AnalyseResultsMulti.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Dataset</th>\n",
       "      <th>Notebooks</th>\n",
       "      <th>KGSize</th>\n",
       "      <th>EmbMethod</th>\n",
       "      <th>InputSize</th>\n",
       "      <th>TestRegime</th>\n",
       "      <th>Method</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Matthews</th>\n",
       "      <th>:Analysis</th>\n",
       "      <th>:Movement</th>\n",
       "      <th>:Preparation</th>\n",
       "      <th>:Reuse</th>\n",
       "      <th>:Visualisation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MultiClassification.csv</td>\n",
       "      <td>interactive-intro-to-dimensionality-reduction....</td>\n",
       "      <td>8150</td>\n",
       "      <td>rdf2vec</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>0.484946</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.780488</td>\n",
       "      <td>0.350000</td>\n",
       "      <td>0.556962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MultiClassification.csv</td>\n",
       "      <td>interactive-intro-to-dimensionality-reduction....</td>\n",
       "      <td>8150</td>\n",
       "      <td>rdf2vec</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>0.688172</td>\n",
       "      <td>0.593386</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>0.680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MultiClassification.csv</td>\n",
       "      <td>interactive-intro-to-dimensionality-reduction....</td>\n",
       "      <td>8150</td>\n",
       "      <td>rdf2vec</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.774194</td>\n",
       "      <td>0.702271</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.920635</td>\n",
       "      <td>0.786885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MultiClassification.csv</td>\n",
       "      <td>interactive-intro-to-dimensionality-reduction....</td>\n",
       "      <td>8150</td>\n",
       "      <td>rdf2vec</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>0.903226</td>\n",
       "      <td>0.875007</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.983607</td>\n",
       "      <td>0.877193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MultiClassification.csv</td>\n",
       "      <td>interactive-intro-to-dimensionality-reduction....</td>\n",
       "      <td>8150</td>\n",
       "      <td>rdf2vec</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>MLPClassifier</td>\n",
       "      <td>0.924731</td>\n",
       "      <td>0.900329</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.883721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.945455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Dataset                                          Notebooks  \\\n",
       "0  MultiClassification.csv  interactive-intro-to-dimensionality-reduction....   \n",
       "1  MultiClassification.csv  interactive-intro-to-dimensionality-reduction....   \n",
       "2  MultiClassification.csv  interactive-intro-to-dimensionality-reduction....   \n",
       "3  MultiClassification.csv  interactive-intro-to-dimensionality-reduction....   \n",
       "4  MultiClassification.csv  interactive-intro-to-dimensionality-reduction....   \n",
       "\n",
       "   KGSize EmbMethod  InputSize  TestRegime              Method  Accuracy  \\\n",
       "0    8150   rdf2vec         10           1       Decision Tree  0.580645   \n",
       "1    8150   rdf2vec         10           1          GaussianNB  0.688172   \n",
       "2    8150   rdf2vec         10           1  LogisticRegression  0.774194   \n",
       "3    8150   rdf2vec         10           1           LinearSVC  0.903226   \n",
       "4    8150   rdf2vec         10           1       MLPClassifier  0.924731   \n",
       "\n",
       "   Matthews  :Analysis  :Movement  :Preparation    :Reuse  :Visualisation  \n",
       "0  0.484946   0.714286   0.666667      0.780488  0.350000        0.556962  \n",
       "1  0.593386   0.714286   0.181818      0.604651  0.947368        0.680000  \n",
       "2  0.702271   0.500000   0.666667      0.628571  0.920635        0.786885  \n",
       "3  0.875007   0.933333   0.823529      0.833333  0.983607        0.877193  \n",
       "4  0.900329   0.800000   0.769231      0.883721  1.000000        0.945455  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"MultiClassificationExperiments.csv\",names=[\"Dataset\", \"Notebooks\", \"KGSize\", \"EmbMethod\", \"InputSize\", \"TestRegime\", \"Method\", \"Accuracy\", \"Matthews\",\":Analysis\",\":Movement\",\":Preparation\",\":Reuse\",\":Visualisation\"])\n",
    "# Preview the first 5 lines of the loaded data \n",
    "data.head()\n",
    "# data=data.fillna(0.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Decision Tree', 'GaussianNB', 'LogisticRegression', 'LinearSVC',\n",
       "       'MLPClassifier', 'RandomForestClassifier'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Method'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  8150,   9684,   9636,   7143,   7300,   8918,   8281,   8489,\n",
       "         6674,  10157,   6799,   8278,   7395,   7543,   5287,   8056,\n",
       "         8901,   7329,   7660,  10072,   7774,  12699,   6592,   8577,\n",
       "         6870,   7567,  13431,   8989,   5600,   7220, 104088,  73999,\n",
       "        94110,  83021,  76274,  85668,  91090,  79834, 107892,  77374,\n",
       "        77463,  76916, 116023,  77313,  92863,  96462,  97013,  76600,\n",
       "        88825,  87208,  85762,  67926,  95168,  80137,  84182,  77800,\n",
       "       109477, 126972, 108493,  80686, 164145, 187246, 180471, 209387,\n",
       "       207657, 167061, 194248, 178525, 203603, 187662, 173160, 160664,\n",
       "       173285, 182292, 182299, 187262, 155723, 172782, 182454, 186500,\n",
       "       183779, 169426, 198517, 201414, 213965, 179948, 186508, 175855,\n",
       "       169117, 178354,   6089,   8318,   7767,   6454,   6356,   8408,\n",
       "         7864,   8079,  10719,   7879,  11189,   8914,   9896,   6362,\n",
       "         7776,   5456,   6401,   9767,  27457,  13660,   7283,   6096,\n",
       "         8333,   9193,   8592,   4768,  15729,   8192,  23058,   8222,\n",
       "        85900, 102347, 111244,  79264,  95619,  99160,  75074, 109797,\n",
       "        72704,  71803,  75388,  89060,  91176,  91523,  92013,  92596,\n",
       "        93476,  87781,  80987,  79054,  80424, 101632,  94962,  95991,\n",
       "       102109,  80956,  71074,  72931, 102921,  96527, 199278, 183401,\n",
       "       191321, 222756, 208009, 195655, 201191, 162910, 152953, 178014,\n",
       "       150222, 192927, 178240, 213204, 192513, 186744, 194907, 204342,\n",
       "       202423, 186456, 181603, 206001, 154511, 197413, 206003, 179575,\n",
       "       175329, 221862])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['KGSize'].unique() # Size of KG / Notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop KGSize\n",
    "data = data.drop(columns='KGSize')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Table(dataframe, dataset, test_regime):\n",
    "    # VP, TestRegime 1\n",
    "    df = dataframe[dataframe['Dataset'] == dataset].drop(columns=['Notebooks','Dataset'])\n",
    "    TR = df.loc[df['TestRegime']==test_regime].drop(columns=['TestRegime'])\n",
    "    return TR.groupby([\"EmbMethod\",'InputSize','Method']).agg(['mean','std'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test regime = 1\n",
    "\n",
    "1. Train and test entities randomly split\n",
    "2. Train and test do not share notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Matthews</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Analysis</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Movement</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Preparation</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Reuse</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Visualisation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmbMethod</th>\n",
       "      <th>InputSize</th>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">bertcode</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.366725</td>\n",
       "      <td>0.283739</td>\n",
       "      <td>0.147699</td>\n",
       "      <td>0.361214</td>\n",
       "      <td>0.096324</td>\n",
       "      <td>0.177369</td>\n",
       "      <td>0.115000</td>\n",
       "      <td>0.185293</td>\n",
       "      <td>0.281064</td>\n",
       "      <td>0.264678</td>\n",
       "      <td>0.325714</td>\n",
       "      <td>0.431881</td>\n",
       "      <td>0.425117</td>\n",
       "      <td>0.347240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.571405</td>\n",
       "      <td>0.254137</td>\n",
       "      <td>0.497932</td>\n",
       "      <td>0.272244</td>\n",
       "      <td>0.210604</td>\n",
       "      <td>0.265315</td>\n",
       "      <td>0.309246</td>\n",
       "      <td>0.313952</td>\n",
       "      <td>0.471307</td>\n",
       "      <td>0.260350</td>\n",
       "      <td>0.733542</td>\n",
       "      <td>0.387666</td>\n",
       "      <td>0.598604</td>\n",
       "      <td>0.281352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.328271</td>\n",
       "      <td>0.413681</td>\n",
       "      <td>0.123009</td>\n",
       "      <td>0.534183</td>\n",
       "      <td>0.174444</td>\n",
       "      <td>0.294999</td>\n",
       "      <td>0.296832</td>\n",
       "      <td>0.384810</td>\n",
       "      <td>0.278400</td>\n",
       "      <td>0.324328</td>\n",
       "      <td>0.387101</td>\n",
       "      <td>0.500082</td>\n",
       "      <td>0.347147</td>\n",
       "      <td>0.442359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.055358</td>\n",
       "      <td>0.024359</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>0.043686</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.342145</td>\n",
       "      <td>0.408488</td>\n",
       "      <td>0.220975</td>\n",
       "      <td>0.461280</td>\n",
       "      <td>0.180615</td>\n",
       "      <td>0.261905</td>\n",
       "      <td>0.298668</td>\n",
       "      <td>0.351925</td>\n",
       "      <td>0.267287</td>\n",
       "      <td>0.338555</td>\n",
       "      <td>0.394733</td>\n",
       "      <td>0.509682</td>\n",
       "      <td>0.340345</td>\n",
       "      <td>0.442365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.415680</td>\n",
       "      <td>0.276577</td>\n",
       "      <td>0.168742</td>\n",
       "      <td>0.402936</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.090351</td>\n",
       "      <td>0.135000</td>\n",
       "      <td>0.286793</td>\n",
       "      <td>0.360183</td>\n",
       "      <td>0.207916</td>\n",
       "      <td>0.377872</td>\n",
       "      <td>0.491033</td>\n",
       "      <td>0.447145</td>\n",
       "      <td>0.345909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.543955</td>\n",
       "      <td>0.292960</td>\n",
       "      <td>0.394909</td>\n",
       "      <td>0.384079</td>\n",
       "      <td>0.251416</td>\n",
       "      <td>0.242408</td>\n",
       "      <td>0.318524</td>\n",
       "      <td>0.304800</td>\n",
       "      <td>0.493616</td>\n",
       "      <td>0.258824</td>\n",
       "      <td>0.579503</td>\n",
       "      <td>0.484648</td>\n",
       "      <td>0.587929</td>\n",
       "      <td>0.237366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.697686</td>\n",
       "      <td>0.221545</td>\n",
       "      <td>0.647460</td>\n",
       "      <td>0.209102</td>\n",
       "      <td>0.467061</td>\n",
       "      <td>0.214158</td>\n",
       "      <td>0.546135</td>\n",
       "      <td>0.281234</td>\n",
       "      <td>0.580998</td>\n",
       "      <td>0.269498</td>\n",
       "      <td>0.891319</td>\n",
       "      <td>0.289525</td>\n",
       "      <td>0.671830</td>\n",
       "      <td>0.135246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.513983</td>\n",
       "      <td>0.434789</td>\n",
       "      <td>0.373858</td>\n",
       "      <td>0.548182</td>\n",
       "      <td>0.380077</td>\n",
       "      <td>0.321506</td>\n",
       "      <td>0.442190</td>\n",
       "      <td>0.362650</td>\n",
       "      <td>0.461938</td>\n",
       "      <td>0.384225</td>\n",
       "      <td>0.593442</td>\n",
       "      <td>0.510768</td>\n",
       "      <td>0.509934</td>\n",
       "      <td>0.434800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.051906</td>\n",
       "      <td>0.004791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098654</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.492495</td>\n",
       "      <td>0.409990</td>\n",
       "      <td>0.348977</td>\n",
       "      <td>0.531672</td>\n",
       "      <td>0.281945</td>\n",
       "      <td>0.243109</td>\n",
       "      <td>0.247414</td>\n",
       "      <td>0.299784</td>\n",
       "      <td>0.431288</td>\n",
       "      <td>0.364523</td>\n",
       "      <td>0.598559</td>\n",
       "      <td>0.515160</td>\n",
       "      <td>0.491121</td>\n",
       "      <td>0.419524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.567090</td>\n",
       "      <td>0.312752</td>\n",
       "      <td>0.393538</td>\n",
       "      <td>0.465667</td>\n",
       "      <td>0.011735</td>\n",
       "      <td>0.027003</td>\n",
       "      <td>0.251274</td>\n",
       "      <td>0.271555</td>\n",
       "      <td>0.488097</td>\n",
       "      <td>0.288649</td>\n",
       "      <td>0.599322</td>\n",
       "      <td>0.515816</td>\n",
       "      <td>0.633568</td>\n",
       "      <td>0.297144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.494300</td>\n",
       "      <td>0.359170</td>\n",
       "      <td>0.317602</td>\n",
       "      <td>0.484808</td>\n",
       "      <td>0.262034</td>\n",
       "      <td>0.262698</td>\n",
       "      <td>0.378258</td>\n",
       "      <td>0.334517</td>\n",
       "      <td>0.450775</td>\n",
       "      <td>0.294624</td>\n",
       "      <td>0.501345</td>\n",
       "      <td>0.515249</td>\n",
       "      <td>0.536273</td>\n",
       "      <td>0.346455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.660016</td>\n",
       "      <td>0.252260</td>\n",
       "      <td>0.611237</td>\n",
       "      <td>0.251395</td>\n",
       "      <td>0.459764</td>\n",
       "      <td>0.250898</td>\n",
       "      <td>0.503772</td>\n",
       "      <td>0.334005</td>\n",
       "      <td>0.498289</td>\n",
       "      <td>0.335135</td>\n",
       "      <td>0.875574</td>\n",
       "      <td>0.310956</td>\n",
       "      <td>0.634360</td>\n",
       "      <td>0.230245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.428398</td>\n",
       "      <td>0.448323</td>\n",
       "      <td>0.249812</td>\n",
       "      <td>0.582755</td>\n",
       "      <td>0.325917</td>\n",
       "      <td>0.336980</td>\n",
       "      <td>0.361534</td>\n",
       "      <td>0.375258</td>\n",
       "      <td>0.369267</td>\n",
       "      <td>0.385648</td>\n",
       "      <td>0.496358</td>\n",
       "      <td>0.522897</td>\n",
       "      <td>0.433029</td>\n",
       "      <td>0.451827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.052442</td>\n",
       "      <td>0.003505</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099639</td>\n",
       "      <td>0.006322</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.398283</td>\n",
       "      <td>0.398552</td>\n",
       "      <td>0.296363</td>\n",
       "      <td>0.447150</td>\n",
       "      <td>0.124516</td>\n",
       "      <td>0.160143</td>\n",
       "      <td>0.142711</td>\n",
       "      <td>0.191811</td>\n",
       "      <td>0.332731</td>\n",
       "      <td>0.351115</td>\n",
       "      <td>0.498893</td>\n",
       "      <td>0.525880</td>\n",
       "      <td>0.400652</td>\n",
       "      <td>0.413847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.519867</td>\n",
       "      <td>0.309610</td>\n",
       "      <td>0.327462</td>\n",
       "      <td>0.451867</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.231816</td>\n",
       "      <td>0.253266</td>\n",
       "      <td>0.446877</td>\n",
       "      <td>0.269109</td>\n",
       "      <td>0.499502</td>\n",
       "      <td>0.526522</td>\n",
       "      <td>0.600561</td>\n",
       "      <td>0.303219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">rdf2vec</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.710983</td>\n",
       "      <td>0.086279</td>\n",
       "      <td>0.614850</td>\n",
       "      <td>0.102383</td>\n",
       "      <td>0.523863</td>\n",
       "      <td>0.227523</td>\n",
       "      <td>0.658750</td>\n",
       "      <td>0.171741</td>\n",
       "      <td>0.668180</td>\n",
       "      <td>0.099786</td>\n",
       "      <td>0.797798</td>\n",
       "      <td>0.182055</td>\n",
       "      <td>0.674315</td>\n",
       "      <td>0.116209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.760073</td>\n",
       "      <td>0.050925</td>\n",
       "      <td>0.676080</td>\n",
       "      <td>0.064208</td>\n",
       "      <td>0.591975</td>\n",
       "      <td>0.261870</td>\n",
       "      <td>0.668401</td>\n",
       "      <td>0.220247</td>\n",
       "      <td>0.709958</td>\n",
       "      <td>0.068046</td>\n",
       "      <td>0.832092</td>\n",
       "      <td>0.090799</td>\n",
       "      <td>0.748113</td>\n",
       "      <td>0.079990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.888429</td>\n",
       "      <td>0.029794</td>\n",
       "      <td>0.849414</td>\n",
       "      <td>0.038465</td>\n",
       "      <td>0.746346</td>\n",
       "      <td>0.275191</td>\n",
       "      <td>0.906335</td>\n",
       "      <td>0.095609</td>\n",
       "      <td>0.868495</td>\n",
       "      <td>0.048715</td>\n",
       "      <td>0.907831</td>\n",
       "      <td>0.036052</td>\n",
       "      <td>0.870738</td>\n",
       "      <td>0.065779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.848175</td>\n",
       "      <td>0.047046</td>\n",
       "      <td>0.795548</td>\n",
       "      <td>0.058885</td>\n",
       "      <td>0.687022</td>\n",
       "      <td>0.163938</td>\n",
       "      <td>0.867557</td>\n",
       "      <td>0.121545</td>\n",
       "      <td>0.822207</td>\n",
       "      <td>0.081737</td>\n",
       "      <td>0.876221</td>\n",
       "      <td>0.045176</td>\n",
       "      <td>0.838720</td>\n",
       "      <td>0.064602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.916063</td>\n",
       "      <td>0.044849</td>\n",
       "      <td>0.886877</td>\n",
       "      <td>0.058029</td>\n",
       "      <td>0.789766</td>\n",
       "      <td>0.155373</td>\n",
       "      <td>0.897534</td>\n",
       "      <td>0.099886</td>\n",
       "      <td>0.922520</td>\n",
       "      <td>0.041638</td>\n",
       "      <td>0.921677</td>\n",
       "      <td>0.068970</td>\n",
       "      <td>0.918274</td>\n",
       "      <td>0.053987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.842239</td>\n",
       "      <td>0.053698</td>\n",
       "      <td>0.783707</td>\n",
       "      <td>0.074778</td>\n",
       "      <td>0.597519</td>\n",
       "      <td>0.231448</td>\n",
       "      <td>0.814330</td>\n",
       "      <td>0.156224</td>\n",
       "      <td>0.781200</td>\n",
       "      <td>0.071645</td>\n",
       "      <td>0.940486</td>\n",
       "      <td>0.033470</td>\n",
       "      <td>0.816505</td>\n",
       "      <td>0.044054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.729491</td>\n",
       "      <td>0.029289</td>\n",
       "      <td>0.641082</td>\n",
       "      <td>0.036087</td>\n",
       "      <td>0.470452</td>\n",
       "      <td>0.112246</td>\n",
       "      <td>0.561421</td>\n",
       "      <td>0.102842</td>\n",
       "      <td>0.686458</td>\n",
       "      <td>0.065122</td>\n",
       "      <td>0.880692</td>\n",
       "      <td>0.043386</td>\n",
       "      <td>0.703283</td>\n",
       "      <td>0.051289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.849262</td>\n",
       "      <td>0.017066</td>\n",
       "      <td>0.797510</td>\n",
       "      <td>0.021953</td>\n",
       "      <td>0.833834</td>\n",
       "      <td>0.048789</td>\n",
       "      <td>0.880261</td>\n",
       "      <td>0.034989</td>\n",
       "      <td>0.855479</td>\n",
       "      <td>0.026910</td>\n",
       "      <td>0.841526</td>\n",
       "      <td>0.025537</td>\n",
       "      <td>0.845304</td>\n",
       "      <td>0.026346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.954143</td>\n",
       "      <td>0.023538</td>\n",
       "      <td>0.938586</td>\n",
       "      <td>0.030978</td>\n",
       "      <td>0.913674</td>\n",
       "      <td>0.073447</td>\n",
       "      <td>0.914356</td>\n",
       "      <td>0.045952</td>\n",
       "      <td>0.956815</td>\n",
       "      <td>0.034066</td>\n",
       "      <td>0.971996</td>\n",
       "      <td>0.010748</td>\n",
       "      <td>0.949791</td>\n",
       "      <td>0.029151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.941755</td>\n",
       "      <td>0.016940</td>\n",
       "      <td>0.921826</td>\n",
       "      <td>0.022854</td>\n",
       "      <td>0.895266</td>\n",
       "      <td>0.075455</td>\n",
       "      <td>0.909817</td>\n",
       "      <td>0.045511</td>\n",
       "      <td>0.950199</td>\n",
       "      <td>0.023805</td>\n",
       "      <td>0.950115</td>\n",
       "      <td>0.010573</td>\n",
       "      <td>0.942159</td>\n",
       "      <td>0.016558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.966120</td>\n",
       "      <td>0.015015</td>\n",
       "      <td>0.954464</td>\n",
       "      <td>0.020288</td>\n",
       "      <td>0.905164</td>\n",
       "      <td>0.124162</td>\n",
       "      <td>0.938486</td>\n",
       "      <td>0.032499</td>\n",
       "      <td>0.964062</td>\n",
       "      <td>0.021779</td>\n",
       "      <td>0.980942</td>\n",
       "      <td>0.008292</td>\n",
       "      <td>0.968165</td>\n",
       "      <td>0.014323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.868734</td>\n",
       "      <td>0.022848</td>\n",
       "      <td>0.824964</td>\n",
       "      <td>0.030070</td>\n",
       "      <td>0.391844</td>\n",
       "      <td>0.245819</td>\n",
       "      <td>0.728863</td>\n",
       "      <td>0.078132</td>\n",
       "      <td>0.833123</td>\n",
       "      <td>0.021670</td>\n",
       "      <td>0.959100</td>\n",
       "      <td>0.014395</td>\n",
       "      <td>0.885092</td>\n",
       "      <td>0.035703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.686009</td>\n",
       "      <td>0.063976</td>\n",
       "      <td>0.587915</td>\n",
       "      <td>0.073111</td>\n",
       "      <td>0.375700</td>\n",
       "      <td>0.077662</td>\n",
       "      <td>0.489756</td>\n",
       "      <td>0.051259</td>\n",
       "      <td>0.673296</td>\n",
       "      <td>0.061738</td>\n",
       "      <td>0.814348</td>\n",
       "      <td>0.118744</td>\n",
       "      <td>0.680800</td>\n",
       "      <td>0.070748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.842247</td>\n",
       "      <td>0.017181</td>\n",
       "      <td>0.786557</td>\n",
       "      <td>0.022924</td>\n",
       "      <td>0.836058</td>\n",
       "      <td>0.061802</td>\n",
       "      <td>0.866749</td>\n",
       "      <td>0.050106</td>\n",
       "      <td>0.821840</td>\n",
       "      <td>0.023982</td>\n",
       "      <td>0.846607</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>0.845496</td>\n",
       "      <td>0.022271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.949151</td>\n",
       "      <td>0.012878</td>\n",
       "      <td>0.931246</td>\n",
       "      <td>0.017398</td>\n",
       "      <td>0.922480</td>\n",
       "      <td>0.025149</td>\n",
       "      <td>0.882372</td>\n",
       "      <td>0.044426</td>\n",
       "      <td>0.934757</td>\n",
       "      <td>0.023541</td>\n",
       "      <td>0.975129</td>\n",
       "      <td>0.011141</td>\n",
       "      <td>0.954580</td>\n",
       "      <td>0.012883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.948986</td>\n",
       "      <td>0.014211</td>\n",
       "      <td>0.930964</td>\n",
       "      <td>0.019118</td>\n",
       "      <td>0.907653</td>\n",
       "      <td>0.056396</td>\n",
       "      <td>0.898263</td>\n",
       "      <td>0.042316</td>\n",
       "      <td>0.946895</td>\n",
       "      <td>0.016503</td>\n",
       "      <td>0.962457</td>\n",
       "      <td>0.012636</td>\n",
       "      <td>0.957351</td>\n",
       "      <td>0.013790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.960099</td>\n",
       "      <td>0.006792</td>\n",
       "      <td>0.946348</td>\n",
       "      <td>0.008896</td>\n",
       "      <td>0.918188</td>\n",
       "      <td>0.038443</td>\n",
       "      <td>0.908062</td>\n",
       "      <td>0.038952</td>\n",
       "      <td>0.954779</td>\n",
       "      <td>0.008173</td>\n",
       "      <td>0.981334</td>\n",
       "      <td>0.006191</td>\n",
       "      <td>0.964207</td>\n",
       "      <td>0.013322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.849412</td>\n",
       "      <td>0.010979</td>\n",
       "      <td>0.797526</td>\n",
       "      <td>0.014492</td>\n",
       "      <td>0.190538</td>\n",
       "      <td>0.144271</td>\n",
       "      <td>0.670460</td>\n",
       "      <td>0.106953</td>\n",
       "      <td>0.815172</td>\n",
       "      <td>0.028896</td>\n",
       "      <td>0.954050</td>\n",
       "      <td>0.015682</td>\n",
       "      <td>0.862221</td>\n",
       "      <td>0.021568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Accuracy            Matthews  \\\n",
       "                                                mean       std      mean   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.366725  0.283739  0.147699   \n",
       "                    GaussianNB              0.571405  0.254137  0.497932   \n",
       "                    LinearSVC               0.328271  0.413681  0.123009   \n",
       "                    LogisticRegression      0.055358  0.024359  0.000000   \n",
       "                    MLPClassifier           0.342145  0.408488  0.220975   \n",
       "                    RandomForestClassifier  0.415680  0.276577  0.168742   \n",
       "          100       Decision Tree           0.543955  0.292960  0.394909   \n",
       "                    GaussianNB              0.697686  0.221545  0.647460   \n",
       "                    LinearSVC               0.513983  0.434789  0.373858   \n",
       "                    LogisticRegression      0.051906  0.004791  0.000000   \n",
       "                    MLPClassifier           0.492495  0.409990  0.348977   \n",
       "                    RandomForestClassifier  0.567090  0.312752  0.393538   \n",
       "          200       Decision Tree           0.494300  0.359170  0.317602   \n",
       "                    GaussianNB              0.660016  0.252260  0.611237   \n",
       "                    LinearSVC               0.428398  0.448323  0.249812   \n",
       "                    LogisticRegression      0.052442  0.003505  0.000000   \n",
       "                    MLPClassifier           0.398283  0.398552  0.296363   \n",
       "                    RandomForestClassifier  0.519867  0.309610  0.327462   \n",
       "rdf2vec   10        Decision Tree           0.710983  0.086279  0.614850   \n",
       "                    GaussianNB              0.760073  0.050925  0.676080   \n",
       "                    LinearSVC               0.888429  0.029794  0.849414   \n",
       "                    LogisticRegression      0.848175  0.047046  0.795548   \n",
       "                    MLPClassifier           0.916063  0.044849  0.886877   \n",
       "                    RandomForestClassifier  0.842239  0.053698  0.783707   \n",
       "          100       Decision Tree           0.729491  0.029289  0.641082   \n",
       "                    GaussianNB              0.849262  0.017066  0.797510   \n",
       "                    LinearSVC               0.954143  0.023538  0.938586   \n",
       "                    LogisticRegression      0.941755  0.016940  0.921826   \n",
       "                    MLPClassifier           0.966120  0.015015  0.954464   \n",
       "                    RandomForestClassifier  0.868734  0.022848  0.824964   \n",
       "          200       Decision Tree           0.686009  0.063976  0.587915   \n",
       "                    GaussianNB              0.842247  0.017181  0.786557   \n",
       "                    LinearSVC               0.949151  0.012878  0.931246   \n",
       "                    LogisticRegression      0.948986  0.014211  0.930964   \n",
       "                    MLPClassifier           0.960099  0.006792  0.946348   \n",
       "                    RandomForestClassifier  0.849412  0.010979  0.797526   \n",
       "\n",
       "                                                     :Analysis            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.361214  0.096324  0.177369   \n",
       "                    GaussianNB              0.272244  0.210604  0.265315   \n",
       "                    LinearSVC               0.534183  0.174444  0.294999   \n",
       "                    LogisticRegression      0.000000  0.104000  0.043686   \n",
       "                    MLPClassifier           0.461280  0.180615  0.261905   \n",
       "                    RandomForestClassifier  0.402936  0.028571  0.090351   \n",
       "          100       Decision Tree           0.384079  0.251416  0.242408   \n",
       "                    GaussianNB              0.209102  0.467061  0.214158   \n",
       "                    LinearSVC               0.548182  0.380077  0.321506   \n",
       "                    LogisticRegression      0.000000  0.098654  0.008628   \n",
       "                    MLPClassifier           0.531672  0.281945  0.243109   \n",
       "                    RandomForestClassifier  0.465667  0.011735  0.027003   \n",
       "          200       Decision Tree           0.484808  0.262034  0.262698   \n",
       "                    GaussianNB              0.251395  0.459764  0.250898   \n",
       "                    LinearSVC               0.582755  0.325917  0.336980   \n",
       "                    LogisticRegression      0.000000  0.099639  0.006322   \n",
       "                    MLPClassifier           0.447150  0.124516  0.160143   \n",
       "                    RandomForestClassifier  0.451867  0.000000  0.000000   \n",
       "rdf2vec   10        Decision Tree           0.102383  0.523863  0.227523   \n",
       "                    GaussianNB              0.064208  0.591975  0.261870   \n",
       "                    LinearSVC               0.038465  0.746346  0.275191   \n",
       "                    LogisticRegression      0.058885  0.687022  0.163938   \n",
       "                    MLPClassifier           0.058029  0.789766  0.155373   \n",
       "                    RandomForestClassifier  0.074778  0.597519  0.231448   \n",
       "          100       Decision Tree           0.036087  0.470452  0.112246   \n",
       "                    GaussianNB              0.021953  0.833834  0.048789   \n",
       "                    LinearSVC               0.030978  0.913674  0.073447   \n",
       "                    LogisticRegression      0.022854  0.895266  0.075455   \n",
       "                    MLPClassifier           0.020288  0.905164  0.124162   \n",
       "                    RandomForestClassifier  0.030070  0.391844  0.245819   \n",
       "          200       Decision Tree           0.073111  0.375700  0.077662   \n",
       "                    GaussianNB              0.022924  0.836058  0.061802   \n",
       "                    LinearSVC               0.017398  0.922480  0.025149   \n",
       "                    LogisticRegression      0.019118  0.907653  0.056396   \n",
       "                    MLPClassifier           0.008896  0.918188  0.038443   \n",
       "                    RandomForestClassifier  0.014492  0.190538  0.144271   \n",
       "\n",
       "                                           :Movement           :Preparation  \\\n",
       "                                                mean       std         mean   \n",
       "EmbMethod InputSize Method                                                    \n",
       "bertcode  10        Decision Tree           0.115000  0.185293     0.281064   \n",
       "                    GaussianNB              0.309246  0.313952     0.471307   \n",
       "                    LinearSVC               0.296832  0.384810     0.278400   \n",
       "                    LogisticRegression      0.000000  0.000000     0.000000   \n",
       "                    MLPClassifier           0.298668  0.351925     0.267287   \n",
       "                    RandomForestClassifier  0.135000  0.286793     0.360183   \n",
       "          100       Decision Tree           0.318524  0.304800     0.493616   \n",
       "                    GaussianNB              0.546135  0.281234     0.580998   \n",
       "                    LinearSVC               0.442190  0.362650     0.461938   \n",
       "                    LogisticRegression      0.000000  0.000000     0.000000   \n",
       "                    MLPClassifier           0.247414  0.299784     0.431288   \n",
       "                    RandomForestClassifier  0.251274  0.271555     0.488097   \n",
       "          200       Decision Tree           0.378258  0.334517     0.450775   \n",
       "                    GaussianNB              0.503772  0.334005     0.498289   \n",
       "                    LinearSVC               0.361534  0.375258     0.369267   \n",
       "                    LogisticRegression      0.000000  0.000000     0.000000   \n",
       "                    MLPClassifier           0.142711  0.191811     0.332731   \n",
       "                    RandomForestClassifier  0.231816  0.253266     0.446877   \n",
       "rdf2vec   10        Decision Tree           0.658750  0.171741     0.668180   \n",
       "                    GaussianNB              0.668401  0.220247     0.709958   \n",
       "                    LinearSVC               0.906335  0.095609     0.868495   \n",
       "                    LogisticRegression      0.867557  0.121545     0.822207   \n",
       "                    MLPClassifier           0.897534  0.099886     0.922520   \n",
       "                    RandomForestClassifier  0.814330  0.156224     0.781200   \n",
       "          100       Decision Tree           0.561421  0.102842     0.686458   \n",
       "                    GaussianNB              0.880261  0.034989     0.855479   \n",
       "                    LinearSVC               0.914356  0.045952     0.956815   \n",
       "                    LogisticRegression      0.909817  0.045511     0.950199   \n",
       "                    MLPClassifier           0.938486  0.032499     0.964062   \n",
       "                    RandomForestClassifier  0.728863  0.078132     0.833123   \n",
       "          200       Decision Tree           0.489756  0.051259     0.673296   \n",
       "                    GaussianNB              0.866749  0.050106     0.821840   \n",
       "                    LinearSVC               0.882372  0.044426     0.934757   \n",
       "                    LogisticRegression      0.898263  0.042316     0.946895   \n",
       "                    MLPClassifier           0.908062  0.038952     0.954779   \n",
       "                    RandomForestClassifier  0.670460  0.106953     0.815172   \n",
       "\n",
       "                                                        :Reuse            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.264678  0.325714  0.431881   \n",
       "                    GaussianNB              0.260350  0.733542  0.387666   \n",
       "                    LinearSVC               0.324328  0.387101  0.500082   \n",
       "                    LogisticRegression      0.000000  0.000000  0.000000   \n",
       "                    MLPClassifier           0.338555  0.394733  0.509682   \n",
       "                    RandomForestClassifier  0.207916  0.377872  0.491033   \n",
       "          100       Decision Tree           0.258824  0.579503  0.484648   \n",
       "                    GaussianNB              0.269498  0.891319  0.289525   \n",
       "                    LinearSVC               0.384225  0.593442  0.510768   \n",
       "                    LogisticRegression      0.000000  0.000000  0.000000   \n",
       "                    MLPClassifier           0.364523  0.598559  0.515160   \n",
       "                    RandomForestClassifier  0.288649  0.599322  0.515816   \n",
       "          200       Decision Tree           0.294624  0.501345  0.515249   \n",
       "                    GaussianNB              0.335135  0.875574  0.310956   \n",
       "                    LinearSVC               0.385648  0.496358  0.522897   \n",
       "                    LogisticRegression      0.000000  0.000000  0.000000   \n",
       "                    MLPClassifier           0.351115  0.498893  0.525880   \n",
       "                    RandomForestClassifier  0.269109  0.499502  0.526522   \n",
       "rdf2vec   10        Decision Tree           0.099786  0.797798  0.182055   \n",
       "                    GaussianNB              0.068046  0.832092  0.090799   \n",
       "                    LinearSVC               0.048715  0.907831  0.036052   \n",
       "                    LogisticRegression      0.081737  0.876221  0.045176   \n",
       "                    MLPClassifier           0.041638  0.921677  0.068970   \n",
       "                    RandomForestClassifier  0.071645  0.940486  0.033470   \n",
       "          100       Decision Tree           0.065122  0.880692  0.043386   \n",
       "                    GaussianNB              0.026910  0.841526  0.025537   \n",
       "                    LinearSVC               0.034066  0.971996  0.010748   \n",
       "                    LogisticRegression      0.023805  0.950115  0.010573   \n",
       "                    MLPClassifier           0.021779  0.980942  0.008292   \n",
       "                    RandomForestClassifier  0.021670  0.959100  0.014395   \n",
       "          200       Decision Tree           0.061738  0.814348  0.118744   \n",
       "                    GaussianNB              0.023982  0.846607  0.015283   \n",
       "                    LinearSVC               0.023541  0.975129  0.011141   \n",
       "                    LogisticRegression      0.016503  0.962457  0.012636   \n",
       "                    MLPClassifier           0.008173  0.981334  0.006191   \n",
       "                    RandomForestClassifier  0.028896  0.954050  0.015682   \n",
       "\n",
       "                                           :Visualisation            \n",
       "                                                     mean       std  \n",
       "EmbMethod InputSize Method                                           \n",
       "bertcode  10        Decision Tree                0.425117  0.347240  \n",
       "                    GaussianNB                   0.598604  0.281352  \n",
       "                    LinearSVC                    0.347147  0.442359  \n",
       "                    LogisticRegression           0.000000  0.000000  \n",
       "                    MLPClassifier                0.340345  0.442365  \n",
       "                    RandomForestClassifier       0.447145  0.345909  \n",
       "          100       Decision Tree                0.587929  0.237366  \n",
       "                    GaussianNB                   0.671830  0.135246  \n",
       "                    LinearSVC                    0.509934  0.434800  \n",
       "                    LogisticRegression           0.000000  0.000000  \n",
       "                    MLPClassifier                0.491121  0.419524  \n",
       "                    RandomForestClassifier       0.633568  0.297144  \n",
       "          200       Decision Tree                0.536273  0.346455  \n",
       "                    GaussianNB                   0.634360  0.230245  \n",
       "                    LinearSVC                    0.433029  0.451827  \n",
       "                    LogisticRegression           0.000000  0.000000  \n",
       "                    MLPClassifier                0.400652  0.413847  \n",
       "                    RandomForestClassifier       0.600561  0.303219  \n",
       "rdf2vec   10        Decision Tree                0.674315  0.116209  \n",
       "                    GaussianNB                   0.748113  0.079990  \n",
       "                    LinearSVC                    0.870738  0.065779  \n",
       "                    LogisticRegression           0.838720  0.064602  \n",
       "                    MLPClassifier                0.918274  0.053987  \n",
       "                    RandomForestClassifier       0.816505  0.044054  \n",
       "          100       Decision Tree                0.703283  0.051289  \n",
       "                    GaussianNB                   0.845304  0.026346  \n",
       "                    LinearSVC                    0.949791  0.029151  \n",
       "                    LogisticRegression           0.942159  0.016558  \n",
       "                    MLPClassifier                0.968165  0.014323  \n",
       "                    RandomForestClassifier       0.885092  0.035703  \n",
       "          200       Decision Tree                0.680800  0.070748  \n",
       "                    GaussianNB                   0.845496  0.022271  \n",
       "                    LinearSVC                    0.954580  0.012883  \n",
       "                    LogisticRegression           0.957351  0.013790  \n",
       "                    MLPClassifier                0.964207  0.013322  \n",
       "                    RandomForestClassifier       0.862221  0.021568  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table(data, 'MultiClassification.csv', 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data[(data['Dataset']=='MultiClassification.csv') & (data['InputSize']==10) & (data['TestRegime']==1) & (data['Method']=='Decision Tree')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test regime = 2\n",
    "\n",
    "1. Train and test entities randomly split\n",
    "2. Train and test do not share notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Matthews</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Analysis</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Movement</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Preparation</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Reuse</th>\n",
       "      <th colspan=\"2\" halign=\"left\">:Visualisation</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmbMethod</th>\n",
       "      <th>InputSize</th>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">bertcode</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.461179</td>\n",
       "      <td>0.223750</td>\n",
       "      <td>0.303444</td>\n",
       "      <td>0.291559</td>\n",
       "      <td>0.134531</td>\n",
       "      <td>0.177373</td>\n",
       "      <td>0.161625</td>\n",
       "      <td>0.182554</td>\n",
       "      <td>0.380770</td>\n",
       "      <td>0.192801</td>\n",
       "      <td>0.599421</td>\n",
       "      <td>0.422215</td>\n",
       "      <td>0.310983</td>\n",
       "      <td>0.349718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.404534</td>\n",
       "      <td>0.319480</td>\n",
       "      <td>0.310002</td>\n",
       "      <td>0.314829</td>\n",
       "      <td>0.083929</td>\n",
       "      <td>0.072523</td>\n",
       "      <td>0.155872</td>\n",
       "      <td>0.244708</td>\n",
       "      <td>0.329621</td>\n",
       "      <td>0.303318</td>\n",
       "      <td>0.454662</td>\n",
       "      <td>0.452452</td>\n",
       "      <td>0.447587</td>\n",
       "      <td>0.356750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.510030</td>\n",
       "      <td>0.350094</td>\n",
       "      <td>0.352118</td>\n",
       "      <td>0.436654</td>\n",
       "      <td>0.176949</td>\n",
       "      <td>0.208743</td>\n",
       "      <td>0.409664</td>\n",
       "      <td>0.289033</td>\n",
       "      <td>0.418064</td>\n",
       "      <td>0.284269</td>\n",
       "      <td>0.644374</td>\n",
       "      <td>0.447520</td>\n",
       "      <td>0.430842</td>\n",
       "      <td>0.328134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.050903</td>\n",
       "      <td>0.033610</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.084548</td>\n",
       "      <td>0.066676</td>\n",
       "      <td>0.010606</td>\n",
       "      <td>0.033539</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.548566</td>\n",
       "      <td>0.355945</td>\n",
       "      <td>0.418003</td>\n",
       "      <td>0.416903</td>\n",
       "      <td>0.186165</td>\n",
       "      <td>0.249183</td>\n",
       "      <td>0.409935</td>\n",
       "      <td>0.235757</td>\n",
       "      <td>0.446226</td>\n",
       "      <td>0.313488</td>\n",
       "      <td>0.671323</td>\n",
       "      <td>0.465762</td>\n",
       "      <td>0.452941</td>\n",
       "      <td>0.345543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.568413</td>\n",
       "      <td>0.304975</td>\n",
       "      <td>0.388577</td>\n",
       "      <td>0.472436</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>0.042164</td>\n",
       "      <td>0.149697</td>\n",
       "      <td>0.247787</td>\n",
       "      <td>0.532656</td>\n",
       "      <td>0.226598</td>\n",
       "      <td>0.669727</td>\n",
       "      <td>0.463450</td>\n",
       "      <td>0.450887</td>\n",
       "      <td>0.345686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.431492</td>\n",
       "      <td>0.307787</td>\n",
       "      <td>0.227233</td>\n",
       "      <td>0.432130</td>\n",
       "      <td>0.139741</td>\n",
       "      <td>0.151951</td>\n",
       "      <td>0.164845</td>\n",
       "      <td>0.201452</td>\n",
       "      <td>0.414110</td>\n",
       "      <td>0.194786</td>\n",
       "      <td>0.490382</td>\n",
       "      <td>0.508144</td>\n",
       "      <td>0.435988</td>\n",
       "      <td>0.343815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.443406</td>\n",
       "      <td>0.250343</td>\n",
       "      <td>0.400965</td>\n",
       "      <td>0.234663</td>\n",
       "      <td>0.235728</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>0.210860</td>\n",
       "      <td>0.303847</td>\n",
       "      <td>0.229837</td>\n",
       "      <td>0.288296</td>\n",
       "      <td>0.654548</td>\n",
       "      <td>0.432454</td>\n",
       "      <td>0.543922</td>\n",
       "      <td>0.197503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.384429</td>\n",
       "      <td>0.400421</td>\n",
       "      <td>0.196020</td>\n",
       "      <td>0.514159</td>\n",
       "      <td>0.209000</td>\n",
       "      <td>0.219085</td>\n",
       "      <td>0.293688</td>\n",
       "      <td>0.302398</td>\n",
       "      <td>0.328149</td>\n",
       "      <td>0.340758</td>\n",
       "      <td>0.476225</td>\n",
       "      <td>0.503652</td>\n",
       "      <td>0.373447</td>\n",
       "      <td>0.388457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.051996</td>\n",
       "      <td>0.012770</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.098600</td>\n",
       "      <td>0.023042</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.409725</td>\n",
       "      <td>0.408198</td>\n",
       "      <td>0.241747</td>\n",
       "      <td>0.516412</td>\n",
       "      <td>0.187605</td>\n",
       "      <td>0.189667</td>\n",
       "      <td>0.306762</td>\n",
       "      <td>0.299052</td>\n",
       "      <td>0.356265</td>\n",
       "      <td>0.343405</td>\n",
       "      <td>0.494786</td>\n",
       "      <td>0.521580</td>\n",
       "      <td>0.401398</td>\n",
       "      <td>0.418304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.445683</td>\n",
       "      <td>0.323091</td>\n",
       "      <td>0.237132</td>\n",
       "      <td>0.449736</td>\n",
       "      <td>0.041026</td>\n",
       "      <td>0.129734</td>\n",
       "      <td>0.096609</td>\n",
       "      <td>0.199492</td>\n",
       "      <td>0.355808</td>\n",
       "      <td>0.281294</td>\n",
       "      <td>0.491460</td>\n",
       "      <td>0.518580</td>\n",
       "      <td>0.526088</td>\n",
       "      <td>0.255547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.549986</td>\n",
       "      <td>0.355217</td>\n",
       "      <td>0.438797</td>\n",
       "      <td>0.416386</td>\n",
       "      <td>0.338333</td>\n",
       "      <td>0.231132</td>\n",
       "      <td>0.368256</td>\n",
       "      <td>0.321560</td>\n",
       "      <td>0.475580</td>\n",
       "      <td>0.288620</td>\n",
       "      <td>0.611977</td>\n",
       "      <td>0.503279</td>\n",
       "      <td>0.594413</td>\n",
       "      <td>0.345126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.566826</td>\n",
       "      <td>0.199935</td>\n",
       "      <td>0.520874</td>\n",
       "      <td>0.186720</td>\n",
       "      <td>0.255202</td>\n",
       "      <td>0.159362</td>\n",
       "      <td>0.350297</td>\n",
       "      <td>0.340211</td>\n",
       "      <td>0.338278</td>\n",
       "      <td>0.300231</td>\n",
       "      <td>0.843098</td>\n",
       "      <td>0.309191</td>\n",
       "      <td>0.630053</td>\n",
       "      <td>0.080424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.514669</td>\n",
       "      <td>0.422670</td>\n",
       "      <td>0.353099</td>\n",
       "      <td>0.560588</td>\n",
       "      <td>0.381029</td>\n",
       "      <td>0.311105</td>\n",
       "      <td>0.395033</td>\n",
       "      <td>0.322570</td>\n",
       "      <td>0.434420</td>\n",
       "      <td>0.355780</td>\n",
       "      <td>0.612264</td>\n",
       "      <td>0.507137</td>\n",
       "      <td>0.530151</td>\n",
       "      <td>0.433459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.048212</td>\n",
       "      <td>0.008231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.091885</td>\n",
       "      <td>0.015029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.511572</td>\n",
       "      <td>0.408131</td>\n",
       "      <td>0.415073</td>\n",
       "      <td>0.468898</td>\n",
       "      <td>0.305875</td>\n",
       "      <td>0.230927</td>\n",
       "      <td>0.262840</td>\n",
       "      <td>0.257188</td>\n",
       "      <td>0.441816</td>\n",
       "      <td>0.357120</td>\n",
       "      <td>0.622922</td>\n",
       "      <td>0.515830</td>\n",
       "      <td>0.522864</td>\n",
       "      <td>0.428981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.542650</td>\n",
       "      <td>0.318015</td>\n",
       "      <td>0.377759</td>\n",
       "      <td>0.446352</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.219781</td>\n",
       "      <td>0.253525</td>\n",
       "      <td>0.461756</td>\n",
       "      <td>0.265801</td>\n",
       "      <td>0.610056</td>\n",
       "      <td>0.506101</td>\n",
       "      <td>0.618390</td>\n",
       "      <td>0.269738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">rdf2vec</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.593236</td>\n",
       "      <td>0.152615</td>\n",
       "      <td>0.469565</td>\n",
       "      <td>0.157476</td>\n",
       "      <td>0.272774</td>\n",
       "      <td>0.275011</td>\n",
       "      <td>0.347798</td>\n",
       "      <td>0.248644</td>\n",
       "      <td>0.510706</td>\n",
       "      <td>0.260166</td>\n",
       "      <td>0.798579</td>\n",
       "      <td>0.132618</td>\n",
       "      <td>0.521433</td>\n",
       "      <td>0.179072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.599732</td>\n",
       "      <td>0.160262</td>\n",
       "      <td>0.485148</td>\n",
       "      <td>0.147632</td>\n",
       "      <td>0.351986</td>\n",
       "      <td>0.350998</td>\n",
       "      <td>0.601884</td>\n",
       "      <td>0.254444</td>\n",
       "      <td>0.443433</td>\n",
       "      <td>0.206216</td>\n",
       "      <td>0.824118</td>\n",
       "      <td>0.129820</td>\n",
       "      <td>0.478920</td>\n",
       "      <td>0.185732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.737641</td>\n",
       "      <td>0.158588</td>\n",
       "      <td>0.674760</td>\n",
       "      <td>0.142616</td>\n",
       "      <td>0.666450</td>\n",
       "      <td>0.325147</td>\n",
       "      <td>0.694879</td>\n",
       "      <td>0.213951</td>\n",
       "      <td>0.626680</td>\n",
       "      <td>0.247838</td>\n",
       "      <td>0.883954</td>\n",
       "      <td>0.076377</td>\n",
       "      <td>0.620245</td>\n",
       "      <td>0.234275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.696170</td>\n",
       "      <td>0.166799</td>\n",
       "      <td>0.608720</td>\n",
       "      <td>0.159141</td>\n",
       "      <td>0.475884</td>\n",
       "      <td>0.345656</td>\n",
       "      <td>0.647919</td>\n",
       "      <td>0.248740</td>\n",
       "      <td>0.564169</td>\n",
       "      <td>0.239027</td>\n",
       "      <td>0.859883</td>\n",
       "      <td>0.087808</td>\n",
       "      <td>0.607112</td>\n",
       "      <td>0.217987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.753797</td>\n",
       "      <td>0.150308</td>\n",
       "      <td>0.697738</td>\n",
       "      <td>0.112164</td>\n",
       "      <td>0.601565</td>\n",
       "      <td>0.350315</td>\n",
       "      <td>0.667082</td>\n",
       "      <td>0.227260</td>\n",
       "      <td>0.651461</td>\n",
       "      <td>0.210310</td>\n",
       "      <td>0.911853</td>\n",
       "      <td>0.065738</td>\n",
       "      <td>0.673085</td>\n",
       "      <td>0.191583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.698755</td>\n",
       "      <td>0.170309</td>\n",
       "      <td>0.615341</td>\n",
       "      <td>0.162082</td>\n",
       "      <td>0.323368</td>\n",
       "      <td>0.309243</td>\n",
       "      <td>0.573041</td>\n",
       "      <td>0.330479</td>\n",
       "      <td>0.550775</td>\n",
       "      <td>0.217250</td>\n",
       "      <td>0.922905</td>\n",
       "      <td>0.064522</td>\n",
       "      <td>0.622374</td>\n",
       "      <td>0.216159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.726987</td>\n",
       "      <td>0.057047</td>\n",
       "      <td>0.641572</td>\n",
       "      <td>0.064109</td>\n",
       "      <td>0.468712</td>\n",
       "      <td>0.100217</td>\n",
       "      <td>0.526740</td>\n",
       "      <td>0.084083</td>\n",
       "      <td>0.681897</td>\n",
       "      <td>0.091191</td>\n",
       "      <td>0.868918</td>\n",
       "      <td>0.106191</td>\n",
       "      <td>0.735795</td>\n",
       "      <td>0.051239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.846486</td>\n",
       "      <td>0.023876</td>\n",
       "      <td>0.795572</td>\n",
       "      <td>0.029595</td>\n",
       "      <td>0.894679</td>\n",
       "      <td>0.064999</td>\n",
       "      <td>0.899924</td>\n",
       "      <td>0.037494</td>\n",
       "      <td>0.837372</td>\n",
       "      <td>0.028665</td>\n",
       "      <td>0.832351</td>\n",
       "      <td>0.056838</td>\n",
       "      <td>0.833913</td>\n",
       "      <td>0.028413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.948288</td>\n",
       "      <td>0.020936</td>\n",
       "      <td>0.930875</td>\n",
       "      <td>0.027849</td>\n",
       "      <td>0.883760</td>\n",
       "      <td>0.125928</td>\n",
       "      <td>0.904125</td>\n",
       "      <td>0.050986</td>\n",
       "      <td>0.944841</td>\n",
       "      <td>0.035463</td>\n",
       "      <td>0.977770</td>\n",
       "      <td>0.012154</td>\n",
       "      <td>0.941985</td>\n",
       "      <td>0.032779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.942537</td>\n",
       "      <td>0.014369</td>\n",
       "      <td>0.923091</td>\n",
       "      <td>0.018438</td>\n",
       "      <td>0.934415</td>\n",
       "      <td>0.032017</td>\n",
       "      <td>0.915109</td>\n",
       "      <td>0.041856</td>\n",
       "      <td>0.942260</td>\n",
       "      <td>0.021403</td>\n",
       "      <td>0.950988</td>\n",
       "      <td>0.017829</td>\n",
       "      <td>0.941255</td>\n",
       "      <td>0.022814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.960437</td>\n",
       "      <td>0.014021</td>\n",
       "      <td>0.947052</td>\n",
       "      <td>0.018136</td>\n",
       "      <td>0.923617</td>\n",
       "      <td>0.031249</td>\n",
       "      <td>0.919694</td>\n",
       "      <td>0.039937</td>\n",
       "      <td>0.950759</td>\n",
       "      <td>0.022558</td>\n",
       "      <td>0.985253</td>\n",
       "      <td>0.008487</td>\n",
       "      <td>0.962906</td>\n",
       "      <td>0.019324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.862960</td>\n",
       "      <td>0.032171</td>\n",
       "      <td>0.818849</td>\n",
       "      <td>0.040215</td>\n",
       "      <td>0.458009</td>\n",
       "      <td>0.258883</td>\n",
       "      <td>0.801835</td>\n",
       "      <td>0.064540</td>\n",
       "      <td>0.826439</td>\n",
       "      <td>0.034952</td>\n",
       "      <td>0.951163</td>\n",
       "      <td>0.024127</td>\n",
       "      <td>0.863521</td>\n",
       "      <td>0.054155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.689154</td>\n",
       "      <td>0.042616</td>\n",
       "      <td>0.590960</td>\n",
       "      <td>0.049535</td>\n",
       "      <td>0.445096</td>\n",
       "      <td>0.102886</td>\n",
       "      <td>0.506604</td>\n",
       "      <td>0.046091</td>\n",
       "      <td>0.628488</td>\n",
       "      <td>0.042622</td>\n",
       "      <td>0.827255</td>\n",
       "      <td>0.089043</td>\n",
       "      <td>0.700401</td>\n",
       "      <td>0.039547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.846285</td>\n",
       "      <td>0.020371</td>\n",
       "      <td>0.794599</td>\n",
       "      <td>0.026777</td>\n",
       "      <td>0.869744</td>\n",
       "      <td>0.058711</td>\n",
       "      <td>0.852587</td>\n",
       "      <td>0.029519</td>\n",
       "      <td>0.824387</td>\n",
       "      <td>0.035658</td>\n",
       "      <td>0.848669</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>0.851543</td>\n",
       "      <td>0.024324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.939936</td>\n",
       "      <td>0.025511</td>\n",
       "      <td>0.920108</td>\n",
       "      <td>0.033141</td>\n",
       "      <td>0.920507</td>\n",
       "      <td>0.039311</td>\n",
       "      <td>0.860392</td>\n",
       "      <td>0.067977</td>\n",
       "      <td>0.927034</td>\n",
       "      <td>0.047036</td>\n",
       "      <td>0.973948</td>\n",
       "      <td>0.012353</td>\n",
       "      <td>0.941300</td>\n",
       "      <td>0.022680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.941619</td>\n",
       "      <td>0.012096</td>\n",
       "      <td>0.922176</td>\n",
       "      <td>0.015589</td>\n",
       "      <td>0.915501</td>\n",
       "      <td>0.032576</td>\n",
       "      <td>0.882649</td>\n",
       "      <td>0.047010</td>\n",
       "      <td>0.936762</td>\n",
       "      <td>0.020846</td>\n",
       "      <td>0.959511</td>\n",
       "      <td>0.015136</td>\n",
       "      <td>0.947612</td>\n",
       "      <td>0.013338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.943088</td>\n",
       "      <td>0.021537</td>\n",
       "      <td>0.924915</td>\n",
       "      <td>0.027837</td>\n",
       "      <td>0.925413</td>\n",
       "      <td>0.049029</td>\n",
       "      <td>0.859595</td>\n",
       "      <td>0.069989</td>\n",
       "      <td>0.933370</td>\n",
       "      <td>0.037995</td>\n",
       "      <td>0.976968</td>\n",
       "      <td>0.012859</td>\n",
       "      <td>0.943115</td>\n",
       "      <td>0.025831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.842044</td>\n",
       "      <td>0.029191</td>\n",
       "      <td>0.791757</td>\n",
       "      <td>0.036439</td>\n",
       "      <td>0.304065</td>\n",
       "      <td>0.189132</td>\n",
       "      <td>0.656287</td>\n",
       "      <td>0.103507</td>\n",
       "      <td>0.795113</td>\n",
       "      <td>0.041963</td>\n",
       "      <td>0.951304</td>\n",
       "      <td>0.019128</td>\n",
       "      <td>0.876723</td>\n",
       "      <td>0.027355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Accuracy            Matthews  \\\n",
       "                                                mean       std      mean   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.461179  0.223750  0.303444   \n",
       "                    GaussianNB              0.404534  0.319480  0.310002   \n",
       "                    LinearSVC               0.510030  0.350094  0.352118   \n",
       "                    LogisticRegression      0.050903  0.033610  0.000000   \n",
       "                    MLPClassifier           0.548566  0.355945  0.418003   \n",
       "                    RandomForestClassifier  0.568413  0.304975  0.388577   \n",
       "          100       Decision Tree           0.431492  0.307787  0.227233   \n",
       "                    GaussianNB              0.443406  0.250343  0.400965   \n",
       "                    LinearSVC               0.384429  0.400421  0.196020   \n",
       "                    LogisticRegression      0.051996  0.012770  0.000000   \n",
       "                    MLPClassifier           0.409725  0.408198  0.241747   \n",
       "                    RandomForestClassifier  0.445683  0.323091  0.237132   \n",
       "          200       Decision Tree           0.549986  0.355217  0.438797   \n",
       "                    GaussianNB              0.566826  0.199935  0.520874   \n",
       "                    LinearSVC               0.514669  0.422670  0.353099   \n",
       "                    LogisticRegression      0.048212  0.008231  0.000000   \n",
       "                    MLPClassifier           0.511572  0.408131  0.415073   \n",
       "                    RandomForestClassifier  0.542650  0.318015  0.377759   \n",
       "rdf2vec   10        Decision Tree           0.593236  0.152615  0.469565   \n",
       "                    GaussianNB              0.599732  0.160262  0.485148   \n",
       "                    LinearSVC               0.737641  0.158588  0.674760   \n",
       "                    LogisticRegression      0.696170  0.166799  0.608720   \n",
       "                    MLPClassifier           0.753797  0.150308  0.697738   \n",
       "                    RandomForestClassifier  0.698755  0.170309  0.615341   \n",
       "          100       Decision Tree           0.726987  0.057047  0.641572   \n",
       "                    GaussianNB              0.846486  0.023876  0.795572   \n",
       "                    LinearSVC               0.948288  0.020936  0.930875   \n",
       "                    LogisticRegression      0.942537  0.014369  0.923091   \n",
       "                    MLPClassifier           0.960437  0.014021  0.947052   \n",
       "                    RandomForestClassifier  0.862960  0.032171  0.818849   \n",
       "          200       Decision Tree           0.689154  0.042616  0.590960   \n",
       "                    GaussianNB              0.846285  0.020371  0.794599   \n",
       "                    LinearSVC               0.939936  0.025511  0.920108   \n",
       "                    LogisticRegression      0.941619  0.012096  0.922176   \n",
       "                    MLPClassifier           0.943088  0.021537  0.924915   \n",
       "                    RandomForestClassifier  0.842044  0.029191  0.791757   \n",
       "\n",
       "                                                     :Analysis            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.291559  0.134531  0.177373   \n",
       "                    GaussianNB              0.314829  0.083929  0.072523   \n",
       "                    LinearSVC               0.436654  0.176949  0.208743   \n",
       "                    LogisticRegression      0.000000  0.084548  0.066676   \n",
       "                    MLPClassifier           0.416903  0.186165  0.249183   \n",
       "                    RandomForestClassifier  0.472436  0.013333  0.042164   \n",
       "          100       Decision Tree           0.432130  0.139741  0.151951   \n",
       "                    GaussianNB              0.234663  0.235728  0.166917   \n",
       "                    LinearSVC               0.514159  0.209000  0.219085   \n",
       "                    LogisticRegression      0.000000  0.098600  0.023042   \n",
       "                    MLPClassifier           0.516412  0.187605  0.189667   \n",
       "                    RandomForestClassifier  0.449736  0.041026  0.129734   \n",
       "          200       Decision Tree           0.416386  0.338333  0.231132   \n",
       "                    GaussianNB              0.186720  0.255202  0.159362   \n",
       "                    LinearSVC               0.560588  0.381029  0.311105   \n",
       "                    LogisticRegression      0.000000  0.091885  0.015029   \n",
       "                    MLPClassifier           0.468898  0.305875  0.230927   \n",
       "                    RandomForestClassifier  0.446352  0.000000  0.000000   \n",
       "rdf2vec   10        Decision Tree           0.157476  0.272774  0.275011   \n",
       "                    GaussianNB              0.147632  0.351986  0.350998   \n",
       "                    LinearSVC               0.142616  0.666450  0.325147   \n",
       "                    LogisticRegression      0.159141  0.475884  0.345656   \n",
       "                    MLPClassifier           0.112164  0.601565  0.350315   \n",
       "                    RandomForestClassifier  0.162082  0.323368  0.309243   \n",
       "          100       Decision Tree           0.064109  0.468712  0.100217   \n",
       "                    GaussianNB              0.029595  0.894679  0.064999   \n",
       "                    LinearSVC               0.027849  0.883760  0.125928   \n",
       "                    LogisticRegression      0.018438  0.934415  0.032017   \n",
       "                    MLPClassifier           0.018136  0.923617  0.031249   \n",
       "                    RandomForestClassifier  0.040215  0.458009  0.258883   \n",
       "          200       Decision Tree           0.049535  0.445096  0.102886   \n",
       "                    GaussianNB              0.026777  0.869744  0.058711   \n",
       "                    LinearSVC               0.033141  0.920507  0.039311   \n",
       "                    LogisticRegression      0.015589  0.915501  0.032576   \n",
       "                    MLPClassifier           0.027837  0.925413  0.049029   \n",
       "                    RandomForestClassifier  0.036439  0.304065  0.189132   \n",
       "\n",
       "                                           :Movement           :Preparation  \\\n",
       "                                                mean       std         mean   \n",
       "EmbMethod InputSize Method                                                    \n",
       "bertcode  10        Decision Tree           0.161625  0.182554     0.380770   \n",
       "                    GaussianNB              0.155872  0.244708     0.329621   \n",
       "                    LinearSVC               0.409664  0.289033     0.418064   \n",
       "                    LogisticRegression      0.010606  0.033539     0.000000   \n",
       "                    MLPClassifier           0.409935  0.235757     0.446226   \n",
       "                    RandomForestClassifier  0.149697  0.247787     0.532656   \n",
       "          100       Decision Tree           0.164845  0.201452     0.414110   \n",
       "                    GaussianNB              0.210860  0.303847     0.229837   \n",
       "                    LinearSVC               0.293688  0.302398     0.328149   \n",
       "                    LogisticRegression      0.000000  0.000000     0.000000   \n",
       "                    MLPClassifier           0.306762  0.299052     0.356265   \n",
       "                    RandomForestClassifier  0.096609  0.199492     0.355808   \n",
       "          200       Decision Tree           0.368256  0.321560     0.475580   \n",
       "                    GaussianNB              0.350297  0.340211     0.338278   \n",
       "                    LinearSVC               0.395033  0.322570     0.434420   \n",
       "                    LogisticRegression      0.000000  0.000000     0.000000   \n",
       "                    MLPClassifier           0.262840  0.257188     0.441816   \n",
       "                    RandomForestClassifier  0.219781  0.253525     0.461756   \n",
       "rdf2vec   10        Decision Tree           0.347798  0.248644     0.510706   \n",
       "                    GaussianNB              0.601884  0.254444     0.443433   \n",
       "                    LinearSVC               0.694879  0.213951     0.626680   \n",
       "                    LogisticRegression      0.647919  0.248740     0.564169   \n",
       "                    MLPClassifier           0.667082  0.227260     0.651461   \n",
       "                    RandomForestClassifier  0.573041  0.330479     0.550775   \n",
       "          100       Decision Tree           0.526740  0.084083     0.681897   \n",
       "                    GaussianNB              0.899924  0.037494     0.837372   \n",
       "                    LinearSVC               0.904125  0.050986     0.944841   \n",
       "                    LogisticRegression      0.915109  0.041856     0.942260   \n",
       "                    MLPClassifier           0.919694  0.039937     0.950759   \n",
       "                    RandomForestClassifier  0.801835  0.064540     0.826439   \n",
       "          200       Decision Tree           0.506604  0.046091     0.628488   \n",
       "                    GaussianNB              0.852587  0.029519     0.824387   \n",
       "                    LinearSVC               0.860392  0.067977     0.927034   \n",
       "                    LogisticRegression      0.882649  0.047010     0.936762   \n",
       "                    MLPClassifier           0.859595  0.069989     0.933370   \n",
       "                    RandomForestClassifier  0.656287  0.103507     0.795113   \n",
       "\n",
       "                                                        :Reuse            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.192801  0.599421  0.422215   \n",
       "                    GaussianNB              0.303318  0.454662  0.452452   \n",
       "                    LinearSVC               0.284269  0.644374  0.447520   \n",
       "                    LogisticRegression      0.000000  0.000000  0.000000   \n",
       "                    MLPClassifier           0.313488  0.671323  0.465762   \n",
       "                    RandomForestClassifier  0.226598  0.669727  0.463450   \n",
       "          100       Decision Tree           0.194786  0.490382  0.508144   \n",
       "                    GaussianNB              0.288296  0.654548  0.432454   \n",
       "                    LinearSVC               0.340758  0.476225  0.503652   \n",
       "                    LogisticRegression      0.000000  0.000000  0.000000   \n",
       "                    MLPClassifier           0.343405  0.494786  0.521580   \n",
       "                    RandomForestClassifier  0.281294  0.491460  0.518580   \n",
       "          200       Decision Tree           0.288620  0.611977  0.503279   \n",
       "                    GaussianNB              0.300231  0.843098  0.309191   \n",
       "                    LinearSVC               0.355780  0.612264  0.507137   \n",
       "                    LogisticRegression      0.000000  0.000000  0.000000   \n",
       "                    MLPClassifier           0.357120  0.622922  0.515830   \n",
       "                    RandomForestClassifier  0.265801  0.610056  0.506101   \n",
       "rdf2vec   10        Decision Tree           0.260166  0.798579  0.132618   \n",
       "                    GaussianNB              0.206216  0.824118  0.129820   \n",
       "                    LinearSVC               0.247838  0.883954  0.076377   \n",
       "                    LogisticRegression      0.239027  0.859883  0.087808   \n",
       "                    MLPClassifier           0.210310  0.911853  0.065738   \n",
       "                    RandomForestClassifier  0.217250  0.922905  0.064522   \n",
       "          100       Decision Tree           0.091191  0.868918  0.106191   \n",
       "                    GaussianNB              0.028665  0.832351  0.056838   \n",
       "                    LinearSVC               0.035463  0.977770  0.012154   \n",
       "                    LogisticRegression      0.021403  0.950988  0.017829   \n",
       "                    MLPClassifier           0.022558  0.985253  0.008487   \n",
       "                    RandomForestClassifier  0.034952  0.951163  0.024127   \n",
       "          200       Decision Tree           0.042622  0.827255  0.089043   \n",
       "                    GaussianNB              0.035658  0.848669  0.027397   \n",
       "                    LinearSVC               0.047036  0.973948  0.012353   \n",
       "                    LogisticRegression      0.020846  0.959511  0.015136   \n",
       "                    MLPClassifier           0.037995  0.976968  0.012859   \n",
       "                    RandomForestClassifier  0.041963  0.951304  0.019128   \n",
       "\n",
       "                                           :Visualisation            \n",
       "                                                     mean       std  \n",
       "EmbMethod InputSize Method                                           \n",
       "bertcode  10        Decision Tree                0.310983  0.349718  \n",
       "                    GaussianNB                   0.447587  0.356750  \n",
       "                    LinearSVC                    0.430842  0.328134  \n",
       "                    LogisticRegression           0.000000  0.000000  \n",
       "                    MLPClassifier                0.452941  0.345543  \n",
       "                    RandomForestClassifier       0.450887  0.345686  \n",
       "          100       Decision Tree                0.435988  0.343815  \n",
       "                    GaussianNB                   0.543922  0.197503  \n",
       "                    LinearSVC                    0.373447  0.388457  \n",
       "                    LogisticRegression           0.000000  0.000000  \n",
       "                    MLPClassifier                0.401398  0.418304  \n",
       "                    RandomForestClassifier       0.526088  0.255547  \n",
       "          200       Decision Tree                0.594413  0.345126  \n",
       "                    GaussianNB                   0.630053  0.080424  \n",
       "                    LinearSVC                    0.530151  0.433459  \n",
       "                    LogisticRegression           0.000000  0.000000  \n",
       "                    MLPClassifier                0.522864  0.428981  \n",
       "                    RandomForestClassifier       0.618390  0.269738  \n",
       "rdf2vec   10        Decision Tree                0.521433  0.179072  \n",
       "                    GaussianNB                   0.478920  0.185732  \n",
       "                    LinearSVC                    0.620245  0.234275  \n",
       "                    LogisticRegression           0.607112  0.217987  \n",
       "                    MLPClassifier                0.673085  0.191583  \n",
       "                    RandomForestClassifier       0.622374  0.216159  \n",
       "          100       Decision Tree                0.735795  0.051239  \n",
       "                    GaussianNB                   0.833913  0.028413  \n",
       "                    LinearSVC                    0.941985  0.032779  \n",
       "                    LogisticRegression           0.941255  0.022814  \n",
       "                    MLPClassifier                0.962906  0.019324  \n",
       "                    RandomForestClassifier       0.863521  0.054155  \n",
       "          200       Decision Tree                0.700401  0.039547  \n",
       "                    GaussianNB                   0.851543  0.024324  \n",
       "                    LinearSVC                    0.941300  0.022680  \n",
       "                    LogisticRegression           0.947612  0.013338  \n",
       "                    MLPClassifier                0.943115  0.025831  \n",
       "                    RandomForestClassifier       0.876723  0.027355  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table(data, 'MultiClassification.csv', 2 )"
   ]
  },
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   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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