000_MASTER_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/maciek/miniconda/lib/python2.7/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# raport = pd.concat(\n",
    "#     (pd.read_csv('dude_horizontal_dock_final.csv', index_col=0),\n",
    "#     pd.read_csv('dude_horizontal_dude_final.csv', index_col=0),\n",
    "#     pd.read_csv('dude_horizontal_vina_final.csv', index_col=0))).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_per_target = pd.read_csv('dude_per_target_final.csv', index_col=0)\n",
    "raport_per_target.drop('head', axis=1, inplace=True)\n",
    "raport_per_target['n_folds'] = 5\n",
    "raport_per_target['kind'] = 'Per Target'\n",
    "\n",
    "raport_per_target['engine'].replace('dock', 'Dock 6.6', inplace=True)\n",
    "raport_per_target['engine'].replace('dude', 'Dock 3.6', inplace=True)\n",
    "raport_per_target['engine'].replace('vina', 'Autodock Vina', inplace=True)\n",
    "\n",
    "raport_per_target['v'] = 'RF_v' + raport_per_target['v'].astype(str)\n",
    "raport_per_target.rename(columns={'v':'score'}, inplace=True)\n",
    "\n",
    "raport_per_target = raport_per_target.groupby(('engine', 'kind', 'score', 'n_folds', 'dude_id')).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_per_target.groupby(('engine', 'kind', 'score', 'n_folds')).mean().to_excel('dude_raport_per_target.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_classic = pd.read_csv('dude_classical_final_nfolds.csv', index_col=0)\n",
    "raport_classic.rename(columns={'auc':'roc_auc', 'log_auc': 'roc_log_auc'}, inplace=True)\n",
    "# add missing colums\n",
    "raport_classic['n_folds'] = 0\n",
    "raport_classic['accuracy'] = None\n",
    "raport_classic['precision'] = None\n",
    "raport_classic['recall'] = None\n",
    "\n",
    "raport_classic['kind'] = 'Horizontal'#'Classical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'engine', u'score', u'ef0.1', u'ef1', u'ef10', u'ef1_perc', u'ef2',\n",
       "       u'ef5', u'mse', u'rk', u'rk_active', u'rk_inactive', u'roc_auc',\n",
       "       u'roc_log_auc', u'rp', u'rp_active', u'rp_inactive', u'rs',\n",
       "       u'rs_active', u'rs_inactive', u'n_folds', u'accuracy', u'precision',\n",
       "       u'recall', u'kind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raport_classic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z7CVpDN7hohLMiPvsJelATeS++L6+Prq6No5Z365d9w/Z7urayPz5C0Ytf8cd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\nGfaSJBXOsJckqXD/H4akOE0ZzMjDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc46499e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x=\"engine\", y=\"ef5\", hue=\"score\", data=raport_classic, size=6, kind=\"bar\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9+oZvwEvjiE3B0vgXEedRXOXed8Tdd/7+wsz8WcsKG8SAlyRNOpk57NH4CNt+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc464eaf50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x=\"engine\", y=\"ef1_perc\", hue=\"score\", data=raport_classic, size=6, kind=\"bar\", palette=\"muted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raport_horizontal = pd.read_csv('dude_horizontal_final_multitarget.csv', index_col=0)\n",
    "raport_horizontal.drop('head',axis=1, inplace=True)\n",
    "\n",
    "# rename 'v' to 'scores'\n",
    "raport_horizontal['v'] = 'RF_v' + raport_horizontal['v'].astype(int).astype(str)\n",
    "raport_horizontal.rename(columns={'v':'score'}, inplace=True)\n",
    "raport_horizontal['n_folds'] = 5\n",
    "\n",
    "raport_horizontal['kind'] = 'Horizontal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_vertical = pd.read_csv('dude_vertical_final.csv', index_col=0)\n",
    "raport_vertical.drop('head',axis=1, inplace=True)\n",
    "\n",
    "#raport_vertical = raport_vertical[raport_vertical.n_folds == 20]\n",
    "\n",
    "# rename 'v' to 'scores'\n",
    "raport_vertical['v'] = 'RF_v' + raport_vertical['v'].astype(int).astype(str)\n",
    "raport_vertical.rename(columns={'v':'score'}, inplace=True)\n",
    "\n",
    "raport_vertical['rk_inactive'] = 0\n",
    "raport_vertical['rp_inactive'] = 0\n",
    "raport_vertical['rs_inactive'] = 0\n",
    "\n",
    "raport_vertical['kind'] = 'Vertical'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raport_vertical_loo = pd.read_csv('dude_vertical_loo_final.csv', index_col=0).dropna()\n",
    "#raport_vertical_loo.drop('head',axis=1, inplace=True)\n",
    "\n",
    "# rename 'v' to 'scores'\n",
    "raport_vertical_loo['v'] = 'RF_v' + raport_vertical_loo['v'].astype(int).astype(str)\n",
    "raport_vertical_loo.rename(columns={'v':'score'}, inplace=True)\n",
    "\n",
    "raport_vertical_loo['kind'] = 'Vertical LOO'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "raport_vertical_loo_seed_10 = pd.read_csv('dude_vertical_loo_seed_final_10proc.csv', index_col=0).dropna()\n",
    "#raport_vertical_loo_seed_10.drop('head',axis=1, inplace=True)\n",
    "\n",
    "# rename 'v' to 'scores'\n",
    "raport_vertical_loo_seed_10['v'] = 'RF_v' + raport_vertical_loo_seed_10['v'].astype(int).astype(str)\n",
    "raport_vertical_loo_seed_10.rename(columns={'v':'score'}, inplace=True)\n",
    "\n",
    "raport_vertical_loo_seed_10['kind'] = 'Vertical LOO + seed (10% actives)'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'accuracy', u'ef0.1', u'ef1', u'ef10', u'ef1_perc', u'ef2', u'ef5',\n",
       "       u'engine', u'mse', u'precision', u'recall', u'rk', u'rk_active',\n",
       "       u'rk_inactive', u'roc_auc', u'roc_log_auc', u'rp', u'rp_active',\n",
       "       u'rp_inactive', u'rs', u'rs_active', u'rs_inactive', u'score',\n",
       "       u'n_folds', u'kind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raport_vertical.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols = raport_vertical.columns\n",
    "#cols = ['engine', 'kind', 'score', 'ef1', 'roc_auc']\n",
    "raport = pd.concat((raport_classic[cols],\n",
    "                    raport_vertical[cols],\n",
    "#                     raport_vertical_loo[cols],\n",
    "#                     raport_vertical_loo_seed_10[cols],\n",
    "                    raport_horizontal[cols],\n",
    "                    raport_per_target[cols],\n",
    "                   ))#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#drop v5\n",
    "raport = raport[raport.score != 'RF_v5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_final = raport.copy()\n",
    "# adjust names\n",
    "raport_final['engine'].replace('dock', 'Dock 6.6', inplace=True)\n",
    "raport_final['engine'].replace('dude', 'Dock 3.6', inplace=True)\n",
    "raport_final['engine'].replace('vina', 'Autodock Vina', inplace=True)\n",
    "\n",
    "raport_final['score'].replace('chemscore', 'Chemscore', inplace=True)\n",
    "raport_final['score'].replace('d_score', 'D score', inplace=True)\n",
    "raport_final['score'].replace('g_score', 'G score', inplace=True)\n",
    "raport_final['score'].replace('pmf_score', 'PMF score', inplace=True)\n",
    "\n",
    "raport_final['score'].replace('native', 'Native', inplace=True)\n",
    "#raport_final.loc[raport_final['score'] == 'native', 'score'] = raport_final[raport_final['score'] == 'native']['engine']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'accuracy', u'ef0.1', u'ef1', u'ef10', u'ef1_perc', u'ef2', u'ef5',\n",
       "       u'engine', u'mse', u'precision', u'recall', u'rk', u'rk_active',\n",
       "       u'rk_inactive', u'roc_auc', u'roc_log_auc', u'rp', u'rp_active',\n",
       "       u'rp_inactive', u'rs', u'rs_active', u'rs_inactive', u'score',\n",
       "       u'n_folds', u'kind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raport.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ef0.1</th>\n",
       "      <th>ef1</th>\n",
       "      <th>ef2</th>\n",
       "      <th>ef5</th>\n",
       "      <th>ef10</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>roc_log_auc</th>\n",
       "      <th>rp</th>\n",
       "      <th>rs</th>\n",
       "      <th>rk</th>\n",
       "      <th>rp_active</th>\n",
       "      <th>rs_active</th>\n",
       "      <th>rk_active</th>\n",
       "      <th>rp_inactive</th>\n",
       "      <th>rs_inactive</th>\n",
       "      <th>rk_inactive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docking Software</th>\n",
       "      <th>Cross Validation</th>\n",
       "      <th>Re-Scoring</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">Autodock Vina</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">Horizontal</th>\n",
       "      <th>Chemscore</th>\n",
       "      <td>8.50</td>\n",
       "      <td>6.24</td>\n",
       "      <td>5.08</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.90</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.27</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D score</th>\n",
       "      <td>4.46</td>\n",
       "      <td>3.28</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G score</th>\n",
       "      <td>5.98</td>\n",
       "      <td>3.58</td>\n",
       "      <td>3.04</td>\n",
       "      <td>2.44</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.21</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native</th>\n",
       "      <td>20.55</td>\n",
       "      <td>10.98</td>\n",
       "      <td>8.20</td>\n",
       "      <td>5.38</td>\n",
       "      <td>3.82</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMF score</th>\n",
       "      <td>11.70</td>\n",
       "      <td>7.78</td>\n",
       "      <td>6.20</td>\n",
       "      <td>4.41</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v1</th>\n",
       "      <td>43.65</td>\n",
       "      <td>26.11</td>\n",
       "      <td>18.15</td>\n",
       "      <td>10.12</td>\n",
       "      <td>6.33</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>51.06</td>\n",
       "      <td>31.76</td>\n",
       "      <td>21.85</td>\n",
       "      <td>11.89</td>\n",
       "      <td>7.11</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>49.61</td>\n",
       "      <td>31.17</td>\n",
       "      <td>21.18</td>\n",
       "      <td>11.54</td>\n",
       "      <td>7.01</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Per Target</th>\n",
       "      <th>RF_v1</th>\n",
       "      <td>51.10</td>\n",
       "      <td>31.73</td>\n",
       "      <td>22.46</td>\n",
       "      <td>12.39</td>\n",
       "      <td>7.42</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>54.98</td>\n",
       "      <td>36.29</td>\n",
       "      <td>25.38</td>\n",
       "      <td>13.38</td>\n",
       "      <td>7.81</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>54.00</td>\n",
       "      <td>36.10</td>\n",
       "      <td>25.22</td>\n",
       "      <td>13.41</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Vertical</th>\n",
       "      <th>RF_v1</th>\n",
       "      <td>16.97</td>\n",
       "      <td>7.61</td>\n",
       "      <td>6.16</td>\n",
       "      <td>4.41</td>\n",
       "      <td>3.34</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>25.00</td>\n",
       "      <td>11.39</td>\n",
       "      <td>8.67</td>\n",
       "      <td>5.85</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>29.23</td>\n",
       "      <td>13.53</td>\n",
       "      <td>10.15</td>\n",
       "      <td>6.55</td>\n",
       "      <td>4.46</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">Dock 3.6</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">Horizontal</th>\n",
       "      <th>Chemscore</th>\n",
       "      <td>17.16</td>\n",
       "      <td>11.21</td>\n",
       "      <td>8.63</td>\n",
       "      <td>5.56</td>\n",
       "      <td>3.87</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.33</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D score</th>\n",
       "      <td>1.61</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.61</td>\n",
       "      <td>1.34</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G score</th>\n",
       "      <td>7.50</td>\n",
       "      <td>5.52</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.57</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native</th>\n",
       "      <td>29.39</td>\n",
       "      <td>16.86</td>\n",
       "      <td>12.04</td>\n",
       "      <td>7.09</td>\n",
       "      <td>4.69</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMF score</th>\n",
       "      <td>11.13</td>\n",
       "      <td>8.49</td>\n",
       "      <td>6.56</td>\n",
       "      <td>4.56</td>\n",
       "      <td>3.44</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v1</th>\n",
       "      <td>50.99</td>\n",
       "      <td>30.70</td>\n",
       "      <td>20.71</td>\n",
       "      <td>11.00</td>\n",
       "      <td>6.56</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.16</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>56.49</td>\n",
       "      <td>37.89</td>\n",
       "      <td>25.26</td>\n",
       "      <td>12.84</td>\n",
       "      <td>7.39</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>54.87</td>\n",
       "      <td>35.34</td>\n",
       "      <td>24.04</td>\n",
       "      <td>12.47</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Per Target</th>\n",
       "      <th>RF_v1</th>\n",
       "      <td>53.27</td>\n",
       "      <td>36.84</td>\n",
       "      <td>25.56</td>\n",
       "      <td>13.46</td>\n",
       "      <td>7.78</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>58.55</td>\n",
       "      <td>43.27</td>\n",
       "      <td>29.55</td>\n",
       "      <td>14.73</td>\n",
       "      <td>8.26</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>57.76</td>\n",
       "      <td>41.80</td>\n",
       "      <td>28.77</td>\n",
       "      <td>14.56</td>\n",
       "      <td>8.16</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Vertical</th>\n",
       "      <th>RF_v1</th>\n",
       "      <td>16.04</td>\n",
       "      <td>7.59</td>\n",
       "      <td>6.20</td>\n",
       "      <td>4.47</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>31.19</td>\n",
       "      <td>13.33</td>\n",
       "      <td>9.75</td>\n",
       "      <td>6.32</td>\n",
       "      <td>4.42</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>37.43</td>\n",
       "      <td>15.28</td>\n",
       "      <td>10.87</td>\n",
       "      <td>6.82</td>\n",
       "      <td>4.69</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">Dock 6.6</th>\n",
       "      <th rowspan=\"8\" valign=\"top\">Horizontal</th>\n",
       "      <th>Chemscore</th>\n",
       "      <td>7.25</td>\n",
       "      <td>4.94</td>\n",
       "      <td>4.21</td>\n",
       "      <td>3.16</td>\n",
       "      <td>2.47</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.24</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D score</th>\n",
       "      <td>2.20</td>\n",
       "      <td>2.46</td>\n",
       "      <td>2.22</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.66</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.18</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G score</th>\n",
       "      <td>4.95</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.35</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.67</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.19</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native</th>\n",
       "      <td>4.61</td>\n",
       "      <td>3.55</td>\n",
       "      <td>3.05</td>\n",
       "      <td>2.33</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.21</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PMF score</th>\n",
       "      <td>13.40</td>\n",
       "      <td>8.29</td>\n",
       "      <td>6.34</td>\n",
       "      <td>4.46</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v1</th>\n",
       "      <td>51.75</td>\n",
       "      <td>23.73</td>\n",
       "      <td>16.28</td>\n",
       "      <td>8.92</td>\n",
       "      <td>5.56</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>65.92</td>\n",
       "      <td>28.66</td>\n",
       "      <td>18.99</td>\n",
       "      <td>10.34</td>\n",
       "      <td>6.28</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>61.91</td>\n",
       "      <td>28.50</td>\n",
       "      <td>18.67</td>\n",
       "      <td>10.10</td>\n",
       "      <td>6.13</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Per Target</th>\n",
       "      <th>RF_v1</th>\n",
       "      <td>66.85</td>\n",
       "      <td>32.98</td>\n",
       "      <td>21.97</td>\n",
       "      <td>11.63</td>\n",
       "      <td>6.94</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.14</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>75.37</td>\n",
       "      <td>38.33</td>\n",
       "      <td>24.62</td>\n",
       "      <td>12.68</td>\n",
       "      <td>7.37</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>71.59</td>\n",
       "      <td>35.83</td>\n",
       "      <td>23.41</td>\n",
       "      <td>12.45</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Vertical</th>\n",
       "      <th>RF_v1</th>\n",
       "      <td>16.23</td>\n",
       "      <td>7.77</td>\n",
       "      <td>6.04</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3.25</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v2</th>\n",
       "      <td>24.31</td>\n",
       "      <td>10.31</td>\n",
       "      <td>7.81</td>\n",
       "      <td>5.31</td>\n",
       "      <td>3.92</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RF_v3</th>\n",
       "      <td>25.04</td>\n",
       "      <td>11.42</td>\n",
       "      <td>8.61</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.02</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              ef0.1    ef1    ef2    ef5  \\\n",
       "Docking Software Cross Validation Re-Scoring                               \n",
       "Autodock Vina    Horizontal       Chemscore    8.50   6.24   5.08   3.84   \n",
       "                                  D score      4.46   3.28   2.59   2.01   \n",
       "                                  G score      5.98   3.58   3.04   2.44   \n",
       "                                  Native      20.55  10.98   8.20   5.38   \n",
       "                                  PMF score   11.70   7.78   6.20   4.41   \n",
       "                                  RF_v1       43.65  26.11  18.15  10.12   \n",
       "                                  RF_v2       51.06  31.76  21.85  11.89   \n",
       "                                  RF_v3       49.61  31.17  21.18  11.54   \n",
       "                 Per Target       RF_v1       51.10  31.73  22.46  12.39   \n",
       "                                  RF_v2       54.98  36.29  25.38  13.38   \n",
       "                                  RF_v3       54.00  36.10  25.22  13.41   \n",
       "                 Vertical         RF_v1       16.97   7.61   6.16   4.41   \n",
       "                                  RF_v2       25.00  11.39   8.67   5.85   \n",
       "                                  RF_v3       29.23  13.53  10.15   6.55   \n",
       "Dock 3.6         Horizontal       Chemscore   17.16  11.21   8.63   5.56   \n",
       "                                  D score      1.61   2.12   2.00   1.61   \n",
       "                                  G score      7.50   5.52   4.50   3.30   \n",
       "                                  Native      29.39  16.86  12.04   7.09   \n",
       "                                  PMF score   11.13   8.49   6.56   4.56   \n",
       "                                  RF_v1       50.99  30.70  20.71  11.00   \n",
       "                                  RF_v2       56.49  37.89  25.26  12.84   \n",
       "                                  RF_v3       54.87  35.34  24.04  12.47   \n",
       "                 Per Target       RF_v1       53.27  36.84  25.56  13.46   \n",
       "                                  RF_v2       58.55  43.27  29.55  14.73   \n",
       "                                  RF_v3       57.76  41.80  28.77  14.56   \n",
       "                 Vertical         RF_v1       16.04   7.59   6.20   4.47   \n",
       "                                  RF_v2       31.19  13.33   9.75   6.32   \n",
       "                                  RF_v3       37.43  15.28  10.87   6.82   \n",
       "Dock 6.6         Horizontal       Chemscore    7.25   4.94   4.21   3.16   \n",
       "                                  D score      2.20   2.46   2.22   1.83   \n",
       "                                  G score      4.95   2.85   2.35   1.94   \n",
       "                                  Native       4.61   3.55   3.05   2.33   \n",
       "                                  PMF score   13.40   8.29   6.34   4.46   \n",
       "                                  RF_v1       51.75  23.73  16.28   8.92   \n",
       "                                  RF_v2       65.92  28.66  18.99  10.34   \n",
       "                                  RF_v3       61.91  28.50  18.67  10.10   \n",
       "                 Per Target       RF_v1       66.85  32.98  21.97  11.63   \n",
       "                                  RF_v2       75.37  38.33  24.62  12.68   \n",
       "                                  RF_v3       71.59  35.83  23.41  12.45   \n",
       "                 Vertical         RF_v1       16.23   7.77   6.04   4.28   \n",
       "                                  RF_v2       24.31  10.31   7.81   5.31   \n",
       "                                  RF_v3       25.04  11.42   8.61   5.68   \n",
       "\n",
       "                                              ef10  roc_auc  roc_log_auc  \\\n",
       "Docking Software Cross Validation Re-Scoring                               \n",
       "Autodock Vina    Horizontal       Chemscore   2.90     0.69         0.27   \n",
       "                                  D score     1.63     0.55         0.19   \n",
       "                                  G score     2.02     0.61         0.21   \n",
       "                                  Native      3.82     0.74         0.33   \n",
       "                                  PMF score   3.29     0.71         0.30   \n",
       "                                  RF_v1       6.33     0.79         0.25   \n",
       "                                  RF_v2       7.11     0.82         0.26   \n",
       "                                  RF_v3       7.01     0.82         0.27   \n",
       "                 Per Target       RF_v1       7.42     0.83         0.30   \n",
       "                                  RF_v2       7.81     0.85         0.30   \n",
       "                                  RF_v3       7.80     0.85         0.32   \n",
       "                 Vertical         RF_v1       3.34     0.65         0.18   \n",
       "                                  RF_v2       4.25     0.70         0.19   \n",
       "                                  RF_v3       4.46     0.70         0.19   \n",
       "Dock 3.6         Horizontal       Chemscore   3.87     0.73         0.33   \n",
       "                                  D score     1.34     0.50         0.16   \n",
       "                                  G score     2.57     0.65         0.25   \n",
       "                                  Native      4.69     0.77         0.39   \n",
       "                                  PMF score   3.44     0.73         0.31   \n",
       "                                  RF_v1       6.56     0.79         0.27   \n",
       "                                  RF_v2       7.39     0.82         0.28   \n",
       "                                  RF_v3       7.24     0.82         0.29   \n",
       "                 Per Target       RF_v1       7.78     0.84         0.33   \n",
       "                                  RF_v2       8.26     0.87         0.33   \n",
       "                                  RF_v3       8.16     0.86         0.34   \n",
       "                 Vertical         RF_v1       3.39     0.65         0.19   \n",
       "                                  RF_v2       4.42     0.70         0.20   \n",
       "                                  RF_v3       4.69     0.71         0.21   \n",
       "Dock 6.6         Horizontal       Chemscore   2.47     0.65         0.24   \n",
       "                                  D score     1.66     0.55         0.18   \n",
       "                                  G score     1.67     0.59         0.19   \n",
       "                                  Native      2.04     0.61         0.21   \n",
       "                                  PMF score   3.24     0.69         0.29   \n",
       "                                  RF_v1       5.56     0.72         0.28   \n",
       "                                  RF_v2       6.28     0.75         0.30   \n",
       "                                  RF_v3       6.13     0.75         0.30   \n",
       "                 Per Target       RF_v1       6.94     0.78         0.34   \n",
       "                                  RF_v2       7.37     0.81         0.34   \n",
       "                                  RF_v3       7.22     0.80         0.34   \n",
       "                 Vertical         RF_v1       3.25     0.62         0.21   \n",
       "                                  RF_v2       3.92     0.65         0.22   \n",
       "                                  RF_v3       4.02     0.66         0.23   \n",
       "\n",
       "                                                rp    rs    rk  rp_active  \\\n",
       "Docking Software Cross Validation Re-Scoring                                \n",
       "Autodock Vina    Horizontal       Chemscore  -0.11 -0.11 -0.09      -0.04   \n",
       "                                  D score    -0.01 -0.02 -0.02       0.00   \n",
       "                                  G score    -0.05 -0.06 -0.05      -0.07   \n",
       "                                  Native     -0.09 -0.10 -0.08      -0.05   \n",
       "                                  PMF score  -0.11 -0.12 -0.09      -0.07   \n",
       "                                  RF_v1       0.44  0.20  0.16       0.22   \n",
       "                                  RF_v2       0.50  0.22  0.18       0.24   \n",
       "                                  RF_v3       0.49  0.22  0.18       0.22   \n",
       "                 Per Target       RF_v1       0.55  0.23  0.19       0.29   \n",
       "                                  RF_v2       0.60  0.24  0.19       0.32   \n",
       "                                  RF_v3       0.60  0.24  0.19       0.30   \n",
       "                 Vertical         RF_v1       0.13  0.10  0.08       0.09   \n",
       "                                  RF_v2       0.18  0.12  0.10       0.09   \n",
       "                                  RF_v3       0.21  0.12  0.10       0.10   \n",
       "Dock 3.6         Horizontal       Chemscore  -0.10 -0.10 -0.08      -0.03   \n",
       "                                  D score     0.01 -0.00 -0.00       0.01   \n",
       "                                  G score    -0.06 -0.06 -0.05      -0.05   \n",
       "                                  Native     -0.08 -0.12 -0.10      -0.07   \n",
       "                                  PMF score  -0.10 -0.10 -0.08      -0.07   \n",
       "                                  RF_v1       0.44  0.16  0.13       0.23   \n",
       "                                  RF_v2       0.51  0.18  0.14       0.26   \n",
       "                                  RF_v3       0.49  0.17  0.14       0.23   \n",
       "                 Per Target       RF_v1       0.55  0.19  0.15       0.29   \n",
       "                                  RF_v2       0.61  0.19  0.16       0.32   \n",
       "                                  RF_v3       0.60  0.19  0.16       0.30   \n",
       "                 Vertical         RF_v1       0.13  0.09  0.08       0.06   \n",
       "                                  RF_v2       0.19  0.12  0.10       0.06   \n",
       "                                  RF_v3       0.22  0.12  0.10       0.07   \n",
       "Dock 6.6         Horizontal       Chemscore  -0.05 -0.06 -0.05      -0.03   \n",
       "                                  D score    -0.02 -0.02 -0.02      -0.03   \n",
       "                                  G score    -0.03 -0.03 -0.02      -0.05   \n",
       "                                  Native     -0.03 -0.04 -0.03      -0.06   \n",
       "                                  PMF score  -0.07 -0.07 -0.06      -0.06   \n",
       "                                  RF_v1       0.30  0.12  0.10       0.17   \n",
       "                                  RF_v2       0.34  0.13  0.11       0.18   \n",
       "                                  RF_v3       0.33  0.13  0.11       0.17   \n",
       "                 Per Target       RF_v1       0.41  0.14  0.12       0.21   \n",
       "                                  RF_v2       0.46  0.15  0.12       0.25   \n",
       "                                  RF_v3       0.45  0.15  0.12       0.24   \n",
       "                 Vertical         RF_v1       0.10  0.07  0.06       0.04   \n",
       "                                  RF_v2       0.13  0.09  0.07       0.05   \n",
       "                                  RF_v3       0.14  0.09  0.07       0.05   \n",
       "\n",
       "                                              rs_active  rk_active  \\\n",
       "Docking Software Cross Validation Re-Scoring                         \n",
       "Autodock Vina    Horizontal       Chemscore       -0.04      -0.03   \n",
       "                                  D score          0.00       0.00   \n",
       "                                  G score         -0.08      -0.06   \n",
       "                                  Native          -0.04      -0.03   \n",
       "                                  PMF score       -0.07      -0.05   \n",
       "                                  RF_v1            0.22       0.15   \n",
       "                                  RF_v2            0.25       0.17   \n",
       "                                  RF_v3            0.22       0.15   \n",
       "                 Per Target       RF_v1            0.27       0.20   \n",
       "                                  RF_v2            0.31       0.22   \n",
       "                                  RF_v3            0.30       0.22   \n",
       "                 Vertical         RF_v1            0.10       0.07   \n",
       "                                  RF_v2            0.10       0.07   \n",
       "                                  RF_v3            0.12       0.08   \n",
       "Dock 3.6         Horizontal       Chemscore       -0.03      -0.02   \n",
       "                                  D score          0.02       0.01   \n",
       "                                  G score         -0.06      -0.04   \n",
       "                                  Native          -0.09      -0.06   \n",
       "                                  PMF score       -0.07      -0.05   \n",
       "                                  RF_v1            0.24       0.16   \n",
       "                                  RF_v2            0.26       0.18   \n",
       "                                  RF_v3            0.23       0.16   \n",
       "                 Per Target       RF_v1            0.28       0.20   \n",
       "                                  RF_v2            0.30       0.22   \n",
       "                                  RF_v3            0.29       0.21   \n",
       "                 Vertical         RF_v1            0.08       0.06   \n",
       "                                  RF_v2            0.08       0.06   \n",
       "                                  RF_v3            0.08       0.05   \n",
       "Dock 6.6         Horizontal       Chemscore       -0.04      -0.02   \n",
       "                                  D score         -0.03      -0.02   \n",
       "                                  G score         -0.06      -0.04   \n",
       "                                  Native          -0.07      -0.05   \n",
       "                                  PMF score       -0.07      -0.06   \n",
       "                                  RF_v1            0.19       0.13   \n",
       "                                  RF_v2            0.20       0.14   \n",
       "                                  RF_v3            0.19       0.13   \n",
       "                 Per Target       RF_v1            0.20       0.14   \n",
       "                                  RF_v2            0.24       0.18   \n",
       "                                  RF_v3            0.23       0.17   \n",
       "                 Vertical         RF_v1            0.07       0.05   \n",
       "                                  RF_v2            0.08       0.06   \n",
       "                                  RF_v3            0.07       0.05   \n",
       "\n",
       "                                              rp_inactive  rs_inactive  \\\n",
       "Docking Software Cross Validation Re-Scoring                             \n",
       "Autodock Vina    Horizontal       Chemscore          -0.0          NaN   \n",
       "                                  D score             0.0          NaN   \n",
       "                                  G score            -0.0          NaN   \n",
       "                                  Native             -0.0          NaN   \n",
       "                                  PMF score          -0.0          NaN   \n",
       "                                  RF_v1               0.0          NaN   \n",
       "                                  RF_v2              -0.0          NaN   \n",
       "                                  RF_v3               0.0          NaN   \n",
       "                 Per Target       RF_v1              -0.0         0.02   \n",
       "                                  RF_v2               0.0         0.03   \n",
       "                                  RF_v3              -0.0         0.01   \n",
       "                 Vertical         RF_v1               0.0         0.00   \n",
       "                                  RF_v2               0.0         0.00   \n",
       "                                  RF_v3               0.0         0.00   \n",
       "Dock 3.6         Horizontal       Chemscore           0.0          NaN   \n",
       "                                  D score            -0.0          NaN   \n",
       "                                  G score             0.0          NaN   \n",
       "                                  Native              0.0          NaN   \n",
       "                                  PMF score          -0.0          NaN   \n",
       "                                  RF_v1              -0.0          NaN   \n",
       "                                  RF_v2               0.0          NaN   \n",
       "                                  RF_v3               0.0          NaN   \n",
       "                 Per Target       RF_v1               0.0         0.02   \n",
       "                                  RF_v2               0.0         0.02   \n",
       "                                  RF_v3               0.0         0.02   \n",
       "                 Vertical         RF_v1               0.0         0.00   \n",
       "                                  RF_v2               0.0         0.00   \n",
       "                                  RF_v3               0.0         0.00   \n",
       "Dock 6.6         Horizontal       Chemscore           0.0          NaN   \n",
       "                                  D score            -0.0          NaN   \n",
       "                                  G score            -0.0          NaN   \n",
       "                                  Native             -0.0          NaN   \n",
       "                                  PMF score          -0.0          NaN   \n",
       "                                  RF_v1               0.0          NaN   \n",
       "                                  RF_v2               0.0          NaN   \n",
       "                                  RF_v3               0.0          NaN   \n",
       "                 Per Target       RF_v1              -0.0         0.00   \n",
       "                                  RF_v2               0.0         0.02   \n",
       "                                  RF_v3               0.0         0.02   \n",
       "                 Vertical         RF_v1               0.0         0.00   \n",
       "                                  RF_v2               0.0         0.00   \n",
       "                                  RF_v3               0.0         0.00   \n",
       "\n",
       "                                              rk_inactive  \n",
       "Docking Software Cross Validation Re-Scoring               \n",
       "Autodock Vina    Horizontal       Chemscore           NaN  \n",
       "                                  D score             NaN  \n",
       "                                  G score             NaN  \n",
       "                                  Native              NaN  \n",
       "                                  PMF score           NaN  \n",
       "                                  RF_v1               NaN  \n",
       "                                  RF_v2               NaN  \n",
       "                                  RF_v3               NaN  \n",
       "                 Per Target       RF_v1              0.02  \n",
       "                                  RF_v2              0.02  \n",
       "                                  RF_v3              0.01  \n",
       "                 Vertical         RF_v1              0.00  \n",
       "                                  RF_v2              0.00  \n",
       "                                  RF_v3              0.00  \n",
       "Dock 3.6         Horizontal       Chemscore           NaN  \n",
       "                                  D score             NaN  \n",
       "                                  G score             NaN  \n",
       "                                  Native              NaN  \n",
       "                                  PMF score           NaN  \n",
       "                                  RF_v1               NaN  \n",
       "                                  RF_v2               NaN  \n",
       "                                  RF_v3               NaN  \n",
       "                 Per Target       RF_v1              0.02  \n",
       "                                  RF_v2              0.02  \n",
       "                                  RF_v3              0.02  \n",
       "                 Vertical         RF_v1              0.00  \n",
       "                                  RF_v2              0.00  \n",
       "                                  RF_v3              0.00  \n",
       "Dock 6.6         Horizontal       Chemscore           NaN  \n",
       "                                  D score             NaN  \n",
       "                                  G score             NaN  \n",
       "                                  Native              NaN  \n",
       "                                  PMF score           NaN  \n",
       "                                  RF_v1               NaN  \n",
       "                                  RF_v2               NaN  \n",
       "                                  RF_v3               NaN  \n",
       "                 Per Target       RF_v1              0.00  \n",
       "                                  RF_v2              0.02  \n",
       "                                  RF_v3              0.02  \n",
       "                 Vertical         RF_v1              0.00  \n",
       "                                  RF_v2              0.00  \n",
       "                                  RF_v3              0.00  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = raport_final.groupby(('engine', 'kind', 'score')).mean()\n",
    "table = table[[\n",
    "               'ef0.1', 'ef1','ef2','ef5','ef10',\n",
    "               'roc_auc','roc_log_auc',\n",
    "               'rp','rs','rk',\n",
    "               'rp_active','rs_active','rk_active',\n",
    "               'rp_inactive','rs_inactive','rk_inactive',\n",
    "              ]].round(2)\n",
    "table.index.rename('Docking Software', level=0, inplace=True)\n",
    "table.index.rename('Cross Validation', level=1, inplace=True)\n",
    "table.index.rename('Re-Scoring', level=2, inplace=True)\n",
    "table.to_excel('dude_raport_all.xls')\n",
    "table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#raport_final.groupby(('engine', 'kind', 'score', 'n_folds')).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc4658c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(col=\"engine\", y=\"ef1\", x='kind', hue=\"score\", data=raport, size=12, kind=\"bar\", palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=raport, size=12, kind=\"bar\", palette=\"Set3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iTrr/FmA8MByYLunOEiF+CLhK0joASWsj4nBJbVodI2Jn4Idk23INJmvJeywi\nbgAOAZqBiyU9GRFfAA4D+gNfk/Td1Pr3RHqPTwGzyXZ+GAB8SNITHfyNt1u5MXD/BFyR+yk+NzMz\ns74nIuK+9DMfODcVNqbjw4AjgWkR0bp9xUpJp6fjgqSFko4BjgNeAq5I9c8pqr93qjNQ0glkOz+d\nLek64GVJp5Elhvek500DPlMm9tcBj+cLipO3ZCpZUnks8F5gbERMBV4j6a1kSdm0iDgC+HtJh6c6\n01OrHsDjkj4MXAbcJek44IPA9WXi6zYlW+BqsVWWmZmZ1dxTKbEBtoyBAzgAWCCpALRExIPA5HTt\ntyWeNR24W9LDEXEK2R6j+fr7p/seSL+XkW1yD1tbAlcDB0fE+4HNZAldKZtJuU3aO/UaYAjwiKRL\nc/ctAD4bETcBP5F0b0R8HHgQQNKvgV9HxEdJO09JWh8RTwKt+6a1vvOhwG4RcXY6H1Imvm7jMXBm\nZmZWiQJte+4GA62tW5uKb06tV4dIOj5XP989m6//aq68uAv3TGCUpMMjYlfgd2VifIIsAZwraSFw\nTNpF6pL8TZKej4jJwDHAB1Kyt5ZteybLxbwp9/tDkn5TJq5uV8lODGZmZrbjKDUG7lFgSkT0i4gB\nZInSo+3dGBEjga8A5xXVf2sl9Yvi2A14Jh2fSjZurZQbybo5x6Q4+gHHks1Izcc3FTguTWz4MPBm\nssTw2HT9gIj4Glkr2zGpbATZzlR/KfrM3wCnpHvekFrtqq7DFriI+Lyky4vKvinpwuqFZWZmZsuX\nL6vFs9qdhSppcUTMAH5FllzNlLQ0Itq7/2JgDHBrRDQAaySdFBEzK6wP8FhELATeDdyeWslmAcsi\n4or24pT0SET8K/DziHiFrDtzIdnkhrz/S7H9G1m365WSHoqIP6UJmwXgg5L+GBEPR8T9ZDnTJyRt\nKIr5q8C3U71+ZAlh1TW0LtdQLPVVvwt4G5DfhWEQcISk8dUPr60VK9Z4Lbo6smbN37joonO2nM+c\n+V/stNPOVatntfX003+h8IebmDS+3PCU9s3//TO8NHk04yd2vu7vH1rEqRMuZd/GfTq+OecvTYvY\nsNtwJk3at+ObrcfU4nvU1e8QbN/3aMyYnUrO9oTarANn9aNcC9zdwIvAW4Bf5so3A1e1W8PMzMy6\nRUq0OlqzzXZQ5WahbgAejIgDJG1MTaBl/2/BzMzMzKqvkkkMl0bEy2QzRJpzv83MzMysBipZRuR9\nwJskLakra/1qAAAgAElEQVR2MGZmZmbWsUpa4P7i5M3MzMys96ikBe7xiPge8D/kFtqTNKtaQZmZ\nmZlZaZUkcHsCrwBvzZUVyNZiMTMzsyqoxTIiETERmCPpoFzZVcAKSTd19PCIeDvQKOkb2xtoRJwq\n6bYy1+eTbUL/ZFGs/yDp4Px9aR/VUs/5R+AuYFdguqQPbG/sPaHDBE7S+Wkl47GSnu+BmMzMzAwm\n/eD952nC6FHd8rAlq1Zzxje+HXS8NEmX11yVdE9X67bjcqBkAlfGoIh4t6QfpvOO3udfgPskvQDU\nRfIGle3EcCzwLbJWuNdFxJeBX0i6o9rBmZmZ7cgmjB7FpLG79fTHllwyLCI+AkwjS4rmSro2ImaT\n7Qc6Gvg5sB9Z4nVNum8ssFTSiWXqLwcOBPYCzgKmApMjYk66/xZgPDCcrJXszjLx/wfwyYi4TdKW\n1saIGA98J332QOBc4DBgCnBnRFwIfA/4DHCypPelerOAHwMvA59L77oUuEhSfg/XHlXJJIbPkb3c\n8nT+H8AVVYvIzMzMaiki4r70M58s0SEiGtma9BwJTIuI1u0rVko6PR0XJC1M3ZbHAS8BV6T65xTV\n3zvVGSjpBLK9TM+WdB3wsqTTyBLDe9LzppElWOW8AMxl29a0ccDVkqYCs8m2yrqVLL85gSwxKwD3\nAEeld+4HHJHKbgROkvQ2so0OTqeGKkng1qZmRQAkvUT2kmZmZtb3PCXp2PRzDFnrF8ABwAJJhdSy\n9SAwOV37bYlnTQfulvRwqr+wqP7+6b4H0u9lwC7puLUlcDVwcET8OsVSyV5pXwIuShvQtz7neeAj\naV/Ty8jGvLV+zpZWR0mbgEci4mDgULLN6kcB+wI/Tknt0WQtgjVTySSGDRFxFNAQEaOAM4CN1Q3L\nzMzMepkCbRt+BgOtXZTbNOxExBHAIZKOz9XPd8/m6+e7Iou7cM8ERkk6PCJ2BX7XUaCS1kXEfwIf\nZ+sYuM+QJZMzIuJU4B/KPOInwEkpxjnp/ZZJOrajz+4plbTAfZDsD3AQ8DRZM+M/VzMoMzMzq5lS\nY+AeBaZERL+IGAAcnMq2EREjga8A5xXVf2sl9Yvi2A14Jh2fCgyq5CWAmWRJ2O655zydjv8p95zN\nbG3Qav3MO8i6eY8E7pL0cnqv16ffl0bEfhXGURWVzEJdCryzB2IxMzOznCWrVtfiWe3O2pS0OCJm\nAL8iS3RmSloaEe3dfzEwBrg17aW+RtJJETGzwvoAj0XEQuDdwO0RMYVsCbNlEXFFiTi3lEl6NSL+\nA/jvVPQN4GsR8QzwVWBGRLyNbJ3bXwPnt9aXtCYiVgHrJb2S6r8PmB0RrwDPpefVTCWzUN9G1gq3\nC237iHtNM6KZmVkf9HRa9qNbn1nuoqTFZC1j+bKrc8c3AzcXXb8gd3xL7tLn23n+TcBNRWX5+neQ\ntX6RJgu0mpw7/n76/dl2nv+ZovM5QP/iZyd7pd+/yJVteXdJJxc96yGySZ29QiVj4G4G/p1sYKGZ\nmZn1gLTgbkdrttkOqpIE7s9FGbXZFi0tLTQ1LWr32vr169qcNzUtYtiw4W3KGhv3oX///lWLz8zM\nrC+qJIGbGRHfBB6i7V6o/1W1qKxuNDUt4q7ff4Gx40duc+2VDW13a3nsxVsYPHRrsvbisy9zIp9g\n0qR9qx6nmZlZX1JJAvcpYB3ZVNpWBcAJnAEwdvxIxk/cdlmeDeua25yP22skQ4cP3HK+uWUzS5Ys\n3qaeW+7MzMzKqySB21RuE1izrlrx/N+Y0H89Q4e1Tdg2b1jf5nzIyvUMzRU1PbsEwC13Zma2w6ok\ngftZRBxDtmJyvgt1c9Wish1G4/gJ7Nu4T5uyv61b0+Z8nwkT2Xn4Tm3KNlQ9MjMzs96rkgTuCrLN\nY/MKpGm5ZmZm1v0aGhr6A5O6+bFPp9mt7YqIicAcSQflyq4CVqQlQMqKiLcDjZK2e420iDhV0m1l\nrs8HLpH0ZFGs7yVbOaMfsBa4UNLz6f4/Sfpg7v5LgRsl9Uu7Tv0IeIJs2bQC2c4NX9zed6mGShby\n3amje8zMzKzbTbrwyhs0euye3fKwVS8+xzc/c1nQ8dIkpRbW7ZCke7patx2XAyUTuDJuaE02I+Ic\nsi20WneQOiAi+qe9WAH+kWxR3lb/I+ndXQ24J1WykO84sv1PR9J2Id8rqxiX9SKzZs3g3nvv5Pjj\n38EFF3gXNTOznjJ67J6MGT+hpz+21FZaRMRHgGlkSd5cSddGxGyyvUJHAz8H9iNLvK5J940Flko6\nsUz95cCBZIvrngVMBSZHxJx0/y1km8cPB6ZLurPCd/kN2Q4LrX4LvB24MyJeAzTTzj6uZd7/OLK1\ncdcDL5C19o1P8fUDFgPnAnuS7RoxiGy/1/elR3wX+BvwtfT7c+nzlwIXScrvCVtWJXuh/hx4A9le\nYS25H9sBbNy4gXnz7gJg3ry72bjRo8/MzPq4iIj70s98soSEiGhMx4eR7RE6LSJaBzGvlHR6Oi5I\nWpgmQB4HvARckeqfU1R/71RnoKQTgBuBsyVdB7ws6TSyxPCe9LxpZC1qlXonWdLWag7wnnQ8Dfhx\nJ54FcCnwLymWH5Dtr/ofwHWSjiJrzTsoxfjNdN/NQOtuFpOBM1MCeiNwUtpx4kXgdDqhkjFw6yRd\n1JmH5kXE9WRbT2wGLpP0cO7aRcAFZJMj/iDp0q5+jlVHc3MzhULWml4obKa5uZkhQ4bWOCozM6ui\np/LbZaZxZQAHAAskFYCWiHiQrVtc/Zb2TScbR/ZwRJwCLCyqv3+674H0exlbt7NqbQlcDRwcEe8n\nyyW2Xbeqrcsi4rRU/8/Ax1J5gWzP029GxBDgVLIE79O5ukdHxH1sHQP3HUmzc9d/CHwjIm4FfiDp\nhYg4EPgwgKTLAdKer5enOvPJ5hMAPC3p5YgYC+wL/DjtFTsMWNHBe7VRSQL3PxHxekl/6syDASLi\nSOC1kg6NiNeRNScemq4NJdug9jBJmyPilxExRdLCzn6OmZmZVV2Btj13g9naI7dNN2REHAEcIun4\nXP1892y+fr7rsLgL90xglKTDI2JX4HcdxLllDFwxSYWIuBe4BFgraVVKoFqVHQMn6bsRcQ9wCtkq\nHaen2It7NDfn3mNQOoetf6dNwLLt2Ve+ZBdqRCyNiCVkfcf/GxHPRcSSXHklpgJzASQ9BYyMiBHp\nfIOk41LyNgzYGXi+qy9iZmZm3aLUGLhHgSkR0S8iBpC1lD3a3o0RMRL4CnBeUf23VlK/KI7dgGfS\n8alkCVFXtD5vDvDJ9LtTIuLTwKuSZgL/DbyeLKGcmq5fHRFTU1lrcnY00Nr72AAg6eV0/+vT70sj\nYr/OxFKuBe7wzjyohD3YGjRk/eB7AP/XWhARnyBrerxBUlM3fKaZmVmfsOrF5zq+qfuf1e4sVEmL\nI2IG8CuyRGSmpKUR0d79FwNjgFtTC9caSSelrsVK6gM8FhELyXrrbo+IKWQ9ecsi4opScVbwXr8i\nW070x0XllVgC/CIiVgOrgC+R5TmzI+ID6fp04EngW2mo2CtkkxgGFX3W+1K9V8jGznVq6ZWSCZyk\nxQAR8QayAYWfTOezges68yE522T1kr4QETcAd0XEryUt6OKzzczM+pKn07If3frMchfTf/sPLiq7\nOnd8M9mg/Pz1C3LHt+Qufb6d598E3FRUlq9/B3BHOn5b7rbJuePvp9+fbef5VxeX5a4dm34XyGa7\ntpbvk37fD9xfqn6657/YdivRZWSTNfKWA+9o5xFb/raSHiKbI9AllYyB+zqQXzJkFtkf/6gK6j5H\n1uLWak+ylyIiRgH7SXpA0isRcRfZzJSSCdyoUcMYMMDrB/ekQYPabrix664j2GWXrUsDrl49IptI\n3cNGjx7BmDFeorA3Wb16BCtrHUQn+XvU+/h7tFVacLejNdtsB1VJAjdAUuvsECQ9EFHx/xDcS9aU\nODPN0nhWUuvGlwOBb0fEGyWtJ8tKi7PaNlavXl/uslXBmjVr25yvXLmWTZu2Dp1ctWptcZUesWrV\nWlasWNPxjdZjavVd2B7+HvU+O9L3yP/zYNujkgTur6lf93/IJj2cAFT0TZW0ICIeSVOFW4BLIuJc\nsrVdfhoRV5PNcm0mW0bk9i69hZmZmdkOpJIE7nyy1ZQ/SDb47iHarmpclqRPFRU9nrvWXl+ymZmZ\nmZVRyV6oK4ALeyAWMzMzM6tAyQQuIv5b0rSIWEo7U2wl9fjmbGbWPby/rZlZfSvXAvfh9Ls71oMz\ns16ieH/bM88829ujmfVCDQ0N/YFJ3fzYp9Ps1nZFxERgjqSDcmVXAStK7W5QVP/tQKOkTq1pVuJZ\np0q6rcz1+cAlkp4sivW9ZEt79AOGAJ+XNDcijgJ+BDzB1q2y7pb0xU7G9WGy5dRGpkmYNVFuHbjW\nxSG+KGlaD8VjZlXm/W3N6sakH1x9mibsvku3PGzJC3/ljKvmBB0vTdLZBXK3kHRPV+u243KgZAJX\nxpattNKSZY+lpcqgg62yOhIRZwNjgWe7+ozuUskkhmci4gKyyQtb9jqTtKhqUZmZmRkTdt+FSeM7\n2ru925XaSouI+AgwjSzJmyvp2rTA/yayTeZ/DuxHlnhdk+4bCyyVdGKZ+suBA8kW2D2LbGuqyREx\nJ91/CzAeGA5Ml3RnJS8iaXVELAfGdeYPEBFvAr4sqXWLrCvJdl6YLWldRLy3M8+rhpJ7oeZMA64A\n7gJ+mX5+Uc2gzMzMrGYiIu5LP/OBc1NhYzo+DDgSmBYR+6Q6KyWdno4LkhZKOoZsh4KXgCtS/XOK\n6u+d6gyUdAJwI9nuT9eRLTl2GllieE963jTgM515EbIEclln/gCS/hcYFxE7p6KTgNtya9nWXCWz\nUPfu6B4zMzPrM55q3XYKtowrAzgAWJC2ompJa7y2bnH12xLPmk42zuzhiDgFWFhUf/90X+uGAcvY\nut1Ua0vgauDgiHg/sJksoSvnsog4DdgFGAy8R9KraROCoyPiPraOgfuOpNklnnM7cEJELAA2SFre\nwef2qA4TuIj4e7JlREaTa1aVdE4V47Id2MABA2loaKBQKNCvoYGBAwbWOiQzM8sSnnzP3WCyRfoh\nN8SqVUQcARwi6fhc/Xz3bL7+q7ny4i7cM4FRkg6PiF2B33UQ5w2SboqIPch6DR/PXevMGLifAJcC\nY9h2LF6Xxwl2l0q6UH9Ilv3ex9Yu1F9WMyjbsQ0dPIR3HD4VgBMPn8rQwUNqHJGZ2Q6l1Bi4R4Ep\nEdEvIgaQtZQ92t6NETES+ApwXlH9t1ZSvyiO3YBn0vGpwKBKXkLS82SbBUyv5P526i8E3kC2Kf2c\nErHVTCWTGJ6XVHF/s1l3uPi0c7j4NDfymtmObckLf63Fs9ptXZK0OCJmAL8iS2BmSloaEe3dfzFZ\ny9WtEdEArJF0UkTMrLA+ZLNHFwLvBm6PiCnALGBZRFxRKs4iXwb+kCZKdMVDwP6SlgFExKfIxvXt\nDtwVEQskXd7FZ2+XShK4uyLieLK9ULc0cUraXK2grG/oP6DfllEGDQ3p3MzMKvV0WvajW59Z7qKk\nxWwdg9ZadnXu+Gbg5qLrF+SOb8ld+nw7z78JuKmoLF//DuCOdPy23G2Tc8ffT78/287zry463wS8\nPp3+Cbi/uE45ki4tOv8c8LnOPKNaKkngPg20zsJo7b8uAP2rFZT1DYMG9+eNh47l8QdfZL9DxzJo\nsL8yZmaVSgvudrRmm22HiNiLrJu1tTWvNce5vzgZ7G0qmYU6sicCsb7p6Hc1cvS7Gmsdxg6npaWF\npqb2l2pcv77tLPimpkUMGza8TVlj4z707++E28z6NklLgWNqHUdXlNsL9WOSvpQ7f4ukh9PxtyS9\nrycCNLPOa2paxOJvf5UJo0dtc63Q/Grb87t+RGHg1n8VLFm1Gs77EJMm7Vv1OM3MrGvKtcD9A/Cl\n3PkXgdZ1YfbZ9nYz600mjB7FpLG7bVO+ZlNzm/PGMbuy0yAv1WJmVk/KjSovniKbP6/5+idmZmZm\nO6pyLXDlkrSar39i3avUmKmOxkstWbI4W4rRzMzMekwls1BbFUocWx/Q1LSIxfdew4Tdd2lTXnil\npe35E7Mo5GaTPvvHZQyaOrFHYjQz25E0NDT0ByZ182OfTrNb2xURE4E5kg7KlV0FrEhLgJQVEW8H\nGiV9Y3sDjYhTJRXvgJC/Ph+4RNKTRbG+l2xLrn7AEODzkuZGxFHAj4An2Drb9G5JX+xETK8BZgMD\nyXafOEvSi51+uW5QLoE7NCKW5M7HpvMGslWRrY+ZsPsuTBrfdou5NRteBbZ+DRrHjWKnobkB7y/8\nlZd6KkAzsx3LpCu//m6NHd89i0G8+OzLfOaSHwYdL03S5UYaSfd0tW47LmfbLawqcUNrshkRo8gW\nBL4rXevMVlrt+XfgPyXdFhEfBD4GfGI7ntdl5RK47l480MzMzDph7PiRjJ/Y0d7t3a7kMKmI+Agw\njSzJmyvp2rTLwSayPdN/DuxHlnhdk+4bCyyVdGKZ+suBA4G9gLOAqcDkiJiT7r8FGA8MB6ZLurOS\nF5G0OiKWA+M68weIiDcBX5Y0NZ1fCawCPgBsTLetAA7ozHO7U8kELq3GbGZmZjuWiIj70nEDMBG4\nLiIagXOBN5N1T/4mIlpbyFZKen9EnAsU0j6ix0REf7L9069I9c8B3pKr37rH6EBJJ0TE+4GzJf1L\nRPybpNMiYgxwj6TvRMTeZN2gFSVwERFkCeSy9B4VkfS/ETEuInaW9DfgJOAfJW1Iz+0HXALUbLHf\nzoyBMzMzs77vKUmty4a1jiuDrLVpgaQC0BIRD7J1i6vflnjWdLJxZg9HxCnAwqL6+6f7Hki/l7F1\nK6/WlsDVwMEpudtM1tJXzmURcRqwC9k0u/dIejXL5Tg6JaetY+C+I6nUPqm3AydExAJgg6Tl6e/R\nD/gO8EtJ8zuIpWqcwJmZmVklCrRdfmww0DohYlPxzRFxBHCIpONz9fPds/n6+RXGi7twzwRGSTo8\nInYFftdBnDdIuiki9iBr/Xs8d60zY+B+AlwKjKHtWLzZgCRtsxdrT/Lu4mZmZpZXagzco8CUiOgX\nEQPIWsoebe/GiBgJfAU4r6j+WyupXxTHbsAz6fhUYFAlLyHpebJ9TqdXcn879RcCbwDeAcwBiIj3\nAq9I+kxXntmd3AJnZmbWS7347Mu1eFa7s1AlLY6IGcCvyJKrmZKWRkR7919M1nJ1a0Q0AGsknRQR\nMyusD9ns0YXAu4HbI2IKMAtYFhFXlIqzyJeBP6SJEl3xELC/pGXp/IPA4LSESQF4UtKlXXz2dnEC\nZ2Zm1js9nZb96NZnlruYJjAeXFR2de74ZuDmousX5I5vyV36fDvPvwm4qagsX/8O4I50/LbcbZNz\nx99Pv7fpwszHms43Aa9Pp38C7i+uU05xcibpsM7UryYncGZmZr1QWnC3ozXbbDtExF5k3aytrXmt\nkxvuL04GexsncGY7mIH9Grb8G6ohnZuZ7YgkLQWOqXUcXeFJDGY7mCEDBnBc42sAOK7xNQwZ4P+P\nMzOrN/43t9kO6Pw3Bue/0ZutmJnVK7fAmZmZmdUZJ3BmZmZmdcZdqGZmZr1QQ0NDf2BSNz/26TS7\ntV0RMRGYI+mgXNlVwIq0BEhZEfF2oFHSN7Y30Ig4VdJtZa7PBy6R9GRRrO8l25KrHzAE+LykuRFx\nFNk+qk+wdbbp3ZK+2ImY3gp8EWgm29T+bEkrO/1y3cAJnJmZWe80ae5/3qrG8RO65WFNzy7h5IvP\nCjpemqSSBXLbJemertZtx+W03cKqUje0JpsRMYpsQeC70rXObKXVnsuAs9KixlcCF9HOenc9wQmc\nmZlZL9U4fgL7Nu7T0x9bcm2hiPgIMI0syZsr6dq0y8Emsk3mfw7sR5Z4XZPuGwsslXRimfrLgQOB\nvYCzgKnA5IiYk+6/BRgPDAemS7qzkheRtDoilgPjOvMHiIg3AV+WNDWdXwmskjQtnTekeB7ozHO7\nk8fAmZmZWV5ExH3pZz5wbipsTMeHAUcC0yKiNbtcKen0dFyQtFDSMcBxwEvAFan+OUX19051Bko6\nAbiRrFvyOuBlSaeRJYb3pOdNAyrehzQigiyBXNbRvXmS/hcYFxE7p6KTSK2BqZv4KWCspFs789zu\n5BY4MzMzy3tK0rGtJ2lcGcABwAJJBaAlIh5k6xZXvy3xrOlk48wejohTgIVF9fdP97W2ZC1j61Ze\nrS2Bq4GDI+L9wGayhK6cyyLiNGAXYDDwHkmvZrkcR0fEfWwdA/cdSaX2Sb0dOCEiFgAbJC2HLd3E\nERGfj4hPSrqmg3iqwgmcmZmZVaJA2567wUDrhIhNxTdHxBHAIZKOz9XPd8/m67+aKy/uwj0TGCXp\n8IjYFfhdB3HeIOmmiNgD+CXweO5aZ8bA/QS4FBjD1ta3kyXNTddvA64qUbfq3IVqZmZmeaXGwD0K\nTImIfhExgKyl7NH2boyIkcBXgPOK6r+1kvpFcewGPJOOTwUGVfISkp4n2+d0eiX3t1N/IfAG4B3A\nnFQ8PY2PAzgEUFee3R3cAmdmZtZLNT27pBbPancWapp5OQP4FVlyNVPS0oho7/6LyVqubk0D/tdI\nOikiZlZYH7LZowuBdwO3R8QUYBawLCKuKBVnkS8Df0gTJbriIWB/Sa1j6C4Abo6IZmADcHYXn7vd\nnMCZmVmPmjVrBvfeeyfHH/8OLrjgn2sdTm/2dFr2o1ufWe6ipMVsHYPWWnZ17vhm4Oai6xfkjm/J\nXdpmeY20vMdNRWX5+ncAd6Tjt+Vum5w7/n76/dl2nn910fkm4PXp9E/A/cV1ypF0adH578kmYdSc\nEzgra2D/hi0jPRsasnMzs67auHED8+ZlS3LNm3c3Z555NkOGDK1xVL1TWnC3ozXbbDtExF5k3ayt\nrXmt/8m7vzgZ7G2cwFlZQwb157jJY7j3Dys47k1jGDKof61DMrM61tzcTKGQ/beyUNhMc3OzEzir\nGUlLgWNqHUdXOIGzDp1/7ATOP7Z7VgI3s9LctWhmlfIsVDOzXqC4a3Hjxg01jsjMejMncGZmvUB7\nXYtmZqU4gTMzMzOrM07gzMzMzOqMEzgzMzOzOuMEzszMzKzOOIEzMzMzqzNO4MzMzMzqjBM4MzMz\nszrjBM7MzMyszjiBMzMzM6szVd8LNSKuB6YAm4HLJD2cu3YM8DngVUCSLqx2PGbV4D0szcysJ1W1\nBS4ijgReK+lQ4ELgxqJb/hN4l6QjgJ0j4oRqxmNWDd7D0szMelq1u1CnAnMBJD0FjIyIEbnrb5a0\nPB2vAHatcjxm3c57WJqZWU+rdgK3B1li1uqlVAaApLUAETEOOA64s8rxmJmZmdW9qo+BK9JQXBAR\nY4GfAR+QtLpc5VGjhjFgQP9qxbZDW716BCtrHUQnjB49gjFjdqp1GAAMGrS5zfmuu45gl11qG9v2\n/vPsyt+33r5D4O9RLXT0nv4emVWm2gncc+Ra3IA9gdYuUyJiJ7JWt09K+mVHD1u9en23B2iZVavW\n1jqETlm1ai0rVqypdRgArFnT9m+3cuVaNm2q7QTv7f3n2ZW/b719h8Dfo1ro6D13pO+Rkz7bHtX+\nt8O9wGkAEXEg8Kykdbnr1wPXS5pX5TjMzMzM+oyqtsBJWhARj0TEg0ALcElEnAu8TJbcnQVMioiL\ngALwPUnfrGZMZmZmZvWu6mPgJH2qqOjx3PHQan++mZn1vJaWFpqaFm1Tvn79ujbnTU2LGDZs+Jbz\nJUsWs1fVozOrfz09icHMzHYATU2L+NZdDzB67J5tyl99ZWOb8zsea2LA4CFbzp/502N89I09EqJZ\nXXMCZ2ZmVTH6/7d353F2lWWCx3+VVGoJECUhSMhCJMKjjWmbrWFUMKyS4IDLDA0qMtKK2O729OK0\nC9pof0ZaHZe2NWrchhbXbtpRMJGgImqrKAOiPiqxEiAQQxICISlSqar+45widSuVVKUqt26dqt/3\n8+GTe96zPXXrcOu5z3nP+x5+JLPnLqhp27mj9mG0WXPm0dI+/fHlzRvWAxvGIjyp0ibeI06SJEkT\nnAmcJElSxXgLVZLGyN469sPQnfsBFi48mqlTHcxckgmcJI2Zjo41XPvj1cw6cs4e67o6azv337T+\nN0xr2925f9P6+3kJsGjRMfUOc1hWrFjOypXf5Nxzl3H55Vc0Ohxp0jGBk4ZppMMigJUT7TbryDk8\n6ag9B8p47NHazv2z582l9aDpe2w3HnR27mDVqhsAWLXqRl784ktpa3NUKGksmcBJw7S36knVKifS\naHV1ddHb2wtAb28PXV1dJnDSGDOBk/bDYNWTKlVOJEkTg0+hSpIkVYwJnCRJUsWYwEmSJFWMCZwk\nSVLFmMBJkiRVjAmcpGFbsWI5F1/8fFasWN7oUCRpUjOBkzQsAwdv7ezc0eCIJGnyMoGTNCyDDd4q\nSWoMEzhJkqSKMYGTJEmqGBM4SZKkijGBkyRJqhgns5ckDaq7u5uOjjV7tG/f/mjNckfHGqZPP6im\nbd26tXWNTZrsTOAkSYPq6FjD2s98mAUzD61p7+3aVbt8w5fpnVb75+S+NR1w2iX1DlGatEzgpFGa\n2twMTUAv0NRULEsTxIKZh7Lo8MNq2h7ZWTuEzMLZszikZVpN27pNW3hgkONNmTqVx/+HaWoqlyXt\nL/vASaPU3NrColNPBmDRqSfR3NrS4Iik8au5pZUFi08BYMHTT6G5pbXBEUnVZKlAOgCOv3AZx1+4\nrNFhSJVw3JILOG7JBY0OQ6o0K3CSJEkVYwInSePA430pwb6UkoZkAidJ44B9KSXtD7/iSdI4YV9K\nScNlBU6SJKliTOAkSZIqxluokmp09/SwfpBpkIaaPmndurXMr3t0kiQwgZM0wH1btvLtu9Yxc2Pt\ndEm7HuusWf7G7R00t7Y9vvz7X93OmxaPSYiSNOmZwEnaw8zDj2T23AU1bTt3bK9ZnjVnHi3t0x9f\n3oay+aEAABY1SURBVLxhPbBhLMKTpEnPPnCSJEkVYwInSZJUMSZwkiRJFWMCJ0mSVDEmcJIkSRVj\nAidJklQxJnCSJEkVYwInSZJUMSZwk8SKFcu5+OLns2LF8kaHIkmSRskEbhLo7NzBqlU3ALBq1Y10\ndu5ocESSJGk0TOAmga6uLnp7ewHo7e2hq6urwRFJqrJpU5poKl83lcuSxpYJnCRpv7Q1N3POwnkA\nnLNwHm3NTqstjTX/r5OkSWzFiuWsXPlNzj13GZdffsWw93v54uDli6OOkUnaFytwkjRJ2T9Wqi4T\nOEmapOwfK1WXCZwkSVLFmMBJGpYpU6dC37OHTU3lsiSpEUzgJA1Lc0srCxafAsCCp59Cc0trgyNS\nHwfqliYfEzipohrxR/u4JRew9HXv4bglF4zZObVvPoggTU4mcFIF+UdbfXwQQZqc6j4OXES8HzgV\n6AHemJk/7beuFfg4cFxmnlzvWKSJYrA/2m1t7Q2OSpI0VupagYuI04GnZOYzgVcAHxqwyTXAz4He\nesYhSZI0kdT7FupZwL8BZOavgSdGxMH91r+lb70kSZKGp94J3BHAxn7LD5ZtAGTmo3U+vyRJ0oQz\n1nOhNo1m50MPnU5zs2NP7a+Wlp6a5VmzDuYJTzikpm3LloPZNJZBjdLMmQcze/YhQ294AG3ZcjDc\nO7J9D3S8Q/1Oq/b7bJSxvo5Gcw3B4PEOdS10d3dz9913D3q8bdu21Sw/9NAGdu3a/b1669aNzBh5\nuJNGIz6PpHoncOvpV3EDjgTuH+nBtmzZPuqAJqNHHqn9kN60aRs7d9YWXzdvrt1mvNu8eRsbNz4y\n5ucczb4HMt6hfqdV+302ylhfR6P9vQwW71DXwt13/5Zbv3cbc+bM2+N4A59evv22jpqHYe644zYu\nnDaqkCeFkV5HJn0ajXoncCuBq4BPRMQJwH2D3DZtYpSVudFYsWI5K1d+k3PPXcbll1/RqDAOiO7u\nbjo61uzRvn177Vve0bGG6dMPqmlbt24t8+sanaRGmTNnHvPnL9yjffv22uRv7tz5TJ++u5vy/fff\nCw9trnd4kkagrglcZv4wIm6LiFuBbuA1EXEZ8FBmXh8RXwLmA8dGxGpgeWZeV8+Y+hs4ltaLX3xp\npYdi6OhYw6duuIWZhx9Z077rsc6a5W/c3kFza1tN2+9/dTtvWlz3ECVJ0gFQ9z5wmfm/BjTd2W/d\nRfU+/75MxLG0Zh5+JLPnLqhp27mj9tbzrDnzaGmfXtO2ecN6YEO9w9N+GmlV1YqqJE1sY/0Qg6T9\n0NGxZtD+SwP7LnXcvZG2tt23w+644y7m23dJkiYsEzhpnBus/5J9lyRpcnMuVEmSpIqxAidJFdDT\n3cO6dWv3aB9Of0hoQ9LEYgJXMRNp2BNJw7dlwwZaj1pPe/vDNe09PbVPmbe1/ZL29t0J24MP3skT\nDzlrTGKUNHZM4Cpkog17Imn/LFx4BMccU/t88cMP1z5lfvTRc5kxY/dT5mvXPsC2rWMSnqQxZB+4\nChls2BNJkjT5mMBJkiRVjAmcVEFTm6fRNwNdU1NTuSxJmixM4KQKam1p5eSTTgPgpBNPo7WltcER\nSZLGkg8xSBV1/tKLOH9pQ2ejkyQ1iBU4SZKkijGBk6RJyr6UUnWZwEnSJGVfSqm67AM3CUyZOpXi\nW3YvNDWVy5JkX0qpqqzATQLNLa0sWHwKAAuefgrNfsuWJKnSJkUFrru7m46ONXu0DzUJNMDChUcz\ndQJUrI5bcgHHLbmg0WFIkqQDYFIkcB0da7j2x6uZdeScmvauztpJoG9a/xumte2eBHrT+vt5CbBo\n0TFjEaYkSdKwTIoEDmDWkXN40lG1k0A/9mjtJNCz582l9aDpSFJVTJvWTFMT9PbClClNTJs2aT7W\npUnNPnCSVGHt7S0sW3YiAEuXnkB7e0uDI5I0FvyqJkkVd+WV53Hllec1OgxJY8gEbhwa6UMX69at\nrXtskiSp8UzgxqGOjjWs/cyHWTDz0Jr23q5dtcs3fJnefv1d7lvTAaddMhYhSpKkBjKBG6cWzDyU\nRYcfVtP2yM6umuWFs2dxSMvuqW/WbdrCA2MSnSRJaiQfYpAkSaoYEzhJkqSKMYGTGmjFiuVcfPHz\nWbFieaNDkSRViAmc1CCdnTtYteoGAFatupHOzh0NjkiSVBUmcFKDdHV10dvbC0Bvbw9dXV1D7CFJ\nUsEEbhS8/SVJkhrBBG6EvP0lSZIaxQRuhEZz+8vKnSRJGg0TuDFm5U6SJI2WCdwYs+O6JEkarUk9\nldbU5mZoAnqBpqZiWTrAerp7WLdu7R7t27c/WrPc0bGG6dMPqmkr9murZ3iSpAqa1BlLc2sLi049\nmbt/+BMWnXoSza0tjQ5JE9CWDRtoPWo97e0P17T39HTWLLe1/ZL29tpk7cEH7+SJh5xV9xglSdUy\nqRM4gOMvXMbxFy5rdBia4BYuPIJjjplf0/bww9trlo8+ei4zZkyvaVu79gG2ba17eJKkirEPnCRJ\nUsVM+grcvuyt7xIM3X+pu7sbaGLq1Cn7tR8U/Z5qazWSJEm7mcDtw976LsHQ/ZduvfVOdmwL5syZ\nV7PdwGFDOu7eSFvbtpq2O+64i/nTRhu9JEmaqEzghjBY3yUYuv9S0XdpHvPnL6zZbvv22mRt7tz5\nTJ9+cE3b/fffCw9t3uOc06Y07X5otlyWJEmTj33gKqStuZlzFhYVvXMWzqPNYU8kSZqUzAAq5uWL\ng5cvjkaHIUmSGsgKnNQg06Y101TeBZ8ypYlp0/w+JUkaHhM4qUHa21tYtuxEAJYuPYH2dgeSliQN\nj1/5R6ivetLbu3/Vk6nN0+ibv6upqalc1mR15ZXnceWV5zU6DElSxViBG6GRVk9aW1o5+aTTADjp\nxNNobWmtW4ySJGlisgI3CiOtnpy/9CLOX3pRHSKSJEmTgRU4SZKkijGBkyRJqhgTOEmSpIoxgZMk\nSaoYEzhJkqSKMYGTJEmqmLoPIxIR7wdOBXqAN2bmT/utOxt4N7ALuCEzr653PJIkSVVX1wpcRJwO\nPCUznwm8AvjQgE0+CLwAeDZwbkQ8tZ7xSJIkTQT1voV6FvBvAJn5a+CJEXEwQEQ8GdiUmeszsxf4\nZrm9JEmS9qHeCdwRwMZ+yw+WbYOt+wMwp87xSJIkVd5YT6XVNMJ1o7Zp/f37vc+WP2yko6N7ROe7\n996N7Nh274j23bhxA+se2bLf+63fupXNf1g/onNu3fwH1jVv3f9zPvgID903su8Bmzc8QsfUdfu9\nX8d963jSYU8b0TlHqyrX0UivIRj5dTTSawgm13U0kmsIRn4dNeKzCKp1HY30GoLGfh5pcmvq7e2t\n28Ej4h3A+sz8RLl8N/DHmfloRBwFfKHsH0dEvB14MDM/WreAJEmSJoB630JdCfw3gIg4AbgvMx8F\nyMy1wCERsSAimoHnldtLkiRpH+pagQOIiPcAzwG6gdcAJwAPZeb1EfFs4L1AL/CVzPxAXYORJEma\nAOqewEmSJOnAciYGSZKkijGBkyRJqhgTOEmSpIoZ63HgNEA5nMqdwE8pEuou4B8yc/V+HufTwJcz\n85t7WT8DuA6YCdwLXJKZXQO2+Z/AS4CdwF9k5m37+eOoQcbwOroA+FvgMYrBty/NzJ0DtvE6Gmci\n4hLgs8ARmbl5iG1flJlfHeZxrwHuzMzP7UcsvweOy8ztg6x7D8VwUu/v13Yu8FrgK8DWzLx+uOeS\nJjIrcOPDrzPzzMxcArwK+HBEPP0An+PvgBsz81TgduAZ/VdGxB8BF1E8JfwqimFdVC1jcR29Djg3\nM88AHgVe2H+l19G4dQnwO8phnfYmIlqAN9c5ln09OfcvFNdPfxcB12bm50zepN2swI0zmbkmIq6m\nGHLl1RHxBuDPKD70rs/M90bEAopv01OAtcD/6Nu/HFPvBuDqzPxuv0P/V+D08hxXD3Lq5wFfKuel\nvb38TxVVr+soM8/pt/4I4L4Bp/Y6Gmci4lDgZOBy4G+A5WX7zcBrMvOXEfEa4DBgNrA4Ij4CvLHc\n9migBXhHZq6KiJcCfw3cA3QCd5bXw2DbngO8G9gFXJeZH6KcdSci5gNfA56XmRsAMvMXEdEWEQsy\nc1153LOB15YDwz8I/IKiItcLBPDVzHxXRJwF/D1FdXgLcFFm7qrLmyqNA1bgxqfbgD+KiIXAZcCz\nKJKviyLiaIoPxH/MzOcA64GTyv2agA8AXxyQvEHxx/bKiPheRPxzREwbsH4hcFRE3BARqyLij+vx\ng2lM1eM6IiIuA+4GfpeZtwxYvRCvo/HmvwNfB74FPCUi9jbndC/FuJy/zszXUlTtdpQV3RcBHym3\nezdwBnAh8JSybW/b/hNwHvBs4OyIaCvP0w58DvjzvuStny+wuwp3NnBLZnb2ixGKhPRS4JkUVWGA\nQym6hpwBPAI8d5/vilRxJnDj0yEUAx8fD/wwM3szsxu4leLW5/HADwAy828z88flfpcB8zPzk4Mc\nsw1YmZmnA1OBVwxY3wRMycylwFXAYMdQtdTjOiIzPws8GZgZERcPWO11NP68mKL61QN8laISuzf9\n56Q+CfgOQGbeD3RGxGzg4czcVFa3vj/Etjsyc3Nm9mTmBWUi1gR8jKISfMcgMVxHkXRCkcj9yyDb\n/CwzHytn9ulL6jYCn4qI7wBLgFn7+DmlyjOBG59OAn4O9FD7O2ot27oZ/HfXBBwdEU8ZZN26fn+g\nVwLHDVi/AfgeQGbeChw14ug1XhzQ6ygiWiPiuQBlMnA9RWWlP6+jcSQi5gKnAO+LiJ9TVKX6ku7+\nfdEGVuT71vdP6Foorpv++00t/+0ZZNtd/dYPdA9waXmLtEY5zeJjEXEsRYVtsCkWB7s1uoLioZkl\nwL/v5bzShGECNz48/sEXEYuAN1HcwrodODUippQfdH8K/Az4CXBmuf07y74fAJ8GXg98apBzrI6I\nJeXrE4EcsP4GilsdRMRTKT5gVS31vo52AZ+IiCPK5VPwOhrvLgE+kpnHl/89laJyejSwFei7nfqs\n8t8edidzP6G4VdrXX60H2AzMiIgZZTeMZ/Xb9sz+22bmFmBKRMyJiKaI+HpEPKHc/q0USdZVe4n7\nOuAfgJvKqvFwzADuiYgnlnG3DHM/qZJM4MaHYyNidUT8ALiW4lvkveU30eUUFY3vAp/IzHsoPvSu\nKDshLwRWU34rzszvAHdFxOsGnOPtwFsi4rvAIspbWxHxr+V+/wGsLWP4FEXnd1VLXa+j8g/pK4Hr\ny+toAfAJ8Doaxy6mSMj7+yzFbdTlwEcj4uvsfhjlfqAlIr5I0RetOSJWU9zGvKJ8OOWdFNfSlyiG\nroEi4Zraf9uy/S8obtt+H1iVmVvZXcF7D3BeRBw/SNxfongg5tp+bUPN+/hRii4BHwP+N8Xn3ZOG\n2EeqLOdClSRJqhgrcJIkSRVjAidJklQxJnCSJEkVYwInSZJUMSZwkiRJFWMCJ0mSVDFOZi8NEBFH\nUQxQ+wOKwXGbgVuAv8/MHSM43s3lvqv7tT0DuDwz33AA4r0UeBWwk2L6rJ8Ab8jMrn3s80LgGuBq\n4EdAW2b+fLSx7K+IOA74EHDevuLdz2NeRjGd18Dxz4ba7yjg/5axPHogYpGkerECJw3uD5l5Zjkx\n9lnAwQw+J+OIZOb/P0DJ21yKycXPKeM9mSLW5w+x6zLgvWWS8wLghNHGsr8iogn4PPDqA5W8QTFX\n6/4mb+V+aykGub3mQMUiSfViBU4aQmbujIg3A78tp4f6DfB/KKYk6wFuzsy3A0TEW4ELKOYZ/Xxm\nfrT/sSJiBbCGoqJ3dWaeVlbovk0x7+MxwDsy8wsR8WSKBKeHoqq2DDg/M9f0O+ShFFMfHQTsKON9\nWb/znQ+8DXgU2E5RqfsvwPnAs8qR6l8NPBQRO4AXZeaLImIG8CBwVmbeEhF/A3QB3wA+Xr6eAbw1\nM1dFxDsoJrhfAPxlue9HgXaKhPLvMvOmAW/thcA9mfmbMtYlwDvKdV3AKzNzbUT8HvggsJRixogr\nM/PmiDiBYjaBRyim8Hpn+T68DZiamW+PiIcoqoxLgSOAizLzrohYDLyP4jNwGvC6zLwd+Azwzoh4\nW2ZuQpLGKStw0jBk5i7gp8Bi4CJgYWY+C3gOcG5EnBYRzwaWZeafAqeV7X1zPxIRVwGPZObVZVP/\naVAOyszzgVcAf122vQu4LjNPp5jQ+5hB4voF8GVgTUT8e0S8KSLmledrp5jq6gWZeRZwI8Wt3K+W\nr6/JzHf1vQa+AvRNa3Q6RVL5nHL5DOBbFEnQWzPzHOANFNMh9VlYVgF/Dvwz8I+ZeTZFovbJiBj4\neXNeee6+WD9WxnoG8BGKBKvP9sx8LkW18fVl2wcpkt0zgIcYfO7LGcAd5c//xfL9hWKKpldl5pkU\n0319snw/dwG3UlRdJWncMoGThu8JFJW1UyiSGzKzh6KadnLZfkvZviszn1/O/Qjwcorkbm+3Tb9T\n/rsWmFm+/pO+9sz8FkUVbQ+Z+XrgWIo5J08AflFW3o4FHsjM+/ud4+S9/XCZuRP4VUQ8jSJh+wBF\nla4ZeHJm3kUxV+ZfRcT3KKqQs/od4kf9Xp9BUcm6mWKezMeAwweccj67J7t/OsXE6l8r9/nLAcf+\nTvnvwPfnu+Xrr+zt5xq4b0TMBgL4VHmuD1JUCem33cJ9HE+SGs5bqNIwRMR04BnAz4BnD1jdxO5q\n2t6+FLVQTBJ+1iC3EgF2DThe37F6+rX3f90/trbMfICi/9ZnI+IVFJPOv73fsQbGuTcrKapvJ1NU\nAt9K8fPeWq7/CHBtZn62fADh6/323dnvdSdFNW3LEOfr8xiwtqyIDWao92fQ92Yv+z4GdO7jXJI0\n7lmBkwb3eOITEdMoqjQrM7ODotJ0TrmumeI2448onlo9KyKmRsS0iLg5Io4oD/Nx4KXA8og4bJgx\n/IqiXxwRcQ61VaK+2K4A/rWMsc/RwG8p+urN7rulCpxNbZWsTw+7bz9+m6IP37bM7Ka4bfxmitun\nUFTRflm+/jOgdS+xfx+4uIzxsIj4wCDb3ENRhaOM9bAyKSQiTi8T0X15/P0BXjjEto/LzIeBjohY\nWp7r2Ih4W79NjgI6hns8SWoEEzhpcIdFxOryVuFtwFbgz8t1XwZ+FxHfB74HfC0zf5iZP6K4jdnX\n/tWyMtYLj/dXex/waWorY3uril0FvDYibqJIEu+ltppEZi4H/gO4NSJuKuOdBVyVmZ1lzF+KiNXA\nmRQVtYHnXA28PSKuLG+TLi7jh+IW5XnAqnL5/cDnI+IGitvFmyPimkF+hjcALyjj+X/AYFXHG8tj\nU8b6Unbf1nwnu2+P7u39+SvggxGxkqKvW295S7u/ve17GfCWiPguxe9jJUBETKVICgeLV5LGjabe\n3qHuqEhqhIg4EWjNzB+UT4v+Eji8rIxVXjmMyE+Bl2Tmr0ew/xJgU2beWT6Rem1mPm2UMb0S+JPM\nfM1ojiNJ9WYfOGn82kZRYYJiqIsrJkryBpCZvRHxMuCfImIkA/l2UVTsOinen1eNJp5yIN+XUVYF\nJWk8swInSZJUMfaBkyRJqhgTOEmSpIoxgZMkSaoYEzhJkqSKMYGTJEmqGBM4SZKkivlPdSWqijWq\nj+YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc43935510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()[raport_final.kind.str.startswith('Classical') | raport_final.kind.str.startswith('Horizontal')]\n",
    "data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "g = sns.factorplot(x=\"engine\", y=\"ef1_perc\", hue=\"score\", data=data2, size=7, kind=\"bar\", palette=\"Set3\")\n",
    "g.set_titles('')\n",
    "g.set_ylabels('Enrichment Factor 1%')\n",
    "g.set_xlabels('Docking Software (engine)')\n",
    "g.savefig('dude_raport_horizontal_ef1.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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grKz+nyikf1YcuwH/jMdTyP+A5TXArLgEi5l1IxSwb7QjdFw6PSo+7PBlwhLx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\nCv1/QMw99EdGk3cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc4230b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=7, kind=\"bar\", palette=sns.color_palette(\"Set3\", 15))\n",
    "g.set_titles('')\n",
    "g.set_ylabels('Enrichment Factor 1%')\n",
    "g.set_xlabels('Docking Software (engine)')\n",
    "g.savefig('dude_raport_all_ef1.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LiCSrrR3G/vtXA7BuXeoexqNGVbHHHpVsbvtn2O6sP/AbM3sP2BMYD/zUzLYC77j7CjP7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\nym3R/dMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc423694d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "g = sns.factorplot(x=\"engine\", y=\"roc_log_auc\", hue=\"score\", data=data2, size=7, kind=\"bar\", palette=\"Set3\")\n",
    "g.set_titles('')\n",
    "g.set_ylabels('log-ROC AUC')\n",
    "g.set_xlabels('Docking Software (engine)')\n",
    "g.savefig('dude_raport_all_roc_log_auc.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ziIhIBt519ynhvxMJer8g6Il60d2j7t4CPE+Q5AC83EZb1wCPufsrYf3FSfUP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc43c399d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "g = sns.factorplot(x=\"engine\", y=\"roc_auc\", hue=\"score\", data=data2, size=7, kind=\"bar\", palette=\"Set3\")\n",
    "g.set_titles('')\n",
    "g.set_ylabels('ROC AUC')\n",
    "g.set_xlabels('Docking Software (engine)')\n",
    "g.savefig('dude_raport_all_roc_auc.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accuracy</th>\n",
       "      <th>ef0.1</th>\n",
       "      <th>ef1</th>\n",
       "      <th>ef10</th>\n",
       "      <th>ef1_perc</th>\n",
       "      <th>ef2</th>\n",
       "      <th>ef5</th>\n",
       "      <th>engine</th>\n",
       "      <th>mse</th>\n",
       "      <th>precision</th>\n",
       "      <th>...</th>\n",
       "      <th>roc_log_auc</th>\n",
       "      <th>rp</th>\n",
       "      <th>rp_active</th>\n",
       "      <th>rp_inactive</th>\n",
       "      <th>rs</th>\n",
       "      <th>rs_active</th>\n",
       "      <th>rs_inactive</th>\n",
       "      <th>score</th>\n",
       "      <th>n_folds</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.842983</td>\n",
       "      <td>22.778608</td>\n",
       "      <td>9.038202</td>\n",
       "      <td>3.486735</td>\n",
       "      <td>0.144710</td>\n",
       "      <td>7.013350</td>\n",
       "      <td>4.786295</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.071097</td>\n",
       "      <td>0.045369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197348</td>\n",
       "      <td>0.144049</td>\n",
       "      <td>0.047154</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.094102</td>\n",
       "      <td>0.007613</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.768345</td>\n",
       "      <td>12.184598</td>\n",
       "      <td>6.353398</td>\n",
       "      <td>3.389563</td>\n",
       "      <td>0.108631</td>\n",
       "      <td>5.309794</td>\n",
       "      <td>4.247709</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.061271</td>\n",
       "      <td>0.0400955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165643</td>\n",
       "      <td>0.125854</td>\n",
       "      <td>0.109282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101092</td>\n",
       "      <td>0.151655</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.807789</td>\n",
       "      <td>22.467610</td>\n",
       "      <td>8.466837</td>\n",
       "      <td>3.131219</td>\n",
       "      <td>0.122334</td>\n",
       "      <td>6.137663</td>\n",
       "      <td>4.205881</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.067617</td>\n",
       "      <td>0.0345132</td>\n",
       "      <td>...</td>\n",
       "      <td>0.176681</td>\n",
       "      <td>0.123444</td>\n",
       "      <td>0.055799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.080725</td>\n",
       "      <td>0.100521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.825112</td>\n",
       "      <td>8.070605</td>\n",
       "      <td>7.040200</td>\n",
       "      <td>3.339524</td>\n",
       "      <td>0.116310</td>\n",
       "      <td>6.204008</td>\n",
       "      <td>4.439912</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.073321</td>\n",
       "      <td>0.0435922</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187772</td>\n",
       "      <td>0.125186</td>\n",
       "      <td>0.099862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.093835</td>\n",
       "      <td>0.103501</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.858557</td>\n",
       "      <td>14.695274</td>\n",
       "      <td>7.048537</td>\n",
       "      <td>3.598345</td>\n",
       "      <td>0.112125</td>\n",
       "      <td>6.351879</td>\n",
       "      <td>4.655717</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.054411</td>\n",
       "      <td>0.0495899</td>\n",
       "      <td>...</td>\n",
       "      <td>0.208230</td>\n",
       "      <td>0.118441</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101271</td>\n",
       "      <td>0.055735</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.830989</td>\n",
       "      <td>21.646891</td>\n",
       "      <td>9.078013</td>\n",
       "      <td>3.407408</td>\n",
       "      <td>0.146958</td>\n",
       "      <td>7.147489</td>\n",
       "      <td>4.802671</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.069827</td>\n",
       "      <td>0.0441808</td>\n",
       "      <td>...</td>\n",
       "      <td>0.191461</td>\n",
       "      <td>0.148290</td>\n",
       "      <td>0.070272</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.097745</td>\n",
       "      <td>0.033157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.805056</td>\n",
       "      <td>13.686941</td>\n",
       "      <td>7.189776</td>\n",
       "      <td>3.474411</td>\n",
       "      <td>0.169063</td>\n",
       "      <td>6.213362</td>\n",
       "      <td>4.643699</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.076082</td>\n",
       "      <td>0.0600118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.181391</td>\n",
       "      <td>0.154878</td>\n",
       "      <td>0.087774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.114933</td>\n",
       "      <td>0.109545</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.780569</td>\n",
       "      <td>25.514491</td>\n",
       "      <td>9.236338</td>\n",
       "      <td>3.311226</td>\n",
       "      <td>0.110563</td>\n",
       "      <td>6.700469</td>\n",
       "      <td>4.436741</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.054675</td>\n",
       "      <td>0.0270526</td>\n",
       "      <td>...</td>\n",
       "      <td>0.165917</td>\n",
       "      <td>0.127200</td>\n",
       "      <td>0.122582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.072942</td>\n",
       "      <td>0.118705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.801104</td>\n",
       "      <td>12.412888</td>\n",
       "      <td>7.211533</td>\n",
       "      <td>3.471256</td>\n",
       "      <td>0.132571</td>\n",
       "      <td>5.820476</td>\n",
       "      <td>4.423288</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.082986</td>\n",
       "      <td>0.0460747</td>\n",
       "      <td>...</td>\n",
       "      <td>0.178287</td>\n",
       "      <td>0.146280</td>\n",
       "      <td>0.177491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.100926</td>\n",
       "      <td>0.165747</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.808791</td>\n",
       "      <td>11.565221</td>\n",
       "      <td>5.318449</td>\n",
       "      <td>3.033981</td>\n",
       "      <td>0.084712</td>\n",
       "      <td>4.902945</td>\n",
       "      <td>3.723900</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.054746</td>\n",
       "      <td>0.0394824</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179322</td>\n",
       "      <td>0.086364</td>\n",
       "      <td>-0.005746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091318</td>\n",
       "      <td>0.089459</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.87493</td>\n",
       "      <td>18.060321</td>\n",
       "      <td>9.119593</td>\n",
       "      <td>3.133602</td>\n",
       "      <td>0.095830</td>\n",
       "      <td>6.927981</td>\n",
       "      <td>4.456678</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.047245</td>\n",
       "      <td>0.0296813</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206727</td>\n",
       "      <td>0.104023</td>\n",
       "      <td>0.051695</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.061752</td>\n",
       "      <td>0.038692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.848248</td>\n",
       "      <td>10.293345</td>\n",
       "      <td>7.004505</td>\n",
       "      <td>3.104431</td>\n",
       "      <td>0.085429</td>\n",
       "      <td>5.475603</td>\n",
       "      <td>4.053720</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.043329</td>\n",
       "      <td>0.0319991</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193681</td>\n",
       "      <td>0.093059</td>\n",
       "      <td>0.040832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.073917</td>\n",
       "      <td>0.082116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.894983</td>\n",
       "      <td>28.851217</td>\n",
       "      <td>10.758532</td>\n",
       "      <td>3.493130</td>\n",
       "      <td>0.087195</td>\n",
       "      <td>7.317625</td>\n",
       "      <td>4.685973</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.038815</td>\n",
       "      <td>0.0278304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224830</td>\n",
       "      <td>0.108660</td>\n",
       "      <td>0.023194</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.059970</td>\n",
       "      <td>0.051084</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.893033</td>\n",
       "      <td>11.664629</td>\n",
       "      <td>6.696172</td>\n",
       "      <td>3.091399</td>\n",
       "      <td>0.092025</td>\n",
       "      <td>5.384838</td>\n",
       "      <td>3.961902</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.067082</td>\n",
       "      <td>0.0421814</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219822</td>\n",
       "      <td>0.104768</td>\n",
       "      <td>0.090803</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.078718</td>\n",
       "      <td>0.141574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.896755</td>\n",
       "      <td>12.272359</td>\n",
       "      <td>5.283876</td>\n",
       "      <td>3.440986</td>\n",
       "      <td>0.051963</td>\n",
       "      <td>5.078392</td>\n",
       "      <td>4.227589</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.036061</td>\n",
       "      <td>0.0338866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226410</td>\n",
       "      <td>0.078017</td>\n",
       "      <td>-0.024987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.077448</td>\n",
       "      <td>0.034278</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v1</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.847891</td>\n",
       "      <td>34.351611</td>\n",
       "      <td>15.904291</td>\n",
       "      <td>4.550797</td>\n",
       "      <td>0.254642</td>\n",
       "      <td>11.366782</td>\n",
       "      <td>6.844403</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.069269</td>\n",
       "      <td>0.0561729</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213384</td>\n",
       "      <td>0.215367</td>\n",
       "      <td>0.063053</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117225</td>\n",
       "      <td>0.063864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.755828</td>\n",
       "      <td>28.198642</td>\n",
       "      <td>11.679808</td>\n",
       "      <td>4.014552</td>\n",
       "      <td>0.199702</td>\n",
       "      <td>8.356397</td>\n",
       "      <td>5.542911</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.059860</td>\n",
       "      <td>0.0435267</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167705</td>\n",
       "      <td>0.168393</td>\n",
       "      <td>0.046307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121309</td>\n",
       "      <td>0.077543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.799882</td>\n",
       "      <td>38.737258</td>\n",
       "      <td>14.525675</td>\n",
       "      <td>4.830210</td>\n",
       "      <td>0.209877</td>\n",
       "      <td>10.604949</td>\n",
       "      <td>6.878584</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.064143</td>\n",
       "      <td>0.0453254</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192435</td>\n",
       "      <td>0.212022</td>\n",
       "      <td>0.098014</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121966</td>\n",
       "      <td>0.160281</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.823712</td>\n",
       "      <td>27.171037</td>\n",
       "      <td>12.677755</td>\n",
       "      <td>4.232401</td>\n",
       "      <td>0.209447</td>\n",
       "      <td>9.211603</td>\n",
       "      <td>6.079928</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.071492</td>\n",
       "      <td>0.0510682</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197696</td>\n",
       "      <td>0.193602</td>\n",
       "      <td>0.127603</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117609</td>\n",
       "      <td>0.137880</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.859522</td>\n",
       "      <td>27.485604</td>\n",
       "      <td>11.884161</td>\n",
       "      <td>4.478123</td>\n",
       "      <td>0.189048</td>\n",
       "      <td>9.220470</td>\n",
       "      <td>6.240410</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.053294</td>\n",
       "      <td>0.0599417</td>\n",
       "      <td>...</td>\n",
       "      <td>0.222080</td>\n",
       "      <td>0.165743</td>\n",
       "      <td>-0.022324</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123782</td>\n",
       "      <td>-0.027357</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.812688</td>\n",
       "      <td>28.774526</td>\n",
       "      <td>12.227528</td>\n",
       "      <td>4.135486</td>\n",
       "      <td>0.197943</td>\n",
       "      <td>8.788764</td>\n",
       "      <td>5.946416</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.068599</td>\n",
       "      <td>0.0474064</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190947</td>\n",
       "      <td>0.184517</td>\n",
       "      <td>0.087798</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.108478</td>\n",
       "      <td>0.050451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.782865</td>\n",
       "      <td>18.086315</td>\n",
       "      <td>9.684188</td>\n",
       "      <td>4.179080</td>\n",
       "      <td>0.227717</td>\n",
       "      <td>7.656623</td>\n",
       "      <td>5.558738</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.074976</td>\n",
       "      <td>0.0650156</td>\n",
       "      <td>...</td>\n",
       "      <td>0.180763</td>\n",
       "      <td>0.188193</td>\n",
       "      <td>0.062393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143379</td>\n",
       "      <td>0.059467</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.750887</td>\n",
       "      <td>45.267645</td>\n",
       "      <td>14.555480</td>\n",
       "      <td>4.239029</td>\n",
       "      <td>0.174235</td>\n",
       "      <td>10.287796</td>\n",
       "      <td>6.135568</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.052597</td>\n",
       "      <td>0.0306938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166373</td>\n",
       "      <td>0.180286</td>\n",
       "      <td>0.123677</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102080</td>\n",
       "      <td>0.150204</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.809868</td>\n",
       "      <td>26.286116</td>\n",
       "      <td>13.215043</td>\n",
       "      <td>4.328085</td>\n",
       "      <td>0.242934</td>\n",
       "      <td>9.737400</td>\n",
       "      <td>6.136946</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.080737</td>\n",
       "      <td>0.0548829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.192024</td>\n",
       "      <td>0.221328</td>\n",
       "      <td>0.189949</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.124855</td>\n",
       "      <td>0.208696</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.819973</td>\n",
       "      <td>6.608698</td>\n",
       "      <td>7.271317</td>\n",
       "      <td>4.372258</td>\n",
       "      <td>0.115817</td>\n",
       "      <td>6.855813</td>\n",
       "      <td>5.486103</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.052970</td>\n",
       "      <td>0.0515919</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200049</td>\n",
       "      <td>0.123873</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.120666</td>\n",
       "      <td>0.053198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.858846</td>\n",
       "      <td>23.964656</td>\n",
       "      <td>11.486510</td>\n",
       "      <td>3.718541</td>\n",
       "      <td>0.120702</td>\n",
       "      <td>8.529424</td>\n",
       "      <td>5.445503</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.046576</td>\n",
       "      <td>0.0322023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205769</td>\n",
       "      <td>0.130995</td>\n",
       "      <td>0.067215</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.074589</td>\n",
       "      <td>0.077311</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.846609</td>\n",
       "      <td>22.361406</td>\n",
       "      <td>9.848974</td>\n",
       "      <td>3.652062</td>\n",
       "      <td>0.120121</td>\n",
       "      <td>7.128951</td>\n",
       "      <td>4.800458</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.042575</td>\n",
       "      <td>0.0360563</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200152</td>\n",
       "      <td>0.120621</td>\n",
       "      <td>0.050867</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088759</td>\n",
       "      <td>0.071820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.874507</td>\n",
       "      <td>40.514476</td>\n",
       "      <td>13.217625</td>\n",
       "      <td>4.686207</td>\n",
       "      <td>0.107125</td>\n",
       "      <td>9.685092</td>\n",
       "      <td>6.297161</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.037881</td>\n",
       "      <td>0.0335881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.231139</td>\n",
       "      <td>0.147821</td>\n",
       "      <td>0.078801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.087848</td>\n",
       "      <td>0.113435</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.90867</td>\n",
       "      <td>17.774673</td>\n",
       "      <td>8.760825</td>\n",
       "      <td>3.454109</td>\n",
       "      <td>0.120399</td>\n",
       "      <td>6.584569</td>\n",
       "      <td>4.665000</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.066670</td>\n",
       "      <td>0.051273</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238074</td>\n",
       "      <td>0.130651</td>\n",
       "      <td>0.104461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090031</td>\n",
       "      <td>0.150728</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.900681</td>\n",
       "      <td>16.947544</td>\n",
       "      <td>8.219363</td>\n",
       "      <td>4.092777</td>\n",
       "      <td>0.080831</td>\n",
       "      <td>7.133233</td>\n",
       "      <td>5.331460</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.035499</td>\n",
       "      <td>0.0407776</td>\n",
       "      <td>...</td>\n",
       "      <td>0.239871</td>\n",
       "      <td>0.104760</td>\n",
       "      <td>-0.040569</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.091547</td>\n",
       "      <td>0.002047</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v2</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.849058</td>\n",
       "      <td>37.658183</td>\n",
       "      <td>16.714231</td>\n",
       "      <td>4.867438</td>\n",
       "      <td>0.267610</td>\n",
       "      <td>11.734937</td>\n",
       "      <td>7.278851</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.068190</td>\n",
       "      <td>0.06003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218972</td>\n",
       "      <td>0.244037</td>\n",
       "      <td>0.074037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.123386</td>\n",
       "      <td>0.042945</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.799452</td>\n",
       "      <td>40.905438</td>\n",
       "      <td>14.900023</td>\n",
       "      <td>4.561200</td>\n",
       "      <td>0.254762</td>\n",
       "      <td>10.297518</td>\n",
       "      <td>6.507350</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.058515</td>\n",
       "      <td>0.0515086</td>\n",
       "      <td>...</td>\n",
       "      <td>0.189825</td>\n",
       "      <td>0.218929</td>\n",
       "      <td>0.068515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.127036</td>\n",
       "      <td>0.127276</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.805173</td>\n",
       "      <td>42.610984</td>\n",
       "      <td>16.648857</td>\n",
       "      <td>4.843160</td>\n",
       "      <td>0.240554</td>\n",
       "      <td>12.236480</td>\n",
       "      <td>7.303315</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.063369</td>\n",
       "      <td>0.045162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.193417</td>\n",
       "      <td>0.235623</td>\n",
       "      <td>0.105917</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.115800</td>\n",
       "      <td>0.146708</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.837015</td>\n",
       "      <td>36.048703</td>\n",
       "      <td>15.752785</td>\n",
       "      <td>4.699071</td>\n",
       "      <td>0.260250</td>\n",
       "      <td>10.830040</td>\n",
       "      <td>6.646381</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.070243</td>\n",
       "      <td>0.0580182</td>\n",
       "      <td>...</td>\n",
       "      <td>0.210765</td>\n",
       "      <td>0.235750</td>\n",
       "      <td>0.117020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125289</td>\n",
       "      <td>0.092802</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.84913</td>\n",
       "      <td>29.934817</td>\n",
       "      <td>12.375920</td>\n",
       "      <td>4.502713</td>\n",
       "      <td>0.196871</td>\n",
       "      <td>9.261450</td>\n",
       "      <td>6.344235</td>\n",
       "      <td>Dock 3.6</td>\n",
       "      <td>0.053499</td>\n",
       "      <td>0.056704</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214913</td>\n",
       "      <td>0.166024</td>\n",
       "      <td>-0.039285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.118437</td>\n",
       "      <td>-0.028676</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.819999</td>\n",
       "      <td>29.038512</td>\n",
       "      <td>14.318382</td>\n",
       "      <td>4.360529</td>\n",
       "      <td>0.231791</td>\n",
       "      <td>10.469748</td>\n",
       "      <td>6.444157</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.068127</td>\n",
       "      <td>0.0509657</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197988</td>\n",
       "      <td>0.203518</td>\n",
       "      <td>0.061666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.119283</td>\n",
       "      <td>0.045838</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.792985</td>\n",
       "      <td>21.263641</td>\n",
       "      <td>12.325331</td>\n",
       "      <td>4.548542</td>\n",
       "      <td>0.289822</td>\n",
       "      <td>9.772591</td>\n",
       "      <td>6.522709</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.073778</td>\n",
       "      <td>0.0693957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.187959</td>\n",
       "      <td>0.226206</td>\n",
       "      <td>0.086448</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146683</td>\n",
       "      <td>0.082203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.777454</td>\n",
       "      <td>41.563929</td>\n",
       "      <td>16.699630</td>\n",
       "      <td>4.832823</td>\n",
       "      <td>0.199901</td>\n",
       "      <td>11.978376</td>\n",
       "      <td>7.339590</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.051968</td>\n",
       "      <td>0.0338935</td>\n",
       "      <td>...</td>\n",
       "      <td>0.179221</td>\n",
       "      <td>0.209303</td>\n",
       "      <td>0.152093</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101927</td>\n",
       "      <td>0.189461</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.824313</td>\n",
       "      <td>29.936965</td>\n",
       "      <td>14.972168</td>\n",
       "      <td>4.610033</td>\n",
       "      <td>0.275236</td>\n",
       "      <td>10.853907</td>\n",
       "      <td>6.788722</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.079749</td>\n",
       "      <td>0.060386</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202790</td>\n",
       "      <td>0.248163</td>\n",
       "      <td>0.206135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.133934</td>\n",
       "      <td>0.206843</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.801271</td>\n",
       "      <td>24.369572</td>\n",
       "      <td>9.348836</td>\n",
       "      <td>3.964956</td>\n",
       "      <td>0.148908</td>\n",
       "      <td>7.666045</td>\n",
       "      <td>5.644036</td>\n",
       "      <td>Autodock Vina</td>\n",
       "      <td>0.052826</td>\n",
       "      <td>0.0438431</td>\n",
       "      <td>...</td>\n",
       "      <td>0.183770</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.107342</td>\n",
       "      <td>0.059034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.868476</td>\n",
       "      <td>27.090481</td>\n",
       "      <td>12.182662</td>\n",
       "      <td>3.777731</td>\n",
       "      <td>0.128018</td>\n",
       "      <td>9.225703</td>\n",
       "      <td>5.675301</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.046468</td>\n",
       "      <td>0.0350063</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214193</td>\n",
       "      <td>0.145080</td>\n",
       "      <td>0.056939</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.077545</td>\n",
       "      <td>0.060535</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.855921</td>\n",
       "      <td>13.842775</td>\n",
       "      <td>8.497851</td>\n",
       "      <td>3.680511</td>\n",
       "      <td>0.103643</td>\n",
       "      <td>6.933393</td>\n",
       "      <td>4.928470</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.042698</td>\n",
       "      <td>0.0382945</td>\n",
       "      <td>...</td>\n",
       "      <td>0.206845</td>\n",
       "      <td>0.118387</td>\n",
       "      <td>0.063446</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.090206</td>\n",
       "      <td>0.085383</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.88628</td>\n",
       "      <td>38.123800</td>\n",
       "      <td>15.065050</td>\n",
       "      <td>4.532503</td>\n",
       "      <td>0.124365</td>\n",
       "      <td>10.760750</td>\n",
       "      <td>6.567799</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.038489</td>\n",
       "      <td>0.0346342</td>\n",
       "      <td>...</td>\n",
       "      <td>0.235431</td>\n",
       "      <td>0.155020</td>\n",
       "      <td>0.059120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.077841</td>\n",
       "      <td>0.075454</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.903512</td>\n",
       "      <td>25.082632</td>\n",
       "      <td>10.821726</td>\n",
       "      <td>3.917411</td>\n",
       "      <td>0.149346</td>\n",
       "      <td>7.812500</td>\n",
       "      <td>5.423689</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.066363</td>\n",
       "      <td>0.0551827</td>\n",
       "      <td>...</td>\n",
       "      <td>0.240128</td>\n",
       "      <td>0.163787</td>\n",
       "      <td>0.130847</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095021</td>\n",
       "      <td>0.154491</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.904504</td>\n",
       "      <td>21.038330</td>\n",
       "      <td>10.509043</td>\n",
       "      <td>4.204345</td>\n",
       "      <td>0.103349</td>\n",
       "      <td>8.307428</td>\n",
       "      <td>5.801192</td>\n",
       "      <td>Dock 6.6</td>\n",
       "      <td>0.035474</td>\n",
       "      <td>0.0426122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244402</td>\n",
       "      <td>0.113968</td>\n",
       "      <td>-0.044724</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.089302</td>\n",
       "      <td>-0.013690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Vertical RF_v3</td>\n",
       "      <td>5</td>\n",
       "      <td>Vertical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    accuracy      ef0.1        ef1      ef10  ef1_perc        ef2       ef5  \\\n",
       "0   0.842983  22.778608   9.038202  3.486735  0.144710   7.013350  4.786295   \n",
       "1   0.768345  12.184598   6.353398  3.389563  0.108631   5.309794  4.247709   \n",
       "2   0.807789  22.467610   8.466837  3.131219  0.122334   6.137663  4.205881   \n",
       "3   0.825112   8.070605   7.040200  3.339524  0.116310   6.204008  4.439912   \n",
       "4   0.858557  14.695274   7.048537  3.598345  0.112125   6.351879  4.655717   \n",
       "5   0.830989  21.646891   9.078013  3.407408  0.146958   7.147489  4.802671   \n",
       "6   0.805056  13.686941   7.189776  3.474411  0.169063   6.213362  4.643699   \n",
       "7   0.780569  25.514491   9.236338  3.311226  0.110563   6.700469  4.436741   \n",
       "8   0.801104  12.412888   7.211533  3.471256  0.132571   5.820476  4.423288   \n",
       "9   0.808791  11.565221   5.318449  3.033981  0.084712   4.902945  3.723900   \n",
       "10   0.87493  18.060321   9.119593  3.133602  0.095830   6.927981  4.456678   \n",
       "11  0.848248  10.293345   7.004505  3.104431  0.085429   5.475603  4.053720   \n",
       "12  0.894983  28.851217  10.758532  3.493130  0.087195   7.317625  4.685973   \n",
       "13  0.893033  11.664629   6.696172  3.091399  0.092025   5.384838  3.961902   \n",
       "14  0.896755  12.272359   5.283876  3.440986  0.051963   5.078392  4.227589   \n",
       "15  0.847891  34.351611  15.904291  4.550797  0.254642  11.366782  6.844403   \n",
       "16  0.755828  28.198642  11.679808  4.014552  0.199702   8.356397  5.542911   \n",
       "17  0.799882  38.737258  14.525675  4.830210  0.209877  10.604949  6.878584   \n",
       "18  0.823712  27.171037  12.677755  4.232401  0.209447   9.211603  6.079928   \n",
       "19  0.859522  27.485604  11.884161  4.478123  0.189048   9.220470  6.240410   \n",
       "20  0.812688  28.774526  12.227528  4.135486  0.197943   8.788764  5.946416   \n",
       "21  0.782865  18.086315   9.684188  4.179080  0.227717   7.656623  5.558738   \n",
       "22  0.750887  45.267645  14.555480  4.239029  0.174235  10.287796  6.135568   \n",
       "23  0.809868  26.286116  13.215043  4.328085  0.242934   9.737400  6.136946   \n",
       "24  0.819973   6.608698   7.271317  4.372258  0.115817   6.855813  5.486103   \n",
       "25  0.858846  23.964656  11.486510  3.718541  0.120702   8.529424  5.445503   \n",
       "26  0.846609  22.361406   9.848974  3.652062  0.120121   7.128951  4.800458   \n",
       "27  0.874507  40.514476  13.217625  4.686207  0.107125   9.685092  6.297161   \n",
       "28   0.90867  17.774673   8.760825  3.454109  0.120399   6.584569  4.665000   \n",
       "29  0.900681  16.947544   8.219363  4.092777  0.080831   7.133233  5.331460   \n",
       "30  0.849058  37.658183  16.714231  4.867438  0.267610  11.734937  7.278851   \n",
       "31  0.799452  40.905438  14.900023  4.561200  0.254762  10.297518  6.507350   \n",
       "32  0.805173  42.610984  16.648857  4.843160  0.240554  12.236480  7.303315   \n",
       "33  0.837015  36.048703  15.752785  4.699071  0.260250  10.830040  6.646381   \n",
       "34   0.84913  29.934817  12.375920  4.502713  0.196871   9.261450  6.344235   \n",
       "35  0.819999  29.038512  14.318382  4.360529  0.231791  10.469748  6.444157   \n",
       "36  0.792985  21.263641  12.325331  4.548542  0.289822   9.772591  6.522709   \n",
       "37  0.777454  41.563929  16.699630  4.832823  0.199901  11.978376  7.339590   \n",
       "38  0.824313  29.936965  14.972168  4.610033  0.275236  10.853907  6.788722   \n",
       "39  0.801271  24.369572   9.348836  3.964956  0.148908   7.666045  5.644036   \n",
       "40  0.868476  27.090481  12.182662  3.777731  0.128018   9.225703  5.675301   \n",
       "41  0.855921  13.842775   8.497851  3.680511  0.103643   6.933393  4.928470   \n",
       "42   0.88628  38.123800  15.065050  4.532503  0.124365  10.760750  6.567799   \n",
       "43  0.903512  25.082632  10.821726  3.917411  0.149346   7.812500  5.423689   \n",
       "44  0.904504  21.038330  10.509043  4.204345  0.103349   8.307428  5.801192   \n",
       "\n",
       "           engine       mse  precision    ...    roc_log_auc        rp  \\\n",
       "0        Dock 3.6  0.071097   0.045369    ...       0.197348  0.144049   \n",
       "1        Dock 3.6  0.061271  0.0400955    ...       0.165643  0.125854   \n",
       "2        Dock 3.6  0.067617  0.0345132    ...       0.176681  0.123444   \n",
       "3        Dock 3.6  0.073321  0.0435922    ...       0.187772  0.125186   \n",
       "4        Dock 3.6  0.054411  0.0495899    ...       0.208230  0.118441   \n",
       "5   Autodock Vina  0.069827  0.0441808    ...       0.191461  0.148290   \n",
       "6   Autodock Vina  0.076082  0.0600118    ...       0.181391  0.154878   \n",
       "7   Autodock Vina  0.054675  0.0270526    ...       0.165917  0.127200   \n",
       "8   Autodock Vina  0.082986  0.0460747    ...       0.178287  0.146280   \n",
       "9   Autodock Vina  0.054746  0.0394824    ...       0.179322  0.086364   \n",
       "10       Dock 6.6  0.047245  0.0296813    ...       0.206727  0.104023   \n",
       "11       Dock 6.6  0.043329  0.0319991    ...       0.193681  0.093059   \n",
       "12       Dock 6.6  0.038815  0.0278304    ...       0.224830  0.108660   \n",
       "13       Dock 6.6  0.067082  0.0421814    ...       0.219822  0.104768   \n",
       "14       Dock 6.6  0.036061  0.0338866    ...       0.226410  0.078017   \n",
       "15       Dock 3.6  0.069269  0.0561729    ...       0.213384  0.215367   \n",
       "16       Dock 3.6  0.059860  0.0435267    ...       0.167705  0.168393   \n",
       "17       Dock 3.6  0.064143  0.0453254    ...       0.192435  0.212022   \n",
       "18       Dock 3.6  0.071492  0.0510682    ...       0.197696  0.193602   \n",
       "19       Dock 3.6  0.053294  0.0599417    ...       0.222080  0.165743   \n",
       "20  Autodock Vina  0.068599  0.0474064    ...       0.190947  0.184517   \n",
       "21  Autodock Vina  0.074976  0.0650156    ...       0.180763  0.188193   \n",
       "22  Autodock Vina  0.052597  0.0306938    ...       0.166373  0.180286   \n",
       "23  Autodock Vina  0.080737  0.0548829    ...       0.192024  0.221328   \n",
       "24  Autodock Vina  0.052970  0.0515919    ...       0.200049  0.123873   \n",
       "25       Dock 6.6  0.046576  0.0322023    ...       0.205769  0.130995   \n",
       "26       Dock 6.6  0.042575  0.0360563    ...       0.200152  0.120621   \n",
       "27       Dock 6.6  0.037881  0.0335881    ...       0.231139  0.147821   \n",
       "28       Dock 6.6  0.066670   0.051273    ...       0.238074  0.130651   \n",
       "29       Dock 6.6  0.035499  0.0407776    ...       0.239871  0.104760   \n",
       "30       Dock 3.6  0.068190    0.06003    ...       0.218972  0.244037   \n",
       "31       Dock 3.6  0.058515  0.0515086    ...       0.189825  0.218929   \n",
       "32       Dock 3.6  0.063369   0.045162    ...       0.193417  0.235623   \n",
       "33       Dock 3.6  0.070243  0.0580182    ...       0.210765  0.235750   \n",
       "34       Dock 3.6  0.053499   0.056704    ...       0.214913  0.166024   \n",
       "35  Autodock Vina  0.068127  0.0509657    ...       0.197988  0.203518   \n",
       "36  Autodock Vina  0.073778  0.0693957    ...       0.187959  0.226206   \n",
       "37  Autodock Vina  0.051968  0.0338935    ...       0.179221  0.209303   \n",
       "38  Autodock Vina  0.079749   0.060386    ...       0.202790  0.248163   \n",
       "39  Autodock Vina  0.052826  0.0438431    ...       0.183770  0.143293   \n",
       "40       Dock 6.6  0.046468  0.0350063    ...       0.214193  0.145080   \n",
       "41       Dock 6.6  0.042698  0.0382945    ...       0.206845  0.118387   \n",
       "42       Dock 6.6  0.038489  0.0346342    ...       0.235431  0.155020   \n",
       "43       Dock 6.6  0.066363  0.0551827    ...       0.240128  0.163787   \n",
       "44       Dock 6.6  0.035474  0.0426122    ...       0.244402  0.113968   \n",
       "\n",
       "    rp_active  rp_inactive        rs  rs_active  rs_inactive           score  \\\n",
       "0    0.047154          0.0  0.094102   0.007613          0.0  Vertical RF_v1   \n",
       "1    0.109282          0.0  0.101092   0.151655          0.0  Vertical RF_v1   \n",
       "2    0.055799          0.0  0.080725   0.100521          0.0  Vertical RF_v1   \n",
       "3    0.099862          0.0  0.093835   0.103501          0.0  Vertical RF_v1   \n",
       "4    0.010700          0.0  0.101271   0.055735          0.0  Vertical RF_v1   \n",
       "5    0.070272          0.0  0.097745   0.033157          0.0  Vertical RF_v1   \n",
       "6    0.087774          0.0  0.114933   0.109545          0.0  Vertical RF_v1   \n",
       "7    0.122582          0.0  0.072942   0.118705          0.0  Vertical RF_v1   \n",
       "8    0.177491          0.0  0.100926   0.165747          0.0  Vertical RF_v1   \n",
       "9   -0.005746          0.0  0.091318   0.089459          0.0  Vertical RF_v1   \n",
       "10   0.051695          0.0  0.061752   0.038692          0.0  Vertical RF_v1   \n",
       "11   0.040832          0.0  0.073917   0.082116          0.0  Vertical RF_v1   \n",
       "12   0.023194          0.0  0.059970   0.051084          0.0  Vertical RF_v1   \n",
       "13   0.090803          0.0  0.078718   0.141574          0.0  Vertical RF_v1   \n",
       "14  -0.024987          0.0  0.077448   0.034278          0.0  Vertical RF_v1   \n",
       "15   0.063053          0.0  0.117225   0.063864          0.0  Vertical RF_v2   \n",
       "16   0.046307          0.0  0.121309   0.077543          0.0  Vertical RF_v2   \n",
       "17   0.098014          0.0  0.121966   0.160281          0.0  Vertical RF_v2   \n",
       "18   0.127603          0.0  0.117609   0.137880          0.0  Vertical RF_v2   \n",
       "19  -0.022324          0.0  0.123782  -0.027357          0.0  Vertical RF_v2   \n",
       "20   0.087798          0.0  0.108478   0.050451          0.0  Vertical RF_v2   \n",
       "21   0.062393          0.0  0.143379   0.059467          0.0  Vertical RF_v2   \n",
       "22   0.123677          0.0  0.102080   0.150204          0.0  Vertical RF_v2   \n",
       "23   0.189949          0.0  0.124855   0.208696          0.0  Vertical RF_v2   \n",
       "24   0.000689          0.0  0.120666   0.053198          0.0  Vertical RF_v2   \n",
       "25   0.067215          0.0  0.074589   0.077311          0.0  Vertical RF_v2   \n",
       "26   0.050867          0.0  0.088759   0.071820          0.0  Vertical RF_v2   \n",
       "27   0.078801          0.0  0.087848   0.113435          0.0  Vertical RF_v2   \n",
       "28   0.104461          0.0  0.090031   0.150728          0.0  Vertical RF_v2   \n",
       "29  -0.040569          0.0  0.091547   0.002047          0.0  Vertical RF_v2   \n",
       "30   0.074037          0.0  0.123386   0.042945          0.0  Vertical RF_v3   \n",
       "31   0.068515          0.0  0.127036   0.127276          0.0  Vertical RF_v3   \n",
       "32   0.105917          0.0  0.115800   0.146708          0.0  Vertical RF_v3   \n",
       "33   0.117020          0.0  0.125289   0.092802          0.0  Vertical RF_v3   \n",
       "34  -0.039285          0.0  0.118437  -0.028676          0.0  Vertical RF_v3   \n",
       "35   0.061666          0.0  0.119283   0.045838          0.0  Vertical RF_v3   \n",
       "36   0.086448          0.0  0.146683   0.082203          0.0  Vertical RF_v3   \n",
       "37   0.152093          0.0  0.101927   0.189461          0.0  Vertical RF_v3   \n",
       "38   0.206135          0.0  0.133934   0.206843          0.0  Vertical RF_v3   \n",
       "39   0.007598          0.0  0.107342   0.059034          0.0  Vertical RF_v3   \n",
       "40   0.056939          0.0  0.077545   0.060535          0.0  Vertical RF_v3   \n",
       "41   0.063446          0.0  0.090206   0.085383          0.0  Vertical RF_v3   \n",
       "42   0.059120          0.0  0.077841   0.075454          0.0  Vertical RF_v3   \n",
       "43   0.130847          0.0  0.095021   0.154491          0.0  Vertical RF_v3   \n",
       "44  -0.044724          0.0  0.089302  -0.013690          0.0  Vertical RF_v3   \n",
       "\n",
       "    n_folds      kind  \n",
       "0         5  Vertical  \n",
       "1         5  Vertical  \n",
       "2         5  Vertical  \n",
       "3         5  Vertical  \n",
       "4         5  Vertical  \n",
       "5         5  Vertical  \n",
       "6         5  Vertical  \n",
       "7         5  Vertical  \n",
       "8         5  Vertical  \n",
       "9         5  Vertical  \n",
       "10        5  Vertical  \n",
       "11        5  Vertical  \n",
       "12        5  Vertical  \n",
       "13        5  Vertical  \n",
       "14        5  Vertical  \n",
       "15        5  Vertical  \n",
       "16        5  Vertical  \n",
       "17        5  Vertical  \n",
       "18        5  Vertical  \n",
       "19        5  Vertical  \n",
       "20        5  Vertical  \n",
       "21        5  Vertical  \n",
       "22        5  Vertical  \n",
       "23        5  Vertical  \n",
       "24        5  Vertical  \n",
       "25        5  Vertical  \n",
       "26        5  Vertical  \n",
       "27        5  Vertical  \n",
       "28        5  Vertical  \n",
       "29        5  Vertical  \n",
       "30        5  Vertical  \n",
       "31        5  Vertical  \n",
       "32        5  Vertical  \n",
       "33        5  Vertical  \n",
       "34        5  Vertical  \n",
       "35        5  Vertical  \n",
       "36        5  Vertical  \n",
       "37        5  Vertical  \n",
       "38        5  Vertical  \n",
       "39        5  Vertical  \n",
       "40        5  Vertical  \n",
       "41        5  Vertical  \n",
       "42        5  Vertical  \n",
       "43        5  Vertical  \n",
       "44        5  Vertical  \n",
       "\n",
       "[45 rows x 25 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[data2.kind == 'Vertical']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rbdu2jVVWVrILFy4wqVTKdu/ezX744Qfm4ODA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc413c23d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2 = data2[(data2.kind.str.startswith('Horizontal'))]\n",
    "#data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "#data2.loc[data2['engine'] == 'Dock 6.6'] = '\\nA'\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "\n",
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=12, kind=\"box\", palette=\"Set3\",)\n",
    "g = sns.FacetGrid(col=\"engine\", data=data2, size=6, sharey=True, sharex=False)\n",
    "g.map(sns.boxplot, \"score\", \"ef1\", palette=\"Set3\", linewidth=1.)\n",
    "\n",
    "g.set_ylabels('Enrichment Factor 1%')\n",
    "g.set_xlabels('{col_name}')\n",
    "\n",
    "g.set_titles('{col_name}')\n",
    "for ax, i in zip(g.axes[0],['A','B','C']):\n",
    "    ax.set_xlabel(ax.title.get_text())\n",
    "    ax.set_title(i)\n",
    "\n",
    "g.set_xticklabels(rotation=45)\n",
    "g.savefig('dude_raport_boxplots_horizontal.svg')\n",
    "g.savefig('dude_raport_boxplots_horizontal.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PEhFRYGAgSaVSevPNN6l79+508uTJWu/bsGED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\nIiJlkAp3IiIiIiIiIiIiZdD/AyBP6CW9lpeMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc43b329d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "#data2 = data2[(data2.engine == 'Dock 3.6') & (data2.kind.str.startswith('Vertical'))]\n",
    "#data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "data2 = data2[data2.kind.str.startswith('Vertical') | data2.kind.str.startswith('Horizontal') & ~data2.score.str.startswith('RF')]\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "\n",
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=7, kind=\"box\", palette=\"Set3\",)\n",
    "g = sns.FacetGrid(col=\"engine\", data=data2, size=6, sharey=True, sharex=False, )\n",
    "g.map(sns.boxplot, \"score\", \"ef0.1\", palette=\"Set3\", linewidth=1.)\n",
    "\n",
    "g.set_ylabels('Enrichment Factor 1%')\n",
    "g.set_xlabels('{col_name}')\n",
    "\n",
    "g.set_titles('{col_name}')\n",
    "for ax, i in zip(g.axes[0],['A','B','C']):\n",
    "    ax.set_xlabel(ax.title.get_text())\n",
    "    ax.set_title(i)\n",
    "\n",
    "\n",
    "g.set_xticklabels(rotation=45)\n",
    "# g.fig.tight_layout()\n",
    "g.savefig('dude_raport_boxplots_vertical.svg')\n",
    "g.savefig('dude_raport_boxplots_vertical.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IiIh0xGIaERERERERERGRjlhMIyIiIiIiIiIi0hGLaURERERERERERDqSVDFNq9Vix44d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SAAAg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\n3MNpzZo1KKUYPHgwzz/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dtOjZYtK8kxY9W+qMEjU4nU7PP9PT09W4ceNUp06d1HvvvaeU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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc438fde50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "\n",
    "data2 = data2[data2.kind.str.startswith('Per Target') | data2.kind.str.startswith('Horizontal') & data2.score.str.startswith('Horizontal RF')]\n",
    "\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "\n",
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=7, kind=\"box\", palette=\"Set3\",)\n",
    "g = sns.FacetGrid(col=\"engine\", data=data2, size=6, sharey=True, sharex=False, ylim=(0,100), aspect=1)\n",
    "g.map(sns.boxplot, \"score\", \"ef1\", palette=\"Set3\", fliersize=0, linewidth=1.)\n",
    "g.map(sns.stripplot, \"score\", \"ef1\", data=data2, jitter=True, size=2, color=\".3\", linewidth=0)\n",
    "\n",
    "g.set_ylabels('Enrichment Factor 1%')\n",
    "g.set_xlabels('Docking Software')\n",
    "\n",
    "g.set_titles('{col_name}')\n",
    "for ax, i in zip(g.axes[0],['A','B','C']):\n",
    "    ax.set_xlabel(ax.title.get_text())\n",
    "    ax.set_title(i)\n",
    "\n",
    "g.set_xticklabels(rotation=60)\n",
    "\n",
    "g.savefig('dude_raport_boxplots_all_wpoints.svg')\n",
    "g.savefig('dude_raport_boxplots_all_wpoints.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Rbdu2jVVWVrILFy4wqVTKdu/ezX744Qfm4ODA\njh49yt22qKiIubi4sG3btint8WUyGWOMsfv377O+ffuyAwcOMMYYCwoKYlKplL311lusR48e7OTJ\nk43ut2HDBjZo0CBWXFystCwvIiEhgb355pssPj6elZaWMj8/PzZmzBj25ZdfMgcHB/bHH380uv2E\nCRPY+++/r9aM8ueYMcZqa2vZ5MmT2bRp07jL1q9fzxwcHNixY8cYY4xlZWWxixcvqjWjMiQkJLCp\nU6eyxMREVlRUxIYMGcImTZrEFi9e/Mht8cYbb7AZM2YoNUNNTQ3379zcXJaRkcEYq98GMTExzN/f\nn7m4uLCDBw+yioqKRvetrq5WapZnJc+cmZnJgoKCWEJCQqPrExMTmZ2dHTty5MhD99V05nv37rFV\nq1ax2NhY7rpNmzYxqVTKDhw4wFJSUti9e/fYN998w6RSKYuIiNBIXlKP+kYx1Df8oem+EWLXMEZ9\nQ9RHk31DXaN61DX02eZJqGuejM64E5CGR1MWL17MnT7cu3dvDBw4EEuWLMGmTZuwcuVK+Pr6cvdJ\nS0uDmZmZ0iYhlR+NysvLw927d+Hh4YFXX30VADBlyhQMGTIEFy5cwODBg+Ho6Mjdr7S0FP/88w+k\nUqlGJnFteATkr7/+QufOndG+fXsYGBjg448/xs2bN7F3714EBATA29sbQP0p8llZWRCLxejRowcA\n9ZyWy/4/R0ZWVhaA+tPJW7Rogfv37wOo/6rAhg0bGh3xu3z5MpYtW4aUlBRenTr8KPJtUVVVhePH\nj8PS0hIWFhYwNDTErFmzcPnyZezevRuffPJJo22RmZkJHR0dpW8L+QStp0+fhqmpKXekRyQSwdnZ\nGXPnzm10dEp+1OzYsWPYsWMH9391kmdOTk7GlClTuL9v+XNSU1MDHR2dh+av4EPm9PR0zJs3j9uO\ndXV1cHR0RHBwMEaNGoVOnTqhffv2GDduHFq1aoWTJ08C4Ncp8U0d9Y1iqG/4g099I8SuaZib+oao\nAh/6hrqGukZRfOoaQJh9Q13zZLTjTkAkEgnS0tIQFBQEsViM/v37cyUxc+ZMeHh4QEdHBxUVFdwp\n3cXFxdi3bx9kMhlsbW2VkkMsFiM7Oxv+/v745ptvUFBQAB0dHW7Amj17NgYOHIjIyEgsW7YMZ8+e\nxfHjx7FixQqcP38eH330kUYmw5Q/f++99x5Xbi1btgRjDP3798cXX3wBLS0tnDlzBkeOHAEApKSk\nYPPmzUhMTMTQoUMBqOe0XJFIhPz8fAwbNgyrVq0CAHh4eODevXuYNGkS1q5di59//hmDBw/m7pOQ\nkICqqiq0a9eOV6cOP4pEIkFqaiqmT5+OS5cuwcbGhlsd6NVXX8Vnn30GsViMqKgonDhxAgBw7949\nbNmyBcnJydybKWX+nitXrsT06dMfOVGss7Mz5syZ06jgfv/9d3z00UeorKzU2CS/K1euxNtvv90o\ns/w5KSoqQl1dXaOvGRw/fpwXmSdPnoyEhAQA9YUlkUjg4eGBXr16cZcB9afEGxkZwcbGBgC/Tolv\n6qhvFEN9wx986xshdg1AfUNUhw99Q11DXaMovnUNIMy+oa55PFpVVkDq6urw5ZdfIi4uDpaWlmjd\nujXEYjEYY5BKpfjwww+xdu1aLFq0CMHBwWjXrh0KCwtx584d7NixQ6mrr0gkElhaWiIpKQnp6emo\nrq6Gvr4+ZDIZOnbsiJUrV2LNmjU4cOAAjh8/DmNjY1hYWCAkJERpH+heRFRUFG7fvg0DAwPuO/3y\nJedHjhwJXV1dLFmyBJ988gkWLlwIY2NjyGQybNu2TeVLdT+ouroarVq1wrVr11BeXo7Ro0cjMjIS\n0dHR8PX1xSuvvAKxuH7fe1FREe7evQtbW1vIZDK15nxRf/75J+Li4tCyZUtUV1cDqD/ypKWlhbFj\nx0JbWxsrVqzAmTNnYGRkxN0uMDAQnTt3VnoeR0dHMMYQExODbt26QSaTcX9fIpEILi4umDdvHr7/\n/nssWLAAMpkMAQEBmD59utKzKJoZqH8TWltbyxXFsWPHMGfOHMyaNQszZszQaGYAuHbtGmxsbLi/\nP5FIxK0wJhKJUFNTg6ioKMhkMu7oFVEf6hvFUd/wB5/6Rohd86TcAPUNUQxf+oa6RvWoa+izzYtm\nBqhraI47gUlLS2Pjx49nUqmUzZw586HrCwoK2A8//MDGjRvHxo4dyxYtWsQSExMVftyGcxLIZWVl\nsY8//phJpVL23nvvcZfX1tZy//7333/ZuXPnWFxcHMvLy1M4hzLs3LmTubu7M6lUyi5dusQYa/z7\nJSYmsqCgILZy5Uq2b98+du/ePbVnlOeJiIhgdnZ2LDw8nDHGWH5+Phs6dCjr3r07mz59OouNjWXH\njx9nX3zxBXN1dWW3b99We1ZFbN68mTk7OzMHBwcWFxfHGGu8LeLi4tjGjRvZkiVLWEhICEtLS1Np\nnmnTpjEvLy+WlZX12Nv88ssvTCqVsh07dnCX1dXVqTTXkzyYWf78/fPPP8zZ2ZlFRUWxU6dOMQcH\nB7Zu3TrufnzKzBhjJSUlLDIykkVERLCrV6+yTZs2MWdnZ7ZlyxaN5WzuqG8UR33DH3zqGyF2DWPU\nN0R1NNE31DXqRV1Tjz7bPB11zaPRjjsee/DFJ/9/RkYGGzduHJNKpWzZsmXc9Q1LpaSkhFVXVytl\nokb5zy0uLmbJycns9u3bLCcnhzHGWE5ODps9ezaTSqUsICCAu09VVZXCj6soeW7581BSUsJdFxoa\nytzc3NjLL7/MLl++zBirf34bPoeqJh+EamtrH9rWMpmMyWQylpWVxfz9/dmIESNYamoqY4yxwsJC\n9vHHH7NevXoxqVTKevXqxYYNG8b+/fdftWV/XvLntaamhtXW1jbaFkFBQczJyYn169eP/fPPP9zt\nVTX4NpysVU6+LcLCwpiLiwvbuXNno8vlIiMjmaOjI/vll1+4y9RREs+TWZ6nuLiY9evXj02aNIk5\nOTmxn376ideZ09LS2KuvvsqkUilzdnZmgwcPZoGBgQ/dl6gG9Y1iqG/4gy99I8SuYYz6puF9iWrw\noW+oa1SDuqYefbZ5Ouqa5+8a2nHHU/LB4P79+2zfvn3cERL50aX09HQ2ZswY5uDgwFasWMHdT9kr\nqshzJCQksIkTJzIvLy/m6OjIfHx8uFWV8vLyHllwj3pxq4s8d3JyMlu0aBEbN24cmzJlSqNBaffu\n3czd3Z15enqy6Ohoxlj9H5m63rA1XHWGsfptmpGR8dAbg6CgIObg4MBOnz7NXVZVVcVSUlLYH3/8\nwf755x/eHPF7lIbbYsmSJWzSpEns/fffZ0FBQdxtgoKCmKurK/Py8mpUcKq0b98+Vlpa+tDrdNy4\ncWzEiBHc/+Wvh+TkZObr68u+//577jp1H9l51sx1dXWsvLycvfHGG0wqlTZacY2vmRmrH2fCw8PZ\n6dOnG71Z0/RZJk0d9Y1iqG/4g499I8SuYYz6hqgGH/qGukZ1qGuCuNvQZ5tnQ13z7CSLFy9erNxv\n+RJFyb8XfevWLbzzzjuIjY1FdHQ0jh07hiNHjsDGxgaOjo545ZVXcOXKFZw8eRLFxcV45ZVXIJFI\nuJWRFMUYg1gsRmJiIiZMmABbW1uMGzcOtra2SExMxJ49e2BgYMBNvJiRkYGjR48iOTkZvr6+3PfR\n1U3+/N2+fRsTJ06ERCKBmZkZ0tLSEBkZiX/++Qfu7u7o3bs3DAwMEBUVhePHj8PZ2RmWlpYAVD9J\n67lz5zBp0iR4enqiXbt2KCgowKhRoxAUFISioiK0bdsWpqamAOonDz116hSio6MxZswYAPXzcBgZ\nGcHa2hpmZmbQ09NTad4X1XBb+Pv7QyaToWXLlrhz5w6OHDmC5ORkuLu7cxMPnz17FidPnoS7uzvM\nzc2V9lp+0MaNG7FixQqcPn0aRUVF6NatG/cctm3bFiEhIdDT04OzszP3+GKxGK6urhg5ciT3u6nz\nNf68mbW1teHo6AhbW1u8+eabvM/MGIOpqSns7OxgZWWFNm3aAPhvHCKqQX2jGOob/uBj3wixa14k\nN/UNeRZ86BvqGtWhrqHPNqrO3Ny7hnbc8ZBIJEJaWhomT56M/v37Y8GCBfjss89gZmaGhIQEhIaG\nwtraGi4uLujTpw+uXr2Kv//+G+np6fDy8lLaYCASiVBaWop58+bB2toay5Ytg4uLC9zd3WFvb4/c\n3Fzs2bMH5ubm6NWrF1xdXZGdnY2IiAjcv3+fW+pa3UQiEbKzs/Hee++hT58+WLx4Md544w0MHDgQ\nrVq1wq+//oqbN2/Cx8cHrq6u0NXVxZUrV7B79254enpyy2WrUmVlJfr37w8PDw8UFRXB2NgYUqkU\nJSUliIyMxIEDByCTyaCjo4O2bdtCR0cHERERsLCwgFQqVXk+ZZEv+/7uu+/C09MTixcvxrhx4zBg\nwADo6ekhLCyMezPk6uoKkUiES5cuYf/+/RgwYADMzMxUksvCwgJ9+/ZFbGwsIiMjER4eDgMDA7Rq\n1QoODg6IiopCamoqBg8eDG1tbTDG0KJFC7Rr1w6AZj5IPW/m2tpatG3bFk5OToLJ/Kixi++riAkd\n9Y3iualv+IGPfSPErnmR3NQ35FnwoW+oa1SHuoY+26g6c3PvGtpxxzPs/6uk7Ny5EwUFBfjiiy9g\naWkJiUQCR0dHdOjQgTsi1KdPH9ja2qJv3744ceIE/v33X/j5+UFfX19pebKzs7Fz506MHj0abm5u\nqK2thVgsRrt27WBlZYW7d+9i79698PLyQufOneHq6oqioiJMnToVrVu3VlqO5xUdHY3Tp09j9uzZ\n6Ny5M8RiMVq1agUbGxu0atUKYWFhyM7OxqBBg9CzZ0+IRCKkpKRg9OjRMDExUUkm9v8VfADA1NQU\nVlZWyMrKQv/+/VFSUoIJEybgtddeg7W1NXR1dbFjxw5ERUWhsLCQW4K+rq5OY28aXtSFCxdw9uxZ\nzJ07F506dYJYLOaWwW7RogX27NmD4uJi9OvXD25ubqiursadO3cwevToRst9K5ORkRE6d+6M0aNH\no2PHjigpKcH27dsRFRUFY2Nj9OrVC5s3b4azszOsra0fGmA18eb+eTNLJBLBZSbqRX2jHNQ3/MG3\nvhFi17xIbuob8jR86hvqGuWhruFH1wDC7Bvqmuf03F+uJWrx0UcfseHDh3P/b/gd6mPHjjF3d3c2\nbdo0VlpayhirXwVJFavSJCcnM0dHx0YrtjT8fv4ff/zBHBwc2NatW7nL+TA/yN69e5lUKuVWTWqY\nOTMzk82bN4/16NGDXb9+nbu8qKhI5bmqqqq4LBUVFaygoIBNmjSJSaVS9u233za67cWLF9n8+fOZ\no6MjmzRpEnvrrbeYVCplf/31l8pzKtOuXbuYVCrlJv1tuC3u37/PAgICmLOzc6Pv/RcUFKg814Pz\nTERERLCZM2cyqVTKJk+ezOzt7dmYMWNYcXGxyrM8K8pMVIH6RjHUN/zBx74R6hgoxNxCzNzc8KFv\nqGuUi7qGH13zYA7GhDEGUuZnRxM58AT7/5Gouro6VFdXQ09PD9nZ2YiLiwMAaGlpoa6uDgDg4+OD\nwYMHIy4uDnV1dWCMwdzcHB06dFAog0wme+gyQ0NDWFpa4ujRo4iNjQVQPwdBbW0tAMDb2xsmJibI\nyMjg9oKr+5RV+XPX8N/y05D37duH0tJSSCQSsPrFWGBhYYGxY8eiuroahYWF3H0NDQ1VnvOdd97B\nV199hX///RdDhw5FbW0tvvrqKwwaNAjbtm3DmjVruNu7u7vjq6++wpEjR2BiYoL09HTo6Oigc+fO\nKs2piCdti/3796OiooKbpwQALC0tMXr0aFRUVDTaFqo6GtXQg0dthgwZghUrVmDPnj0QiUTQ19fH\nsGHD0KpVK5VneVaUmSgD9c2Lo77hD6H0jVDHQCHmFmLmpk7TfUNdQ12jKKF0DSDMMZAyPwel7gYk\nz63hst6M/XfkKSoqikmlUrZo0SKWnZ3N3V5+/aZNm5ivr6/S9uTKc2RmZrLjx4+zoKAgbinxP/74\ng9nZ2bFZs2Y1OnJQVVXFkpOT2bBhw9ju3buVkuNFcxcVFbHMzMxG+fz9/ZmHhwf77bffWGVlJWPs\nvyNm58+fZ15eXo2OSqnD+fPnmaOjI3NycmKfffYZl+vWrVts+vTpTCqVNlrZp+GRyJiYGJaZmanW\nvM9Dvi2Ki4tZbm4ut0JYXV0dGzlyJOvXrx87fvw4t7KUfFucOXOm0YpLilDWqlmlpaVcfmX+3Eeh\nzOrJTKhvlJWb+kbzNN03Qh0DhZhbiJkJP/qGukY9qGvos82DKLNqMtMcdxpUV1cHiUSCu3fvYs2a\nNdi2bRt27tyJDh06oE+fPqipqcGuXbtQU1ODLl26wMjICGKxGCUlJQgODoaRkRFef/11aGlpKZSj\n4eo406ZNw9mzZxEZGYkzZ85gyJAhcHJyQosWLRASEoL79++jVatWsLKyQnp6OrZt24Z///0Xs2fP\nhpGRkZKemWcjf/4SExMxd+5cBAUFYevWrbh79y58fHy4FYv++usvGBsbw9raGtra2igsLMS2bdtQ\nWFiIt956S62rFhUWFmL//v2oqamBm5sbN9muqakpbGxskJ2dzV3v4eEBsViM6upqSCQStG3bFi1b\ntlRb1ufRcFt8+umnCAwMxKZNm5CRkQFvb2/07NkTR48exfnz59G6dWt07doVWlpaKCoqQmBgIIqL\nizFp0iSFtkV2djbCwsLQrVs36OjovPDPkclk0NXV5eYxUeXEp5RZPZkJ9Y2iqG/4Q9N9I9QxUIi5\nhZiZ8KNvqGuoaxSl6a4BhDkGUmbVZaYddxrCGixHPnHiRJiamsLAwABaWlrw8vJC27ZtYWVlhdra\nWoSGhiIhIQFVVVXIzMxEYGAgzp49i++//x4WFhYKZ5GvjvP222/Dw8MDn332Gd58802MGTMG7du3\nBwDY29vD3NwcoaGhOHLkCPbv34+TJ0/ixo0b2LJlC7p27apwjuclFouRkpICf39/uLq6YtiwYejX\nrx8GDx4MCwsLmJiYoEePHjh37hwOHDiA6Oho3Lx5E3v37kVUVBQ2bNiAjh07qiWrfOnvEydOoEOH\nDrCxscHevXtRUlKCPn36QCwWw9TUFLa2tsjJyWlUcA+ejstHYrEYd+/ehb+/P3r27AlfX1/06dOH\n2xampqZwcHDA6dOncfjwYcTExCA+Ph579uzB+fPnFd4W2dnZ8PX1hY6ODl577TWFnjN1TdZKmTU/\nKW5zQX2jOOob/tBk3wh1DBRibiFmJvzpG+oa1aOuoc82D6LMKs6stHP3yHMrLS1lU6dOZQEBAdzk\nofJTbeWnVebl5bHdu3czd3d3Zm9vz/r3789GjRrV6LRpZYiMjGRDhgxhCQkJjS6vra1tdIrnrVu3\n2KZNm9iXX37JduzYwVJTU5Wa43nU1dWxVatWMX9/f25yULmGk0aWlJSwJUuWsDfeeIONGDGCzZkz\n56HfU5UZHyUpKYktWrSISaVS9s033zS63a1bt9isWbOYVCptNHEun9XV1bHly5ezyZMns9zc3EbX\nNdwWeXl57PPPP2fDhg1jQ4cOZR999JHC2yIrK4t5eHiwgIAAVlhYqNDPUhfKTNSN+kYx1Df8oam+\nEeoYKMTcQsxM/sOXvqGuUV3GR6Guoc82lFm1FPvOC1FIWVkZkpOT8c4773CTh2prawP47xTvqqoq\njB8/Hv3790deXh50dHRgbm6u9Aku09LSkJaWxk0AKz+1U77X+e+//0Z8fDzef/99dOvWjTdfMbh5\n8yb09fXRpk0bAP/llue7cuUKzM3NsWjRIpSVlUEkEkFLS0uh02CflfwU66ysLFy/fh1FRUWQSqVw\ncnKCtbU13nrrLQDAzp07IZPJ8MUXXwAAbG1tMW/ePOjo6MDHx0flOZWBMYabN2/C2NgYpqam3GUi\nkYjbFjExMbC0tMTy5ctRWloKANDR0VFoW2RnZ8PPzw9ubm5YunSp0ibhra6uRlZWFiwtLZV+VJAy\n/0eVmUlj1DeKo77hB030jVDHQCHmFmJm0hhf+oa6Rvmoa+izzaNQ5v+otGs0udewuUtLS2Oenp5s\n7dq1j7z+33//ZW5ubuzGjRtKfdyGR5nk//7999+Zk5MTCw0NfeioGGOMffrpp+yll15iVVVVvFgS\nnbH6CW/nzp3Lhg4d+sil4ouLi1mvXr3Y8uXL1Z5NfiTm1q1bbPDgweyVV15hUqmUvfzyy42ONCUm\nJnJHp5YtW8bOnj3LAgIC2LVr13jzPD+LqqoqFhAQwEaOHPnISWYLCgqYs7Nzo8lpFSU/SjJz5sxH\nHiV5cKnuZ1VRUcE+/fRTNn78eFZeXq5ozEYo839UmZk8jPpGMdQ3/KHuvhHqGCjE3ELMTB6mib6h\nrlE96prG6LNNPcr8H1V3DT8OLTQDj1qO3MLCApaWloiIiOCWI294W5lMhtLSUiQlJSktR11dHUQi\nEUpLS1FYWIg7d+4AALy8vNCuXTts374dFy5c4G7H/r/stampKTp06AAtLS2NHJGSPyeswZLc3nuU\nxwAAIABJREFU2tracHNzQ0JCAvbv34/c3NxG99HT00OHDh1QVlam9pwSiQQpKSl488030adPH/zw\nww84cOAA2rRpg6CgIHz//fcAgK5du+Ktt97C2LFjERwcjA8//BCnT5+Gnp4eb478PehRr2UdHR24\nuLjg5s2bOHDgAAoKCgD8t7309fVhaWmJoqIipWTIz8+Hn58fevXqhW+++eahyYPlRwTj4+Nx6tSp\nZ/65lZWV+Pbbb3H8+HF8/vnnSp3clzKrJzOhvlEU9Q1/aLpvhDoGCjG3EDMTfvQNdY16clLX0Gcb\nyqyZzHL8/OtpYurq6iAWi5GRkYGwsDCsWbMG0dHR0NbWxoIFC1BYWIjvvvsON2/eRE1NTaNBzdLS\nEmZmZkrLIZFIkJSUhJkzZ2LEiBGYMWMG4uLioK+vj1WrVqGsrAyrVq3CsWPHANRPqlhZWYl79+7B\nwsICtbW1jQpGHeTPX0pKCr7++muMHz8eK1euRF1dHcaPH48RI0YgMDAQu3fvxv3797n7lZaWQiKR\nwNLSUuUZ4+PjUVBQALFYDJlMBplMhj179qBbt26YNWsW3Nzc4ODgAA8PD5SVlSE4OBg//vgjgPqC\nmzp1Kr799luMHDkShw4dgq2trcozvwj5tkhNTcWyZcswadIk/PDDDwCAKVOmwMfHB5s2bcL+/fuR\nlZXFTcpZWloKXV1dbkJgRTNs2bIF+fn5sLGxeejUZvnr/NatW/Dz88Pff//9TD+3srISK1euxMGD\nBxESEgInJyeFs1Jm9WYm1DfKyE19ww+a7huhjoFCzC3EzIQffUNdozrUNfTZhjJrPnNDtKqsijVc\njvzdd99FTEwM7ty5g/bt28POzg6tW7eGmZkZDh48iNjYWBgYGMDKygpZWVkIDg5GSkoK3nvvPYWX\nymb/X+UpISEB/v7+6N69O7y9vTF69GhYW1tDT08PFhYWkEqliIyMxO+//44bN24gISEBoaGhiIqK\nwnfffQdzc3O1rsbV8Pl78803IZFIYGJiAicnJ3To0AF6enpwdXVFamoq9uzZg3v37qFly5a4f/8+\ngoODceXKFSxYsEDpczQ1dP36dUyePBmlpaVwcnKCnp4eamtrsW3bNohEIkycOBEikQjr169HUFAQ\nFi5ciISEBJw6dQo1NTWwsbFBSUkJPD094eXlBRMTE5VlVcSD26Kurg4tW7aEg4NDo21x+/Zt7Nmz\nB1lZWTA2Nsb9+/exa9cuxMTE4LPPPlN4W4jFYujp6aGsrAy7d+/mjk6KRKJGR0nGjx+PsWPH4vPP\nP3/qHAPyATc8PBy7d++Gvb29Qhkps2YyN3fUN4qhvuEPPvSNUMdAIeYWYubmjg99Q11DXaMoPnQN\nIMwxkDKrJ3MjSv/yLXlIcnIy8/T0ZAsXLmSJiYksOzubVVZWNrrN0aNHWb9+/ZhUKmXu7u5syJAh\nrE+fPuyff/5RWo68vDw2ZswYtmjRIpafn89dLpPJWE5ODrt79y5jrP573zNmzGADBw5kPj4+bNq0\naezWrVtKy/G87t+/z7y9vdmiRYtYTk5Oo++dy/9dV1fH1qxZwxwdHZlUKmWenp7stddeU/pqiI8z\na9Ys5unpyVauXMny8vIYY4ytXLmSvffee4wxxoKDg5mLiwuLiIhgjDF28eJF5uTkxJydnVn37t3Z\ngAEDWFFRUaO5N/jo3r17bNCgQeyLL75g2dnZrKamhrtOPm9FVVUVW7p0KevevTu3LXx9fdnNmzeV\nmiUxMZF9+OGHTCqVsg0bNrDq6mrGWP3cKT179mRffvklN6fJk1RUVLCvvvqKOTk5KfXvjTJrLnNz\nRn2jGOob/uBL3wh1DBRibiFmbs740DfUNapDXUOfbSgzfzIzRqvKqlxdXR0CAwPh6OiITz/9lDsl\nMyMjAwkJCbhy5QqcnZ3h6+sLd3d37N27F8XFxbCwsMDAgQPRsWNHpWXJyclBYWEhvL29uSMfZ8+e\nxcmTJ3Hs2DFUVFRg6NChmD9/PtauXYuioiKIRCLo6emhRYsWSsvxvM6cOYNWrVph+vTp3ApLN27c\nQHR0NCIjI9G2bVtMnjwZn3zyCYYMGYL8/Hzo6+ujY8eO3CpAqiLfO//TTz9h7ty5CA8PBwDMmDED\n77zzDiorK1FUVISwsDCMHz8egwYNAgC0a9cO3bt3h5OTE/Lz8/Hee+8pbTUbVTp9+jSMjY0xc+ZM\n7isON2/exJUrVxAZGYlOnTrB398fX3zxBUaMGIG8vDy0bNkSVlZW3LZ7EYWFhdi1axdu3LgBmUwG\nHx8fDB8+HPPnz4dIJMKPP/6IVq1awc3NjfuKwcKFC5+6qlNlZSVWrVqlkqMklFk9mcl/qG8UR33D\nH5roG6GOgULMLcTM5D986RvqGuWjrqHPNpSZn11DO+7UICUlBR07duQmKQwPD8e+ffsQExMDXV1d\nVFVVYerUqZg3bx6mTZumstO18/LykJ6ejtTUVMTGxuLUqVPYtWsX9PT04OXlhZYtWyI4OBgdO3bE\n+++/j9atW6skx/NKT09HcXExiouL0aZNGwQHByM4OBjZ2dmwsbHB1atXERsbi3379sHOzk4tmdj/\nlwOXSCRcwX333XeYO3cuDh48CJFIhA8++ABmZmaIjY1FYmIiRowYAV1dXQDAnTt3YGtriwULFqC2\nthZaWsL4U0xPT0dpaSk3Ke7OnTsRHByMjIwMWFtb488//0RMTAz27t2LHj16KOUxs7OzMXnyZOjq\n6kJLSwtFRUVYtGgR7t27h48//hgzZsyASCTC0qVLAQATJkzA559//tQBt7a2FkuWLMGhQ4cQFham\n1AGXMqsnM3kY9Y1iqG/4Q919I9QxUIi5hZiZPIwPfUNdozzUNfTZhjJrPvOTCOMvSkDkg56cRCJB\n69at8eeff2L58uVIS0tDVFQUXFxcsHLlSri5uSEsLAyBgYEYPXo0rKysHvlzlKFPnz7o168fli1b\nBolEAm1tbfj7+2Pw4MFwdnYGUH+E4caNG0p9XEX17t0bmzZtwvvvvw+xWIzs7Gz4+vpixIgR6Nev\nH+7cuYPXX38dJ0+exPjx49WSSb5t5HMjVFdXQ0dHp1HBMca4Nyo1NTW4du0aUlNTuddDSUkJqqur\noa2trZbMyuDq6opt27bhww8/hEwmQ3p6Onx9fbFw4UL0798f8fHx8PPzw+nTpzFy5EiFHy87Oxt+\nfn5wdnbGnDlz0KFDB9TU1OCDDz7A/v37MWnSJNjb2+PDDz+Enp4eIiIiYGpq+tQBFwAqKirQsmVL\n7N27V+klQZlVn5lQ36gC9Q1/qLNvhDoGCjG3EDMT/vYNdY3yUNfQZxvKzPOuUfmXcZsR+ZwEhYWF\nLCcnh+Xm5jLG6udVmDx5Muvbty979dVX2aFDh1hKSgp3v927dzMfH59GczMom/w7+owxFh4ezsLD\nw1lqamqjORUyMzOZv78/W7duncpyPC/5vAjHjh1js2bNYnPmzGGXL19mBQUF3G2uX7/OXn31VXb+\n/HmV5/njjz/YypUr2aVLl9i9e/cee7vZs2czd3d3tnr1alZVVcW+/fZbJpVKmbe3Nxs5ciRzd3dn\nt2/fVnne55Wenv7U7/SHh4ezadOmsYCAAHbhwgVu3gvGGLty5Qrz9fVlly9fVjhLVlYW8/DwYDNn\nzmRFRUWNrrtw4QKTSqXs6NGj3GVxcXFs5syZTCqVso0bNz7TnBoN57FQBsqsnsyE+kYVqG/Uiy99\nI9QxUIi5hZiZ8LdvqGuUg7qGPttQZs1nfhZ0xp2SyI9OJCUlYeHChcjJyUHnzp0xceJEeHt7Y+fO\nnUhPT4eRkREMDAy4+5SVleHmzZto3779C59SzB44eiX/f8PL5Ut5i8Vi+Pn5PXSfgoICbNiwAenp\n6Rg+fLgCz4RyyX8PHx8fDBo0CIyxRs9TSUkJDh8+DB0dHVhbW6s0S2xsLGbOnAkAOHXqFLKysuDm\n5gZHR0f07dsX3bp1g56eHnR1dfH999/j888/R2hoKABg+vTpMDc3x5kzZ2BkZIRVq1ahW7duKs37\nvEJCQhASEoLAwEC0a9fusbfz8/PD0KFDAeChbREREQE9PT106tRJoSw5OTkYNWoU3Nzc8M0333Bz\nZMhP3a+pqYGpqSk6duzIXebo6IgPPvgAALB27VrU1tZixowZT3wcZZ7GT5nVk5lQ36gK9Y368KVv\nhDoGCjG3EDMTzfUNdQ11jTLwpWsAYY6BlFk9mZ8VtZuSiMVipKen491330Xnzp3h7OyMI0eO4M6d\nOyguLsaoUaNgaWkJoL5ITExMuKWljx49ipCQELRq1eqFHlskEqG8vBxFRUXckuLa2toQiURcockz\nNrxPVlYWDh48iPLyciQkJCAmJgbbt29XeGB6HjU1NWCMPfG0VHkJy5djzsjIQH5+Pu7fv4/Tp0/j\n+PHjCAkJgYWFhUqzWlhY4PXXX8epU6cglUrxySefYN++fQgNDUVQUBD09PTg7OwMJycnvPLKK1i8\neDHKy8tx/PhxSCQSTJ8+HVOmTOHlvA/79+/H8uXLMW/evCcWm5w8f0ZGBsrKypCamooTJ07g2LFj\nCAkJgbm5+QtnKS0txZQpUwAAS5cu5Qbchs/buXPnUFtbC0tLy0avawcHB3zwwQeorq7GunXr0K9f\nP6XNR0GZNZ+Z1KO+eTHUN/zAl74R6hgoxNxCzEzqaapvqGuoaxTFl64BhDkGUmb+dY1k8eLFizUd\nQsgaHt25ceMGCgoKsGzZMvj5+cHFxQXHjx/HpUuXYGxsjO7du6Oqqgq//PILPvnkE5w7dw4pKSnY\nvHkzpFLpCz1+dHQ0du7cia+++grbt29HUFAQYmJiUFtbC3t7+yfOI5Gbm4sDBw4gLi4OHTp0wIoV\nK2Bra/tCOZ7X1atXER4ejh9//BE7d+5EQUEB3N3dn3q/2tpa/PXXX5gxYwauXr2KmpoarF+/Xi0T\nt7Zs2RIuLi5IT0/H0aNHMXToUMyePRuTJk2Ci4sLLCwscP/+fRw5cgQHDhzA77//jhYtWuDmzZu4\nevUqAMDd3R1isVhlE8K/iP3792PRokVYtGgRJk+e/Mz3q62txYkTJ/DBBx/gypUrqKiowM8//6zw\ntigpKUF0dDTi4+Ohq6sLd3f3RgPut99+i6CgIFRVVeH06dP4888/UVVVhZKSEnTo0AHm5ubo2bMn\n+vTpg969eyuUhTLzK3NzR33zYqhvqG8eRahjoBBzCzFzc6fJvqGuoa5RFJ+6BhDmGEiZ+dc1IsYY\n03QIoZKfXpmfn4+CggKcPn0aRUVFmDNnDnebc+fOYf78+dDS0sLHH38MPz8/JCQk4Pfff4ednR2c\nnZ2f6SjAoxw6dAg//fQTunTpAnt7e7Rt2xb379/Hr7/+itzcXEyZMgXz5s2DRCJ57GSwMpkMtbW1\nEIvFajtSEh4ejjVr1sDQ0BCmpqaoqanBG2+8AT8/P2hpaT3TwH/t2jWYmprC0NAQRkZGakj9n9zc\nXHz99df4448/sGTJEowdO7bR9Tk5OUhISMDp06eRlZWFv/76C3p6eti9eze6dOmi1qxPIy+2L7/8\nEhMnTmx03eNeMw/e5vLlyzAzM4OxsTFMTEyUkis3NxfLli3D0aNHMX36dAQEBACoH3D37duHpUuX\ngjGGv//+G1euXMG9e/cgEonQoUMHWFpa4pdffmn0lY2GR1RUhTKrJ3NzRX3zYqhv+IOPfSPUMVCI\nuYWYubnSZN9Q11DXKIqPXQMIcwykzDzrGtVMndf0ySctvH37NvP29ma9e/dm3bt3Z2+//TbLzMxs\ndNuzZ88yT09P1rdvX/brr78q5fH37dvHunfvzrZs2cJSU1MbXXf9+nU2d+5cJpVK2ZIlSx7KrEnh\n4eHMwcGB/fzzzywxMZExxlhRURGrrq5udDt51oYTz/JJTk4OmzVrFpNKpSwsLIy7/MHJT2UyGUtN\nTX3oNcEHYWFhrHv37iwkJOSh6xo+7+Xl5eqMxcnJyWEfffQRk0qlLCgoiK1cuZI5OzuzP//8s9Ht\nCgsL2ZUrV9j69evZmDFj2IYNGzSSlzHKTFSD+ubFUN/wB5/7RqhjoBBzCzFzc6PJvqGu0SzqGtUT\n4hhImfmDdty9APnAm5+fz7y8vFhAQAD7+uuv2cSJE5mDgwPbuXMnq6ysbHSfs2fPsgEDBjAnJyeF\ny23//v3coNRwEGq4itLdu3fZggULmFQqZTt37lTo8ZTl8uXLzMvLi23btu2hEmj4exw+fJitW7eO\nW61FaAUnk8m4zHx4Q/Eo58+fZ1KplK1YseKh6xo+36tXr2YTJkx46M2HuuTk5LCAgADWvXt3JpVK\n2ZkzZ7jr5K/3x70+NPXcU2aiTNQ3L4b6hj+E0DdCHQOFmFuImZsLTfYNdQ0/UNeonhDHQMrMDzTH\n3XOSnzIpn0C0qKgIX375JXx8fGBra4vMzEzs2bMHZmZmsLGx4U7R7tSpE6ysrBAXF4d33nkHxsbG\nL/T4f//9N2bPno2ZM2di6tSpjU73bXgqp7GxMdq3b4/o6Ghcv34dAwYM4CZoVDf2/9OSDx48iIKC\nAkybNg2tW7fmrm94GuqGDRuwbNky3LlzB1paWnBwcODdZKdy+vr6eOmll3Dv3j1s2bIF7dq14+be\nkP/OfJrvQY4xhosXLyI+Ph5JSUno2bMn2rZtC+DhbbFt2zYEBATA3t5eI1n19fXh5uaG/Px8xMfH\no3PnznjppZcgEom4nPLnmDU4/Z09w6nwlFnYmZsD6pvnR33DL0LpG6GOgULMLcTMzYEm+4a6hj+o\na1RPiGMgZeYH2nH3nOQrFk2cOBHXrl1DbW0t3njjDQD1K/PY2NggPT0dISEhD5WblZUVRo0aBTMz\nsxd6bPmEmdeuXUNZWRk8PDyeWFht2rSBtrY2wsPD4ePjw636pG4ikQjV1dX45ptv0LVrV4wePfqh\n6wFg/fr12Lp1K77++mvk5+fj+PHjAAA3Nze1Z35WDxZc+/bt0b17d97+wVdWVkJbWxs2NjawsLBA\nVFQUIiIiuLlI5Ll/+eUX/PLLL1izZg2GDBmi0cwGBgbo2bMnMjIyEBoaCpFIhF69ej30HDf8v6af\nf8pMlIH65vlR3/CH0PpGqGOgEHMLMXNTp6m+oa7hH+oa1RPiGEiZNY923L0APT09nDp1Cjdu3EBJ\nSQmGDx8OfX19APWF0q1bN67cLCws0LVrV67cnnWC0kcRi8WwtbWFiYkJjhw5gosXL8LDw+ORE5iy\n/685oqenh5CQELi6umrsyAIAVFRUYM+ePejcuTO8vLxQXV3NLYEO1E/qunr1aqxcuRIjRozAoEGD\nEBERgZqaGgwePJhfE0M+QF5wGRkZ2LhxIzp37vzCqzaq0smTJ3Ho0CF06tQJJiYm6NKlC8zMzHD5\n8mVERETA1dUVbdu2bVRsvr6+mo4NoH7g7dWrF9LT0xEaGgqxWMwdNeErykyUgfrm+VHfaJ5Q+0ao\nY6AQcwsxc1Onib6hruEn6hrVE+IYSJk1i3bcPYMHT58Ui8UYPnw4EhMTERMTgzt37sDT0xN6enoA\n/iu3rKwsbNu2DZ06dUL37t0BKL4XV1dXF127doWhoSGOHj2KS5cuoU+fPo88OiUSiaCtrY3NmzfD\ny8sLDg4OCj22InR0dPD7778jIyMD48aNg0QigUwm456P9PR0jB07FgMGDOBuv2vXLnTq1Ak+Pj4a\ny/2s9PX14erqioKCAvj6+jY6XZ4Prl69iqlTpyIhIQE6Ojro1KkTjI2NYW1tjTZt2uDSpUs4duwY\nEhMTERISgu+++443xSanr6+PXr16ISsrC8HBwaiqqkKfPn00HeuJKDN5XtQ3iqO+0Syh941Qx0Ah\n5hZi5qaEL31DXcNP1DWqJ8QxkDJrDu24e4q6ujqIxWKUl5ejpKQEJSUlqK2thb6+PgYNGoTExESc\nOnUK9+7dw8svv4wWLVoAqC+3zp07o7S0FMOGDVPqYKejo4Nu3bo9seDkpREREYHr169jxowZSlvO\n+nnJ3xhkZmYiIiIChoaG6NmzJ0QiEff8Wltbo3379tx9jhw5gsuXL2P69Olo3749r79vLmdgYAAv\nLy+0adNG01EeoqOjgyNHjnDf8xeJRLCysoKRkRGsra1hZmaG2NhYnD17FgEBARg3bpymIz+S/E1E\nUlIS7Ozs4OzsrOlIT0WZybOivlEc9Y3mNYW+EeoYKMTcQszcFPCtb6hr+Im6RvWEOAZSZs2gHXdP\nUFdXB4lEgqSkJMyfPx9bt27Fxo0bcfXqVRgYGMDW1haDBw9GUlISjh07hrS0NHh4eHDlZm5uDi8v\nrxeeY+hJnqXgioqKsGPHDhgYGGDUqFFcLnWTl5KlpSUiIyPxzz//wNLSEtbW1hCLxaitrW002Wl+\nfj527NgBfX19jBs3Di1atOB9scnx8bT3uro6GBgYAKgftFq2bInffvsNOjo6sLa25gquVatW3FFW\nFxcXblJXvjEwMIC3tzd69eql6SjPjDKTp6G+UQ7qG81qSn0j1DFQiLmFmFnI+No31DX8RF2jekIc\nAymz+tGOu8eQnzKemJiI8ePHo1u3bvD19YWzszNu3bqFXbt2oWPHjrC3t4ePjw9XbhkZGXjppZe4\n08obznWgbA8W3MWLF+Hp6QlDQ0NUVlZi9erVOHHiBNauXauxyVsbMjQ0hJOTE/bu3YsbN26gRYsW\nsLe3h1gs5sorOzsb69atw19//YXvvvuOF7mFTl64FRUVCA0NxezZs9G+fXts2bIF2traXMF17doV\nrVu3xqVLlxrNC8FH2traAPi98s+DKDN5HOob5aO+0Yym1jdCHQOFmFuImYWI731DXUOeRVPrGkCY\nYyBlVi/acfcYIpEI5eXlWLBgAbp06YJly5bh5ZdfxksvvYSrV68iOTkZw4YNg5WVFSQSCXx8fHD3\n7l3ulN2BAweq5cXQsOCOHTuGixcvolevXti4cSMOHjyIkJAQ2NnZqTzHs7K0tISTkxPCwsJw4sQJ\n7tTmkpIShIeHY9++fbhw4QK2bt0KW1tbTccVpMcNRB07dsSdO3cQGhqK9evXo7KyEoGBgQ8VXJs2\nbRATE4OdO3fC09MTFhYWGvgtno3QBlyAMpOHUd+oBvWN6jWXvhHqGCjE3ELMLCRC6BvqGvKg5tI1\ngDDHQMqsHrTj7gny8/MRGBgIX19feHh4AKhfSjo0NBQ//PADvL29UVRUhMLCQhgaGmLQoEHIyMjA\nu+++C1NTU7XlbFhwJ0+eRGBgIBITExEaGqrR1ZYep2PHjvD29kZeXh6io6Nx8OBBHDhwAKmpqbCy\nssLy5cup2BQgXxZdTiaTcYVnbGyMU6dOQUdHB9OmTUN2djZ27drFFZyhoSG6du2Kli1bIi0tDSNG\njICxsbEGfxtCmgfqG9WgvlEt6htChEcIfUNdQxqiriEEACOPlZqaytzd3dm6desYY4z9/PPPzMHB\ngR09epQxxlhNTQ1bvXo127p1KysrK9NkVMYYYyUlJWzDhg1s5MiRLD4+XtNxnqqiooKVlJSwa9eu\nsZiYGFZeXs4qKys1HUvQDh8+zF555RV24MABFhcX98jbTJs2jQ0dOpT7/6JFi5hUKmXfffcdy8zM\nZIzVv7ZLSkrUkpkQQn2jatQ3ykd9Q4gwCalvqGsIdQ0h9eiMu/9ruHy3nLa2NiIjI5GZmYn4+HgE\nBQXhxx9/5JbwFovFWL9+PfLy8jBy5EhNxG5ER0cHdnZ2GDZsGDp06KDpOE+lpaUFHR0dtG3bFm3b\ntoW2tja0tLQ0HUuw7ty5g/nz5yM7OxuJiYm4evUqbt68iW7dukFPT4+bj8TOzg7bt2+HWCyGq6sr\nvLy8kJubi+DgYNTU1EAqlcLQ0BA6Ojoa/o0IaZqob9SP+ka5qG8IEQah9w11TfNGXUPIf5r9jru6\nujowxiCRSJCVlYVz586hvLwcYrEYxsbGaN++Pfbu3Yvo6GjMnz8fo0aNAgDU1NQgNzcXkZGRePnl\nl3mzOomOjo7GVlgimiVfvSojIwNFRUUYPnw4fvvtN4SFheHWrVuwsbGBgYEBWrdujRs3biA5ORn9\n+vWDnp4eBgwYgJSUFERGRmLKlCnc5MOEEOWhviFNBfUNIfzWlPqGuqb5oq4h5D/NdsddcnIyTExM\nIBaLIRaLcfv2bbz99ts4dOgQwsPDkZ6eDhsbG7i4uEBHRwdXrlxBVVUV9PT00KVLF6SkpGD79u2I\njo7G559/Tt+VJxrXokULdO3aFWKxGNHR0WCMYefOnSgvL8fZs2exefNmpKamok2bNnB2dsbatWvx\n0ksvwcrKCgAwePBgjBw5Em3atNHsL0JIE0N9Q5oa6htC+In6hjQl1DWE/KdZ7rg7c+YM3nnnHYhE\nIri5uaG4uBgTJkxAz5498fbbb0NHRwcRERFISkqCi4sLBgwYAGNjYxw+fBiHDh3Cjh07cOLECSQk\nJGDLli2wsbHR9K9Emqni4mLk5uaitLQUIpEIRkZGsLKygoGBAfbt24e0tDQsXrwYY8eOBQBcvHgR\n27ZtQ11dHUpKSnD79m0MGDAA+vr6AAADAwNN/jqENDnUN6SpoL4hhN+ob0hTQF1DyKM1yx13lZWV\nOHHiBGJiYiCRSGBhYYGcnBwsXLgQLi4u8PHxQUlJCSIiIpCQkABnZ2f07dsXr732GgwNDWFjY4MB\nAwbgk08+QdeuXTX965Bm6tixY1i5ciV+/PFH7N27F1FRUXB2dkb79u3RpUsXmJiYYNeuXYiNjYWf\nnx969+6NXr16wcnJCYcPH0ZJSQmSk5PRt29fdOzYUdO/DiFNEvUNaQqobwjhP+obInTUNYQ8XrPb\ncVdXVwczMzMMHDgQJ0+exNmzZ5GUlATGGPz8/FBTUwOJRIK+ffuirKwMv//+OxITE9G9e3dYW1vD\nzc0Nffv2RY8ePWBoaKjpX4c0U/v378fChQvh6uqKIUOGwNjYGGfOnMHhw4fh7e2Ndu1SqX54AAAb\nxklEQVTawdraGsbGxti/fz+uXLmCESNGwNTUFHZ2dhg6dCisrKxQXl6OoUOH0lchCFEB6hvSFFDf\nEMJ/1DdE6KhrCHmyZrPj7vbt2zh37hy2bt2K3377DdbW1hgzZgxOnjyJ+Ph4mJqaYvjw4ZBIJKiu\nroZEIsErr7zClVtaWhqkUinMzc0BAIyxh1ZpIkQd9u/fj0WLFmHBggX48MMP4e7uDm9vbxgZGeH0\n6dNITU3FoEGD0KpVK3Tr1g2GhoY4cOAAYmJiMHz4cACAnp4epFIphg0bBhMTEw3/RoQ0LdQ3pKmg\nviGE36hvSFNAXUPI0zWLHXdHjhzB0qVLcerUKeTk5ODevXtwdXWFu7s7evfujfPnz+PatWsAAHd3\n94fKraKiAocPH0ZWVhZ8fHwgkUio1IhGhIWF4auvvsIXX3wBf39/6OjoQCaTQSQSwcnJCQkJCbh2\n7RpGjRoFAwMD6OrqcgUXFhaGuLg4DB06lPt59DomRLmob0hTQX1DCL9R35CmgLqGkGfT5HfchYeH\n46uvvsLIkSPxwQcf4PPPP4e3tzfs7Oygq6uL1q1bo3fv3rh06RKioqLAGIObm1ujcvP8X3v3HlVl\nlf9x/P1wUxERNS00tcQMG0XHC16wxtuAZiaCmgZqTg5Gml3GLLQacpG6NEMUTDRrRd4QCbxhEpaI\nTTrYjFlKWko2yTSjaQjE3fP7w3XOz9PhYpMiHj6vtVir9Zz97LOfs4KP5/vsZ28/PyoqKnjsscdo\n3br1zb4kaaD279/Ps88+y6xZs3j88cctwWQYhuURiO+//55jx44RHByMm5sbJpPJEnAeHh6sX7+e\nb775hhEjRtzkqxGxP8obsRfKG5H6TXkj9kBZI3Lt7Lpwd/jwYV599VXCw8OZNm0aHTp0AMDDw4NG\njRoBV9aEaNWqFb6+vuzfv5+srCwAm3Dr378/LVu2vGnXIg1bRUUFGRkZHDlyhKKiIgYMGGBZg+Ty\n5cs4OTkB8Pbbb1NRUUFISAhOTk4YhmEJuE6dOnHHHXcQHBys/5dFrjPljdgL5Y1I/aa8EXugrBH5\ndeyycGdenyEpKYlLly4RHh5e7S+zYRgYhkHLli3p378/Bw4cYN++fTg6OtKrVy8cHR3rePQithwc\nHOjSpQstWrQgLS2NQ4cOMWDAAJo3b265OxUXF0dycjIxMTHceeedlt8Dc8A1btyY7t27K9hEriPl\njdgb5Y1I/aS8EXuirBH5deyycGcYBmVlZSxcuBAvLy+Cg4NrbAuQnp5OSUkJ06ZNY//+/Wzfvh03\nNzd69uxZV8MWqVGjRo3w8vLC3d2dDz74gEOHDuHn54e7uzurVq3izTff5I033sDPz89mceGrp56L\nyPWjvBF7pLwRqX+UN2JvlDUi187pZg/gRikrK6OiosIyzba8vBxnZ+cq25aUlJCZmWmZSr5o0SJe\nffVVHnjggbocskit3NzcLP9QW7FiBXPnzqV79+5s2LCBZcuW4e/vDyjEROqS8kbskfJGpP5R3oi9\nUdaIXBu7Ldy5ubnRokULcnJyAHB2duby5cs4ODhYtTNPs23bti0VFRUUFBTQtWtXEhIScHFxuRlD\nF6nR1QG3du1aPvvsM/76178SEBBwk0cm0jApb8ReKW9E6hfljdgjZY1I7ezyUVnzVNoffviBtLQ0\ny5Rw8/PwVU2zTUxMxDAMQkJCgCvP3auyL/WVi4sLXl5eNGnShOPHj3P27FnL1HIRqTvKG7F3yhuR\n+kF5I/ZMWSNSM7ss3JkDqW3btuzevZtjx47Rrl07OnXqhGEYVFZWWha2BMjNzWXXrl34+fnh6+tr\nE34i9ZF5K3RXV1fLuhADBw5UwInUIeWNNATKG5GbT3kj9k5ZI1I9uyzcmbm7u+Pj48OWLVv48ssv\nadKkCV27drW623ThwgVWr15NTk4Oc+fOtdrJRqS+c3FxoXPnzri7u5ORkUF6ejrDhg2jWbNmN3to\nIg2K8kbsnfJGpH5Q3og9U9aIVM2uC3dw5a6Uj48PW7duJSMjgxMnTuDo6EhRURE7d+5k8+bN7N+/\nnzVr1uDl5XWzhyvyq5kDzsnJic8//5zAwECaN29+s4cl0uAob8TeKW9E6gfljdgzZY2ILcNkMplu\n9iDqwqlTp4iJiSE7O5uLFy8C0KZNG3r06MEzzzyjUJNbXmFhIRUVFXh4eNzsoYg0aMobsXfKG5H6\nQXkj9kxZI/L/GkzhDqC4uJiSkhJOnjzJ5cuX6dq1Ky4uLri6ut7soYmIiB1R3oiISF1Q3oiI2L8G\nVbgTERERERERERG5VTjc7AGIiIiIiIiIiIiILRXuRERERERERERE6iEV7kREREREREREROohFe5E\nRERERERERETqIRXuRERERERERERE6iEV7kREREREREREROohFe5ERERERERERETqIRXuRERERERE\nRERE6iEV7kREREREREREROohFe5E7NDKlSvx9vYmOzv7Zg9FRETsmPJGRER+jZSUFLy9vUlNTb2h\n7+Pt7c2UKVNu6HtcLTY2VnkoN4zTzR6AiL1ISUkhIiLC6pizszMtWrTgvvvuY/jw4YwePZpGjRrd\n8LEYhoFhGL+pj/z8fJYvX87evXv56aef8PT0ZPTo0YSHh+Po6HhNfWzdupUNGzZw+vRpmjZtiq+v\nL88++ywdO3b8TWMTEWnI7ClvTCYTKSkpbNmyhdzcXEpKSmjfvj2jRo1i+vTpODs7X1M/yhsRacgi\nIiJISUmhY8eO7Nmz57r0uWHDBnr37o23t/d16e9qv/V7yo00Y8YMMjMz2bJlCz4+PtW2u3TpEoMG\nDeK2227jo48+YuTIkXTp0oXOnTvX4WiloVDhTuQ6Gzt2LEOGDAGgrKyMvLw8Dhw4wEsvvcTatWuJ\niYm5IQF4PRUWFjJx4kTOnTvHn/70J9q1a8fevXuJjY0lLy+PhQsX1trHsmXLWLt2LSNHjmTatGn8\n61//Yt26dRw+fJjt27fTsmXLOrgSERH7ZQ95ExkZSWJiIsOGDWPcuHFUVlaye/duYmJiOHbsGLGx\nsbX2obwRkYassLCQDz74AEdHR7777jsOHTpEv379flOfZWVlLFq0iKioqHqfI9fb+PHjyczMJDk5\nucbC3Y4dOygvL2fcuHEAeHl54eXlVVfDlAZGhTuR66xLly74+/tbHQsLC+Nvf/sbTz31FNOnTyc5\nOZnbb7/9Jo2wditXruTMmTNs3LiRnj17AjBmzBhmzpxJTk4OhYWFuLm5VXt+Tk4Ob731FhMnTiQy\nMtJy/O6772bJkiUcOnSIkSNH3ujLEBGxa7d63hw5coTExERGjRrFsmXLLMcnTJjAuHHj2Lt3L19/\n/TX33HNPtX0ob0Skodu+fTslJSVMmjSJjRs3kpSU9JsLd1999RUVFRXXaYS3liFDhtC6dWvS0tKY\nP38+Li4uVbZLTk7G0dGR4ODgOh6hNERa406kjgwcOJAFCxZw/vx5YmJirF4rKSlh+fLljBgxgu7d\nu9O7d29CQ0PJyMiw6ae8vJzY2FhGjBiBj48PgwYNYtGiRVy6dKnWMbzyyit4e3uzefPmatuUl5eT\nnJyMn5+fpWhnFhcXR0pKSo1FO4BNmzZhGAYzZ860Ov7ggw+yb98+fYkSEbmBbpW8adKkCc899xxh\nYWFWxw3DoH///gDk5eXV+D7KGxFp6JKSknB0dGTWrFl07NiRjIwMCgoKbNq9+OKLeHt72/xdvXjx\notV6cBEREUyYMAHDMHjxxRfp2rWr1bptH330EZMnT6Z37974+PgQEBBAdHQ0xcXFVv1WVFQQHR3N\n4MGD8fHx4aGHHiIpKQm4skzCLx0+fJjp06fj6+tLt27dGDp0KFFRUVy4cMGm7eeff05YWBj9+vXD\nx8eHiRMncuDAgVo/q1OnTtG3b19GjRpVbZY5OjoSFBREYWFhtY8dnzx5kuPHj3P//fdbbo5Vtear\nt7c3TzzxBHl5ecycOZN+/frh6+vL5MmT+eKLL6z6NJlMrFu3jsDAQHr27EmvXr0YM2YM69evp7Ky\nstZrE/umwp1IHRo1ahTt2rVj9+7dlJeXA1f+SP/5z38mPj6e3//+90RGRvL0009TXFzMrFmz2Lp1\nq1Uf4eHhxMfHM2zYMBYuXEhQUBCbNm3i8ccft/RZlbfeeostW7YQHh7OxIkTq2139OhRCgsL8fPz\nsxwrLS39Vdd58OBBvLy8aN26NXAluBU4IiJ151bIm3vvvZewsDDuvfdem9dyc3NxcHCocbYdKG9E\npGE7duwYOTk5DBo0iJYtWzJmzBhKS0vZtm2bTdtrXZM0NDSUkJAQy3/HxMRY1m1LSkriySefpLCw\nkKeeeorIyEj69u1LfHy8zU2YxYsXEx8fj5eXFy+//DLBwcEkJCSQmppqM46srCwee+wxzpw5Q1hY\nGFFRUfzxj39k8+bNhIaGUlJSYml7+PBhQkJCOH/+PHPmzOGVV16x5Ft6enq113XhwgXCwsJwdXVl\n3bp1uLu7V9t2/PjxALz//vtVvp6cnIxhGEyYMMFyrLrPt6ioiKlTp+Lp6UlERARjx47ls88+Izw8\nnLKyMku7JUuWsHTpUjp06EBkZCQvv/wybdu2JSoqiri4uGrHKg2DHpUVqWO+vr6kpqZy4sQJunXr\nxs6dO8nOzmbq1KlWi42PGzeOgIAAXn/9dQIDA3FycuLDDz/kwIEDzJkzh+nTp1vaurm5ER0dze7d\nu3n44Ydt3jM9PZ1ly5YRFBTE008/XeP4Tp8+jWEYeHp6Eh0dTVJSEhcuXKBZs2Y8+OCDzJ07l6ZN\nm1Z7fnl5Od9//z3Dhg0jKyuL6OhocnJyMAyD3r178/zzz9e4XoSIiFwf9T1vrlZWVkZxcTE//PAD\nGzdu5OOPP2b27Nm0bdu22nOUNyLS0CUmJmIYBmPHjgWurH26cuVKtm7dSmho6P/U5+9+9ztOnjwJ\nQLdu3SxLMpSUlLBkyRI8PT3ZuHEjTZo0ASAoKIjGjRuzYcMG0tPT8ff35+LFi2zevJl77rmHtWvX\n4uBwZb6QOW9+KSoqChcXFzZs2ECbNm0ACAwMxNPTk8WLF7Np0yamTZsGwIIFC2jevDnr16/H1dUV\ngJEjRzJ8+HCWLVtms4QEXMmY8PBwLl26xIYNG7jjjjtq/AzuvPNOBg4cyKeffkpeXp5VFlVUVLBj\nxw7atGnD4MGDa/08Dx8+zCuvvMKkSZMs15WXl0dGRgb//Oc/LY81nz9/nmHDhrFixQrLuYGBgQwf\nPpyEhARmz55d63uJ/dKMO5E6Zp4V8OOPPwKQkZGBYRg88sgjVu1cXV0JCAggPz+fI0eOAPDBBx9g\nGAYPPfSQVdtHH32UTZs2Wc2SMzt69Chz587l/vvvJyoqqtbx5efnYzKZiIuLIzs7m/nz5xMTE8OA\nAQNITExkxowZNZ5fUFDA5cuXyc3NJSIiglGjRrF69WpmzZrFF198wZQpUyz/GBARkRunvufN1TZt\n2kS/fv0YM2YMWVlZxMXFER4eXuM5yhsRaciKi4vZtWsX7u7uDB06FABPT0/69+/PiRMnbB7F/K0O\nHTpEQUEBo0ePthTtzMaOHYvJZGLfvn3AlWJVRUUFAQEBlqIdQLNmzRgxYoTVuadOneLMmTMMGTLE\nUrQzCwwMBLD0++2333Ly5EkGDx5sKdoBNG3alHfeecdmeQiz559/npycHGJjY+nSpcs1Xe/48eO5\nfPmyzay7jz/+mAsXLhAcHHxNMxidnZ2tZuYB3HfffQCcO3fOcmzp0qWWmXUVFRUUFBRQUFBAhw4d\nKCoq4vz589c0brFPmnEnUsfMjxc5OV359fv2229xcHCgU6dONm3vvvtu4MojQ3369OHrr7/G0dHR\n5i6Rm5ubzXp0AGfPnmXp0qXcfvvtxMTEWAVnbeNzcHAgISHBMs6AgACeeOIJMjMz2bdvX7V3mMzn\nnzp1isTERMtshz/84Q907tyZ2bNnExsba3U3SURErr/6njdXGzFiBN7e3pw/f549e/Ywc+ZMJk+e\nzLx582q9PuWNiDREO3fupKioiJCQEJydnS3Hg4KC+PTTT0lKSqJ79+7X7f1yc3MxDKPGDPn2228B\n+P777zEMg/bt29u0/eXOq7m5uVZ9XM3DwwMPDw9Lv19//TVwZUbcL1W3++0bb7zBnj17WLBgwa/a\ntGP48OG0atWK1NRUZs2aZTmenJyMg4ODZTfZ2nh6euLo6Gh1rFGjRgBWG4CcP3+e6OhosrKyOHfu\nnNU6gIZhWD1WKw2PZtyJ1DHzgrDmO0rFxcXV7lbUqFEjTCaTZbHX0tJSyxew2phMJhYsWMCFCxfI\ny8vj9OnT13Se+e7VsGHDbN4rKCgIk8nE3//+91rP9/T0tHlEyd/fH1dXV6tFW0VE5Mao73lztdtv\nv51+/foxatQoVqxYwdSpU0lISCAtLa3ac5Q3ItKQJSUlWZYG+O677yw/Xbp0oVGjRqSlpVmtDfdb\nmfOhcePGNq+Zj5nb1NTWnDdmP//8M4DNLL6r+zb3Z76eqwuVNfnyyy9Zs2YNhmFc0+YVV3NyciIw\nMJCzZ89y8OBB4Epx7cCBA/j5+dW4lMPVqsvdq5WWlhISEsL777/PwIEDWb58OQkJCbz33ntV3iyT\nhkeFO5E6VF5eTnZ2Nh4eHpYFt11dXSktLa1yd6Xi4mIMw7CsKdeyZUtKS0trXBT8anfddRfvvPMO\nTk5OzJkz55rCu127dkDVgdiyZUsACgsLqz2/WbNmNGvWrNpAbdWqVY3ni4jIb3cr5E1NzDMZsrKy\nqm2jvBGRhurkyZMcPXoUgOeeew5/f3/Lz5gxYygrK6OoqKjGmx9m1zqTy3yz5Je7x8L/F9/MGWIu\n2FW1wd3PP/9s9YhpTf2aj5v7bdWqFUCVu+ZWpbS0lHnz5jFu3DjS09Mtu9peqwkTJmAymSyPy6am\nplJZWWnz6OtvtXfvXs6cOUNwcDCLFy8mICAAX19f+vbtazNbTxomFe5E6lBiYiIXLlywmlrt5eWF\nyWSqcoaCeTq4eUq6uah26tQpq3aFhYWkpKTw+eefW44ZhsG8efPo378/L7zwArm5uSxcuLDWMfbo\n0QPDMPjqq69sXvvl7I3q9OzZk7y8PJsvTJWVlfznP/+p9XwREfltboW8WblyJf369bN8+bya+UvZ\n1Y8RVUV5IyIN0ZYtWzAMg9DQUFasWGHzM3/+fEwmk1WhyjyL+peFuu++++6a3rNz586YTCa++eYb\nm9fMWWHOEE9PT0wmk+W7w9V+ufaoecfaqvo9f/48+fn5NtlUVdvs7GxSUlKsrq93795MmTKFiIgI\nOnTowKJFizhz5sw1XS9Ax44d8fX1JSMjg5KSEtLS0mjVqpVlTcHrxfxo8YABA6yOFxYWVvmdTBoe\nFe5E6shHH33E66+/Tvv27a22Sw8ICMBkMpGYmGjVPj8/nw8//JA77riDHj16ADB06FBMJhPJyclW\nbbdt20ZERITNFyzzrIqJEycyePBgkpKS2Lt3b43jbN26NX5+fmRkZFi+yJn7Mu9cdfX6dv/+9785\nffq01QyOsWPHUl5eTnx8vFXfycnJlJWVMWTIkBrHICIi/7tbJW+6dOlCfn4+7777rs1r27ZtszwC\nZqa8ERG5UnjbsWMHTZo04emnn7aabWf+CQ0N5b777uPIkSOWv9fmDYu+/PJLq/7ee+89m/dwcHDA\nZDJZzZjr27cvLVq0YOfOnTaz48yFRPOOsX369MEwDNLT063aXbx4kQ8//NDq2F133cU999xDZmYm\n//3vf2vst2PHjnTq1IlPPvnEamOHkpIS5s6dy8qVK6t8NNXV1ZWlS5dSWlrKnDlzqKysrOKTrdqE\nCRMoLi4mISGB48ePExQUdN1nwd12222YTCbOnj1rdXzp0qWWNWOv52PPcuvR5hQi19mJEyfYs2cP\nAJcvX+bcuXNkZmbyySefcNddd7F69WqaNWtmae/v78+gQYN47733KCoqok+fPly8eJH333+fgoIC\nXnvtNcsf7JEjR5KYmMiGDRsoLy+nV69efPPNNyQkJNC9e3cefvjhasf12muvMXr0aF566SW6d+9e\n4yyEefPmMWnSJKZMmcKMGTNwdXVl586d/OMf/2DcuHF069bN0nbu3LlkZ2dz8OBBPDw8AHjwwQdJ\nTU3lrbfe4qeffqJPnz4cP37csv16bTsFiohI7W71vPH398fPz4+0tDR++uknhg8fjpOTE5mZmWRk\nZHDvvfcSFBRkaa+8ERGB3bt3k5+fT0hICG5ubtW2CwkJYf78+WzdupUXXniBoUOH8uabb7J48WJ+\n/PFHmjZtSkZGBk5OTjbry5k3f1i/fj3FxcX06tULHx8f5s2bxwsvvEBISAhBQUE0atSIgwcPsmvX\nLkaMGGHZcbx169YEBgaSmprKjBkzGD58OJcuXSIlJYUePXqwf/9+q/d7+eWXmT59OqGhoUyaNInm\nzZtz9OhRtmzZQs+ePQkODra0femllwgLCyM0NJSpU6fi7OxMcnIyP/zwAytXrqz28+jRowdPPPEE\ncXFxxMTE8Nxzz13T5+3v709UVBRxcXE4ODgwfvz4azqvNlffhHrggQdo0qQJ69atw9nZmebNm5OW\nlobJZGLy5MmsWrWKNWvWMH78eKsbWtJwqHAnch0ZhsG2bdvYtm2b5Zi7uzudO3dm/vz5PPLII1Xe\nBTL/Md65cyc7duygcePG9OjRg8jISPr06WPV/5o1a1i1ahVpaWkkJyfTvHlzJk6cyFNPPVXjQuKt\nWrUiKiqKmTNnEhERwbp166pt26lTJ5KSkoiOjiY+Pp6ioiI6duxIREQEU6ZMsWlf1e6Bq1atYu3a\ntaSmprJt2zbc3d0ZM2YMs2fPtqxPISIi/xt7yBvze7z77rvs2LGDJUuWUFlZSdu2bQkLCyMsLMzm\nGpQ3ItLQbd26FQcHBx599NEa2z300EMsWbKE7du385e//IVu3boRExPD8uXLef3112nRogUjRoxg\nzpw59OvXz2rdud69exMcHExaWhpvvvkmkZGR+Pj4MHr0aDw8PFizZg3R0dFUVFTQoUMHnn/+eaZN\nm2b1/q+++irNmzdn165dfPrpp7Rv357HHnuMNm3a2Kxf6uvry/r164mNjWX16tX8/PPPliyYMWOG\n1Qy3gQMHkpCQQFxcHG+88Qbl5eV07dqVt99+2+pRU8MwrK4J4MknnyQrK4t169bxwAMPWOVedVxc\nXBgzZgwJCQn079+/yp1ya/LLMVR1/LbbbiM+Pp4lS5awYsUKWrRoQUBAAM888wwXL17k448/Zvfu\n3Xh4eKhw10AZpqpWKBYREREREREREZGbSmvciYiIiIiIiIiI1EMq3ImIiIiIiIiIiNRDKtyJiIiI\niIiIiIjUQyrciYiIiIiIiIiI1EMq3ImIiIiIiIiIiNRDKtyJiIiIiIiIiIjUQyrciYiIiIiIiIiI\n1EMq3ImIiIiIiIiIiNRDKtyJiIiIiIiIiIjUQyrciYiIiIiIiIiI1EP/B9vOBAvcvIcdAAAAAElF\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc467d12d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2 = data2[(data2.kind.str.startswith('Horizontal'))]\n",
    "#data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "#data2.loc[data2['engine'] == 'Dock 6.6'] = '\\nA'\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "\n",
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=12, kind=\"box\", palette=\"Set3\",)\n",
    "g = sns.FacetGrid(col=\"engine\", data=data2, size=6, sharey=True, sharex=False)\n",
    "g.map(sns.boxplot, \"score\", \"ef0.1\", palette=\"Set3\", linewidth=1.)\n",
    "\n",
    "g.set_ylabels('Enrichment Factor 0.1%')\n",
    "g.set_xlabels('{col_name}')\n",
    "\n",
    "g.set_titles('{col_name}')\n",
    "for ax, i in zip(g.axes[0],['A','B','C']):\n",
    "    ax.set_xlabel(ax.title.get_text())\n",
    "    ax.set_title(i)\n",
    "\n",
    "g.set_xticklabels(rotation=45)\n",
    "# g.savefig('dude_raport_boxplots_horizontal.svg')\n",
    "g.savefig('dude_raport_boxplots_horizontal_0.1perc.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LyzX2nqpKSUlBZmYmunbtCiMjI1RVVQEAfH19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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc4154fb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "#data2 = data2[(data2.engine == 'Dock 3.6') & (data2.kind.str.startswith('Vertical'))]\n",
    "#data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "data2 = data2[data2.kind.str.startswith('Vertical') | data2.kind.str.startswith('Horizontal') & ~data2.score.str.startswith('RF')]\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "\n",
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=7, kind=\"box\", palette=\"Set3\",)\n",
    "g = sns.FacetGrid(col=\"engine\", data=data2, size=6, sharey=True, sharex=False, )\n",
    "g.map(sns.boxplot, \"score\", \"ef0.1\", palette=\"Set3\", linewidth=1.)\n",
    "\n",
    "g.set_ylabels('Enrichment Factor 0.1%')\n",
    "g.set_xlabels('{col_name}')\n",
    "\n",
    "g.set_titles('{col_name}')\n",
    "for ax, i in zip(g.axes[0],['A','B','C']):\n",
    "    ax.set_xlabel(ax.title.get_text())\n",
    "    ax.set_title(i)\n",
    "\n",
    "\n",
    "g.set_xticklabels(rotation=45)\n",
    "# g.fig.tight_layout()\n",
    "# g.savefig('dude_raport_boxplots_vertical.svg')\n",
    "g.savefig('dude_raport_boxplots_vertical_0.1perc.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mZGTIarUqLGyxXFxK//GZ3VcBmM3Jk19p+/Ytmjhxqpo2bWZ0HMA0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\naEl8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc438f3610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = raport_final.copy()\n",
    "data2['score'] = data2['kind'] + ' ' + data2['score']\n",
    "\n",
    "data2 = data2[data2.kind.str.startswith('Per Target') | data2.kind.str.startswith('Horizontal') & data2.score.str.startswith('Horizontal RF')]\n",
    "\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "\n",
    "#g = sns.factorplot(x=\"engine\", y=\"ef1\", hue=\"score\", data=data2, size=7, kind=\"box\", palette=\"Set3\",)\n",
    "g = sns.FacetGrid(col=\"engine\", data=data2, size=6, sharey=True, sharex=False, aspect=1)\n",
    "g.map(sns.boxplot, \"score\", \"ef0.1\", palette=\"Set3\", fliersize=0, linewidth=1.)\n",
    "g.map(sns.stripplot, \"score\", \"ef0.1\", data=data2, jitter=True, size=2, color=\".3\", linewidth=0)\n",
    "\n",
    "g.set_ylabels('Enrichment Factor 0.1%')\n",
    "g.set_xlabels('Docking Software')\n",
    "\n",
    "g.set_titles('{col_name}')\n",
    "for ax, i in zip(g.axes[0],['A','B','C']):\n",
    "    ax.set_xlabel(ax.title.get_text())\n",
    "    ax.set_title(i)\n",
    "\n",
    "g.set_xticklabels(rotation=60)\n",
    "\n",
    "# g.savefig('dude_raport_boxplots_all_wpoints.svg')\n",
    "g.savefig('dude_raport_boxplots_all_wpoints_0.1perc.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'accuracy', u'ef0.1', u'ef1', u'ef10', u'ef1_perc', u'ef2', u'ef5',\n",
       "       u'engine', u'mse', u'precision', u'recall', u'rk', u'rk_active',\n",
       "       u'rk_inactive', u'roc_auc', u'roc_log_auc', u'rp', u'rp_active',\n",
       "       u'rp_inactive', u'rs', u'rs_active', u'rs_inactive', u'score',\n",
       "       u'n_folds', u'kind'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2[data2.kind.str.startswith('Per Target') & (data2['ef1'] > 60)].columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from matplotlib.pylab import plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dude_ids = ['aa2ar', 'abl1', 'ace', 'aces', 'ada', 'ada17', 'adrb1', 'adrb2', 'akt1', 'akt2', 'aldr', 'ampc', 'andr', 'aofb', 'bace1', 'braf', 'cah2', 'casp3', 'cdk2', 'comt', 'cp2c9', 'cp3a4', 'csf1r', 'cxcr4', 'def', 'dhi1', 'dpp4', 'drd3', 'dyr', 'egfr', 'esr1', 'esr2', 'fa10', 'fa7', 'fabp4', 'fak1', 'fgfr1', 'fkb1a', 'fnta', 'fpps', 'gcr', 'glcm', 'gria2', 'grik1', 'hdac2', 'hdac8', 'hivint', 'hivpr', 'hivrt', 'hmdh', 'hs90a', 'hxk4', 'igf1r', 'inha', 'ital', 'jak2', 'kif11', 'kit', 'kith', 'kpcb', 'lck', 'lkha4', 'mapk2', 'mcr', 'met', 'mk01', 'mk10', 'mk14', 'mmp13', 'mp2k1', 'nos1', 'nram', 'pa2ga', 'parp1', 'pde5a', 'pgh1', 'pgh2', 'plk1', 'pnph', 'ppara', 'ppard', 'pparg', 'prgr', 'ptn1', 'pur2', 'pygm', 'pyrd', 'reni', 'rock1', 'rxra', 'sahh', 'src', 'tgfr1', 'thb', 'thrb', 'try1', 'tryb1', 'tysy', 'urok', 'vgfr2', 'wee1', 'xiap']\n",
    "dude_weights = pd.DataFrame({'dude_id':dude_id, 'actives_num': len(pd.read_csv('/home/maciek/scratch/Pobrane/dude/all/%s/actives_final.ism' % (dude_id), header=None))} for dude_id in dude_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-39-7bd540e011a2>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-39-7bd540e011a2>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    engine = 'Autodock Vina\u001b[0m\n\u001b[1;37m                          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "engine = 'Autodock Vina\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, di"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "out = []\n",
    "for engine in ['Autodock Vina', 'Dock 3.6', 'Dock 6.6']:\n",
    "    over = raport_per_target[(raport_per_target['ef1'] > 50) & (raport_per_target['engine'] == engine)]['dude_id'].unique()\n",
    "    under = raport_per_target[(raport_per_target['ef1'] < 15) & (raport_per_target['engine'] == engine)]['dude_id'].unique()\n",
    "    \n",
    "    for dude_id in over:\n",
    "        out.append({'engine': engine, 'kind': 'over', 'dude_id': dude_id})\n",
    "    for dude_id in under:\n",
    "        out.append({'engine': engine, 'kind': 'under', 'dude_id': dude_id})\n",
    "    \n",
    "    print(engine, \"All\")\n",
    "    dude_weights.hist()\n",
    "    plt.show()\n",
    "\n",
    "    print(engine, \"Over\")\n",
    "    dude_weights[dude_weights.dude_id.isin(over)].hist()\n",
    "    plt.show()\n",
    "\n",
    "    print(engine, \"Under\")\n",
    "    dude_weights[dude_weights.dude_id.isin(under)].hist()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from itertools import combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "out2 = pd.DataFrame(out)\n",
    "for kind in ['over', 'under']:\n",
    "    for engine1, engine2 in combinations(['Autodock Vina', 'Dock 3.6', 'Dock 6.6'], 2):\n",
    "        s1 = set(out2[(out2['engine'] == engine1) & (out2['kind'] == kind)]['dude_id'])\n",
    "        s2 = set(out2[(out2['engine'] == engine2) & (out2['kind'] == kind)]['dude_id'])\n",
    "        intersection = len(s1.intersection(s2))\n",
    "        print(engine1,\n",
    "              engine2,\n",
    "              kind,\n",
    "              intersection,\n",
    "              len(s1), round(intersection/float(len(s1)), 2),\n",
    "              len(s2), round(intersection/float(len(s2)), 2),\n",
    "              sep='\\t')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t = out2['dude_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sns.countplot(x=\"actives_num\", hue=\"class\", data=raport_per_target, palette=\"Greens_d\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_per_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "raport_classic_per_target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols = ['dude_id', 'ef1', 'ef0.1']\n",
    "data1 = raport_per_target[(raport_per_target['score'] == 'RF_v3') & (raport_per_target['engine'] == 'Autodock Vina')]#[cols]\n",
    "\n",
    "raport_classic_per_target = pd.read_csv('dude_classical_final.csv', index_col=0)\n",
    "raport_classic_per_target['engine'].replace('dock', 'Dock 6.6', inplace=True)\n",
    "raport_classic_per_target['engine'].replace('dude', 'Dock 3.6', inplace=True)\n",
    "raport_classic_per_target['engine'].replace('vina', 'Autodock Vina', inplace=True)\n",
    "\n",
    "data2 = raport_classic_per_target[(raport_classic_per_target['engine'] == 'vina') & (raport_classic_per_target['score'] == 'native')]#[cols]\n",
    "\n",
    "data3 = pd.merge(data1, data2, on='dude_id', suffixes=('_per_target', '_classical'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data3.plot(x='roc_auc_per_target', y='roc_auc_classical',kind='scatter')\n",
    "# plt.plot(x=[0,60], y=[0,60], s='-')\n",
    "lim = (0, 1)\n",
    "plt.plot(lim, lim, c=\".2\", ls=\"--\")\n",
    "plt.xlim(lim)\n",
    "plt.ylim(lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data3.plot(x='ef1_per_target', y='ef1_classical',kind='scatter')\n",
    "# plt.plot(x=[0,60], y=[0,60], s='-')\n",
    "lim = (0, 100)\n",
    "plt.plot(lim, lim, c=\".2\", ls=\"--\")\n",
    "plt.xlim(lim)\n",
    "plt.ylim(lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data4 = pd.merge(raport_per_target, raport_classic_per_target, on=['dude_id', 'engine'], suffixes=('_per_target', '_classical'))\n",
    "data4 = pd.merge(data4, pd.read_csv('dude_target_classes.csv'), on='dude_id', how='left')\n",
    "data4['target_class'].fillna('other', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"ticks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g = sns.FacetGrid(data4[data4['score_classical'] == 'native'],\n",
    "                  col='score_per_target',\n",
    "                  row='engine',\n",
    "                  hue='target_class',\n",
    "                  sharex=False,\n",
    "                  sharey=False,\n",
    "                  margin_titles=True,\n",
    "                  hue_kws={'marker': ['^', 'v','s', 'o', 'D']},\n",
    "                  size=4)\n",
    "\n",
    "# g.map(sns.kdeplot,\n",
    "#       'ef1_per_target',\n",
    "#       'ef1_classical',\n",
    "#       alpha=.1,\n",
    "#      )\n",
    "\n",
    "g.map(plt.scatter,\n",
    "      'ef1_per_target',\n",
    "      'ef1_classical',\n",
    "      s=30\n",
    "     )\n",
    "\n",
    "\n",
    "lim = (0, 100)\n",
    "for ax in g.axes.flat:\n",
    "    ax.plot(lim, lim, c=\".2\", ls=\"--\")\n",
    "g.set(xlim=lim, ylim=lim)\n",
    "g.add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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