EDA_Final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Once deleted, variables cannot be recovered. Proceed (y/[n])? y\n"
     ]
    }
   ],
   "source": [
    "%reset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
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       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import time\n",
    "from plotly import tools\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import datetime\n",
    "import random\n",
    "\n",
    "import re\n",
    "sns.set()\n",
    "\n",
    "from scipy.stats.mstats import winsorize\n",
    "import scipy.stats\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 500)\n",
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.width', 1000)\n",
    "\n",
    "\n",
    "#%matplotlib notebook\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('Downloads_Hackathon19/train.csv')\n",
    "test = pd.read_csv('Downloads_Hackathon19/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['random']=np.random.randint(1, 6, train.shape[0])\n",
    "test['random']=np.random.randint(1, 6, test.shape[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "specific_death\n",
      "months_survival\n"
     ]
    }
   ],
   "source": [
    "for element in train.columns:\n",
    "    if element in test.columns:\n",
    "        s=1\n",
    "        #print(element)\n",
    "    else:\n",
    "        print(element)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make it easier to split\n",
    "train['train']=1\n",
    "test['train']=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = train.append(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mean Encoding Function\n",
    "def Target_encoding(df,element,target):\n",
    "    #create aux    \n",
    "    aux_df = pd.DataFrame(df.groupby(element)[target].mean())\n",
    "\n",
    "    #change name\n",
    "    new_variable=element+'_ENCODED'+target\n",
    "    aux_df[new_variable] = aux_df[target]\n",
    "    aux_df= aux_df.drop(target, axis=1)\n",
    "\n",
    "    #join\n",
    "    df = df.join(aux_df, how = 'left', on = element) \n",
    "    print(np.corrcoef(df[target], df[new_variable])[0][1])\n",
    "    return df\n",
    "def splited_target_encoding(df,query_train,query_test,element,target):\n",
    "    entrenamiento=df.query(query_train)\n",
    "    testeo=df.query(query_test)\n",
    "    \n",
    "    entrenamiento=Target_encoding(entrenamiento,element,target)\n",
    "    \n",
    "    inputZIP1=element\n",
    "    inputZIP2=element+'_ENCODED'+target\n",
    "    \n",
    "    mydict=dict(zip(entrenamiento[inputZIP1],entrenamiento[inputZIP2]))\n",
    "    testeo[element+'_ENCODED'+target]=testeo[element].map(mydict)\n",
    "    return entrenamiento.append(testeo)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features categoricas binarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cutaneous_biopsy_vascular_invasion.fillna('not_taken',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['paso_previo_metastasis']= np.where(df['cutaneous_biopsy_vascular_invasion']=='present',1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cutaneous_biopsy_ulceration.fillna('not_taken',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cutaneous_biopsy_satellitosis.fillna('not_taken',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cutaneous_biopsy_neurotropism.fillna('not_taken',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cutaneous_biopsy_lymphatic_invasion.fillna('not_taken',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.cutaneous_biopsy_associated_nevus.fillna('not_taken',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Did this person take the LAB test\n",
    "df['LAB_taken_13']  =np.where(df['LAB1300'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2404']= np.where(df['LAB2404'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2405'] = np.where(df['LAB2405'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2406'] = np.where(df['LAB2406'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2407'] = np.where(df['LAB2407'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2419'] = np.where(df['LAB2419'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2422'] = np.where(df['LAB2422'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2467'] = np.where(df['LAB2467'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2469'] = np.where(df['LAB2469'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2476'] = np.where(df['LAB2476'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2498'] = np.where(df['LAB2498'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2544'] = np.where(df['LAB2544'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB2679'] = np.where(df['LAB2679'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LAB4176'] = np.where(df['LAB2544'].fillna(9999)==9999,1,0)\n",
    "\n",
    "df['LAB_taken_LABGF_filtrat_glomerular'] = np.where(df['LABGF_filtrat_glomerular'].fillna(99999)==99999,1,0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risky fill na"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['MC1R'].fillna(df.MC1R.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['count_inv_prec_tumour'].fillna(df.count_inv_prec_tumour.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['count_situ_prec_tumour'].fillna(df.count_situ_prec_tumour.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_histological_subtype'].fillna('superficial_spreading',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_mitotic_index'].fillna(df['cutaneous_biopsy_mitotic_index'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_predominant_cell_type'].fillna('epitheloid',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_regression'].fillna('missing',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_eye_color'].fillna('brown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_gender'].fillna('female',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_eye_color'].fillna('notknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_hair_color'].fillna('notknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_phototype_unk']=df['patient_phototype'].fillna('notknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_phototype_common_2'] = df['patient_phototype'].fillna('2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['patient_phototype_common_3'] = df['patient_phototype'].fillna('3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['primary_tumour_location_coded'].fillna('trunk',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['scenario'].fillna('notknown',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['slnb_ldn_location_total'].fillna(df['slnb_ldn_location_total'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_count_slnb_ldn'].fillna(df['total_count_slnb_ldn'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_positives_slnb_ldn'].fillna(df['total_positives_slnb_ldn'].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['visceral_metastasis_location'].fillna('missing',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['paso_previo_metastasis'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna(),yticklabels=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.limfocits_per_monocits.fillna(df.limfocits_per_monocits.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.neutrofils_per_limfocits.fillna(df.neutrofils_per_limfocits.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.patient_phototype.fillna(df.patient_phototype.median(),inplace=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Winsorization\n",
    "df['LAB1301']=scipy.stats.mstats.winsorize(df['LAB1301'], limits=0.03,inplace=True)\n",
    "\n",
    "df['LAB2405']=scipy.stats.mstats.winsorize(df['LAB1301'], limits=0.03,inplace=True)\n",
    "\n",
    "df['LAB2406']=scipy.stats.mstats.winsorize(df['LAB2406'], limits=0.03,inplace=True)\n",
    "\n",
    "df['LAB2419']=scipy.stats.mstats.winsorize(df['LAB2419'], limits=0.05,inplace=True)\n",
    "\n",
    "\n",
    "df['LAB2422']=scipy.stats.mstats.winsorize(df['LAB2422'], limits=0.03,inplace=True)\n",
    "\n",
    "\n",
    "df['LAB2476']=scipy.stats.mstats.winsorize(df['LAB2476'], limits=0.03,inplace=True)\n",
    "\n",
    "\n",
    "df['cutaneous_biopsy_mitotic_index']=scipy.stats.mstats.winsorize(df['cutaneous_biopsy_mitotic_index'], limits=0.02,inplace=True)\n",
    "\n",
    "\n",
    "df['limfocits_per_monocits']=scipy.stats.mstats.winsorize(df['limfocits_per_monocits'], limits=0.03,inplace=True)\n",
    "\n",
    "\n",
    "df['LABGF_filtrat_glomerular']=scipy.stats.mstats.winsorize(df['LABGF_filtrat_glomerular'], limits=0.05,inplace=True)\n",
    "\n",
    "df['LAB1316']=scipy.stats.mstats.winsorize(df['LAB1316'], limits=0.01,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Winsorization\n",
    "df['LAB1301']=scipy.stats.mstats.winsorize(df['LAB1301'], limits=0.05,inplace=True)\n",
    "\n",
    "df['LAB2405']=scipy.stats.mstats.winsorize(df['LAB1301'], limits=0.05,inplace=True)\n",
    "\n",
    "df['LAB2406']=scipy.stats.mstats.winsorize(df['LAB2406'], limits=0.05,inplace=True)\n",
    "\n",
    "df['LAB2419']=scipy.stats.mstats.winsorize(df['LAB2419'], limits=0.05,inplace=True)\n",
    "\n",
    "\n",
    "df['LAB2422']=scipy.stats.mstats.winsorize(df['LAB2422'], limits=0.05,inplace=True)\n",
    "\n",
    "\n",
    "df['LAB2476']=scipy.stats.mstats.winsorize(df['LAB2476'], limits=0.05,inplace=True)\n",
    "\n",
    "\n",
    "df['cutaneous_biopsy_mitotic_index']=scipy.stats.mstats.winsorize(df['cutaneous_biopsy_mitotic_index'], limits=0.05,inplace=True)\n",
    "\n",
    "\n",
    "df['limfocits_per_monocits']=scipy.stats.mstats.winsorize(df['limfocits_per_monocits'], limits=0.05,inplace=True)\n",
    "\n",
    "\n",
    "df['LABGF_filtrat_glomerular']=scipy.stats.mstats.winsorize(df['LABGF_filtrat_glomerular'], limits=0.05,inplace=True)\n",
    "\n",
    "df['LAB1316']=scipy.stats.mstats.winsorize(df['LAB1316'], limits=0.05,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# obtain the x variables that are categorical\n",
    "categorical_feature_mask = df.dtypes==object\n",
    "\n",
    "# Filter categorical columns using mask and turn it into a list\n",
    "categorical_cols = df.columns[categorical_feature_mask].tolist()\n",
    "\n",
    "for col in df.columns:\n",
    "    if col not in categorical_cols:\n",
    "        if col != 'specific_death' and col != 'months_survival':\n",
    "            df[col] = df[col].fillna(df[col].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3068, 74)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('bigger.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LAB1300</th>\n",
       "      <th>LAB1301</th>\n",
       "      <th>LAB1307</th>\n",
       "      <th>LAB1309</th>\n",
       "      <th>LAB1311</th>\n",
       "      <th>LAB1313</th>\n",
       "      <th>LAB1314</th>\n",
       "      <th>LAB1316</th>\n",
       "      <th>LAB2404</th>\n",
       "      <th>LAB2405</th>\n",
       "      <th>LAB2406</th>\n",
       "      <th>LAB2407</th>\n",
       "      <th>LAB2419</th>\n",
       "      <th>LAB2422</th>\n",
       "      <th>LAB2467</th>\n",
       "      <th>LAB2469</th>\n",
       "      <th>LAB2476</th>\n",
       "      <th>LAB2498</th>\n",
       "      <th>LAB2544</th>\n",
       "      <th>LAB2679</th>\n",
       "      <th>LAB4176</th>\n",
       "      <th>LABGF_filtrat_glomerular</th>\n",
       "      <th>MC1R</th>\n",
       "      <th>T0_date</th>\n",
       "      <th>age</th>\n",
       "      <th>count_inv_prec_tumour</th>\n",
       "      <th>count_situ_prec_tumour</th>\n",
       "      <th>cutaneous_biopsy_associated_nevus</th>\n",
       "      <th>cutaneous_biopsy_breslow</th>\n",
       "      <th>cutaneous_biopsy_histological_subtype</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasion</th>\n",
       "      <th>cutaneous_biopsy_mitotic_index</th>\n",
       "      <th>cutaneous_biopsy_neurotropism</th>\n",
       "      <th>cutaneous_biopsy_predominant_cell_type</th>\n",
       "      <th>cutaneous_biopsy_regression</th>\n",
       "      <th>cutaneous_biopsy_satellitosis</th>\n",
       "      <th>cutaneous_biopsy_ulceration</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasion</th>\n",
       "      <th>limfocits_per_monocits</th>\n",
       "      <th>months_survival</th>\n",
       "      <th>neutrofils_per_limfocits</th>\n",
       "      <th>patient_eye_color</th>\n",
       "      <th>patient_gender</th>\n",
       "      <th>patient_hair_color</th>\n",
       "      <th>patient_phototype</th>\n",
       "      <th>primary_tumour_location_coded</th>\n",
       "      <th>random</th>\n",
       "      <th>scenario</th>\n",
       "      <th>slnb_ldn_location_total</th>\n",
       "      <th>specific_death</th>\n",
       "      <th>total_count_slnb_ldn</th>\n",
       "      <th>total_positives_slnb_ldn</th>\n",
       "      <th>train</th>\n",
       "      <th>visceral_metastasis_location</th>\n",
       "      <th>paso_previo_metastasis</th>\n",
       "      <th>LAB_taken_13</th>\n",
       "      <th>LAB_taken_LAB2404</th>\n",
       "      <th>LAB_taken_LAB2405</th>\n",
       "      <th>LAB_taken_LAB2406</th>\n",
       "      <th>LAB_taken_LAB2407</th>\n",
       "      <th>LAB_taken_LAB2419</th>\n",
       "      <th>LAB_taken_LAB2422</th>\n",
       "      <th>LAB_taken_LAB2467</th>\n",
       "      <th>LAB_taken_LAB2469</th>\n",
       "      <th>LAB_taken_LAB2476</th>\n",
       "      <th>LAB_taken_LAB2498</th>\n",
       "      <th>LAB_taken_LAB2544</th>\n",
       "      <th>LAB_taken_LAB2679</th>\n",
       "      <th>LAB_taken_LAB4176</th>\n",
       "      <th>LAB_taken_LABGF_filtrat_glomerular</th>\n",
       "      <th>patient_phototype_unk</th>\n",
       "      <th>patient_phototype_common_2</th>\n",
       "      <th>patient_phototype_common_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID2</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>76.692145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004-03-02</td>\n",
       "      <td>64.251366</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.90</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>extensive</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2.282609</td>\n",
       "      <td>brown</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>trunk</td>\n",
       "      <td>1</td>\n",
       "      <td>scenario2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID4</td>\n",
       "      <td>7.314448</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>4.468256</td>\n",
       "      <td>2.110679</td>\n",
       "      <td>0.373566</td>\n",
       "      <td>0.172969</td>\n",
       "      <td>14.118785</td>\n",
       "      <td>0.038357</td>\n",
       "      <td>23.83014</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>29.416104</td>\n",
       "      <td>0.708325</td>\n",
       "      <td>338.722488</td>\n",
       "      <td>102.14691</td>\n",
       "      <td>0.963757</td>\n",
       "      <td>207.917879</td>\n",
       "      <td>112.238666</td>\n",
       "      <td>72.643528</td>\n",
       "      <td>1.931261</td>\n",
       "      <td>0.102403</td>\n",
       "      <td>7.603459</td>\n",
       "      <td>82.481334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006-09-13</td>\n",
       "      <td>44.613699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>3.75</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>partial</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>present</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.120606</td>\n",
       "      <td>green</td>\n",
       "      <td>male</td>\n",
       "      <td>red</td>\n",
       "      <td>2.0</td>\n",
       "      <td>trunk</td>\n",
       "      <td>1</td>\n",
       "      <td>scenario3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID6</td>\n",
       "      <td>9.640000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>81.00000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>91.545283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2013-02-13</td>\n",
       "      <td>35.956164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.50</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>partial</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.826087</td>\n",
       "      <td>brown</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>trunk</td>\n",
       "      <td>3</td>\n",
       "      <td>scenario1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID9</td>\n",
       "      <td>10.080000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>64.014945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009-10-06</td>\n",
       "      <td>33.347945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>0.50</td>\n",
       "      <td>spitzoid</td>\n",
       "      <td>absent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.034483</td>\n",
       "      <td>blue</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>3.0</td>\n",
       "      <td>lower limbs</td>\n",
       "      <td>4</td>\n",
       "      <td>scenario1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID17</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>88.316054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2009-03-23</td>\n",
       "      <td>32.060274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>0.60</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>partial</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>brown</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>lower limbs</td>\n",
       "      <td>3</td>\n",
       "      <td>scenario1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID    LAB1300     LAB1301   LAB1307   LAB1309   LAB1311   LAB1313    LAB1314   LAB1316   LAB2404     LAB2405    LAB2406   LAB2407     LAB2419    LAB2422   LAB2467     LAB2469     LAB2476    LAB2498   LAB2544   LAB2679   LAB4176  LABGF_filtrat_glomerular  MC1R     T0_date        age  count_inv_prec_tumour  count_situ_prec_tumour cutaneous_biopsy_associated_nevus  cutaneous_biopsy_breslow cutaneous_biopsy_histological_subtype cutaneous_biopsy_lymphatic_invasion  cutaneous_biopsy_mitotic_index cutaneous_biopsy_neurotropism cutaneous_biopsy_predominant_cell_type cutaneous_biopsy_regression cutaneous_biopsy_satellitosis cutaneous_biopsy_ulceration cutaneous_biopsy_vascular_invasion  limfocits_per_monocits  months_survival  neutrofils_per_limfocits patient_eye_color patient_gender patient_hair_color  patient_phototype primary_tumour_location_coded  random   scenario  slnb_ldn_location_total  specific_death  total_count_slnb_ldn  total_positives_slnb_ldn  train  \\\n",
       "0   ID2   6.600000  260.000000  4.200000  1.840000  0.190000  0.280000  13.400000  0.020000  21.00000  260.000000  17.000000  0.400000  308.000000   95.00000  0.800000  320.000000   72.000000  65.000000  1.600000  0.120000  7.000000                 76.692145   0.0  2004-03-02  64.251366                    0.0                     0.0                            absent                      0.90                 superficial_spreading                              absent                             0.0                        absent                             epitheloid                   extensive                        absent                      absent                             absent                9.650000            179.0                  2.282609             brown         female              brown                2.0                         trunk       1  scenario2                      1.0             0.0                   1.0                       0.0      1   \n",
       "1   ID4   7.314448  252.010132  4.468256  2.110679  0.373566  0.172969  14.118785  0.038357  23.83014  252.010132  29.416104  0.708325  338.722488  102.14691  0.963757  207.917879  112.238666  72.643528  1.931261  0.102403  7.603459                 82.481334   0.0  2006-09-13  44.613699                    0.0                     0.0                            absent                      3.75                 superficial_spreading                           not_taken                             0.0                     not_taken                             epitheloid                     partial                     not_taken                     present                          not_taken                5.666667            127.0                  2.120606             green           male                red                2.0                         trunk       1  scenario3                      1.0             0.0                  16.0                       2.0      1   \n",
       "2   ID6   9.640000  169.000000  6.500000  2.300000  0.500000  0.200000  13.400000  0.000000  22.00000  169.000000  12.000000  0.700000  477.000000   81.00000  0.760000  172.000000   53.000000  73.000000  1.800000  0.040000  6.200000                 91.545283   0.0  2013-02-13  35.956164                    0.0                     0.0                            absent                      0.50                 superficial_spreading                              absent                             0.0                        absent                             epitheloid                     partial                        absent                      absent                             absent                4.600000             67.0                  2.826087             brown         female              brown                2.0                         trunk       3  scenario1                      1.0             0.0                   3.0                       0.0      1   \n",
       "3   ID9  10.080000  280.000000  5.900000  2.900000  0.600000  0.400000  13.600000  0.000000  19.00000  280.000000   9.000000  0.400000  382.000000   90.00000  1.050000  193.000000   51.000000  71.000000  1.700000  0.080000  6.600000                 64.014945   0.0  2009-10-06  33.347945                    0.0                     0.0                         not_taken                      0.50                              spitzoid                              absent                             2.0                        absent                             epitheloid                      absent                        absent                      absent                          not_taken                4.833333            111.0                  2.034483              blue         female              brown                3.0                   lower limbs       4  scenario1                      1.0             0.0                   3.0                       0.0      1   \n",
       "4  ID17   6.810000  283.000000  4.000000  2.000000  0.400000  0.200000  12.600000  0.000000  21.00000  283.000000   9.000000  1.000000  298.000000  100.00000  0.800000  208.000000   56.000000  71.000000  1.300000  0.090000  4.800000                 88.316054   0.0  2009-03-23  32.060274                    0.0                     0.0                         not_taken                      0.60                 superficial_spreading                              absent                             0.0                        absent                             epitheloid                     partial                        absent                      absent                          not_taken                5.000000            118.0                  2.000000             brown         female              brown                2.0                   lower limbs       3  scenario1                      1.0             0.0                   3.0                       0.0      1   \n",
       "\n",
       "  visceral_metastasis_location  paso_previo_metastasis  LAB_taken_13  LAB_taken_LAB2404  LAB_taken_LAB2405  LAB_taken_LAB2406  LAB_taken_LAB2407  LAB_taken_LAB2419  LAB_taken_LAB2422  LAB_taken_LAB2467  LAB_taken_LAB2469  LAB_taken_LAB2476  LAB_taken_LAB2498  LAB_taken_LAB2544  LAB_taken_LAB2679  LAB_taken_LAB4176  LAB_taken_LABGF_filtrat_glomerular patient_phototype_unk patient_phototype_common_2 patient_phototype_common_3  \n",
       "0                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     2                          2                          2  \n",
       "1                      missing                       0             1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                                   1                     2                          2                          2  \n",
       "2                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     2                          2                          2  \n",
       "3                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     3                          3                          3  \n",
       "4                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     2                          2                          2  "
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9/IwxpidVpZefl+9jlc7QVsg3tPNhFbfAtL5xDZVVOh73ycrCt/yMVVGc4A1PVbnl54TKGDN4cuJefsYY04vKbhvVFidUxpjB45lSjDGmJ1xDZYwxPeEaKmOM6UlV6ZTigf2s0hnaQHpDOx/GA/sZ0xqP+2Rl4Vt+xqooTvCGp7JrntrihMoYM3hURdpQOaEyxgweTz1ljDE9qSq9/JxQGWMGj1ebYowxPeFefsYY05Oq0svPA/tZpTO0gfSGdj6s+JZf20dl4hoqq3Q87pOVhXv5GauiOMEbHrmCe/kZY0wvKvtWXlucUBljBo9v+RljTE+4hsoYY3rC41AZY0xPeOopY4zpSVW55eeB/Ywxg1dRK/bHxcVhyJAhGDhwIDZv3qz2+tWrV+Ho6AhLS0v4+flBJpOVWh4nVMZU8Ewpw1MRM6UyMzOxYsUKbNmyBbGxsYiKisLNmzdFMT4+Ppg/fz4SEhJARIiOji61TE6ojKnggf2GR5eEmpeXh7S0NLVHXl6eqMykpCSYm5ujfv36qFWrFiwtLREfHy+8np6ejpcvX6JDhw4AAAcHB9HrUsrVhiorTC/P2xiTZGjXk6GdDwOKdPg3WbNmDcLCwtSOe3p6wsvLS3ielZWFhg0bCs9NTU1x4cIFja83bNgQmZmZpX42d0oxxt4o48aNg729vdrxunXrip5LNQ8YGRlp/boUTqiMsTdK3bp11ZKnFDMzMyQnJwvPs7KyYGpqKnr90aNHwvPs7GzR61K4DZUx9lbq0aMHjh07htzcXBQUFGDfvn2wsLAQXm/UqBHeeecdnD59GgCwa9cu0etSjKiqDPBijDE9i4uLQ3h4OIqKiuDk5AQ3Nze4ubnB29sb7dq1Q0pKCvz9/fH8+XO0bdsWS5cuRY0aNTSWxwmVMcb0hG/5GWNMTzihMsaYnnBCZYwxPeGEyhhjesIJlTHG9IQTKjMoKSkplX0KjJWbzgn1+vXriI+Px+HDh3H//n3JGA8PD42vqUpKSsLFixchk8mwYsUKeHh44JdffoFcLtf11CrF48eP/+ef+fvvv2sdW1hYiLVr12LOnDl49uwZwsLCUFhYqNfzefbsGW7cuKGXsmbMmKFTfFpaGg4fPgy5XF7qNVdUVITr16/j6tWrZS7Bpou8vDxs3rwZP/30E8LCwoRHVVAZ1+6bTuuEmpOTgzFjxsDZ2Rm//vor/v3vf8PBwQFubm5qq7icP38eEydOxPr161FUVKSxzB9++AFr1qzBd999B3d3dzx8+BDffvstbt++jSVLlpR5Ts7OzpLHra2tcfbsWa2+ly6xDx8+hI+PDxYsWID79+/D2tpaWEtRtWaVn5+PH3/8ERkZGcjLy8O8efMwbNgwzJ07F7m5uZLlp6enY8KECRg0aBCysrLg4uKCtLQ0tTipdRs1Wbx4MQoKCnDlyhUYGxsjNTUVfn5+krG3b9/GsmXLMG/ePNFDyrZt2zBv3jzk5uZiyJAh8Pb2xooVKyRjL1y4gIiICBQWFsLV1RXm5uZISEiQjG3RogXCwsJw5MgRnDp1SnhI2bNnDyZPnozAwEA8efIEo0aNQmxsrFrcxYsXYWlpCV9fX8ybNw99+vTB+fPnJcsEiv8dgoOD8d1335X5c5g2bRpOnDgBhY7bHBvqtavtdZuSkgJbW1t069YNfn5+ePbsmfCa1Dz6twZpadq0afTjjz9SYWGhcOzVq1cUGhpKc+fOFcXa2tpSdnY2+fj4UJ8+fSg8PJzS0tLUyhw6dCgpFArKzc2lTp06kVwuJyIihUJBNjY2oth+/fqpPb744gvhv0vq1asXDRs2jBYvXkw5OTmlfi9dYseOHUu//fYbhYeHU/fu3SkhIYGIiJKTk+nbb78VxU6cOJF+/PFHys/Pp9mzZ9PKlSvp5s2bFB4eThMnTpQs39XVlY4cOUJ2dnakUCgoKiqKRo8erRY3ceJEGjt2LIWGhtKaNWuEhxQ7OzsiKv43ISr+2Q4dOlQydvDgwbRmzRrauXOn6CHF3t6eHj9+TJGRkbRo0SIqKioie3t7ydjhw4fTyZMnaffu3TR58mR68OABOTg4SMY6OzurPcaOHavxu+Xn5wvfLTMzk4YMGaIWN3LkSDp37pzw/OzZs+To6ChZJhGRk5MTLVu2jHbs2FHmz2HYsGEay1GqSteuttftyJEj6dixY5Sbm0sLFy4ke3t7evbsGRH9c629jbROqFZWVpLHFQoFWVtbi44pf4mJiO7cuUPBwcE0aNAg6t27N40cOVJ4bejQofTy5UsiIoqOjhaO5+fnq/1iHDp0iAYPHkx//vknpaWl0f3792nIkCGUlpamlqzt7OyooKCAVqxYQT169KAFCxbQiRMn6NWrV2rnr0tsyQulV69eotdU/wCUfK7689H0S6hMSCU/R7VcIhIl0bISqr29Pb169Ur4N8nJydH4+SX/bcqiPFdXV1c6fPgwEZFkMiMiIXnNnDmTYmJiiKjsX7r8/Hx6+vRpqTHKpFyyLKnvpvrz1xSnVPL6LYuPjw9dvXq11JiqdO1qe92q/vstW7aMxo4dS4WFhW91QtV6tal33nlH8riRkRGqVRO3HFCJ2axNmzbFnDlzMGfOHDx+/FjUzjV69GjY2Nhgz549GD58OADgzJkzmD17Njw8PERl9unTB+3atYOfnx9u376NKVOmoEaNGmjUqJHkeb377ruYPn06Jk6ciN27d+Pnn3/G5cuXUb16dRw9erRcsXXq1MHWrVvx7NkzyOVyHDp0CH379sWZM2fUfj5169bF33//jZ49e6Jt27a4cuUK2rZti2vXruHdd9/VeM4ZGRnCEmHJycmS84Y9PT0l3y/FxcUFEyZMQHZ2NoKCgpCYmIipU6dKxtrb22PFihUwNzeHick/l0aXLl3UYlu0aAF3d3ekpaWhe/fumDZtGr788kvJcmvWrIn169fjxIkTWLBgASIjI1G7dm3J2Pv372PGjBm4f/8+iAiffPIJVq5ciaZNm6rFtmzZEps2bYJMJsPVq1exZcsWtGnTRi2uXr16SExMxIABAwAAiYmJqF+/vuTnA0CnTp1w8OBBfPPNN6XO2waAGzduwN7eHg0aNMA777wDIoKRkREOHDggxFSla1fb67Z27dr466+/YGFhASMjI8ydOxezZs2Cl5cXCgoKSv2ZvdG0zbyl/dVWfU1ZY9HG/fv3Rc8fPnxI169fL/U9GzduJGdnZ7K0tJR8vbS/kKq3RrrEpqWlkY+PD82aNYtSU1Pp22+/pW7dulHv3r3pwoULothbt26RpaUl2dvbk5ubG3Xo0IFsbW2pV69edP78ecnPO3/+PNnY2FCHDh3IxsaGevfuLbpVVWrdujW1adNG9LCwsND4PW7cuEGbNm2ijRs3llqbmjlzJg0cOFCr2+2ioiI6efIkPX78mIiIDhw4QDKZTDI2IyOD1qxZQ6dPnyYiopCQEMrIyJCMHT9+PO3du1d4/ueff5Kzs7Nk7PPnzyk0NJQcHBzI3t6eli1bRvn5+Wpxt2/fJicnJ+ratSt16dKFHB0d6datWxp/Dj179qTWrVuLHm3atJGMVdYyVR+aGPq1q+11e/PmTfr222+FOw4iIplMRoGBgdS2bVuN5/Wm03pxlC+//BJmZmZSCRnZ2dm4ePGi6PitW7eQkJCAjIwMVKtWDaampujVqxfatWunlz8EN27cQEJCgmRtLSUlRbKmIkWXWCm5ubn44IMPNL5++fJl3Lt3D3K5HB9++CG+/vprjbX9p0+folatWrh79y7kcjmaNWuG7OxsjTUZoLj3OjExEefOnZPsOCkqKsLmzZtx/PhxmJiYwMLCAsOHD5dcKNfS0lJjZ5FSVFQURo4cqbEnW+rfIyAgAPPnzxcdmzt3LoKDg9Vi7ezssGvXLtExa2trxMXFSX5eTk4OkpOTYWJigs6dO6NevXoaz/3FixdQKBSoU6eOxhhdERF+//13HD9+HDKZDObm5nB2dla7ayupKly7uly3mspds2aNaIX8t4HWt/xl/aKVtGXLFkRFRcHS0lJIoNnZ2Zg/fz5sbGzg6uoKAHjw4EGp5XzyySei50eOHEF8fLwoSSckJMDS0lIU16ZNG8lYCwsLtdgWLVpg69atGDhwIN577z2sW7cOFy9exBdffAF3d3fRRaRQKLBx40YcOHAA2dnZqF69Oj777DMMHToUQ4YMEZXr4eEBPz8/fPHFF/jiiy9K/Z4PHz4EEWHSpEn45ZdfhNvhzMxMuLm5lbqPTfXq1TF48GD8/PPPkq/7+/vj5cuXGDFiBBQKBWJjY3Hjxg3Jnv5WrVqV+Uuq5d9fAICfnx/u37+PS5cuiYZVyeVytZEhSjVq1MDly5eFn9mlS5dQs2ZNydjY2FiEhISgU6dOkMvlWLRoEQIDA9G7d28AwPz58xEQEICxY8dK/gHZuHGj6HlZw52kEmBISAju3bsHR0dHEBF27tyJtLQ0fPfdd6K4qnLt6nLdaqJM0gcPHnzrEmqFLN9naWmJXbt2qf0iFBQUwN7eXkgQ1tbWuHv3LkxNTdV+UVXboVatWoULFy7AxsZGWDU7KysLf/zxB1q0aIG5c+eWK3bWrFkAihNPWFgYXrx4ASsrKxw+fBh5eXn48ccfhdglS5agqKgIffr0QUJCAtq0aYOPP/4YGzduhLm5uahtsnv37njvvffw7bffwtnZGdWrV9f485o3bx5OnDihtmK4iYkJ+vTpo/bLWbIGR0S4ceMGTp48ie3bt6uVbWVlJUrICoUCw4YNw549e9Ri7ezscO3aNTRs2BDVq1eXbA8sqbCwEDVq1MC9e/dw584dWFhYiGpmaWlpSE9PR1BQEPz9/YXjxsbGaN68uWQ75rlz5zBz5kzUr18fRISnT59i+fLlwkZpqt8tMjJSuHNKT0+Hh4eHUJu9dOkSvvzySyQlJYnahJW6du0qeq5MqBcuXEBGRgasrKxgYmKC/fv3o1GjRli5cqVaGTY2Nti1a5fwvWUyGaytrbF3714hpipdu7pct2WRutt442nbNiDVbtemTRvJ9qUhQ4ZQXl6eWhm5ubminsL8/Hyytram5OTkMj9/0KBBwrCqkmQymdoIBF1iS56PtbU1KRQK4fngwYNFsSV7PeVyOY0aNYqIioePqbaJaTt0rKTw8PBSX1fy9fUVPYKDgyk1NVUydvz48XT37l3heWZmJo0fP14yVpf2wLCwMPLx8aH09HTq2bMnjR8/nvz9/UUxWVlZRESUnp4u+dCksLCQrl+/TikpKZI91kqOjo5q/85SQ7d06bUnKh7t8OLFC+H5y5cvacSIEZKxQ4YMEZ3jy5cv1YalVaVrtzzXrSa6/tzfBFrf8usyJdDDwwN2dnbo3r27sGtgdnY2jh8/LpoJU6dOHQQGBmLbtm3o1KlTqWW+8847yMjIUGsGePDggVpPrC6xtWrVwo0bN9CyZUt89tlnePjwIT755BNkZmaqxcrlcuTk5KBBgwbIzs7Gy5cvARS3U6rWgIyMjPDhhx8iJCQEd+/eRXR0NFxdXfHq1St89NFH2Lp1q9p3dHBwwIYNG/D8+XMQERQKBdLS0hASEiKKW7p0KYqKinDnzh3I5XK0bNlSsgYGFNeYbG1t0blzZ5iYmOD06dNo2LAhXFxcAIhve2vXro0rV66gR48eCA8Px+XLl+Ht7S1Z7oEDB7B161Zs2LABNjY2mDNnDhwcHEQx/v7+CA8PlxzErlrz1TRwvuR3VtWqVSu4ubnB0dERxsbG2Lt3L0xNTYVakZ2dHQCgQYMGSE5ORvv27cvstQeKZxCVbCIoKirCkydPJGOtra3h4uKCoUOHAgD+/PNP4b+VqtK1W57rlv2jQjbps7a2RteuXXHs2DFkZWWBiNC5c2d4eXmpdWy1b98e7du3L7NMX19fjBkzBk2bNhUl6bt376r9sukaO2HCBHz99deoXbs2RowYga+++gqXL1/G999/L4qdOHEiHBwc0LFjR5w/fx6zZs3CvXv3MG7cOLW2ItJy6FhJXl5e+Oyzz3Du3DkMGDAAf//9t2R75qVLl+Dt7Y369etDoVDg0aNH+Omnn/DVV19JllmSsv1ayqxZs9C3b18AQHx8PMaNG4eFCxdKzsxSKBSoUaMVmEx8AAAgAElEQVQGDh06hOnTp0OhUKgNlwkPDwdQ3JZWVFSE6tWro6ioCIWFhWrDplRvv7VBRDA1NcWRI0cAFA/PqlmzJk6cOAHgn4R66dIltaRuZGSEq1evSpY7fPhwODo6wsLCAgqFAocPHxb+AKny8PDA559/juPHj4OI4OHhgT59+ohiqtK1W57rlv2jwrZAefHiBUxMTFCjRg0kJSXh2rVr+Prrr9V+6bWNA4BXr17hwoULQpI2MzPDV199JVnr0CX22bNnSEpKEvVqfvPNN/joo4/UYu/cuYNr166hTZs2aNq0KQoLC/HixQu19sC//vpL6BzRlrK9Mzg4GFZWVmjWrBnGjx+PHTt2iOJGjRqFefPmCT+jc+fOITAwULIN1c3NDQ4ODhgwYECZ7WFOTk7Yvn07AgIC0KRJE7i4uMDBwQE7d+5Uiw0ODsaRI0fw7rvvIjo6Gs7OzujYsSN8fHzUYvfu3Yt///vfiIuLQ2pqKsaOHYv58+cL40JVXb9+HSdPnoRMJkO3bt3w+eefS8bt3btXq+9VHpcuXcLJkydhZGSE7t27q/1hU3acaZoWqzp2t6pcu+W5bjWZPXs2QkND9VJWVVEhCfWPP/5AQEAAatSogZEjRyIhIQG9evXC0aNHMXbsWGEQv7Zxmqxfv77UGpehxZZl5MiRiIqKQnR0NIgII0eOhK2trdr8dBsbG+zevVt0TNPQolOnTiEmJgbHjx9H7969YW9vr/GOwMHBAYsXL8bUqVOxadMmPHv2DL6+vpLz44Hi29CPPvoI1apVw9WrVzUmPmtra0RERODDDz8EUDzUydXVVbLcXbt2ISwsDAMGDIBCocCBAwcwefJkODk5qcUqO/PK+l45OTmIi4srsymlrA4UZY0XKG7OCAwMxNixY9XijIyM1EYQqDKE61Ef1256ejo2bdqEp0+fimq3Uk00b4MKueVft24d4uPjkZmZieHDh+Po0aOoV68epkyZgjFjxgiJUts4QHpIy9atW/HixQsA4iEthhCr65AwADA3N4e3tzfmzp0LV1dXXL58WXLsny4zf7p06YIuXbrg5cuXiI+Ph5eXF9577z04OTlh9OjRolqPj48PQkJCMGHCBDRu3BgjRoyAr6+vZLm5ubkIDg7G8ePHIZfL0a1bN3z//fdC0iypqKhIdLxBgwYah19FRERg27ZteP/99wEU31K7uLhIJtSlS5fixYsX2L9/P9asWYOcnBwMHToUdnZ2aNCggRDn6empVVOKsqlAk5IJNTAwEEDx0KxWrVqJ4s6dOyd6bgjXo7axul6306dPR+fOndG5c2fJoWlvmwpJqESE999/H++//z5sbW2Fwda1a9cWLcunbRzwz23gqFGjNHbAGFKsu7u71kPClGbMmIHU1FQ0atQIy5cvx6lTpySniQYEBMDHxwd+fn4gInz22Wdqta2STpw4gdjYWPz999+wsLDAkCFDkJSUhMmTJ+PXX38V4rp3747u3bsLz6OjozWWuWDBAnTs2BFBQUFQKBSIioqCn5+f0G5aUqdOnTBz5kxYW1sDKL5VlxoGBRS3zSqTKVA8prG0X9RatWqhUaNG+Pjjj3Hv3j1cu3YN48ePx8iRI4V208ePH+P3339HcHAwBg0aBA8PD4wfP16tLG1qVcqxradPn4ZCoYC/vz+CgoKEf2OZTIZFixaJxm0bwvWobayu161MJhMN5XrrVcTQgfnz59PMmTNFwz/u3r1LU6dOpYULF+ocp7Rt2zYaM2YM3bx5k4hKn3pX2bG6DAmLiYkp9aHJ8+fPJadaltSnTx9ydnammJgYKigoEI7L5XJhWIvy/5VD4LSZcim1aIumBUdevXpF//nPf8jd3Z08PT0pIiJC43CoWbNmUWBgIKWkpFBKSgoFBATQ7NmzJWOXL19O/fr1IxcXF4qNjRUW2snPz6euXbsKccohT1FRUbR161aN568N5c9q9erV5OzsTB06dBBN1R0/fjz9+uuvau+r7OtR21hdrlsiooCAADpw4ECpw9veJhVSQ124cCHi4uJEg7yfPXuGHj16YNSoUTrHKTk5OaFLly7w9/dXmzViaLG6DAlT3mqmpqbi3r176N27N4yNjXH06FG0aNFCdKsJFC+aEhkZiadPn4qOS7XbhYeHS96SdujQATExMQAg/L8uQ+OMjIzw8OFDfPzxxwCKbxU11Xxq1KiBXr16oUaNGkLzgKbhS4GBgcIauUQEc3NzLFy4UDK2WrVq2LBhAxo3biw6XqdOHfzyyy/Cc22bUnSh7BnftWuX2r+PlMq+HrWN1eW6BYpHg2zatEl0rLQRFG+8ys7o5SGTyWj58uUalxQ0tFhtOTs7ixa1ePLkCY0ZM0Ytrn///hQTE0MnTpwQPUpKTk6mkydP0qBBg+jUqVN08uRJOnnyJCUlJdGgQYMkP//p06cUGBhIw4YNIzs7O1q+fLmoVlvSwYMHqVevXuTp6UlTp06lb775hg4dOiQZGxMTQ/3796elS5dSUFAQ9evXj7Zt2yYZK5PJKDExkYiKF/jYtm2baMB6SZ6enmrHXFxcJGPv3btHRESXLl2iiIgIyszMlIwri+pg9Vu3blFAQADNmzePfH19ac6cOZJr2CoZwvVYEdcuK1Zhw6YqQm5uLrKzs9GyZUtRrbbk3G9DilX1xx9/YNiwYRpft7S0xN69e4VyCwsLYW1trbaOwpgxY8pctX/NmjU4efKkMP1SycTEBL169ZLs3XV3d0ezZs1gZ2cHIsKOHTuQm5srmsJYUk5ODi5evAiFQoGvvvoK7733nmTN09bWFhs2bBDaRnNzc+Hi4oI//vhDLXbevHlQKBQIDg5Gbm4uli5dipo1a2Lx4sVCzNSpU5GSkqI2VVcul+Pjjz9W2yJGJpPh6NGjaoPztalZqrK3txdq9Mrv1r9/fxw6dAj29vb473//i08//RSLFi0Svc8QrsfyXrulXbcFBQUICwvDsWPHIJfLYW5ujmnTpqFWrVoay3uTVcgt/7BhwyTXRCSVueHaxgHF210sXboU9evXR2FhIdasWSPcyvr7+4suckOIlRqCs3r1amE/I6lf5j59+ghboCgUCsTHx2Pw4MFqcWPHjsXs2bPV1i0tWaaut6RA8RCYkp1Kfn5+Gn+RlEO8lIPYFQoFbG1tJYdu6dLRdOnSJaGMDz74AD/88IPQmaUUHByMJ0+eqK0RYGJiIurdV5o1axYePHiA5s2biz63PAlVtf6hUCjg7e0NmUyGtm3bYtSoUWrNVYZwPWobq+t1u3jxYtSsWVPYsig6OhoLFy7EDz/8oO2P9I1SIQn1xx9/hJubG5YvXy60sb1OHAD8/PPPiI2NxQcffIA9e/Zg4sSJiIiIQIsWLdQuckOI3bp1K+7evSvMPAKA58+fq83iKWnevHlISEgQBpS7urqif//+AIpnyyhnzmzZsgUAcPr0adH7pcrs168fli1bhpMnT8LExAQ9evSAu7u75ApOTZo0QXJyMjp37gyguE21SZMmohgXFxecPHkSQPHKSMoEZWxsjH79+qmVCQCtW7dGUFCQMPRp+/btGle0UigUoppnTk6O2lJ4derUQZ06dbB27Vr89ddfoqXzlD+vkq5du4a9e/fqZVhPjx49RM9r1qyJwsJCNG3aFJcvX0bnzp3x6tUrUYwhXI/axup63V6+fFk0JnrBggVqK6+9VSqqLSEmJoa8vLz0Fqe6HcOePXuob9++lJGRodauZQixMpmMVq5cSVOnThXaRV9na4iS5evS9uXl5UUhISGUkpJCV69epcDAQJo1a5Zk7LBhw6h169ZkZWVFQ4cOpbZt21L37t2pb9++ansfBQQEaH0OBQUFFBwcXOZC0EREu3fvph49epCXlxd5enqShYWFsP+Rql9++YWcnJwoMjKSNmzYQI6OjrR27Vq1uClTpujUZvrw4UOaOnUqdenShXr06EGzZs3SuGfTb7/9RhMmTKCcnBwaMGAATZw4kSZMmCCKMYTrUdtYXa/bYcOGibaqefr0qVb7bL2pKrRTqqwhPbrETZs2jYKDg+nhw4fCsY0bN1L//v3pm2++MbhYpeTkZBoxYgQdPHjwtVbfKXlR+/v708GDB6moqKjM9+kyvEnTalMlV506ePAgERHt3LlTpyFeusjIyKD4+HhKTEwUJULlZ5f8HiU7zV68eCH5x8bV1ZU6duxII0eOpLFjxwoPTSZMmECbN2+m/Px8ys/Pp4iICJo0aZLGeOX1m56eTvv27aPnz5+LXjeE61HXa1fb63b79u00cOBAWrp0KS1ZsoQGDhyoscPxbfA/6+VXbpVR3rjnz5/TihUrKCkpSXR8//79aknDEGJLUu4gqSnpaqPkRa3LFh3Tpk2jM2fOCM+vXr0q2TtOVLzh4ubNm8nLy4smT55MkZGRakvJrVq1iojUlxBUPqTs2LGDunbtWuqSj9pQ/cVWXSZPLpdL/rFQHQ0hNSqiJKkamaZ/32PHjgmbG966dYv69u0rbPWiZAjXY3muXW2v22vXrglb7KSkpJQa+6arkF7+x48f49dff8WHH34IKysruLq64vbt2/jkk0+wYsUKYVEPbeM0efLkSambrRlabGZmpuQ2MtpQ7V0uS79+/WBkZIRXr14hJycHzZo1Q7Vq1XD79m00adJEcoHp4OBgtdXnP/30U7UFrnXVv39/rF27Vm08rK5UFywODAxEZmamsA/8rl27YGpqKuqoUjp9+jSuX78OR0dHnD9/XnLjQaUFCxagW7duwjJ8hw4dQmJiIoKCgtRi7e3tERwcLHy3W7duYc6cOWoL2qgyhOtR29jSrtvCwkIcPXpUbQeG8nT4vQkqpFPK19cXTZo0wY0bNxAZGYn/+7//g62tLZKSkhAQECCsiqRtHKBb8jXU2Dt37uDjjz/W6o9FabTZz+m3337Tudy///5btPp8nz591HrYlYlaE6kptWZmZq+dTAGofa6fnx+2bNmCXbt2CZMApCaEREZGIjExEVlZWbCyssKCBQvg5OSEiRMniuKUnWxEhOjoaPj7+8PIyAgvXrxAvXr1JBPqq1evRN+tefPmQo+4kqFej9pWckq7bt3c3EBEavueva0JtUJqqMOGDcMff/wBhUKB3r17C+tVAuJVkbSNA4rHSDZp0gTPnz9HUlKSKPmuW7dOlHyrWqw2StbOSibUoqIiHDlyBF999ZXkjCJNKyhJXfBDhw5FTEyMMJb01atXcHR0FI0XTU9PL/U8pTYUDAoKQmZmJnr27CmaoaTrL51qLT08PBzu7u6imOXLl2PmzJmiY3Z2doiOjsaIESOwa9cuPH/+HMOHD5espevK09MTTZo0ga2tLYDiBabv3r2LVatWCTGGcI1pG6vrdSu18tnbrEJqqMqxkdWqVVNbfajkuElt44B/xkgqk++YMWMAAIMGDcKaNWuqdCxQPFzozJkzyMzMhJGREUxNTUUrzJe8jVXdLG7q1Kkal2EruYJSUVERTp8+jc6dO0smM6nV51XHoWpa/1NJKqE+e/YMtWvXVluFqby1mNDQUOTk5ODgwYO4e/eucFwul+P8+fNqCbVatWqiCQfvvPMOjI2NNZafl5eHuLg4PHnyRDSkSGqTvqCgIKxatQqzZs1C9erV0blzZ2ElKiVDuMa0jdX1ujU3N0dSUhLMzc1L3en1bVEhCVUmk+Hhw4dQKBQoKioSdvUEin+pdY0DdEu+VS32zJkz8PX1RaNGjYTY7Oxs3Lt3D0uWLEH37t2FsaFSnj9/rnHZNdUVlJ48eSLahqYkNze3Mlef12WJu5LnoO2WLaVRXhuDBg3CrVu3cPz4cdFK/8bGxpgyZYra+7p27Yrg4GAUFBQgMTERUVFRMDc31/g506ZNw3vvvYeWLVuWOXa1Xr168PT0xLlz56BQKNChQwe89957ohhDuMYqopIDFC/n5+rqKvyc6P9Pynlb5/JXSEJ98eIFnJ2dhV8A5V85QNwOpm0coFvyrWqxCxYswLp169C0aVPR8Xv37sHT01Nt9lHJdkwiQl5entYLBdeqVUvjbbuTkxNiYmJKXbFdNUE/ffpUWHZRE122bAGAuLg43Lx5Ex4eHkhISBCSdFRUFIB/ts0ZMGAAioqKcP78ecjlcnTo0EFyPdY5c+YgOjoarVu3xq5du9CnTx+MHDlS4/k+evQIERERpX4npSNHjuC7775Dhw4doFAosGDBAgQFBYkGxhvCNVYRlRygeEGegwcPSq7v+zaqkIR68OBBja/duXNH5zhAt+Rb1WLlcrlaMgWAxo0bSy7EXLLDycjICHXr1kWdOnXU4gCI9qQnIqSlpWlMmLpsZpeSkoLp06fj5cuXiIqKgrOzM1auXCk5JzwwMFDUoXHu3Dm1Tkel0NBQZGRk4PLly3Bzc8OOHTuQkpICX19ftRWizp07V2YyA4rnm8vlcqxevRqZmZnYunWr5MaKSp9//jlSUlI0zuYqacWKFdiyZYuw4tX9+/fh6ekpOgdDuMYqopIDAKamplqPKngrVOCQLEFRURH9+eefNHbsWOrQocNrx6m6fft2lY5dtmwZubu7U2xsLCUlJVFSUhLt3r2b3NzcKDg4WO39uqyyVHLc5cmTJ+nGjRsaz6tbt26ica2ljRcdPXo03bx5UxizefToUXJ0dJSMVZ2lQ6R5coGtrS0pFAqh3KKiIrUtkZXs7e1F22enpqZKjql0d3en5cuXE1Hx2Mrly5drHItLVDzetU2bNtSzZ0/q16+f5Eyx8nw3KYZ4PeoS5+3tTX379qVZs2aVOR75bVAhNVSl+/fvY+vWrYiJiUFeXh48PDywcuXKcseVJJPJsG/fPmzduhUXL17E2bNnq2xsq1at0KFDBxw+fBhZWVkAiv/yOzg4wMrKSogrucpSyTnrcrlcclM2AOjQoQNu376NNm3aIC4uDgcPHsSECRNEqzQpHT9+XO1YYWGhZLkFBQVo3ry58Lxnz54IDg6WjNVlyxZlx4ayNlRYWKixs0Mmk4nWQm3cuDEUCoVa3IMHD/Dzzz8DKF4HYMaMGUKvvBRNw9KkfPLJJ9iwYYNonQKpjjnV8zbk61GXuD59+qi1s7/VKiJL79u3j1xdXalbt27k7+9PR48epb59+5Y7rqTU1FQKCQmh7t270xdffEFr1qzROM+6qsRqOyU1Pz+f7t+/Tx4eHqIpoRkZGcI01KysLNF7vL29aeXKlXT+/HkaOHAghYWFqc01V1KubK+kaeYRUfH0zKtXrwrnHhsbS87OzpKxd+7cIScnJ+ratSt16dKFHB0d6datW5Kx4eHhQq0nIiKC7O3tJefnExXXPCMiIkRTRN3d3dXibGxsRDN4bt68SQ4ODmpx5dk54dGjRzRt2jTq1q0bde3alby9vTWuG1BVrkddy8zPz6f09HTR421VIeNQ27RpAysrK8yYMUNYrah///5qg761jQOA/fv3Y+vWrbh8+TIGDhwIKysrzJ8/X7IdtqrF6joLqjSqZTk6OmLHjh0ICQlB/fr1MWnSJOGYUskVpEoyMTFBv379sHr1arXXUlNTMXfuXFy8eBHvvvsumjRpgtDQUPzrX//SeG4vXryAQqHQ2N6rdOTIESQlJUGhUMDc3FytTVQpJycHAQEBwqgEc3Nz+Pn5qdW+k5KS4OPjI8z2efz4MX744Qe1kRPz5s0r9bzKu5OnIVxj2sbqUiZQPLsuOjpauOMgiaU33yYVcsu/e/duxMTEYPTo0WjUqBGGDh2qtumeLnFA8fqeVlZWiIqKEpKvpiEtVS32xo0bksvOlefiVP37KJfLkZubiwMHDmDNmjXIzs7Gy5cvRTHKrVMCAwMlp21KefXqFX7//XdRklQdZ6qky5YtkyZNgr29PWbOnInq1auXeg4NGjQos2kIKL7NHz9+PL7++musWrUK6enpyMnJUYuTSpjPnj3Dw4cP0bJlS9FxXWaMGcI1pm2sLmUqv+d///tf1K5dW2PM26RCEmqrVq0wd+5czJ49G4cOHUJMTAwePXqESZMmYfTo0UKbi7ZxgG7Jt6rFNmnSBOvWrdPw09SN6sU/ceJEjBgxAv369UOrVq1gaWmJadOmSb73u+++w5YtW0Trizo7O4vaMHXZ7VPJ19cXnp6eWg2tcXNzQ0xMDH744Qf07t0b9vb2aN++vWTs4cOH8dNPP+Hx48eiPySqf4ACAwPh4+ODBw8eoE6dOti1axc8PT017qu0bds2nDlzBj4+PrCzs0Pt2rUxaNAg0fhdbab2KlfCN4RrrCIqOUDxWreFhYWcUJX+V20LOTk5tH79eo37GWkbJ5PJaP/+/TRlyhT64osvyM3NTeNeRlUl9nXWSVVVVnusTCYT/tvf31/02rJly2jy5MmUmJhI+/fvp8mTJ1NgYKAoRtfdPomo1D2WNCkoKKCYmBiysLCgoUOHSu6UOmDAADp06BDdv39fbZnBkpSjD2bOnCm0hZb2M7e3t6fHjx9TZGQkLVq0iIqKisje3l7n7yC1Rm5VuB51idu/fz917tyZRo8erdXSiG+6//kmfR07dtRbnLZJ2tBjv//++zLfqy1d1lyVWoS45HJ9RUVFGhez1mXt071799KsWbNo27ZtWq2devz4cZo3bx5ZWFiQv78/JSUlUWhoKLm6uoritE1yzs7O9Ouvv1LPnj3pyZMntGHDhlKTvLJcV1dXOnz4MBERDRkyRKvPKqm0pK28FqSGXRlabGlxffv2LXPDyLdJhQ6bqmgffPABJkyYIDnHuCrFLliwoMz3aoteo49RLpdDJpMJg/rlcrnGOe/t27dHYGAgXrx4ASKCQqFAWlqa5OaBumzZ0rdvX3z66adwdHTEggUL8O677wIAunXrBkdHRwD/rCfQvHlzBAYGon///qJB+qpL84WGhmLbtm1YvXo16tWrh6ysLI0bDwJAixYt4O7ujrS0NHTv3h3Tpk1Du3btNMZrUlrbo/JauHfvXpnlVHZsaXHvvffeW7uylJQqnVDfVs+fP8euXbswZswYYebPpEmTULNmTa07laRosziK0owZM9C/f3+cPn1a2O1TteNGKTs7G3v37tXqHCIjI/HZZ5+pHa9WrZoweqHkqIOMjAxcu3ZNeG5kZKTW2WVmZiZa2MTHx6fUc1iyZAnOnj2Lli1bokaNGrC1tYWFhQWA4rVRNY06KI/du3er7ZBqqLFScZ06dYKXlxcsLCxEnYhva5KtkIRacrpjSUQk2sBM2zgmNmvWLLRu3RoAULt2bSgUCsyZMwdr1qwpdRGVsmizOIqSNrt9KnXu3BmHDh1Cr169ylwUxdjYGBMmTEB6ejo2bdqE2bNnIygoSDSAX7VD6MmTJzA2NlZblKS8TExMRLXckpsPrl69Wq8JVZc7isqOlYorKChAnTp1cObMGdFxTqh6pNzCWF9xgG7Jt6rF6krXmT+aqP6CaLM4ipI2u30qHTp0CNu2bdNqRaIFCxZg4sSJCA0NRcOGDTFs2DD4+vpKNiWkpKRgzpw5yMzMBBGhWbNmCAkJkazh6ou+E5UuO7FWdqxUnL5WEntTVMg3L7mkmj7iAN2Sb1WL1ZWRkRGuXbsm1FJv3bpVrotYdUtkXRZHsbGxgYeHB0JDQzFy5EgcOXJE4zYZR48e1ViO6i3048eP8c033yA0NBRGRkYYMWKEZDIFiod5zZgxQ3j//v374evrK7TZVgTVpFJYWCia2nvlyhVhaq+yrdwQ/mhX1F2jriuJvemqzJ8SXZJvVYvV1dy5c+Hq6qo286ckTb8YShs3bsScOXNExy5dugRnZ2fRMU01SWdnZ9jZ2aFOnTr47bffcOnSJbUErQ3VW+h3330XGRkZwrknJydrTO5EJHrvwIED8dNPP+l8Dq/Dx8cHzZo1w6tXr7BmzRrY2trC19cX69evF5optPnjmp2djYYNG1Z6rC5lArqtJPZW+F8NJ2D69erVK7p48SJdvXpVbXwmkeadPvU1rOXevXsUGxtLCoWC/P39ycHBgU6dOqVzOapDiy5cuEA2NjbUoUMHsrGxod69e9O5c+ck3xsYGEg//fQTZWdnU25uLm3atIlmzpxZofPJVYeaKdcECA4OpvDwcNGx1ynXkGNLxr3ualtvmipTQ2X/ePr0KX744QekpqZi1apVWLhwIXx9fUULPZesIV+5ckUY3iSXy5GWliZZg9Zm8z+lefPmwdnZGQcOHMDdu3cxb948hISEIDo6WqfvolqLbteuHbZv3467d+9CLpejWbNmQg01KipKtDC0ckaUam3I2dm5wuaTUzmm9panXEOOLRmnupLY/v373+r1UTmhVkHz589Hz549ceHCBdSuXRumpqbw8fGRnL46d+5cnD17Fk+fPkWzZs2QkpKCr7/+WlhuTpOSm/9JefXqFQYPHgw/Pz9YW1ujc+fOart9llf16tUlh2Bt3bpVlFBLW6B869at5f58Ly8vtbHC48aNQ2RkpLBrgJIuU3tLU9kdTrrElowLCAiAj48P/Pz8ABQvoRgSEqL1Z75pOKFWQWlpaRg5ciR+//131KhRAzNmzICNjY1k7KlTp5CQkICAgAC4uLiAiLB48WLJWF02/zM2NkZCQgIOHz6MadOmITExscI3adOlthUVFaVxGJcm2qw3q7prgLW1tWir7T179pS6AeCbpmnTpli7di1q1aoFhUKBnJwcYVGVtxEn1CrI2NgY+fn5Qk3h7t27GpOZqakpqlevjubNm+PatWsYOnQonj9/rtXnlLb53+LFi7FhwwYsWLAApqam+PPPP9V2+1TKzc3FBx98IPmaLklSl9qWLuUqBQcH48mTJwgKChJNkDAxMUGDBg1Ese7u7ggPD9e46tTbsnzdxo0bERMTg5iYGKSnp8PDwwPjx48vdc+uNxkn1CrI29sbY8eOxcOHDzFlyhScO3cOS5YskYw1MzNDeHg4unfvLowEePHihWSs1OZ/EydOlIxt3bq1aFhCYU8AACAASURBVLm7FStWCP+tuibrmDFjNM6UUr2F1hddkq9SnTp1UKdOHaxduxY3btzA06dPhcScmpoqGuwfEBAAQLtVp7RR2e2iusSWjIuOjhbazRs1aoSdO3dixIgRnFBZ1dGwYUOsX78eFy5cgFwux+LFiyV3+wSK943/66+/0L59ewwaNAh//vmnxmmGumz+VxrVX8w2bdpg165daN++vTA3HyjePkT1FtoQLF68GAcPHhTNzlKd0qpcxLp27dq4cuUKevTogfDwcFy+fBne3t4ay3727Bny8/NFP6NPPvlEcspwZcdqE1dUVCQa1lbWGrZvOk6oVdCMGTOwd+9erfbyMTIywpMnTwAU72efk5OjsaOpUaNGGrdw1oVq7fD8+fM4f/68Woy2t8WFhYWoUaOG3qaWluXo0aOIj48XJX9NZs2aJYyFjY+Px7hx47Bw4ULJyQg///wz1q1bJ+oFV/4cVKcMV3astnEDBgzAuHHjMHjwYADAvn37JBdLf1twQq2CWrRogbCwMHz11VeiX3rVVZYA9Xn/RCTM+1dV2hbOr6O03nhVI0eOFDUDKBQKODo6Ii4uTnKFf1X6SL6atu+W8vTpUzg7OyMgIAD29vaws7PTeJ7bt29HYmKixvZkQ4rVNs7Hxwfx8fE4deoUTExM4OLiIgyhKjkB4G3BCbUKevLkCU6cOIETJ04Ix6RWWQJ0m/d/9OhRxMTEwN7eHnXq1EFERARsbGxeO6GqjpsNCQnBvHnzULduXSGm5L5Wbdq0EY4r97WS8rrJV5N69eph6NCh6Nixo+h2VmqLFIVCgUuXLiExMRGbNm3C1atXNa5w//HHH4vGCpemsmN1KdPKykq0O6/SpEmT9LZXWlXBCbUKUrZ1arPKki7z/nXZwrk0qrU7qXGzs2fPFo2b1WVfq/IkX1306tULvXr10irWx8cHISEhmDBhAho3bowRI0Zo3OyvadOmGD16NLp16yZK1FITJyo7VpcyNSnPSIuqjhNqFaTLKktS8/41Dby2srLC9OnT8fTpU2zYsAG7d+/WuB7qf/7zH9ja2kre0k2aNEn0XJdxs3PnzsXhw4eFdl+lkm255dlUUBfdunXTOrZ79+7o3r278Hzr1q1IT0+XjDUzM9O4gIyhxepSpiblGWlR1VXINtKsYjk4OMDLy0u0ylJERITGVZYKCwtx/fp1mJiYiKZyStF2C+ewsDDExcWhSZMmsLe3x4ABAzT28A4fPhzr16+Hi4sLYmJicPfuXUyfPh27du1Si502bRoePHiA5s2bi34hpW63i4qK8Pfff5eafMtDOXyMiCCTyfDo0SN8/vnnoq23lTZt2oTly5ejoKBAONaoUSMkJiZKlv3ixQukpqaiVatWePnyJWrVqqXxPCo7Vpcypehze/Qqo+KXC2D6JrWIheoiI6tXryYiIl9fX8mHJtevX6dTp07RyZMnhUdpTp06RQsXLqTBgwfT999/T1euXFGL+e9//0u2trbUtWtXmjx5MnXv3l3jJnKWlpalfl5J3t7e5OTkRHPnztXqu5XX+fPnac6cOZKv9e3bl1JTU2nmzJl0//592rFjB82cOVMyNikpiQYMGEB9+/alzMxM6tatGx05csQgY3UpUxNdFmZ5U3BCrYK0WWXpwIEDRES0c+dOyYeURYsWUe/evUW7mZa2g+WLFy8oJiaGXF1daejQobRy5UoaM2YMhYaGqsXm5OTQoUOH6MCBA5Sdna2xzClTplBmZqZWPwddku/rGjp0qORxJycnIiIKDw8XfuaaNg90cnKirKws4Y/fjRs3NG6QV9mxupSpiT53860quA21CtJmlSVl54xqe6CRkZHGwfR///23TuMvT5w4AQsLC0yePFkYm1hYWIhvvvkGs2bNEmLz8vKwdu1aHD9+HCYmJsJ7pD7n5cuXsLKyQqtWrURNE1K99s2bN0dWVpYwyF5fVFfdunnzptrUU6WaNWvi+PHjaN26NRITE9GuXTvk5eVJxioUClGbc4sWLTSeQ2XH6lJmWloabt68iV69euHBgwfChIiKaN82dJxQqyBdVlmaOnUqrl+/jtatW4OIcOPGDTRs2BDGxsYICAgQdajoMv6ye/fuCAgIUGtXq1GjBv7880/RMeUizKGhoSAi7NixA35+fpI7j7q7u2v1+YBuyfd1dOnSRdi4UNX8+fOxbds2+Pr6Yvv27Rg8eLDGnvCPPvoIhw4dgpGREfLy8rB582Z88sknBhmrbdyePXuwdu1aFBQUCAvSzJkzB7a2tq+1v1mVVck1ZKZnqu1W7u7udPHiReF5SkoKeXp60oMHD8jR0VEUO2PGDOrbty/NnDmzzDbJu3fvar3AtNTtsqZbaCKi5ORk2rJlC7169arUNtyKWjybqLiJ4uDBg7R///5SmyiOHj2qdiwhIUEy9tGjRzRjxgzq1q0bde3alby8vCgrK8sgY7WNs7Ozo/z8fOH2PjMzk4YMGSL52W8DrqG+YUilhpmeno4vv/xSeN66dWukpqbi448/VhuArsv4y++++07rBaabNGmC5ORkocaSkpKicYm3yMhIJCYmIisrC1ZWVliwYAGcnJwkF2np2rUrTp8+jevXr8PR0RHnz5+XnC2mqyNHjuC7775Dhw4doFAosGDBAgQFBYlGPOzZsweFhYVYvXq1aO6+TCZDeHg4Bg0apFZutWrVsHz5ctExTdtSV3asLnEl13swNTWt8GUcDRkn1DeM6ti/xo0bIzQ0FLa2tlAoFPjjjz/QpEkTnD17Vu3Ct7e311iu6hAYXRaYTk1NhbOzM/71r3/B2NgYd+7cQb169YThSSXn9MfExCA6OhojRozA+++/j+3bt2P48OGSCVWX5KuLFStWYMuWLUJb4P379+Hp6SlKJs+ePcPZs2fx/Plz0Yw1Y2NjzJgxQ7LcCRMmYP369fjggw+QnZ2NgIAA3Lx5UzLxVXastnEtW7bEpk2bIJPJcPXqVWzZskU02eKtU9lVZKZfqrf8+fn5tGzZMrK2tiZ7e3v64YcfKD8/n2JjY0VNAWVR7bEdMWIExcfHU48ePSgzM5P279+v1oSglJaWpvGheg7KHnLl5xUVFWnco8jW1pZevXolxD579owGDx6s9XfSRJd9kpKSkkTP8/PzNZYbHx9Ptra2FBERQT179qSwsDAqLCw0yFht454/f06hoaHk4OBA9vb2tGzZslJ/Bm86TqhvGKmxfzk5OXTgwIEy2wN1KTclJYV8fX2F9sLp06fT1atXX7vcpUuX0rJly2jQoEG0f/9++r//+z8KDAyUfK8uyVcX7u7uFBERQfn5+ZSfn08RERHk7u4uGXvgwAEKCQmhZ8+ekZWVFXXt2pU2bdqkseyjR49Sx44d6dixY2WeR2XHahv36NEjio+Pp8TERHry5EmZn/8m45lSbxgXFxdRL7dqe+DZs2fV2gO1ITXr5cqVKzhx4gSMjY3Rs2dPNG/eXOfztbOzE82YUigUiI6OFs3WGjVqlOT6A8uWLYORkREOHjwIHx8fREVFoWnTpsL+RuWVk5ODgIAAHD9+HEQEc3Nz+Pn5SQ7PcnR0REhICM6cOYPk5GQsWLAAY8eOxc6dO4UY1YW7Hz9+DGNjY2HxkZJNHpUdq0uZABAbG4uQkBB06tQJcrkcFy5cQGBgIHr37q3DT/zNwQm1CtJld1IHBwesWrVKrT0wNjZWp89UTai//voroqOj0bdvXygUChw+fBju7u5wdHQsV7nKpd40bbkiNWRHl+SrqytXrqBt27bIz8/HpUuXRMPLSnJ0dMSOHTswdepU2NjYwNLSEtbW1oiLixNiNM3tV2rUqJHBxOpSJlC8/kNkZKQw71+5DUrJ7/824U6pKq6s3UllMplo5fnGjRtDoVDo/Dmqf3ejo6OxY8cOoYd36tSp+Pbbb3VOqEr+/v4IDw8XJieU/FzVjitl8s3IyICFhQUsLCz+X3vnHRbVtf39LyUUJZeokdjQhxiVXHNtAQRbIiABaQKKDbDEggrWiC3gjYAR0aiIUYwKYiMQAQWJItKMFbxCFAMSYwExEAtgQWBm9vsH75wfM3MGGUQOc2Z/nofnYc6s2awBXbPP2mt9F/NcRUWF3BrM5rJlyxbcunULBw4cQE1NDX788Ufk5ubC19dXxvbDDz9EYGAgbt68idDQUGzatEnm54uDUF1dHbKyspiZXuKR3o2npHJtq8iaQIMkZOMGgJ49e6q0aj8NqEqIItNJe/TogaioKGZs9C+//CKzyxCjiIKUvr6+xE6wQ4cO6Nixo0LvozEREREAgPj4eJm57qWlpRKPFQm+LSEzM5PZwRsYGCAyMhIuLi6sAXXr1q1IS0uDl5cXOnToAENDQ1Y7oOHvVlNTgwcPHsDExAQ5OTkYMmRIu7Rtrl3//v0xd+5cuLm5QUNDA7/++isMDAyYNM7bCtUoG6pbMMYjmppOGhwcjLy8PFhbW8PKygrXr1+XO0b69evX8PDwwLx58/Drr7+ivr6eeW78+PEStoaGhpg8eTL27t3LKEnp6ekhPDxcbkqCDfHO99GjRygrK4OHhwfzfVlZGUpKSjBnzhyJ1zQOvufOnWO+0tPTcfDgwWb/bHkIBAK8fv2aedz49yCNlpYWOnbsiOvXryMxMRE6OjrYt28fq+3du3cRHR2NcePGYc6cOYiLi0NFRUW7tG2uHSEEBgYGOH/+PDIzM6Grq4tOnTrJCKCrCnSHqoSwTSeVt0Pt0qULtm/f3qx1fXx84OPjg9zcXCQnJ2Pnzp0wNzfHpEmT8Omnn0rYGhkZwcjICHV1dairq8PIkSPlrltXV4e//voLxsbGSEpKwq1btzBr1iwYGBgwo1jCwsJw5coVVFRUYPr06cxrNTU1ZWZnPXr0CIQQzJs3Dz/99BMTlIVCIebOnYvTp0836/3KY8qUKXB1dWX0ELKzsyV8aowiu8MuXbpATU0NRkZGKCoqwoQJE1BXV9cubZtrN2bMmCalG1UNGlCVkOZMJ5U3L16MvNvimpoalJaWoqSkBOrq6tDX10dwcDCGDh0qIXji4+ODp0+fIj8/H0KhEEOGDJE7eVXcy19bW4udO3fC2dkZq1evxoEDB5j8rljvdO/evTLpBWkUCb4tYebMmRg2bBhyc3OhqamJ0NBQ/Pvf/wYAFBQUYODAgYzt3bt3kZqaiuDgYLi5ucHPz08mzyimX79+CAwMxNSpU/HNN9+goqJC7u6Xa9vm2mVnZyM0NBRffPEFXFxcMGjQINafqzK0dZ0W5e3x8fGRuebl5SXxuKli+tLSUtZ1ly9fTkaOHEnWrFkj0ZdfW1tLTE1NJWyzs7PJqFGjiI+PD1m4cCGxsLAg6enprOu6uroSQggJCQkhEREREtek+eeff0hkZCQJDw8nO3fuJDt27CArV65ktRWv1ZZI181OnjyZEELI4cOHSUJCAiFEvnyfQCBgfq9paWkkMDCQ3L59u13aKrLmy5cvSWJiIpkzZw5xcXEh+/btI48fP2a15Ts0oCoRCxcuJJaWlmTgwIHE0tKS+RozZgzzH1sakUhEjhw5Qnx9fcmCBQvIwYMHiVAoZLWNi4sjL1++ZH1OWhjDxcWFPHjwgHn84MED4uTkxPpaZ2dn8uTJE2JjY0OKiopIRUWFXAGNKVOmED8/P2JjY0M2b95MnJ2dyapVq1htFQm+rYV0x9i3335LNmzYQIqLi4mzszOJiIiQ21ywYcMGmWvyhKu5tlVkTUIahMb9/f3JuHHjyMqVK4mDgwM5dOiQXHu+Qm/5lYiQkBBUVlYiODhYQmtSU1NTrl7n5s2bcf/+fbi5uYEQgvj4eJSWlmLt2rUytqampkhLS4OjoyMCAgJw69YtrFmzBiYmJjIn/4qUY82ZMwfu7u6wtLRE//798dVXX8m9LX727BmOHTuGkJAQ2NjYwNvbGzNnzmS19fX1Re/evZlDtwsXLrzzPnLpNMp///tfXL9+HZ988gl8fX1x6dIlGVGRdevWoaSkBDdv3kRxcTFzXSgUymincm2ryJpAg+5BcnIyevXqBTc3N6xbtw7a2tp48eIFrKys4OHhIftL5DNcR3RK87l58yYhpEG2rvGIkqZGlTg6OkrsSOvr64mtrS2r7bRp00hKSgo5e/Ys8fDwIDk5OWTSpEmstoq0Z0pL3AkEArkSd+7u7oQQQn7++WcSExNDCCFyd75ixf5NmzaRvLw8Ul1dLTeV0FpI3/I3ZydXUlJCLl++TBwdHSVkBnNzc8mzZ8/ala0iaxJCyPbt2yXuVBqTn5/Pep3P0B2qEnHs2DEEBQUxJ+ONUVNTYxVWFgqFEAgEjACzUCiEhoYG6/qKKEgFBwcjMDAQe/bsYdozpcuxWiJxZ25ujsWLF2P16tWYNWsWCgoK5E4YELdDGhkZobCwEIMHD5brb2ujyE6uV69e6NWrF06ePCl3PXHHWHuxbY4d0DDNoPGdCgDMmDEDBw8eVMkDKhpQlYigoCAAkqf8QIOUnPQpvxhHR0d4eXkxivOnTp2Sqz6voaGBM2fOIDMzE0uWLEFaWppcbcvmlGO1ROJu0aJFiI6OxoYNG2BkZITevXvLVcBXJPi2FuT/l2gtWLAADx8+RHBwsIR/GhoaLdI0IAp0gHNtSwjBokWLUFhYiIqKClhZWTHPCYVCdO/evdk/k2/QXn4lJCMjA7m5uVi4cCEmTpyIp0+fYvHixXJrJbOysiSEPuSVFhUVFSEqKgpjx46FjY0Nli1bhvnz50vkJefPn4+IiAi5ZVls5ViXLl2S2wsvzapVq1BbWwsnJyeIRCKcOHEC3bp1YxU8qaurQ3R0NHJzcwEAJiYmcHJyavGMqZycnCafNzU1RUlJicyOTN5MJUVQZOQy17YuLi44dOhQk/n81tBTUEZU810rOeHh4di8eTNSUlIwaNAgRuGILaAGBgbC399fQv1n1apVCAkJkbEdMGAAPD09ceXKFURHR8PHx0dmtxUYGAhAdpfMhr+/PwIDA/Hjjz9i9+7dMs+zpSjy8/MlCvMtLS3h4OAgd/3a2lq4u7szwbe8vLzFalNhYWFynxOnVKSDZVMzlfiMnp4e9PT0sHv3buYDWyAQwNzcXGLHqmrQgKqk9O3bFz/88AOcnJzQsWNHmaJreTk+gUCA58+fs64prSC1YMECGQUp8e6vZ8+eSE9Px9WrV6GpqYkRI0ZgxIgREutNnjwZAOT2trPRvXt33L9/nxmR8vjxY0bJSBpFgm9zaM6HhDQ//fQTjh07Bg8PD3Tp0gUJCQmYNWsW7wOqmH379uHMmTNwdHQEIQS7d+9GcXExvL29uXaNE2hAVUKao3DUkhyfIgpSW7duxbVr12BnZwdCCHbs2IEbN25ITC0Vz7Lq06cPoqOjsXLlSpSUlGDnzp3w8/Nj9UEgEDATMzU1NXHt2jV07doVXl5eACR3tYoEX0XIzc3F/v378erVKxBCIBKJUFZWxjpttrVmKnGdF1XEtrHdiRMnEBcXx4wEd3d3h6urKw2oFOWBTeFIHDTFrZGNT3XFs+tzc3NRWFgo05cvRhEFqczMTMTHxzM93JMnT4abmxvrGOhvvvmGOQj76KOPYGJiAj8/Pxw4cEDGVno3K0+jAFAs+CrCt99+i7lz5yIhIQGenp7Izs5mWk+laelMpcrKSglVrababbm2bcqOEMIEUwDQ1tZW2fwpQA+leIf0wcL69euhrq6O6dOn4+uvv8bIkSPx/Plz1tKrFStW4M8//4S9vT00NTVx9uxZ6Ojo4PPPPwcgKRvo4eGB8PBw5j/aixcvMGfOHMTExMis6+TkJFOGo8hhiTyuXr3a5PNmZmYtWlc8RSAsLAympqYwNzeHq6srq7+vXr3C7t27JUSuFy1aJLFrffbsGfbv348PP/wQtra2mD17Nv766y/06NED27Ztk9Cy5dpWkTWBhsqT8vJyZsBjYmIiDAwMJA6qVAnV/SjhKdKfjzdu3MDx48cRHh6OiRMnwtfXV64IdHMUpNasWQOgQS3f2dkZlpaW0NDQQHZ2Nj7++GPWdXV0dJCVlcUcjF26dAm6urpv8zYBtDxgvgltbW1UVlbCyMgI+fn5sLCwwKtXr1htO3TogBUrVkgIx0izevVq9OnTB8XFxTh48CDmzJkDZ2dnXLx4EYGBgfjll1/aja0iawINufqjR48iMTGRqSKZMmVKi37vvKANmwgobYB0J4+TkxMRCATE2dmZ5OXlkVevXjU5GfTJkyckPT1d7kC/+Pj4Jr/YuHXrFrG3tydmZmbEzMyMODs7k6Kiord7o++QlJQUMnPmTPL8+XPy1VdfkfHjx5Ply5ez2kZGRhJTU1NibGxMjI2NyYABA4ixsbGEjb29PSGEEKFQSEaNGiXxnHTfP9e2iqxJCCF79uyRubZ161aZa6oC3aHynAkTJmDUqFEYNmwYBg8eDDs7O7k7COmBfgEBATID/cS3dk0hfTv/6aefIjk5GWVlZVBTU2v3hd8jRoyAra0t1NTUEB8fj3v37uH9999ntY2OjkZiYmKTY1fEOUV1dXUZiUPpfCPXts2127JlC548eYL09HTcu3ePuS4UCpGfn4/ly5dDFaEBlefMmjULXl5eTLvpkSNH0LlzZ1bbbdu24ejRozID/RSdkEqk0g4PHjzA8uXLUVJSAkIIk48zMjJqwTt6d8gTrn7//fflClf37dtXrg6sGIFAgEePHkEkEqG+vp75OYDsNACubZtrZ2Njgzt37uDy5csSqRcNDQ0sXLiwyd8Hn6EBlWeI//GLi+o9PT1ZO5rYTsBba6Cf9M9bv3495syZA1tbWwANxfABAQEtqvt8l7REuNrT0xOOjo4YPHiwhEaCWDAbaDi48vDwYP42jdeW/l1xbdtcu0GDBmHQoEGwtrZGfX19s4TGVQEaUJUQcfdTY8TdT+LT+5YU1Ssy0E8Rnj17xgRToGE+FVvnFNcoMjVATHBwMBwdHZv8PbHVr4q5e/duu7JVZE0AyMvLe2OaSJWgAVWJaE73k3iHKS6qNzMzw507d/Ds2bM3Fm43R0GqJWhpaUmMDrl582arnPK/Kzw8PBAaGopLly5BKBTC3NwcS5YsQYcOHWRstbS05Iq3yEMgECA1NRUxMTG4ceMGrl+/3m5t32TXWmkivkDrUJWI0tJSpvupcZ2fuPtJevwyAAQEBCArKwu9e/dmrsmT+mstxHWcYvLz87Fs2TJ88MEHIISgqqqKtaaxvbBmzRro6urC3d0dQEMH2fPnzxEaGipj+/3330NNTQ1jxoyRGFRnamoqY1tSUoKYmBgkJCSguroa3t7emDZtGmtOm2vb5tqx1Rg7OjoiKSlJ5merAjSgKikvXrzA8+fPJXadbCfN1tbWSElJYfRQ2WiJghTQoPakpaWF+/fv4+7duxgzZgzU1dWRkpKC8ePHIzQ0FCtXrkR2djYsLCxw7949iEQiGBkZNekP17AFifHjxyMlJUXG1tPTU+aa9AfW2bNnERMTg4KCAowbNw62trbw9/dnvb3m2laRNQHA29sb5ubmEmmiy5cvY8+ePaz2vKcNS7QorcSePXvI0KFDydixY5kvS0tLVlsPDw9SXV3d5Hrl5eWEEPmD/djYuXMnWblyJXn48CEZOXIkmTFjBlm3bp2EzdixY8mFCxeIjY0NycnJadaEgfaAg4MDqaqqYh5XVVXJnRPVFGFhYYQQQgYMGECWLFlC7t27xzwn7+/Fta0iaxJCyOPHj8mSJUvI8OHDiZmZGVm8eDHz70kVoTlUJSQuLg5paWlyy5+A/+toEgqFTL+7vFNoRRSkxKSnpyMmJgZRUVFwcnKCn58fXF1dJWy8vb0RERGBiooK7NixQ+K5d512eBtmzZqFiRMnwtLSEoQQZGRkNPuQqjHp6enw9fXFyZMnkZCQgGnTpqFnz56wt7eHUChkfQ3XtoqsCTRPaFyVoLf8SoinpyeioqLkjjIB8MY+ebYCfWkFqVOnTsHS0pJV8EScJ506dSqWLl0KU1NT2Nvb49dff5Wx3bVrFxYtWtSMd9Y+8PX1xeLFi3H16lUQQmBqaoqNGzfi4MGDCq0jnUsWCoXIyMhAQkICsrKyMGLECEybNo21JItr2+baZWZmYteuXTKHnvLSRHyHBlQlxN/fH7dv38bw4cMlcpGKnjZLdzQ5OjpKKEjV1tbCzc0NycnJMq8NCQnB+fPnoaOjg9jYWHh4eGDIkCGssnxPnjxBSkoKqqqqJK4r6u+7pvFYDwMDAyZAiEQidO/eHceOHVNovaYEYJ4+fYoTJ04gJiYGZ86caXIdrm2bshs3bhzWrVuHTz75RCL/3hrldsoIveVXQj766KNW0f2U/izV19fHy5cvmWqB+vp6ubOqVq1aBU9PT3Tr1g3q6urw9/eXKws4b9489O/fv93/J2vJmO6W0rlzZ8yaNYtV9au92TZl9/7778ttelBFaEBVQlprZyfeUbREQUr8Gmka52abc7090XisB6VpxPO3+vbti6CgIFhZWUn0+rOVjakCNKAqIcbGxjLlTQYGBsjKymrReuJebGk5PHEhflOvARqKv8+dOyc3+FpbWyMuLg7m5uYSed+mBEX4QEumnyoLjedv/f333ygqKmIet+cDx3cNDahKSGFhIfN9fX090tLSkJeX1+L1WqIgJf2aiRMnYurUqayvff78Ofbu3YtOnTox19TU1JT64ELeDl3M999/jy1btgCAXD0FQghqa2slrnFt21w7aR2GyspKaGhoyFXlUhVoQFVy3nvvPdjZ2bWokFqR88g32d65cwcVFRWsz6WmpuLSpUsSozKUHUXErRXRU+DaVpE1gYYPdz8/P5SXl4MQgo8//hibN2+W6MxTJWhAVUIal+IQQlBcXCzR9tiYffv2wdnZGV27dpV5TpHaSuldizjtIA60nTt3lquBaWhoiKqqKl4F1MY79MrKStTU1IAQAqFQiNLSUglb51X/egAAE39JREFURYIv17aKTkFYu3Ytli1bxvTunz17FqtXr8bRo0cVWocv0ICqhFy5ckXicadOnbBt2zZW29evX8PDwwN9+vSBi4sLrK2tmeA7fvz4FvvQOO3wJtTU1GBvb49+/fpJBH4+5Nl++OEHHDlyBAKBAJ06dUJ5eTk+++wzxMXFce1am0AIkRBCGTduHHbt2sWhR9xC61CVlPr6ety9exdCoRD9+vV746TJ3NxcJCcn4+rVqzA3N8ekSZPkljmxIZ1DrampQXh4eLMUmeQN03tXM6HaEktLS5w8eRLBwcFYsGABysrKEBkZiYiICK5daxOCg4PRqVMnuLu7Q0NDAykpKfjf//7HzNji+8GjNHSHqoTcvHkTixcvxgcffACRSITHjx9j165dctWbampqUFpaipKSEqirq0NfXx/BwcEYOnRok8PlGiP9ubthwwbo6upi48aNABoUmdavXy+hyCSW7GM75OALBgYG0NPTQ79+/VBYWAgbGxtWVSq+Ij5YlB7e5+HhofQHjy2BBlQlJCgoSEL+Li8vj3UiJdAwGvrKlSsYM2YMFixYABMTEwANSlGjRo2SCajyFKSk860FBQUSikwBAQEyKYSYmBgEBgYiLCys2VMDlA09PT0kJiZi4MCBOHz4MAwMDFBdXc21W21GU4LUbCPF+Y461w5QFOfVq1cSu9EhQ4bIlMmIsbCwQGpqKjZu3MgEU6BBGPnUqVMStuHh4fj2229RVlaG6dOnIyoqCgEBAQBk862EEInAUV1dLaMtcO/ePXh5eTH2jb/4QnBwMJ4+fYrhw4ejZ8+eCAgIwNKlS7l2q13w888/c+1Cm0N3qEqIvr4+0tLSYG1tDQBIS0tjFZcGGjpW0tLS4OjoiICAANy6dQtr1qyBiYmJzMl/cxSkxMycOZNRZBK/VnoXq2gJjjKyfft2pgts9erVHHvTvuDTB2dzoQFVCQkMDMT8+fOxbt065pq826u1a9fCw8MD586dw71797BmzRps3rwZsbGxMrYikQhaWlrIyMjA0qVLIRKJUFNTw7qum5sb/vOf/yAnJwcikQg7d+7EgAEDJGz4cOj0Jm7fvo2XL1+iY8eOXLvS7uBz7lweNKAqIdnZ2dDV1UVCQgIePHiAZcuW4erVq6xjmWtra2FnZ4d169bB0dERJiYmEAgErOtaWFjAwcEBOjo6MDU1hYeHR5OzgYqKilBRUYH58+cjNTVVJqCqAurq6hg7diyMjIygra3NXOdDfpiiODSgKiGxsbGIi4uDrq4ujI2NER8fD3d3d2bSaWM0NDRw5swZZGZmYsmSJUhLS4O6OnvqXBEFqS1btuDvv/9GQUEB5s6di+PHj6OwsFDlbntXrlzJtQuUdgQNqEpIfX29RIG8vC4poKG8KSoqCuvXr4eBgQFOnTqFoKAgVltFFKR+++03JCQkwMXFBXp6eoiMjISTk5PKBVQzMzNcu3YNt2/fhpubG/Lz81VWaUmMuFJEFfv6aUBVQqytrTFjxgzY2dkBaOiVt7KyYrUdMGAAPD09ceXKFURHR8PHx0euCpIiClLiXa44T1ZXVyd358tnDh48iLS0NFRUVMDW1hYBAQGYOHEivv76a65daxMmT54scZovEong5uaGpKQklUx70E4pJeX06dPIycmBpqYmTE1NmRN/afbv34/Y2FiMHTsWIpEImZmZmD9/Ptzc3N74MwghmDp1KuuB1969e1FQUIAbN27Ay8sLJ0+ehI2NDby9vd/6vSkTEyZMQGxsLNzd3ZGYmIiXL19i0qRJrBNS+YSXlxdrB5ympiYsLS0l5P1UCbpDVVJsbW1ha2v7RrvY2FgcP36cUd5ftGgRpk6d2qyA2pSC1Lx583D+/Hn06NEDjx49gq+vb5MHWHxFXV1dYgyNtrZ2k7O++IJ49xkUFCQx3UDVoQGV5+jr60v0+Xfo0EFuiY8iClIA0K1bN1hZWTH2OTk5Kpc/NDMzQ0hICGpqapCWloaff/4Z5ubmXLvVZqxatQqZmZmorKyUuD5hwgSOPOIWesvPc1asWIE///wT9vb20NTUxNmzZ6Gjo4PPP/8cQMvHqXz33XfIyMiAoaEhc00VldpFIhFiY2Nx8eJFiEQiWFhYYPLkyW8Uq+ELS5YsQVlZGfr27StRd6oMI2/eBarxV1dhjIyMYGRkhLq6OtTV1WHkyJFybRVRkLpw4QJOnz7NK43TllBTUwOhUIiwsDCUl5cjJiYG9fX1KhNQi4qKcPr0aa7daDeoxl9dhfHx8cHTp0+Rn58PoVCIIUOG4MMPP2S1bY6ClBhDQ0OVbC2UZsWKFUxDQ8eOHSESieDn59esqaN8oG/fvszYbQoNqLzn/PnzWLt2LYYMGQKRSISAgAAEBwezHiA1R0FKjL6+Puzt7TF06FCJQxlVu9UrKytjxs/o6elh2bJlcHZ25tirtuP169ewtbVF//79Jf4dqFrqRwwNqDxn27ZtOHr0KJPrLCkpgY+PD2tAFStI/etf/wLAriAlZvTo0Rg9evS7c1xJUFNTQ1FREbNLvXPnjsrc7gPA/PnzuXahXaE6f3kVRSAQSBwcGRoaQiQSsdo2R0FKjIuLyxtnKakCq1atwuzZs/HRRx8BAJ49e6ZSAtO0U0wSesrPc7y9vWFubo6JEycCaFBWv3z5stwpqbdv32YUpMzMzOQKnqj6LKXG1NXV4fbt29DU1MTHH38scevLdxp3isXExGDatGkq1Skmjer1CqoYwcHByMvLg7W1NaysrHD9+nVs2LBBrr1YQcrNzQ1//PGHXLvk5GRkZWVh/PjxiI6ORmRkJDp37vwu3kK75vfff8eRI0fQv39/bN68GWPGjMGZM2e4dqvNSEhIwP79+6Grq4tOnTrhl19+wfHjx7l2izNoQOU5Xbp0wfbt23H58mVcuXIFO3bskHsiu2XLFmRlZSE1NRUikQjHjx/Hpk2bWG2lZymZm5vj8ePH7/KttEuCgoIwcOBAnDlzBjo6OoiPj8fevXu5dqvNUNVOMXnQgMpTxIcFlpaWsLKykvli47fffkNoaCi0tbUZBans7GxW28azlJKSkpCXl6dSs5TEiFMjmZmZsLGxQY8ePSAUCrl2q82Q7hRbsGCBSnWKSUMPpXhKYGAgAODQoUPNfo0iClLBwcE4deoUJkyYgIyMDAQEBGDZsmVv6bXyoauriwMHDuDy5csICAjAwYMHVUq938/PD7GxsRgwYAASExPxxRdfYMqUKVy7xRn0UEoFSE9Px9WrV6GpqYkRI0ZgxIgRrHaKKEiJtVAbc+TIEUyfPv2dvIf2Snl5OeLi4jBixAgMGzYMoaGh8PLyYk79+co///yDrl27oqysjPX5Hj16tLFH7QMaUHnO1q1bce3aNdjZ2YEQglOnTsHS0lJu/eD58+eZvnRzc3OZetWoqCi8ePECMTExEjsRoVCIpKQkpKWlvdP3014oKCjAwIEDkZOTw/o830uH5s+fj4iICFhaWkr08BNCoKamhnPnznHoHXfQgMpzHB0dER8fz6j619bWws3NDcnJyaz2xcXFqKqqkmgrbRwcMjIyUFBQIBNQNTQ0YGpqKjGqms/4+/sjMDAQn3/+OQYOHAjg/6Z8qpJITGVlpczE3dLSUvTq1Ysjj7iF5lB5jr6+Pl6+fMn8o6+vr2e0UaVpjoLU2LFjMXbsWNjZ2clV/lcFxDnqPn364OnTp3BycoKjoyO6d+/OsWdtw6NHj0AIwbx58/DTTz8xHyZCoRBz585VWcEUGlB5ing+lEgkgrOzMywtLaGhoYHs7Gy5Y00UUZCaO3cu65hgVbvVi4+Px/3795GcnIx58+bhgw8+gJOTEyZNmsS1a++UsLAwXLlyBRUVFRJ5c01NTXz55ZfcOcYx9JafpyQkJDT5vPSBEgB8/fXXCA8Ph66u7hvXf/jwIfO9QCDA2bNnUVdXh4ULFyruLA949eoVzp07h8jISLx48QKpqalcu9Qm7N27V257sipCA6oK4+LiIhF4ly9fjry8vBYrSLm6uiI+Pr7V/WzPpKamIjk5Gb///ju+/PJLODk5YdiwYVy71WY8fvwYycnJePnyJQghEIlEKC0txebNm7l2jRPoLb8KI/1ZqoiCVOPTbUIIiouLUVtb26r+KQNJSUlwdnbG1q1bmxznzVd8fX3Ru3dvpr35woULMDY25totzqABVYWRzoEqoiAVFhaGJ0+eoEuXLlBTU4O+vj5CQkLawu12haoIScvj2bNnOHbsGEJCQpia5ZkzZ3LtFmfQgEphUERBaty4cYiPj8ehQ4dQWlqKuXPnoqCgAJ999hkHnlO4Ql9fH0DDqJ3CwkIMHjwYAoGAY6+4g/byUxgUUZCKjY3FsWPHAAC9evVCfHw8Dh8+3JbuUtoB5ubmWLx4MUaNGoUDBw4gICAA2traXLvFGTSgqjDSOVRFFKTq6+slcoaqmD+kAIsWLcKgQYOwYcMGGBkZoXfv3ggPD+faLc6gt/wqQF1dHbS0tHD//n3cvXsXY8aMgbq6uky5S2MFqcOHD8PAwECugpS1tTVmzJgBOzs7AA2n3fJUrCj8xd/fH7W1tXB3d4dIJMKJEydQXl6OdevWce0aJ9CyKZ4THh6OBw8eYOnSpXB3d8cnn3yCXr16ISgoSMa2vLwcp06dwuzZs7Fp0yZcvHgR3t7ecgf1nT59Gjk5OdDU1ISpqSmsra3f9duhtDNsbW0luqJEIhEcHByQkpLCoVfcQXeoPCc9PR0xMTGIioqCk5MT/Pz84Orqymp78eJFzJ49GwCwevVqAA0KUvKwtbWFra1t6ztNURq6d++O+/fvo0+fPgAa6lL5rrTVFDSg8hyRSAQtLS1kZGRg6dKlEIlEqKmpkbBprCDVuANKrCClapJ8lOYjEAjg7OwMExMTaGpq4tq1a+jatSu8vLwAqN44aRpQeY6FhQUcHBygo6MDU1NTeHh4yEjy9enTBwUFBTKv1dLSkjsChUIBGgr7GyO+w1FVaA5VBSgrK0O3bt2grq6OP/74A59++imr3Z07d1RaQYpCeVtoQOU5YtUpadj686XFgsWomoIUhdJS6C0/zzEzM2O+FwgEOHfunFz5vsbzpxorSFEolOZBd6gqBiEEU6dORUxMTLPsVVFBikJpKXSHqmLcuXMHFRUVrM9RBSkK5e2gAZXnGBsbQ01NjWkz7dy5M5YvX85qSxWkKJS3gwZUnlNYWNhsW6ogRaG8HTSHynNqamoQHh6OS5cuQSgUwtzcHEuWLEGHDh1kbB0cHBAXF8eMQKmpqYG7uzuSkpLa2m0KRSmhalM8Z8OGDaipqcHGjRsREhKC+vp6rF+/ntWWKkhRKG8HveXnOQUFBTh58iTzOCAgQK7YCVWQolDeDhpQeQ4hBNXV1fjXv/4FAKiuroaGhgar7cqVKyUUpLy8vKiCFIWiADSHynOOHz+OiIgIWFpaAmhQn5o3bx4mTpzIsWcUCv+gAVUFuH37NnJyciASiWBmZoYBAwZw7RKFwkvooZQKUFRUhIqKCri5ueGPP/7g2h0KhbfQgMpztmzZgqysLKSmpkIkEuH48eNUko9CeUfQgMpzfvvtN4SGhkJbWxt6enqIjIxEdnY2125RKLyEBlSeo67e8CcWy/LV1dUx1ygUSutCy6Z4jq2tLZYuXYqqqipERUXh5MmTcHBw4NotCoWX0FN+FeD8+fO4ePEiRCIRzM3NZUagUCiU1oEGVBWguLgYVVVVaPynNjU15dAjCoWf0Ft+nvPdd98hIyMDhoaGzDU1NTWVm0ZJobQFNKDynAsXLuD06dPQ0dHh2hUKhffQ416eY2hoCJrVoVDaBrpD5Tn6+vqwt7fH0KFDoaWlxVxnm3pKoVDeDhpQec7o0aMxevRort2gUFQCesqvAlRWVqKmpgaEEAiFQpSWlsLCwoJrtygU3kF3qDznhx9+wJEjRyAQCNCpUyeUl5fjs88+Q1xcHNeuUSi8gx5K8Zzk5GRkZWVh/PjxiI6ORmRkJDp37sy1WxQKL6EBlecYGBhAT08P/fr1Q2FhIczNzfH48WOu3aJQeAm95ec5enp6SExMxMCBA3H48GEYGBigurqaa7coFF5Cd6g8Jzg4GE+fPsXw4cPRs2dPBAQEYNmyZVy7RaHwEnrKz3MSEhLg4uIice3IkSOYPn06Rx5RKPyF3vLzlKioKLx48QIxMTF4+PAhc10oFCIpKYkGVArlHUBv+XlKnz59WK9raWnRESgUyjuC3vLznDt37qBv375cu0GhqAQ0oPIcS0tLZvxJY86dO8eBNxQKv6E5VJ5z6NAh5nuBQICzZ8+irq6OQ48oFP5Cd6gqiKurK+Lj47l2g0LhHXSHynNycnKY7wkhKC4uRm1tLYceUSj8hQZUnhMWFoYnT56gS5cuUFNTg76+PkJCQrh2i0LhJbRsiueMGzcOWlpaOHToEDZu3Ig///wTBQUFXLtFofASmkPlOQ4ODoiLi4Ouri4AoKamBu7u7khKSuLYMwqFf9AdKs+pr6/He++9xzxu/D2FQmldaA6V51hbW2PGjBmws7MDAKSmpsLKyopjrygUfkJv+VWA06dPIycnB5qamjA1NYW1tTXXLlEovIQGVAqFQmklaA6VQqFQWgkaUCkUCqWVoAGVQqFQWgkaUCkUCqWVoAGVQqFQWon/B90EMz9mb0OeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isna(),yticklabels=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "def date_to_month(x):\n",
    "    year = int(x[:4])\n",
    "    month = int(x[5:7])\n",
    "    return 12 * (year - 1998) + month\n",
    "\n",
    "df['T0_date'] = [date_to_month(v) for v in df['T0_date'].values]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean Encodings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1., nan])"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.specific_death.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets=['specific_death','months_survival']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "cats=['cutaneous_biopsy_associated_nevus',\n",
    " 'cutaneous_biopsy_histological_subtype',\n",
    " 'cutaneous_biopsy_lymphatic_invasion',\n",
    " 'cutaneous_biopsy_neurotropism',\n",
    " 'cutaneous_biopsy_predominant_cell_type',\n",
    " 'cutaneous_biopsy_regression',\n",
    " 'cutaneous_biopsy_satellitosis',\n",
    " 'cutaneous_biopsy_ulceration',\n",
    " 'cutaneous_biopsy_vascular_invasion',\n",
    " 'patient_eye_color',\n",
    " 'patient_gender',\n",
    " 'patient_hair_color',\n",
    " 'primary_tumour_location_coded',\n",
    " 'scenario',\n",
    " 'visceral_metastasis_location',\n",
    " 'patient_phototype_unk',\n",
    " 'patient_phototype_common_2',\n",
    " 'patient_phototype_common_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03713417871640304\n",
      "0.3117536827506859\n",
      "0.15695906986330568\n",
      "0.12454004424877697\n",
      "0.07784173935353905\n",
      "0.1482049216735401\n",
      "0.18867037458693886\n",
      "0.333323283100396\n",
      "0.1113749234830956\n",
      "0.09472040744385524\n",
      "0.09005669918856885\n",
      "0.16423717211201327\n",
      "0.15639452998757372\n",
      "0.37361357461022765\n",
      "0.24236341838766215\n",
      "0.15895623129380582\n",
      "0.15895623129380582\n",
      "0.15895623129380582\n",
      "0.20604245419169018\n",
      "0.19829345794635558\n",
      "0.11389256676125058\n",
      "0.09242546033483291\n",
      "0.11705566983815904\n",
      "0.2420757440057372\n",
      "0.11085390568575121\n",
      "0.20053460216961613\n",
      "0.07706235307068983\n",
      "0.06651077106891432\n",
      "0.08476801131532931\n",
      "0.09011500033774983\n",
      "0.14453046594230218\n",
      "0.21573132001329542\n",
      "0.14185367687940528\n",
      "0.16522749372825282\n",
      "0.16522749372825282\n",
      "0.16522749372825282\n"
     ]
    }
   ],
   "source": [
    "for targ in targets:\n",
    "    for columnas in cats:\n",
    "        df=splited_target_encoding(df,'train==1','train==0',columnas,targ)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_encoding = [\n",
    "    'cutaneous_biopsy_ulceration',\n",
    "    'cutaneous_biopsy_satellitosis',\n",
    "    'cutaneous_biopsy_vascular_invasion',\n",
    "    'cutaneous_biopsy_neurotropism',\n",
    "    'cutaneous_biopsy_lymphatic_invasion',\n",
    "    'cutaneous_biopsy_associated_nevus',\n",
    "    'MC1R']\n",
    "\n",
    "for col in columns_encoding:\n",
    "    variables = df[col].unique()\n",
    "    for v in variables:\n",
    "        df[col + str(v)] = [1 if row == v else 0 for row in df[col].values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>LAB1300</th>\n",
       "      <th>LAB1301</th>\n",
       "      <th>LAB1307</th>\n",
       "      <th>LAB1309</th>\n",
       "      <th>LAB1311</th>\n",
       "      <th>LAB1313</th>\n",
       "      <th>LAB1314</th>\n",
       "      <th>LAB1316</th>\n",
       "      <th>LAB2404</th>\n",
       "      <th>LAB2405</th>\n",
       "      <th>LAB2406</th>\n",
       "      <th>LAB2407</th>\n",
       "      <th>LAB2419</th>\n",
       "      <th>LAB2422</th>\n",
       "      <th>LAB2467</th>\n",
       "      <th>LAB2469</th>\n",
       "      <th>LAB2476</th>\n",
       "      <th>LAB2498</th>\n",
       "      <th>LAB2544</th>\n",
       "      <th>LAB2679</th>\n",
       "      <th>LAB4176</th>\n",
       "      <th>LABGF_filtrat_glomerular</th>\n",
       "      <th>MC1R</th>\n",
       "      <th>T0_date</th>\n",
       "      <th>age</th>\n",
       "      <th>count_inv_prec_tumour</th>\n",
       "      <th>count_situ_prec_tumour</th>\n",
       "      <th>cutaneous_biopsy_associated_nevus</th>\n",
       "      <th>cutaneous_biopsy_breslow</th>\n",
       "      <th>cutaneous_biopsy_histological_subtype</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasion</th>\n",
       "      <th>cutaneous_biopsy_mitotic_index</th>\n",
       "      <th>cutaneous_biopsy_neurotropism</th>\n",
       "      <th>cutaneous_biopsy_predominant_cell_type</th>\n",
       "      <th>cutaneous_biopsy_regression</th>\n",
       "      <th>cutaneous_biopsy_satellitosis</th>\n",
       "      <th>cutaneous_biopsy_ulceration</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasion</th>\n",
       "      <th>limfocits_per_monocits</th>\n",
       "      <th>months_survival</th>\n",
       "      <th>neutrofils_per_limfocits</th>\n",
       "      <th>patient_eye_color</th>\n",
       "      <th>patient_gender</th>\n",
       "      <th>patient_hair_color</th>\n",
       "      <th>patient_phototype</th>\n",
       "      <th>primary_tumour_location_coded</th>\n",
       "      <th>random</th>\n",
       "      <th>scenario</th>\n",
       "      <th>slnb_ldn_location_total</th>\n",
       "      <th>specific_death</th>\n",
       "      <th>total_count_slnb_ldn</th>\n",
       "      <th>total_positives_slnb_ldn</th>\n",
       "      <th>train</th>\n",
       "      <th>visceral_metastasis_location</th>\n",
       "      <th>paso_previo_metastasis</th>\n",
       "      <th>LAB_taken_13</th>\n",
       "      <th>LAB_taken_LAB2404</th>\n",
       "      <th>LAB_taken_LAB2405</th>\n",
       "      <th>LAB_taken_LAB2406</th>\n",
       "      <th>LAB_taken_LAB2407</th>\n",
       "      <th>LAB_taken_LAB2419</th>\n",
       "      <th>LAB_taken_LAB2422</th>\n",
       "      <th>LAB_taken_LAB2467</th>\n",
       "      <th>LAB_taken_LAB2469</th>\n",
       "      <th>LAB_taken_LAB2476</th>\n",
       "      <th>LAB_taken_LAB2498</th>\n",
       "      <th>LAB_taken_LAB2544</th>\n",
       "      <th>LAB_taken_LAB2679</th>\n",
       "      <th>LAB_taken_LAB4176</th>\n",
       "      <th>LAB_taken_LABGF_filtrat_glomerular</th>\n",
       "      <th>patient_phototype_unk</th>\n",
       "      <th>patient_phototype_common_2</th>\n",
       "      <th>patient_phototype_common_3</th>\n",
       "      <th>cutaneous_biopsy_associated_nevus_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_histological_subtype_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasion_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_neurotropism_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_predominant_cell_type_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_regression_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_satellitosis_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_ulceration_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasion_ENCODEDspecific_death</th>\n",
       "      <th>patient_eye_color_ENCODEDspecific_death</th>\n",
       "      <th>patient_gender_ENCODEDspecific_death</th>\n",
       "      <th>patient_hair_color_ENCODEDspecific_death</th>\n",
       "      <th>primary_tumour_location_coded_ENCODEDspecific_death</th>\n",
       "      <th>scenario_ENCODEDspecific_death</th>\n",
       "      <th>visceral_metastasis_location_ENCODEDspecific_death</th>\n",
       "      <th>patient_phototype_unk_ENCODEDspecific_death</th>\n",
       "      <th>patient_phototype_common_2_ENCODEDspecific_death</th>\n",
       "      <th>patient_phototype_common_3_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_associated_nevus_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_histological_subtype_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasion_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_neurotropism_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_predominant_cell_type_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_regression_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_satellitosis_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_ulceration_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasion_ENCODEDmonths_survival</th>\n",
       "      <th>patient_eye_color_ENCODEDmonths_survival</th>\n",
       "      <th>patient_gender_ENCODEDmonths_survival</th>\n",
       "      <th>patient_hair_color_ENCODEDmonths_survival</th>\n",
       "      <th>primary_tumour_location_coded_ENCODEDmonths_survival</th>\n",
       "      <th>scenario_ENCODEDmonths_survival</th>\n",
       "      <th>visceral_metastasis_location_ENCODEDmonths_survival</th>\n",
       "      <th>patient_phototype_unk_ENCODEDmonths_survival</th>\n",
       "      <th>patient_phototype_common_2_ENCODEDmonths_survival</th>\n",
       "      <th>patient_phototype_common_3_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_ulcerationabsent</th>\n",
       "      <th>cutaneous_biopsy_ulcerationpresent</th>\n",
       "      <th>cutaneous_biopsy_ulcerationnot_taken</th>\n",
       "      <th>cutaneous_biopsy_satellitosisabsent</th>\n",
       "      <th>cutaneous_biopsy_satellitosisnot_taken</th>\n",
       "      <th>cutaneous_biopsy_satellitosispresent</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasionabsent</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasionnot_taken</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasionpresent</th>\n",
       "      <th>cutaneous_biopsy_neurotropismabsent</th>\n",
       "      <th>cutaneous_biopsy_neurotropismnot_taken</th>\n",
       "      <th>cutaneous_biopsy_neurotropismpresent</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasionabsent</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasionnot_taken</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasionpresent</th>\n",
       "      <th>cutaneous_biopsy_associated_nevusabsent</th>\n",
       "      <th>cutaneous_biopsy_associated_nevusnot_taken</th>\n",
       "      <th>cutaneous_biopsy_associated_nevuspresent</th>\n",
       "      <th>MC1R0.0</th>\n",
       "      <th>MC1R1.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ID2</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>1.840000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>308.000000</td>\n",
       "      <td>95.00000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>76.692145</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>64.251366</td>\n",
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       "      <td>absent</td>\n",
       "      <td>0.90</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>extensive</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>179.0</td>\n",
       "      <td>2.282609</td>\n",
       "      <td>brown</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>trunk</td>\n",
       "      <td>1</td>\n",
       "      <td>scenario2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.173196</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.163685</td>\n",
       "      <td>0.197849</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.146959</td>\n",
       "      <td>0.157744</td>\n",
       "      <td>0.153186</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>108.732990</td>\n",
       "      <td>103.315088</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>113.458564</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>98.379059</td>\n",
       "      <td>95.734050</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>97.872466</td>\n",
       "      <td>98.603250</td>\n",
       "      <td>105.952206</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ID4</td>\n",
       "      <td>7.314448</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>4.468256</td>\n",
       "      <td>2.110679</td>\n",
       "      <td>0.373566</td>\n",
       "      <td>0.172969</td>\n",
       "      <td>14.118785</td>\n",
       "      <td>0.038357</td>\n",
       "      <td>23.83014</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>29.416104</td>\n",
       "      <td>0.708325</td>\n",
       "      <td>338.722488</td>\n",
       "      <td>102.14691</td>\n",
       "      <td>0.963757</td>\n",
       "      <td>207.917879</td>\n",
       "      <td>112.238666</td>\n",
       "      <td>72.643528</td>\n",
       "      <td>1.931261</td>\n",
       "      <td>0.102403</td>\n",
       "      <td>7.603459</td>\n",
       "      <td>82.481334</td>\n",
       "      <td>0.0</td>\n",
       "      <td>105</td>\n",
       "      <td>44.613699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.75</td>\n",
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       "      <td>127.0</td>\n",
       "      <td>2.120606</td>\n",
       "      <td>green</td>\n",
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       "      <td>red</td>\n",
       "      <td>2.0</td>\n",
       "      <td>trunk</td>\n",
       "      <td>1</td>\n",
       "      <td>scenario3</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.102456</td>\n",
       "      <td>0.218349</td>\n",
       "      <td>0.202830</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.092233</td>\n",
       "      <td>0.163934</td>\n",
       "      <td>0.404612</td>\n",
       "      <td>0.172819</td>\n",
       "      <td>0.113636</td>\n",
       "      <td>0.208047</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>0.157744</td>\n",
       "      <td>0.365942</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>108.732990</td>\n",
       "      <td>103.315088</td>\n",
       "      <td>91.007339</td>\n",
       "      <td>97.731132</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>89.300971</td>\n",
       "      <td>93.180328</td>\n",
       "      <td>72.081761</td>\n",
       "      <td>93.095638</td>\n",
       "      <td>99.522727</td>\n",
       "      <td>90.375859</td>\n",
       "      <td>103.261538</td>\n",
       "      <td>98.603250</td>\n",
       "      <td>75.684783</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ID6</td>\n",
       "      <td>9.640000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.00000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>477.000000</td>\n",
       "      <td>81.00000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>91.545283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>182</td>\n",
       "      <td>35.956164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.50</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>partial</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.826087</td>\n",
       "      <td>brown</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>trunk</td>\n",
       "      <td>3</td>\n",
       "      <td>scenario1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.173196</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.092233</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.163685</td>\n",
       "      <td>0.197849</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.146959</td>\n",
       "      <td>0.157744</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>108.732990</td>\n",
       "      <td>103.315088</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>89.300971</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>98.379059</td>\n",
       "      <td>95.734050</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>97.872466</td>\n",
       "      <td>98.603250</td>\n",
       "      <td>98.893617</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID9</td>\n",
       "      <td>10.080000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.00000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>382.000000</td>\n",
       "      <td>90.00000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>193.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.700000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>64.014945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142</td>\n",
       "      <td>33.347945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>0.50</td>\n",
       "      <td>spitzoid</td>\n",
       "      <td>absent</td>\n",
       "      <td>2.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>4.833333</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.034483</td>\n",
       "      <td>blue</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>3.0</td>\n",
       "      <td>lower limbs</td>\n",
       "      <td>4</td>\n",
       "      <td>scenario1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.210756</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.172819</td>\n",
       "      <td>0.135693</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.146959</td>\n",
       "      <td>0.133803</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.157601</td>\n",
       "      <td>0.157601</td>\n",
       "      <td>0.157601</td>\n",
       "      <td>79.917824</td>\n",
       "      <td>104.235294</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>76.549419</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>93.095638</td>\n",
       "      <td>94.404130</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>97.872466</td>\n",
       "      <td>106.847418</td>\n",
       "      <td>98.893617</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>102.882845</td>\n",
       "      <td>102.882845</td>\n",
       "      <td>102.882845</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ID17</td>\n",
       "      <td>6.810000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>283.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>298.000000</td>\n",
       "      <td>100.00000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>208.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>88.316054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>135</td>\n",
       "      <td>32.060274</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>0.60</td>\n",
       "      <td>superficial_spreading</td>\n",
       "      <td>absent</td>\n",
       "      <td>0.0</td>\n",
       "      <td>absent</td>\n",
       "      <td>epitheloid</td>\n",
       "      <td>partial</td>\n",
       "      <td>absent</td>\n",
       "      <td>absent</td>\n",
       "      <td>not_taken</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>brown</td>\n",
       "      <td>female</td>\n",
       "      <td>brown</td>\n",
       "      <td>2.0</td>\n",
       "      <td>lower limbs</td>\n",
       "      <td>3</td>\n",
       "      <td>scenario1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>missing</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.092233</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.172819</td>\n",
       "      <td>0.197849</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.146959</td>\n",
       "      <td>0.133803</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>79.917824</td>\n",
       "      <td>103.315088</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>89.300971</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>93.095638</td>\n",
       "      <td>95.734050</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>97.872466</td>\n",
       "      <td>106.847418</td>\n",
       "      <td>98.893617</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID    LAB1300     LAB1301   LAB1307   LAB1309   LAB1311   LAB1313    LAB1314   LAB1316   LAB2404     LAB2405    LAB2406   LAB2407     LAB2419    LAB2422   LAB2467     LAB2469     LAB2476    LAB2498   LAB2544   LAB2679   LAB4176  LABGF_filtrat_glomerular  MC1R  T0_date        age  count_inv_prec_tumour  count_situ_prec_tumour cutaneous_biopsy_associated_nevus  cutaneous_biopsy_breslow cutaneous_biopsy_histological_subtype cutaneous_biopsy_lymphatic_invasion  cutaneous_biopsy_mitotic_index cutaneous_biopsy_neurotropism cutaneous_biopsy_predominant_cell_type cutaneous_biopsy_regression cutaneous_biopsy_satellitosis cutaneous_biopsy_ulceration cutaneous_biopsy_vascular_invasion  limfocits_per_monocits  months_survival  neutrofils_per_limfocits patient_eye_color patient_gender patient_hair_color  patient_phototype primary_tumour_location_coded  random   scenario  slnb_ldn_location_total  specific_death  total_count_slnb_ldn  total_positives_slnb_ldn  train  \\\n",
       "0   ID2   6.600000  260.000000  4.200000  1.840000  0.190000  0.280000  13.400000  0.020000  21.00000  260.000000  17.000000  0.400000  308.000000   95.00000  0.800000  320.000000   72.000000  65.000000  1.600000  0.120000  7.000000                 76.692145   0.0       75  64.251366                    0.0                     0.0                            absent                      0.90                 superficial_spreading                              absent                             0.0                        absent                             epitheloid                   extensive                        absent                      absent                             absent                9.650000            179.0                  2.282609             brown         female              brown                2.0                         trunk       1  scenario2                      1.0             0.0                   1.0                       0.0      1   \n",
       "1   ID4   7.314448  252.010132  4.468256  2.110679  0.373566  0.172969  14.118785  0.038357  23.83014  252.010132  29.416104  0.708325  338.722488  102.14691  0.963757  207.917879  112.238666  72.643528  1.931261  0.102403  7.603459                 82.481334   0.0      105  44.613699                    0.0                     0.0                            absent                      3.75                 superficial_spreading                           not_taken                             0.0                     not_taken                             epitheloid                     partial                     not_taken                     present                          not_taken                5.666667            127.0                  2.120606             green           male                red                2.0                         trunk       1  scenario3                      1.0             0.0                  16.0                       2.0      1   \n",
       "2   ID6   9.640000  169.000000  6.500000  2.300000  0.500000  0.200000  13.400000  0.000000  22.00000  169.000000  12.000000  0.700000  477.000000   81.00000  0.760000  172.000000   53.000000  73.000000  1.800000  0.040000  6.200000                 91.545283   0.0      182  35.956164                    0.0                     0.0                            absent                      0.50                 superficial_spreading                              absent                             0.0                        absent                             epitheloid                     partial                        absent                      absent                             absent                4.600000             67.0                  2.826087             brown         female              brown                2.0                         trunk       3  scenario1                      1.0             0.0                   3.0                       0.0      1   \n",
       "3   ID9  10.080000  280.000000  5.900000  2.900000  0.600000  0.400000  13.600000  0.000000  19.00000  280.000000   9.000000  0.400000  382.000000   90.00000  1.050000  193.000000   51.000000  71.000000  1.700000  0.080000  6.600000                 64.014945   0.0      142  33.347945                    0.0                     0.0                         not_taken                      0.50                              spitzoid                              absent                             2.0                        absent                             epitheloid                      absent                        absent                      absent                          not_taken                4.833333            111.0                  2.034483              blue         female              brown                3.0                   lower limbs       4  scenario1                      1.0             0.0                   3.0                       0.0      1   \n",
       "4  ID17   6.810000  283.000000  4.000000  2.000000  0.400000  0.200000  12.600000  0.000000  21.00000  283.000000   9.000000  1.000000  298.000000  100.00000  0.800000  208.000000   56.000000  71.000000  1.300000  0.090000  4.800000                 88.316054   0.0      135  32.060274                    0.0                     0.0                         not_taken                      0.60                 superficial_spreading                              absent                             0.0                        absent                             epitheloid                     partial                        absent                      absent                          not_taken                5.000000            118.0                  2.000000             brown         female              brown                2.0                   lower limbs       3  scenario1                      1.0             0.0                   3.0                       0.0      1   \n",
       "\n",
       "  visceral_metastasis_location  paso_previo_metastasis  LAB_taken_13  LAB_taken_LAB2404  LAB_taken_LAB2405  LAB_taken_LAB2406  LAB_taken_LAB2407  LAB_taken_LAB2419  LAB_taken_LAB2422  LAB_taken_LAB2467  LAB_taken_LAB2469  LAB_taken_LAB2476  LAB_taken_LAB2498  LAB_taken_LAB2544  LAB_taken_LAB2679  LAB_taken_LAB4176  LAB_taken_LABGF_filtrat_glomerular patient_phototype_unk patient_phototype_common_2 patient_phototype_common_3  cutaneous_biopsy_associated_nevus_ENCODEDspecific_death  cutaneous_biopsy_histological_subtype_ENCODEDspecific_death  cutaneous_biopsy_lymphatic_invasion_ENCODEDspecific_death  cutaneous_biopsy_neurotropism_ENCODEDspecific_death  cutaneous_biopsy_predominant_cell_type_ENCODEDspecific_death  cutaneous_biopsy_regression_ENCODEDspecific_death  cutaneous_biopsy_satellitosis_ENCODEDspecific_death  cutaneous_biopsy_ulceration_ENCODEDspecific_death  cutaneous_biopsy_vascular_invasion_ENCODEDspecific_death  patient_eye_color_ENCODEDspecific_death  \\\n",
       "0                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     2                          2                          2                                           0.173196                                                 0.102456                                                     0.144524                                                   0.155462                                             0.163607                                                      0.060773                                           0.161828                                             0.099157                                           0.163685                                        0.197849   \n",
       "1                      missing                       0             1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                  1                                   1                     2                          2                          2                                           0.173196                                                 0.102456                                                     0.218349                                                   0.202830                                             0.163607                                                      0.092233                                           0.163934                                             0.404612                                           0.172819                                        0.113636   \n",
       "2                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     2                          2                          2                                           0.173196                                                 0.102456                                                     0.144524                                                   0.155462                                             0.163607                                                      0.092233                                           0.161828                                             0.099157                                           0.163685                                        0.197849   \n",
       "3                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     3                          3                          3                                           0.182870                                                 0.000000                                                     0.144524                                                   0.155462                                             0.163607                                                      0.210756                                           0.161828                                             0.099157                                           0.172819                                        0.135693   \n",
       "4                      missing                       0             0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                  0                                   0                     2                          2                          2                                           0.182870                                                 0.102456                                                     0.144524                                                   0.155462                                             0.163607                                                      0.092233                                           0.161828                                             0.099157                                           0.172819                                        0.197849   \n",
       "\n",
       "   patient_gender_ENCODEDspecific_death  patient_hair_color_ENCODEDspecific_death  primary_tumour_location_coded_ENCODEDspecific_death  scenario_ENCODEDspecific_death  visceral_metastasis_location_ENCODEDspecific_death  patient_phototype_unk_ENCODEDspecific_death  patient_phototype_common_2_ENCODEDspecific_death  patient_phototype_common_3_ENCODEDspecific_death  cutaneous_biopsy_associated_nevus_ENCODEDmonths_survival  cutaneous_biopsy_histological_subtype_ENCODEDmonths_survival  cutaneous_biopsy_lymphatic_invasion_ENCODEDmonths_survival  cutaneous_biopsy_neurotropism_ENCODEDmonths_survival  cutaneous_biopsy_predominant_cell_type_ENCODEDmonths_survival  cutaneous_biopsy_regression_ENCODEDmonths_survival  cutaneous_biopsy_satellitosis_ENCODEDmonths_survival  cutaneous_biopsy_ulceration_ENCODEDmonths_survival  cutaneous_biopsy_vascular_invasion_ENCODEDmonths_survival  patient_eye_color_ENCODEDmonths_survival  patient_gender_ENCODEDmonths_survival  \\\n",
       "0                              0.139947                                  0.146959                                           0.157744                          0.153186                                           0.161473                                      0.133787                                          0.133787                                          0.133787                                         108.732990                                                103.315088                                                     99.582631                                                   97.132053                                             95.739201                                                     113.458564                                           98.426807                                            104.014906                                           98.379059                                         95.734050                             101.536758   \n",
       "1                              0.208047                                  0.184615                                           0.157744                          0.365942                                           0.161473                                      0.133787                                          0.133787                                          0.133787                                         108.732990                                                103.315088                                                     91.007339                                                   97.731132                                             95.739201                                                      89.300971                                           93.180328                                             72.081761                                           93.095638                                         99.522727                              90.375859   \n",
       "2                              0.139947                                  0.146959                                           0.157744                          0.075028                                           0.161473                                      0.133787                                          0.133787                                          0.133787                                         108.732990                                                103.315088                                                     99.582631                                                   97.132053                                             95.739201                                                      89.300971                                           98.426807                                            104.014906                                           98.379059                                         95.734050                             101.536758   \n",
       "3                              0.139947                                  0.146959                                           0.133803                          0.075028                                           0.161473                                      0.157601                                          0.157601                                          0.157601                                          79.917824                                                104.235294                                                     99.582631                                                   97.132053                                             95.739201                                                      76.549419                                           98.426807                                            104.014906                                           93.095638                                         94.404130                             101.536758   \n",
       "4                              0.139947                                  0.146959                                           0.133803                          0.075028                                           0.161473                                      0.133787                                          0.133787                                          0.133787                                          79.917824                                                103.315088                                                     99.582631                                                   97.132053                                             95.739201                                                      89.300971                                           98.426807                                            104.014906                                           93.095638                                         95.734050                             101.536758   \n",
       "\n",
       "   patient_hair_color_ENCODEDmonths_survival  primary_tumour_location_coded_ENCODEDmonths_survival  scenario_ENCODEDmonths_survival  visceral_metastasis_location_ENCODEDmonths_survival  patient_phototype_unk_ENCODEDmonths_survival  patient_phototype_common_2_ENCODEDmonths_survival  patient_phototype_common_3_ENCODEDmonths_survival  cutaneous_biopsy_ulcerationabsent  cutaneous_biopsy_ulcerationpresent  cutaneous_biopsy_ulcerationnot_taken  cutaneous_biopsy_satellitosisabsent  cutaneous_biopsy_satellitosisnot_taken  cutaneous_biopsy_satellitosispresent  cutaneous_biopsy_vascular_invasionabsent  cutaneous_biopsy_vascular_invasionnot_taken  cutaneous_biopsy_vascular_invasionpresent  cutaneous_biopsy_neurotropismabsent  cutaneous_biopsy_neurotropismnot_taken  cutaneous_biopsy_neurotropismpresent  cutaneous_biopsy_lymphatic_invasionabsent  cutaneous_biopsy_lymphatic_invasionnot_taken  cutaneous_biopsy_lymphatic_invasionpresent  cutaneous_biopsy_associated_nevusabsent  \\\n",
       "0                                  97.872466                                          98.603250                          105.952206                                          97.345137                                       91.566893                                          91.566893                                          91.566893                                  1                                   0                                     0                                    1                                       0                                     0                                         1                                            0                                          0                                    1                                       0                                     0                                          1                                             0                                           0                                        1   \n",
       "1                                 103.261538                                          98.603250                           75.684783                                          97.345137                                       91.566893                                          91.566893                                          91.566893                                  0                                   1                                     0                                    0                                       1                                     0                                         0                                            1                                          0                                    0                                       1                                     0                                          0                                             1                                           0                                        1   \n",
       "2                                  97.872466                                          98.603250                           98.893617                                          97.345137                                       91.566893                                          91.566893                                          91.566893                                  1                                   0                                     0                                    1                                       0                                     0                                         1                                            0                                          0                                    1                                       0                                     0                                          1                                             0                                           0                                        1   \n",
       "3                                  97.872466                                         106.847418                           98.893617                                          97.345137                                      102.882845                                         102.882845                                         102.882845                                  1                                   0                                     0                                    1                                       0                                     0                                         0                                            1                                          0                                    1                                       0                                     0                                          1                                             0                                           0                                        0   \n",
       "4                                  97.872466                                         106.847418                           98.893617                                          97.345137                                       91.566893                                          91.566893                                          91.566893                                  1                                   0                                     0                                    1                                       0                                     0                                         0                                            1                                          0                                    1                                       0                                     0                                          1                                             0                                           0                                        0   \n",
       "\n",
       "   cutaneous_biopsy_associated_nevusnot_taken  cutaneous_biopsy_associated_nevuspresent  MC1R0.0  MC1R1.0  \n",
       "0                                           0                                         0        1        0  \n",
       "1                                           0                                         0        1        0  \n",
       "2                                           0                                         0        1        0  \n",
       "3                                           1                                         0        1        0  \n",
       "4                                           1                                         0        1        0  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bined_splited_target_encoding(df,query_train,query_test,element,target,bin_num):\n",
    "    element_bin = element+'_bin'\n",
    "    df[element_bin] = pd.cut(df[element],bin_num,labels=np.array(range(0,bin_num)))\n",
    "    return splited_target_encoding(df,query_train,query_test,element_bin,target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.41825758003022473\n"
     ]
    }
   ],
   "source": [
    "df = bined_splited_target_encoding(df,'train==1','train==0','cutaneous_biopsy_breslow','specific_death',30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_breslow_w']=scipy.stats.mstats.winsorize(df['cutaneous_biopsy_breslow'], limits=0.05,inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LAB1300</th>\n",
       "      <th>LAB1301</th>\n",
       "      <th>LAB1307</th>\n",
       "      <th>LAB1309</th>\n",
       "      <th>LAB1311</th>\n",
       "      <th>LAB1313</th>\n",
       "      <th>LAB1314</th>\n",
       "      <th>LAB1316</th>\n",
       "      <th>LAB2404</th>\n",
       "      <th>LAB2405</th>\n",
       "      <th>LAB2406</th>\n",
       "      <th>LAB2407</th>\n",
       "      <th>LAB2419</th>\n",
       "      <th>LAB2422</th>\n",
       "      <th>LAB2467</th>\n",
       "      <th>LAB2469</th>\n",
       "      <th>LAB2476</th>\n",
       "      <th>LAB2498</th>\n",
       "      <th>LAB2544</th>\n",
       "      <th>LAB2679</th>\n",
       "      <th>LAB4176</th>\n",
       "      <th>LABGF_filtrat_glomerular</th>\n",
       "      <th>MC1R</th>\n",
       "      <th>T0_date</th>\n",
       "      <th>age</th>\n",
       "      <th>count_inv_prec_tumour</th>\n",
       "      <th>count_situ_prec_tumour</th>\n",
       "      <th>cutaneous_biopsy_breslow</th>\n",
       "      <th>cutaneous_biopsy_mitotic_index</th>\n",
       "      <th>limfocits_per_monocits</th>\n",
       "      <th>months_survival</th>\n",
       "      <th>neutrofils_per_limfocits</th>\n",
       "      <th>patient_phototype</th>\n",
       "      <th>random</th>\n",
       "      <th>slnb_ldn_location_total</th>\n",
       "      <th>specific_death</th>\n",
       "      <th>total_count_slnb_ldn</th>\n",
       "      <th>total_positives_slnb_ldn</th>\n",
       "      <th>train</th>\n",
       "      <th>paso_previo_metastasis</th>\n",
       "      <th>LAB_taken_13</th>\n",
       "      <th>LAB_taken_LAB2404</th>\n",
       "      <th>LAB_taken_LAB2405</th>\n",
       "      <th>LAB_taken_LAB2406</th>\n",
       "      <th>LAB_taken_LAB2407</th>\n",
       "      <th>LAB_taken_LAB2419</th>\n",
       "      <th>LAB_taken_LAB2422</th>\n",
       "      <th>LAB_taken_LAB2467</th>\n",
       "      <th>LAB_taken_LAB2469</th>\n",
       "      <th>LAB_taken_LAB2476</th>\n",
       "      <th>LAB_taken_LAB2498</th>\n",
       "      <th>LAB_taken_LAB2544</th>\n",
       "      <th>LAB_taken_LAB2679</th>\n",
       "      <th>LAB_taken_LAB4176</th>\n",
       "      <th>LAB_taken_LABGF_filtrat_glomerular</th>\n",
       "      <th>cutaneous_biopsy_associated_nevus_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_histological_subtype_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasion_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_neurotropism_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_predominant_cell_type_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_regression_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_satellitosis_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_ulceration_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasion_ENCODEDspecific_death</th>\n",
       "      <th>patient_eye_color_ENCODEDspecific_death</th>\n",
       "      <th>patient_gender_ENCODEDspecific_death</th>\n",
       "      <th>patient_hair_color_ENCODEDspecific_death</th>\n",
       "      <th>primary_tumour_location_coded_ENCODEDspecific_death</th>\n",
       "      <th>scenario_ENCODEDspecific_death</th>\n",
       "      <th>visceral_metastasis_location_ENCODEDspecific_death</th>\n",
       "      <th>patient_phototype_unk_ENCODEDspecific_death</th>\n",
       "      <th>patient_phototype_common_2_ENCODEDspecific_death</th>\n",
       "      <th>patient_phototype_common_3_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_associated_nevus_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_histological_subtype_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasion_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_neurotropism_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_predominant_cell_type_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_regression_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_satellitosis_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_ulceration_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasion_ENCODEDmonths_survival</th>\n",
       "      <th>patient_eye_color_ENCODEDmonths_survival</th>\n",
       "      <th>patient_gender_ENCODEDmonths_survival</th>\n",
       "      <th>patient_hair_color_ENCODEDmonths_survival</th>\n",
       "      <th>primary_tumour_location_coded_ENCODEDmonths_survival</th>\n",
       "      <th>scenario_ENCODEDmonths_survival</th>\n",
       "      <th>visceral_metastasis_location_ENCODEDmonths_survival</th>\n",
       "      <th>patient_phototype_unk_ENCODEDmonths_survival</th>\n",
       "      <th>patient_phototype_common_2_ENCODEDmonths_survival</th>\n",
       "      <th>patient_phototype_common_3_ENCODEDmonths_survival</th>\n",
       "      <th>cutaneous_biopsy_ulcerationabsent</th>\n",
       "      <th>cutaneous_biopsy_ulcerationpresent</th>\n",
       "      <th>cutaneous_biopsy_ulcerationnot_taken</th>\n",
       "      <th>cutaneous_biopsy_satellitosisabsent</th>\n",
       "      <th>cutaneous_biopsy_satellitosisnot_taken</th>\n",
       "      <th>cutaneous_biopsy_satellitosispresent</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasionabsent</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasionnot_taken</th>\n",
       "      <th>cutaneous_biopsy_vascular_invasionpresent</th>\n",
       "      <th>cutaneous_biopsy_neurotropismabsent</th>\n",
       "      <th>cutaneous_biopsy_neurotropismnot_taken</th>\n",
       "      <th>cutaneous_biopsy_neurotropismpresent</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasionabsent</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasionnot_taken</th>\n",
       "      <th>cutaneous_biopsy_lymphatic_invasionpresent</th>\n",
       "      <th>cutaneous_biopsy_associated_nevusabsent</th>\n",
       "      <th>cutaneous_biopsy_associated_nevusnot_taken</th>\n",
       "      <th>cutaneous_biopsy_associated_nevuspresent</th>\n",
       "      <th>MC1R0.0</th>\n",
       "      <th>MC1R1.0</th>\n",
       "      <th>cutaneous_biopsy_breslow_bin_ENCODEDspecific_death</th>\n",
       "      <th>cutaneous_biopsy_breslow_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>2148.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>2148.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.00000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3067.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3067.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "      <td>3068.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.314448</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>4.468256</td>\n",
       "      <td>2.110679</td>\n",
       "      <td>0.373566</td>\n",
       "      <td>0.172969</td>\n",
       "      <td>14.118785</td>\n",
       "      <td>0.038357</td>\n",
       "      <td>23.830140</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>29.416104</td>\n",
       "      <td>0.708325</td>\n",
       "      <td>338.722488</td>\n",
       "      <td>102.146910</td>\n",
       "      <td>0.963757</td>\n",
       "      <td>207.917879</td>\n",
       "      <td>112.238666</td>\n",
       "      <td>72.643528</td>\n",
       "      <td>1.931261</td>\n",
       "      <td>0.102403</td>\n",
       "      <td>7.603459</td>\n",
       "      <td>82.481334</td>\n",
       "      <td>0.158409</td>\n",
       "      <td>111.945893</td>\n",
       "      <td>54.231454</td>\n",
       "      <td>0.026402</td>\n",
       "      <td>0.023468</td>\n",
       "      <td>1.994968</td>\n",
       "      <td>1.583931</td>\n",
       "      <td>5.829190</td>\n",
       "      <td>96.242086</td>\n",
       "      <td>2.338329</td>\n",
       "      <td>2.437419</td>\n",
       "      <td>3.013038</td>\n",
       "      <td>1.012712</td>\n",
       "      <td>0.172253</td>\n",
       "      <td>4.519231</td>\n",
       "      <td>0.359518</td>\n",
       "      <td>0.700130</td>\n",
       "      <td>0.020535</td>\n",
       "      <td>0.356584</td>\n",
       "      <td>0.370600</td>\n",
       "      <td>0.370274</td>\n",
       "      <td>0.421121</td>\n",
       "      <td>0.377445</td>\n",
       "      <td>0.386897</td>\n",
       "      <td>0.356584</td>\n",
       "      <td>0.356258</td>\n",
       "      <td>0.372881</td>\n",
       "      <td>0.374511</td>\n",
       "      <td>0.375489</td>\n",
       "      <td>0.423403</td>\n",
       "      <td>0.41395</td>\n",
       "      <td>0.423403</td>\n",
       "      <td>0.445567</td>\n",
       "      <td>0.172291</td>\n",
       "      <td>0.172091</td>\n",
       "      <td>0.172043</td>\n",
       "      <td>0.172048</td>\n",
       "      <td>0.172769</td>\n",
       "      <td>0.172179</td>\n",
       "      <td>0.172324</td>\n",
       "      <td>0.171728</td>\n",
       "      <td>0.172384</td>\n",
       "      <td>0.171763</td>\n",
       "      <td>0.172665</td>\n",
       "      <td>0.172339</td>\n",
       "      <td>0.172004</td>\n",
       "      <td>0.171819</td>\n",
       "      <td>0.172496</td>\n",
       "      <td>0.171906</td>\n",
       "      <td>0.171906</td>\n",
       "      <td>0.171906</td>\n",
       "      <td>96.349558</td>\n",
       "      <td>96.226651</td>\n",
       "      <td>96.264717</td>\n",
       "      <td>96.263992</td>\n",
       "      <td>96.371557</td>\n",
       "      <td>96.291655</td>\n",
       "      <td>96.244918</td>\n",
       "      <td>96.301886</td>\n",
       "      <td>96.239034</td>\n",
       "      <td>96.288204</td>\n",
       "      <td>96.174579</td>\n",
       "      <td>96.245312</td>\n",
       "      <td>96.230327</td>\n",
       "      <td>96.390939</td>\n",
       "      <td>96.237025</td>\n",
       "      <td>96.205357</td>\n",
       "      <td>96.205357</td>\n",
       "      <td>96.205357</td>\n",
       "      <td>0.721643</td>\n",
       "      <td>0.220991</td>\n",
       "      <td>0.057366</td>\n",
       "      <td>0.755541</td>\n",
       "      <td>0.225228</td>\n",
       "      <td>0.019231</td>\n",
       "      <td>0.704694</td>\n",
       "      <td>0.274772</td>\n",
       "      <td>0.020535</td>\n",
       "      <td>0.777053</td>\n",
       "      <td>0.196545</td>\n",
       "      <td>0.026402</td>\n",
       "      <td>0.722621</td>\n",
       "      <td>0.249022</td>\n",
       "      <td>0.028357</td>\n",
       "      <td>0.456975</td>\n",
       "      <td>0.398957</td>\n",
       "      <td>0.144068</td>\n",
       "      <td>0.841591</td>\n",
       "      <td>0.158409</td>\n",
       "      <td>0.174459</td>\n",
       "      <td>1.994968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.805866</td>\n",
       "      <td>49.029458</td>\n",
       "      <td>1.524091</td>\n",
       "      <td>1.929019</td>\n",
       "      <td>0.129385</td>\n",
       "      <td>0.110248</td>\n",
       "      <td>1.166038</td>\n",
       "      <td>0.103243</td>\n",
       "      <td>9.050384</td>\n",
       "      <td>49.029458</td>\n",
       "      <td>40.121593</td>\n",
       "      <td>0.297855</td>\n",
       "      <td>56.955188</td>\n",
       "      <td>24.259247</td>\n",
       "      <td>0.339600</td>\n",
       "      <td>31.916938</td>\n",
       "      <td>68.796844</td>\n",
       "      <td>3.589170</td>\n",
       "      <td>1.319795</td>\n",
       "      <td>0.283679</td>\n",
       "      <td>3.083257</td>\n",
       "      <td>13.980997</td>\n",
       "      <td>0.365184</td>\n",
       "      <td>60.988774</td>\n",
       "      <td>17.280302</td>\n",
       "      <td>0.188400</td>\n",
       "      <td>0.173488</td>\n",
       "      <td>1.890258</td>\n",
       "      <td>2.841830</td>\n",
       "      <td>1.622174</td>\n",
       "      <td>65.760954</td>\n",
       "      <td>1.457288</td>\n",
       "      <td>0.689240</td>\n",
       "      <td>1.416227</td>\n",
       "      <td>0.128324</td>\n",
       "      <td>0.377688</td>\n",
       "      <td>6.020990</td>\n",
       "      <td>1.408953</td>\n",
       "      <td>0.458275</td>\n",
       "      <td>0.141843</td>\n",
       "      <td>0.479069</td>\n",
       "      <td>0.483044</td>\n",
       "      <td>0.482957</td>\n",
       "      <td>0.493819</td>\n",
       "      <td>0.484827</td>\n",
       "      <td>0.487119</td>\n",
       "      <td>0.479069</td>\n",
       "      <td>0.478971</td>\n",
       "      <td>0.483650</td>\n",
       "      <td>0.484075</td>\n",
       "      <td>0.484328</td>\n",
       "      <td>0.494179</td>\n",
       "      <td>0.49262</td>\n",
       "      <td>0.494179</td>\n",
       "      <td>0.497109</td>\n",
       "      <td>0.013933</td>\n",
       "      <td>0.117195</td>\n",
       "      <td>0.059530</td>\n",
       "      <td>0.046600</td>\n",
       "      <td>0.030187</td>\n",
       "      <td>0.055966</td>\n",
       "      <td>0.071516</td>\n",
       "      <td>0.125719</td>\n",
       "      <td>0.042587</td>\n",
       "      <td>0.035984</td>\n",
       "      <td>0.034030</td>\n",
       "      <td>0.061892</td>\n",
       "      <td>0.058021</td>\n",
       "      <td>0.137553</td>\n",
       "      <td>0.093233</td>\n",
       "      <td>0.060117</td>\n",
       "      <td>0.060117</td>\n",
       "      <td>0.060117</td>\n",
       "      <td>13.543861</td>\n",
       "      <td>12.966714</td>\n",
       "      <td>7.525314</td>\n",
       "      <td>6.011470</td>\n",
       "      <td>7.594833</td>\n",
       "      <td>15.899763</td>\n",
       "      <td>7.313048</td>\n",
       "      <td>13.168882</td>\n",
       "      <td>5.115608</td>\n",
       "      <td>4.452771</td>\n",
       "      <td>5.577088</td>\n",
       "      <td>5.713259</td>\n",
       "      <td>9.486483</td>\n",
       "      <td>13.888915</td>\n",
       "      <td>9.357879</td>\n",
       "      <td>10.822187</td>\n",
       "      <td>10.822187</td>\n",
       "      <td>10.822187</td>\n",
       "      <td>0.448263</td>\n",
       "      <td>0.414982</td>\n",
       "      <td>0.232579</td>\n",
       "      <td>0.429836</td>\n",
       "      <td>0.417801</td>\n",
       "      <td>0.137358</td>\n",
       "      <td>0.456255</td>\n",
       "      <td>0.446472</td>\n",
       "      <td>0.141843</td>\n",
       "      <td>0.416291</td>\n",
       "      <td>0.397450</td>\n",
       "      <td>0.160352</td>\n",
       "      <td>0.447778</td>\n",
       "      <td>0.432517</td>\n",
       "      <td>0.166018</td>\n",
       "      <td>0.498227</td>\n",
       "      <td>0.489764</td>\n",
       "      <td>0.351216</td>\n",
       "      <td>0.365184</td>\n",
       "      <td>0.365184</td>\n",
       "      <td>0.160609</td>\n",
       "      <td>1.890258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.800000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>169.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>58.379878</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.920765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.140127</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060773</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.163685</td>\n",
       "      <td>0.105263</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>79.917824</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>57.883333</td>\n",
       "      <td>59.793103</td>\n",
       "      <td>57.272727</td>\n",
       "      <td>76.549419</td>\n",
       "      <td>46.414634</td>\n",
       "      <td>72.081761</td>\n",
       "      <td>64.860465</td>\n",
       "      <td>69.083333</td>\n",
       "      <td>90.375859</td>\n",
       "      <td>86.076923</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>40.554054</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.780000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>13.700000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>228.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>314.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>0.870000</td>\n",
       "      <td>196.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>77.875316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>40.221918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.069371</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>1.883049</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173196</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.092233</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.163685</td>\n",
       "      <td>0.135693</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.146959</td>\n",
       "      <td>0.133803</td>\n",
       "      <td>0.075028</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>0.133787</td>\n",
       "      <td>79.917824</td>\n",
       "      <td>79.084034</td>\n",
       "      <td>91.007339</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>76.549419</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>92.578125</td>\n",
       "      <td>93.095638</td>\n",
       "      <td>95.734050</td>\n",
       "      <td>90.375859</td>\n",
       "      <td>94.957784</td>\n",
       "      <td>97.294118</td>\n",
       "      <td>98.893617</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.079122</td>\n",
       "      <td>0.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.314448</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>4.468256</td>\n",
       "      <td>2.110679</td>\n",
       "      <td>0.373566</td>\n",
       "      <td>0.172969</td>\n",
       "      <td>14.118785</td>\n",
       "      <td>0.038357</td>\n",
       "      <td>23.830140</td>\n",
       "      <td>252.010132</td>\n",
       "      <td>29.416104</td>\n",
       "      <td>0.708325</td>\n",
       "      <td>338.722488</td>\n",
       "      <td>102.146910</td>\n",
       "      <td>0.963757</td>\n",
       "      <td>207.917879</td>\n",
       "      <td>112.238666</td>\n",
       "      <td>72.643528</td>\n",
       "      <td>1.931261</td>\n",
       "      <td>0.102403</td>\n",
       "      <td>7.603459</td>\n",
       "      <td>82.481334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>54.456902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>2.120606</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.173196</td>\n",
       "      <td>0.102456</td>\n",
       "      <td>0.144524</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.202999</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.163685</td>\n",
       "      <td>0.197849</td>\n",
       "      <td>0.139947</td>\n",
       "      <td>0.146959</td>\n",
       "      <td>0.157744</td>\n",
       "      <td>0.153186</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.157601</td>\n",
       "      <td>0.157601</td>\n",
       "      <td>0.157601</td>\n",
       "      <td>102.573248</td>\n",
       "      <td>103.315088</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>89.300971</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>98.379059</td>\n",
       "      <td>95.734050</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>97.872466</td>\n",
       "      <td>98.603250</td>\n",
       "      <td>98.893617</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>91.566893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.079122</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>2.140000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>14.500000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>23.830140</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>29.416104</td>\n",
       "      <td>0.708325</td>\n",
       "      <td>341.250000</td>\n",
       "      <td>102.146910</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>112.238666</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>1.931261</td>\n",
       "      <td>0.102403</td>\n",
       "      <td>7.603459</td>\n",
       "      <td>82.481334</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>67.792466</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.308355</td>\n",
       "      <td>139.250000</td>\n",
       "      <td>2.365555</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.210526</td>\n",
       "      <td>0.218349</td>\n",
       "      <td>0.155462</td>\n",
       "      <td>0.163607</td>\n",
       "      <td>0.210756</td>\n",
       "      <td>0.161828</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>0.172819</td>\n",
       "      <td>0.197849</td>\n",
       "      <td>0.208047</td>\n",
       "      <td>0.184615</td>\n",
       "      <td>0.157744</td>\n",
       "      <td>0.153186</td>\n",
       "      <td>0.161473</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>108.732990</td>\n",
       "      <td>103.315088</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.132053</td>\n",
       "      <td>95.739201</td>\n",
       "      <td>111.573241</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>98.379059</td>\n",
       "      <td>95.734050</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>97.872466</td>\n",
       "      <td>98.603250</td>\n",
       "      <td>105.952206</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>102.882845</td>\n",
       "      <td>102.882845</td>\n",
       "      <td>102.882845</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>123.900000</td>\n",
       "      <td>722.000000</td>\n",
       "      <td>44.200000</td>\n",
       "      <td>101.700000</td>\n",
       "      <td>3.340000</td>\n",
       "      <td>2.490000</td>\n",
       "      <td>19.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>722.000000</td>\n",
       "      <td>1746.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>1230.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>377.000000</td>\n",
       "      <td>1942.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>42.400000</td>\n",
       "      <td>9.540000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>234.446109</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>97.597260</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.650000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>42.166667</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.182870</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.431034</td>\n",
       "      <td>0.291139</td>\n",
       "      <td>0.210756</td>\n",
       "      <td>0.682927</td>\n",
       "      <td>0.404612</td>\n",
       "      <td>0.465116</td>\n",
       "      <td>0.197849</td>\n",
       "      <td>0.208047</td>\n",
       "      <td>0.304843</td>\n",
       "      <td>0.481481</td>\n",
       "      <td>0.729730</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>108.732990</td>\n",
       "      <td>111.350877</td>\n",
       "      <td>99.582631</td>\n",
       "      <td>97.731132</td>\n",
       "      <td>134.935484</td>\n",
       "      <td>113.458564</td>\n",
       "      <td>98.426807</td>\n",
       "      <td>104.014906</td>\n",
       "      <td>98.379059</td>\n",
       "      <td>118.052632</td>\n",
       "      <td>101.536758</td>\n",
       "      <td>231.000000</td>\n",
       "      <td>106.847418</td>\n",
       "      <td>105.952206</td>\n",
       "      <td>97.345137</td>\n",
       "      <td>125.493506</td>\n",
       "      <td>125.493506</td>\n",
       "      <td>125.493506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LAB1300      LAB1301      LAB1307      LAB1309      LAB1311      LAB1313      LAB1314      LAB1316      LAB2404      LAB2405      LAB2406      LAB2407      LAB2419      LAB2422      LAB2467      LAB2469      LAB2476      LAB2498      LAB2544      LAB2679      LAB4176  LABGF_filtrat_glomerular         MC1R      T0_date          age  count_inv_prec_tumour  count_situ_prec_tumour  cutaneous_biopsy_breslow  cutaneous_biopsy_mitotic_index  limfocits_per_monocits  months_survival  neutrofils_per_limfocits  patient_phototype       random  slnb_ldn_location_total  specific_death  total_count_slnb_ldn  total_positives_slnb_ldn        train  paso_previo_metastasis  LAB_taken_13  LAB_taken_LAB2404  LAB_taken_LAB2405  LAB_taken_LAB2406  LAB_taken_LAB2407  LAB_taken_LAB2419  LAB_taken_LAB2422  LAB_taken_LAB2467  LAB_taken_LAB2469  LAB_taken_LAB2476  LAB_taken_LAB2498  LAB_taken_LAB2544  LAB_taken_LAB2679  LAB_taken_LAB4176  LAB_taken_LABGF_filtrat_glomerular  \\\n",
       "count  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000  3068.000000               3068.000000  3068.000000  3068.000000  3068.000000            3068.000000             3068.000000               3068.000000                     3068.000000             3068.000000      2148.000000               3068.000000        3068.000000  3068.000000              3068.000000     2148.000000           3068.000000               3068.000000  3068.000000             3068.000000   3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000        3068.000000         3068.00000        3068.000000                         3068.000000   \n",
       "mean      7.314448   252.010132     4.468256     2.110679     0.373566     0.172969    14.118785     0.038357    23.830140   252.010132    29.416104     0.708325   338.722488   102.146910     0.963757   207.917879   112.238666    72.643528     1.931261     0.102403     7.603459                 82.481334     0.158409   111.945893    54.231454               0.026402                0.023468                  1.994968                        1.583931                5.829190        96.242086                  2.338329           2.437419     3.013038                 1.012712        0.172253              4.519231                  0.359518     0.700130                0.020535      0.356584           0.370600           0.370274           0.421121           0.377445           0.386897           0.356584           0.356258           0.372881           0.374511           0.375489           0.423403            0.41395           0.423403                            0.445567   \n",
       "std       2.805866    49.029458     1.524091     1.929019     0.129385     0.110248     1.166038     0.103243     9.050384    49.029458    40.121593     0.297855    56.955188    24.259247     0.339600    31.916938    68.796844     3.589170     1.319795     0.283679     3.083257                 13.980997     0.365184    60.988774    17.280302               0.188400                0.173488                  1.890258                        2.841830                1.622174        65.760954                  1.457288           0.689240     1.416227                 0.128324        0.377688              6.020990                  1.408953     0.458275                0.141843      0.479069           0.483044           0.482957           0.493819           0.484827           0.487119           0.479069           0.478971           0.483650           0.484075           0.484328           0.494179            0.49262           0.494179                            0.497109   \n",
       "min       1.800000   169.000000     0.140000     0.180000     0.100000     0.000000     7.900000     0.000000     4.000000   169.000000     9.000000     0.200000   258.000000    80.000000     0.300000    75.000000    49.000000    54.000000     0.100000     0.000000     1.500000                 58.379878     0.000000     1.000000     6.920765               0.000000                0.000000                  0.400000                        0.000000                3.000000         0.000000                  0.038997           1.000000     1.000000                 1.000000        0.000000              1.000000                  0.000000     0.000000                0.000000      0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000            0.00000           0.000000                            0.000000   \n",
       "25%       6.500000   228.000000     3.800000     1.780000     0.300000     0.100000    13.700000     0.020000    21.000000   228.000000    17.000000     0.600000   314.000000    92.000000     0.870000   196.000000    81.000000    71.000000     1.600000     0.060000     6.700000                 77.875316     0.000000    59.000000    40.221918               0.000000                0.000000                  0.700000                        0.000000                5.069371        44.000000                  1.883049           2.000000     2.000000                 1.000000        0.000000              2.000000                  0.000000     0.000000                0.000000      0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000            0.00000           0.000000                            0.000000   \n",
       "50%       7.314448   252.010132     4.468256     2.110679     0.373566     0.172969    14.118785     0.038357    23.830140   252.010132    29.416104     0.708325   338.722488   102.146910     0.963757   207.917879   112.238666    72.643528     1.931261     0.102403     7.603459                 82.481334     0.000000   112.000000    54.456902               0.000000                0.000000                  1.200000                        0.000000                5.666667        82.000000                  2.120606           2.000000     3.000000                 1.000000        0.000000              3.000000                  0.000000     1.000000                0.000000      0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000           0.000000            0.00000           0.000000                            0.000000   \n",
       "75%       7.400000   262.000000     4.600000     2.140000     0.400000     0.190000    14.500000     0.040000    23.830140   262.000000    29.416104     0.708325   341.250000   102.146910     0.990000   216.000000   112.238666    74.000000     1.931261     0.102403     7.603459                 82.481334     0.000000   165.000000    67.792466               0.000000                0.000000                  2.500000                        2.000000                6.308355       139.250000                  2.365555           3.000000     4.000000                 1.000000        0.000000              3.000000                  0.000000     1.000000                0.000000      1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000            1.00000           1.000000                            1.000000   \n",
       "max     123.900000   722.000000    44.200000   101.700000     3.340000     2.490000    19.500000     5.500000   172.000000   722.000000  1746.000000    10.500000  1230.000000   416.000000    10.000000   377.000000  1942.000000    94.000000    42.400000     9.540000   105.000000                234.446109     1.000000   216.000000    97.597260               4.000000                3.000000                  7.100000                       10.000000                9.650000       253.000000                 42.166667           5.000000     5.000000                 4.000000        1.000000             88.000000                 31.000000     1.000000                1.000000      1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000           1.000000            1.00000           1.000000                            1.000000   \n",
       "\n",
       "       cutaneous_biopsy_associated_nevus_ENCODEDspecific_death  cutaneous_biopsy_histological_subtype_ENCODEDspecific_death  cutaneous_biopsy_lymphatic_invasion_ENCODEDspecific_death  cutaneous_biopsy_neurotropism_ENCODEDspecific_death  cutaneous_biopsy_predominant_cell_type_ENCODEDspecific_death  cutaneous_biopsy_regression_ENCODEDspecific_death  cutaneous_biopsy_satellitosis_ENCODEDspecific_death  cutaneous_biopsy_ulceration_ENCODEDspecific_death  cutaneous_biopsy_vascular_invasion_ENCODEDspecific_death  patient_eye_color_ENCODEDspecific_death  patient_gender_ENCODEDspecific_death  patient_hair_color_ENCODEDspecific_death  primary_tumour_location_coded_ENCODEDspecific_death  scenario_ENCODEDspecific_death  visceral_metastasis_location_ENCODEDspecific_death  patient_phototype_unk_ENCODEDspecific_death  patient_phototype_common_2_ENCODEDspecific_death  patient_phototype_common_3_ENCODEDspecific_death  cutaneous_biopsy_associated_nevus_ENCODEDmonths_survival  \\\n",
       "count                                        3068.000000                                              3068.000000                                                  3068.000000                                                3068.000000                                          3068.000000                                                   3068.000000                                        3068.000000                                          3068.000000                                        3068.000000                                     3068.000000                           3068.000000                               3068.000000                                        3068.000000                       3068.000000                                        3067.000000                                   3068.000000                                       3068.000000                                       3068.000000                                        3068.000000          \n",
       "mean                                            0.172291                                                 0.172091                                                     0.172043                                                   0.172048                                             0.172769                                                      0.172179                                           0.172324                                             0.171728                                           0.172384                                        0.171763                              0.172665                                  0.172339                                           0.172004                          0.171819                                           0.172496                                      0.171906                                          0.171906                                          0.171906                                          96.349558          \n",
       "std                                             0.013933                                                 0.117195                                                     0.059530                                                   0.046600                                             0.030187                                                      0.055966                                           0.071516                                             0.125719                                           0.042587                                        0.035984                              0.034030                                  0.061892                                           0.058021                          0.137553                                           0.093233                                      0.060117                                          0.060117                                          0.060117                                          13.543861          \n",
       "min                                             0.140127                                                 0.000000                                                     0.144524                                                   0.155462                                             0.000000                                                      0.060773                                           0.161828                                             0.099157                                           0.163685                                        0.105263                              0.139947                                  0.000000                                           0.000000                          0.075028                                           0.161473                                      0.133787                                          0.133787                                          0.133787                                          79.917824          \n",
       "25%                                             0.173196                                                 0.102456                                                     0.144524                                                   0.155462                                             0.163607                                                      0.092233                                           0.161828                                             0.099157                                           0.163685                                        0.135693                              0.139947                                  0.146959                                           0.133803                          0.075028                                           0.161473                                      0.133787                                          0.133787                                          0.133787                                          79.917824          \n",
       "50%                                             0.173196                                                 0.102456                                                     0.144524                                                   0.155462                                             0.163607                                                      0.202999                                           0.161828                                             0.099157                                           0.163685                                        0.197849                              0.139947                                  0.146959                                           0.157744                          0.153186                                           0.161473                                      0.157601                                          0.157601                                          0.157601                                         102.573248          \n",
       "75%                                             0.182870                                                 0.210526                                                     0.218349                                                   0.155462                                             0.163607                                                      0.210756                                           0.161828                                             0.187500                                           0.172819                                        0.197849                              0.208047                                  0.184615                                           0.157744                          0.153186                                           0.161473                                      0.166667                                          0.166667                                          0.166667                                         108.732990          \n",
       "max                                             0.182870                                                 0.428571                                                     0.466667                                                   0.431034                                             0.291139                                                      0.210756                                           0.682927                                             0.404612                                           0.465116                                        0.197849                              0.208047                                  0.304843                                           0.481481                          0.729730                                           1.000000                                      0.500000                                          0.500000                                          0.500000                                         108.732990          \n",
       "\n",
       "       cutaneous_biopsy_histological_subtype_ENCODEDmonths_survival  cutaneous_biopsy_lymphatic_invasion_ENCODEDmonths_survival  cutaneous_biopsy_neurotropism_ENCODEDmonths_survival  cutaneous_biopsy_predominant_cell_type_ENCODEDmonths_survival  cutaneous_biopsy_regression_ENCODEDmonths_survival  cutaneous_biopsy_satellitosis_ENCODEDmonths_survival  cutaneous_biopsy_ulceration_ENCODEDmonths_survival  cutaneous_biopsy_vascular_invasion_ENCODEDmonths_survival  patient_eye_color_ENCODEDmonths_survival  patient_gender_ENCODEDmonths_survival  patient_hair_color_ENCODEDmonths_survival  primary_tumour_location_coded_ENCODEDmonths_survival  scenario_ENCODEDmonths_survival  visceral_metastasis_location_ENCODEDmonths_survival  patient_phototype_unk_ENCODEDmonths_survival  patient_phototype_common_2_ENCODEDmonths_survival  patient_phototype_common_3_ENCODEDmonths_survival  cutaneous_biopsy_ulcerationabsent  cutaneous_biopsy_ulcerationpresent  cutaneous_biopsy_ulcerationnot_taken  \\\n",
       "count                                        3068.000000                                                   3068.000000                                                 3068.000000                                           3068.000000                                                    3068.000000                                         3068.000000                                           3068.000000                                         3068.000000                                       3068.000000                            3068.000000                                3068.000000                                        3068.000000                         3068.000000                                        3067.000000                                     3068.000000                                        3068.000000                                        3068.000000                        3068.000000                         3068.000000                           3068.000000   \n",
       "mean                                           96.226651                                                     96.264717                                                   96.263992                                             96.371557                                                      96.291655                                           96.244918                                             96.301886                                           96.239034                                         96.288204                              96.174579                                  96.245312                                          96.230327                           96.390939                                          96.237025                                       96.205357                                          96.205357                                          96.205357                           0.721643                            0.220991                              0.057366   \n",
       "std                                            12.966714                                                      7.525314                                                    6.011470                                              7.594833                                                      15.899763                                            7.313048                                             13.168882                                            5.115608                                          4.452771                               5.577088                                   5.713259                                           9.486483                           13.888915                                           9.357879                                       10.822187                                          10.822187                                          10.822187                           0.448263                            0.414982                              0.232579   \n",
       "min                                            47.500000                                                     57.883333                                                   59.793103                                             57.272727                                                      76.549419                                           46.414634                                             72.081761                                           64.860465                                         69.083333                              90.375859                                  86.076923                                          35.000000                           40.554054                                           8.666667                                       48.500000                                          48.500000                                          48.500000                           0.000000                            0.000000                              0.000000   \n",
       "25%                                            79.084034                                                     91.007339                                                   97.132053                                             95.739201                                                      76.549419                                           98.426807                                             92.578125                                           93.095638                                         95.734050                              90.375859                                  94.957784                                          97.294118                           98.893617                                          97.345137                                       91.566893                                          91.566893                                          91.566893                           0.000000                            0.000000                              0.000000   \n",
       "50%                                           103.315088                                                     99.582631                                                   97.132053                                             95.739201                                                      89.300971                                           98.426807                                            104.014906                                           98.379059                                         95.734050                             101.536758                                  97.872466                                          98.603250                           98.893617                                          97.345137                                       91.566893                                          91.566893                                          91.566893                           1.000000                            0.000000                              0.000000   \n",
       "75%                                           103.315088                                                     99.582631                                                   97.132053                                             95.739201                                                     111.573241                                           98.426807                                            104.014906                                           98.379059                                         95.734050                             101.536758                                  97.872466                                          98.603250                          105.952206                                          97.345137                                      102.882845                                         102.882845                                         102.882845                           1.000000                            0.000000                              0.000000   \n",
       "max                                           111.350877                                                     99.582631                                                   97.731132                                            134.935484                                                     113.458564                                           98.426807                                            104.014906                                           98.379059                                        118.052632                             101.536758                                 231.000000                                         106.847418                          105.952206                                          97.345137                                      125.493506                                         125.493506                                         125.493506                           1.000000                            1.000000                              1.000000   \n",
       "\n",
       "       cutaneous_biopsy_satellitosisabsent  cutaneous_biopsy_satellitosisnot_taken  cutaneous_biopsy_satellitosispresent  cutaneous_biopsy_vascular_invasionabsent  cutaneous_biopsy_vascular_invasionnot_taken  cutaneous_biopsy_vascular_invasionpresent  cutaneous_biopsy_neurotropismabsent  cutaneous_biopsy_neurotropismnot_taken  cutaneous_biopsy_neurotropismpresent  cutaneous_biopsy_lymphatic_invasionabsent  cutaneous_biopsy_lymphatic_invasionnot_taken  cutaneous_biopsy_lymphatic_invasionpresent  cutaneous_biopsy_associated_nevusabsent  cutaneous_biopsy_associated_nevusnot_taken  cutaneous_biopsy_associated_nevuspresent      MC1R0.0      MC1R1.0  cutaneous_biopsy_breslow_bin_ENCODEDspecific_death  cutaneous_biopsy_breslow_w  \n",
       "count                          3068.000000                             3068.000000                           3068.000000                               3068.000000                                  3068.000000                                3068.000000                          3068.000000                             3068.000000                           3068.000000                                3068.000000                                   3068.000000                                 3068.000000                              3068.000000                                 3068.000000                               3068.000000  3068.000000  3068.000000                                        3068.000000                  3068.000000  \n",
       "mean                              0.755541                                0.225228                              0.019231                                  0.704694                                     0.274772                                   0.020535                             0.777053                                0.196545                              0.026402                                   0.722621                                      0.249022                                    0.028357                                 0.456975                                    0.398957                                  0.144068     0.841591     0.158409                                           0.174459                     1.994968  \n",
       "std                               0.429836                                0.417801                              0.137358                                  0.456255                                     0.446472                                   0.141843                             0.416291                                0.397450                              0.160352                                   0.447778                                      0.432517                                    0.166018                                 0.498227                                    0.489764                                  0.351216     0.365184     0.365184                                           0.160609                     1.890258  \n",
       "min                               0.000000                                0.000000                              0.000000                                  0.000000                                     0.000000                                   0.000000                             0.000000                                0.000000                              0.000000                                   0.000000                                      0.000000                                    0.000000                                 0.000000                                    0.000000                                  0.000000     0.000000     0.000000                                           0.000000                     0.400000  \n",
       "25%                               1.000000                                0.000000                              0.000000                                  0.000000                                     0.000000                                   0.000000                             1.000000                                0.000000                              0.000000                                   0.000000                                      0.000000                                    0.000000                                 0.000000                                    0.000000                                  0.000000     1.000000     0.000000                                           0.079122                     0.700000  \n",
       "50%                               1.000000                                0.000000                              0.000000                                  1.000000                                     0.000000                                   0.000000                             1.000000                                0.000000                              0.000000                                   1.000000                                      0.000000                                    0.000000                                 0.000000                                    0.000000                                  0.000000     1.000000     0.000000                                           0.079122                     1.200000  \n",
       "75%                               1.000000                                0.000000                              0.000000                                  1.000000                                     1.000000                                   0.000000                             1.000000                                0.000000                              0.000000                                   1.000000                                      0.000000                                    0.000000                                 1.000000                                    1.000000                                  0.000000     1.000000     0.000000                                           0.300000                     2.500000  \n",
       "max                               1.000000                                1.000000                              1.000000                                  1.000000                                     1.000000                                   1.000000                             1.000000                                1.000000                              1.000000                                   1.000000                                      1.000000                                    1.000000                                 1.000000                                    1.000000                                  1.000000     1.000000     1.000000                                           1.000000                     7.100000  "
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1719628974436349\n"
     ]
    }
   ],
   "source": [
    " df = bined_splited_target_encoding(df,'train==1','train==0','T0_date','specific_death',50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06763531557639553\n"
     ]
    }
   ],
   "source": [
    "df = bined_splited_target_encoding(df,'train==1','train==0','LAB1300','specific_death',5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12632181980185456\n"
     ]
    }
   ],
   "source": [
    "df = bined_splited_target_encoding(df,'train==1','train==0','age','specific_death',10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11910128759773285\n"
     ]
    }
   ],
   "source": [
    "df = bined_splited_target_encoding(df,'train==1','train==0','LAB1314','specific_death',30);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_breslow_w_2'] = df['cutaneous_biopsy_breslow_w']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['cutaneous_biopsy_breslow_w_log'] = np.log(df['cutaneous_biopsy_breslow_w'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.35687036033291986\n",
      "0.21342192574977503\n"
     ]
    }
   ],
   "source": [
    "for targ in targets:\n",
    "    df=splited_target_encoding(df,'train==1','train==0','total_positives_slnb_ldn',targ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('numeric_full.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_orig=df.query('train==1')\n",
    "train_orig=train_orig._get_numeric_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2148, 55)"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "target=train_orig['specific_death']\n",
    "target_2=train_orig['months_survival']\n",
    "train_orig.drop(columns=['specific_death','months_survival'],inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test, y_train, y_test = train_test_split(train_orig, target, stratify = target, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LAB1300', 'LAB1301', 'LAB1307', 'LAB1309', 'LAB1311', 'LAB1313', 'LAB1314', 'LAB1316', 'LAB2404', 'LAB2405',\n",
       "       ...\n",
       "       'cutaneous_biopsy_breslow_bin_ENCODEDspecific_death', 'cutaneous_biopsy_breslow_w', 'T0_date_bin_ENCODEDspecific_death', 'LAB1300_bin_ENCODEDspecific_death', 'age_bin_ENCODEDspecific_death', 'LAB1314_bin_ENCODEDspecific_death', 'cutaneous_biopsy_breslow_w_2', 'cutaneous_biopsy_breslow_w_log', 'total_positives_slnb_ldn_ENCODEDspecific_death', 'total_positives_slnb_ldn_ENCODEDmonths_survival'], dtype='object', length=119)"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "\n",
    "\n",
    "# Initialize CatBoostClassifier\n",
    "cb = CatBoostClassifier()\n",
    "\n",
    "#Parameters\n",
    "params = {#'depth': [3,4, 7],\n",
    "          #'learning_rate' : [0.001,0.03, 0.1],\n",
    "          'l2_leaf_reg': [4],\n",
    "         'depth': [3],\n",
    "          'learning_rate' : [0.01],\n",
    "         #'l2_leaf_reg': [1],\n",
    "         'iterations': [200]}\n",
    "\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# This search space takes way longer\n",
    "params = {'depth': [3,4, 7,9,11,15],\n",
    "          'learning_rate' : [0.001,0.005,0.01,0.03, 0.1, 0.15],\n",
    "          'l2_leaf_reg': [1,4,9,12,15],\n",
    "         'iterations': [1000]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:\tlearn: 0.6846249\ttotal: 70ms\tremaining: 13.9s\n",
      "1:\tlearn: 0.6760507\ttotal: 119ms\tremaining: 11.8s\n",
      "2:\tlearn: 0.6691422\ttotal: 165ms\tremaining: 10.8s\n",
      "3:\tlearn: 0.6618030\ttotal: 203ms\tremaining: 9.93s\n",
      "4:\tlearn: 0.6536990\ttotal: 249ms\tremaining: 9.71s\n",
      "5:\tlearn: 0.6474692\ttotal: 286ms\tremaining: 9.24s\n",
      "6:\tlearn: 0.6405178\ttotal: 314ms\tremaining: 8.66s\n",
      "7:\tlearn: 0.6348281\ttotal: 344ms\tremaining: 8.26s\n",
      "8:\tlearn: 0.6282533\ttotal: 367ms\tremaining: 7.78s\n",
      "9:\tlearn: 0.6218590\ttotal: 393ms\tremaining: 7.46s\n",
      "10:\tlearn: 0.6156587\ttotal: 414ms\tremaining: 7.11s\n",
      "11:\tlearn: 0.6097090\ttotal: 434ms\tremaining: 6.79s\n",
      "12:\tlearn: 0.6043407\ttotal: 462ms\tremaining: 6.64s\n",
      "13:\tlearn: 0.5988901\ttotal: 483ms\tremaining: 6.42s\n",
      "14:\tlearn: 0.5927144\ttotal: 514ms\tremaining: 6.34s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "196:\tlearn: 0.3471737\ttotal: 3.49s\tremaining: 53.2ms\n",
      "197:\tlearn: 0.3466868\ttotal: 3.5s\tremaining: 35.4ms\n",
      "198:\tlearn: 0.3461522\ttotal: 3.51s\tremaining: 17.6ms\n",
      "199:\tlearn: 0.3459771\ttotal: 3.52s\tremaining: 0us\n",
      "The time elapsed is 21.46328377723694s\n"
     ]
    }
   ],
   "source": [
    "tic=time.time()\n",
    "# GridSearchCV\n",
    "catb_model = GridSearchCV(cb, params, scoring=\"f1\", cv=3, refit=True, n_jobs=-1)\n",
    "catb_model.fit(train,y_train)\n",
    "\n",
    "\n",
    "# Get predicted classes\n",
    "preds_class = catb_model.predict(test)\n",
    "\n",
    "print('The time elapsed is {}s'.format(np.abs(tic-time.time())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3485254691689008"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train\n",
    "f1_score(catb_model.predict(train),y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2765957446808511"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test\n",
    "f1_score(catb_model.predict(test),y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'depth': 3, 'iterations': 200, 'l2_leaf_reg': 4, 'learning_rate': 0.01}"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catb_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([14.9257973]),\n",
       " 'std_fit_time': array([0.03800865]),\n",
       " 'mean_score_time': array([0.02377097]),\n",
       " 'std_score_time': array([0.00167328]),\n",
       " 'param_depth': masked_array(data=[3],\n",
       "              mask=[False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_iterations': masked_array(data=[200],\n",
       "              mask=[False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_l2_leaf_reg': masked_array(data=[4],\n",
       "              mask=[False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_learning_rate': masked_array(data=[0.01],\n",
       "              mask=[False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'depth': 3,\n",
       "   'iterations': 200,\n",
       "   'l2_leaf_reg': 4,\n",
       "   'learning_rate': 0.01}],\n",
       " 'split0_test_score': array([0.25954198]),\n",
       " 'split1_test_score': array([0.25862069]),\n",
       " 'split2_test_score': array([0.30894309]),\n",
       " 'mean_test_score': array([0.27568257]),\n",
       " 'std_test_score': array([0.02350122]),\n",
       " 'rank_test_score': array([1], dtype=int32),\n",
       " 'split0_train_score': array([0.37815126]),\n",
       " 'split1_train_score': array([0.37837838]),\n",
       " 'split2_train_score': array([0.34262948]),\n",
       " 'mean_train_score': array([0.36638637]),\n",
       " 'std_train_score': array([0.01679892])}"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "catb_model.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances_dict = {train.columns[i] : (catb_model.best_estimator_.feature_importances_)[i] for i in range(len(train.columns))}\n",
    "\n",
    "importances_dict_filtered = {k : v for k, v in importances_dict.items() if v > 1}\n",
    "\n",
    "importances_dict_filtered = dict(sorted(importances_dict_filtered.items(),  key=lambda kv: abs(kv[1])))\n",
    "\n",
    "plt.barh(range(len(importances_dict_filtered)), list(importances_dict_filtered.values()))\n",
    "plt.yticks(range(len(importances_dict_filtered)), list(importances_dict_filtered.keys()))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGB regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "\n",
    "from math import sqrt\n",
    "\n",
    "\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
      "       colsample_bytree=0.4, gamma=0, learning_rate=0.07, max_delta_step=0,\n",
      "       max_depth=3, min_child_weight=1.5, missing=None, n_estimators=10000,\n",
      "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
      "       reg_alpha=0.75, reg_lambda=0.45, scale_pos_weight=1, seed=42,\n",
      "       silent=True, subsample=0.6)\n"
     ]
    }
   ],
   "source": [
    "#Fitting XGB regressor \n",
    "model = xgb.XGBRegressor(colsample_bytree=0.4,\n",
    "                 gamma=0,                 \n",
    "                 learning_rate=0.07,\n",
    "                 max_depth=3,\n",
    "                 min_child_weight=1.5,\n",
    "                 n_estimators=10000,                                                                    \n",
    "                 reg_alpha=0.75,\n",
    "                 reg_lambda=0.45,\n",
    "                 subsample=0.6,\n",
    "                 seed=42)\n",
    "model.fit(train,y_train)\n",
    "print (model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3778188041426868"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(y_test, model.predict(test)))\n",
    "rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances_dict = {train.columns[i] : (model.feature_importances_)[i] for i in range(len(train.columns))}\n",
    "\n",
    "importances_dict_filtered = {k : v for k, v in importances_dict.items() if v > 0.015}\n",
    "\n",
    "importances_dict_filtered = dict(sorted(importances_dict_filtered.items(),  key=lambda kv: abs(kv[1])))\n",
    "\n",
    "plt.barh(range(len(importances_dict_filtered)), list(importances_dict_filtered.values()))\n",
    "plt.yticks(range(len(importances_dict_filtered)), list(importances_dict_filtered.keys()))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}