{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Urgences - Image CC0 - pexels.com](img/pexels-pixabay-263402.jpg \"Urgences\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Biology Order Prescription\n",
    "*Levi-Dan Azoulay*  \n",
    "*Shana Zirah*  \n",
    "*Nathane Berrebi*  \n",
    "*Gaspard André*  \n",
    "*Jona Benhamou*    \n",
    "*Ali Bellamine*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plan du document"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les étapes clés de ce projet sont les suivantes :\n",
    "\n",
    "- I. Introduction\n",
    "- II. Téléchargement des données et transformation\n",
    "- III. Exploration et visualisation les données\n",
    "- IV. Sélection des variables d'interêts\n",
    "- V. Définition et entrainement d'une solution d'apprentissage statistique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ## I.1 Contexte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chaque jour, environ 50 000 personnes se présentent dans un service d'accueil des urgences (SAU) en France. En moyenne, 75% des patients retournent à domicile, et 20% sont hospitalisés. La durée moyenne de présence au SAU est longue. On estime que seulement 20% attendront moins d'une heure, tandis que ~30% attendront entre 1h et 2H et ~30% attendront en 2 et 4H. Enfin, un peu plus de 10% resteront au SAU entre 4 et 6H. Dans un contexte de pénurie de soignants, le recours à la consultation au SAU est en constante augmentation depuis plusieurs années. L'optimisation du circuit des urgences est une problématique centrale. Le cout humain et financier des dysfonctionnements du circuit et de l'offre de soin est important. \n",
    "\n",
    "Le parcours classique du circuit des urgences est le suivant :  \n",
    "1. **Premier contact d'ordre administratif**\n",
    "2. **Premier contact soignant avec une infirmière d'accueil et d'orientation (IAO) (~M30) avec**  :\n",
    "    - Recueil du motif de consultation\n",
    "    - Prise des constantes\n",
    "    - Recueil de quelques antécédents et de l'ordonnance du patient \n",
    "    - Eventuellement ECG  \n",
    "\n",
    "\n",
    "Le patient est classé selon un score de gravité (bleu, vert, jaune, orange, rouge, ou 1-2-3-4-5)\n",
    "\n",
    "3. **Premier contact médical avec un médecin (~H1)** :\n",
    "    - Interrogatoire\n",
    "    - Examen clinique\n",
    "\n",
    "    \n",
    "A la suite de cette consultation, plusieurs cas de figures selon la situation.\n",
    "Le patient peut sortir avec ou sans ordonnance si le diagnostic est posé par l'examen clinique et ne nécéssite ni examen, ni hospitalisation.\n",
    "Le patient peut nécéssiter la réalisation d'examens (prise de sang, radiographie, scanner) ou motiver un avis d'un spécialiste. Auquel cas il doit attendre\n",
    "\n",
    "4. **Réalisation des examens complémentaire ou d'un avis (prescription, réalisation, récupération)**\n",
    "5. **Décision finale : Conclusion une fois les examens récupérés. (~H3)**\n",
    "\n",
    "Entre chaque étape, le patient attend pendant une durée plus ou moins longue. Le médecin lui « jongle » avec plusieurs patients à la fois à des étapes différentes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I.2 Objectifs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous proposons d'aider à raccourcir le temps entre l'arrivée du patient et sa sortie, en ne subordonnant pas la décision de réaliser un examen biologique à l'examen clinique du médecin. Nous savons que le temps entre l'arrivée au SAU et la première visite avec le médecin est le temps le plus long et le plus mal vécu par les patients. \n",
    "\n",
    "Nous proposons à l'aide d'un algorithme d'apprentissage statistique de prédire, dès les données fournies par l'IAO, la nécéssité de réaliser un examen de biologie médicale, afin de permettre aux IDE de prélever cet examen juste après l'IAO, de sorte que le médecin dès sa première visite peut conclure avec les résultats de la biologie, qu'il aurait sans cela, demandé et attendu de récuperer avant de conclure et de prendre en charge le patient. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table markdown=\"1\">\n",
    "     <TR>\n",
    "       <TH>Données d'entrée</TH>\n",
    "       <TH>Algorithme</TH>\n",
    "       <TH>Données de sortie</TH>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD></TD>\n",
    "          <TD></TD>\n",
    "          <TD>Vecteur {0,1}^d d'examens de biologie associée à sa réalisation (1) ou non (0)</TD>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD>Age</TD>\n",
    "          <TD ROWSPAN=\"6\">MLP<br \\>NLP (Embeddings, Word2Vec ...) <br \\>Autres</TD>\n",
    "          <TD>Ionogramme Complet - {0,1}</TD>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD>Sexe</TD>\n",
    "          <TD>Bilan hépato-biliaire - {0,1}</TD>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD>Motif de consultation</TD>\n",
    "          <TD>Numération sanguine (NFS) - {0,1}</TD>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD>Paramètres vitaux (FC, SpO2, PA, T°, FR, EVA)</TD>\n",
    "          <TD>Glycémie - {0,1}</TD>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD>Ordonnance d'entrée du patient</TD>\n",
    "          <TD>Hémostase - {0,1}</TD>\n",
    "     </TR>\n",
    "     <TR>\n",
    "          <TD></TD>\n",
    "          <TD>...</TD>\n",
    "     </TR>\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I.3 Définition des métriques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous proposons d'effectuer la tache suivante : **prédire les examens biologiques qui seront réalisés lors de l'arrivé d'un patient aux urgences**  \n",
    "Les métriques d'évaluation des performances seront :\n",
    "- L'**accuracy**\n",
    "- La **precision**\n",
    "- L'**aire sous la courbe (AUC)**\n",
    "\n",
    "Nous attachons une importance particulière à la précision. En effet, une sur-prescription d'examen biologique non indiqué pourrait entrainer un effet contraire à l'effet escompté, en prolongant le temps de prise en charge des personnes concernées."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# II.  Téléchargement des données et transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données sont issus du projet MIMIC-IV.  \n",
    "Le projet MIMIC est un projet d'open-data médical initié par l'hopital _Beth Israel Deaconess_ à Boston.  \n",
    "Initialement, seul des données de réanimation été accessible.\n",
    "\n",
    "Pour sa 4ème édition, a été mis à disposition un jeu de données couvrant un spectre bien plus large :\n",
    "- Données relatives aux passages aux urgences\n",
    "- Données relatives aux hospitalisations\n",
    "- Données relatives aux séjour en réanimation\n",
    "- Données de radiographie thoracique avec compte rendu associé"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'ensemble de ces données ont été mis à disposition dans le cadre de projets complémentaires :\n",
    "- [MIMIC-IV](https://physionet.org/content/mimiciv/0.4/) : hospitalisation et réanimation\n",
    "- [MIMIC-IV-ED](https://physionet.org/content/mimic-iv-ed/1.0/) : urgences\n",
    "- [MIMIC-IV-CXR](https://physionet.org/content/mimic-cxr/2.0.0/) : radiographie thoracique\n",
    "\n",
    "Ces bases sont complémentaires dans le sens où chaque collecte a été faite durant une période temporelle spécifique, qui se recoupe plus où moins.  \n",
    "Certains éléments nécessaires à l'exploitation de MIMIC-IV-ED sont présent dans MIMIC-IV.  \n",
    "La lecture de la documentation de MIMIC-IV et de MIMIC-IV-ED est vivement recommandé (lien ci-dessus).\n",
    "\n",
    "En complément, un certains nombre de ressources est disponible sur le site du projet [MIMIC-IV](https://mimic.mit.edu/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II.1 Téléchargement des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La base de données de biologie étant volumineuse (nous y reviendrons plus bas), un pré-traitement des données a été effectué.  \n",
    "Le pré-traitement est le suivant :\n",
    "- Intégration de l'ensemble des données utiles au sein d'une base de données SQLITE\n",
    "- Tri des lignes de biologies afin de ne conserver que celles répondant au critères suivants :\n",
    "  - Date de réalisation >= date de début du passage aux urgences\n",
    "  - Date de réalisation <= date de fin du passage aux urgences\n",
    "\n",
    "*Le script de transformation peut être consulté dans `database_constitution/database_constitution.py`*\n",
    "\n",
    "Un token de téléchargement des données vous a normallement été mis à disposition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "    # Commande à executer dans le terminal\n",
    "    pip install -r requirements.txt\n",
    "    python download_data.py [TOKEN]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II.2 Transformation des données au format tabulaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données sont extraites depuis la base de données SQLITE de la façon suivante :\n",
    "  - Récupération et aggrégation des informations suivantes pour chaque consultation aux urgence identifié par un identifiant unique *stay_id* :\n",
    "    - Date de passage *intime*\n",
    "    - Genre *gender*\n",
    "    - Age *age*\n",
    "    - Température à l'accueil **temperature**\n",
    "    - Fréquence Cardiaque à l'accueil **heartrate**\n",
    "    - Fréquence respiratoire à l'accueil **resprate**\n",
    "    - Saturation en Oxygène à l'accueil **o2sat**\n",
    "    - Pression artérielle Systolique à l'accueil (**sbp**) et Diastolique (**dbp**)\n",
    "    - Cotation de douleur à l'accueil **pain**\n",
    "    - Motif de consultation à l'accueil **chiefcomplaint**\n",
    "    - Consultation dans les 7 derniers jours **last_7** ou 30 derniers jours **last_30**\n",
    "    - Antécédents connus au moment de la consultation selon la Classification Internationale des Maladies (CIM) : CIM-9 **icd9** ou CIM10 **icd10**\n",
    "    - Traitements habituels du patient lors de sa consultation, Generic Sequence Number (GSN) (**gsn**)\n",
    "  - Récupération des examens prescrits pour chaque consultation aux urgences :\n",
    "    - Les examens ont été regroupés par paquet correspondant aux techniques de laboratoire et aux organes / aspects fonctionnels explorés sur base de connaissance métier.\n",
    "    - Il s'agit pour chaque paquet d'examen d'une variables binaire indiquant si l'examen a été prescrit au moins une fois durant le passage aux urgences\n",
    "    - La prescription est identifié par la présence d'un résultat d'examen dans la table de résultats d'examens biologiques **labevents**\n",
    "    - Les différentes modalités d'examens sont :\n",
    "      - Cardiaque (**Cardiaque**)\n",
    "      - Coagulation (**Coagulation**)\n",
    "      - Gazométrie (**Gazometrie**)\n",
    "      - Glycemie Sanguine (**Glycemie_Sanguine**)\n",
    "      - Hépato-biliaire (**Hepato-Biliaire**)\n",
    "      - Ionogramme Complet (**IonoC**)\n",
    "      - Lipase (**Lipase**)\n",
    "      - Numération de Formule Sanguine (**NFS**)\n",
    "      - Phospho-Calcique (**Phospho-Calcique**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts import preprocessing\n",
    "\n",
    "lab_dictionnary = pd.read_csv(\"./config/lab_items.csv\").set_index(\"item_id\")[\"3\"].to_dict()\n",
    "get_drugs, get_diseases = True, True\n",
    "\n",
    "X = preprocessing.generate_features_dataset(\n",
    "    database=\"./data/mimic-iv.sqlite\",\n",
    "    get_drugs=get_drugs,\n",
    "    get_diseases=get_diseases\n",
    ")\n",
    "\n",
    "y = preprocessing.generate_labels_dataset(\n",
    "    database=\"./data/mimic-iv.sqlite\",\n",
    "    lab_dictionnary=lab_dictionnary,\n",
    ")\n",
    "\n",
    "# Par conception, last_7 et last_30 doivent valoir 0 lorsque manquant\n",
    "X[\"last_7\"] = X[\"last_7\"].fillna(0)\n",
    "X[\"last_30\"] = X[\"last_30\"].fillna(0)\n",
    "\n",
    "assert((X[\"stay_id\"] != y[\"stay_id\"]).sum() == 0) # Sanity check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train - test split\n",
    "# Nous gardons 10 000 lignes p\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=10000\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# III. Exploration et visualisation les données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avant d'explorer en détail les données, nous procédons à une identification et un nettoyage des données abérrantes.\n",
    "Ce nettoyage est effectué à partir de la documentation de MIMIC, de la visualisation des données et de connaissances métier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III.1. Identification et suppression des outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scripts.visualisation import plot_all_scatter, plot_missing_outcome\n",
    "from scripts.preprocessing import remove_outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "variables = [\"temperature\", \"heartrate\", \"resprate\", \"o2sat\", \"sbp\", \"dbp\", \"pain\"]\n",
    "plot_all_scatter(X_train, variables, ncols=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "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>n</th>\n",
       "      <th>total</th>\n",
       "      <th>pourcentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>temperature</th>\n",
       "      <td>511</td>\n",
       "      <td>414709</td>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heartrate</th>\n",
       "      <td>29</td>\n",
       "      <td>421209</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resprate</th>\n",
       "      <td>78</td>\n",
       "      <td>417862</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>o2sat</th>\n",
       "      <td>140</td>\n",
       "      <td>417598</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sbp</th>\n",
       "      <td>263</td>\n",
       "      <td>419915</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dbp</th>\n",
       "      <td>623</td>\n",
       "      <td>419090</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pain</th>\n",
       "      <td>11648</td>\n",
       "      <td>409552</td>\n",
       "      <td>2.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 n   total  pourcentage\n",
       "temperature    511  414709         0.12\n",
       "heartrate       29  421209         0.01\n",
       "resprate        78  417862         0.02\n",
       "o2sat          140  417598         0.03\n",
       "sbp            263  419915         0.06\n",
       "dbp            623  419090         0.15\n",
       "pain         11648  409552         2.84"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "variables_ranges = {\n",
    "    \"temperature\":[60,130],\n",
    "    \"heartrate\":[20, 300],\n",
    "    \"resprate\":[5, 50],\n",
    "    \"o2sat\":[20, 100],\n",
    "    \"sbp\":[40, 250],\n",
    "    \"dbp\":[20, 200],\n",
    "    \"pain\":[0,10]\n",
    "}\n",
    "\n",
    "X_train_clean, outliers = remove_outliers(X_train, variables_ranges)\n",
    "outliers.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JmtaamoLVi+exevG8RldFkiRJkqRpZ1QpMiLi6Ii4LiJuLZ6fEBHvGJuqSZIkSZIkSZIms9HmYP4I8DagGyAzbwbOHW2lJEmSJEmSJEmT32gDzHMz8ycDynpGuUxJkiRJkiRJ0hQw2gDzwxHxZCABIuIVwJZR10qSJEmSJEmSNOmNdpC/NwKXA8dExAPAPcDvjbpWkiRJkiRJkqRJr+4Ac0Q0A2/IzN+OiHagKTN3j13VJEmqXW9vsmlHJ1t3dbF0QRurOtppaoqaX5ckSZIkSSNXd4A5Mw9ExDOLx51jVyVJkkamtze55raHuOjKm+jq7qWttYlLzzmR9WsPp6kpDvq6JElqjKn4A/BUrLMkSeNptCky/icirgL+HegLMmfmf4xyuZIk1WzTjs6+4DFAV3cvF115E8dccCqrF8876OuSNFYi4khgTWZ+JyLmAC3e5SdVNxV/AJ6KdZYkabyNdpC/w4AdwPOBlxR/Z4y2UpIkjcTWXV19weOyru5etu3uqul1SRoLEfHHwJeAfyuKVgBfbViFpEluqB+AN+2YvDfITsU6S5I03kYVYM7M11f5+8Ph5omIIyLiuxFxR0TcFhEXFuWHRcS1EXFn8X9hxTxvi4i7IuKXEfGiivJnRsQtxWuXRUQU5bMj4oqi/McRsWo02ylJmtyWLmijrbX/V1pbaxNL5rfV9LokjZE3Ar8J7ALIzDuBJQ2tkTSJTcUfgKdinSVJGm+jCjBHxCci4uMD/w4yWw/wF5l5LHAK8MaIOA54K3BdZq4BriueU7x2LrAWWA/8SzHAIMCHgfOBNcXf+qL8PODRzHwK8H7gktFspyRpclvV0c6l55zYF0Qu3666qqO9ptclaYzsy8z95ScR0QJkA+sjTWpT8QfgqVhnSZLG22hzMH+j4nEb8DLgweFmyMwtwJbi8e6IuANYDpwJPLeY7FPA94C3FOVfzMx9wD0RcRdwckRsAhZk5g0AEfFp4CzgW8U87yqW9SXgQxERmWkDX5KmoaamYP3awznmglPZtruLJfP7D7hzsNclaYz8V0S8HZgTES8A/hfw9QbXSZq0yj8AD8xnXOsPwI0YbG+0dZYkaToaVYA5M79c+TwivgB8p9b5i9QVzwB+DCwtgs9k5paIKN9OuBz4UcVsm4uy7uLxwPLyPPcXy+qJiJ1AB/DwgPWfT6kHNCtXrqy12pKkSaipKVi9eN6Qg/Yd7HVJGgNvpXQn3S3AnwBXZ+ZHGlslaXIZGBR+4bFLubqOH4AbNdieP1pLkjTYaHswD7QGqClSGxHzgC8Db87MXUX65KqTVinLYcqHm6d/QeblwOUA69ats3ezJEmSRuPPMvMDQF9QOSIuLMqkGW+4oPBIfwAearC9Yy44ddx/TPZHa0mS+httDubdEbGr/EfpFsC31DBfK6Xg8ucy8z+K4q0Rsax4fRmwrSjfDBxRMfsKSmk4NhePB5b3m6fIfXcI8MjIt1CSNJX19iYbt+/hhrsfZuP2PfT2ZtUySRojr6tS9gcTXQlpshoqKLxpR+eIlzXawfYme3tgstdPkqRKo02RMX+k80Spq/LHgDsy89KKl66i1Cj/++L/1yrKPx8RlwJPotRL+ieZeaAIcJ9CKcXGa4EPDljWDcArgOvNvyxJM0u1XlIfevUz2N+TE347raTpLSJeBbwaOCoirqp4aT6wozG1kiaf4YLCI+0NXB5sr3J5Qw22NzAtx8qFc/n2HVsnbXugUek/JEmq12h7MF9XS9kAvwm8Bnh+RNxU/J1OKbD8goi4E3hB8ZzMvA24ErgduAZ4Y2YeKJb1BuCjwF3A3ZQG+INSALujGBDwIkr58CRJM0i1XlI3b945Zj2nJKnCD4F/BH5R/C///QWwfrgZI+LjEbEtIm6tKDssIq6NiDuL/wsrXntbRNwVEb+MiBdVlD8zIm4pXrus6NRBRMyOiCuK8h8XY6BIDVEOClcaKih8MOXB9srLG2qwvXKw9vTLfsCrPvJjTr/sB3zz1i1ccs0dk7Y9MJY9vSVJmgh19WCOiDZgLrCoaPCWf0ZdQKmX8ZAy8/9RPUcywGlDzHMxcHGV8g3A8VXKu4Czh6uHJGl6q9ZLqjcZs55TklSWmfcC9wK/XsfsnwQ+BHy6ouytwHWZ+fcR8dbi+Vsi4jjgXGAtpTb3dyLi6KLzxYcpDV79I+BqSoHtb1EadPDRzHxKRJwLXAK8so56SqNWDgoP7JlbLShc2eO42iB6tQ62Vy1Y+5Yv38x5z17NP3/3rr7pJlN7YCx7ekuSNBHqTZHxJ8CbKTVsb+SJgPEu4J9HXy1Jkkan2q2zzUHNt9NK0kgVqds+CBwLzAKagc7MXDDUPJn5/Sq9is8Enls8/hTwPUrjnJwJfDEz9wH3FHfrnRwRm4AFmXlDUY9PA2dRCjCfCbyrWNaXgA9FRJg+To1QS1B4qPQQLzx2Kfc9undQ0Plgg+0NFaxtHnAv72RqD4wk/YckSZNBXSkyMvMDmXkU8L8zc3VmHlX8PT0zPzTGdZQkacSq3Tr7tBWH1HQ7rSTV6UPAq4A7gTnAH/HEGCEjsTQztwAU/5cU5cuB+yum21yULS8eDyzvN09m9gA7gY5qK42I8yNiQ0Rs2L59ex3Vlg6uHBQ+ZfUiVi+eV1OP40uuuYNv3rqlX5qLa257qKaB74ZKy7HuyMMGtQdWLpw7KQbWqzX9hyRJk8VoB/n7YEQcDxwHtFWUf3rouSRJGn9D9ZICDno7rSTVKzPviojmIm3FJyLih2O4+GofVjlM+XDzDC7MvBy4HGDdunX2cFZDVOtxfMYJy3nLl28elJP4mAtOPWjKiKHScvzG6g6urmgPTKaB/2pN/yFJ0mQxqgBzRLyT0u17x1HK9fZi4P/RP4ecJEkNMdStswe7nVaS6rQ3ImYBN0XEPwBbgHq6HG6NiGWZuSUilgHbivLNwBEV060AHizKV1Qpr5xnc0S0AIcAj9RRJ2lCVE1x1VT/GArDBWsr2wMbt++pOrBeLUHs8VBL+g9JkiaLulJkVHgFpYH5HsrM1wNPB2aPulaSJI2h3t6cFLe8Spr2XkOpff0moJNSYPd361jOVcDrisevA75WUX5uRMyOiKOANcBPijQauyPilIgI4LUD5ikv6xXA9eZf1mRWLT3Er1WksygbSU7ig6XlgOEH1pMkScMbVQ9m4PHM7I2InohYQKl3xeoxqJckSWNiqMGCGnHLq6TpKyKagYsz8/eBLuDdNc73BUp3BC6KiM3AO4G/B66MiPOA+4CzATLztoi4Ergd6AHeWKTiAHgD8ElKuZ+/VfwBfAz4TDEg4CPAuaPbUml8VetxvHLh3KppLsYyJ7ED60mSVL8YTQeGiPgX4O2UGqp/AewBbip6M08p69atyw0bNjS6GpKkMbZx+x5Ov+wHgy4Yr27QLa+SJpeIuDEz143Rsv4TeElm7h+L5TWSbWNNNr29yaYdneOWk9gfpCVJqr9tXHcP5uL2u7/LzMeAf42Ia4AFmXlzvcuUJGmsDXfLqwFmSWNsE/DfEXEVpRQZAGTmpQ2rkTRNjHdOYgfWkySpfnUHmDMzI+KrwDOL55vGqE6SJI1KuZfT1l1dzJ3VwpEdc7h3x+N9r3vLq6Rx8mDx1wTML8rMdyzVqPL7e+mCiQ/wOrCeJEn1GW0O5h9FxK9l5k/HpDaSJNWh8oJ0yfw27tmxhzd9/n/6bnF931nH88Hr7+TeHY+PS95GSSrcnpn/XlkQEWc3qjLSVDIeKSrqDVg3OtAtSdJUM9oA8/OAP42ITZRuAwxKnZtPGG3FJEkaysECyheetoaFc2exZWcpPcY7vnorV5x/Co93H/CWV0nj6W3Av9dQJmmATTs6+4LLUEpnddGVN3FMnWMm1BuwNhezJEkjN9oA84vHpBaSJNWo2oXfwIDyB667k/OevZp//u5dQOki9fHuA5yyelGDay9pOoqIFwOnA8sj4rKKlxYAPY2plTSxRtvrd6zHTKg3YD3WgW5JkmaCUQWYM/PeiHg2sCYzPxERiwG/dSVJ46bahV85oPwfP9vMy09aQQQ8del8lh3SxpadXeZcljTeHgQ2AC8Fbqwo3w38eUNqJE2goXr9vvDYpdz36N6ags5LF7TR1trUL8g8mu/vegPW9c5nWg1J0kw2qgBzRLwTWAc8FfgE0Ap8FvjN0VdNkqTBhrrwm9/WzGtOOZLLrr+z7+L2guev4YoN9/GW9ceac1nSuMnMn0fErcALM/NTja6PNNGq/fh7yTV30H2gl7d8+eaaUk2s6mjn0nNOHBSkrvf7u96AdT3zjSSthoFoSdJ01DTK+V9GqadGJ0BmPsgTI2ZLkjTmyhd+ldpam1hx6Ny+4DKULm4vu/5OLjv3GeZNlDTuMvMA0BERsxpdF2miVfvx94wTlvcFl+GJVBObdnRWXUZTU7B+7eFcfcGpfPH8Z3H1BaeO6vu7HLAutxmGC1j39iYbt+/hhrsfJhM+9Opn1DRf2VBpNe57pLNvuRu376Gnp5drbnuI0y/7Aa/6yI85/bIfcM1tD9Hbm3VtoyRJk8VoczDvz8yMiASICLuHSZLG1VA9nOYM6G0ET+ReNrgsaYLcC/x3RFxF0QEDIDMvbVyVpPFXrddvcxMjTjXR1BSsXjxvTHIdlwPWx1xwKtt2dw05yO9QvY+vufBUHto19HyVqgXYF86dxc/ue4y3f+UWFs6dxdnrVvCUJfN48NHH+40bYX5nSdJ0MNoA85UR8W/AoRHxx8AfAh8ZfbUkSapuqAvGTTs6xzR3oyTV4cHirwnv6tMUVG/6hmo//v7akYeNyfdytToBNdWzloD1UL2Pr77g1JoHB64WYD973Yq+4HK1FF6f+dG9fUHmegcylCRpshjtIH//NyJeAOwCjgb+JjOvHZOaSZI0hGoXjLXkbjTvoaTxlJnvbnQdpHqNJI/wQNV+/F25cO6ocypXq9OHXv0M9vdkXfWspt5B/SpVa4McvWQ+Xd29vPykFVVTeJ337NX883fv8sdwSdK0MNoezAC3AHOALB5LkjThDnYrbOVFavlW1aOXzOfYZQs4apGBZkmjFxGLgf8DrAX6IkaZ+fyGVUqq0VA9eWtN31Dtx99aUlSMtE43b97J5d/fOKiex114Kr3JiH9ErnVQv+F+pK7WBsksLSeieqqQiNryO0uSNBWMKsAcEX8E/A1wPRDAByPiPZn58bGonCRJIzHcrbDli9Rqt6qOpueTJFX4HHAFcAbwp8DrgO0NrZFUo7HoyTvQaHMqV6tTbw4O2FbmOx7pd3utd0AdrHf3wG3t7U0uPedEfvnQrqoB7FOfsoiXP2O5d1NJkqaF0fZg/kvgGZm5AyAiOoAfAgaYJUnjbiQpL8oXqdVuVXWAHUljpCMzPxYRF2bmfwH/FRH/1ehKSbWotSdvo+vUXPT8rZbvuJbv9mpth4P1tK6nd3e5V/Nxy+ZzZEf7oOD3r606zMCyJGnaGG2AeTOwu+L5buD+US5TkqSDGmmuyPJF6lC3qjrAjqQx0F383xIRv0NpwL8VDayPVLNaevJOhjo9bcUhQ+Y7rlTtu324tsNwPa3r7d3d1BSsWjSPlYe1c+IRhw4ZwHaMCEnSVDfaAPMDwI8j4muUcjCfCfwkIi4CyMxLR7l8SZKqGtibaOHcWfyiuA11VUf7oIuz8kXqULeqOsCOpDHwvog4BPgL4IPAAuDPG1slqTYHG8tgMtUJqJrv+GDf7fXmmR5t7+7hUoWMZnBFSZImi9EGmO8u/sq+VvyfP8rlSpI0rMreRMsOaTtoXuWD3arqADuSRiszv1E83Ak8r5F1kepRa87kiexxO1SdquU7Pljv63p7Io9n7+7RDq4oSdJkMKoAc2a+e6wqIknSSFT2Jqo1r3Ktt6pKUj0i4mjgw8DSzDw+Ik4AXpqZ72tw1aQxMxl73Nba+7rensjj2bt7PAZXlCRpojWNZuaIWBcRX4mIn0XEzeW/saqcJElDKfcmOlhe5eFkjmcNJc1AHwHeRpGLOTNvBs5taI2kMTZUj9tNOzobWq9yT+dTVi9i9eJ5VYO/lW0HYEQ9kWtZfj3KQe9Kpu6SJE01o02R8TngL4FbgN6DTCtJ0pip7E20fc8+PvqDjTX1SJqMPa8kTRtzM/MnEf0+S3oaVRlpPAxMUfXyk1YQAdv37Jv0dwRNxjzTjRpc0YEFJUljabQB5u2ZedWY1ESSpBEq9yYaycWZuQ4ljaOHI+LJlAa/JiJeAWxpbJWksVXucbtw7qx+4x989Acbp8QPtrXmmZ4ojQh6+2O7JGmsjTbA/M6I+ChwHbCvXJiZ/zHK5UqSVLORXJyZ61DSOHojcDlwTEQ8ANwD/F5jqySNrfKPur94aFfV8Q+Ou/BUepOD9oy1B+0Tagl6j+X+8sd2SdJYG22A+fXAMUArT6TISGDYAHNEfBw4A9iWmccXZYcBVwCrgE3AOZn5aPHa24DzgAPABZn5n0X5M4FPAnOAq4ELMzMjYjbwaeCZwA7glZm5aZTbKkmaxGrtkVTvAD+SVIMHgE8A3wUOA3YBrwPe08hKSWOp/KPuwO9SgIVzZ/Gz+x7j7V+5ZdiesdO5B+14BM7Hen/5Y7skaayNapA/4OmZuS4zX5eZry/+/rCG+T4JrB9Q9lbgusxcQ6lH9FsBIuI4SoOjrC3m+ZeIaC7m+TBwPrCm+Csv8zzg0cx8CvB+4JJ6N1CSNL2MZoAfSTqIrwEvoTTI34PAHqCxI59J46CpKVjV0T5ocLqz163oCy7D0AMATtaBAkerHAg+/bIf8KqP/JjTL/sB19z2EL29oxtVeKz3lwMLSpLG2mgDzD8qAsAjkpnfBx4ZUHwm8Kni8aeAsyrKv5iZ+zLzHuAu4OSIWAYsyMwbMjMp9Vg+q8qyvgScFgNGW5EkzUzlnldXX3AqX/rTU7ji/FNYOLeVTTs6R30BKGnGW5GZ52bmP2TmP5b/Gl0paTxU+8H26CXzh+wZW2m4HrRTSW9vsnH7Hm64+2E2bt/DfY+MT+B8rPeXP7ZLksbaaFNkPBt4XUTcQykHcwCZmSfUsaylmbmF0gK2RMSSonw58KOK6TYXZd3F44Hl5XnuL5bVExE7gQ7g4TrqJUmaZso9r37x0O5peXuupIb5YUQ8LTNvaXRFpPFWbfyDTGpKQzUd0lVVS1vxty97GgvnzmLLzicCv2ORemKs91cjBhaUJE1vo+3BvJ5SaooXUrod8Izi/1iq9i2Xw5QPN0//BUecHxEbImLD9u3bR1FFSVKjDOw9VGsv5Ol6e66kiRcRt0TEzZQ6X/wsIn4ZETdXlNe73E3FMm6KiA1F2WERcW1E3Fn8X1gx/dsi4q5i/S+qKH9msZy7IuIy7+zTWCmPf3DK6kWsXjyPoxbV1jN2OvSgrdaOePtXbuHsdSv6TTdUIHgk7Zfx2F8Dj53BZUnSaIyqB3Nm3hsRzwbWZOYnImIxUO9Ps1sjYlnRe3kZsK0o3wwcUTHdCko57TYXjweWV86zOSJagEMYnJKDzLyc0kjfrFu3zvuiJWmKGc2gNw5wI2kMnTGOy35eZlbehVcet+TvI+KtxfO3DBi35EnAdyLi6Mw8wBPjlvyI0sDY64FvjWOdNUPV2jN2OvSgHaodcfTS+X29jYcKBI+0/TId9pckaXobVYA5It4JrAOeSmnE7Fbgs8Bv1rG4qyiNsv33xf+vVZR/PiIupdRYXgP8JDMPRMTuiDgF+DHwWuCDA5Z1A/AK4PoiT7MkaRoZqhfyMRecetAg8XS4PVfS5JCZ907g6s4Enls8/hTwPeAtVIxbAtwTEeVxSzZRjFsCEBHlcUsMMKsmvb3Jph2dbN3VxdIFBw9slnvGHux7uNbpJquh2hHHHr6Aqw8SCK6n/VJtf4302EiSNF5GmyLjZcBLKUbHzswHgfkHmykivkAp+PvUiNgcEedRCiy/ICLuBF5QPCczbwOuBG4HrgHeWPTEAHgD8FFKA//dzRMN5Y8BHUXD+iJKPTskSdPMaAa9mQ6350qa9hL4dkTcGBHnF2X9xi0BKsctub9i3vL4JMsZetySfkwfp4HKPW1Pv+wHvOojP+b0y37ANbc9NOJBcetNZzWZDdWOOGpR+0FTT4zFoH1jdWwkSRoLox3kb39mZkQkQETUdFWema8a4qXThpj+YuDiKuUbgOOrlHcBZ9dSF0nS1DWaXsjebippCvjNzHywGPz62oj4xTDT1jNuSf9C08dpgNHcKVQ2mnRWk9lo2hFDtV8OX9DGxu17auqRPBbHRpKksTLaHsxXRsS/AYdGxB8D3wE+MvpqSZJ0cKPthewAN5Ims+LuQDJzG/AV4GSKcUsAxmDcEmlYY9HTdjoPqltvO6Ja++VDr34Gt2/ZXVOP5N7eZPvuffzRqat50/OfwrJDSj+sj/TYSJI0Vkbbg3kx8CVgF6U8zH8D/PZoKyVJUi1G03vIvIWSJrPizsCmzNxdPH4h8B7GdtwSaVhjMV6Bg+oOVq39kgm/88EfHLRHcrUe4Rc8fw2f+dG9PLp3v2NJSJIaYrQB5hdk5luAa8sFEfGPlAYakSRp3AwMEJ+8qqPmAPF0vV1X0rSyFPhKRECpzf75zLwmIn5K6S7C84D7KNLCZeZtEVEet6SHweOWfBKYQ2nMEgf4U03KPW0Hfl+OZLwCB9WtbuCgfTfc/XBNgfhqPcIvu/5Ozn/Oao45fEHVY+OP6pKk8VZXgDki3gD8L2B1RNxc8dJ84L/HomKSJA1ltAFi8xZKmuwycyPw9CrlOxijcUs0s9USdByL8QrGIkg9E9QaiB+qR/gzjjiU3zp6yaBj44/qkqSJUG8P5s9T6vnwd8BbK8p3Z+Yjo66VJEnDGG2A2Nt1JUkz2UiCjuWetqs62tm0o5Mf37NjRL1gHVS3NkMF4lcunNtv4L9lh1QPRB85xD71R3VJ0kSoK8CcmTuBncCrxrY6kiQd3GgDxN6uK0mayUYadBxtL9iB6SA0WLVA/MqFc/n2HVsH7fcPvfoZvOnz/1NTj3B/VJckTYSmRldAkqRa9fYmG7fv4fHuA1x42hOjpsPIAsQDR28/smMOl79mHVt3dbFx+56qI7ZLkjRdDBd0rGaogPSmHZ3jXteZpByIP2X1IlYvnsd9j+6tut+P6pjH1RecyhfPfxZXX3Aq69ceDsDG7Xv46aYd/Pz+R7nh7ofZuH1PX4/nSv6oLkkaa6Md5E+SpAlRrffUhaet4dM3lEZNH0k+x8peQo907uOBx7o4/zMbzE0oSZoRRnonj71gG2Oo/b59T1dfEBqeaCNdcs0dvHLdSi67/s66ezxLklQPA8ySpCmhWu+pD1x3J596/cksnj97xPkcy72EAH7/Yz8xN6EkacYY6cB7ppZqjFr3e7mNdN6zV/cFl+GJNs03/+xUrjYHtiRpHBlgliRNCUP14klyVIFge2VJkmaakQ68N9KAtMZGrfu93JaJoKYez0Pp7U027ejsG1DQQLQkqVYGmCVJU8J49Z6yV5YkaSYabuC9aoHGkQSkNTZq/SGg3JYBDtqmGSqIPNqBHKsxYC1JM0dkOpARwLp163LDhg2NroYkaQjjceED0NPTyw837mDDvY/Qm/D1nz/AW9Yfaw5maYaIiBszc12j6zHZ2Daeucbr+1bjZ6gczEd2zOG9Zz6N1uZg6YI2Vi6cy7fv2Fr12G7a0cnpl/1gUHD66jpThnkeSdLUVG/b2B7MkqQpYaS389aitzcHXWhd8rsn8MJjl3rxI0maEQb2Mm0KBo154NgEk1tfG+nw+TzSuY8rzj+F/Qd6eXDAIMaX/O4JXHrtL6se27FOGVZt7AzPI0mavgwwS5KmjOFu563HwIufhXNncff2PXz/ru2s6mj3Vk5J0rQwkrQIf/uyp7Fw7iy27Ozqm9+xCSa/gW2kjdv38JoBgxi/5cs3c96zV/PP372rb77ysR3rlGGOcSFJM4sBZknSlDKW+fwqL36WHdLGa045su+2Um/llCRNB8OlKqjWy/TtX7mF85+zmsuueyII6dgEU89QAd7mpv7TlY9tLQMKVrbB5s5qYf+BA3S0zx42L7RjXEjSzGCAWZI06VVe0PQcSN7xtVu4d8fjow4CV178vPykFX3BZfBWTknS9DBcqoKhgpBHL53f9/1YLdCoyW+oAO+6Iw+remwPloqs2g8VFzx/DVdsuI+3rD+WFx67lPse3dvXAWDlwrkHDVhLkqYPA8ySpEmtcuCaM05YTnMTvHX9sXz4e3dx8wO7RhUEruytE4G3ckqSpp2Bd+u8/KQVRMD2PftYdkj1IOSxhy/g6jEc80ATb6geyb+xumPQsYVSSo1ycPjkVR39AsubdnSyaUcnv3xoV1/6lK7uXi67/k7Oe/ZqLrnmDroPlFJwVK7rhccu9TySpBnCALMkaVLbtKNz0Kjoba1N/PUZx7H9+rvYsrOr7iBwZW+dR/fupymgN2He7GZ6DiT7D/Qyp7WF3t70gkiSNCWVe7IunDurXyqoj/5gI5eecyIfevUzeNPn/6dfYPCoRe19OX01NQ3XI7kyV/NwKVSAqr2WP/Oje/uCzBFwxgnL+4LL8EQv+auLDgCeR5I0/TUdfBJJkhpn664uzjhh+aD0Fe/9xu28/KQVo87n19QUrOpoZ+uufVz+/Y18+cbNZMKHvnsXl113F6+8/Aauue0hentzrDZJkqQJ0dubZML/fcXT+avfOZYrNtw3KAh4VMc8rr7gVL54/rO4+oJTHXtgGikHk09ZvYjVi+dVPa5DpVAp91oe+Npl19/Jy09aAZR6u2dCc9PQd4FJkmYGezBLkiaVgYP4LTukbcgLl+YmxiSfX+UF1MtPWsEHrjMXsyRpahpu3ILK3qdQ+o7bvqerLwCpmWeoPNyPdO7jQC/80amrAfjyjZv79Voun0/X/+Ih/uKFxwClu8DK0zmgnyTNLAaYJUmTRrXbND/06mfwm09eVHp9wIXLaccs4WnLDx11T6vKi6v5bc2c9+zVRDyRKqOrp5fte/aZO1CSNKkM/FF25cK5fPuOrUOmNCjnzP3n794FYBBQVQcDPLJjDg881tUvp3L5PHp0735OfcoiXnDsEjKT5Qvn8PpP/rTqwH8O6CdJM4cBZknSpDHwVsyFc2fx4KOP83DnTnoTmgP+9Dmr+fgP7+Et648dk+Ay9M9POb+tlX/6zp0snDuL1/76kXzou3f1y1XprcOSpEaoJZh8ye+ewKXX/nJQSoNyULl89w/Ql2vXIODMVm0wwPee+TTO/8yGQefR+c9ZzTGHL+DXVh1GU1Owcfse3vLlHw+a7orzTxmzNpokaWowwCxJmjQGjnT/N2ccR+f+HqJzP1++cTOP7t3Phaet4Z9ffRLHLTtkzC5cyhdXv3hoF+/9xu39UmUsnDuLl5+0ggj45UO7OG7ZfFYt8jZiSdLEqXaHT7Vg8lu+fHO/Hsrl8ii+Lst3//zGkzv6DfqmmavaYIBDpc14xhGH8ltHL+k7Z4aa7vHuA0OeVwN/KCn/wFEumzurhf0HDtDRPtvzU5KmEAPMkqRJY+BI939e5RbfD1x3JyetXDimFxzli6vKW0TntzXzpuc9haUL2tj86F6u3FAKcB/Z0c7Kw7zgkSRNnGqDrQ0MJi87pI2Xn7SClYfN4U3Pf0q/lFKZT/RYtmepBioPBliZh3tg2oy21iaOHBDwrZZeY7i0K0OlQtvfk4PSupTTbHjnmCRNDU2NroAkSWXlnsRnr1vBZdeXeg+/8XlP4Y9OXc2+ngO89tePpKu7l737e8Z83U1NwaqOdtpam1h2SBvz21r59xvvZ9OOvSTwN2ccx9FL5vH2r9zCph2dY75+SZKGMrCn6LJD2jjv2av7gsknLF/Aa045ko/9v4285cu38NEfbOQ1pxzJkR1zuOR3T+B5T13E1RecarBONSm3x9paS+GCodKpVJvuQ69+Bplww90Ps3H7Hnp7s2/6aj+U3Lx556Cyy66/k3N/bSW/eGgX3/vVtr7l9PYmG7fvqbpsSVJj2YNZkjRplHsSz29rZlbz4N7Df33GcRzZMYeVh41PvsjKVBmXf/9uXrluJZddfydd3b0c2TGHvz5jLbc/uMsB/yRJE6qyp+iyQ9p4zSlH9n0/tbU28ddnHMfl37/bXLgaE9XSZlRr9wyc7vAFbdy+ZTe/88Ef9J2bleNXDEyF9vKTVrD8kDn80amr+3rcQ2kMjgVzWvnAdXcO29P50nNO5IXHLuW+R/f2S7nh+S5JE88AsyRp0nmks7tvcL3K9Bjv/cbtXP6adRy1aHwCzJWpMg700nfxfsLyBfzpc5/CzZsfY3ZLEz+/71G279rHscsWcNQiL2QkSeOrciC2l5+0ou/7CUrB5Pd+4/aquZeHy4UrDada2oyDTbdx+55BvZEvuvImjrvwVHoTHu8+wIWnPYXv/mIb649f1u9HknJbb8vOLs5et6JvTIzycm7evJPLv7+x70eWl5+0go3b9/Dfs5r5m6tu5d4djw8KaI+FajmjfU9J0mAGmCVJk8qmHZ285cs3D+qFVb5wbm2OcW3Yl1Nl3HT/Y30XMa88eSUXXXkTC+fO4ux1K1h62Fy27uriwcf28qRD57L2SQvoTdi224sPSdLoVQtqlXuK/mrr7qoDqzUPSH44XC5caTxUG/Rv4dxZ/Oy+x3j7V245aI/78569mo/9v40csXDukD2dv//L4YPTlQHtakHhkQSMq+WMHusAtiRNFwaYJUmTylAjkkeULpaXLhj/i+VVHe382pGH0dbaxMtPKvWiKQ88WM4Nffa6FaxZMo+21ibu2r6H2S1NPLxnP4893s1d2/bw5MXz7N0sSeqnMri1ZH4bzU2wfc8+ZjU3sXf/AZYuaGPlwrl8+46tfUGtIzvm8N4zn0Zrc7B0QRvHHD6/6sBq64rvrcpA2MCcudJ4qjbo39nrVvQFl2H4HvdPW76AK84/hf09vf0Gfa4lHUx5eZUB7XJ77fgnHcLS+bPp3H+AA73JO752C/t7krPXreDoJfP77kgD+gWfm4KqPbKPueDUYXt2j7TXs72kJU0HBpglSROm3IDe0bmPebOb2fX4AR7es49F82fT1tLEY3v309bawmXnPp37H32cz/7oPrbs7KKttYmmYMIulpuagl9f3cElv3sCd2/fQ1d3b98tyeWLnSs23Mcr163s9/+ME5ZzSFszxz3pEH7x0C627e5if88B5sxq4fH9PbS1trCvp4d5s1uLi5xe2me1sHd/D3NntfDY4920z26mrbWJyGBv9wH2dfdyZEe7wWpJGoXRBHAqv7vKgeBycHjLzieWB/2DUysXzu3LDbvskDZ6DiS/eGg3d27b3Te2wEUvOJrZzU383TW/6Asmv/ulx7Nx+55+vTXP/8yGfoHjD736Gbzp8//Tr+w3Vndw9UFy5krjqTKVS/ncPHrJ/Jp73B+9dD6rF8+jtzf7xsSoNR3M/LZm3vi8p7DysDncu6OTo5fMY/3xy7hiw320tTRzwRefeL/8+W8fTVtL//fde898Gru7uvnV1tL7c1ZL8K6XHs//eu5T2H+gly/fuBmAl5+0gru27WZ3Vw/7Dxxg8bzZHOgt3cU2d1YL+w8coGt/76AgduXdbuXpOtpnD/pBqVpu6crpq33W1PJeN4gtabxN6wBzRKwHPgA0Ax/NzL9vcJX6jNUHfC3LGWqa8f5ldSTrhcFfktXKhrt9aWCDftOOTu7Z0UlbazML57ZyzNIFtLQ0VZ2/FqPZ16PZj0Pp6enlti072bKzi2WHzGHtsurbV+2iqHIf3/dIJ1t37WP/gQMsmN3K3u4DI26o7Ojcx+zmUu/NtllNzG5upmPeLFYeNjbndeUFWrmBddjc2VV7/VTWu7yPtu7qoqN9Nr1kX8Os8ja5ex7u5N5HOmmf1cLSBbNZcWhpfdUuJB/du5/eXti6u4tF82bTc+AALc3N9GYvc1paeGTvfubNbqEpYPe+Hma3NLNnXw8d7bNIkv09yZ6uHg5rn0VrS7BzbzetLU3sfrxU1tQETQSP7N3P7NYn9mW5Tjsf39/XiD2sfRbzZrXQm0nn/gMsO6SNA72wo3MfQWnfLJo3m+4DPbQ0NZe2pfsAe/cfYF5bC7OamjisfRYR/fdjeTnlBvDj3d00RxOP7u1m7qxmWpuD1uYm9h840LfN7bNb2Nd9gEPnzqL7QC9bdnbRMW8Ws1uayF7Yva+bttYWdj3ezby2Fh7v7qFrfy93bNnJJ354L4/u3c87X7KW9tbg3sf3cmRHO7Namvm/Z5/AoXNa6dx3gK2797Fo3ixu3/Ioe/cn23bvY+n82XQfOMDslhY69/fwePcBFrXPZs/+bua2tvDo3v20tTYzb3YLh8xp4uE9PTy6t5t5s1uYM6uJJoIHd3Zx+CFtZMLDe/ZxyNxW9nX3sKBtFovntbJy4WJOXnUYu7t6+Ox5J9PSlHz3lzs499dW8oHr7uTC09YA8KbnrWHHnn3Mbmnmr756C69ct5J/+M9fDApC/+FvHEUEdO4/wOL5s/nF7t20z27h4qvvYOHcWbz2149k+aFzmDurmUc697FkwRx+tXU3W3c/TltLM13dPbQ2N7Ojs3SuzZ3VTGbyeHcvPb29LGhrZV9PL3u6ejikvYXWaKKrp5fO4jzs6U0e7tzPoXNaaGspLWfBnFbaZzXzSOd+Zrc0s//AAeYXnwfVgij13GY6kvf/PQ938uDOvcxubqZzfw8rDxs6wO4F0/TgcRx/06FdPFzv3/0Hegd9Z5df69w3RK/Fw+fT3Bz9vgMrP/Mqg0UfuO6XPP+Yw/v1pLzwtDV865YtPPeYJRy9ZD4L21t5+1dK63jtrx/Jkw6dw8bte/juL7bx4qct6zdgWfmW/kuv/RXnP2f1EymZ1q3kTz9747C9NS+68ia++WenVg0m15IzV4018Dye1Qpbd+5nV9d+5rS2sPPxbjrmzWJBWzNPXjS665eJVm1wwExG3OO+ckyMWoLTR3bMYX5bK//0ncE9nc84YfmgIPX7vzP4fVf5I87b1h9DV08vb6h4L5bL3v+dX/Ur23egl0uv/VW/93a5vVeevty+q/wMeNv6Y9jbfYDlC+fw4KOPs3DuLIAhc0tf8Pw1XP+Lh7jgtKcOCoS/98ynMWdW04g/A8tB70f2PjFd++z+11qVn4XVgumjKat2PVe+DinXabjP9mrXLZXrKi9jqM/2g9Wz1u+jgderY7Guer4Xq8VRatn/tbbxK5c71B049fx4O5pUNpO9/Vhv/Sb7dg1l2gaYI6IZ+GfgBcBm4KcRcVVm3t7Ymo1dLqdaljPUNC88dmnVX0qHqsNI6zyS9Q41IvCslhjUM6Pa+gau68iOOVxw2tH8VUWerwtPW8M9D3fy4rXL6mqkjWZfj3SaWvT09PLVnz/AO756a99y3nfW8Zz19OX9tq+8vkuuuYNXrlvZ76Lo0nNOpH12E7c/uJsv/vS+qq8PV6/KZb/65CP7NbYuPG0N7bOaedLCPTz/qUtHfV6/76zj+eD1d/ZrYF2x4T7O+82jeLy7f0OvXO/e3hy0j8rzvWX9saxfezjAoHW9/cXHMHd2Cx+8/s5B++Q9Lz2O3gze9fXb+sreecZavvvLLTzvqct49zeeKB/YI6mttYl3v3Qt//K9u9jfk7z+N1fxuR/f228dR3bM4U+f85R+y7nwtDV0tLeyoelRvviTe/ndk1b2e71yma/99SOrHst3vWQtLc2wfff+fg3bi15Q6r3R1trEo3t7hmwAv/Mla/nX/7qrb/+X6zRwX5T377m/tpJP31ARNJ4VbNm5v99xKk/7e886kj99zmr+9fsb+df/uou/eOFT6dx/gD8pGvRHdszhfz33Kbzzqtv6nr/xuU/hbyqev/m0o3lo166+Oh/ZMYc3/NZTeNfXf9ZvWw8/pI3/86WbB52nX7pxM2c8/UlVLw7e9Lw1PL6/k7/91hPH8b1nHs/8WTCnrZWjl8xjwZzWvoFoysstB5/Pe/bqvts2y//39RzgQNJvH1942pq+njYD9/07Ky4u3vE7xzKrualv+8vzHn5IG5/70SZecsLyvgudhXNn8YbfWk1XzxPPBx7bC09b03esyufsx394z6BzqHK68nus2vtntHkJD/aZNXDZ5kacHjyO4286tIurTff2Fx/D4929fP4n9w76zKj2PTyw1+JQ01V+5pW/D85/zpMHBXq/+NNSeeV3QLV1HOyW/t4sbeNIBu/bvqeLU1YvMpg8xVQ7j99z5vFc+dN7B/2A8d4zj+fBnV2c+uQlUy7IXPlDR7k38sD3+MF63JfHxKglOP3ulx7f98MM9H/vRFA1SD3c+27H3v19AwqOtKz83q587eUnrehrf0EpL/Xe7gP92mTVPjsqc0uXP4eqBcL/+mu3DPoMLH8+Vra/K9dRbhdWu3ao1qavFkyvtezPf/to5rQ29WtPV7ueG1incgeNobarWtu2vK6P/ffw7dla61nL99GRHXP4s+ev6XfdOZp1QW1t7GqfJ9ViK8Pt/8pjfLD1D4zPVDvHam2/DffdP5rtn0ztx3rrN9m3azhT55tq5E4G7srMjZm5H/gicGaD6wSUfvWplstp047OMV/OUNPctmXniOow0jqPZL03b65el5s376xpfQPXdcYJy/uCy+V5P3Ddndy5bQ+3bdlZ6+4d8faP1TS1uG3Lzr4vsPJy3vHVWwdtX3l91X65v+jKm9j9eKlhM9Trw9WrctnlL5XyvB+47k4e7tzPzZt3jsl5/Y6v3soZJyzve37Z9aU6P9y5f9C6y/Wuto/K85Wnqbauhzv3961v4D6ZO6u1L6BaLnv3N27j9045qi/oWy6/9NpfsWPv/n5l77zqNs44YTkvP2kFl177q0HrOOOE5YOW84Hr7mTurFbe8dVbee1vrB70euUyhzqW7/r6bbS1tPRr2Jbr+HDnfubOau3bjwMbwF3dvbz767f12//lOg3cF+X9+4Hr7uTlJ63om/fQubMHHafytOX99PKTVnDGCcu5a9uefus/44TlfcHl8vO/GfD8nh2dg+YZWLdLr/0Vd23bU/U8/aPnPLmvwTewfn/9tVt5uLP/cfzrr93KCUcs4r3fuL1fYKFyuSuKwWnKFzWV/5cdOnfQPv7AdXfyR8958qBjN3Dfb9u9r9/2l+e95+FOXvsbpYuZ8ra8/KQVPNzZ/3m19ZaPVflYVDuHKqcb7v1Tz+dZpYN9Zg1c9njUQRPP4zghpny7eKjv7Pd/Z/D36VDfw+//zuCyatNVfuaVvw/e+43b+z6Py8rlB1tHtXnL3wnlFFDAkIEwB++bPqqdx3/ztVIbb+A5/Ndfu5XmaKr7+mWyKPdGvvqCU/ni+c/i6gtOZf3aw2lpaWL14nl9P5RUC5yUU260tZbeBAOD0+XlzW4Zvqdzef6yg73verP+svJ7u/K1geuo1iar9tlx2fWlzyKo/nmzY+/Qn4HVrpMq1zHctUO1Nn1lG7Ny/bWUvf87vxrUnq52PTewTgfbrqH248OdB2/P1lrPWr6Pzjhh+aDrztGsazTfi9ViK8Pt/5G08QfGZ4a7Fj+Y4bZxNNs/mdqP9dZvsm/XcKZzgHk5cH/F881FWZ+IOD8iNkTEhu3bt09YxYYawGrb7q4xX85Q02zZObI6jLTOI1nvUF/M5V+VD7a+gesa7lfqh3aObB8PtY5q9RmraWox1PEbuH3l9Q21Tzr39wz7+nD1Otiye5O+PGMjMdwAbwOfD3XubNvdNeQ+Ktd32+6uqusqL7PadnXu66m6zMf2dtd8DkcwKOhYdrDj9PgQ6x9umQOXUa2Ola8NNf/A/T/UvqisR7nskc7q+6c8bW+W1tvcNPiYHmwfVTsPDtZTZWDZcPt1qPm27i6dOwcyq867d39Pv4uhyv+PD3Eshiqv3PfDfV4+vr9n0MXMcBc3A5dfeSwONt1Q7596Ps8qHexzZeCyx6MOmngexwlx0HYxNKZtXOvxH+l39nCf+7WUVX7mlZc/MNDb3FT7eqsFiZsCLnrB0XTMnTXou6JyupNWLhwUYHPwvqlpqPN9qLbIo3u7675+mUzKvZqHCyYPNV8tweny4IKV2lqbeMbKhXz95w9wwfPX9HsP/cPvnsBTl84f8n3XHPWXtbU2kTn4tcrHI/nMKn8WVfu8Ge4z8GDX2Ae7dhjYph9t0H2obavWVh2ubrW0bWtpz44mBlFr/KHedY3F92It66p23Tbc+gcuY7hr8YMZbhtHs/2Tqf1Yb/0m+3YNZzoHmKt9a/V7S2Tm5Zm5LjPXLV68eIKqxZBfgCPtiVDLcoaaZtkhI6vDSOs8kvUO9cU8sN0x1PqGWle15R1+SH29PUazr0c6TS2WHTKn6nIGbl/l+qpN3z6rZdjXh6vXwZbdFNAUjNl5nTn4+VDnzpL5bUPuo3IeuCXz26quq3KZA19rb2upusxD57bWfA6Xt2O4fTfwefk4zZ1dff21LLPyWA+s48DXatn/Q+2L8v6trNNh7dX3T3na8rny9BWHDnlMh3te6zzVjkdTMOx+HWq+pfPbhv0sfaSzdFt1+aKm8v/CikBC5TxDHaPKfT/c5+XcWS2DXh/u4mbg8iu39WDTDfX+qefzrNLBPlcGLns86qCJ53GcEAdtF0Nj2sa1Hv+RfmcP97lfS1nlZ175++CYwxf0W9+xyxbUvN5jB8z7ty97Gi87cTkvPG4pa5cv4IrzT+G3jl7EJb97Qr/pLj3nRJ795EWDAmyT/TZZVTfU+T5UW2Th3Na6r1+mi1qC00P1dD71yYv4xB+czMlHLeSK80/hC39ceg+dccKTePHxy7j6glN5bpX33dFL5/MPA8qetuKQQes4bO6sUsq5irILnr+Gb9z8AMcuW8DfvuxptLU28eUbN3PhaU8EuUdy/Vv+/Kn8DCkb7jOwlnUM9/k5sE0/2qD7UNs2VFu11u0abl3DtWdHE4OoNf5Q77rG6nvxYOuqdt023PoHLmO4a/GDGW4bR7P9k6n9WG/9Jvt2DWc6B5g3A0dUPF8BPNiguvQz1BfgSHsi1LKcoaZZu2zwF+RwdRhpnUey3mpf1peecyInrDikpvUNXNfXf/4AFxdf5uV5LzxtDWuWzGPtskNq3b0j3v6xmqYWa5ct4H1nHd9vOe876/hB21deX7Vf7i8950Tmz2nmwtPWDPn6cPWqXPaf//bRg/b3ovZZnLDikDE5r9931vF84+YH+p6XG24d7bMGrbtc72r7qDxfeZpq6+pon8X7zjq+6j7Zu6+bd71kbb+yd56xls/96B7eeUb/8mo9kt790rV84+YH+PKNm7noBUcPWsfXf/7AoOVceNoa9u7v5n1nHc+nfrhx0OuVyxzqWL7rJWvp6unp17At13FR+yz27u/u248DG8BtraU8wJX7/8LT1lTdF+X9e+Fpa/iPn23um/exvfsGHafytBe94GiesmQev7VmMUvnz2bNkvn91v/1nz/Au1+6tt/z9wx4vqqjfdA8A+tWXk+18/Qj3797yIuD9555PIva+x/H9555PDff/zCXnnMiJx2xcNA59LcvexqnPmURJ6xYwP/3iqez9knzn/j/u09nXlsTfzfgM+ris57GysPmDDpnB+77xfNn99v+8nYctaidT/1wY78LnS/fuJmO9v7PBx7bymNVPmernUOV0w33/hltr7qDfWYNXPZ41EETz+M4IaZ8u3io7+w//+3B36dDfQ9X67VYbbrKz7zy98Elv3sCTzpkdl+Q6pt/dirHLZvfF0Aabh1/+7KncdyT5vPNP3siSHzWics5avE8Vi2ax7pVHTz9iIX82qoOXnLCk+pKJaCpodp5/J4zS228gefwe888ngPZW/f1y0xysJ7O5ffYrz/5ifdQOXC9rsr77sXHL+OMAWXPf+rSvnV84Y+fxRXnn8La5Qt44XFL+eafPVF28lEL+cQfnMwLjzucs05cztUXnMr7X/l0Xnz84X3TveC4pYM+O6pd/17yuyfw3Kcu4orzT2HZIbOHDIRX+wysdp1U+fk03LVDtTZ9tWB6rWV//ttHD2pPV7ueG1ing21XtbZteV0Ha8/WWs9avo++/vMHBrXhR7Ou0XwvVoutDLf/R9LGH3h+DnctfjDDbeNotn8ytR/rrd9k367hROagzgvTQkS0AL8CTgMeAH4KvDozb6s2/bp163LDhg0TVr/yqJBDDWowlssZapqR1mGspq9WDtRUNtyAc5XTrlw4l007OrlnRydtrc0snNvKMUtHNwrzaPb1aPbjUHp6erlty04e2tnF4Ye0sXbZIVW3r7y+Rzr30TpghFeA+x7pZOuufXQfOMD82a3s7a59BNjKZc9qbuLhPfuZ3dpEW0szHfNmsfKwsTmvK0flnTurme4DvSwsRr19eM/g7Sqvs7yPtu7ax2Hts0iSjvbZg0aovefhTu57pJO5s1pYumA2Kw4tra9yn5VHyn1s734O9MLW3V0sap9NT+8BWpqbyexldksLj+3dT/vsUk/SXft6mN3SzJ6uHg5rnwWR7O/JvuetLcHOvd20tjSx+/FSWVMTNBE8snc/s1qe2JflOu16fD89vaXUI4e1z2LerBZ6M+kcMOoyWRrVd9G82XQfOEBLU2lk6ce7D9C57wDz2lqY1dzEYe2ziOi/H/uNxtzaTFdPD03RxGN7u5kzq5nW5qC1qYnu3gN921wejXjBnFZ6DiRbdnbRMW8Ws1uayF7Yva+bttYWdj3ezYI5rXR19zB3VguL58/ud5709ib3PdLJQ7v2sevxbtpnN3PonFY69x1g2+59LFkwm7mzgr37k+2797Fkfmn7Zre09KX6OKx9Fp37u5nb2sKje7tpa21i3uwWDpnTxMN7enh0bzfzZrcwt7WJiODBnV0cvqCNBHbs2ceCOa3s6ym9H/b19DCntYXO/QfY3dXDonmzOKy9mYiWuj9Lq53j1Za1eN4TI1Dv7uph3uxm2lqb6eruobW5mR2d+0vbMauZzOTx7l56entZ0NbKvp5e9nT1sGBuC7OamujqKaU16WifRU9vsqNzPwvaWpjTWlrOgjmttM9q5tG9+5nV3Mz+is+D8rn/0K7B2zdWn2cD9809D3eyZedeZjU307m/h5WHtXPUouFHjx7LOmjiNfI4RsSNmbluQlbWICNtF8PEto1rPf7VPiPL31/7D/T2jWRf/tyq1kYAhlxG5bxbdj7R5jhsQNvhYHWvXIefSxpo4Hk8qxW27tzP7q79fW2lw9pnsWBOM09eNLrrF01e9Xx2DDdPteuWg30GHr6g1OZ/dO8T0w281qr8LFw0b/YT1whjUFbteq58HVKu08E+2/tdtwxYV+V2VftsH66e2/fU/n1Ueb06sA1f77rq+V4ceE5UXjsPt/9rbeNXnjvVvj9rjSEMV/ex2P7J9H1bb/0avV31to2nbYAZICJOB/4JaAY+npkXDzXtRAeYJUmS1HgzIcAMI2sXg21jSZKkmajetnHLeFRmssjMq4GrG10PSZIkqZFsF0uSJGm8TOsezCMREduBexuw6kXAww1Y70zkvp447uuJ476eOO7rieX+njgzfV8fmZkTN9rzFNHAtrEObqa/Z2caj/fM4bGeOTzWM8tUO951tY0NMDdYRGyYCbdlTgbu64njvp447uuJ476eWO7vieO+lqYW37Mzi8d75vBYzxwe65llphxvRwyQJEmSJEmSJNXFALMkSZIkSZIkqS4GmBvv8kZXYAZxX08c9/XEcV9PHPf1xHJ/Txz3tTS1+J6dWTzeM4fHeubwWM8sM+J4m4NZkiRJkiRJklQXezBLkiRJkiRJkupigFmSJEmSJEmSVBcDzBMoIg6NiC9FxC8i4o6I+PWIOCwiro2IO4v/Cxtdz6kuIp4aETdV/O2KiDe7r8dHRPx5RNwWEbdGxBcios19PT4i4sJiP98WEW8uytzXYyQiPh4R2yLi1oqyIfdvRLwtIu6KiF9GxIsaU+upaYh9fXZxbvdGxLoB07uv6zTEvv7/irbIzRHxlYg4tOI197U0yUTEpoi4pWjXbijK/P6fBmx7zBxDHOt3RcQDFdetp1e85rGeoiLiiIj4bhHzuS0iLizKfW9PQ8Mc7xn3/jbAPLE+AFyTmccATwfuAN4KXJeZa4Driucahcz8ZWaemJknAs8E9gJfwX095iJiOXABsC4zjweagXNxX4+5iDge+GPgZEqfH2dExBrc12Ppk8D6AWVV929EHEfpXF9bzPMvEdE8cVWd8j7J4H19K/By4PuVhe7rUfskg/f1tcDxmXkC8CvgbeC+lia55xXt2/IPcH7/Tw+fxLbHTPFJBh9rgPeXr10z82rwWE8DPcBfZOaxwCnAG4tj6nt7ehrqeMMMe38bYJ4gEbEAeA7wMYDM3J+ZjwFnAp8qJvsUcFYj6jeNnQbcnZn34r4eLy3AnIhoAeYCD+K+Hg/HAj/KzL2Z2QP8F/Ay3NdjJjO/DzwyoHio/Xsm8MXM3JeZ9wB3UQr+qwbV9nVm3pGZv6wyuft6FIbY198uPkcAfgSsKB67r6Wpw+//acC2x8wxxLEeisd6CsvMLZn5s+LxbkodC5fje3taGuZ4D2XaHm8DzBNnNbAd+ERE/E9EfDQi2oGlmbkFSicmsKSRlZyGzgW+UDx2X4+xzHwA+L/AfcAWYGdmfhv39Xi4FXhORHRExFzgdOAI3Nfjbaj9uxy4v2K6zQzfkFD93Nfj6w+BbxWP3dfS5JTAtyPixog4vyjz+3/6su0xs7ypSFn18YqUCR7raSIiVgHPAH6M7+1pb8Dxhhn2/jbAPHFagJOAD2fmM4BOvJVtXEXELOClwL83ui7TVfEheSZwFPAkoD0ifr+xtZqeMvMO4BJKt7ZfA/yc0u04aoyoUpYTXouZwX09TiLiryh9jnyuXFRlMve11Hi/mZknAS+mdOvtcxpdITWEn9HTz4eBJwMnUuqs849Fucd6GoiIecCXgTdn5q7hJq1S5vGeYqoc7xn3/jbAPHE2A5szs/xLxpcoBZy3RsQygOL/tgbVbzp6MfCzzNxaPHdfj73fBu7JzO2Z2Q38B/AbuK/HRWZ+LDNPysznULrF7k7c1+NtqP27mVIP8rIVlNLDaOy5r8dBRLwOOAP4vcwsN2rd19IklJkPFv+3URpX5GT8/p/ObHvMEJm5NTMPZGYv8BGeuE3eYz3FRUQrpWDj5zLzP4pi39vTVLXjPRPf3waYJ0hmPgTcHxFPLYpOA24HrgJeV5S9DvhaA6o3Xb2KJ9JjgPt6PNwHnBIRcyMiKJ3Xd+C+HhcRsaT4v5LSYGhfwH093obav1cB50bE7Ig4ClgD/KQB9ZsJ3NdjLCLWA28BXpqZeytecl9Lk0xEtEfE/PJj4IWU0mb5/T992faYIcrBxsLLKL23wWM9pRXXxR8D7sjMSyte8r09DQ11vGfi+zue6LSi8RYRJwIfBWYBG4HXUwryXwmspBSsOzsza03+ryEUOWrvB1Zn5s6irAP39ZiLiHcDr6R0m/X/AH8EzMN9PeYi4gdAB9ANXJSZ13lej52I+ALwXGARsBV4J/BVhti/RXqBP6R07r85M781eKmqZoh9/QjwQWAx8BhwU2a+qJjefV2nIfb124DZwI5ish9l5p8W07uvpUkkIlZT6rUMpZR7n8/Mi/3+nx5se8wcQxzr51K6fT6BTcCflHP0eqynroh4NvAD4Bagtyh+O6W8vL63p5lhjvermGHvbwPMkiRJkiRJkqS6mCJDkiRJkiRJklQXA8ySJEmSJEmSpLoYYJYkSZIkSZIk1cUAsyRJkiRJkiSpLgaYJUmSJEmSJEl1McAsSZIkSZIkSaqLAWZJkiRJkiRJUl0MMEuSJEmSJEmS6mKAWZIkSZIkSZJUFwPMkiRJkiRJkqS6GGCWJEmSJEmSJNXFALMkSZIkSZIkqS4GmCVJkiRJ0rQXEX8ZEbdGxO6IuCci/nIMlrkqIjIiWsaijpI0FRlgliSNWEQ8NyI2N7oekiRJ0ggE8FpgIbAeeFNEnNvYKknS1GeAWZKmkInoGRElfj9IkiRpSoqIYyPiexHxWETcFhEvBcjMf8jMn2VmT2b+Evga8JvFPBER74+IbRGxMyJujojji9d+JyL+JyJ2RcT9EfGuitV9v/j/WETsiYhfn8BNlaRJwQCCJE1yEbEpIt4SETcDnRHx7Ij4YdFg/nlEPLdi2j+IiI0Vt/39XkX5f0fEB4sG8y8i4rSK+b4XERdHxH8De4HVEfH6iLijWNbGiPiTYtp24FvAk4pG9J6IeFJENEXEWyPi7ojYERFXRsRhE7irJEmSNMNFRCvwdeDbwBLgz4DPRcRTB0wXwKnAbUXRC4HnAEcDhwKvBHYUr3VS6vl8KPA7wBsi4qzitecU/w/NzHmZecOYb5QkTXIGmCVpangVpcbsako9Ld4HHAb8b+DLEbG4CPxeBrw4M+cDvwHcVLGMZwEbgUXAO4H/GBAAfg1wPjAfuBfYBpwBLABeD7w/Ik7KzE7gxcCDRSN6XmY+CFwAnAX8FvAk4FHgn8d4P0iSJEnDOQWYB/x9Zu7PzOuBb1BqT1d6F6WYyCeK592U2sHHAJGZd2TmFoDM/F5m3pKZvZl5M/AFSm1eSRIGmCVpqrgsM+8Hfh+4OjOvLhq41wIbgNOL6XqB4yNiTmZuyczbKpaxDfinzOzOzCuAX1IKWpd9MjNvK24Z7M7Mb2bm3VnyX5R6gZw6TB3/BPirzNycmfsoNdpf4YAnkiRJmkBPAu7PzN6KsnuB5eUnEfEmSj2Sf6dot1IEoj9EqYPE1oi4PCIWFNM/KyK+GxHbI2In8KeUOm1IkjDALElTxf3F/yOBs4v0GI9FxGPAs4FlRc/iV1Jq8G6JiG9GxDEVy3ggM7Pi+b2UGuAD1wFARLw4In4UEY8U6zmd4RvSRwJfqajXHcABYOkIt1WSJEmq14PAEQPGFFkJPAAQEX8IvBU4LTP7DVqdmZdl5jOBtZRSZfxl8dLngauAIzLzEOBfKQ0YCFDZvpakGckAsyRNDeWG6/3AZzLz0Iq/9sz8e4DM/M/MfAGwDPgF8JGKZSwvcs2VraTUAB+4DiJiNvBl4P8CSzPzUOBqhm9I308pPUdl3doy84F6N1qSJEkaoR9Typn8fyKitRiv5CXAF4vxSf4WeEFmbqycKSJ+reip3FrM30WpswSUUmc8kpldEXEy8OqKWbdTuotw9ThukyRNagaYJWlq+Szwkoh4UUQ0R0RbRDw3IlZExNKIeGmRi3kfsIcnGsVQGuTkgqKhfTZwLKWgcTWzgNmUGsw9EfFiSgOflG0FOiLikIqyfwUujogjAYq80GeOwTZLkiRJNcnM/cBLKY0Z8jDwL8BrM/MXlMYx6QB+WjFY9b8Wsy6g1DnjUUp3+u2g1NkC4H8B74mI3cDfAFdWrG8vcDHw38WdfKeM9zZK0mRjXkxJmkIy8/4iaPsPlAYXOQD8BHgDpR8N/wL4DKUexjdRagyX/RhYQ6mhvRV4RWbuoIrM3B0RF1BqPM+mNBL3VRWv/yIivgBsjIhm4DjgA5R6OH87Ip5EKefzFZQGJZQkSZImRDEOyaBB+DLzqGHmuQ44YYjXvgR8aZh5/4ZS4FmSZqTon45TkjQdRcQfAH+Umc9udF0kSZIkSdL0YYoMSZIkSZIkSVJdDDBLkiRJkiRJkupiigxJkiRJkiRJUl3swSxJkiRJkiRJqktLoyswWSxatChXrVrV6GpIkiRpAt14440PZ+biRtdjsrFtLEmSNPPU2zY2wFxYtWoVGzZsaHQ1JEmSNIEi4t5G12Eysm0sSZI089TbNjZFhiRJkiRJkiSpLvZglmaQ3t5k045Otu7qYumCNlZ1tNPUFI2uliRJkiRJ0ow3VeM2BpilGaK3N7nmtoe46Mqb6Orupa21iUvPOZH1aw+fEh9WkiRJkiRJ09VUjtuYIkOaITbt6Oz7kALo6u7loitvYtOOzgbXTJIkSZIkaWabynEbA8zSDLF1V1ffh1RZV3cv23Z3NahGkiRJkiRJgqkdtzHALM0QSxe00dba/y3f1trEkvltDaqRJEmSJEmSYGrHbQwwSzPEqo52Lj3nxL4Pq3Iun1Ud7Q2umSRJkiRJ0sw2leM2DvInzRBNTcH6tYdzzAWnsm13F0vmT53RSCVJkiRJkqazqRy3McAszSBNTcHqxfNYvXheo6siSZIkSZKkClM1bmOKDEmSJEmSJElSXQwwS5IkSZIkSZLqMm4B5oj4eERsi4hbK8reFREPRMRNxd/pFa+9LSLuiohfRsSLKsqfGRG3FK9dFhFRlM+OiCuK8h9HxKqKeV4XEXcWf68br22UJEmSJEmSpJlsPHswfxJYX6X8/Zl5YvF3NUBEHAecC6wt5vmXiGgupv8wcD6wpvgrL/M84NHMfArwfuCSYlmHAe8EngWcDLwzIhaO/eZJkiRJkiRJ0sw2bgHmzPw+8EiNk58JfDEz92XmPcBdwMkRsQxYkJk3ZGYCnwbOqpjnU8XjLwGnFb2bXwRcm5mPZOajwLVUD3RLkiRJkiRJkkahETmY3xQRNxcpNMo9i5cD91dMs7koW148Hljeb57M7AF2Ah3DLGuQiDg/IjZExIbt27ePbqskSZIkSZIkaYaZ6ADzh4EnAycCW4B/LMqjyrQ5THm98/QvzLw8M9dl5rrFixcPU21pfPX2Jhu37+GGux9m4/Y99PZWPWUlSZIkSZKkSaVlIleWmVvLjyPiI8A3iqebgSMqJl0BPFiUr6hSXjnP5ohoAQ6hlJJjM/DcAfN8b6y2QRprvb3JNbc9xEVX3kRXdy9trU1ces6JrF97OE1N1X4vkSRJkiRJkiaHCe3BXORULnsZcGvx+Crg3IiYHRFHURrM7yeZuQXYHRGnFPmVXwt8rWKe1xWPXwFcX+Rp/k/ghRGxsEjB8cKiTJqUNu3o7AsuA3R193LRlTexaUdng2smSZIkSZIkDW/cejBHxBco9SReFBGbgXcCz42IEymlrNgE/AlAZt4WEVcCtwM9wBsz80CxqDcAnwTmAN8q/gA+BnwmIu6i1HP53GJZj0TEe4GfFtO9JzNrHWxQmnBbd3X1BZfLurp72ba7i9WL5zWoVpIkSZIkSdLBjVuAOTNfVaX4Y8NMfzFwcZXyDcDxVcq7gLOHWNbHgY/XXFmpgZYuaKOttalfkLmttYkl89saWCtJkjSWIuLjwBnAtsw8vih7F/DHQHm06bdn5tXFa28DzgMOABdk5n8W5c/kic4XVwMXZmZGxGzg08AzgR3AKzNzUzHP64B3FOt4X2Z+alw3VpIkSTPKRA/yJ2mAVR3tXHrOibS1lt6O5RzMqzraG1wzSZI0hj4JrK9S/v7MPLH4KweXj6N0d97aYp5/iYjmYvoPA+dTSim3pmKZ5wGPZuZTgPcDlxTLOozSnYTPAk4G3lmkkZMkSZLGxIQO8idpsKamYP3awznmglPZtruLJfPbWNXR7gB/kiRNI5n5/YhYVePkZwJfzMx9wD1FSriTI2ITsCAzbwCIiE8DZ1FKIXcm8K5i/i8BHyrGMHkRcG05ZVxEXEspKP2FMdgsSZIkyR7M0mTQ1BSsXjyPU1YvYvXieQaXJUmaOd4UETdHxMcrehYvB+6vmGZzUba8eDywvN88mdkD7AQ6hlnWIBFxfkRsiIgN27dvrzaJJEmSNIgBZkmSJKkxPgw8GTgR2AL8Y1Fe7ZfmHKa83nn6F2ZenpnrMnPd4sWLh6m2JEmS9AQDzJIkSVIDZObWzDyQmb3ARyjlSIZSL+MjKiZdATxYlK+oUt5vnohoAQ4BHhlmWZIkSdKYMMAsSZIkNUBELKt4+jLg1uLxVcC5ETE7Io6iNJjfTzJzC7A7Ik4p8iu/FvhaxTyvKx6/Arg+MxP4T+CFEbGwSMHxwqJMkiRJGhMO8idJkiSNs4j4AvBcYFFEbAbeCTw3Ik6klLJiE/AnAJl5W0RcCdwO9ABvzMwDxaLeAHwSmENpcL9vFeUfAz5TDAj4CHBusaxHIuK9wE+L6d5THvBPkiRJGgsGmCVJkqRxlpmvqlL8sWGmvxi4uEr5BuD4KuVdwNlDLOvjwMdrrqwkSZI0AqbIkCRJkiRJkiTVxQCzJEmSJEmSJKkuBpglSZIkSZIkSXUxwCxJkiRJkiRJqosBZkmSJEmSJElSXQwwS5IkSZIkSZLqYoBZkiRJkiRJklQXA8ySJEmSJEmSpLoYYJYkSZIkSZIk1cUAsyRJkiRJkiSpLgaYJUmSJEmSJEl1McAsSZIkSZIkSaqLAWZJkiRJkiRJUl0MMEuSJEmSJEmS6mKAWZIkSZIkSZJUFwPMkiRJkiRJkqS6GGCWJEmSJEmSJNXFALMkSZIkSZIkqS41B5gjYk5EPHU8KyNJkiRJkiRJmjpqCjBHxEuAm4BriucnRsRV41gvSZIkSZIkSdIkV2sP5ncBJwOPAWTmTcCq4WaIiI9HxLaIuLWi7LCIuDYi7iz+L6x47W0RcVdE/DIiXlRR/syIuKV47bKIiKJ8dkRcUZT/OCJWVczzumIdd0bE62rcRkmSJEmSJEnSCNQaYO7JzJ0jXPYngfUDyt4KXJeZa4DriudExHHAucDaYp5/iYjmYp4PA+cDa4q/8jLPAx7NzKcA7wcuKZZ1GPBO4FmUguLvrAxkS5IkSZIkSZLGRq0B5lsj4tVAc0SsiYgPAj8cbobM/D7wyIDiM4FPFY8/BZxVUf7FzNyXmfcAdwEnR8QyYEFm3pCZCXx6wDzlZX0JOK3o3fwi4NrMfCQzHwWuZXCgW5IkSZIkSZI0SrUGmP+MUu/ifcDngZ3AhXWsb2lmbgEo/i8pypcD91dMt7koW148Hljeb57M7Cnq1DHMsgaJiPMjYkNEbNi+fXsdmyNJkiRJkiRJM1etAebfycy/ysxfK/7eAbx0DOsRVcpymPJ65+lfmHl5Zq7LzHWLFy+uqaKSJEmSJEmSpJJaA8xvq7HsYLYWaS8o/m8ryjcDR1RMtwJ4sChfUaW83zwR0QIcQiklx1DLkiRJkkYlIs6upazKNA6ALUmSpGlp2ABzRLy4yLe8vGjAlv8+CfTUsb6rgHKj9nXA1yrKzy0axkdRGszvJ0Uajd0RcUrReH7tgHnKy3oFcH2Rp/k/gRdGxMKikf7CokySJEkarXo7XnwSB8CWJEnSNNRykNcfBDZQSodxY0X5buDPh5sxIr4APBdYFBGbKTVs/x64MiLOA+4DzgbIzNsi4krgdkqB6zdm5oFiUW+g1CCfA3yr+AP4GPCZiLiLUs/lc4tlPRIR7wV+Wkz3nswcONigJEmSVLOIeDFwOkXHi4qXFlBDx4vM/H5lr+LCmZTay1AavPp7wFuoGAAbuKdo754cEZsoBsAu6lQeAPtbxTzvKpb1JeBDAwfALuYpD4D9hdq2XJIkSRresAHmzPw58POI+Hxmdo9kwZn5qiFeOm2I6S8GLq5SvgE4vkp5F0WAusprHwc+XnNlJUmSpOHV3fFiGP0GwI6IygGwf1QxXXnQ6m5qHAA7IuoaAJtS72hWrlxZ5yZJkiRppjlYD+ayVRHxd8BxQFu5MDNXj0utJEmSpElkNB0v6tCwAbCBywHWrVtXdRpJkiRpoFoH+fsEpXxvPcDzgE8DnxmvSkmSJEmT1KqI+FJE3B4RG8t/dS7LAbAlSZI05dUaYJ6TmdcBkZn3Zua7gOePX7UkSZKkSWksO144ALYkSZKmvFpTZHRFRBNwZ0S8CXgAWHKQeSRJkqTpZk5mXhcRkZn3Au+KiB9QGtB6SA6ALUmSpOmq1gDzm4G5wAXAeyn11njdcDNIkiRJ01BdHS8cAFuSJEnT1UEDzBHRDJyTmX8J7AFeP+61kiRJkianN9O/48XzseOFJEmSZrCDBpgz80BEPLO4DdDRpCVJkjRjZWY51cSeIrXFvMzc1cg6SZIkSY1U6yB//wN8LSJeExEvL/+NZ8UkSZKkySYiPh8RCyKinVKO5F9GxF82ul6SJElSo9QaYD4M2EHpFsCXFH9njFelJEmSpEnquKLH8lnA1cBK4DUNrZEkSZLUQDUN8peZ5l2WJEmSoDUiWikFmD+Umd0RYRo5SZIkzVg19WCOiNUR8fWI2B4R2yLiaxFx1HhXTpIkSZpk/g3YBLQD34+IIwFzMEuSJGnGqjVFxueBK4FlwJOAfwe+OF6VkiRJkiajzLwsM5dn5unFANj3Ac9rdL0kSZKkRqk1wByZ+ZnM7Cn+Pgt4K6AkSZJmjIg4JiJOi4h55bIiyPzbDayWJEmS1FC1Bpi/GxFvjYhVEXFkRPwf4JsRcVhEHDaeFZQkSZIaLSIuAL4G/Blwa0ScWfHy3zamVpIkSVLj1TTIH/DK4v+fDCj/Q0o9mVePWY0kSZKkyeePgWdm5p6IWAV8KSJWZeYHgGhs1SRJkqTGqSnAnJkO6CdJkqSZrDkz9wBk5qaIeC6lIPORGGCWJEnSDFZTioyIODsi5heP3xER/xERzxjfqkmSJEmTxkMRcWL5SRFsPgNYBDytUZWSJEmSGq3WHMx/nZm7I+LZwIuATwH/On7VkiRJkiaV1wIPAUTE0yPiTcCfAv8IPKeRFZMkSZIaqdYA84Hi/+8AH87MrwGzxqdKkiRJ0uSSmZsz86GIuBD4HLCk+PsscFJDKydJkqRJq7c32bh9Dzfc/TAbt++htzcbXaUxV+sgfw9ExL8Bvw1cEhGzqT04LUmSJE0X5wHPysxOgIi4BLgB+GBDayVJkqRJp7c3uea2h7joypvo6u6lrbWJS885kfVrD6epafoM41FrkPgc4D+B9Zn5GHAY8JfjVSlJkiRpkgqeuLuP4vH0uTqQJEnSQdXaK3nTjs6+4DJAV3cvF115E5t2dE5kdcddTT2YM3NvRGwDng3cCfQU/yVJkqSZ5BPAjyPiK8Xzs4CPNa46kiRJmkgj6ZW8dVdXX3C5rKu7l227u1i9eN5EVntc1dSDOSLeCbwFeFtR1Eop35wkSZI0Y2TmpcDrgUeAR4HXZ+Y/NbRSkiRJmjAj6ZW8dEEbba39w69trU0smd82IXWdKLWmyHgZ8FKgEyAzHwTmj1elJEmSpMkqM3+WmZdl5gcy838aXR9JkiRNnOF6JQ+0qqOdS885sS/IXO7tvKqjfULqOlFqHeRvf2ZmRCRAREyvvSBJkiRJkiRJB1HulVwZZB6qV3JTU7B+7eEcc8GpbNvdxZL5bazqaJ9WA/xBDT2YIyKAb0TEvwGHRsQfA98BPjLelZMkSZIkSZKkyWKkvZKbmoLVi+dxyupFrF48b9oFl6GGHsxFz+WzKOVg3gU8FfibzLx2nOsmSZIkSZIkSZPGTOmVPBK1psi4AXgsM/9yPCsjSZIkSZIkSZNZuVfy6sXzGl2VSaHWQf6eB9wQEXdHxM3lv3pXGhGbIuKWiLgpIjYUZYdFxLURcWfxf2HF9G+LiLsi4pcR8aKK8mcWy7krIi4r0nkQEbMj4oqi/McRsareukqSJEmSJEmSqqu1B/OLx2Hdz8vMhyuevxW4LjP/PiLeWjx/S0QcB5wLrAWeBHwnIo7OzAPAh4HzgR8BVwPrgW8B5wGPZuZTIuJc4BLgleOwDZIkSZIkSZI0Y9XUgzkz7632N8Z1ORP4VPH4U8BZFeVfzMx9mXkPcBdwckQsAxZk5g2ZmcCnB8xTXtaXgNPKvZslSZKkycS7+yRJkjSV1ZoiY6wl8O2IuDEizi/KlmbmFoDi/5KifDlwf8W8m4uy5cXjgeX95snMHmAn0DGwEhFxfkRsiIgN27dvH5MNkyRJkurwvMw8MTPXFc/Ld/etAa4rnjPg7r71wL9ERHMxT/nuvjXF3/qivO/uPuD9lO7ukyRJksZEowLMv5mZJ1FKvfHGiHjOMNNW63mcw5QPN0//gszLM3NdZq5bvHjxweosaQR6e5ON2/dww90Ps3H7Hnp7B70FJUnS0Ly7T5IkSVNCrTmYx1RmPlj83xYRXwFOBrZGxLLM3FI0kLcVk28GjqiYfQXwYFG+okp55TybI6IFOAR4ZLy2R1J/vb3JNbc9xEVX3kRXdy9trU1ces6JrF97OE1NXs9KkjRA+e6+BP4tMy9nwN19EVF5d9+PKuYt38XXTY1390VE+e6+yvFQJEmSpLpMeA/miGiPiPnlx8ALgVuBq4DXFZO9Dvha8fgq4Nwid9xRlG73+0nR4N4dEacUPTBeO2Ce8rJeAVxf9OSQNAE27ejsCy4DdHX3ctGVN7FpR2eDayZJ0qQ0Ke7uM32cJEmS6tGIHsxLga8Ud+W1AJ/PzGsi4qfAlRFxHnAfcDZAZt4WEVcCtwM9wBsz80CxrDcAnwTmAN8q/gA+BnwmIu6i1HP53InYMEklW3d19QWXy7q6e9m2u4vVi+c1qFaSJE1Ok+XuvqLn9OUA69ats3OGJEmSajLhAebM3Ag8vUr5DuC0Iea5GLi4SvkG4Pgq5V0UAWpJE2/pgjbaWpv6BZnbWptYMr+tgbWSJGnyKe7oa8rM3RV3972HJ+7I+3sG3933+Yi4FHgST9zddyAidkfEKcCPKd3d98GKeV4H3IB390mSJFXV25ts2tHJ1l1dLF3QxqqOdtN81qghOZglTW+rOtq59JwTB+VgXtXR3uiqSZI02Xh3nyRJUoM5ltTohJ0XStatW5cbNmxodDWkaaP8y9+23V0sme8vf5KkySkibszMdY2ux2Rj21iSJM0kG7fv4fTLfjDoTuyrLzh1RqX6rLdtbA9mSeOiqSlYvXjejPogliRJkiRJU49jSY1OU6MrIEmSJEmSJEmNUh5LqpJjSdXOALMkSZIkSZKkGas8llQ5yOxYUiNjigxJkiRJkiRJM1ZTU7B+7eEcc8GpjiVVBwPMkiRJkiRJkmY0x5KqnykyJEmSJEmSJEl1sQezJEmSJEmSpGmntzfZtKOTrbu6WLrAtBfjxQCzJEmSJEmSpGmltze55raHuOjKm+jq7u0buG/92sMNMo8xU2RIkiRJkiRJmlY27ejsCy4DdHX3ctGVN7FpR2eDazb9GGCWJEmSJEmSNK1s3dXVF1wu6+ruZdvurgbVaPoywCxJkiRJkiRpWlm6oI221v6hz7bWJpbMb2tQjaYvA8ySJEmSJEmSppVVHe1ces6JfUHmcg7mVR3tDa7Z9OMgf5IkSZIkSZKmlaamYP3awznmglPZtruLJfPbWNXR7gB/48AAsyRJkiRJkqRpp6kpWL14HqsXz2t0VaY1A8ySJEmSJEmSJr3e3mTTjk627upi6QJ7JE8WBpilIfihJUmSJEmSNDn09ibX3PYQF115E13dvX05ldevPdx4TYM5yJ9URflD6/TLfsCrPvJjTr/sB1xz20P09majqyZJkiRJkjTjbNrR2RdcBujq7uWiK29i047OBtdMBpilKvzQkiRJkiRJmjy27urqi9OUdXX3sm13V4NqpDIDzFIVU+FDq7c32bh9Dzfc/TAbt++xd7UkSZIkSZq2li5oo621fyizrbWJJfPbGlQjlRlglqqY7B9apvCQJEmSJEkzyaqOdi4958S+eE05B/OqjvYG10wO8idVUf7QGpg4vpYPrYkYHHCoFB7HXHAqqxfPG9N1TXYOxihJkiRJ0tRV63V9U1Owfu3hHHPBqWzb3cWS+cYAJgsDzFIV9X5oTdSIpsOl8BiPAPNkDeI6gqwkSZIkSVPXSK/rm5qC1YvnzbjOdZOdKTKkIZQ/tE5ZvYjVi+fVFLCcqMEBJzKFx2ROxzHRgzGa91qSJEmSpLEz0df1Gh8GmKUxNFGDA05k3qHJ/GE/kYMxTuZAuyRJkiRJU9FEXtdr/JgiQ1POZE3XAE/0LK78cByPnsUTmXdootNxjMRE7W8w77UkSZIkSbWqNXYzkdf1Gj/2YNaUMtl7kU5kz+J6UnjUYyLTcYzURO7vie4tPVGpOEz7IUmSJEmqRa3XjyOJ3Uzkdb3GT2RO32BCRKwHPgA0Ax/NzL8fatp169blhg0bJqxu9fTCnczzTNS6Nm7fw+mX/WDQL1tXH6QX6UT2ei6va7qMaDqRA+mN5hwa6f6erOdevfu73n03keuaTp9FbtPoTeZt8thOjW0aKxFxY2aum5CVNdBI2sUwsW3jkRz/er5Xx+PcGs86j1c9xmu5k2X7ptq0k6Uebp/b5/ZNjTpPhn0xkuvHkV4/T5U4Sm9vcs/Dndy7o5O2WU3Mbm5m7uwmdncdYM++Ho7qaOdAb3Lfo3tpn9VC++xmHu8+wKzmZvYfOEBH+2xWLpzLfY/unZR35UP9beNpmyIjIpqBfwZeAGwGfhoRV2Xm7Y2tWX1Bnck8z0Suq550DRMZIIXpN6LpRKXjqPc41bO/61lX+VfVgfMM96tqPeupJxVHvftuotY1HT+L3KbRmczb5LGdGtukkZku7eKRnivjdW6NZ53Hqx7Tffum2rSTpR5un9vn9k2NOk+WfTGS68eRxm6mQhyl2r666AVHs3j+bN72H7ewcO4sXv+bq7j02l/1vX7haWuY29rMx394D69ct5IrNtzHnz1/DR+8/k7u3fH4tGr3TucUGScDd2XmxszcD3wROLPBdQLqGzRtMs8zkeuqJ13DZB6kbqoof9iPZzqOiTxO9ayrHGi/+oJT+eL5z+LqC06tO4A73HrqScVR776bqHVNx88it2l0JvM2eWynxjZpxKZFu3ik58p4nVvjWefxqsd4LXeybN9Um3ay1MPtc/vcvqlR58myL0Zy/TiZU23Wq9q+uvTaX3HPw510dffy8pNW9AWXy69/4Lo72bF3P2ecsJzLrr+TM05Yzju+eitnnLC8b5rp0u6dzgHm5cD9Fc83F2V9IuL8iNgQERu2b98+YRWrJ6gzmeeZyHXVk5vHEUmnhok8TvWua6SB9nrWU88Xcb3bM1Hrmo6fRW7T6EzmbfLY1j/PRK9LI3LQdjE0pm08kuM/0nNlvM6t8azzeNVjvJY7WbZvqk07Werh9tU37WSph9tX37STpR6TYdqRTj+S68fpmFd5qH3VW2QejmDI18uvVf6vnGY6tHunc4C5WuSnX8LpzLw8M9dl5rrFixdPULXqC+pM5nkmcl319CKdjr+cTUcTeZwmal31rKeeL+J6t2ei1jUdP4vcptGZzNvksa1/nolel0bkoO1iaEzbeCTHf6TnynidW+NZ5/Gqx3gtd7Js31SbdrLUw+2rb9rJUg+3r75pJ0s9JsO0I51+JNeP9cRuJruh9lXlJg31embpceX/ymmmQ7t3OgeYNwNHVDxfATzYoLr0U09QZzLPM9HrGmkv0un4y9l0NJHHaaLWVc966vkiHs17aSLWNR0/i9ym0ZnM2+SxnRrbpBGbFu3ikZ4r43VujWedx6se47XcybJ9U23ayVIPt8/tc/umRp0ny74Y6fXjRKTanEjV9tVFLziaoxa109baxJdv3MxFLzi63+sXnraGjrmz+MbND3DB89fwjZsf4H1nHc83bn6gb5rp0u6NzEGdF6aFiGgBfgWcBjwA/BR4dWbeVm36iRwpG+obIXMyzzPR6xqpqTIi6Uw3kcdpup17k33fTcfPIrdpdCbzNnlsp8Y2jZV6R8qeSkbaLoaJbRuP5PiP9FwZr3NrPOs8XvUYr+VOlu2batNOlnq4fW6f2zc16jxZ9sVM19ub3PNwJ/c90smslibaWpqZO7uJPV0H2LOvh1Ud7RzoTe5/dC9zZ7XQPruZx7sPMKu5ie4DvRzWPpuVC+dy36N7J+3+rrdtPG0DzAARcTrwT0Az8PHMvHioaSc6wCxJkqTGmwkBZhhZuxhsG0uSJM1E9baNW8ajMpNFZl4NXN3oekiSJEmNZLtYkiRJ42Va92AeiYjYDtzb6HpME4uAhxtdCU0ang+q5PmggTwnVKkR58ORmTlxoz1PEdOwbexnzdTm8ZvaPH5Tm8dv6vLYTW2NOn51tY0NMGvMRcSGmXCrqWrj+aBKng8ayHNClTwfNF48t6Y2j9/U5vGb2jx+U5fHbmqbasevqdEVkCRJkiRJkiRNTQaYJUmSJEmSJEl1McCs8XB5oyugScXzQZU8HzSQ54QqeT5ovHhuTW0ev6nN4ze1efymLo/d1Daljp85mCVJkiRJkiRJdbEHsyRJkiRJkiSpLgaYJUmSJEmSJEl1McCsukXExyNiW0TcWlF2WERcGxF3Fv8XNrKOmjgRcUREfDci7oiI2yLiwqLcc2KGioi2iPhJRPy8OCfeXZR7TsxgEdEcEf8TEd8onns+zFARsSkibomImyJiQ1Hm+aBR89ya2iLi0Ij4UkT8omhX/rrHb2qIiKcW77vy366IeLPHb+qIiD8v2u23RsQXiva8x2+KiIgLi2N3W0S8uSjz+E1SI42pRcTbIuKuiPhlRLyoMbUemgFmjcYngfUDyt4KXJeZa4DriueaGXqAv8jMY4FTgDdGxHF4Tsxk+4DnZ+bTgROB9RFxCp4TM92FwB0Vzz0fZrbnZeaJmbmueO75oLHiuTV1fQC4JjOPAZ5O6TvD4zcFZOYvi/fdicAzgb3AV/D4TQkRsRy4AFiXmccDzcC5ePymhIg4Hvhj4GRKn51nRMQaPH6T2SepMaZWxFbOBdYW8/xLRDRPXFUPzgCz6paZ3wceGVB8JvCp4vGngLMmsk5qnMzckpk/Kx7vpnQxsBzPiRkrS/YUT1uLv8RzYsaKiBXA7wAfrSj2fFAlzweNF8+tKSAiFgDPAT4GkJn7M/MxPH5T0WnA3Zl5Lx6/qaQFmBMRLcBc4EE8flPFscCPMnNvZvYA/wW8DI/fpDXCmNqZwBczc19m3gPcRenHhEnDALPG2tLM3AKlgCOwpMH1UQNExCrgGcCP8ZyY0Yp0CDcB24BrM9NzYmb7J+D/AL0VZZ4PM1cC346IGyPi/KLM80FjwXNr6loNbAc+UaRT+mhEtOPxm4rOBb5QPPb4TQGZ+QDwf4H7gC3Azsz8Nh6/qeJW4DkR0RERc4HTgSPw+E01Qx2v5cD9FdNtLsomjZZGV0DS9BIR84AvA2/OzF0R0egqqYEy8wBwYkQcCnyluHVLM1BEnAFsy8wbI+K5Da6OJoffzMwHI2IJcG1E/KLRFdK04bk1dbUAJwF/lpk/jogP4O3cU05EzAJeCryt0XVR7Ypcr2cCRwGPAf8eEb/f0EqpZpl5R0RcAlwL7AF+TimNpaaHaoGVnPBaDMMezBprWyNiGUDxf1uD66MJFBGtlILLn8vM/yiKPSdEcXvr9yjli/KcmJl+E3hpRGwCvgg8PyI+i+fDjJWZDxb/t1HK0Xkyng8aA55bU9pmYHNxxxPAlygFnD1+U8uLgZ9l5tbiucdvavht4J7M3J6Z3cB/AL+Bx2/KyMyPZeZJmfkcSqkX7sTjN9UMdbw2U+qRXraCUgqbScMAs8baVcDrisevA77WwLpoAkWpq/LHgDsy89KKlzwnZqiIWFz0XCYi5lBqtP4Cz4kZKTPflpkrMnMVpdtmr8/M38fzYUaKiPaImF9+DLyQ0q2dng8aFc+tqS0zHwLuj4inFkWnAbfj8ZtqXsUT6THA4zdV3AecEhFzi2u70yiNq+PxmyKKO3eIiJXAyym9Dz1+U8tQx+sq4NyImB0RRwFrgJ80oH5DisxJ1aNaU0hEfAF4LrAI2Aq8E/gqcCWwktIX1NmZOTBpuaahiHg28APgFp7Ir/p2SnmYPSdmoIg4gdLABM2UftC8MjPfExEdeE7MaEWKjP+dmWd4PsxMEbGaUs9SKN0S//nMvNjzQaPluTX1RcSJlAaDnQVsBF5P0Y7A4zfpFblf7wdWZ+bOosz33xQREe8GXkkptcL/AH8EzMPjNyVExA+ADqAbuCgzr/P9N3mNNKYWEX8F/CGl9+ebM/NbE1/roRlgliRJkiRJkiTVxRQZkiRJkiRJkqS6GGCWJEmSJEmSJNXFALMkSZIkSZIkqS4GmCVJkiRJkiRJdTHALEmSJEmSJEmqiwFmSZIkSZIkSVJdDDBLkiRJkiRJkupigFmSJEmSJEmSVBcDzJIkSZIkSZKkuhhgliRJkiRJkiTVxQCzJEmSJEmSJKkuBpglSZIkSZIkSXUxwCxJM1BEvCsiPtvoekiSJEmNEhGfjIj3RcRzI2Jzo+sjSVOVAWZJkiRJkiRJUl0MMEuSJEmSJEmS6mKAWZKmuYh4S0Q8EBG7I+KXEXFa8VJbRFxRlP8sIp5eMc+miHhbRNweEY9GxCcioq1BmyBJkiSNWkQ8o2j37o6IK4C2Aa+/PSIeLtrCv1dR/smI+NeIuLaY978i4sgJ3wBJmqQMMEvSNBYRTwXeBPxaZs4HXgRsKl4+E/h34DDg88BXI6K1YvbfK6Z/MnA08I4JqrYkSZI0piJiFvBV4DOU2r//DvxuxSSHA4uA5cDrgMuLtnTZ7wHvLaa5CfjcuFdakqYIA8ySNL0dAGYDx0VEa2Zuysy7i9duzMwvZWY3cCmlHhynVMz7ocy8PzMfAS4GXjWhNZckSZLGzilAK/BPmdmdmV8Cfjpgmr/OzH2Z+V/AN4FzKl77ZmZ+PzP3AX8F/HpEHDEhNZekSc4AsyRNY5l5F/Bm4F3Atoj4YkQ8qXj5/orpeoHNwJMqZr+/4vG9A16TJEmSppInAQ9kZlaU3Vvx+NHM7BzwWtW2cWbuAR7B9rEkAQaYJWnay8zPZ+azgSOBBC4pXurrcRERTcAK4MGKWSt7ZKwc8JokSZI0lWwBlkdEVJStrHi8MCLaB7xWtW0cEfMopdmwfSxJGGCWpGktIp4aEc+PiNlAF/z/7f15mFzleeD9f+/WQmtHu2QJScgWxmzGogM4sRwHYiwTJ9hgtiSGOHg0dmyLDPObwfbrGXtCyGuyEKPYsYOXsCQ21hg7EEfgBeyBvMMmORgQ2EgIgQXaEFgrraX7+f1RVU119anu6u7qrqW/n+vSpapT51Q9p+p01fPc5z73w6vkymYAnBYR50fEaHJZzgeBB4s2/2hEzI+IacCngG8NY9MlSZKkanoAOAKsjIjREXE+cHrJOv8rIsZGxDLgPeTqNBecGxFvy9dyvgZ4KKX0SyRJBpglqckdBXwOeAnYBswiFywGuAO4GHgF+ABwfr4ec8E3gB8Am/L//nyY2ixJkiRVVUrpEHA+8Efk+r8XA98pWmVbfvmL5Cbw+3BK6edFj38D+Ay50hinkZv0T5IERPfyQ5IkQURsBj6UUvpRrdsiSZIk1VJE3ARsSSl9utZtkaR6ZAazJEmSJEmSJGlADDBLkiRJkiRJkgbEEhmSJEmSJEmSpAExg1mSJEmSJEmSNCCja92AejFjxoy0aNGiWjdDkiRJw2jdunUvpZRm1rod9ca+sSRJ0sgz0L6xAea8RYsWsXbt2lo3Q5IkScMoIp6rdRvqkX1jSZKkkWegfWNLZEiSJEmSJEmSBsQMZkkaAp2dic279rN9TzuzJ7eyaPoEWlqi1s2SJElSnbL/KElqVAaYJanKOjsTd6/fxlWrH6X9cCetY1q4/qJTWX7iHAcJkiRJ6sH+oySpkVkiQ5KqbPOu/V2DA4D2w51ctfpRNu/aX+OWSZIkqR7Zf5QkNTIDzJJUZdv3tHcNDgraD3eyY297jVokSZKkemb/UZLUyAwwS1KVzZ7cSuuY7l+vrWNamDWptUYtkiRJUj2z/yhJamQGmCWpyhZNn8D1F53aNUgo1NBbNH1CjVsmSZKkemT/UZLUyJzkT5KqrKUlWH7iHI5fuYwde9uZNclZwCVJklSe/UdJUiMzwCxJQ6ClJVg8cyKLZ06sdVMkSZLUAOw/SpIalQFmSZIkSdKI19mZ2LxrP9v3tDN7shnEkiRVygCzJEmSJGlE6+xM3L1+G1etfpT2w51dNZCXnzin6kFmA9mSpGZjgFmSJEmSNKJt3rW/K7gM0H64k6tWP8rxK5cNqGRFuSDycAayJUkaLi21boAkSZIkSbW0fU97V3C5oP1wJzv2tvf7uQpB5HNX3c+lX3mIc1fdz93rt3UFnbMC2Zt37a/KfkiSVAsGmCVpkDo7E5t27uOBZ15i0859dHamqqwrSZKk4TF7ciutY7oPj1vHtDBrUmu/n6u3IHI1A9mSJNULS2RI0iD05zJHL4mUJEmqT4umT+D6i07t0U9bNH1Cv5+rtyByIZBd/PhAA9mSJNULM5glqUR/soz7c5mjl0RKkiTVp5aWYPmJc1izchm3rTiDNSuXDTgJoLds6EIgu/D4YALZkiTVCzOYJalIf7OMe8tQKZ0Qpj/rSpIkaXi1tASLZ04cdL+st2zoQiD7+JXL2LG3vSvo7NVskqRGZoBZkor0NoP4oukTeswG3p/LHL0kUpIkqfn1FUSuViBbkqR6YYBZkoqUyzJ+ef9Bfr5tb49MlHPeNLvien3lslkWTB3Ppp37ugWu+5vFUpiVfDDPIUmSpP4p1wcziCxJGknqLsAcEf8F+BCQgMeBDwLjgW8Bi4DNwEUppVfy638SuALoAFamlL6fX34acBMwDlgDXJlSKl9IVZIon2U8ZlRLZmZzoT5fJZc5ZmWzLJg6nh88tT2zJAdQUdDYyQMlSZKGn30wSZJy6mqSv4iYB6wE2lJKJwGjgEuATwD3pJSWAPfk7xMRJ+QfPxFYDvx9RIzKP92XgBXAkvy/5cO4K5IaVLmJVw4c6ihbP7mQoXLm4hksnjmx1wFFYd3TF00H4L6NO/nFtj1MHT+26zmvWv0oz7+8n7vXb+PcVfdz6Vce4txV93P3+m2ZEw46eaAkSdLQKTcBtH0wSZJy6i6DmVybxkXEYXKZyy8CnwTekX/8ZuAnwNXAecBtKaWDwLMRsRE4PSI2A5NTSg8ARMQtwHuBu4ZtLyQ1hKzLGrMykjfv2t8ts3nulFYubJvPgUMdbNq5r18lKbKyXVaetYRbH3yOrbtzJTq27zlYtha0kwdKkiQNj96ylO2DSZKUU1cZzCmlF4C/Bp4HtgK7U0o/AGanlLbm19kKzMpvMg/4ZdFTbMkvm5e/Xbq8m4hYERFrI2Ltzp07q707kupIVuZJYcBQmiUM9MhILs5snjullcveupAb79vEH9+0ttfs4ixZ2S6r7t3A+UvnA7ms6f2HjpStBV26H4WyHsWcPFCSJGnwestS7k8frFwWtCRJzaCuMpgjYiq5rORjgV8B/zsi/rC3TTKWpV6Wd1+Q0o3AjQBtbW3+wktNqlzmyQlzJ1WcJVxcP3nn3oNc/o8P99juhCuX0Znos2ZyuWyXiNdKciycNqFHLeiF08fxwq/a+cOvPTzgiQYlSZJUud6ylE9fNL1bH2zh9HFcc97JbN/TDtDVF7RWsySp2dVVgBn4beDZlNJOgIj4DvDrwPaImJtS2hoRc4Ed+fW3AMcUbT+fXEmNLfnbpcsljUDlMk9u/uDp/bqssVA/OWugMXX8WH76/K/41Hcf73OyvrlTsicSXPaGGbx/6Tw6OmHH3na+8oE2Pn3H4zy361Vax7RwzXkns+LWtYOaaFCSJEmVKzcB9KxJrd0SEF7ef5AXftXe1Vcr7guW64tmJTWUk1XWzb6eJKle1FuA+XngzIgYD7wKnA2sBfYDlwOfy/9/R379O4FvRMT1wOvITeb3cEqpIyL2RsSZwEPAZcDfDeueSKob5TJPDhw6UnbA0JusgcaFbfO7gsuF5y9kNT+5dW+PjJUv/P5b+Ng3/qPbstMWTOUHT23vtu51F5zCvKNbmTbhqD7r/BX+SZIkqToKZdLKXSlWSEAAuq4yg+5B5N76cIW5PnoLHJsBLUmqd3UVYE4pPRQR3wZ+ChwB/oNcCYuJwOqIuIJcEPrC/PrrI2I18GR+/Y+mlDryT/cR4CZgHLnJ/ZzgTxqhymWeLJg2oc/LGoEenf6sgcZxsyZlDhzKTdb3bx9fxpqMiQRL17369sdYU5TdMpCAuCRJkgamOEu5tyvFegsil+uLzpncWlHguBoZ0JIkDaW6CjADpJQ+A3ymZPFBctnMWetfC1ybsXwtcFLVGyip4ZTLPDl2xgSOnTGh18sax46OHpnGy0+c02OgkVJ28LfcZH0797V3TSJY0FeGcl8ZNJIkSaq+QpZyb8Hc3kpplOvDdXRSUeC4rz6iJEm1VncBZkmqtr4yT3q7rHHF2xeX7fQXDzQ6O1PmwCFrsr5yWce9DUwKdfemjh/Dt1a8lcMdHUybcJT19yRJkoZZVj3k3hIByvVFH3p2V0WB4976iJIk1QMDzJJGhL4yT8plhnQmeizLyhYpN3AAKs46LjcwWTB1fOblk0sXTDO4LEmSNIx6q4fcW0JDVl+00sCxV7FJkuqdAWZJTWcgs2yX6+CXbtZbtki5IHYldfsK22eta909SZKk+tBXv6w/ky5XGjju7Wq8gfR7JUmqNgPMkprKQGfZLtfBHzs6ugLPA80WqaRuX2/rWndPkiSpPlSzX1bpBIKFdUv7iAPt90qSVG0GmCU1lUqyfctlepQrcbGmgk7/ULLuniRJUn2odr+sP4kIpbzKTZJUL1pq3QBJqqbeskrgtUyPc1fdz6VfeYhzV93P3eu30dmZujr4Zy6eweKZE2lpicxlw62QXd06JveVbd09SZKkodXZmdi0cx8PPPMSm3buozM/Mcdw9svKtaGgr36vJEnDxQxmSU2lr6ySRsz06C27etPOfdbck6QGFhFfB94D7EgpnZRfNg34FrAI2AxclFJ6Jf/YJ4ErgA5gZUrp+/nlpwE3AeOANcCVKaUUEUcBtwCnAbuAi1NKm4dp96SG1FfpiUrLWgxVGyDXp331cAdXnv0GVq/dwtbduaCyV7lJkmrBDGZJTaWvrJJGzfQozaQGymZiS5Iayk3A8pJlnwDuSSktAe7J3yciTgAuAU7Mb/P3ETEqv82XgBXAkvy/wnNeAbySUnoD8LfAdUO2J1ID6CsrGMonJGzetR/o2S8bihP85drw/Mv7u/qAf3zTWv7hvk1c9taFzJ3S6lVukqSaMYNZUlPpK6ukWeoZD6bWtCSpfqSU7ouIRSWLzwPekb99M/AT4Or88ttSSgeBZyNiI3B6RGwGJqeUHgCIiFuA9wJ35bf5bP65vg18ISIipeQZSY04lU6KVw8TLJdrw/Y9B3v0AW+4ZwM3f/B0Zk46yv6eJKkmzGCW1HR6yypplnrGg6k1LUmqe7NTSlsB8v/Pyi+fB/yyaL0t+WXz8rdLl3fbJqV0BNgNTM960YhYERFrI2Ltzp07q7QrUv3oKzO5oJCQUGy4ExLKtWH/oSOZfcBEqtl8IZIkGWCWNKIUMpzXrFzGbSvOYM3KZT2yVhpBXwOfSgdQkqSGkvVjlXpZ3ts2PRemdGNKqS2l1DZz5swBNlGqX5WWSquHhIRybVg4bULFwe9KyoFIklQNlsiQ1NTKlYlYPHNi3U7qV4nCoKP0Es9F0yfQ2ZnYufcgH1q2GIDb1+UmfhnuSzslSQO2PSLmppS2RsRcYEd++RbgmKL15gMv5pfPz1hevM2WiBgNTAFeHsrGS/Wq0lJpwzWRX296m+S5XB+wWKXlQEpZYk2SNBAGmCU1rYF2rBtBb4OO0n1eedYS7n5iK+84fhYHDnXwzI59jGqBrbsdOEhSnboTuBz4XP7/O4qWfyMirgdeR24yv4dTSh0RsTcizgQeAi4D/q7kuR4A3g/ca/1ljVS9naAvVQ8JCeXaUEnwu5L5Oko1c99ZkjS0wv5lTltbW1q7dm2tmyFpgLKyLTbv2s+5q+7vkaWyppeOdaPbtHNfj31eOH0cK97+eq753pNdg4Urz17CLQ88xysHDjlwkDSiRcS6lFJbDV//m+Qm9JsBbAc+A/wLsBpYADwPXJhSejm//v8D/DFwBPjTlNJd+eVtwE3AOHKT+308pZQiohW4FXgLuczlS1JKm/pql31jNatCn7EQnF0wdTzPv3KgaTJ2C/v39Pa9PPHinq4r2QpuW3EGZy6ekbltVj+y2fvOkqTuBto3NoNZUsMqDiof6Uh8+o7HeW7Xq13ZFjMnja35DODDLau24HtOmdcVXIbXZhu/4m2L+eKPN/aZzSJJGjoppUvLPHR2mfWvBa7NWL4WOCljeTtw4WDaKDWT4qzgZsvYzdqflWct4dYHn2Pr7vY+JyrsrUa1/URJUm+c5E9SQyp0oM9ddT+XfuUh/tOta7m4bQFzp7R2XQI4dlRLzWcAH25Zk/+NaiFzsBDx2u3SyW0kSZKaXbNNipy1P6vu3cD5S+dXNFFhX5NIS5JUjgFmSQ2ptw504f6BQx01nwF8uGXNOP5rC6dlDhYKFZIcOEiSpJGot4zdRlRuf06ZN5m7r1zGG2dP4qFnd/HMjn1sfmkfDzzzEpt27qOzM9cpzOpHNnvfWZJUHXVXIiMijga+Su4Sv0SuxtwvgG8Bi4DNwEUppVfy638SuALoAFamlL6fX34ar9WhWwNc6YQmUvMo14EuZOW2jmlh9uRWzjh2ek1nAB9uWZP/LZg6vseENoUazIWBw4Kp49m0c1/T1B+UJEkqyJqro6UlujJ2S2sON+qJ93L788Y5k3hy697MvmDpfByVTCAoSVKpupvkLyJuBu5PKX01IsYC44FPAS+nlD4XEZ8ApqaUro6IE4BvAqeTm0n7R8Bx+Zm0HwauBB4kF2BeVZgEJYsTmUiNpdwkJFe8bTFf+/dNDV0/bygUT2gzc2Iro1pg25525kxu5UhH4ufb9rJhx15Wr91S0cR/5QZqktRoaj3JX72yb6xm0VudZaDpazBff9GpvHH2JH7n77L7zV/88UYn8pMkdWmKSf4iYjLwduCPAFJKh4BDEXEeudm1AW4GfgJcDZwH3JZSOgg8GxEbgdMjYjMwOaX0QP55bwHeS25GbUlNoHAJX3EH+roLTmHe0a1csHSeAc8SxRPaFCyYNqHsRDC9TfzXbBPiSJKk5lWuznKhn9NMGbvlMpAfenZXRfNxGGCWJA1UXQWYgcXATuAfI+LNwDpyWcizU0pbAVJKWyNiVn79eeQylAu25Jcdzt8uXd5NRKwAVgAsWLCgunsiqaqKM2bnTmmloxOmjh/Dt1a8lcMdHUybcFRDDwhqoVwd60I2y4697SyaPqFHpnJfAzVJkqRaK/Qdn96+lw8tW8zt67awdXeutnJxQDXrJHwjy9qfcqUznI9DklQt9RZgHg0sBT6eUnooIm4APtHL+lmRpNTL8u4LUroRuBFylwH2v7mShkNxxuzU8WO57K0LueGeDd2yZ5cumGZwuZ96q2PdOqaFOZNbMzOVZ04aW3ZCnGYZnEmSpMaVdbVV4SqtrbvbR1xANevKv9L5OJzIT5I0GPUWYN4CbEkpPZS//21yAebtETE3n708F9hRtP4xRdvPB17ML5+fsVxSAyrOmD1/6fyu4DKYPTsY5bJZWgKuv+hUOjrJzFT+1oozm2pCHEmS1Fx6u0qrMFfHSAqolpbOKMzH8ZYFRzd8WRBJUn1oqXUDiqWUtgG/jIg35hedDTwJ3Alcnl92OXBH/vadwCURcVREHAssAR7Ol9PYGxFnRkQAlxVtI6nBFGfaRlA2e1b9U8hmaR2T+yloHdPCX7zvZC5YOo83zp7Ehh25S0rnTnktcNx+uJMDhzp6bDfSBmqSJKl+lbtK65R5k1mzctmInDeiUDrjzMUzeP2siSyakbtdKBMiSdJg1FsGM8DHgX+OiLHAJuCD5ALhqyPiCuB54EKAlNL6iFhNLgh9BPhoSqkj/zwfAW4CxpGb3M8J/qQGVZppa/ZsdWRNBLNg6nh+8NT2Xi8pnT25lTOOnd40E+JIkqTmUu4qrSWzJ3nFmyRJQyBSsvQw5Gowr127ttbNkEa84sn8CpPKAX3WYB6JmShDYdPOfZy76v4eA7LiS0p9ryU1k4hYl1Jqq3U76o19YzWCrH5jS0tk1mC2DyNJUt8G2jeuxwxmSSNE6aAgK3u2MBgozrSdM7mVc06Yw859Zs9WW1+XlPpeS5KketBXELn0Ki37MOWVC9RLklQpA8ySaiJrUHDdBadw/Q9/UXYCv8K/gtfP8hLHavOSUkmS1AiyJvIr7jcWag7bf+md2d6SpGqoq0n+JI0cWYOCq29/jPecMq/bek7gN7yyJv5zAj9JklRvsq66mjp+LDv3HuSBZ15i0859dHZaDrIv5QL1m3ftr3HLJEmNxAxmSTVRrhTDqKLTXnOntHJh23wOHOpg0859Xq43DLykVJIkNYLSq67mTmnlsrcu5PJ/fNhM3H4o1yffsbfd7G9JUsWGNIM5IsZGxCkRcXJEjB3K15LUWAqDgmKtY1poWziN1jEtXYOEG+/bxB/ftJZzV93P3eu3mYkyDAqXlJ65eEbXJaaQu4Ry0859ZgVJUi8iYk5E/F5E/G5EzKl1e6RmVXrV1YVt87smgQYzcStVrk8+a1JrjVokSWpEQxZgjojfAZ4BVgFfADZGxLuH6vUkNZZypRh+ffF01qxcxucvPtVBQh0p1Oc7d9X9XPqVhwz4S1KGiPgQ8DBwPvB+4MGI+OPatkpqToWrrtasXMZtK87g1GOOLpuJq/IsjyZJqoahLJHxN8BvpZQ2AkTE64F/A+4awteU1CB6K8WweOZEL9erM31NpCNJAuC/AW9JKe0CiIjpwP8Fvl7TVklNqngiv00792VOVGwmbu8sjyZJqoahLJGxoxBcztsE7BjC15PUYMqVYgAv16s3vQX8JUldtgB7i+7vBX5Zo7ZII4qZuAPXW59ckqRKDGUG8/qIWAOsBhJwIfBIRJwPkFL6zhC+tqQGVxgkFLJmHSTUVulEOpAbuM2Z3MqmnfvYvqeduVNa6eiEHXvbmT3Z7BdJI9ILwEMRcQe5/u95wMMRcRVASun6WjZOamZm4kqSVDtDGWBuBbYDv5m/vxOYBvwuuQ63AWZJZTlIqC9ZAf8v/P5beHLrXq5a/ShTx4/lsrcu7Kqb7cztkkaoZ/L/Cu7I/z+pBm2RmlJnZ2Lzrv1s39PzhHZxyQxJkjR8hizAnFL64FA9t6SRwUFC/cgK+KcEv/N399N+uJPzl2bP3G6NZkkjSUrpfwFExOTc3bS3j00k5fUWOC5e5+7123pc4eYJ7aFXyecjSRq5hizAHBGLgRuAM8llLD8A/GlK6dmhek1JjckOa2MoDfg/8MxLXQHlCMrWaF40fYKfr6QRISLagH8kn7EcEbuBP04pratpw6Q611fguNBX3LxrP7/Ytoep48eydXe7J7SHiYF9SVJfhrJExjeALwLvy9+/BLgNOGMIX1NSg7HD2rhK6zIX3547pZUL2+bz6qEO/r+NL/HpOx7nuV2v+vlKanZfB/4kpXQ/QES8jVzA+ZSatkqqc5t37e/qC0L3K6EWTZ/Qo6+48qwl3Prgc11B5h172w0wDwED+5KkSrUM4XNHSunWlNKR/L9/IpfJLEldyg0oNu/aX+OWqS/Fs7Xfvm4LV569hNYxLcyd0splb13Ijfdt4oM3reU/3bqWi9sWMHdKq5+vpGa3txBcBkgp/TtgmQypD9v3tJe9Eiqrr7jq3g2cv3Q+kDvBPWtS67C3udkVkkDOXXU/f3zTWv7hvk184MyFzJ2Se68Ln48kSTAEGcwRMS1/88cR8QlyWcsJuBj4t2q/nqTG1tuAwoyI+lZal3nO5FbOOWEOL+07yOX/+HCPgeAVb1vMF3+80c9XUtOJiKX5mw9HxD8A3+S1/u9PatUuqVGUXhUFrwWOy/UVI+i6MmrR9AnD3eSmVy6wX+jPGdiXJBUbihIZ68h1qAvXPv/novsJuGYIXlNSHeutxnJvAwrVv6yJGHfszR4ITmodxUd/6w2MaoFxY0bT2ZkqKpNhjW5JDeBv6N7//Z/5/wv9X0m9KFwVVVoyrRA4zuorLnvDDM5/yzz7BUPEwL4kqT+qHmBOKR0LEBEXAXenlPZExP8AllJBcDkiRgFrgRdSSu/JZ0R/C1gEbAYuSim9kl/3k8AVQAewMqX0/fzy04CbgHHAGuDKlJKde6kG+qqx3NeAQo0n66TBwunjmNQ6hs//aAPthzu549EXuOa8kxkzKnoNGlujW1KD+F7R7eJAs/1PqQKlV0XNmvRa36BcX/HXFk2zLzCEyiWBGNiXJGWJoYq7RsRjKaVT8pOb/AW5zI5PpZR6neQvIq4C2oDJ+QDzXwIvp5Q+ly+5MTWldHVEnEDu8sPTgdcBPwKOSyl1RMTDwJXAg+QCzKtSSnf19rptbW1p7dq1g9tpST1s2rmPc1fd36NzuqZoUpBChmrpgEKNKSso/OU/PI0P/9M62g93MndKKx84cyGr7t3QZ9C4kuNHkgYjItallNoG+Ryfyd98I/BrwB3kgsy/C9yXUvrQ4Fo5/Owbq57YVxx+nuSXpJFpoH3joSiRUdCR//93gC+nlO6IiM/2tkFEzM+vfy1wVX7xecA78rdvJlfH7ur88ttSSgeBZyNiI3B6RGwmF5x+IP+ctwDvBXoNMEsaGqWX182d0sr5S+fz9PbcnEeFAUJpmQU1rqwspOLj4Pyl87uCy9B9pvjSY8Aa3ZIaQUrpfwFExA+ApSmlvfn7nwX+dw2bJjUF+4rDr7esckmSSg1lgPmF/CQnvw1cFxFHAS19bPN54L8Dk4qWzU4pbQVIKW2NiFn55fPIZSgXbMkvO5y/XbpcUg0UX17Xn8xVNbasgWDhOIig4qCxNbolNZgFwKGi+4fIlXmTpIbTW2DfOTIkScX6CvgOxkXA94HlKaVfAdOA/1Zu5Yh4D7AjpbSuwufP+vVKvSzPes0VEbE2Itbu3LmzwpeV1B+FunmtY1rKZq5u3rW/xq3UUCs+DoCu/yGX1b7y7Ddw4FAHm1/axzM79vHAMy+xaec+Fkwd32M7a3RLqmO3Ag9HxGfzZTMeIncFniQ1jUL5jHNX3c+lX3mID970MP++8aWu/ltnp+XnJWmkGbIM5pTSAeA7Rfe3Alt72eQ3gN+LiHOBVmByRPwTsD0i5uazl+cCO/LrbwGOKdp+PvBifvn8jOVZbbwRuBFydeb6sXuSysjKZihcXvf09r2WOxihii+zfHn/QZbMmsjVtz/G1PFjueytC7nhng3dbhdnuJ/zptms8fJMSQ0gpXRtRNwFLMsv+mBK6T9q2SapUZkhW78279rfVZt57pRWLm5bwIpb13qFoiSNYENZIqNfUkqfBD4JEBHvAP5/KaU/jIi/Ai4HPpf//478JncC34iI68lN8rcEeDg/yd/eiDiTXNbIZcDfDee+SCNVb5OBFALIljsYuYovs1zamTh53hR27j3I5f/4MO2HOzl/6fyu4DK8luFemNDPkxCSGkFK6afAT2vdDqmROcFcfRvo3BqV8MSCJDWmoSyRUS2fA94ZERuAd+bvk1JaD6wGngTuBj6aUipMLPgR4KvARuAZnOBPGhal2QxXvG0xP9+2h8df2E1nZ8osk2C5g5GpEGzuTKlrQNJbbWZJkjRyFPcpwbJq9aYwRwZUt/9WWnrj3FX3c/f6bZbckKQGUDcZzMVSSj8BfpK/vQs4u8x61wLXZixfC5w0dC2UlKWQzVA6md+N923qyjpxNmoVK53Ezwx3SXpNRGwG9gIdwJGUUltETAO+RW7ywM3ARSmlV/LrfxK4Ir/+ypTS9/PLTwNuAsYBa4ArU0pGbFS3ijNkCyyrVj8KSSNXrX4UqF7/rdyJhYFmQ0uShk8jZDBLahCFYGFvk/kVMlfPXDyDxTMnGlwe4Yqz2m9ft4Urz15ihrskdfdbKaVTU0pt+fufAO5JKS0B7snfJyJOAC4BTgSWA38fEaPy23wJWEGupNyS/ONS3SrOkC3wpHP9KMytsWblMt5x3Ayuu+CUrs9r4fRx3PiBNrbvae/3hH+9nViQJNW3usxgltSYCsHCn2/bY9aJKlI8+d+Ove3MmdzKOSfMYee+3O2OTnjo2V3W4JOk15wHvCN/+2ZyV/1dnV9+W0rpIPBsRGwETs9nQU9OKT0AEBG3AO/FEnKqY8UZssU1mD3pXD+y5tZ4ef9BXvhV+4An/Cu9sg08sSBJjcIAs6SqKQQL5x09jhvv22TnUBUpHqAUHDtjgpP7SBIk4AcRkYB/SCndCMxOKW0FSCltjYhZ+XXnAQ8Wbbslv+xw/nbp8h4iYgW5TGcWLFhQzf2Q+qX0BLRl1epboS8H8Idfe7hrDDB1/Fh+vm0PrWNaWDR9Qp+foScWJKlxGWCWVFUtLcHJ86bYOdSgWINPkgD4jZTSi/kg8g8j4ue9rJsVtUm9LO+5MBfAvhGgra3NGs2qqawT0KpvxSUuSudkqSRZwBMLktS4DDBLqjo7hxosJ/eRJEgpvZj/f0dEfBc4HdgeEXPz2ctzgR351bcAxxRtPh94Mb98fsZyqSY6OxObd+1n+5525k7JlcPasbfdclhNoLjERbk5WQrJAsXHQfFn74kFSWpMBpglDUhxp3DWpFZGtcDW3d07iHYONVDW4JM00kXEBKAlpbQ3f/sc4M+AO4HLgc/l/78jv8mdwDci4nrgdeQm83s4pdQREXsj4kzgIeAy4O+Gd2+knM7O1FUCa+r4sVz21oXccE/lGa6qb8UlLiIomyywaLql0CSp2bT0vYokdVcYHJy76n4u/cpD/M7f3c9dT2zjqtU/49xV93P3+m39mjFaKlUYoBRmJLfMiqQRaDbw7xHxM+Bh4N9SSneTCyy/MyI2AO/M3yeltB5YDTwJ3A18NKXUkX+ujwBfBTYCz+AEf6qR4hJY5y+d3xVchtcyXDfv2l/jVmqgClcxrlm5jGVLZnT14woWTh/HuDGj+MnTO/jFtj1MHT8W8LOXpGZgBrOkfsuqj3vDPRu44m2L+eKPN1orV4NmmRVJI11KaRPw5ozlu4Czy2xzLXBtxvK1wEnVbqPUX8UlsHrLcLUP2bgKVzGWTti3cPo4Pn7WEi6+8cGurOWVZy3h1gefY+vudj97SWpwBpgl9Vu5+rgRr922g6jByiqzUklpFkmSVJ9KS2BZDqt5lSYLjBszqiu4DLnxwqp7X0tQ8bOXpMZmiQxJXTo7E5t27uOBZ15i0859ZctcFAYHxVrHtJDSa7ftIKraLM0iSVJjKvQxt+9p5ysfaGPh9HHcvm4LV569xHJYTayQLHDm4hkcONRRNkHFz16SGp8ZzJKA7pOuFE+2cc6bZvP8Kwd6zPT9lQ+08ek7Hue5Xa/SOqaFK89ewi0PPGcHUUOmktIsJ1y5jM5EjxnJJUnS8Cm94ujZXfv42Df+o6uPed0FpzDv6FZmTDyKc06Yw859lsNqduUmcF72hhm8f+k8OjrhoWd32X+TpAZlgFkSkB28u+7upzjc0cnVtz+WOdN3YXAwdfxRjGqBtyw42sGBhkxvpVnmTmnlD85YwGNbdvPMzn2sXruFVw4cckZySZKGWVbSwpVnL2Hq+LFdtXavvv0x1qxcxqIZuTJYr59lWbVmV1qTuZCUctqCqfzgqe09ltt/k6TGYokMSUB28O49p8zj6tsfKzvT99W3P8a0CUfx+lkTWTQjd/nb4pkT7QxqSJQrzTJh7Cg+cOZCvvDjjay87VH+4b5NfODMhUwdP9YZySVJGmblrjg6f+n8rnUK83Vo5CjUZF6zchm3rTiDNSuXsfzEOTz/yoEex4v9N0lqPAaYJQHZwbtRLa/N7t3bTN/ScChkvhTXarzy7CV0dCZW3buhx6Qx5y+d7zEqSdIw62syaHC+jpGquCZzISml3PFi/02SGoslMiQB2Zet/drCac70rbpROhv5zImtjGqBn2/bmzkwmdQ6ipVnv4EDhzrYtHOfpVskSRoG5WrtFn6Cna9DxcodL44xJKmxGGCWBPQM3s2Z3MqRjsRfv//NbNixlx//fAdXnr2kWw1mBwcaboXMl8UzX6vV2Jl6nvxYOH0ck1rH8Pkf5Y7XhdPHcc15JzNmVDh5jCRJQ6B4Yr/SyaCvv+hUTpg7iV9//XTn61A35WozO8aQpMZigFlSl0LwbtH0CT0mZ/mL953MaQuPdqZv1Z2sgclnf+8kPvJP62g/3MncKa1c3LaAFbeurWjymOIBssFoSZL6ljWxX2Ey6GkTjur6LS1M6icVlCa5OMaQpMZUVzWYI+KYiPhxRDwVEesj4sr88mkR8cOI2JD/f2rRNp+MiI0R8YuIeFfR8tMi4vH8Y6siwl8oqUJZk7N86ruP09GZm+XbyfxUT7ImjWkd/VpG8/lL5/eo0VyYPKazM7Fp5z4eeOYlNu3cx5Ejndy9fhvnrrqfS7/yEOeuup+712+jszPVchclSapLhd/Rnzy9g19s28PU8WOB7pNB22dUX7JqM0uSGku9ZTAfAf5rSumnETEJWBcRPwT+CLgnpfS5iPgE8Ang6og4AbgEOBF4HfCjiDgupdQBfAlYATwIrAGWA3cN+x5JDai3yTaKSxNI9SKrdEahbEbpBJVzp7Ry/tL5bNyxlxdeebXbJbzXXXAK1//wFz2C0cevXOaxL0lSkays5ZVnLeHWB59j6+52+46SJI0gdZXBnFLamlL6af72XuApYB5wHnBzfrWbgffmb58H3JZSOphSehbYCJweEXOBySmlB1JKCbilaBtJfShMtlHMyTbUSAplMwrHceH/uVNa+cCZC/nav29ixa0/5T/dupaL2xYwd0prV7bVe06Z1+25nMlckqSesq54W3XvBs5fOh+w7yhJ0khSbxnMXSJiEfAW4CFgdkppK+SC0BExK7/aPHIZygVb8ssO52+XLpdUASfbUKMrruf38v6DLJk1katvfyyzXMaqezdw5dlL2Hewgwg4fs4k5k5pZevuXFDZAbIkST2Vu+ItAvuOkiSNMHUZYI6IicDtwJ+mlPb0Uj4564HUy/LS11lBrowGCxYsGFhjpSbkZBtqBsVlM5Z2Jk6eN4Wnt+/tMRieOn4sk8eN4YZ7NnSdULny7CXc8sBzvHLgkANkSdKIVm7y28IVb8W/q61jWlj2hhmc/5Z59h0lSRpB6i7AHBFjyAWX/zml9J384u0RMTefvTwX2JFfvgU4pmjz+cCL+eXzM5Z3k1K6EbgRoK2tzRmcNGIUDxTmTmmloxN27O0+aMiqaSs1qsLxDPQYDF/YNp9rvvdkt6zmG+7ZwM0fPJ2Zk45ygCxJGrGy6ix/4fffwrHTJ7Jjbztf+UBbt7kMrr/oVH5t0TR/NyVJGmHqKsAcuVTlrwFPpZSuL3roTuBy4HP5/+8oWv6NiLie3CR/S4CHU0odEbE3Is4kV2LjMuDvhmk3pLpWPFCYOn4sl711YbfMzesvOpXlJ85xYKCmlFX+5Q2zJmZe4ptI3U6wlMvgkiSpGWT9zpXWWZ46fiwbtu/jY9/4j67f0esuOIV5R7cybYInZSVJGqnqKsAM/AbwAeDxiHg0v+xT5ALLqyPiCuB54EKAlNL6iFgNPAkcAT6aUurIb/cR4CZgHHBX/p804hUPFM5fOr8ruAy5oNpVqx/l+JXLzFxWU8oq/5JSz6zmQt3l4sH2kY7UI0vLkzGSpGaQlal8/UWnMnPS2G6/j1l9x6tvf4w19h0lSRrR6irAnFL6d7LrJwOcXWaba4FrM5avBU6qXuukxlSajbJr/8GuQUEEmZmbO/a2O0hQ0yot/9LZmTIntVwwdXyPwfbKs5Zw64PPsXV3uydjJElNozRTuZB08K0VZ3Y7CWvfUcOtktJ+kqTaq6sAs6TqyspGue6CU1g4fRzP7XoVKJ+5KY0U5Sa1zBpsr7p3A1eevYR9BzuIgFcOHOKZHfvYsXfggx5Lb0iSam37nvbMwPGBQx3dTsKOCvuOGj6W9pOkxmGAWWpiWXXzntm5j08sfxM/37aHH/98B1eevaRHR23R9Ak1brk0vLImtcwabE8dP5bJ48Zwwz0bmDp+LBPGjuq6XTzoWTh9HNecdzJjRkWvQeNylyQ7WJIkDafZk1u7BY7nTmnlwrb5vHq4gzfOnsTdVy5j25525kxu5Y1zJvf43bLvqKFgaT9JahwGmKUmVhwgmzullQ+cuZBV974WTP6L953MaQuP5pwT5rBz32uZmwa2pJ6DbYAL2+Zzzfee7DHQKb49d0orF7ctYMWta5k6fiwXts3nuFmTOPF1k+lM3TOcy12S7GBJkjSciifB7StTdMG0CT2u+rHvqKFQPJYpLc8yd0or5y+dz9Pb99ISWDpDkmrMALPUhAqX3L96uIMrz34Dq9du4fyl87uCy5DroH3qu4+zZuUyXj9rIq+fZTBLKlY82C4MsN8wa2LmQKf4duFvber4sV0ndcoN1ksnTwJrWUqShl9xuaidew9y+T8+XPbkZ9ZVP9JQKD3ZX7hdnDhj6QxJqg8GmKUmUVzH9UhH4tN3PM5zu16ldUwLV569xElZpH7Kqs2cEpkDneLbhb+14pM6pZd1Th0/lp9v28OYUVO6TgJt3d3e9TzWspQkDbdC4LhcPWb7jBpuxSf7b1+3pau0X299LK8Gk6TaMMAsNYGsOq4rz1rCrQ8+x9bd7dxwzwZu+ePTnZRF6qfSLK3OzpQ50Cm+Dbm/rXIZzlnlaq48ewm3PPAcrxw4ZC1LSdKQKZ1YdsHU8Tz/yoFuE81mlYiyz6haKD3ZP2dyK+ecMIcNO/aWLZ0BuRP5O/cedAJlSRpGBpilBlQ6OGgJetRxXXXvBq5422K++OONtB/upKMoMOakLNLAlBvo7Nz32u1XDhxkyayJbNq5LzPDOatczQ33bODmD57O7MlH0dEJ655/mbGjWjhwqMOBkSSpKkoTEhZOH8fHz1rCp//liW59w3PeNNs+o+pGVkmWiPJXlM2d0splb13YVeal0omXi5WOteyHSVLfDDBLdSyrcwP0GBx89vdO4k/e8QYOdXRy+7rcpfaFS/Uh1+maPbmVM46d7qQs0iBlDXS61zCfyNLOxPMv72fh9Al86ruPd8twLleuZlQLPLl1L9fd/RQXty3oluFcqCUIOOCRJA1I6cSy7zllXldwGV4rLbBm5bIeJaL8vVE9KVc6o/1wJxe2ZU+8XGmwOevKUGs6S1LfDDBLdapc5+aEuZO6lhU6TR/5p3U9SmO8cuBQV73YQtaJk7JIw6OlJVg0YyILpk3g1GOO7pbt/NL+g3z1/k09Lj0eM6qFq1Y/yhVvW9wtw7lQr3ncmBZGt7R01VcfSEYOmJUjSSNB8Xf9rEmtjGqBp7fv5UPLFnclI/Q1P4d9RtWr3q4oO3Coo8fEy/0JNpeeiLGmsyRVxgCzVKdKOzeFIFNL0DU4yLrUftW9G1jx9sW8fuZE5h3dygVL5xlAkmok66TOsZ0TMi89LgyI+qrXvPKsJdz9xFaWnzSXFbeuZer4sVzYNp/jZk3ixNdNpjPBjr3ZwWOzciSpOZUGlJ/dtY+PfeM/Mmv9F5IRAGstq2GVu6KsuERZcZ+qt2Bz65gWvvD7b+HY6RPZsKP7iRhwkktJqoQBZqlOFc/gXS7I1NKSnXnylmOO5jePm2XASKpDpVk3hUuPN+/aT+uYFqD3es2r7t3AX77/zfz3b/+MqePHdn03TB0/lsveurDrstCszByzciSpeRQHlY90pK4rXAoB5anjx3aVTbvhntfm5ijM0/GvP3uBP3/vST1qMFtrWY2suHwG0GewGXKJPBu2dz8pUzxhuideJKlvBpilOlU8g3e5INNfvf/NmZknC81YlupaVtZNYUB03d1PsfKsJay6t3y95lcPHunx3XD+0r5rDrYfPtKVlQO5AVYEvHLgEM/s2Fc281mSVF+yrkgpDogVB5SBbnNztB/u5JR5k7lg6TwWTB3P0gVTrbWsplF8Iv/l/bmJl6++/TEgO9gMdOtDQfcJ07/32Atcc97JbN+Ty2b2b0SSshlglupU8dn3ckGmo0a3cN0Fp3D17Y+ZeSI1uK4B0ZxJvLz/IN9acSbthzsz6zWPP2o0rWNaun039Ocy0E8uP572I5387Y+eZur4sUwYO6prYFXNshnWe5akyvT2fZlVT3nTS/v5xbY93bKUCwGxL/54Y7eAMuR+O1J67faS2ZO6TnJaa1nNpvhE/tLOxMnzppQNNgNlx1ptC6dw3OyJ/ZokUJJGKgPMUp3IGlgUzr7v3Jc9KdgbZk1k0fQJnDxvipknUhMozWzu7Ew96jVfd8EpHDO1lesuOKVbnUGo7DLQ9sOd7DpwiBvv29Qj8xleq/feOqaFxTMm0NGZq+k8fuxoDnV0MHPiUV3LigdXpd9hC6aO5wdPbbfesyT1oTQbuTiIVWk95UKQuRBUbh3TQkvR7cI2JiNopOkr2Nx+uJNRkV2PfNqEo/jIPz/YZ93mHXvbmTulNbN/JEkjhQFmaQhlBY2BrmXFHZFC7bxDR1LXhF1vmjuZY2dM6JbNXJqpnHWpvaTmUK5ec0tLcMr8xPMv72fh9Al86ruPc/u6LVx59hJuuGcDUP4yUIDOlJ35XFzvvbSmcyHz+WBHJ9f/8OluQZDRLUFHZ/fvsDfMmsiLr7zaLbvOes+S1FNxffysIFYl9ZS/+OONXVnKhX7iCXMn8euvn87Mibms57csONpkBI1oWcHmHXvbmTO5lTfOmVx2AmbovW5zb/NglAaeF0wdz/OvHOh2RcLW3b2ftPdvVlIjMMAs9UPxj31xZ6G4c1B8OytoPHXCGD713dyy0uDNf/nt42gd3cL/e/fPe2T8lQsySWpu5U4itbQEi2ZMZMG0CZx6zNFdA6RzTpjDKwfKXwYK9MjUyar3nlWPsDjzua/SG6U1QSE3OHt6+15agq7vz8J36csHDjJ2VAsHDnWU/X7ta5DlgExSvcm6uqMQXCp8123YsberPn7WVSd91VOO/Hf6dRecwryjW7lg6byu779FM4pq/c/w5J5UUNq/WjBtQtkJmPuq21xuHoyswPPHz1rSbWLNwtUFY0dHj5P2hUk7vQJMUiMwwKyGN9iAQrmgcWmAoyVg/Yt72LBjLz/++Q7effLcbsHh4ssVi29nBY1XnrWECHoEb/72R0+z4u2Luy0rzvgzU1lSqewAdPnLQFvHtHDy/CldV0UUZz6Xq+lcUJz53FfpjcKyVfdu4Mqzl5ASPTKjC7dve+R5Lm5bUDZzOmvwNeGoniU7Cif1ntv1amb2UHEQuzhwbUBb0mBl1UkuTjYoBIr+/L0n8Xf3bshMNFh51hJaWrJrwfZWT3nZG2Zw/lvm+R0kDUJvEzBftfpRoHzd5nKlyUpP1r/nlHldwWV47QRSoZ9UfNK+uPzNdXc/xbyjW3vtv/SWKd1b+Q77MpKqpakDzBGxHLgBGAV8NaX0uRo3qUs1v8j7eq5KAqjFtwt1NqeNPyozK7evUg+V/Ohl1fIslwXcV3uyAgrlgg+lbTvSkfj5tr09gsa9BTheOXCI//GeE7jxvmfKZpcU384KGq+6dwN/9f43Zw4gOhM9lu3Y216VwHJnZ+K5Xft5cferHDjYwdQJYzjSmXhl/2FmTBzL4Y4ORrWMYvbko1gwzc5FOXbE1AjKXQZayMoBujJ1CpnPL+3vXu+9t8znvgLQBe2HO5k/dTz/7ds/6zHYKty+4m2Le82cLjf4+i+/fRzjxrTwF3d1P4F39xNbWX7S3G7ZQ8VB7OLv9bse35p5wvCux7fyjuNncdysSZz4usl0pp6/I1lXqZRbd6C/h32tW8nvb6Xb7dxXeQC+knX7s11/AvsamEbpF5ceC+UuJy933FW6TW+vU8lVaqXblAaSy9VJ/vS/PMEVb1sM9Ew0KPQPs2rB9lZP+dcWTfPvRRoCxaXK+qrbnNU/Ku0rlZtMsLifVFhWKH/znZ9u4eK2BVx844Nl+y9ZY9dyJ7N6q/Fe+lh/v2N7O2Ff7jt2INtU2raBvM5gA/iD/Z0ZyOv0tk250ivVfp2BvG+9PXclJ0EqHZNXchV6NY7rgbzX5bZp1H5x0waYI2IU8EXgncAW4JGIuDOl9GRtW9ZzIo/BXPbS13MVP56VNVbuB2nlWUv41trnueTXFmRm5V5/0amMHR2ZNacq/dHLCgyUywLuqz2lAYW+XiMroFAcNC4X4CgEja/53pPdLlUsrFN6uWLhdlbQ+MChI70OIIqXzZrU2q/jotyxcu8vtrNh+76ygfPP/u6JfHvd85z9pjksmT2Rs944uyG+yIZTNf9+peFSrsxG6bJjOydkZjYXjvVp48dy1TuP4/ofPg30XXqjsM6YUdFrxk9fmdPlBl/lTuD95fvfzH8vCWgXB7EL695wT/d1C8tve+R5Vrz99VzzvSf7POFYXBak2r+HWb9VxXWwK3m9rNfI2u6qdx7HUaO6X21T7rk+9e7jefVw91Iolexnue3K9TP8Xq2eRusXFx8LhSBJVuC23DHa1zZZt3vLLO5rm8LrFAeSy9VJLu0jFms/3MlRo3OlLoqvOrGeslQ7ldRtLjcPRuntcvcPHDxS9sqFcmVzsvo6xetknczqrcY7kPlY6XdsuTIf5U7YF65AK2zTVxyir20qbdtAXqfSPk251+ntN2Mg+9PXVS+V/gZef9GpnPOm2V2Tb/fnhMRgjoNKS8Rk7UOhHwhkjr2L96e3MXm5eNhAjoNKtunPe13u88k6jhqlX9xS6wYModOBjSmlTSmlQ8BtwHk1bhPQfSIPeK0MwuZd+6v+XMWPl6sTlfWDtOreDbznlHnccM8Gzl86v+uHrHD7qtWP8tiW3b0GYz/09tf3+hp/+6OneWn/oR7bZb1eX+1ZdW/u9Up/fMu9RnHbCsuv+d6TvOeUeUD5AEfxgGBUyV9P6eWKxbezgsY79x7kyrOX0DqmpWvZX15wCm+cPanbsmrN9L15134e27I787MqvI+f/df1XPbri7nhng08tmX3gI7JZlfNv1+p3hQyddasXMbfXvxm3n3SHP7t48v45n86g2+tOJMT503mnBNm828fX8Y7jpvBdRec0u376k1zJ/MX7zu5x3fYklmvfa8VlpfeLvd44X65wVfWCbxXi9bNCmKXW7fgPafM45rvPdnrb1zhe3PXgUNdAdNq/x5m/VbtOnCoa5LFSl4v6zWytrv+h0+z60BlbXhp/2v73J/9LLdduX6G36tV1VD94uJj4dP/8kRX/yzrmMk6RvvaJut2YZu+/uZ7a9uqe3OPFe5nJR6U9hGLtY5p4Q2zJvK7p7yONSuXcduKM1izchnLT5zDohkTOXPxDF4/a2LX7cUzJ9b9YFNqJoVg85mLZ7BoxsTMPlNx/6gQeC78rf/rz17gz997Ure+z5VnL+Gl/Qczvw9SglFlyuZk9XVK14koX76jsE7hO63cY6XfseXKfGR9Fxeeu3ibvuIQfW1TadsG8jrlnru3UieV/mYMZH8q+W2q5DfwqtWPsn7r7sy40EBfZ7DvW1/7UOgHlht7F+9P6TbFysXDBnIcVLJNf97rvvanEfvFTZvBDMwDfll0fwtwRvEKEbECWAGwYMGCYWvY9j3tmT8AAymD0NdzFT9eLkOstx+kcp3j9sOvDerLDtoP9f2jlxUYKPd6fbUnK0hQ7jWK21a8vDhonHV2uXhA8KY5k7vWKb1csfj2X15wCqNaotu6hUlYZkw8inNOmMPOfd0vW3/T3MlVn8xv+572spevd3sf8+9N4RJvaz53V82/X6keZWU7v35W1rFdvvRGYdLB4mVZmdGF27c98jwrz1rCqns3ZGZOFw++Sr+Xs07gjT9qdI/socL/pduXrgvdB3N9nXAs/l6tJIjdn9/DrN+1/r5e1mtUsw3d1u3jd7+S36Di236vVlWf/WKoTd+43O9q1nGR9Vi54723bSq5XUl7enudrMSD0iyx0u+6QlJBuatOJNWXvubBKC5DVhjvLZg6nqULprJjb3vXFQkv7TvI/JIrFwrjxTGjRnWb3wLK93Wyxq6FSUCLx9PF+vruK/2O7W/fo/S5K41DlNum22seKt+2gbxOuefurdRJJc890P3pbzuztinc37o7Oy400NcZSAyov8fOjr3tpDJ9x+L9Kd2m+O+xXDxsoPtdyTb9ea/72p9G6xc3c4A5KyLXbeiUUroRuBGgra0tZaw/JGZPbs38ARhIGYS+nqv08UpuF+4XOsKVZOVmDtrH9v2jlxUYKPd6fbUnK0hQNvgwNnvdQtC4XICjEDT+i/edzAmvm8S/fXwZO/e1d7tcMevSReg9aFwavBmKQcXsya1lL18vfh/H5d+blqAqpTmaTTX/fqVGV2npDaCrhmHpYKtw+5UDB/nWijM5cKiDuVNee7y3wVeh7EPpCbxjprZ2XWKeFcQu/l7/yn3P9Pi+f9PcyX3+fhW+N8vVYCxet5JBYCWB8oG+XtZrVLMNXev28btfyW9Q8W2/V6uqz34x1KZvXO53Neu4yHqs3PHe2za93Y5+HqdZr1PabywEii5YOq9bcKk08GS5C6k59HWyvsdkgjOyT9oXSl4WTtKX679kjV2zTmZB+d//ztS/PkWl62V9r/Y3MF72NcuM6QfzOuWeu9LXKX7uau1Pf9pZ+vqF+3OnjKvq+1HpPgzm2Cn0A7MeK92f0m0KeouHDfb9LbfN3CmVvWYl+9No/eJIaVj6jsMuIt4KfDal9K78/U8CpJT+36z129ra0tq1a4elbdZgrn4N5usuOIWF08bx3MuvZgYfKqlr+RfvO5nTFh7NkQ66gh4dnXQLcGzb07gDAGswV4c1mKXaKZ78YvzYURzu6GRGfuK60gBNYd1CEKejE145cJAxJRPMbdvT8/t+9ChY99yv+NR3H++ztt7nLz6VA4c6el3XGsz1XYM5ItallNqG/IVqqL/9Yhi+vnEz1WAuBJKn5iembuR+o6T6UdynKR6XFvdfim8XMqWff+VAj37QC79qz6zx/tK+no9Zg9kazIM5DqzB3Lg1mAfaN27mAPNo4GngbOAF4BHg91NK67PWH84AM3T/kRhsx7Ov58oaZJf+CBVu52abzw3aizvHWQFWIPN5+/rRK36N4sBA8XaF21t3V96e0pk3+3qN0h/hkdD57+xMPLdrP1t3v8r+gx1MHT+GIynxqwOHmT5hLIc7OhjVMorZk49iwbTmfz8Gqpp/v5LqU7nfzuH+Pexr3b5erz/bvbSvsgB8pev2Z7tansgdIQHmfvWLYfiTL7KCJ6VBkqy+YlafrpJtenudcn9XvW1jf0BSI+htHFNJILu379je+kvlvmMHsk2lbRvI6wwmgN+f34zB7k+l2/SVfFGt1xnI+1ZpbKbcMVvpmLzSPv1gj+uBvNfltql1gqMB5gwRcS7weWAU8PWU0rXl1h3uALMkSZJqbyQEmKF//WKwbyxJkjQSDbRv3Mw1mEkprQHW1LodkiRJUi3ZL5YkSdJQaeoM5v6IiJ3Ac7VuxxCbAbxU60aoIXisqFIeK6qEx4kqVYtjZWFKaeYwv2bda9C+sd811eH7OHi+h4Pne1gdvo+D53s4eL6H1TFc7+OA+sYGmEeQiFg7Ei4B1eB5rKhSHiuqhMeJKuWxosHw+KkO38fB8z0cPN/D6vB9HDzfw8HzPayOen8fW2rdAEmSJEmSJElSYzLALEmSJEmSJEkaEAPMI8uNtW6AGobHiirlsaJKeJyoUh4rGgyPn+rwfRw838PB8z2sDt/HwfM9HDzfw+qo6/fRGsySJEmSJEmSpAExg1mSJEmSJEmSNCAGmCVJkiRJkiRJA2KAuYlFxOaIeDwiHo2Itfll0yLihxGxIf//1Fq3U8MvIr4eETsi4omiZWWPjYj4ZERsjIhfRMS7atNqDbcyx8lnI+KF/PfKoxFxbtFjHicjUEQcExE/joinImJ9RFyZX+53irrp5Vjxe0X9NpDjST05Xhi8iHhj0fH2aETsiYg/9VjsneORwSvzHv5VRPw8Ih6LiO9GxNH55Ysi4tWi4/HLNWt4nXHMM3hl3sNvFb1/myPi0fxyj8UMzTCmsgZzE4uIzUBbSumlomV/CbycUvpcRHwCmJpSurpWbVRtRMTbgX3ALSmlk/LLMo+NiDgB+CZwOvA64EfAcSmljho1X8OkzHHyWWBfSumvS9b1OBmhImIuMDel9NOImASsA94L/BF+p6hIL8fKRfi9on7q7/GkbI4XqisiRgEvAGcAH8RjsSzHI4NX5j08B7g3pXQkIq4DyL+Hi4DvFdbTaxzzDF7We1jy+N8Au1NKf+axmK0ZxlRmMI885wE352/fTO6A1QiTUroPeLlkcblj4zzgtpTSwZTSs8BGcl9ianJljpNyPE5GqJTS1pTST/O39wJPAfPwO0UlejlWyvFYUVkDOJ5UOccLA3c28ExK6blaN6TeOR4ZvKz3MKX0g5TSkfzdB4H5w96wBuOYZ/B6ew8jIsid/P3msDaqwTTDmMoAc3NLwA8iYl1ErMgvm51S2gq5AxiYVbPWqd6UOzbmAb8sWm8LDuBGuo/lL7v7etElOh4nIp+R8BbgIfxOUS9KjhXwe0WDUOHxpGyOF6rrEroHUTwW+8e+Q3X9MXBX0f1jI+I/IuL/RMSyWjWqgdg3qY5lwPaU0oaiZR6LvWjUMZUB5ub2GymlpcC7gY/mL1uQ+isylllbZ+T6EvB64FRgK/A3+eUeJyNcREwEbgf+NKW0p7dVM5Z5rIwgGceK3ysasH4cT8rmeKFKImIs8HvA/84v8lisHn8P+iki/h/gCPDP+UVbgQUppbcAVwHfiIjJtWpfA7BvUj2X0v3Em8diLxp5TGWAuYmllF7M/78D+C65dPnt+douhRovO2rXQtWZcsfGFuCYovXmAy8Oc9tUJ1JK21NKHSmlTuArvHYZjsfJCBYRY8h1hP45pfSd/GK/U9RD1rHi94oGqp/HkzI4XqiqdwM/TSltB4/FAbLvUAURcTnwHuAPUn7Srfxl9Lvyt9cBzwDH1a6V9c2+SXVExGjgfOBbhWUei+U1+pjKAHOTiogJ+cLgRMQE4BzgCeBO4PL8apcDd9SmhapD5Y6NO4FLIuKoiDgWWAI8XIP2qQ4Uftzy3kfuewU8TkasfF21rwFPpZSuL3rI7xR1U+5Y8XtFAzGA40klHC9UXbcsPY/FAbHvMEgRsRy4Gvi9lNKBouUz85NQEhGLyb2Hm2rTyvpn36Rqfhv4eUppS2GBx2K2ZhhTja7li2tIzQa+mztGGQ18I6V0d0Q8AqyOiCuA54ELa9hG1UhEfBN4BzAjIrYAnwE+R8axkVJaHxGrgSfJXWb10ZE+S+5IUeY4eUdEnEru8pvNwH8Gj5MR7jeADwCPR8Sj+WWfwu8U9VTuWLnU7xUNQL+OJ2VyvFAlETEeeCfdj7e/9Fgsz/HI4JV5Dz8JHAX8MP+3/WBK6cPA24E/i4gjQAfw4ZRSpRPbNTXHPIOX9R6mlL5Gz7r04LFYTsOPqSJ/xYQkSZIkSZIkSf1iiQxJkiRJkiRJ0oAYYJYkSZIkSZIkDYgBZkmSJEmSJEnSgBhgliRJkiRJkiQNiAFmSZIkSZIkSdKAGGCWJEmSJEmSJA2IAWZJkiRJkiRJ0oAYYJYkSZIkSZIkDYgBZkmSJEmSJEnSgBhgliRJkiRJkiQNiAFmSZIkSZIkSdKAGGCWJEmSJEmSJA2IAWZJUpeIuCsiLq91OyRJkiRJUmOIlFKt2yBJkiRJkiRJakBmMEuSJEmSJEmSBsQAsyQ1oYjYHBGfjIgnI+KViPjHiGiNiKkR8b2I2Jlf/r2ImF+03U8i4kP5238UEf8eEX+dX/fZiHh37fZKkiRJkiTVGwPMktS8/gB4F/B64Djg0+S+9/8RWAgsAF4FvtDLc5wB/AKYAfwl8LWIiCFssyRJkiRJaiAGmCWpeX0hpfTLlNLLwLXApSmlXSml21NKB1JKe/PLf7OX53gupfSVlFIHcDMwF5g99E2XJEmSJEmNYHStGyBJGjK/LLr9HPC6iBgP/C2wHJiaf2xSRIzKB5FLbSvcSCkdyCcvTxyi9kqSJEmSpAZjBrMkNa9jim4vAF4E/ivwRuCMlNJk4O35xy17IUmSJEmS+s0AsyQ1r49GxPyImAZ8CvgWMIlc3eVf5Zd/ppYNlCRJkiRJjc0AsyQ1r28APwA25f/9OfB5YBzwEvAgcHetGidJkiRJkhpfpJRq3QZJUpVFxGbgQymlH9W6LZIkSZIkqXmZwSxJkiRJkiRJGhADzJIkSVKDioivR8SOiHiizOMREasiYmNEPBYRS4e7jZIkSWpuBpglqQmllBZZHkOSRoSbgOW9PP5uYEn+3wrgS8PQJkmSJI0gQxZgzsqmiIjPRsQLEfFo/t+5RY99Mp9Z8YuIeFfR8tMi4vH8Y6siIvLLj4qIb+WXPxQRi4q2uTwiNuT/XT5U+yhJkiTVUkrpPuDlXlY5D7gl5TwIHB0Rc4endZIkSRoJRg/hc98EfAG4pWT536aU/rp4QUScAFwCnAi8DvhRRByXUuogl2WxAngQWEMuQ+Mu4ArglZTSGyLiEuA64OKImAZ8BmgDErAuIu5MKb3SW2NnzJiRFi1aNIjdlSRJUqNZt27dSymlmbVuxxCaB/yy6P6W/LKtpStGxApy/W4mTJhw2vHHHz8sDZQkSVJ9GGjfeMgCzCml+4qzivtwHnBbSukg8GxEbAROj4jNwOSU0gMAEXEL8F5yAebzgM/mt/828IV8dvO7gB+mlF7Ob/NDckHpb/bWgEWLFrF27dqK90+SJEmNLyKeq3UbhlhkLEtZK6aUbgRuBGhra0v2jSVJkkaWgfaNa1GD+WP5CUa+HhFT88vKZVbMy98uXd5tm5TSEWA3ML2X5+ohIlZExNqIWLtz587B7ZUkSZJUf7YAxxTdnw+8WKO2SJIkqQkNd4D5S8DrgVPJXZb3N/nl5TIresu4GMg23RemdGNKqS2l1DZz5vBeGdnZmdi0cx8PPPMSm3buo7Mzs4mSJEnSYNwJXBY5ZwK7U0o9ymNIkiRJAzWUNZh7SCltL9yOiK8A38vfLZdZsSV/u3R58TZbImI0MIXcBCdbgHeUbPOTau1DNXR2Ju5ev42rVj9K++FOWse0cP1Fp7L8xDm0tGTFxyVJkqSeIuKb5Pq+MyJiC7m5SMYApJS+TG4Ok3OBjcAB4IO1aakkSZKa1bBmMJfMWP0+4In87TuBSyLiqIg4FlgCPJzPrtgbEWfm6ytfBtxRtM3l+dvvB+5NKSXg+8A5ETE1X4LjnPyyurF51/6u4DJA++FOrlr9KJt37a9xyyRJktRIUkqXppTmppTGpJTmp5S+llL6cj64TMr5aErp9Smlk1NKFlaWJElSVQ1ZBnOZbIp3RMSp5EpWbAb+M0BKaX1ErAaeBI4AH00pdeSf6iPATcA4cpP73ZVf/jXg1vyEgC8Dl+Sf6+WIuAZ4JL/enxUm/KsX2/e0dwWXC9oPd7JjbzuLZ06sUaskSZIkSZIkqX+GLMCcUro0Y/HXeln/WuDajOVrgZMylrcDF5Z5rq8DX6+4scNs9uRWWse0dAsyt45pYdak1hq2SpIkSZIkSZL6Z7gn+ROwaPoErr/oVFrH5N7+Qg3mRdMn1LhlkiRJkiRJklS5YZ3kTzktLcHyE+dw/Mpl7NjbzqxJrSyaPsEJ/iRJkiRJkiQ1FAPMNdLSEiyeOdGay5IkSZIkSZIaliUyJEmSJEmSJEkDYoBZkiRJkiRJkjQgBpglSZIkSZIkSQNigFmSJEmSJEmSNCAGmCVJkiRJkiRJA2KAWZIkSZIkSZI0IAaYJUmSJEmSJEkDYoBZkiRJkiRJkjQgBpglSZIkSZIkSQNigFmSJEmSJEmSNCAGmCVJkiRJkiRJA2KAWZIkSZIkSZI0IAaYJUmSJEmSJEkDYoBZkiRJamARsTwifhERGyPiExmPT4mIf42In0XE+oj4YC3aKUmSpOZkgFmSJElqUBExCvgi8G7gBODSiDihZLWPAk+mlN4MvAP4m4gYO6wNlSRJUtMywCxJkiQ1rtOBjSmlTSmlQ8BtwHkl6yRgUkQEMBF4GTgyvM2UJElSszLALEmSJDWuecAvi+5vyS8r9gXgTcCLwOPAlSmlztIniogVEbE2Itbu3LlzqNorSZKkJmOAWZIkSWpckbEsldx/F/Ao8DrgVOALETG5x0Yp3ZhSaksptc2cObPa7ZQkSVKTMsAsSZIkNa4twDFF9+eTy1Qu9kHgOylnI/AscPwwtU+SJElNzgCzJEmS1LgeAZZExLH5ifsuAe4sWed54GyAiJgNvBHYNKytlCRJUtMaXesGSJIkSRqYlNKRiPgY8H1gFPD1lNL6iPhw/vEvA9cAN0XE4+RKalydUnqpZo2WJElSUzHALEmSJDWwlNIaYE3Jsi8X3X4ROGe42yVJkqSRwRIZkiRJkiRJkqQBMcAsSZIkSZIkSRoQA8ySJEmSJEmSpAExwCxJkiRJkiRJGhADzJIkSZIkSZKkATHALEmSJEmSJEkaEAPMkiRJkiRJkqQBMcAsSZIkSZIkSRqQIQswR8TXI2JHRDxRtGxaRPwwIjbk/59a9NgnI2JjRPwiIt5VtPy0iHg8/9iqiIj88qMi4lv55Q9FxKKibS7Pv8aGiLh8qPZRkiRJkiRJkkayocxgvglYXrLsE8A9KaUlwD35+0TECcAlwIn5bf4+Ikblt/kSsAJYkv9XeM4rgFdSSm8A/ha4Lv9c04DPAGcApwOfKQ5kS5IkSZIkSZKqY8gCzCml+4CXSxafB9ycv30z8N6i5bellA6mlJ4FNgKnR8RcYHJK6YGUUgJuKdmm8FzfBs7OZze/C/hhSunllNIrwA/pGeiWJEmSJEmSJA3ScNdgnp1S2gqQ/39Wfvk84JdF623JL5uXv126vNs2KaUjwG5gei/P1UNErIiItRGxdufOnYPYLUmSJEmSJEkaeeplkr/IWJZ6WT7QbbovTOnGlFJbSqlt5syZFTVUkiRJkiRJkpQz3AHm7fmyF+T/35FfvgU4pmi9+cCL+eXzM5Z32yYiRgNTyJXkKPdckiRJkiRJkqQqGu4A853A5fnblwN3FC2/JCKOiohjyU3m93C+jMbeiDgzX1/5spJtCs/1fuDefJ3m7wPnRMTU/OR+5+SXSZIkSZIkSZKqaPRQPXFEfBN4BzAjIrYAnwE+B6yOiCuA54ELAVJK6yNiNfAkcAT4aEqpI/9UHwFuAsYBd+X/AXwNuDUiNpLLXL4k/1wvR8Q1wCP59f4spVQ62aAkSZIkSZIkaZCGLMCcUrq0zENnl1n/WuDajOVrgZMylreTD1BnPPZ14OsVN1aSJEmSJEmS1G/1MsmfJEmSpAGIiOUR8YuI2BgRnyizzjsi4tGIWB8R/2e42yhJkqTmNWQZzJIkSZKGVkSMAr4IvJPcZNePRMSdKaUni9Y5Gvh7YHlK6fmImFWTxkqSJKkpmcEsSZIkNa7TgY0ppU0ppUPAbcB5Jev8PvCdlNLzACmlHcPcRkmSJDUxA8ySJElS45oH/LLo/pb8smLHAVMj4icRsS4iLhu21kmSJKnpWSJDkiRJalyRsSyV3B8NnEZusu1xwAMR8WBK6eluTxSxAlgBsGDBgiFoqiRJkpqRGcySJElS49oCHFN0fz7wYsY6d6eU9qeUXgLuA95c+kQppRtTSm0ppbaZM2cOWYMlSZLUXAwwS5IkSY3rEWBJRBwbEWOBS4A7S9a5A1gWEaMjYjxwBvDUMLdTkiRJTcoSGZIkSVKDSikdiYiPAd8HRgFfTymtj4gP5x//ckrpqYi4G3gM6AS+mlJ6onatliRJUjMxwCxJkiQ1sJTSGmBNybIvl9z/K+CvhrNdkiRJGhkskSFJkiRJkiRJGpCKMpgj4jjgvwELi7dJKZ01RO2SJEmSJEmSJNW5Sktk/G/gy8BXgI6ha44kSZIkSZIkqVFUGmA+klL60pC2RJIkSZIkSZLUUCqtwfyvEfEnETE3IqYV/g1pyyRJkiRJkiRJda3SDObL8///t6JlCVhc3eZIkiRJkiRJkhpFRQHmlNKxQ90QSZIkSZIkSVJj6TXAHBFnpZTujYjzsx5PKX1naJolSZIkSZIkSap3fWUw/yZwL/C7GY8lwACzJEmSJEmSJI1QvQaYU0qfyf//weFpjiRJkiRJkiSpUVQ6yR8R8TvAiUBrYVlK6c+GolGSJEmSJEmSpPrXUslKEfFl4GLg40AAFwILh7BdkiRJkiRJkqQ6V1GAGfj1lNJlwCsppf8FvBU4ZuiaJUmSJEmSJEmqd5UGmF/N/38gIl4HHAaOHZomSZIkSZIkSZIaQaU1mL8XEUcDfwmsyy/76pC0SJIkSZIkSZLUECoNMP818BFgGfAAcD/wpaFqlCRJkiRJkiSp/lUaYL4Z2Ausyt+/FLgFuGgoGiVJkiRJkiRJqn+VBpjfmFJ6c9H9H0fEz4aiQZIkSZIkSZKkxlDpJH//ERFnFu5ExBnA/zc0TZIkSZJUqYhYHhG/iIiNEfGJXtb7tYjoiIj3D2f7JEmS1NwqzWA+A7gsIp7P318APBURjwMppXTKkLROkiRJUlkRMQr4IvBOYAvwSETcmVJ6MmO964DvD38rJUmS1MwqDTAvH9JWSJIkSRqI04GNKaVNABFxG3Ae8GTJeh8Hbgd+bXibJ0mSpGZXUYA5pfTcUDdEkiRJUr/NA35ZdH8LuasPu0TEPOB9wFn0EmCOiBXACoAFCxZUvaGSJElqTpXWYJYkSZJUfyJjWSq5/3ng6pRSR29PlFK6MaXUllJqmzlzZrXaJ0mSpCZXaYkMSZIkSfVnC3BM0f35wIsl67QBt0UEwAzg3Ig4klL6l2FpoSRJkppaTTKYI2JzRDweEY9GxNr8smkR8cOI2JD/f2rR+p/Mz4r9i4h4V9Hy0/LPszEiVkW+1xwRR0XEt/LLH4qIRcO+k5IkSdLQewRYEhHHRsRY4BLgzuIVUkrHppQWpZQWAd8G/sTgsiRJkqqlliUyfiuldGpKqS1//xPAPSmlJcA9+ftExAnkOsonkpts8O/zs2ADfIlcnbgl+X+FyQivAF5JKb0B+FtyM2ZLkiRJTSWldAT4GPB94ClgdUppfUR8OCI+XNvWSZIkaSSopxIZ5wHvyN++GfgJcHV++W0ppYPAsxGxETg9IjYDk1NKDwBExC3Ae4G78tt8Nv9c3wa+EBGRUiqtRydJkiQ1tJTSGmBNybIvl1n3j4ajTZIkSRo5apXBnIAfRMS6/GzVALNTSlsB8v/Pyi/Pmhl7Xv7flozl3bbJZ3XsBqaXNiIiVkTE2ohYu3PnzqrsmCRJkiRJkiSNFLXKYP6NlNKLETEL+GFE/LyXdcvNjN3bjNmVzKZNSulG4EaAtrY2s5slSZIkSZIkqR9qksGcUnox//8O4LvA6cD2iJgLkP9/R371cjNjb8nfLl3ebZuIGA1MAV4ein2RJEmSJEmSpJFq2APMETEhIiYVbgPnAE+Qm+368vxqlwN35G/fCVwSEUdFxLHkJvN7OF9GY29EnBkRAVxWsk3hud4P3Gv9ZUmSmkNnZ2LTzn088MxLbNq5j85Of+IlSZIkqVZqUSJjNvDdXEyY0cA3Ukp3R8QjwOqIuAJ4HrgQID8L9mrgSeAI8NGUUkf+uT4C3ASMIze531355V8Dbs1PCPgycMlw7JgkSRpanZ2Ju9dv46rVj9J+uJPWMS1cf9GpLD9xDi0tWRWy1Ag6OxObd+1n+552Zk9uZdH0CX6ekiRJUoMY9gBzSmkT8OaM5buAs8tscy1wbcbytcBJGcvbyQeoJUlS89i8a39XcBmg/XAnV61+lONXLmPxzIk1bp0GwpMGkiRJUmOrSQ1mSZKkgdi+p70ruFzQfriTHXvba9QiDVa5kwabd+2vccskSZIkVcIAsyRJahizJ7fSOqZ796V1TAuzJrXWqEUaLE8aSJIkSY3NALMkSWoYi6ZP4PqLTu0KMhfKKSyaPqHGLdNAedJAkiRJamy1mORPkiRpQFpaguUnzuH4lcvYsbedWZOcEK7RFU4alNZg9qSBJEmS1BgMMEuSpIbS0hIsnjnRSf2ahCcNJEmSpMZmgFmSJEk15UkDSZIkqXEZYJYkqUl0diY279rP9j3tzJ5sFqgkSZIkaegZYJYkqQl0dibuXr+tRx3b5SfOMcgsSZIkSRoyLX2vIklqVp2diU079/HAMy+xaec+OjtTrZukAdq8a39XcBmg/XAnV61+lM279te4ZZIkSZKkZmYGsySNUGa8Npfte9q7gssF7Yc72bG33bq2kiRJkqQhYwazJI1QZrw2l9mTW2kd0/1nvXVMC7MmtdaoRZIkSZKkkcAAsySNUL1lvDabkVAKZNH0CVx/0aldQeZCRvqi6RNq3DJJkiRJUjOzRIYkjVCFjNfiIHMzZryOlFIgLS3B8hPncPzKZezY286sSa0smj6hqfZRUraIWA7cAIwCvppS+lzJ438AXJ2/uw/4SErpZ8PbSkmSJDUrM5glaYQaKRmvI6kUSEtLsHjmRM5cPIPFMycaXJZGgIgYBXwReDdwAnBpRJxQstqzwG+mlE4BrgFuHN5WSpIkqZmZwSxJI9RIyXh18jtJTe50YGNKaRNARNwGnAc8WVghpfR/i9Z/EJg/rC2UJElSUzPALEkjWCHjtZkDrSOlFIiaU2dnYvOu/Wzf087syc15EkiDNg/4ZdH9LcAZvax/BXBX1gMRsQJYAbBgwYJqtU+SJElNzhIZklRiJEwIN5KMlFIgaj6F+uHnrrqfS7/yEOeuup+712/zO0mlss44ZB4kEfFb5ALMV2c9nlK6MaXUllJqmzlzZhWbKEmSpGZmBrMkFRkpE8KNJCOlFIiaT7n64cevXNbUVx2o37YAxxTdnw+8WLpSRJwCfBV4d0pp1zC1TZIkSSOAGcySVGQkTQg3kjj5nRpRb/XDpSKPAEsi4tiIGAtcAtxZvEJELAC+A3wgpfR0DdooSZKkJmYGs6SKjYRaoE4IJ6leWD9clUgpHYmIjwHfB0YBX08prY+ID+cf/zLwP4HpwN9HBMCRlFJbrdosSZKk5mKAWVJFRkrpCAM6kupFoX546feu9cNVKqW0BlhTsuzLRbc/BHxouNslSZKkkcEAs1QFIyGzd6TUAjWgI6leWD9ckiRJUiMwwCwN0kjJ7B0ppSMM6EiqJ4X64c30PStJkiSpuTjJnzRII2VSuELpiGLNWjrCCeEkSZIkSZIqY4BZGqTeMnubSaF0RCHIbOkISZIkSZIkWSJDGqSRMimcpSMkSZIkSZJUygxmaZBGUmavpSMkSZIkSZJUzAxmaZDM7JUkSZIkSdJIZYBZQ6qzM7F5136272ln9uTmDbwWMnsXz5xY66ZIkiRJkiRJw8YAs4ZMZ2fi7vXbuGr1o7Qf7uwqHbH8xDlNGWSWJEmSJEmSRhprMGvIbN61vyu4DNB+uJOrVj/K5l37a9wySZIkSZIkSdVggFlDZvue9q7gckH74U527G2vUYskSZIkSZIkVZMBZg2Z2ZNbaR3T/RBrHdPCrEmtNWqRJEmSJEmSpGpq6hrMEbEcuAEYBXw1pfS5Gjepqvo7gd5wT7i3aPoErr/o1B41mBdNnzCk7RspEwu6n42r3D41475mqcZ+NsJ7Vc02DtX+Fp531/6DjB3VwoFDHf1+/kY4nqv1e5m1vLMzsX7rbrbubmfulHGcOHcyo0e31P1vdH/3s1xbitedNamVUS2wdXf57erpuJAkSZJUHU0bYI6IUcAXgXcCW4BHIuLOlNKTtW1ZdfR3Ar1aTLjX0hIsP3EOx69cxo69uYFnuYFktdo3UiYWdD8bdz/L7dM5b5rND57a3lT7mqUan2kjHBfVbONQ7W/hea+7+ykublvAqns39Pv5G+F4rtbvZdY+/cMHlrJz7yE+/S9PdC378/eexO+d/Dp+9Isddf0bXY3PLus5rjx7Cbc88ByvHDjUY7tG+NuVJEmS1H/NXCLjdGBjSmlTSukQcBtwXo3bVDX9nUCvVhPutbQEi2dO5MzFM1g8c2LZAWS12jdSJhZ0Pxt3P8vt0/qtu5tuX7NU4zNthOOimm0cqv0tPO97TpnXFVzu7/M3wvFcrd/LrH3a+2pHV3C5sOzT//IEj73Yv/2vxTFdjc8u6zluuGcD5y+dn7ldI/ztSpIkSeq/Zg4wzwN+WXR/S35Zl4hYERFrI2Ltzp07h7Vxg9XfCfTqfcK9arWv3vezWtzPxt3Pcvu0dXfz7WuWanymjXBcVLONQ7W/heeNYMDP3wjHc7V+L7P2af/BI5nrbmuA3+hqfHblniMie7tG+NuVJEmS1H/NHGDOSpVN3e6kdGNKqS2l1DZz5sxhalZ19HcCvXqfcK9a7av3/awW97Nx97PcPs2d0nz7mqUan2kjHBfVbONQ7W/x8w70+csfz+Pq5jOq1u9l1t/ohNbRmevOaYDf6Gp8F5V7jpSyt2uEv11JkiRJ/dfMAeYtwDFF9+cDL9aoLVVXmECvODjQ2wR6/V1/uFWrffW+n9XifjbufpbbpxPnTmm6fc1Sjc+0EY6LarZxqPa38Lz/+rMXWHnWkgE9f/njeXLdfEbV+r3M+hud1DqKP3/vSd2W/fl7T+KU1/Xv77kWx3Q1vouynuPKs5fwnZ9uydyuEf52JUmSJPVfpJT6XqsBRcRo4GngbOAF4BHg91NK67PWb2trS2vXrh3GFg5eYSb2vibQG+j6w61a7av3/awW97NxldunZtzXLNXYz0Z4r6rZxqHa38Lzvrz/IGNGtXDgUAezJ/fv+RvheK7W72XW8s7OxPqtu9m2u505U1o5ce4URo9uaYjf6Gp8dsXrzpzYyqgW2Lan/Hb1dFwURMS6lFJbTRsxSBGxHLgBGAV8NaX0uZLHI//4ucAB4I9SSj/t7TkbsW8sSZKkwRlo37hpA8wAEXEu8Hlyne2vp5SuLbeunWhJkqSRp9EDzBExilxSxTvJXcH3CHBpSunJonXOBT5OLsB8BnBDSumM3p7XvrEkSdLIM9C+8eihaEy9SCmtAdbUuh2SJEnSEDkd2JhS2gQQEbcB5wFPFq1zHnBLymWWPBgRR0fE3JTS1uFvriRJkppNUweY+2PdunUvRcRzNXjpGcBLNXhdDQ0/z+bi59l8/Eybi59n86nFZ7pwmF+v2uYBvyy6v4VclnJf68wDugWYI2IFsCJ/92BEPFHdpqqB+P06svn5j2x+/iObn7/eOJCNDDDnpZRm1uJ1I2JtI1+Wqe78PJuLn2fz8TNtLn6ezcfPdECyiliX1sCrZB1SSjcCN4KfxUjn5z+y+fmPbH7+I5ufvyJiQDXSWqrdEEmSJEnDZgtwTNH9+cCLA1hHkiRJGhADzJIkSVLjegRYEhHHRsRY4BLgzpJ17gQui5wzgd3WX5YkSVK1WCKj9m6sdQNUVX6ezcXPs/n4mTYXP8/m42faTymlIxHxMeD7wCjg6yml9RHx4fzjXyY36fW5wEbgAPDBCp7az2Jk8/Mf2fz8RzY//5HNz18DOgYiN5m0JEmSJEmSJEn9Y4kMSZIkSZIkSdKAGGCWJEmSJEmSJA2IAeYaiYjlEfGLiNgYEZ+odXs0OBFxTET8OCKeioj1EXFlrdukwYuIURHxHxHxvVq3RYMXEUdHxLcj4uf5v9W31rpNGriI+C/579snIuKbEdFa6zapfyLi6xGxIyKeKFo2LSJ+GBEb8v9PrWUbR4K++qT5iQFX5R9/LCKW1qKdGhoVfP5/kP/cH4uI/xsRb65FOzU0Kh2TRsSvRURHRLx/ONunoVXJ5x8R74iIR/N9rv8z3G3U0Kng+39KRPxrRPws//lXMn+DGkRWP7zk8X73/www10BEjAK+CLwbOAG4NCJOqG2rNEhHgP+aUnoTcCbwUT/TpnAl8FStG6GquQG4O6V0PPBm/GwbVkTMA1YCbSmlk8hNbHZJbVulAbgJWF6y7BPAPSmlJcA9+fsaIhX2Sd8NLMn/WwF8aVgbqSFT4ef/LPCbKaVTgGtw8qemUemYNL/edeQmElWTqOTzj4ijgb8Hfi+ldCJw4XC3U0Ojwr//jwJPppTeDLwD+JuIGDusDdVQuome/fBi/e7/GWCujdOBjSmlTSmlQ8BtwHk1bpMGIaW0NaX00/ztveQCV/Nq2yoNRkTMB34H+Gqt26LBi4jJwNuBrwGklA6llH5V00ZpsEYD4yJiNDAeeLHG7VE/pZTuA14uWXwecHP+9s3Ae4ezTSNQJX3S84BbUs6DwNERMXe4G6oh0efnn1L6vymlV/J3HwTmD3MbNXQqHZN+HLgd2DGcjdOQq+Tz/33gOyml5wFSSh4DzaOSzz8BkyIigInk+mxHhreZGipl+uHF+t3/M8BcG/OAXxbd34LByKYREYuAtwAP1bgpGpzPA/8d6KxxO1Qdi4GdwD/my558NSIm1LpRGpiU0gvAXwPPA1uB3SmlH9S2VaqS2SmlrZA7eQvMqnF7ml0lfVL7rc2rv5/tFcBdQ9oiDac+P//8FUPvA748jO3S8Kjk7/84YGpE/CQi1kXEZcPWOg21Sj7/LwBvIpfE8ThwZUrJsfHI0e/+nwHm2oiMZWnYW6Gqi4iJ5M7w/2lKaU+t26OBiYj3ADtSSutq3RZVzWhgKfCllNJbgP146X3DytflPQ84FngdMCEi/rC2rZIaUiV9UvutzavizzYifotcgPnqIW2RhlMln//ngatTSh1D3xwNs0o+/9HAaeSu6nwX8D8i4rihbpiGRSWf/7uAR8n1tU8FvpC/KlQjQ7/7fwaYa2MLcEzR/fl4aW/Di4gx5ILL/5xS+k6t26NB+Q3g9yJiM7nLhc6KiH+qbZM0SFuALSmlwpUF3yYXcFZj+m3g2ZTSzpTSYeA7wK/XuE2qju2Fy+/y/3s57tCqpE9qv7V5VfTZRsQp5EqGnZdS2jVMbdPQq+TzbwNuy/eJ3w/8fUS8d1hap6FW6ff/3Sml/Smll4D7yM1josZXyef/QXIlUlJKaSO5mvzHD1P7VHv97v8ZYK6NR4AlEXFsvkj6JcCdNW6TBiFfl+hrwFMppetr3R4NTkrpkyml+SmlReT+Pu9NKZkd2cBSStuAX0bEG/OLzgaerGGTNDjPA2dGxPj89+/ZOGljs7gTuDx/+3Lgjhq2ZSSopE96J3BZfjbxM8mVpNk63A3VkOjz84+IBeRO4n0gpfR0DdqoodPn559SOjaltCjfJ/428CcppX8Z9pZqKFTy/X8HsCwiRkfEeOAM7G81i0o+/+fJ9bGJiNnAG4FNw9pK1VK/+3+jh6ddKpZSOhIRHyM3E+8o4OsppfU1bpYG5zeADwCPR8Sj+WWfSimtqV2TJJX4OPDP+U7UJnJn5dWAUkoPRcS3gZ+Sm2zkP4Aba9sq9VdEfJPcrOQzImIL8Bngc8DqiLiC3MDGGeuHULk+aUR8OP/4l4E1wLnARuAAfnc2jQo///8JTCeXuQpwJKXUVqs2q3oq/PzVpCr5/FNKT0XE3cBj5Oal+WpK6YnatVrVUuHf/zXATRHxOLlyCVfnM9nVBMr0w8fAwPt/kZIl1CRJkiRJkiRJ/WeJDEmSJEmSJEnSgBhgliRJkiRJkiQNiAFmSZIkSZIkSdKAGGCWJEmSJEmSJA2IAWZJkiRJkiRJ0oAYYJYkSZIkSZIkDYgBZkmSJEmSJEnSgPz/AfgWKmX8WcC+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1440x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_scatter(X_train_clean, variables, ncols=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III.2. Analyse des valeurs manquantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_features = ['gender']\n",
    "continuous_features = ['age', 'temperature', 'heartrate', 'resprate', 'o2sat', 'sbp', 'dbp', 'pain']\n",
    "features = categorical_features+continuous_features\n",
    "labels = y_train.columns.values[1:].tolist()\n",
    "\n",
    "plot_missing_outcome(X_train_clean, y_train, features, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "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>Cardiaque</th>\n",
       "      <th>Coagulation</th>\n",
       "      <th>Gazometrie</th>\n",
       "      <th>Glycemie_Sanguine</th>\n",
       "      <th>Hepato-Biliaire</th>\n",
       "      <th>IonoC</th>\n",
       "      <th>Lipase</th>\n",
       "      <th>NFS</th>\n",
       "      <th>Phospho-Calcique</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_NA</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</th>\n",
       "      <td>0.145400</td>\n",
       "      <td>0.226537</td>\n",
       "      <td>0.441106</td>\n",
       "      <td>0.013475</td>\n",
       "      <td>0.182567</td>\n",
       "      <td>0.440376</td>\n",
       "      <td>0.126376</td>\n",
       "      <td>0.440122</td>\n",
       "      <td>0.151331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.212756</td>\n",
       "      <td>0.319305</td>\n",
       "      <td>0.558329</td>\n",
       "      <td>0.031010</td>\n",
       "      <td>0.243085</td>\n",
       "      <td>0.558215</td>\n",
       "      <td>0.163798</td>\n",
       "      <td>0.558357</td>\n",
       "      <td>0.213353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.251205</td>\n",
       "      <td>0.368129</td>\n",
       "      <td>0.570636</td>\n",
       "      <td>0.049662</td>\n",
       "      <td>0.259643</td>\n",
       "      <td>0.574735</td>\n",
       "      <td>0.182257</td>\n",
       "      <td>0.575217</td>\n",
       "      <td>0.248795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.322435</td>\n",
       "      <td>0.462232</td>\n",
       "      <td>0.700113</td>\n",
       "      <td>0.072153</td>\n",
       "      <td>0.328072</td>\n",
       "      <td>0.705750</td>\n",
       "      <td>0.225479</td>\n",
       "      <td>0.704622</td>\n",
       "      <td>0.330327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.413729</td>\n",
       "      <td>0.538033</td>\n",
       "      <td>0.768089</td>\n",
       "      <td>0.105751</td>\n",
       "      <td>0.380334</td>\n",
       "      <td>0.775510</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.773655</td>\n",
       "      <td>0.371058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.389085</td>\n",
       "      <td>0.510563</td>\n",
       "      <td>0.751761</td>\n",
       "      <td>0.070423</td>\n",
       "      <td>0.336268</td>\n",
       "      <td>0.757042</td>\n",
       "      <td>0.258803</td>\n",
       "      <td>0.762324</td>\n",
       "      <td>0.364437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.357053</td>\n",
       "      <td>0.521139</td>\n",
       "      <td>0.685224</td>\n",
       "      <td>0.147342</td>\n",
       "      <td>0.291754</td>\n",
       "      <td>0.712013</td>\n",
       "      <td>0.244454</td>\n",
       "      <td>0.716618</td>\n",
       "      <td>0.314357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.313561</td>\n",
       "      <td>0.680876</td>\n",
       "      <td>0.738734</td>\n",
       "      <td>0.410292</td>\n",
       "      <td>0.281224</td>\n",
       "      <td>0.774896</td>\n",
       "      <td>0.458762</td>\n",
       "      <td>0.784353</td>\n",
       "      <td>0.273157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Cardiaque  Coagulation  Gazometrie  Glycemie_Sanguine  Hepato-Biliaire  \\\n",
       "n_NA                                                                           \n",
       "0      0.145400     0.226537    0.441106           0.013475         0.182567   \n",
       "1      0.212756     0.319305    0.558329           0.031010         0.243085   \n",
       "2      0.251205     0.368129    0.570636           0.049662         0.259643   \n",
       "3      0.322435     0.462232    0.700113           0.072153         0.328072   \n",
       "4      0.413729     0.538033    0.768089           0.105751         0.380334   \n",
       "5      0.389085     0.510563    0.751761           0.070423         0.336268   \n",
       "6      0.357053     0.521139    0.685224           0.147342         0.291754   \n",
       "7      0.313561     0.680876    0.738734           0.410292         0.281224   \n",
       "\n",
       "         IonoC    Lipase       NFS  Phospho-Calcique  \n",
       "n_NA                                                  \n",
       "0     0.440376  0.126376  0.440122          0.151331  \n",
       "1     0.558215  0.163798  0.558357          0.213353  \n",
       "2     0.574735  0.182257  0.575217          0.248795  \n",
       "3     0.705750  0.225479  0.704622          0.330327  \n",
       "4     0.775510  0.272727  0.773655          0.371058  \n",
       "5     0.757042  0.258803  0.762324          0.364437  \n",
       "6     0.712013  0.244454  0.716618          0.314357  \n",
       "7     0.774896  0.458762  0.784353          0.273157  "
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stay_id</th>\n",
       "      <th>intime</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>temperature</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>resprate</th>\n",
       "      <th>o2sat</th>\n",
       "      <th>sbp</th>\n",
       "      <th>dbp</th>\n",
       "      <th>...</th>\n",
       "      <th>gsn</th>\n",
       "      <th>Cardiaque</th>\n",
       "      <th>Coagulation</th>\n",
       "      <th>Gazometrie</th>\n",
       "      <th>Glycemie_Sanguine</th>\n",
       "      <th>Hepato-Biliaire</th>\n",
       "      <th>IonoC</th>\n",
       "      <th>Lipase</th>\n",
       "      <th>NFS</th>\n",
       "      <th>Phospho-Calcique</th>\n",
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       "      <th>0</th>\n",
       "      <td>36785614</td>\n",
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       "      <td>18.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>33230507</td>\n",
       "      <td>2112-12-10 20:34:00</td>\n",
       "      <td>M</td>\n",
       "      <td>57</td>\n",
       "      <td>98.3</td>\n",
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       "      <td>16.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>94.0</td>\n",
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       "      <td>35705580</td>\n",
       "      <td>2131-05-18 11:54:00</td>\n",
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       "      <td>20</td>\n",
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       "      <td>66.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>71.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>36008650</td>\n",
       "      <td>2177-01-23 23:52:00</td>\n",
       "      <td>F</td>\n",
       "      <td>40</td>\n",
       "      <td>98.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>...</td>\n",
       "      <td>[28090, 59178, 13649, 58577, 46087, 63025, 462...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34420405</td>\n",
       "      <td>2127-08-04 16:04:00</td>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>103.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>97.0</td>\n",
       "      <td>55.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>438967</th>\n",
       "      <td>33657802</td>\n",
       "      <td>2113-10-27 13:08:00</td>\n",
       "      <td>M</td>\n",
       "      <td>61</td>\n",
       "      <td>97.3</td>\n",
       "      <td>89.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>44.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>438968</th>\n",
       "      <td>35305460</td>\n",
       "      <td>2123-11-03 20:43:00</td>\n",
       "      <td>F</td>\n",
       "      <td>91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>438969</th>\n",
       "      <td>39978305</td>\n",
       "      <td>2110-04-27 21:04:00</td>\n",
       "      <td>M</td>\n",
       "      <td>48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
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       "      <td>36696016</td>\n",
       "      <td>2130-03-15 10:44:00</td>\n",
       "      <td>M</td>\n",
       "      <td>46</td>\n",
       "      <td>97.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>...</td>\n",
       "      <td>[7371, 15568, 29928, 25703, 16579, 6749]</td>\n",
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       "    <tr>\n",
       "      <th>438971</th>\n",
       "      <td>31234612</td>\n",
       "      <td>2184-11-27 15:20:00</td>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>98.8</td>\n",
       "      <td>72.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>119.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>438972 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stay_id               intime gender  age temperature heartrate  \\\n",
       "0       36785614  2142-08-23 08:31:00      F   28        99.9      82.0   \n",
       "1       33230507  2112-12-10 20:34:00      M   57        98.3      98.0   \n",
       "2       35705580  2131-05-18 11:54:00      F   20        98.0      66.0   \n",
       "3       36008650  2177-01-23 23:52:00      F   40        98.0      92.0   \n",
       "4       34420405  2127-08-04 16:04:00      F   55       103.0     122.0   \n",
       "...          ...                  ...    ...  ...         ...       ...   \n",
       "438967  33657802  2113-10-27 13:08:00      M   61        97.3      89.0   \n",
       "438968  35305460  2123-11-03 20:43:00      F   91         NaN       NaN   \n",
       "438969  39978305  2110-04-27 21:04:00      M   48         NaN      96.0   \n",
       "438970  36696016  2130-03-15 10:44:00      M   46        97.6      96.0   \n",
       "438971  31234612  2184-11-27 15:20:00      F   22        98.8      72.0   \n",
       "\n",
       "       resprate  o2sat    sbp   dbp  ...  \\\n",
       "0          18.0  100.0  125.0  82.0  ...   \n",
       "1          16.0   99.0   94.0  51.0  ...   \n",
       "2          18.0  100.0  114.0  71.0  ...   \n",
       "3          18.0   97.0  128.0  60.0  ...   \n",
       "4          22.0   97.0   97.0  55.0  ...   \n",
       "...         ...    ...    ...   ...  ...   \n",
       "438967     18.0   97.0  105.0  44.0  ...   \n",
       "438968      NaN    NaN    NaN   NaN  ...   \n",
       "438969      NaN    NaN  130.0  70.0  ...   \n",
       "438970     18.0   96.0  126.0  84.0  ...   \n",
       "438971     16.0  100.0  119.0  63.0  ...   \n",
       "\n",
       "                                                      gsn Cardiaque  \\\n",
       "0                                                     NaN         0   \n",
       "1                             [16926, 6649, 47586, 33530]         1   \n",
       "2                                                     NaN         0   \n",
       "3       [28090, 59178, 13649, 58577, 46087, 63025, 462...         0   \n",
       "4       [46229, 4061, 4222, 4222, 46242, 28465, 4560, ...         0   \n",
       "...                                                   ...       ...   \n",
       "438967  [28090, 16926, 15864, 2173, 3009, 15586, 386, ...         1   \n",
       "438968                                                NaN         1   \n",
       "438969             [3758, 3758, 34188, 34188, 3690, 3690]         0   \n",
       "438970           [7371, 15568, 29928, 25703, 16579, 6749]         0   \n",
       "438971                                                NaN         0   \n",
       "\n",
       "        Coagulation  Gazometrie Glycemie_Sanguine Hepato-Biliaire IonoC  \\\n",
       "0                 0           0                 0               0     0   \n",
       "1                 1           1                 0               0     1   \n",
       "2                 0           0                 0               0     0   \n",
       "3                 0           0                 0               0     0   \n",
       "4                 0           1                 0               1     1   \n",
       "...             ...         ...               ...             ...   ...   \n",
       "438967            0           1                 0               1     1   \n",
       "438968            1           1                 1               0     1   \n",
       "438969            1           1                 0               0     1   \n",
       "438970            0           0                 0               0     0   \n",
       "438971            0           0                 0               0     0   \n",
       "\n",
       "        Lipase  NFS  Phospho-Calcique  \n",
       "0            0    0                 0  \n",
       "1            0    1                 1  \n",
       "2            0    0                 0  \n",
       "3            0    0                 0  \n",
       "4            0    1                 1  \n",
       "...        ...  ...               ...  \n",
       "438967       0    1                 1  \n",
       "438968       1    1                 0  \n",
       "438969       0    1                 0  \n",
       "438970       0    0                 0  \n",
       "438971       0    0                 0  \n",
       "\n",
       "[438972 rows x 26 columns]"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy_train_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cardiaque             70001\n",
       "Coagulation          111073\n",
       "Gazometrie           203744\n",
       "Glycemie_Sanguine     12843\n",
       "Hepato-Biliaire       84595\n",
       "IonoC                204075\n",
       "Lipase                62329\n",
       "NFS                  204133\n",
       "Phospho-Calcique      71558\n",
       "n_NA                 166161\n",
       "dtype: int64"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy_train_clean[labels].join(\n",
    "    pd.DataFrame(Xy_train_clean[features].isna().astype(\"int\").sum(axis=1))\n",
    ").rename(columns={0:\"n_NA\"}).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Gazometrie</th>\n",
       "      <th>Glycemie_Sanguine</th>\n",
       "      <th>Hepato-Biliaire</th>\n",
       "      <th>IonoC</th>\n",
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       "      <th>Phospho-Calcique</th>\n",
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       "    <tr>\n",
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       "</div>"
      ],
      "text/plain": [
       "      Cardiaque  Coagulation  Gazometrie  Glycemie_Sanguine  Hepato-Biliaire  \\\n",
       "n_NA                                                                           \n",
       "0         55375        86276      167994               5132            69530   \n",
       "1          7492        11244       19661               1092             8560   \n",
       "2          1042         1527        2367                206             1077   \n",
       "3           286          410         621                 64              291   \n",
       "4           223          290         414                 57              205   \n",
       "5           221          290         427                 40              191   \n",
       "6           853         1245        1637                352              697   \n",
       "7          4509         9791       10623               5900             4044   \n",
       "\n",
       "       IonoC  Lipase     NFS  Phospho-Calcique  \n",
       "n_NA                                            \n",
       "0     167716   48130  167619             57634  \n",
       "1      19657    5768   19662              7513  \n",
       "2       2384     756    2386              1032  \n",
       "3        626     200     625               293  \n",
       "4        418     147     417               200  \n",
       "5        430     147     433               207  \n",
       "6       1701     584    1712               751  \n",
       "7      11143    6597   11279              3928  "
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xy_train_clean[labels].join(\n",
    "    pd.DataFrame(Xy_train_clean[features].isna().astype(\"int\").sum(axis=1))\n",
    ").rename(columns={0:\"n_NA\"}) \\\n",
    " .groupby(\"n_NA\") \\\n",
    " .agg(lambda x: x.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [],
   "source": [
    "toto = Xy_train_clean.join(Xy_train_clean[variables].isna().sum(axis=1).reset_index())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>539 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stay_id               intime gender  age temperature heartrate  \\\n",
       "417     31963073  2144-10-11 15:31:00      F   63         NaN      75.0   \n",
       "866     36057564  2133-12-12 14:58:00      F   56        97.2      96.0   \n",
       "1563    36378590  2137-06-13 13:23:00      M   45         NaN     117.0   \n",
       "1765    39926343  2135-11-29 12:23:00      F   50         NaN     147.0   \n",
       "2541    33566771  2132-12-17 00:34:00      M   28         NaN     130.0   \n",
       "...          ...                  ...    ...  ...         ...       ...   \n",
       "437713  33936558  2185-11-03 02:15:00      F   78         NaN       NaN   \n",
       "438110  38818858  2142-07-19 10:03:00      M   61         NaN      81.0   \n",
       "438115  39422563  2166-10-21 04:36:00      F   34         NaN     173.0   \n",
       "438826  39922925  2200-10-17 10:35:00      F   62         NaN       NaN   \n",
       "438969  39978305  2110-04-27 21:04:00      M   48         NaN      96.0   \n",
       "\n",
       "       resprate  o2sat    sbp    dbp  ... Coagulation Gazometrie  \\\n",
       "417         NaN    NaN   86.0   54.0  ...           1          1   \n",
       "866         NaN  100.0    NaN    NaN  ...           1          0   \n",
       "1563       18.0    NaN    NaN    NaN  ...           0          0   \n",
       "1765        NaN    NaN  124.0   79.0  ...           1          1   \n",
       "2541       35.0  100.0    NaN    NaN  ...           0          1   \n",
       "...         ...    ...    ...    ...  ...         ...        ...   \n",
       "437713     24.0   98.0    NaN    NaN  ...           0          1   \n",
       "438110     18.0  100.0    NaN    NaN  ...           1          1   \n",
       "438115      NaN    NaN  169.0  105.0  ...           0          1   \n",
       "438826     24.0    NaN  124.0   73.0  ...           0          1   \n",
       "438969      NaN    NaN  130.0   70.0  ...           1          1   \n",
       "\n",
       "        Glycemie_Sanguine  Hepato-Biliaire IonoC Lipase NFS  Phospho-Calcique  \\\n",
       "417                     0                0     1      0   1                 0   \n",
       "866                     1                0     1      0   1                 0   \n",
       "1563                    0                0     0      0   0                 0   \n",
       "1765                    0                0     1      0   1                 0   \n",
       "2541                    0                0     1      0   1                 1   \n",
       "...                   ...              ...   ...    ...  ..               ...   \n",
       "437713                  0                1     1      1   1                 1   \n",
       "438110                  0                0     1      0   1                 1   \n",
       "438115                  1                0     1      0   1                 1   \n",
       "438826                  0                1     1      0   1                 0   \n",
       "438969                  0                0     1      0   1                 0   \n",
       "\n",
       "         index  0  \n",
       "417        417  4  \n",
       "866        866  4  \n",
       "1563      1563  4  \n",
       "1765      1765  4  \n",
       "2541      2541  4  \n",
       "...        ... ..  \n",
       "437713  437713  4  \n",
       "438110  438110  4  \n",
       "438115  438115  4  \n",
       "438826  438826  4  \n",
       "438969  438969  4  \n",
       "\n",
       "[539 rows x 28 columns]"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toto[\n",
    "    toto[0] == 4\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\4078182\\Miniconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12352/327606526.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m  \u001b[1;33m.\u001b[0m\u001b[0magg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\seaborn\\_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     44\u001b[0m             )\n\u001b[0;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mbarplot\u001b[1;34m(x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge, ax, **kwargs)\u001b[0m\n\u001b[0;32m   3180\u001b[0m ):\n\u001b[0;32m   3181\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3182\u001b[1;33m     plotter = _BarPlotter(x, y, hue, data, order, hue_order,\n\u001b[0m\u001b[0;32m   3183\u001b[0m                           \u001b[0mestimator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mci\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_boot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3184\u001b[0m                           \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[0;32m   1582\u001b[0m                  errwidth, capsize, dodge):\n\u001b[0;32m   1583\u001b[0m         \u001b[1;34m\"\"\"Initialize the plotter.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1584\u001b[1;33m         self.establish_variables(x, y, hue, data, orient,\n\u001b[0m\u001b[0;32m   1585\u001b[0m                                  order, hue_order, units)\n\u001b[0;32m   1586\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m             orient = infer_orient(\n\u001b[0m\u001b[0;32m    157\u001b[0m                 \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrequire_numeric\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrequire_numeric\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m             )\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36minfer_orient\u001b[1;34m(x, y, orient, require_numeric)\u001b[0m\n\u001b[0;32m   1309\u001b[0m     \"\"\"\n\u001b[0;32m   1310\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1311\u001b[1;33m     \u001b[0mx_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mvariable_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1312\u001b[0m     \u001b[0my_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mvariable_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\seaborn\\_core.py\u001b[0m in \u001b[0;36mvariable_type\u001b[1;34m(vector, boolean_type)\u001b[0m\n\u001b[0;32m   1227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1228\u001b[0m     \u001b[1;31m# Special-case all-na data, which is always \"numeric\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1229\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvector\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1230\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"numeric\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1231\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1535\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1536\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1537\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m   1538\u001b[0m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1539\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0'>"
      ]
     },
     "execution_count": 367,
     "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": [
    "categorical_features = ['gender']\n",
    "continuous_features = ['age', 'temperature', 'heartrate', 'resprate', 'o2sat', 'sbp', 'dbp', 'pain']\n",
    "features = categorical_features+continuous_features\n",
    "labels = y_train.columns.values[1:].tolist()\n",
    "\n",
    "Xy_train_clean = pd.merge(\n",
    "    X_train_clean,\n",
    "    y_train,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "toto = (Xy_train_clean[categorical_features+continuous_features].isna()*1).sum(axis=1).reset_index().join(\n",
    "    y_train\n",
    ").drop(columns=[\"index\", \"stay_id\"])\n",
    "\n",
    "(toto.groupby(0).sum().T/toto.groupby(0)[\"NFS\"].count().values).T.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy_train = pd.merge(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap((Xy_train[categorical_features+continuous_features].isna()*1).corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 128,
     "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": [
    "(Xy_train[categorical_features+continuous_features].isna()*1).sum(axis=1).value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "toto = (Xy_train[categorical_features+continuous_features].isna()*1).sum(axis=1).reset_index().join(\n",
    "    y_train\n",
    ").drop(columns=[\"index\", \"stay_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='0'>"
      ]
     },
     "execution_count": 168,
     "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": [
    "(toto.groupby(0).sum().T/toto.groupby(0)[\"NFS\"].count().values).T.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 101,
     "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": [
    "(X[categorical_features+continuous_features].isna()*1).mean().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualisation de la distribution des NA\n",
    "# Visualisation de la distribution des features selon les labels\n",
    "# Visualisation des corrélations\n",
    "# Visualisation du texte\n",
    "# Analyser des ATCD\n",
    "# Analyse des traitements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. 2. Exploration des labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualisation de la fréquence des labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IV. Sélection des variables d'interêts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Définir la stratégie de sélection des features (voir sklearn)\n",
    "# L'appliquer et sortir un dataset pertinent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# V. Définition et entrainement d'une solution d'apprentissage statistique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prévoir la pipeline de traitement\n",
    "# Algorithmes à produire :\n",
    "# Tree classifier simple : argument, explicabilité\n",
    "# MLP\n",
    "# Ajouter les traitements et voir\n",
    "# Ajouter les ATCD et voir\n",
    "# Ajouter le texte et voir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OLD - History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import SimpleImputer, MissingIndicator, KNNImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.multioutput import MultiOutputClassifier\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OrdinalEncoder, StandardScaler, PolynomialFeatures\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xy[\"time\"] = pd.to_datetime(Xy[\"intime\"])\n",
    "Xy[\"time\"] = Xy[\"time\"].dt.hour\n",
    "#X[\"temperature\"] = ((X[\"temperature\"]-32)*(5/9))-37 # On prend 37 comme norme\n",
    "#X[\"heartrate\"] = X[\"heartrate\"]-80\n",
    "#X[\"resprate\"] = X[\"resprate\"]-18\n",
    "#X[\"o2sat\"] = X[\"o2sat\"]-100\n",
    "#X[\"sbp\"] = X[\"sbp\"]-120\n",
    "#X[\"dbp\"] = X[\"dbp\"]-80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;31mSignature:\u001b[0m\n",
      "\u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[1;33m*\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mweights\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mstat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'count'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'auto'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbinwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mbinrange\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mdiscrete\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcumulative\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcommon_bins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcommon_norm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mmultiple\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'layer'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0melement\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'bars'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mfill\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mshrink\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mkde_kws\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mline_kws\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mthresh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mpthresh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mpmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcbar\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcbar_ax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcbar_kws\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mpalette\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mhue_order\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mhue_norm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mlog_scale\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0mlegend\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m    \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\n",
      "\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDocstring:\u001b[0m\n",
      "Plot univariate or bivariate histograms to show distributions of datasets.\n",
      "\n",
      "A histogram is a classic visualization tool that represents the distribution\n",
      "of one or more variables by counting the number of observations that fall within\n",
      "disrete bins.\n",
      "\n",
      "This function can normalize the statistic computed within each bin to estimate\n",
      "frequency, density or probability mass, and it can add a smooth curve obtained\n",
      "using a kernel density estimate, similar to :func:`kdeplot`.\n",
      "\n",
      "More information is provided in the :ref:`user guide <tutorial_hist>`.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "data : :class:`pandas.DataFrame`, :class:`numpy.ndarray`, mapping, or sequence\n",
      "    Input data structure. Either a long-form collection of vectors that can be\n",
      "    assigned to named variables or a wide-form dataset that will be internally\n",
      "    reshaped.\n",
      "x, y : vectors or keys in ``data``\n",
      "    Variables that specify positions on the x and y axes.\n",
      "hue : vector or key in ``data``\n",
      "    Semantic variable that is mapped to determine the color of plot elements.\n",
      "weights : vector or key in ``data``\n",
      "    If provided, weight the contribution of the corresponding data points\n",
      "    towards the count in each bin by these factors.\n",
      "stat : str\n",
      "    Aggregate statistic to compute in each bin.\n",
      "    \n",
      "    - `count`: show the number of observations in each bin\n",
      "    - `frequency`: show the number of observations divided by the bin width\n",
      "    - `probability`: or `proportion`: normalize such that bar heights sum to 1\n",
      "    - `percent`: normalize such that bar heights sum to 100\n",
      "    - `density`: normalize such that the total area of the histogram equals 1\n",
      "bins : str, number, vector, or a pair of such values\n",
      "    Generic bin parameter that can be the name of a reference rule,\n",
      "    the number of bins, or the breaks of the bins.\n",
      "    Passed to :func:`numpy.histogram_bin_edges`.\n",
      "binwidth : number or pair of numbers\n",
      "    Width of each bin, overrides ``bins`` but can be used with\n",
      "    ``binrange``.\n",
      "binrange : pair of numbers or a pair of pairs\n",
      "    Lowest and highest value for bin edges; can be used either\n",
      "    with ``bins`` or ``binwidth``. Defaults to data extremes.\n",
      "discrete : bool\n",
      "    If True, default to ``binwidth=1`` and draw the bars so that they are\n",
      "    centered on their corresponding data points. This avoids \"gaps\" that may\n",
      "    otherwise appear when using discrete (integer) data.\n",
      "cumulative : bool\n",
      "    If True, plot the cumulative counts as bins increase.\n",
      "common_bins : bool\n",
      "    If True, use the same bins when semantic variables produce multiple\n",
      "    plots. If using a reference rule to determine the bins, it will be computed\n",
      "    with the full dataset.\n",
      "common_norm : bool\n",
      "    If True and using a normalized statistic, the normalization will apply over\n",
      "    the full dataset. Otherwise, normalize each histogram independently.\n",
      "multiple : {\"layer\", \"dodge\", \"stack\", \"fill\"}\n",
      "    Approach to resolving multiple elements when semantic mapping creates subsets.\n",
      "    Only relevant with univariate data.\n",
      "element : {\"bars\", \"step\", \"poly\"}\n",
      "    Visual representation of the histogram statistic.\n",
      "    Only relevant with univariate data.\n",
      "fill : bool\n",
      "    If True, fill in the space under the histogram.\n",
      "    Only relevant with univariate data.\n",
      "shrink : number\n",
      "    Scale the width of each bar relative to the binwidth by this factor.\n",
      "    Only relevant with univariate data.\n",
      "kde : bool\n",
      "    If True, compute a kernel density estimate to smooth the distribution\n",
      "    and show on the plot as (one or more) line(s).\n",
      "    Only relevant with univariate data.\n",
      "kde_kws : dict\n",
      "    Parameters that control the KDE computation, as in :func:`kdeplot`.\n",
      "line_kws : dict\n",
      "    Parameters that control the KDE visualization, passed to\n",
      "    :meth:`matplotlib.axes.Axes.plot`.\n",
      "thresh : number or None\n",
      "    Cells with a statistic less than or equal to this value will be transparent.\n",
      "    Only relevant with bivariate data.\n",
      "pthresh : number or None\n",
      "    Like ``thresh``, but a value in [0, 1] such that cells with aggregate counts\n",
      "    (or other statistics, when used) up to this proportion of the total will be\n",
      "    transparent.\n",
      "pmax : number or None\n",
      "    A value in [0, 1] that sets that saturation point for the colormap at a value\n",
      "    such that cells below is constistute this proportion of the total count (or\n",
      "    other statistic, when used).\n",
      "cbar : bool\n",
      "    If True, add a colorbar to annotate the color mapping in a bivariate plot.\n",
      "    Note: Does not currently support plots with a ``hue`` variable well.\n",
      "cbar_ax : :class:`matplotlib.axes.Axes`\n",
      "    Pre-existing axes for the colorbar.\n",
      "cbar_kws : dict\n",
      "    Additional parameters passed to :meth:`matplotlib.figure.Figure.colorbar`.\n",
      "palette : string, list, dict, or :class:`matplotlib.colors.Colormap`\n",
      "    Method for choosing the colors to use when mapping the ``hue`` semantic.\n",
      "    String values are passed to :func:`color_palette`. List or dict values\n",
      "    imply categorical mapping, while a colormap object implies numeric mapping.\n",
      "hue_order : vector of strings\n",
      "    Specify the order of processing and plotting for categorical levels of the\n",
      "    ``hue`` semantic.\n",
      "hue_norm : tuple or :class:`matplotlib.colors.Normalize`\n",
      "    Either a pair of values that set the normalization range in data units\n",
      "    or an object that will map from data units into a [0, 1] interval. Usage\n",
      "    implies numeric mapping.\n",
      "color : :mod:`matplotlib color <matplotlib.colors>`\n",
      "    Single color specification for when hue mapping is not used. Otherwise, the\n",
      "    plot will try to hook into the matplotlib property cycle.\n",
      "log_scale : bool or number, or pair of bools or numbers\n",
      "    Set axis scale(s) to log. A single value sets the data axis for univariate\n",
      "    distributions and both axes for bivariate distributions. A pair of values\n",
      "    sets each axis independently. Numeric values are interpreted as the desired\n",
      "    base (default 10). If `False`, defer to the existing Axes scale.\n",
      "legend : bool\n",
      "    If False, suppress the legend for semantic variables.\n",
      "ax : :class:`matplotlib.axes.Axes`\n",
      "    Pre-existing axes for the plot. Otherwise, call :func:`matplotlib.pyplot.gca`\n",
      "    internally.\n",
      "kwargs\n",
      "    Other keyword arguments are passed to one of the following matplotlib\n",
      "    functions:\n",
      "\n",
      "    - :meth:`matplotlib.axes.Axes.bar` (univariate, element=\"bars\")\n",
      "    - :meth:`matplotlib.axes.Axes.fill_between` (univariate, other element, fill=True)\n",
      "    - :meth:`matplotlib.axes.Axes.plot` (univariate, other element, fill=False)\n",
      "    - :meth:`matplotlib.axes.Axes.pcolormesh` (bivariate)\n",
      "\n",
      "Returns\n",
      "-------\n",
      ":class:`matplotlib.axes.Axes`\n",
      "    The matplotlib axes containing the plot.\n",
      "\n",
      "See Also\n",
      "--------\n",
      "displot : Figure-level interface to distribution plot functions.\n",
      "kdeplot : Plot univariate or bivariate distributions using kernel density estimation.\n",
      "rugplot : Plot a tick at each observation value along the x and/or y axes.\n",
      "ecdfplot : Plot empirical cumulative distribution functions.\n",
      "jointplot : Draw a bivariate plot with univariate marginal distributions.\n",
      "\n",
      "Notes\n",
      "-----\n",
      "\n",
      "The choice of bins for computing and plotting a histogram can exert\n",
      "substantial influence on the insights that one is able to draw from the\n",
      "visualization. If the bins are too large, they may erase important features.\n",
      "On the other hand, bins that are too small may be dominated by random\n",
      "variability, obscuring the shape of the true underlying distribution. The\n",
      "default bin size is determined using a reference rule that depends on the\n",
      "sample size and variance. This works well in many cases, (i.e., with\n",
      "\"well-behaved\" data) but it fails in others. It is always a good to try\n",
      "different bin sizes to be sure that you are not missing something important.\n",
      "This function allows you to specify bins in several different ways, such as\n",
      "by setting the total number of bins to use, the width of each bin, or the\n",
      "specific locations where the bins should break.\n",
      "\n",
      "Examples\n",
      "--------\n",
      "\n",
      ".. include:: ../docstrings/histplot.rst\n",
      "\u001b[1;31mFile:\u001b[0m      c:\\users\\4078182\\miniconda3\\lib\\site-packages\\seaborn\\distributions.py\n",
      "\u001b[1;31mType:\u001b[0m      function\n"
     ]
    }
   ],
   "source": [
    "?sns.histplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='Percent'>"
      ]
     },
     "execution_count": 27,
     "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": [
    "sns.histplot(data=Xy, x=\"time\", hue=\"IonoC\", multiple=\"dodge\", stat=\"percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='time', ylabel='Density'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "sns.kdeplot(data=Xy, x=\"time\", hue=\"IonoC\", multiple=\"layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='last_7', ylabel='Percent'>"
      ]
     },
     "execution_count": 32,
     "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": [
    "sns.histplot(data=Xy, x=\"last_7\", hue=\"IonoC\", multiple=\"dodge\", stat=\"percent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='last_7', ylabel='Density'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "sns.kdeplot(data=Xy, x=\"last_7\", hue=\"IonoC\", multiple=\"layer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "toto = pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.histplot(data=penguins, x=\"flipper_length_mm\", hue=\"species\", multiple=\"stack\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IonoC\n",
       "0    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "1    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: age, dtype: object"
      ]
     },
     "execution_count": 5,
     "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": [
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"age\"].plot(kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.loc[(X[\"temperature\"] >= 107),\"temperature\"] = 107"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac92863fa0>"
      ]
     },
     "execution_count": 71,
     "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": [
    "X[\"temperature_norm\"] = (X[\"temperature\"]-X[\"temperature\"].mean())/(X[\"temperature\"].max()-X[\"temperature\"].min())\n",
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"temperature_norm\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(-0.1, 0.1)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>stay_id</th>\n",
       "      <th>intime</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>temperature</th>\n",
       "      <th>heartrate</th>\n",
       "      <th>resprate</th>\n",
       "      <th>o2sat</th>\n",
       "      <th>sbp</th>\n",
       "      <th>dbp</th>\n",
       "      <th>pain</th>\n",
       "      <th>chiefcomplaint</th>\n",
       "      <th>last_7</th>\n",
       "      <th>last_30</th>\n",
       "      <th>icd9</th>\n",
       "      <th>icd10</th>\n",
       "      <th>gsn</th>\n",
       "      <th>time</th>\n",
       "      <th>temperature_norm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30000012</td>\n",
       "      <td>2126-02-14 20:22:00</td>\n",
       "      <td>F</td>\n",
       "      <td>65</td>\n",
       "      <td>98.8</td>\n",
       "      <td>96.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>CHANGE IN MENTAL STATUS</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[2761, 4589, 5712, 5990, 7804]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8209, 21413, 2510, 27462, 66295, 6818, 6818, ...</td>\n",
       "      <td>73320</td>\n",
       "      <td>0.007457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30000017</td>\n",
       "      <td>2185-06-18 11:51:00</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ETOH, Unable to ambulate</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[07070, 30300, V600]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42660</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30000038</td>\n",
       "      <td>2152-12-07 16:37:00</td>\n",
       "      <td>F</td>\n",
       "      <td>80</td>\n",
       "      <td>97.1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Cough</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[9221, 92231, 92232, 95901, E8889]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[16925, 15864, 16278, 17037, 17037, 21413, 289...</td>\n",
       "      <td>59820</td>\n",
       "      <td>-0.008446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30000039</td>\n",
       "      <td>2165-10-06 11:47:00</td>\n",
       "      <td>M</td>\n",
       "      <td>80</td>\n",
       "      <td>98.6</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>s/p Fall</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8209, 19293]</td>\n",
       "      <td>42420</td>\n",
       "      <td>0.005586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30000055</td>\n",
       "      <td>2155-07-18 17:03:00</td>\n",
       "      <td>F</td>\n",
       "      <td>63</td>\n",
       "      <td>99.4</td>\n",
       "      <td>85.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>L Ear pain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[3804]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8182]</td>\n",
       "      <td>61380</td>\n",
       "      <td>0.013070</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>448967</th>\n",
       "      <td>39999939</td>\n",
       "      <td>2155-08-04 11:15:00</td>\n",
       "      <td>M</td>\n",
       "      <td>57</td>\n",
       "      <td>98.4</td>\n",
       "      <td>84.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Chest pain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[8182, 16578, 22159, 22159, 27475, 27475]</td>\n",
       "      <td>40500</td>\n",
       "      <td>0.003715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448968</th>\n",
       "      <td>39999953</td>\n",
       "      <td>2152-06-22 14:08:00</td>\n",
       "      <td>F</td>\n",
       "      <td>36</td>\n",
       "      <td>98.2</td>\n",
       "      <td>108.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Palpitations, Dizziness, Headache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[6655]</td>\n",
       "      <td>50880</td>\n",
       "      <td>0.001844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448969</th>\n",
       "      <td>39999961</td>\n",
       "      <td>2145-05-16 17:16:00</td>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>99.3</td>\n",
       "      <td>119.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Chest pain, Cough, Dyspnea</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[4019, 7295, 7820, 78909, E9208]</td>\n",
       "      <td>[C50919, H6692, I10, L308, L538, R112, R29810,...</td>\n",
       "      <td>[16927, 16995, 16995, 13109, 2173, 2169, 18368...</td>\n",
       "      <td>62160</td>\n",
       "      <td>0.012134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448970</th>\n",
       "      <td>39999964</td>\n",
       "      <td>2130-06-05 11:53:00</td>\n",
       "      <td>M</td>\n",
       "      <td>50</td>\n",
       "      <td>98.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>127.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>SI, Depression</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>[71946, 7242, 78659, 9221, 92231, 92411, 95901...</td>\n",
       "      <td>[F329, H5711, M25561, M549, M79662, R45851, R5...</td>\n",
       "      <td>[44633, 44633, 4561, 4561, 4561, 4540, 4540]</td>\n",
       "      <td>42780</td>\n",
       "      <td>0.005586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448971</th>\n",
       "      <td>39999965</td>\n",
       "      <td>2125-09-14 00:46:00</td>\n",
       "      <td>F</td>\n",
       "      <td>30</td>\n",
       "      <td>97.5</td>\n",
       "      <td>65.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Labor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2760</td>\n",
       "      <td>-0.004704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>448972 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stay_id               intime gender  age  temperature  heartrate  \\\n",
       "0       30000012  2126-02-14 20:22:00      F   65         98.8       96.0   \n",
       "1       30000017  2185-06-18 11:51:00      M   39          NaN       73.0   \n",
       "2       30000038  2152-12-07 16:37:00      F   80         97.1       54.0   \n",
       "3       30000039  2165-10-06 11:47:00      M   80         98.6       85.0   \n",
       "4       30000055  2155-07-18 17:03:00      F   63         99.4       85.0   \n",
       "...          ...                  ...    ...  ...          ...        ...   \n",
       "448967  39999939  2155-08-04 11:15:00      M   57         98.4       84.0   \n",
       "448968  39999953  2152-06-22 14:08:00      F   36         98.2      108.0   \n",
       "448969  39999961  2145-05-16 17:16:00      F   55         99.3      119.0   \n",
       "448970  39999964  2130-06-05 11:53:00      M   50         98.6       64.0   \n",
       "448971  39999965  2125-09-14 00:46:00      F   30         97.5       65.0   \n",
       "\n",
       "        resprate  o2sat    sbp    dbp  pain  \\\n",
       "0           18.0   93.0  160.0   54.0   0.0   \n",
       "1           18.0   97.0  156.0  112.0   0.0   \n",
       "2           18.0   95.0  143.0   73.0   0.0   \n",
       "3           16.0   98.0  189.0   96.0   0.0   \n",
       "4           16.0  100.0    NaN    NaN   0.0   \n",
       "...          ...    ...    ...    ...   ...   \n",
       "448967      16.0   97.0  152.0   90.0   2.0   \n",
       "448968      18.0  100.0  155.0   94.0   5.0   \n",
       "448969      22.0    NaN  132.0   74.0   7.0   \n",
       "448970      18.0   99.0  127.0   64.0   4.0   \n",
       "448971      16.0  100.0  132.0   77.0   0.0   \n",
       "\n",
       "                           chiefcomplaint  last_7  last_30  \\\n",
       "0                 CHANGE IN MENTAL STATUS     0.0      1.0   \n",
       "1                ETOH, Unable to ambulate     1.0      1.0   \n",
       "2                                   Cough     NaN      NaN   \n",
       "3                                s/p Fall     NaN      NaN   \n",
       "4                              L Ear pain     NaN      NaN   \n",
       "...                                   ...     ...      ...   \n",
       "448967                         Chest pain     NaN      NaN   \n",
       "448968  Palpitations, Dizziness, Headache     NaN      NaN   \n",
       "448969         Chest pain, Cough, Dyspnea     0.0      1.0   \n",
       "448970                     SI, Depression     0.0      1.0   \n",
       "448971                              Labor     NaN      NaN   \n",
       "\n",
       "                                                     icd9  \\\n",
       "0                          [2761, 4589, 5712, 5990, 7804]   \n",
       "1                                    [07070, 30300, V600]   \n",
       "2                      [9221, 92231, 92232, 95901, E8889]   \n",
       "3                                                     NaN   \n",
       "4                                                  [3804]   \n",
       "...                                                   ...   \n",
       "448967                                                NaN   \n",
       "448968                                                NaN   \n",
       "448969                   [4019, 7295, 7820, 78909, E9208]   \n",
       "448970  [71946, 7242, 78659, 9221, 92231, 92411, 95901...   \n",
       "448971                                                NaN   \n",
       "\n",
       "                                                    icd10  \\\n",
       "0                                                     NaN   \n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "3                                                     NaN   \n",
       "4                                                     NaN   \n",
       "...                                                   ...   \n",
       "448967                                                NaN   \n",
       "448968                                                NaN   \n",
       "448969  [C50919, H6692, I10, L308, L538, R112, R29810,...   \n",
       "448970  [F329, H5711, M25561, M549, M79662, R45851, R5...   \n",
       "448971                                                NaN   \n",
       "\n",
       "                                                      gsn   time  \\\n",
       "0       [8209, 21413, 2510, 27462, 66295, 6818, 6818, ...  73320   \n",
       "1                                                     NaN  42660   \n",
       "2       [16925, 15864, 16278, 17037, 17037, 21413, 289...  59820   \n",
       "3                                           [8209, 19293]  42420   \n",
       "4                                                  [8182]  61380   \n",
       "...                                                   ...    ...   \n",
       "448967          [8182, 16578, 22159, 22159, 27475, 27475]  40500   \n",
       "448968                                             [6655]  50880   \n",
       "448969  [16927, 16995, 16995, 13109, 2173, 2169, 18368...  62160   \n",
       "448970       [44633, 44633, 4561, 4561, 4561, 4540, 4540]  42780   \n",
       "448971                                                NaN   2760   \n",
       "\n",
       "        temperature_norm  \n",
       "0               0.007457  \n",
       "1                    NaN  \n",
       "2              -0.008446  \n",
       "3               0.005586  \n",
       "4               0.013070  \n",
       "...                  ...  \n",
       "448967          0.003715  \n",
       "448968          0.001844  \n",
       "448969          0.012134  \n",
       "448970          0.005586  \n",
       "448971         -0.004704  \n",
       "\n",
       "[448972 rows x 19 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "X"
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  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac923802e0>"
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     "execution_count": 76,
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"heartrate\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(0, 150)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac922ea850>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K5M2B3iHGp5JsW+SeX5k62mNMJZ1njqhrsSye0Hp/ufukmd0N/AyoBB5295fM7M5g+wNmtgbYCzQBSTO7B9gCvBf4HLDPzLqCQ/6xuz8BfNXMtpGqSnsN+GJY1yCyUIv9JH02V1+8nMYlVew+3MfNV62deweRPAgtqQAESeCJaeseyFh+k1S12HS/IHubDO7+uXzGKBKG7p4EqxqXsHZZbWQxVFdW8IHLmtl1sA93D23WSZFMeqJeJARdIU8fnKvO9lWcGBjlaN9wpHFI+VBSEcmzgZEJjvWdjeT5lOk62lsA+LuD6gUmi0NJRSTP9sVTDxwu9vAs2bSuqGdTbKnGAZNFo6Qikmfp6YOvag13TvpcdbTHeO5YP6MTU1GHImVASUUkz7p6ElwaW8qyuuqoQwFSz6uMTSZ57lXNBinhU1IRySN3p6snEenzKdNdv7GZmqoKdqldRRaBkopIHr05OErf0Fikz6dMV1dTyfUbV7L7sJKKhE9JRSSPut5IANE+9JhNZ3uMI6eGOZ4YiToUKXFKKiJ51BVPUF1pXLG2MepQ3qEjPRukBpiUkCmpiORRd0+CLWubWFK1eNMH56JtVQNrl9WqXUVCp6QikidTSWdffKDgqr4AzFKzQf79kdNMTCWjDkdKmJKKSJ4c7Rvm7PhUQTxJn03n5hhDY5N0BYNdioRBSUUkT7oKYGTi2XxwUwsVpnYVCVdOScXMfmBm/9DMlIREZtDdk6CxtoqNzUujDiWrZfXVXH3xCk0xLKHKNUncD/wT4LCZ3Wdml4cYk0hR6o4n2Nq6nIoIpg/OVUdbjH3HB+gfHos6FClROSUVd/8/7v5PgWtITYz1lJk9Y2b/PJj2V6SsjU5M8UrvEFvXF8Z4XzPp3BzDHX6h2SAlJDlXZ5lZM/AF4HeBXwN/RirJPBVKZCJF5KUTg0wmvSBGJp7NVeuWsby+WlVgEpqcZn40sx8ClwN/Cfwjd+8NNv1PM9sbVnAixSI9fXCh9vxKq6wwPtwWY/eh0ySTXtBVdVKccr1T+Qt33+Lu/yWdUMxsCYC7b59pJzO7ycwOmtkRM7s3y/bLzexZMxszsz/MZV8zW2lmT5nZ4eB9RY7XIBKarp4Ea5fVsqopuumDc9XR1sLp4TEOvDkYdShSgnJNKn+SZd2zs+1gZpXAt4GbgS3AbWa2ZVqxt4DfA742j33vBZ529zbg6eCzSKTSjfTFoPP8kC1qV5H8mzWpmNkaM7sWqDOzq83smuD1EaB+jmNfBxxx92PuPg48BuzILODup9x9DzAxj313AI8Ey48An5ojDpFQnTk7zuv95wr2+ZTpVjXVcvmaRnYdOhV1KFKC5mpT+QSpxvlW4BsZ64eAP55j33VAT8bnOHB9jnHNtu/qdBWcu/ea2apsBzCzO4A7AC6++OIcTysyf+mZHgu951emzs0xHv7FqwyPTdKwJKemVZGczHqn4u6PuPtHgS+4+0czXp909x/OcexsLYCeY1wXsm+qsPuD7r7d3bfHYrH57CoyL909A5ilelYVi862GBNTzrNH+6MORUrMrH+imNk/c/f/Dmwws381fbu7fyPLbmlxYH3G51bgRI5xzbbvSTNbG9ylrAV0Dy+R6o4naFvVQGNt8Tyyde2GFdTXVLL7UB83blkddThSQuZqqE+PN9EANGZ5zWYP0GZmG82sBrgV2JljXLPtuxO4PVi+HfhxjscUyTt3p7uneBrp05ZUVXLDpc16XkXybtY7FXf/TvD+lfke2N0nzexu4GdAJfCwu79kZncG2x8wszXAXqAJSJrZPcAWdx/Mtm9w6PuAx83sd4A3gM/MNzaRfImfGaH/7HjRNNJn6twc4+lXTvHa6bNsaCnM8cqk+OT68ONXSXUrHgGeBLYC9wRVYzNy9yeAJ6ateyBj+U1SVVs57Rus7wc+lkvcImFLN9IX+kOP2XS0pdoadx3qU1KRvMn1OZWPu/sg8P+Qau9oB/5NaFGJFInungQ1VRVsXlNY0wfnYkPLUi5prtdQ+JJXuSaVdAvkLcCj7v5WSPGIFJXungGuvKiJ6srinBWioy3GM0f7GZucijoUKRG5/hJ+YmavANuBp80sBoyGF5ZI4ZucSrLveGFOH5yrjvYYIxNTPP/amahDkRKR69D39wI3ANvdfQI4y7Sn40XKzaGTw4xMFO70wbm4YVMz1ZWmXmCSN/O5Z78C+H/N7PPAbwEfDyckkeJw/kn6IutOnKlhSRXXXqLZICV/cp1O+C9JDfr4IeB9wWvG0YlFykF3T4JlddVc0jzXMHiFrbN9Fa+8OcTJQdVoy4XLddCf7aSeH5nXUCkipayrJ8HW9csxK+45STraW/ivT8LuQ318Zvv6uXcQmUWu1V/7gTVhBiJSTM6NT3Lo5BDbWotnvK+ZbFnbRKxxCbsPayh8uXC53qm0AC+b2a+AsfRKd/9kKFGJFLj9xwdJOkXd8yvNzPhwWwv//yunmEo6lZoNUi5Arknly2EGIVJs0tMHl0JSgdTEXT984Tj7jg8UdW82iV6uXYp3Aa8B1cHyHuCFEOMSKWhd8QStK+poaVgSdSh58eG2GGaw66B6gcmFybX3178A/jfwnWDVOuCvQopJpOB1B430pWLl0hreu24Zuw8rqciFybWh/i7gg8AggLsfBrLOuChS6k4PjxE/M8K2In4+JZuO9hi/fuMMA+emz+4tkrtck8pYMFc8AGZWxTxnYhQpFS+enz54eaRx5Ftne4ykw98fVS8wWbhck8ouM/tjoM7MbgT+F/CT8MISKVxdbySoMLhyXVPUoeTVtvXLaaytUruKXJBck8q9QB+wD/giqXlO/l1YQYkUsq74AO2rG6mvybXzZHGoqqzgQ5e1sPtwH3rOWRYq195fSVIN819y999y94f0dL2Uo/T0waXa7bajPUbvwCiHTw1HHYoUqVmTiqV82cxOA68AB82sz8z+fS4HN7ObzOygmR0xs3tnOP6fB9tfNLNrgvWbzawr4zUYTDVMEM/xjG23zPuqRRbo9f5zDIxMlFx7SlpHe2o2SE3cJQs1153KPaR6fb3P3ZvdfSVwPfBBM/uD2XY0s0rg28DNwBbgNjPbMq3YzUBb8LoDuB/A3Q+6+zZ33wZcC5wDfpSx3zfT24Nph0UWRSmMTDybdcvruGxVg0YtlgWbK6l8HrjN3V9Nr3D3Y8A/C7bN5jrgiLsfC3qOPca752DZAXzPU34JLDeztdPKfAw46u6vz3E+kdB19SSora6gfXVD1KGEprM9xnOvvsXIuGaDlPmbK6lUu/u7+he6ex9vTzE8k3VAT8bneLBuvmVuBR6dtu7uoLrsYTNbMUccInnT3ZPgqnXLqCrS6YNz0dEeY3wyyS9f7Y86FClCc/0yxhe4DSDbqHTTG/dnLWNmNcAnSXVhTrsf2ARsA3qBr2c9udkdZrbXzPb29elWXi7cxFSS/ScGS7aRPu36jStZUlWhdhVZkLn6RG41s8Es6w2onWPfOJA5OUMrcGKeZW4GXnD3k+kVmctm9hDw02wnd/cHgQcBtm/frp5qcsEOvjnE+GSyZBvp02qrK7n+0ma1q8iCzHqn4u6V7t6U5dXo7nNVf+0B2sxsY3DHcSuwc1qZncDng15g7wcG3L03Y/ttTKv6mtbm8mlSc72IhK4rPTJxiTbSZ+poa+FY31niZ85FHYoUmdAqht19Ergb+BlwAHjc3V8yszvN7M6g2BPAMeAI8BDwpfT+ZlYP3Aj8cNqhv2pm+8zsReCjwKy90ETypbsnQfPSGlpX1EUdSug+sjndtVhDtsj8hPpIcNDd94lp6x7IWHZSg1Vm2/cc0Jxl/efyHKZITkpl+uBcbIo1cNGyWnYdOsU/uf7iqMORIlK6XVhE8mhodIIjfcNlUfUFqdkgOzfHeOZIPxNTyajDkSKipCKSg33HB3CHreuLf076XHW0xRgam+TXbySiDkWKiJKKSA66ewaA8mikT/vAZS1UVpi6Fsu8KKmI5KC7J8ElzfWsWFoTdSiLZlldNVevX66uxTIvSioiOeiOJ8rqLiWtsz3GvuMDnB4eizoUKRJKKiJzODk4Su/AaMk/9JhNetTiXxxW12LJjZKKyBy6g4cet5VRI33aVeuWsXJpjdpVJGdKKiJz6I4nqKow3nNR+SWVigo7PxtkMqnRjmRuSioic+juGeDytY3UVldGHUokOttjnB4e5+XebMMAiryTkorILJJJL9tG+rQPt7cAqBeY5ERJRWQWr/afZWh0siwb6dNWNdayZW2T2lUkJ0oqIrPoCp4mL/U5VObS0R7j+dfPMDQ6EXUoUuCUVERm0R1PsLSmkk2x0p0+OBed7TEmk86zRzUbpMxOSUVkFt09Ca5qXUZlRemPTDybay9ZwdKaSrWryJyUVERmMDY5xcu9g2XdnpJWU1XBDZta2HWoj9SMFSLZKamIzOBA7xATU862Mu75lamzvYX4mRFePX026lCkgCmpiMwg/SS97lRSOttXAagXmMxKSUVkBt09CWKNS1i7rDbqUArCxc31bGiuZ7fGAZNZhJpUzOwmMztoZkfM7N4s283M/jzY/qKZXZOx7bVgLvouM9ubsX6lmT1lZoeD9xVhXoOUr67gocdymD44Vx3tMZ492s/Y5FTUoUiBCi2pmFkl8G3gZmALcJuZbZlW7GagLXjdAdw/bftH3X2bu2/PWHcv8LS7twFPB59F8mpgZIJjfWe5+uLlUYdSUDrbY4xMTLH3tTNRhyIFKsw7leuAI+5+zN3HgceAHdPK7AC+5ym/BJab2do5jrsDeCRYfgT4VB5jFgFgX7z8ZnrMxfsvbaa60tS1WGYUZlJZB/RkfI4H63It48DfmNnzZnZHRpnV7t4LELyvynZyM7vDzPaa2d6+Pv0AZH664wkArmotv5GJZ7N0SRXv27BSjfUyozCTSraK6Okd3Gcr80F3v4ZUFdldZtYxn5O7+4Puvt3dt8disfnsKsKv30hwaWwpy+qqow6l4HS0x3jlzSEOnRyKOhQpQGEmlTiwPuNzK3Ai1zLunn4/BfyIVHUawMl0FVnwfirvkUtZc3e6ehJ6PmUGn756Hc1La/jiXz5P4tx41OFIgQkzqewB2sxso5nVALcCO6eV2Ql8PugF9n5gwN17zWypmTUCmNlS4OPA/ox9bg+Wbwd+HOI1SBnqHRjl9PCYnk+ZweqmWr7zuWs5fmaEL33/BSamklGHJAUktKTi7pPA3cDPgAPA4+7+kpndaWZ3BsWeAI4BR4CHgC8F61cDvzCzbuBXwP/n7k8G2+4DbjSzw8CNwWeRvNFDj3PbvmEl/+U3r+KZo/185ScvRR2OFJCqMA/u7k+QShyZ6x7IWHbgriz7HQO2znDMfuBj+Y1U5G1d8QTVlcYVaxujDqWg/eNrWzl0aojv7DpG++pGPn/DhqhDkgKgJ+pFpunuSbBlbRNLqspz+uD5+LefuJzfuGI1X/nJy/z8sHqEiZKKyDtMJZ198QFVfeWossL401u30baqgS99/wWO9g1HHZJETElFJMPRvmHOjk/pocd5aFhSxV/cvp2aygp+95G96hFW5pRURDJ0qZF+QVpX1KtHmABKKiLv0N2ToLG2iktblkYdStFRjzCBkHt/iRSb7mBk4ooynz54odQjTHSnIhIYnZjild4htq7XeF8XQj3CypuSikjgpRMDTCZdjfQXSD3CypuSikigqyc13P02NdJfMPUIK19KKiKB7p4Ea5fVsqpJ0wfng3qElSclFZFAupFe8kc9wsqPkooIcObsOK/3n9PzKSH4x9e28sXOS/nvv3yD7z37WtThSMiUVER4e6ZH9fwKh3qElQ8lFRGgu2cAM7hqnZJKGNQjrHwoqYiQulO5LNZAY62mDw6LeoSVByUVKXvuTndPQu0pi0A9wkqfkoqUvfiZEfrPjiupLBL1CCttGvtLyl66kf5qJZVFozHCSleodypmdpOZHTSzI2Z2b5btZmZ/Hmx/0cyuCdavN7O/NbMDZvaSmf1+xj5fNrPjZtYVvG4J8xqk9HW9kaCmqoLNazR98GJSj7DSFFpSMbNK4NvAzcAW4DYz2zKt2M1AW/C6A7g/WD8J/Gt3vwJ4P3DXtH2/6e7bgtcTYV2DlIfueIIrL2qiulK1wYtJPcJKU5i/ouuAI+5+zN3HgceAHdPK7AC+5ym/BJab2Vp373X3FwDcfQg4AKwLMVYpU5NTSfYd1/TBUVGPsNITZlJZB/RkfI7z7sQwZxkz2wBcDTyXsfruoLrsYTNbke3kZnaHme01s719fbq1luwOnRxmdCKpQSQjpB5hpSXMpJJtliOfTxkzawB+ANzj7oPB6vuBTcA2oBf4eraTu/uD7r7d3bfHYrF5hi7l4vyT9BrzK1LqEVY6wuz9FQfWZ3xuBU7kWsbMqkkllO+7+w/TBdz9ZHrZzB4CfprfsKWcdPckWFZXzSXN9VGHUvbUI6w0hHmnsgdoM7ONZlYD3ArsnFZmJ/D5oBfY+4EBd+81MwO+Cxxw929k7mBmazM+fhrYH94lSKnrCh56TP1fTqKmHmHFL7Sk4u6TwN3Az0g1tD/u7i+Z2Z1mdmdQ7AngGHAEeAj4UrD+g8DngH+QpevwV81sn5m9CHwU+IOwrkFK27nxSQ6dHGJbq8b7KhTqEVb8Qn34Meju+8S0dQ9kLDtwV5b9fkH29hbc/XN5DlPK1P7jgyQd9fwqMOkeYTu+9ff87iN7+dGXPsDy+pqow5IcqWO+lK3ungQA71UjfcFRj7DipaQiZasrnmDd8jpijUuiDkWyUI+w4qSxv6Rsdb2RYNvFy6MOQ2ahHmHFR3cqUpb6hsY4nhhhm6q+Cp56hBUXJRUpSy+enz54eaRxyNzUI6y4KKlIWeruSVBhcOW6pqhDkRxojLDioaQiZakrPkD76kbqa9SsWCzUI6w4KKlI2UlPH6xBJIuPeoQVPv2ZJmXn9f5zDIxMqD2lSKlHWGHTnYqUHY1MXPzUI6xwKalI2enqSVBbXUH76oaoQ5EFUo+wwqWkImWnuyfBVeuWUaXpg4uaeoQVJv2qpKxMTCXZf2JQVV8lQj3CCo+SipSVV3qHGJ9MqpG+hKhHWGFR7y8pK11BI726E5cW9QgrHEoqUlKSSef08Bg9Z0aInzlH/MwIxxMjxNOf3xqheWkNrSvqog5V8uzffuJyjp46y1d+8jIbW5by4bZY1CGVJSUVKSrJpNM3PHY+Ybz9OsfxMyPEEyOMT76zXn1lkEQuX9PIb1yxmo62mKYPLkHpHmG/df8zfOn7L/C/7/wAbasaqKjQd72YLDX5Ymnbvn277927N+ow5uYevJJzvHIpk49jBGWSU7kdAwcMrCJ42dvv2LR1FVnKGkk3BkYn6RueoG94nFPDE5waHufU0Dgnh1Lv41OQxHCMJEZTXQ2rmmpZ1VTPqqY6Vi+rZfWyelY31bF6eR11NdXZ45oz1lzKSqGJnznHjm/9Pf1nxzFL9RJrqq2mYUkVjbWpV0Nt9fnlxiVVNAafGzKWU6/UcnWZ9hQ0s+fdffu89gkzqZjZTcCfAZXAX7j7fdO2W7D9FuAc8AV3f2G2fc1sJfA/gQ3Aa8Bn3f3MbHFs39Tie//zzTn+Y3qh/2DP8/iZ/2BT+gm+9OSWLLMnpellmSWBZa5nlnNVTItptrjS62a7hunrci3LDOda6B8c8yt7cmiMfccHOTfhjEwkOTfhnJtIvr08PsW5iSTDE87ElJPESAb9lpJeEfzRwvl1lZWV1C2pob6mktol1dTXVFNXU03dkirql1RRX1NN/ZIali6por42vVydetXW0FBbTV1NFXY+zgv5b7B4FpJUQqv+MrNK4NvAjUAc2GNmO9395YxiNwNtwet64H7g+jn2vRd42t3vM7N7g89/NGswEyNw+nD2H2q2V0UlWPUsZSwoM8v22Y6ftcz04811jFyu4QKP8Y4yM13vO/8hmUom6R8e482Bc5wcOMebiRFODb79Oj04ymQyScX5+wxYWV/JqsYlrG6sYVVjNasal7CqoZrVDdU0L62htsoyErIDnj1Jv2t9LmUz3nMq6zOca4bzZz3X9LLMEtc8zn9+ffBK/8GSS9kZ/xtmlsvTf+9F+ONpdfCaU2XwysUUMBK8IpT61VTgBk4FTuoPjdR7Req/rqV+YW+/p5cz/tjg3QnMrSJVNXwBCSzMNpXrgCPufgzAzB4DdgCZSWUH8D1P3S790syWm9laUnchM+27A/hIsP8jwN8xR1I55K3cOPZf83NVEXIgfWfpwf+k16Xe0+X87d94xnaCMue3A+5JYCpj33ceL3PfbOdLb00fbyrpTCXf+Y9GS0M9rSuaaV1fx+YV9bSuqAte9axbXkddTa6/aikJsyWgrElpoWUvNAHmXjaZdMYmJhgdn2R0YpLR8fTyFGPjk4xNTjCWXp6YYmxigonJSaaSb9daeHAu9yQkU8f2jPO5O+ZJ3JPYtHWpWKCC9B9sb78qglfmZ8OpsMx16VSVfEe5hQgzqawDejI+x0ndjcxVZt0c+652914Ad+81s1XZTm5mdwB3ADRddCltJTIkh2Fg6WUws+D97c/pbamilrHt7WOc/2MlYzvnj2PvPt70fdNlg/OnT1pVYaxdpqQhs3j7/3zkfptQ2CqAuuAVFffUH3RT7iSTMJlMkkzCVHr9+W1vL59fn3SS7kwm37mdP5l/D7owk0q2e6fpqW+mMrnsOyt3fxB4EFIN9f/tn147n91FRIqKmVFVaRn/qEeTsCtCPHYcWJ/xuRU4kWOZ2fY9GVSREbyfymPMIiJyAcJMKnuANjPbaGY1wK3AzmlldgKft5T3AwNB1dZs++4Ebg+Wbwd+HOI1iIjIPIRW/eXuk2Z2N/AzUvdhD7v7S2Z2Z7D9AeAJUt2Jj5DqUvzPZ9s3OPR9wONm9jvAG8BnwroGERGZHz38KCIiWS3kOZUwq79ERKTMKKmIiEjeKKmIiEjeKKmIiEjelEVDvZkNAQejjiNELcDpqIMIUSlfXylfG+j6it1md2+czw7lMp/Kwfn2YCgmZrZX11ecSvnaQNdX7Mxs3t1mVf0lIiJ5o6QiIiJ5Uy5J5cGoAwiZrq94lfK1ga6v2M37+sqioV5ERBZHudypiIjIIlBSERGRvCnppGJmN5nZQTM7EsxnX1LM7DUz22dmXQvp+ldozOxhMztlZvsz1q00s6fM7HDwviLKGC/EDNf3ZTM7HnyHXWZ2S5QxXggzW29mf2tmB8zsJTP7/WB90X+Hs1xbSXx/ZlZrZr8ys+7g+r4SrJ/3d1eybSpmVgkcAm4kNenXHuA2d3850sDyyMxeA7a7e0k8fGVmHcAw8D13vzJY91XgLXe/L/jDYIW7/1GUcS7UDNf3ZWDY3b8WZWz5EEyat9bdXzCzRuB54FPAFyjy73CWa/ssJfD9WWre8KXuPmxm1cAvgN8HfpN5fnelfKdyHXDE3Y+5+zjwGLAj4phkFu6+G3hr2uodwCPB8iOkfshFaYbrKxnu3uvuLwTLQ8ABYB0l8B3Ocm0lwVOGg4/VwctZwHdXykllHdCT8TlOCf2fIODA35jZ82Z2R9TBhGR1MBsowfuqiOMJw91m9mJQPVZ0VUPZmNkG4GrgOUrsO5x2bVAi35+ZVZpZF6kp2p9y9wV9d6WcVCzLulKr6/ugu18D3AzcFVSvSHG5H9gEbAN6ga9HGk0emFkD8APgHncfjDqefMpybSXz/bn7lLtvA1qB68zsyoUcp5STShxYn/G5FTgRUSyhcPcTwfsp4EekqvxKzcmgPjtdr30q4njyyt1PBj/mJPAQRf4dBvXxPwC+7+4/DFaXxHeY7dpK7fsDcPcE8HfATSzguyvlpLIHaDOzjWZWA9wK7Iw4prwxs6VBgyFmthT4OLB/9r2K0k7g9mD5duDHEcaSd+kfbODTFPF3GDT2fhc44O7fyNhU9N/hTNdWKt+fmcXMbHmwXAf8BvAKC/juSrb3F0DQve9PgUrgYXf/T9FGlD9mdimpuxNIjTb9P4r9+szsUeAjpIYTPwn8B+CvgMeBi4E3gM+4e1E2ds9wfR8hVXXiwGvAF9N12MXGzD4E/BzYBySD1X9Mqu2hqL/DWa7tNkrg+zOz95JqiK8kdbPxuLv/RzNrZp7fXUknFRERWVylXP0lIiKLTElFRETyRklFRETyRklFRETyRklFRETyRklFRETyRklFRETy5v8CteJnFZt0tcgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"resprate\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(0, 30)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac91e23cd0>"
      ]
     },
     "execution_count": 81,
     "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": [
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"o2sat\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(60, 100)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac9191ccd0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"sbp\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(0, 300)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac91a19ac0>"
      ]
     },
     "execution_count": 85,
     "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": [
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"dbp\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(0, 200)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ")[[\"IonoC\",\"last_7\"]].fillna(0).groupby('IonoC')[\"last_7\"].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ")[[\"IonoC\",\"last_7\"]].fillna(0).groupby('IonoC')[\"last_30\"].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fac913a2df0>"
      ]
     },
     "execution_count": 93,
     "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": [
    "ax = plt.subplot()\n",
    "pd.merge(\n",
    "    X,\n",
    "    y,\n",
    "    left_on=\"stay_id\",\n",
    "    right_on=\"stay_id\"\n",
    ").groupby('IonoC')[\"pain\"].plot(kind='kde', ax=ax)\n",
    "ax.set_xlim(0, 20)\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical_features = [\n",
    "    \"gender\",\n",
    "    \"last_7\",\n",
    "    \"last_30\"\n",
    "]\n",
    "\n",
    "continuous_features = [\n",
    "    \"pain\",\n",
    "    \"time\",\n",
    "    \"age\",\n",
    "    \"temperature\",\n",
    "    \"heartrate\",\n",
    "    \"resprate\",\n",
    "    \"o2sat\",\n",
    "    \"sbp\",\n",
    "    \"dbp\"\n",
    "]+X_train.columns[14:-1].tolist()\n",
    "\n",
    "continuous_features = [\n",
    "    \"pain\",\n",
    "    \"time\",\n",
    "    \"age\",\n",
    "    \"temperature\",\n",
    "    \"heartrate\",\n",
    "    \"resprate\",\n",
    "    \"o2sat\",\n",
    "    \"sbp\",\n",
    "    \"dbp\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_preprocessing = ColumnTransformer([\n",
    "    (\"binary_encoder\", OrdinalEncoder(), categorical_features),\n",
    "    (\"identity\", StandardScaler(), continuous_features),\n",
    "    (\"missing\", MissingIndicator(), continuous_features),\n",
    "    (\"nlp\", Pipeline([\n",
    "        (\"cv\", CountVectorizer(ngram_range=(1,1), max_features=200)),\n",
    "        (\"tf-idf\", TfidfTransformer())\n",
    "    ]), \"chiefcomplaint\"),\n",
    "])\n",
    "\n",
    "features_preprocessing_without_nlp = ColumnTransformer([\n",
    "    (\"binary_encoder\", OrdinalEncoder(), categorical_features),\n",
    "    (\"identity\", StandardScaler(), continuous_features),\n",
    "    (\"missing\", MissingIndicator(), continuous_features)\n",
    "])\n",
    "\n",
    "full_preprocessing = Pipeline([\n",
    "    (\"features\", features_preprocessing_without_nlp),\n",
    "    (\"imputer\", SimpleImputer(strategy=\"median\"))\n",
    "])\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"preprocessing\", full_preprocessing),\n",
    "    (\"mlp\", MLPClassifier(hidden_layer_sizes=(100,20), verbose=True, learning_rate_init=1e-3, batch_size=64, max_iter=100))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocesser = full_preprocessing.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer, BertModel\n",
    "import pickle\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_name = \"dmis-lab/biobert-v1.1\"\n",
    "drug_name = \"./models/ATC_2\"\n",
    "\n",
    "biobert_tokenizer = BertTokenizer.from_pretrained(bert_name)\n",
    "\n",
    "with open(drug_name+\"_encoder.model\", \"rb\") as f:\n",
    "    drug_encoder = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drug_columns = X_train.columns[14:-1]\n",
    "\n",
    "#columns_id = drug_encoder.transform(\n",
    "#    np.expand_dims(np.array(X_train[drug_columns].columns), 1)\n",
    "#).flatten().astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_drug_token_list (df):\n",
    "\n",
    "    df_drug_tokens = (df[drug_columns] >= 1)*1\n",
    "    df_drug_tokens = df_drug_tokens.rename(columns=dict(zip(drug_columns, columns_id)))\n",
    "\n",
    "    df_drug_tokens_list = (df_drug_tokens*(df_drug_tokens.columns+1)).apply(lambda x: list(set(x.tolist()))[1:], axis=1) \\\n",
    "        .tolist()\n",
    "\n",
    "    df_drug_tokens_list = [torch.tensor(x)-1 for x in df_drug_tokens_list]\n",
    "\n",
    "    return df_drug_tokens_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_preprocess = torch.tensor(preprocesser.transform(X_train), dtype=torch.float32)\n",
    "X_train_tokens = biobert_tokenizer(X_train[\"chiefcomplaint\"].tolist())[\"input_ids\"]\n",
    "#X_train_tokens_drug = get_drug_token_list(X_train)\n",
    "y_train_preprocess = torch.tensor(y_train.iloc[:,1:].values, dtype=torch.float32)\n",
    "X_test_preprocess = torch.tensor(preprocesser.transform(X_test), dtype=torch.float32)\n",
    "X_test_tokens = biobert_tokenizer(X_test[\"chiefcomplaint\"].tolist())[\"input_ids\"]\n",
    "#X_test_tokens_drug = get_drug_token_list(X_test)\n",
    "y_test_preprocess = torch.tensor(y_test.iloc[:,1:].values, dtype=torch.float32)\n",
    "X_train_tokens = [torch.tensor(x) for x in X_train_tokens]\n",
    "X_test_tokens = [torch.tensor(x) for x in X_test_tokens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn, optim\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "import torch\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class neural_net (nn.Module):\n",
    "    def __init__(self, n_features, n_outputs, device=\"cpu\"):\n",
    "        super().__init__()\n",
    "\n",
    "        self.embedding_encoder = nn.Sequential(*[\n",
    "            nn.Linear(768, 250),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(250, 50),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(50, 20),\n",
    "            nn.ReLU()\n",
    "        ])\n",
    "\n",
    "        self.network = nn.Sequential(*[\n",
    "            nn.Linear(n_features+20, 100),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(100, 50),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(50, n_outputs),\n",
    "            nn.Sigmoid()\n",
    "        ])\n",
    "\n",
    "        self.biobert_model = BertModel.from_pretrained(bert_name).to(device)\n",
    "        self.drug_embedding = torch.load(f\"{drug_name}_embedding.model\").to(device)     \n",
    "\n",
    "        for x in self.biobert_model.parameters():\n",
    "            x.requires_grad = False\n",
    "\n",
    "        #self.drug_embedding.requires_grad = True\n",
    "        #self.biobert_model.embeddings.requires_grad = True\n",
    "            \n",
    "        #self.loss = nn.BCELoss(weight=torch.tensor(y_train.iloc[:,1:].mean().values))\n",
    "        self.loss = nn.BCELoss()\n",
    "        #self.loss = nn.MultiLabelSoftMarginLoss()\n",
    "        #self.loss = nn.CrossEntropyLoss(weight=torch.tensor(y_train.iloc[:,1:].mean().values))\n",
    "        self.optimizer = optim.Adam(self.parameters(), lr=1e-3)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \n",
    "        x_data = x[0]\n",
    "        x_tokens = x[1]\n",
    "        #x_drugs = x[2]\n",
    "\n",
    "        x_bert = self.biobert_model.embeddings.word_embeddings(x_tokens)\n",
    "        x_bert_mask = (x_tokens != 0).unsqueeze(2)*1\n",
    "        x_bert = (x_bert*x_bert_mask).sum(axis=1)/x_bert_mask.sum(axis=1)\n",
    "\n",
    "        #x_drugs_embeddings = self.drug_embedding(x_drugs)\n",
    "        #x_drugs_embeddings_mask = (x_drugs != self.drug_embedding.weight.shape[0]-1).unsqueeze(2)*1\n",
    "        #x_drugs_embeddings_mask = x_drugs_embeddings_mask + 1e-8\n",
    "        #x_drugs_embeddings = (x_drugs_embeddings*x_drugs_embeddings_mask).sum(axis=1)/x_drugs_embeddings_mask.sum(axis=1)\n",
    "\n",
    "        x_embedding_encoded = self.embedding_encoder(x_bert)\n",
    "        x = torch.concat([x_data, x_embedding_encoded], axis=1)\n",
    "\n",
    "        y_hat = self.network(x)\n",
    "\n",
    "        return y_hat\n",
    "    \n",
    "    def fit(self, x, y):\n",
    "        \n",
    "        self.train()\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        y_hat = self.forward(x)\n",
    "\n",
    "        loss = self.loss(y_hat, y)\n",
    "\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "\n",
    "        return loss\n",
    "\n",
    "    def last_hidden_layer (self, x):\n",
    "        \n",
    "        self.eval()\n",
    "        \n",
    "        with torch.no_grad():          \n",
    "            x_data = x[0]\n",
    "            x_tokens = x[1]\n",
    "            #x_drugs = x[2]\n",
    "\n",
    "            x_bert = self.biobert_model.embeddings.word_embeddings(x_tokens)\n",
    "            x_bert_mask = (x_tokens != 0).unsqueeze(2)*1\n",
    "            x_bert = (x_bert*x_bert_mask).sum(axis=1)/x_bert_mask.sum(axis=1)\n",
    "\n",
    "            x_embedding_encoded = self.embedding_encoder(x_bert)\n",
    "            x = torch.concat([x_data, x_embedding_encoded], axis=1)\n",
    "\n",
    "            y_hat = network.network[:-3](x)\n",
    "\n",
    "            return y_hat\n",
    "\n",
    "    def predict(self, x):\n",
    "        \n",
    "        self.eval()\n",
    "        \n",
    "        with torch.no_grad():            \n",
    "            y_hat = self.forward(x)\n",
    "\n",
    "        return y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = \"cuda:0\"\n",
    "#device = \"cpu\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#network = neural_net(X_train_preprocess.shape[1], y_train_preprocess.shape[1], device=device)\n",
    "network = neural_net(X_train_preprocess.shape[1], 1, device=device)\n",
    "network = network.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torchvision.ops.focal_loss import sigmoid_focal_loss\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = DataLoader(range(X_train_preprocess.shape[0]), shuffle=True, batch_size=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.691106379032135\n",
      "On test : precision = 0.5, recall = 0.00028876696505919725\n",
      "0.6006030204272507\n",
      "On test : precision = 0.7114660697934683, recall = 0.6731157955529887\n",
      "0.575636284564858\n",
      "On test : precision = 0.7091583562171797, recall = 0.7084416209452306\n",
      "0.5641533084882058\n",
      "On test : precision = 0.7105074026160701, recall = 0.7136875541438059\n",
      "Epoch 0 - loss : 0.5566418178473846\n",
      "0.5154001116752625\n",
      "On test : precision = 0.7277034414536367, recall = 0.6726826451054\n",
      "0.5331675112247467\n",
      "On test : precision = 0.7340632731483918, recall = 0.6711425546250842\n",
      "0.5320046168951252\n",
      "On test : precision = 0.7303906490310673, recall = 0.6856771585330638\n",
      "0.5311751908242108\n",
      "On test : precision = 0.7530828460603254, recall = 0.6260467802483396\n",
      "Epoch 1 - loss : 0.5314786310437359\n",
      "0.5191752910614014\n",
      "On test : precision = 0.7150190114068441, recall = 0.7240350370584272\n",
      "0.5276658352058713\n",
      "On test : precision = 0.7223930122480847, recall = 0.7124843584560593\n",
      "0.5285998987617777\n",
      "On test : precision = 0.7104558877814692, recall = 0.74102416016941\n",
      "0.5277084383457602\n",
      "On test : precision = 0.7457774871962515, recall = 0.6587737029550486\n",
      "Epoch 2 - loss : 0.5267519308796412\n",
      "0.5291849374771118\n",
      "On test : precision = 0.7194110920526015, recall = 0.7266820675714698\n",
      "0.5206360940886016\n",
      "On test : precision = 0.7333908980089394, recall = 0.6949177014149581\n",
      "0.5217355852992973\n",
      "On test : precision = 0.7369723210143807, recall = 0.688131677736067\n",
      "0.521929228622652\n",
      "On test : precision = 0.7239691845535152, recall = 0.7191259986524209\n",
      "Epoch 3 - loss : 0.5215651713594606\n",
      "0.5235981941223145\n",
      "On test : precision = 0.7369445013311489, recall = 0.6927519491770141\n",
      "0.5186061596516336\n",
      "On test : precision = 0.7351477607393114, recall = 0.696794686687843\n",
      "0.5180819096849926\n",
      "On test : precision = 0.7319912079128784, recall = 0.7052170565020695\n",
      "0.5182426832245037\n",
      "On test : precision = 0.7510562235944102, recall = 0.6673404562518048\n",
      "Epoch 4 - loss : 0.5190195072300826\n",
      "0.5245155096054077\n",
      "On test : precision = 0.7069435141772717, recall = 0.7619597651362018\n",
      "0.5169979248306539\n",
      "On test : precision = 0.7158938869665513, recall = 0.7467994994705939\n",
      "0.5154471167580998\n",
      "On test : precision = 0.7308615369544987, recall = 0.7181634421022235\n",
      "0.5157833989474464\n",
      "On test : precision = 0.7234719058466211, recall = 0.7337087303879103\n",
      "Epoch 5 - loss : 0.515931355500523\n",
      "0.4985155463218689\n",
      "On test : precision = 0.7207000093835038, recall = 0.7392915583790548\n",
      "0.5164604614866842\n",
      "On test : precision = 0.7275522459764593, recall = 0.7288478198094138\n",
      "0.5142399074129798\n",
      "On test : precision = 0.7449022055763629, recall = 0.6891904899412841\n",
      "0.5145066314163398\n",
      "On test : precision = 0.7475281552371384, recall = 0.6804312253344884\n",
      "Epoch 6 - loss : 0.5147569981556904\n",
      "0.49890613555908203\n",
      "On test : precision = 0.7430380398414578, recall = 0.6947251901049186\n",
      "0.5157188932494362\n",
      "On test : precision = 0.7427693882774508, recall = 0.6983347771681586\n",
      "0.5156954976160135\n",
      "On test : precision = 0.7385383567862006, recall = 0.7047357782269709\n",
      "0.515681599164722\n",
      "On test : precision = 0.74251832247557, recall = 0.7021368755414381\n",
      "Epoch 7 - loss : 0.5150245293786254\n",
      "0.49726372957229614\n",
      "On test : precision = 0.7318327350344426, recall = 0.7260564058138416\n",
      "0.513713940240369\n",
      "On test : precision = 0.7359238699444886, recall = 0.7146019828664935\n",
      "0.5135435411586097\n",
      "On test : precision = 0.7411841972314843, recall = 0.706083357397247\n",
      "0.5129304822299171\n",
      "On test : precision = 0.7369464967521198, recall = 0.7152757724516315\n",
      "Epoch 8 - loss : 0.5133467673500882\n",
      "0.5323821902275085\n",
      "On test : precision = 0.7329362366956352, recall = 0.7224949465781114\n",
      "0.510783731052191\n",
      "On test : precision = 0.7271386430678466, recall = 0.7355375878332852\n",
      "0.5100371665622464\n",
      "On test : precision = 0.7245657568238213, recall = 0.7448262585426894\n",
      "0.5103289775080063\n",
      "On test : precision = 0.7371029287873532, recall = 0.7158533063817499\n",
      "Epoch 9 - loss : 0.5115820087964021\n",
      "0.5278018712997437\n",
      "On test : precision = 0.7349821594408329, recall = 0.7236981422658582\n",
      "0.509880670521519\n",
      "On test : precision = 0.7158084914182475, recall = 0.7627298103763596\n",
      "0.5081589534804596\n",
      "On test : precision = 0.7391979949874686, recall = 0.7097410722879969\n",
      "0.5089700956677281\n",
      "On test : precision = 0.7339721762817395, recall = 0.7262007892963711\n",
      "Epoch 10 - loss : 0.5090479979032203\n",
      "0.519418478012085\n",
      "On test : precision = 0.7202764976958526, recall = 0.7522379439792087\n",
      "0.5076188123462224\n",
      "On test : precision = 0.7349367960257734, recall = 0.7191259986524209\n",
      "0.5096459284943727\n",
      "On test : precision = 0.7378349093774625, recall = 0.7210029839253056\n",
      "0.5096920878190139\n",
      "On test : precision = 0.7328155339805825, recall = 0.7265376840889403\n",
      "Epoch 11 - loss : 0.5086245969126496\n",
      "0.5080099105834961\n",
      "On test : precision = 0.7295591469828823, recall = 0.7343343921455385\n",
      "0.5070724266000314\n",
      "On test : precision = 0.7358842285994603, recall = 0.721965540475503\n",
      "0.5059742887518299\n",
      "On test : precision = 0.7328332930075006, recall = 0.7288478198094138\n",
      "0.5064577097908602\n",
      "On test : precision = 0.7308452673706544, recall = 0.7294734815670421\n",
      "Epoch 12 - loss : 0.5078333315970023\n",
      "0.49358904361724854\n",
      "On test : precision = 0.7290276453765491, recall = 0.7361151217634035\n",
      "0.5067540569470661\n",
      "On test : precision = 0.7323598635861472, recall = 0.73380498604293\n",
      "0.5065122897648693\n",
      "On test : precision = 0.7542148234545647, recall = 0.6846664741553566\n",
      "0.5084922199827492\n",
      "On test : precision = 0.7233796296296297, recall = 0.7519973048416595\n",
      "Epoch 13 - loss : 0.5075114444086823\n",
      "0.49666109681129456\n",
      "On test : precision = 0.7252100840336134, recall = 0.7476176725382616\n",
      "0.5051100498969012\n",
      "On test : precision = 0.7369399334768147, recall = 0.7250938492636443\n",
      "0.5057181871649045\n",
      "On test : precision = 0.7466612294831277, recall = 0.7049764173645202\n",
      "0.5072105418011992\n",
      "On test : precision = 0.7283676553939165, recall = 0.7421792280296468\n",
      "Epoch 14 - loss : 0.5062240871447551\n",
      "0.5191906690597534\n",
      "On test : precision = 0.7395544554455445, recall = 0.7189816151698912\n",
      "0.504310804723513\n",
      "On test : precision = 0.7552971164569061, recall = 0.684522090672827\n",
      "0.5053237096883765\n",
      "On test : precision = 0.7340909090909091, recall = 0.7306285494272788\n",
      "0.5062640938053892\n",
      "On test : precision = 0.736219124480313, recall = 0.7244200596785061\n",
      "Epoch 15 - loss : 0.5053795011737678\n",
      "0.4989502429962158\n",
      "On test : precision = 0.7325291977790542, recall = 0.7365482722109924\n",
      "0.5064039799836603\n",
      "On test : precision = 0.7364736765349278, recall = 0.729136586774473\n",
      "0.505478177497636\n",
      "On test : precision = 0.7345682005527808, recall = 0.7290884589469632\n",
      "0.5045830322262457\n",
      "On test : precision = 0.7247833943381365, recall = 0.7528636057368371\n",
      "Epoch 16 - loss : 0.5043610495102556\n",
      "0.4920410215854645\n",
      "On test : precision = 0.7243070658460969, recall = 0.7533448840119357\n",
      "0.5003653608336307\n",
      "On test : precision = 0.7195271096616388, recall = 0.7645105399942247\n",
      "0.5010813141047065\n",
      "On test : precision = 0.7379865508270751, recall = 0.7236018866108384\n",
      "0.5018343973991483\n",
      "On test : precision = 0.7353953556018811, recall = 0.7300510154971604\n",
      "Epoch 17 - loss : 0.5035921111137052\n",
      "0.4888218641281128\n",
      "On test : precision = 0.7268319970165952, recall = 0.7504090865338339\n",
      "0.5015489895745079\n",
      "On test : precision = 0.7332950136513867, recall = 0.7367889113485417\n",
      "0.5033437172275278\n",
      "On test : precision = 0.7282874905802562, recall = 0.7442005967850611\n",
      "0.5026719430554348\n",
      "On test : precision = 0.7377786468517794, recall = 0.7263451727789008\n",
      "Epoch 18 - loss : 0.5028010086168216\n",
      "0.5066063404083252\n",
      "On test : precision = 0.7345524542829643, recall = 0.7346231591105977\n",
      "0.5018388912229255\n",
      "On test : precision = 0.719209142027934, recall = 0.763307344306478\n",
      "0.5002150563754846\n",
      "On test : precision = 0.7373129950132004, recall = 0.7258157666762922\n",
      "0.5022425656500845\n",
      "On test : precision = 0.7278510838831291, recall = 0.7433342958898835\n",
      "Epoch 19 - loss : 0.5019486363175549\n",
      "0.47926339507102966\n",
      "On test : precision = 0.7323501427212179, recall = 0.7408797766868803\n",
      "0.49971008772897246\n",
      "On test : precision = 0.7300813777441333, recall = 0.7426605063047454\n",
      "0.5002152393409862\n",
      "On test : precision = 0.7273622970817303, recall = 0.7461257098854558\n",
      "0.5013288241684238\n",
      "On test : precision = 0.7416225313072315, recall = 0.7210992395803253\n",
      "Epoch 20 - loss : 0.5008858129193511\n",
      "0.5151246786117554\n",
      "On test : precision = 0.735239852398524, recall = 0.728799691981904\n",
      "0.4983244101599892\n",
      "On test : precision = 0.7248408818374689, recall = 0.7563769371450573\n",
      "0.4970505050758817\n",
      "On test : precision = 0.730860225229488, recall = 0.7433824237173934\n",
      "0.49717872661609586\n",
      "On test : precision = 0.7449337881219904, recall = 0.714746366349023\n",
      "Epoch 21 - loss : 0.49780065651181377\n",
      "0.48739099502563477\n",
      "On test : precision = 0.7354018115243784, recall = 0.7346231591105977\n",
      "0.49334852795789735\n",
      "On test : precision = 0.7303307264675398, recall = 0.7460775820579459\n",
      "0.49463323218312427\n",
      "On test : precision = 0.7429808841099164, recall = 0.7183078255847531\n",
      "0.49579841146041387\n",
      "On test : precision = 0.7449392712550608, recall = 0.7084416209452306\n",
      "Epoch 22 - loss : 0.4965084141568293\n",
      "0.4926077127456665\n",
      "On test : precision = 0.7217624590760277, recall = 0.7639330060641063\n",
      "0.49502673007474085\n",
      "On test : precision = 0.7273743537193162, recall = 0.7515641543940706\n",
      "0.4956098737111732\n",
      "On test : precision = 0.7344678811121764, recall = 0.7373664452786601\n",
      "0.4958594325570965\n",
      "On test : precision = 0.7506115014311736, recall = 0.6941476561748002\n",
      "Epoch 23 - loss : 0.495923723374741\n",
      "0.47399887442588806\n",
      "On test : precision = 0.7381651017214398, recall = 0.7264414284339205\n",
      "0.4940861616984452\n",
      "On test : precision = 0.7452663468821005, recall = 0.7103667340456252\n",
      "0.49426327445613805\n",
      "On test : precision = 0.7333174224343676, recall = 0.7393878140340745\n",
      "0.4953075092892314\n",
      "On test : precision = 0.7655076495132128, recall = 0.662238906535759\n",
      "Epoch 24 - loss : 0.4953661680221558\n",
      "0.5228779315948486\n",
      "On test : precision = 0.7442258340461934, recall = 0.7118105688709212\n",
      "0.4923725653402876\n",
      "On test : precision = 0.7376028030561098, recall = 0.7294734815670421\n",
      "0.4930079595663061\n",
      "On test : precision = 0.7340628882729618, recall = 0.7392915583790548\n",
      "0.49445695774103715\n",
      "On test : precision = 0.7341409901745684, recall = 0.7407835210318606\n",
      "Epoch 25 - loss : 0.49466084581387193\n",
      "0.46513304114341736\n",
      "On test : precision = 0.7360903344110409, recall = 0.7341418808354991\n",
      "0.49388582281547017\n",
      "On test : precision = 0.7195767195767195, recall = 0.765809991336991\n",
      "0.49362160183897064\n",
      "On test : precision = 0.7461990675045611, recall = 0.7086341322552699\n",
      "0.49338837715478434\n",
      "On test : precision = 0.7481032639136412, recall = 0.7070940417749543\n",
      "Epoch 26 - loss : 0.49349285349061217\n",
      "0.4880646765232086\n",
      "On test : precision = 0.7410621825533803, recall = 0.7232649918182693\n",
      "0.49176528194163105\n",
      "On test : precision = 0.7345693837360103, recall = 0.7360188661083839\n",
      "0.49261100775566863\n",
      "On test : precision = 0.7456571944974395, recall = 0.7147944941765328\n",
      "0.49288865616947314\n",
      "On test : precision = 0.7505957931820537, recall = 0.6972759649629415\n",
      "Epoch 27 - loss : 0.4931577115873747\n",
      "0.47572997212409973\n",
      "On test : precision = 0.7506438652518801, recall = 0.7013668303012802\n",
      "0.49050306595198\n",
      "On test : precision = 0.7380428941325907, recall = 0.7270670901915488\n",
      "0.4920397731498699\n",
      "On test : precision = 0.7261525681849647, recall = 0.7572913658677447\n",
      "0.492620616260161\n",
      "On test : precision = 0.7270705947748749, recall = 0.75541438059486\n",
      "Epoch 28 - loss : 0.4932937731471243\n",
      "0.5012404918670654\n",
      "On test : precision = 0.7346705417123255, recall = 0.7421311002021369\n",
      "0.4935308385013354\n",
      "On test : precision = 0.7320115733055068, recall = 0.7427567619597651\n",
      "0.4925164138499777\n",
      "On test : precision = 0.7216824049997719, recall = 0.7613822312060834\n",
      "0.4938466421195439\n",
      "On test : precision = 0.750325436084353, recall = 0.693521994417172\n",
      "Epoch 29 - loss : 0.4946022235894505\n",
      "0.4953339099884033\n",
      "On test : precision = 0.7297094305966296, recall = 0.7481470786408702\n",
      "0.49041805763055785\n",
      "On test : precision = 0.7502446561936646, recall = 0.7010299355087112\n",
      "0.4931423207420615\n",
      "On test : precision = 0.7352813956841447, recall = 0.736307633073443\n",
      "0.4925843425763406\n",
      "On test : precision = 0.7460094368035338, recall = 0.7152757724516315\n",
      "Epoch 30 - loss : 0.4933103129833559\n",
      "0.5755341053009033\n",
      "On test : precision = 0.7485953621411788, recall = 0.7053614399845991\n",
      "0.4932693898087681\n",
      "On test : precision = 0.7366245136186771, recall = 0.7288959476369237\n",
      "0.49100364855865936\n",
      "On test : precision = 0.7430106457068948, recall = 0.7188372316873616\n",
      "0.49268141141365535\n",
      "On test : precision = 0.7314221048660883, recall = 0.7465588603330445\n",
      "Epoch 31 - loss : 0.49313025632991064\n",
      "0.46600431203842163\n",
      "On test : precision = 0.7224534601838722, recall = 0.7601790355183367\n",
      "0.4903270124208809\n",
      "On test : precision = 0.7441521203279843, recall = 0.7119549523534507\n",
      "0.49055656909349543\n",
      "On test : precision = 0.7316389548693587, recall = 0.7412166714794494\n",
      "0.492653287625392\n",
      "On test : precision = 0.7404246971703202, recall = 0.72663393974396\n",
      "Epoch 32 - loss : 0.4919591864453086\n",
      "0.4467436969280243\n",
      "On test : precision = 0.7414435618319114, recall = 0.7277408797766869\n",
      "0.4866951137486071\n",
      "On test : precision = 0.7230283334096215, recall = 0.7602271633458466\n",
      "0.4884573491058539\n",
      "On test : precision = 0.735220368405825, recall = 0.73380498604293\n",
      "0.49062324639570676\n",
      "On test : precision = 0.7195598472939592, recall = 0.7710559245355665\n",
      "Epoch 33 - loss : 0.49098759293556216\n",
      "0.4772811532020569\n",
      "On test : precision = 0.7395291809710642, recall = 0.7257195110212725\n",
      "0.4887826596156205\n",
      "On test : precision = 0.7256522142790828, recall = 0.7523341996342285\n",
      "0.49171305325493886\n",
      "On test : precision = 0.728898459809934, recall = 0.7493502743286168\n",
      "0.49049466204801667\n",
      "On test : precision = 0.7414505450744189, recall = 0.7168639907594572\n",
      "Epoch 34 - loss : 0.49134464648705495\n",
      "0.48381781578063965\n",
      "On test : precision = 0.7229911835914302, recall = 0.7617191259986524\n",
      "0.49350475320721615\n",
      "On test : precision = 0.7532263963634463, recall = 0.6938107613822312\n",
      "0.4918556806459949\n",
      "On test : precision = 0.7408834865105247, recall = 0.7216286456829338\n",
      "0.4905527135066416\n",
      "On test : precision = 0.735855421686747, recall = 0.7348637982481471\n",
      "Epoch 35 - loss : 0.49083545019355\n",
      "0.50368332862854\n",
      "On test : precision = 0.7351413733410271, recall = 0.7357782269708345\n",
      "0.490445864082563\n",
      "On test : precision = 0.7325553703262682, recall = 0.7402059871017422\n",
      "0.48895341009642945\n",
      "On test : precision = 0.742154915590864, recall = 0.7193666377899701\n",
      "0.4895016640127695\n",
      "On test : precision = 0.7154373775164796, recall = 0.7730772932909808\n",
      "Epoch 36 - loss : 0.49018767596800117\n",
      "0.4943732023239136\n",
      "On test : precision = 0.741195092995647, recall = 0.7211473674078352\n",
      "0.48728429179380434\n",
      "On test : precision = 0.7321445599313435, recall = 0.7390509192415055\n",
      "0.4889129802065702\n",
      "On test : precision = 0.7385675200312684, recall = 0.7275483684666474\n",
      "0.4895449759952254\n",
      "On test : precision = 0.7478923311325546, recall = 0.7087303879102897\n",
      "Epoch 37 - loss : 0.4894779199286352\n",
      "0.5134391784667969\n",
      "On test : precision = 0.7131474103585658, recall = 0.7753393011839446\n",
      "0.48750126656919424\n",
      "On test : precision = 0.7298979687353739, recall = 0.7505534700163634\n",
      "0.4876820467301269\n",
      "On test : precision = 0.7359180335754486, recall = 0.736307633073443\n",
      "0.48870848401440337\n",
      "On test : precision = 0.7342398616182971, recall = 0.7354413321782655\n",
      "Epoch 38 - loss : 0.4888177595561064\n",
      "0.5162264704704285\n",
      "On test : precision = 0.7294762998734118, recall = 0.7488208682260082\n",
      "0.4837004595463819\n",
      "On test : precision = 0.7297081355454247, recall = 0.7472326499181827\n",
      "0.4879101812839508\n",
      "On test : precision = 0.7376440711076382, recall = 0.7269227067090192\n",
      "0.4885243367515133\n",
      "On test : precision = 0.7336136059621632, recall = 0.7390509192415055\n",
      "Epoch 39 - loss : 0.48852996003778676\n",
      "0.4767839014530182\n",
      "On test : precision = 0.7173160756819918, recall = 0.7681682548849745\n",
      "0.48714618989736724\n",
      "On test : precision = 0.7371397922863133, recall = 0.7275964962941572\n",
      "0.48659720883440616\n",
      "On test : precision = 0.7303243472923847, recall = 0.7444893637501203\n",
      "0.4855928495278786\n",
      "On test : precision = 0.7257344737820751, recall = 0.7513716430840311\n",
      "Epoch 40 - loss : 0.4853414050385922\n",
      "0.4991248846054077\n",
      "On test : precision = 0.7392603477834925, recall = 0.7263451727789008\n",
      "0.4837396457643792\n",
      "On test : precision = 0.7260178471834914, recall = 0.75180479353162\n",
      "0.4851182410076483\n",
      "On test : precision = 0.7457893668831169, recall = 0.7075271922225431\n",
      "0.4848854331875164\n",
      "On test : precision = 0.7241426737575647, recall = 0.7601790355183367\n",
      "Epoch 41 - loss : 0.48461107302315626\n",
      "0.4812166690826416\n",
      "On test : precision = 0.7516588652847076, recall = 0.7032919434016749\n",
      "0.4824957245647317\n",
      "On test : precision = 0.7348794687966126, recall = 0.7350563095581866\n",
      "0.48231442339384734\n",
      "On test : precision = 0.7364314789687924, recall = 0.7313985946674367\n",
      "0.48301191365600027\n",
      "On test : precision = 0.7260126936718313, recall = 0.7487246125709885\n",
      "Epoch 42 - loss : 0.48370890670184846\n",
      "0.4642280340194702\n",
      "On test : precision = 0.7284904688304997, recall = 0.7485802290884589\n",
      "0.4810808053111086\n",
      "On test : precision = 0.7414962618210625, recall = 0.7207623447877562\n",
      "0.4822801913491529\n",
      "On test : precision = 0.7300451807228916, recall = 0.7465588603330445\n",
      "0.4838001663700687\n",
      "On test : precision = 0.7426664674535585, recall = 0.7176821638271248\n",
      "Epoch 43 - loss : 0.4833500043500828\n",
      "0.48344671726226807\n",
      "On test : precision = 0.7282776710852967, recall = 0.7482914621233997\n",
      "0.47828448113828603\n",
      "On test : precision = 0.7443261699136373, recall = 0.7134469150062566\n",
      "0.47993328796690377\n",
      "On test : precision = 0.7382389011528084, recall = 0.7242756761959765\n",
      "0.482173868984083\n",
      "On test : precision = 0.7241951849460382, recall = 0.7557031475599192\n",
      "Epoch 44 - loss : 0.48240686473967154\n",
      "0.4691382050514221\n",
      "On test : precision = 0.7398253662868137, recall = 0.7217730291654635\n",
      "0.4799038248487038\n",
      "On test : precision = 0.7180549302116164, recall = 0.7675425931273462\n",
      "0.48127377181503905\n",
      "On test : precision = 0.7440377566902646, recall = 0.7132062758687073\n",
      "0.48116818308038173\n",
      "On test : precision = 0.7240358827337464, recall = 0.753585523149485\n",
      "Epoch 45 - loss : 0.4820579810233056\n",
      "0.4957138001918793\n",
      "On test : precision = 0.7318316030098104, recall = 0.739580325344114\n",
      "0.4815530251748491\n",
      "On test : precision = 0.7397954165437198, recall = 0.7239869092309174\n",
      "0.4814220647610242\n",
      "On test : precision = 0.7348794687966126, recall = 0.7350563095581866\n",
      "0.4813806482921803\n",
      "On test : precision = 0.7248872111223644, recall = 0.7578207719703532\n",
      "Epoch 46 - loss : 0.48105589106113095\n",
      "0.4731432795524597\n",
      "On test : precision = 0.7445958763915931, recall = 0.7178265473096545\n",
      "0.47872081457978427\n",
      "On test : precision = 0.7366201279813845, recall = 0.7313023390124169\n",
      "0.4795397832915558\n",
      "On test : precision = 0.72129574633344, recall = 0.7597940128982578\n",
      "0.4801403841505019\n",
      "On test : precision = 0.7380836100800938, recall = 0.727355857156608\n",
      "Epoch 47 - loss : 0.48055834536310993\n",
      "0.47339731454849243\n",
      "On test : precision = 0.7346831070383342, recall = 0.7369814226585812\n",
      "0.4796473847167327\n",
      "On test : precision = 0.738722447583207, recall = 0.7274521128116277\n",
      "0.4792541035668767\n",
      "On test : precision = 0.735829812035553, recall = 0.729136586774473\n",
      "0.4792395104403512\n",
      "On test : precision = 0.7345323741007195, recall = 0.7370776783136009\n",
      "Epoch 48 - loss : 0.479706547079207\n",
      "0.45964089035987854\n",
      "On test : precision = 0.7396514161220044, recall = 0.7189334873423814\n",
      "0.4777260641060253\n",
      "On test : precision = 0.7351035016444186, recall = 0.7314948503224564\n",
      "0.4780597803901084\n",
      "On test : precision = 0.7253739637845598, recall = 0.7538261622870344\n",
      "0.4789733001560072\n",
      "On test : precision = 0.7376690339527191, recall = 0.729858504187121\n",
      "Epoch 49 - loss : 0.4793024491660203\n"
     ]
    }
   ],
   "source": [
    "n_epochs = 50\n",
    "n_epoch_print = 1\n",
    "n_batch_print = 100\n",
    "\n",
    "for i in range(n_epochs):\n",
    "\n",
    "    losses = []\n",
    "\n",
    "    j = 0\n",
    "    for indices in data_loader:\n",
    "        X_tensor = X_train_preprocess[indices,:].to(device)\n",
    "        X_train_tokens_indices = list(operator.itemgetter(*indices)(X_train_tokens))\n",
    "        X_train_tokens_indices = pad_sequence(X_train_tokens_indices, batch_first=True, padding_value=biobert_tokenizer(\"[PAD]\")[\"input_ids\"][1]).to(device)\n",
    "        #X_train_drug_tokens_indices = list(operator.itemgetter(*indices)(X_train_tokens_drug))\n",
    "        #X_train_drug_tokens_indices = pad_sequence(X_train_drug_tokens_indices, batch_first=True, padding_value=len(drug_encoder.categories_[0])).to(device)\n",
    "        #X_train_drug_tokens_indices = X_train_drug_tokens_indices.int()\n",
    "\n",
    "        y_tensor = y_train_preprocess[indices,5].unsqueeze(1).to(device)\n",
    "        #y_tensor = y_train_preprocess[indices,:].to(device)\n",
    "\n",
    "        loss = network.fit((X_tensor, X_train_tokens_indices), y_tensor).detach().cpu().item()\n",
    "\n",
    "        losses.append(loss)\n",
    "\n",
    "        if j%n_batch_print == 0:\n",
    "            print(np.array(losses).mean())\n",
    "\n",
    "            y_test_hat, y_true = get_test_evaluation(X_test_preprocess, X_test_tokens)\n",
    "            prec = precision_score(y_true, y_test_hat, zero_division=0)\n",
    "            rec = recall_score(y_true, y_test_hat, zero_division=0)\n",
    "            \n",
    "            print(f\"On test : precision = {prec}, recall = {rec}\")\n",
    "\n",
    "        j += 1\n",
    "\n",
    "    if (i%n_epoch_print) == 0:\n",
    "        mean_loss = np.array(losses).mean()\n",
    "        print(f\"Epoch {i} - loss : {mean_loss}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyse des faux positifs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_hats = []\n",
    "y_val = []\n",
    "\n",
    "data_loader_noshuffle = DataLoader(range(X_train_preprocess.shape[0]), shuffle=False, batch_size=1024)\n",
    "\n",
    "for indices in data_loader_noshuffle:\n",
    "    X_tensor = X_train_preprocess[indices,:].to(device)\n",
    "    X_train_tokens_indices = list(operator.itemgetter(*indices)(X_train_tokens))\n",
    "    X_train_tokens_indices = pad_sequence(X_train_tokens_indices, batch_first=True, padding_value=biobert_tokenizer(\"[PAD]\")[\"input_ids\"][1]).to(device)\n",
    "\n",
    "    y_hats.append(\n",
    "        network.predict((X_tensor, X_train_tokens_indices)).detach().cpu()\n",
    "    )\n",
    "    y_val.append(y_train_preprocess[indices,5])\n",
    "\n",
    "y_hat = np.concatenate(y_hats)\n",
    "y_val = np.concatenate(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fp = (y_hat >= 0.65)*1 & ((y_val == 0)*1).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "416252                      UNRESPPONSIVE\n",
       "441947    Abd pain, Abnormal CT, Transfer\n",
       "189529      Chest pain, Palpitations, ILI\n",
       "240265                 Weakness, Transfer\n",
       "291786                     Hallucinations\n",
       "                       ...               \n",
       "252801                           s/p Fall\n",
       "239629                           Abd pain\n",
       "278167            Hyperglycemia, Weakness\n",
       "137337                         Wound eval\n",
       "131932               s/p Fall, R Hip pain\n",
       "Name: chiefcomplaint, Length: 24690, dtype: string"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.iloc[np.where(fp == 1)[0],:][\"chiefcomplaint\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(\n",
    "    y_val,\n",
    "    y_hat\n",
    ")\n",
    "\n",
    "auc = roc_auc_score(y_val, y_hat)\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "\n",
    "plt.plot(fpr,tpr,label=\"AUC=\"+str(auc))\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.legend(loc=4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 256\n",
    "\n",
    "def get_test_evaluation(X_test_preprocess, X_test_tokens):\n",
    "    y_hats = []\n",
    "\n",
    "    for idx in range(0, X_test_preprocess.shape[0], batch_size):\n",
    "        X_test_tensor = X_test_preprocess[idx:idx+batch_size].to(device)\n",
    "        X_test_tokens_indices = X_test_tokens[idx:idx+batch_size]\n",
    "        X_test_tokens_indices = pad_sequence(X_test_tokens_indices, batch_first=True, padding_value=biobert_tokenizer(\"[PAD]\")[\"input_ids\"][1]).to(device)\n",
    "        #X_test_drug_tokens_indices = X_test_tokens_drug[idx:idx+batch_size]\n",
    "        #X_test_drug_tokens_indices = pad_sequence(X_test_drug_tokens_indices, batch_first=True, padding_value=len(drug_encoder.categories_[0])).to(device)\n",
    "        #X_test_drug_tokens_indices = X_test_drug_tokens_indices.int()\n",
    "\n",
    "        y_hat_ = ((network.predict((X_test_tensor, X_test_tokens_indices)).detach().cpu()) >= 0.65)*1\n",
    "\n",
    "        y_hats.append(y_hat_)\n",
    "\n",
    "    y_hat = torch.concat(y_hats, axis=0).numpy()\n",
    "    y_true = y_test_preprocess[:,[5]]\n",
    "    #y_true = y_test_preprocess[:,:]\n",
    "\n",
    "    return y_hat, y_true\n",
    "\n",
    "y_hat, y_true = get_test_evaluation(X_test_preprocess, X_test_tokens)\n",
    "titles = y_train.columns.tolist()[1:]\n",
    "titles = y_train.columns[[6]].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x2160 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_cols = 3\n",
    "n_rows = len(titles) // n_cols + int((len(titles)%n_cols) != 0)\n",
    "\n",
    "figs, axs = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(30,30))\n",
    "axs_flatten = axs.flatten()\n",
    "\n",
    "for i in range(len(titles)):\n",
    "    cm_plot = ConfusionMatrixDisplay(confusion_matrix(y_true[:,i], y_hat[:,i]))\n",
    "    cm_plot.plot(ax=axs_flatten[i])\n",
    "    axs_flatten[i].set_title(titles[i])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IonoC</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       precision  recall  f1_score\n",
       "IonoC        0.8    0.58      0.67"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(\n",
    "    dict(zip(titles, np.concatenate([\n",
    "        precision_score(y_true, y_hat, average=\"binary\", zero_division=0).reshape(1,-1),\n",
    "        recall_score(y_true, y_hat, average=\"binary\", zero_division=0).reshape(1,-1),\n",
    "        f1_score(y_true, y_hat, average=\"binary\").reshape(1,-1)\n",
    "    ], axis=0).T)),\n",
    "    orient=\"index\",\n",
    "    columns=[\"precision\",\"recall\",\"f1_score\"]\n",
    ").round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "interpreter": {
   "hash": "28b293e0c0671e44c7281dde6399c7c7419d3faca031d22494da8635907ada72"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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 },
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}