corda-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>data es</th>\n",
       "      <th>ID TRACK</th>\n",
       "      <th>DATA NASCITA</th>\n",
       "      <th>image_id</th>\n",
       "      <th>covid</th>\n",
       "      <th>rx</th>\n",
       "      <th>CLINICA</th>\n",
       "      <th>NOTE</th>\n",
       "      <th>...</th>\n",
       "      <th>Lung Lesion</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>9.003902e+09</td>\n",
       "      <td>01/03/1946</td>\n",
       "      <td>corda1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080021</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>0.057013</td>\n",
       "      <td>0.039196</td>\n",
       "      <td>0.025258</td>\n",
       "      <td>0.035283</td>\n",
       "      <td>0.074448</td>\n",
       "      <td>0.061089</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>0.048945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001680e+09</td>\n",
       "      <td>04/04/1968</td>\n",
       "      <td>corda2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031187</td>\n",
       "      <td>0.930774</td>\n",
       "      <td>0.350397</td>\n",
       "      <td>0.184001</td>\n",
       "      <td>0.144656</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.758117</td>\n",
       "      <td>0.005987</td>\n",
       "      <td>0.013088</td>\n",
       "      <td>0.059794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.003353e+09</td>\n",
       "      <td>12/09/0199</td>\n",
       "      <td>corda3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036552</td>\n",
       "      <td>0.003615</td>\n",
       "      <td>0.030241</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.261099</td>\n",
       "      <td>0.054235</td>\n",
       "      <td>0.026397</td>\n",
       "      <td>0.020180</td>\n",
       "      <td>0.091661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000477e+09</td>\n",
       "      <td>28/05/1946</td>\n",
       "      <td>corda4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063948</td>\n",
       "      <td>0.448018</td>\n",
       "      <td>0.187096</td>\n",
       "      <td>0.298485</td>\n",
       "      <td>0.056434</td>\n",
       "      <td>0.030715</td>\n",
       "      <td>0.036530</td>\n",
       "      <td>0.027439</td>\n",
       "      <td>0.093709</td>\n",
       "      <td>0.029209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000981e+09</td>\n",
       "      <td>10/04/1957</td>\n",
       "      <td>corda5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135531</td>\n",
       "      <td>0.265945</td>\n",
       "      <td>0.265859</td>\n",
       "      <td>0.226268</td>\n",
       "      <td>0.292798</td>\n",
       "      <td>0.111357</td>\n",
       "      <td>0.197117</td>\n",
       "      <td>0.042322</td>\n",
       "      <td>0.082476</td>\n",
       "      <td>0.047800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
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      ],
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       "   Unnamed: 0  Unnamed: 0.1     data es      ID TRACK DATA NASCITA image_id  \\\n",
       "0           0             0  23/03/2020  9.003902e+09   01/03/1946   corda1   \n",
       "1           1             1         NaN  9.001680e+09   04/04/1968   corda2   \n",
       "2           2             2         NaN  9.003353e+09   12/09/0199   corda3   \n",
       "3           3             3         NaN  9.000477e+09   28/05/1946   corda4   \n",
       "4           4             4         NaN  9.000981e+09   10/04/1957   corda5   \n",
       "\n",
       "   covid   rx  CLINICA NOTE  ... Lung Lesion     Edema  Consolidation  \\\n",
       "0    0.0  0.0      0.0  NaN  ...    0.080021  0.002013       0.057013   \n",
       "1    0.0  1.0      0.0  NaN  ...    0.031187  0.930774       0.350397   \n",
       "2    0.0  0.0      0.0  NaN  ...    0.036552  0.003615       0.030241   \n",
       "3    1.0  1.0      1.0  NaN  ...    0.063948  0.448018       0.187096   \n",
       "4    1.0  1.0      1.0  NaN  ...    0.135531  0.265945       0.265859   \n",
       "\n",
       "   Pneumonia  Atelectasis  Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "0   0.039196     0.025258      0.035283          0.074448       0.061089   \n",
       "1   0.184001     0.144656      0.009302          0.758117       0.005987   \n",
       "2   0.018732     0.006805      0.261099          0.054235       0.026397   \n",
       "3   0.298485     0.056434      0.030715          0.036530       0.027439   \n",
       "4   0.226268     0.292798      0.111357          0.197117       0.042322   \n",
       "\n",
       "   Fracture  Support Devices  \n",
       "0  0.078508         0.048945  \n",
       "1  0.013088         0.059794  \n",
       "2  0.020180         0.091661  \n",
       "3  0.093709         0.029209  \n",
       "4  0.082476         0.047800  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('predictions.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.dataset == 'CORDA-dataset'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>CORDA-dataset</th>\n",
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       "      <td>447</td>\n",
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      "text/plain": [
       "               Unnamed: 0  Unnamed: 0.1  data es  ID TRACK  DATA NASCITA  \\\n",
       "dataset                                                                    \n",
       "CORDA-dataset         447           447       17       447           447   \n",
       "\n",
       "               image_id  covid   rx  CLINICA  NOTE  ...  Lung Lesion  Edema  \\\n",
       "dataset                                             ...                       \n",
       "CORDA-dataset       447    447  447      447   135  ...          447    447   \n",
       "\n",
       "               Consolidation  Pneumonia  Atelectasis  Pneumothorax  \\\n",
       "dataset                                                              \n",
       "CORDA-dataset            447        447          447           447   \n",
       "\n",
       "               Pleural Effusion  Pleural Other  Fracture  Support Devices  \n",
       "dataset                                                                    \n",
       "CORDA-dataset               447            447       447              447  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('dataset').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
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       "      <td>249</td>\n",
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       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
       "      <td>249</td>\n",
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      "text/plain": [
       "     Unnamed: 0  Unnamed: 0.1  data es  ID TRACK  DATA NASCITA  image_id  \\\n",
       "rx                                                                         \n",
       "0.0         198           198        9       198           198       198   \n",
       "1.0         249           249        8       249           249       249   \n",
       "\n",
       "     covid  CLINICA  NOTE  dataset  ...  Lung Lesion  Edema  Consolidation  \\\n",
       "rx                                  ...                                      \n",
       "0.0    198      198    43      198  ...          198    198            198   \n",
       "1.0    249      249    92      249  ...          249    249            249   \n",
       "\n",
       "     Pneumonia  Atelectasis  Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "rx                                                                           \n",
       "0.0        198          198           198               198            198   \n",
       "1.0        249          249           249               249            249   \n",
       "\n",
       "     Fracture  Support Devices  \n",
       "rx                              \n",
       "0.0       198              198  \n",
       "1.0       249              249  \n",
       "\n",
       "[2 rows x 25 columns]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.groupby('rx').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>No Finding</th>\n",
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       "      <th>No Finding</th>\n",
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       "      <td>-0.331294</td>\n",
       "      <td>-0.680895</td>\n",
       "      <td>-0.835388</td>\n",
       "      <td>-0.689813</td>\n",
       "      <td>-0.540793</td>\n",
       "      <td>0.065305</td>\n",
       "      <td>-0.593863</td>\n",
       "      <td>0.290473</td>\n",
       "      <td>0.318004</td>\n",
       "      <td>-0.321832</td>\n",
       "      <td>-0.332273</td>\n",
       "      <td>-0.615599</td>\n",
       "      <td>-0.298985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enlarged Cardiomediastinum</th>\n",
       "      <td>-0.136742</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341358</td>\n",
       "      <td>-0.010113</td>\n",
       "      <td>0.118687</td>\n",
       "      <td>0.089084</td>\n",
       "      <td>-0.047904</td>\n",
       "      <td>-0.091178</td>\n",
       "      <td>0.166590</td>\n",
       "      <td>-0.143359</td>\n",
       "      <td>-0.028625</td>\n",
       "      <td>0.007736</td>\n",
       "      <td>0.262864</td>\n",
       "      <td>-0.216359</td>\n",
       "      <td>0.017157</td>\n",
       "      <td>0.089657</td>\n",
       "      <td>-0.014301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <td>-0.445735</td>\n",
       "      <td>0.341358</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.281013</td>\n",
       "      <td>-0.131975</td>\n",
       "      <td>0.622757</td>\n",
       "      <td>0.204744</td>\n",
       "      <td>0.181882</td>\n",
       "      <td>0.232167</td>\n",
       "      <td>-0.341792</td>\n",
       "      <td>0.194460</td>\n",
       "      <td>-0.317668</td>\n",
       "      <td>-0.243591</td>\n",
       "      <td>-0.087137</td>\n",
       "      <td>0.030188</td>\n",
       "      <td>0.222584</td>\n",
       "      <td>0.038983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung Opacity</th>\n",
       "      <td>-0.899130</td>\n",
       "      <td>-0.010113</td>\n",
       "      <td>0.281013</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.358650</td>\n",
       "      <td>0.705666</td>\n",
       "      <td>0.905047</td>\n",
       "      <td>0.839520</td>\n",
       "      <td>0.309188</td>\n",
       "      <td>-0.047059</td>\n",
       "      <td>0.516295</td>\n",
       "      <td>-0.329125</td>\n",
       "      <td>-0.452970</td>\n",
       "      <td>0.437516</td>\n",
       "      <td>0.340364</td>\n",
       "      <td>0.604547</td>\n",
       "      <td>0.317907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lung Lesion</th>\n",
       "      <td>-0.331294</td>\n",
       "      <td>0.118687</td>\n",
       "      <td>-0.131975</td>\n",
       "      <td>0.358650</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.058354</td>\n",
       "      <td>0.436690</td>\n",
       "      <td>0.525971</td>\n",
       "      <td>-0.055749</td>\n",
       "      <td>0.054243</td>\n",
       "      <td>-0.003706</td>\n",
       "      <td>0.285206</td>\n",
       "      <td>0.168896</td>\n",
       "      <td>-0.053227</td>\n",
       "      <td>0.204380</td>\n",
       "      <td>0.276772</td>\n",
       "      <td>0.156587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Edema</th>\n",
       "      <td>-0.680895</td>\n",
       "      <td>0.089084</td>\n",
       "      <td>0.622757</td>\n",
       "      <td>0.705666</td>\n",
       "      <td>-0.058354</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.541494</td>\n",
       "      <td>0.569381</td>\n",
       "      <td>0.214032</td>\n",
       "      <td>-0.306928</td>\n",
       "      <td>0.399091</td>\n",
       "      <td>-0.421607</td>\n",
       "      <td>-0.467326</td>\n",
       "      <td>0.255867</td>\n",
       "      <td>0.214347</td>\n",
       "      <td>0.451295</td>\n",
       "      <td>0.214701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Consolidation</th>\n",
       "      <td>-0.835388</td>\n",
       "      <td>-0.047904</td>\n",
       "      <td>0.204744</td>\n",
       "      <td>0.905047</td>\n",
       "      <td>0.436690</td>\n",
       "      <td>0.541494</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818624</td>\n",
       "      <td>0.337711</td>\n",
       "      <td>-0.089819</td>\n",
       "      <td>0.555135</td>\n",
       "      <td>-0.325222</td>\n",
       "      <td>-0.438147</td>\n",
       "      <td>0.371712</td>\n",
       "      <td>0.281840</td>\n",
       "      <td>0.558248</td>\n",
       "      <td>0.253863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumonia</th>\n",
       "      <td>-0.689813</td>\n",
       "      <td>-0.091178</td>\n",
       "      <td>0.181882</td>\n",
       "      <td>0.839520</td>\n",
       "      <td>0.525971</td>\n",
       "      <td>0.569381</td>\n",
       "      <td>0.818624</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047148</td>\n",
       "      <td>-0.218724</td>\n",
       "      <td>0.178463</td>\n",
       "      <td>-0.223299</td>\n",
       "      <td>-0.349257</td>\n",
       "      <td>0.232634</td>\n",
       "      <td>0.293369</td>\n",
       "      <td>0.501363</td>\n",
       "      <td>0.270265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Atelectasis</th>\n",
       "      <td>-0.540793</td>\n",
       "      <td>0.166590</td>\n",
       "      <td>0.232167</td>\n",
       "      <td>0.309188</td>\n",
       "      <td>-0.055749</td>\n",
       "      <td>0.214032</td>\n",
       "      <td>0.337711</td>\n",
       "      <td>0.047148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.083024</td>\n",
       "      <td>0.446481</td>\n",
       "      <td>-0.257815</td>\n",
       "      <td>-0.010029</td>\n",
       "      <td>0.033967</td>\n",
       "      <td>0.122643</td>\n",
       "      <td>0.277141</td>\n",
       "      <td>0.094480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pneumothorax</th>\n",
       "      <td>0.065305</td>\n",
       "      <td>-0.143359</td>\n",
       "      <td>-0.341792</td>\n",
       "      <td>-0.047059</td>\n",
       "      <td>0.054243</td>\n",
       "      <td>-0.306928</td>\n",
       "      <td>-0.089819</td>\n",
       "      <td>-0.218724</td>\n",
       "      <td>-0.083024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>0.044571</td>\n",
       "      <td>-0.018709</td>\n",
       "      <td>0.154384</td>\n",
       "      <td>0.059064</td>\n",
       "      <td>-0.022847</td>\n",
       "      <td>0.042597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <td>-0.593863</td>\n",
       "      <td>-0.028625</td>\n",
       "      <td>0.194460</td>\n",
       "      <td>0.516295</td>\n",
       "      <td>-0.003706</td>\n",
       "      <td>0.399091</td>\n",
       "      <td>0.555135</td>\n",
       "      <td>0.178463</td>\n",
       "      <td>0.446481</td>\n",
       "      <td>0.010312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.157437</td>\n",
       "      <td>-0.355183</td>\n",
       "      <td>0.340889</td>\n",
       "      <td>0.071072</td>\n",
       "      <td>0.318361</td>\n",
       "      <td>0.091028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleural Other</th>\n",
       "      <td>0.290473</td>\n",
       "      <td>0.007736</td>\n",
       "      <td>-0.317668</td>\n",
       "      <td>-0.329125</td>\n",
       "      <td>0.285206</td>\n",
       "      <td>-0.421607</td>\n",
       "      <td>-0.325222</td>\n",
       "      <td>-0.223299</td>\n",
       "      <td>-0.257815</td>\n",
       "      <td>0.044571</td>\n",
       "      <td>-0.157437</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.566075</td>\n",
       "      <td>-0.292327</td>\n",
       "      <td>-0.016113</td>\n",
       "      <td>-0.207860</td>\n",
       "      <td>-0.013051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fracture</th>\n",
       "      <td>0.318004</td>\n",
       "      <td>0.262864</td>\n",
       "      <td>-0.243591</td>\n",
       "      <td>-0.452970</td>\n",
       "      <td>0.168896</td>\n",
       "      <td>-0.467326</td>\n",
       "      <td>-0.438147</td>\n",
       "      <td>-0.349257</td>\n",
       "      <td>-0.010029</td>\n",
       "      <td>-0.018709</td>\n",
       "      <td>-0.355183</td>\n",
       "      <td>0.566075</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.463289</td>\n",
       "      <td>-0.011413</td>\n",
       "      <td>-0.196068</td>\n",
       "      <td>-0.065270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Support Devices</th>\n",
       "      <td>-0.321832</td>\n",
       "      <td>-0.216359</td>\n",
       "      <td>-0.087137</td>\n",
       "      <td>0.437516</td>\n",
       "      <td>-0.053227</td>\n",
       "      <td>0.255867</td>\n",
       "      <td>0.371712</td>\n",
       "      <td>0.232634</td>\n",
       "      <td>0.033967</td>\n",
       "      <td>0.154384</td>\n",
       "      <td>0.340889</td>\n",
       "      <td>-0.292327</td>\n",
       "      <td>-0.463289</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.157155</td>\n",
       "      <td>0.232912</td>\n",
       "      <td>0.159531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covid</th>\n",
       "      <td>-0.332273</td>\n",
       "      <td>0.017157</td>\n",
       "      <td>0.030188</td>\n",
       "      <td>0.340364</td>\n",
       "      <td>0.204380</td>\n",
       "      <td>0.214347</td>\n",
       "      <td>0.281840</td>\n",
       "      <td>0.293369</td>\n",
       "      <td>0.122643</td>\n",
       "      <td>0.059064</td>\n",
       "      <td>0.071072</td>\n",
       "      <td>-0.016113</td>\n",
       "      <td>-0.011413</td>\n",
       "      <td>0.157155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.424979</td>\n",
       "      <td>0.802825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rx</th>\n",
       "      <td>-0.615599</td>\n",
       "      <td>0.089657</td>\n",
       "      <td>0.222584</td>\n",
       "      <td>0.604547</td>\n",
       "      <td>0.276772</td>\n",
       "      <td>0.451295</td>\n",
       "      <td>0.558248</td>\n",
       "      <td>0.501363</td>\n",
       "      <td>0.277141</td>\n",
       "      <td>-0.022847</td>\n",
       "      <td>0.318361</td>\n",
       "      <td>-0.207860</td>\n",
       "      <td>-0.196068</td>\n",
       "      <td>0.232912</td>\n",
       "      <td>0.424979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.407707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLINICA</th>\n",
       "      <td>-0.298985</td>\n",
       "      <td>-0.014301</td>\n",
       "      <td>0.038983</td>\n",
       "      <td>0.317907</td>\n",
       "      <td>0.156587</td>\n",
       "      <td>0.214701</td>\n",
       "      <td>0.253863</td>\n",
       "      <td>0.270265</td>\n",
       "      <td>0.094480</td>\n",
       "      <td>0.042597</td>\n",
       "      <td>0.091028</td>\n",
       "      <td>-0.013051</td>\n",
       "      <td>-0.065270</td>\n",
       "      <td>0.159531</td>\n",
       "      <td>0.802825</td>\n",
       "      <td>0.407707</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            No Finding  Enlarged Cardiomediastinum  \\\n",
       "No Finding                    1.000000                   -0.136742   \n",
       "Enlarged Cardiomediastinum   -0.136742                    1.000000   \n",
       "Cardiomegaly                 -0.445735                    0.341358   \n",
       "Lung Opacity                 -0.899130                   -0.010113   \n",
       "Lung Lesion                  -0.331294                    0.118687   \n",
       "Edema                        -0.680895                    0.089084   \n",
       "Consolidation                -0.835388                   -0.047904   \n",
       "Pneumonia                    -0.689813                   -0.091178   \n",
       "Atelectasis                  -0.540793                    0.166590   \n",
       "Pneumothorax                  0.065305                   -0.143359   \n",
       "Pleural Effusion             -0.593863                   -0.028625   \n",
       "Pleural Other                 0.290473                    0.007736   \n",
       "Fracture                      0.318004                    0.262864   \n",
       "Support Devices              -0.321832                   -0.216359   \n",
       "covid                        -0.332273                    0.017157   \n",
       "rx                           -0.615599                    0.089657   \n",
       "CLINICA                      -0.298985                   -0.014301   \n",
       "\n",
       "                            Cardiomegaly  Lung Opacity  Lung Lesion     Edema  \\\n",
       "No Finding                     -0.445735     -0.899130    -0.331294 -0.680895   \n",
       "Enlarged Cardiomediastinum      0.341358     -0.010113     0.118687  0.089084   \n",
       "Cardiomegaly                    1.000000      0.281013    -0.131975  0.622757   \n",
       "Lung Opacity                    0.281013      1.000000     0.358650  0.705666   \n",
       "Lung Lesion                    -0.131975      0.358650     1.000000 -0.058354   \n",
       "Edema                           0.622757      0.705666    -0.058354  1.000000   \n",
       "Consolidation                   0.204744      0.905047     0.436690  0.541494   \n",
       "Pneumonia                       0.181882      0.839520     0.525971  0.569381   \n",
       "Atelectasis                     0.232167      0.309188    -0.055749  0.214032   \n",
       "Pneumothorax                   -0.341792     -0.047059     0.054243 -0.306928   \n",
       "Pleural Effusion                0.194460      0.516295    -0.003706  0.399091   \n",
       "Pleural Other                  -0.317668     -0.329125     0.285206 -0.421607   \n",
       "Fracture                       -0.243591     -0.452970     0.168896 -0.467326   \n",
       "Support Devices                -0.087137      0.437516    -0.053227  0.255867   \n",
       "covid                           0.030188      0.340364     0.204380  0.214347   \n",
       "rx                              0.222584      0.604547     0.276772  0.451295   \n",
       "CLINICA                         0.038983      0.317907     0.156587  0.214701   \n",
       "\n",
       "                            Consolidation  Pneumonia  Atelectasis  \\\n",
       "No Finding                      -0.835388  -0.689813    -0.540793   \n",
       "Enlarged Cardiomediastinum      -0.047904  -0.091178     0.166590   \n",
       "Cardiomegaly                     0.204744   0.181882     0.232167   \n",
       "Lung Opacity                     0.905047   0.839520     0.309188   \n",
       "Lung Lesion                      0.436690   0.525971    -0.055749   \n",
       "Edema                            0.541494   0.569381     0.214032   \n",
       "Consolidation                    1.000000   0.818624     0.337711   \n",
       "Pneumonia                        0.818624   1.000000     0.047148   \n",
       "Atelectasis                      0.337711   0.047148     1.000000   \n",
       "Pneumothorax                    -0.089819  -0.218724    -0.083024   \n",
       "Pleural Effusion                 0.555135   0.178463     0.446481   \n",
       "Pleural Other                   -0.325222  -0.223299    -0.257815   \n",
       "Fracture                        -0.438147  -0.349257    -0.010029   \n",
       "Support Devices                  0.371712   0.232634     0.033967   \n",
       "covid                            0.281840   0.293369     0.122643   \n",
       "rx                               0.558248   0.501363     0.277141   \n",
       "CLINICA                          0.253863   0.270265     0.094480   \n",
       "\n",
       "                            Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "No Finding                      0.065305         -0.593863       0.290473   \n",
       "Enlarged Cardiomediastinum     -0.143359         -0.028625       0.007736   \n",
       "Cardiomegaly                   -0.341792          0.194460      -0.317668   \n",
       "Lung Opacity                   -0.047059          0.516295      -0.329125   \n",
       "Lung Lesion                     0.054243         -0.003706       0.285206   \n",
       "Edema                          -0.306928          0.399091      -0.421607   \n",
       "Consolidation                  -0.089819          0.555135      -0.325222   \n",
       "Pneumonia                      -0.218724          0.178463      -0.223299   \n",
       "Atelectasis                    -0.083024          0.446481      -0.257815   \n",
       "Pneumothorax                    1.000000          0.010312       0.044571   \n",
       "Pleural Effusion                0.010312          1.000000      -0.157437   \n",
       "Pleural Other                   0.044571         -0.157437       1.000000   \n",
       "Fracture                       -0.018709         -0.355183       0.566075   \n",
       "Support Devices                 0.154384          0.340889      -0.292327   \n",
       "covid                           0.059064          0.071072      -0.016113   \n",
       "rx                             -0.022847          0.318361      -0.207860   \n",
       "CLINICA                         0.042597          0.091028      -0.013051   \n",
       "\n",
       "                            Fracture  Support Devices     covid        rx  \\\n",
       "No Finding                  0.318004        -0.321832 -0.332273 -0.615599   \n",
       "Enlarged Cardiomediastinum  0.262864        -0.216359  0.017157  0.089657   \n",
       "Cardiomegaly               -0.243591        -0.087137  0.030188  0.222584   \n",
       "Lung Opacity               -0.452970         0.437516  0.340364  0.604547   \n",
       "Lung Lesion                 0.168896        -0.053227  0.204380  0.276772   \n",
       "Edema                      -0.467326         0.255867  0.214347  0.451295   \n",
       "Consolidation              -0.438147         0.371712  0.281840  0.558248   \n",
       "Pneumonia                  -0.349257         0.232634  0.293369  0.501363   \n",
       "Atelectasis                -0.010029         0.033967  0.122643  0.277141   \n",
       "Pneumothorax               -0.018709         0.154384  0.059064 -0.022847   \n",
       "Pleural Effusion           -0.355183         0.340889  0.071072  0.318361   \n",
       "Pleural Other               0.566075        -0.292327 -0.016113 -0.207860   \n",
       "Fracture                    1.000000        -0.463289 -0.011413 -0.196068   \n",
       "Support Devices            -0.463289         1.000000  0.157155  0.232912   \n",
       "covid                      -0.011413         0.157155  1.000000  0.424979   \n",
       "rx                         -0.196068         0.232912  0.424979  1.000000   \n",
       "CLINICA                    -0.065270         0.159531  0.802825  0.407707   \n",
       "\n",
       "                             CLINICA  \n",
       "No Finding                 -0.298985  \n",
       "Enlarged Cardiomediastinum -0.014301  \n",
       "Cardiomegaly                0.038983  \n",
       "Lung Opacity                0.317907  \n",
       "Lung Lesion                 0.156587  \n",
       "Edema                       0.214701  \n",
       "Consolidation               0.253863  \n",
       "Pneumonia                   0.270265  \n",
       "Atelectasis                 0.094480  \n",
       "Pneumothorax                0.042597  \n",
       "Pleural Effusion            0.091028  \n",
       "Pleural Other              -0.013051  \n",
       "Fracture                   -0.065270  \n",
       "Support Devices             0.159531  \n",
       "covid                       0.802825  \n",
       "rx                          0.407707  \n",
       "CLINICA                     1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes = [\n",
    "    'No Finding', 'Enlarged Cardiomediastinum', 'Cardiomegaly', 'Lung Opacity',\n",
    "    'Lung Lesion', 'Edema', 'Consolidation', 'Pneumonia', 'Atelectasis',\n",
    "    'Pneumothorax', 'Pleural Effusion', 'Pleural Other', 'Fracture',\n",
    "    'Support Devices'\n",
    "]\n",
    "val_classes = ['No Finding', 'Atelectasis', 'Cardiomegaly', 'Consolidation', 'Edema', 'Pleural Effusion']\n",
    "\n",
    "df[[*classes, 'covid', 'rx', 'CLINICA']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//407PxU7mWYup1uSvH2+3wNND9I/pvlZPBL4zwUcQ5p4VXUSzcSqr6F5o3I9zd/oP9G8Afod4Haa64yz5zjWv9DUqU/QTNT+ie1s+680b8A+RFMff46mZizke/g8zd/sO2nq7DW0E8dWM/fbb9HMz3ErTUPOR7izhr+N+dXV62mGuhXN5PWSRuOjNI23Wx+vo/k//0s0w0w/zhw1qldbr95OMyfrNTR//3Bnbejf/mya3jf/m+ba4Cs0teOJ7TXUVpfSXG99h2ZOsme1c5XdTvPB3Dk0dep36BlyWlWfpZ1omuYa9d+4a89taOrql4EbkvReo53Xbn9e2+tcQ5Bth/9J3dG2Or+nqmYqJJIWKM2do26lGULRP45ckjThklxKc420vcbo7e3/PuBbVfWa4SaTNC5pbul+FXDPmeZUG/AYxwAvqKpfGWa2eZz/G8DvDTC/rQZkDyF1RpIdkzyj7Q69J02X54X0npDUJ8lvtEMpdqIZ+30lfbcClSRNpiS/muRn2muko2luPb3dnkDbOdZqmh5Hpw4xoqQxSPKbSe6Z5H40PZn/eaGNQeOW5H/S9FyctWem5s8GIXVJaIbC3EIzZOxqmuEVkhbvUJqhT9+i6QZ8RNmFVJK64uE0w0pupRkW+6yF3IEnyV/Q9CD4G3uISsvC79FMA/ANmmk25ponbSIlmaa5CcCLquonc2yueXDImCRJkiRJ0gpjDyFJkiRJkqQVZtW4AwDsvvvutXr16jm3u+OOO9hpp53m3G7cupCzCxmhGzmXW8bLLrvsO1X1gCWO1BmD1idYfr8L49KFjNCNnF3ICIPntD5ty/o0el3ICN3I2YWMYH1aKOvT6HUhI3QjZxcywpDqU1WN/fGLv/iLNYhLLrlkoO3GrQs5u5Cxqhs5l1tG4PM1AXVhUh6D1qeq5fe7MC5dyFjVjZxdyFg1eE7rk/Vp3LqQsaobObuQscr6tNCH9Wn0upCxqhs5u5Cxajj1ySFjkiRJkiRJK4wNQpIkSZIkSSuMDUKSJEmSJEkrjA1CkiRJkiRJK4wNQpIkSZIkSSuMDUKSJElDluR9STYluWqGdccnqSS7t6+T5O1JrklyRZLHjj6xJElaaVaNO8B8XLnhNo454cJtlq078eAxpZGkpbXaeid12WnAO4EP9C5MsjfwNOC/ehY/Hdi3fTwOeHf7dWj6r6GsJ5JmkmRX4L3Ao4ACng98DTgbWA2sAw6vqluWKoPXP9Lo2ENIkiRpyKrqk8DNM6x6K/AKmjdaWx0KfKAanwF2TbLHCGJKUr+TgY9V1c8DjwauBk4ALq6qfYGL29eSlgEbhCRJkkYgyaHAhqr6Ut+qPYHre16vb5dJ0sgk2QV4EnAqQFX9sKpupWm0Pr3d7HTgsHHkkzR8nRoyJkmDSvK/gRfQfAp/JfA8YA/gLGA34DLgqKr64dhCSloxktwbeDXNcLHFHOc44DiAqakppqenB9pvakc4fr8tP3096H6jtHnz5onM1asLGaEbObuQEbqTc0j2Ab4NvD/Jo2mulV4KTFXVxnabG4CpmXZeaH3q/xn31iqYjHrVhd+DLmSEbuTsQkYYTk4bhCQtO0n2BP4QeERVfT/JOcARwDOAt1bVWUneAxxLM1eHJC21n6N5s/WlJAB7AV9IcgCwAdi7Z9u92mV3UVWnAKcArFmzptauXTvQyd9xxvmcdOWdl33rjhxsv1Ganp5m0O9nXLqQEbqRswsZoTs5h2QV8FjgJVV1aZKT6RseVlWVpGbaeaH1qf9nfJc5YyegXnXh96ALGaEbObuQEYaT0yFjkparVcCOSVYB9wY2Ak8Bzm3X2+VZ0shU1ZVV9cCqWl1Vq2mGhT22qm4ALgCe295t7PHAbT2fxkvSqKwH1lfVpe3rc2kaiG7cOq9Z+3XTmPJJGjJ7CEladqpqQ5I309zF5/vAx2m6Pd9aVVv7Ic86R8ewujzP15Ubbtvm9fH7bbt+GF1Xu9AFtgsZoRs5u5ARupNzPpKcCawFdk+yHnhtVZ06y+YfpenBeA3wPZohrpI0UlV1Q5Lrkzy8qr4GHAh8pX0cDZzYfj1/jDElDZENQpKWnST3o5kAcR/gVuAfgYMG3X9YXZ7nq7+LdL9hdJnuQhfYLmSEbuTsQkboTs75qKpnz7F+dc/zAl601JkkaQAvAc5Icg/gWpoG6rsB5yQ5FrgOOHyM+SQNkQ1CkpajXwO+WVXfBkjyYeCJNLdyXtX2Epp1jg5JkqSVqKouB9bMsOrAEUeRNALOISRpOfov4PFJ7p1m9tatXZ4vAZ7VbmOXZ0mSJEkr1oIbhJLcK8lnk3wpyZeT/Hm7/LQk30xyefvYf2hpJWkA7WSI5wJfoLnl/N1ohoC9Enh5kmtobj0/23wekiRJkrSsLWbI2A+Ap1TV5iR3Bz6V5F/adX9cVeduZ19JWlJV9VrgtX2LrwUOGEMcSZIkSZooC24QaidA3Ny+vHv7qGGEkiRJkiRJ0tJZ1KTSSXaguZXzQ4F3VdWlSX4feGOSPwMuBk6oqh/MsO+8b+s8tSMcv9+WbZZN4m1qu3D73C5khG7kNKMkSZIkqWsW1SBUVT8G9k+yK3BekkcBrwJuAO7BnXN2vH6Gfed9W+d3nHE+J125beRh3IZ52Lpw+9wuZIRu5DSjJEmSJKlrhnKXsaq6lebuPQdV1cZq/AB4P87XIUmSJEmSNFEWc5exB7Q9g0iyI/BU4KtJ9miXBTgMuGrxMSVJkiRJkjQsixkytgdwejuP0N2Ac6rqI0k+keQBQIDLgRcuPqYkSZIkSZKGZTF3GbsCeMwMy5+yqESSJEmSJElaUkOZQ0iSJEmSJEndYYOQJEmSJEnSCmODkCRJkiRJ0gpjg5AkSdKQJXlfkk1JrupZ9jdJvprkiiTnbb1ba7vuVUmuSfK1JL8+ltCSJGlFsUFIkiRp+E4DDupbdhHwqKr6H8D/A14FkOQRwBHAI9t9/k97F1dJkqQlY4OQJEnSkFXVJ4Gb+5Z9vKq2tC8/A+zVPj8UOKuqflBV3wSuAQ4YWVhJkrQiLfi285IkSVqw5wNnt8/3pGkg2mp9u+wukhwHHAcwNTXF9PT0QCeb2hGO32/LT18Put8obd68eSJz9epCRuhGzi5khO7klKSFsEFIkiRphJL8CbAFOGO++1bVKcApAGvWrKm1a9cOtN87zjifk66887Jv3ZGD7TdK09PTDPr9jEsXMkI3cnYhI3Qn5zAlWQfcDvwY2FJVa5Lcn6YRezWwDji8qm4ZV0ZJw+GQMUmSpBFJcgxwCHBkVVW7eAOwd89me7XLJGlcnlxV+1fVmvb1CcDFVbUvcHH7WlLH2SAkSZI0AkkOAl4BPLOqvtez6gLgiCT3TLIPsC/w2XFklKRZHAqc3j4/HThsfFEkDYtDxiRJkoYsyZnAWmD3JOuB19LcVeyewEVJAD5TVS+sqi8nOQf4Cs1QshdV1Y/Hk1ySKODjSQr423ao6lRVbWzX3wBM9e+00DnO+udp6p3vDCZjzrMuzCXVhYzQjZxdyAjDybngBqEk9wI+SXNhswo4t6pe236ydRawG3AZcFRV/XBRKSVJkjqkqp49w+JTt7P9G4E3Ll0iSRrYr1TVhiQPpGnA/mrvyqqqtrGIvuULmuOsf56mY064cJv1kzDnWRfmkupCRuhGzi5khOHkXMyQsR8AT6mqRwP7AwcleTzwJuCtVfVQ4Bbg2EUllCRJkiSNRFVtaL9uAs4DDgBuTLIHQPt10/gSShqWBTcIVWNz+/Lu7aOApwDntssdXypJkiRJHZBkpyQ7b30OPA24imaus6PbzY4Gzh9PQknDtKg5hJLsQDMs7KHAu4BvALdW1daBn+uBPReVUJIkSZI0ClPAee08Z6uAD1bVx5J8DjgnybHAdcDhY8woaUgW1SDUTni4f5JdaboT/vyg+y5k0rGpHSdzkrF+XZiEqgsZoRs5zShJkqTloKquBR49w/KbgANHn0jSUhrKXcaq6tYklwBPAHZNsqrtJbQXsGGWfeY96dg7zjifk67cNvIkTDLWrwuTUHUhI3QjpxklSZIkSV2z4DmEkjyg7RlEkh2BpwJXA5cAz2o3c3ypJEmSJEnShFlMD6E9gNPbeYTuBpxTVR9J8hXgrCRvAL7Idm6xKkkr2eq+26pKkiRJ0qgsuEGoqq4AHjPD8mtpbk0oSZIkSZKkCbTgIWOSJEmSJEnqJhuEJEmSJEmSVhgbhCQtS0l2TXJukq8muTrJE5LcP8lFSb7efr3fuHNKkiRJ0jjYICRpuToZ+FhV/TzwaJq7IJ4AXFxV+wIXt68lSZIkacWxQUjSspNkF+BJtHc5rKofVtWtwKHA6e1mpwOHjSOfJEmSJI3bYm47L0mTah/g28D7kzwauAx4KTBVVRvbbW4ApmbaOclxwHEAU1NTTE9PD3TSzZs3D7wtwPH7bRl4W2Bex57NfDOOQxcyQjdydiEjdCfnfCR5H3AIsKmqHtUuuz9wNrAaWAccXlW3JAlNr8ZnAN8DjqmqL4wjtyRJWjlsEJK0HK0CHgu8pKouTXIyfcPDqqqS1Ew7V9UpwCkAa9asqbVr1w500unpaQbdFuCYEy4ceFuAdUcOfuzZzDfjOHQhI3QjZxcyQndyztNpwDuBD/Qs2zps9cQkJ7SvXwk8Hdi3fTwOeHf7VZIkack4ZEzScrQeWF9Vl7avz6VpILoxyR4A7ddNY8onaZmrqk8CN/ctnm3Y6qHAB6rxGWDXrbVKkiRpqdhDSNKyU1U3JLk+ycOr6mvAgcBX2sfRwInt1/PHGFPSyjPbsNU9get7tlvfLttIn4UOaZ3acdthqpM4RK8LQwe7kBG6kbMLGaE7OSVpIWwQkrRcvQQ4I8k9gGuB59H0ijwnybHAdcDhY8wnaQXb3rDVOfZb0JDWd5xxPiddeedl3zCGoA5bF4YOdiEjdCNnFzJCd3JK0kLYICRpWaqqy4E1M6w6cMRRJGmrG5PsUVUb+4atbgD27tlur3aZJEnSknEOIUmSpNG4gGa4Kmw7bPUC4LlpPB64rWdomSRJ0pJYcINQkr2TXJLkK0m+nOSl7fLXJdmQ5PL28YzhxZUkSZp8Sc4EPg08PMn6dqjqicBTk3wd+LX2NcBHaYa2XgP8HfAHY4gsSZJWmMUMGdsCHF9VX0iyM3BZkovadW+tqjcvPp4kSVL3VNWzZ1l1l2GrVVXAi5Y2kSQNJskOwOeBDVV1SJJ9gLOA3YDLgKOq6ofjzChpOBbcQ6iqNlbVF9rntwNX09wRQ5IkSZLUTS+leW+31ZtoPvB/KHALcOxYUkkauqHMIZRkNfAY4NJ20YuTXJHkfUnuN4xzSJIkSZKWTpK9gIOB97avAzwFOLfd5HTgsLGEkzR0i77LWJL7AB8CXlZV303ybuAvgGq/ngQ8f4b9jgOOA5iammJ6enrOc03tCMfvt2WbZYPsN2qbN2+eyFy9upARupHTjJIkSVom3ga8Ati5fb0bcGtVbX0Ttp5ZRoUs5P0d3PU6tf/93jvOOH+b1/vtuctAxx2mLlxLdyEjdCNnFzLCcHIuqkEoyd1pGoPOqKoPA1TVjT3r/w74yEz7VtUpwCkAa9asqbVr1855vneccT4nXblt5HVHzr3fqE1PTzPI9zNOXcgI3chpRkmSJHVdkkOATVV1WZK1891/Ie/v4K7XqceccOF2tx/H+78uXEt3ISN0I2cXMsJwci64QajtPngqcHVVvaVn+R49t0r9TeCqRSWUJEmSJC21JwLPbO8SfS/gvsDJwK5JVrW9hPYCNowxo6QhWswcQk8EjgKe0neL+b9OcmWSK4AnA/97GEElSZIkSUujql5VVXtV1WrgCOATVXUkcAnwrHazo4HzZzmEpI5ZcA+hqvoUkBlWfXThcSRJkiRJE+SVwFlJ3gB8kWaUiKRlYNGTSkuSJEmSlo+qmgam2+fXAgeMM4+kpTGU285LkiRJkiSpO2wQkiRJkiRJWmEcMiZJHbG67zas6048eExJJEmSJHWdPYQkSZIkSZJWGBuEJEmSJEmSVhgbhCRJkkYoyf9O8uUkVyU5M8m9kuyT5NIk1yQ5O8k9xp1TkiQtb8tuDiHn2JAkSZMqyZ7AHwKPqKrvJzkHOAJ4BvDWqjoryXuAY4F3jzGqJEla5uwhJEmSNFqrgB2TrALuDWwEngKc264/HThsPNEkSdJKYYOQJEnSiFTVBuDNwH/RNATdBlwG3FpVW9rN1gN7jiehJElaKZbdkDFJkqRJleR+wKHAPsCtwD8CB81j/+OA4wCmpqaYnp4eaL+pHeH4/bb89PWg+43S5s2bJzJXry5khG7k7EJG6E7OlcQpQqTh6XyDUH9BkCRJmmC/Bnyzqr4NkOTDwBOBXZOsansJ7QVsmGnnqjoFOAVgzZo1tXbt2oFO+o4zzuekK++87Ft35GD7jdL09DSDfj/j0oWM0I2cXcgI3ckpSQux4CFjSfZOckmSr7R3ynhpu/z+SS5K8vX26/2GF1eSJKnT/gt4fJJ7JwlwIPAV4BLgWe02RwPnjymfJElaIRYzh9AW4PiqegTweOBFSR4BnABcXFX7Ahe3ryVJkla8qrqUZvLoLwBX0lyLnQK8Enh5kmuA3YBTxxZSkiStCAseMlZVG2kmQ6Sqbk9yNc0EiIcCa9vNTgemaS5yJEmSVryqei3w2r7F1wIHjCGOJElaoYYyh1CS1cBjgEuBqbaxCOAGYGqWfeY9KWL/hIiDGMckcF2YfK4LGaEbOc0oSZIkSeqaRTcIJbkP8CHgZVX13WY4fKOqKknNtN9CJkXsnxBxEOOYNLELk891ISN0I6cZJUmSJElds5g5hEhyd5rGoDOq6sPt4huT7NGu3wPYtLiIkiRJkiRJGqbF3GUsNBMeXl1Vb+lZdQHN3THAu2RIkiRJUickuVeSzyb5Unsn6T9vl++T5NIk1yQ5O8k9xp1V0uItpofQE4GjgKckubx9PAM4EXhqkq8Dv9a+lqSRS7JDki8m+Uj72osZSZKk2f0AeEpVPRrYHzgoyeOBNwFvraqHArcAx44voqRhWcxdxj4FZJbVBy70uNJyt/qEC++ybN2JB48hyYrwUuBq4L7t660XM2cleQ/Nxcy7xxVOkiRpklRVAZvbl3dvHwU8BfiddvnpwOvwGkrqvKHcZUySJk2SvYCDgTcCL2+HuS6rixkbFyVJ0rAl2QG4DHgo8C7gG8CtVbX1ds/rgT1n2G/ed5GGu94NdxLvKt2FO/Z2ISN0I2cXMsJwctogJGm5ehvwCmDn9vVuDHAxA8O7oJnLfC94BjHX+bvwH1wXMkI3cnYhI3QnpyStBFX1Y2D/JLsC5wE/P+B+876LNNz1brjHzPCB1/aM4q7SXbhjbxcyQjdydiEjDCenDUKSlp0khwCbquqyJGvnu/+wLmjmMt8LnkHMdVHUhf/gupARupGzCxmhOzklaSWpqluTXAI8Adg1yar2g7W9gA3jTSdpGBZ123lJmlBPBJ6ZZB1wFs1QsZNpL2babbyYkSRJ6pHkAW3PIJLsCDyVZj7GS4BntZt5J2lpmbBBSNKyU1Wvqqq9qmo1cATwiao6Ei9mJEmStmcP4JIkVwCfAy6qqo8Ar6SZk/EammH4p44xo6QhcciYpJXklcBZSd4AfBEvZiRJkn6qqq4AHjPD8muBA0afSNJSskFI0rJWVdPAdPvcixlJkiRJYgU0CPXfltlbMkuSJEmSpJXOOYQkSZJGKMmuSc5N8tUkVyd5QpL7J7koydfbr/cbd05JkrS82SAkSZI0WicDH6uqnwceTXMHnxOAi6tqX+Di9rUkSdKSsUFIkiRpRJLsAjyJdlL7qvphVd0KHAqc3m52OnDYOPJJkqSVY9nPISRJkjRB9gG+Dbw/yaOBy4CXAlNVtbHd5gZgaqadkxwHHAcwNTXF9PT0QCed2hGO32/LT18Put8obd68eSJz9epCRuhGzi5khO7klKSFWHCDUJL3AYcAm6rqUe2y1wG/S3OhA/DqqvroYkNKkiQtE6uAxwIvqapLk5xM3/CwqqokNdPOVXUKcArAmjVrau3atQOd9B1nnM9JV9552bfuyMH2G6Xp6WkG/X7GpQsZoRs5u5ARupNTkhZiMUPGTgMOmmH5W6tq//ZhY5AkSdKd1gPrq+rS9vW5NA1ENybZA6D9umlM+SRJ0gqx4AahqvokcPMQs0iSJC1rVXUDcH2Sh7eLDgS+AlwAHN0uOxo4fwzxJEnSCrIUcwi9OMlzgc8Dx1fVLTNttJAx8P3j3xei/zxXbrhtm9f77bnLoo4P3Rhr3IWM0I2c/Rnn+p2a6Xd4ru9xsb+nXfg5StIK8hLgjCT3AK4FnkfzId05SY4FrgMOH2M+SZK0Agy7QejdwF8A1X49CXj+TBsuZAx8//j3hegfM3/MCRdud/1CdGGscRcyQjdy9mec63eqf/1M2/Rb7O9pF36OkrRSVNXlwJoZVh044iiSJGkFG+pt56vqxqr6cVX9BPg74IBhHl+SJEmSJEmLN9QGoa2TIbZ+E7hqmMeXJEmSJEnS4i3mtvNnAmuB3ZOsB14LrE2yP82QsXXA7y0+oiQtD6tnGC4oSePQX4/WnXjwmJJIkqRxWXCDUFU9e4bFpy4iiyRJkiRJkkZgqEPGJEmSJEmSNPlsEJIkSZIkSVphbBCSJEmSpBUuyd5JLknylSRfTvLSdvn9k1yU5Ovt1/uNO6uk4VjwHELSSjHXRMCnHbTTyM8pSZIkDdkW4Piq+kKSnYHLklwEHANcXFUnJjkBOAF45RhzShoSewhJkiRJ0gpXVRur6gvt89uBq4E9gUOB09vNTgcOG0tASUNnDyFJkiRJ0k8lWQ08BrgUmKqqje2qG4CpWfY5DjgOYGpqiunp6YHOtXnz5m22PX6/LfPK+o4zzt/m9X577jKv/QfRn3ESdSEjdCNnFzLCcHLaICRJkiRJAiDJfYAPAS+rqu8m+em6qqokNdN+VXUKcArAmjVrau3atQOdb3p6mt5tj1nk1AnrjhzsvPPRn3ESdSEjdCNnFzLCcHKuuAahueZm6V+/7sSDlzKOtCD+nkqSJGnYktydpjHojKr6cLv4xiR7VNXGJHsAm8aXcG4zvd/zWlmamXMISZIkSdIKl6Yr0KnA1VX1lp5VFwBHt8+PBs7v31dSN624HkKSJEnjlmQH4PPAhqo6JMk+wFnAbsBlwFFV9cNR5bHnqSTgicBRwJVJLm+XvRo4ETgnybHAdcDh44knadhsEJIkSRq9l9Lcwee+7es3AW+tqrOSvAc4Fnj3uMJJWnmq6lNAZll94CizSBoNG4S04vmpqJYTf5+lyZdkL+Bg4I3Ay9thGk8Bfqfd5HTgddggJEmSltCiGoSSvA84BNhUVY9ql90fOBtYDawDDq+qWxYXU5Ikadl4G/AKYOf29W7ArVW19V7L64E9Z9pxobd1ntpxfrdyHsftdrtwm98uZIRu5OxCRuhOTklaiMX2EDoNeCfwgZ5lJwAXV9WJSU5oX79ykeeRJEnqvCRbP0i7LMna+e6/0Ns6v+OM8znpysEv+5bits1z6cJtfruQEbqRswsZoTs5JWkhFnWXsar6JHBz3+JDabo60349bDHnkCRJWkaeCDwzyTqaSaSfApwM7Jpka4vNXsCG8cSTJEkrxVLMITRVVRvb5zcAUzNttJAuz/Pt7jwMC+ki2oWupZOa8coNt23zep9ddljynP2/U/3nm+t3rv9nuZDjzfecc+3fb1L/vZdKkr1pei5OAQWcUlUnO6RV0rhV1auAVwG0PYT+qKqOTPKPwLNoGom8rbMkSVpySzqpdFVVkppl3by7PM+3u/MwLKTLdBe6lk5qxmP6JsQ97aCdljxn/zn7/8371/frz7iQ4833nHPt329S/72X0Bbg+Kr6QpKdgcuSXAQcg0NaJU2mVwJnJXkD8EXg1DHnkSRJy9xStK7cmGSPqtqYZA9g0xKcQ5Jm1fZS3Ng+vz3J1TQTtB4KrG03Ox2YxgYhSWNSVdM0dYiquhY4YJx5JEnSyrKoOYRmcQFNV2ewy7OkMUuyGngMcCkDDmmVJEmSpOVusbedP5Pm0/bdk6wHXgucCJyT5FjgOuDwxYbsmis33LbNkJ91Jx683e1X9w8xmmN73Wm+P7v+7SfFpObquiT3AT4EvKyqvpvkp+u2N6R1obd1nmuuplHPgQZ3nV+qC/NJdSEjdCNnFzJCd3JKkiQtJ4tqEKqqZ8+y6sDFHFeSFivJ3Wkag86oqg+3iwca0rrQ2zrPNVfTfOeGGob++aW6MJ9UFzJCN3J2ISN0J6ckqZv8AF6a2VIMGZOksUrTFehU4OqqekvPKoe0SpIkSRJLfJcxSRqTJwJHAVcmubxd9moc0ipJkiRJgA1CE2mm+WRW6jxE852PaRz6M/ZzfqDRq6pPAZlltUNaJUmSJK14NghJ0jLW3yB52kE7jSmJJEmSpEniHEKSJEmSJEkrjD2EJEmStI3lOhRd0uSZa/qFUbDmaaWyh5AkSZIkSdIKYw+hOQwywXP/Nsfvt6SRJsZ8W9JteZckSZImV5L3AYcAm6rqUe2y+wNnA6uBdcDhVXXLuDKOQxdudCMthD2EJEmSJEkApwEH9S07Abi4qvYFLm5fS1oG7CEkSSuYPfckSdJWVfXJJKv7Fh8KrG2fnw5MA68cXSpJS8UGIUmSpBFJsjfwAWAKKOCUqjrZIRmSJthUVW1sn99AU7/uIslxwHEAU1NTTE9PD3bwHeH4/bYMIebg+rP1n/8dZ5y/zev+jIN+b6O0efPmiczVbylyXrnhtm1e77fnLos63kr6WdogtAAzzSs0yv2HYbmOg52En60kSduxBTi+qr6QZGfgsiQXAcfQDMk4MckJNEMy/ARe0kSpqkpSs6w7BTgFYM2aNbV27dqBjvmOM87npCtH+7Z03ZFrt3k9113Ojt9vyzYZ+/efBNPT0wz6Mx+npcjZ/++32H+flfSzXLK/vCTrgNuBHwNbqmrNUp1LkiSpC9pP2Te2z29PcjWwJw7JkDS5bkyyR1VtTLIHsGncgbQwK+WmQHPl7ur3tRSWuin2yVX1nSU+hyRNJHusSdqedp6OxwCXMuFDMkbRdb4LXfS7kBG6kbMLGaE7OZfYBcDRwInt1/O3v7mkrnDImCRJ0ogluQ/wIeBlVfXdJD9dN4lDMkYxPKILXfS7kBG6kbMLGaE7OYclyZk0vRV3T7IeeC1NQ9A5SY4FrgMOH19CScO0lA1CBXy8vaD52/YC5qcW8gnXOCYcW4i5Jh1byPfQP7FZ/0RZ/cec6+c5yMRoc03ONd9zDrJ9/zZL8bOcS//P+vj9tr/9JPxezvWz99MtSZocSe5O0xh0RlV9uF3skAxJY1dVz55l1YEjDTLhhjHkaBKGNY1j6NQ45rK11/7slrJB6FeqakOSBwIXJflqVX1y68qFfMI1jgnHFmKuScfmmrRsEHMdc65P8vp/ljNtP9cx53vOQbbv32YUP8vF6s84DnP97Ffap1uSNKnSdAU6Fbi6qt7Ss8ohGZIkaaSW7F1sVW1ov25Kch5wAPDJ7e8lSZK0rD0ROAq4Msnl7bJXM+FDMpyAU5Kk5WdJGoSS7ATcrb17xk7A04DXL8W5JEmSuqKqPgVkltWdGZJhA5Ekzc+why31H++0g3Za8nOuVDP9HJfL/3tL1UNoCjivnSBxFfDBqvrYEp1r4q2UP8T5fp8r5ecyCl6Ya1D947YlSZIkrUxL0iBUVdcCj16KY0uSJGmyLOdPTyUtP0vdWwfmXwPnyuSH6ZOtqx/Q323cASRJkiRJkjRak3/LLkmSJC07g8x/IUmSlo4NQgLsgihJkiRJXTWKeSIHec847KFy/ccbxfvWlTTnpg1CkiRJmnhdnZ9BkqRJZYOQJEmShs4GHEkrWRdHYExi5lFk6j/H8fst+Sknhg1CkiRJ6hzvbCZJ0uLYINRR820pHUar5yS2GEuSpG7wOkKSpMlig5Ak6acW8obNT+QlSZI0k+U6HGu5fMhhg5AkSZLGrv+uLgtpbJ7v3WokSVrJbBCSJEnSxBnHRKJzNRjNdCtiG5kkSV1lg5AkSZI0g+U61KGrvHOdNPmWy1CqxepKvbJBaJlarn+Iy/X7krpsrv/wuvIfoqTlrwvXEYNkXEhPpvnsP0gua7kkdd/dluKgSQ5K8rUk1yQ5YSnOIUkLZY2SNKmsT5ImlfVJWrjVJ1y4zWNSDL2HUJIdgHcBTwXWA59LckFVfWXY55Kk+bJGLb25/pMbxafMi+21NNP3sBw/DV8p32dXWJ/GbyEX6YudyHopzrnYoW3DyDTfXkzWnslmfZKWp6UYMnYAcE1VXQuQ5CzgUMBiIWkSWKMkTSrr0zI0SZ8EbzWMhvn5fl+jmI/Ju8wtKeuTtAylqoZ7wORZwEFV9YL29VHA46rqxX3bHQcc1758OPC1AQ6/O/CdIcZdKl3I2YWM0I2cyy3jg6vqAUsZZpwGqVELrE+w/H4XxqULGaEbObuQEQbPaX2yPo1bFzJCN3J2ISNYnwDrE2Ycpi7k7EJGGEJ9Gtuk0lV1CnDKfPZJ8vmqWrNEkYamCzm7kBG6kdOMy89C6hN04+dsxuHpQs4uZITu5JwE1qfx6kJG6EbOLmSE7uScBNan8epCRuhGzi5khOHkXIpJpTcAe/e83qtdJkmTwBolaVJZnyRNKuuTtAwtRYPQ54B9k+yT5B7AEcAFS3AeSVoIa5SkSWV9kjSprE/SMjT0IWNVtSXJi4H/C+wAvK+qvjykw8+7C+KYdCFnFzJCN3KasUOsUWYcoi7k7EJG6E7OJWV9MuMQdSFnFzJCd3IuKeuTGYeoCzm7kBGGkHPok0pLkiRJkiRpsi3FkDFJkiRJkiRNMBuEJEmSJEmSVpiJbBBKclCSryW5JskJM6y/Z5Kz2/WXJlk9hpiD5Hx5kq8kuSLJxUkePGkZe7b7n0kqychvrzdIxiSHtz/LLyf54Kgzthnm+vf+2SSXJPli+2/+jDFkfF+STUmummV9kry9/R6uSPLYUWfsOuvT6DL2bGd9moP1SWB9GmXGnu3GVp/a8098jbI+CaxPo8zYs531aZEZV0R9qqqJetBMUvYN4CHAPYAvAY/o2+YPgPe0z48Azp7QnE8G7t0+//1R5xwkY7vdzsAngc8AayYtI7Av8EXgfu3rB07ov/cpwO+3zx8BrBtDzicBjwWummX9M4B/AQI8Hrh01Bm7/LA+jTZju531aTg5rU/L/GF9Gm3Gdrux1ad5/CzHWqOsTz7m8XtgfRpSxnY769NwMi77+jSJPYQOAK6pqmur6ofAWcChfdscCpzePj8XODBJRpgRBshZVZdU1ffal58B9pq0jK2/AN4E/Pcow7UGyfi7wLuq6haAqto04owwWM4C7ts+3wX41gjzNQGqPgncvJ1NDgU+UI3PALsm2WM06ZYF69MIM7asT3OzPgmsTyPN2BpnfYJu1Cjrk8D6NNKMLevTcDIu+/o0iQ1CewLX97xe3y6bcZuq2gLcBuw2knQzZGjNlLPXsTQtd6M0Z8a2S9neVXXhKIP1GOTn+DDgYUn+I8lnkhw0snR3GiTn64DnJFkPfBR4yWiizct8f2+1LevT8Fifhsf6JLA+DVMX6hN0o0ZZnwTWp2GyPg2P9QlYNfQ4uoskzwHWAL867iy9ktwNeAtwzJijzGUVTZfCtTSt8J9Msl9V3TrOUDN4NnBaVZ2U5AnA3yd5VFX9ZNzBpNlYnxbN+iQtEevTUHShRlmf1DnWp6GwPk2ASewhtAHYu+f1Xu2yGbdJsoqm+9ZNI0k3Q4bWTDlJ8mvAnwDPrKofjCjbVnNl3Bl4FDCdZB3NmMMLRjzx2CA/x/XABVX1o6r6JvD/aIrHKA2S81jgHICq+jRwL2D3kaQb3EC/t5qV9Wl4rE/DY30SWJ+GqQv1CbpRo6xPAuvTMFmfhsf6BBM5qfQq4FpgH+6c3OmRfdu8iG0nHTtnQnM+hmaiqn0n9WfZt/00o5+0dZCf40HA6e3z3Wm6xO02gTn/BTimff4LNGNMM4Z/99XMPunYwWw76dhnR52vyw/r02gz9m1vfVpcTuvTMn9Yn0absW/7kdenefwsx1qjrE8+5vF7YH0aUsa+7a1Pi8u47OvTSL+ReXzDz6BpIfwG8CftstfTtMJC0zL3j8A1wGeBh0xozn8FbgQubx8XTFrGvm3HVTDm+jmGpuvjV4ArgSMm9N/7EcB/tMXkcuBpY8h4JrAR+BFNq/uxwAuBF/b8LN/Vfg9XjuPfu+sP69PoMvZta31aXE7r0wp4WJ9Gl7Fv27HUpwF/lmOvUdYnHwP+HlifhpSxb1vr0+IyLvv6lPYgkiRJkiRJWiEmcQ4hSZIkSZIkLSEbhCRJkiRJklYYG4QkSZIkSZJWGBuEJEmSJEmSVhgbhCRJkiRJklYYG4QkSZIkSZJWGBuEJEmSJEmSVhgbhCRJkiRJklYYG4QkSZIkSZJWGBuEJEmSJEmSVhgbhCRJkiRJklYYG4QkSZIkSZJWGBuEJEmSJEmSVhgbhCRJkiRJklYYG4QkSZIkSZJWGBuEJEmSJEmSVhgbhCRJkiRJklYYG4QkSZIkSZJWGBuENHGSvCfJn447h6TJl2R1kkqyatxZJGlQbd16aPt8u9c9vdsu4DxHJvn4QnNK0lJJ8i9Jjh53jpUuVTXuDFqEJOuAF1TVv44xwzTwD1X13nFlkLS8tLVtCvhxz+LTqurFfdutBr4J3L2qtowsoKTOSfI7wMuBnwduBy4H3lhVnxpDlgL2raprhrWt9VBafvquh+4A/gV4cVVtHmcuLR/2EJIkTarfqKr79DxePPcuknRXSV4OvA34S5o3Vz8L/B/g0DHGkqRB/EZV3Qd4LLAGeM2Y82gZsUFomUpyWpI39Lxem2R9z+t1Sf4oyRVJbktydpJ79ax/RZKNSb6V5AUL7a6c5PlJrk5yS5L/m+TB7fIkeWuSTUm+m+TKJI+aJfvvJrkmyc1JLkjyoJ51leSFSb6e5NYk70qS+f/EJHVBkh2SvDnJd5JcCxzct36XJKe29WtDkjck2aFdd0yS/2hrz61Jrk3yy+3y69t6dHTPsQ5O8sW2Rl2f5HWj/W4lDUOSXYDXAy+qqg9X1R1V9aOq+ueq+uMk90zytvaa51vt83u2+65Nsj7J8W2N2JjkeT3HfkaSryS5va05f9Szbtbrl758/dc9f9xzDfb8vm23V5c+2X69NcnmJE9o69unevb/5SSfa6/9Ppfkl3vWTSf5i7ZO3p7k40l2X9APXdLQVdUGmh5Cj5rrPdB23oPdZah9+7f/gvb5fK+VdknygSTfTnJdktckuVvPsT7VXrfdkuSbSZ4+y3l/LsknktzUXuOdkWTXJf6RChuEVrrDgYOAfYD/ARwDkOQgmi7VvwY8FFi7kIMnORR4NfBbwAOAfwfObFc/DXgS8DBglzbLTTMc4ynAX7Xr9wCuA87q2+wQ4Jfa7+Fw4NcXkldSJ/wuzd/8Y2g+JXtW3/rTgC00tesxNLXmBT3rHwdcAewGfJCmnvxSu/1zgHcmuU+77R3Ac4FdaRqefj/JYUP+fiQtvScA9wLOm2X9nwCPB/YHHg0cwLafwP8MzbXKnsCxwLuS3K9ddyrwe1W1M/Ao4BMw8PXLXbTXYH8EPBXYl+ZarNf26tKT2q+7tr0qP9137PsDFwJvp6mBbwEuTLJbz2a/AzwPeCBwjzaLpAmQZG/gGcAX20Uzvgea4z3YIOZzrfQOmvr4EOBXaerT8/qO9TVgd+CvgVN7G656vz2amvkg4BeAvYHXzSOzFsgGoZXt7VX1raq6GfhnmgshaArK+6vqy1X1PRb+x/hC4K+q6up2LPtfAvu3LdQ/AnamGcefdpuNMxzjSOB9VfWFqvoB8CrgCWnGyW91YlXdWlX/BVzS831I6rZ/aj+d2vr4XZr69Laqur6tXX+1deMkUzQXSi9rewBsAt4KHNFzzG9W1fur6sfA2TQXHK+vqh9U1ceBH9Jc8FBV01V1ZVX9pKquoLmY+tURfN+Shms34DvbmVfnSJo6sKmqvg38OXBUz/oftet/VFUfBTYDD+9Z94gk962qW6rqCz3HnOv6ZSZbr8Guqqo76LsGW2RdOhj4elX9fVVtqaozga8Cv9Gzzfur6v9V1feBc/CaSpoE/5TkVuBTwL/RvKeC2d8Dbe892CAGulZK0wP7COBVVXV7Va0DTmLb+nldVf1de6zTaRrIp/pPWFXXVNVF7Tm+TdNg7TXXCNggtLLd0PP8e8DWlt4HAdf3rOt9Ph8PBk7e+mYOuJmm9XfPqvoE8E7gXcCmJKckue8Mx3gQzadqALQTqN1E8yndXN+HpG47rKp27Xn8HXetT9f1PH8wcHdgY0/d+VuaT7q3urHn+fcBqqp/2X0AkjwuySVtN+jbaC6wHD4hdc9NwO6Z/W6E21xrtM97h3fd1NeY1Hut8T9pGqKvS/JvSZ4w0zFnuX6ZLctsNW6xdan/+9x6fK+ppMm29XrowVX1B22DLcz+9zrre7ABzzfotdLuNNdd/fVzxprSdjSAGepKkqkkZ6UZevtd4B/wmmskbBBavu4A7t3z+mfmse9GYK+e13svMMP1NN2oe9/Q7VhV/wlQVW+vql8EHkEzdOyPZzjGt2iKGgBJdqL5pG/DAjNJ6raNbFuTfrbn+fXAD4Dde2rOfavqkQs81weBC4C9q2oX4D00F1SSuuXTNLXhsFnWb3OtQVNXvjXIgavqc1V1KE3D8z/R9Kq5yzHncf2yvRoH269Lc906uP/73Hp8r6mk5WV778HuaLdZ6PvEXt+h6SXZXz8XUlP+kqaG7VdV96UZmuY11wjYILQ83D3JvXoeq2hupfqMJPdP8jPAy+ZxvHOA5yX5hST3Bv50gH1W9WW4O81FyquSPBJ+OunYb7fPf6n9lOvuNIXpv4GfzHDcM9ss+6eZ4PEvgUvbLomSVp5zgD9Mslc7h8cJW1e0w04/DpyU5L5J7tZOUrjQLsc7AzdX1X8nOYBmbg1JHVNVtwF/RjP3z2FJ7p3k7kmenuSvaa41XpPkAWkmUf4zmk+ntyvJPZIcmWSXqvoR8F3uvJZZ6PXLOcAxSR7RXoO9tm/99urSt9vzP2SWY38UeFiS30myKsn/ovlQ7iNzfa+SOmXW92DtcKwNwHPS3Kjj+cDPLeQk7TCwc4A3Jtm5HZL2cgaonzPYmWY47m1J9mTmjgJaAjYILQ8fpem6t/XxOuDvgS8B62jeIJ096MGq6l9oJhy8BLgG+Ey76gfb2e3dfRneX1XnAW8Czmq7/l0FbJ1Z/r7A3wG30HQtvAn4mxmy/CtNg9SHaD41+zm2nQ9E0vL1z2nulLP1cR5N3fi/NPXtC8CH+/Z5Ls1EqF+hqS/n0oxXX4g/AF6f5HaaN4jnzLG9pAlVVSfRvFF5DU3DyfXAi2l69bwB+DzNJKpX0tSWN8x4oLs6CljXXue8kGbuoAVfv7TXYG+jmZz6mvZrr1nrUjsc443Af7RDRR7fd+ybaCahPZ7muusVwCFV9Z0Bv1dJHTDHezBobtDxxzR14JHAfy7idC+h+XD/Wpo5jj4IvG8Bx/lz4LHAbTST3/df32mJpGqu3qVa6ZL8Ak0hued2JmSUJEmSJEkdYQ8hzSjJbya5Zzsk403AP9sYJEmSJEnS8mCDkGbze8Am4BvAj4HfH28cSZIkSZI0LA4ZkyRJGqEk64DbaT5w2VJVa5Lcn2a+v9U08/8dXlW3jCujJEla/uwhJEmSNHpPrqr9q2pN+/oE4OKq2he4mJ476EmSJC2FieghtPvuu9fq1avn3O6OO+5gp512WvpAi9SFnF3ICN3IudwyXnbZZd+pqgcscaTOGLQ+wfL7XRiXLmSEbuTsQkYYPOdyqU9tD6E1vXd3SvI1YG1VbUyyBzBdVQ/f3nGsT6PXhYzQjZxdyAgrrz4Ni/Vp9LqQEbqRswsZYTj1adXQUy3A6tWr+fznPz/ndtPT06xdu3bpAy1SF3J2ISN0I+dyy5jkuqVNMzxJ3kdzC91NVfWodtnZwNY3UbsCt1bV/klWA1cDX2vXfaaqXjjXOQatT7D8fhfGpQsZoRs5u5ARBs/Zpfo0hwI+nqSAv62qU4CpqtrYrr8BmJppxyTHAccBTE1N8eY3v3mgE27evJn73Oc+iw6+lMw4PF3I2YWMMHjOJz/5yculPg2F10+j14WM0I2cXcgIw7l+mogGIUlaoNOAdwIf2Lqgqv7X1udJTgJu69n+G1W1/6jCSdIsfqWqNiR5IHBRkq/2rqyqahuL7qJtPDoFYM2aNTXoBWsXLm7NODxdyNmFjNCdnJK0EM4hJKmzquqTwM0zrUsS4HDgzJGGkqQ5VNWG9usm4DzgAODGdqgY7ddN40soSZJWAnsISVqu/j/gxqr6es+yfZJ8Efgu8Jqq+veZduwfkjE9PT3QCTdv3jzwtuNixuHpQs4uZITu5ByGJDsBd6uq29vnTwNeD1wAHA2c2H49f3wpJUnSSmCDkKTl6tls2ztoI/CzVXVTkl8E/inJI6vqu/07OiRjvLqQEbqRswsZoTs5h2QKOK/pxMgq4INV9bEknwPOSXIscB1ND0dJkqQlY4OQpGUnySrgt4Bf3Lqsqn4A/KB9flmSbwAPAwab8VCShqCqrgUePcPym4ADR59IkiStVM4hJGk5+jXgq1W1fuuCJA9IskP7/CHAvsC1Y8onSZIkSWO17HsIrT7hwm1erzvx4DElkTRsSc4E1gK7J1kPvLaqTgWO4K6TST8JeH2SHwE/AV5YVTNOSL1QV264jWN6ao71RtKk8vpI0qSyPkmjs+wbhCQtX1X17FmWHzPDsg8BH1rqTJIkSZLUBQ4ZkyRJkiRJWmFsEJIkSZIkSVphbBCSJEmSJJHkfyf5cpKrkpyZ5F5J9klyaZJrkpyd5B7jzilpOBbcIJTk4Uku73l8N8nLktw/yUVJvt5+vd8wA0uSJEmShivJnsAfAmuq6lHADjQ36ngT8NaqeihwC3Ds+FJKGqYFTypdVV8D9gdob+W8ATgPOAG4uKpOTHJC+/qVi486mP5Z6SVJkiRJA1kF7NjelfXewEbgKcDvtOtPB14HvHss6SQN1bDuMnYg8I2qui7JoTS3gYamYEwzwgYhSZIkSdL8VNWGJG8G/gv4PvBx4DLg1qra0m62Hthzpv2THAccBzA1NcX09PRA5928efM22x6/35Zt1g96nKXUn3ESdSEjdCNnFzLCcHIOq0HoCODM9vlUVW1sn98ATM20w0IKxiDfcH8B6feOM87f5vV+e+4y53nnqwu/QF3ICN3IaUZJkiR1XTvVx6HAPsCtwD8CBw26f1WdApwCsGbNmlq7du1A+01PT9O77TF9Iz7WHTnYcZZSf8ZJ1IWM0I2cXcgIw8m56AahdlKxZwKv6l9XVZWkZtpvIQVjkG+4v4DMZSkKTBd+gbqQEbqR04ySJElaBn4N+GZVfRsgyYeBJwK7JlnV9hLai2aqEEnLwDB6CD0d+EJV3di+vjHJHlW1MckewKYhnEOSJElLpH8OxnUnHjymJJLG6L+Axye5N82QsQOBzwOXAM8CzgKOBs6f9QiSOmUYt51/NncOFwO4gKZQgAVDkiRJkiZeVV0KnAt8AbiS5r3iKTTzwb48yTXAbsCpYwspaagW1UMoyU7AU4Hf61l8InBOkmOB64DDF3MOSZIkSdLSq6rXAq/tW3wtcMAY4khaYotqEKqqO2haiXuX3UTTvVCSJEmSJEkTaBhDxiRJkiRJktQhNghJkiRJkiStMDYISeqsJO9LsinJVT3LXpdkQ5LL28czeta9Ksk1Sb6W5NfHk1qSJEmSxs8GIUlddhpw0AzL31pV+7ePjwIkeQRwBPDIdp//k2SHkSWVJEmSpAlig5CkzqqqTwI3D7j5ocBZVfWDqvomcA3eMUOSJEnSCrWou4xJ0oR6cZLnAp8Hjq+qW4A9gc/0bLO+XXYXSY4DjgOYmppienp6oJNO7QjH77flp68H3W+UNm/ePJG5enUhI3QjZxcyQndySpIkLSc2CElabt4N/AVQ7deTgOfP5wBVdQpwCsCaNWtq7dq1A+33jjPO56Qr7yyr644cbL9Rmp6eZtDvZ1y6kBG6kbMLGaE7OSVJkpYTh4xJWlaq6saq+nFV/QT4O+4cFrYB2Ltn073aZZIkSZK04tggJGlZSbJHz8vfBLbegewC4Igk90yyD7Av8NlR55MkSZKkSeCQMUmdleRMYC2we5L1wGuBtUn2pxkytg74PYCq+nKSc4CvAFuAF1XVj8cQW5Jo73L4eWBDVR3SNlSfBewGXAYcVVU/HGdGSZK0vNkgJKmzqurZMyw+dTvbvxF449IlkqSBvRS4Grhv+/pNwFur6qwk7wGOpZkTTZIkaUk4ZEySJGmEkuwFHAy8t30d4CnAue0mpwOHjSWcJElaMRbVQyjJrjQXM4+iGZ7xfOBrwNnAaprhGoe3t3yWJEkSvA14BbBz+3o34Naq2tK+Xg/sOdOOSY4DjgOYmppienp6oBNu3rx5m22P32/L7BvDwMcdpv6Mk6gLGaEbObuQEbqTczlbfcKF27xed+LBY0oiLT+LHTJ2MvCxqnpWknsA9wZeDVxcVScmOQE4AXjlIs8jSZLUeUkOATZV1WVJ1s53/6o6BTgFYM2aNbV27WCHmJ6epnfbY/reYPVbd+S8oy1af8ZJ1IWM0I2cXcgI3ckpSQux4AahJLsATwKOAWgnPvxhkkNpJnmFpsvzNDYISZIkATwReGaSZwD3oplD6GRg1ySr2l5CewEbxphRkiStAIuZQ2gf4NvA+5N8Mcl7k+wETFXVxnabG4CpxYaUJElaDqrqVVW1V1WtBo4APlFVRwKXAM9qNzsaOH9MESVJ0gqxmCFjq4DHAi+pqkuTnEwzPOynqqqS1Ew7L2QM/CBjeOcaE99vKcYEd2GscRcyQjdymlGSNASvBM5K8gbgi2znjomSJEnDsJgGofXA+qq6tH19Lk2D0I1J9qiqjUn2ADbNtPNCxsAPMoZ3rjHx/ZZijHwXxhp3ISN0I6cZJUkLUVXTNEPrqaprgQPGmUeSJK0sCx4yVlU3ANcneXi76EDgK8AFNF2dwS7PkiRJkiRJE2exdxl7CXBGe4exa4Hn0TQynZPkWOA64PBFnkOSJEmSJElDtKgGoaq6HFgzw6oDF3NcSZIkSZIkLZ3F3GVMkiRJkiRJHWSDkCRJkiRJ0gpjg5AkSZIkSdIKs9hJpTtvdd9t6tedePCYkkiSJEmSJI2GPYQkdVaS9yXZlOSqnmV/k+SrSa5Icl6SXdvlq5N8P8nl7eM9YwsuSZIkSWNmg5CkLjsNOKhv2UXAo6rqfwD/D3hVz7pvVNX+7eOFI8ooSZIkSRPHBiFJnVVVnwRu7lv28ara0r78DLDXyINJkiRJ0oRb8XMISVrWng+c3fN6nyRfBL4LvKaq/n2mnZIcBxwHMDU1xfT09EAnm9oRjt9vy09fD7rfKG3evHkic/XqQkboRs4uZITu5JSk5a4dav9e4FFA0VxLfY3memo1sA44vKpuGU9CScNkg5CkZSnJnwBbgDPaRRuBn62qm5L8IvBPSR5ZVd/t37eqTgFOAVizZk2tXbt2oHO+44zzOenKO8vquiMH22+UpqenGfT7GZcuZIRu5OxCRuhOTklaAU4GPlZVz0pyD+DewKuBi6vqxCQnACcArxxnSEnD4ZAxSctOkmOAQ4Ajq6oAquoHVXVT+/wy4BvAw8YWUpIkaYIk2QV4EnAqQFX9sKpuBQ4FTm83Ox04bBz5JA2fPYQkLStJDgJeAfxqVX2vZ/kDgJur6sdJHgLsC1w7ppiSJEmTZh/g28D7kzwauAx4KTBVVRvbbW4ApmbaeaFD7vuHDfcOv5/JOIYYd2FocxcyQjdydiEjDCenDUKSOivJmcBaYPck64HX0txV7J7ARUkAPtPeUexJwOuT/Aj4CfDCqrp5xgNLkiStPKuAxwIvqapLk5xMMzzsp6qqktRMOy90yH3/sOFjTrhwu9uPY0h+F4Y2dyEjdCNnFzLCcHLaICSps6rq2TMsPnWWbT8EfGhpE0mSJHXWemB9VV3avj6XpkHoxiR7VNXGJHsAm8aWUNJQLWoOoSTrklyZ5PIkn2+X3T/JRUm+3n6933CiSpIkSZKWQlXdAFyf5OHtogOBrwAXAEe3y44Gzh9DPElLYBg9hJ5cVd/peX0CzkIvSZLUWav7hmysO/HgMSWRNGIvAc5o7zB2LfA8mk4E5yQ5FrgOOHyM+SQN0VIMGTuUZk4PaGahn8YGIUmSJEmaaFV1ObBmhlUHjjiKpBFYbINQAR9vJxb723YisSWbhX6QWbTnmpV+LsOYTbwLs5J3ISN0I6cZJUmSJElds9gGoV+pqg1JHkhzR5+v9q4c9iz0g8yiPdes9HMZxqz1XZiVvAsZoRs5zShJkiRJ6ppFTSpdVRvar5uA84ADaGehB3AWekmSJEmSpMmz4AahJDsl2Xnrc+BpwFU4C70kSZIkSdJEW8yQsSngvCRbj/PBqvpYks/hLPSSJEmSJEkTa8ENQlV1LfDoGZbfhLPQS5Ik3UWSewGfBO5Jcx12blW9Nsk+wFnAbsBlwFFV9cPxJZUkScvdouYQkiRJ0rz8AHhKVT0a2B84KMnjgTcBb62qhwK3AMeOL6IkSVoJbBCSJEkakWpsbl/evX0U8BTg3Hb56cBho08nSZJWksXedl6SJEnzkGQHmmFhDwXeBXwDuLWqtrSbrAf2nGXf44DjAKamppienh7onJs3b95m2+P32zL7xjMY9DyL0Z9xEnUhI3QjZxcyQndyStJC2CAkSZI0QlX1Y2D/JLsC5wE/P499TwFOAVizZk2tXbt2oP2mp6fp3faYEy4cOC/AuiMHO89i9GecRF3ICN3I2YWM0J2ckrQQDhmTJEkag6q6FbgEeAKwa5KtH9TtBWwYVy5JkrQy2CAkqbOSvC/JpiRX9Sy7f5KLkny9/Xq/dnmSvD3JNUmuSPLY8SWXtFIleUDbM4gkOwJPBa6maRh6VrvZ0cD5YwkoSZJWDBuEJHXZacBBfctOAC6uqn2Bi9vXAE8H9m0fxwHvHlFGSeq1B3BJkiuAzwEXVdVHgFcCL09yDc2t508dY0ZJkrQCOIeQpM6qqk8mWd23+FBgbfv8dGCa5o3WocAHqqqAzyTZNckeVbVxRHEliaq6AnjMDMuvBQ4YfSJJkrRS2SAkabmZ6mnkuQGYap/vCVzfs93Wu/jcpUFooXfxmdpx2zv3TOJdSbpwt5QuZIRu5OxCRuhOTkmSpOXEBiFJy1ZVVZJawH4LuovPO844n5OuvLOsjuKuPPPVhbuldCEjdCNnFzJCd3JKkiQtJ84hJGm5uTHJHgDt103t8g3A3j3beRcfSZIkSSuWDUKSlpsLaO7QA9veqecC4Lnt3cYeD9zm/EGSJEmSVqpFDxlLsgPweWBDVR2SZB/gLJo7ZFwGHFVVP1zseSSpX5IzaSaQ3j3JeuC1wInAOUmOBa4DDm83/yjwDOAa4HvA80YeWJIkSYuy+oQLt3m97sSDx5RE6r5hzCH0UuBq4L7t6zcBb62qs5K8BzgWb+8saQlU1bNnWXXgDNsW8KKlTSRJkiRJ3bCoIWNJ9gIOBt7bvg7wFODcdpPTgcMWcw5JkiRJkiQN12J7CL0NeAWwc/t6N+DWqtp63+Wtt3W+i4Xc1nmQ29L23vJ5IYZx29su3D63CxmhGznNKEmSJEnqmgU3CCU5BNhUVZclWTvf/RdyW+dBbkt7TN+Y0vkaxm2iu3D73C5khG7kNKMkSZIkqWsW00PoicAzkzwDuBfNHEInA7smWdX2EvK2zpIkSZIkSRNmwQ1CVfUq4FUAbQ+hP6qqI5P8I/AsmjuN9d7yeUn0zzIvSZIkSZKk7VvUpNKzeCXw8iTX0MwpdOoSnEOSJEmSJEkLNIzbzlNV08B0+/xa4IBhHFeSJEmSJEnDtxQ9hCRJkiRJkjTBbBCSJEmSJAGQZIckX0zykfb1PkkuTXJNkrOT3GPcGSUNhw1CkiRJkqStXgpc3fP6TcBbq+qhwC3AsWNJJWnobBCSJEmSJJFkL+Bg4L3t6wBPAc5tNzkdOGws4SQNnQ1CkiRJkiSAtwGvAH7Svt4NuLWqtrSv1wN7jiGXpCUwlLuMSZIkSZK6K8khwKaquizJ2gXsfxxwHMDU1BTT09MD7bd58+Zttj1+vy2zbzyDQc+zGP0ZJ1EXMkI3cnYhIwwnpw1CkiRJkqQnAs9M8gzgXsB9gZOBXZOsansJ7QVsmGnnqjoFOAVgzZo1tXbt2oFOOj09Te+2x5xw4bxCrztysPMsRn/GSdSFjNCNnF3ICMPJ6ZAxSctOkocnubzn8d0kL0vyuiQbepY/Y9xZJUmSJkFVvaqq9qqq1cARwCeq6kjgEuBZ7WZHA+ePKaKkIbOHkKRlp6q+BuwPza1TaT7JOg94Hs1dMt48vnSS1D2r5/mJPcC6Ew+e1/ZXbrhtm54B891f0pJ5JXBWkjcAXwROHXMeSUNig5Ck5e5A4BtVdV1zowxJkiRtT1VNA9Pt82uBA8aZR9LScMiYpOXuCODMntcvTnJFkvclud+4QkmSJEnSONlDSNKyleQewDOBV7WL3g38BVDt15OA58+w34LukjG147Z3xpjEuxN04a4JXcgI3cjZhYzQnZzDkGRv4APAFE0tOqWqTk5yf+BsYDWwDji8qm4ZV05JkrT82SAkaTl7OvCFqroRYOtXgCR/B3xkpp0WepeMd5xxPiddeWdZHcVdL+arC3dN6EJG6EbOLmSE7uQcki3A8VX1hSQ7A5cluQg4Bri4qk5McgJwAs28HZK0rPXPHyZpdBY8ZCzJvZJ8NsmXknw5yZ+3y/dJcmmSa5Kc3X5CL0nj8Gx6hosl2aNn3W8CV408kaQVrao2VtUX2ue3A1cDewKHAqe3m50OHDaWgJIkacVYTA+hHwBPqarNSe4OfCrJvwAvp7mLz1lJ3gMcSzNMQ5JGJslOwFOB3+tZ/NdJ9qcZprGub50kjVSS1cBjgEuBqara2K66gWZI2Uz7LGhIa/+wvN7hrUtlvsMAHXY7PF3I2YWM0J2ckrQQC24QqqoCNrcv794+CngK8Dvt8tOB12GDkKQRq6o7gN36lh01pjiStI0k9wE+BLysqr7bexfEqqokNdN+Cx3S2j8sbxTDM+Y7bNZht8PThZxdyAjdySlJC7GoOYSS7ABcBjwUeBfwDeDWqtr68c56mm7QM+0770+4ZmqhH/YnXO844/xtXu+35y7zPkYXPknoQkboRk4zSpLmo+1Z/SHgjKr6cLv4xiR7VNXGdnjrpvEllCRJK8GiGoSq6sfA/kl2Bc4Dfn4e+877E66ZWuiX+hOuhXw61YVPErqQEbqR04ySpEGl6Qp0KnB1Vb2lZ9UFwNHAie3X82fYXZIkaWiGcpexqro1ySXAE4Bdk6xqewntBWwYxjkkSZKWgScCRwFXJrm8XfZqmoagc5IcC1wHHD6eeJIkaaVYcINQkgcAP2obg3akmbz1TcAlwLOAs/ATLkmSpJ+qqk8BmWX1gaPMIkmSVrbF9BDaAzi9nUfobsA5VfWRJF8BzkryBuCLNN2iJUmSJEmSNCEWc5exK2huldq//FrggMWEkiRJUretnmOex3UnHjyiJJIkaSZ3G3cASZIkSZIkjZYNQpIkSZIkSSvMUO4yJkmSJM1H/5Cy4/cbUxBJklYoewhJkiRJkiStMDYISZIkSZIkrTAOGZMkSZIkLQsz3eHQuxpKM7OHkCRJkiRJ0gpjDyFJkiRJUifN1CNI0mDsISRJkiRJkrTC2ENI0rKUZB1wO/BjYEtVrUlyf+BsYDWwDji8qm4ZV0ZJkiRJGhd7CElazp5cVftX1Zr29QnAxVW1L3Bx+1qSJEmSVhwbhCStJIcCp7fPTwcOG18USZIkSRqfBQ8ZS7I38AFgCijglKo62SEZkiZEAR9PUsDfVtUpwFRVbWzX30BTv+4iyXHAcQBTU1NMT08PdMKpHeH4/bb89PWg+43S5s2bJzJXry5khG7k7EJG6E5OSZKk5WQxcwhtAY6vqi8k2Rm4LMlFwDE0QzJOTHICzZCMVy4+qiTNy69U1YYkDwQuSvLV3pVVVW1j0V20jUenAKxZs6bWrl070Anfccb5nHTlnWV13ZGD7TdK09PTDPr9jEsXMkI3cnYhI3QnpyRJ0nKy4CFjVbWxqr7QPr8duBrYE4dkSJoAVbWh/boJOA84ALgxyR4A7ddN40soSZIkSeMzlLuMJVkNPAa4lCUckjFTl/Le4RlLYZBcV264bZvX++yyw8R3fe9K9/wu5DTj5EmyE3C3qrq9ff404PXABcDRwInt1/PHl1KSJEmSxmfRDUJJ7gN8CHhZVX03yU/XDXtIxkxdyo854cKFRh/IIEM++jOcdtBOE9/1vSvd87uQ04wTaQo4r61Hq4APVtXHknwOOCfJscB1wOFjzChJkiRJY7OoBqEkd6dpDDqjqj7cLr4xyR5VtXE5DMlYPUOD07oTDx5DEkmDqqprgUfPsPwm4MDRJ5IkSZps3jRIWnkWc5exAKcCV1fVW3pWOSRDkrhrg7KNyZIkaYJ50yBphVlMD6EnAkcBVya5vF32apqGIIdkSJIkaaLM1VBvQ75WsnYe2I3t89uT9N40aG272enANDYIScvCghuEqupTQGZZ7ZAMSZIkSeqgUd00CGBqx8m4UdD2dOEGLV3ICN3I2YWMMJycQ7nLmCRJkiSp+0Z50yCAd5xxPiddubRvSwe5UdD2dOEGLV3ICN3I2YWMMJycNghJkiSNUJL3AYcAm6rqUe0yJ21dAjPdHGTSOWxN47Rcbxrk35U0s7uNO4AkSdIKcxpwUN+yE2gmbd0XuLh9LUkjM8BNg8CbBknLig1CkiRJI1RVnwRu7lt8KM1krbRfDxtlJknizpsGPSXJ5e3jGTQ3DXpqkq8Dv9a+lrQMOGRMkiRp/JZ00tZNN9/GO86480P94/dbTNSl0T+xbG9egP323GWb11duuO0ux+jfZq6Javt/fv3b92fYZ5cdlnyi0f4MCzlfFyZE7UJG6E7OYfCmQdLKY4OQJEnSBOnqpK2Ldfx+W7absX9S2GNmmB9okG0Ws/1pB+205BON9mdYyGS4XZgQtQsZoTs5JWkhHDImSZI0fje2k7XS1UlbJUlSt0z2R0WSJEkrw9ZJW0/ESVtHZinuQubdjKTJ59+p1LCHkCRJ0gglORP4NPDwJOuTHIuTtkqSpBGzh5AkSdIIVdWzZ1nlpK2SJGlk7CEkSZIkSZK0wthDSNKyk2Rv4AM0t20u4JSqOjnJ64DfBb7dbvrqqvroeFI6fl2S5mMp5vuZrys33HbXu4BZuyVJHWWDkKTlaAtwfFV9IcnOwGVJLmrXvbWq3jzGbJIkSZI0dosaMpbkfUk2JbmqZ9n9k1yU5Ovt1/stPqYkDa6qNlbVF9rntwNXA3uON5UkSZIkTY7F9hA6DXgnzdCMrU4ALq6qE5Oc0L5+5SLPs6w5bERaOklWA48BLgWeCLw4yXOBz9P0Irplhn2OA44DmJqaYnp6eqBzTe0Ix++3Zdb1/cfp33bQ8yzG5s2bR3KexehCRuhGzi5khO7klGYyCUPZJA1X/9/1aQftNKYk0tJaVINQVX2yfbPV61Bgbfv8dGAaG4QkjUGS+wAfAl5WVd9N8m7gL2jmFfoL4CTg+f37VdUpwCkAa9asqbVr1w50vneccT4nXTl7WV135LbHucs8FH3rl8L09DSDfj/j0oWM0I2cXcgI3ckpSZK0nCzFHEJTVbWxfX4DzaSud7GQT+A33Xwb7zjj/G2WHb/fYqIuzFyf8s/3k057CcyuCznNOJmS3J2mMeiMqvowQFXd2LP+74CPjCmeJEmSJoQ9/bRSLemk0lVVSWqWdfP+BH6uT99HZa5P+U87aKd5fdJpL4HZdSGnGSdPkgCnAldX1Vt6lu/R02D9m8BVM+0vSeom39QtjNMXSNLKtBStKzdufdOVZA9g0xKcY6y82JAm3hOBo4Ark1zeLns18Owk+9MMGVsH/N44wkmSJEnSuC1Fg9AFwNHAie3X87e/uSQNV1V9CsgMqz466iySJEmSNIkW1SCU5EyaCaR3T7IeeC1NQ9A5SY4FrgMOX2zIrrlyw23bDAOz260kSZIkSZoki73L2LNnWXXgYo4rSZIkLQddnJ9npukRupBbkjQ/45+heQVwziFJYC2QJElaDhbS0NvFxmEtf3cbdwBJkiRJkiSNlj2EJEmSpDGZq/eow7ek8eufI7bfpP6d2itJc7GHkCRJkiRJ0gpjDyFJmlB+qiNJkrQyjeM60GvPlccGIUnSkvLiQpLuNIwbDMx1jNMO2mnJM8zF2i9Jk88GIUlaRrwAlyRJmnyLbZjt33+uhuCZ9vE6UTYIrVD9E6NZDKTxm4Tb0nuhIEmSJK0MNghJUkcsRYPRJDRCSZIkLTfL8RprIR8cLqQnk0bHBiFJWsb6/xM+fr8t271t6lz7d7XH0HL5PiRpubC3ujT5lmOjlrZlg5AkaWCTcmEwCXfe6G9c882MJEmSusQGoQk00xuu+b7RmIRPwychg6TJM9+744xiqFx/fRrHOSVJkmYz17VJf6+7YRxzEnj9tLSWpEEoyUHAycAOwHur6sSlOI8kLYQ1SqPQhYssTR7rk4ZhIW8M57LUNW0YH4hqaVmfpOVn6A1CSXYA3gU8FVgPfC7JBVX1lWGfayVZ7KfZdx3qsPgM/eb6T9vWXU0Ca9TkW4o3MnMZxhud+R5jIeecq/ZbV7vN+iRpUlmfNJNJveHJfN87r+Trp6XoIXQAcE1VXQuQ5CzgUMBiIWkSWKOWoZXyH/tiL5KW4ue02A8LtA3rk6RJZX2SlqFU1XAPmDwLOKiqXtC+Pgp4XFW9uG+744Dj2pcPB742wOF3B74zxLhLpQs5u5ARupFzuWV8cFU9YCnDjNMgNWqB9QmW3+/CuHQhI3QjZxcywuA5rU/Wp3HrQkboRs4uZATrE2B9wozD1IWcXcgIQ6hPY5tUuqpOAU6Zzz5JPl9Va5Yo0tB0IWcXMkI3cppx+VlIfYJu/JzNODxdyNmFjNCdnJPA+jReXcgI3cjZhYzQnZyTwPo0Xl3ICN3I2YWMMJycdxtWmB4bgL17Xu/VLpOkSWCNkjSprE+SJpX1SVqGlqJB6HPAvkn2SXIP4AjggiU4jyQthDVK0qSyPkmaVNYnaRka+pCxqtqS5MXA/6W5JeH7qurLQzr8vLsgjkkXcnYhI3Qjpxk7xBplxiHqQs4uZITu5FxS1iczDlEXcnYhI3Qn55KyPplxiLqQswsZYQg5hz6ptCRJkiRJkibbUgwZkyRJkiRJ0gSzQUiSJEmSJGmFmcgGoSQHJflakmuSnDDD+nsmObtdf2mS1WOIOUjOlyf5SpIrklyc5MGTlrFnu/+ZpJKM/PZ6g2RMcnj7s/xykg+OOmObYa5/759NckmSL7b/5s8YQ8b3JdmU5KpZ1ifJ29vv4Yokjx11xq6zPo0uY8921qc5WJ8E1qdRZuzZbmz1qT3/xNco65PA+jTKjD3bWZ8WmXFF1KeqmqgHzSRl3wAeAtwD+BLwiL5t/gB4T/v8CODsCc35ZODe7fPfH3XOQTK22+0MfBL4DLBm0jIC+wJfBO7Xvn7ghP57nwL8fvv8EcC6MeR8EvBY4KpZ1j8D+BcgwOOBS0edscsP69NoM7bbWZ+Gk9P6tMwf1qfRZmy3G1t9msfPcqw1yvrkYx6/B9anIWVst7M+DSfjsq9Pk9hD6ADgmqq6tqp+CJwFHNq3zaHA6e3zc4EDk2SEGWGAnFV1SVV9r335GWCvScvY+gvgTcB/jzJca5CMvwu8q6puAaiqTSPOCIPlLOC+7fNdgG+NMF8ToOqTwM3b2eRQ4APV+Aywa5I9RpNuWbA+jTBjy/o0N+uTwPo00oytcdYn6EaNsj4JrE8jzdiyPg0n47KvT5PYILQncH3P6/Xtshm3qaotwG3AbiNJN0OG1kw5ex1L03I3SnNmbLuU7V1VF44yWI9Bfo4PAx6W5D+SfCbJQSNLd6dBcr4OeE6S9cBHgZeMJtq8zPf3VtuyPg2P9Wl4rE8C69MwdaE+QTdqlPVJYH0aJuvT8FifgFVDj6O7SPIcYA3wq+PO0ivJ3YC3AMeMOcpcVtF0KVxL0wr/yST7VdWt4ww1g2cDp1XVSUmeAPx9kkdV1U/GHUyajfVp0axP0hKxPg1FF2qU9UmdY30aCuvTBJjEHkIbgL17Xu/VLptxmySraLpv3TSSdDNkaM2UkyS/BvwJ8Myq+sGIsm01V8adgUcB00nW0Yw5vGDEE48N8nNcD1xQVT+qqm8C/4+meIzSIDmPBc4BqKpPA/cCdh9JusEN9HurWVmfhsf6NDzWJ4H1aZi6UJ+gGzXK+iSwPg2T9Wl4rE8wkZNKrwKuBfbhzsmdHtm3zYvYdtKxcyY052NoJqrad1J/ln3bTzP6SVsH+TkeBJzePt+dpkvcbhOY81+AY9rnv0AzxjRj+HdfzeyTjh3MtpOOfXbU+br8sD6NNmPf9tanxeW0Pi3zh/VptBn7th95fZrHz3KsNcr65GMevwfWpyFl7Nve+rS4jMu+Po30G5nHN/wMmhbCbwB/0i57PU0rLDQtc/8IXAN8FnjIhOb8V+BG4PL2ccGkZezbdlwFY66fY2i6Pn4FuBI4YkL/vR8B/EdbTC4HnjaGjGcCG4Ef0bS6Hwu8EHhhz8/yXe33cOU4/r27/rA+jS5j37bWp8XltD6tgIf1aXQZ+7YdS30a8Gc59hplffIx4O+B9WlIGfu2tT4tLuOyr09pDyJJkiRJkqQVYhLnEJIkSZIkSdISskFIkiRJkiRphbFBSJIkSZIkaYWxQUiSJEmSJGmFsUFIkiRJkiRphbFBSJIkSZIkaYWxQUiSJEmSJGmFsUFIkiRJkiRphbFBSJIkSZIkaYWxQUiSJEmSJGmFsUFIkiRJkiRphbFBSJIkSZIkaYWxQUiSJEmSJGmFsUFIkiRJkiRphbFBSJIkSZIkaYWxQUiSJEmSJGmFsUFIkiRJkiRphbFBSJIkSZIkaYWxQUgzSjKd5AXjzrFVki8nWTvuHJKWrySnJXnDuHNIGr5xXtckWZfk1xa4728muT7J5iSPSfLwJJcnuT3JHy4i03uS/OlC95c0Ol2tX9s55uuS/MMwj6mFs0FoGWqLxi1J7tmzbOh/zAPkGNofe1U9sqqmh3EsScPR1pXvt29UbmwbVO4z7lyDSHJMkk+NO4ek4eliTWoz/rDNvPXxpZ5N3gy8uKruU1VfBF4BXFJVO1fV2xd63qp6YVX9xWLzSxqOLtYvgCS/nOQTbSP1bUn+OckjetavTbJ+nBm1fTYILTNJVgP/H1DAM8ebRtIK8BtVdR/gscAa4DVjzjN2afj/qzQeI61JSVYN4TB/3Tb4bH08umfdg4Evb+e1pOWjU/UryROAjwPnAw8C9gG+BPxHkocsPuFAGYZRg1c0L1iXn+cCnwFOA44GSPL3wM8C/9y2Or+iXf74JP+Z5NYkX9rekKwkz09yddvz6P8meXDPukcmuSjJzW2L9quTHAS8GvhfvZ92JXlee5zbk1yb5Pd6jrN7ko+0eW5O8u9b31T19nBKckCSzyf5bnu+twzzByhp/qpqA/AvwKOSVJIXJvl6+/f8riTZuu1s9STJ6nbfVT3b/rSbdNur5z+SvLU97rXtJ1PHpBlSsSnJ0T377pLkA0m+neS6JK9JcrckvwC8B3hCW59u7flW7pfkwrZGXZrk53qO98tJPtd+Ava5JL/cl/ONSf4D+B7wkDnq3Svb469qX/9+mqGx9xrWv4m0kvXWpJnWD6kO3QS8LsnPpfmE/KYk30lyRpJdF5M/yT2TbAZ2AL6U5BtJPgE8GXhnW7selr6hJOnp/ZjGW9va+N0kVyZ5VLtumyGySX43yTXt9dcFSR7Us267NV3ScHWofv018IGqOrmqbq+qm6vqNTTvRV+XZKf2+3hQ7uwBubW23KO9Rru9vf5Z05P5QUk+1F6/fTM9w2PTjEA5N8k/JPkucMyAWTULG4SWn+cCZ7SPX08yVVVHAf9F2+pcVX+dZE/gQuANwP2BPwI+lOQB/QdMcihN485vAQ8A/h04s123M/CvwMdoWoYfClxcVR8D/hI4u+/Trk3AIcB9gecBb03y2Hbd8cD69hxT7Tlrhu/xZODkqrov8HPAOQv6SUkamiR7A88AvtguOgT4JeB/AIcDv95uN2s9GdDjgCuA3YAPAme153ko8ByaN0pbu1i/A9gFeAjwqzT18XlVdTXwQuDTbX3atef4RwB/DtwPuAZ4Y5v7/jQ18+3tud8CXJhkt559jwKOA3YGrmP79e5vgB8Ar0myL029fE5V/fc8fhaSZjFDTepdN4w6dC3NtcobgQB/RXMd9AvA3sDrFp4equoHbU8BgEdX1c9V1VParFuHkP2/OQ7zNOBJwMNoauHhwE39GyV5Spv/cGAPmvp1Vt9mM9Z0ScPXhfqV5N7ALwP/OMPqc4CnVtUdwNOBb/X0gPxWu80zaerMrsAFwDvb494N+GeankZ7AgcCL0vSW3MOBc5t9z1jsG9bs7FBaBlJ8is0XYnPqarLgG8AvzPL5s8BPlpVH62qn1TVRcDnaYpPvxcCf1VVV1fVFpo3Lvu3rdGHADdU1UlV9d9t6/Cls2Wsqgur6hvV+Deabob/X7v6RzQXIg+uqh9V1b9X1UwNQj8CHppk96raXFWfmeNHI2np/FOaHjafAv6Npj4AnFhVt1bVfwGXAPu3y7dXTwbxzap6f1X9GDib5sLl9e2bp48DP6SpDzvQNO68qq1L64CTaBpttue8qvpsm+2MntwHA1+vqr+vqi1VdSbwVeA3evY9raq+3K7/0fbqXVX9hKaB6g9pLoT+up0fRNLizFaTei22Dn2rqt7R/q1/v6quqaqL2jr0bZoG41+dR+Y/anvebH2cPo99t+dHNA3UPw+k/X43zrDdkcD7quoLVfUD4FU0PShX92wzW02XNDxdql/3p2lLmKmmbAR2n2P/T7XvQ38M/D2wtfPALwEPqKrXV9UPq+pa4O9orum2+nRV/VP7Hvb7A2TVdtggtLwcDXy8qr7Tvv5gu2wmDwZ+u/cCBPgVmgaZmbY9uWe7m2lak/ekeTP2jUEDJnl6ks+0XZJvpWmA2low/obmE/mPpxleccIshzmW5tOur6YZtnHIoOeXNHSHVdWuVfXgqvqDnv+Yb+jZ5nvA1k+6t1dPBnFjz/PvA1RV/7L70NSVu9N80r3VdQOcZ7bcD+o71kzHu7535Rz1jraR6hJgNfCuOXJJGsxsNanXYutQ/9/6VJKzkmxohzD8A3O/Ger15jbz1sds127zUlWfoPnU/V3ApiSnJLnvDJtuU9+qajNNT6Len8dstVHS8HSpft0C/ISZ3zvuAXxnhuW9+mvKvdqhbg+mGWLW+x711TQ9mmb8HrQ4NggtE0l2pOnC+6tJbkhyA/C/gUcneTR3HXp1PfD3fRcgO1XViTMc/nrg9/q23bGq/rNdN9ukYducM81dzz5Ec8eMqWqGaXyUpojRfop/fFU9hKYb4cuTHHiXg1Z9vaqeDTwQeBNwbjtGVdLk2149uaPd5t492//MAs/zHZpPx3s/MftZYEP7fKbeh9vzrb5j9R9vm2POVe/abQ4GngBcTNMgLmk0FluH+uvHX7bL9qtmOPtz6PlbX2J3sJ2sVfX2qvpF4BE0H6b98QzH2Ka+tddUu7FtfZM0GSaiflUzHOzTwG/PsPpwmmubmc43l+tpeoP3fn87V1XvKJb5HlPbYYPQ8nEY8GOa//D3bx+/QDOu9Lk0n6r3Ntz8A/AbSX49yQ5J7pXmtoB7zXDs9wCvSvJI+OlErVv/+D8C7JHkZWkmQNw5yePadTcCq3Pn3XbuAdwT+DawJcnTaca30x73kCQPTRLgtvb7+Ul/mCTPSfKAdsjFre3iu2wnaSLNWk/arsobgOe0den5NPOEzVvbBfkc4I1tXXow8HKa2gdNfdoryT0GPORHgYcl+Z0kq5L8L5p6+5FZtp+r3u0OvBd4AU1Pzt9IMtOQXUnDN+w6tDOwGbitnaNxpkaXpXI58FtJ7p3koTS9qAFI8ktJHpfk7jRvFP+bma+XzgSel2T/tjH7L4FL216MkibLJNWvE4Cjk/xhe611vzST1T+BZj5GaK63dkuyy4DH/Cxwe5qbb+zYfh+PSvJL88ilebBBaPk4Gnh/Vf1XVd2w9UHTVfhImsnCXtN2vfujqrqeZkKuV9O8YbmepgDc5Xeiqs6j6YlzVtuV8CqaCcKoqtuBp9LMo3ED8HWaO2DAnZOM3ZTkC+22f0jzJu0WmvmNLug51b40E1Rvpmlx/j9VdckM3+tBwJfT3H3jZOAIx49K3bC9etL6XZpadBPwSOA/F3G6l9C8CbqWZjz+B4H3tes+QXPr5huSzNWtmaq6iWbOtOPbbK8ADukZotu//Vz17hTg/Hb8/E00b+Lem20nqZa0BJagDv05zW2ib6OZfP7D84z0itx5B57Ng9SkHm+lmTvtRuB0tp1g9b40c2/cQjMk7CZm6I1YVf8K/ClNr8aNNG8gj+jfTtL4TVL9qqpP0Uww/1s0teM64DHAr1TV19ttvkrT6Hxt+z70QbMdr93+xzTXW/sD36Tp8f1emonxtQRSM87ZK0mSJEmSpOXKHkKSJEmSJEkrjA1CkiRJkiRJK8yqcQeQpKWQZB1wO83k5Fuqak2S+wNn09zmex1weFXdMq6MkiRJkjQui+ohlGTXJOcm+WqSq5M8Icn9k1yU5Ovt1/sNK6wkzdOTq2r/qlrTvj4BuLiq9qW5HeYJ44smSZI0eknel2RTkqt6lv1N+57uiiTnJdm1Z92rklyT5GtJfn0soSUtiUVNKp3kdODfq+q97a17701z16qbq+rEJCcA96uqV27vOLvvvnutXr16zvPdcccd7LTTTgvOOypdyNmFjNCNnMst42WXXfadqnrAEkdacm0PoTW9d4FK8jVgbVVtTLIHMF1VD9/ecQatT7D8fhfGpQsZoRs5u5ARBs+5XOrTsFifRq8LGaEbObuQEZZnfUryJJq7+n6gqh7VLnsa8Imq2pLkTQBV9cokj6C5S9QBwINo7gj8sPZuULOyPo1eFzJCN3J2ISMMpz4teMhYkl2AJwHHAFTVD4EfJjkUWNtudjowDWy3QWj16tV8/vOfn/Oc09PTrF27ds7txq0LObuQEbqRc7llTHLd0qYZmQI+nqSAv62qU4CpqtrYrr8BmJppxyTHAccBTE1N8eY3v3mgE27evJn73Oc+iw6+lMw4PF3I2YWMMHjOJz/5yculPg3FoNdPsPz+rxqXLmSEbuTsQkYYPGeXrp+q6pNJVvct+3jPy88Az2qfHwqcVVU/AL6Z5BqaxqFPb+8c1qfR60JG6EbOLmSE4dSnxcwhtA/wbeD9SR4NXAa8lAHfcEnSEvuVqtqQ5IHARUm+2ruyqqptLLqLtvHoFIA1a9bUoP8hdOE/DzMOTxdydiEjdCenJK0Qz6eZcxFgT5oGoq3Wt8skLQOLaRBaBTwWeElVXZrkZPrm49jeG67+T+Cnp6fnPOHmzZsH2m7cupCzCxmhGznNOJmqakP7dVOS82g+zboxyR49Q8Y2jTWkJEnSBEnyJ8AW4IwF7Dvv93fQjetUMw5PF3J2ISMMJ+diGoTWA+ur6tL29bk0DUIDveFayCfwXfkEsQs5u5ARupHTjJMnyU7A3arq9vb504DXAxcARwMntl/PH19KSZKkyZHkGOAQ4MC6c6LZDcDePZvt1S67C3tYj1cXMkI3cnYhIwwn54LvMlZVNwDXJ9k6IeuBwFe48w0X+IZL0nhMAZ9K8iXgs8CFVfUxmoagpyb5OvBr7WtJkqQVLclBwCuAZ1bV93pWXQAckeSeSfYB9qW5tpK0DCymhxDAS4Az2juMXQs8j6aR6ZwkxwLXAYcv8hySNC9VdS3w6BmW30TTeC1JkrQiJTmT5iZAuydZD7wWeBVwT5p5FwE+U1UvrKovJzmH5oP/LcCL5rrDmKTuWFSDUFVdDqyZYZVvuCRJkiRpwlTVs2dYfOp2tn8j8MalSyRpXBbbQ2jsVp9w4Tav15148JiSSNL2Wa8kTSrrk6RJYT2SRmfBcwhJkiRJkiSpm/7/9u493LK7rvP8+0OKkHBLQoKnYxKpOAQ0UsOlq7kMtn0g4MSEIXlGJo1GTOzY1dqgKOWYQrsbVJwuZuQSMwx0NcEUGnIhoskQFemYY9oLkYQABYlICAVUUSQoSaAQhdJv/7FX4anNqTr7nH1de71fz7Ofs9dl7/U5u05+2b/v+v3WsiAkSZIkSZLUMRaEJEmSJEmSOsaCkCRJkiRJUse0/qLS/bwImSRJkiRJ0pE5QkiSJEmSJKljLAhJkiRJkiR1TKumjO3a+xAX900JkyRJkiRJ0to4QkiSJEmSJKljWjVCSJIkSZI0P5wFIk2PI4QkSZIkSZI6xoKQJEmSJElSxzhlTJIkSUe0sW86x+7t504piSRJGhVHCEmSJEmSJHWMBSFJkiRJkqSOsSAkSZIkSZLUMRaEJEmSJEmSOsaCkCRJkiRJUscMVRBKsjvJriQfTnJ7s+5xSd6f5JPNzxNGE1WSJEmSNKwk70hyf5KPLVu3Yj8uPb+e5J4kH03yjOkllzRKoxgh9LyqelpVbW6WtwE3V9UZwM3NsiRJkhpJjkpyZ5L3NsunJ7mt6XBdm+ToaWeUNNeuBM7uW3e4ftwPAGc0jy3AWyeUUdKYjWPK2HnAzub5TuD8MRxDkiSpzV4J3L1s+fXAm6rqicADwCVTSSWpE6rqVuBLfasP1487D3hn9XwAOD7JyRMJKmmsNgz5+gL+MEkB/6WqdgALVbWv2f4FYGGlFybZQq/CzMLCAktLS6sebOFY2LrpwJoCDvK+o7Z///6pHHct2pAR2pHTjJKktUhyKnAu8KvAq5IEeD7ww80uO4HX4ll4SZN1uH7cKcDnlu23p1m3b9m6dfXvYPU+3ix8h23Dd+k2ZIR25GxDRhhNzmELQt9bVXuTfBvw/iR/uXxjVVVTLPoWTfFoB8DmzZtrcXFx1YNdftUNvGHX2iLvvnD19x21paUlBvl9pqkNGaEdOc04u5IcBdwO7K2qFyU5HbgGOBG4A3hZVX19mhklddKbgZ8HHtMsnwg8WFUHe0QHO1uSNBVH6scd4TVr7t/B6n28afTn+rXhu3QbMkI7crYhI4wm51AFoara2/y8P8nvAM8E7ktyclXta4YS3j9UQklav4NTMh7bLB+cknFNkrfRm5LhGXhJE5PkRcD9VXVHksV1vH5dZ+BXO4vYf3a+f9/Vto9CG87ItiEjtCNnGzJCe3KOyOH6cXuB05btd2qzTlLLrbsglORRwMOq6ivN8+8Hfhm4EbgI2N78vGEUQSVpLZySIWlGPRd4cZJzgGPoFawvo3dNjg3NKKHDdrbWewZ+tbOIF2+76ZDl/jPyq20fhTackW1DRmhHzjZkhPbkHJHD9eNuBF6R5BrgWcBDy6aWSWqxYUYILQC/0+tjsQF4V1X9QZIPAtcluQT4DHDB8DElac3ezDqnZEzqDPzlVx25Xr7plOMGOu5atOFMZxsyQjtytiEjtCfnKFTVq4FXAzQjhH6uqi5M8m7gJfSmtXpCTdJYJbkaWAROSrIHeA29QtBK/bjfA84B7gH+FvixiQeWNBbrLghV1b3AU1dY/zfAWcOEkqRhDDslY1Jn4FfjGfjZ1oacbcgI7ck5ZpcC1yR5HXAncMU0w2xcY3slqV2q6ocOs+lb+nFVVcDLx5tI0jQMe1FpSZpFQ03JkKRJqKolYKl5fi+9azFKkiRNxMOmHUCSRq2qXl1Vp1bVRuClwB9V1YXALfSmZIBTMiRJkiR1mAUhSV1yKb0LTN9D75pCU52SIUmSJEnT4pQxSXPNKRmSJEmS9K0cISRJkiRJktQxFoQkSZIkSZI6xoKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR3jXcYkSZK0Jhu33XTI8u7t504piSRJWi9HCEmSJEmSJHWMBSFJkiRJkqSOsSAkSZIkSZLUMXN/DSHnuEuSJEmSJB3KEUKSJEmSJEkdM/cjhCRpWvpHKEqSJEnSrHCEkCRJkiRJUsdYEJIkSZIkSeqYoQtCSY5KcmeS9zbLpye5Lck9Sa5NcvTwMSVJkiRJ45TkZ5N8PMnHklyd5Bj7d9L8GsUIoVcCdy9bfj3wpqp6IvAAcMkIjiFJkiRJGpMkpwA/DWyuqqcARwEvxf6dNLeGKgglORU4F3h7sxzg+cD1zS47gfOHOYYkSZIkaSI2AMcm2QA8EtiH/Ttpbg17l7E3Az8PPKZZPhF4sKoONMt7gFNWemGSLcAWgIWFBZaWllY92MKxsHXTgVX3O5JBjjOs/fv3T+Q4w2hDRmhHTjNKkiSp7apqb5JfAz4LfA34Q+AOxti/g9X7eLPwHbYN36XbkBHakbMNGWE0OdddEEryIuD+qrojyeJaX19VO4AdAJs3b67FxdXf4vKrbuANu4arYe2+cPXjDGtpaYlBfp9pakNGaEdOM0qSJKntkpwAnAecDjwIvBs4e9DXr6d/B6v38SbRf1tNG75LtyEjtCNnGzLCaHIOU115LvDiJOcAxwCPBS4Djk+yoakinwrsHSqhJEmSZtrGbTd9y7rd28+dQhJJQ3gB8Omq+iJAkvfQ6/PZv5Pm1LqvIVRVr66qU6tqI72Ljf1RVV0I3AK8pNntIuCGoVNKkiRJksbps8CzkzyyuTbsWcBd2L+T5tYo7jLW71LgVUnuoXdNoSvGcAxJkiRJ0ohU1W30Lh79IWAXvb7iDuzfSXNr2ItKA1BVS8BS8/xe4JmjeF9JkqR5kuQY4FbgEfS+h11fVa9JcjpwDb3O1h3Ay6rq69NLKqmLquo1wGv6Vtu/k+bUSApCbdY/59357pIkaYz+Hnh+Ve1P8nDgT5L8PvAq4E1VdU2StwGXAG+dZlBJmgX216TxGceUMUmaqiTHJPmLJB9J8vEkv9SsPz3JbUnuSXJtkqOnnVVSt1TP/mbx4c2jgOfTm6oBsBM4f/LpJElSl3RuhNBKd8GQNHc8Ay9pZiU5it60sCcCbwE+BTzY3MEHYA9wymFeuwXYArCwsMDS0tJAx9y/f/8R99266cBht63XoNkOWi3jLGhDRmhHzjZkhPbklKT16FxBSNL8q6oCDncG/oeb9TuB12JBSNKEVdU/AE9LcjzwO8B3reG1O+hd5JXNmzfX4uLiQK9bWlriSPtePIYTZrsvPPzxVrJaxlnQhozQjpxtyAjtySlJ62FBSNJcmoUz8MOecR/HGck2nOlsQ0ZoR842ZIT25By1qnowyS3Ac4Djk2xo2qhTgb3TTSdJkuadBSFJc2kWzsAPe8Z9rWfXB9GGM51tyAjtyNmGjNCenKOQ5PHAN5pi0LHAC4HXA7cAL6F3p7GLgBuml1KSJHWBBSFJc80z8JJmzMnAzmYU48OA66rqvUnuAq5J8jrgTuCKaYaUJEnzz4KQpLnjGXhJs6qqPgo8fYX19wLPnHwiSZLUVRaEJM0jz8BLkiRJ0hFYEJI0dzwDL0mSJElH9rBpB5AkSZIkSdJkOUJIkiRJktQKG/vu4rp7+7lTSiK1nyOEJEmSJEmSOsaCkCRJkiRJUsc4ZUySWqp/yDQ4bFqSJEnSYBwhJEmSJEmS1DEWhCRJkiRJkjrGgpAkSZIkSVLHrLsglOSYJH+R5CNJPp7kl5r1pye5Lck9Sa5NcvTo4kqSJEmSxiHJ8UmuT/KXSe5O8pwkj0vy/iSfbH6eMO2ckkZjmBFCfw88v6qeCjwNODvJs4HXA2+qqicCDwCXDJ1SkiRJkjRulwF/UFXfBTwVuBvYBtxcVWcANzfLkubAugtC1bO/WXx48yjg+cD1zfqdwPnDBJQkSZIkjVeS44DvA64AqKqvV9WDwHn0+nVg/06aK0Pddj7JUcAdwBOBtwCfAh6sqgPNLnuAUw7z2i3AFoCFhQWWlpZWPd7CsbB104FV9xtGf45dex/6ln02nXLcEd9j//79A/0+09SGjNCOnGbUQbv2PsTFK9wKfpL6b0XvbeglSdKATge+CPxGkqfS6+e9Elioqn3NPl8AFlZ68Xr6dzB8H28S33Hb8F26DRmhHTnbkBFGk3OoglBV/QPwtCTHA78DfNcaXrsD2AGwefPmWlxcXPU1l191A2/YNVTkVe2+8NAcK3Xu+vfpt7S0xCC/zzS1ISO0I6cZJUmSNAc2AM8AfqqqbktyGX3Tw6qqktRKL15P/w6G7+Ot1jcbhTZ8l25DRmhHzjZkhNHkHMldxpqhhLcAzwGOT3Lwv+hTgb2jOIYkSZIkaWz2AHuq6rZm+Xp6BaL7kpwM0Py8f0r5JI3YMHcZe3wzMogkxwIvpHfRsVuAlzS7XQTcMGRGSZIkSdIYVdUXgM8leXKz6izgLuBGev06sH8nzZVh5l+dDOxsriP0MOC6qnpvkruAa5K8DriT5qJkkiRJkqSZ9lPAVUmOBu4Ffoymr5fkEuAzwAVTzCdphNZdEKqqjwJPX2H9vcAzhwklSZKk8em/AP0kjuFF7qXZV1UfBjavsOmsCUeRNAHjvUJzC03iC5IkSZIkSdI0jeSi0pIkSZIkSWoPC0KSJEmSJEkdY0FIkiRJkiSpYywISZIkSZIkdYwFIUmSJEmSpI7xLmOSJEkTkuQ04J3AAlDAjqq6LMnjgGuBjcBu4IKqemBaOSeh/86uV579qCklkSSpmxwhJGnuJDktyS1J7kry8SSvbNY/Lsn7k3yy+XnCtLNK6pwDwNaqOhN4NvDyJGcC24Cbq+oM4OZmWZIkaWwsCEmaR3a4JM2kqtpXVR9qnn8FuBs4BTgP2NnsthM4fyoBJUlSZzhlTNLcqap9wL7m+VeSLO9wLTa77QSWgEunEFGSSLIReDpwG7DQtF0AX6A3pWyl12wBtgAsLCywtLQ00LH2799/yL5bNx1YZ+r168/an6E/4yxqQ0ZoR842ZIT25JSk9bAgJGmuTbLDtXDsaDtZl191wxG3b920+nv0Z2/DF9s2ZIR25GxDRmhPzlFK8mjgt4GfqaovJ/nmtqqqJLXS66pqB7ADYPPmzbW4uDjQ8ZaWlli+78V91++ZhN0XLh6y3J/hyrMfxaC/z7T0f46zqg0525AR2pNTktbDgtA69F8Ecff2c6eURNKRTLrDdflVN/CGXbPVrPZ3wNrwxbYNGaEdOduQEdqTc1SSPJxe23RVVb2nWX1fkpOral+Sk4H7p5dQkiR1gdcQkjSXjtTharbb4ZI0celVpq8A7q6qNy7bdCNwUfP8IuDIQwQlSZKGZEFI0tyxwyVphj0XeBnw/CQfbh7nANuBFyb5JPCCZlmSJGlsZmtugySNxsEO164kH27W/QK9DtZ1SS4BPgNcMJ14krqqqv4EyGE2nzXJLJIkqdssCEmaO3a4JEmSJOnInDImSZIkSZLUMRaEJEmSJEmSOsaCkCRJkiRJUses+xpCSU4D3gksAAXsqKrLkjwOuBbYCOwGLqiqB4aPKkmSpLbauO2maUeQNIAkRwG3A3ur6kVJTgeuAU4E7gBeVlVfn2ZGSaMxzAihA8DWqjoTeDbw8iRnAtuAm6vqDODmZlmSJEmSNPteCdy9bPn1wJuq6onAA8AlU0klaeTWXRCqqn1V9aHm+VfoNRqnAOcBO5vddgLnD5lRkiRJkjRmSU4FzgXe3iwHeD5wfbOL/TtpjozktvNJNgJPB24DFqpqX7PpC/SmlK30mi3AFoCFhQWWlpZWPc7CsbB104ERJB6t/uz79+8f6PeZpjZkhHbkNKMkSZLmxJuBnwce0yyfCDxYVQc7YXvoDQL4Fuvp38HwfbxJfMdtw3fpNmSEduRsQ0YYTc6hC0JJHg38NvAzVfXlXhG5p6oqSa30uqraAewA2Lx5cy0uLq56rMuvuoE37BpJDWukdl+4eMjy0tISg/w+09SGjNCOnGaUJElS2yV5EXB/Vd2RZHGtr19P/w6G7+P198XGoQ3fpduQEdqRsw0ZYTQ5h6quJHk4vWLQVVX1nmb1fUlOrqp9SU4G7h8qoSRJkiRp3J4LvDjJOcAxwGOBy4Djk2xoRgmdCuydYkZJI7Tuawg180mvAO6uqjcu23QjcFHz/CLghvXHkyRJkiSNW1W9uqpOraqNwEuBP6qqC4FbgJc0u9m/k+bIMCOEngu8DNiV5MPNul8AtgPXJbkE+AxwwVAJJUmSNPd27X2Ii5fdmn739nMP2d5/2/r+7ZLG5lLgmiSvA+6kNyhA0hxYd0Goqv4EyGE2n7Xe95UkSZIkTU9VLQFLzfN7gWdOM4+k8Vj3lDFJkiRJkiS10+zdskuSJEmSpAE4nVRaPwtCkjTH+r8kbd104IjX6JAkSZLUDU4ZkyRJkiRJ6hgLQpIkSZIkSR1jQUiSJEmSJKljLAhJkiRJkiR1jBeVHoH+i7ZeefajppREkiRJkiRpdY4QkiRJkiRJ6hgLQpIkSZIkSR3jlLEx2LX3IS5eNo1s9/Zzp5hGkiRJkiTpUI4QkiRJkiRJ6hhHCEmaS0neAbwIuL+qntKsexxwLbAR2A1cUFUPTCvjLOq/SL4jHKXRs32SJEmzwBFCE7Bx202HPCRNxJXA2X3rtgE3V9UZwM3NsiRN2pXYPkmSpCmzICRpLlXVrcCX+lafB+xsnu8Ezp9kJkkC2ydJkjQbnDImqUsWqmpf8/wLwMJKOyXZAmwBWFhYYGlpabA3Pxa2bjowgpjj05/x8qtuOGT71k2H7j/o7z6MXXsfOmT59OOOmshxh7V///6Zz9mGjNCenGM21vap/zOexbaqv33q/936M0/jb6Ytf6ttyNmGjNCenJK0HhaEJHVSVVWSOsy2HcAOgM2bN9fi4uJA73n5VTfwhl2z3axu3XRgTRl3X7g4vjCNi/um0l559qMY9DOfpqWlpZnP2YaM0J6ckzKO9qn/M+7/724W9LdP/e1Pf+ZJtE/92vK32oacbcgI7ckpSevhlDFJXXJfkpMBmp/3TzmPJB1k+yRJkiZqqFPZ3iVDUsvcCFwEbG9+3nDk3SVpYsbaPu3a+9BMjgoatTbeKbGNmTWfkpwGvJPelNUCdlTVZfbvpPk17AihK/EuGZJmUJKrgT8HnpxkT5JL6HW0Xpjkk8ALmmVJmijbJ0kz6gCwtarOBJ4NvDzJmdi/k+bWUCOEqurWJBv7Vp8HLDbPdwJLwKXDHEeS1qqqfugwm86aaBBJ6mP7JGkWNRe239c8/0qSu4FTsH8nza1xXP10bHfJaMMdfGD1nLNwp4K23DGhDTnNKEmSpHnSnPR/OnAbLbtL6zi+87bhu3QbMkI7crYhI4wm51hvhzPqu2S04Q4+sPpdfKZxV4x+bbljQhtymlE6Mq+PIUlSeyR5NPDbwM9U1ZeTfHNbG+7SOo6+Vhu+S7chI7QjZxsywmhyjuMuY94lQ5IkSZJaJsnD6RWDrqqq9zSr7d9Jc2ocBaGDd8kA7+IjSZIkSTMvvaFAVwB3V9Ubl22yfyfNqWFvO381vQuMnZRkD/AaenfFuK65Y8ZngAuGDSlJmg39079WMo0pYU5Lk+bPau3NIO3RNNgeqcWeC7wM2JXkw826X8D+nTS3hr3LmHfJkCRJkqSWq6o/AXKYzfbvpDk0jiljkiRJkiRJmmGzf8suSZIkaQxWm3bWP91r196HuNgpYZKkOWFBaAY411xSl6z1uh+zep0QSZIkqc2cMiZJkiRJktQxFoQkSZIkSZI6xiljU+D0B0mabSu107M4ndcpx9LaDDtldeumUaaZXW1pA6VB+f9LaWUWhCRJkiRJc8GT79LgnDImSZIkSZLUMY4QmkEO05U0K+b1LNs4fq82Dkf3/zfS+M1i2zCvbXu/WfzsJWmWOEJIkiRJkiSpYywISZIkSZIkdYxTxlpitaG9DoGVJEmaP4NM7ezf58qzHzXWTKOwa+9DXLws90rfZUc95cspZJJ0KAtCkiTNqDZ0XtqQUZIkSd/KKWOSJEmSJEkdY0FIkiRJkiSpY5wyJkkaqWFvZ9x/XYlRGMUtlvvfY+umA0fMOY6pVKv9HrN4HZFBPvtZyKn5NInbq4/6GIO831qPOQufw2pt4HoyOmVV6+X1WaUeC0JzYq3/E/XLtyRJkiRJ3WVBSJIkSZKkxiyOuJXGYSwFoSRnA5cBRwFvr6rt4ziOJK2HbdT8m8T0iGFNY6rDOKbOjeMYXWb7NP8m8d/IOKbeDutbp90O/x5r3b7a/k4TOjLbJ2n+jLwglOQo4C3AC4E9wAeT3FhVd436WFq/9XxR6P+fpP8TVRvZRkmaVbZPkmZV19unUfSdpFk0jhFCzwTuqap7AZJcA5wHdKKxkDTzbKMkzSrbJ0mzyvZpSJ5M1yxKVY32DZOXAGdX1Y83yy8DnlVVr+jbbwuwpVl8MvCJAd7+JOCvRxh3XNqQsw0ZoR055y3jE6rq8eMMM02DtFHrbJ9g/v4WpqUNGaEdOduQEQbPaftk+zRtbcgI7cjZhoxg+wTYPmHGUWpDzjZkhBG0T1O7qHRV7QB2rOU1SW6vqs1jijQybcjZhozQjpxmnD/raZ+gHZ+zGUenDTnbkBHak3MW2D5NVxsyQjtytiEjtCfnLLB9mq42ZIR25GxDRhhNzoeNKswye4HTli2f2qyTpFlgGyVpVtk+SZpVtk/SHBpHQeiDwBlJTk9yNPBS4MYxHEeS1sM2StKssn2SNKtsn6Q5NPIpY1V1IMkrgPfRuyXhO6rq4yN6+zUPQZySNuRsQ0ZoR04ztohtlBlHqA0525AR2pNzrGyfzDhCbcjZhozQnpxjZftkxhFqQ842ZIQR5Bz5RaUlSZIkSZI028YxZUySJEmSJEkzzIKQJEmSJElSx8xkQSjJ2Uk+keSeJNtW2P6IJNc2229LsnEKMQfJ+aokdyX5aJKbkzxh1jIu2+8Hk1SSid9eb5CMSS5oPsuPJ3nXpDM2GVb79/6OJLckubP5Nz9nChnfkeT+JB87zPYk+fXmd/hokmdMOmPb2T5NLuOy/WyfVmH7JLB9mmTGZftNrX1qjj/zbZTtk8D2aZIZl+1n+zRkxk60T1U1Uw96Fyn7FPCdwNHAR4Az+/b598DbmucvBa6d0ZzPAx7ZPP/JSeccJGOz32OAW4EPAJtnLSNwBnAncEKz/G0z+u+9A/jJ5vmZwO4p5Pw+4BnAxw6z/Rzg94EAzwZum3TGNj9snyabsdnP9mk0OW2f5vxh+zTZjM1+U2uf1vBZTrWNsn3ysYa/A9unEWVs9rN9Gk3GuW+fZnGE0DOBe6rq3qr6OnANcF7fPucBO5vn1wNnJckEM8IAOavqlqr622bxA8Cps5ax8SvA64G/m2S4xiAZ/y3wlqp6AKCq7p9wRhgsZwGPbZ4fB3x+gvl6AapuBb50hF3OA95ZPR8Ajk9y8mTSzQXbpwlmbNg+rc72SWD7NNGMjWm2T9CONsr2SWD7NNGMDdun0WSc+/ZpFgtCpwCfW7a8p1m34j5VdQB4CDhxIulWyNBYKedyl9Cr3E3SqhmbIWWnVdVNkwy2zCCf45OAJyX50yQfSHL2xNL9k0Fyvhb4kSR7gN8Dfmoy0dZkrX+3OpTt0+jYPo2O7ZPA9mmU2tA+QTvaKNsnge3TKNk+jY7tE7Bh5HH0LZL8CLAZ+FfTzrJckocBbwQunnKU1WygN6RwkV4V/tYkm6rqwWmGWsEPAVdW1RuSPAf4zSRPqap/nHYw6XBsn4Zm+ySNie3TSLShjbJ9UuvYPo2E7dMMmMURQnuB05Ytn9qsW3GfJBvoDd/6m4mkWyFDY6WcJHkB8IvAi6vq7yeU7aDVMj4GeAqwlGQ3vTmHN074wmODfI57gBur6htV9Wngr+g1HpM0SM5LgOsAqurPgWOAkyaSbnAD/d3qsGyfRsf2aXRsnwS2T6PUhvYJ2tFG2T4JbJ9GyfZpdGyfYCYvKr0BuBc4nX+6uNP39O3zcg696Nh1M5rz6fQuVHXGrH6WffsvMfmLtg7yOZ4N7Gyen0RvSNyJM5jz94GLm+ffTW+Oaabw776Rw1907FwOvejYX0w6X5sftk+Tzdi3v+3TcDltn+b8Yfs02Yx9+0+8fVrDZznVNsr2ycca/g5sn0aUsW9/26fhMs59+zTRX2QNv/A59CqEnwJ+sVn3y/SqsNCrzL0buAf4C+A7ZzTnfwPuAz7cPG6ctYx9+06rwVjtcwy9oY93AbuAl87ov/eZwJ82jcmHge+fQsargX3AN+hV3S8BfgL4iWWf5Vua32HXNP692/6wfZpcxr59bZ+Gy2n71IGH7dPkMvbtO5X2acDPcuptlO2TjwH/DmyfRpSxb1/bp+Eyzn37lOZNJEmSJEmS1BGzeA0hSZIkSZIkjZEFIUmSJEmSpI6xICRJkiRJktQxFoQkSZIkSZI6xoKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR1jQUiSJEmSJKljLAhJkiRJkiR1jAUhSZIkSZKkjrEgJEmSJEmS1DEWhCRJkiRJkjrGgpAkSZIkSVLHWBCSJEmSJEnqGAtCkiRJkiRJHWNBSJIkSZIkqWMsCEmSJEmSJHWMBSFJkiRJkqSOsSAkSdIIJfl4ksVp55AkSZKOxIJQhyTZneRrSfYve3z7CN+/kjxxVO8naXYk+d4kf5bkoSRfSvKnSf7FtHP1S7KYZM8q+1yZ5OtJvtI8PpbkPyc5bhQZqup7qmppFO8lSZIkjYsFoe7536rq0csenz+4IcmGaQZLctQ0jy9pZUkeC7wXuBx4HHAK8EvA308zV781tmH/d1U9Bng88GPAs4E/TfKosYSTJEmSZowFoY5rRvW8PMkngU826y5L8rkkX05yR5J/uWz/o5L8QpJPNWfW70hyWpJbm10+0ow8+tdJLk7yJysc74nN8yuTvDXJ7yX5KvC8JN+e5LeTfDHJp5P89IQ+CkmH9ySAqrq6qv6hqr5WVX9YVR8FSPLaJL91cOckG5v/1jc0y0vNCJy/aNqVG5I8rm/fLUk+n2Rfkp9b9l6PSPLmZtvnm+ePaLYtJtmT5NIkXwCuBn4f+PZBR0FW1d9V1QeBFwMn0isOHTz2v0lyd5IHkrwvyROa9W9N8mvL36f5nV7VPN+d5AXN8xXbzGbbdyV5fzPi6hNJLlj2fuckuat5zd7ln4kkSZI0ChaEBHA+8CzgzGb5g8DT6I0EeBfw7iTHNNteBfwQcA7wWODfAH9bVd/XbH9qM/Lo2gGP/cPArwKPAf4M+P+Bj9AbgXAW8DNJ/td1/2aSRuGvgH9IsjPJDyQ5YR3v8aP02ouTgQPAr/dtfx5wBvD9wKUHCyrAL9IbvfM04KnAM4H/sOx1/4xeW/WE5hg/AHx+pVGQR1JVXwHeD/xLgCTnAb8A/O/0RhH9d3oFJ5qf/zpJmn1PaHJfs8Jbr9hmNiOR3k+vjf024KXA/5fkYDt8BfDvmlFMTwH+aJDfQ5IkSRqUBaHu+d0kDzaP323W/eeq+lJVfQ2gqn6rqv6mqg5U1RuARwBPbvb9ceA/VNUnqucjVfU3Q+S5oar+tKr+EdgEPL6qfrmqvl5V9wL/lV5HSdKUVNWXge8Fit5/k19McmOShTW8zW9W1ceq6qvAfwQu6Jsm+ktV9dWq2gX8Br0iCsCFwC9X1f1V9UV6U9Vetux1/wi8pqr+/mAbNoTP0ysuAfwEvbbx7qo6APxfwNOaUUL/nd5ncXD05EuAPz9M8elwbeaLgN1V9RtNW3sn8NvA/9G87hvAmUkeW1UPVNWHhvzdJEmSpENYEOqe86vq+OZxfrPuc8t3SPJzzTSJh5I8CBwHnNRsPg341AjzLD/2E+hN9ThYsHqQ3hn6tXQ6JY1BUxi5uKpOpTdi5duBN6/hLZb/t/4Z4OH8U7uy0vaDU72+vVleaRvAF6vq79aQ40hOAb7UPH8CcNmytuhLQIBTqqrojQY6WLT6YeCqw7zn4drMJwDP6mvvLqQ34gngB+mNKvpMkj9O8pyhfjNJkiSpjwUhQe9MNwDN9YJ+HrgAOKGqjgceotcRgl6n7X8a8H2/Cjxy2Xv/sxX2qWXPPwd8elnB6viqekxVnTPwbyJp7KrqL4Er6RWGoO+/df6pqLHcacuefwe9ETB/fYTtB0fbfJ5e8WSlbXBoG7LS8kCSPBp4Ab3RP9Brj/5dX3t0bFX9WbP9auAlzYihZ9Eb3bOSw7WZnwP+uO/9H11VPwlQVR+sqvPoTSf7XeC69fxekiRJ0uFYEFK/x9C7vscXgQ1J/hO9614c9HbgV5KckZ7/OcmJzbb7gO9ctu9HgO9J8rTmGkSvXeXYfwF8pblA7LHNxVifkhm8tbXUJc3Fj7cmObVZPo3e6JgPNLt8GPi+JN+R3q3bX73C2/xIkjOTPBL4ZeD6qvqHZdv/Y5JHJvkeehd2PngdsquB/5Dk8UlOAv4T8Fsc3n3AiRnwFvLNRav/Ob2iywP0pqsBvA14dZOHJMclOTidi2aK11/TaxPfV1UPHuYQh2sz3ws8KcnLkjy8efyLJN+d5OgkFyY5rqq+AXyZ3tQ4SZIkaWQsCKnf+4A/oHcR2c8Af8ehUzneSO9M9R/S66RcARzbbHstsLOZ/nBBVf0VvY7ff6N3B7ND7jjWr+kcvojexWM/zT91tgbq2Ekam6/QGwVzW3p3BPwA8DFgK0BVvZ9eAeejwB30ih39fpPeqKIvAMcA/XcQ/GPgHuBm4Neq6g+b9a8Dbm/eexfwoWbdiprRS1cD9zZt0eHuMvbzSb4C/A3wzib3/9Jc44iq+h3g9cA1Sb7c/L4/0Pce76I3quhdh8vDYdrM5iLW30/vGmmfp/e5vJ7eNdugd52k3c2xf4LedDJJkiRpZNK7FIIkSeORZAn4rap6+wrbNtIrAD+8uXizJEmSpAlwhJAkSZIkSVLHWBCSJEmSJEnqGKeMSZIkSZIkdYwjhCRJkiRJkjpmw7QDAJx00km1cePGVff76le/yqMe9ajxBxpSG3K2ISO0I+e8Zbzjjjv+uqoeP+ZIrTFo+wTz97cwLW3ICO3I2YaMMHhO2ydJkqTRmYmC0MaNG7n99ttX3W9paYnFxcXxBxpSG3K2ISO0I+e8ZUzymfGmaZdB2yeYv7+FaWlDRmhHzjZkhMFz2j5JkiSNjlPGJEmSJEmSOsaCkCRJkiRJUsdYEJIkSZIkSeoYC0KSWivJO5Lcn+Rjy9Y9Lsn7k3yy+XlCsz5Jfj3JPUk+muQZ00suSZIkSdNlQUhSm10JnN23bhtwc1WdAdzcLAP8AHBG89gCvHVCGSVJkiRp5lgQktRaVXUr8KW+1ecBO5vnO4Hzl61/Z/V8ADg+yckTCSpJkiRJM2bV284neQfwIuD+qnpKs+5xwLXARmA3cEFVPZAkwGXAOcDfAhdX1YfGE71n47abDlnevf3ccR5O0uxbqKp9zfMvAAvN81OAzy3bb0+zbh99kmyhN4qIhYUFlpaWBjrw/v37j7jvrr0PHbK86ZTjBnrfUVot4yxoQ0ZoR842ZIT25JQkSZonqxaE6E3J+H+Bdy5bd3BKxvYk25rlSzl0Ssaz6E3JeNYoA0vSoKqqktQ6XrcD2AGwefPmWlxcHOh1S0tLHGnfi/sL2BcO9r6jtFrGWdCGjNCOnG3ICO3JKUmSNE9WnTLmlAxJLXPfwXan+Xl/s34vcNqy/U5t1kmSJElS5wwyQmglU5mSsdKQ8q2bDhyyPAtDztsw9L0NGaEdOc04c24ELgK2Nz9vWLb+FUmuoTdy8aFl7ZgkSZIkdcp6C0LfNMkpGSsNKZ+FKRj92jD0vQ0ZoR05zTg9Sa4GFoGTkuwBXkOvEHRdkkuAzwAXNLv/Hr3rm91D7xpnPzbxwJIkSZI0I9ZbELovyclVtc8pGZKmpap+6DCbzlph3wJePt5EkiRJktQO673t/MEpGfCtUzJ+ND3PxikZkiRJkiRJM2eQ2847JUOSJEmSJGmOrFoQckqGJEmSJEnSfFnvlDFJkiRJkiS1lAUhSZIkSZKkjrEgJEmSJEmS1DEWhCRJkiRJkjrGgpAkSZIkSVLHrHqXsbbZuO2mQ5Z3bz93SkkkSZIkSZJmkyOEJEmSJEmSOsaCkCRJkiRJUsdYEJIkSZIkSeoYC0KSJEmSJEkdY0FIkiRJkiSpYywISZIkSZIkdYwFIUmSJEmSpI6xICRJkiRJktQxFoQkSZIkSZI6xoKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR1jQUiSJEmSJKljLAhJkiRJkiR1jAUhSXMpyc8m+XiSjyW5OskxSU5PcluSe5Jcm+ToaeeUJEmSpGmwICRp7iQ5BfhpYHNVPQU4Cngp8HrgTVX1ROAB4JLppZQkSZKk6RmqIOQZeEkzbANwbJINwCOBfcDzgeub7TuB86cTTZIkSZKma8N6X7jsDPyZVfW1JNfROwN/Dr0z8NckeRu9M/BvHUlaSRpAVe1N8mvAZ4GvAX8I3AE8WFUHmt32AKes9PokW4AtAAsLCywtLQ103P379x+y7669Dx2yfeumQ/cf9H1HqT/jLGpDRmhHzjZkhPbklCRJmifrLggte/2xSb7BoWfgf7jZvhN4LRaEJE1QkhOA84DTgQeBdwNnD/r6qtoB7ADYvHlzLS4uDvS6paUllu978babjrj/7gsHe99R6s84i9qQEdqRsw0ZoT05JUmS5sm6C0LTOAO/0hnErZsOrLxzwzPwK2tDRmhHTjPOpBcAn66qLwIkeQ/wXOD4JBuaNupUYO8UM0qSJEnS1AwzZWziZ+BXOoPoGfj1aUNGaEdOM86kzwLPTvJIegXrs4DbgVuAlwDXABcBN0wtoSRJkiRN0TAXlf7mGfiq+gZwyBn4Zh/PwEuauKq6jd7Foz8E7KLX1u0ALgVeleQe4ETgiqmFlCRJkqQpGuYaQp6BlzSzquo1wGv6Vt8LPHMKcSRJkiRppqx7hJBn4CVJkiRJktppqLuMeQZekiRJkiSpfYa5hpAkSZIkSZJayIKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR0z1EWl22DjtpsOWd69/dwpJZEkSZIkSZoNjhCSJEmSJEnqGAtCkiRJkiRJHWNBSJIkSZIkqWMsCEmSJEmSJHWMBSFJkiRJkqSOsSAkSZIkSZLUMRaEJEmSJEmSOsaCkCRJkiRJUsdYEJIkSZIkSeoYC0KSJEmSJEkdY0FIkiRJkiSpYywISZIkSZIkdYwFIUmSJEmSpI6xICRJkiRJktQxG6YdYC127X2Ii7fdNO0YkiRJkiRJreYIIUmSJEmSpI6xICRpLiU5Psn1Sf4yyd1JnpPkcUnen+STzc8Tpp1TkiRJkqZhqIKQHS5JM+wy4A+q6ruApwJ3A9uAm6vqDODmZlmSJEmSOmfYEUJ2uCTNnCTHAd8HXAFQVV+vqgeB84CdzW47gfOnkU+SJEmSpm3dF5Ve1uG6GHodLuDrSc4DFpvddgJLwKXDhJSkNTod+CLwG0meCtwBvBJYqKp9zT5fABZWenGSLcAWgIWFBZaWlgY66P79+w/Zd+umA0fcf9D3HaX+jLOoDRmhHTnbkBHak1OSJGmeDHOXsaE6XJI0RhuAZwA/VVW3JbmMvtGKVVVJaqUXV9UOYAfA5s2ba3FxcaCDLi0tsXzf1e6KuPvCwd53lPozzqI2ZIR25GxDRmhPTkmSpHkyTEFoqA7Xes7ALxy7+hn31UziDGQbznS2ISO0I6cZZ9IeYE9V3dYsX0+vfbovyclVtS/JycD9U0soSZIkSVM0TEFoqA7Xes7AX37VDbxh1zCRJ3NGvg1nOtuQEdqR04yzp6q+kORzSZ5cVZ8AzgLuah4XAdubnzdMMaYkSZIkTc26qyt2uCTNuJ8CrkpyNHAv8GP0LqR/XZJLgM8AF0wxnyRJkiRNzXDDbexwSZpRVfVhYPMKm86acBRJkiRJmjlDFYTscEmSJEmSJLXPw6YdQJIkSZIkSZNlQUiSJEmSJKljhr2GUOts3HbTIcu7t587pSSSJEmSJEnT4QghSZIkSZKkjrEgJEmSJEmS1DEWhCRJkiRJkjrGgpAkSZIkSVLHWBCSJEmSJEnqGAtCkiRJkiRJHdO5285L0qzYuO2mQ5Z3bz93SkkkSZIkdY0jhCRJkiRJkjrGgpAkSZIkSVLHWBCSJEmSJEnqGAtCkiRJkiRJHWNBSJIkSZIkqWMsCEmSJEmSJHWMBSFJkiRJkqSOsSAkSZIkSZLUMRaEJEmSJEmSOsaCkCRJkiRJUsdYEJIkSZIkSeoYC0KS5laSo5LcmeS9zfLpSW5Lck+Sa5McPe2MkiRJkjQNQxeE7HBJmmGvBO5etvx64E1V9UTgAeCSqaSSJEmSpCkbxQghO1ySZk6SU4Fzgbc3ywGeD1zf7LITOH8q4SRJkiRpyjYM8+JlHa5fBV61rMP1w80uO4HXAm8d5jiStA5vBn4eeEyzfCLwYFUdaJb3AKes9MIkW4AtAAsLCywtLQ10wP379x+y79ZNBw6/8woGPc4w+jPOojZkhHbkbENGaE9OSZKkeTJUQYghOlySNC5JXgTcX1V3JFlc6+uragewA2Dz5s21uDjYWywtLbF834u33bSm4+6+cLDjDKM/4yxqQ0ZoR842ZIT25JQkSZon6y4IDdvhWs8Z+IVj137GfTXjOCPZhjOdbcgI7chpxpn0XODFSc4BjgEeC1wGHJ9kQ1O0PhXYO8WMkiRJkjQ1w4wQGqrDtZ4z8JdfdQNv2DXsoKZDjeOMfBvOdLYhI7QjpxlnT1W9Gng1QFOw/rmqujDJu4GXANcAFwE3TCujJEmSJE3TuqsrdrgktdClwDVJXgfcCVwx5TxHtLFvytnu7edOKYkkSZKkeTPa4TY9repwSZpvVbUELDXP7wWeOc08kiRJkjQLRlIQssMlSZIkSZLUHuMYISRJWof+KWKSJEmSNC4Pm3YASZIkSZIkTZYFIUmSJEmSpI6xICRJkiRJktQxFoQkSZIkSZI6xoKQJEmSJElSx3T+LmP9d/XZvf3cKSWRJEmSJEmaDEcISZIkSZIkdYwFIUmSJEmSpI6xICRJkiRJktQxFoQkSZIkSZI6xoKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR1jQUiSJEmSJKljLAhJkiRJkiR1jAUhSZIkSZKkjrEgJEmSJEmS1DEWhCRJkiRJkjrGgpAkSZIkSVLHWBCSJEmSJEnqGAtCkiRJkiRJHWNBSNLcSXJakluS3JXk40le2ax/XJL3J/lk8/OEaWeVJEmSpGlYd0HIDpekGXYA2FpVZwLPBl6e5ExgG3BzVZ0B3NwsS5IkSVLnDDNCyA6XpJlUVfuq6kPN868AdwOnAOcBO5vddgLnTyWgJEmSJE3ZhvW+sKr2Afua519JsrzDtdjsthNYAi4dKqUkrVOSjcDTgduAhabtAvgCsHCY12wBtgAsLCywtLQ00LH2799/yL5bNx1YZ+qVDZrjSPozzqI2ZIR25GxDRmhPTkmSpHmSqhr+TXodrluBpwCfrarjm/UBHji43Pea5R2uf37NNdesepz7v/QQ931t6LhHtOmU44Z+j/379/PoRz96BGnGpw0ZoR055y3j8573vDuqavOYI01EkkcDfwz8alW9J8mDy9ujJA9U1RGntW7evLluv/32gY63tLTE4uLiN5c3brtpPbEPa/f2c4d+j/6Ms6gNGaEdOduQEQbPmWRu2idJkqRpW/cIoYOaDtdvAz9TVV/u1YB6qqqSrFhxqqodwA7odbgG+SJ4+VU38IZdQ0c+ot0Xrp5jNW34At6GjNCOnGacTUkeTq9tuqqq3tOsvi/JyVW1L8nJwP3TSyhJkiRJ0zPUXcaO1OFqttvhkjRxzejEK4C7q+qNyzbdCFzUPL8IuGHS2SRJkiRpFgxzlzE7XJJm1XOBlwHPT/Lh5nEOsB14YZJPAi9oliVJkiSpc4aZf3Www7UryYebdb9Ar4N1XZJLgM8AFwyVUJLWqKr+BMhhNp81ySySJEmSNIuGucuYHS5JkiRJkqQWGuoaQpIkSZIkSWofC0KSJEmSJEkdY0FIkiRJkiSpY4a5qPRc2rjtpkOWd28/d0pJJOlQtk+SJEmSRsWCkCS1VH+BCCwSSZIkSRqMU8YkSZIkSZI6xoKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR3jRaUlaUR27X2Ii1e40LMkSZIkzRpHCEmSJEmSJHWMBSFJkiRJkqSOccrYKjauMP1j9/Zzp5BEkiRJkiRpNBwhJEmSJEmS1DGOEJKkOdI/qtERjZIkSZJWYkFIkuZYf4HoyrMfNaUkWo1TlCVJkjRJThmTJEmSJEnqGEcISZK+ySlns23U/z6DjEryb0KSJGk+WRCSJGkddu19iIuXFUvGUShZqWAjSZIkjYJTxiRJkiRJkjrGEUIj4EVbJbVF/6iWURj1lKJRjLxZa6ZR/A7DHnM9Vjvmej7L1XI5hUySJGk+OEJIkiRJkiSpY8YyQijJ2cBlwFHA26tq+ziOI0nrYRs1XsOOlNm6afTHmAWzcD2gcWRY7T3b8G8jSZLURSMvCCU5CngL8EJgD/DBJDdW1V2jPta0zMKXeknr04U2apTGMa1pHKZxjNUKV9P4f8V6imvj1sbinSRJUheMY8rYM4F7qureqvo6cA1w3hiOI0nrYRslSZIkqfNSVaN9w+QlwNlV9ePN8suAZ1XVK/r22wJsaRafDHxigLc/CfjrEcYdlzbkbENGaEfOecv4hKp6/DjDTNMgbdQ62yeYv7+FaWlDRmhHzjZkhMFzznX7JEmSNElTu8tYVe0AdqzlNUlur6rNY4o0Mm3I2YaM0I6cZpw/62mfoB2fsxlHpw0525AR2pNTkiRpnoxjythe4LRly6c26yRpFthGSZIkSeq8cRSEPgickeT0JEcDLwVuHMNxJGk9bKMkSZIkdd7Ip4xV1YEkrwDeR++Wzu+oqo+P6O3XPIVjStqQsw0ZoR05zdgitlFmHKE25GxDRmhPTkmSpLkx8otKS5IkSZIkabaNY8qYJEmSJEmSZpgFIUmSJEmSpI6ZyYJQkrOTfCLJPUm2rbD9EUmubbbflmTjFGIOkvNVSe5K8tEkNyd5wqxlXLbfDyapJBO/7e8gGZNc0HyWH0/yrklnbDKs9u/9HUluSXJn829+zhQyviPJ/Uk+dpjtSfLrze/w0STPmHTGtrN9mlzGZfvZPq3C9kmSJElrVlUz9aB3kddPAd8JHA18BDizb59/D7ytef5S4NoZzfk84JHN85+cdM5BMjb7PQa4FfgAsHnWMgJnAHcCJzTL3zaj/947gJ9snp8J7J5Czu8DngF87DDbzwF+HwjwbOC2SWds88P2abIZm/1sn0aT0/bJhw8fPnz48OHDxyGPWRwh9Ezgnqq6t6q+DlwDnNe3z3nAzub59cBZSTLBjDBAzqq6par+tln8AHDqrGVs/ArweuDvJhmuMUjGfwu8paoeAKiq+yecEQbLWcBjm+fHAZ+fYL5egKpbgS8dYZfzgHdWzweA45OcPJl0c8H2aYIZG7ZPq7N9kiRJ0prNYkHoFOBzy5b3NOtW3KeqDgAPASdOJN0KGRor5VzuEnpnPidp1YzNkPzTquqmSQZbZpDP8UnAk5L8aZIPJDl7Yun+ySA5Xwv8SJI9wO8BPzWZaGuy1r9bHcr2aXRsn0bH9kmSJElrtmHaAbogyY8Am4F/Ne0syyV5GPBG4OIpR1nNBnrTMhbpjWK4NcmmqnpwmqFW8EPAlVX1hiTPAX4zyVOq6h+nHUw6HNunodk+SZIkqZVmcYTQXuC0ZcunNutW3CfJBnrD3/9mIulWyNBYKSdJXgD8IvDiqvr7CWU7aLWMjwGeAiwl2U3vmg03TvjCrYN8jnuAG6vqG1X1aeCv6HXAJmmQnJcA1wFU1Z8DxwAnTSTd4Ab6u9Vh2T6Nju3T6Ng+SZIkac1msSD0QeCMJKcnOZreRVlv7NvnRuCi5vlLgD+qqppgRhggZ5KnA/+FXmdrGteVOGLGqnqoqk6qqo1VtZHedUReXFW3z0rGxu/SO/tOkpPoTdG4d4IZYbCcnwXOAkjy3fQ6XF+caMrV3Qj8aHM3n2cDD1XVvmmHahHbpwlltH1aE9snSZIkrdnMTRmrqgNJXgG8j96dU95RVR9P8svA7VV1I3AFveHu99C7QOVLZzTn/wM8Gnh3c03Zz1bVi2cs41QNmPF9wPcnuQv4B+D/rKqJjrgYMOdW4L8m+Vl6F3C9eNKFgCRX0+ucntRcK+Q1wMOb3+Ft9K4dcg5wD/C3wI9NMl/b2T5NPONU2T6Nlu2TJEnSbMnkT1xLkiRJkiRpmmZxypgkSZIkSZLGyIKQJEmSJElSx1gQkiRJkiRJ6hgLQpIkSZIkSR1jQUiSJEmSJKljLAhJkiRJkiR1jAUhSZIkSZKkjvkf9R7EXot6n+wAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x1080 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[classes].hist(figsize=(20, 15), bins=50, range=(0,1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "covid_corr = df[[*classes, 'covid']].corr().loc['covid', :]\n",
    "covid_corr.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['No Finding',\n",
       " 'Enlarged Cardiomediastinum',\n",
       " 'Cardiomegaly',\n",
       " 'Lung Opacity',\n",
       " 'Lung Lesion',\n",
       " 'Edema',\n",
       " 'Consolidation',\n",
       " 'Pneumonia',\n",
       " 'Atelectasis',\n",
       " 'Pneumothorax',\n",
       " 'Pleural Effusion',\n",
       " 'Pleural Other',\n",
       " 'Fracture',\n",
       " 'Support Devices']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = [*classes, 'covid', 'rx'] #, 'CLINICA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111)\n",
    "cax = ax.matshow(df[index].corr())\n",
    "fig.colorbar(cax)\n",
    "\n",
    "ax.set_xticks(range(len(index)))\n",
    "ax.set_yticks(range(len(index)))\n",
    "ax.set_xticklabels(index, rotation='vertical')\n",
    "ax.set_yticklabels(index)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No Finding</th>\n",
       "      <th>Enlarged Cardiomediastinum</th>\n",
       "      <th>Cardiomegaly</th>\n",
       "      <th>Lung Opacity</th>\n",
       "      <th>Lung Lesion</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.872418</td>\n",
       "      <td>0.077172</td>\n",
       "      <td>0.010822</td>\n",
       "      <td>0.039960</td>\n",
       "      <td>0.080021</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>0.057013</td>\n",
       "      <td>0.039196</td>\n",
       "      <td>0.025258</td>\n",
       "      <td>0.035283</td>\n",
       "      <td>0.074448</td>\n",
       "      <td>0.061089</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>0.048945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006387</td>\n",
       "      <td>0.130256</td>\n",
       "      <td>0.861003</td>\n",
       "      <td>0.484288</td>\n",
       "      <td>0.031187</td>\n",
       "      <td>0.930774</td>\n",
       "      <td>0.350397</td>\n",
       "      <td>0.184001</td>\n",
       "      <td>0.144656</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.758117</td>\n",
       "      <td>0.005987</td>\n",
       "      <td>0.013088</td>\n",
       "      <td>0.059794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.957775</td>\n",
       "      <td>0.060137</td>\n",
       "      <td>0.029840</td>\n",
       "      <td>0.019996</td>\n",
       "      <td>0.036552</td>\n",
       "      <td>0.003615</td>\n",
       "      <td>0.030241</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.261099</td>\n",
       "      <td>0.054235</td>\n",
       "      <td>0.026397</td>\n",
       "      <td>0.020180</td>\n",
       "      <td>0.091661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.179418</td>\n",
       "      <td>0.112782</td>\n",
       "      <td>0.205861</td>\n",
       "      <td>0.359551</td>\n",
       "      <td>0.063948</td>\n",
       "      <td>0.448018</td>\n",
       "      <td>0.187096</td>\n",
       "      <td>0.298485</td>\n",
       "      <td>0.056434</td>\n",
       "      <td>0.030715</td>\n",
       "      <td>0.036530</td>\n",
       "      <td>0.027439</td>\n",
       "      <td>0.093709</td>\n",
       "      <td>0.029209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.061596</td>\n",
       "      <td>0.100192</td>\n",
       "      <td>0.068573</td>\n",
       "      <td>0.496562</td>\n",
       "      <td>0.135531</td>\n",
       "      <td>0.265945</td>\n",
       "      <td>0.265859</td>\n",
       "      <td>0.226268</td>\n",
       "      <td>0.292798</td>\n",
       "      <td>0.111357</td>\n",
       "      <td>0.197117</td>\n",
       "      <td>0.042322</td>\n",
       "      <td>0.082476</td>\n",
       "      <td>0.047800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>0.002730</td>\n",
       "      <td>0.084995</td>\n",
       "      <td>0.814987</td>\n",
       "      <td>0.642096</td>\n",
       "      <td>0.028012</td>\n",
       "      <td>0.951643</td>\n",
       "      <td>0.205296</td>\n",
       "      <td>0.192721</td>\n",
       "      <td>0.071620</td>\n",
       "      <td>0.005308</td>\n",
       "      <td>0.825061</td>\n",
       "      <td>0.005452</td>\n",
       "      <td>0.005476</td>\n",
       "      <td>0.304919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>0.339105</td>\n",
       "      <td>0.051394</td>\n",
       "      <td>0.011973</td>\n",
       "      <td>0.396348</td>\n",
       "      <td>0.043572</td>\n",
       "      <td>0.356680</td>\n",
       "      <td>0.268396</td>\n",
       "      <td>0.138872</td>\n",
       "      <td>0.106578</td>\n",
       "      <td>0.097473</td>\n",
       "      <td>0.240197</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.039479</td>\n",
       "      <td>0.748487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>0.022768</td>\n",
       "      <td>0.079083</td>\n",
       "      <td>0.248876</td>\n",
       "      <td>0.873053</td>\n",
       "      <td>0.176306</td>\n",
       "      <td>0.411610</td>\n",
       "      <td>0.531252</td>\n",
       "      <td>0.679463</td>\n",
       "      <td>0.035929</td>\n",
       "      <td>0.085284</td>\n",
       "      <td>0.109297</td>\n",
       "      <td>0.070532</td>\n",
       "      <td>0.016029</td>\n",
       "      <td>0.089341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>0.015286</td>\n",
       "      <td>0.221574</td>\n",
       "      <td>0.532350</td>\n",
       "      <td>0.821619</td>\n",
       "      <td>0.102327</td>\n",
       "      <td>0.733764</td>\n",
       "      <td>0.511534</td>\n",
       "      <td>0.279175</td>\n",
       "      <td>0.077693</td>\n",
       "      <td>0.058971</td>\n",
       "      <td>0.693585</td>\n",
       "      <td>0.028290</td>\n",
       "      <td>0.009405</td>\n",
       "      <td>0.121630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>0.075183</td>\n",
       "      <td>0.217475</td>\n",
       "      <td>0.330327</td>\n",
       "      <td>0.606322</td>\n",
       "      <td>0.174199</td>\n",
       "      <td>0.457557</td>\n",
       "      <td>0.552521</td>\n",
       "      <td>0.169278</td>\n",
       "      <td>0.163936</td>\n",
       "      <td>0.115372</td>\n",
       "      <td>0.681655</td>\n",
       "      <td>0.016696</td>\n",
       "      <td>0.026880</td>\n",
       "      <td>0.098483</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>447 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     No Finding  Enlarged Cardiomediastinum  Cardiomegaly  Lung Opacity  \\\n",
       "0      0.872418                    0.077172      0.010822      0.039960   \n",
       "1      0.006387                    0.130256      0.861003      0.484288   \n",
       "2      0.957775                    0.060137      0.029840      0.019996   \n",
       "3      0.179418                    0.112782      0.205861      0.359551   \n",
       "4      0.061596                    0.100192      0.068573      0.496562   \n",
       "..          ...                         ...           ...           ...   \n",
       "442    0.002730                    0.084995      0.814987      0.642096   \n",
       "443    0.339105                    0.051394      0.011973      0.396348   \n",
       "444    0.022768                    0.079083      0.248876      0.873053   \n",
       "445    0.015286                    0.221574      0.532350      0.821619   \n",
       "446    0.075183                    0.217475      0.330327      0.606322   \n",
       "\n",
       "     Lung Lesion     Edema  Consolidation  Pneumonia  Atelectasis  \\\n",
       "0       0.080021  0.002013       0.057013   0.039196     0.025258   \n",
       "1       0.031187  0.930774       0.350397   0.184001     0.144656   \n",
       "2       0.036552  0.003615       0.030241   0.018732     0.006805   \n",
       "3       0.063948  0.448018       0.187096   0.298485     0.056434   \n",
       "4       0.135531  0.265945       0.265859   0.226268     0.292798   \n",
       "..           ...       ...            ...        ...          ...   \n",
       "442     0.028012  0.951643       0.205296   0.192721     0.071620   \n",
       "443     0.043572  0.356680       0.268396   0.138872     0.106578   \n",
       "444     0.176306  0.411610       0.531252   0.679463     0.035929   \n",
       "445     0.102327  0.733764       0.511534   0.279175     0.077693   \n",
       "446     0.174199  0.457557       0.552521   0.169278     0.163936   \n",
       "\n",
       "     Pneumothorax  Pleural Effusion  Pleural Other  Fracture  Support Devices  \n",
       "0        0.035283          0.074448       0.061089  0.078508         0.048945  \n",
       "1        0.009302          0.758117       0.005987  0.013088         0.059794  \n",
       "2        0.261099          0.054235       0.026397  0.020180         0.091661  \n",
       "3        0.030715          0.036530       0.027439  0.093709         0.029209  \n",
       "4        0.111357          0.197117       0.042322  0.082476         0.047800  \n",
       "..            ...               ...            ...       ...              ...  \n",
       "442      0.005308          0.825061       0.005452  0.005476         0.304919  \n",
       "443      0.097473          0.240197       0.018597  0.039479         0.748487  \n",
       "444      0.085284          0.109297       0.070532  0.016029         0.089341  \n",
       "445      0.058971          0.693585       0.028290  0.009405         0.121630  \n",
       "446      0.115372          0.681655       0.016696  0.026880         0.098483  \n",
       "\n",
       "[447 rows x 14 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, 12:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RX+ vs RX- on CORDA (Molinette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "molinette_df = df[df.dataset == 'CORDA-dataset']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8560098981785729"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(molinette_df.rx.values, 1-molinette_df.iloc[:, 12].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_normal = (np.argmax(molinette_df.iloc[:, 12:].values, axis=1) > 0).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[156,  42],\n",
       "       [ 63, 186]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cm = confusion_matrix(molinette_df.rx.values, predicted_normal)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAZRklEQVR4nO3deZxU5Z3v8c+3m00EFQERBRUQE3EZZYgkMY5O3DAvtywqUWIMKs4Y5nrjEmOSSxhN4jZOZvSFGiYXNd5R1Ix6MXKHuIwTE5dAglEBCQQVGhDZZIlg01W/+0cVTYHdVdVQdWrh+/Z1XtY556mnfo3Fz6d/53nOUURgZmbJaKh0AGZmuxMnXTOzBDnpmpklyEnXzCxBTrpmZgly0jUzS5CTrplZOyRNkfS+pDfbOS9Jd0paKOl1ScML9emka2bWvvuBUXnOnwEMzW7jgHsKdeika2bWjoj4NbAmT5NzgJ9HxivAPpL65+uzUykDbMuH917lJW/2MWNufrvSIVgVevzdadrVPrasWlR0zunSd8gVZEaoW02OiMkd+LgDgSU5+03ZY8vbe0PZk66ZWbXKJtiOJNld5qRrZvUlnUry05YCA3P2B2SPtcs1XTOrL6mW4rddNw24ODuL4dPAuohot7QAHumaWZ2JSJesL0kPAycBfSQ1AT8AOmc+J+4FpgNfABYCHwLfKNSnk66Z1Zd06ZJuRHy1wPkAvtmRPp10zay+lHCkWw5OumZWX5K9kNZhTrpmVl880jUzS06UZlZC2Tjpmll9KeGFtHJw0jWz+uLygplZgnwhzcwsQR7pmpklyBfSzMwS5AtpZmbJiXBN18wsOa7pmpklyOUFM7MEeaRrZpag1JZKR5CXk66Z1ReXF8zMEuTygplZgjzSNTNLkJOumVlywhfSzMwS5JqumVmCXF4wM0uQR7pmZgnySNfMLEEe6ZqZJajFNzE3M0uOR7pmZglyTdfMLEEe6ZqZJcgjXTOzBHmka2aWIM9eMDNLUESlI8jLSdfM6otrumZmCarypNtQ6QDMzEoq0sVvBUgaJWm+pIWSvtPG+YMk/Zek2ZJel/SFQn16pGtm9SWVKkk3khqBScCpQBMwU9K0iJib0+z7wKMRcY+kYcB04JB8/Trpmll9KV154ThgYUQsApA0FTgHyE26AeyVfb03sKxQp066ZlZfOpB0JY0DxuUcmhwRk7OvDwSW5JxrAkbu0MVE4FeS/gHYEzil0Gc66ZpZfenA4ohsgp1csGH7vgrcHxF3SPoM8KCkIyPaD8JJ18zqSqRLNk93KTAwZ39A9liuS4FRABHxsqRuQB/g/fY69ewFM6sv6XTxW34zgaGSBknqAowGpu3QZjFwMoCkw4FuwMp8nXqka2b1pUSzFyKiRdJ4YAbQCEyJiDmSbgRmRcQ04Brg3yR9i8xFtUsi8i+Jc9I1s/pSwsURETGdzDSw3GMTcl7PBY7vSJ9OumZWX6p8RZqTbon89p2V3P7CPNJpOPfIAYw9bvB25//phXnMbFoDwOYtKdZsaubFKzOzS/71xfm8+HamDHT5yCGc/on+yQZvZXPsicMZ+4PLaGhs5Nmpv+KJe/5ju/NnXXYOp4w+lVRLmvVr1jHpujtZuTTzXfhfD0zksGMPY96sefx47E2VCL82+YY39S+VDm55fi73fOlT9OvZjYseepkTh+zHkN49Wttce9Lhra8fnv0u81euB+DFRe8z7/31TB3zWbak0lz22O84/pC+9Ojq/zS1rqGhgctvuoJ/vGgCq99bzW3T7mDms7+jacG2qZ9vz1nEdWdeTfPmZk4fcwYX33AJd4y/HYAnJz9O125dOe2iUZX6EWpTlY90C85ekLRB0vodtiWSnpA0uND7dwdvvvcBA/fpzoB9utO5sYHTP7E/L/x5Rbvt/3P+ckZlR7OL1vyF4Qf2olNDA3t07sTQPj156Z28Fz+tRhx6zFCWv7OcFUtW0LKlhd889SLHnbr93Po3X36D5s3NAPxp9nx69+/Teu6N377Opr9sSjTmupCO4rcKKGbK2L8A15FZnTEAuBZ4CJgKTClbZDXk/Y0f0a/nHq37/Xp0Y+XGj9psu2z9Jpat28SnBvYG4LC+PXnpnVVs2pJi7aZmZi1Zw3sbNycSt5VX7/17s3r5qtb91ctXse/+vdttf/IFp/KHF36fRGj1LZUqfquAYpLu2RHx04jYEBHrsys4To+IR4Bebb1B0jhJsyTNmvLimyUNuNbNmL+ckw/rR2ODAPjMwX343KC+XPLIK9ww/Y8cfcA+NEoVjtKS9jdfPIlDjzqUJ3/6eKVDqXmRThe9VUIxSfdDSedLashu5wNbh2Jtjs8jYnJEjIiIEWNPOLJkwVar/Xp0ZcWGbb8Grti4mb49urbZdkZOaWGry0YO4ZExx3Pvlz9FBBzUa8+yxmvJWP3e6u3KBb3792HNe6s/1u7o4/+Kr4w/j5sv+yEtzdX9qJmaUAflhYuAr5FZ1rYi+3qMpD2A8WWMrWYcsf/eLF77IUvXfciWVJoZ89/jpMH7fazd22s2sv6jLfxV/31aj6XSwQebsjW9lRtYsGoDnzm4/V9BrXYs/OMC+g86gP0G9qNT50587qwTmPnMq9u1GXTEYP7u5iu5+dIfsm71ugpFWmdKeD/dcih4iTx7W7Oz2jn9m9KGU5s6NTRw/eeHceXjs0hHcM4RAxjSpyd3v7SAYf325qQhmQQ8Y/5yTj+sP8opH7Sk04x9NPMXsUeXTvxo1NF0avDq7HqQTqX52YSfMuHnE2lobOC5R59lyYIljL76Qv78+kJmPvs7Lv7uJXTrvgfX3n09AKuWreTmy34EwA8fu5kDhwyg257d+LdXpjDp23fx2q9nV/AnqhEVGsEWSwVWrCGpL3A5mRvztibpiBhbzAd8eO9V1f0nYBUx5ua3Kx2CVaHH3522yxc0/jJhdNE5Z88bpyZ+AaWYyaD/F3gReBaozOU+M7NiVahsUKxikm73iLi+7JGYmZVClZcXiike/rKYh62ZmVWDap8yVsxI9yrgu5I+ArYAAiIi9sr/NjOzCqjykW4xsxd6JhGImVlJ1GrSlfTJiHhL0vC2zkfEH8oXlpnZTqrQ8t5i5RvpXkNmqtgdbZwL4PNlicjMbBeU8BlpZdFu0o2Iy7P//tvkwjEz20W1mnQlfSnfGyPCd+Yws+pT5ffTzVde2Lr0dz/gs8Dz2f2/BV4CnHTNrPrU6kg3Ir4BIOlXwLCIWJ7d7w/cn0h0ZmYdVatJN8fArQk3awVwUJniMTPbJZGq3fLCVs9JmgE8nN2/gMx9GMzMqk+tj3QjYnz2otoJ2UOTI+KJ8oZlZrZzanbKWK7sTAVfODOz6lflSbeYpwF/SdICSeuyTwLeIGl9EsGZmXVYugNbBRQz0r0NOCsi5pU7GDOzXRUttX8hbYUTrpnVjOrOuUUl3VmSHgGeBD7aetAr0sysGtXDhbS9gA+B03KOBb6wZmbVqNZHultXppmZ1YKaHelK+nZE3CbpLjIj2+1ExP8oa2RmZjujhke6c7P/npVEIGZmpRAtlY4gv3xJ9yvALyPiAUlfj4gHkgrKzGxnVfkT2PMujjg65/VV5Q7EzKwkSrg4QtIoSfMlLZT0nXbanC9prqQ5kh4q1GdRy4DNzGpFqUa6khqBScCpQBMwU9K0iJib02YocANwfESslbRfoX7zJd0Bku4k88j1ra9b+UKamVWjEpYXjgMWRsQiAElTgXPYdr0LMs+RnBQRawEi4v1CneZLutflvPbFNDOrCZFS0W0ljQPG5RyaHBGTs68PBJbknGsCRu7QxWHZfn4LNAITI+I/831mvidH+MKZmdWcjox0swl2csGG7esEDAVOAgYAv5Z0VER8kO8NZmZ1I9LFj3QLWAoMzNkfkD2Wqwl4NSK2AG9L+hOZJDyzvU4L3trRzKyWRLr4rYCZwFBJgyR1AUYD03Zo8ySZUS6S+pApNyzK16lHumZWVyJKM9KNiBZJ44EZZOq1UyJijqQbgVkRMS177jRJc4EUcF1ErM7Xb8GkK2kAcBfwOTLLgV8EroqIpl36iczMyqCUiyMiYjowfYdjE3JeB3B1ditKMeWF+8gMqfsDBwBPZY+ZmVWddEpFb5VQTNLtGxH3RURLdrsf6FvmuMzMdkqkVfRWCcUk3dWSxkhqzG5jgLw1CzOzSqmHpDsWOB94D1hO5kY4vseumVWliOK3SijmJubvAmcnEIuZ2S6r1Ai2WPluYj6hvXNkLtrdVIZ4zMx2SammjJVLvpHuX9o4tidwKdAbcNI1s6qTqtCshGLlu/fCHVtfS+pJ5p663wCmAne09z4zs0qq5ZEukvYlM+n3IuABYPjWW5iZmVWjWq7p3g58icwdeI6KiI2JRWVmtpMqNSuhWPlGutcAHwHfB74ntf7fQ2QupO1V5tjMzDqsZke6EeE7kJlZzUmlqzt1+S5jZlZXarm8YGZWc9K1PHvBzKzW1PSUMTOzWrPblxfG37LjI4XM4MG/61XpEKxOubxgZpYgz14wM0tQlVcXnHTNrL64vGBmliDPXjAzS1AJHwZcFk66ZlZXAo90zcwS0+LygplZcjzSNTNLkGu6ZmYJ8kjXzCxBHumamSUo5ZGumVlyqvxpPU66ZlZf0h7pmpklxze8MTNLkC+kmZklKC2XF8zMEpOqdAAFVPct1s3MOiit4rdCJI2SNF/SQknfydPuy5JC0ohCfXqka2Z1pVSzFyQ1ApOAU4EmYKakaRExd4d2PYGrgFeL6dcjXTOrK9GBrYDjgIURsSgimoGpwDlttLsJuBXYXEx8TrpmVlc6Ul6QNE7SrJxtXE5XBwJLcvabssdaSRoODIyIp4uNz+UFM6srHZkyFhGTgck78zmSGoB/Bi7pyPucdM2srqRKN2NsKTAwZ39A9thWPYEjgReUmaa2PzBN0tkRMau9Tp10zayulHBxxExgqKRBZJLtaODCrScjYh3QZ+u+pBeAa/MlXHBN18zqTLoDWz4R0QKMB2YA84BHI2KOpBslnb2z8Xmka2Z1pZSPSIuI6cD0HY5NaKftScX06aRrZnXF914wM0tQtS8DdtI1s7rim5ibmSXI5QUzswQ56ZqZJchPjjAzS5BrumZmCfLsBTOzBKWrvMDgpGtmdcUX0szMElTd41wnXTOrMx7pmpklqEXVPdZ10jWzulLdKddJ18zqjMsLZmYJ8pQxM7MEVXfKddI1szrj8oKZWYJSVT7WddI1s7rika6ZWYLCI10zs+R4pLubOPLEY7hwwjdQYwMvPvIc0+95crvzp116Jn8z+mRSLWk2rFnPfd+exOqlqwD42Z8foWn+YgBWL13FXZffmnT4ViaNg4+iyykXQUMDLa/9N1teeXq7811OvpCGgz8JgDp3Rd178uFPrgSg+/X3kV65BIBYv4aPfvEvicZeqzxlbDeghgbG3HgZd4y5kTXvrWHCtFt47ZlZLFvY1Npm8dy3ufGs62ne3MxJY07jvBu+xr3jfwJA8+ZmJn7hukqFb+Ui0eW0i9k89TZi/Rq6XTKRlgWzidXLWps0P/dQ6+tOf30KDf0O3vb+lmY2T5mQZMR1obpTLjRUOoB6MPiYQ3n/3fdYueR9UltaePWp33LMaZ/ars1bL8+heXMzAItmL6DX/r0rEaolqOGAwaTXriA+WAnpFKl5r9LpsOHttu807NO0zH0lwQjrUwtR9FYJBUe6khqAT0fESwnEU5P26bcva5atat1fu3w1g48Z2m77E87/PG+8MLt1v3PXLkyYdiupVIrp9zzB7F/NLGu8lgz16EWsX9O6HxvW0HDAkLbb7tUb7dOX9Ltztx3s1Jlul0yEdIotLz9NasEfyhxxfaj5C2kRkZY0CTi22E4ljQPGAXx232P5RM/BOx9hnfn0uSdwyNFDuPWCbb82Xnf83/PBijX0Hbgf1z08kaa3FrNy8YoKRmlJ6zRsJKm3ZkJsSxibJl1DbFyL9ulLt69ez+aVTcQH71cwytpQ7RfSii0vPCfpy5KKeuRbREyOiBERMWJ3SLgfrFjDvgf0ad3v1b83a1es+Vi7YccfxZnjv8ydl91CS3PLdu8HWLnkfd56ZQ4HHTGo/EFb2cXGtWivfVv31XNfYsPaNts2Hv7x0kJszLSND1aSWvwWDf0OKl+wdSQ68E8lFJt0rwAeA5olrZe0QdL6MsZVU97+40L6HdKfPgP2o7FzJ0aedTyvPbN9ieCgIwZx8Y+v4M7LbmHD6m1/dN332pNOXTK/cPTo1ZOhf/1Jli9owmpfetnbNPTqh/buAw2NNB4+kpYFsz/WTvv2R926k166cNvBbt2hMfuL6B49aBwwlPSqZR97r31cugNbJRQ1eyEiepY7kFqWTqX5PxN+xtU//z4NjQ385tHnWbagiXO/dQHvvPFnXnt2Fuff8DW6du/GlXdfA2ybGtb/0AF8/cfjiAgkMf2eJ7ab9WA1LNI0P/Mg3UZfB2qg5fVfE6uW0vmEL5Je/g6phZkE3GnYSFrmvbrdWxt6H0DXUZe0fi+2vPz0drMerH2pqO6arqKIALNlhYuAQRFxk6SBQP+I+F2h94495CvV/SdgFXHXFXtWOgSrQnve8EBRJcx8Ljz4i0XnnIfefWKXP6+jii0v3A18Brgwu78RmFSWiMzMdkG113SLXRwxMiKGS5oNEBFrJXUpY1xmZjul2mcvFJt0t0hqJLvYQ1Jfqv9nM7PdULUvAy62vHAn8ASwn6QfAb8Bfly2qMzMdlIpywuSRkmaL2mhpO+0cf5qSXMlvS7pOUkHt9VPrmJnL/y7pN8DJwMCzo2IecW818wsSaWavZD97X4ScCrQBMyUNC0icpYNMhsYEREfSvp74Dbggnz9duSGNwuA9VvfI+mgiFjcgfebmZVdCcsLxwELI2IRgKSpwDlAa9KNiP/Kaf8KMKZQp0UlXUn/APwAWAGkyIx2Azi6yODNzBLRkYtNubcsyJocEZOzrw8EluScawJG5unuUuD/FfrMYke6VwGfiIjVRbY3M6uIjkwFyybYyQUbFiBpDDACOLFQ22KT7hJg3a4EZWaWhBKWF5YCA3P2B2SPbUfSKcD3gBMj4qNCneZNupKuzr5cBLwg6WmgtdOI+OfCcZuZJaeYVbZFmgkMlTSITLIdzbYFYgBIOhb4KTAqIoq6BVyhke7Wey4szm5dshtU/w3azWw3VKpHsEdEi6TxwAygEZgSEXMk3QjMiohpwO1AD+Cx7E0YF0fE2fn6zZt0I+IfASSdFxGP5Z6TdN5O/zRmZmVSysURETEdmL7DsQk5r0/paJ/FLo64ochjZmYVFRFFb5VQqKZ7BvAF4EBJd+ac2gtoaftdZmaVU+3LgAvVdJcBs4DzgD9lj7WQma/7rTLGZWa2U2r9GWlzydxHtwswNnvsIOA+4JdljMvMbKdU+03MC9V0bwN6AQdHxPCIGA4MBvYG/qncwZmZdVSaKHqrhEIj3TOBwyKn4hwR67M3dniLzEo1M7OqUes13Yg2LvFFREpSdf9kZrZbqtSshGIVKi/MlXTxjgez64zfKk9IZmY7r9bLC98EHpc0Fvh99tgIYA/gi+UMzMxsZ9T07IWIWAqMlPR54Ijs4ekR8VzZIzMz2wmpqO4niRX75IjngefLHIuZ2S6r9ppuR54cYWZW9Wp99oKZWU2p6ZqumVmtSbu8YGaWHI90zcwSVBezF8zMaoXLC2ZmCXJ5wcwsQR7pmpklyCNdM7MEpSJV6RDyctI1s7riZcBmZgnyMmAzswR5pGtmliDPXjAzS5BnL5iZJcjLgM3MEuSarplZglzTNTNLkEe6ZmYJ8jxdM7MEeaRrZpYgz14wM0uQL6SZmSWo2ssLDZUOwMyslKID/xQiaZSk+ZIWSvpOG+e7Snoke/5VSYcU6tNJ18zqSkQUveUjqRGYBJwBDAO+KmnYDs0uBdZGxKHAT4BbC8XnpGtmdSUdUfRWwHHAwohYFBHNwFTgnB3anAM8kH39C+BkScrXadlrulPe+UXeAHYnksZFxORKx2HVxd+L0mppXlp0zpE0DhiXc2hyzn+LA4ElOeeagJE7dNHaJiJaJK0DegOr2vtMj3STNa5wE9sN+XtRIRExOSJG5Gxl/5+fk66ZWduWAgNz9gdkj7XZRlInYG9gdb5OnXTNzNo2ExgqaZCkLsBoYNoObaYBX8++/grwfBS4Qud5usly3c7a4u9FFcrWaMcDM4BGYEpEzJF0IzArIqYB/xt4UNJCYA2ZxJyXqn0isZlZPXF5wcwsQU66ZmYJctItMUkpSa9JelPSU5L2yR4fIWlOtiCPpCGSFknaq6IBW9n4u2BtcdItvU0RcUxEHEmmsP5NgIiYBfw3cG223STgexGxvjJhWgJ26bsgaaKkSxKM1xLg2Qvl9TJwdM7+d4HZklqAThHxcGXCsgrwd8EAJ92yyd4s42QyU0oAiIgPJN0C3E3mBhq2G/B3wXI56ZbeHpJeI7Mmex7wzA7nzwBWkPmLNj/Z0CxhHf4uSDoKeDB7fn+gWdL/zO6fHBF5VztZ9XNNt/Q2RcQxwMGAyNbxACSdSWaZ4OnA7ZK6SxqZvdjymqSzKxKxlUuHvgsAEfFGtg58DHAvMGHrvhNuffDiiBKTtDEiemRfHws8CQwBOgN/BM6NiLmS7gA2R8T3KhasldWufhckTQTeiYj7k4zbystJt8Ry/6Jl958CHgUOBxoj4vrs8Z5k/uKdHhELKhKsldWufhecdOuTk66ZWYJc0zUzS5CTrplZgpx0zcwS5KRrZpYgJ10zswQ56ZqZJchJ18wsQf8fcmwPpijVTHEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlabels = ['RX-', 'RX+']\n",
    "ylabels = ['No Finding', 'Other']\n",
    "ax = sns.heatmap(\n",
    "    cm / cm.sum(axis=1)[:, np.newaxis], annot=True, fmt=\".2f\",\n",
    "    xticklabels=xlabels, yticklabels=ylabels,\n",
    "    vmin=0., vmax=1.\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[155,  43],\n",
       "       [ 58, 191]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_normal = (molinette_df.iloc[:, 12].values <= 0.5).astype('int')\n",
    "cm = confusion_matrix(molinette_df.rx.values, predicted_normal)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['RX-', 'RX+']\n",
    "ax = sns.heatmap(\n",
    "    cm / cm.sum(axis=1)[:, np.newaxis], annot=True, fmt=\".2f\",\n",
    "    xticklabels=labels, yticklabels=labels,\n",
    "    vmin=0., vmax=1.\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>data es</th>\n",
       "      <th>ID TRACK</th>\n",
       "      <th>DATA NASCITA</th>\n",
       "      <th>image_id</th>\n",
       "      <th>covid</th>\n",
       "      <th>rx</th>\n",
       "      <th>CLINICA</th>\n",
       "      <th>NOTE</th>\n",
       "      <th>...</th>\n",
       "      <th>Lung Lesion</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>9.003902e+09</td>\n",
       "      <td>01/03/1946</td>\n",
       "      <td>corda1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080021</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>0.057013</td>\n",
       "      <td>0.039196</td>\n",
       "      <td>0.025258</td>\n",
       "      <td>0.035283</td>\n",
       "      <td>0.074448</td>\n",
       "      <td>0.061089</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>0.048945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001680e+09</td>\n",
       "      <td>04/04/1968</td>\n",
       "      <td>corda2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031187</td>\n",
       "      <td>0.930774</td>\n",
       "      <td>0.350397</td>\n",
       "      <td>0.184001</td>\n",
       "      <td>0.144656</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.758117</td>\n",
       "      <td>0.005987</td>\n",
       "      <td>0.013088</td>\n",
       "      <td>0.059794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.003353e+09</td>\n",
       "      <td>12/09/0199</td>\n",
       "      <td>corda3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036552</td>\n",
       "      <td>0.003615</td>\n",
       "      <td>0.030241</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.261099</td>\n",
       "      <td>0.054235</td>\n",
       "      <td>0.026397</td>\n",
       "      <td>0.020180</td>\n",
       "      <td>0.091661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000477e+09</td>\n",
       "      <td>28/05/1946</td>\n",
       "      <td>corda4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063948</td>\n",
       "      <td>0.448018</td>\n",
       "      <td>0.187096</td>\n",
       "      <td>0.298485</td>\n",
       "      <td>0.056434</td>\n",
       "      <td>0.030715</td>\n",
       "      <td>0.036530</td>\n",
       "      <td>0.027439</td>\n",
       "      <td>0.093709</td>\n",
       "      <td>0.029209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000981e+09</td>\n",
       "      <td>10/04/1957</td>\n",
       "      <td>corda5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135531</td>\n",
       "      <td>0.265945</td>\n",
       "      <td>0.265859</td>\n",
       "      <td>0.226268</td>\n",
       "      <td>0.292798</td>\n",
       "      <td>0.111357</td>\n",
       "      <td>0.197117</td>\n",
       "      <td>0.042322</td>\n",
       "      <td>0.082476</td>\n",
       "      <td>0.047800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1     data es      ID TRACK DATA NASCITA image_id  \\\n",
       "0           0             0  23/03/2020  9.003902e+09   01/03/1946   corda1   \n",
       "1           1             1         NaN  9.001680e+09   04/04/1968   corda2   \n",
       "2           2             2         NaN  9.003353e+09   12/09/0199   corda3   \n",
       "3           3             3         NaN  9.000477e+09   28/05/1946   corda4   \n",
       "4           4             4         NaN  9.000981e+09   10/04/1957   corda5   \n",
       "\n",
       "   covid   rx  CLINICA NOTE  ... Lung Lesion     Edema  Consolidation  \\\n",
       "0    0.0  0.0      0.0  NaN  ...    0.080021  0.002013       0.057013   \n",
       "1    0.0  1.0      0.0  NaN  ...    0.031187  0.930774       0.350397   \n",
       "2    0.0  0.0      0.0  NaN  ...    0.036552  0.003615       0.030241   \n",
       "3    1.0  1.0      1.0  NaN  ...    0.063948  0.448018       0.187096   \n",
       "4    1.0  1.0      1.0  NaN  ...    0.135531  0.265945       0.265859   \n",
       "\n",
       "   Pneumonia  Atelectasis  Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "0   0.039196     0.025258      0.035283          0.074448       0.061089   \n",
       "1   0.184001     0.144656      0.009302          0.758117       0.005987   \n",
       "2   0.018732     0.006805      0.261099          0.054235       0.026397   \n",
       "3   0.298485     0.056434      0.030715          0.036530       0.027439   \n",
       "4   0.226268     0.292798      0.111357          0.197117       0.042322   \n",
       "\n",
       "   Fracture  Support Devices  \n",
       "0  0.078508         0.048945  \n",
       "1  0.013088         0.059794  \n",
       "2  0.020180         0.091661  \n",
       "3  0.093709         0.029209  \n",
       "4  0.082476         0.047800  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.703988685037822"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca_components = pca.fit_transform(df.iloc[:, 12:])\n",
    "pca.explained_variance_ratio_.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(447, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>covid</th>\n",
       "      <th>rx</th>\n",
       "      <th>pca_comp0</th>\n",
       "      <th>pca_comp1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.591973</td>\n",
       "      <td>-0.102283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.887707</td>\n",
       "      <td>-0.641959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.671364</td>\n",
       "      <td>0.045775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.236226</td>\n",
       "      <td>-0.349480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.370901</td>\n",
       "      <td>-0.152383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dataset  covid   rx  pca_comp0  pca_comp1\n",
       "0  CORDA-dataset    0.0  0.0  -0.591973  -0.102283\n",
       "1  CORDA-dataset    0.0  1.0   0.887707  -0.641959\n",
       "2  CORDA-dataset    0.0  0.0  -0.671364   0.045775\n",
       "3  CORDA-dataset    1.0  1.0   0.236226  -0.349480\n",
       "4  CORDA-dataset    1.0  1.0   0.370901  -0.152383"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca_df = df[['dataset', 'covid', 'rx']].copy()\n",
    "print(pca_df.shape)\n",
    "for i in range(pca_components.shape[1]):\n",
    "    pca_df[f'pca_comp{i}'] = pca_components[:, i]\n",
    "pca_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.05, 'PCA (2-comp)')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(pca_df.drop(['rx', 'dataset'], axis=1), hue='covid')\n",
    "g.fig.set_size_inches(10, 10)\n",
    "g.fig.suptitle(f'PCA (2-comp)', y=1.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=2, perplexity=100, n_iter=1000)\n",
    "embedded = tsne.fit_transform(df.iloc[:, 12:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>covid</th>\n",
       "      <th>rx</th>\n",
       "      <th>tsne_comp0</th>\n",
       "      <th>tsne_comp1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.256965</td>\n",
       "      <td>-4.364223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.785978</td>\n",
       "      <td>8.787773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.811397</td>\n",
       "      <td>-6.804569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.038612</td>\n",
       "      <td>4.131247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CORDA-dataset</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.061280</td>\n",
       "      <td>3.715339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dataset  covid   rx  tsne_comp0  tsne_comp1\n",
       "0  CORDA-dataset    0.0  0.0    5.256965   -4.364223\n",
       "1  CORDA-dataset    0.0  1.0   -2.785978    8.787773\n",
       "2  CORDA-dataset    0.0  0.0    4.811397   -6.804569\n",
       "3  CORDA-dataset    1.0  1.0   -1.038612    4.131247\n",
       "4  CORDA-dataset    1.0  1.0   -2.061280    3.715339"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_df = df[['dataset', 'covid', 'rx']].copy()\n",
    "for i in range(embedded.shape[1]):\n",
    "    tsne_df[f'tsne_comp{i}'] = embedded[:, i]\n",
    "tsne_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.pairplot(tsne_df, hue='covid')\n",
    "g.fig.set_size_inches(10, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Covid prediction from radiological findings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Unnamed: 0.1', 'data es', 'ID TRACK', 'DATA NASCITA',\n",
       "       'image_id', 'covid', 'rx', 'CLINICA', 'NOTE', 'dataset', 'view',\n",
       "       'No Finding', 'Enlarged Cardiomediastinum', 'Cardiomegaly',\n",
       "       'Lung Opacity', 'Lung Lesion', 'Edema', 'Consolidation', 'Pneumonia',\n",
       "       'Atelectasis', 'Pneumothorax', 'Pleural Effusion', 'Pleural Other',\n",
       "       'Fracture', 'Support Devices'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = df[df.dataset == 'CORDA-dataset'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "447"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>data es</th>\n",
       "      <th>ID TRACK</th>\n",
       "      <th>DATA NASCITA</th>\n",
       "      <th>image_id</th>\n",
       "      <th>covid</th>\n",
       "      <th>rx</th>\n",
       "      <th>CLINICA</th>\n",
       "      <th>NOTE</th>\n",
       "      <th>...</th>\n",
       "      <th>Lung Lesion</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23/03/2020</td>\n",
       "      <td>9.003902e+09</td>\n",
       "      <td>01/03/1946</td>\n",
       "      <td>corda1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080021</td>\n",
       "      <td>0.002013</td>\n",
       "      <td>0.057013</td>\n",
       "      <td>0.039196</td>\n",
       "      <td>0.025258</td>\n",
       "      <td>0.035283</td>\n",
       "      <td>0.074448</td>\n",
       "      <td>0.061089</td>\n",
       "      <td>0.078508</td>\n",
       "      <td>0.048945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001680e+09</td>\n",
       "      <td>04/04/1968</td>\n",
       "      <td>corda2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031187</td>\n",
       "      <td>0.930774</td>\n",
       "      <td>0.350397</td>\n",
       "      <td>0.184001</td>\n",
       "      <td>0.144656</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>0.758117</td>\n",
       "      <td>0.005987</td>\n",
       "      <td>0.013088</td>\n",
       "      <td>0.059794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.003353e+09</td>\n",
       "      <td>12/09/0199</td>\n",
       "      <td>corda3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036552</td>\n",
       "      <td>0.003615</td>\n",
       "      <td>0.030241</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.261099</td>\n",
       "      <td>0.054235</td>\n",
       "      <td>0.026397</td>\n",
       "      <td>0.020180</td>\n",
       "      <td>0.091661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000477e+09</td>\n",
       "      <td>28/05/1946</td>\n",
       "      <td>corda4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063948</td>\n",
       "      <td>0.448018</td>\n",
       "      <td>0.187096</td>\n",
       "      <td>0.298485</td>\n",
       "      <td>0.056434</td>\n",
       "      <td>0.030715</td>\n",
       "      <td>0.036530</td>\n",
       "      <td>0.027439</td>\n",
       "      <td>0.093709</td>\n",
       "      <td>0.029209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000981e+09</td>\n",
       "      <td>10/04/1957</td>\n",
       "      <td>corda5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135531</td>\n",
       "      <td>0.265945</td>\n",
       "      <td>0.265859</td>\n",
       "      <td>0.226268</td>\n",
       "      <td>0.292798</td>\n",
       "      <td>0.111357</td>\n",
       "      <td>0.197117</td>\n",
       "      <td>0.042322</td>\n",
       "      <td>0.082476</td>\n",
       "      <td>0.047800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1     data es      ID TRACK DATA NASCITA image_id  \\\n",
       "0           0             0  23/03/2020  9.003902e+09   01/03/1946   corda1   \n",
       "1           1             1         NaN  9.001680e+09   04/04/1968   corda2   \n",
       "2           2             2         NaN  9.003353e+09   12/09/0199   corda3   \n",
       "3           3             3         NaN  9.000477e+09   28/05/1946   corda4   \n",
       "4           4             4         NaN  9.000981e+09   10/04/1957   corda5   \n",
       "\n",
       "   covid   rx  CLINICA NOTE  ... Lung Lesion     Edema  Consolidation  \\\n",
       "0    0.0  0.0      0.0  NaN  ...    0.080021  0.002013       0.057013   \n",
       "1    0.0  1.0      0.0  NaN  ...    0.031187  0.930774       0.350397   \n",
       "2    0.0  0.0      0.0  NaN  ...    0.036552  0.003615       0.030241   \n",
       "3    1.0  1.0      1.0  NaN  ...    0.063948  0.448018       0.187096   \n",
       "4    1.0  1.0      1.0  NaN  ...    0.135531  0.265945       0.265859   \n",
       "\n",
       "   Pneumonia  Atelectasis  Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "0   0.039196     0.025258      0.035283          0.074448       0.061089   \n",
       "1   0.184001     0.144656      0.009302          0.758117       0.005987   \n",
       "2   0.018732     0.006805      0.261099          0.054235       0.026397   \n",
       "3   0.298485     0.056434      0.030715          0.036530       0.027439   \n",
       "4   0.226268     0.292798      0.111357          0.197117       0.042322   \n",
       "\n",
       "   Fracture  Support Devices  \n",
       "0  0.078508         0.048945  \n",
       "1  0.013088         0.059794  \n",
       "2  0.020180         0.091661  \n",
       "3  0.093709         0.029209  \n",
       "4  0.082476         0.047800  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(train_df, test_size=0.3, random_state=42, stratify=train_df.covid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>data es</th>\n",
       "      <th>ID TRACK</th>\n",
       "      <th>DATA NASCITA</th>\n",
       "      <th>image_id</th>\n",
       "      <th>covid</th>\n",
       "      <th>rx</th>\n",
       "      <th>CLINICA</th>\n",
       "      <th>NOTE</th>\n",
       "      <th>...</th>\n",
       "      <th>Lung Lesion</th>\n",
       "      <th>Edema</th>\n",
       "      <th>Consolidation</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>316</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.003901e+09</td>\n",
       "      <td>21/01/1959</td>\n",
       "      <td>borda16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106938</td>\n",
       "      <td>0.008967</td>\n",
       "      <td>0.203726</td>\n",
       "      <td>0.081905</td>\n",
       "      <td>0.092515</td>\n",
       "      <td>0.266311</td>\n",
       "      <td>0.248240</td>\n",
       "      <td>0.100553</td>\n",
       "      <td>0.050994</td>\n",
       "      <td>0.035300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>350</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000970e+09</td>\n",
       "      <td>01/04/1923</td>\n",
       "      <td>borda54</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254394</td>\n",
       "      <td>0.171296</td>\n",
       "      <td>0.674627</td>\n",
       "      <td>0.265448</td>\n",
       "      <td>0.098607</td>\n",
       "      <td>0.245364</td>\n",
       "      <td>0.333252</td>\n",
       "      <td>0.044471</td>\n",
       "      <td>0.017755</td>\n",
       "      <td>0.453705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>210</td>\n",
       "      <td>239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000713e+09</td>\n",
       "      <td>29/06/1962</td>\n",
       "      <td>corda234</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128189</td>\n",
       "      <td>0.006237</td>\n",
       "      <td>0.048474</td>\n",
       "      <td>0.051157</td>\n",
       "      <td>0.016407</td>\n",
       "      <td>0.083171</td>\n",
       "      <td>0.053604</td>\n",
       "      <td>0.133908</td>\n",
       "      <td>0.078491</td>\n",
       "      <td>0.049397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>446</td>\n",
       "      <td>161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001387e+09</td>\n",
       "      <td>14/11/1944</td>\n",
       "      <td>borda157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>comorbilita'</td>\n",
       "      <td>...</td>\n",
       "      <td>0.174199</td>\n",
       "      <td>0.457557</td>\n",
       "      <td>0.552521</td>\n",
       "      <td>0.169278</td>\n",
       "      <td>0.163936</td>\n",
       "      <td>0.115372</td>\n",
       "      <td>0.681655</td>\n",
       "      <td>0.016696</td>\n",
       "      <td>0.026880</td>\n",
       "      <td>0.098483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>85</td>\n",
       "      <td>20/03/2020</td>\n",
       "      <td>9.004187e+09</td>\n",
       "      <td>20/02/1992</td>\n",
       "      <td>corda83</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109971</td>\n",
       "      <td>0.018418</td>\n",
       "      <td>0.088666</td>\n",
       "      <td>0.055536</td>\n",
       "      <td>0.090340</td>\n",
       "      <td>0.568278</td>\n",
       "      <td>0.125891</td>\n",
       "      <td>0.058869</td>\n",
       "      <td>0.070843</td>\n",
       "      <td>0.047627</td>\n",
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       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>225</td>\n",
       "      <td>255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.004080e+09</td>\n",
       "      <td>01/03/1967</td>\n",
       "      <td>corda249</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096386</td>\n",
       "      <td>0.009374</td>\n",
       "      <td>0.153679</td>\n",
       "      <td>0.060142</td>\n",
       "      <td>0.420276</td>\n",
       "      <td>0.591948</td>\n",
       "      <td>0.285536</td>\n",
       "      <td>0.043923</td>\n",
       "      <td>0.074513</td>\n",
       "      <td>0.104268</td>\n",
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       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>416</td>\n",
       "      <td>126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.004009e+09</td>\n",
       "      <td>21/11/1993</td>\n",
       "      <td>borda123</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>operatore sanitario</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045381</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>0.025971</td>\n",
       "      <td>0.020074</td>\n",
       "      <td>0.010246</td>\n",
       "      <td>0.180533</td>\n",
       "      <td>0.045105</td>\n",
       "      <td>0.064096</td>\n",
       "      <td>0.040790</td>\n",
       "      <td>0.102560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>291</td>\n",
       "      <td>335</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000628e+09</td>\n",
       "      <td>30/08/1970</td>\n",
       "      <td>corda327</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.117816</td>\n",
       "      <td>0.026619</td>\n",
       "      <td>0.201703</td>\n",
       "      <td>0.114311</td>\n",
       "      <td>0.182916</td>\n",
       "      <td>0.137782</td>\n",
       "      <td>0.453120</td>\n",
       "      <td>0.103603</td>\n",
       "      <td>0.081952</td>\n",
       "      <td>0.022225</td>\n",
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       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>262</td>\n",
       "      <td>301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000274e+09</td>\n",
       "      <td>08/06/1952</td>\n",
       "      <td>corda294</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060750</td>\n",
       "      <td>0.426791</td>\n",
       "      <td>0.287538</td>\n",
       "      <td>0.191276</td>\n",
       "      <td>0.362595</td>\n",
       "      <td>0.060158</td>\n",
       "      <td>0.082688</td>\n",
       "      <td>0.012249</td>\n",
       "      <td>0.042295</td>\n",
       "      <td>0.060549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>223</td>\n",
       "      <td>253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001367e+09</td>\n",
       "      <td>30/09/1982</td>\n",
       "      <td>corda247</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>tc neg</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065714</td>\n",
       "      <td>0.008896</td>\n",
       "      <td>0.041176</td>\n",
       "      <td>0.020002</td>\n",
       "      <td>0.021574</td>\n",
       "      <td>0.358133</td>\n",
       "      <td>0.103004</td>\n",
       "      <td>0.026893</td>\n",
       "      <td>0.042233</td>\n",
       "      <td>0.064981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>120</td>\n",
       "      <td>139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001137e+09</td>\n",
       "      <td>12/04/1943</td>\n",
       "      <td>corda136</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.237929</td>\n",
       "      <td>0.351718</td>\n",
       "      <td>0.381442</td>\n",
       "      <td>0.236494</td>\n",
       "      <td>0.225992</td>\n",
       "      <td>0.086778</td>\n",
       "      <td>0.296815</td>\n",
       "      <td>0.024907</td>\n",
       "      <td>0.044091</td>\n",
       "      <td>0.270294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.001771e+09</td>\n",
       "      <td>30/04/1954</td>\n",
       "      <td>corda47</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.079485</td>\n",
       "      <td>0.037578</td>\n",
       "      <td>0.085092</td>\n",
       "      <td>0.063273</td>\n",
       "      <td>0.073490</td>\n",
       "      <td>0.014201</td>\n",
       "      <td>0.124479</td>\n",
       "      <td>0.056502</td>\n",
       "      <td>0.091202</td>\n",
       "      <td>0.085916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>392</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000867e+09</td>\n",
       "      <td>26/03/1971</td>\n",
       "      <td>borda98</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>controllo</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130572</td>\n",
       "      <td>0.257209</td>\n",
       "      <td>0.756484</td>\n",
       "      <td>0.434690</td>\n",
       "      <td>0.133568</td>\n",
       "      <td>0.168778</td>\n",
       "      <td>0.844525</td>\n",
       "      <td>0.019250</td>\n",
       "      <td>0.022692</td>\n",
       "      <td>0.739298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>162</td>\n",
       "      <td>188</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.002079e+09</td>\n",
       "      <td>21/06/1941</td>\n",
       "      <td>corda184</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107588</td>\n",
       "      <td>0.042840</td>\n",
       "      <td>0.089312</td>\n",
       "      <td>0.073469</td>\n",
       "      <td>0.109662</td>\n",
       "      <td>0.018742</td>\n",
       "      <td>0.058246</td>\n",
       "      <td>0.081940</td>\n",
       "      <td>0.089465</td>\n",
       "      <td>0.051841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>433</td>\n",
       "      <td>148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000214e+09</td>\n",
       "      <td>26/07/1984</td>\n",
       "      <td>borda144</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>operatore sanitario</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075855</td>\n",
       "      <td>0.010501</td>\n",
       "      <td>0.140024</td>\n",
       "      <td>0.040440</td>\n",
       "      <td>0.049574</td>\n",
       "      <td>0.350089</td>\n",
       "      <td>0.184445</td>\n",
       "      <td>0.032021</td>\n",
       "      <td>0.031170</td>\n",
       "      <td>0.044518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>384</td>\n",
       "      <td>92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000529e+09</td>\n",
       "      <td>11/09/1944</td>\n",
       "      <td>borda90</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>controllo</td>\n",
       "      <td>...</td>\n",
       "      <td>0.523374</td>\n",
       "      <td>0.038245</td>\n",
       "      <td>0.536752</td>\n",
       "      <td>0.438293</td>\n",
       "      <td>0.035435</td>\n",
       "      <td>0.246089</td>\n",
       "      <td>0.188768</td>\n",
       "      <td>0.034717</td>\n",
       "      <td>0.041106</td>\n",
       "      <td>0.094582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>155</td>\n",
       "      <td>179</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.004165e+09</td>\n",
       "      <td>25/11/1966</td>\n",
       "      <td>corda175</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>OLT</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065057</td>\n",
       "      <td>0.013726</td>\n",
       "      <td>0.072816</td>\n",
       "      <td>0.071745</td>\n",
       "      <td>0.045117</td>\n",
       "      <td>0.040916</td>\n",
       "      <td>0.083517</td>\n",
       "      <td>0.057525</td>\n",
       "      <td>0.095339</td>\n",
       "      <td>0.042157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>393</td>\n",
       "      <td>101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000558e+09</td>\n",
       "      <td>17/08/1933</td>\n",
       "      <td>borda99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28/3 tampone positivo</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076704</td>\n",
       "      <td>0.601400</td>\n",
       "      <td>0.450648</td>\n",
       "      <td>0.317324</td>\n",
       "      <td>0.154870</td>\n",
       "      <td>0.015408</td>\n",
       "      <td>0.260726</td>\n",
       "      <td>0.028210</td>\n",
       "      <td>0.037502</td>\n",
       "      <td>0.027032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>265</td>\n",
       "      <td>304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000678e+09</td>\n",
       "      <td>29/08/1946</td>\n",
       "      <td>corda297</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>controllo post-toracentesi</td>\n",
       "      <td>...</td>\n",
       "      <td>0.056290</td>\n",
       "      <td>0.410358</td>\n",
       "      <td>0.389113</td>\n",
       "      <td>0.084233</td>\n",
       "      <td>0.241889</td>\n",
       "      <td>0.175081</td>\n",
       "      <td>0.885062</td>\n",
       "      <td>0.013141</td>\n",
       "      <td>0.026249</td>\n",
       "      <td>0.044379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>244</td>\n",
       "      <td>277</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.002188e+09</td>\n",
       "      <td>16/06/1948</td>\n",
       "      <td>corda271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>controllo</td>\n",
       "      <td>...</td>\n",
       "      <td>0.244910</td>\n",
       "      <td>0.063696</td>\n",
       "      <td>0.213642</td>\n",
       "      <td>0.117842</td>\n",
       "      <td>0.040866</td>\n",
       "      <td>0.162228</td>\n",
       "      <td>0.094065</td>\n",
       "      <td>0.047104</td>\n",
       "      <td>0.074104</td>\n",
       "      <td>0.715537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Unnamed: 0.1     data es      ID TRACK DATA NASCITA  \\\n",
       "316         316            15         NaN  9.003901e+09   21/01/1959   \n",
       "350         350            55         NaN  9.000970e+09   01/04/1923   \n",
       "210         210           239         NaN  9.000713e+09   29/06/1962   \n",
       "446         446           161         NaN  9.001387e+09   14/11/1944   \n",
       "71           71            85  20/03/2020  9.004187e+09   20/02/1992   \n",
       "225         225           255         NaN  9.004080e+09   01/03/1967   \n",
       "416         416           126         NaN  9.004009e+09   21/11/1993   \n",
       "291         291           335         NaN  9.000628e+09   30/08/1970   \n",
       "262         262           301         NaN  9.000274e+09   08/06/1952   \n",
       "223         223           253         NaN  9.001367e+09   30/09/1982   \n",
       "120         120           139         NaN  9.001137e+09   12/04/1943   \n",
       "41           41            47         NaN  9.001771e+09   30/04/1954   \n",
       "392         392           100         NaN  9.000867e+09   26/03/1971   \n",
       "162         162           188         NaN  9.002079e+09   21/06/1941   \n",
       "433         433           148         NaN  9.000214e+09   26/07/1984   \n",
       "384         384            92         NaN  9.000529e+09   11/09/1944   \n",
       "155         155           179         NaN  9.004165e+09   25/11/1966   \n",
       "393         393           101         NaN  9.000558e+09   17/08/1933   \n",
       "265         265           304         NaN  9.000678e+09   29/08/1946   \n",
       "244         244           277         NaN  9.002188e+09   16/06/1948   \n",
       "\n",
       "     image_id  covid   rx  CLINICA                        NOTE  ...  \\\n",
       "316   borda16    0.0  0.0      0.0                         NaN  ...   \n",
       "350   borda54    1.0  1.0      1.0                         NaN  ...   \n",
       "210  corda234    0.0  0.0      0.0                         NaN  ...   \n",
       "446  borda157    0.0  1.0      1.0                comorbilita'  ...   \n",
       "71    corda83    1.0  0.0      1.0                         NaN  ...   \n",
       "225  corda249    1.0  0.0      1.0                         NaN  ...   \n",
       "416  borda123    1.0  0.0      1.0         operatore sanitario  ...   \n",
       "291  corda327    0.0  0.0      0.0                         NaN  ...   \n",
       "262  corda294    1.0  1.0      1.0                         NaN  ...   \n",
       "223  corda247    1.0  0.0      1.0                      tc neg  ...   \n",
       "120  corda136    1.0  1.0      1.0                         NaN  ...   \n",
       "41    corda47    1.0  0.0      1.0                         NaN  ...   \n",
       "392   borda98    1.0  1.0      1.0                   controllo  ...   \n",
       "162  corda184    0.0  0.0      0.0                         NaN  ...   \n",
       "433  borda144    1.0  0.0      1.0         operatore sanitario  ...   \n",
       "384   borda90    1.0  1.0      1.0                   controllo  ...   \n",
       "155  corda175    0.0  0.0      0.0                        OLT   ...   \n",
       "393   borda99    0.0  1.0      1.0       28/3 tampone positivo  ...   \n",
       "265  corda297    0.0  1.0      0.0  controllo post-toracentesi  ...   \n",
       "244  corda271    1.0  1.0      1.0                   controllo  ...   \n",
       "\n",
       "    Lung Lesion     Edema  Consolidation  Pneumonia  Atelectasis  \\\n",
       "316    0.106938  0.008967       0.203726   0.081905     0.092515   \n",
       "350    0.254394  0.171296       0.674627   0.265448     0.098607   \n",
       "210    0.128189  0.006237       0.048474   0.051157     0.016407   \n",
       "446    0.174199  0.457557       0.552521   0.169278     0.163936   \n",
       "71     0.109971  0.018418       0.088666   0.055536     0.090340   \n",
       "225    0.096386  0.009374       0.153679   0.060142     0.420276   \n",
       "416    0.045381  0.003360       0.025971   0.020074     0.010246   \n",
       "291    0.117816  0.026619       0.201703   0.114311     0.182916   \n",
       "262    0.060750  0.426791       0.287538   0.191276     0.362595   \n",
       "223    0.065714  0.008896       0.041176   0.020002     0.021574   \n",
       "120    0.237929  0.351718       0.381442   0.236494     0.225992   \n",
       "41     0.079485  0.037578       0.085092   0.063273     0.073490   \n",
       "392    0.130572  0.257209       0.756484   0.434690     0.133568   \n",
       "162    0.107588  0.042840       0.089312   0.073469     0.109662   \n",
       "433    0.075855  0.010501       0.140024   0.040440     0.049574   \n",
       "384    0.523374  0.038245       0.536752   0.438293     0.035435   \n",
       "155    0.065057  0.013726       0.072816   0.071745     0.045117   \n",
       "393    0.076704  0.601400       0.450648   0.317324     0.154870   \n",
       "265    0.056290  0.410358       0.389113   0.084233     0.241889   \n",
       "244    0.244910  0.063696       0.213642   0.117842     0.040866   \n",
       "\n",
       "     Pneumothorax  Pleural Effusion  Pleural Other  Fracture  Support Devices  \n",
       "316      0.266311          0.248240       0.100553  0.050994         0.035300  \n",
       "350      0.245364          0.333252       0.044471  0.017755         0.453705  \n",
       "210      0.083171          0.053604       0.133908  0.078491         0.049397  \n",
       "446      0.115372          0.681655       0.016696  0.026880         0.098483  \n",
       "71       0.568278          0.125891       0.058869  0.070843         0.047627  \n",
       "225      0.591948          0.285536       0.043923  0.074513         0.104268  \n",
       "416      0.180533          0.045105       0.064096  0.040790         0.102560  \n",
       "291      0.137782          0.453120       0.103603  0.081952         0.022225  \n",
       "262      0.060158          0.082688       0.012249  0.042295         0.060549  \n",
       "223      0.358133          0.103004       0.026893  0.042233         0.064981  \n",
       "120      0.086778          0.296815       0.024907  0.044091         0.270294  \n",
       "41       0.014201          0.124479       0.056502  0.091202         0.085916  \n",
       "392      0.168778          0.844525       0.019250  0.022692         0.739298  \n",
       "162      0.018742          0.058246       0.081940  0.089465         0.051841  \n",
       "433      0.350089          0.184445       0.032021  0.031170         0.044518  \n",
       "384      0.246089          0.188768       0.034717  0.041106         0.094582  \n",
       "155      0.040916          0.083517       0.057525  0.095339         0.042157  \n",
       "393      0.015408          0.260726       0.028210  0.037502         0.027032  \n",
       "265      0.175081          0.885062       0.013141  0.026249         0.044379  \n",
       "244      0.162228          0.094065       0.047104  0.074104         0.715537  \n",
       "\n",
       "[20 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>data es</th>\n",
       "      <th>ID TRACK</th>\n",
       "      <th>DATA NASCITA</th>\n",
       "      <th>image_id</th>\n",
       "      <th>rx</th>\n",
       "      <th>CLINICA</th>\n",
       "      <th>NOTE</th>\n",
       "      <th>dataset</th>\n",
       "      <th>...</th>\n",
       "      <th>Lung Lesion</th>\n",
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       "      <th>Consolidation</th>\n",
       "      <th>Pneumonia</th>\n",
       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covid</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",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>25</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>55</td>\n",
       "      <td>207</td>\n",
       "      <td>...</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Unnamed: 0.1  data es  ID TRACK  DATA NASCITA  image_id  \\\n",
       "covid                                                                        \n",
       "0.0           105           105        7       105           105       105   \n",
       "1.0           207           207        4       207           207       207   \n",
       "\n",
       "        rx  CLINICA  NOTE  dataset  ...  Lung Lesion  Edema  Consolidation  \\\n",
       "covid                               ...                                      \n",
       "0.0    105      105    25      105  ...          105    105            105   \n",
       "1.0    207      207    55      207  ...          207    207            207   \n",
       "\n",
       "       Pneumonia  Atelectasis  Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "covid                                                                          \n",
       "0.0          105          105           105               105            105   \n",
       "1.0          207          207           207               207            207   \n",
       "\n",
       "       Fracture  Support Devices  \n",
       "covid                             \n",
       "0.0         105              105  \n",
       "1.0         207              207  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby('covid').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_df = pd.concat((\n",
    "#    train_df[train_df.covid == 0].sample(n=105, random_state=42),\n",
    "#    train_df[train_df.covid == 1].sample(n=105, random_state=42)\n",
    "#))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>data es</th>\n",
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       "      <th>Atelectasis</th>\n",
       "      <th>Pneumothorax</th>\n",
       "      <th>Pleural Effusion</th>\n",
       "      <th>Pleural Other</th>\n",
       "      <th>Fracture</th>\n",
       "      <th>Support Devices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covid</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>7</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>25</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>4</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>55</td>\n",
       "      <td>207</td>\n",
       "      <td>...</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  Unnamed: 0.1  data es  ID TRACK  DATA NASCITA  image_id  \\\n",
       "covid                                                                        \n",
       "0.0           105           105        7       105           105       105   \n",
       "1.0           207           207        4       207           207       207   \n",
       "\n",
       "        rx  CLINICA  NOTE  dataset  ...  Lung Lesion  Edema  Consolidation  \\\n",
       "covid                               ...                                      \n",
       "0.0    105      105    25      105  ...          105    105            105   \n",
       "1.0    207      207    55      207  ...          207    207            207   \n",
       "\n",
       "       Pneumonia  Atelectasis  Pneumothorax  Pleural Effusion  Pleural Other  \\\n",
       "covid                                                                          \n",
       "0.0          105          105           105               105            105   \n",
       "1.0          207          207           207               207            207   \n",
       "\n",
       "       Fracture  Support Devices  \n",
       "covid                             \n",
       "0.0         105              105  \n",
       "1.0         207              207  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.groupby('covid').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, train_Y = train_df[classes], train_df.covid\n",
    "test_X, test_Y = test_df[classes], test_df.covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc:  0.7111111111111111\n",
      "ba: 0.6833333333333333\n",
      "auc: 0.6980246913580248\n"
     ]
    }
   ],
   "source": [
    "tree_model = DecisionTreeClassifier(random_state=0, max_depth=4, min_samples_split=10).fit(train_X, train_Y)\n",
    "\n",
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n",
    "\n",
    "print('acc: ', accuracy_score(test_Y, (tree_model.predict_proba(test_X)[:,1] > 0.5).astype('int')))\n",
    "print('ba:', balanced_accuracy_score(test_Y, (tree_model.predict_proba(test_X)[:,1] > 0.5).astype('int')))\n",
    "print('auc:', roc_auc_score(test_Y, tree_model.predict_proba(test_X)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27, 18],\n",
       "       [21, 69]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "y = tree_model.predict(test_X)\n",
    "cm = confusion_matrix(test_Y, y)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAAD8CAYAAADUv3dIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Il7ecAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYaUlEQVR4nO3de5RU5Znv8e+vmptcFGhRkO4Romi86wjEiTkREj0SV5S4ksmAZnk5KpkknERzTAaPHp3FmGQSlzlxPCbazvEWL4xmopKELIyKx6hRafESIUEQQWhBEFBB5dJVz/mjirZo6K5qumpXdfH7sPZatd/97reekvLx9dnv3qWIwMzMkpGqdABmZnsTJ10zswQ56ZqZJchJ18wsQU66ZmYJctI1M0uQk66ZWQck3SZpraRXOzguSf8maamkVyT9baExnXTNzDp2BzCpk+NfAMbktmnALwoN6KRrZtaBiHgS2NBJl8nAXZH1LDBY0ojOxuxVygB35+0JE3zLm+1i7pKGSodgVei8lrvV3TG2v7Os6JzTZ9ghXyc7Q92hKSKauvB2I4GVefurcm2rOzqh7EnXzKxa5RJsV5JstznpmlltyaSTfLcWoDFvvyHX1iHXdM2stqRbi9+6bzZwXm4Vw0nAexHRYWkBPNM1sxoTkSnZWJLuAyYA+0taBVwD9M6+T9wMzAHOAJYCHwIXFhrTSdfMakumdEk3IqYWOB7At7oyppOumdWWEs50y8FJ18xqS7IX0rrMSdfMaotnumZmyYnSrEooGyddM6stJbyQVg5OumZWW1xeMDNLkC+kmZklyDNdM7ME+UKamVmCfCHNzCw5Ea7pmpklxzVdM7MEubxgZpYgz3TNzBKU3l7pCDrlpGtmtcXlBTOzBLm8YGaWIM90zcwS5KRrZpac8IU0M7MEuaZrZpYglxfMzBLkma6ZWYI80zUzS5BnumZmCWr1Q8zNzJLjma6ZWYJc0zUzS5BnumZmCfJM18wsQZ7pmpklyKsXzMwSFFHpCDrlpGtmtcU1XTOzBFV50k1VOgAzs5KKTPFbAZImSVosaamkGbs5/jeS5kl6UdIrks4oNKZnumZWW9LpkgwjqQ64CTgNWAXMlzQ7IhbldbsKuD8ifiHpSGAOMKqzcZ10zay2lK68MB5YGhHLACTNAiYD+Uk3gH1zr/cD3io0qJOumdWWLiRdSdOAaXlNTRHRlHs9EliZd2wV8Kl2Q/wz8Iik/w4MAE4t9J5OumZWW7pwc0QuwTYV7NixqcAdEXG9pL8Dfinp6IiOg3DSNbOaEpmSrdNtARrz9htybfkuAiYBRMSfJPUD9gfWdjSoVy+YWW3JZIrfOjcfGCNptKQ+wBRgdrs+bwKfB5B0BNAPWNfZoJ7pmlltKdHqhYholTQdmAvUAbdFxEJJM4HmiJgN/A/gVkmXkb2odkFE57fEOemaWW0p4c0RETGH7DKw/Lar814vAk7uyphOumZWW3xH2t6hz/jx1N91F/X33EP/c87ZbZ++EyZQf8cd1N9+O/tedVVbe7/TT6f+7rupv/tu+p1+elIhWwIOmnAsk5+8ji89dT1Hf+vMDvv9zRnjOK/lbuqPHd3WdvT0M/nSU9cz+cnrOOiUY5IItzZEFL9VgGe6pZBKMeg73+Hdyy8nvW4dQ2++ma1PP016xYq2LnUjRzLg3HPZMH06sXkzGjwYAA0axIDzz2fD178OEQxtamLr008TmzdX6MNYqSglPvWD8/nD1H/lw9UbOGPOTFY+8gLvLdl5/XyvAf044qLTWbdgaVvbfmMOYtTkk5j9uX+i/4FDOG3WDB76L5eX8sp87fJMt/b1/uQnSbe0kF69Glpb2fL44/Q9eecyzz5f/CIfPfRQWzKNd98FoO+4cWxrbiY2bSI2b2ZbczN9x49P+iNYGdSfcAiblr/N5jfXkdmeZvnDz9J4+om79Dv++1/h1Z//lvSW7W1tjaefyPKHnyWzrZXNK9exafnb1J9wSJLh91yZKH6rgC4nXUm/LUcgPVlq2DAy6z5eJZJZt466YcN26lPX2EhdQwNDbryRIT//OX1yiXV356banWs9U//hQ/jgrQ1t+x+u3kD/4UN26jP06FEMGDGUlsde6vTcD3ZzrnUgnS5+q4A9memOLNRB0jRJzZKaf/lWwVuR9wqqq6OuoYGNl17KezNnsu/ll6OBAysdllWSxNhrzqV55r2VjqSmRCZT9FYJndZ0JQ2PiDXtml8sNGj+rXVvT5hQ80Wo9rPT1LBhpNftvD46vW4d2xctgnSazJo1tK5cSd3IkWTWraP38cfvdO72l15KKHIrpw/XbGTAQUPb9vuPGMqHaza27fce2I/Bn2zg9F9dCcA+w/Zj4u3fZd6FP93l3AHtzrVOVHndu9BM9yVJj0q6SNJggIj4b+UPq2fZvngxdQ0NpIYPh1696Pe5z7H1mWd26rP1qafok0uu2m8/ejU2kl69mq3z59N33Dg0cCAaOJC+48axdf78CnwKK7X1Ly1j0OjhDGwcRqp3HaMmn8TKRxa0Hd++6SPuP+Yb/Pqky/j1SZexbsHrzLvwp6x/5Q1WPrKAUZNPItWnFwMbhzFo9HDWv/h6BT9ND1LC5+mWQ6HVCyPJPjVnCvBDSc8C9wEPR8RH5Q6ux0in2XTDDQy57jpIpdjy+9+TXr6cARdeSOvixWx95hm2Pf88fcaOpf6OO4hMhk0330y8/z4Am++6i6G33JJ9feedxKZNlfw0ViKRzvD8VXdy6r3fR6kUS//j//Heay0cd/mXWf/yG6z6w4IOz33vtRZW/OY5Js/7MZl0hueuvMMrF4pV5f+cVOCOtY87Zu89/gLZBDwReCwizi103t5QXrCum7ukodIhWBU6r+VudXeMD66eUnTOGTBzVrffr6uKvpAWEdvIPrz3L8D7wBHlCsrMbI/18PICkhrJzm6nkn1I733AWRHx1zLHZmbWdVVeXii0euEZsnXdB4BLIuKFRKIyM9tDlVoKVqxCM90ZwB8jIiQNlDQwInx/qplVryqf6XZa042IJ4F/lPQmsAJ4U9IKSd9MJDozs66q8tuAC5UXriT7rMgJeb+I+QngBklDI+LaBGI0MytehW7vLVah8sJ5wHERsWVHQ0Qsk/RV4GXASdfMqkq1r2culHQjP+HmNX4kqbqr1Wa2d6rypFtonW6LpM+3b8y1rS5PSGZm3VC6H6Ysi0Iz3W8DD0t6CtixXGws2Trv5HIGZma2R6p8plso6W4FLgAOA47KtT0J3ArsUnYwM6u4Hp50fwZcERG35TdKOiZ3rOMffTIzq4BIV/flpkJJ98CI+HP7xoj4s6RR5QnJzKwbevhMd3Anx/YpYRxmZiVR7UvGCq1eaJZ0SftGSRfz8YU1M7Pq0ZPvSAMuBR6UdC47r17oA5xdxrjMzPZMdZd0O0+6EfE28GlJE4Gjc82/i4jHyx6ZmdkeiNbqzroFn6cLEBHzgHlljsXMrPuqO+cWl3TNzHqKar+Q5qRrZrXFM10zs+R4pmtmliTPdM3MkhOtlY6gc066ZlZTKvTL6kUrdEeamVnPkunCVoCkSZIWS1oqaUYHfb4qaZGkhZLuLTSmZ7pmVlNKNdOVVAfcBJwGrALmS5odEYvy+owBrgBOjoiNkg4oNK5numZWUyJT/FbAeGBpRCyLiG3ALHb98YZLgJsiYiNARKwtNKiTrpnVlEir6E3SNEnNedu0vKFGAivz9lfl2vIdBhwm6WlJz0qaVCg+lxfMrKZ0pbwQEU1AUzferhcwBpgANABPSjomIt7t7AQzs5oRGZVqqBagMW+/IdeWbxXwXERsB96Q9BrZJDy/o0FdXjCzmlLCmu58YIyk0ZL6AFOA2e36PER2louk/cmWG5Z1NqhnumZWUyJKM9ONiFZJ04G5QB1wW0QslDQTaI6I2blj/1XSIiANfC8i1nc2rpOumdWUUt4cERFzgDnt2q7Oex3Ad3NbUZx0zaymZNIlq+mWhZOumdWUEl5IKwsnXTOrKU66ZmYJiup+nK6TrpnVFs90zcwSVKolY+XipGtmNSXt1QtmZsnxTNfMLEGu6ZqZJcirF8zMEuSZrplZgtKZ6n54opOumdUUlxfMzBKU8eoFM7PkeMmYmVmC9vrywmVvDCn3W1gP9O/fG1DpEKxGubxgZpYgr14wM0tQlVcXnHTNrLa4vGBmliCvXjAzS1AJfwy4LJx0zaymBJ7pmpklptXlBTOz5Hima2aWINd0zcwS5JmumVmCPNM1M0tQ2jNdM7PkVPmv9TjpmlltyXima2aWHD/wxswsQb6QZmaWoIxcXjAzS0y60gEUUN2PWDcz66KMit8KkTRJ0mJJSyXN6KTflyWFpLGFxvRM18xqSqlWL0iqA24CTgNWAfMlzY6IRe36DQK+AzxXzLie6ZpZTYkubAWMB5ZGxLKI2AbMAibvpt+/AD8GthQTn5OumdWUrpQXJE2T1Jy3TcsbaiSwMm9/Va6tjaS/BRoj4nfFxufygpnVlK4sGYuIJqBpT95HUgr4KXBBV85z0jWzmpIu3YqxFqAxb78h17bDIOBo4Alll6kNB2ZLOisimjsa1EnXzGpKCW+OmA+MkTSabLKdApyz42BEvAfsv2Nf0hPA5Z0lXHBN18xqTKYLW2ciohWYDswF/gLcHxELJc2UdNaexueZrpnVlFL+RFpEzAHmtGu7uoO+E4oZ00nXzGqKn71gZpagar8N2EnXzGqKH2JuZpYglxfMzBLkpGtmliD/coSZWYJc0zUzS5BXL5iZJShT5QUGJ10zqym+kGZmlqDqnuc66ZpZjfFM18wsQa2q7rmuk66Z1ZTqTrlOumZWY1xeMDNLkJeMmZklqLpTrpOumdUYlxfMzBKUrvK5rpOumdUUz3TNzBIUnumamSXHM929xLGnnMB511xEqi7FvFmP8ptf/Hqn42dcfBYTppxKpjXN+xvep+l7/4d3Wtax/8hhXNb0T0gpevWuY+4dc3jsnrkV+hRWaqmDj6LPKV+FVIrWV5+itXnnv9ven/176hoPz+706oP6D+KjX1xGquGw7Hk5GjKcbb+/lfTrLycZfo/kJWN7AaVSXPgv0/jRuf/M+jXruXb2T1jw6PO0LFnV1mf5wmVc9cXL2bZlG6d+7XSmXnEeN06/no1rN3LN2TNo3dZK3/79+MkjN/DCH57n3bUbK/iJrCQk+kycytZf/4zYvJF+U68gvewVYsPqti7bn3yA7bnXvY6bSOqARgAyq15jyz3XZg/07c8+F15LesWihD9Az1TdKRdSlQ6gFhx6/BjeXr6atSvfJr29lT/95ilOPG38Tn0W/elVtm3ZBsCSF19j6Ih6ANLbW2nd1gpA7z69UarKH3tvRUsNH028t5Z4/x3IpGl9rZm6Q47rsH/d4eNoXTx/1/YxJ5Je/iq0bt/NWdZeK1H0Vgme6ZbAkOFDWb/6nbb9DavXc+gJh3XYf+I/nMrLTyxo2x86op7v334VB44awb0/vNOz3BqhAYOJTR//XcamjaSGj95930FDSe23P5mVf93lWK/Dx7J9waNli7PWVPuFtC7NdCXdWGS/aZKaJTUv3bx8jwKrVSeffQqjjzmE397yUFvbhtXrmTHpMi777Df47Jcnsu/++1UuQKuIusPH0bpkAUS7hNF/X1L1I8msWFiZwHqgTBe2SuhqeeHkYjpFRFNEjI2IsYcOHNX1qHqYjWs2UD9i/7b9oSPq2bBm/S79jj75WL40/Stcf/GP2koK+d5du5GVr73JJ8cfWdZ4LRnxwbto0JC2fQ0aQnzw7m779jpsLOnFz+++/fWXIFPt1+SrR3ThTyW4plsCr7+8hOGjRzCs8QDqevfi7878DC/8Yefa3MFHjeaiH32D6y/6Ie+vf6+tfejwenr37QPAgH0HcPjYI1j9ekui8Vt5ZNYsR4MPQPvWQ6oul0B3XX2gIQdCv/5kVi/b5Vi2zrtrMraOVftMt2BNV9IbZC8IChghaVnudUTEJ8ocX4+QSWe44+pbmXHXNaTqUjxx/2O0LFnJV747lWWvLGXBo/M593+eT7/+/fj2z78HwPq31nH9xT/ioEMb+NpVFxARSOJ3TQ+xcvGbFf5EVhKRYdu8WfQ9+zugFK0LnyY2rKb3SWeSWbuC9LJXAOh1+DjSi5t3OV371qNBQ8isWpJ05D1aun2JpsoouhCgpBcj4oSuvME5B59d3f8ErCL+/bIDKh2CVaH+l97S7eU7Xck59654MPHlQl69YGY1pdpXL3Q16T5QlijMzEqk2i85dpp0JQ2PiDU79iPih+UPycxsz1X7bcCFVi+8JOlRSRdJGpxEQGZm3VHKJWOSJklaLGmppBm7Of5dSYskvSLpMUkHFxqzUNIdCVwHfAZYLOlhSVMk7VMwWjOzCkhHFL11RlIdcBPwBeBIYKqk9ovoXwTGRsSxwK+AnxSKr9OkGxHpiJgbERcCjcBtwGTgDUn3FBrczCxpGaLorYDxwNKIWBYR24BZZPNfm4iYFxEf5nafBRoKDVr0zRG5N10E/AV4Hzii2HPNzJLSlZsj8h9ZkNum5Q01EliZt78q19aRi4DfF4qvmJsjGoEpwFRgAHAfcFZE7PpkDjOzCuvKkrGIaAKauvuekr4GjAVOKdS30OqFZ8hm9geASyLihe4GZ2ZWTiVcvdBCtqy6Q0OubSeSTgWuBE6JiK2FBi00050B/DEiQtJASQMjYnMXgjYzS1RX7rItYD4wRtJossl2CnBOfgdJJwC3AJMiYm0xgxa6kPYk8I+S3gRWAG9KWiHpm3vwAczMyi5NFL11JiJagenAXLLXsu6PiIWSZko6K9ftOmAg8ICklyTNLhRfofLClWQf5zghIpbl2j4B3CBpaERcW+gNzMySVMqbIyJiDjCnXdvVea9P7eqYhcoL5wHHRcSWvDdZJumrwMuAk66ZVZUSlhfKolDSjfyEm9f4kaRqv8XZzPZCPf024BZJn2/fmGtbvZv+ZmYVVe2/HFFopvtt4GFJTwE7louNJVvnndzhWWZmFVLtDzEvlHS3AhcAhwFH5dqeBG4Fdik7mJlVWrWXFwol3Z8BV0TEbfmNko7JHTuzPGGZme2Znp50D4yIP7dvjIg/SxpVnpDMzPZcT1+9MLiTY368o5lVnWqf6RZavdAs6ZL2jZIu5uMLa2ZmVaOnr164FHhQ0rnsvHqhD3B2GeMyM9sj6ajuWwg6TboR8TbwaUkTgaNzzb+LiMfLHpmZ2R7o6TVdIPt0dGBemWMxM+u2aq/pdvUn2M3MqlqlarXFctI1s5qSqYXygplZT+GZrplZgnr06gUzs57G5QUzswS5vGBmliDPdM3MEuSZrplZgtKRrnQInXLSNbOaUhO3AZuZ9RS+DdjMLEGe6ZqZJcirF8zMEuTVC2ZmCfJtwGZmCXJN18wsQa7pmpklyDNdM7MEeZ2umVmCPNM1M0uQVy+YmSXIF9LMzBJU7eWFVKUDMDMrpejCn0IkTZK0WNJSSTN2c7yvpP/IHX9O0qhCYzrpmllNiYiit85IqgNuAr4AHAlMlXRku24XARsj4lDgfwM/LhSfk66Z1ZRMRNFbAeOBpRGxLCK2AbOAye36TAbuzL3+FfB5Seps0LLXdO9d8WCnAexNJE2LiKZKx2HVxd+L0mrd1lJ0zpE0DZiW19SU93cxEliZd2wV8Kl2Q7T1iYhWSe8B9cA7Hb2nZ7rJmla4i+2F/L2okIhoioixeVvZ/+PnpGtmtnstQGPefkOubbd9JPUC9gPWdzaok66Z2e7NB8ZIGi2pDzAFmN2uz2zg/NzrrwCPR4ErdF6nmyzX7Wx3/L2oQrka7XRgLlAH3BYRCyXNBJojYjbwf4FfSloKbCCbmDulal9IbGZWS1xeMDNLkJOumVmCnHS7SdJwSbMkvS7pBUlzJB0m6ShJj+duIVwi6X8p6xRJf2o3Ri9Jb0s6qFKfw0rD3wcrxEm3G3J3njwIPBERh0TEicAVwIFkr2r+a0QcDhwHfBr4JvBHoEHSwXlDnQosjIi3Ev0AVlLl/j5IWl7+T2Hl5qTbPROB7RFx846GiHgZOAx4OiIeybV9CEwHZkREBrifna9yTgHuSyxqKxd/H6wgJ93uORp4YTftR7Vvj4jXgYGS9iX7L9QUyD6lCDgD+M/yhmoJ8PfBCvI63QqIiGZJAyUdDhwBPBcRGyodl1VGZ98HSVcCf5/repCkl3Kvn46IbyUfrXWXk273LCR7F0p7i4DP5jdI+gSwOSLezzXtmN0ckXuNpNuBE4C3IuKMcgVtZVPS7wNARPwA+EHunOURcXzpw7YkubzQPY8DfXNPKgJA0rHAYuAzkk7Nte0D/Bvwk7xz7wO+BnwOeBggIi6MiOOdcHuskn4frDb5jrRuyi3r+RlwIrAFWA5cCvQDbgRGkL2F8JfAzPz7snP/q/jXiCh466D1DOX8PuRmuqPKFrwlwknXzCxBLi+YmSXISdfMLEFOumZmCXLSNTNLkJOumVmCnHTNzBLkpGtmlqD/D7Py5o1PZCb+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['COV-', 'COV+']\n",
    "ax = sns.heatmap(\n",
    "    cm / cm.sum(axis=1)[:, np.newaxis], annot=True, fmt=\".2f\",\n",
    "    xticklabels=labels, yticklabels=labels,\n",
    "    vmin=0., vmax=1.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree - RX+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_rx_X, test_rx_Y = test_df[test_df.rx == 1][classes], test_df[test_df.rx == 1].covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc:  0.7945205479452054\n",
      "ba: 0.5799120234604105\n",
      "auc: 0.6209677419354839\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, balanced_accuracy_score\n",
    "\n",
    "print('acc: ', accuracy_score(test_rx_Y, (tree_model.predict_proba(test_rx_X)[:,1] > 0.5).astype('int')))\n",
    "print('ba:', balanced_accuracy_score(test_rx_Y, (tree_model.predict_proba(test_rx_X)[:,1] > 0.5).astype('int')))\n",
    "print('auc:', roc_auc_score(test_rx_Y, tree_model.predict_proba(test_rx_X)[:,1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  8],\n",
       "       [ 7, 55]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = tree_model.predict(test_rx_X)\n",
    "cm = confusion_matrix(test_rx_Y, y)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['COV-', 'COV+']\n",
    "ax = sns.heatmap(\n",
    "    cm / cm.sum(axis=1)[:, np.newaxis], annot=True, fmt=\".2f\",\n",
    "    xticklabels=labels, yticklabels=labels,\n",
    "    vmin=0., vmax=1.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tree - RX-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "acc:  0.6129032258064516\n",
      "ba: 0.6029411764705883\n",
      "auc: 0.5724789915966386\n",
      "[[24 10]\n",
      " [14 14]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_rx_X, test_rx_Y = test_df[test_df.rx == 0][classes], test_df[test_df.rx == 0].covid\n",
    "print('acc: ', accuracy_score(test_rx_Y, (tree_model.predict_proba(test_rx_X)[:,1] > 0.5).astype('int')))\n",
    "print('ba:', balanced_accuracy_score(test_rx_Y, (tree_model.predict_proba(test_rx_X)[:,1] > 0.5).astype('int')))\n",
    "print('auc:', roc_auc_score(test_rx_Y, tree_model.predict_proba(test_rx_X)[:,1]))\n",
    "y = tree_model.predict(test_rx_X)\n",
    "cm = confusion_matrix(test_rx_Y, y)\n",
    "print(cm)\n",
    "labels = ['COV-', 'COV+']\n",
    "ax = sns.heatmap(\n",
    "    cm / cm.sum(axis=1)[:, np.newaxis], annot=True, fmt=\".2f\",\n",
    "    xticklabels=labels, yticklabels=labels,\n",
    "    vmin=0., vmax=1.\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.447</text>\n<text text-anchor=\"middle\" x=\"914\" y=\"-423.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 312</text>\n<text text-anchor=\"middle\" x=\"914\" y=\"-408.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [105, 207]</text>\n</g>\n<!-- 1 -->\n<g id=\"node2\" class=\"node\">\n<title>1</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"797.5,-365 610.5,-365 610.5,-297 797.5,-297 797.5,-365\"/>\n<text text-anchor=\"middle\" x=\"704\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\">Pleural Other &lt;= 0.063</text>\n<text text-anchor=\"middle\" x=\"704\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"704\" y=\"-319.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 170</text>\n<text text-anchor=\"middle\" x=\"704\" y=\"-304.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [86, 84]</text>\n</g>\n<!-- 0&#45;&gt;1 -->\n<g id=\"edge1\" class=\"edge\">\n<title>0&#45;&gt;1</title>\n<path fill=\"none\" stroke=\"black\" d=\"M845.82,-400.88C825.27,-390.9 802.56,-379.87 781.45,-369.62\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"782.72,-366.35 772.2,-365.12 779.66,-372.64 782.72,-366.35\"/>\n<text text-anchor=\"middle\" x=\"780.38\" y=\"-385.05\" font-family=\"Times,serif\" font-size=\"14.00\">True</text>\n</g>\n<!-- 16 -->\n<g id=\"node17\" class=\"node\">\n<title>16</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1064,-365 870,-365 870,-297 1064,-297 1064,-365\"/>\n<text text-anchor=\"middle\" x=\"967\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\">Pneumothorax &lt;= 0.008</text>\n<text text-anchor=\"middle\" x=\"967\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.232</text>\n<text text-anchor=\"middle\" x=\"967\" y=\"-319.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 142</text>\n<text text-anchor=\"middle\" x=\"967\" y=\"-304.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [19, 123]</text>\n</g>\n<!-- 0&#45;&gt;16 -->\n<g id=\"edge16\" class=\"edge\">\n<title>0&#45;&gt;16</title>\n<path fill=\"none\" stroke=\"black\" d=\"M931.21,-400.88C935.6,-392.42 940.39,-383.21 944.99,-374.35\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"948.19,-375.79 949.7,-365.3 941.98,-372.56 948.19,-375.79\"/>\n<text text-anchor=\"middle\" x=\"957.23\" y=\"-385.44\" font-family=\"Times,serif\" font-size=\"14.00\">False</text>\n</g>\n<!-- 2 -->\n<g id=\"node3\" class=\"node\">\n<title>2</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"504,-261 364,-261 364,-193 504,-193 504,-261\"/>\n<text text-anchor=\"middle\" x=\"434\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\">Edema &lt;= 0.041</text>\n<text text-anchor=\"middle\" x=\"434\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.483</text>\n<text text-anchor=\"middle\" x=\"434\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 108</text>\n<text text-anchor=\"middle\" x=\"434\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [64, 44]</text>\n</g>\n<!-- 1&#45;&gt;2 -->\n<g id=\"edge2\" class=\"edge\">\n<title>1&#45;&gt;2</title>\n<path fill=\"none\" stroke=\"black\" d=\"M616.34,-296.88C583.26,-284.39 545.85,-270.25 513.56,-258.05\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"514.74,-254.76 504.15,-254.5 512.27,-261.31 514.74,-254.76\"/>\n</g>\n<!-- 9 -->\n<g id=\"node10\" class=\"node\">\n<title>9</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"792.5,-261 615.5,-261 615.5,-193 792.5,-193 792.5,-261\"/>\n<text text-anchor=\"middle\" x=\"704\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\">Lung Lesion &lt;= 0.101</text>\n<text text-anchor=\"middle\" x=\"704\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.458</text>\n<text text-anchor=\"middle\" x=\"704\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 62</text>\n<text text-anchor=\"middle\" x=\"704\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [22, 40]</text>\n</g>\n<!-- 1&#45;&gt;9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>1&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M704,-296.88C704,-288.78 704,-279.98 704,-271.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"707.5,-271.3 704,-261.3 700.5,-271.3 707.5,-271.3\"/>\n</g>\n<!-- 3 -->\n<g id=\"node4\" class=\"node\">\n<title>3</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"310,-157 102,-157 102,-89 310,-89 310,-157\"/>\n<text text-anchor=\"middle\" x=\"206\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Support Devices &lt;= 0.052</text>\n<text text-anchor=\"middle\" x=\"206\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.405</text>\n<text text-anchor=\"middle\" x=\"206\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 71</text>\n<text text-anchor=\"middle\" x=\"206\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [51, 20]</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M363.73,-194.56C340.18,-184.03 313.69,-172.18 289.24,-161.24\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"290.54,-157.99 279.98,-157.1 287.68,-164.38 290.54,-157.99\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"528.5,-157 339.5,-157 339.5,-89 528.5,-89 528.5,-157\"/>\n<text text-anchor=\"middle\" x=\"434\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Cardiomegaly &lt;= 0.108</text>\n<text text-anchor=\"middle\" x=\"434\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.456</text>\n<text text-anchor=\"middle\" x=\"434\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 37</text>\n<text text-anchor=\"middle\" x=\"434\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [13, 24]</text>\n</g>\n<!-- 2&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>2&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M434,-192.88C434,-184.78 434,-175.98 434,-167.47\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"437.5,-167.3 434,-157.3 430.5,-167.3 437.5,-167.3\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"122,-53 0,-53 0,0 122,0 122,-53\"/>\n<text text-anchor=\"middle\" x=\"61\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.172</text>\n<text text-anchor=\"middle\" x=\"61\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 21</text>\n<text text-anchor=\"middle\" x=\"61\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [19, 2]</text>\n</g>\n<!-- 3&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>3&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M155.29,-88.95C140.27,-79.16 123.87,-68.48 109.03,-58.8\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"110.78,-55.76 100.49,-53.24 106.96,-61.63 110.78,-55.76\"/>\n</g>\n<!-- 5 -->\n<g id=\"node6\" class=\"node\">\n<title>5</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"271.5,-53 140.5,-53 140.5,0 271.5,0 271.5,-53\"/>\n<text text-anchor=\"middle\" x=\"206\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.461</text>\n<text text-anchor=\"middle\" x=\"206\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 50</text>\n<text text-anchor=\"middle\" x=\"206\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [32, 18]</text>\n</g>\n<!-- 3&#45;&gt;5 -->\n<g id=\"edge5\" class=\"edge\">\n<title>3&#45;&gt;5</title>\n<path fill=\"none\" stroke=\"black\" d=\"M206,-88.95C206,-80.72 206,-71.85 206,-63.48\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"209.5,-63.24 206,-53.24 202.5,-63.24 209.5,-63.24\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"412,-53 290,-53 290,0 412,0 412,-53\"/>\n<text text-anchor=\"middle\" x=\"351\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"351\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 11</text>\n<text text-anchor=\"middle\" x=\"351\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [0, 11]</text>\n</g>\n<!-- 6&#45;&gt;7 -->\n<g id=\"edge7\" class=\"edge\">\n<title>6&#45;&gt;7</title>\n<path fill=\"none\" stroke=\"black\" d=\"M404.97,-88.95C397.02,-79.89 388.39,-70.07 380.41,-60.99\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"382.83,-58.44 373.6,-53.24 377.57,-63.06 382.83,-58.44\"/>\n</g>\n<!-- 8 -->\n<g id=\"node9\" class=\"node\">\n<title>8</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"561.5,-53 430.5,-53 430.5,0 561.5,0 561.5,-53\"/>\n<text text-anchor=\"middle\" x=\"496\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"496\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 26</text>\n<text text-anchor=\"middle\" x=\"496\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [13, 13]</text>\n</g>\n<!-- 6&#45;&gt;8 -->\n<g id=\"edge8\" class=\"edge\">\n<title>6&#45;&gt;8</title>\n<path fill=\"none\" stroke=\"black\" d=\"M455.68,-88.95C461.45,-80.17 467.68,-70.66 473.49,-61.82\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"476.56,-63.52 479.11,-53.24 470.7,-59.68 476.56,-63.52\"/>\n</g>\n<!-- 10 -->\n<g id=\"node11\" class=\"node\">\n<title>10</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"744.5,-157 557.5,-157 557.5,-89 744.5,-89 744.5,-157\"/>\n<text text-anchor=\"middle\" x=\"651\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Pleural Other &lt;= 0.073</text>\n<text text-anchor=\"middle\" x=\"651\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.5</text>\n<text text-anchor=\"middle\" x=\"651\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 32</text>\n<text text-anchor=\"middle\" x=\"651\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [16, 16]</text>\n</g>\n<!-- 9&#45;&gt;10 -->\n<g id=\"edge10\" class=\"edge\">\n<title>9&#45;&gt;10</title>\n<path fill=\"none\" stroke=\"black\" d=\"M686.79,-192.88C682.4,-184.42 677.61,-175.21 673.01,-166.35\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"676.02,-164.56 668.3,-157.3 669.81,-167.79 676.02,-164.56\"/>\n</g>\n<!-- 13 -->\n<g id=\"node14\" class=\"node\">\n<title>13</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1062,-157 762,-157 762,-89 1062,-89 1062,-157\"/>\n<text text-anchor=\"middle\" x=\"912\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Enlarged Cardiomediastinum &lt;= 0.202</text>\n<text text-anchor=\"middle\" x=\"912\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.32</text>\n<text text-anchor=\"middle\" x=\"912\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 30</text>\n<text text-anchor=\"middle\" x=\"912\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [6, 24]</text>\n</g>\n<!-- 9&#45;&gt;13 -->\n<g id=\"edge13\" class=\"edge\">\n<title>9&#45;&gt;13</title>\n<path fill=\"none\" stroke=\"black\" d=\"M771.53,-192.88C791.89,-182.9 814.38,-171.87 835.29,-161.62\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"837.01,-164.67 844.45,-157.12 833.93,-158.39 837.01,-164.67\"/>\n</g>\n<!-- 11 -->\n<g id=\"node12\" class=\"node\">\n<title>11</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"692.5,-53 579.5,-53 579.5,0 692.5,0 692.5,-53\"/>\n<text text-anchor=\"middle\" x=\"636\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.219</text>\n<text text-anchor=\"middle\" x=\"636\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 8</text>\n<text text-anchor=\"middle\" x=\"636\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [1, 7]</text>\n</g>\n<!-- 10&#45;&gt;11 -->\n<g id=\"edge11\" class=\"edge\">\n<title>10&#45;&gt;11</title>\n<path fill=\"none\" stroke=\"black\" d=\"M645.75,-88.95C644.43,-80.62 643.01,-71.65 641.67,-63.2\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"645.11,-62.56 640.09,-53.24 638.2,-63.66 645.11,-62.56\"/>\n</g>\n<!-- 12 -->\n<g id=\"node13\" class=\"node\">\n<title>12</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"833,-53 711,-53 711,0 833,0 833,-53\"/>\n<text text-anchor=\"middle\" x=\"772\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.469</text>\n<text text-anchor=\"middle\" x=\"772\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 24</text>\n<text text-anchor=\"middle\" x=\"772\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [15, 9]</text>\n</g>\n<!-- 10&#45;&gt;12 -->\n<g id=\"edge12\" class=\"edge\">\n<title>10&#45;&gt;12</title>\n<path fill=\"none\" stroke=\"black\" d=\"M693.32,-88.95C705.5,-79.43 718.77,-69.07 730.88,-59.62\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"733.32,-62.15 739.05,-53.24 729.01,-56.63 733.32,-62.15\"/>\n</g>\n<!-- 14 -->\n<g id=\"node15\" class=\"node\">\n<title>14</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"973,-53 851,-53 851,0 973,0 973,-53\"/>\n<text text-anchor=\"middle\" x=\"912\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.245</text>\n<text text-anchor=\"middle\" x=\"912\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 28</text>\n<text text-anchor=\"middle\" x=\"912\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [4, 24]</text>\n</g>\n<!-- 13&#45;&gt;14 -->\n<g id=\"edge14\" class=\"edge\">\n<title>13&#45;&gt;14</title>\n<path fill=\"none\" stroke=\"black\" d=\"M912,-88.95C912,-80.72 912,-71.85 912,-63.48\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"915.5,-63.24 912,-53.24 908.5,-63.24 915.5,-63.24\"/>\n</g>\n<!-- 15 -->\n<g id=\"node16\" class=\"node\">\n<title>15</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1104.5,-53 991.5,-53 991.5,0 1104.5,0 1104.5,-53\"/>\n<text text-anchor=\"middle\" x=\"1048\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"1048\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"middle\" x=\"1048\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [2, 0]</text>\n</g>\n<!-- 13&#45;&gt;15 -->\n<g id=\"edge15\" class=\"edge\">\n<title>13&#45;&gt;15</title>\n<path fill=\"none\" stroke=\"black\" d=\"M959.57,-88.95C973.52,-79.25 988.74,-68.68 1002.56,-59.07\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1004.75,-61.82 1010.96,-53.24 1000.75,-56.07 1004.75,-61.82\"/>\n</g>\n<!-- 17 -->\n<g id=\"node18\" class=\"node\">\n<title>17</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1023.5,-253.5 910.5,-253.5 910.5,-200.5 1023.5,-200.5 1023.5,-253.5\"/>\n<text text-anchor=\"middle\" x=\"967\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.0</text>\n<text text-anchor=\"middle\" x=\"967\" y=\"-223.3\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 2</text>\n<text text-anchor=\"middle\" x=\"967\" y=\"-208.3\" font-family=\"Times,serif\" font-size=\"14.00\">value = [2, 0]</text>\n</g>\n<!-- 16&#45;&gt;17 -->\n<g id=\"edge17\" class=\"edge\">\n<title>16&#45;&gt;17</title>\n<path fill=\"none\" stroke=\"black\" d=\"M967,-296.88C967,-286.33 967,-274.6 967,-263.85\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"970.5,-263.52 967,-253.52 963.5,-263.52 970.5,-263.52\"/>\n</g>\n<!-- 18 -->\n<g id=\"node19\" class=\"node\">\n<title>18</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1313.5,-261 1146.5,-261 1146.5,-193 1313.5,-193 1313.5,-261\"/>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\">Atelectasis &lt;= 0.379</text>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.213</text>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 140</text>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [17, 123]</text>\n</g>\n<!-- 16&#45;&gt;18 -->\n<g id=\"edge18\" class=\"edge\">\n<title>16&#45;&gt;18</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1052.39,-296.88C1079.36,-286.42 1109.29,-274.82 1136.77,-264.16\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1138.33,-267.31 1146.39,-260.43 1135.8,-260.78 1138.33,-267.31\"/>\n</g>\n<!-- 19 -->\n<g id=\"node20\" class=\"node\">\n<title>19</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1380,-157 1080,-157 1080,-89 1380,-89 1380,-157\"/>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\">Enlarged Cardiomediastinum &lt;= 0.338</text>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.194</text>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 138</text>\n<text text-anchor=\"middle\" x=\"1230\" y=\"-96.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [15, 123]</text>\n</g>\n<!-- 18&#45;&gt;19 -->\n<g id=\"edge19\" class=\"edge\">\n<title>18&#45;&gt;19</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1230,-192.88C1230,-184.78 1230,-175.98 1230,-167.47\"/>\n<polygon fill=\"black\" 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1397.01,-157.06\"/>\n</g>\n<!-- 20 -->\n<g id=\"node21\" class=\"node\">\n<title>20</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1282,-53 1142,-53 1142,0 1282,0 1282,-53\"/>\n<text text-anchor=\"middle\" x=\"1212\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\">gini = 0.183</text>\n<text text-anchor=\"middle\" x=\"1212\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\">samples = 137</text>\n<text text-anchor=\"middle\" x=\"1212\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\">value = [14, 123]</text>\n</g>\n<!-- 19&#45;&gt;20 -->\n<g id=\"edge20\" class=\"edge\">\n<title>19&#45;&gt;20</title>\n<path fill=\"none\" stroke=\"black\" d=\"M1223.7,-88.95C1222.12,-80.62 1220.41,-71.65 1218.8,-63.2\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"1222.21,-62.41 1216.9,-53.24 1215.34,-63.72 1222.21,-62.41\"/>\n</g>\n<!-- 21 -->\n<g id=\"node22\" class=\"node\">\n<title>21</title>\n<polygon fill=\"none\" stroke=\"black\" points=\"1413.5,-53 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      "text/plain": [
       "<graphviz.files.Source at 0x7f2f66dc73d0>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import pydotplus\n",
    "import graphviz\n",
    "\n",
    "tree_graph = tree.export_graphviz(tree_model, out_file=None, feature_names=classes)\n",
    "pydot_graph = pydotplus.graph_from_dot_data(tree_graph)\n",
    "pydot_graph.set_size('\"10,20!\"')\n",
    "\n",
    "pydot_graph.write_png('tree.png')\n",
    "pydot_graph.write_pdf('tree.pdf')\n",
    "\n",
    "gvz_graph = graphviz.Source(pydot_graph.to_string())\n",
    "gvz_graph"
   ]
  }
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