AnalyseResultsBinary.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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21540"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BC = pd.read_csv(\"ClassificationExperiments.csv\",names=[\"Dataset\", \"Notebooks\", \"EmbMethod\", \"InputSize\", \"TestRegime\", \"Method\", \"Accuracy\", \"Matthews\", \"F1:A\", \"F1:B\" ])\n",
    "# Preview the first 5 lines of the loaded data \n",
    "# data.head()\n",
    "BC.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Decision Tree', 'GaussianNB', 'LogisticRegression', 'LinearSVC',\n",
       "       'MLPClassifier', 'RandomForestClassifier'], dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BC['Method'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group data by Dataset/InputSize/TestRegime/Method and compute AVG & STD on each Measures\n",
    "# avg = data[(data['Dataset']=='BC_VisualisationPreparation.csv') & (data['InputSize']==10) & (data['TestRegime']==1) & (data['Method']=='Decision Tree')].mean()\n",
    "# std = data[(data['Dataset']=='BC_VisualisationPreparation.csv') & (data['InputSize']==10) & (data['TestRegime']==1) & (data['Method']=='Decision Tree')].std()\n",
    "# avg, std\n",
    "# # https://stackoverflow.com/questions/36106490/how-to-get-unique-values-from-multiple-columns-in-a-pandas-groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
    "Train and test entities randomly split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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\">F1:A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">F1:B</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",
       "    </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",
       "    </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>0.577051</td>\n",
       "      <td>0.252038</td>\n",
       "      <td>0.179078</td>\n",
       "      <td>0.397626</td>\n",
       "      <td>0.467030</td>\n",
       "      <td>0.359543</td>\n",
       "      <td>0.463225</td>\n",
       "      <td>0.403142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.622766</td>\n",
       "      <td>0.276111</td>\n",
       "      <td>0.387714</td>\n",
       "      <td>0.375738</td>\n",
       "      <td>0.685924</td>\n",
       "      <td>0.228461</td>\n",
       "      <td>0.440648</td>\n",
       "      <td>0.432474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.472725</td>\n",
       "      <td>0.438203</td>\n",
       "      <td>0.009850</td>\n",
       "      <td>0.862283</td>\n",
       "      <td>0.470314</td>\n",
       "      <td>0.414787</td>\n",
       "      <td>0.454224</td>\n",
       "      <td>0.466559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.386693</td>\n",
       "      <td>0.158639</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.539984</td>\n",
       "      <td>0.173280</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.599950</td>\n",
       "      <td>0.276495</td>\n",
       "      <td>0.169645</td>\n",
       "      <td>0.551101</td>\n",
       "      <td>0.308873</td>\n",
       "      <td>0.414337</td>\n",
       "      <td>0.700317</td>\n",
       "      <td>0.231390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.663972</td>\n",
       "      <td>0.197592</td>\n",
       "      <td>0.225267</td>\n",
       "      <td>0.396973</td>\n",
       "      <td>0.498571</td>\n",
       "      <td>0.368476</td>\n",
       "      <td>0.601780</td>\n",
       "      <td>0.352356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.801900</td>\n",
       "      <td>0.218581</td>\n",
       "      <td>0.526294</td>\n",
       "      <td>0.491070</td>\n",
       "      <td>0.654591</td>\n",
       "      <td>0.385062</td>\n",
       "      <td>0.824114</td>\n",
       "      <td>0.259461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.593144</td>\n",
       "      <td>0.283216</td>\n",
       "      <td>0.385194</td>\n",
       "      <td>0.381653</td>\n",
       "      <td>0.656348</td>\n",
       "      <td>0.180427</td>\n",
       "      <td>0.444783</td>\n",
       "      <td>0.458167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.619094</td>\n",
       "      <td>0.435600</td>\n",
       "      <td>0.248237</td>\n",
       "      <td>0.847587</td>\n",
       "      <td>0.625458</td>\n",
       "      <td>0.405666</td>\n",
       "      <td>0.596423</td>\n",
       "      <td>0.477577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.332707</td>\n",
       "      <td>0.034260</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.498396</td>\n",
       "      <td>0.038853</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.676572</td>\n",
       "      <td>0.356131</td>\n",
       "      <td>0.313236</td>\n",
       "      <td>0.737459</td>\n",
       "      <td>0.541497</td>\n",
       "      <td>0.465692</td>\n",
       "      <td>0.737327</td>\n",
       "      <td>0.304262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.831633</td>\n",
       "      <td>0.185968</td>\n",
       "      <td>0.589493</td>\n",
       "      <td>0.455977</td>\n",
       "      <td>0.681149</td>\n",
       "      <td>0.388190</td>\n",
       "      <td>0.867030</td>\n",
       "      <td>0.171784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.743837</td>\n",
       "      <td>0.261029</td>\n",
       "      <td>0.475650</td>\n",
       "      <td>0.507818</td>\n",
       "      <td>0.626452</td>\n",
       "      <td>0.378426</td>\n",
       "      <td>0.761027</td>\n",
       "      <td>0.300291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.512475</td>\n",
       "      <td>0.263456</td>\n",
       "      <td>0.235290</td>\n",
       "      <td>0.378940</td>\n",
       "      <td>0.616087</td>\n",
       "      <td>0.161668</td>\n",
       "      <td>0.274426</td>\n",
       "      <td>0.441948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.582551</td>\n",
       "      <td>0.441095</td>\n",
       "      <td>0.159407</td>\n",
       "      <td>0.870649</td>\n",
       "      <td>0.566053</td>\n",
       "      <td>0.409514</td>\n",
       "      <td>0.580456</td>\n",
       "      <td>0.469237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.347399</td>\n",
       "      <td>0.029663</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.515027</td>\n",
       "      <td>0.031934</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.629239</td>\n",
       "      <td>0.398259</td>\n",
       "      <td>0.236111</td>\n",
       "      <td>0.811552</td>\n",
       "      <td>0.540705</td>\n",
       "      <td>0.462845</td>\n",
       "      <td>0.686091</td>\n",
       "      <td>0.347271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.798654</td>\n",
       "      <td>0.230223</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>0.457387</td>\n",
       "      <td>0.730557</td>\n",
       "      <td>0.291162</td>\n",
       "      <td>0.825653</td>\n",
       "      <td>0.221960</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>0.534203</td>\n",
       "      <td>0.224473</td>\n",
       "      <td>0.123282</td>\n",
       "      <td>0.320532</td>\n",
       "      <td>0.478875</td>\n",
       "      <td>0.222165</td>\n",
       "      <td>0.448268</td>\n",
       "      <td>0.368171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.690987</td>\n",
       "      <td>0.133617</td>\n",
       "      <td>0.406752</td>\n",
       "      <td>0.228141</td>\n",
       "      <td>0.593253</td>\n",
       "      <td>0.245654</td>\n",
       "      <td>0.707625</td>\n",
       "      <td>0.191443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.652293</td>\n",
       "      <td>0.118661</td>\n",
       "      <td>0.055932</td>\n",
       "      <td>0.143138</td>\n",
       "      <td>0.147740</td>\n",
       "      <td>0.279748</td>\n",
       "      <td>0.708965</td>\n",
       "      <td>0.261083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.659509</td>\n",
       "      <td>0.120594</td>\n",
       "      <td>0.070758</td>\n",
       "      <td>0.169168</td>\n",
       "      <td>0.142185</td>\n",
       "      <td>0.272733</td>\n",
       "      <td>0.718783</td>\n",
       "      <td>0.263675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.642308</td>\n",
       "      <td>0.140824</td>\n",
       "      <td>0.115363</td>\n",
       "      <td>0.199119</td>\n",
       "      <td>0.265339</td>\n",
       "      <td>0.270097</td>\n",
       "      <td>0.671128</td>\n",
       "      <td>0.292840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.601550</td>\n",
       "      <td>0.188000</td>\n",
       "      <td>0.146671</td>\n",
       "      <td>0.328337</td>\n",
       "      <td>0.494427</td>\n",
       "      <td>0.185673</td>\n",
       "      <td>0.511122</td>\n",
       "      <td>0.373816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.572162</td>\n",
       "      <td>0.175765</td>\n",
       "      <td>0.133173</td>\n",
       "      <td>0.266208</td>\n",
       "      <td>0.474642</td>\n",
       "      <td>0.111566</td>\n",
       "      <td>0.587623</td>\n",
       "      <td>0.264917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.685992</td>\n",
       "      <td>0.031916</td>\n",
       "      <td>0.289639</td>\n",
       "      <td>0.057168</td>\n",
       "      <td>0.515862</td>\n",
       "      <td>0.043711</td>\n",
       "      <td>0.766071</td>\n",
       "      <td>0.033024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.733157</td>\n",
       "      <td>0.063893</td>\n",
       "      <td>0.384801</td>\n",
       "      <td>0.158147</td>\n",
       "      <td>0.529422</td>\n",
       "      <td>0.171525</td>\n",
       "      <td>0.808576</td>\n",
       "      <td>0.043771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.722392</td>\n",
       "      <td>0.060492</td>\n",
       "      <td>0.344682</td>\n",
       "      <td>0.153393</td>\n",
       "      <td>0.487063</td>\n",
       "      <td>0.165925</td>\n",
       "      <td>0.804814</td>\n",
       "      <td>0.043671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.624887</td>\n",
       "      <td>0.167011</td>\n",
       "      <td>0.232325</td>\n",
       "      <td>0.249973</td>\n",
       "      <td>0.486488</td>\n",
       "      <td>0.165714</td>\n",
       "      <td>0.639575</td>\n",
       "      <td>0.263343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.537218</td>\n",
       "      <td>0.166822</td>\n",
       "      <td>0.125351</td>\n",
       "      <td>0.230447</td>\n",
       "      <td>0.499242</td>\n",
       "      <td>0.109001</td>\n",
       "      <td>0.515219</td>\n",
       "      <td>0.271171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.515500</td>\n",
       "      <td>0.170929</td>\n",
       "      <td>0.065055</td>\n",
       "      <td>0.286752</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.096645</td>\n",
       "      <td>0.518311</td>\n",
       "      <td>0.238964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.680087</td>\n",
       "      <td>0.022199</td>\n",
       "      <td>0.274577</td>\n",
       "      <td>0.044559</td>\n",
       "      <td>0.496846</td>\n",
       "      <td>0.028861</td>\n",
       "      <td>0.765276</td>\n",
       "      <td>0.018961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.635269</td>\n",
       "      <td>0.175882</td>\n",
       "      <td>0.274812</td>\n",
       "      <td>0.261311</td>\n",
       "      <td>0.527896</td>\n",
       "      <td>0.134382</td>\n",
       "      <td>0.656212</td>\n",
       "      <td>0.256356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.679942</td>\n",
       "      <td>0.148312</td>\n",
       "      <td>0.340061</td>\n",
       "      <td>0.218088</td>\n",
       "      <td>0.548881</td>\n",
       "      <td>0.110073</td>\n",
       "      <td>0.726276</td>\n",
       "      <td>0.198476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.630925</td>\n",
       "      <td>0.183350</td>\n",
       "      <td>0.258421</td>\n",
       "      <td>0.289231</td>\n",
       "      <td>0.519503</td>\n",
       "      <td>0.151941</td>\n",
       "      <td>0.656101</td>\n",
       "      <td>0.254168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.578445</td>\n",
       "      <td>0.195247</td>\n",
       "      <td>0.218753</td>\n",
       "      <td>0.294248</td>\n",
       "      <td>0.574979</td>\n",
       "      <td>0.111058</td>\n",
       "      <td>0.544844</td>\n",
       "      <td>0.296816</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           0.577051  0.252038  0.179078   \n",
       "                    GaussianNB              0.622766  0.276111  0.387714   \n",
       "                    LinearSVC               0.472725  0.438203  0.009850   \n",
       "                    LogisticRegression      0.386693  0.158639  0.000000   \n",
       "                    MLPClassifier           0.599950  0.276495  0.169645   \n",
       "                    RandomForestClassifier  0.663972  0.197592  0.225267   \n",
       "          100       Decision Tree           0.801900  0.218581  0.526294   \n",
       "                    GaussianNB              0.593144  0.283216  0.385194   \n",
       "                    LinearSVC               0.619094  0.435600  0.248237   \n",
       "                    LogisticRegression      0.332707  0.034260  0.000000   \n",
       "                    MLPClassifier           0.676572  0.356131  0.313236   \n",
       "                    RandomForestClassifier  0.831633  0.185968  0.589493   \n",
       "          200       Decision Tree           0.743837  0.261029  0.475650   \n",
       "                    GaussianNB              0.512475  0.263456  0.235290   \n",
       "                    LinearSVC               0.582551  0.441095  0.159407   \n",
       "                    LogisticRegression      0.347399  0.029663  0.000000   \n",
       "                    MLPClassifier           0.629239  0.398259  0.236111   \n",
       "                    RandomForestClassifier  0.798654  0.230223  0.578840   \n",
       "rdf2vec   10        Decision Tree           0.534203  0.224473  0.123282   \n",
       "                    GaussianNB              0.690987  0.133617  0.406752   \n",
       "                    LinearSVC               0.652293  0.118661  0.055932   \n",
       "                    LogisticRegression      0.659509  0.120594  0.070758   \n",
       "                    MLPClassifier           0.642308  0.140824  0.115363   \n",
       "                    RandomForestClassifier  0.601550  0.188000  0.146671   \n",
       "          100       Decision Tree           0.572162  0.175765  0.133173   \n",
       "                    GaussianNB              0.685992  0.031916  0.289639   \n",
       "                    LinearSVC               0.733157  0.063893  0.384801   \n",
       "                    LogisticRegression      0.722392  0.060492  0.344682   \n",
       "                    MLPClassifier           0.624887  0.167011  0.232325   \n",
       "                    RandomForestClassifier  0.537218  0.166822  0.125351   \n",
       "          200       Decision Tree           0.515500  0.170929  0.065055   \n",
       "                    GaussianNB              0.680087  0.022199  0.274577   \n",
       "                    LinearSVC               0.635269  0.175882  0.274812   \n",
       "                    LogisticRegression      0.679942  0.148312  0.340061   \n",
       "                    MLPClassifier           0.630925  0.183350  0.258421   \n",
       "                    RandomForestClassifier  0.578445  0.195247  0.218753   \n",
       "\n",
       "                                                          F1:A            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.397626  0.467030  0.359543   \n",
       "                    GaussianNB              0.375738  0.685924  0.228461   \n",
       "                    LinearSVC               0.862283  0.470314  0.414787   \n",
       "                    LogisticRegression      0.000000  0.539984  0.173280   \n",
       "                    MLPClassifier           0.551101  0.308873  0.414337   \n",
       "                    RandomForestClassifier  0.396973  0.498571  0.368476   \n",
       "          100       Decision Tree           0.491070  0.654591  0.385062   \n",
       "                    GaussianNB              0.381653  0.656348  0.180427   \n",
       "                    LinearSVC               0.847587  0.625458  0.405666   \n",
       "                    LogisticRegression      0.000000  0.498396  0.038853   \n",
       "                    MLPClassifier           0.737459  0.541497  0.465692   \n",
       "                    RandomForestClassifier  0.455977  0.681149  0.388190   \n",
       "          200       Decision Tree           0.507818  0.626452  0.378426   \n",
       "                    GaussianNB              0.378940  0.616087  0.161668   \n",
       "                    LinearSVC               0.870649  0.566053  0.409514   \n",
       "                    LogisticRegression      0.000000  0.515027  0.031934   \n",
       "                    MLPClassifier           0.811552  0.540705  0.462845   \n",
       "                    RandomForestClassifier  0.457387  0.730557  0.291162   \n",
       "rdf2vec   10        Decision Tree           0.320532  0.478875  0.222165   \n",
       "                    GaussianNB              0.228141  0.593253  0.245654   \n",
       "                    LinearSVC               0.143138  0.147740  0.279748   \n",
       "                    LogisticRegression      0.169168  0.142185  0.272733   \n",
       "                    MLPClassifier           0.199119  0.265339  0.270097   \n",
       "                    RandomForestClassifier  0.328337  0.494427  0.185673   \n",
       "          100       Decision Tree           0.266208  0.474642  0.111566   \n",
       "                    GaussianNB              0.057168  0.515862  0.043711   \n",
       "                    LinearSVC               0.158147  0.529422  0.171525   \n",
       "                    LogisticRegression      0.153393  0.487063  0.165925   \n",
       "                    MLPClassifier           0.249973  0.486488  0.165714   \n",
       "                    RandomForestClassifier  0.230447  0.499242  0.109001   \n",
       "          200       Decision Tree           0.286752  0.480000  0.096645   \n",
       "                    GaussianNB              0.044559  0.496846  0.028861   \n",
       "                    LinearSVC               0.261311  0.527896  0.134382   \n",
       "                    LogisticRegression      0.218088  0.548881  0.110073   \n",
       "                    MLPClassifier           0.289231  0.519503  0.151941   \n",
       "                    RandomForestClassifier  0.294248  0.574979  0.111058   \n",
       "\n",
       "                                                F1:B            \n",
       "                                                mean       std  \n",
       "EmbMethod InputSize Method                                      \n",
       "bertcode  10        Decision Tree           0.463225  0.403142  \n",
       "                    GaussianNB              0.440648  0.432474  \n",
       "                    LinearSVC               0.454224  0.466559  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.700317  0.231390  \n",
       "                    RandomForestClassifier  0.601780  0.352356  \n",
       "          100       Decision Tree           0.824114  0.259461  \n",
       "                    GaussianNB              0.444783  0.458167  \n",
       "                    LinearSVC               0.596423  0.477577  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.737327  0.304262  \n",
       "                    RandomForestClassifier  0.867030  0.171784  \n",
       "          200       Decision Tree           0.761027  0.300291  \n",
       "                    GaussianNB              0.274426  0.441948  \n",
       "                    LinearSVC               0.580456  0.469237  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.686091  0.347271  \n",
       "                    RandomForestClassifier  0.825653  0.221960  \n",
       "rdf2vec   10        Decision Tree           0.448268  0.368171  \n",
       "                    GaussianNB              0.707625  0.191443  \n",
       "                    LinearSVC               0.708965  0.261083  \n",
       "                    LogisticRegression      0.718783  0.263675  \n",
       "                    MLPClassifier           0.671128  0.292840  \n",
       "                    RandomForestClassifier  0.511122  0.373816  \n",
       "          100       Decision Tree           0.587623  0.264917  \n",
       "                    GaussianNB              0.766071  0.033024  \n",
       "                    LinearSVC               0.808576  0.043771  \n",
       "                    LogisticRegression      0.804814  0.043671  \n",
       "                    MLPClassifier           0.639575  0.263343  \n",
       "                    RandomForestClassifier  0.515219  0.271171  \n",
       "          200       Decision Tree           0.518311  0.238964  \n",
       "                    GaussianNB              0.765276  0.018961  \n",
       "                    LinearSVC               0.656212  0.256356  \n",
       "                    LogisticRegression      0.726276  0.198476  \n",
       "                    MLPClassifier           0.656101  0.254168  \n",
       "                    RandomForestClassifier  0.544844  0.296816  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train and test entities randomly split\n",
    "Table(BC, 'BC_OutputTemporary.csv', 1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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\">F1:A</th>\n",
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       "      <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",
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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>0.675211</td>\n",
       "      <td>0.216436</td>\n",
       "      <td>0.405456</td>\n",
       "      <td>0.366951</td>\n",
       "      <td>0.644882</td>\n",
       "      <td>0.267113</td>\n",
       "      <td>0.615498</td>\n",
       "      <td>0.312945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.599964</td>\n",
       "      <td>0.247060</td>\n",
       "      <td>0.334643</td>\n",
       "      <td>0.387103</td>\n",
       "      <td>0.694617</td>\n",
       "      <td>0.183410</td>\n",
       "      <td>0.375905</td>\n",
       "      <td>0.420926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.684054</td>\n",
       "      <td>0.290823</td>\n",
       "      <td>0.382693</td>\n",
       "      <td>0.587334</td>\n",
       "      <td>0.666403</td>\n",
       "      <td>0.350745</td>\n",
       "      <td>0.668343</td>\n",
       "      <td>0.283653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.498224</td>\n",
       "      <td>0.194070</td>\n",
       "      <td>0.083797</td>\n",
       "      <td>0.264990</td>\n",
       "      <td>0.641779</td>\n",
       "      <td>0.149845</td>\n",
       "      <td>0.091803</td>\n",
       "      <td>0.290307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.659562</td>\n",
       "      <td>0.168940</td>\n",
       "      <td>0.265328</td>\n",
       "      <td>0.353737</td>\n",
       "      <td>0.290857</td>\n",
       "      <td>0.362720</td>\n",
       "      <td>0.760191</td>\n",
       "      <td>0.140395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.767949</td>\n",
       "      <td>0.193718</td>\n",
       "      <td>0.545409</td>\n",
       "      <td>0.349107</td>\n",
       "      <td>0.731994</td>\n",
       "      <td>0.220430</td>\n",
       "      <td>0.740598</td>\n",
       "      <td>0.282695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.683299</td>\n",
       "      <td>0.168713</td>\n",
       "      <td>0.374693</td>\n",
       "      <td>0.344508</td>\n",
       "      <td>0.669442</td>\n",
       "      <td>0.213853</td>\n",
       "      <td>0.648134</td>\n",
       "      <td>0.266176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.683423</td>\n",
       "      <td>0.109741</td>\n",
       "      <td>0.448375</td>\n",
       "      <td>0.245034</td>\n",
       "      <td>0.751805</td>\n",
       "      <td>0.062476</td>\n",
       "      <td>0.530093</td>\n",
       "      <td>0.287541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.587500</td>\n",
       "      <td>0.343584</td>\n",
       "      <td>0.166774</td>\n",
       "      <td>0.696872</td>\n",
       "      <td>0.548173</td>\n",
       "      <td>0.377948</td>\n",
       "      <td>0.615643</td>\n",
       "      <td>0.325243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.469520</td>\n",
       "      <td>0.035593</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.638294</td>\n",
       "      <td>0.032919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.675232</td>\n",
       "      <td>0.150482</td>\n",
       "      <td>0.382490</td>\n",
       "      <td>0.339200</td>\n",
       "      <td>0.484806</td>\n",
       "      <td>0.420326</td>\n",
       "      <td>0.718708</td>\n",
       "      <td>0.083495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.686963</td>\n",
       "      <td>0.241533</td>\n",
       "      <td>0.394193</td>\n",
       "      <td>0.478305</td>\n",
       "      <td>0.710601</td>\n",
       "      <td>0.251171</td>\n",
       "      <td>0.596416</td>\n",
       "      <td>0.372225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.774282</td>\n",
       "      <td>0.157056</td>\n",
       "      <td>0.532384</td>\n",
       "      <td>0.356993</td>\n",
       "      <td>0.744323</td>\n",
       "      <td>0.258633</td>\n",
       "      <td>0.743309</td>\n",
       "      <td>0.270800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.631763</td>\n",
       "      <td>0.159509</td>\n",
       "      <td>0.333212</td>\n",
       "      <td>0.314442</td>\n",
       "      <td>0.729494</td>\n",
       "      <td>0.090097</td>\n",
       "      <td>0.383404</td>\n",
       "      <td>0.373893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.724890</td>\n",
       "      <td>0.275513</td>\n",
       "      <td>0.450126</td>\n",
       "      <td>0.555432</td>\n",
       "      <td>0.710740</td>\n",
       "      <td>0.280303</td>\n",
       "      <td>0.734914</td>\n",
       "      <td>0.276728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.476051</td>\n",
       "      <td>0.019843</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.644809</td>\n",
       "      <td>0.018471</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.740743</td>\n",
       "      <td>0.118415</td>\n",
       "      <td>0.521482</td>\n",
       "      <td>0.276688</td>\n",
       "      <td>0.657982</td>\n",
       "      <td>0.347375</td>\n",
       "      <td>0.744151</td>\n",
       "      <td>0.042275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.770841</td>\n",
       "      <td>0.215494</td>\n",
       "      <td>0.537041</td>\n",
       "      <td>0.471607</td>\n",
       "      <td>0.763665</td>\n",
       "      <td>0.276013</td>\n",
       "      <td>0.735040</td>\n",
       "      <td>0.291753</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>0.521368</td>\n",
       "      <td>0.158236</td>\n",
       "      <td>0.035137</td>\n",
       "      <td>0.268449</td>\n",
       "      <td>0.244091</td>\n",
       "      <td>0.311676</td>\n",
       "      <td>0.572452</td>\n",
       "      <td>0.175176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.547842</td>\n",
       "      <td>0.121535</td>\n",
       "      <td>0.078666</td>\n",
       "      <td>0.251524</td>\n",
       "      <td>0.367316</td>\n",
       "      <td>0.322288</td>\n",
       "      <td>0.541121</td>\n",
       "      <td>0.196124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.616405</td>\n",
       "      <td>0.145448</td>\n",
       "      <td>0.282352</td>\n",
       "      <td>0.212142</td>\n",
       "      <td>0.621008</td>\n",
       "      <td>0.237027</td>\n",
       "      <td>0.441853</td>\n",
       "      <td>0.275942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.658181</td>\n",
       "      <td>0.124353</td>\n",
       "      <td>0.375546</td>\n",
       "      <td>0.180945</td>\n",
       "      <td>0.527688</td>\n",
       "      <td>0.304331</td>\n",
       "      <td>0.644751</td>\n",
       "      <td>0.115382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.643995</td>\n",
       "      <td>0.113988</td>\n",
       "      <td>0.233854</td>\n",
       "      <td>0.250524</td>\n",
       "      <td>0.624688</td>\n",
       "      <td>0.284902</td>\n",
       "      <td>0.455634</td>\n",
       "      <td>0.267631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.659661</td>\n",
       "      <td>0.086972</td>\n",
       "      <td>0.258381</td>\n",
       "      <td>0.220803</td>\n",
       "      <td>0.530185</td>\n",
       "      <td>0.328926</td>\n",
       "      <td>0.562086</td>\n",
       "      <td>0.279058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.525259</td>\n",
       "      <td>0.088481</td>\n",
       "      <td>0.052372</td>\n",
       "      <td>0.176599</td>\n",
       "      <td>0.511250</td>\n",
       "      <td>0.101356</td>\n",
       "      <td>0.532132</td>\n",
       "      <td>0.099231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.528554</td>\n",
       "      <td>0.037094</td>\n",
       "      <td>0.069034</td>\n",
       "      <td>0.070502</td>\n",
       "      <td>0.550588</td>\n",
       "      <td>0.044189</td>\n",
       "      <td>0.501718</td>\n",
       "      <td>0.042570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.676048</td>\n",
       "      <td>0.095164</td>\n",
       "      <td>0.378331</td>\n",
       "      <td>0.181741</td>\n",
       "      <td>0.615667</td>\n",
       "      <td>0.146844</td>\n",
       "      <td>0.684490</td>\n",
       "      <td>0.152926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.663943</td>\n",
       "      <td>0.067331</td>\n",
       "      <td>0.348803</td>\n",
       "      <td>0.130162</td>\n",
       "      <td>0.587081</td>\n",
       "      <td>0.123376</td>\n",
       "      <td>0.693799</td>\n",
       "      <td>0.110009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.698129</td>\n",
       "      <td>0.121591</td>\n",
       "      <td>0.420556</td>\n",
       "      <td>0.226301</td>\n",
       "      <td>0.658683</td>\n",
       "      <td>0.156851</td>\n",
       "      <td>0.695839</td>\n",
       "      <td>0.172674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.594515</td>\n",
       "      <td>0.084589</td>\n",
       "      <td>0.188959</td>\n",
       "      <td>0.164218</td>\n",
       "      <td>0.472693</td>\n",
       "      <td>0.129480</td>\n",
       "      <td>0.659460</td>\n",
       "      <td>0.090563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.566372</td>\n",
       "      <td>0.063330</td>\n",
       "      <td>0.122542</td>\n",
       "      <td>0.128121</td>\n",
       "      <td>0.523272</td>\n",
       "      <td>0.106691</td>\n",
       "      <td>0.577205</td>\n",
       "      <td>0.112919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.549235</td>\n",
       "      <td>0.021922</td>\n",
       "      <td>0.117638</td>\n",
       "      <td>0.042326</td>\n",
       "      <td>0.580365</td>\n",
       "      <td>0.033714</td>\n",
       "      <td>0.511080</td>\n",
       "      <td>0.026111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.675659</td>\n",
       "      <td>0.097787</td>\n",
       "      <td>0.358131</td>\n",
       "      <td>0.189392</td>\n",
       "      <td>0.595306</td>\n",
       "      <td>0.148783</td>\n",
       "      <td>0.725929</td>\n",
       "      <td>0.073307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.694450</td>\n",
       "      <td>0.095393</td>\n",
       "      <td>0.394703</td>\n",
       "      <td>0.188672</td>\n",
       "      <td>0.618964</td>\n",
       "      <td>0.135487</td>\n",
       "      <td>0.742757</td>\n",
       "      <td>0.071608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.693254</td>\n",
       "      <td>0.097000</td>\n",
       "      <td>0.393050</td>\n",
       "      <td>0.186904</td>\n",
       "      <td>0.620009</td>\n",
       "      <td>0.154680</td>\n",
       "      <td>0.739038</td>\n",
       "      <td>0.066696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.638065</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>0.287916</td>\n",
       "      <td>0.116527</td>\n",
       "      <td>0.585629</td>\n",
       "      <td>0.091809</td>\n",
       "      <td>0.668067</td>\n",
       "      <td>0.069636</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           0.675211  0.216436  0.405456   \n",
       "                    GaussianNB              0.599964  0.247060  0.334643   \n",
       "                    LinearSVC               0.684054  0.290823  0.382693   \n",
       "                    LogisticRegression      0.498224  0.194070  0.083797   \n",
       "                    MLPClassifier           0.659562  0.168940  0.265328   \n",
       "                    RandomForestClassifier  0.767949  0.193718  0.545409   \n",
       "          100       Decision Tree           0.683299  0.168713  0.374693   \n",
       "                    GaussianNB              0.683423  0.109741  0.448375   \n",
       "                    LinearSVC               0.587500  0.343584  0.166774   \n",
       "                    LogisticRegression      0.469520  0.035593  0.000000   \n",
       "                    MLPClassifier           0.675232  0.150482  0.382490   \n",
       "                    RandomForestClassifier  0.686963  0.241533  0.394193   \n",
       "          200       Decision Tree           0.774282  0.157056  0.532384   \n",
       "                    GaussianNB              0.631763  0.159509  0.333212   \n",
       "                    LinearSVC               0.724890  0.275513  0.450126   \n",
       "                    LogisticRegression      0.476051  0.019843  0.000000   \n",
       "                    MLPClassifier           0.740743  0.118415  0.521482   \n",
       "                    RandomForestClassifier  0.770841  0.215494  0.537041   \n",
       "rdf2vec   10        Decision Tree           0.521368  0.158236  0.035137   \n",
       "                    GaussianNB              0.547842  0.121535  0.078666   \n",
       "                    LinearSVC               0.616405  0.145448  0.282352   \n",
       "                    LogisticRegression      0.658181  0.124353  0.375546   \n",
       "                    MLPClassifier           0.643995  0.113988  0.233854   \n",
       "                    RandomForestClassifier  0.659661  0.086972  0.258381   \n",
       "          100       Decision Tree           0.525259  0.088481  0.052372   \n",
       "                    GaussianNB              0.528554  0.037094  0.069034   \n",
       "                    LinearSVC               0.676048  0.095164  0.378331   \n",
       "                    LogisticRegression      0.663943  0.067331  0.348803   \n",
       "                    MLPClassifier           0.698129  0.121591  0.420556   \n",
       "                    RandomForestClassifier  0.594515  0.084589  0.188959   \n",
       "          200       Decision Tree           0.566372  0.063330  0.122542   \n",
       "                    GaussianNB              0.549235  0.021922  0.117638   \n",
       "                    LinearSVC               0.675659  0.097787  0.358131   \n",
       "                    LogisticRegression      0.694450  0.095393  0.394703   \n",
       "                    MLPClassifier           0.693254  0.097000  0.393050   \n",
       "                    RandomForestClassifier  0.638065  0.058910  0.287916   \n",
       "\n",
       "                                                          F1:A            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.366951  0.644882  0.267113   \n",
       "                    GaussianNB              0.387103  0.694617  0.183410   \n",
       "                    LinearSVC               0.587334  0.666403  0.350745   \n",
       "                    LogisticRegression      0.264990  0.641779  0.149845   \n",
       "                    MLPClassifier           0.353737  0.290857  0.362720   \n",
       "                    RandomForestClassifier  0.349107  0.731994  0.220430   \n",
       "          100       Decision Tree           0.344508  0.669442  0.213853   \n",
       "                    GaussianNB              0.245034  0.751805  0.062476   \n",
       "                    LinearSVC               0.696872  0.548173  0.377948   \n",
       "                    LogisticRegression      0.000000  0.638294  0.032919   \n",
       "                    MLPClassifier           0.339200  0.484806  0.420326   \n",
       "                    RandomForestClassifier  0.478305  0.710601  0.251171   \n",
       "          200       Decision Tree           0.356993  0.744323  0.258633   \n",
       "                    GaussianNB              0.314442  0.729494  0.090097   \n",
       "                    LinearSVC               0.555432  0.710740  0.280303   \n",
       "                    LogisticRegression      0.000000  0.644809  0.018471   \n",
       "                    MLPClassifier           0.276688  0.657982  0.347375   \n",
       "                    RandomForestClassifier  0.471607  0.763665  0.276013   \n",
       "rdf2vec   10        Decision Tree           0.268449  0.244091  0.311676   \n",
       "                    GaussianNB              0.251524  0.367316  0.322288   \n",
       "                    LinearSVC               0.212142  0.621008  0.237027   \n",
       "                    LogisticRegression      0.180945  0.527688  0.304331   \n",
       "                    MLPClassifier           0.250524  0.624688  0.284902   \n",
       "                    RandomForestClassifier  0.220803  0.530185  0.328926   \n",
       "          100       Decision Tree           0.176599  0.511250  0.101356   \n",
       "                    GaussianNB              0.070502  0.550588  0.044189   \n",
       "                    LinearSVC               0.181741  0.615667  0.146844   \n",
       "                    LogisticRegression      0.130162  0.587081  0.123376   \n",
       "                    MLPClassifier           0.226301  0.658683  0.156851   \n",
       "                    RandomForestClassifier  0.164218  0.472693  0.129480   \n",
       "          200       Decision Tree           0.128121  0.523272  0.106691   \n",
       "                    GaussianNB              0.042326  0.580365  0.033714   \n",
       "                    LinearSVC               0.189392  0.595306  0.148783   \n",
       "                    LogisticRegression      0.188672  0.618964  0.135487   \n",
       "                    MLPClassifier           0.186904  0.620009  0.154680   \n",
       "                    RandomForestClassifier  0.116527  0.585629  0.091809   \n",
       "\n",
       "                                                F1:B            \n",
       "                                                mean       std  \n",
       "EmbMethod InputSize Method                                      \n",
       "bertcode  10        Decision Tree           0.615498  0.312945  \n",
       "                    GaussianNB              0.375905  0.420926  \n",
       "                    LinearSVC               0.668343  0.283653  \n",
       "                    LogisticRegression      0.091803  0.290307  \n",
       "                    MLPClassifier           0.760191  0.140395  \n",
       "                    RandomForestClassifier  0.740598  0.282695  \n",
       "          100       Decision Tree           0.648134  0.266176  \n",
       "                    GaussianNB              0.530093  0.287541  \n",
       "                    LinearSVC               0.615643  0.325243  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.718708  0.083495  \n",
       "                    RandomForestClassifier  0.596416  0.372225  \n",
       "          200       Decision Tree           0.743309  0.270800  \n",
       "                    GaussianNB              0.383404  0.373893  \n",
       "                    LinearSVC               0.734914  0.276728  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.744151  0.042275  \n",
       "                    RandomForestClassifier  0.735040  0.291753  \n",
       "rdf2vec   10        Decision Tree           0.572452  0.175176  \n",
       "                    GaussianNB              0.541121  0.196124  \n",
       "                    LinearSVC               0.441853  0.275942  \n",
       "                    LogisticRegression      0.644751  0.115382  \n",
       "                    MLPClassifier           0.455634  0.267631  \n",
       "                    RandomForestClassifier  0.562086  0.279058  \n",
       "          100       Decision Tree           0.532132  0.099231  \n",
       "                    GaussianNB              0.501718  0.042570  \n",
       "                    LinearSVC               0.684490  0.152926  \n",
       "                    LogisticRegression      0.693799  0.110009  \n",
       "                    MLPClassifier           0.695839  0.172674  \n",
       "                    RandomForestClassifier  0.659460  0.090563  \n",
       "          200       Decision Tree           0.577205  0.112919  \n",
       "                    GaussianNB              0.511080  0.026111  \n",
       "                    LinearSVC               0.725929  0.073307  \n",
       "                    LogisticRegression      0.742757  0.071608  \n",
       "                    MLPClassifier           0.739038  0.066696  \n",
       "                    RandomForestClassifier  0.668067  0.069636  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table(BC, 'BC_VisualisationPreparation.csv', 1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Regime 2\n",
    "\n",
    "Train and test do not share notebooks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th colspan=\"2\" halign=\"left\">Accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Matthews</th>\n",
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       "      <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",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th rowspan=\"18\" valign=\"top\">bertcode</th>\n",
       "      <th rowspan=\"6\" valign=\"top\">10</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.657277</td>\n",
       "      <td>0.278830</td>\n",
       "      <td>0.207870</td>\n",
       "      <td>0.382163</td>\n",
       "      <td>0.461404</td>\n",
       "      <td>0.430373</td>\n",
       "      <td>0.668945</td>\n",
       "      <td>0.248973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.741040</td>\n",
       "      <td>0.261107</td>\n",
       "      <td>0.361188</td>\n",
       "      <td>0.400612</td>\n",
       "      <td>0.761049</td>\n",
       "      <td>0.213134</td>\n",
       "      <td>0.489012</td>\n",
       "      <td>0.469107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.645625</td>\n",
       "      <td>0.355712</td>\n",
       "      <td>0.137641</td>\n",
       "      <td>0.642979</td>\n",
       "      <td>0.547884</td>\n",
       "      <td>0.430812</td>\n",
       "      <td>0.551750</td>\n",
       "      <td>0.392228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.588706</td>\n",
       "      <td>0.277454</td>\n",
       "      <td>0.126008</td>\n",
       "      <td>0.265665</td>\n",
       "      <td>0.686137</td>\n",
       "      <td>0.217984</td>\n",
       "      <td>0.181086</td>\n",
       "      <td>0.359336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.583337</td>\n",
       "      <td>0.339475</td>\n",
       "      <td>0.252823</td>\n",
       "      <td>0.547273</td>\n",
       "      <td>0.424759</td>\n",
       "      <td>0.384858</td>\n",
       "      <td>0.670515</td>\n",
       "      <td>0.322852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.735126</td>\n",
       "      <td>0.260813</td>\n",
       "      <td>0.298031</td>\n",
       "      <td>0.439431</td>\n",
       "      <td>0.679612</td>\n",
       "      <td>0.331009</td>\n",
       "      <td>0.588356</td>\n",
       "      <td>0.372940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.668346</td>\n",
       "      <td>0.190340</td>\n",
       "      <td>0.257420</td>\n",
       "      <td>0.425264</td>\n",
       "      <td>0.431519</td>\n",
       "      <td>0.368311</td>\n",
       "      <td>0.748626</td>\n",
       "      <td>0.150604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.586171</td>\n",
       "      <td>0.237804</td>\n",
       "      <td>0.347711</td>\n",
       "      <td>0.328861</td>\n",
       "      <td>0.640532</td>\n",
       "      <td>0.137758</td>\n",
       "      <td>0.424698</td>\n",
       "      <td>0.422098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.395752</td>\n",
       "      <td>0.409383</td>\n",
       "      <td>-0.208316</td>\n",
       "      <td>0.806010</td>\n",
       "      <td>0.394394</td>\n",
       "      <td>0.374522</td>\n",
       "      <td>0.383287</td>\n",
       "      <td>0.440457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.376000</td>\n",
       "      <td>0.068855</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.543234</td>\n",
       "      <td>0.072801</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.507187</td>\n",
       "      <td>0.322200</td>\n",
       "      <td>-0.053913</td>\n",
       "      <td>0.672987</td>\n",
       "      <td>0.293730</td>\n",
       "      <td>0.393672</td>\n",
       "      <td>0.609174</td>\n",
       "      <td>0.281792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.682601</td>\n",
       "      <td>0.237283</td>\n",
       "      <td>0.278205</td>\n",
       "      <td>0.507707</td>\n",
       "      <td>0.516068</td>\n",
       "      <td>0.376402</td>\n",
       "      <td>0.713575</td>\n",
       "      <td>0.268087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.658982</td>\n",
       "      <td>0.180402</td>\n",
       "      <td>0.252300</td>\n",
       "      <td>0.340566</td>\n",
       "      <td>0.394295</td>\n",
       "      <td>0.341254</td>\n",
       "      <td>0.723078</td>\n",
       "      <td>0.200949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.432876</td>\n",
       "      <td>0.192634</td>\n",
       "      <td>0.130758</td>\n",
       "      <td>0.267594</td>\n",
       "      <td>0.557488</td>\n",
       "      <td>0.106121</td>\n",
       "      <td>0.157872</td>\n",
       "      <td>0.334062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.329513</td>\n",
       "      <td>0.384261</td>\n",
       "      <td>-0.332695</td>\n",
       "      <td>0.747868</td>\n",
       "      <td>0.334469</td>\n",
       "      <td>0.350175</td>\n",
       "      <td>0.310264</td>\n",
       "      <td>0.414841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.339239</td>\n",
       "      <td>0.043889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.505158</td>\n",
       "      <td>0.049433</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.388532</td>\n",
       "      <td>0.352593</td>\n",
       "      <td>-0.248319</td>\n",
       "      <td>0.720774</td>\n",
       "      <td>0.263204</td>\n",
       "      <td>0.407621</td>\n",
       "      <td>0.473695</td>\n",
       "      <td>0.313537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.707063</td>\n",
       "      <td>0.190379</td>\n",
       "      <td>0.293424</td>\n",
       "      <td>0.427143</td>\n",
       "      <td>0.480304</td>\n",
       "      <td>0.347145</td>\n",
       "      <td>0.776159</td>\n",
       "      <td>0.175456</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>0.587671</td>\n",
       "      <td>0.228369</td>\n",
       "      <td>0.083074</td>\n",
       "      <td>0.249813</td>\n",
       "      <td>0.354532</td>\n",
       "      <td>0.301522</td>\n",
       "      <td>0.502143</td>\n",
       "      <td>0.368355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.490339</td>\n",
       "      <td>0.256558</td>\n",
       "      <td>0.163302</td>\n",
       "      <td>0.204350</td>\n",
       "      <td>0.530520</td>\n",
       "      <td>0.245106</td>\n",
       "      <td>0.313664</td>\n",
       "      <td>0.331305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.627078</td>\n",
       "      <td>0.227112</td>\n",
       "      <td>0.099588</td>\n",
       "      <td>0.169544</td>\n",
       "      <td>0.129091</td>\n",
       "      <td>0.188090</td>\n",
       "      <td>0.733841</td>\n",
       "      <td>0.199968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.609751</td>\n",
       "      <td>0.237436</td>\n",
       "      <td>-0.003032</td>\n",
       "      <td>0.036881</td>\n",
       "      <td>0.003175</td>\n",
       "      <td>0.010039</td>\n",
       "      <td>0.730115</td>\n",
       "      <td>0.201192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.674990</td>\n",
       "      <td>0.183749</td>\n",
       "      <td>0.086271</td>\n",
       "      <td>0.198157</td>\n",
       "      <td>0.303115</td>\n",
       "      <td>0.325691</td>\n",
       "      <td>0.620632</td>\n",
       "      <td>0.370041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.522366</td>\n",
       "      <td>0.233084</td>\n",
       "      <td>0.041937</td>\n",
       "      <td>0.254396</td>\n",
       "      <td>0.494497</td>\n",
       "      <td>0.291751</td>\n",
       "      <td>0.284650</td>\n",
       "      <td>0.370640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.484477</td>\n",
       "      <td>0.155729</td>\n",
       "      <td>0.038990</td>\n",
       "      <td>0.167542</td>\n",
       "      <td>0.403994</td>\n",
       "      <td>0.068494</td>\n",
       "      <td>0.500745</td>\n",
       "      <td>0.242501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.691150</td>\n",
       "      <td>0.063054</td>\n",
       "      <td>0.269111</td>\n",
       "      <td>0.087385</td>\n",
       "      <td>0.468684</td>\n",
       "      <td>0.051773</td>\n",
       "      <td>0.777618</td>\n",
       "      <td>0.068934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.686346</td>\n",
       "      <td>0.055618</td>\n",
       "      <td>0.267512</td>\n",
       "      <td>0.123168</td>\n",
       "      <td>0.411061</td>\n",
       "      <td>0.170941</td>\n",
       "      <td>0.777942</td>\n",
       "      <td>0.052921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.695263</td>\n",
       "      <td>0.080808</td>\n",
       "      <td>0.253623</td>\n",
       "      <td>0.146112</td>\n",
       "      <td>0.348346</td>\n",
       "      <td>0.164959</td>\n",
       "      <td>0.798258</td>\n",
       "      <td>0.061591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.542268</td>\n",
       "      <td>0.162506</td>\n",
       "      <td>0.153351</td>\n",
       "      <td>0.151191</td>\n",
       "      <td>0.402036</td>\n",
       "      <td>0.134220</td>\n",
       "      <td>0.564722</td>\n",
       "      <td>0.260320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.561082</td>\n",
       "      <td>0.171484</td>\n",
       "      <td>0.154384</td>\n",
       "      <td>0.155006</td>\n",
       "      <td>0.445238</td>\n",
       "      <td>0.108369</td>\n",
       "      <td>0.564194</td>\n",
       "      <td>0.281145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.529637</td>\n",
       "      <td>0.172165</td>\n",
       "      <td>0.036350</td>\n",
       "      <td>0.236561</td>\n",
       "      <td>0.404628</td>\n",
       "      <td>0.098662</td>\n",
       "      <td>0.561237</td>\n",
       "      <td>0.251692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.686489</td>\n",
       "      <td>0.029965</td>\n",
       "      <td>0.268121</td>\n",
       "      <td>0.051426</td>\n",
       "      <td>0.475765</td>\n",
       "      <td>0.043482</td>\n",
       "      <td>0.774976</td>\n",
       "      <td>0.029254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.704932</td>\n",
       "      <td>0.062050</td>\n",
       "      <td>0.301368</td>\n",
       "      <td>0.158421</td>\n",
       "      <td>0.450604</td>\n",
       "      <td>0.149284</td>\n",
       "      <td>0.795579</td>\n",
       "      <td>0.041173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.693467</td>\n",
       "      <td>0.077561</td>\n",
       "      <td>0.257898</td>\n",
       "      <td>0.198170</td>\n",
       "      <td>0.399454</td>\n",
       "      <td>0.183358</td>\n",
       "      <td>0.792446</td>\n",
       "      <td>0.050402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.659024</td>\n",
       "      <td>0.127386</td>\n",
       "      <td>0.260196</td>\n",
       "      <td>0.183492</td>\n",
       "      <td>0.468810</td>\n",
       "      <td>0.122586</td>\n",
       "      <td>0.721250</td>\n",
       "      <td>0.183244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.682264</td>\n",
       "      <td>0.075649</td>\n",
       "      <td>0.311353</td>\n",
       "      <td>0.145701</td>\n",
       "      <td>0.527934</td>\n",
       "      <td>0.119908</td>\n",
       "      <td>0.751287</td>\n",
       "      <td>0.080017</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           0.657277  0.278830  0.207870   \n",
       "                    GaussianNB              0.741040  0.261107  0.361188   \n",
       "                    LinearSVC               0.645625  0.355712  0.137641   \n",
       "                    LogisticRegression      0.588706  0.277454  0.126008   \n",
       "                    MLPClassifier           0.583337  0.339475  0.252823   \n",
       "                    RandomForestClassifier  0.735126  0.260813  0.298031   \n",
       "          100       Decision Tree           0.668346  0.190340  0.257420   \n",
       "                    GaussianNB              0.586171  0.237804  0.347711   \n",
       "                    LinearSVC               0.395752  0.409383 -0.208316   \n",
       "                    LogisticRegression      0.376000  0.068855  0.000000   \n",
       "                    MLPClassifier           0.507187  0.322200 -0.053913   \n",
       "                    RandomForestClassifier  0.682601  0.237283  0.278205   \n",
       "          200       Decision Tree           0.658982  0.180402  0.252300   \n",
       "                    GaussianNB              0.432876  0.192634  0.130758   \n",
       "                    LinearSVC               0.329513  0.384261 -0.332695   \n",
       "                    LogisticRegression      0.339239  0.043889  0.000000   \n",
       "                    MLPClassifier           0.388532  0.352593 -0.248319   \n",
       "                    RandomForestClassifier  0.707063  0.190379  0.293424   \n",
       "rdf2vec   10        Decision Tree           0.587671  0.228369  0.083074   \n",
       "                    GaussianNB              0.490339  0.256558  0.163302   \n",
       "                    LinearSVC               0.627078  0.227112  0.099588   \n",
       "                    LogisticRegression      0.609751  0.237436 -0.003032   \n",
       "                    MLPClassifier           0.674990  0.183749  0.086271   \n",
       "                    RandomForestClassifier  0.522366  0.233084  0.041937   \n",
       "          100       Decision Tree           0.484477  0.155729  0.038990   \n",
       "                    GaussianNB              0.691150  0.063054  0.269111   \n",
       "                    LinearSVC               0.686346  0.055618  0.267512   \n",
       "                    LogisticRegression      0.695263  0.080808  0.253623   \n",
       "                    MLPClassifier           0.542268  0.162506  0.153351   \n",
       "                    RandomForestClassifier  0.561082  0.171484  0.154384   \n",
       "          200       Decision Tree           0.529637  0.172165  0.036350   \n",
       "                    GaussianNB              0.686489  0.029965  0.268121   \n",
       "                    LinearSVC               0.704932  0.062050  0.301368   \n",
       "                    LogisticRegression      0.693467  0.077561  0.257898   \n",
       "                    MLPClassifier           0.659024  0.127386  0.260196   \n",
       "                    RandomForestClassifier  0.682264  0.075649  0.311353   \n",
       "\n",
       "                                                          F1:A            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.382163  0.461404  0.430373   \n",
       "                    GaussianNB              0.400612  0.761049  0.213134   \n",
       "                    LinearSVC               0.642979  0.547884  0.430812   \n",
       "                    LogisticRegression      0.265665  0.686137  0.217984   \n",
       "                    MLPClassifier           0.547273  0.424759  0.384858   \n",
       "                    RandomForestClassifier  0.439431  0.679612  0.331009   \n",
       "          100       Decision Tree           0.425264  0.431519  0.368311   \n",
       "                    GaussianNB              0.328861  0.640532  0.137758   \n",
       "                    LinearSVC               0.806010  0.394394  0.374522   \n",
       "                    LogisticRegression      0.000000  0.543234  0.072801   \n",
       "                    MLPClassifier           0.672987  0.293730  0.393672   \n",
       "                    RandomForestClassifier  0.507707  0.516068  0.376402   \n",
       "          200       Decision Tree           0.340566  0.394295  0.341254   \n",
       "                    GaussianNB              0.267594  0.557488  0.106121   \n",
       "                    LinearSVC               0.747868  0.334469  0.350175   \n",
       "                    LogisticRegression      0.000000  0.505158  0.049433   \n",
       "                    MLPClassifier           0.720774  0.263204  0.407621   \n",
       "                    RandomForestClassifier  0.427143  0.480304  0.347145   \n",
       "rdf2vec   10        Decision Tree           0.249813  0.354532  0.301522   \n",
       "                    GaussianNB              0.204350  0.530520  0.245106   \n",
       "                    LinearSVC               0.169544  0.129091  0.188090   \n",
       "                    LogisticRegression      0.036881  0.003175  0.010039   \n",
       "                    MLPClassifier           0.198157  0.303115  0.325691   \n",
       "                    RandomForestClassifier  0.254396  0.494497  0.291751   \n",
       "          100       Decision Tree           0.167542  0.403994  0.068494   \n",
       "                    GaussianNB              0.087385  0.468684  0.051773   \n",
       "                    LinearSVC               0.123168  0.411061  0.170941   \n",
       "                    LogisticRegression      0.146112  0.348346  0.164959   \n",
       "                    MLPClassifier           0.151191  0.402036  0.134220   \n",
       "                    RandomForestClassifier  0.155006  0.445238  0.108369   \n",
       "          200       Decision Tree           0.236561  0.404628  0.098662   \n",
       "                    GaussianNB              0.051426  0.475765  0.043482   \n",
       "                    LinearSVC               0.158421  0.450604  0.149284   \n",
       "                    LogisticRegression      0.198170  0.399454  0.183358   \n",
       "                    MLPClassifier           0.183492  0.468810  0.122586   \n",
       "                    RandomForestClassifier  0.145701  0.527934  0.119908   \n",
       "\n",
       "                                                F1:B            \n",
       "                                                mean       std  \n",
       "EmbMethod InputSize Method                                      \n",
       "bertcode  10        Decision Tree           0.668945  0.248973  \n",
       "                    GaussianNB              0.489012  0.469107  \n",
       "                    LinearSVC               0.551750  0.392228  \n",
       "                    LogisticRegression      0.181086  0.359336  \n",
       "                    MLPClassifier           0.670515  0.322852  \n",
       "                    RandomForestClassifier  0.588356  0.372940  \n",
       "          100       Decision Tree           0.748626  0.150604  \n",
       "                    GaussianNB              0.424698  0.422098  \n",
       "                    LinearSVC               0.383287  0.440457  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.609174  0.281792  \n",
       "                    RandomForestClassifier  0.713575  0.268087  \n",
       "          200       Decision Tree           0.723078  0.200949  \n",
       "                    GaussianNB              0.157872  0.334062  \n",
       "                    LinearSVC               0.310264  0.414841  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.473695  0.313537  \n",
       "                    RandomForestClassifier  0.776159  0.175456  \n",
       "rdf2vec   10        Decision Tree           0.502143  0.368355  \n",
       "                    GaussianNB              0.313664  0.331305  \n",
       "                    LinearSVC               0.733841  0.199968  \n",
       "                    LogisticRegression      0.730115  0.201192  \n",
       "                    MLPClassifier           0.620632  0.370041  \n",
       "                    RandomForestClassifier  0.284650  0.370640  \n",
       "          100       Decision Tree           0.500745  0.242501  \n",
       "                    GaussianNB              0.777618  0.068934  \n",
       "                    LinearSVC               0.777942  0.052921  \n",
       "                    LogisticRegression      0.798258  0.061591  \n",
       "                    MLPClassifier           0.564722  0.260320  \n",
       "                    RandomForestClassifier  0.564194  0.281145  \n",
       "          200       Decision Tree           0.561237  0.251692  \n",
       "                    GaussianNB              0.774976  0.029254  \n",
       "                    LinearSVC               0.795579  0.041173  \n",
       "                    LogisticRegression      0.792446  0.050402  \n",
       "                    MLPClassifier           0.721250  0.183244  \n",
       "                    RandomForestClassifier  0.751287  0.080017  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train and test entities randomly split\n",
    "# Train and test do not share notebooks\n",
    "# Train and test do not have nodes sharing the same direct arc\n",
    "Table(BC, 'BC_OutputTemporary.csv', 2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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\">F1:A</th>\n",
       "      <th colspan=\"2\" halign=\"left\">F1:B</th>\n",
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       "    <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",
       "    </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",
       "    </tr>\n",
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       "  <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>0.630357</td>\n",
       "      <td>0.229825</td>\n",
       "      <td>0.301891</td>\n",
       "      <td>0.355974</td>\n",
       "      <td>0.542232</td>\n",
       "      <td>0.305562</td>\n",
       "      <td>0.597466</td>\n",
       "      <td>0.339416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.559373</td>\n",
       "      <td>0.254666</td>\n",
       "      <td>0.292170</td>\n",
       "      <td>0.358384</td>\n",
       "      <td>0.620276</td>\n",
       "      <td>0.222360</td>\n",
       "      <td>0.391160</td>\n",
       "      <td>0.388188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.596609</td>\n",
       "      <td>0.287449</td>\n",
       "      <td>0.260648</td>\n",
       "      <td>0.520643</td>\n",
       "      <td>0.580356</td>\n",
       "      <td>0.327256</td>\n",
       "      <td>0.525760</td>\n",
       "      <td>0.373359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.527183</td>\n",
       "      <td>0.276668</td>\n",
       "      <td>0.208374</td>\n",
       "      <td>0.386783</td>\n",
       "      <td>0.616725</td>\n",
       "      <td>0.227146</td>\n",
       "      <td>0.263238</td>\n",
       "      <td>0.431597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.694572</td>\n",
       "      <td>0.180949</td>\n",
       "      <td>0.182513</td>\n",
       "      <td>0.337758</td>\n",
       "      <td>0.198462</td>\n",
       "      <td>0.351830</td>\n",
       "      <td>0.800761</td>\n",
       "      <td>0.134266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.651188</td>\n",
       "      <td>0.215337</td>\n",
       "      <td>0.230882</td>\n",
       "      <td>0.433494</td>\n",
       "      <td>0.434745</td>\n",
       "      <td>0.349796</td>\n",
       "      <td>0.704132</td>\n",
       "      <td>0.218967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.593983</td>\n",
       "      <td>0.146478</td>\n",
       "      <td>0.284752</td>\n",
       "      <td>0.251832</td>\n",
       "      <td>0.558706</td>\n",
       "      <td>0.245871</td>\n",
       "      <td>0.562877</td>\n",
       "      <td>0.220748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.670926</td>\n",
       "      <td>0.154908</td>\n",
       "      <td>0.443545</td>\n",
       "      <td>0.262550</td>\n",
       "      <td>0.741005</td>\n",
       "      <td>0.118239</td>\n",
       "      <td>0.520113</td>\n",
       "      <td>0.294621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.507821</td>\n",
       "      <td>0.325638</td>\n",
       "      <td>0.016018</td>\n",
       "      <td>0.650066</td>\n",
       "      <td>0.501982</td>\n",
       "      <td>0.298658</td>\n",
       "      <td>0.502964</td>\n",
       "      <td>0.348507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.474168</td>\n",
       "      <td>0.087730</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.638790</td>\n",
       "      <td>0.084528</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.572959</td>\n",
       "      <td>0.138078</td>\n",
       "      <td>0.236613</td>\n",
       "      <td>0.305598</td>\n",
       "      <td>0.360991</td>\n",
       "      <td>0.386130</td>\n",
       "      <td>0.629413</td>\n",
       "      <td>0.069591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.567695</td>\n",
       "      <td>0.233393</td>\n",
       "      <td>0.143050</td>\n",
       "      <td>0.514305</td>\n",
       "      <td>0.572963</td>\n",
       "      <td>0.279702</td>\n",
       "      <td>0.496116</td>\n",
       "      <td>0.297244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.553775</td>\n",
       "      <td>0.223985</td>\n",
       "      <td>0.119340</td>\n",
       "      <td>0.460604</td>\n",
       "      <td>0.575873</td>\n",
       "      <td>0.264289</td>\n",
       "      <td>0.440089</td>\n",
       "      <td>0.337512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.694811</td>\n",
       "      <td>0.151450</td>\n",
       "      <td>0.476588</td>\n",
       "      <td>0.277347</td>\n",
       "      <td>0.760055</td>\n",
       "      <td>0.089925</td>\n",
       "      <td>0.555673</td>\n",
       "      <td>0.322617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.533477</td>\n",
       "      <td>0.357185</td>\n",
       "      <td>0.064300</td>\n",
       "      <td>0.715792</td>\n",
       "      <td>0.515255</td>\n",
       "      <td>0.367093</td>\n",
       "      <td>0.546414</td>\n",
       "      <td>0.350684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.462890</td>\n",
       "      <td>0.028298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.632392</td>\n",
       "      <td>0.026255</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.651107</td>\n",
       "      <td>0.192619</td>\n",
       "      <td>0.297004</td>\n",
       "      <td>0.453207</td>\n",
       "      <td>0.455236</td>\n",
       "      <td>0.433023</td>\n",
       "      <td>0.699422</td>\n",
       "      <td>0.125377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.671252</td>\n",
       "      <td>0.236587</td>\n",
       "      <td>0.370657</td>\n",
       "      <td>0.474227</td>\n",
       "      <td>0.639732</td>\n",
       "      <td>0.335196</td>\n",
       "      <td>0.597927</td>\n",
       "      <td>0.368123</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>0.545121</td>\n",
       "      <td>0.182512</td>\n",
       "      <td>0.032743</td>\n",
       "      <td>0.235999</td>\n",
       "      <td>0.415646</td>\n",
       "      <td>0.292066</td>\n",
       "      <td>0.398993</td>\n",
       "      <td>0.353103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.608697</td>\n",
       "      <td>0.208084</td>\n",
       "      <td>0.075567</td>\n",
       "      <td>0.211757</td>\n",
       "      <td>0.280532</td>\n",
       "      <td>0.336769</td>\n",
       "      <td>0.587660</td>\n",
       "      <td>0.318013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.562627</td>\n",
       "      <td>0.210367</td>\n",
       "      <td>-0.037529</td>\n",
       "      <td>0.112358</td>\n",
       "      <td>0.194918</td>\n",
       "      <td>0.262815</td>\n",
       "      <td>0.562010</td>\n",
       "      <td>0.326898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.600318</td>\n",
       "      <td>0.209236</td>\n",
       "      <td>-0.021410</td>\n",
       "      <td>0.095474</td>\n",
       "      <td>0.120225</td>\n",
       "      <td>0.242188</td>\n",
       "      <td>0.626608</td>\n",
       "      <td>0.325796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.605811</td>\n",
       "      <td>0.232157</td>\n",
       "      <td>0.063970</td>\n",
       "      <td>0.230682</td>\n",
       "      <td>0.311157</td>\n",
       "      <td>0.356690</td>\n",
       "      <td>0.499105</td>\n",
       "      <td>0.427645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.441795</td>\n",
       "      <td>0.203979</td>\n",
       "      <td>0.009922</td>\n",
       "      <td>0.206943</td>\n",
       "      <td>0.291612</td>\n",
       "      <td>0.284315</td>\n",
       "      <td>0.413641</td>\n",
       "      <td>0.278187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.514275</td>\n",
       "      <td>0.073742</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.128544</td>\n",
       "      <td>0.505615</td>\n",
       "      <td>0.100796</td>\n",
       "      <td>0.498357</td>\n",
       "      <td>0.107981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.527879</td>\n",
       "      <td>0.071046</td>\n",
       "      <td>0.057338</td>\n",
       "      <td>0.149045</td>\n",
       "      <td>0.573330</td>\n",
       "      <td>0.084764</td>\n",
       "      <td>0.459861</td>\n",
       "      <td>0.088393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.561163</td>\n",
       "      <td>0.084231</td>\n",
       "      <td>0.135281</td>\n",
       "      <td>0.152839</td>\n",
       "      <td>0.416815</td>\n",
       "      <td>0.167481</td>\n",
       "      <td>0.631956</td>\n",
       "      <td>0.087220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.555676</td>\n",
       "      <td>0.085088</td>\n",
       "      <td>0.128467</td>\n",
       "      <td>0.165232</td>\n",
       "      <td>0.379643</td>\n",
       "      <td>0.160457</td>\n",
       "      <td>0.646228</td>\n",
       "      <td>0.072800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.618546</td>\n",
       "      <td>0.098161</td>\n",
       "      <td>0.236652</td>\n",
       "      <td>0.199730</td>\n",
       "      <td>0.540902</td>\n",
       "      <td>0.203278</td>\n",
       "      <td>0.632450</td>\n",
       "      <td>0.124962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.506205</td>\n",
       "      <td>0.090606</td>\n",
       "      <td>-0.004520</td>\n",
       "      <td>0.189640</td>\n",
       "      <td>0.419678</td>\n",
       "      <td>0.127904</td>\n",
       "      <td>0.532990</td>\n",
       "      <td>0.162685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.515430</td>\n",
       "      <td>0.088256</td>\n",
       "      <td>0.035520</td>\n",
       "      <td>0.184878</td>\n",
       "      <td>0.475097</td>\n",
       "      <td>0.131097</td>\n",
       "      <td>0.540331</td>\n",
       "      <td>0.079720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.536311</td>\n",
       "      <td>0.048601</td>\n",
       "      <td>0.093044</td>\n",
       "      <td>0.094449</td>\n",
       "      <td>0.569146</td>\n",
       "      <td>0.047546</td>\n",
       "      <td>0.495315</td>\n",
       "      <td>0.064333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.561839</td>\n",
       "      <td>0.085024</td>\n",
       "      <td>0.089786</td>\n",
       "      <td>0.205107</td>\n",
       "      <td>0.415014</td>\n",
       "      <td>0.191905</td>\n",
       "      <td>0.638567</td>\n",
       "      <td>0.050875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.563627</td>\n",
       "      <td>0.080993</td>\n",
       "      <td>0.090555</td>\n",
       "      <td>0.192602</td>\n",
       "      <td>0.372997</td>\n",
       "      <td>0.180246</td>\n",
       "      <td>0.659092</td>\n",
       "      <td>0.051711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.557564</td>\n",
       "      <td>0.073622</td>\n",
       "      <td>0.084580</td>\n",
       "      <td>0.178112</td>\n",
       "      <td>0.438547</td>\n",
       "      <td>0.165697</td>\n",
       "      <td>0.615788</td>\n",
       "      <td>0.087716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.542781</td>\n",
       "      <td>0.088816</td>\n",
       "      <td>0.077411</td>\n",
       "      <td>0.182283</td>\n",
       "      <td>0.367208</td>\n",
       "      <td>0.138771</td>\n",
       "      <td>0.630242</td>\n",
       "      <td>0.098070</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           0.630357  0.229825  0.301891   \n",
       "                    GaussianNB              0.559373  0.254666  0.292170   \n",
       "                    LinearSVC               0.596609  0.287449  0.260648   \n",
       "                    LogisticRegression      0.527183  0.276668  0.208374   \n",
       "                    MLPClassifier           0.694572  0.180949  0.182513   \n",
       "                    RandomForestClassifier  0.651188  0.215337  0.230882   \n",
       "          100       Decision Tree           0.593983  0.146478  0.284752   \n",
       "                    GaussianNB              0.670926  0.154908  0.443545   \n",
       "                    LinearSVC               0.507821  0.325638  0.016018   \n",
       "                    LogisticRegression      0.474168  0.087730  0.000000   \n",
       "                    MLPClassifier           0.572959  0.138078  0.236613   \n",
       "                    RandomForestClassifier  0.567695  0.233393  0.143050   \n",
       "          200       Decision Tree           0.553775  0.223985  0.119340   \n",
       "                    GaussianNB              0.694811  0.151450  0.476588   \n",
       "                    LinearSVC               0.533477  0.357185  0.064300   \n",
       "                    LogisticRegression      0.462890  0.028298  0.000000   \n",
       "                    MLPClassifier           0.651107  0.192619  0.297004   \n",
       "                    RandomForestClassifier  0.671252  0.236587  0.370657   \n",
       "rdf2vec   10        Decision Tree           0.545121  0.182512  0.032743   \n",
       "                    GaussianNB              0.608697  0.208084  0.075567   \n",
       "                    LinearSVC               0.562627  0.210367 -0.037529   \n",
       "                    LogisticRegression      0.600318  0.209236 -0.021410   \n",
       "                    MLPClassifier           0.605811  0.232157  0.063970   \n",
       "                    RandomForestClassifier  0.441795  0.203979  0.009922   \n",
       "          100       Decision Tree           0.514275  0.073742  0.016893   \n",
       "                    GaussianNB              0.527879  0.071046  0.057338   \n",
       "                    LinearSVC               0.561163  0.084231  0.135281   \n",
       "                    LogisticRegression      0.555676  0.085088  0.128467   \n",
       "                    MLPClassifier           0.618546  0.098161  0.236652   \n",
       "                    RandomForestClassifier  0.506205  0.090606 -0.004520   \n",
       "          200       Decision Tree           0.515430  0.088256  0.035520   \n",
       "                    GaussianNB              0.536311  0.048601  0.093044   \n",
       "                    LinearSVC               0.561839  0.085024  0.089786   \n",
       "                    LogisticRegression      0.563627  0.080993  0.090555   \n",
       "                    MLPClassifier           0.557564  0.073622  0.084580   \n",
       "                    RandomForestClassifier  0.542781  0.088816  0.077411   \n",
       "\n",
       "                                                          F1:A            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.355974  0.542232  0.305562   \n",
       "                    GaussianNB              0.358384  0.620276  0.222360   \n",
       "                    LinearSVC               0.520643  0.580356  0.327256   \n",
       "                    LogisticRegression      0.386783  0.616725  0.227146   \n",
       "                    MLPClassifier           0.337758  0.198462  0.351830   \n",
       "                    RandomForestClassifier  0.433494  0.434745  0.349796   \n",
       "          100       Decision Tree           0.251832  0.558706  0.245871   \n",
       "                    GaussianNB              0.262550  0.741005  0.118239   \n",
       "                    LinearSVC               0.650066  0.501982  0.298658   \n",
       "                    LogisticRegression      0.000000  0.638790  0.084528   \n",
       "                    MLPClassifier           0.305598  0.360991  0.386130   \n",
       "                    RandomForestClassifier  0.514305  0.572963  0.279702   \n",
       "          200       Decision Tree           0.460604  0.575873  0.264289   \n",
       "                    GaussianNB              0.277347  0.760055  0.089925   \n",
       "                    LinearSVC               0.715792  0.515255  0.367093   \n",
       "                    LogisticRegression      0.000000  0.632392  0.026255   \n",
       "                    MLPClassifier           0.453207  0.455236  0.433023   \n",
       "                    RandomForestClassifier  0.474227  0.639732  0.335196   \n",
       "rdf2vec   10        Decision Tree           0.235999  0.415646  0.292066   \n",
       "                    GaussianNB              0.211757  0.280532  0.336769   \n",
       "                    LinearSVC               0.112358  0.194918  0.262815   \n",
       "                    LogisticRegression      0.095474  0.120225  0.242188   \n",
       "                    MLPClassifier           0.230682  0.311157  0.356690   \n",
       "                    RandomForestClassifier  0.206943  0.291612  0.284315   \n",
       "          100       Decision Tree           0.128544  0.505615  0.100796   \n",
       "                    GaussianNB              0.149045  0.573330  0.084764   \n",
       "                    LinearSVC               0.152839  0.416815  0.167481   \n",
       "                    LogisticRegression      0.165232  0.379643  0.160457   \n",
       "                    MLPClassifier           0.199730  0.540902  0.203278   \n",
       "                    RandomForestClassifier  0.189640  0.419678  0.127904   \n",
       "          200       Decision Tree           0.184878  0.475097  0.131097   \n",
       "                    GaussianNB              0.094449  0.569146  0.047546   \n",
       "                    LinearSVC               0.205107  0.415014  0.191905   \n",
       "                    LogisticRegression      0.192602  0.372997  0.180246   \n",
       "                    MLPClassifier           0.178112  0.438547  0.165697   \n",
       "                    RandomForestClassifier  0.182283  0.367208  0.138771   \n",
       "\n",
       "                                                F1:B            \n",
       "                                                mean       std  \n",
       "EmbMethod InputSize Method                                      \n",
       "bertcode  10        Decision Tree           0.597466  0.339416  \n",
       "                    GaussianNB              0.391160  0.388188  \n",
       "                    LinearSVC               0.525760  0.373359  \n",
       "                    LogisticRegression      0.263238  0.431597  \n",
       "                    MLPClassifier           0.800761  0.134266  \n",
       "                    RandomForestClassifier  0.704132  0.218967  \n",
       "          100       Decision Tree           0.562877  0.220748  \n",
       "                    GaussianNB              0.520113  0.294621  \n",
       "                    LinearSVC               0.502964  0.348507  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.629413  0.069591  \n",
       "                    RandomForestClassifier  0.496116  0.297244  \n",
       "          200       Decision Tree           0.440089  0.337512  \n",
       "                    GaussianNB              0.555673  0.322617  \n",
       "                    LinearSVC               0.546414  0.350684  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.699422  0.125377  \n",
       "                    RandomForestClassifier  0.597927  0.368123  \n",
       "rdf2vec   10        Decision Tree           0.398993  0.353103  \n",
       "                    GaussianNB              0.587660  0.318013  \n",
       "                    LinearSVC               0.562010  0.326898  \n",
       "                    LogisticRegression      0.626608  0.325796  \n",
       "                    MLPClassifier           0.499105  0.427645  \n",
       "                    RandomForestClassifier  0.413641  0.278187  \n",
       "          100       Decision Tree           0.498357  0.107981  \n",
       "                    GaussianNB              0.459861  0.088393  \n",
       "                    LinearSVC               0.631956  0.087220  \n",
       "                    LogisticRegression      0.646228  0.072800  \n",
       "                    MLPClassifier           0.632450  0.124962  \n",
       "                    RandomForestClassifier  0.532990  0.162685  \n",
       "          200       Decision Tree           0.540331  0.079720  \n",
       "                    GaussianNB              0.495315  0.064333  \n",
       "                    LinearSVC               0.638567  0.050875  \n",
       "                    LogisticRegression      0.659092  0.051711  \n",
       "                    MLPClassifier           0.615788  0.087716  \n",
       "                    RandomForestClassifier  0.630242  0.098070  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table(BC, 'BC_VisualisationPreparation.csv', 2 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Regime 3\n",
    "\n",
    "Train and test do not have nodes sharing the same direct arc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Matthews</th>\n",
       "      <th colspan=\"2\" halign=\"left\">F1:A</th>\n",
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       "      <th>mean</th>\n",
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       "      <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",
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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>0.598523</td>\n",
       "      <td>0.229993</td>\n",
       "      <td>0.151653</td>\n",
       "      <td>0.373776</td>\n",
       "      <td>0.362578</td>\n",
       "      <td>0.384144</td>\n",
       "      <td>0.648533</td>\n",
       "      <td>0.170012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.668127</td>\n",
       "      <td>0.246055</td>\n",
       "      <td>0.288115</td>\n",
       "      <td>0.459596</td>\n",
       "      <td>0.651670</td>\n",
       "      <td>0.351916</td>\n",
       "      <td>0.461328</td>\n",
       "      <td>0.435401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.534226</td>\n",
       "      <td>0.360969</td>\n",
       "      <td>0.017025</td>\n",
       "      <td>0.702596</td>\n",
       "      <td>0.560667</td>\n",
       "      <td>0.352294</td>\n",
       "      <td>0.414116</td>\n",
       "      <td>0.382641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.548043</td>\n",
       "      <td>0.241415</td>\n",
       "      <td>0.006670</td>\n",
       "      <td>0.372672</td>\n",
       "      <td>0.647987</td>\n",
       "      <td>0.237051</td>\n",
       "      <td>0.113780</td>\n",
       "      <td>0.309548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.539413</td>\n",
       "      <td>0.266130</td>\n",
       "      <td>0.062996</td>\n",
       "      <td>0.390422</td>\n",
       "      <td>0.165136</td>\n",
       "      <td>0.350351</td>\n",
       "      <td>0.639948</td>\n",
       "      <td>0.217266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.679987</td>\n",
       "      <td>0.197326</td>\n",
       "      <td>0.309993</td>\n",
       "      <td>0.340566</td>\n",
       "      <td>0.631622</td>\n",
       "      <td>0.303053</td>\n",
       "      <td>0.494469</td>\n",
       "      <td>0.383530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.643638</td>\n",
       "      <td>0.140073</td>\n",
       "      <td>0.345317</td>\n",
       "      <td>0.242765</td>\n",
       "      <td>0.591630</td>\n",
       "      <td>0.271175</td>\n",
       "      <td>0.615166</td>\n",
       "      <td>0.220976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.585287</td>\n",
       "      <td>0.108802</td>\n",
       "      <td>0.193939</td>\n",
       "      <td>0.250403</td>\n",
       "      <td>0.577788</td>\n",
       "      <td>0.201863</td>\n",
       "      <td>0.365589</td>\n",
       "      <td>0.383282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.553995</td>\n",
       "      <td>0.311350</td>\n",
       "      <td>0.121423</td>\n",
       "      <td>0.652891</td>\n",
       "      <td>0.518450</td>\n",
       "      <td>0.393853</td>\n",
       "      <td>0.567241</td>\n",
       "      <td>0.243926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.497655</td>\n",
       "      <td>0.039154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.663750</td>\n",
       "      <td>0.035258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.621646</td>\n",
       "      <td>0.188117</td>\n",
       "      <td>0.241935</td>\n",
       "      <td>0.417611</td>\n",
       "      <td>0.344401</td>\n",
       "      <td>0.383623</td>\n",
       "      <td>0.728157</td>\n",
       "      <td>0.123297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.754944</td>\n",
       "      <td>0.183970</td>\n",
       "      <td>0.519236</td>\n",
       "      <td>0.363271</td>\n",
       "      <td>0.616590</td>\n",
       "      <td>0.395164</td>\n",
       "      <td>0.806276</td>\n",
       "      <td>0.108516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.650976</td>\n",
       "      <td>0.151525</td>\n",
       "      <td>0.302746</td>\n",
       "      <td>0.315922</td>\n",
       "      <td>0.630367</td>\n",
       "      <td>0.271262</td>\n",
       "      <td>0.625391</td>\n",
       "      <td>0.117666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.621687</td>\n",
       "      <td>0.107577</td>\n",
       "      <td>0.214501</td>\n",
       "      <td>0.280356</td>\n",
       "      <td>0.716804</td>\n",
       "      <td>0.062939</td>\n",
       "      <td>0.316620</td>\n",
       "      <td>0.342436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.528041</td>\n",
       "      <td>0.304277</td>\n",
       "      <td>0.046193</td>\n",
       "      <td>0.628510</td>\n",
       "      <td>0.461881</td>\n",
       "      <td>0.405384</td>\n",
       "      <td>0.560766</td>\n",
       "      <td>0.223355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.523414</td>\n",
       "      <td>0.036278</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.686490</td>\n",
       "      <td>0.031234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.592291</td>\n",
       "      <td>0.167384</td>\n",
       "      <td>0.198395</td>\n",
       "      <td>0.365340</td>\n",
       "      <td>0.288542</td>\n",
       "      <td>0.383420</td>\n",
       "      <td>0.703769</td>\n",
       "      <td>0.097938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.712919</td>\n",
       "      <td>0.232598</td>\n",
       "      <td>0.434666</td>\n",
       "      <td>0.454384</td>\n",
       "      <td>0.475247</td>\n",
       "      <td>0.485788</td>\n",
       "      <td>0.793107</td>\n",
       "      <td>0.143850</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>0.581698</td>\n",
       "      <td>0.129341</td>\n",
       "      <td>0.118699</td>\n",
       "      <td>0.224214</td>\n",
       "      <td>0.610105</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>0.344706</td>\n",
       "      <td>0.276487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.566203</td>\n",
       "      <td>0.117964</td>\n",
       "      <td>-0.059831</td>\n",
       "      <td>0.208402</td>\n",
       "      <td>0.619618</td>\n",
       "      <td>0.239484</td>\n",
       "      <td>0.184063</td>\n",
       "      <td>0.296037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.445138</td>\n",
       "      <td>0.129138</td>\n",
       "      <td>-0.007075</td>\n",
       "      <td>0.153160</td>\n",
       "      <td>0.191971</td>\n",
       "      <td>0.284484</td>\n",
       "      <td>0.507686</td>\n",
       "      <td>0.180755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.394540</td>\n",
       "      <td>0.121100</td>\n",
       "      <td>-0.048431</td>\n",
       "      <td>0.099086</td>\n",
       "      <td>0.011874</td>\n",
       "      <td>0.028640</td>\n",
       "      <td>0.552839</td>\n",
       "      <td>0.122202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.438550</td>\n",
       "      <td>0.169759</td>\n",
       "      <td>-0.110666</td>\n",
       "      <td>0.226026</td>\n",
       "      <td>0.303697</td>\n",
       "      <td>0.352401</td>\n",
       "      <td>0.340954</td>\n",
       "      <td>0.243534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.500809</td>\n",
       "      <td>0.121474</td>\n",
       "      <td>-0.027646</td>\n",
       "      <td>0.199133</td>\n",
       "      <td>0.587744</td>\n",
       "      <td>0.231487</td>\n",
       "      <td>0.192836</td>\n",
       "      <td>0.144571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.557555</td>\n",
       "      <td>0.078883</td>\n",
       "      <td>0.111929</td>\n",
       "      <td>0.140349</td>\n",
       "      <td>0.634019</td>\n",
       "      <td>0.090783</td>\n",
       "      <td>0.404729</td>\n",
       "      <td>0.151142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.354301</td>\n",
       "      <td>0.048539</td>\n",
       "      <td>-0.291174</td>\n",
       "      <td>0.092948</td>\n",
       "      <td>0.370831</td>\n",
       "      <td>0.062062</td>\n",
       "      <td>0.334577</td>\n",
       "      <td>0.043069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.482088</td>\n",
       "      <td>0.093851</td>\n",
       "      <td>-0.037938</td>\n",
       "      <td>0.243897</td>\n",
       "      <td>0.300234</td>\n",
       "      <td>0.206922</td>\n",
       "      <td>0.575358</td>\n",
       "      <td>0.075559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.450890</td>\n",
       "      <td>0.053497</td>\n",
       "      <td>-0.113431</td>\n",
       "      <td>0.171691</td>\n",
       "      <td>0.177231</td>\n",
       "      <td>0.124872</td>\n",
       "      <td>0.581053</td>\n",
       "      <td>0.062896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.542365</td>\n",
       "      <td>0.137269</td>\n",
       "      <td>0.089073</td>\n",
       "      <td>0.333889</td>\n",
       "      <td>0.443808</td>\n",
       "      <td>0.272178</td>\n",
       "      <td>0.576382</td>\n",
       "      <td>0.109381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.518592</td>\n",
       "      <td>0.041845</td>\n",
       "      <td>0.021479</td>\n",
       "      <td>0.139955</td>\n",
       "      <td>0.650326</td>\n",
       "      <td>0.039586</td>\n",
       "      <td>0.209651</td>\n",
       "      <td>0.114655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.508477</td>\n",
       "      <td>0.104715</td>\n",
       "      <td>0.025185</td>\n",
       "      <td>0.208306</td>\n",
       "      <td>0.548265</td>\n",
       "      <td>0.131226</td>\n",
       "      <td>0.445941</td>\n",
       "      <td>0.097362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.369547</td>\n",
       "      <td>0.038100</td>\n",
       "      <td>-0.261387</td>\n",
       "      <td>0.075384</td>\n",
       "      <td>0.356683</td>\n",
       "      <td>0.032986</td>\n",
       "      <td>0.378453</td>\n",
       "      <td>0.064440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.444100</td>\n",
       "      <td>0.068049</td>\n",
       "      <td>-0.101428</td>\n",
       "      <td>0.154550</td>\n",
       "      <td>0.329827</td>\n",
       "      <td>0.096245</td>\n",
       "      <td>0.514787</td>\n",
       "      <td>0.092999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.427760</td>\n",
       "      <td>0.068626</td>\n",
       "      <td>-0.149375</td>\n",
       "      <td>0.148185</td>\n",
       "      <td>0.279591</td>\n",
       "      <td>0.119585</td>\n",
       "      <td>0.516756</td>\n",
       "      <td>0.082141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.475502</td>\n",
       "      <td>0.082199</td>\n",
       "      <td>-0.048819</td>\n",
       "      <td>0.169257</td>\n",
       "      <td>0.439018</td>\n",
       "      <td>0.151587</td>\n",
       "      <td>0.476655</td>\n",
       "      <td>0.109600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.487486</td>\n",
       "      <td>0.077815</td>\n",
       "      <td>-0.037838</td>\n",
       "      <td>0.153192</td>\n",
       "      <td>0.570047</td>\n",
       "      <td>0.142549</td>\n",
       "      <td>0.289437</td>\n",
       "      <td>0.120139</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           0.598523  0.229993  0.151653   \n",
       "                    GaussianNB              0.668127  0.246055  0.288115   \n",
       "                    LinearSVC               0.534226  0.360969  0.017025   \n",
       "                    LogisticRegression      0.548043  0.241415  0.006670   \n",
       "                    MLPClassifier           0.539413  0.266130  0.062996   \n",
       "                    RandomForestClassifier  0.679987  0.197326  0.309993   \n",
       "          100       Decision Tree           0.643638  0.140073  0.345317   \n",
       "                    GaussianNB              0.585287  0.108802  0.193939   \n",
       "                    LinearSVC               0.553995  0.311350  0.121423   \n",
       "                    LogisticRegression      0.497655  0.039154  0.000000   \n",
       "                    MLPClassifier           0.621646  0.188117  0.241935   \n",
       "                    RandomForestClassifier  0.754944  0.183970  0.519236   \n",
       "          200       Decision Tree           0.650976  0.151525  0.302746   \n",
       "                    GaussianNB              0.621687  0.107577  0.214501   \n",
       "                    LinearSVC               0.528041  0.304277  0.046193   \n",
       "                    LogisticRegression      0.523414  0.036278  0.000000   \n",
       "                    MLPClassifier           0.592291  0.167384  0.198395   \n",
       "                    RandomForestClassifier  0.712919  0.232598  0.434666   \n",
       "rdf2vec   10        Decision Tree           0.581698  0.129341  0.118699   \n",
       "                    GaussianNB              0.566203  0.117964 -0.059831   \n",
       "                    LinearSVC               0.445138  0.129138 -0.007075   \n",
       "                    LogisticRegression      0.394540  0.121100 -0.048431   \n",
       "                    MLPClassifier           0.438550  0.169759 -0.110666   \n",
       "                    RandomForestClassifier  0.500809  0.121474 -0.027646   \n",
       "          100       Decision Tree           0.557555  0.078883  0.111929   \n",
       "                    GaussianNB              0.354301  0.048539 -0.291174   \n",
       "                    LinearSVC               0.482088  0.093851 -0.037938   \n",
       "                    LogisticRegression      0.450890  0.053497 -0.113431   \n",
       "                    MLPClassifier           0.542365  0.137269  0.089073   \n",
       "                    RandomForestClassifier  0.518592  0.041845  0.021479   \n",
       "          200       Decision Tree           0.508477  0.104715  0.025185   \n",
       "                    GaussianNB              0.369547  0.038100 -0.261387   \n",
       "                    LinearSVC               0.444100  0.068049 -0.101428   \n",
       "                    LogisticRegression      0.427760  0.068626 -0.149375   \n",
       "                    MLPClassifier           0.475502  0.082199 -0.048819   \n",
       "                    RandomForestClassifier  0.487486  0.077815 -0.037838   \n",
       "\n",
       "                                                          F1:A            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.373776  0.362578  0.384144   \n",
       "                    GaussianNB              0.459596  0.651670  0.351916   \n",
       "                    LinearSVC               0.702596  0.560667  0.352294   \n",
       "                    LogisticRegression      0.372672  0.647987  0.237051   \n",
       "                    MLPClassifier           0.390422  0.165136  0.350351   \n",
       "                    RandomForestClassifier  0.340566  0.631622  0.303053   \n",
       "          100       Decision Tree           0.242765  0.591630  0.271175   \n",
       "                    GaussianNB              0.250403  0.577788  0.201863   \n",
       "                    LinearSVC               0.652891  0.518450  0.393853   \n",
       "                    LogisticRegression      0.000000  0.663750  0.035258   \n",
       "                    MLPClassifier           0.417611  0.344401  0.383623   \n",
       "                    RandomForestClassifier  0.363271  0.616590  0.395164   \n",
       "          200       Decision Tree           0.315922  0.630367  0.271262   \n",
       "                    GaussianNB              0.280356  0.716804  0.062939   \n",
       "                    LinearSVC               0.628510  0.461881  0.405384   \n",
       "                    LogisticRegression      0.000000  0.686490  0.031234   \n",
       "                    MLPClassifier           0.365340  0.288542  0.383420   \n",
       "                    RandomForestClassifier  0.454384  0.475247  0.485788   \n",
       "rdf2vec   10        Decision Tree           0.224214  0.610105  0.247676   \n",
       "                    GaussianNB              0.208402  0.619618  0.239484   \n",
       "                    LinearSVC               0.153160  0.191971  0.284484   \n",
       "                    LogisticRegression      0.099086  0.011874  0.028640   \n",
       "                    MLPClassifier           0.226026  0.303697  0.352401   \n",
       "                    RandomForestClassifier  0.199133  0.587744  0.231487   \n",
       "          100       Decision Tree           0.140349  0.634019  0.090783   \n",
       "                    GaussianNB              0.092948  0.370831  0.062062   \n",
       "                    LinearSVC               0.243897  0.300234  0.206922   \n",
       "                    LogisticRegression      0.171691  0.177231  0.124872   \n",
       "                    MLPClassifier           0.333889  0.443808  0.272178   \n",
       "                    RandomForestClassifier  0.139955  0.650326  0.039586   \n",
       "          200       Decision Tree           0.208306  0.548265  0.131226   \n",
       "                    GaussianNB              0.075384  0.356683  0.032986   \n",
       "                    LinearSVC               0.154550  0.329827  0.096245   \n",
       "                    LogisticRegression      0.148185  0.279591  0.119585   \n",
       "                    MLPClassifier           0.169257  0.439018  0.151587   \n",
       "                    RandomForestClassifier  0.153192  0.570047  0.142549   \n",
       "\n",
       "                                                F1:B            \n",
       "                                                mean       std  \n",
       "EmbMethod InputSize Method                                      \n",
       "bertcode  10        Decision Tree           0.648533  0.170012  \n",
       "                    GaussianNB              0.461328  0.435401  \n",
       "                    LinearSVC               0.414116  0.382641  \n",
       "                    LogisticRegression      0.113780  0.309548  \n",
       "                    MLPClassifier           0.639948  0.217266  \n",
       "                    RandomForestClassifier  0.494469  0.383530  \n",
       "          100       Decision Tree           0.615166  0.220976  \n",
       "                    GaussianNB              0.365589  0.383282  \n",
       "                    LinearSVC               0.567241  0.243926  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.728157  0.123297  \n",
       "                    RandomForestClassifier  0.806276  0.108516  \n",
       "          200       Decision Tree           0.625391  0.117666  \n",
       "                    GaussianNB              0.316620  0.342436  \n",
       "                    LinearSVC               0.560766  0.223355  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.703769  0.097938  \n",
       "                    RandomForestClassifier  0.793107  0.143850  \n",
       "rdf2vec   10        Decision Tree           0.344706  0.276487  \n",
       "                    GaussianNB              0.184063  0.296037  \n",
       "                    LinearSVC               0.507686  0.180755  \n",
       "                    LogisticRegression      0.552839  0.122202  \n",
       "                    MLPClassifier           0.340954  0.243534  \n",
       "                    RandomForestClassifier  0.192836  0.144571  \n",
       "          100       Decision Tree           0.404729  0.151142  \n",
       "                    GaussianNB              0.334577  0.043069  \n",
       "                    LinearSVC               0.575358  0.075559  \n",
       "                    LogisticRegression      0.581053  0.062896  \n",
       "                    MLPClassifier           0.576382  0.109381  \n",
       "                    RandomForestClassifier  0.209651  0.114655  \n",
       "          200       Decision Tree           0.445941  0.097362  \n",
       "                    GaussianNB              0.378453  0.064440  \n",
       "                    LinearSVC               0.514787  0.092999  \n",
       "                    LogisticRegression      0.516756  0.082141  \n",
       "                    MLPClassifier           0.476655  0.109600  \n",
       "                    RandomForestClassifier  0.289437  0.120139  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table(BC, 'BC_OutputTemporary.csv', 3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th colspan=\"2\" halign=\"left\">Accuracy</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Matthews</th>\n",
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       "      <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",
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       "      <th></th>\n",
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       "  <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>0.564900</td>\n",
       "      <td>0.171080</td>\n",
       "      <td>-0.103612</td>\n",
       "      <td>0.155699</td>\n",
       "      <td>0.474509</td>\n",
       "      <td>0.327521</td>\n",
       "      <td>0.302640</td>\n",
       "      <td>0.346021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.524171</td>\n",
       "      <td>0.171608</td>\n",
       "      <td>0.001385</td>\n",
       "      <td>0.174955</td>\n",
       "      <td>0.645235</td>\n",
       "      <td>0.161534</td>\n",
       "      <td>0.121571</td>\n",
       "      <td>0.219412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.517413</td>\n",
       "      <td>0.181368</td>\n",
       "      <td>0.014886</td>\n",
       "      <td>0.323867</td>\n",
       "      <td>0.508284</td>\n",
       "      <td>0.207670</td>\n",
       "      <td>0.417724</td>\n",
       "      <td>0.267211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.550720</td>\n",
       "      <td>0.165002</td>\n",
       "      <td>-0.039084</td>\n",
       "      <td>0.117060</td>\n",
       "      <td>0.626081</td>\n",
       "      <td>0.238335</td>\n",
       "      <td>0.138053</td>\n",
       "      <td>0.225296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.448322</td>\n",
       "      <td>0.181513</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>0.149450</td>\n",
       "      <td>0.129732</td>\n",
       "      <td>0.208961</td>\n",
       "      <td>0.553772</td>\n",
       "      <td>0.193065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.462137</td>\n",
       "      <td>0.162956</td>\n",
       "      <td>-0.005686</td>\n",
       "      <td>0.208835</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>0.318490</td>\n",
       "      <td>0.342857</td>\n",
       "      <td>0.283814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.509046</td>\n",
       "      <td>0.062078</td>\n",
       "      <td>-0.021076</td>\n",
       "      <td>0.150209</td>\n",
       "      <td>0.421481</td>\n",
       "      <td>0.268183</td>\n",
       "      <td>0.368784</td>\n",
       "      <td>0.318709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.470157</td>\n",
       "      <td>0.045299</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.379583</td>\n",
       "      <td>0.328805</td>\n",
       "      <td>0.258860</td>\n",
       "      <td>0.334646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.509573</td>\n",
       "      <td>0.118108</td>\n",
       "      <td>0.030653</td>\n",
       "      <td>0.230720</td>\n",
       "      <td>0.537006</td>\n",
       "      <td>0.112050</td>\n",
       "      <td>0.463363</td>\n",
       "      <td>0.157062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.487025</td>\n",
       "      <td>0.053428</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.653459</td>\n",
       "      <td>0.048720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.538124</td>\n",
       "      <td>0.041382</td>\n",
       "      <td>0.023241</td>\n",
       "      <td>0.039578</td>\n",
       "      <td>0.167648</td>\n",
       "      <td>0.299100</td>\n",
       "      <td>0.555902</td>\n",
       "      <td>0.286502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.492212</td>\n",
       "      <td>0.048783</td>\n",
       "      <td>-0.013241</td>\n",
       "      <td>0.056470</td>\n",
       "      <td>0.499020</td>\n",
       "      <td>0.251806</td>\n",
       "      <td>0.255291</td>\n",
       "      <td>0.280646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.507219</td>\n",
       "      <td>0.066192</td>\n",
       "      <td>0.055953</td>\n",
       "      <td>0.154564</td>\n",
       "      <td>0.591159</td>\n",
       "      <td>0.086300</td>\n",
       "      <td>0.256554</td>\n",
       "      <td>0.250509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.490386</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.012708</td>\n",
       "      <td>0.027120</td>\n",
       "      <td>0.324759</td>\n",
       "      <td>0.342453</td>\n",
       "      <td>0.336016</td>\n",
       "      <td>0.347464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.491944</td>\n",
       "      <td>0.075766</td>\n",
       "      <td>-0.021065</td>\n",
       "      <td>0.150728</td>\n",
       "      <td>0.494557</td>\n",
       "      <td>0.074680</td>\n",
       "      <td>0.469623</td>\n",
       "      <td>0.130114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.490723</td>\n",
       "      <td>0.022381</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.658102</td>\n",
       "      <td>0.019782</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.507545</td>\n",
       "      <td>0.036732</td>\n",
       "      <td>0.030393</td>\n",
       "      <td>0.059014</td>\n",
       "      <td>0.311303</td>\n",
       "      <td>0.331795</td>\n",
       "      <td>0.409777</td>\n",
       "      <td>0.333887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.543404</td>\n",
       "      <td>0.063341</td>\n",
       "      <td>0.085101</td>\n",
       "      <td>0.151955</td>\n",
       "      <td>0.507790</td>\n",
       "      <td>0.176283</td>\n",
       "      <td>0.468299</td>\n",
       "      <td>0.246976</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>0.453187</td>\n",
       "      <td>0.199206</td>\n",
       "      <td>-0.049420</td>\n",
       "      <td>0.160938</td>\n",
       "      <td>0.308158</td>\n",
       "      <td>0.273787</td>\n",
       "      <td>0.394730</td>\n",
       "      <td>0.280213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.498889</td>\n",
       "      <td>0.207983</td>\n",
       "      <td>-0.036215</td>\n",
       "      <td>0.182453</td>\n",
       "      <td>0.186948</td>\n",
       "      <td>0.270693</td>\n",
       "      <td>0.517328</td>\n",
       "      <td>0.302166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.491131</td>\n",
       "      <td>0.216976</td>\n",
       "      <td>-0.098738</td>\n",
       "      <td>0.197935</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.090351</td>\n",
       "      <td>0.603721</td>\n",
       "      <td>0.267550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.505121</td>\n",
       "      <td>0.237059</td>\n",
       "      <td>-0.077826</td>\n",
       "      <td>0.203015</td>\n",
       "      <td>0.019048</td>\n",
       "      <td>0.060234</td>\n",
       "      <td>0.624834</td>\n",
       "      <td>0.252230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.583953</td>\n",
       "      <td>0.185790</td>\n",
       "      <td>-0.078269</td>\n",
       "      <td>0.225509</td>\n",
       "      <td>0.202529</td>\n",
       "      <td>0.337702</td>\n",
       "      <td>0.585241</td>\n",
       "      <td>0.275402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.617057</td>\n",
       "      <td>0.156574</td>\n",
       "      <td>0.087958</td>\n",
       "      <td>0.274904</td>\n",
       "      <td>0.254790</td>\n",
       "      <td>0.331891</td>\n",
       "      <td>0.645227</td>\n",
       "      <td>0.257876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">100</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.501555</td>\n",
       "      <td>0.113272</td>\n",
       "      <td>0.007756</td>\n",
       "      <td>0.232605</td>\n",
       "      <td>0.431772</td>\n",
       "      <td>0.142056</td>\n",
       "      <td>0.542375</td>\n",
       "      <td>0.114097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.518631</td>\n",
       "      <td>0.054483</td>\n",
       "      <td>0.045430</td>\n",
       "      <td>0.104220</td>\n",
       "      <td>0.587855</td>\n",
       "      <td>0.050806</td>\n",
       "      <td>0.412301</td>\n",
       "      <td>0.090914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.542639</td>\n",
       "      <td>0.090164</td>\n",
       "      <td>0.095354</td>\n",
       "      <td>0.187593</td>\n",
       "      <td>0.300640</td>\n",
       "      <td>0.197253</td>\n",
       "      <td>0.652795</td>\n",
       "      <td>0.063657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.537846</td>\n",
       "      <td>0.067516</td>\n",
       "      <td>0.082915</td>\n",
       "      <td>0.138654</td>\n",
       "      <td>0.244688</td>\n",
       "      <td>0.151533</td>\n",
       "      <td>0.663508</td>\n",
       "      <td>0.050385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.521841</td>\n",
       "      <td>0.117357</td>\n",
       "      <td>0.066283</td>\n",
       "      <td>0.256900</td>\n",
       "      <td>0.377027</td>\n",
       "      <td>0.228666</td>\n",
       "      <td>0.574400</td>\n",
       "      <td>0.133601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.508073</td>\n",
       "      <td>0.097446</td>\n",
       "      <td>0.016046</td>\n",
       "      <td>0.197368</td>\n",
       "      <td>0.372670</td>\n",
       "      <td>0.163452</td>\n",
       "      <td>0.564845</td>\n",
       "      <td>0.135250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">200</th>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.529154</td>\n",
       "      <td>0.091355</td>\n",
       "      <td>0.055814</td>\n",
       "      <td>0.189328</td>\n",
       "      <td>0.530879</td>\n",
       "      <td>0.122103</td>\n",
       "      <td>0.505031</td>\n",
       "      <td>0.122745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GaussianNB</th>\n",
       "      <td>0.549291</td>\n",
       "      <td>0.037235</td>\n",
       "      <td>0.097155</td>\n",
       "      <td>0.063976</td>\n",
       "      <td>0.613375</td>\n",
       "      <td>0.041323</td>\n",
       "      <td>0.456552</td>\n",
       "      <td>0.043867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LinearSVC</th>\n",
       "      <td>0.489477</td>\n",
       "      <td>0.076079</td>\n",
       "      <td>-0.024970</td>\n",
       "      <td>0.151666</td>\n",
       "      <td>0.386531</td>\n",
       "      <td>0.168115</td>\n",
       "      <td>0.515030</td>\n",
       "      <td>0.141247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogisticRegression</th>\n",
       "      <td>0.503967</td>\n",
       "      <td>0.068291</td>\n",
       "      <td>0.010199</td>\n",
       "      <td>0.150022</td>\n",
       "      <td>0.343759</td>\n",
       "      <td>0.162173</td>\n",
       "      <td>0.584036</td>\n",
       "      <td>0.068676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLPClassifier</th>\n",
       "      <td>0.499241</td>\n",
       "      <td>0.069569</td>\n",
       "      <td>-0.008334</td>\n",
       "      <td>0.142860</td>\n",
       "      <td>0.433361</td>\n",
       "      <td>0.170201</td>\n",
       "      <td>0.495393</td>\n",
       "      <td>0.162417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RandomForestClassifier</th>\n",
       "      <td>0.498594</td>\n",
       "      <td>0.089448</td>\n",
       "      <td>-0.013399</td>\n",
       "      <td>0.198713</td>\n",
       "      <td>0.419499</td>\n",
       "      <td>0.175352</td>\n",
       "      <td>0.519019</td>\n",
       "      <td>0.149093</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           0.564900  0.171080 -0.103612   \n",
       "                    GaussianNB              0.524171  0.171608  0.001385   \n",
       "                    LinearSVC               0.517413  0.181368  0.014886   \n",
       "                    LogisticRegression      0.550720  0.165002 -0.039084   \n",
       "                    MLPClassifier           0.448322  0.181513 -0.000551   \n",
       "                    RandomForestClassifier  0.462137  0.162956 -0.005686   \n",
       "          100       Decision Tree           0.509046  0.062078 -0.021076   \n",
       "                    GaussianNB              0.470157  0.045299  0.000000   \n",
       "                    LinearSVC               0.509573  0.118108  0.030653   \n",
       "                    LogisticRegression      0.487025  0.053428  0.000000   \n",
       "                    MLPClassifier           0.538124  0.041382  0.023241   \n",
       "                    RandomForestClassifier  0.492212  0.048783 -0.013241   \n",
       "          200       Decision Tree           0.507219  0.066192  0.055953   \n",
       "                    GaussianNB              0.490386  0.021480  0.012708   \n",
       "                    LinearSVC               0.491944  0.075766 -0.021065   \n",
       "                    LogisticRegression      0.490723  0.022381  0.000000   \n",
       "                    MLPClassifier           0.507545  0.036732  0.030393   \n",
       "                    RandomForestClassifier  0.543404  0.063341  0.085101   \n",
       "rdf2vec   10        Decision Tree           0.453187  0.199206 -0.049420   \n",
       "                    GaussianNB              0.498889  0.207983 -0.036215   \n",
       "                    LinearSVC               0.491131  0.216976 -0.098738   \n",
       "                    LogisticRegression      0.505121  0.237059 -0.077826   \n",
       "                    MLPClassifier           0.583953  0.185790 -0.078269   \n",
       "                    RandomForestClassifier  0.617057  0.156574  0.087958   \n",
       "          100       Decision Tree           0.501555  0.113272  0.007756   \n",
       "                    GaussianNB              0.518631  0.054483  0.045430   \n",
       "                    LinearSVC               0.542639  0.090164  0.095354   \n",
       "                    LogisticRegression      0.537846  0.067516  0.082915   \n",
       "                    MLPClassifier           0.521841  0.117357  0.066283   \n",
       "                    RandomForestClassifier  0.508073  0.097446  0.016046   \n",
       "          200       Decision Tree           0.529154  0.091355  0.055814   \n",
       "                    GaussianNB              0.549291  0.037235  0.097155   \n",
       "                    LinearSVC               0.489477  0.076079 -0.024970   \n",
       "                    LogisticRegression      0.503967  0.068291  0.010199   \n",
       "                    MLPClassifier           0.499241  0.069569 -0.008334   \n",
       "                    RandomForestClassifier  0.498594  0.089448 -0.013399   \n",
       "\n",
       "                                                          F1:A            \\\n",
       "                                                 std      mean       std   \n",
       "EmbMethod InputSize Method                                                 \n",
       "bertcode  10        Decision Tree           0.155699  0.474509  0.327521   \n",
       "                    GaussianNB              0.174955  0.645235  0.161534   \n",
       "                    LinearSVC               0.323867  0.508284  0.207670   \n",
       "                    LogisticRegression      0.117060  0.626081  0.238335   \n",
       "                    MLPClassifier           0.149450  0.129732  0.208961   \n",
       "                    RandomForestClassifier  0.208835  0.390789  0.318490   \n",
       "          100       Decision Tree           0.150209  0.421481  0.268183   \n",
       "                    GaussianNB              0.000000  0.379583  0.328805   \n",
       "                    LinearSVC               0.230720  0.537006  0.112050   \n",
       "                    LogisticRegression      0.000000  0.653459  0.048720   \n",
       "                    MLPClassifier           0.039578  0.167648  0.299100   \n",
       "                    RandomForestClassifier  0.056470  0.499020  0.251806   \n",
       "          200       Decision Tree           0.154564  0.591159  0.086300   \n",
       "                    GaussianNB              0.027120  0.324759  0.342453   \n",
       "                    LinearSVC               0.150728  0.494557  0.074680   \n",
       "                    LogisticRegression      0.000000  0.658102  0.019782   \n",
       "                    MLPClassifier           0.059014  0.311303  0.331795   \n",
       "                    RandomForestClassifier  0.151955  0.507790  0.176283   \n",
       "rdf2vec   10        Decision Tree           0.160938  0.308158  0.273787   \n",
       "                    GaussianNB              0.182453  0.186948  0.270693   \n",
       "                    LinearSVC               0.197935  0.028571  0.090351   \n",
       "                    LogisticRegression      0.203015  0.019048  0.060234   \n",
       "                    MLPClassifier           0.225509  0.202529  0.337702   \n",
       "                    RandomForestClassifier  0.274904  0.254790  0.331891   \n",
       "          100       Decision Tree           0.232605  0.431772  0.142056   \n",
       "                    GaussianNB              0.104220  0.587855  0.050806   \n",
       "                    LinearSVC               0.187593  0.300640  0.197253   \n",
       "                    LogisticRegression      0.138654  0.244688  0.151533   \n",
       "                    MLPClassifier           0.256900  0.377027  0.228666   \n",
       "                    RandomForestClassifier  0.197368  0.372670  0.163452   \n",
       "          200       Decision Tree           0.189328  0.530879  0.122103   \n",
       "                    GaussianNB              0.063976  0.613375  0.041323   \n",
       "                    LinearSVC               0.151666  0.386531  0.168115   \n",
       "                    LogisticRegression      0.150022  0.343759  0.162173   \n",
       "                    MLPClassifier           0.142860  0.433361  0.170201   \n",
       "                    RandomForestClassifier  0.198713  0.419499  0.175352   \n",
       "\n",
       "                                                F1:B            \n",
       "                                                mean       std  \n",
       "EmbMethod InputSize Method                                      \n",
       "bertcode  10        Decision Tree           0.302640  0.346021  \n",
       "                    GaussianNB              0.121571  0.219412  \n",
       "                    LinearSVC               0.417724  0.267211  \n",
       "                    LogisticRegression      0.138053  0.225296  \n",
       "                    MLPClassifier           0.553772  0.193065  \n",
       "                    RandomForestClassifier  0.342857  0.283814  \n",
       "          100       Decision Tree           0.368784  0.318709  \n",
       "                    GaussianNB              0.258860  0.334646  \n",
       "                    LinearSVC               0.463363  0.157062  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.555902  0.286502  \n",
       "                    RandomForestClassifier  0.255291  0.280646  \n",
       "          200       Decision Tree           0.256554  0.250509  \n",
       "                    GaussianNB              0.336016  0.347464  \n",
       "                    LinearSVC               0.469623  0.130114  \n",
       "                    LogisticRegression      0.000000  0.000000  \n",
       "                    MLPClassifier           0.409777  0.333887  \n",
       "                    RandomForestClassifier  0.468299  0.246976  \n",
       "rdf2vec   10        Decision Tree           0.394730  0.280213  \n",
       "                    GaussianNB              0.517328  0.302166  \n",
       "                    LinearSVC               0.603721  0.267550  \n",
       "                    LogisticRegression      0.624834  0.252230  \n",
       "                    MLPClassifier           0.585241  0.275402  \n",
       "                    RandomForestClassifier  0.645227  0.257876  \n",
       "          100       Decision Tree           0.542375  0.114097  \n",
       "                    GaussianNB              0.412301  0.090914  \n",
       "                    LinearSVC               0.652795  0.063657  \n",
       "                    LogisticRegression      0.663508  0.050385  \n",
       "                    MLPClassifier           0.574400  0.133601  \n",
       "                    RandomForestClassifier  0.564845  0.135250  \n",
       "          200       Decision Tree           0.505031  0.122745  \n",
       "                    GaussianNB              0.456552  0.043867  \n",
       "                    LinearSVC               0.515030  0.141247  \n",
       "                    LogisticRegression      0.584036  0.068676  \n",
       "                    MLPClassifier           0.495393  0.162417  \n",
       "                    RandomForestClassifier  0.519019  0.149093  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Table(BC, 'BC_VisualisationPreparation.csv', 3 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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