diff --git "a/notebooks/feature_selection.ipynb" "b/notebooks/feature_selection.ipynb"
deleted file mode 100644--- "a/notebooks/feature_selection.ipynb"
+++ /dev/null
@@ -1,4557 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4fa5c9aa",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(34, 178)\n",
-      "['ratio_unique_traces_per_trace', 'trace_len_hist2', 'trace_len_hist3', 'trace_len_hist4', 'trace_len_hist5', 'trace_len_hist7', 'trace_len_hist8', 'trace_len_hist9', 'ratio_most_common_variant', 'ratio_top_1_variants', 'ratio_top_5_variants', 'ratio_top_10_variants', 'ratio_top_20_variants', 'ratio_top_50_variants', 'ratio_top_75_variants', 'normalized_variant_entropy', 'normalized_sequence_entropy', 'normalized_sequence_eventropy_linear_forgetting', 'normalized_sequence_eventropy_exponential_forgetting']\n",
-      "(34, 102)\n",
-      "Index(['log', 'n_traces', 'n_unique_traces', 'ratio_unique_traces_per_trace',\n",
-      "       'trace_len_min', 'trace_len_max', 'trace_len_mean', 'trace_len_median',\n",
-      "       'trace_len_mode', 'trace_len_std',\n",
-      "       ...\n",
-      "       'eventropy_knn_7', 'Log Nature', 'variant_entropy',\n",
-      "       'normalized_variant_entropy', 'sequence_entropy',\n",
-      "       'normalized_sequence_entropy', 'sequence_eventropy_linear_forgetting',\n",
-      "       'normalized_sequence_eventropy_linear_forgetting',\n",
-      "       'sequence_eventropy_exponential_forgetting',\n",
-      "       'normalized_sequence_eventropy_exponential_forgetting'],\n",
-      "      dtype='object', length=102)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>log</th>\n",
-       "      <th>n_traces</th>\n",
-       "      <th>n_unique_traces</th>\n",
-       "      <th>ratio_unique_traces_per_trace</th>\n",
-       "      <th>trace_len_min</th>\n",
-       "      <th>trace_len_max</th>\n",
-       "      <th>trace_len_mean</th>\n",
-       "      <th>trace_len_median</th>\n",
-       "      <th>trace_len_mode</th>\n",
-       "      <th>trace_len_std</th>\n",
-       "      <th>...</th>\n",
-       "      <th>eventropy_knn_7</th>\n",
-       "      <th>Log Nature</th>\n",
-       "      <th>variant_entropy</th>\n",
-       "      <th>normalized_variant_entropy</th>\n",
-       "      <th>sequence_entropy</th>\n",
-       "      <th>normalized_sequence_entropy</th>\n",
-       "      <th>sequence_eventropy_linear_forgetting</th>\n",
-       "      <th>normalized_sequence_eventropy_linear_forgetting</th>\n",
-       "      <th>sequence_eventropy_exponential_forgetting</th>\n",
-       "      <th>normalized_sequence_eventropy_exponential_forgetting</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>BPIC15_2</td>\n",
-       "      <td>832</td>\n",
-       "      <td>828</td>\n",
-       "      <td>0.995192</td>\n",
-       "      <td>1</td>\n",
-       "      <td>132</td>\n",
-       "      <td>53.310096</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>61</td>\n",
-       "      <td>19.894977</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.721</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.405122e+05</td>\n",
-       "      <td>0.627973</td>\n",
-       "      <td>2.858769e+05</td>\n",
-       "      <td>0.602371</td>\n",
-       "      <td>1.505466e+05</td>\n",
-       "      <td>0.317217</td>\n",
-       "      <td>1.853129e+05</td>\n",
-       "      <td>0.390473</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>BPI_Challenge_2018</td>\n",
-       "      <td>43809</td>\n",
-       "      <td>28457</td>\n",
-       "      <td>0.649570</td>\n",
-       "      <td>24</td>\n",
-       "      <td>2973</td>\n",
-       "      <td>57.391541</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>49</td>\n",
-       "      <td>34.872131</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.067</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.156384e+07</td>\n",
-       "      <td>0.712079</td>\n",
-       "      <td>2.114626e+07</td>\n",
-       "      <td>0.570688</td>\n",
-       "      <td>1.414023e+07</td>\n",
-       "      <td>0.381612</td>\n",
-       "      <td>1.557608e+07</td>\n",
-       "      <td>0.420362</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Receipt_WABO_CoSeLoG</td>\n",
-       "      <td>1434</td>\n",
-       "      <td>116</td>\n",
-       "      <td>0.080893</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25</td>\n",
-       "      <td>5.981172</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.166129</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.584</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.382326e+03</td>\n",
-       "      <td>0.689363</td>\n",
-       "      <td>1.829627e+04</td>\n",
-       "      <td>0.235532</td>\n",
-       "      <td>7.814868e+03</td>\n",
-       "      <td>0.100603</td>\n",
-       "      <td>1.072870e+04</td>\n",
-       "      <td>0.138113</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>BPIC15_3</td>\n",
-       "      <td>1409</td>\n",
-       "      <td>1349</td>\n",
-       "      <td>0.957417</td>\n",
-       "      <td>3</td>\n",
-       "      <td>124</td>\n",
-       "      <td>42.356991</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>44</td>\n",
-       "      <td>16.138406</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.900</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.981464e+05</td>\n",
-       "      <td>0.661781</td>\n",
-       "      <td>3.975043e+05</td>\n",
-       "      <td>0.605676</td>\n",
-       "      <td>2.241393e+05</td>\n",
-       "      <td>0.341521</td>\n",
-       "      <td>2.657571e+05</td>\n",
-       "      <td>0.404934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>BPI_Challenge_2019</td>\n",
-       "      <td>251734</td>\n",
-       "      <td>11973</td>\n",
-       "      <td>0.047562</td>\n",
-       "      <td>1</td>\n",
-       "      <td>990</td>\n",
-       "      <td>6.339720</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>13.057417</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.601</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.690369e+06</td>\n",
-       "      <td>0.645530</td>\n",
-       "      <td>7.477256e+06</td>\n",
-       "      <td>0.328029</td>\n",
-       "      <td>7.298458e+06</td>\n",
-       "      <td>0.320185</td>\n",
-       "      <td>7.300663e+06</td>\n",
-       "      <td>0.320282</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>RequestForPayment</td>\n",
-       "      <td>6886</td>\n",
-       "      <td>89</td>\n",
-       "      <td>0.012925</td>\n",
-       "      <td>1</td>\n",
-       "      <td>20</td>\n",
-       "      <td>5.343596</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.467384</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.312</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.322840e+03</td>\n",
-       "      <td>0.703735</td>\n",
-       "      <td>7.313156e+04</td>\n",
-       "      <td>0.189048</td>\n",
-       "      <td>3.774501e+04</td>\n",
-       "      <td>0.097572</td>\n",
-       "      <td>4.593493e+04</td>\n",
-       "      <td>0.118744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>PrepaidTravelCost</td>\n",
-       "      <td>2099</td>\n",
-       "      <td>202</td>\n",
-       "      <td>0.096236</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21</td>\n",
-       "      <td>8.692711</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2.253873</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.987</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>5.488774e+03</td>\n",
-       "      <td>0.723785</td>\n",
-       "      <td>5.675857e+04</td>\n",
-       "      <td>0.317044</td>\n",
-       "      <td>3.309789e+04</td>\n",
-       "      <td>0.184879</td>\n",
-       "      <td>3.838045e+04</td>\n",
-       "      <td>0.214387</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>DomesticDeclarations</td>\n",
-       "      <td>10500</td>\n",
-       "      <td>99</td>\n",
-       "      <td>0.009429</td>\n",
-       "      <td>1</td>\n",
-       "      <td>24</td>\n",
-       "      <td>5.374952</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.486345</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.315</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.649696e+03</td>\n",
-       "      <td>0.696474</td>\n",
-       "      <td>1.017330e+05</td>\n",
-       "      <td>0.164758</td>\n",
-       "      <td>5.275579e+04</td>\n",
-       "      <td>0.085439</td>\n",
-       "      <td>6.445733e+04</td>\n",
-       "      <td>0.104389</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>BPIC15_4</td>\n",
-       "      <td>1053</td>\n",
-       "      <td>1049</td>\n",
-       "      <td>0.996201</td>\n",
-       "      <td>1</td>\n",
-       "      <td>116</td>\n",
-       "      <td>44.912631</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>42</td>\n",
-       "      <td>14.947644</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.729</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.357325e+05</td>\n",
-       "      <td>0.652985</td>\n",
-       "      <td>3.074084e+05</td>\n",
-       "      <td>0.603866</td>\n",
-       "      <td>1.811907e+05</td>\n",
-       "      <td>0.355927</td>\n",
-       "      <td>2.101610e+05</td>\n",
-       "      <td>0.412835</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>BPI_Challenge_2012</td>\n",
-       "      <td>13087</td>\n",
-       "      <td>4366</td>\n",
-       "      <td>0.333614</td>\n",
-       "      <td>3</td>\n",
-       "      <td>175</td>\n",
-       "      <td>20.035149</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>19.943559</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5.335</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>4.749288e+05</td>\n",
-       "      <td>0.708280</td>\n",
-       "      <td>1.384057e+06</td>\n",
-       "      <td>0.423074</td>\n",
-       "      <td>7.397790e+05</td>\n",
-       "      <td>0.226133</td>\n",
-       "      <td>9.014471e+05</td>\n",
-       "      <td>0.275551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Hospital_log</td>\n",
-       "      <td>1143</td>\n",
-       "      <td>981</td>\n",
-       "      <td>0.858268</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1814</td>\n",
-       "      <td>131.488189</td>\n",
-       "      <td>55.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>202.528729</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.931</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>8.469167e+05</td>\n",
-       "      <td>0.517443</td>\n",
-       "      <td>9.191069e+05</td>\n",
-       "      <td>0.513032</td>\n",
-       "      <td>4.798126e+05</td>\n",
-       "      <td>0.267825</td>\n",
-       "      <td>5.941966e+05</td>\n",
-       "      <td>0.331672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>BPIC15_5</td>\n",
-       "      <td>1156</td>\n",
-       "      <td>1153</td>\n",
-       "      <td>0.997405</td>\n",
-       "      <td>5</td>\n",
-       "      <td>154</td>\n",
-       "      <td>51.109862</td>\n",
-       "      <td>50.0</td>\n",
-       "      <td>44</td>\n",
-       "      <td>16.029953</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.841</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>3.031393e+05</td>\n",
-       "      <td>0.648702</td>\n",
-       "      <td>3.915923e+05</td>\n",
-       "      <td>0.603260</td>\n",
-       "      <td>2.222674e+05</td>\n",
-       "      <td>0.342410</td>\n",
-       "      <td>2.626238e+05</td>\n",
-       "      <td>0.404580</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>CoSeLoG_WABO_2</td>\n",
-       "      <td>645</td>\n",
-       "      <td>644</td>\n",
-       "      <td>0.998450</td>\n",
-       "      <td>1</td>\n",
-       "      <td>124</td>\n",
-       "      <td>51.741085</td>\n",
-       "      <td>51.0</td>\n",
-       "      <td>44</td>\n",
-       "      <td>20.373556</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.430</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.744120e+05</td>\n",
-       "      <td>0.618455</td>\n",
-       "      <td>2.064846e+05</td>\n",
-       "      <td>0.594035</td>\n",
-       "      <td>1.123546e+05</td>\n",
-       "      <td>0.323233</td>\n",
-       "      <td>1.355134e+05</td>\n",
-       "      <td>0.389858</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Road_Traffic_Fine_Management_Process</td>\n",
-       "      <td>150370</td>\n",
-       "      <td>231</td>\n",
-       "      <td>0.001536</td>\n",
-       "      <td>2</td>\n",
-       "      <td>20</td>\n",
-       "      <td>3.733923</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.641012</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.790</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>1.892307e+03</td>\n",
-       "      <td>0.769353</td>\n",
-       "      <td>8.319835e+05</td>\n",
-       "      <td>0.111932</td>\n",
-       "      <td>3.908680e+05</td>\n",
-       "      <td>0.052586</td>\n",
-       "      <td>5.087233e+05</td>\n",
-       "      <td>0.068442</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>BPI_Challenge_2017_Offer_log</td>\n",
-       "      <td>42995</td>\n",
-       "      <td>16</td>\n",
-       "      <td>0.000372</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4.508641</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.560753</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.618</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>4.232263e+01</td>\n",
-       "      <td>0.813479</td>\n",
-       "      <td>2.481151e+05</td>\n",
-       "      <td>0.105130</td>\n",
-       "      <td>1.243092e+05</td>\n",
-       "      <td>0.052672</td>\n",
-       "      <td>1.557649e+05</td>\n",
-       "      <td>0.066000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>Sepsis_Cases_Event_Log</td>\n",
-       "      <td>1050</td>\n",
-       "      <td>846</td>\n",
-       "      <td>0.805714</td>\n",
-       "      <td>3</td>\n",
-       "      <td>185</td>\n",
-       "      <td>14.489524</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>11.470475</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.191</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>4.062449e+04</td>\n",
-       "      <td>0.695759</td>\n",
-       "      <td>7.652868e+04</td>\n",
-       "      <td>0.522343</td>\n",
-       "      <td>3.213928e+04</td>\n",
-       "      <td>0.219365</td>\n",
-       "      <td>4.388054e+04</td>\n",
-       "      <td>0.299505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>CoSeLoG_WABO_3</td>\n",
-       "      <td>1087</td>\n",
-       "      <td>1032</td>\n",
-       "      <td>0.949402</td>\n",
-       "      <td>3</td>\n",
-       "      <td>124</td>\n",
-       "      <td>41.215271</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>46</td>\n",
-       "      <td>15.780335</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.537</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.127409e+05</td>\n",
-       "      <td>0.654296</td>\n",
-       "      <td>2.861474e+05</td>\n",
-       "      <td>0.596367</td>\n",
-       "      <td>1.334475e+05</td>\n",
-       "      <td>0.278121</td>\n",
-       "      <td>1.710260e+05</td>\n",
-       "      <td>0.356439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>BPI_Challenge_2013_closed_problems</td>\n",
-       "      <td>1487</td>\n",
-       "      <td>183</td>\n",
-       "      <td>0.123067</td>\n",
-       "      <td>1</td>\n",
-       "      <td>35</td>\n",
-       "      <td>4.478816</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3.170888</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.386</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>3.502264e+03</td>\n",
-       "      <td>0.705383</td>\n",
-       "      <td>1.823159e+04</td>\n",
-       "      <td>0.310940</td>\n",
-       "      <td>1.679949e+04</td>\n",
-       "      <td>0.286515</td>\n",
-       "      <td>1.690901e+04</td>\n",
-       "      <td>0.288383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>BPI_Challenge_2013_incidents</td>\n",
-       "      <td>7554</td>\n",
-       "      <td>1511</td>\n",
-       "      <td>0.200026</td>\n",
-       "      <td>1</td>\n",
-       "      <td>123</td>\n",
-       "      <td>8.675271</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7.625071</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.083</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>8.867742e+04</td>\n",
-       "      <td>0.717846</td>\n",
-       "      <td>2.940925e+05</td>\n",
-       "      <td>0.404651</td>\n",
-       "      <td>2.842417e+05</td>\n",
-       "      <td>0.391097</td>\n",
-       "      <td>2.846260e+05</td>\n",
-       "      <td>0.391625</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>PermitLog</td>\n",
-       "      <td>7065</td>\n",
-       "      <td>1478</td>\n",
-       "      <td>0.209200</td>\n",
-       "      <td>3</td>\n",
-       "      <td>90</td>\n",
-       "      <td>12.254919</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>5.584833</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.725</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>9.661835e+04</td>\n",
-       "      <td>0.733653</td>\n",
-       "      <td>4.135643e+05</td>\n",
-       "      <td>0.420150</td>\n",
-       "      <td>1.351355e+05</td>\n",
-       "      <td>0.137287</td>\n",
-       "      <td>2.121125e+05</td>\n",
-       "      <td>0.215490</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>BPIC15_1</td>\n",
-       "      <td>1199</td>\n",
-       "      <td>1170</td>\n",
-       "      <td>0.975813</td>\n",
-       "      <td>2</td>\n",
-       "      <td>101</td>\n",
-       "      <td>43.550459</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>44</td>\n",
-       "      <td>16.981536</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4.931</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.772222e+05</td>\n",
-       "      <td>0.652855</td>\n",
-       "      <td>3.461843e+05</td>\n",
-       "      <td>0.610294</td>\n",
-       "      <td>1.532922e+05</td>\n",
-       "      <td>0.270241</td>\n",
-       "      <td>2.064350e+05</td>\n",
-       "      <td>0.363928</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>InternationalDeclarations</td>\n",
-       "      <td>6449</td>\n",
-       "      <td>753</td>\n",
-       "      <td>0.116762</td>\n",
-       "      <td>3</td>\n",
-       "      <td>27</td>\n",
-       "      <td>11.187936</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>2.740671</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.986</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>3.072202e+04</td>\n",
-       "      <td>0.758268</td>\n",
-       "      <td>2.739197e+05</td>\n",
-       "      <td>0.339380</td>\n",
-       "      <td>1.175256e+05</td>\n",
-       "      <td>0.145611</td>\n",
-       "      <td>1.563815e+05</td>\n",
-       "      <td>0.193753</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>BPI_Challenge_2017</td>\n",
-       "      <td>31509</td>\n",
-       "      <td>15930</td>\n",
-       "      <td>0.505570</td>\n",
-       "      <td>10</td>\n",
-       "      <td>180</td>\n",
-       "      <td>38.156305</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>24</td>\n",
-       "      <td>16.715043</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6.297</td>\n",
-       "      <td>Real</td>\n",
-       "      <td>2.602767e+06</td>\n",
-       "      <td>0.741706</td>\n",
-       "      <td>7.768787e+06</td>\n",
-       "      <td>0.461565</td>\n",
-       "      <td>3.903568e+06</td>\n",
-       "      <td>0.231922</td>\n",
-       "      <td>4.888915e+06</td>\n",
-       "      <td>0.290464</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>BPI2016_Complaints</td>\n",
-       "      <td>226</td>\n",
-       "      <td>99</td>\n",
-       "      <td>0.438053</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.278761</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.642995</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3.272</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5.219700e+02</td>\n",
-       "      <td>0.899497</td>\n",
-       "      <td>1.119782e+03</td>\n",
-       "      <td>0.683796</td>\n",
-       "      <td>6.627117e+02</td>\n",
-       "      <td>0.404685</td>\n",
-       "      <td>7.698614e+02</td>\n",
-       "      <td>0.470116</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>BPI2016_Questions</td>\n",
-       "      <td>21533</td>\n",
-       "      <td>17171</td>\n",
-       "      <td>0.797427</td>\n",
-       "      <td>1</td>\n",
-       "      <td>102</td>\n",
-       "      <td>5.730878</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5.485213</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8.054</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.500998e+05</td>\n",
-       "      <td>0.813468</td>\n",
-       "      <td>1.093881e+06</td>\n",
-       "      <td>0.756132</td>\n",
-       "      <td>6.147088e+05</td>\n",
-       "      <td>0.424910</td>\n",
-       "      <td>7.321911e+05</td>\n",
-       "      <td>0.506118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>BPI2016_Werkmap_Messages</td>\n",
-       "      <td>16653</td>\n",
-       "      <td>48</td>\n",
-       "      <td>0.002882</td>\n",
-       "      <td>1</td>\n",
-       "      <td>61</td>\n",
-       "      <td>3.966733</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.203692</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.055</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000e+00</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>BPI_Challenge_2013_open_problems</td>\n",
-       "      <td>819</td>\n",
-       "      <td>108</td>\n",
-       "      <td>0.131868</td>\n",
-       "      <td>1</td>\n",
-       "      <td>22</td>\n",
-       "      <td>2.870574</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.082628</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.259</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.200861e+02</td>\n",
-       "      <td>0.702960</td>\n",
-       "      <td>5.051026e+03</td>\n",
-       "      <td>0.276771</td>\n",
-       "      <td>4.783176e+03</td>\n",
-       "      <td>0.262094</td>\n",
-       "      <td>4.800238e+03</td>\n",
-       "      <td>0.263029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>CoSeLoG_WABO_1</td>\n",
-       "      <td>937</td>\n",
-       "      <td>916</td>\n",
-       "      <td>0.977588</td>\n",
-       "      <td>2</td>\n",
-       "      <td>95</td>\n",
-       "      <td>41.562433</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>40</td>\n",
-       "      <td>16.678023</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.951662e+05</td>\n",
-       "      <td>0.646697</td>\n",
-       "      <td>2.476248e+05</td>\n",
-       "      <td>0.601566</td>\n",
-       "      <td>1.205360e+05</td>\n",
-       "      <td>0.292824</td>\n",
-       "      <td>1.548878e+05</td>\n",
-       "      <td>0.376276</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>CoSeLoG_WABO_4</td>\n",
-       "      <td>787</td>\n",
-       "      <td>781</td>\n",
-       "      <td>0.992376</td>\n",
-       "      <td>2</td>\n",
-       "      <td>93</td>\n",
-       "      <td>44.279543</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>39</td>\n",
-       "      <td>13.950744</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.675112e+05</td>\n",
-       "      <td>0.644399</td>\n",
-       "      <td>2.176264e+05</td>\n",
-       "      <td>0.597109</td>\n",
-       "      <td>1.362814e+05</td>\n",
-       "      <td>0.373920</td>\n",
-       "      <td>1.539968e+05</td>\n",
-       "      <td>0.422526</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>CoSeLoG_WABO_5</td>\n",
-       "      <td>892</td>\n",
-       "      <td>879</td>\n",
-       "      <td>0.985426</td>\n",
-       "      <td>5</td>\n",
-       "      <td>100</td>\n",
-       "      <td>49.210762</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>44</td>\n",
-       "      <td>14.946856</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.097776e+05</td>\n",
-       "      <td>0.642668</td>\n",
-       "      <td>2.779969e+05</td>\n",
-       "      <td>0.592454</td>\n",
-       "      <td>1.627439e+05</td>\n",
-       "      <td>0.346832</td>\n",
-       "      <td>1.885039e+05</td>\n",
-       "      <td>0.401731</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>Detail_Change</td>\n",
-       "      <td>18000</td>\n",
-       "      <td>872</td>\n",
-       "      <td>0.048444</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1007</td>\n",
-       "      <td>1.681944</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7.979279</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>Detail_Incident_Activity</td>\n",
-       "      <td>46616</td>\n",
-       "      <td>23161</td>\n",
-       "      <td>0.496847</td>\n",
-       "      <td>1</td>\n",
-       "      <td>178</td>\n",
-       "      <td>10.012378</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9.653109</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7.647</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>Detail_Interaction</td>\n",
-       "      <td>147004</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.000041</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>finale</td>\n",
-       "      <td>4580</td>\n",
-       "      <td>226</td>\n",
-       "      <td>0.049345</td>\n",
-       "      <td>2</td>\n",
-       "      <td>15</td>\n",
-       "      <td>4.661135</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.179897</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.748</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.724970e+03</td>\n",
-       "      <td>0.799120</td>\n",
-       "      <td>5.406829e+04</td>\n",
-       "      <td>0.254066</td>\n",
-       "      <td>2.521350e+04</td>\n",
-       "      <td>0.118478</td>\n",
-       "      <td>3.289574e+04</td>\n",
-       "      <td>0.154576</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>34 rows × 102 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     log  n_traces  n_unique_traces   \n",
-       "0                               BPIC15_2       832              828  \\\n",
-       "1                     BPI_Challenge_2018     43809            28457   \n",
-       "2                   Receipt_WABO_CoSeLoG      1434              116   \n",
-       "3                               BPIC15_3      1409             1349   \n",
-       "4                     BPI_Challenge_2019    251734            11973   \n",
-       "5                      RequestForPayment      6886               89   \n",
-       "6                      PrepaidTravelCost      2099              202   \n",
-       "7                   DomesticDeclarations     10500               99   \n",
-       "8                               BPIC15_4      1053             1049   \n",
-       "9                     BPI_Challenge_2012     13087             4366   \n",
-       "10                          Hospital_log      1143              981   \n",
-       "11                              BPIC15_5      1156             1153   \n",
-       "12                        CoSeLoG_WABO_2       645              644   \n",
-       "13  Road_Traffic_Fine_Management_Process    150370              231   \n",
-       "14          BPI_Challenge_2017_Offer_log     42995               16   \n",
-       "15                Sepsis_Cases_Event_Log      1050              846   \n",
-       "16                        CoSeLoG_WABO_3      1087             1032   \n",
-       "17    BPI_Challenge_2013_closed_problems      1487              183   \n",
-       "18          BPI_Challenge_2013_incidents      7554             1511   \n",
-       "19                             PermitLog      7065             1478   \n",
-       "20                              BPIC15_1      1199             1170   \n",
-       "21             InternationalDeclarations      6449              753   \n",
-       "22                    BPI_Challenge_2017     31509            15930   \n",
-       "23                    BPI2016_Complaints       226               99   \n",
-       "24                     BPI2016_Questions     21533            17171   \n",
-       "25              BPI2016_Werkmap_Messages     16653               48   \n",
-       "26      BPI_Challenge_2013_open_problems       819              108   \n",
-       "27                        CoSeLoG_WABO_1       937              916   \n",
-       "28                        CoSeLoG_WABO_4       787              781   \n",
-       "29                        CoSeLoG_WABO_5       892              879   \n",
-       "30                         Detail_Change     18000              872   \n",
-       "31              Detail_Incident_Activity     46616            23161   \n",
-       "32                    Detail_Interaction    147004                6   \n",
-       "33                                finale      4580              226   \n",
-       "\n",
-       "    ratio_unique_traces_per_trace  trace_len_min  trace_len_max   \n",
-       "0                        0.995192              1            132  \\\n",
-       "1                        0.649570             24           2973   \n",
-       "2                        0.080893              1             25   \n",
-       "3                        0.957417              3            124   \n",
-       "4                        0.047562              1            990   \n",
-       "5                        0.012925              1             20   \n",
-       "6                        0.096236              1             21   \n",
-       "7                        0.009429              1             24   \n",
-       "8                        0.996201              1            116   \n",
-       "9                        0.333614              3            175   \n",
-       "10                       0.858268              1           1814   \n",
-       "11                       0.997405              5            154   \n",
-       "12                       0.998450              1            124   \n",
-       "13                       0.001536              2             20   \n",
-       "14                       0.000372              3              5   \n",
-       "15                       0.805714              3            185   \n",
-       "16                       0.949402              3            124   \n",
-       "17                       0.123067              1             35   \n",
-       "18                       0.200026              1            123   \n",
-       "19                       0.209200              3             90   \n",
-       "20                       0.975813              2            101   \n",
-       "21                       0.116762              3             27   \n",
-       "22                       0.505570             10            180   \n",
-       "23                       0.438053              1              5   \n",
-       "24                       0.797427              1            102   \n",
-       "25                       0.002882              1             61   \n",
-       "26                       0.131868              1             22   \n",
-       "27                       0.977588              2             95   \n",
-       "28                       0.992376              2             93   \n",
-       "29                       0.985426              5            100   \n",
-       "30                       0.048444              1           1007   \n",
-       "31                       0.496847              1            178   \n",
-       "32                       0.000041              1              1   \n",
-       "33                       0.049345              2             15   \n",
-       "\n",
-       "    trace_len_mean  trace_len_median  trace_len_mode  trace_len_std  ...   \n",
-       "0        53.310096              54.0              61      19.894977  ...  \\\n",
-       "1        57.391541              49.0              49      34.872131  ...   \n",
-       "2         5.981172               6.0               6       2.166129  ...   \n",
-       "3        42.356991              42.0              44      16.138406  ...   \n",
-       "4         6.339720               5.0               5      13.057417  ...   \n",
-       "5         5.343596               5.0               5       1.467384  ...   \n",
-       "6         8.692711               8.0               8       2.253873  ...   \n",
-       "7         5.374952               5.0               5       1.486345  ...   \n",
-       "8        44.912631              44.0              42      14.947644  ...   \n",
-       "9        20.035149              11.0               3      19.943559  ...   \n",
-       "10      131.488189              55.0               2     202.528729  ...   \n",
-       "11       51.109862              50.0              44      16.029953  ...   \n",
-       "12       51.741085              51.0              44      20.373556  ...   \n",
-       "13        3.733923               5.0               2       1.641012  ...   \n",
-       "14        4.508641               5.0               5       0.560753  ...   \n",
-       "15       14.489524              13.0               8      11.470475  ...   \n",
-       "16       41.215271              41.0              46      15.780335  ...   \n",
-       "17        4.478816               3.0               2       3.170888  ...   \n",
-       "18        8.675271               6.0               3       7.625071  ...   \n",
-       "19       12.254919              11.0              10       5.584833  ...   \n",
-       "20       43.550459              44.0              44      16.981536  ...   \n",
-       "21       11.187936              10.0              10       2.740671  ...   \n",
-       "22       38.156305              35.0              24      16.715043  ...   \n",
-       "23        1.278761               1.0               1       0.642995  ...   \n",
-       "24        5.730878               4.0               1       5.485213  ...   \n",
-       "25        3.966733               3.0               1       4.203692  ...   \n",
-       "26        2.870574               2.0               2       2.082628  ...   \n",
-       "27       41.562433              43.0              40      16.678023  ...   \n",
-       "28       44.279543              44.0              39      13.950744  ...   \n",
-       "29       49.210762              46.0              44      14.946856  ...   \n",
-       "30        1.681944               1.0               1       7.979279  ...   \n",
-       "31       10.012378               7.0               5       9.653109  ...   \n",
-       "32        1.000000               1.0               1       0.000000  ...   \n",
-       "33        4.661135               4.0               4       1.179897  ...   \n",
-       "\n",
-       "    eventropy_knn_7  Log Nature  variant_entropy  normalized_variant_entropy   \n",
-       "0             4.721        Real     2.405122e+05                    0.627973  \\\n",
-       "1             7.067        Real     1.156384e+07                    0.712079   \n",
-       "2             2.584        Real     2.382326e+03                    0.689363   \n",
-       "3             4.900        Real     2.981464e+05                    0.661781   \n",
-       "4             6.601        Real     1.690369e+06                    0.645530   \n",
-       "5             2.312        Real     1.322840e+03                    0.703735   \n",
-       "6             2.987        Real     5.488774e+03                    0.723785   \n",
-       "7             2.315        Real     1.649696e+03                    0.696474   \n",
-       "8             4.729        Real     2.357325e+05                    0.652985   \n",
-       "9             5.335        Real     4.749288e+05                    0.708280   \n",
-       "10            4.931        Real     8.469167e+05                    0.517443   \n",
-       "11            4.841        Real     3.031393e+05                    0.648702   \n",
-       "12            4.430        Real     1.744120e+05                    0.618455   \n",
-       "13            2.790        Real     1.892307e+03                    0.769353   \n",
-       "14            0.618        Real     4.232263e+01                    0.813479   \n",
-       "15            4.191        Real     4.062449e+04                    0.695759   \n",
-       "16            4.537        Real     2.127409e+05                    0.654296   \n",
-       "17            2.386        Real     3.502264e+03                    0.705383   \n",
-       "18            4.083        Real     8.867742e+04                    0.717846   \n",
-       "19            4.725        Real     9.661835e+04                    0.733653   \n",
-       "20            4.931        Real     2.772222e+05                    0.652855   \n",
-       "21            3.986        Real     3.072202e+04                    0.758268   \n",
-       "22            6.297        Real     2.602767e+06                    0.741706   \n",
-       "23            3.272         NaN     5.219700e+02                    0.899497   \n",
-       "24            8.054         NaN     8.500998e+05                    0.813468   \n",
-       "25            1.055         NaN     0.000000e+00                    0.000000   \n",
-       "26            2.259         NaN     9.200861e+02                    0.702960   \n",
-       "27            0.000         NaN     1.951662e+05                    0.646697   \n",
-       "28            0.000         NaN     1.675112e+05                    0.644399   \n",
-       "29            0.000         NaN     2.097776e+05                    0.642668   \n",
-       "30            0.000         NaN              NaN                         NaN   \n",
-       "31            7.647         NaN              NaN                         NaN   \n",
-       "32            0.000         NaN              NaN                         NaN   \n",
-       "33            2.748         NaN     3.724970e+03                    0.799120   \n",
-       "\n",
-       "    sequence_entropy  normalized_sequence_entropy   \n",
-       "0       2.858769e+05                     0.602371  \\\n",
-       "1       2.114626e+07                     0.570688   \n",
-       "2       1.829627e+04                     0.235532   \n",
-       "3       3.975043e+05                     0.605676   \n",
-       "4       7.477256e+06                     0.328029   \n",
-       "5       7.313156e+04                     0.189048   \n",
-       "6       5.675857e+04                     0.317044   \n",
-       "7       1.017330e+05                     0.164758   \n",
-       "8       3.074084e+05                     0.603866   \n",
-       "9       1.384057e+06                     0.423074   \n",
-       "10      9.191069e+05                     0.513032   \n",
-       "11      3.915923e+05                     0.603260   \n",
-       "12      2.064846e+05                     0.594035   \n",
-       "13      8.319835e+05                     0.111932   \n",
-       "14      2.481151e+05                     0.105130   \n",
-       "15      7.652868e+04                     0.522343   \n",
-       "16      2.861474e+05                     0.596367   \n",
-       "17      1.823159e+04                     0.310940   \n",
-       "18      2.940925e+05                     0.404651   \n",
-       "19      4.135643e+05                     0.420150   \n",
-       "20      3.461843e+05                     0.610294   \n",
-       "21      2.739197e+05                     0.339380   \n",
-       "22      7.768787e+06                     0.461565   \n",
-       "23      1.119782e+03                     0.683796   \n",
-       "24      1.093881e+06                     0.756132   \n",
-       "25      0.000000e+00                     0.000000   \n",
-       "26      5.051026e+03                     0.276771   \n",
-       "27      2.476248e+05                     0.601566   \n",
-       "28      2.176264e+05                     0.597109   \n",
-       "29      2.779969e+05                     0.592454   \n",
-       "30               NaN                          NaN   \n",
-       "31               NaN                          NaN   \n",
-       "32               NaN                          NaN   \n",
-       "33      5.406829e+04                     0.254066   \n",
-       "\n",
-       "    sequence_eventropy_linear_forgetting   \n",
-       "0                           1.505466e+05  \\\n",
-       "1                           1.414023e+07   \n",
-       "2                           7.814868e+03   \n",
-       "3                           2.241393e+05   \n",
-       "4                           7.298458e+06   \n",
-       "5                           3.774501e+04   \n",
-       "6                           3.309789e+04   \n",
-       "7                           5.275579e+04   \n",
-       "8                           1.811907e+05   \n",
-       "9                           7.397790e+05   \n",
-       "10                          4.798126e+05   \n",
-       "11                          2.222674e+05   \n",
-       "12                          1.123546e+05   \n",
-       "13                          3.908680e+05   \n",
-       "14                          1.243092e+05   \n",
-       "15                          3.213928e+04   \n",
-       "16                          1.334475e+05   \n",
-       "17                          1.679949e+04   \n",
-       "18                          2.842417e+05   \n",
-       "19                          1.351355e+05   \n",
-       "20                          1.532922e+05   \n",
-       "21                          1.175256e+05   \n",
-       "22                          3.903568e+06   \n",
-       "23                          6.627117e+02   \n",
-       "24                          6.147088e+05   \n",
-       "25                          0.000000e+00   \n",
-       "26                          4.783176e+03   \n",
-       "27                          1.205360e+05   \n",
-       "28                          1.362814e+05   \n",
-       "29                          1.627439e+05   \n",
-       "30                                   NaN   \n",
-       "31                                   NaN   \n",
-       "32                                   NaN   \n",
-       "33                          2.521350e+04   \n",
-       "\n",
-       "    normalized_sequence_eventropy_linear_forgetting   \n",
-       "0                                          0.317217  \\\n",
-       "1                                          0.381612   \n",
-       "2                                          0.100603   \n",
-       "3                                          0.341521   \n",
-       "4                                          0.320185   \n",
-       "5                                          0.097572   \n",
-       "6                                          0.184879   \n",
-       "7                                          0.085439   \n",
-       "8                                          0.355927   \n",
-       "9                                          0.226133   \n",
-       "10                                         0.267825   \n",
-       "11                                         0.342410   \n",
-       "12                                         0.323233   \n",
-       "13                                         0.052586   \n",
-       "14                                         0.052672   \n",
-       "15                                         0.219365   \n",
-       "16                                         0.278121   \n",
-       "17                                         0.286515   \n",
-       "18                                         0.391097   \n",
-       "19                                         0.137287   \n",
-       "20                                         0.270241   \n",
-       "21                                         0.145611   \n",
-       "22                                         0.231922   \n",
-       "23                                         0.404685   \n",
-       "24                                         0.424910   \n",
-       "25                                         0.000000   \n",
-       "26                                         0.262094   \n",
-       "27                                         0.292824   \n",
-       "28                                         0.373920   \n",
-       "29                                         0.346832   \n",
-       "30                                              NaN   \n",
-       "31                                              NaN   \n",
-       "32                                              NaN   \n",
-       "33                                         0.118478   \n",
-       "\n",
-       "    sequence_eventropy_exponential_forgetting   \n",
-       "0                                1.853129e+05  \\\n",
-       "1                                1.557608e+07   \n",
-       "2                                1.072870e+04   \n",
-       "3                                2.657571e+05   \n",
-       "4                                7.300663e+06   \n",
-       "5                                4.593493e+04   \n",
-       "6                                3.838045e+04   \n",
-       "7                                6.445733e+04   \n",
-       "8                                2.101610e+05   \n",
-       "9                                9.014471e+05   \n",
-       "10                               5.941966e+05   \n",
-       "11                               2.626238e+05   \n",
-       "12                               1.355134e+05   \n",
-       "13                               5.087233e+05   \n",
-       "14                               1.557649e+05   \n",
-       "15                               4.388054e+04   \n",
-       "16                               1.710260e+05   \n",
-       "17                               1.690901e+04   \n",
-       "18                               2.846260e+05   \n",
-       "19                               2.121125e+05   \n",
-       "20                               2.064350e+05   \n",
-       "21                               1.563815e+05   \n",
-       "22                               4.888915e+06   \n",
-       "23                               7.698614e+02   \n",
-       "24                               7.321911e+05   \n",
-       "25                               0.000000e+00   \n",
-       "26                               4.800238e+03   \n",
-       "27                               1.548878e+05   \n",
-       "28                               1.539968e+05   \n",
-       "29                               1.885039e+05   \n",
-       "30                                        NaN   \n",
-       "31                                        NaN   \n",
-       "32                                        NaN   \n",
-       "33                               3.289574e+04   \n",
-       "\n",
-       "    normalized_sequence_eventropy_exponential_forgetting  \n",
-       "0                                            0.390473     \n",
-       "1                                            0.420362     \n",
-       "2                                            0.138113     \n",
-       "3                                            0.404934     \n",
-       "4                                            0.320282     \n",
-       "5                                            0.118744     \n",
-       "6                                            0.214387     \n",
-       "7                                            0.104389     \n",
-       "8                                            0.412835     \n",
-       "9                                            0.275551     \n",
-       "10                                           0.331672     \n",
-       "11                                           0.404580     \n",
-       "12                                           0.389858     \n",
-       "13                                           0.068442     \n",
-       "14                                           0.066000     \n",
-       "15                                           0.299505     \n",
-       "16                                           0.356439     \n",
-       "17                                           0.288383     \n",
-       "18                                           0.391625     \n",
-       "19                                           0.215490     \n",
-       "20                                           0.363928     \n",
-       "21                                           0.193753     \n",
-       "22                                           0.290464     \n",
-       "23                                           0.470116     \n",
-       "24                                           0.506118     \n",
-       "25                                           0.000000     \n",
-       "26                                           0.263029     \n",
-       "27                                           0.376276     \n",
-       "28                                           0.422526     \n",
-       "29                                           0.401731     \n",
-       "30                                                NaN     \n",
-       "31                                                NaN     \n",
-       "32                                                NaN     \n",
-       "33                                           0.154576     \n",
-       "\n",
-       "[34 rows x 102 columns]"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "dmf = pd.read_csv(\"/Users/andreamaldonado/Downloads/benchmark_features_final.csv\", index_col=None)\n",
-    "#dmf = dmf.drop(['Unnamed: 0'], axis=1)\n",
-    "print(dmf.shape)\n",
-    "describe = dmf.describe().transpose()\n",
-    "print(describe[describe['max']<1].index[:19].tolist())\n",
-    "selected_ft = ['log', 'ratio_unique_traces_per_trace', 'ratio_most_common_variant', 'ratio_top_1_variants',\n",
-    "               'ratio_top_5_variants', 'ratio_top_10_variants', 'ratio_top_20_variants', 'ratio_top_50_variants',\n",
-    "               'ratio_top_75_variants', 'normalized_variant_entropy', 'normalized_sequence_entropy', \n",
-    "               'normalized_sequence_entropy_linear_forgetting', 'normalized_sequence_entropy_exponential_forgetting']\n",
-    "selected_ft = dmf.columns[:102]\n",
-    "#print(selected_ft)\n",
-    "ft_preselection = dmf[selected_ft]\n",
-    "#ft_preselection['log'] = dmf['log']\n",
-    "print(ft_preselection.shape)\n",
-    "print(ft_preselection.columns)\n",
-    "\n",
-    "ft_preselection\n",
-    "# Include complexity features"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "39182cd7",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before: 19 columns in 34 rows\n",
-      "After: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "cols_to_drop = ft_preselection.select_dtypes(include=['object']).columns\n",
-    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
-    "imp_mean.fit(ft_preselection.drop(cols_to_drop, axis=1))\n",
-    "imp_df = imp_mean.transform(ft_preselection.drop(cols_to_drop, axis=1))\n",
-    "imp_df = pd.DataFrame(imp_df, columns = ft_preselection.columns[2:])\n",
-    "print(\"Before:\", len(ft_preselection.loc[:, ft_preselection.isna().any()].columns), \"columns in\", len(ft_preselection.loc[:, ft_preselection.isna().any()]), 'rows')\n",
-    "print(\"After:\", len(imp_df.loc[:, imp_df.isna().any()].columns))\n",
-    "\n",
-    "#col_to_drop = ft_preselection.select_dtypes(include=['object']).columns\n",
-    "#scaled_dmf = scaleColumns(ft_preselection, ft_preselection.drop(col_to_drop, axis=1).columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ef4741c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/andreamaldonado/miniconda3/envs/py39/lib/python3.9/site-packages/sklearn/base.py:457: UserWarning: X has feature names, but PCA was fitted without feature names\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.preprocessing import Normalizer\n",
-    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
-    "from sklearn.decomposition import PCA\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "X = imp_df\n",
-    "scaler = StandardScaler()\n",
-    "minmaxscaler = MinMaxScaler()\n",
-    "scaled_X_train = scaler.fit_transform(X)\n",
-    "normed_X = Normalizer(norm=\"l2\").fit_transform(X)\n",
-    "minmaxscaled_X = minmaxscaler.fit_transform(X)\n",
-    "\n",
-    "\n",
-    "import pandas as pd\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "pca = PCA(n_components=2).fit(minmaxscaled_X)\n",
-    "minmax_pca =  PCA(n_components=2).fit(X)\n",
-    "scaled_pca = PCA(n_components=2).fit(scaled_X_train)\n",
-    "norm_pca = PCA(n_components=2).fit(normed_X)\n",
-    "\n",
-    "\n",
-    "X_train_transformed = pca.transform(X)\n",
-    "X_train_minmax_transformed = pca.transform(minmaxscaled_X)\n",
-    "X_train_std_transformed = scaled_pca.transform(scaled_X_train)\n",
-    "X_train_norm_transformed = norm_pca.transform(normed_X)\n",
-    "\n",
-    "\n",
-    "first_pca_component = pd.DataFrame(\n",
-    "    pca.components_[0], index=X.columns, columns=[\"without scaling\"]\n",
-    ")\n",
-    "first_pca_component[\"with scaling\"] = scaled_pca.components_[0]\n",
-    "first_pca_component[\"with normalization\"] = norm_pca.components_[0]\n",
-    "first_pca_component.plot.bar(\n",
-    "    title=\"Weights of the first principal component\", figsize=(30, 8)\n",
-    ")\n",
-    "\n",
-    "_ = plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cb1665e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(100,)\n",
-      "10: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min']\n",
-      "20: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting']\n",
-      "30: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants']\n",
-      "40: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std']\n",
-      "50: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min']\n",
-      "60: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence']\n",
-      "70: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean']\n",
-      "80: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist']\n",
-      "90: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean']\n",
-      "100: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "110: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "120: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "130: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "140: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "150: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "160: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n",
-      "170: ['trace_len_min', 'mean_variant_occurrence', 'ratio_top_75_variants', 'ratio_top_50_variants', 'normalized_sequence_entropy', 'eventropy_k_block_diff_1', 'ratio_top_1_variants', 'normalized_sequence_eventropy_exponential_forgetting', 'eventropy_k_block_ratio_3', 'activities_min', 'end_activities_median', 'eventropy_k_block_diff_5', 'trace_len_mode', 'trace_len_geometric_std', 'eventropy_k_block_ratio_1', 'eventropy_k_block_ratio_5', 'end_activities_iqr', 'eventropy_knn_3', 'ratio_top_20_variants', 'normalized_sequence_eventropy_linear_forgetting', 'trace_len_hist1', 'eventropy_global_block', 'trace_len_q3', 'trace_len_std', 'trace_len_median', 'activities_iqr', 'end_activities_kurtosis', 'trace_len_iqr', 'trace_len_skewness', 'ratio_top_10_variants', 'eventropy_prefix', 'end_activities_variance', 'end_activities_skewness', 'start_activities_median', 'eventropy_lempel_ziv', 'start_activities_mean', 'start_activities_iqr', 'activities_median', 'n_unique_traces', 'activities_std', 'activities_skewness', 'end_activities_q1', 'end_activities_mean', 'start_activities_std', 'start_activities_q3', 'start_activities_max', 'activities_kurtosis', 'eventropy_trace', 'eventropy_k_block_diff_3', 'end_activities_min', 'start_activities_kurtosis', 'eventropy_knn_7', 'ratio_top_5_variants', 'Log Nature', 'eventropy_knn_5', 'skewness_variant_occurrence', 'end_activities_std', 'trace_len_hist3', 'start_activities_skewness', 'std_variant_occurrence', 'trace_len_hist7', 'n_unique_start_activities', 'start_activities_variance', 'trace_len_geometric_mean', 'trace_len_variance', 'activities_q3', 'activities_variance', 'n_unique_end_activities', 'start_activities_q1', 'activities_mean', 'end_activities_q3', 'trace_len_harmonic_mean', 'start_activities_min', 'activities_max', 'end_activities_max', 'trace_len_hist10', 'trace_len_q1', 'trace_len_hist8', 'kurtosis_variant_occurrence', 'trace_len_skewness_hist', 'trace_len_hist6', 'trace_len_hist4', 'activities_q1', 'trace_len_kurtosis', 'trace_len_kurtosis_hist', 'ratio_unique_traces_per_trace', 'trace_len_hist9', 'ratio_most_common_variant', 'trace_len_coefficient_variation', 'trace_len_mean', 'trace_len_entropy', 'trace_len_max', 'sequence_eventropy_linear_forgetting', 'normalized_variant_entropy', 'sequence_eventropy_exponential_forgetting', 'n_unique_activities', 'variant_entropy', 'trace_len_hist2', 'sequence_entropy', 'trace_len_hist5']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "['trace_len_min',\n",
-       " 'mean_variant_occurrence',\n",
-       " 'ratio_top_75_variants',\n",
-       " 'ratio_top_50_variants',\n",
-       " 'normalized_sequence_entropy',\n",
-       " 'eventropy_k_block_diff_1',\n",
-       " 'ratio_top_1_variants',\n",
-       " 'normalized_sequence_eventropy_exponential_forgetting',\n",
-       " 'eventropy_k_block_ratio_3',\n",
-       " 'activities_min',\n",
-       " 'end_activities_median',\n",
-       " 'eventropy_k_block_diff_5',\n",
-       " 'trace_len_mode',\n",
-       " 'trace_len_geometric_std',\n",
-       " 'eventropy_k_block_ratio_1',\n",
-       " 'eventropy_k_block_ratio_5',\n",
-       " 'end_activities_iqr',\n",
-       " 'eventropy_knn_3',\n",
-       " 'ratio_top_20_variants',\n",
-       " 'normalized_sequence_eventropy_linear_forgetting']"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weights = scaled_pca.components_[0]\n",
-    "series = pd.Series(weights, index=X.columns)\n",
-    "pca_weights = pd.DataFrame([series]).T\n",
-    "pca_weights.columns = ['weight']\n",
-    "\n",
-    "pca_weights['abs_weight'] = pca_weights.apply(lambda x: abs(x['weight']), axis=1)\n",
-    "ranked_features = pca_weights.sort_values('abs_weight', ascending = False).index\n",
-    "print(ranked_features.shape)\n",
-    "\n",
-    "for i in range(10,176,10):\n",
-    "    print(f\"{i}: {list(ranked_features[:i])}\")\n",
-    "    \n",
-    "list(ranked_features[:20])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "eac5f4c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_21410/2487822438.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>n_traces</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.097870</td>\n",
-       "      <td>0.213476</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.003322</td>\n",
-       "      <td>0.012379</td>\n",
-       "      <td>0.069331</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>n_unique_traces</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.122521</td>\n",
-       "      <td>0.249317</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.006388</td>\n",
-       "      <td>0.029981</td>\n",
-       "      <td>0.045631</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_unique_traces_per_trace</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.466611</td>\n",
-       "      <td>0.415471</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.057282</td>\n",
-       "      <td>0.386407</td>\n",
-       "      <td>0.956895</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_min</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.075448</td>\n",
-       "      <td>0.181112</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.086957</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_max</th>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.091313</td>\n",
-       "      <td>0.202147</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.007823</td>\n",
-       "      <td>0.033479</td>\n",
-       "      <td>0.049630</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>normalized_sequence_entropy</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.571459</td>\n",
-       "      <td>0.259735</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.388629</td>\n",
-       "      <td>0.610429</td>\n",
-       "      <td>0.792636</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sequence_eventropy_linear_forgetting</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.068522</td>\n",
-       "      <td>0.200469</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.002505</td>\n",
-       "      <td>0.009557</td>\n",
-       "      <td>0.017976</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>normalized_sequence_eventropy_linear_forgetting</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.579532</td>\n",
-       "      <td>0.279733</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.332892</td>\n",
-       "      <td>0.635997</td>\n",
-       "      <td>0.804794</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>sequence_eventropy_exponential_forgetting</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.069387</td>\n",
-       "      <td>0.198277</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.002883</td>\n",
-       "      <td>0.010980</td>\n",
-       "      <td>0.017668</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>normalized_sequence_eventropy_exponential_forgetting</th>\n",
-       "      <td>31.0</td>\n",
-       "      <td>0.577105</td>\n",
-       "      <td>0.262115</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.403206</td>\n",
-       "      <td>0.632820</td>\n",
-       "      <td>0.783765</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>100 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    count      mean       std   \n",
-       "n_traces                                             34.0  0.097870  0.213476  \\\n",
-       "n_unique_traces                                      34.0  0.122521  0.249317   \n",
-       "ratio_unique_traces_per_trace                        34.0  0.466611  0.415471   \n",
-       "trace_len_min                                        34.0  0.075448  0.181112   \n",
-       "trace_len_max                                        34.0  0.091313  0.202147   \n",
-       "...                                                   ...       ...       ...   \n",
-       "normalized_sequence_entropy                          31.0  0.571459  0.259735   \n",
-       "sequence_eventropy_linear_forgetting                 31.0  0.068522  0.200469   \n",
-       "normalized_sequence_eventropy_linear_forgetting      31.0  0.579532  0.279733   \n",
-       "sequence_eventropy_exponential_forgetting            31.0  0.069387  0.198277   \n",
-       "normalized_sequence_eventropy_exponential_forge...   31.0  0.577105  0.262115   \n",
-       "\n",
-       "                                                    min       25%       50%   \n",
-       "n_traces                                            0.0  0.003322  0.012379  \\\n",
-       "n_unique_traces                                     0.0  0.006388  0.029981   \n",
-       "ratio_unique_traces_per_trace                       0.0  0.057282  0.386407   \n",
-       "trace_len_min                                       0.0  0.000000  0.000000   \n",
-       "trace_len_max                                       0.0  0.007823  0.033479   \n",
-       "...                                                 ...       ...       ...   \n",
-       "normalized_sequence_entropy                         0.0  0.388629  0.610429   \n",
-       "sequence_eventropy_linear_forgetting                0.0  0.002505  0.009557   \n",
-       "normalized_sequence_eventropy_linear_forgetting     0.0  0.332892  0.635997   \n",
-       "sequence_eventropy_exponential_forgetting           0.0  0.002883  0.010980   \n",
-       "normalized_sequence_eventropy_exponential_forge...  0.0  0.403206  0.632820   \n",
-       "\n",
-       "                                                         75%  max  \n",
-       "n_traces                                            0.069331  1.0  \n",
-       "n_unique_traces                                     0.045631  1.0  \n",
-       "ratio_unique_traces_per_trace                       0.956895  1.0  \n",
-       "trace_len_min                                       0.086957  1.0  \n",
-       "trace_len_max                                       0.049630  1.0  \n",
-       "...                                                      ...  ...  \n",
-       "normalized_sequence_entropy                         0.792636  1.0  \n",
-       "sequence_eventropy_linear_forgetting                0.017976  1.0  \n",
-       "normalized_sequence_eventropy_linear_forgetting     0.804794  1.0  \n",
-       "sequence_eventropy_exponential_forgetting           0.017668  1.0  \n",
-       "normalized_sequence_eventropy_exponential_forge...  0.783765  1.0  \n",
-       "\n",
-       "[100 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn import preprocessing\n",
-    "scaler = preprocessing.MinMaxScaler()\n",
-    "min_max_scaler = preprocessing.MinMaxScaler()\n",
-    "\n",
-    "def scaleColumns(df, cols_to_scale):\n",
-    "    for col in cols_to_scale:\n",
-    "        df[col] = pd.DataFrame(min_max_scaler.fit_transform(pd.DataFrame(df[col])),columns=[col])\n",
-    "    return df\n",
-    "\n",
-    "scaled_dmf = scaleColumns(ft_preselection, ft_preselection.drop(cols_to_drop, axis=1).columns)\n",
-    "stats = ft_preselection.describe().transpose()\n",
-    "#stats = scaled_dmf.describe().transpose()\n",
-    "\n",
-    "#print(stats.index)\n",
-    "stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "54c59f5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "36"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "THRESHOLD= 0.25\n",
-    "stats['var'] = stats.apply(lambda x: x['std']**2, axis=1)\n",
-    "sorted_features = stats.sort_values(\"var\", ascending=True)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(15, 10))\n",
-    "plt.xlabel(\"sorted feature\")\n",
-    "plt.ylabel(\"std\")\n",
-    "#plt.xticks(sorted_features.index)\n",
-    "\n",
-    "plt.plot(range(len(sorted_features)), sorted_features['std'], marker='o')\n",
-    "plt.axhline(y=0.25, color='r', linestyle='-')\n",
-    "\n",
-    "filtered_feat = sorted_features[sorted_features['std']>=THRESHOLD].index\n",
-    "len(filtered_feat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "692820ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 2000x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>activities_q3</th>\n",
-       "      <th>ratio_most_common_variant</th>\n",
-       "      <th>normalized_sequence_entropy</th>\n",
-       "      <th>normalized_sequence_eventropy_exponential_forgetting</th>\n",
-       "      <th>trace_len_entropy</th>\n",
-       "      <th>trace_len_hist9</th>\n",
-       "      <th>normalized_sequence_eventropy_linear_forgetting</th>\n",
-       "      <th>eventropy_knn_3</th>\n",
-       "      <th>trace_len_hist3</th>\n",
-       "      <th>kurtosis_variant_occurrence</th>\n",
-       "      <th>...</th>\n",
-       "      <th>trace_len_geometric_mean</th>\n",
-       "      <th>eventropy_k_block_diff_5</th>\n",
-       "      <th>eventropy_k_block_ratio_1</th>\n",
-       "      <th>eventropy_k_block_diff_3</th>\n",
-       "      <th>trace_len_median</th>\n",
-       "      <th>ratio_top_20_variants</th>\n",
-       "      <th>trace_len_q1</th>\n",
-       "      <th>ratio_top_50_variants</th>\n",
-       "      <th>ratio_top_75_variants</th>\n",
-       "      <th>ratio_unique_traces_per_trace</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>activities_q3</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.299914</td>\n",
-       "      <td>-0.535848</td>\n",
-       "      <td>-0.500003</td>\n",
-       "      <td>0.651857</td>\n",
-       "      <td>-0.298666</td>\n",
-       "      <td>-0.446838</td>\n",
-       "      <td>-0.062271</td>\n",
-       "      <td>-0.163765</td>\n",
-       "      <td>0.192853</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.178143</td>\n",
-       "      <td>-0.331395</td>\n",
-       "      <td>-0.372845</td>\n",
-       "      <td>-0.360271</td>\n",
-       "      <td>-0.204937</td>\n",
-       "      <td>0.379360</td>\n",
-       "      <td>-0.207547</td>\n",
-       "      <td>0.392501</td>\n",
-       "      <td>0.393849</td>\n",
-       "      <td>-0.394220</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_most_common_variant</th>\n",
-       "      <td>0.299914</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.872166</td>\n",
-       "      <td>-0.820146</td>\n",
-       "      <td>0.455871</td>\n",
-       "      <td>-0.239070</td>\n",
-       "      <td>-0.744763</td>\n",
-       "      <td>-0.399730</td>\n",
-       "      <td>0.387022</td>\n",
-       "      <td>-0.248842</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.680095</td>\n",
-       "      <td>-0.484408</td>\n",
-       "      <td>-0.505069</td>\n",
-       "      <td>-0.484158</td>\n",
-       "      <td>-0.677083</td>\n",
-       "      <td>0.772018</td>\n",
-       "      <td>-0.640762</td>\n",
-       "      <td>0.809234</td>\n",
-       "      <td>0.808176</td>\n",
-       "      <td>-0.808280</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>normalized_sequence_entropy</th>\n",
-       "      <td>-0.535848</td>\n",
-       "      <td>-0.872166</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.949180</td>\n",
-       "      <td>-0.513211</td>\n",
-       "      <td>0.220806</td>\n",
-       "      <td>0.867450</td>\n",
-       "      <td>0.391840</td>\n",
-       "      <td>-0.235271</td>\n",
-       "      <td>0.196209</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.664062</td>\n",
-       "      <td>0.591652</td>\n",
-       "      <td>0.595375</td>\n",
-       "      <td>0.587182</td>\n",
-       "      <td>0.659965</td>\n",
-       "      <td>-0.866084</td>\n",
-       "      <td>0.647014</td>\n",
-       "      <td>-0.876646</td>\n",
-       "      <td>-0.877751</td>\n",
-       "      <td>0.878053</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>normalized_sequence_eventropy_exponential_forgetting</th>\n",
-       "      <td>-0.500003</td>\n",
-       "      <td>-0.820146</td>\n",
-       "      <td>0.949180</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.453465</td>\n",
-       "      <td>0.184055</td>\n",
-       "      <td>0.979552</td>\n",
-       "      <td>0.393510</td>\n",
-       "      <td>-0.273272</td>\n",
-       "      <td>0.295912</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.592721</td>\n",
-       "      <td>0.519657</td>\n",
-       "      <td>0.491600</td>\n",
-       "      <td>0.504442</td>\n",
-       "      <td>0.578747</td>\n",
-       "      <td>-0.770970</td>\n",
-       "      <td>0.577783</td>\n",
-       "      <td>-0.784374</td>\n",
-       "      <td>-0.785651</td>\n",
-       "      <td>0.786009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_entropy</th>\n",
-       "      <td>0.651857</td>\n",
-       "      <td>0.455871</td>\n",
-       "      <td>-0.513211</td>\n",
-       "      <td>-0.453465</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.401086</td>\n",
-       "      <td>-0.392114</td>\n",
-       "      <td>0.115983</td>\n",
-       "      <td>-0.177211</td>\n",
-       "      <td>0.518292</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.421136</td>\n",
-       "      <td>-0.374418</td>\n",
-       "      <td>-0.411303</td>\n",
-       "      <td>-0.389937</td>\n",
-       "      <td>-0.459727</td>\n",
-       "      <td>0.556509</td>\n",
-       "      <td>-0.414980</td>\n",
-       "      <td>0.573152</td>\n",
-       "      <td>0.573851</td>\n",
-       "      <td>-0.573412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_hist9</th>\n",
-       "      <td>-0.298666</td>\n",
-       "      <td>-0.239070</td>\n",
-       "      <td>0.220806</td>\n",
-       "      <td>0.184055</td>\n",
-       "      <td>-0.401086</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.152934</td>\n",
-       "      <td>-0.357692</td>\n",
-       "      <td>-0.104087</td>\n",
-       "      <td>-0.248786</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.327961</td>\n",
-       "      <td>-0.047815</td>\n",
-       "      <td>0.009878</td>\n",
-       "      <td>0.016813</td>\n",
-       "      <td>0.331725</td>\n",
-       "      <td>-0.306775</td>\n",
-       "      <td>0.406222</td>\n",
-       "      <td>-0.308349</td>\n",
-       "      <td>-0.309056</td>\n",
-       "      <td>0.309232</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>normalized_sequence_eventropy_linear_forgetting</th>\n",
-       "      <td>-0.446838</td>\n",
-       "      <td>-0.744763</td>\n",
-       "      <td>0.867450</td>\n",
-       "      <td>0.979552</td>\n",
-       "      <td>-0.392114</td>\n",
-       "      <td>0.152934</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.367694</td>\n",
-       "      <td>-0.276891</td>\n",
-       "      <td>0.341992</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.522601</td>\n",
-       "      <td>0.445527</td>\n",
-       "      <td>0.400941</td>\n",
-       "      <td>0.426984</td>\n",
-       "      <td>0.500775</td>\n",
-       "      <td>-0.672905</td>\n",
-       "      <td>0.511506</td>\n",
-       "      <td>-0.686745</td>\n",
-       "      <td>-0.688042</td>\n",
-       "      <td>0.688380</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_knn_3</th>\n",
-       "      <td>-0.062271</td>\n",
-       "      <td>-0.399730</td>\n",
-       "      <td>0.391840</td>\n",
-       "      <td>0.393510</td>\n",
-       "      <td>0.115983</td>\n",
-       "      <td>-0.357692</td>\n",
-       "      <td>0.367694</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.114732</td>\n",
-       "      <td>0.670754</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.194343</td>\n",
-       "      <td>0.470102</td>\n",
-       "      <td>0.548088</td>\n",
-       "      <td>0.544745</td>\n",
-       "      <td>0.176249</td>\n",
-       "      <td>-0.200954</td>\n",
-       "      <td>0.103769</td>\n",
-       "      <td>-0.260643</td>\n",
-       "      <td>-0.262890</td>\n",
-       "      <td>0.264034</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_hist3</th>\n",
-       "      <td>-0.163765</td>\n",
-       "      <td>0.387022</td>\n",
-       "      <td>-0.235271</td>\n",
-       "      <td>-0.273272</td>\n",
-       "      <td>-0.177211</td>\n",
-       "      <td>-0.104087</td>\n",
-       "      <td>-0.276891</td>\n",
-       "      <td>-0.114732</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.258008</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.352139</td>\n",
-       "      <td>-0.089258</td>\n",
-       "      <td>-0.051290</td>\n",
-       "      <td>-0.017674</td>\n",
-       "      <td>-0.352342</td>\n",
-       "      <td>0.365283</td>\n",
-       "      <td>-0.300585</td>\n",
-       "      <td>0.350999</td>\n",
-       "      <td>0.349343</td>\n",
-       "      <td>-0.349657</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>kurtosis_variant_occurrence</th>\n",
-       "      <td>0.192853</td>\n",
-       "      <td>-0.248842</td>\n",
-       "      <td>0.196209</td>\n",
-       "      <td>0.295912</td>\n",
-       "      <td>0.518292</td>\n",
-       "      <td>-0.248786</td>\n",
-       "      <td>0.341992</td>\n",
-       "      <td>0.670754</td>\n",
-       "      <td>-0.258008</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.103149</td>\n",
-       "      <td>-0.033833</td>\n",
-       "      <td>-0.054358</td>\n",
-       "      <td>-0.001832</td>\n",
-       "      <td>0.042782</td>\n",
-       "      <td>-0.003638</td>\n",
-       "      <td>0.037692</td>\n",
-       "      <td>-0.042264</td>\n",
-       "      <td>-0.043031</td>\n",
-       "      <td>0.044279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_knn_7</th>\n",
-       "      <td>-0.055360</td>\n",
-       "      <td>-0.416660</td>\n",
-       "      <td>0.413630</td>\n",
-       "      <td>0.414553</td>\n",
-       "      <td>0.142646</td>\n",
-       "      <td>-0.348102</td>\n",
-       "      <td>0.387390</td>\n",
-       "      <td>0.997943</td>\n",
-       "      <td>-0.142479</td>\n",
-       "      <td>0.704145</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.207681</td>\n",
-       "      <td>0.455547</td>\n",
-       "      <td>0.529663</td>\n",
-       "      <td>0.527873</td>\n",
-       "      <td>0.187323</td>\n",
-       "      <td>-0.215919</td>\n",
-       "      <td>0.116926</td>\n",
-       "      <td>-0.274286</td>\n",
-       "      <td>-0.276444</td>\n",
-       "      <td>0.277664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_knn_5</th>\n",
-       "      <td>-0.062258</td>\n",
-       "      <td>-0.411045</td>\n",
-       "      <td>0.408061</td>\n",
-       "      <td>0.409052</td>\n",
-       "      <td>0.128484</td>\n",
-       "      <td>-0.349866</td>\n",
-       "      <td>0.382163</td>\n",
-       "      <td>0.999150</td>\n",
-       "      <td>-0.128360</td>\n",
-       "      <td>0.690184</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.203716</td>\n",
-       "      <td>0.460452</td>\n",
-       "      <td>0.536569</td>\n",
-       "      <td>0.534144</td>\n",
-       "      <td>0.184209</td>\n",
-       "      <td>-0.211173</td>\n",
-       "      <td>0.113142</td>\n",
-       "      <td>-0.270523</td>\n",
-       "      <td>-0.272753</td>\n",
-       "      <td>0.273977</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>skewness_variant_occurrence</th>\n",
-       "      <td>0.088042</td>\n",
-       "      <td>-0.373731</td>\n",
-       "      <td>0.329533</td>\n",
-       "      <td>0.404550</td>\n",
-       "      <td>0.399360</td>\n",
-       "      <td>-0.193931</td>\n",
-       "      <td>0.426785</td>\n",
-       "      <td>0.741303</td>\n",
-       "      <td>-0.315236</td>\n",
-       "      <td>0.966532</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.216999</td>\n",
-       "      <td>0.056393</td>\n",
-       "      <td>0.059318</td>\n",
-       "      <td>0.098855</td>\n",
-       "      <td>0.164644</td>\n",
-       "      <td>-0.132644</td>\n",
-       "      <td>0.141057</td>\n",
-       "      <td>-0.184708</td>\n",
-       "      <td>-0.185789</td>\n",
-       "      <td>0.187322</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>activities_mean</th>\n",
-       "      <td>0.917156</td>\n",
-       "      <td>0.199631</td>\n",
-       "      <td>-0.423045</td>\n",
-       "      <td>-0.346623</td>\n",
-       "      <td>0.699155</td>\n",
-       "      <td>-0.319872</td>\n",
-       "      <td>-0.276812</td>\n",
-       "      <td>0.093266</td>\n",
-       "      <td>-0.214655</td>\n",
-       "      <td>0.471373</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.097058</td>\n",
-       "      <td>-0.342978</td>\n",
-       "      <td>-0.390862</td>\n",
-       "      <td>-0.352554</td>\n",
-       "      <td>-0.141924</td>\n",
-       "      <td>0.333256</td>\n",
-       "      <td>-0.136567</td>\n",
-       "      <td>0.341059</td>\n",
-       "      <td>0.342069</td>\n",
-       "      <td>-0.341861</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_top_1_variants</th>\n",
-       "      <td>0.206981</td>\n",
-       "      <td>0.256647</td>\n",
-       "      <td>-0.359378</td>\n",
-       "      <td>-0.258761</td>\n",
-       "      <td>0.470289</td>\n",
-       "      <td>-0.159045</td>\n",
-       "      <td>-0.192220</td>\n",
-       "      <td>0.299416</td>\n",
-       "      <td>0.040655</td>\n",
-       "      <td>0.437257</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.429185</td>\n",
-       "      <td>-0.392627</td>\n",
-       "      <td>-0.331039</td>\n",
-       "      <td>-0.305745</td>\n",
-       "      <td>-0.453129</td>\n",
-       "      <td>0.588358</td>\n",
-       "      <td>-0.451532</td>\n",
-       "      <td>0.522835</td>\n",
-       "      <td>0.520572</td>\n",
-       "      <td>-0.519027</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_lempel_ziv</th>\n",
-       "      <td>-0.369278</td>\n",
-       "      <td>-0.585507</td>\n",
-       "      <td>0.718598</td>\n",
-       "      <td>0.647689</td>\n",
-       "      <td>-0.398436</td>\n",
-       "      <td>-0.123402</td>\n",
-       "      <td>0.566472</td>\n",
-       "      <td>0.618973</td>\n",
-       "      <td>-0.138655</td>\n",
-       "      <td>0.092295</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.451688</td>\n",
-       "      <td>0.897458</td>\n",
-       "      <td>0.911113</td>\n",
-       "      <td>0.892299</td>\n",
-       "      <td>0.482644</td>\n",
-       "      <td>-0.690318</td>\n",
-       "      <td>0.414984</td>\n",
-       "      <td>-0.694109</td>\n",
-       "      <td>-0.694258</td>\n",
-       "      <td>0.694278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_global_block</th>\n",
-       "      <td>-0.143753</td>\n",
-       "      <td>-0.519100</td>\n",
-       "      <td>0.475344</td>\n",
-       "      <td>0.446875</td>\n",
-       "      <td>-0.077549</td>\n",
-       "      <td>-0.280870</td>\n",
-       "      <td>0.399285</td>\n",
-       "      <td>0.893677</td>\n",
-       "      <td>-0.263814</td>\n",
-       "      <td>0.503937</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.480625</td>\n",
-       "      <td>0.577783</td>\n",
-       "      <td>0.669330</td>\n",
-       "      <td>0.659452</td>\n",
-       "      <td>0.488968</td>\n",
-       "      <td>-0.444348</td>\n",
-       "      <td>0.350320</td>\n",
-       "      <td>-0.486711</td>\n",
-       "      <td>-0.488084</td>\n",
-       "      <td>0.488692</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_harmonic_mean</th>\n",
-       "      <td>-0.116906</td>\n",
-       "      <td>-0.645260</td>\n",
-       "      <td>0.629948</td>\n",
-       "      <td>0.570846</td>\n",
-       "      <td>-0.334337</td>\n",
-       "      <td>0.346679</td>\n",
-       "      <td>0.512861</td>\n",
-       "      <td>0.171198</td>\n",
-       "      <td>-0.322361</td>\n",
-       "      <td>0.155284</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.958903</td>\n",
-       "      <td>0.342788</td>\n",
-       "      <td>0.386648</td>\n",
-       "      <td>0.403107</td>\n",
-       "      <td>0.917264</td>\n",
-       "      <td>-0.785392</td>\n",
-       "      <td>0.976809</td>\n",
-       "      <td>-0.797166</td>\n",
-       "      <td>-0.797082</td>\n",
-       "      <td>0.797317</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_trace</th>\n",
-       "      <td>-0.089274</td>\n",
-       "      <td>-0.558403</td>\n",
-       "      <td>0.549376</td>\n",
-       "      <td>0.506727</td>\n",
-       "      <td>-0.063832</td>\n",
-       "      <td>-0.301461</td>\n",
-       "      <td>0.450011</td>\n",
-       "      <td>0.931933</td>\n",
-       "      <td>-0.233797</td>\n",
-       "      <td>0.532930</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.402992</td>\n",
-       "      <td>0.606042</td>\n",
-       "      <td>0.683355</td>\n",
-       "      <td>0.668513</td>\n",
-       "      <td>0.387461</td>\n",
-       "      <td>-0.441856</td>\n",
-       "      <td>0.308826</td>\n",
-       "      <td>-0.480534</td>\n",
-       "      <td>-0.481789</td>\n",
-       "      <td>0.482509</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_k_block_diff_1</th>\n",
-       "      <td>-0.218326</td>\n",
-       "      <td>-0.236374</td>\n",
-       "      <td>0.376392</td>\n",
-       "      <td>0.337745</td>\n",
-       "      <td>-0.238610</td>\n",
-       "      <td>0.063024</td>\n",
-       "      <td>0.285660</td>\n",
-       "      <td>0.303359</td>\n",
-       "      <td>0.072237</td>\n",
-       "      <td>0.016650</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.109747</td>\n",
-       "      <td>0.693135</td>\n",
-       "      <td>0.589455</td>\n",
-       "      <td>0.608730</td>\n",
-       "      <td>0.131434</td>\n",
-       "      <td>-0.319323</td>\n",
-       "      <td>0.147371</td>\n",
-       "      <td>-0.308105</td>\n",
-       "      <td>-0.307244</td>\n",
-       "      <td>0.307021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_prefix</th>\n",
-       "      <td>-0.154083</td>\n",
-       "      <td>-0.543751</td>\n",
-       "      <td>0.535002</td>\n",
-       "      <td>0.488804</td>\n",
-       "      <td>-0.135577</td>\n",
-       "      <td>-0.267297</td>\n",
-       "      <td>0.430069</td>\n",
-       "      <td>0.901190</td>\n",
-       "      <td>-0.236717</td>\n",
-       "      <td>0.453405</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.484517</td>\n",
-       "      <td>0.633532</td>\n",
-       "      <td>0.729512</td>\n",
-       "      <td>0.710391</td>\n",
-       "      <td>0.486056</td>\n",
-       "      <td>-0.479836</td>\n",
-       "      <td>0.364837</td>\n",
-       "      <td>-0.519542</td>\n",
-       "      <td>-0.520845</td>\n",
-       "      <td>0.521518</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_mode</th>\n",
-       "      <td>-0.202287</td>\n",
-       "      <td>-0.616403</td>\n",
-       "      <td>0.626516</td>\n",
-       "      <td>0.553293</td>\n",
-       "      <td>-0.400258</td>\n",
-       "      <td>0.387157</td>\n",
-       "      <td>0.484338</td>\n",
-       "      <td>0.114816</td>\n",
-       "      <td>-0.290794</td>\n",
-       "      <td>0.025402</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.914711</td>\n",
-       "      <td>0.495384</td>\n",
-       "      <td>0.488929</td>\n",
-       "      <td>0.510358</td>\n",
-       "      <td>0.890032</td>\n",
-       "      <td>-0.800977</td>\n",
-       "      <td>0.987385</td>\n",
-       "      <td>-0.806671</td>\n",
-       "      <td>-0.806296</td>\n",
-       "      <td>0.806604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_top_5_variants</th>\n",
-       "      <td>0.219209</td>\n",
-       "      <td>0.494400</td>\n",
-       "      <td>-0.691534</td>\n",
-       "      <td>-0.591511</td>\n",
-       "      <td>0.393169</td>\n",
-       "      <td>-0.223787</td>\n",
-       "      <td>-0.501746</td>\n",
-       "      <td>0.068793</td>\n",
-       "      <td>0.372263</td>\n",
-       "      <td>0.153116</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.659600</td>\n",
-       "      <td>-0.539024</td>\n",
-       "      <td>-0.496584</td>\n",
-       "      <td>-0.485880</td>\n",
-       "      <td>-0.679181</td>\n",
-       "      <td>0.854760</td>\n",
-       "      <td>-0.656847</td>\n",
-       "      <td>0.784494</td>\n",
-       "      <td>0.782390</td>\n",
-       "      <td>-0.780722</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_top_10_variants</th>\n",
-       "      <td>0.266050</td>\n",
-       "      <td>0.525677</td>\n",
-       "      <td>-0.792878</td>\n",
-       "      <td>-0.694187</td>\n",
-       "      <td>0.394550</td>\n",
-       "      <td>-0.242386</td>\n",
-       "      <td>-0.598864</td>\n",
-       "      <td>-0.007579</td>\n",
-       "      <td>0.398718</td>\n",
-       "      <td>0.091337</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.718275</td>\n",
-       "      <td>-0.547724</td>\n",
-       "      <td>-0.512481</td>\n",
-       "      <td>-0.500971</td>\n",
-       "      <td>-0.736846</td>\n",
-       "      <td>0.907611</td>\n",
-       "      <td>-0.708694</td>\n",
-       "      <td>0.848190</td>\n",
-       "      <td>0.846992</td>\n",
-       "      <td>-0.845994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_k_block_ratio_3</th>\n",
-       "      <td>-0.284616</td>\n",
-       "      <td>-0.458070</td>\n",
-       "      <td>0.530260</td>\n",
-       "      <td>0.413134</td>\n",
-       "      <td>-0.277666</td>\n",
-       "      <td>-0.158166</td>\n",
-       "      <td>0.316485</td>\n",
-       "      <td>0.727363</td>\n",
-       "      <td>0.012534</td>\n",
-       "      <td>0.131184</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.431902</td>\n",
-       "      <td>0.825771</td>\n",
-       "      <td>0.922590</td>\n",
-       "      <td>0.903515</td>\n",
-       "      <td>0.455753</td>\n",
-       "      <td>-0.499033</td>\n",
-       "      <td>0.387944</td>\n",
-       "      <td>-0.522102</td>\n",
-       "      <td>-0.523124</td>\n",
-       "      <td>0.522969</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_k_block_ratio_5</th>\n",
-       "      <td>-0.326463</td>\n",
-       "      <td>-0.418030</td>\n",
-       "      <td>0.502240</td>\n",
-       "      <td>0.376855</td>\n",
-       "      <td>-0.324748</td>\n",
-       "      <td>-0.101061</td>\n",
-       "      <td>0.277739</td>\n",
-       "      <td>0.631913</td>\n",
-       "      <td>0.059331</td>\n",
-       "      <td>-0.004747</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.379651</td>\n",
-       "      <td>0.877190</td>\n",
-       "      <td>0.961157</td>\n",
-       "      <td>0.939838</td>\n",
-       "      <td>0.404277</td>\n",
-       "      <td>-0.487833</td>\n",
-       "      <td>0.388326</td>\n",
-       "      <td>-0.504889</td>\n",
-       "      <td>-0.505709</td>\n",
-       "      <td>0.505480</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_geometric_mean</th>\n",
-       "      <td>-0.178143</td>\n",
-       "      <td>-0.680095</td>\n",
-       "      <td>0.664062</td>\n",
-       "      <td>0.592721</td>\n",
-       "      <td>-0.421136</td>\n",
-       "      <td>0.327961</td>\n",
-       "      <td>0.522601</td>\n",
-       "      <td>0.194343</td>\n",
-       "      <td>-0.352139</td>\n",
-       "      <td>0.103149</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.377851</td>\n",
-       "      <td>0.431359</td>\n",
-       "      <td>0.429419</td>\n",
-       "      <td>0.990961</td>\n",
-       "      <td>-0.846993</td>\n",
-       "      <td>0.946175</td>\n",
-       "      <td>-0.858941</td>\n",
-       "      <td>-0.858895</td>\n",
-       "      <td>0.859037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_k_block_diff_5</th>\n",
-       "      <td>-0.331395</td>\n",
-       "      <td>-0.484408</td>\n",
-       "      <td>0.591652</td>\n",
-       "      <td>0.519657</td>\n",
-       "      <td>-0.374418</td>\n",
-       "      <td>-0.047815</td>\n",
-       "      <td>0.445527</td>\n",
-       "      <td>0.470102</td>\n",
-       "      <td>-0.089258</td>\n",
-       "      <td>-0.033833</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.377851</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.925266</td>\n",
-       "      <td>0.924130</td>\n",
-       "      <td>0.403825</td>\n",
-       "      <td>-0.607564</td>\n",
-       "      <td>0.441321</td>\n",
-       "      <td>-0.599378</td>\n",
-       "      <td>-0.598815</td>\n",
-       "      <td>0.598346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_k_block_ratio_1</th>\n",
-       "      <td>-0.372845</td>\n",
-       "      <td>-0.505069</td>\n",
-       "      <td>0.595375</td>\n",
-       "      <td>0.491600</td>\n",
-       "      <td>-0.411303</td>\n",
-       "      <td>0.009878</td>\n",
-       "      <td>0.400941</td>\n",
-       "      <td>0.548088</td>\n",
-       "      <td>-0.051290</td>\n",
-       "      <td>-0.054358</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.431359</td>\n",
-       "      <td>0.925266</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.983618</td>\n",
-       "      <td>0.454950</td>\n",
-       "      <td>-0.596477</td>\n",
-       "      <td>0.455977</td>\n",
-       "      <td>-0.595245</td>\n",
-       "      <td>-0.595384</td>\n",
-       "      <td>0.594868</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>eventropy_k_block_diff_3</th>\n",
-       "      <td>-0.360271</td>\n",
-       "      <td>-0.484158</td>\n",
-       "      <td>0.587182</td>\n",
-       "      <td>0.504442</td>\n",
-       "      <td>-0.389937</td>\n",
-       "      <td>0.016813</td>\n",
-       "      <td>0.426984</td>\n",
-       "      <td>0.544745</td>\n",
-       "      <td>-0.017674</td>\n",
-       "      <td>-0.001832</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.429419</td>\n",
-       "      <td>0.924130</td>\n",
-       "      <td>0.983618</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.445314</td>\n",
-       "      <td>-0.582388</td>\n",
-       "      <td>0.474504</td>\n",
-       "      <td>-0.581416</td>\n",
-       "      <td>-0.581487</td>\n",
-       "      <td>0.581037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_median</th>\n",
-       "      <td>-0.204937</td>\n",
-       "      <td>-0.677083</td>\n",
-       "      <td>0.659965</td>\n",
-       "      <td>0.578747</td>\n",
-       "      <td>-0.459727</td>\n",
-       "      <td>0.331725</td>\n",
-       "      <td>0.500775</td>\n",
-       "      <td>0.176249</td>\n",
-       "      <td>-0.352342</td>\n",
-       "      <td>0.042782</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.990961</td>\n",
-       "      <td>0.403825</td>\n",
-       "      <td>0.454950</td>\n",
-       "      <td>0.445314</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.862281</td>\n",
-       "      <td>0.919761</td>\n",
-       "      <td>-0.872477</td>\n",
-       "      <td>-0.872387</td>\n",
-       "      <td>0.872451</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_top_20_variants</th>\n",
-       "      <td>0.379360</td>\n",
-       "      <td>0.772018</td>\n",
-       "      <td>-0.866084</td>\n",
-       "      <td>-0.770970</td>\n",
-       "      <td>0.556509</td>\n",
-       "      <td>-0.306775</td>\n",
-       "      <td>-0.672905</td>\n",
-       "      <td>-0.200954</td>\n",
-       "      <td>0.365283</td>\n",
-       "      <td>-0.003638</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.846993</td>\n",
-       "      <td>-0.607564</td>\n",
-       "      <td>-0.596477</td>\n",
-       "      <td>-0.582388</td>\n",
-       "      <td>-0.862281</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.824921</td>\n",
-       "      <td>0.991745</td>\n",
-       "      <td>0.991361</td>\n",
-       "      <td>-0.991075</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>trace_len_q1</th>\n",
-       "      <td>-0.207547</td>\n",
-       "      <td>-0.640762</td>\n",
-       "      <td>0.647014</td>\n",
-       "      <td>0.577783</td>\n",
-       "      <td>-0.414980</td>\n",
-       "      <td>0.406222</td>\n",
-       "      <td>0.511506</td>\n",
-       "      <td>0.103769</td>\n",
-       "      <td>-0.300585</td>\n",
-       "      <td>0.037692</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.946175</td>\n",
-       "      <td>0.441321</td>\n",
-       "      <td>0.455977</td>\n",
-       "      <td>0.474504</td>\n",
-       "      <td>0.919761</td>\n",
-       "      <td>-0.824921</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.832498</td>\n",
-       "      <td>-0.832272</td>\n",
-       "      <td>0.832481</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_top_50_variants</th>\n",
-       "      <td>0.392501</td>\n",
-       "      <td>0.809234</td>\n",
-       "      <td>-0.876646</td>\n",
-       "      <td>-0.784374</td>\n",
-       "      <td>0.573152</td>\n",
-       "      <td>-0.308349</td>\n",
-       "      <td>-0.686745</td>\n",
-       "      <td>-0.260643</td>\n",
-       "      <td>0.350999</td>\n",
-       "      <td>-0.042264</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.858941</td>\n",
-       "      <td>-0.599378</td>\n",
-       "      <td>-0.595245</td>\n",
-       "      <td>-0.581416</td>\n",
-       "      <td>-0.872477</td>\n",
-       "      <td>0.991745</td>\n",
-       "      <td>-0.832498</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999979</td>\n",
-       "      <td>-0.999957</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_top_75_variants</th>\n",
-       "      <td>0.393849</td>\n",
-       "      <td>0.808176</td>\n",
-       "      <td>-0.877751</td>\n",
-       "      <td>-0.785651</td>\n",
-       "      <td>0.573851</td>\n",
-       "      <td>-0.309056</td>\n",
-       "      <td>-0.688042</td>\n",
-       "      <td>-0.262890</td>\n",
-       "      <td>0.349343</td>\n",
-       "      <td>-0.043031</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.858895</td>\n",
-       "      <td>-0.598815</td>\n",
-       "      <td>-0.595384</td>\n",
-       "      <td>-0.581487</td>\n",
-       "      <td>-0.872387</td>\n",
-       "      <td>0.991361</td>\n",
-       "      <td>-0.832272</td>\n",
-       "      <td>0.999979</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.999989</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ratio_unique_traces_per_trace</th>\n",
-       "      <td>-0.394220</td>\n",
-       "      <td>-0.808280</td>\n",
-       "      <td>0.878053</td>\n",
-       "      <td>0.786009</td>\n",
-       "      <td>-0.573412</td>\n",
-       "      <td>0.309232</td>\n",
-       "      <td>0.688380</td>\n",
-       "      <td>0.264034</td>\n",
-       "      <td>-0.349657</td>\n",
-       "      <td>0.044279</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.859037</td>\n",
-       "      <td>0.598346</td>\n",
-       "      <td>0.594868</td>\n",
-       "      <td>0.581037</td>\n",
-       "      <td>0.872451</td>\n",
-       "      <td>-0.991075</td>\n",
-       "      <td>0.832481</td>\n",
-       "      <td>-0.999957</td>\n",
-       "      <td>-0.999989</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>36 rows × 36 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                    activities_q3   \n",
-       "activities_q3                                            1.000000  \\\n",
-       "ratio_most_common_variant                                0.299914   \n",
-       "normalized_sequence_entropy                             -0.535848   \n",
-       "normalized_sequence_eventropy_exponential_forge...      -0.500003   \n",
-       "trace_len_entropy                                        0.651857   \n",
-       "trace_len_hist9                                         -0.298666   \n",
-       "normalized_sequence_eventropy_linear_forgetting         -0.446838   \n",
-       "eventropy_knn_3                                         -0.062271   \n",
-       "trace_len_hist3                                         -0.163765   \n",
-       "kurtosis_variant_occurrence                              0.192853   \n",
-       "eventropy_knn_7                                         -0.055360   \n",
-       "eventropy_knn_5                                         -0.062258   \n",
-       "skewness_variant_occurrence                              0.088042   \n",
-       "activities_mean                                          0.917156   \n",
-       "ratio_top_1_variants                                     0.206981   \n",
-       "eventropy_lempel_ziv                                    -0.369278   \n",
-       "eventropy_global_block                                  -0.143753   \n",
-       "trace_len_harmonic_mean                                 -0.116906   \n",
-       "eventropy_trace                                         -0.089274   \n",
-       "eventropy_k_block_diff_1                                -0.218326   \n",
-       "eventropy_prefix                                        -0.154083   \n",
-       "trace_len_mode                                          -0.202287   \n",
-       "ratio_top_5_variants                                     0.219209   \n",
-       "ratio_top_10_variants                                    0.266050   \n",
-       "eventropy_k_block_ratio_3                               -0.284616   \n",
-       "eventropy_k_block_ratio_5                               -0.326463   \n",
-       "trace_len_geometric_mean                                -0.178143   \n",
-       "eventropy_k_block_diff_5                                -0.331395   \n",
-       "eventropy_k_block_ratio_1                               -0.372845   \n",
-       "eventropy_k_block_diff_3                                -0.360271   \n",
-       "trace_len_median                                        -0.204937   \n",
-       "ratio_top_20_variants                                    0.379360   \n",
-       "trace_len_q1                                            -0.207547   \n",
-       "ratio_top_50_variants                                    0.392501   \n",
-       "ratio_top_75_variants                                    0.393849   \n",
-       "ratio_unique_traces_per_trace                           -0.394220   \n",
-       "\n",
-       "                                                    ratio_most_common_variant   \n",
-       "activities_q3                                                        0.299914  \\\n",
-       "ratio_most_common_variant                                            1.000000   \n",
-       "normalized_sequence_entropy                                         -0.872166   \n",
-       "normalized_sequence_eventropy_exponential_forge...                  -0.820146   \n",
-       "trace_len_entropy                                                    0.455871   \n",
-       "trace_len_hist9                                                     -0.239070   \n",
-       "normalized_sequence_eventropy_linear_forgetting                     -0.744763   \n",
-       "eventropy_knn_3                                                     -0.399730   \n",
-       "trace_len_hist3                                                      0.387022   \n",
-       "kurtosis_variant_occurrence                                         -0.248842   \n",
-       "eventropy_knn_7                                                     -0.416660   \n",
-       "eventropy_knn_5                                                     -0.411045   \n",
-       "skewness_variant_occurrence                                         -0.373731   \n",
-       "activities_mean                                                      0.199631   \n",
-       "ratio_top_1_variants                                                 0.256647   \n",
-       "eventropy_lempel_ziv                                                -0.585507   \n",
-       "eventropy_global_block                                              -0.519100   \n",
-       "trace_len_harmonic_mean                                             -0.645260   \n",
-       "eventropy_trace                                                     -0.558403   \n",
-       "eventropy_k_block_diff_1                                            -0.236374   \n",
-       "eventropy_prefix                                                    -0.543751   \n",
-       "trace_len_mode                                                      -0.616403   \n",
-       "ratio_top_5_variants                                                 0.494400   \n",
-       "ratio_top_10_variants                                                0.525677   \n",
-       "eventropy_k_block_ratio_3                                           -0.458070   \n",
-       "eventropy_k_block_ratio_5                                           -0.418030   \n",
-       "trace_len_geometric_mean                                            -0.680095   \n",
-       "eventropy_k_block_diff_5                                            -0.484408   \n",
-       "eventropy_k_block_ratio_1                                           -0.505069   \n",
-       "eventropy_k_block_diff_3                                            -0.484158   \n",
-       "trace_len_median                                                    -0.677083   \n",
-       "ratio_top_20_variants                                                0.772018   \n",
-       "trace_len_q1                                                        -0.640762   \n",
-       "ratio_top_50_variants                                                0.809234   \n",
-       "ratio_top_75_variants                                                0.808176   \n",
-       "ratio_unique_traces_per_trace                                       -0.808280   \n",
-       "\n",
-       "                                                    normalized_sequence_entropy   \n",
-       "activities_q3                                                         -0.535848  \\\n",
-       "ratio_most_common_variant                                             -0.872166   \n",
-       "normalized_sequence_entropy                                            1.000000   \n",
-       "normalized_sequence_eventropy_exponential_forge...                     0.949180   \n",
-       "trace_len_entropy                                                     -0.513211   \n",
-       "trace_len_hist9                                                        0.220806   \n",
-       "normalized_sequence_eventropy_linear_forgetting                        0.867450   \n",
-       "eventropy_knn_3                                                        0.391840   \n",
-       "trace_len_hist3                                                       -0.235271   \n",
-       "kurtosis_variant_occurrence                                            0.196209   \n",
-       "eventropy_knn_7                                                        0.413630   \n",
-       "eventropy_knn_5                                                        0.408061   \n",
-       "skewness_variant_occurrence                                            0.329533   \n",
-       "activities_mean                                                       -0.423045   \n",
-       "ratio_top_1_variants                                                  -0.359378   \n",
-       "eventropy_lempel_ziv                                                   0.718598   \n",
-       "eventropy_global_block                                                 0.475344   \n",
-       "trace_len_harmonic_mean                                                0.629948   \n",
-       "eventropy_trace                                                        0.549376   \n",
-       "eventropy_k_block_diff_1                                               0.376392   \n",
-       "eventropy_prefix                                                       0.535002   \n",
-       "trace_len_mode                                                         0.626516   \n",
-       "ratio_top_5_variants                                                  -0.691534   \n",
-       "ratio_top_10_variants                                                 -0.792878   \n",
-       "eventropy_k_block_ratio_3                                              0.530260   \n",
-       "eventropy_k_block_ratio_5                                              0.502240   \n",
-       "trace_len_geometric_mean                                               0.664062   \n",
-       "eventropy_k_block_diff_5                                               0.591652   \n",
-       "eventropy_k_block_ratio_1                                              0.595375   \n",
-       "eventropy_k_block_diff_3                                               0.587182   \n",
-       "trace_len_median                                                       0.659965   \n",
-       "ratio_top_20_variants                                                 -0.866084   \n",
-       "trace_len_q1                                                           0.647014   \n",
-       "ratio_top_50_variants                                                 -0.876646   \n",
-       "ratio_top_75_variants                                                 -0.877751   \n",
-       "ratio_unique_traces_per_trace                                          0.878053   \n",
-       "\n",
-       "                                                    normalized_sequence_eventropy_exponential_forgetting   \n",
-       "activities_q3                                                                               -0.500003     \\\n",
-       "ratio_most_common_variant                                                                   -0.820146      \n",
-       "normalized_sequence_entropy                                                                  0.949180      \n",
-       "normalized_sequence_eventropy_exponential_forge...                                           1.000000      \n",
-       "trace_len_entropy                                                                           -0.453465      \n",
-       "trace_len_hist9                                                                              0.184055      \n",
-       "normalized_sequence_eventropy_linear_forgetting                                              0.979552      \n",
-       "eventropy_knn_3                                                                              0.393510      \n",
-       "trace_len_hist3                                                                             -0.273272      \n",
-       "kurtosis_variant_occurrence                                                                  0.295912      \n",
-       "eventropy_knn_7                                                                              0.414553      \n",
-       "eventropy_knn_5                                                                              0.409052      \n",
-       "skewness_variant_occurrence                                                                  0.404550      \n",
-       "activities_mean                                                                             -0.346623      \n",
-       "ratio_top_1_variants                                                                        -0.258761      \n",
-       "eventropy_lempel_ziv                                                                         0.647689      \n",
-       "eventropy_global_block                                                                       0.446875      \n",
-       "trace_len_harmonic_mean                                                                      0.570846      \n",
-       "eventropy_trace                                                                              0.506727      \n",
-       "eventropy_k_block_diff_1                                                                     0.337745      \n",
-       "eventropy_prefix                                                                             0.488804      \n",
-       "trace_len_mode                                                                               0.553293      \n",
-       "ratio_top_5_variants                                                                        -0.591511      \n",
-       "ratio_top_10_variants                                                                       -0.694187      \n",
-       "eventropy_k_block_ratio_3                                                                    0.413134      \n",
-       "eventropy_k_block_ratio_5                                                                    0.376855      \n",
-       "trace_len_geometric_mean                                                                     0.592721      \n",
-       "eventropy_k_block_diff_5                                                                     0.519657      \n",
-       "eventropy_k_block_ratio_1                                                                    0.491600      \n",
-       "eventropy_k_block_diff_3                                                                     0.504442      \n",
-       "trace_len_median                                                                             0.578747      \n",
-       "ratio_top_20_variants                                                                       -0.770970      \n",
-       "trace_len_q1                                                                                 0.577783      \n",
-       "ratio_top_50_variants                                                                       -0.784374      \n",
-       "ratio_top_75_variants                                                                       -0.785651      \n",
-       "ratio_unique_traces_per_trace                                                                0.786009      \n",
-       "\n",
-       "                                                    trace_len_entropy   \n",
-       "activities_q3                                                0.651857  \\\n",
-       "ratio_most_common_variant                                    0.455871   \n",
-       "normalized_sequence_entropy                                 -0.513211   \n",
-       "normalized_sequence_eventropy_exponential_forge...          -0.453465   \n",
-       "trace_len_entropy                                            1.000000   \n",
-       "trace_len_hist9                                             -0.401086   \n",
-       "normalized_sequence_eventropy_linear_forgetting             -0.392114   \n",
-       "eventropy_knn_3                                              0.115983   \n",
-       "trace_len_hist3                                             -0.177211   \n",
-       "kurtosis_variant_occurrence                                  0.518292   \n",
-       "eventropy_knn_7                                              0.142646   \n",
-       "eventropy_knn_5                                              0.128484   \n",
-       "skewness_variant_occurrence                                  0.399360   \n",
-       "activities_mean                                              0.699155   \n",
-       "ratio_top_1_variants                                         0.470289   \n",
-       "eventropy_lempel_ziv                                        -0.398436   \n",
-       "eventropy_global_block                                      -0.077549   \n",
-       "trace_len_harmonic_mean                                     -0.334337   \n",
-       "eventropy_trace                                             -0.063832   \n",
-       "eventropy_k_block_diff_1                                    -0.238610   \n",
-       "eventropy_prefix                                            -0.135577   \n",
-       "trace_len_mode                                              -0.400258   \n",
-       "ratio_top_5_variants                                         0.393169   \n",
-       "ratio_top_10_variants                                        0.394550   \n",
-       "eventropy_k_block_ratio_3                                   -0.277666   \n",
-       "eventropy_k_block_ratio_5                                   -0.324748   \n",
-       "trace_len_geometric_mean                                    -0.421136   \n",
-       "eventropy_k_block_diff_5                                    -0.374418   \n",
-       "eventropy_k_block_ratio_1                                   -0.411303   \n",
-       "eventropy_k_block_diff_3                                    -0.389937   \n",
-       "trace_len_median                                            -0.459727   \n",
-       "ratio_top_20_variants                                        0.556509   \n",
-       "trace_len_q1                                                -0.414980   \n",
-       "ratio_top_50_variants                                        0.573152   \n",
-       "ratio_top_75_variants                                        0.573851   \n",
-       "ratio_unique_traces_per_trace                               -0.573412   \n",
-       "\n",
-       "                                                    trace_len_hist9   \n",
-       "activities_q3                                             -0.298666  \\\n",
-       "ratio_most_common_variant                                 -0.239070   \n",
-       "normalized_sequence_entropy                                0.220806   \n",
-       "normalized_sequence_eventropy_exponential_forge...         0.184055   \n",
-       "trace_len_entropy                                         -0.401086   \n",
-       "trace_len_hist9                                            1.000000   \n",
-       "normalized_sequence_eventropy_linear_forgetting            0.152934   \n",
-       "eventropy_knn_3                                           -0.357692   \n",
-       "trace_len_hist3                                           -0.104087   \n",
-       "kurtosis_variant_occurrence                               -0.248786   \n",
-       "eventropy_knn_7                                           -0.348102   \n",
-       "eventropy_knn_5                                           -0.349866   \n",
-       "skewness_variant_occurrence                               -0.193931   \n",
-       "activities_mean                                           -0.319872   \n",
-       "ratio_top_1_variants                                      -0.159045   \n",
-       "eventropy_lempel_ziv                                      -0.123402   \n",
-       "eventropy_global_block                                    -0.280870   \n",
-       "trace_len_harmonic_mean                                    0.346679   \n",
-       "eventropy_trace                                           -0.301461   \n",
-       "eventropy_k_block_diff_1                                   0.063024   \n",
-       "eventropy_prefix                                          -0.267297   \n",
-       "trace_len_mode                                             0.387157   \n",
-       "ratio_top_5_variants                                      -0.223787   \n",
-       "ratio_top_10_variants                                     -0.242386   \n",
-       "eventropy_k_block_ratio_3                                 -0.158166   \n",
-       "eventropy_k_block_ratio_5                                 -0.101061   \n",
-       "trace_len_geometric_mean                                   0.327961   \n",
-       "eventropy_k_block_diff_5                                  -0.047815   \n",
-       "eventropy_k_block_ratio_1                                  0.009878   \n",
-       "eventropy_k_block_diff_3                                   0.016813   \n",
-       "trace_len_median                                           0.331725   \n",
-       "ratio_top_20_variants                                     -0.306775   \n",
-       "trace_len_q1                                               0.406222   \n",
-       "ratio_top_50_variants                                     -0.308349   \n",
-       "ratio_top_75_variants                                     -0.309056   \n",
-       "ratio_unique_traces_per_trace                              0.309232   \n",
-       "\n",
-       "                                                    normalized_sequence_eventropy_linear_forgetting   \n",
-       "activities_q3                                                                             -0.446838  \\\n",
-       "ratio_most_common_variant                                                                 -0.744763   \n",
-       "normalized_sequence_entropy                                                                0.867450   \n",
-       "normalized_sequence_eventropy_exponential_forge...                                         0.979552   \n",
-       "trace_len_entropy                                                                         -0.392114   \n",
-       "trace_len_hist9                                                                            0.152934   \n",
-       "normalized_sequence_eventropy_linear_forgetting                                            1.000000   \n",
-       "eventropy_knn_3                                                                            0.367694   \n",
-       "trace_len_hist3                                                                           -0.276891   \n",
-       "kurtosis_variant_occurrence                                                                0.341992   \n",
-       "eventropy_knn_7                                                                            0.387390   \n",
-       "eventropy_knn_5                                                                            0.382163   \n",
-       "skewness_variant_occurrence                                                                0.426785   \n",
-       "activities_mean                                                                           -0.276812   \n",
-       "ratio_top_1_variants                                                                      -0.192220   \n",
-       "eventropy_lempel_ziv                                                                       0.566472   \n",
-       "eventropy_global_block                                                                     0.399285   \n",
-       "trace_len_harmonic_mean                                                                    0.512861   \n",
-       "eventropy_trace                                                                            0.450011   \n",
-       "eventropy_k_block_diff_1                                                                   0.285660   \n",
-       "eventropy_prefix                                                                           0.430069   \n",
-       "trace_len_mode                                                                             0.484338   \n",
-       "ratio_top_5_variants                                                                      -0.501746   \n",
-       "ratio_top_10_variants                                                                     -0.598864   \n",
-       "eventropy_k_block_ratio_3                                                                  0.316485   \n",
-       "eventropy_k_block_ratio_5                                                                  0.277739   \n",
-       "trace_len_geometric_mean                                                                   0.522601   \n",
-       "eventropy_k_block_diff_5                                                                   0.445527   \n",
-       "eventropy_k_block_ratio_1                                                                  0.400941   \n",
-       "eventropy_k_block_diff_3                                                                   0.426984   \n",
-       "trace_len_median                                                                           0.500775   \n",
-       "ratio_top_20_variants                                                                     -0.672905   \n",
-       "trace_len_q1                                                                               0.511506   \n",
-       "ratio_top_50_variants                                                                     -0.686745   \n",
-       "ratio_top_75_variants                                                                     -0.688042   \n",
-       "ratio_unique_traces_per_trace                                                              0.688380   \n",
-       "\n",
-       "                                                    eventropy_knn_3   \n",
-       "activities_q3                                             -0.062271  \\\n",
-       "ratio_most_common_variant                                 -0.399730   \n",
-       "normalized_sequence_entropy                                0.391840   \n",
-       "normalized_sequence_eventropy_exponential_forge...         0.393510   \n",
-       "trace_len_entropy                                          0.115983   \n",
-       "trace_len_hist9                                           -0.357692   \n",
-       "normalized_sequence_eventropy_linear_forgetting            0.367694   \n",
-       "eventropy_knn_3                                            1.000000   \n",
-       "trace_len_hist3                                           -0.114732   \n",
-       "kurtosis_variant_occurrence                                0.670754   \n",
-       "eventropy_knn_7                                            0.997943   \n",
-       "eventropy_knn_5                                            0.999150   \n",
-       "skewness_variant_occurrence                                0.741303   \n",
-       "activities_mean                                            0.093266   \n",
-       "ratio_top_1_variants                                       0.299416   \n",
-       "eventropy_lempel_ziv                                       0.618973   \n",
-       "eventropy_global_block                                     0.893677   \n",
-       "trace_len_harmonic_mean                                    0.171198   \n",
-       "eventropy_trace                                            0.931933   \n",
-       "eventropy_k_block_diff_1                                   0.303359   \n",
-       "eventropy_prefix                                           0.901190   \n",
-       "trace_len_mode                                             0.114816   \n",
-       "ratio_top_5_variants                                       0.068793   \n",
-       "ratio_top_10_variants                                     -0.007579   \n",
-       "eventropy_k_block_ratio_3                                  0.727363   \n",
-       "eventropy_k_block_ratio_5                                  0.631913   \n",
-       "trace_len_geometric_mean                                   0.194343   \n",
-       "eventropy_k_block_diff_5                                   0.470102   \n",
-       "eventropy_k_block_ratio_1                                  0.548088   \n",
-       "eventropy_k_block_diff_3                                   0.544745   \n",
-       "trace_len_median                                           0.176249   \n",
-       "ratio_top_20_variants                                     -0.200954   \n",
-       "trace_len_q1                                               0.103769   \n",
-       "ratio_top_50_variants                                     -0.260643   \n",
-       "ratio_top_75_variants                                     -0.262890   \n",
-       "ratio_unique_traces_per_trace                              0.264034   \n",
-       "\n",
-       "                                                    trace_len_hist3   \n",
-       "activities_q3                                             -0.163765  \\\n",
-       "ratio_most_common_variant                                  0.387022   \n",
-       "normalized_sequence_entropy                               -0.235271   \n",
-       "normalized_sequence_eventropy_exponential_forge...        -0.273272   \n",
-       "trace_len_entropy                                         -0.177211   \n",
-       "trace_len_hist9                                           -0.104087   \n",
-       "normalized_sequence_eventropy_linear_forgetting           -0.276891   \n",
-       "eventropy_knn_3                                           -0.114732   \n",
-       "trace_len_hist3                                            1.000000   \n",
-       "kurtosis_variant_occurrence                               -0.258008   \n",
-       "eventropy_knn_7                                           -0.142479   \n",
-       "eventropy_knn_5                                           -0.128360   \n",
-       "skewness_variant_occurrence                               -0.315236   \n",
-       "activities_mean                                           -0.214655   \n",
-       "ratio_top_1_variants                                       0.040655   \n",
-       "eventropy_lempel_ziv                                      -0.138655   \n",
-       "eventropy_global_block                                    -0.263814   \n",
-       "trace_len_harmonic_mean                                   -0.322361   \n",
-       "eventropy_trace                                           -0.233797   \n",
-       "eventropy_k_block_diff_1                                   0.072237   \n",
-       "eventropy_prefix                                          -0.236717   \n",
-       "trace_len_mode                                            -0.290794   \n",
-       "ratio_top_5_variants                                       0.372263   \n",
-       "ratio_top_10_variants                                      0.398718   \n",
-       "eventropy_k_block_ratio_3                                  0.012534   \n",
-       "eventropy_k_block_ratio_5                                  0.059331   \n",
-       "trace_len_geometric_mean                                  -0.352139   \n",
-       "eventropy_k_block_diff_5                                  -0.089258   \n",
-       "eventropy_k_block_ratio_1                                 -0.051290   \n",
-       "eventropy_k_block_diff_3                                  -0.017674   \n",
-       "trace_len_median                                          -0.352342   \n",
-       "ratio_top_20_variants                                      0.365283   \n",
-       "trace_len_q1                                              -0.300585   \n",
-       "ratio_top_50_variants                                      0.350999   \n",
-       "ratio_top_75_variants                                      0.349343   \n",
-       "ratio_unique_traces_per_trace                             -0.349657   \n",
-       "\n",
-       "                                                    kurtosis_variant_occurrence   \n",
-       "activities_q3                                                          0.192853  \\\n",
-       "ratio_most_common_variant                                             -0.248842   \n",
-       "normalized_sequence_entropy                                            0.196209   \n",
-       "normalized_sequence_eventropy_exponential_forge...                     0.295912   \n",
-       "trace_len_entropy                                                      0.518292   \n",
-       "trace_len_hist9                                                       -0.248786   \n",
-       "normalized_sequence_eventropy_linear_forgetting                        0.341992   \n",
-       "eventropy_knn_3                                                        0.670754   \n",
-       "trace_len_hist3                                                       -0.258008   \n",
-       "kurtosis_variant_occurrence                                            1.000000   \n",
-       "eventropy_knn_7                                                        0.704145   \n",
-       "eventropy_knn_5                                                        0.690184   \n",
-       "skewness_variant_occurrence                                            0.966532   \n",
-       "activities_mean                                                        0.471373   \n",
-       "ratio_top_1_variants                                                   0.437257   \n",
-       "eventropy_lempel_ziv                                                   0.092295   \n",
-       "eventropy_global_block                                                 0.503937   \n",
-       "trace_len_harmonic_mean                                                0.155284   \n",
-       "eventropy_trace                                                        0.532930   \n",
-       "eventropy_k_block_diff_1                                               0.016650   \n",
-       "eventropy_prefix                                                       0.453405   \n",
-       "trace_len_mode                                                         0.025402   \n",
-       "ratio_top_5_variants                                                   0.153116   \n",
-       "ratio_top_10_variants                                                  0.091337   \n",
-       "eventropy_k_block_ratio_3                                              0.131184   \n",
-       "eventropy_k_block_ratio_5                                             -0.004747   \n",
-       "trace_len_geometric_mean                                               0.103149   \n",
-       "eventropy_k_block_diff_5                                              -0.033833   \n",
-       "eventropy_k_block_ratio_1                                             -0.054358   \n",
-       "eventropy_k_block_diff_3                                              -0.001832   \n",
-       "trace_len_median                                                       0.042782   \n",
-       "ratio_top_20_variants                                                 -0.003638   \n",
-       "trace_len_q1                                                           0.037692   \n",
-       "ratio_top_50_variants                                                 -0.042264   \n",
-       "ratio_top_75_variants                                                 -0.043031   \n",
-       "ratio_unique_traces_per_trace                                          0.044279   \n",
-       "\n",
-       "                                                    ...   \n",
-       "activities_q3                                       ...  \\\n",
-       "ratio_most_common_variant                           ...   \n",
-       "normalized_sequence_entropy                         ...   \n",
-       "normalized_sequence_eventropy_exponential_forge...  ...   \n",
-       "trace_len_entropy                                   ...   \n",
-       "trace_len_hist9                                     ...   \n",
-       "normalized_sequence_eventropy_linear_forgetting     ...   \n",
-       "eventropy_knn_3                                     ...   \n",
-       "trace_len_hist3                                     ...   \n",
-       "kurtosis_variant_occurrence                         ...   \n",
-       "eventropy_knn_7                                     ...   \n",
-       "eventropy_knn_5                                     ...   \n",
-       "skewness_variant_occurrence                         ...   \n",
-       "activities_mean                                     ...   \n",
-       "ratio_top_1_variants                                ...   \n",
-       "eventropy_lempel_ziv                                ...   \n",
-       "eventropy_global_block                              ...   \n",
-       "trace_len_harmonic_mean                             ...   \n",
-       "eventropy_trace                                     ...   \n",
-       "eventropy_k_block_diff_1                            ...   \n",
-       "eventropy_prefix                                    ...   \n",
-       "trace_len_mode                                      ...   \n",
-       "ratio_top_5_variants                                ...   \n",
-       "ratio_top_10_variants                               ...   \n",
-       "eventropy_k_block_ratio_3                           ...   \n",
-       "eventropy_k_block_ratio_5                           ...   \n",
-       "trace_len_geometric_mean                            ...   \n",
-       "eventropy_k_block_diff_5                            ...   \n",
-       "eventropy_k_block_ratio_1                           ...   \n",
-       "eventropy_k_block_diff_3                            ...   \n",
-       "trace_len_median                                    ...   \n",
-       "ratio_top_20_variants                               ...   \n",
-       "trace_len_q1                                        ...   \n",
-       "ratio_top_50_variants                               ...   \n",
-       "ratio_top_75_variants                               ...   \n",
-       "ratio_unique_traces_per_trace                       ...   \n",
-       "\n",
-       "                                                    trace_len_geometric_mean   \n",
-       "activities_q3                                                      -0.178143  \\\n",
-       "ratio_most_common_variant                                          -0.680095   \n",
-       "normalized_sequence_entropy                                         0.664062   \n",
-       "normalized_sequence_eventropy_exponential_forge...                  0.592721   \n",
-       "trace_len_entropy                                                  -0.421136   \n",
-       "trace_len_hist9                                                     0.327961   \n",
-       "normalized_sequence_eventropy_linear_forgetting                     0.522601   \n",
-       "eventropy_knn_3                                                     0.194343   \n",
-       "trace_len_hist3                                                    -0.352139   \n",
-       "kurtosis_variant_occurrence                                         0.103149   \n",
-       "eventropy_knn_7                                                     0.207681   \n",
-       "eventropy_knn_5                                                     0.203716   \n",
-       "skewness_variant_occurrence                                         0.216999   \n",
-       "activities_mean                                                    -0.097058   \n",
-       "ratio_top_1_variants                                               -0.429185   \n",
-       "eventropy_lempel_ziv                                                0.451688   \n",
-       "eventropy_global_block                                              0.480625   \n",
-       "trace_len_harmonic_mean                                             0.958903   \n",
-       "eventropy_trace                                                     0.402992   \n",
-       "eventropy_k_block_diff_1                                            0.109747   \n",
-       "eventropy_prefix                                                    0.484517   \n",
-       "trace_len_mode                                                      0.914711   \n",
-       "ratio_top_5_variants                                               -0.659600   \n",
-       "ratio_top_10_variants                                              -0.718275   \n",
-       "eventropy_k_block_ratio_3                                           0.431902   \n",
-       "eventropy_k_block_ratio_5                                           0.379651   \n",
-       "trace_len_geometric_mean                                            1.000000   \n",
-       "eventropy_k_block_diff_5                                            0.377851   \n",
-       "eventropy_k_block_ratio_1                                           0.431359   \n",
-       "eventropy_k_block_diff_3                                            0.429419   \n",
-       "trace_len_median                                                    0.990961   \n",
-       "ratio_top_20_variants                                              -0.846993   \n",
-       "trace_len_q1                                                        0.946175   \n",
-       "ratio_top_50_variants                                              -0.858941   \n",
-       "ratio_top_75_variants                                              -0.858895   \n",
-       "ratio_unique_traces_per_trace                                       0.859037   \n",
-       "\n",
-       "                                                    eventropy_k_block_diff_5   \n",
-       "activities_q3                                                      -0.331395  \\\n",
-       "ratio_most_common_variant                                          -0.484408   \n",
-       "normalized_sequence_entropy                                         0.591652   \n",
-       "normalized_sequence_eventropy_exponential_forge...                  0.519657   \n",
-       "trace_len_entropy                                                  -0.374418   \n",
-       "trace_len_hist9                                                    -0.047815   \n",
-       "normalized_sequence_eventropy_linear_forgetting                     0.445527   \n",
-       "eventropy_knn_3                                                     0.470102   \n",
-       "trace_len_hist3                                                    -0.089258   \n",
-       "kurtosis_variant_occurrence                                        -0.033833   \n",
-       "eventropy_knn_7                                                     0.455547   \n",
-       "eventropy_knn_5                                                     0.460452   \n",
-       "skewness_variant_occurrence                                         0.056393   \n",
-       "activities_mean                                                    -0.342978   \n",
-       "ratio_top_1_variants                                               -0.392627   \n",
-       "eventropy_lempel_ziv                                                0.897458   \n",
-       "eventropy_global_block                                              0.577783   \n",
-       "trace_len_harmonic_mean                                             0.342788   \n",
-       "eventropy_trace                                                     0.606042   \n",
-       "eventropy_k_block_diff_1                                            0.693135   \n",
-       "eventropy_prefix                                                    0.633532   \n",
-       "trace_len_mode                                                      0.495384   \n",
-       "ratio_top_5_variants                                               -0.539024   \n",
-       "ratio_top_10_variants                                              -0.547724   \n",
-       "eventropy_k_block_ratio_3                                           0.825771   \n",
-       "eventropy_k_block_ratio_5                                           0.877190   \n",
-       "trace_len_geometric_mean                                            0.377851   \n",
-       "eventropy_k_block_diff_5                                            1.000000   \n",
-       "eventropy_k_block_ratio_1                                           0.925266   \n",
-       "eventropy_k_block_diff_3                                            0.924130   \n",
-       "trace_len_median                                                    0.403825   \n",
-       "ratio_top_20_variants                                              -0.607564   \n",
-       "trace_len_q1                                                        0.441321   \n",
-       "ratio_top_50_variants                                              -0.599378   \n",
-       "ratio_top_75_variants                                              -0.598815   \n",
-       "ratio_unique_traces_per_trace                                       0.598346   \n",
-       "\n",
-       "                                                    eventropy_k_block_ratio_1   \n",
-       "activities_q3                                                       -0.372845  \\\n",
-       "ratio_most_common_variant                                           -0.505069   \n",
-       "normalized_sequence_entropy                                          0.595375   \n",
-       "normalized_sequence_eventropy_exponential_forge...                   0.491600   \n",
-       "trace_len_entropy                                                   -0.411303   \n",
-       "trace_len_hist9                                                      0.009878   \n",
-       "normalized_sequence_eventropy_linear_forgetting                      0.400941   \n",
-       "eventropy_knn_3                                                      0.548088   \n",
-       "trace_len_hist3                                                     -0.051290   \n",
-       "kurtosis_variant_occurrence                                         -0.054358   \n",
-       "eventropy_knn_7                                                      0.529663   \n",
-       "eventropy_knn_5                                                      0.536569   \n",
-       "skewness_variant_occurrence                                          0.059318   \n",
-       "activities_mean                                                     -0.390862   \n",
-       "ratio_top_1_variants                                                -0.331039   \n",
-       "eventropy_lempel_ziv                                                 0.911113   \n",
-       "eventropy_global_block                                               0.669330   \n",
-       "trace_len_harmonic_mean                                              0.386648   \n",
-       "eventropy_trace                                                      0.683355   \n",
-       "eventropy_k_block_diff_1                                             0.589455   \n",
-       "eventropy_prefix                                                     0.729512   \n",
-       "trace_len_mode                                                       0.488929   \n",
-       "ratio_top_5_variants                                                -0.496584   \n",
-       "ratio_top_10_variants                                               -0.512481   \n",
-       "eventropy_k_block_ratio_3                                            0.922590   \n",
-       "eventropy_k_block_ratio_5                                            0.961157   \n",
-       "trace_len_geometric_mean                                             0.431359   \n",
-       "eventropy_k_block_diff_5                                             0.925266   \n",
-       "eventropy_k_block_ratio_1                                            1.000000   \n",
-       "eventropy_k_block_diff_3                                             0.983618   \n",
-       "trace_len_median                                                     0.454950   \n",
-       "ratio_top_20_variants                                               -0.596477   \n",
-       "trace_len_q1                                                         0.455977   \n",
-       "ratio_top_50_variants                                               -0.595245   \n",
-       "ratio_top_75_variants                                               -0.595384   \n",
-       "ratio_unique_traces_per_trace                                        0.594868   \n",
-       "\n",
-       "                                                    eventropy_k_block_diff_3   \n",
-       "activities_q3                                                      -0.360271  \\\n",
-       "ratio_most_common_variant                                          -0.484158   \n",
-       "normalized_sequence_entropy                                         0.587182   \n",
-       "normalized_sequence_eventropy_exponential_forge...                  0.504442   \n",
-       "trace_len_entropy                                                  -0.389937   \n",
-       "trace_len_hist9                                                     0.016813   \n",
-       "normalized_sequence_eventropy_linear_forgetting                     0.426984   \n",
-       "eventropy_knn_3                                                     0.544745   \n",
-       "trace_len_hist3                                                    -0.017674   \n",
-       "kurtosis_variant_occurrence                                        -0.001832   \n",
-       "eventropy_knn_7                                                     0.527873   \n",
-       "eventropy_knn_5                                                     0.534144   \n",
-       "skewness_variant_occurrence                                         0.098855   \n",
-       "activities_mean                                                    -0.352554   \n",
-       "ratio_top_1_variants                                               -0.305745   \n",
-       "eventropy_lempel_ziv                                                0.892299   \n",
-       "eventropy_global_block                                              0.659452   \n",
-       "trace_len_harmonic_mean                                             0.403107   \n",
-       "eventropy_trace                                                     0.668513   \n",
-       "eventropy_k_block_diff_1                                            0.608730   \n",
-       "eventropy_prefix                                                    0.710391   \n",
-       "trace_len_mode                                                      0.510358   \n",
-       "ratio_top_5_variants                                               -0.485880   \n",
-       "ratio_top_10_variants                                              -0.500971   \n",
-       "eventropy_k_block_ratio_3                                           0.903515   \n",
-       "eventropy_k_block_ratio_5                                           0.939838   \n",
-       "trace_len_geometric_mean                                            0.429419   \n",
-       "eventropy_k_block_diff_5                                            0.924130   \n",
-       "eventropy_k_block_ratio_1                                           0.983618   \n",
-       "eventropy_k_block_diff_3                                            1.000000   \n",
-       "trace_len_median                                                    0.445314   \n",
-       "ratio_top_20_variants                                              -0.582388   \n",
-       "trace_len_q1                                                        0.474504   \n",
-       "ratio_top_50_variants                                              -0.581416   \n",
-       "ratio_top_75_variants                                              -0.581487   \n",
-       "ratio_unique_traces_per_trace                                       0.581037   \n",
-       "\n",
-       "                                                    trace_len_median   \n",
-       "activities_q3                                              -0.204937  \\\n",
-       "ratio_most_common_variant                                  -0.677083   \n",
-       "normalized_sequence_entropy                                 0.659965   \n",
-       "normalized_sequence_eventropy_exponential_forge...          0.578747   \n",
-       "trace_len_entropy                                          -0.459727   \n",
-       "trace_len_hist9                                             0.331725   \n",
-       "normalized_sequence_eventropy_linear_forgetting             0.500775   \n",
-       "eventropy_knn_3                                             0.176249   \n",
-       "trace_len_hist3                                            -0.352342   \n",
-       "kurtosis_variant_occurrence                                 0.042782   \n",
-       "eventropy_knn_7                                             0.187323   \n",
-       "eventropy_knn_5                                             0.184209   \n",
-       "skewness_variant_occurrence                                 0.164644   \n",
-       "activities_mean                                            -0.141924   \n",
-       "ratio_top_1_variants                                       -0.453129   \n",
-       "eventropy_lempel_ziv                                        0.482644   \n",
-       "eventropy_global_block                                      0.488968   \n",
-       "trace_len_harmonic_mean                                     0.917264   \n",
-       "eventropy_trace                                             0.387461   \n",
-       "eventropy_k_block_diff_1                                    0.131434   \n",
-       "eventropy_prefix                                            0.486056   \n",
-       "trace_len_mode                                              0.890032   \n",
-       "ratio_top_5_variants                                       -0.679181   \n",
-       "ratio_top_10_variants                                      -0.736846   \n",
-       "eventropy_k_block_ratio_3                                   0.455753   \n",
-       "eventropy_k_block_ratio_5                                   0.404277   \n",
-       "trace_len_geometric_mean                                    0.990961   \n",
-       "eventropy_k_block_diff_5                                    0.403825   \n",
-       "eventropy_k_block_ratio_1                                   0.454950   \n",
-       "eventropy_k_block_diff_3                                    0.445314   \n",
-       "trace_len_median                                            1.000000   \n",
-       "ratio_top_20_variants                                      -0.862281   \n",
-       "trace_len_q1                                                0.919761   \n",
-       "ratio_top_50_variants                                      -0.872477   \n",
-       "ratio_top_75_variants                                      -0.872387   \n",
-       "ratio_unique_traces_per_trace                               0.872451   \n",
-       "\n",
-       "                                                    ratio_top_20_variants   \n",
-       "activities_q3                                                    0.379360  \\\n",
-       "ratio_most_common_variant                                        0.772018   \n",
-       "normalized_sequence_entropy                                     -0.866084   \n",
-       "normalized_sequence_eventropy_exponential_forge...              -0.770970   \n",
-       "trace_len_entropy                                                0.556509   \n",
-       "trace_len_hist9                                                 -0.306775   \n",
-       "normalized_sequence_eventropy_linear_forgetting                 -0.672905   \n",
-       "eventropy_knn_3                                                 -0.200954   \n",
-       "trace_len_hist3                                                  0.365283   \n",
-       "kurtosis_variant_occurrence                                     -0.003638   \n",
-       "eventropy_knn_7                                                 -0.215919   \n",
-       "eventropy_knn_5                                                 -0.211173   \n",
-       "skewness_variant_occurrence                                     -0.132644   \n",
-       "activities_mean                                                  0.333256   \n",
-       "ratio_top_1_variants                                             0.588358   \n",
-       "eventropy_lempel_ziv                                            -0.690318   \n",
-       "eventropy_global_block                                          -0.444348   \n",
-       "trace_len_harmonic_mean                                         -0.785392   \n",
-       "eventropy_trace                                                 -0.441856   \n",
-       "eventropy_k_block_diff_1                                        -0.319323   \n",
-       "eventropy_prefix                                                -0.479836   \n",
-       "trace_len_mode                                                  -0.800977   \n",
-       "ratio_top_5_variants                                             0.854760   \n",
-       "ratio_top_10_variants                                            0.907611   \n",
-       "eventropy_k_block_ratio_3                                       -0.499033   \n",
-       "eventropy_k_block_ratio_5                                       -0.487833   \n",
-       "trace_len_geometric_mean                                        -0.846993   \n",
-       "eventropy_k_block_diff_5                                        -0.607564   \n",
-       "eventropy_k_block_ratio_1                                       -0.596477   \n",
-       "eventropy_k_block_diff_3                                        -0.582388   \n",
-       "trace_len_median                                                -0.862281   \n",
-       "ratio_top_20_variants                                            1.000000   \n",
-       "trace_len_q1                                                    -0.824921   \n",
-       "ratio_top_50_variants                                            0.991745   \n",
-       "ratio_top_75_variants                                            0.991361   \n",
-       "ratio_unique_traces_per_trace                                   -0.991075   \n",
-       "\n",
-       "                                                    trace_len_q1   \n",
-       "activities_q3                                          -0.207547  \\\n",
-       "ratio_most_common_variant                              -0.640762   \n",
-       "normalized_sequence_entropy                             0.647014   \n",
-       "normalized_sequence_eventropy_exponential_forge...      0.577783   \n",
-       "trace_len_entropy                                      -0.414980   \n",
-       "trace_len_hist9                                         0.406222   \n",
-       "normalized_sequence_eventropy_linear_forgetting         0.511506   \n",
-       "eventropy_knn_3                                         0.103769   \n",
-       "trace_len_hist3                                        -0.300585   \n",
-       "kurtosis_variant_occurrence                             0.037692   \n",
-       "eventropy_knn_7                                         0.116926   \n",
-       "eventropy_knn_5                                         0.113142   \n",
-       "skewness_variant_occurrence                             0.141057   \n",
-       "activities_mean                                        -0.136567   \n",
-       "ratio_top_1_variants                                   -0.451532   \n",
-       "eventropy_lempel_ziv                                    0.414984   \n",
-       "eventropy_global_block                                  0.350320   \n",
-       "trace_len_harmonic_mean                                 0.976809   \n",
-       "eventropy_trace                                         0.308826   \n",
-       "eventropy_k_block_diff_1                                0.147371   \n",
-       "eventropy_prefix                                        0.364837   \n",
-       "trace_len_mode                                          0.987385   \n",
-       "ratio_top_5_variants                                   -0.656847   \n",
-       "ratio_top_10_variants                                  -0.708694   \n",
-       "eventropy_k_block_ratio_3                               0.387944   \n",
-       "eventropy_k_block_ratio_5                               0.388326   \n",
-       "trace_len_geometric_mean                                0.946175   \n",
-       "eventropy_k_block_diff_5                                0.441321   \n",
-       "eventropy_k_block_ratio_1                               0.455977   \n",
-       "eventropy_k_block_diff_3                                0.474504   \n",
-       "trace_len_median                                        0.919761   \n",
-       "ratio_top_20_variants                                  -0.824921   \n",
-       "trace_len_q1                                            1.000000   \n",
-       "ratio_top_50_variants                                  -0.832498   \n",
-       "ratio_top_75_variants                                  -0.832272   \n",
-       "ratio_unique_traces_per_trace                           0.832481   \n",
-       "\n",
-       "                                                    ratio_top_50_variants   \n",
-       "activities_q3                                                    0.392501  \\\n",
-       "ratio_most_common_variant                                        0.809234   \n",
-       "normalized_sequence_entropy                                     -0.876646   \n",
-       "normalized_sequence_eventropy_exponential_forge...              -0.784374   \n",
-       "trace_len_entropy                                                0.573152   \n",
-       "trace_len_hist9                                                 -0.308349   \n",
-       "normalized_sequence_eventropy_linear_forgetting                 -0.686745   \n",
-       "eventropy_knn_3                                                 -0.260643   \n",
-       "trace_len_hist3                                                  0.350999   \n",
-       "kurtosis_variant_occurrence                                     -0.042264   \n",
-       "eventropy_knn_7                                                 -0.274286   \n",
-       "eventropy_knn_5                                                 -0.270523   \n",
-       "skewness_variant_occurrence                                     -0.184708   \n",
-       "activities_mean                                                  0.341059   \n",
-       "ratio_top_1_variants                                             0.522835   \n",
-       "eventropy_lempel_ziv                                            -0.694109   \n",
-       "eventropy_global_block                                          -0.486711   \n",
-       "trace_len_harmonic_mean                                         -0.797166   \n",
-       "eventropy_trace                                                 -0.480534   \n",
-       "eventropy_k_block_diff_1                                        -0.308105   \n",
-       "eventropy_prefix                                                -0.519542   \n",
-       "trace_len_mode                                                  -0.806671   \n",
-       "ratio_top_5_variants                                             0.784494   \n",
-       "ratio_top_10_variants                                            0.848190   \n",
-       "eventropy_k_block_ratio_3                                       -0.522102   \n",
-       "eventropy_k_block_ratio_5                                       -0.504889   \n",
-       "trace_len_geometric_mean                                        -0.858941   \n",
-       "eventropy_k_block_diff_5                                        -0.599378   \n",
-       "eventropy_k_block_ratio_1                                       -0.595245   \n",
-       "eventropy_k_block_diff_3                                        -0.581416   \n",
-       "trace_len_median                                                -0.872477   \n",
-       "ratio_top_20_variants                                            0.991745   \n",
-       "trace_len_q1                                                    -0.832498   \n",
-       "ratio_top_50_variants                                            1.000000   \n",
-       "ratio_top_75_variants                                            0.999979   \n",
-       "ratio_unique_traces_per_trace                                   -0.999957   \n",
-       "\n",
-       "                                                    ratio_top_75_variants   \n",
-       "activities_q3                                                    0.393849  \\\n",
-       "ratio_most_common_variant                                        0.808176   \n",
-       "normalized_sequence_entropy                                     -0.877751   \n",
-       "normalized_sequence_eventropy_exponential_forge...              -0.785651   \n",
-       "trace_len_entropy                                                0.573851   \n",
-       "trace_len_hist9                                                 -0.309056   \n",
-       "normalized_sequence_eventropy_linear_forgetting                 -0.688042   \n",
-       "eventropy_knn_3                                                 -0.262890   \n",
-       "trace_len_hist3                                                  0.349343   \n",
-       "kurtosis_variant_occurrence                                     -0.043031   \n",
-       "eventropy_knn_7                                                 -0.276444   \n",
-       "eventropy_knn_5                                                 -0.272753   \n",
-       "skewness_variant_occurrence                                     -0.185789   \n",
-       "activities_mean                                                  0.342069   \n",
-       "ratio_top_1_variants                                             0.520572   \n",
-       "eventropy_lempel_ziv                                            -0.694258   \n",
-       "eventropy_global_block                                          -0.488084   \n",
-       "trace_len_harmonic_mean                                         -0.797082   \n",
-       "eventropy_trace                                                 -0.481789   \n",
-       "eventropy_k_block_diff_1                                        -0.307244   \n",
-       "eventropy_prefix                                                -0.520845   \n",
-       "trace_len_mode                                                  -0.806296   \n",
-       "ratio_top_5_variants                                             0.782390   \n",
-       "ratio_top_10_variants                                            0.846992   \n",
-       "eventropy_k_block_ratio_3                                       -0.523124   \n",
-       "eventropy_k_block_ratio_5                                       -0.505709   \n",
-       "trace_len_geometric_mean                                        -0.858895   \n",
-       "eventropy_k_block_diff_5                                        -0.598815   \n",
-       "eventropy_k_block_ratio_1                                       -0.595384   \n",
-       "eventropy_k_block_diff_3                                        -0.581487   \n",
-       "trace_len_median                                                -0.872387   \n",
-       "ratio_top_20_variants                                            0.991361   \n",
-       "trace_len_q1                                                    -0.832272   \n",
-       "ratio_top_50_variants                                            0.999979   \n",
-       "ratio_top_75_variants                                            1.000000   \n",
-       "ratio_unique_traces_per_trace                                   -0.999989   \n",
-       "\n",
-       "                                                    ratio_unique_traces_per_trace  \n",
-       "activities_q3                                                           -0.394220  \n",
-       "ratio_most_common_variant                                               -0.808280  \n",
-       "normalized_sequence_entropy                                              0.878053  \n",
-       "normalized_sequence_eventropy_exponential_forge...                       0.786009  \n",
-       "trace_len_entropy                                                       -0.573412  \n",
-       "trace_len_hist9                                                          0.309232  \n",
-       "normalized_sequence_eventropy_linear_forgetting                          0.688380  \n",
-       "eventropy_knn_3                                                          0.264034  \n",
-       "trace_len_hist3                                                         -0.349657  \n",
-       "kurtosis_variant_occurrence                                              0.044279  \n",
-       "eventropy_knn_7                                                          0.277664  \n",
-       "eventropy_knn_5                                                          0.273977  \n",
-       "skewness_variant_occurrence                                              0.187322  \n",
-       "activities_mean                                                         -0.341861  \n",
-       "ratio_top_1_variants                                                    -0.519027  \n",
-       "eventropy_lempel_ziv                                                     0.694278  \n",
-       "eventropy_global_block                                                   0.488692  \n",
-       "trace_len_harmonic_mean                                                  0.797317  \n",
-       "eventropy_trace                                                          0.482509  \n",
-       "eventropy_k_block_diff_1                                                 0.307021  \n",
-       "eventropy_prefix                                                         0.521518  \n",
-       "trace_len_mode                                                           0.806604  \n",
-       "ratio_top_5_variants                                                    -0.780722  \n",
-       "ratio_top_10_variants                                                   -0.845994  \n",
-       "eventropy_k_block_ratio_3                                                0.522969  \n",
-       "eventropy_k_block_ratio_5                                                0.505480  \n",
-       "trace_len_geometric_mean                                                 0.859037  \n",
-       "eventropy_k_block_diff_5                                                 0.598346  \n",
-       "eventropy_k_block_ratio_1                                                0.594868  \n",
-       "eventropy_k_block_diff_3                                                 0.581037  \n",
-       "trace_len_median                                                         0.872451  \n",
-       "ratio_top_20_variants                                                   -0.991075  \n",
-       "trace_len_q1                                                             0.832481  \n",
-       "ratio_top_50_variants                                                   -0.999957  \n",
-       "ratio_top_75_variants                                                   -0.999989  \n",
-       "ratio_unique_traces_per_trace                                            1.000000  \n",
-       "\n",
-       "[36 rows x 36 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "\n",
-    "\n",
-    "selected_per_category_highest_variance = ['ratio_unique_traces_per_trace', # simple_statistics\n",
-    "                                         'trace_len_hist8',# trace_length\n",
-    "                                         'skewness_variant_occurrence',# trace_variant\n",
-    "                                         'activities_mean',# activities\n",
-    "                                         'start_activities_kurtosis',# start_activities\n",
-    "                                         'end_activities_median',# end_activities\n",
-    "                                         'entropy_k_block_diff_1',# entropies\n",
-    "                                         'n_traces', 'n_unique_traces'\n",
-    "                                         ]\n",
-    "def plot_miner_correlation(input_df):\n",
-    "    #df = input_df.loc[:, input_df.columns.isin(selected_per_category_highest_variance)]\n",
-    "    df = input_df.loc[:,:]\n",
-    "    #df = input_df.loc[:, sorted_features]\n",
-    "    #df = input_df.loc[:, input_df.columns[3:]]\n",
-    "\n",
-    "    corr = df.corr()\n",
-    "    fig, ax = plt.subplots(figsize=(20,20)) \n",
-    "    sns.set(font_scale = 2)\n",
-    "    b= sns.heatmap(corr,\n",
-    "                ax=ax,\n",
-    "                vmin=-1,\n",
-    "                vmax=1,\n",
-    "                xticklabels=corr.columns.values,\n",
-    "                yticklabels=corr.columns.values)\n",
-    "    #b.set_yticklabels(b.get_yticks(), size = 15)\n",
-    "    plt.show()\n",
-    "    return corr\n",
-    "\n",
-    "#scaled_dmf = scaled_dmf.drop(['log', 'Log Nature'], axis=1)\n",
-    "scaled_dmf = scaled_dmf[filtered_feat]\n",
-    "corr_df = plot_miner_correlation(scaled_dmf)\n",
-    "stats = stats.loc[filtered_feat]\n",
-    "corr_df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "16ea5dad",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot: xlabel='disconnection', ylabel='std'>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#feature_metrics= corr_df.loc[:,:]\n",
-    "feature_metrics = pd.DataFrame()\n",
-    "feature_metrics['disconnection'] = pd.Series(min_max_scaler.fit_transform((corr_df.abs()).sum(axis=1).to_numpy().reshape(-1, 1)).reshape(len(corr_df))).apply(lambda x: 1-x)   \n",
-    "#TODO: MinMaxScaler ONLY before std computation\n",
-    "#feature_metrics['std'] = pd.Series(min_max_scaler.fit_transform(stats['std'].to_numpy().reshape(-1, 1)).reshape(len(corr_df)))\n",
-    "feature_metrics['std'] = pd.Series(stats['std'].to_numpy().reshape(-1, 1).reshape(len(corr_df)))\n",
-    "#feature_metrics['score'] = feature_metrics.apply(lambda x: x['disconnection'], axis=1) #TODO mean or prod?\n",
-    "feature_metrics['score'] = feature_metrics.apply(lambda x: x.prod(), axis=1) #TODO mean or prod?\n",
-    "feature_metrics.index = corr_df.index\n",
-    "#pd.concat([feature_metrics, stats['std']], axis=1)\n",
-    "#feature_metrics = scaleColumns(feature_metrics, feature_metrics.columns)\n",
-    "\n",
-    "\n",
-    "feature_metrics.plot.scatter(x=\"disconnection\", y=\"std\")\n",
-    "\n",
-    "#top_k_score = feature_metrics.sort_values([\"score\"], ascending=False).head(TOP_K).index[:5].tolist()\n",
-    "#other = ['variant_entropy', 'normalized_variant_entropy', 'sequence_entropy', 'normalized_sequence_entropy','sequence_entropy_linear_forgetting','normalized_sequence_entropy_linear_forgetting','sequence_entropy_exponential_forgetting','normalized_sequence_entropy_exponential_forgetting'] \n",
-    "#selected_highest_score = top_k_score+other"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "f8643aee",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "                                                    disconnection       std   \n",
-      "trace_len_hist3                                          1.000000  0.281492  \\\n",
-      "trace_len_hist9                                          0.950974  0.276533   \n",
-      "eventropy_k_block_diff_1                                 0.803846  0.325423   \n",
-      "kurtosis_variant_occurrence                              0.923120  0.281577   \n",
-      "activities_mean                                          0.869433  0.289788   \n",
-      "skewness_variant_occurrence                              0.764267  0.283716   \n",
-      "activities_q3                                            0.824492  0.253252   \n",
-      "ratio_top_1_variants                                     0.683999  0.290271   \n",
-      "trace_len_entropy                                        0.576229  0.270546   \n",
-      "eventropy_knn_3                                          0.491725  0.280572   \n",
-      "eventropy_knn_5                                          0.481065  0.281888   \n",
-      "eventropy_knn_7                                          0.475904  0.281818   \n",
-      "ratio_top_5_variants                                     0.363393  0.339036   \n",
-      "trace_len_q1                                             0.286775  0.402506   \n",
-      "eventropy_k_block_ratio_5                                0.294932  0.346309   \n",
-      "trace_len_harmonic_mean                                  0.326012  0.308559   \n",
-      "ratio_top_10_variants                                    0.290012  0.340644   \n",
-      "trace_len_mode                                           0.294336  0.330072   \n",
-      "eventropy_k_block_diff_5                                 0.244431  0.374350   \n",
-      "normalized_sequence_eventropy_linear_forgetting          0.321928  0.279733   \n",
-      "trace_len_median                                         0.224263  0.378282   \n",
-      "eventropy_k_block_ratio_3                                0.237459  0.345849   \n",
-      "eventropy_global_block                                   0.269850  0.304198   \n",
-      "trace_len_geometric_mean                                 0.230946  0.353671   \n",
-      "eventropy_trace                                          0.260809  0.312554   \n",
-      "eventropy_k_block_diff_3                                 0.212719  0.376743   \n",
-      "eventropy_k_block_ratio_1                                0.193213  0.374408   \n",
-      "eventropy_prefix                                         0.211474  0.329233   \n",
-      "ratio_most_common_variant                                0.183727  0.253617   \n",
-      "normalized_sequence_eventropy_exponential_forge...       0.160069  0.262115   \n",
-      "eventropy_lempel_ziv                                     0.075249  0.293915   \n",
-      "ratio_top_20_variants                                    0.030526  0.390934   \n",
-      "normalized_sequence_entropy                              0.030098  0.259735   \n",
-      "ratio_top_50_variants                                    0.001179  0.414572   \n",
-      "ratio_top_75_variants                                    0.000317  0.415087   \n",
-      "ratio_unique_traces_per_trace                            0.000000  0.415471   \n",
-      "\n",
-      "                                                       score  \n",
-      "trace_len_hist3                                     0.281492  \n",
-      "trace_len_hist9                                     0.262976  \n",
-      "eventropy_k_block_diff_1                            0.261590  \n",
-      "kurtosis_variant_occurrence                         0.259929  \n",
-      "activities_mean                                     0.251952  \n",
-      "skewness_variant_occurrence                         0.216835  \n",
-      "activities_q3                                       0.208804  \n",
-      "ratio_top_1_variants                                0.198545  \n",
-      "trace_len_entropy                                   0.155896  \n",
-      "eventropy_knn_3                                     0.137964  \n",
-      "eventropy_knn_5                                     0.135606  \n",
-      "eventropy_knn_7                                     0.134118  \n",
-      "ratio_top_5_variants                                0.123203  \n",
-      "trace_len_q1                                        0.115429  \n",
-      "eventropy_k_block_ratio_5                           0.102138  \n",
-      "trace_len_harmonic_mean                             0.100594  \n",
-      "ratio_top_10_variants                               0.098791  \n",
-      "trace_len_mode                                      0.097152  \n",
-      "eventropy_k_block_diff_5                            0.091503  \n",
-      "normalized_sequence_eventropy_linear_forgetting     0.090054  \n",
-      "trace_len_median                                    0.084835  \n",
-      "eventropy_k_block_ratio_3                           0.082125  \n",
-      "eventropy_global_block                              0.082088  \n",
-      "trace_len_geometric_mean                            0.081679  \n",
-      "eventropy_trace                                     0.081517  \n",
-      "eventropy_k_block_diff_3                            0.080140  \n",
-      "eventropy_k_block_ratio_1                           0.072340  \n",
-      "eventropy_prefix                                    0.069624  \n",
-      "ratio_most_common_variant                           0.046596  \n",
-      "normalized_sequence_eventropy_exponential_forge...  0.041956  \n",
-      "eventropy_lempel_ziv                                0.022117  \n",
-      "ratio_top_20_variants                               0.011934  \n",
-      "normalized_sequence_entropy                         0.007817  \n",
-      "ratio_top_50_variants                               0.000489  \n",
-      "ratio_top_75_variants                               0.000131  \n",
-      "ratio_unique_traces_per_trace                       0.000000  \n"
-     ]
-    },
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 2000x2000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "TOP_K = 10\n",
-    "selected_highest_score = feature_metrics.sort_values([\"score\"], ascending=False)\n",
-    "print(selected_highest_score)\n",
-    "selected_highest_score = selected_highest_score.head(TOP_K).index\n",
-    "\n",
-    "feature_metrics.loc[selected_highest_score,:]\n",
-    "corr_sel_df = plot_miner_correlation(scaled_dmf[selected_highest_score])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "98414438",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Before: 0 columns in 34 rows\n",
-      "After: 0\n"
-     ]
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "df= dmf.loc[:,dmf.columns.isin(selected_highest_score)]\n",
-    "\n",
-    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
-    "imp_mean.fit(df)\n",
-    "imp_df = imp_mean.transform(df)\n",
-    "imp_df = pd.DataFrame(imp_df, columns = df.columns)\n",
-    "print(\"Before:\", len(df.loc[:, df.isna().any()].columns), \"columns in\", len(df.loc[:, df.isna().any()]), 'rows')\n",
-    "print(\"After:\", len(imp_df.loc[:, imp_df.isna().any()].columns))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "c645fdeb",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/andreamaldonado/miniconda3/envs/py39/lib/python3.9/site-packages/sklearn/base.py:457: UserWarning: X has feature names, but PCA was fitted without feature names\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 3000x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.preprocessing import Normalizer\n",
-    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
-    "from sklearn.decomposition import PCA\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "X = imp_df\n",
-    "scaler = StandardScaler()\n",
-    "minmaxscaler = MinMaxScaler()\n",
-    "scaled_X_train = scaler.fit_transform(X)\n",
-    "normed_X = Normalizer(norm=\"l2\").fit_transform(X)\n",
-    "minmaxscaled_X = minmaxscaler.fit_transform(X)\n",
-    "\n",
-    "\n",
-    "import pandas as pd\n",
-    "from sklearn.decomposition import PCA\n",
-    "\n",
-    "pca = PCA(n_components=2).fit(minmaxscaled_X)\n",
-    "minmax_pca =  PCA(n_components=2).fit(X)\n",
-    "scaled_pca = PCA(n_components=2).fit(scaled_X_train)\n",
-    "norm_pca = PCA(n_components=2).fit(normed_X)\n",
-    "\n",
-    "\n",
-    "X_train_transformed = pca.transform(X)\n",
-    "X_train_minmax_transformed = pca.transform(minmaxscaled_X)\n",
-    "X_train_std_transformed = scaled_pca.transform(scaled_X_train)\n",
-    "X_train_norm_transformed = norm_pca.transform(normed_X)\n",
-    "\n",
-    "\n",
-    "first_pca_component = pd.DataFrame(\n",
-    "    pca.components_[0], index=X.columns, columns=[\"without scaling\"]\n",
-    ")\n",
-    "first_pca_component[\"with scaling\"] = scaled_pca.components_[0]\n",
-    "first_pca_component[\"with normalization\"] = norm_pca.components_[0]\n",
-    "first_pca_component.plot.bar(\n",
-    "    title=\"Weights of the first principal component\", figsize=(30, 8)\n",
-    ")\n",
-    "\n",
-    "_ = plt.tight_layout()\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "79720cd5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weights = pca.components_[0]#*np.sqrt(pca.explained_variance_)\n",
-    "len(weights)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "a90487c3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 2400x800 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.preprocessing import Normalizer\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.decomposition import PCA\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "pca = PCA(n_components=2)\n",
-    "\n",
-    "# Normalizer: By default, L2 normalization is applied to each observation so the that the values in a row have a unit norm. \n",
-    "    # Unit norm with L2 means that if each element were squared and summed, the total would equal 1. \n",
-    "    #Normalizer does transform all the features to values between -1 and 1. \n",
-    "#norm_data = Normalizer(norm=\"l2\").fit_transform(df.drop([\"log\"], axis=1))\n",
-    "norm_data = Normalizer(norm=\"l2\").fit_transform(X)\n",
-    "\n",
-    "#StandardScaler standardizes a feature by subtracting the mean and then scaling to unit variance. \n",
-    "    #Unit variance means dividing all the values by the standard deviation.\n",
-    "scaler = StandardScaler()\n",
-    "sca_data = scaler.fit_transform(X)\n",
-    "#norm_data = Normalizer(norm=\"l2\").fit_transform(norm_data)\n",
-    "\n",
-    "sns.set_theme()\n",
-    "fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(24, 8))\n",
-    "\n",
-    "#X_new = pca.fit_transform(X)\n",
-    "X_new = pca.fit_transform(minmaxscaled_X)\n",
-    "df_pca = pd.DataFrame(X_new, columns=[\"PC1\", \"PC2\"])\n",
-    "sns.scatterplot(ax=ax1, data=df_pca, x=\"PC1\", y=\"PC2\", palette=\"bright\", hue=[0]*len(df_pca), alpha=0.9, s=100)\n",
-    "ax1.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
-    "ax1.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
-    "\n",
-    "X_new_norm = pca.fit_transform(norm_data)\n",
-    "df_pca_norm = pd.DataFrame(X_new_norm, columns=[\"PC1\", \"PC2\"])\n",
-    "sns.scatterplot(ax=ax2, data=df_pca_norm, x=\"PC1\", y=\"PC2\", palette=\"bright\", hue=[0]*len(df_pca), alpha=0.9, s=100)\n",
-    "ax2.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
-    "ax2.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
-    "\n",
-    "X_new_sca = pca.fit_transform(sca_data)\n",
-    "df_pca_sca = pd.DataFrame(X_new_sca, columns=[\"PC1\", \"PC2\"])\n",
-    "sns.scatterplot(ax=ax3, data=df_pca_sca, x=\"PC1\", y=\"PC2\", palette=\"bright\", hue=[0]*len(df_pca), alpha=0.9, s=100)\n",
-    "ax3.set_xlabel(f\"PC1 ({np.round(pca.explained_variance_ratio_[0]*100, 2)}% explained variance)\")\n",
-    "ax3.set_ylabel(f\"PC2 ({np.round(pca.explained_variance_ratio_[1]*100, 2)}% explained variance)\")\n",
-    "plt.tight_layout()\n",
-    "# plt.show()\n",
-    "# plt.close()\n",
-    "\n",
-    "#ratio number of activities and traces... "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "2d77ab0d",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.936564226105012\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1500x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "from sklearn.neighbors import NearestNeighbors\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from random import sample\n",
-    "from numpy.random import uniform\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "\n",
-    "# Clustering tendencies\n",
-    "# function to compute hopkins's statistic for the dataframe X\n",
-    "def hopkins_statistic(X):\n",
-    "    #X=X.values  #convert dataframe to a numpy array\n",
-    "    sample_size = int(X.shape[0]*0.05) #0.05 (5%) based on paper by Lawson and Jures\n",
-    "    \n",
-    "    #a uniform random sample in the original data space\n",
-    "    X_uniform_random_sample = uniform(X.min(axis=0), X.max(axis=0) ,(sample_size , X.shape[1]))\n",
-    "    \n",
-    "    #a random sample of size sample_size from the original data X\n",
-    "    random_indices=sample(range(0, X.shape[0], 1), sample_size)\n",
-    "    X_sample = X[random_indices]\n",
-    "    #initialise unsupervised learner for implementing neighbor searches\n",
-    "    neigh = NearestNeighbors(n_neighbors=2)\n",
-    "    nbrs=neigh.fit(X)\n",
-    "    #u_distances = nearest neighbour distances from uniform random sample\n",
-    "    u_distances , u_indices = nbrs.kneighbors(X_uniform_random_sample , n_neighbors=2)\n",
-    "    u_distances = u_distances[: , 0] #distance to the first (nearest) neighbour\n",
-    "    \n",
-    "    #w_distances = nearest neighbour distances from a sample of points from original data X\n",
-    "    w_distances , w_indices = nbrs.kneighbors(X_sample , n_neighbors=2)\n",
-    "    #distance to the second nearest neighbour (as the first neighbour will be the point itself, with distance = 0)\n",
-    "    w_distances = w_distances[: , 1]\n",
-    "    u_sum = np.sum(u_distances)\n",
-    "    w_sum = np.sum(w_distances)\n",
-    "    \n",
-    "    #compute and return hopkins' statistic\n",
-    "    H = u_sum/ (u_sum + w_sum)\n",
-    "    return H\n",
-    "\n",
-    "#https://github.com/prathmachowksey/Hopkins-Statistic-Clustering-Tendency\n",
-    "def hopkins_wrapper(dmf, k):\n",
-    "    selection = feature_metrics.sort_values([\"score\"], ascending=False).head(k).index\n",
-    "    selected_df= dmf.loc[:,dmf.columns.isin(selection)]\n",
-    "\n",
-    "    imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
-    "    imp_mean.fit(selected_df)\n",
-    "\n",
-    "    selected_df = imp_mean.transform(selected_df)\n",
-    "\n",
-    "    selected_df = Normalizer(norm=\"l2\").fit_transform(selected_df)\n",
-    "    return hopkins_statistic(selected_df)\n",
-    "\n",
-    "print(hopkins_statistic(norm_data))\n",
-    "\n",
-    "hopkins_stats = []\n",
-    "for k in range(99):\n",
-    "    hopkins_stats.append(hopkins_wrapper(dmf, k+2))\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(15, 10))\n",
-    "plt.xlabel(\"number of features\")\n",
-    "plt.ylabel(\"hopkins statistic\")\n",
-    "plt.xticks(np.arange(0, 100, 5))\n",
-    "#ax = plt.axes()\n",
-    "\n",
-    "plt.plot(np.arange(2, 101),hopkins_stats, marker='o')\n",
-    "plt.axhline(y=0.75, color='r', linestyle='-')\n",
-    "plt.axhline(y=0.85, color='r', linestyle='-')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25abbd0a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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