diff --git "a/notebooks/feature_selection.ipynb" "b/notebooks/feature_selection.ipynb"
new file mode 100644--- /dev/null
+++ "b/notebooks/feature_selection.ipynb"
@@ -0,0 +1,1936 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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_entropy_linear_forgetting', 'normalized_sequence_entropy_exponential_forgetting']\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>ratio_unique_traces_per_trace</th>\n",
+       "      <th>ratio_most_common_variant</th>\n",
+       "      <th>ratio_top_1_variants</th>\n",
+       "      <th>ratio_top_5_variants</th>\n",
+       "      <th>ratio_top_10_variants</th>\n",
+       "      <th>ratio_top_20_variants</th>\n",
+       "      <th>ratio_top_50_variants</th>\n",
+       "      <th>ratio_top_75_variants</th>\n",
+       "      <th>normalized_variant_entropy</th>\n",
+       "      <th>normalized_sequence_entropy</th>\n",
+       "      <th>normalized_sequence_entropy_linear_forgetting</th>\n",
+       "      <th>normalized_sequence_entropy_exponential_forgetting</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BPIC15_2</td>\n",
+       "      <td>0.995192</td>\n",
+       "      <td>0.002404</td>\n",
+       "      <td>0.014423</td>\n",
+       "      <td>0.054087</td>\n",
+       "      <td>0.103365</td>\n",
+       "      <td>0.203125</td>\n",
+       "      <td>0.502404</td>\n",
+       "      <td>0.751202</td>\n",
+       "      <td>0.627973</td>\n",
+       "      <td>0.602371</td>\n",
+       "      <td>0.317217</td>\n",
+       "      <td>0.390473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BPI_Challenge_2018</td>\n",
+       "      <td>0.649570</td>\n",
+       "      <td>0.026981</td>\n",
+       "      <td>0.290374</td>\n",
+       "      <td>0.373006</td>\n",
+       "      <td>0.415371</td>\n",
+       "      <td>0.480335</td>\n",
+       "      <td>0.675204</td>\n",
+       "      <td>0.837590</td>\n",
+       "      <td>0.712079</td>\n",
+       "      <td>0.570688</td>\n",
+       "      <td>0.381612</td>\n",
+       "      <td>0.420362</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Receipt_WABO_CoSeLoG</td>\n",
+       "      <td>0.080893</td>\n",
+       "      <td>0.497211</td>\n",
+       "      <td>0.497211</td>\n",
+       "      <td>0.796374</td>\n",
+       "      <td>0.887029</td>\n",
+       "      <td>0.930265</td>\n",
+       "      <td>0.959554</td>\n",
+       "      <td>0.979777</td>\n",
+       "      <td>0.689363</td>\n",
+       "      <td>0.235532</td>\n",
+       "      <td>0.100603</td>\n",
+       "      <td>0.138113</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BPIC15_3</td>\n",
+       "      <td>0.957417</td>\n",
+       "      <td>0.010646</td>\n",
+       "      <td>0.049681</td>\n",
+       "      <td>0.090135</td>\n",
+       "      <td>0.137686</td>\n",
+       "      <td>0.233499</td>\n",
+       "      <td>0.520937</td>\n",
+       "      <td>0.760114</td>\n",
+       "      <td>0.661781</td>\n",
+       "      <td>0.605676</td>\n",
+       "      <td>0.341521</td>\n",
+       "      <td>0.404934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BPI_Challenge_2019</td>\n",
+       "      <td>0.047562</td>\n",
+       "      <td>0.199758</td>\n",
+       "      <td>0.871424</td>\n",
+       "      <td>0.929990</td>\n",
+       "      <td>0.946368</td>\n",
+       "      <td>0.959767</td>\n",
+       "      <td>0.976217</td>\n",
+       "      <td>0.988106</td>\n",
+       "      <td>0.645530</td>\n",
+       "      <td>0.328029</td>\n",
+       "      <td>0.320185</td>\n",
+       "      <td>0.320282</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>RequestForPayment</td>\n",
+       "      <td>0.012925</td>\n",
+       "      <td>0.437264</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.827621</td>\n",
+       "      <td>0.933488</td>\n",
+       "      <td>0.964711</td>\n",
+       "      <td>0.992884</td>\n",
+       "      <td>0.996660</td>\n",
+       "      <td>0.703735</td>\n",
+       "      <td>0.189048</td>\n",
+       "      <td>0.097572</td>\n",
+       "      <td>0.118744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PrepaidTravelCost</td>\n",
+       "      <td>0.096236</td>\n",
+       "      <td>0.271081</td>\n",
+       "      <td>0.419247</td>\n",
+       "      <td>0.737494</td>\n",
+       "      <td>0.822773</td>\n",
+       "      <td>0.884707</td>\n",
+       "      <td>0.951882</td>\n",
+       "      <td>0.975703</td>\n",
+       "      <td>0.723785</td>\n",
+       "      <td>0.317044</td>\n",
+       "      <td>0.184879</td>\n",
+       "      <td>0.214387</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>DomesticDeclarations</td>\n",
+       "      <td>0.009429</td>\n",
+       "      <td>0.439810</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.862667</td>\n",
+       "      <td>0.950095</td>\n",
+       "      <td>0.980857</td>\n",
+       "      <td>0.994857</td>\n",
+       "      <td>0.997619</td>\n",
+       "      <td>0.696474</td>\n",
+       "      <td>0.164758</td>\n",
+       "      <td>0.085439</td>\n",
+       "      <td>0.104389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BPIC15_4</td>\n",
+       "      <td>0.996201</td>\n",
+       "      <td>0.002849</td>\n",
+       "      <td>0.013295</td>\n",
+       "      <td>0.053181</td>\n",
+       "      <td>0.102564</td>\n",
+       "      <td>0.202279</td>\n",
+       "      <td>0.501425</td>\n",
+       "      <td>0.750237</td>\n",
+       "      <td>0.652985</td>\n",
+       "      <td>0.603866</td>\n",
+       "      <td>0.355927</td>\n",
+       "      <td>0.412835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BPI_Challenge_2012</td>\n",
+       "      <td>0.333614</td>\n",
+       "      <td>0.262016</td>\n",
+       "      <td>0.560862</td>\n",
+       "      <td>0.643921</td>\n",
+       "      <td>0.686254</td>\n",
+       "      <td>0.733094</td>\n",
+       "      <td>0.833193</td>\n",
+       "      <td>0.916558</td>\n",
+       "      <td>0.708280</td>\n",
+       "      <td>0.423074</td>\n",
+       "      <td>0.226133</td>\n",
+       "      <td>0.275551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Hospital_log</td>\n",
+       "      <td>0.858268</td>\n",
+       "      <td>0.035871</td>\n",
+       "      <td>0.103237</td>\n",
+       "      <td>0.184602</td>\n",
+       "      <td>0.227472</td>\n",
+       "      <td>0.313211</td>\n",
+       "      <td>0.570429</td>\n",
+       "      <td>0.784777</td>\n",
+       "      <td>0.517443</td>\n",
+       "      <td>0.513032</td>\n",
+       "      <td>0.267825</td>\n",
+       "      <td>0.331672</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>BPIC15_5</td>\n",
+       "      <td>0.997405</td>\n",
+       "      <td>0.001730</td>\n",
+       "      <td>0.012111</td>\n",
+       "      <td>0.051903</td>\n",
+       "      <td>0.102076</td>\n",
+       "      <td>0.201557</td>\n",
+       "      <td>0.500865</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.648702</td>\n",
+       "      <td>0.603260</td>\n",
+       "      <td>0.342410</td>\n",
+       "      <td>0.404580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>CoSeLoG_WABO_2</td>\n",
+       "      <td>0.998450</td>\n",
+       "      <td>0.003101</td>\n",
+       "      <td>0.010853</td>\n",
+       "      <td>0.051163</td>\n",
+       "      <td>0.100775</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.500775</td>\n",
+       "      <td>0.750388</td>\n",
+       "      <td>0.618455</td>\n",
+       "      <td>0.594035</td>\n",
+       "      <td>0.323233</td>\n",
+       "      <td>0.389858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Road_Traffic_Fine_Management_Process</td>\n",
+       "      <td>0.001536</td>\n",
+       "      <td>0.375620</td>\n",
+       "      <td>0.683999</td>\n",
+       "      <td>0.983594</td>\n",
+       "      <td>0.993104</td>\n",
+       "      <td>0.996881</td>\n",
+       "      <td>0.999122</td>\n",
+       "      <td>0.999614</td>\n",
+       "      <td>0.769353</td>\n",
+       "      <td>0.111932</td>\n",
+       "      <td>0.052586</td>\n",
+       "      <td>0.068442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>BPI_Challenge_2017_Offer_log</td>\n",
+       "      <td>0.000372</td>\n",
+       "      <td>0.380626</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.380626</td>\n",
+       "      <td>0.841865</td>\n",
+       "      <td>0.989836</td>\n",
+       "      <td>0.997511</td>\n",
+       "      <td>0.813479</td>\n",
+       "      <td>0.105130</td>\n",
+       "      <td>0.052672</td>\n",
+       "      <td>0.066000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Sepsis_Cases_Event_Log</td>\n",
+       "      <td>0.805714</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>0.120000</td>\n",
+       "      <td>0.215238</td>\n",
+       "      <td>0.274286</td>\n",
+       "      <td>0.355238</td>\n",
+       "      <td>0.597143</td>\n",
+       "      <td>0.798095</td>\n",
+       "      <td>0.695759</td>\n",
+       "      <td>0.522343</td>\n",
+       "      <td>0.219365</td>\n",
+       "      <td>0.299505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>CoSeLoG_WABO_3</td>\n",
+       "      <td>0.949402</td>\n",
+       "      <td>0.011960</td>\n",
+       "      <td>0.051518</td>\n",
+       "      <td>0.097516</td>\n",
+       "      <td>0.145354</td>\n",
+       "      <td>0.240110</td>\n",
+       "      <td>0.525299</td>\n",
+       "      <td>0.762649</td>\n",
+       "      <td>0.654296</td>\n",
+       "      <td>0.596367</td>\n",
+       "      <td>0.278121</td>\n",
+       "      <td>0.356439</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>BPI_Challenge_2013_closed_problems</td>\n",
+       "      <td>0.123067</td>\n",
+       "      <td>0.331540</td>\n",
+       "      <td>0.331540</td>\n",
+       "      <td>0.776732</td>\n",
+       "      <td>0.840619</td>\n",
+       "      <td>0.886348</td>\n",
+       "      <td>0.938130</td>\n",
+       "      <td>0.969065</td>\n",
+       "      <td>0.705383</td>\n",
+       "      <td>0.310940</td>\n",
+       "      <td>0.286515</td>\n",
+       "      <td>0.288383</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>BPI_Challenge_2013_incidents</td>\n",
+       "      <td>0.200026</td>\n",
+       "      <td>0.232195</td>\n",
+       "      <td>0.595976</td>\n",
+       "      <td>0.744241</td>\n",
+       "      <td>0.794414</td>\n",
+       "      <td>0.839952</td>\n",
+       "      <td>0.899921</td>\n",
+       "      <td>0.949960</td>\n",
+       "      <td>0.717846</td>\n",
+       "      <td>0.404651</td>\n",
+       "      <td>0.391097</td>\n",
+       "      <td>0.391625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>PermitLog</td>\n",
+       "      <td>0.209200</td>\n",
+       "      <td>0.135315</td>\n",
+       "      <td>0.483369</td>\n",
+       "      <td>0.682803</td>\n",
+       "      <td>0.757537</td>\n",
+       "      <td>0.819816</td>\n",
+       "      <td>0.895400</td>\n",
+       "      <td>0.947629</td>\n",
+       "      <td>0.733653</td>\n",
+       "      <td>0.420150</td>\n",
+       "      <td>0.137287</td>\n",
+       "      <td>0.215490</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>BPIC15_1</td>\n",
+       "      <td>0.975813</td>\n",
+       "      <td>0.006672</td>\n",
+       "      <td>0.030025</td>\n",
+       "      <td>0.072560</td>\n",
+       "      <td>0.121768</td>\n",
+       "      <td>0.219349</td>\n",
+       "      <td>0.512093</td>\n",
+       "      <td>0.755630</td>\n",
+       "      <td>0.652855</td>\n",
+       "      <td>0.610294</td>\n",
+       "      <td>0.270241</td>\n",
+       "      <td>0.363928</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>InternationalDeclarations</td>\n",
+       "      <td>0.116762</td>\n",
+       "      <td>0.212281</td>\n",
+       "      <td>0.506125</td>\n",
+       "      <td>0.722903</td>\n",
+       "      <td>0.811289</td>\n",
+       "      <td>0.876260</td>\n",
+       "      <td>0.941541</td>\n",
+       "      <td>0.970693</td>\n",
+       "      <td>0.758268</td>\n",
+       "      <td>0.339380</td>\n",
+       "      <td>0.145611</td>\n",
+       "      <td>0.193753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>BPI_Challenge_2017</td>\n",
+       "      <td>0.505570</td>\n",
+       "      <td>0.033514</td>\n",
+       "      <td>0.341934</td>\n",
+       "      <td>0.470850</td>\n",
+       "      <td>0.531340</td>\n",
+       "      <td>0.595544</td>\n",
+       "      <td>0.747215</td>\n",
+       "      <td>0.873592</td>\n",
+       "      <td>0.741706</td>\n",
+       "      <td>0.461565</td>\n",
+       "      <td>0.231922</td>\n",
+       "      <td>0.290464</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>BPI2016_Complaints</td>\n",
+       "      <td>0.438053</td>\n",
+       "      <td>0.101770</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.283186</td>\n",
+       "      <td>0.424779</td>\n",
+       "      <td>0.579646</td>\n",
+       "      <td>0.778761</td>\n",
+       "      <td>0.889381</td>\n",
+       "      <td>0.899497</td>\n",
+       "      <td>0.683796</td>\n",
+       "      <td>0.404685</td>\n",
+       "      <td>0.470116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>BPI2016_Questions</td>\n",
+       "      <td>0.797427</td>\n",
+       "      <td>0.015650</td>\n",
+       "      <td>0.171504</td>\n",
+       "      <td>0.242419</td>\n",
+       "      <td>0.282311</td>\n",
+       "      <td>0.362049</td>\n",
+       "      <td>0.601263</td>\n",
+       "      <td>0.800632</td>\n",
+       "      <td>0.813468</td>\n",
+       "      <td>0.756132</td>\n",
+       "      <td>0.424910</td>\n",
+       "      <td>0.506118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>BPI2016_Werkmap_Messages</td>\n",
+       "      <td>0.002882</td>\n",
+       "      <td>0.295803</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.490722</td>\n",
+       "      <td>0.714106</td>\n",
+       "      <td>0.917793</td>\n",
+       "      <td>0.994656</td>\n",
+       "      <td>0.999099</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>BPI_Challenge_2013_open_problems</td>\n",
+       "      <td>0.131868</td>\n",
+       "      <td>0.217338</td>\n",
+       "      <td>0.217338</td>\n",
+       "      <td>0.630037</td>\n",
+       "      <td>0.769231</td>\n",
+       "      <td>0.855922</td>\n",
+       "      <td>0.934066</td>\n",
+       "      <td>0.967033</td>\n",
+       "      <td>0.702960</td>\n",
+       "      <td>0.276771</td>\n",
+       "      <td>0.262094</td>\n",
+       "      <td>0.263029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>CoSeLoG_WABO_1</td>\n",
+       "      <td>0.977588</td>\n",
+       "      <td>0.009605</td>\n",
+       "      <td>0.032017</td>\n",
+       "      <td>0.070438</td>\n",
+       "      <td>0.119530</td>\n",
+       "      <td>0.217716</td>\n",
+       "      <td>0.511206</td>\n",
+       "      <td>0.755603</td>\n",
+       "      <td>0.646697</td>\n",
+       "      <td>0.601566</td>\n",
+       "      <td>0.292824</td>\n",
+       "      <td>0.376276</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>CoSeLoG_WABO_4</td>\n",
+       "      <td>0.992376</td>\n",
+       "      <td>0.002541</td>\n",
+       "      <td>0.016518</td>\n",
+       "      <td>0.057179</td>\n",
+       "      <td>0.106734</td>\n",
+       "      <td>0.205845</td>\n",
+       "      <td>0.503177</td>\n",
+       "      <td>0.750953</td>\n",
+       "      <td>0.644399</td>\n",
+       "      <td>0.597109</td>\n",
+       "      <td>0.373920</td>\n",
+       "      <td>0.422526</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>CoSeLoG_WABO_5</td>\n",
+       "      <td>0.985426</td>\n",
+       "      <td>0.004484</td>\n",
+       "      <td>0.023543</td>\n",
+       "      <td>0.062780</td>\n",
+       "      <td>0.112108</td>\n",
+       "      <td>0.210762</td>\n",
+       "      <td>0.506726</td>\n",
+       "      <td>0.753363</td>\n",
+       "      <td>0.642668</td>\n",
+       "      <td>0.592454</td>\n",
+       "      <td>0.346832</td>\n",
+       "      <td>0.401731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Detail_Change</td>\n",
+       "      <td>0.048444</td>\n",
+       "      <td>0.074944</td>\n",
+       "      <td>0.311722</td>\n",
+       "      <td>0.626778</td>\n",
+       "      <td>0.765056</td>\n",
+       "      <td>0.878778</td>\n",
+       "      <td>0.970222</td>\n",
+       "      <td>0.987889</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>0.496847</td>\n",
+       "      <td>0.037455</td>\n",
+       "      <td>0.414643</td>\n",
+       "      <td>0.510490</td>\n",
+       "      <td>0.552836</td>\n",
+       "      <td>0.602518</td>\n",
+       "      <td>0.751566</td>\n",
+       "      <td>0.875772</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>0.000041</td>\n",
+       "      <td>0.787081</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.787081</td>\n",
+       "      <td>0.999633</td>\n",
+       "      <td>0.999959</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>0.049345</td>\n",
+       "      <td>0.516594</td>\n",
+       "      <td>0.637118</td>\n",
+       "      <td>0.848908</td>\n",
+       "      <td>0.906332</td>\n",
+       "      <td>0.944323</td>\n",
+       "      <td>0.975328</td>\n",
+       "      <td>0.987555</td>\n",
+       "      <td>0.799120</td>\n",
+       "      <td>0.254066</td>\n",
+       "      <td>0.118478</td>\n",
+       "      <td>0.154576</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     log  ratio_unique_traces_per_trace   \n",
+       "0                               BPIC15_2                       0.995192  \\\n",
+       "1                     BPI_Challenge_2018                       0.649570   \n",
+       "2                   Receipt_WABO_CoSeLoG                       0.080893   \n",
+       "3                               BPIC15_3                       0.957417   \n",
+       "4                     BPI_Challenge_2019                       0.047562   \n",
+       "5                      RequestForPayment                       0.012925   \n",
+       "6                      PrepaidTravelCost                       0.096236   \n",
+       "7                   DomesticDeclarations                       0.009429   \n",
+       "8                               BPIC15_4                       0.996201   \n",
+       "9                     BPI_Challenge_2012                       0.333614   \n",
+       "10                          Hospital_log                       0.858268   \n",
+       "11                              BPIC15_5                       0.997405   \n",
+       "12                        CoSeLoG_WABO_2                       0.998450   \n",
+       "13  Road_Traffic_Fine_Management_Process                       0.001536   \n",
+       "14          BPI_Challenge_2017_Offer_log                       0.000372   \n",
+       "15                Sepsis_Cases_Event_Log                       0.805714   \n",
+       "16                        CoSeLoG_WABO_3                       0.949402   \n",
+       "17    BPI_Challenge_2013_closed_problems                       0.123067   \n",
+       "18          BPI_Challenge_2013_incidents                       0.200026   \n",
+       "19                             PermitLog                       0.209200   \n",
+       "20                              BPIC15_1                       0.975813   \n",
+       "21             InternationalDeclarations                       0.116762   \n",
+       "22                    BPI_Challenge_2017                       0.505570   \n",
+       "23                    BPI2016_Complaints                       0.438053   \n",
+       "24                     BPI2016_Questions                       0.797427   \n",
+       "25              BPI2016_Werkmap_Messages                       0.002882   \n",
+       "26      BPI_Challenge_2013_open_problems                       0.131868   \n",
+       "27                        CoSeLoG_WABO_1                       0.977588   \n",
+       "28                        CoSeLoG_WABO_4                       0.992376   \n",
+       "29                        CoSeLoG_WABO_5                       0.985426   \n",
+       "30                         Detail_Change                       0.048444   \n",
+       "31              Detail_Incident_Activity                       0.496847   \n",
+       "32                    Detail_Interaction                       0.000041   \n",
+       "33                                finale                       0.049345   \n",
+       "\n",
+       "    ratio_most_common_variant  ratio_top_1_variants  ratio_top_5_variants   \n",
+       "0                    0.002404              0.014423              0.054087  \\\n",
+       "1                    0.026981              0.290374              0.373006   \n",
+       "2                    0.497211              0.497211              0.796374   \n",
+       "3                    0.010646              0.049681              0.090135   \n",
+       "4                    0.199758              0.871424              0.929990   \n",
+       "5                    0.437264              0.000000              0.827621   \n",
+       "6                    0.271081              0.419247              0.737494   \n",
+       "7                    0.439810              0.000000              0.862667   \n",
+       "8                    0.002849              0.013295              0.053181   \n",
+       "9                    0.262016              0.560862              0.643921   \n",
+       "10                   0.035871              0.103237              0.184602   \n",
+       "11                   0.001730              0.012111              0.051903   \n",
+       "12                   0.003101              0.010853              0.051163   \n",
+       "13                   0.375620              0.683999              0.983594   \n",
+       "14                   0.380626              0.000000              0.000000   \n",
+       "15                   0.033333              0.120000              0.215238   \n",
+       "16                   0.011960              0.051518              0.097516   \n",
+       "17                   0.331540              0.331540              0.776732   \n",
+       "18                   0.232195              0.595976              0.744241   \n",
+       "19                   0.135315              0.483369              0.682803   \n",
+       "20                   0.006672              0.030025              0.072560   \n",
+       "21                   0.212281              0.506125              0.722903   \n",
+       "22                   0.033514              0.341934              0.470850   \n",
+       "23                   0.101770              0.000000              0.283186   \n",
+       "24                   0.015650              0.171504              0.242419   \n",
+       "25                   0.295803              0.000000              0.490722   \n",
+       "26                   0.217338              0.217338              0.630037   \n",
+       "27                   0.009605              0.032017              0.070438   \n",
+       "28                   0.002541              0.016518              0.057179   \n",
+       "29                   0.004484              0.023543              0.062780   \n",
+       "30                   0.074944              0.311722              0.626778   \n",
+       "31                   0.037455              0.414643              0.510490   \n",
+       "32                   0.787081              0.000000              0.000000   \n",
+       "33                   0.516594              0.637118              0.848908   \n",
+       "\n",
+       "    ratio_top_10_variants  ratio_top_20_variants  ratio_top_50_variants   \n",
+       "0                0.103365               0.203125               0.502404  \\\n",
+       "1                0.415371               0.480335               0.675204   \n",
+       "2                0.887029               0.930265               0.959554   \n",
+       "3                0.137686               0.233499               0.520937   \n",
+       "4                0.946368               0.959767               0.976217   \n",
+       "5                0.933488               0.964711               0.992884   \n",
+       "6                0.822773               0.884707               0.951882   \n",
+       "7                0.950095               0.980857               0.994857   \n",
+       "8                0.102564               0.202279               0.501425   \n",
+       "9                0.686254               0.733094               0.833193   \n",
+       "10               0.227472               0.313211               0.570429   \n",
+       "11               0.102076               0.201557               0.500865   \n",
+       "12               0.100775               0.200000               0.500775   \n",
+       "13               0.993104               0.996881               0.999122   \n",
+       "14               0.380626               0.841865               0.989836   \n",
+       "15               0.274286               0.355238               0.597143   \n",
+       "16               0.145354               0.240110               0.525299   \n",
+       "17               0.840619               0.886348               0.938130   \n",
+       "18               0.794414               0.839952               0.899921   \n",
+       "19               0.757537               0.819816               0.895400   \n",
+       "20               0.121768               0.219349               0.512093   \n",
+       "21               0.811289               0.876260               0.941541   \n",
+       "22               0.531340               0.595544               0.747215   \n",
+       "23               0.424779               0.579646               0.778761   \n",
+       "24               0.282311               0.362049               0.601263   \n",
+       "25               0.714106               0.917793               0.994656   \n",
+       "26               0.769231               0.855922               0.934066   \n",
+       "27               0.119530               0.217716               0.511206   \n",
+       "28               0.106734               0.205845               0.503177   \n",
+       "29               0.112108               0.210762               0.506726   \n",
+       "30               0.765056               0.878778               0.970222   \n",
+       "31               0.552836               0.602518               0.751566   \n",
+       "32               0.000000               0.787081               0.999633   \n",
+       "33               0.906332               0.944323               0.975328   \n",
+       "\n",
+       "    ratio_top_75_variants  normalized_variant_entropy   \n",
+       "0                0.751202                    0.627973  \\\n",
+       "1                0.837590                    0.712079   \n",
+       "2                0.979777                    0.689363   \n",
+       "3                0.760114                    0.661781   \n",
+       "4                0.988106                    0.645530   \n",
+       "5                0.996660                    0.703735   \n",
+       "6                0.975703                    0.723785   \n",
+       "7                0.997619                    0.696474   \n",
+       "8                0.750237                    0.652985   \n",
+       "9                0.916558                    0.708280   \n",
+       "10               0.784777                    0.517443   \n",
+       "11               0.750000                    0.648702   \n",
+       "12               0.750388                    0.618455   \n",
+       "13               0.999614                    0.769353   \n",
+       "14               0.997511                    0.813479   \n",
+       "15               0.798095                    0.695759   \n",
+       "16               0.762649                    0.654296   \n",
+       "17               0.969065                    0.705383   \n",
+       "18               0.949960                    0.717846   \n",
+       "19               0.947629                    0.733653   \n",
+       "20               0.755630                    0.652855   \n",
+       "21               0.970693                    0.758268   \n",
+       "22               0.873592                    0.741706   \n",
+       "23               0.889381                    0.899497   \n",
+       "24               0.800632                    0.813468   \n",
+       "25               0.999099                    0.000000   \n",
+       "26               0.967033                    0.702960   \n",
+       "27               0.755603                    0.646697   \n",
+       "28               0.750953                    0.644399   \n",
+       "29               0.753363                    0.642668   \n",
+       "30               0.987889                         NaN   \n",
+       "31               0.875772                         NaN   \n",
+       "32               0.999959                         NaN   \n",
+       "33               0.987555                    0.799120   \n",
+       "\n",
+       "    normalized_sequence_entropy   \n",
+       "0                      0.602371  \\\n",
+       "1                      0.570688   \n",
+       "2                      0.235532   \n",
+       "3                      0.605676   \n",
+       "4                      0.328029   \n",
+       "5                      0.189048   \n",
+       "6                      0.317044   \n",
+       "7                      0.164758   \n",
+       "8                      0.603866   \n",
+       "9                      0.423074   \n",
+       "10                     0.513032   \n",
+       "11                     0.603260   \n",
+       "12                     0.594035   \n",
+       "13                     0.111932   \n",
+       "14                     0.105130   \n",
+       "15                     0.522343   \n",
+       "16                     0.596367   \n",
+       "17                     0.310940   \n",
+       "18                     0.404651   \n",
+       "19                     0.420150   \n",
+       "20                     0.610294   \n",
+       "21                     0.339380   \n",
+       "22                     0.461565   \n",
+       "23                     0.683796   \n",
+       "24                     0.756132   \n",
+       "25                     0.000000   \n",
+       "26                     0.276771   \n",
+       "27                     0.601566   \n",
+       "28                     0.597109   \n",
+       "29                     0.592454   \n",
+       "30                          NaN   \n",
+       "31                          NaN   \n",
+       "32                          NaN   \n",
+       "33                     0.254066   \n",
+       "\n",
+       "    normalized_sequence_entropy_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",
+       "    normalized_sequence_entropy_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   "
+      ]
+     },
+     "execution_count": 10,
+     "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",
+    "#print(selected_ft)\n",
+    "ft_preselection = dmf[selected_ft]\n",
+    "#ft_preselection['log'] = dmf['log']\n",
+    "ft_preselection\n",
+    "# Include complexity features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "39182cd7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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_17178/3210665172.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",
+       "    }\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>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>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>ratio_most_common_variant</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.222764</td>\n",
+       "      <td>0.253617</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.011771</td>\n",
+       "      <td>0.110304</td>\n",
+       "      <td>0.366578</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_1_variants</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.263653</td>\n",
+       "      <td>0.290271</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.015581</td>\n",
+       "      <td>0.128088</td>\n",
+       "      <td>0.479785</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_5_variants</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.425974</td>\n",
+       "      <td>0.339036</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.072152</td>\n",
+       "      <td>0.428965</td>\n",
+       "      <td>0.746087</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_10_variants</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.498102</td>\n",
+       "      <td>0.340644</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.126621</td>\n",
+       "      <td>0.481379</td>\n",
+       "      <td>0.812674</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                               count      mean       std  min       25%   \n",
+       "ratio_unique_traces_per_trace   34.0  0.466611  0.415471  0.0  0.057282  \\\n",
+       "ratio_most_common_variant       34.0  0.222764  0.253617  0.0  0.011771   \n",
+       "ratio_top_1_variants            34.0  0.263653  0.290271  0.0  0.015581   \n",
+       "ratio_top_5_variants            34.0  0.425974  0.339036  0.0  0.072152   \n",
+       "ratio_top_10_variants           34.0  0.498102  0.340644  0.0  0.126621   \n",
+       "\n",
+       "                                    50%       75%  max  \n",
+       "ratio_unique_traces_per_trace  0.386407  0.956895  1.0  \n",
+       "ratio_most_common_variant      0.110304  0.366578  1.0  \n",
+       "ratio_top_1_variants           0.128088  0.479785  1.0  \n",
+       "ratio_top_5_variants           0.428965  0.746087  1.0  \n",
+       "ratio_top_10_variants          0.481379  0.812674  1.0  "
+      ]
+     },
+     "execution_count": 11,
+     "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(['log'], axis=1).columns)\n",
+    "stats = ft_preselection.describe().transpose()\n",
+    "#stats = scaled_dmf.describe().transpose()\n",
+    "\n",
+    "#print(stats.index)\n",
+    "stats.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "54c59f5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 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": 13,
+   "id": "692820ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x1440 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>ratio_most_common_variant</th>\n",
+       "      <th>normalized_sequence_entropy</th>\n",
+       "      <th>normalized_sequence_entropy_exponential_forgetting</th>\n",
+       "      <th>normalized_sequence_entropy_linear_forgetting</th>\n",
+       "      <th>ratio_top_1_variants</th>\n",
+       "      <th>ratio_top_5_variants</th>\n",
+       "      <th>ratio_top_10_variants</th>\n",
+       "      <th>ratio_top_20_variants</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>ratio_most_common_variant</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.872166</td>\n",
+       "      <td>-0.820146</td>\n",
+       "      <td>-0.744763</td>\n",
+       "      <td>0.256647</td>\n",
+       "      <td>0.494400</td>\n",
+       "      <td>0.525677</td>\n",
+       "      <td>0.772018</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>-0.872166</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.949180</td>\n",
+       "      <td>0.867450</td>\n",
+       "      <td>-0.359378</td>\n",
+       "      <td>-0.691534</td>\n",
+       "      <td>-0.792878</td>\n",
+       "      <td>-0.866084</td>\n",
+       "      <td>-0.876646</td>\n",
+       "      <td>-0.877751</td>\n",
+       "      <td>0.878053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_sequence_entropy_exponential_forgetting</th>\n",
+       "      <td>-0.820146</td>\n",
+       "      <td>0.949180</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.979552</td>\n",
+       "      <td>-0.258761</td>\n",
+       "      <td>-0.591511</td>\n",
+       "      <td>-0.694187</td>\n",
+       "      <td>-0.770970</td>\n",
+       "      <td>-0.784374</td>\n",
+       "      <td>-0.785651</td>\n",
+       "      <td>0.786009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>normalized_sequence_entropy_linear_forgetting</th>\n",
+       "      <td>-0.744763</td>\n",
+       "      <td>0.867450</td>\n",
+       "      <td>0.979552</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.192220</td>\n",
+       "      <td>-0.501746</td>\n",
+       "      <td>-0.598864</td>\n",
+       "      <td>-0.672905</td>\n",
+       "      <td>-0.686745</td>\n",
+       "      <td>-0.688042</td>\n",
+       "      <td>0.688380</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_1_variants</th>\n",
+       "      <td>0.256647</td>\n",
+       "      <td>-0.359378</td>\n",
+       "      <td>-0.258761</td>\n",
+       "      <td>-0.192220</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.766335</td>\n",
+       "      <td>0.704883</td>\n",
+       "      <td>0.588358</td>\n",
+       "      <td>0.522835</td>\n",
+       "      <td>0.520572</td>\n",
+       "      <td>-0.519027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_5_variants</th>\n",
+       "      <td>0.494400</td>\n",
+       "      <td>-0.691534</td>\n",
+       "      <td>-0.591511</td>\n",
+       "      <td>-0.501746</td>\n",
+       "      <td>0.766335</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.979064</td>\n",
+       "      <td>0.854760</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>0.525677</td>\n",
+       "      <td>-0.792878</td>\n",
+       "      <td>-0.694187</td>\n",
+       "      <td>-0.598864</td>\n",
+       "      <td>0.704883</td>\n",
+       "      <td>0.979064</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.907611</td>\n",
+       "      <td>0.848190</td>\n",
+       "      <td>0.846992</td>\n",
+       "      <td>-0.845994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_20_variants</th>\n",
+       "      <td>0.772018</td>\n",
+       "      <td>-0.866084</td>\n",
+       "      <td>-0.770970</td>\n",
+       "      <td>-0.672905</td>\n",
+       "      <td>0.588358</td>\n",
+       "      <td>0.854760</td>\n",
+       "      <td>0.907611</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.991745</td>\n",
+       "      <td>0.991361</td>\n",
+       "      <td>-0.991075</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ratio_top_50_variants</th>\n",
+       "      <td>0.809234</td>\n",
+       "      <td>-0.876646</td>\n",
+       "      <td>-0.784374</td>\n",
+       "      <td>-0.686745</td>\n",
+       "      <td>0.522835</td>\n",
+       "      <td>0.784494</td>\n",
+       "      <td>0.848190</td>\n",
+       "      <td>0.991745</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>0.808176</td>\n",
+       "      <td>-0.877751</td>\n",
+       "      <td>-0.785651</td>\n",
+       "      <td>-0.688042</td>\n",
+       "      <td>0.520572</td>\n",
+       "      <td>0.782390</td>\n",
+       "      <td>0.846992</td>\n",
+       "      <td>0.991361</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>-0.808280</td>\n",
+       "      <td>0.878053</td>\n",
+       "      <td>0.786009</td>\n",
+       "      <td>0.688380</td>\n",
+       "      <td>-0.519027</td>\n",
+       "      <td>-0.780722</td>\n",
+       "      <td>-0.845994</td>\n",
+       "      <td>-0.991075</td>\n",
+       "      <td>-0.999957</td>\n",
+       "      <td>-0.999989</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                    ratio_most_common_variant   \n",
+       "ratio_most_common_variant                                            1.000000  \\\n",
+       "normalized_sequence_entropy                                         -0.872166   \n",
+       "normalized_sequence_entropy_exponential_forgetting                  -0.820146   \n",
+       "normalized_sequence_entropy_linear_forgetting                       -0.744763   \n",
+       "ratio_top_1_variants                                                 0.256647   \n",
+       "ratio_top_5_variants                                                 0.494400   \n",
+       "ratio_top_10_variants                                                0.525677   \n",
+       "ratio_top_20_variants                                                0.772018   \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",
+       "ratio_most_common_variant                                             -0.872166  \\\n",
+       "normalized_sequence_entropy                                            1.000000   \n",
+       "normalized_sequence_entropy_exponential_forgetting                     0.949180   \n",
+       "normalized_sequence_entropy_linear_forgetting                          0.867450   \n",
+       "ratio_top_1_variants                                                  -0.359378   \n",
+       "ratio_top_5_variants                                                  -0.691534   \n",
+       "ratio_top_10_variants                                                 -0.792878   \n",
+       "ratio_top_20_variants                                                 -0.866084   \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_entropy_exponential_forgetting   \n",
+       "ratio_most_common_variant                                                                   -0.820146   \\\n",
+       "normalized_sequence_entropy                                                                  0.949180    \n",
+       "normalized_sequence_entropy_exponential_forgetting                                           1.000000    \n",
+       "normalized_sequence_entropy_linear_forgetting                                                0.979552    \n",
+       "ratio_top_1_variants                                                                        -0.258761    \n",
+       "ratio_top_5_variants                                                                        -0.591511    \n",
+       "ratio_top_10_variants                                                                       -0.694187    \n",
+       "ratio_top_20_variants                                                                       -0.770970    \n",
+       "ratio_top_50_variants                                                                       -0.784374    \n",
+       "ratio_top_75_variants                                                                       -0.785651    \n",
+       "ratio_unique_traces_per_trace                                                                0.786009    \n",
+       "\n",
+       "                                                    normalized_sequence_entropy_linear_forgetting   \n",
+       "ratio_most_common_variant                                                               -0.744763  \\\n",
+       "normalized_sequence_entropy                                                              0.867450   \n",
+       "normalized_sequence_entropy_exponential_forgetting                                       0.979552   \n",
+       "normalized_sequence_entropy_linear_forgetting                                            1.000000   \n",
+       "ratio_top_1_variants                                                                    -0.192220   \n",
+       "ratio_top_5_variants                                                                    -0.501746   \n",
+       "ratio_top_10_variants                                                                   -0.598864   \n",
+       "ratio_top_20_variants                                                                   -0.672905   \n",
+       "ratio_top_50_variants                                                                   -0.686745   \n",
+       "ratio_top_75_variants                                                                   -0.688042   \n",
+       "ratio_unique_traces_per_trace                                                            0.688380   \n",
+       "\n",
+       "                                                    ratio_top_1_variants   \n",
+       "ratio_most_common_variant                                       0.256647  \\\n",
+       "normalized_sequence_entropy                                    -0.359378   \n",
+       "normalized_sequence_entropy_exponential_forgetting             -0.258761   \n",
+       "normalized_sequence_entropy_linear_forgetting                  -0.192220   \n",
+       "ratio_top_1_variants                                            1.000000   \n",
+       "ratio_top_5_variants                                            0.766335   \n",
+       "ratio_top_10_variants                                           0.704883   \n",
+       "ratio_top_20_variants                                           0.588358   \n",
+       "ratio_top_50_variants                                           0.522835   \n",
+       "ratio_top_75_variants                                           0.520572   \n",
+       "ratio_unique_traces_per_trace                                  -0.519027   \n",
+       "\n",
+       "                                                    ratio_top_5_variants   \n",
+       "ratio_most_common_variant                                       0.494400  \\\n",
+       "normalized_sequence_entropy                                    -0.691534   \n",
+       "normalized_sequence_entropy_exponential_forgetting             -0.591511   \n",
+       "normalized_sequence_entropy_linear_forgetting                  -0.501746   \n",
+       "ratio_top_1_variants                                            0.766335   \n",
+       "ratio_top_5_variants                                            1.000000   \n",
+       "ratio_top_10_variants                                           0.979064   \n",
+       "ratio_top_20_variants                                           0.854760   \n",
+       "ratio_top_50_variants                                           0.784494   \n",
+       "ratio_top_75_variants                                           0.782390   \n",
+       "ratio_unique_traces_per_trace                                  -0.780722   \n",
+       "\n",
+       "                                                    ratio_top_10_variants   \n",
+       "ratio_most_common_variant                                        0.525677  \\\n",
+       "normalized_sequence_entropy                                     -0.792878   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.694187   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.598864   \n",
+       "ratio_top_1_variants                                             0.704883   \n",
+       "ratio_top_5_variants                                             0.979064   \n",
+       "ratio_top_10_variants                                            1.000000   \n",
+       "ratio_top_20_variants                                            0.907611   \n",
+       "ratio_top_50_variants                                            0.848190   \n",
+       "ratio_top_75_variants                                            0.846992   \n",
+       "ratio_unique_traces_per_trace                                   -0.845994   \n",
+       "\n",
+       "                                                    ratio_top_20_variants   \n",
+       "ratio_most_common_variant                                        0.772018  \\\n",
+       "normalized_sequence_entropy                                     -0.866084   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.770970   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.672905   \n",
+       "ratio_top_1_variants                                             0.588358   \n",
+       "ratio_top_5_variants                                             0.854760   \n",
+       "ratio_top_10_variants                                            0.907611   \n",
+       "ratio_top_20_variants                                            1.000000   \n",
+       "ratio_top_50_variants                                            0.991745   \n",
+       "ratio_top_75_variants                                            0.991361   \n",
+       "ratio_unique_traces_per_trace                                   -0.991075   \n",
+       "\n",
+       "                                                    ratio_top_50_variants   \n",
+       "ratio_most_common_variant                                        0.809234  \\\n",
+       "normalized_sequence_entropy                                     -0.876646   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.784374   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.686745   \n",
+       "ratio_top_1_variants                                             0.522835   \n",
+       "ratio_top_5_variants                                             0.784494   \n",
+       "ratio_top_10_variants                                            0.848190   \n",
+       "ratio_top_20_variants                                            0.991745   \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",
+       "ratio_most_common_variant                                        0.808176  \\\n",
+       "normalized_sequence_entropy                                     -0.877751   \n",
+       "normalized_sequence_entropy_exponential_forgetting              -0.785651   \n",
+       "normalized_sequence_entropy_linear_forgetting                   -0.688042   \n",
+       "ratio_top_1_variants                                             0.520572   \n",
+       "ratio_top_5_variants                                             0.782390   \n",
+       "ratio_top_10_variants                                            0.846992   \n",
+       "ratio_top_20_variants                                            0.991361   \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",
+       "ratio_most_common_variant                                               -0.808280  \n",
+       "normalized_sequence_entropy                                              0.878053  \n",
+       "normalized_sequence_entropy_exponential_forgetting                       0.786009  \n",
+       "normalized_sequence_entropy_linear_forgetting                            0.688380  \n",
+       "ratio_top_1_variants                                                    -0.519027  \n",
+       "ratio_top_5_variants                                                    -0.780722  \n",
+       "ratio_top_10_variants                                                   -0.845994  \n",
+       "ratio_top_20_variants                                                   -0.991075  \n",
+       "ratio_top_50_variants                                                   -0.999957  \n",
+       "ratio_top_75_variants                                                   -0.999989  \n",
+       "ratio_unique_traces_per_trace                                            1.000000  "
+      ]
+     },
+     "execution_count": 13,
+     "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": 14,
+   "id": "16ea5dad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='disconnection', ylabel='std'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 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": 15,
+   "id": "f8643aee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                                                    disconnection       std   \n",
+      "ratio_top_1_variants                                 1.000000e+00  0.290271  \\\n",
+      "normalized_sequence_entropy_linear_forgetting        4.804416e-01  0.279733   \n",
+      "ratio_top_5_variants                                 3.173670e-01  0.339036   \n",
+      "ratio_most_common_variant                            4.022142e-01  0.253617   \n",
+      "normalized_sequence_entropy_exponential_forgetting   2.653517e-01  0.262115   \n",
+      "ratio_top_10_variants                                1.782057e-01  0.340644   \n",
+      "normalized_sequence_entropy                          1.010703e-01  0.259735   \n",
+      "ratio_unique_traces_per_trace                        2.942524e-02  0.415471   \n",
+      "ratio_top_75_variants                                2.850613e-02  0.415087   \n",
+      "ratio_top_50_variants                                2.761956e-02  0.414572   \n",
+      "ratio_top_20_variants                                2.220446e-16  0.390934   \n",
+      "\n",
+      "                                                           score  \n",
+      "ratio_top_1_variants                                2.902711e-01  \n",
+      "normalized_sequence_entropy_linear_forgetting       1.343956e-01  \n",
+      "ratio_top_5_variants                                1.075989e-01  \n",
+      "ratio_most_common_variant                           1.020082e-01  \n",
+      "normalized_sequence_entropy_exponential_forgetting  6.955255e-02  \n",
+      "ratio_top_10_variants                               6.070473e-02  \n",
+      "normalized_sequence_entropy                         2.625147e-02  \n",
+      "ratio_unique_traces_per_trace                       1.222533e-02  \n",
+      "ratio_top_75_variants                               1.183253e-02  \n",
+      "ratio_top_50_variants                               1.145029e-02  \n",
+      "ratio_top_20_variants                               8.680468e-17  \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1440x1440 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "TOP_K = 6\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": 16,
+   "id": "98414438",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before: 2 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": 17,
+   "id": "c645fdeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/andreamaldonado/miniconda3/lib/python3.9/site-packages/sklearn/base.py:443: UserWarning: X has feature names, but PCA was fitted without feature names\n",
+      "  warnings.warn(\n",
+      "/var/folders/d0/btmbyskx4t106_l2zghzln2w0000gn/T/ipykernel_17178/722551070.py:40: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations.\n",
+      "  _ = plt.tight_layout()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2160x576 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": 18,
+   "id": "a90487c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1728x576 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": 19,
+   "id": "2d77ab0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8997756887175077\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 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": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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+}