data_analysis_fixed_text.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nltk\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "import language_tool_python\n",
    "import scipy.stats as stats\n",
    "\n",
    "tool = language_tool_python.LanguageTool('en-US')\n",
    "is_bad_rule = lambda rule: rule.message == 'Possible spelling mistake found.' and len(rule.replacements) and rule.replacements[0][0].isupper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data importation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_fixed = pd.read_csv('data/fixed_text_typing_dataset.csv', sep=';')\n",
    "df_freq = pd.read_csv('data/frequency_dataset.csv', sep=';')\n",
    "df_user = pd.read_csv('data/participants_information.csv', sep=';')\n",
    "\n",
    "df_freq = df_freq.rename(columns={'User ID':'userId'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sentences reconstruction and feature extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reconstruct users' sentences from the keycode in df_fixed\n",
    "\n",
    "sentence = ''\n",
    "uppercase = False\n",
    "uppercase_tmp = False\n",
    "i_start = 0\n",
    "i_end = 0\n",
    "text_fixed = pd.DataFrame(columns=['sentence', 'idx_start', 'idx_end'])\n",
    "\n",
    "df_fixed.keyCode = df_fixed.keyCode.astype(str)\n",
    "\n",
    "for i in range(len(df_fixed)):\n",
    "    if i > 0 and (df_fixed.userId[i] != df_fixed.userId[i-1] or df_fixed.emotionIndex[i] != df_fixed.emotionIndex[i-1]): #df_fixed._id[i][:-2] != df_fixed._id[i-1][:-2]:\n",
    "        i_end = i-1\n",
    "        if len(text_fixed) == 62:\n",
    "            ids_start = [i for i in range(len(sentence)) if sentence[i:i+4] == 'once']\n",
    "            text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start, i_start+ids_start[1]-1]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "            text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start+ids_start[1], i_start+ids_start[2]-1]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "            text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start+ids_start[2], i_end]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "        elif len(text_fixed) == 134 or len(text_fixed) == 190:\n",
    "            ids_start = [i for i in range(len(sentence)) if sentence[i:i+4] == 'Once']\n",
    "            text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start, i_start+ids_start[1]-1]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "            text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start+ids_start[1], i_end]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "        else:\n",
    "            if sentence[0].islower() and sentence[1].isupper():\n",
    "                sentence = sentence.swapcase()\n",
    "            text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start, i_end]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)\n",
    "        i_start = i\n",
    "        sentence = ''\n",
    "        uppercase = False\n",
    "        uppercase_tmp = False\n",
    "    if df_fixed.keyCode[i] == '\\\\b':    # delete key\n",
    "        sentence = sentence[:max(0,len(sentence)-1)]\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '\\\\u0014':    # caps lock key\n",
    "        uppercase = not uppercase\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '\\\\u0010' and not uppercase:     # shift key\n",
    "        uppercase_tmp = True\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '¼':     # comma key\n",
    "        sentence += ','\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == 'ß':     # exclamation mark key\n",
    "        sentence += '!'\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '¾':     # dot key\n",
    "        sentence += '.'\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '4    ':     # apostrophe key\n",
    "        sentence += \"'\"\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '6    ':     # dash key\n",
    "        sentence += \"-\"\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if df_fixed.keyCode[i] == '¿':     # colon key\n",
    "        sentence += \":\"\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    if uppercase:\n",
    "        sentence += df_fixed.keyCode[i].upper()\n",
    "        continue\n",
    "    if uppercase_tmp:\n",
    "        sentence += df_fixed.keyCode[i].upper()\n",
    "        uppercase_tmp = False\n",
    "        continue\n",
    "    sentence += df_fixed.keyCode[i]    \n",
    "\n",
    "text_fixed = text_fixed.append(pd.DataFrame([[sentence, i_start, i]], columns=['sentence', 'idx_start', 'idx_end']), ignore_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fixed text that users have been asked to write\n",
    "\n",
    "gold_fixed = {\n",
    "    'N': 'Once there was a cat and a mouse. Usually, cats eat mice, and mice run away from cats. But this cat and this mouse liked each other very much. They liked each other so much that they lived together.',\n",
    "    'H': 'We can not help falling in love with cute and funny babies. Their beautiful and joyful laughter makes us happy.',\n",
    "    'C': 'Beautiful nature and calm music are always relaxing. The soft sounds are so pleasant to listen to. Watching the superb nature calms our body and soul.',\n",
    "    'S': 'The boy yells at his father to wake him up. But the father is dead. The poor boy is very sad. He realized that his father is gone forever.',\n",
    "    'A': 'Jake has a horrible temper, especially when he drinks alcohol. He gets andgry and agressive when he is drunk. He savagely  beats and violeates his wife.'\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>emotionIndex</th>\n",
       "      <th>index</th>\n",
       "      <th>keyCode</th>\n",
       "      <th>keyDown</th>\n",
       "      <th>keyUp</th>\n",
       "      <th>D1U1</th>\n",
       "      <th>D1U2</th>\n",
       "      <th>D1D2</th>\n",
       "      <th>U1D2</th>\n",
       "      <th>U1U2</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>3448</td>\n",
       "      <td>o</td>\n",
       "      <td>1.580000e+12</td>\n",
       "      <td>1.580000e+12</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2556.0</td>\n",
       "      <td>2479.0</td>\n",
       "      <td>2389.0</td>\n",
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       "      <td>3449</td>\n",
       "      <td>n</td>\n",
       "      <td>1.580000e+12</td>\n",
       "      <td>1.580000e+12</td>\n",
       "      <td>77.0</td>\n",
       "      <td>258.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>1.580000e+12</td>\n",
       "      <td>127.0</td>\n",
       "      <td>613.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>392.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>3451</td>\n",
       "      <td>e</td>\n",
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       "      <td>94.0</td>\n",
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       "      <td>106.0</td>\n",
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       "      <td>N</td>\n",
       "      <td>3452</td>\n",
       "      <td></td>\n",
       "      <td>1.580000e+12</td>\n",
       "      <td>1.580000e+12</td>\n",
       "      <td>76.0</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>2028.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId emotionIndex  index keyCode       keyDown         keyUp   D1U1  \\\n",
       "0     100            N   3448       o  1.580000e+12  1.580000e+12   90.0   \n",
       "1     100            N   3449       n  1.580000e+12  1.580000e+12   77.0   \n",
       "2     100            N   3450       c  1.580000e+12  1.580000e+12  127.0   \n",
       "3     100            N   3451       e  1.580000e+12  1.580000e+12   94.0   \n",
       "4     100            N   3452          1.580000e+12  1.580000e+12   76.0   \n",
       "\n",
       "     D1U2    D1D2    U1D2    U1U2 answer  \n",
       "0  2556.0  2479.0  2389.0  2466.0    NaN  \n",
       "1   258.0   131.0    54.0   181.0    NaN  \n",
       "2   613.0   519.0   392.0   486.0    NaN  \n",
       "3   276.0   200.0   106.0   182.0    NaN  \n",
       "4  2104.0  2032.0  1956.0  2028.0    NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# functions to extract features from the df_fixed table\n",
    "df_fixed = df_fixed.drop(['D1U3','D1D3'], axis=1)\n",
    "df_fixed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_editDistance_fixed(gold_fixed, emotion, sentence):\n",
    "    return nltk.edit_distance(sentence, gold_fixed[emotion])\n",
    "\n",
    "def extract_nbKeystroke(val, start, end):\n",
    "    return val[end] - val[start] + 1\n",
    "\n",
    "def extract_mean(val, start, end):\n",
    "    return np.mean(val[start:end+1])\n",
    "\n",
    "def extract_std(val, start, end):\n",
    "    return np.std(val[start:end+1])\n",
    "\n",
    "def extract_answer(val, start, end):\n",
    "    for i in range(start, end+1):\n",
    "        if val[i] != np.nan:\n",
    "            return val[i]\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>idx_start</th>\n",
       "      <th>idx_end</th>\n",
       "      <th>userId</th>\n",
       "      <th>emotionIndex</th>\n",
       "      <th>sentence</th>\n",
       "      <th>editDistance</th>\n",
       "      <th>nbKeystroke</th>\n",
       "      <th>answer</th>\n",
       "      <th>D1U1_mean</th>\n",
       "      <th>D1U1_std</th>\n",
       "      <th>D1U2_mean</th>\n",
       "      <th>D1U2_std</th>\n",
       "      <th>D1D2_mean</th>\n",
       "      <th>D1D2_std</th>\n",
       "      <th>U1D2_mean</th>\n",
       "      <th>U1D2_std</th>\n",
       "      <th>U1U2_mean</th>\n",
       "      <th>U1U2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>100</td>\n",
       "      <td>N</td>\n",
       "      <td>once there was a cat and a mouse. usually , ca...</td>\n",
       "      <td>26</td>\n",
       "      <td>283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.074380</td>\n",
       "      <td>31.715592</td>\n",
       "      <td>884.215768</td>\n",
       "      <td>1157.264475</td>\n",
       "      <td>683.060284</td>\n",
       "      <td>1101.553201</td>\n",
       "      <td>694.609959</td>\n",
       "      <td>1157.337134</td>\n",
       "      <td>677.178571</td>\n",
       "      <td>1108.432937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>283</td>\n",
       "      <td>422</td>\n",
       "      <td>100</td>\n",
       "      <td>H</td>\n",
       "      <td>We can not help falling in love with cute and ...</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>R</td>\n",
       "      <td>99.071429</td>\n",
       "      <td>22.182246</td>\n",
       "      <td>813.769784</td>\n",
       "      <td>701.131104</td>\n",
       "      <td>714.784173</td>\n",
       "      <td>699.459223</td>\n",
       "      <td>615.776978</td>\n",
       "      <td>696.222821</td>\n",
       "      <td>714.762590</td>\n",
       "      <td>697.865776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>423</td>\n",
       "      <td>656</td>\n",
       "      <td>113</td>\n",
       "      <td>N</td>\n",
       "      <td>Once there was a cat and a mouse\\u0010. Usaual...</td>\n",
       "      <td>23</td>\n",
       "      <td>234</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.440171</td>\n",
       "      <td>35.663291</td>\n",
       "      <td>449.802575</td>\n",
       "      <td>381.758170</td>\n",
       "      <td>361.309013</td>\n",
       "      <td>379.970339</td>\n",
       "      <td>272.832618</td>\n",
       "      <td>380.091995</td>\n",
       "      <td>361.326180</td>\n",
       "      <td>381.705475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>657</td>\n",
       "      <td>840</td>\n",
       "      <td>113</td>\n",
       "      <td>C</td>\n",
       "      <td>Beautiful nature and calm music are always rel...</td>\n",
       "      <td>6</td>\n",
       "      <td>184</td>\n",
       "      <td>R</td>\n",
       "      <td>92.587571</td>\n",
       "      <td>51.886713</td>\n",
       "      <td>526.255682</td>\n",
       "      <td>613.476557</td>\n",
       "      <td>426.710383</td>\n",
       "      <td>601.426508</td>\n",
       "      <td>347.096591</td>\n",
       "      <td>614.503538</td>\n",
       "      <td>421.160221</td>\n",
       "      <td>614.552941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>841</td>\n",
       "      <td>978</td>\n",
       "      <td>113</td>\n",
       "      <td>H</td>\n",
       "      <td>We can not help falling in love with cute and ...</td>\n",
       "      <td>2</td>\n",
       "      <td>138</td>\n",
       "      <td>R</td>\n",
       "      <td>92.086957</td>\n",
       "      <td>54.058138</td>\n",
       "      <td>443.934307</td>\n",
       "      <td>332.686657</td>\n",
       "      <td>352.138686</td>\n",
       "      <td>325.048838</td>\n",
       "      <td>260.189781</td>\n",
       "      <td>325.204406</td>\n",
       "      <td>351.985401</td>\n",
       "      <td>332.486627</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  idx_start idx_end  userId emotionIndex  \\\n",
       "0         0     282     100            N   \n",
       "1       283     422     100            H   \n",
       "2       423     656     113            N   \n",
       "3       657     840     113            C   \n",
       "4       841     978     113            H   \n",
       "\n",
       "                                            sentence  editDistance  \\\n",
       "0  once there was a cat and a mouse. usually , ca...            26   \n",
       "1  We can not help falling in love with cute and ...             2   \n",
       "2  Once there was a cat and a mouse\\u0010. Usaual...            23   \n",
       "3  Beautiful nature and calm music are always rel...             6   \n",
       "4  We can not help falling in love with cute and ...             2   \n",
       "\n",
       "   nbKeystroke answer  D1U1_mean   D1U1_std   D1U2_mean     D1U2_std  \\\n",
       "0          283    NaN  96.074380  31.715592  884.215768  1157.264475   \n",
       "1          140      R  99.071429  22.182246  813.769784   701.131104   \n",
       "2          234    NaN  88.440171  35.663291  449.802575   381.758170   \n",
       "3          184      R  92.587571  51.886713  526.255682   613.476557   \n",
       "4          138      R  92.086957  54.058138  443.934307   332.686657   \n",
       "\n",
       "    D1D2_mean     D1D2_std   U1D2_mean     U1D2_std   U1U2_mean     U1U2_std  \n",
       "0  683.060284  1101.553201  694.609959  1157.337134  677.178571  1108.432937  \n",
       "1  714.784173   699.459223  615.776978   696.222821  714.762590   697.865776  \n",
       "2  361.309013   379.970339  272.832618   380.091995  361.326180   381.705475  \n",
       "3  426.710383   601.426508  347.096591   614.503538  421.160221   614.552941  \n",
       "4  352.138686   325.048838  260.189781   325.204406  351.985401   332.486627  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key_features = ['D1U1','D1U2','D1D2','U1D2','U1U2']\n",
    "\n",
    "text_fixed['userId'] = df_fixed.userId[text_fixed.idx_start].values\n",
    "text_fixed['emotionIndex'] = df_fixed.emotionIndex[text_fixed.idx_start].values\n",
    "text_fixed = text_fixed.reindex(columns=['idx_start','idx_end','userId','emotionIndex','sentence'])\n",
    "text_fixed['editDistance'] = text_fixed.apply(lambda x: extract_editDistance_fixed(gold_fixed, x['emotionIndex'], x['sentence']), axis=1)\n",
    "text_fixed['nbKeystroke'] = text_fixed.apply(lambda x: extract_nbKeystroke(df_fixed.index, x['idx_start'], x['idx_end']), axis=1)\n",
    "text_fixed['answer'] = text_fixed.apply(lambda x: extract_answer(df_fixed.answer, x['idx_start'], x['idx_end']), axis=1)\n",
    "\n",
    "for feat in key_features:\n",
    "    df_fixed[feat] = df_fixed[feat].apply(lambda x: np.nan if np.abs(x) > 1570000000000 else x)\n",
    "    text_fixed[feat+'_mean'] = text_fixed.apply(lambda x: extract_mean(df_fixed[feat], x['idx_start'], x['idx_end']), axis=1)\n",
    "    text_fixed[feat+'_std'] = text_fixed.apply(lambda x: extract_std(df_fixed[feat], x['idx_start'], x['idx_end']), axis=1)\n",
    "\n",
    "\n",
    "text_fixed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## correlation matrix\n",
    "\n",
    "# Numeric columns of the dataset\n",
    "text_fixed.emotionIndex = text_fixed.emotionIndex.map({'N':0,'H':1,'C':2,'S':3,'A':4})\n",
    "numeric_col = ['emotionIndex','editDistance','nbKeystroke'] + [col for col in text_fixed.columns if '_mean' in col] + [col for col in text_fixed.columns if '_std' in col]\n",
    "\n",
    "# Correlation Matrix formation\n",
    "corr_matrix = text_fixed.loc[:, numeric_col].corr()\n",
    "text_fixed.emotionIndex = text_fixed.emotionIndex.map({0:'N',1:'H',2:'C',3:'S',4:'A'})\n",
    " \n",
    "#Using heatmap to visualize the correlation matrix\n",
    "plt.figure(figsize = (15,15))\n",
    "sn.heatmap(corr_matrix, annot=True)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join tables: text_fixed with df_freq_fixed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filter fixed-text experiments in df_freq\n",
    "df_freq_fixed = df_freq[df_freq.textIndex == 'FI'].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correction of expections to align df_freq_fixed and text_fixed tables\n",
    "text_fixed.loc[44,'userId'] = 94\n",
    "tmp = text_fixed.loc[30]\n",
    "text_fixed.loc[30] = text_fixed.loc[31]\n",
    "text_fixed.loc[31] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alignment of df_freq_fixed and text_fixed tables\n",
    "text_fixed['text_index'] = -1\n",
    "df_freq_fixed['text_index'] = -1\n",
    "index = 0\n",
    "\n",
    "i = 0\n",
    "j = 0\n",
    "while i<len(df_freq_fixed) and j<len(text_fixed):\n",
    "    if df_freq_fixed.userId[i] == text_fixed.userId[j] and df_freq_fixed.emotionIndex[i] == text_fixed.emotionIndex[j]:\n",
    "        df_freq_fixed.loc[i,'text_index'] = index\n",
    "        text_fixed.loc[j,'text_index'] = index\n",
    "        index += 1\n",
    "        i += 1\n",
    "        j += 1\n",
    "    elif j != len(text_fixed)-1 and df_freq_fixed.userId[i] == text_fixed.userId[j+1] and df_freq_fixed.emotionIndex[i] == text_fixed.emotionIndex[j+1]:\n",
    "        df_freq_fixed.loc[i,'text_index'] = index\n",
    "        text_fixed.loc[j+1,'text_index'] = index\n",
    "        index += 1\n",
    "        i += 1\n",
    "        j += 2\n",
    "    elif i != len(df_freq_fixed)-1 and df_freq_fixed.userId[i+1] == text_fixed.userId[j] and df_freq_fixed.emotionIndex[i+1] == text_fixed.emotionIndex[j]:\n",
    "        df_freq_fixed.loc[i+1,'text_index'] = index\n",
    "        text_fixed.loc[j,'text_index'] = index\n",
    "        index += 1\n",
    "        i += 2\n",
    "        j += 1\n",
    "    else:\n",
    "        i += 1\n",
    "        j += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# correction of expections to align df_freq_fixed and df_user tables\n",
    "tmp = df_user.loc[92]\n",
    "df_user.loc[92] = df_user.loc[91]\n",
    "df_user.loc[91] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## alignment of df_freq_index and df_user tables\n",
    "df_freq_fixed['user_index'] = -1\n",
    "index = 0\n",
    "\n",
    "j = 0\n",
    "for i in range(len(df_freq_fixed)):\n",
    "    if i in [39,59,71,145,156,159,179,235]: \n",
    "        j += 1\n",
    "    if j < len(df_user) and df_freq_fixed.userId[i] == df_user.userId[j]:\n",
    "        df_freq_fixed.loc[i,'user_index'] = j\n",
    "        i += 1\n",
    "    else:\n",
    "        if j < len(df_user)-1 and df_freq_fixed.userId[i] == df_user.userId[j+1]:\n",
    "            df_freq_fixed.loc[i,'user_index'] = j+1\n",
    "            i += 1\n",
    "            j += 1\n",
    "        else:\n",
    "            for k in range(1,6):\n",
    "                if df_freq_fixed.userId[i] == df_freq_fixed.userId[i-k]:\n",
    "                    df_freq_fixed.loc[i,'user_index'] = df_freq_fixed.loc[i-k,'user_index']\n",
    "                    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>userId</th>\n",
       "      <th>textIndex</th>\n",
       "      <th>emotionIndex</th>\n",
       "      <th>delFreq</th>\n",
       "      <th>leftFreq</th>\n",
       "      <th>TotTime</th>\n",
       "      <th>text_index</th>\n",
       "      <th>user_index</th>\n",
       "      <th>idx_start</th>\n",
       "      <th>idx_end</th>\n",
       "      <th>...</th>\n",
       "      <th>U1U2_mean</th>\n",
       "      <th>U1U2_std</th>\n",
       "      <th>typeWith</th>\n",
       "      <th>typistType</th>\n",
       "      <th>pcTimeAverage</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>gender</th>\n",
       "      <th>status</th>\n",
       "      <th>degree</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>FI</td>\n",
       "      <td>N</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>192762.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>282</td>\n",
       "      <td>...</td>\n",
       "      <td>677.178571</td>\n",
       "      <td>1108.432937</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Touch Typist</td>\n",
       "      <td>More than 3 hours per day</td>\n",
       "      <td>16-19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>FI</td>\n",
       "      <td>H</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>99463.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>283</td>\n",
       "      <td>422</td>\n",
       "      <td>...</td>\n",
       "      <td>714.762590</td>\n",
       "      <td>697.865776</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Touch Typist</td>\n",
       "      <td>More than 3 hours per day</td>\n",
       "      <td>16-19</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113</td>\n",
       "      <td>FI</td>\n",
       "      <td>N</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>84265.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>423</td>\n",
       "      <td>656</td>\n",
       "      <td>...</td>\n",
       "      <td>361.326180</td>\n",
       "      <td>381.705475</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Two Finger Typist</td>\n",
       "      <td>less than an hour per day</td>\n",
       "      <td>20-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>113</td>\n",
       "      <td>FI</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>78186.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>657</td>\n",
       "      <td>840</td>\n",
       "      <td>...</td>\n",
       "      <td>421.160221</td>\n",
       "      <td>614.552941</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Two Finger Typist</td>\n",
       "      <td>less than an hour per day</td>\n",
       "      <td>20-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>113</td>\n",
       "      <td>FI</td>\n",
       "      <td>H</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>48354.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>841</td>\n",
       "      <td>978</td>\n",
       "      <td>...</td>\n",
       "      <td>351.985401</td>\n",
       "      <td>332.486627</td>\n",
       "      <td>2 hands</td>\n",
       "      <td>Two Finger Typist</td>\n",
       "      <td>less than an hour per day</td>\n",
       "      <td>20-29</td>\n",
       "      <td>Female</td>\n",
       "      <td>Student</td>\n",
       "      <td>College/University</td>\n",
       "      <td>Tunisia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId textIndex emotionIndex  delFreq  leftFreq   TotTime  text_index  \\\n",
       "0     100        FI            N        8         1  192762.0           0   \n",
       "1     100        FI            H       11         0   99463.0           1   \n",
       "2     113        FI            N       10         0   84265.0           2   \n",
       "3     113        FI            C        7         0   78186.0           3   \n",
       "4     113        FI            H        9         0   48354.0           4   \n",
       "\n",
       "   user_index idx_start idx_end  ...   U1U2_mean     U1U2_std  typeWith  \\\n",
       "0           0         0     282  ...  677.178571  1108.432937   2 hands   \n",
       "1           0       283     422  ...  714.762590   697.865776   2 hands   \n",
       "2           1       423     656  ...  361.326180   381.705475   2 hands   \n",
       "3           1       657     840  ...  421.160221   614.552941   2 hands   \n",
       "4           1       841     978  ...  351.985401   332.486627   2 hands   \n",
       "\n",
       "          typistType              pcTimeAverage  ageRange  gender   status  \\\n",
       "0       Touch Typist  More than 3 hours per day     16-19  Female  Student   \n",
       "1       Touch Typist  More than 3 hours per day     16-19  Female  Student   \n",
       "2  Two Finger Typist  less than an hour per day     20-29  Female  Student   \n",
       "3  Two Finger Typist  less than an hour per day     20-29  Female  Student   \n",
       "4  Two Finger Typist  less than an hour per day     20-29  Female  Student   \n",
       "\n",
       "               degree  country  \n",
       "0  College/University  Tunisia  \n",
       "1  College/University  Tunisia  \n",
       "2  College/University  Tunisia  \n",
       "3  College/University  Tunisia  \n",
       "4  College/University  Tunisia  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge all tables\n",
    "df_fixed_all = df_freq_fixed.join(text_fixed, on='text_index', how='left', rsuffix='_right')\n",
    "df_fixed_all = df_fixed_all.drop(['userId_right', 'emotionIndex_right', 'text_index_right'], axis=1)\n",
    "df_fixed_all = df_fixed_all.join(df_user.reset_index().rename(columns={'index':'user_index'}), on='user_index', how='left', rsuffix='_right')\n",
    "df_fixed_all = df_fixed_all.drop(['user_index_right','userId_right'], axis=1)\n",
    "df_fixed_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Useful functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import metrics, model_selection\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "import xgboost as xgb\n",
    "import shap\n",
    "from tqdm import trange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Receiving a dictionary of performance metrics and printing them\n",
    "def show_metrics(performance_metrics):\n",
    "    for metric_name, metric in performance_metrics.items():\n",
    "        if metric_name.startswith(\"Confusion\"):\n",
    "            print(\"Confusion Matrix: \")\n",
    "            if len(metric) != 5:\n",
    "                print(pd.DataFrame(metric))\n",
    "            else:\n",
    "                print(pd.DataFrame(metric, \n",
    "                  index=list(label_dictionary.values()), \n",
    "                  columns=list(label_dictionary.values())))\n",
    "        else:\n",
    "            print(\"Metric : % s, Score : % 5.2f\" %(metric_name, metric))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting the Confusion Matrix as a heatmap\n",
    "label_dictionary = {1: 'N', 2: 'H', 3: 'C', 4: 'S', 5: 'A'}\n",
    "\n",
    "def plot_confusion_matrix(confusion_matrix):\n",
    "    if len(confusion_matrix) != 5:\n",
    "        df_cm = pd.DataFrame(confusion_matrix)\n",
    "    else:\n",
    "        df_cm = pd.DataFrame(confusion_matrix, \n",
    "                         index = [i for i in label_dictionary.values()],\n",
    "                         columns = [i for i in label_dictionary.values()])\n",
    "    plt.figure()\n",
    "    sn.set(font_scale=1.4) # for label size\n",
    "    sn.heatmap(df_cm, annot=True, annot_kws={\"size\": 16}, cmap=\"Blues\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Computing the different Performance Metrics\n",
    "def compute_metrics(clf, dataX, dataY, show=False):\n",
    "    # Using Confusion Metrics to compute metrics\n",
    "    def performance_measures(y_true, y_pred):\n",
    "        cnf_matrix = metrics.confusion_matrix(y_true, y_pred, normalize='true')\n",
    "\n",
    "        FP = cnf_matrix.sum(axis=0) - np.diag(cnf_matrix) \n",
    "        FN = cnf_matrix.sum(axis=1) - np.diag(cnf_matrix)\n",
    "        TP = np.diag(cnf_matrix)\n",
    "        TN = cnf_matrix.sum() - (FP + FN + TP)\n",
    "\n",
    "        FP = FP.astype(float)\n",
    "        FN = FN.astype(float)\n",
    "        TP = TP.astype(float)\n",
    "        TN = TN.astype(float)\n",
    "\n",
    "        acc = np.mean((TP + TN) / (TP + FP + FN + TN))\n",
    "        TP_rate = np.mean(TP / (TP + FN))\n",
    "        FP_rate = np.mean(FP / (FP + TN))\n",
    "        precision = np.mean(TP / (TP + FP))\n",
    "        f_measure = 2 * precision * TP_rate / (precision + TP_rate)\n",
    "        f_micro = metrics.f1_score(y_true, y_pred, average='micro')\n",
    "        f_macro = metrics.f1_score(y_true, y_pred, average='macro')\n",
    "        \n",
    "        # Returning \n",
    "        return (acc, TP_rate, FP_rate, precision, f_measure, f_micro, f_macro,\n",
    "                np.round(cnf_matrix, 2))\n",
    "\n",
    "    acc, tp_rate, fp_rate, precision, f_measure, f_micro, f_macro, confusion_matrix = performance_measures(y_true=dataY, y_pred=clf.predict(dataX))\n",
    "    \n",
    "    \n",
    "    performance_metrics = {\"ACC\": acc, \"TP_Rate\": tp_rate, \"FP_Rate\": fp_rate,\n",
    "            \"Precision\": precision, \"F_Measure\": f_measure, \"F_Micro\": f_micro, \"F_Macro\": f_macro,\n",
    "            \"Confusion Matrix\": confusion_matrix} \n",
    "    if show:\n",
    "        show_metrics(performance_metrics)\n",
    "\n",
    "    return performance_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_fixed = df_fixed_all.loc[:,df_fixed_all.columns.difference(['user_index','emotionIndex','userId','textIndex','text_index','idx_start','idx_end','sentence'])]\n",
    "label_fixed = df_fixed_all.emotionIndex.map({'N':0,'H':1,'C':2,'S':3,'A':4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert categorical features into numerical features\n",
    "data_fixed.gender = (data_fixed.gender == 'Male')\n",
    "data_fixed.ageRange = data_fixed.ageRange.map({'16-19':1,'20-29':2,'30-39':3,'>=50':4})\n",
    "data_fixed.answer = data_fixed.answer.map({'R':1, 'W':0})\n",
    "data_fixed.degree = data_fixed.degree.map({'High School':1, 'College/University':2})\n",
    "data_fixed.pcTimeAverage = data_fixed.pcTimeAverage.map({'less than an hour per day':1, 'between 1 hour and 3 hours per day':2, 'More than 3 hours per day':3})\n",
    "data_fixed.status = data_fixed.status.map({'Student':1, 'Professional':2})\n",
    "data_fixed.typeWith = data_fixed.typeWith.map({'1 hand':1, '2 hands':2})\n",
    "data_fixed = pd.get_dummies(data_fixed, columns=['country','typistType'])\n",
    "data_fixed = data_fixed.fillna(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_fixed_filter = data_fixed\n",
    "#label_fixed = (label_fixed == 1)  ## for binary models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     243.000000\n",
       "mean      527.396707\n",
       "std       265.232255\n",
       "min        -1.000000\n",
       "25%       382.898585\n",
       "50%       461.966216\n",
       "75%       577.206529\n",
       "max      2831.904167\n",
       "Name: D1U2_mean, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_fixed.D1U2_mean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "typeWith: F_onewayResult(statistic=1.0625981968164069, pvalue=0.36566561357609456)\n",
      "gender: F_onewayResult(statistic=0.756571665449794, pvalue=0.5195069267139654)\n",
      "ageRange: F_onewayResult(statistic=0.9274717411270018, pvalue=0.4281108442943701)\n",
      "degree: F_onewayResult(statistic=0.5685627041613412, pvalue=0.6362275327388697)\n",
      "pcTimeAverage: F_onewayResult(statistic=1.6099404272995905, pvalue=0.1877059793560216)\n",
      "status: F_onewayResult(statistic=1.999588159091468, pvalue=0.11471258141355736)\n",
      "country: F_onewayResult(statistic=8.5255690311875, pvalue=2.1121969455679765e-05)\n",
      "typistType: F_onewayResult(statistic=0.7377996733969245, pvalue=0.5304116249059669)\n"
     ]
    }
   ],
   "source": [
    "# only for demographic features\n",
    "personal_col = ['typeWith','gender','ageRange','degree','pcTimeAverage','status','country','typistType']\n",
    "for col in personal_col:\n",
    "    anova = stats.f_oneway(data_fixed.loc[data_fixed.D1U2_mean < 383, col],\n",
    "               data_fixed.loc[(data_fixed.D1U2_mean >= 383) & (data_fixed.D1U2_mean < 462), col],\n",
    "               data_fixed.loc[(data_fixed.D1U2_mean >= 462) & (data_fixed.D1U2_mean < 577), col],\n",
    "               data_fixed.loc[data_fixed.D1U2_mean >= 577, col])\n",
    "    print(col + ': ' + str(anova))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-class models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define classification models\n",
    "classifiers_name = ['LogReg','RF','XGB','SVM','MLP']\n",
    "\n",
    "classifiers = [\n",
    "    LogisticRegression(multi_class='auto', max_iter=500, solver='newton-cg', class_weight={0:0.1,1:1,2:1,3:1,4:1}), \n",
    "    RandomForestClassifier(n_estimators=200, max_depth=5, class_weight={0:0.1,1:1,2:1,3:1,4:1}),\n",
    "    xgb.XGBClassifier(objective='multi:softmax', eval_metric='mlogloss',use_label_encoder=False),\n",
    "    SVC(kernel='rbf',decision_function_shape='ovr', probability=True),#, class_weight={0:0.1,1:1,2:1,3:1,4:1}),\n",
    "    MLPClassifier(alpha=1, max_iter=500)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg\n",
      "RF\n",
      "XGB\n",
      "SVM\n",
      "MLP\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LogReg': [0.365, 0.1476896144697461],\n",
       " 'RF': [0.35083333333333344, 0.0826096913873373],\n",
       " 'XGB': [0.5025000000000001, 0.06790209455123197],\n",
       " 'SVM': [0.49816666666666665, 0.017036725037400818],\n",
       " 'MLP': [0.3520666666666667, 0.14220918082568054]}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# micro-F1 scores with a 10-fold cross-validation method\n",
    "\n",
    "model_scores_micro = {}\n",
    "for i in range(len(classifiers)):\n",
    "    model_scores_micro[classifiers_name[i]] = []\n",
    "    print(classifiers_name[i])\n",
    "    for j in range(10):\n",
    "        model = classifiers[i]\n",
    "        scores = model_selection.cross_val_score(model, X=data_fixed_filter, y=label_fixed, cv=10, n_jobs=1, scoring='f1_micro')\n",
    "        model_scores_micro[classifiers_name[i]] += list(scores)\n",
    "    mean = np.nanmean(model_scores_micro[classifiers_name[i]])\n",
    "    std = np.nanstd(model_scores_micro[classifiers_name[i]])\n",
    "    model_scores_micro[classifiers_name[i]] = [mean,std]\n",
    "\n",
    "model_scores_micro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogReg\n",
      "RF\n",
      "XGB\n",
      "SVM\n",
      "MLP\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'LogReg': [0.3371320643580706, 0.15172195155212775],\n",
       " 'RF': [0.3509732681663023, 0.09421859553089508],\n",
       " 'XGB': [0.3644627221520774, 0.09506165389113862],\n",
       " 'SVM': [0.14263320463320459, 0.03154441162617255],\n",
       " 'MLP': [0.1490770423239987, 0.059395200715720575]}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# macro-F1 scores with a 10-fold cross-validation method\n",
    "\n",
    "model_scores_macro = {}\n",
    "for i in range(len(classifiers)):\n",
    "    model_scores_macro[classifiers_name[i]] = []\n",
    "    print(classifiers_name[i])\n",
    "    for j in range(10):\n",
    "        model = classifiers[i]\n",
    "        scores = model_selection.cross_val_score(model, X=data_fixed_filter, y=label_fixed, cv=10, n_jobs=1, scoring='f1_macro')\n",
    "        model_scores_macro[classifiers_name[i]] += list(scores)\n",
    "    mean = np.nanmean(model_scores_macro[classifiers_name[i]])\n",
    "    std = np.nanstd(model_scores_macro[classifiers_name[i]])\n",
    "    model_scores_macro[classifiers_name[i]] = [mean,std]\n",
    "\n",
    "model_scores_macro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best multi-class model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric : ACC, Score :  0.83\n",
      "Metric : TP_Rate, Score :  0.58\n",
      "Metric : FP_Rate, Score :  0.10\n",
      "Metric : Precision, Score :  0.68\n",
      "Metric : F_Measure, Score :  0.63\n",
      "Metric : F_Micro, Score :  0.61\n",
      "Metric : F_Macro, Score :  0.61\n",
      "Confusion Matrix: \n",
      "      N     H     C     S     A\n",
      "N  0.76  0.05  0.14  0.05  0.00\n",
      "H  0.25  0.75  0.00  0.00  0.00\n",
      "C  0.50  0.00  0.33  0.17  0.00\n",
      "S  0.45  0.00  0.18  0.36  0.00\n",
      "A  0.29  0.00  0.00  0.00  0.71\n",
      "here 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = 'XGB'\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_fixed_filter, label_fixed, test_size=0.2)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = classifiers[classifiers_name.index(best_model)]\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "performance_metrics = compute_metrics(model, X_test, Y_test, show=True)\n",
    "plot_confusion_matrix(performance_metrics[\"Confusion Matrix\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class N:\n",
      "0.658 0.12\n",
      "0.655 0.121\n",
      "0.6 0.128\n",
      "\n",
      "\n",
      "Class H:\n",
      "0.844 0.056\n",
      "0.644 0.1\n",
      "0.342 0.173\n",
      "\n",
      "\n",
      "Class C:\n",
      "0.86 0.042\n",
      "0.507 0.096\n",
      "0.067 0.133\n",
      "\n",
      "\n",
      "Class S:\n",
      "0.844 0.059\n",
      "0.566 0.126\n",
      "0.167 0.167\n",
      "\n",
      "\n",
      "Class A:\n",
      "0.893 0.019\n",
      "0.516 0.091\n",
      "0.083 0.171\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## binary classification with XGBoost\n",
    "for emotion_index in range(5):\n",
    "    f1_micro = np.array([])\n",
    "    f1_macro = np.array([])\n",
    "    recall = np.array([])\n",
    "    \n",
    "    print(\"Class \" + label_dictionary[emotion_index+1] + \":\")\n",
    "    label_fixed_binary = (label_fixed == emotion_index).astype(int)\n",
    "    \n",
    "    for i in range(10):\n",
    "        model = xgb.XGBClassifier(objective='binary:logistic',eval_metric='mlogloss',use_label_encoder=False)\n",
    "        scores = model_selection.cross_validate(model, X=data_fixed_filter, y=label_fixed_binary, cv=10, n_jobs=1, scoring=['f1_micro','f1_macro','recall'])\n",
    "        \n",
    "        f1_micro = np.concatenate([f1_micro, scores['test_f1_micro']])\n",
    "        f1_macro = np.concatenate([f1_macro, scores['test_f1_macro']])\n",
    "        recall = np.concatenate([recall, scores['test_recall']])\n",
    "        \n",
    "    print(np.round(np.nanmean(f1_micro),3), np.round(np.nanstd(f1_micro),3))\n",
    "    print(np.round(np.nanmean(f1_macro),3), np.round(np.nanstd(f1_macro),3))\n",
    "    print(np.round(np.nanmean(recall),3), np.round(np.nanstd(recall),3))\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class N:\n",
      "0.579 0.077\n",
      "0.498 0.122\n",
      "0.961 0.055\n",
      "\n",
      "\n",
      "Class H:\n",
      "0.845 0.06\n",
      "0.605 0.137\n",
      "0.249 0.215\n",
      "\n",
      "\n",
      "Class C:\n",
      "0.819 0.065\n",
      "0.549 0.112\n",
      "0.243 0.262\n",
      "\n",
      "\n",
      "Class S:\n",
      "0.803 0.125\n",
      "0.622 0.14\n",
      "0.384 0.215\n",
      "\n",
      "\n",
      "Class A:\n",
      "0.891 0.029\n",
      "0.567 0.135\n",
      "0.223 0.332\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## binary classification with Random Forest\n",
    "for emotion_index in range(5):\n",
    "    f1_micro = np.array([])\n",
    "    f1_macro = np.array([])\n",
    "    recall = np.array([])\n",
    "    \n",
    "    print(\"Class \" + label_dictionary[emotion_index+1] + \":\")\n",
    "    label_fixed_binary = (label_fixed == emotion_index).astype(int)\n",
    "    \n",
    "    for i in range(10):\n",
    "        model = RandomForestClassifier(n_estimators=200, max_depth=5, class_weight={0:0.1,1:1})\n",
    "        scores = model_selection.cross_validate(model, X=data_fixed_filter, y=label_fixed_binary, cv=10, n_jobs=1, scoring=['f1_micro','f1_macro','recall'])\n",
    "        \n",
    "        f1_micro = np.concatenate([f1_micro, scores['test_f1_micro']])\n",
    "        f1_macro = np.concatenate([f1_macro, scores['test_f1_macro']])\n",
    "        recall = np.concatenate([recall, scores['test_recall']])\n",
    "        \n",
    "    print(np.round(np.nanmean(f1_micro),3), np.round(np.nanstd(f1_micro),3))\n",
    "    print(np.round(np.nanmean(f1_macro),3), np.round(np.nanstd(f1_macro),3))\n",
    "    print(np.round(np.nanmean(recall),3), np.round(np.nanstd(recall),3))\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class N:\n",
      "0.669 0.112\n",
      "0.648 0.128\n",
      "0.542 0.259\n",
      "\n",
      "\n",
      "Class H:\n",
      "0.852 0.019\n",
      "0.46 0.006\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class C:\n",
      "0.872 0.011\n",
      "0.466 0.003\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class S:\n",
      "0.868 0.014\n",
      "0.465 0.004\n",
      "0.0 0.0\n",
      "\n",
      "\n",
      "Class A:\n",
      "0.902 0.019\n",
      "0.474 0.005\n",
      "0.0 0.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## binary classification with SVM\n",
    "for emotion_index in range(5):\n",
    "    f1_micro = np.array([])\n",
    "    f1_macro = np.array([])\n",
    "    recall = np.array([])\n",
    "    \n",
    "    print(\"Class \" + label_dictionary[emotion_index+1] + \":\")\n",
    "    label_fixed_binary = (label_fixed == emotion_index).astype(int)\n",
    "    \n",
    "    for i in range(10):\n",
    "        model = SVC(kernel='rbf',decision_function_shape='ovr', probability=True)\n",
    "        scores = model_selection.cross_validate(model, X=data_fixed_filter, y=label_fixed_binary, cv=10, n_jobs=1, scoring=['f1_micro','f1_macro','recall'])\n",
    "        \n",
    "        f1_micro = np.concatenate([f1_micro, scores['test_f1_micro']])\n",
    "        f1_macro = np.concatenate([f1_macro, scores['test_f1_macro']])\n",
    "        recall = np.concatenate([recall, scores['test_recall']])\n",
    "        \n",
    "    print(np.round(np.nanmean(f1_micro),3), np.round(np.nanstd(f1_micro),3))\n",
    "    print(np.round(np.nanmean(f1_macro),3), np.round(np.nanstd(f1_macro),3))\n",
    "    print(np.round(np.nanmean(recall),3), np.round(np.nanstd(recall),3))\n",
    "    \n",
    "    print()\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data standardization\n",
    "scaler = MinMaxScaler()\n",
    "data_fixed = pd.DataFrame(scaler.fit_transform(data_fixed), columns=data_fixed.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chi-squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ordered features by importance with their corresponding chi2 score\n",
      "['country' 'pcTimeAverage' 'status' 'typistType' 'degree' 'typeWith'\n",
      " 'gender' 'ageRange']\n",
      "[18.781  6.822  5.795  3.739  1.871  1.854  1.506  1.474]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "demo_features = np.array(['typeWith','gender','ageRange','degree','pcTimeAverage','status','country','typistType'])\n",
    "\n",
    "# Feature extraction\n",
    "test = SelectKBest(score_func=chi2, k=8)\n",
    "fit = test.fit(data_fixed.loc[:,demo_features].fillna(100).replace(-1,16), pd.cut(data_fixed.D1U2_mean.fillna(0),bins=[0,372,445,573,4306],labels=[1,2,3,4]).fillna(1))\n",
    "\n",
    "# Summarize scores\n",
    "np.set_printoptions(precision=3)\n",
    "print(\"Ordered features by importance with their corresponding chi2 score\")\n",
    "print(demo_features[np.argsort(fit.scores_)][::-1])\n",
    "print(fit.scores_[np.argsort(fit.scores_)][::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D1U2_mean        1.000000\n",
      "U1D2_mean        0.972890\n",
      "U1U2_mean        0.915055\n",
      "D1D2_mean        0.911282\n",
      "D1U2_std         0.790524\n",
      "U1D2_std         0.783093\n",
      "U1U2_std         0.749809\n",
      "D1D2_std         0.740526\n",
      "editDistance     0.455710\n",
      "nbKeystroke      0.427721\n",
      "TotTime          0.301473\n",
      "typistType       0.149490\n",
      "D1U1_std         0.125646\n",
      "D1U1_mean        0.113105\n",
      "status          -0.100291\n",
      "answer          -0.092404\n",
      "pcTimeAverage   -0.086800\n",
      "country          0.081245\n",
      "gender          -0.060056\n",
      "degree          -0.014117\n",
      "delFreq          0.011358\n",
      "leftFreq        -0.011001\n",
      "typeWith        -0.003644\n",
      "ageRange         0.000889\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "importances = data_fixed.apply(lambda x: x.corr(data_fixed.D1U2_mean))\n",
    "indices = np.argsort(importances)\n",
    "indices_abs = np.argsort(np.abs(importances))[::-1]\n",
    "print(importances[indices_abs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.barh(range(len(indices)), importances[indices], color='g', align='center')\n",
    "plt.yticks(range(len(indices)), importances[indices].index.values)\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Information Gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D1D2_mean</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U2_std</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1D2_mean</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1D2_std</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1U2_mean</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>U1U2_std</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U2_mean</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U1_std</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1U1_mean</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D1D2_std</th>\n",
       "      <td>5.493061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotTime</th>\n",
       "      <td>5.475947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nbKeystroke</th>\n",
       "      <td>4.708146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>editDistance</th>\n",
       "      <td>3.419528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delFreq</th>\n",
       "      <td>2.919546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <td>1.271756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pcTimeAverage</th>\n",
       "      <td>1.035956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answer</th>\n",
       "      <td>0.791728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <td>0.714501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typistType</th>\n",
       "      <td>0.686954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>0.630656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leftFreq</th>\n",
       "      <td>0.619030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ageRange</th>\n",
       "      <td>0.611192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>typeWith</th>\n",
       "      <td>0.205801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>degree</th>\n",
       "      <td>0.114121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      1\n",
       "0                      \n",
       "D1D2_mean      5.493061\n",
       "D1U2_std       5.493061\n",
       "U1D2_mean      5.493061\n",
       "U1D2_std       5.493061\n",
       "U1U2_mean      5.493061\n",
       "U1U2_std       5.493061\n",
       "D1U2_mean      5.493061\n",
       "D1U1_std       5.493061\n",
       "D1U1_mean      5.493061\n",
       "D1D2_std       5.493061\n",
       "TotTime        5.475947\n",
       "nbKeystroke    4.708146\n",
       "editDistance   3.419528\n",
       "delFreq        2.919546\n",
       "country        1.271756\n",
       "pcTimeAverage  1.035956\n",
       "answer         0.791728\n",
       "status         0.714501\n",
       "typistType     0.686954\n",
       "gender         0.630656\n",
       "leftFreq       0.619030\n",
       "ageRange       0.611192\n",
       "typeWith       0.205801\n",
       "degree         0.114121"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from info_gain import info_gain \n",
    "\n",
    "importances_ig = []\n",
    "for col in data_fixed.columns:\n",
    "    importances_ig.append(info_gain.info_gain(data_fixed[col],data_fixed.D1U2_mean))\n",
    "indices_ig = np.argsort(importances_ig)[::-1]\n",
    "pd.concat([pd.Series(data_fixed.columns[indices_ig]), pd.Series(np.sort(importances_ig)[::-1])], axis=1).set_index(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mutual information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LIiKirfRMRURERLSQZCoiIiKihSRTERERES0kmYqIiIhoYUoD0CU9FfgoMA/4rO33DTy/FnA0sANwA/B827+q29SImIquB7dPJ34GzkfE6mDSZErSPOC/gV2Bq4HzJB1v+7K+l+0H3Gj7HyTtBbwfeH4XDY6IVVeXiVqXSWYS2IjV21R6pnYErrB9JYCkrwDPAvqTqWcBBzfffw34hCTZdsW2RkREZUlgI9rTZPmOpD2Ap9p+efP4RcCjbL+u7zWXNK+5unn8i+Y11w/E2h/Yv3n4IOBn02jrJsD1k75qNHM1dtfxE3vm4yf2zMdP7JmPn9gzHz+x28e/r+35w56Y0aKdtg8HDh/lZyUtsr2wcpPmdOyu4yf2zMdP7JmPn9gzHz+xZz5+Yncbfyqz+a4Btuh7vHmzbehrJK0BbEgZiB4RERGxSptKMnUesJWk+0m6O7AXcPzAa44HXtJ8vwfww4yXioiIiNXBpLf5bN8h6XXA9ymlEY60famkQ4BFto8HjgC+IOkK4PeUhKu2kW4PruKxu46f2DMfP7FnPn5iz3z8xJ75+IndYfxJB6BHRERExMRSAT0iIiKihSRTERERES0kmYqIiIhoIclUxCyS9EBJpzSFb5G0jaR3zHa7VlUqtpj8lVGLpHmS3jjb7WhD0jqSHjTb7VgdSbqnpG1mux2TGfsB6JI2A+5L38xD26dXij0feAWwYCD+yyrE3pmyxE6v7Sqhff+2sZv4VY+LpI1X9rzt348au4n/pknif6hF7IuBCU9k2yO/EbtsdxP/R8C/Ap+2vV2z7RLbW7eJO2Q/tc+Xro+LgBcC97d9iKQtgfvYPrdN3Cb2xbYf3jbOyuIz/HzsfQa0OR+/QZk9faLtv40aZ4LYXR7zc23v2LqRK9/HLsBWtj/XfLbfw/YvK8TdDfggcHfb95P0COAQ27u3jd2lLs/DJn6X58tpwO6Uz6vFwHXAj22v9HNnirHXBd4MbGn7FZK2Ah5k+4Q2cWe0Avp0SeotmHwZcGez2UCVZAr4FnAG8IO++LUcAbyRciJUjd3RcVncxBCwJXBj8/1GwG+A+7WIDbB+8++DgEeyvFbZbkDbN98zm39f2/z7hebfF7aMC922G2Bd2+eWz6Vl7qgQd5mOzpeuj8sngb8BTwQOAW4Bvt7sq63zJT3S9nkVYg1zYvPv4Hn4qQqxPwm8FPiYpGOBz9mezrJck8Xu6pj/WNIngGOAP/Y22j6/QmwkvRNYSDkfPwesCfwPsHOF8AdT1qg9DcD2EkltPw8BkPTPwPuBe1M+b3uJzgYVwnd5HkK358uGtm+W9HLgaNvvlHRRhbhQzo/FwKObx9cAxwKtkilsj+0XZe2+tTqMv6TD2OfMxeMCfAZ4et/jp1F6TWrFPx1Yv+/x+sDplWJfMGTb+ePcbsoH3gN67aQUvT1xDp0vXR2X3vG4oG/bhZXa/FNKwvoL4CLgYuCiisfkgiHbqpyHffE2BF4FXAWcRUmw1hzjY37qkK8fVjweSyiJSH/bq/xOgbOHHJdasa8AHlLz3OiLfcGQbdXOw47Pl4uBTYGTgEdWPuaLumj3WPdMAVdSrjBu7yj+CZKebvu7HcQ+VdKhwDfoa7/rXIl1eVx2sv2K3gPbJ0r6QMX4fwf8pe/xX5ptNUjSzrZ/3Dx4DPXGBXbV7tdSisY9WNI1wC+BfSrE7dfl+dLVcfmrpHk0tyma2za1bms9pVKciXR5HiLpXpRz5EXABcAXgV0oq1A8vkXozo657SfUiLMSf7FtSb22r1cx9qWSXgDMa24JvYGSwNbwf7YvrxRrUKfnId2+Rw+hFAo/0/Z5ku4P/LxS7L9IWofl7X4AFT4bxz2Z+hOwRNIprJiQvKFS/AOAt0u6HfgrdbtYH9X827+Aoildom11eVx+2wyA/p/m8QuB31aI23M0cK6kbzaPnw18vlLs/YAjJW3YPP4D0Hr8W6OTdtu+Evin5sP/brZvaRtziC7Pl65+nx8DvgncW9J/UnrsqgzMt/3rYeNrasRudHYeNsf5QZRbN7vZvrZ56hhJi1qG7+yYS/o74D3A39t+mqSHAo+2fUSN+MBXJX0a2EjSKyjH+zOVYr8e+HfKe+fLlD/y724TsLm9B7BI0jHAcaz43vxGm/iNLj8Podv36LGUW2+9x1cCz60RG3gn8D1gC0lfpNwK3rdt0LEegC7pJcO22671x3dO6vK4NAPR3wn8I8vH1bzL9o1tY/ftY3vgsc3D021fUCnu/Wz/svfhYfum3rZK8au3W9JGwIu56ySIWhcMnb+POjoua1HG6T2JcpFzCuUqvtVEiCb2svE1th8o6e+BY23XGF/Tv59l52GleHcD3m77P2rEm2AfD6bvmNfqNZF0ImWsyr/b3lbSGpTbLNUmAkjaFXgype3ft31yrdhN/A0oF9utL3gkfW4lT9sVJkH17avqeTgQu6vz5XMMGTxf67g0vbs7Udp9tu3rW8cc52RqJki6J7AVsHZvm+vNFnwG8LCB2IfUiN0VSXs2VwUr3dYi/hdsv2iybSPGPt/29gPbFtveoULsTtot6SzgbMoYgWVd5HPlgqHD4/Id4Nm2/9o83hQ4odLvcgmwHWXMR28G5UVuObupL35nvTCSLui1uTZJOwGX9pKFJnl4iO1zKsQ+z/Yj+9svaYntR7SN3beP+1J6G3/QzNiaVynxeSRwJMsnXdwEvMz24gqxl92GW9m2EWN32huo4TPAb+m9Z1vG7u+FWht4DvDbGheZzcXfoJuAX9seffJP20FXXX5RkpyvUWYhXdn7qhj/5ZQ/YjdSBkTeRqVBkcBhlFsgV1F6ei4Gjhj348KQAYrDttWKT1k8+7KWMR9M6QL+BfDPfV/7Uv44jGW7ax/bcThfKh6XV1BuIcyj9NpdBDy5UpvP7W87sB51B6CfCDyPZlArpcfx4kqxP9ic6+rgPLmgPy5lfE2tCRynAffqO+Y7AT+q2PZXAOcBv2geb0XpKakR+yLgsX2Pd6l1vnT5edvledjE+xVldvD1wA3N99cA5wM71NpPs6+7AWdVinU2ZWznIsqsvtubNv+izWfMuI+Z+hwlEfkw8ATKjJWaA+gOoEzjPNv2E5ouy/dUiv0Y29s0V7zvkvRfLJ+q2lb14yLpacDTgc0kfazvqQ2oMFVf0tuAtwPrSLq5t5lyUrddtftBlPIIG1Gm5vfcQvmQHVnH7Qb4QjPG4wRWHDPR+nZWny7Ol06Pi+3PSLo7ZSzJAuCVtmsN+u1yfA3AJra/2hwjbN8hqVZ5lFcCbwLukPRn6o7zlJu/NpSgf2tux9XwJkr5jAdI+jEwnzLGppbXUsoXnANg++eS7l0p9p22z+g9sH2mpFafiZIeDTwGmK8Va7ZtQLmAqKHL8xDgZOBrtr8PIOnJlET/c5SyCY9ayc9O11aU8hE1/BbYz/alAE2P3SHAv1EmjJ00StBxT6bWsX2KJNn+NXCwpMXAQZXi/9n2nyUhaS3bP1W9Kre3Nf/+qRmTcQNlqmcNXRyX31Iy9d0p2XrPLZR6Wa3Yfi/wXknvtf22tvEGYn8L+JakR9v+SeXYnbW78RfgUMoA194fMgNVirs2qp8vXR2XgT8svZpnS4CdJO3klsVAAWx/sBlfczMlET/IdcfX/LEZk9GbLbQT5TZCa7bXn/xVI7tS0htYXofoNZRezNZsny/pcZTjLeBnrnA7qM/ttv+ipl5bkwTWGsPyoyb5/nIT8/nAab3bRR5thvbdKZMe1mD57UMo52StJLOz87AxOPP7JEkftP3KZszjyCTdwoq/v98Bb2kTs88De4kUgO3LJD3Y9pVasd7ftIx7MnV7M+jy55JeR+lCrDnr5upmAPBxwMmSbgR+XSn2CU3sQyldiAY+Wyl29eNi+0LgQklf8vJxKvcEtnDFweeU47Ke7T9K2gfYHvho80e+redIupSSyH4P2AZ4o+3/WfmPTUlX7X4z8A+uMAByJbp8H9U+LoPJwjcm2D4ySftRBsr/a62YAzrrhZF0iu0nTbZtRK+izNB6B+Xz6hRg/zYBtXzW2qAHSsJ1Zq1BSXh6PaW7UhLBb1eKvW3z7zsHtm/HiDO0bf+I0uajKn32DdN1b+C1kt4CfKV5/Hzg/1TKJbQqkdDxRcOlkj7Fiu2+rEkAR07wx3oAejPw73LK7Zt3U7pAD7V9dgf7ehylEN73bP9lstdPM/ZawNquN6uns+Oi4WX8z7JdZW0tlSq221ISnaMoCebzbD+uQuwlth8h6TmU235vovzR3HaSH51K7E7aLekkykDrP7Vt40r20eX50tnvs4l/DwDbt9aI18R8F2X24QLKOX46cIbtJRViz6PUIfo4FXthJK0NrEsZ2/n4Ji6U3+X3bD+4TfyuaPmstXtTbmv9sHn8BMrnyjOH/uD09yPKGNhls/mAz7rCHzhJ82zXXsXiI7YPlPRths9aa7VUTVfn4cA+NqEkmLs0m34MvIvS+7Wl7StaxO7sokGlxtRrWLHdnwT+TFmRYqTPmrFOpnokrdvVHxt1t55TJ+v/DO6j9nFRM9tGpYz/Fm7K+LveTKfzbW8v6SDgGttHaMgsvBFjX2r7YZI+S7mX/z1JF1ZKpjppt0rdoIdR/kh2UUutf19dnC9dHZetKbWUejOGrgde3N8931bzofoK4F+AzWxXGauiDtahk3QAcCDw95SexV4ydTPwGdufqLCPLtcqPQl4iZu6WCqzM4+y3bqAapM4XNpVQinpSsoyKUe63tT/HWwvbi7i76LpuWq7j87XQ6xtrl40wJjf5lMZpHcE5ZbElpK2pQxEfU2l+MvqzVB/PafP0cX6P3R+XNZoPuieRxnHU9stKgMi9wH+sbn9tGal2N+W9FPKbb5XN38c/lwpdlftPq756kzH50tXx+Vw4E22TwWQ9HjKIPHHtA2sUpR2Z8rxuICSTJ2x0h+anurr0Nn+KPBRSa+3/fEKbRymy7VKt/DyAqMA/0cZD9ea7Tsl/UzSlrZ/UyPmgG2BvYAjmvP7SOArtm9e+Y+t1ItUKm//2PY1NRo5RNfrIc6nDNoeLP/TpjD1K1l+0bCYFS8aWl8wQCk9QVlv8b6seNHQapzqWPdMSTqHco/3eC+vTXKJ7a0rxV9CR/VmJC2yvVAr1lWp1UvS2XGRtCfw/yhl/F+jUsb/UNtVqs9Kug/wAuA822eorDT+eNtHV4q/MXBT8wG7HmXduN81z+3qEQcad93uLnV8vnRyXIa9V2r2MlJmqH4H+BHwE9vVltqRdOqQzW75R6Y//mO4a+9R6/NQles+DcT+BGVG1pebTc8HrrD9+krxT6d8lp/LiolDq9tlQ/bzOOBLlFvmXwPePcrtLJWxi49h+cXBWc3XjymlDFovyzID5+FJlETtXyjj7V4CLLXdeqB4lxcNzQX3GynJ2rKLBts3tIo77smU7Ud1kZA0sc61vWPfrYr1KB+sNZKpsyiVYX/cxH4A8OUa3a5dH5dJ9v02l5lcXcX/ie1HT/7KkWJXuZ04QexptVvSV20/T9LFDB8zUeW2arOv2TxfRvp9Nrc/z2f5ivf7UGrXPKdSuzag9E7tAuwJXGd7l5X/1KQxX9e73SbpYTVvSfbt4wuUhbGXsPwPgWvcFpb0H5RxTF2sVdobjN5fKf+bFWNXv10maQ2XcgLzgGdQSoosoJyTX6T8X95j+4Gj7qPZz9+zPLHaHbi3W5S6mInzsIm92PYO/R0QaoqzVoq/NfBQVuz1qnHRcI7tmmUbgDG/zQdc1VyFWdKalLpQNReF7LLezDvpYP2fRtfHZWX2BDpLpuh743Rg9Hmvk5tuuz/c/FtlAO4kZvN8GfX3+TLKYNZvUJLNM6i3vt3WlD+Ej6Pc5r+KOrf5XsbyWxFfoMxsrG0h8FB3cxV8AN2tVdqbuVdr9t5g7NZjjIY4l/I7/DllDM+hXrHW2dck/eOowSUJeDglidqZkjhcwfILiFHNxHkIy2e+Xauy2sdvWT7GsZVmCM7jKcfku8DTgDMphbDbOlXSoZRzsX+caqvbn+OeTL0K+CiwGWXM0UmU4mxVuMN6M7ZPbm4n9Nb/OcD1pr93elwm0WVCAvVqw4x77P8Gtnd306L7zeb5Mu1j3vQEfMP2EzpoD8D7KDP4Pka5PVmz3lFPV++TS4D7ANdO9sLpcofT0ZteqfdTZvWJyoma7lqXCMqsskXAm10Wyp122ObfbTzBDK9RewQlnUwZVL2EUpH7PbUGtw/uqoOYPf+hsu7fmymzBjegQk3Cxh6UsWoX2H6pytI4NUrcwPJiogv7to1U4qLfWCdTTfLxwo73cXIzpmQNKGNuXK/69GaUarZrUAbnVqmrMhPHZWW7n6X9rmq6TkqXmeXzZdqa8W5/k7ShO1ic1X3T8SXdU9IWti+qEHojlbIcdwM20ECNpRrvfWATSk2cc1nxqrrK2CB1t1bpB4DdOkoYAD4CXE0ZzyTKgPEHUG4VH0np5ZiuZdXJNaSYo9sVkL2SUk5kK0pB5+slLa10wd35edhc8GzlMjv9Jkqpi5puc6nAf0dzS/46YIsagbu6SBvrZErS/YDXc9fBlrU+OF5JuZXwZ0qRMVGp+rSkIylvlktZXsDMVOjm7vq4TLb7ORz/Vx3Gnm67B5ftWUGNMTA9c/R8uRW4uLmC7x9QXGNs0GkM1FKTVKOW2o+auFB6vvqXNqry3qfMQuqESjmUA4DNaarOAz+h5RV74/86TKQAdh8YA3h4M6D+LSrFPEcxjzLjs/pnku1XwrKxeztRbvW9VmWG3CW2X9IifOfnYXPBszfLhyvUtkil6PVnKO/RWynnYhXNbcnBWYiHtIk51skUZcr4EZRKtq1nNwzxL8DWFW+/9dvJ9kM7iAvdH5eVObbj+C8a5YeaD6X5tn8xsH2bXq+D7YmqMdcw3XbfxorL9nTpOGbvfBnp90n5wB/80K/VK7qh7Zub5OFoN7XU2ga1/VIoyasHatU1CW1rHY0N6ulyrdJFko6hnIv9PWq1xlD9SdLzKDPsoNwm6pVFGfW8ubbtH9gpuB34E+Xz4HZKInv3NgF75+EM6Kz0gpeXbTlM0veADSr1HiPpMEotqydQigzvQRkf18q4J1N/tj3h1XsFv6CcyF34iaSH2r6sg9idHBdJT6G8mU+x/au+7S+zfSSA7ZE+XCVtQVlaZzPKgs+HevmyNcfZfnYT/5IRYj+P0s1/XTPAel/b5zVPH0WLAZgdtvsG258ftV3T1PX7aAWSLrb9cBjt99nYyKW2Un/cA1o3rui6ltrXues59zVgh7aBB8YG3Z1S0+uPlcYedblW6QaUz9on922r1VsH5Tb2RymVrE0Zh7SPSmHW140Ys7NeckkfpvRGbUWpdfYT4DBKYdM/tIz9ppU93/L2ZL9HNP/2J5ytxx4BqK/aee9vkeotm/QY29s0sxDfJem/KJ/trYx7MvXRZlT/SVQcdd/nbcBZzZip2tWnj6YkVL9rYvcGXNaY8l79uEh6D2Wa+PmUGT0f8fI6H6+jjDto40jKH5mzgf0o61Lt5lLb474tY7+dMm3+Wkk7Al9QKeHwTdp/IHbV7qpLFk2ii/Nlol4+UQZIt/USyh/HfvsO2TaKQyjLjZxp+zyVWmo/bxu06cl5GLDhwPHZgEqzVPsHiTezwZ5FuU1UQ2drlXbdW9IMMN9tgqfPHDHsk6CUo7C9Qg/rsG3T9EvKgOolrrxUDRXXsVyZLsYeaXkF9E2a8Xv9FdA3q7Sb25p//6RSluIGYNO2Qcc9mXo45TbBE1lx3FGVomPApylrRV1M/dsfR1Da3kXsLo7LbsB2LnVVDga+JOn+zTiSGldo820f1nz/epVFcU+XtDvtb9/Mc1Nd2fa5kp5AWYB3iwqxO2m37Z1g+NVWxSuwni7Ol2MotXaGHYORE4dmHMYLgPtJOr7vqQ2AKhNDbB9L3+3q5g9xjaK0D6KUutiIFf+w30JZpqUq2waOaxLlt1aI16vhdbBKwccNqXDFDiDpgcCngL+zvbWkbSjjnP5jXOP3TUR62MC+5tGyl7HXU6xiH+D+tg9RKXp7H9sj33ay/a42bZsqlRl27wH+3vbTJD0UeLTtI1qEHVYB3ZT3UK0inic0Fw2HUjoPTLnd147tsf2i1Ny4e4fxL+gw9k/m0nEBLh94PI+SEB5LWfeqbfxLKYs992/7p+b/cm3L2GcBDxjYtj5l1fvbx7HdlIRjY+BC4J7N9xtTBon/dA6cL4sp4w2HPXdVi7j3pcy8+gmlDlTva3tgjUptn0/pzTyc0vN4JGXdtbZxv9D8+/aax3pgH//c97UHpcxDlc+aXvsn2zZi7B8BO/Z/5lIGWtc6LtXjU+5c3EKpln9z83ULpSfjvZXa/SlKmZTLm8f3pJTrqBH7gc1n4CXN422Ad1Q85idSbpVf2DxeA7i4UuyDKOOkoKzI8U1KKZkqbe/bz1qUi4b12sa6G+PtEspVXldOlLS/pE0lbdz7qhT7AklfkrS3pH/ufVWK3cVx+YX6qgjbvtP2fsDPgIdUiP9Zltf36O3jB5QioKOOq+l5Nax4Ltu+BXgq7Qs9dtXuV1ISkgdTro4WN1/fotIaVH26OF8OpPxxGWbkKuW2f237NErCeobLgOtrKWP5ao1h+RblA/QHlCVlel9t7dDcNni+SsmFjTv4XNmt7+splD/uz6oUu3oPTJ91fdfeljsqxe4kvu33utxWPdT2Bs3X+rbvZfttbWL3eZTt19IMlrd9Iy0HoPf5DCUh/GsT+yJKyYhaNrH9VZrebtt3UG9Nxz1cJonsQulB/ywl8WxF0maSFkrqHeMNgbdQ4Tb/uN/m2wj4qaTz6KCmCrB382//G6NKaQRgHUqbuxhwuRH1j8uewzbafoek1iex7aFTaG1fAOzaMvaFAE3X7VbN5v91qVH0xZaxO2m3Z2bh2p6NqHy+2J6wYrjtRaPG7XM68Nhm3MRJwHmU9dxq1Mta1xXWDxviMEpPwP0pCXK/Kp8r7mDskcpC1W8H1pHUS5BFGdd3eKXdXK+ypJabfe5B3cKjXcY/QdJ6tv/Y3JLbHvio6xTc/WuTtPbaPZ96w0LWdRn20L+tZgL7R0n3Ynnbd6LUnKqhl5Q9A/iM7e+oLHc0MkkHUiacXAGsJemTlEKyR1NjckjT1TWW1MF6S9Pc/8gL404h9shr3HVxXCQNzj4ycL3tq0aNORB/cDaZgeuBU22POkC0F3styvi3Z1MGdopyu+ibwKtstxrs3YzBej1lTAyUpVg+0fSgtNJcIb0K6C1LcRrwaVesyt3R+fJxVhwvVe332cTvrZf5emAd2x9QpYV41f0adJ+y/eqOYm9OGTuyc7PpDMrqCldXiP3eij0ug7HvT0nMHgPcSHmfvrBSQtJpfJWyGdtSbpMdRekleZ7toe+racZ+IeUiYYcm9h6UW3GtS9BIOpEyeejY5r20B7Cf7ae1jd3E34GyisDWlN7v+ZQepdYlDCSdQFmtYVdK8nobcK5brCcq6TJgF9u/b8am/S+ws+0qJWrGOpmajDpcFLeJ3+XCuGOz6G7zM6cO2bwxpct5b9tLWrZpWBG6jSn33I+x/ZEWsd9Nuep/VXN7D0nrU8Yi/Nr2/2sR+xmU226HUHobRHlzvwN4Xds/yJI+S5ne3iuT8CLgTtsvbxN3mm0Y5Xzp7PfZxL8AeA2lKOB+ti9VX8mFlrFvAdaj9NJVX4Ou2cculArRn5O0CbC+B2pPjRj3ZEqV7/4FoF9ou1XvbhN7Z8rssuo9MFo+XX8dyi35P1J6MRa3+WzRXcsA9MfHFcoA9CX2BwHX2D6i5ue3yizQ3oSTH7pScdOuE9hmH2tQLjIF/KzWRaCkdSnDNC62/XOVUiYPt31Si5gr/M5UebH3uZ5MXWB7u7kYf67ElrQQ+JDtkRf0nCT+OpRegpHbK+kSYEfbfxrYfg9KAcKtW8Q+jXLlf+HA9m2Aj496darlK9Lf5Q1d+00+hbbUPF9a/z6bOP9IKar7Y9vvb/4wHOhKleGbMUyDy6ZU6fFWmV23EHiQ7Qc246iOtb3zJD86ldh36Z2r2GPXZQ/MlyjH5HjKH95nAhdRJlwca/sDI8Z9Z/PtgygFR7/VxN+N0pOxT7uWg6QfURatfymlB/k6yqDr1ol9E397SlkaU873VqV/ZiLBbPZzEfAVysXTLyZ7/WyTdB2lvT179T9u+9ky7mOmJtN1Jthl/DkR2/aiJinphO3bNGTdq2n622Ai1cS+VVLbY3GfwUSqiX2RytTgUfVWpL9T0gN6H0ZN0lC77sxkap4vNX6fADf2j+lyKV9QK5EatmzKWSzvHWjrOcB2NOOmbP+26Smt4Yam1+jLzeO9KbPLarjDtiU9i3Ib+whJ+1WKvTllNtatsCwJ+g4lOVlMWbtv2tyUAZB0ehO/1zN9MHUmFUC5DfcCSg/p75pbRIfWCNz0du1JqWUn4HOSjnW7khG9c20wwXwRFSp999mNcmy+KulvlHIpX7X9m4r7qOlfBx5XXYFiridTc9mMLXTbRpMwdJL4NV3EL6IsUNqGtWKBt35tB3P+ccTnJtNr678Ap0rqrWq/gHIFPOdU/H0CfLIZC3cU8EXXXfD4ALpbNgXgL01S0huYu17F2C+jjJn6MOV9eRalmGkNt6gMRt+HsjD73Si3oGu4N32THyi3V/+uSb5vn+BnpuPvWLEQ7l+aba3Z/h3wob7Hv6EMWq7hhcC2tv8MIOl9lAS/TX2smUgwaW4XfgD4gKStKCUM3k8pqzN2PLDihKR1h12Ej2quJ1NdJyS/6jB2l2vcTfu4DBlQDGUMzGMof3zaNWjFZTB6bqPUh3lly/AbsrzA26C2ieADtGLxyB7RbnbWshXpKYPnex9Ad1J6NYaNYevKKOdLl79PbD9WpRDjS4HFks4FjmozZqJPl8umQLlS/zSwkaRXUBKgz1SKfQhlyZEbYdntyg/SvgQIdNgDQ5lVe46kbzWPd6MUBl4PqLHk1tHAuZK+2Tx+NiURH5mkM23v0neuq//fSmPsfku51dxbR3AtysDrGjpLMHsk3Zdy3jyf8tn1bzXjd0HSoyk1FO8BbClpW+CVXr4e4Ghx5/iYqa094tpfmsLCuC3aNekad10a5bgMGVBsyu2D82xfV61xc4wmmAnXM+o4G0nXUuqmDE1kPENVjJu2jPw+6prKtPFnU2YN3Uw5Xm93iwVymz+4L6XUynoiZXDumraf3ra9ffvYlVIWRcD3XWlW8LDxbV2PHa2lGX/ZGzf2Y9cpodEff3vgsc3D013Kl4w1ScdReklPpnzm7kq5FXc1tBvHI+nfKRNC+hPMYzziLPIh8c+h9Fwe28S9cpIfGQtNu/cAju+9byRd0mZsLYxpMqUpLi7bIv6yhXEpJ8O+bhbGbTtLQyuucbcb8BE3dYQqxO70uMwWSQ+2/dO5FruNtudChf1XmRk3QezWx7wZ4P9SSp2Zk4EjbJ/fDOb+ie37VmhqL1neEPieW5bQmAmSLgQeP9Az9aM2v8shPTDLnqLyLMe5amCQ+Jm1ErUhF7ErGLw1NUL8zhJMSQ+y/bPm+/UoYwX3tv2MWvvogqRzbD+q/yKkxqSfcb3N1+WiuNDtwrhdrnHX9XEZStKJrlSbZAInAVvOtdgtj0vnY+bU/WLEE6lxzD9OmU32dtu9hUl7g7nf0TL2MrVm8MGEtz6hblLyX5QF1HvDBPYE/rNNQNu7NP/OyAK5c03fIPFeb+hRFQaJ9/we+I7t2uu3AssWM281O3AlfinpOZRbw0+h/G06bOU/MhaukvQYyljbNSnDWFqXoxjXZKrLRXGh24Vx13Apq4/tP0jaDTi8+fBru0xAZ8dFdy3auewp4BFtYjfxB4t29sffaIxjd3Vcai5kPJFOFiOGbo85gO3HqZRZ2JKypFH/c18Y/lOzayaSEdtHS1rE8kWq/9l2jTFHSHo4ZXkjgMtsX1oj7iqg+iDxPs8HPiLp65T1IceuF32QpCdTZpE+mTK282jgke6gOn9HXgV8lHKH5xrKxd9r2wYd12RqTUlr905e2/8j6XfA9ynF9tq6RX3T0ZseqscDxzGwPtUIfiHpcb0rXtt3AvupVF1uuzJ9l8flPMrg4WE9Jhu1jA3lls2bWXFGT8/eQ7aNS+xOjouXr0jfpYuADw4bDyXpn1rG7vKY01yEfJByAXI/SY8ADnG9paTmrCZ5qpJAAUjakDJ9fkvKwtsCHi7pN8CzbE+0BuPqorNB4rb3acbv7k3p8TLwOeDLvVl4Y+h7lMr7u7gpRCvpo7PbpKmzfT11lqVawbiOmXojcP5gF7yk7YAPuGW132b0/p9s/3xg+5qUInUjr+fWXE3Tf2ui77nNbI/8JuzyuKgUvnzO4DFpnrvK9hajxm5i/JCyTMJZQ577pe37jWnsTo9LlyQ9llIB/i51XyQtbDMAuMtj3sRYTOl9Oa1vXENn47xWZ00v41+Af+vdblIpi/A+ylI+r5/N9s22LgeJ9+3jXpSyIgdSbjn9A/Axd79u57Q1FzZ7UW59XkkpfHlQrXGMXZugV/0mYJHtbw15bmpxxzGZmikavjBu25idrnHXFZV1my7uDSgceO7Zto9rGX9jypT0anU9Zih2p8dlrurymDfxz7a908Ag0Ytsb9PF/lZnKmuWbdMbntC3fQ3Kuf+Q2WnZeOhykLhKgdR9KcnT0cDnbV+nspzKZbYXjBp7JjRjj/am3HW5EPim7VqLY3dC0uGU29m9cYfPpSy1cy/gStsHjhR3HJOpIZlj7UVUO1sYV92vcdfJoruS3szwhWvPdIU1xQb2NR/A9tKacbuIPZPHpTZ1vBhx336q/z4lHQGcAryV8mH3Bkr5glfV2kcUWslyNCt7bnXR3HLuZJC4pGOA/7Z9et+299t+i6Qn2T6l9j670PRkPonyN65GzbPOSDqbssDxnc3jNWhuW1IuHh46Sty71WtiVYsHvs4HbgUOlXRghfjvoJRE2ML2ds2HxZaUMWQjL4oLYPsJQ762pXThTjRod0pUFt09Evg2ZQbFC4HvAkdKalsj5x6UZQh6XxtQ1tI6UdJeLWOj4mBJ11MGFP+vpKXNTJmxjU3Hx6Vji+jofdTxMYdywfAwypisL1FqTM1Y7a3VzNqStpO0/cDXDpTxQau75wM/l/QBlWr5NW3Vn0g1ngYw7olU8xmwj6SDmkTzZ8yN2Xz3pHyu96wHbNwkVyNX4x/LnqmJqN4iqp0tjDvJftvWmTqNDhbdnWSfGwM/aNPuJs6bKB8S+/cNWrw/pXDl92x/eBxjr2SfVY7LbKjxPurqmEv6rO2XD9m+BXBiV+/N1dkEvenL2H7CTLVlXGn5IPGXUnp4Ww0Sl/Rq4DWUVRT6C0evTylo2nqB5q5J+hRlua4n2n6IypJeJ9l+5Cw3baVU1pt8B3Aa5a7UP1KWk/oycLDtwTX8phZ3LiVTAKpQ7XdlYy+6GuSqssbdd23v0CLGT20PvTJa2XNtVTrmFwC7NjMp+rfPp7wB2/xh7yz2ZPvtKnbX2ra9q2Mu6ShKD/GL+wZDPwQ4AXi37aNGbXOsnPpmCvdtW8t2jbXz5jxVHCSuMoPynsB7Kbeye27xzMz0ba3XOaDKxS9ngkrx3xdRfo/3AK4e0kM4LeNaGuEuVHcR1c4Wxh0yTgXqrXHX1aK7E2rGaN1YIdSag394oYyzUZlFOa6xh6p4XGZUxfdRV8f8pZTxjMc0t1EfRamX9WrbJ7SIG5M7Cxjsaf3JkG2rlSGDxHfsHyROKTA7Lc1kp5uoUEZkFv1VZbmn3oLe82m/sHznJL2c8rd4c0q9sJ0o5/kTV/JjkxrLZEodL6JKtwvjDk43761x9ya3X+Ouq0V3kXQxw5PA3wIvbhO7sbJB/W2X8egs9gwcl850/D7q5Ji7dJXvrzIJ5TTKxJA9bZ89asxYOUn3oRQwXEelzErvc3EDYN1Za9j4eAHw4QkGie83i+2abR+jTNq6t6T/pKx3V211gg4dQCl1cbbtJzTj4N7TNuicu823OlNHi+42sQdrhBi4wXaVHi9JdzK890zA2rZH7s3oOHanx2Wu6uqY9/XsivJH7Hz6lnpwhZo+sSKVqf/7UiZW9F8M3gIc5RaLSq8Kho11XdlQkdVJk4g8ifJ+PcV262VZuibpPNuPlLQEeJTt2yVdartVwe45l0yp44Vru4yv7te4i5iSrt9Ho1LHC7/GxCQ91/bXZ7sd42JVGCTetWa4zBb03eVyWQ9wbEn6JmU4wYGUW3s3UoYttJoRPxeTqd/Y7mpR3NbxtfK13E6wvemosSfZbxK1mLKu30e1NLOoPOqsqZicpH1clqYarKkGgO0PzUKzZt2qMEi8S5LeTenR/AXLzxvbbjX2aCY1d3s2pMxAbjUkZFzHTHW6iGrH8Ttb426SRO0RbWLHqqfr91GXJC2kTD9fvzzUH4CX2V48qw1bNfXW9bzHSl+1mllFBol36XnAA9omIbOpzdCYQWPZM9UMnJ1oEdX/sr3JuMZXh2u5NeNUJkrUdrK9zqixY9XT9fuoS5IuAl5r+4zm8S7AJzNOJWI8SPo6ZZZt24lVq4Sx7Jmi9O5c4uGLqB485vEPZuLK8m0XDL0ceOVEiVrL2LHq6fp91KU7e4kUgO0zJd2xsh+I0aykBxPIoP+Y0HuBC5oOhGUXbLZ3n70mzZ5x7ZnqehHVLhfG7WwtN2XR3ZiGrt9HXei7lf1iYB1KVWJTlvT4s+03zVbbVlV9g/53Bh5KqesFsCdlsd2shxh3IelSSk24i+mrL1Xz1tlcMpbJVD91uChuF/ElvXPI5o2Bp1BK1X+lRew5u+huzK6u30e16K5Lm/TOdzHHBrfONSoLwO5i+47m8ZrAGbZ3mt2WxTjqlRiY7XaMi7G8zSdJwDuB11Fumanp4v+47UPGOb7toYuxNr0EPwBGTqYYPkB0AfDvklolarHq6fp91AU368BJWht4LuX87n1OjfeV39x3T0qhzt5MtXs02yKGOUPSe4HjWfE231iXRujKWCZTwBspXc6P9MAiqpLe6PYL13Yd/y5s/77549YmRpeJWqx6Zvw8r+g44A+Uop299eKSTHXrfcD5Kguq9xaAPXg2GxRjrbf+Zn/PpWm5LMtcNZa3+dTxwrVdx59gn08A/l9Xtyk0hxfdjW7Mxnlei6RLbG892+1YnTQXe72FfA+mrFt2H9vnzl6rIuaGce2Z6nrh2s7iz8Zabpqji+5G52Z8AeiKzpL0cNsXz3ZDViOfpAwkXsf28U11669T1jGLAFYo8jp0MsjqWuR1XJOpLhfF7Tr+MwceV1vLbTYStZjTun4fdWkXYF9Jv6SMx+gNQE+dqe48yvb2TY8mtm+UdPfZblSMnd7i1+vPaivGzLgmU9tKunnIdgFrj3N8279u8/OT6CxRi1VS1++jLmVppJn3V0nzaC7YmtvBf1v5j8Rq6DHA4RON4V1djWUyZXveXI7flY4TtVjFzNXzHHKuz5KPAd8E7i3pP4E9gHfMbpNiDKV3eIixHIAeEREzT9KDgSdRei9PsX35LDcpxoykn1LWKxw6O311LY2QZCoiIiKmpFnz8zyGJ1OrbWHdsbzNFxEREWPpitU1YVqZiRbkjYiIiIgpSDIVERERU/UWAEnrSVqWQ0iaJ2ndiX9s1ZZkKiIiIqbE9knNt6ewvOYUwDqUZc1WS0mmIiIiYrrWtn1r70HzfXqmIiIiIqboj5K27z2QtBC4bRbbM6symy8iIiKm60DgWEm/bR5vCjx/9pozu9IzFREREdN1MXAYZe3MpcCngUtntUWzKEU7IyIiYlokfRW4Gfhis+kFwEa295y9Vs2eJFMRERExLZIus/3QybatLnKbLyIiIqbrfEk79R5IehSwaBbbM6vSMxURERHTIuly4EHAb5pNWwI/A+6grNG3zWy1bTYkmYqIiIhpkXTflT1v+9cz1ZZxkGQqIiIiooWMmYqIiIhoIclURERERAtJpiIiIiJaSDIVERER0cL/B6rRexvYucHMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_regression\n",
    "\n",
    "mi = mutual_info_regression(data_fixed, data_fixed.D1U2_mean)\n",
    "mi = pd.Series(mi)\n",
    "mi.index = data_fixed.columns\n",
    "mi.sort_values(ascending=False)\n",
    "mi.sort_values(ascending=False).plot.bar(figsize=(10, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recursive Feature Elimination (RFE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Ranking: [14 20 15 16 17 11 24 13 19 18 12  5  6 10  2  8  9 21  7 22  4  3  1 23]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression(max_iter=1000) \n",
    "rfe = RFE(model, 1)\n",
    "fit = rfe.fit(data_fixed, label_fixed)\n",
    " \n",
    "print(\"Feature Ranking: %s\"% fit.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['country',\n",
       " 'nbKeystroke',\n",
       " 'degree',\n",
       " 'delFreq',\n",
       " 'editDistance',\n",
       " 'U1U2_std',\n",
       " 'typistType',\n",
       " 'answer',\n",
       " 'leftFreq',\n",
       " 'gender',\n",
       " 'ageRange',\n",
       " 'D1U2_mean',\n",
       " 'D1U2_std',\n",
       " 'U1U2_mean',\n",
       " 'D1D2_std',\n",
       " 'U1D2_mean',\n",
       " 'U1D2_std',\n",
       " 'pcTimeAverage',\n",
       " 'TotTime',\n",
       " 'status',\n",
       " 'D1U1_std',\n",
       " 'D1U1_mean',\n",
       " 'D1D2_mean',\n",
       " 'typeWith']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data_fixed.columns[fit.ranking_ - 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SHAP values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1288.8 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DF, based on which importance is checked\n",
    "X_importance = pd.DataFrame(data_fixed, columns=data_fixed.columns)\n",
    "\n",
    "# Explain model predictions using shap library:\n",
    "explainer = shap.TreeExplainer(model)\n",
    "shap_values = explainer.shap_values(X_importance)\n",
    "\n",
    "# Plot summary_plot\n",
    "shap.summary_plot(shap_values, X_importance, max_display=len(data_fixed.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RF feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric : ACC, Score :  0.80\n",
      "Metric : TP_Rate, Score :  0.61\n",
      "Metric : FP_Rate, Score :  0.13\n",
      "Metric : Precision, Score :  0.50\n",
      "Metric : F_Measure, Score :  0.55\n",
      "Metric : F_Micro, Score :  0.49\n",
      "Metric : F_Macro, Score :  0.50\n",
      "Confusion Matrix: \n",
      "      N     H     C     S     A\n",
      "N  0.32  0.11  0.50  0.86  1.33\n",
      "H  0.05  0.78  0.12  0.00  0.00\n",
      "C  0.05  0.00  0.38  0.43  0.33\n",
      "S  0.05  0.11  0.12  0.57  0.00\n",
      "A  0.00  0.00  0.00  0.00  1.00\n",
      "here 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = 'RF'\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_fixed, label_fixed, test_size=0.2)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = classifiers[classifiers_name.index(best_model)]\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "performance_metrics = compute_metrics(model, X_test, Y_test, show=True)\n",
    "plot_confusion_matrix(performance_metrics[\"Confusion Matrix\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "pd.Series(model.feature_importances_, index=data_fixed.columns).nlargest(data_fixed.shape[1])[::-1].plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGB feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric : ACC, Score :  0.80\n",
      "Metric : TP_Rate, Score :  0.42\n",
      "Metric : FP_Rate, Score :  0.15\n",
      "Metric : Precision, Score :  0.47\n",
      "Metric : F_Measure, Score :  0.44\n",
      "Metric : F_Micro, Score :  0.49\n",
      "Metric : F_Macro, Score :  0.43\n",
      "Confusion Matrix: \n",
      "      N     H     C     S     A\n",
      "N  0.70  0.12  0.33  0.22  0.00\n",
      "H  0.20  0.50  0.00  0.00  0.00\n",
      "C  0.20  0.00  0.22  0.22  0.33\n",
      "S  0.25  0.00  0.00  0.33  0.33\n",
      "A  0.05  0.12  0.00  0.00  0.33\n",
      "here 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = 'XGB'\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(data_fixed, label_fixed, test_size=0.2)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "model = classifiers[classifiers_name.index(best_model)]\n",
    "model.fit(X_train, Y_train)\n",
    "\n",
    "performance_metrics = compute_metrics(model, X_test, Y_test, show=True)\n",
    "plot_confusion_matrix(performance_metrics[\"Confusion Matrix\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "pd.Series(model.feature_importances_, index=data_fixed.columns).nlargest(data_fixed.shape[1])[::-1].plot(kind='barh')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}