_test = x_test[x_column_names] #subset columns\n",
    "x_train = x_train[x_column_names]\n",
    "\n",
    "#remove null columns\n",
    "x_test[x_test.columns[x_test.isnull().any()]] = 0 #set nulls to 0\n",
    "x_train[x_train.columns[x_train.isnull().any()]] = 0 #set nulls to 0\n",
    "\n",
    "#get y labels\n",
    "y_test = normalized_features[normalized_features[\"speaker\"] == speaker]['emotion_y']\n",
    "y_train = normalized_features[normalized_features[\"speaker\"] != speaker]['emotion_y']\n",
    "\n",
    "y_train = y_train.map(emotions_dict).values.ravel() # map to numbers\n",
    "y_test = y_test.map(emotions_dict).values.ravel() # map to numbers\n",
    "\n",
    "#generate prediction\n",
    "clf = MLPClassifier(random_state=1,hidden_layer_sizes=(200,),\n",
    "                    max_iter=300).fit(x_train, y_train)\n",
    "y_pred=clf.predict(x_test)\n",
    "report = classification_report(y_test, y_pred, \n",
    "                               target_names = emotions,\n",
    "                              output_dict = True)\n",
    "\n",
    "\n",
    "print(x_train.shape,y_train.shape)\n",
    "print(x_test.shape,y_test.shape)\n",
    "print(clf.n_layers_)\n",
    "print(clf.n_iter_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "id": "1434c2aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.31788079470198677\n"
     ]
    }
   ],
   "source": [
    "print(report['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "id": "98863659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7dd572efd0>"
      ]
     },
     "execution_count": 627,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(clf, x_test, y_true= y_test, display_labels = emotions, xticks_rotation = 90)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}