Trabajo accidentes cardiovasculares.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "42691b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load ../../standard_import.txt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics, neighbors, preprocessing\n",
    "\n",
    "pd.set_option('display.notebook_repr_html', False)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 150)\n",
    "pd.set_option('display.max_seq_items', None)\n",
    "\n",
    " \n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from scipy.optimize import minimize\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26cd65be",
   "metadata": {},
   "source": [
    "# Clasificación de accidentes cardiovasculares"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf58d4b",
   "metadata": {},
   "source": [
    "## Información sobre el dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de45f8e",
   "metadata": {},
   "source": [
    "De acuerdo a la Organización Mundial de la Salud los derrames son la segunda causa de muerte a nivel global, responsable de aproximadamente el 11% de las muertes. Nuestra intención con este trabajo es poder clasificar si un individuo podría llegar a tener o ha tenido un derrame cerebral para poder tratar este tipo de casos con mayor facilidad. Gracias al conjunto de datos    [**Stroke Prediction Dataset**](https://www.kaggle.com/fedesoriano/stroke-prediction-dataset) obtenido en [**kaggle**](https://www.kaggle.com/) hemos podido plantear este problema de clasificación. A continuación, trataremos las características de los individuos que se tomaron para formar el conjunto de datos, todos ligados a si han tenido un derrame cerebral o no. \n",
    "\n",
    " **Caracteristicas** : \n",
    "\n",
    "*  **ID**: Número que identifica a cada individuo de manera única\n",
    "*  **Gender**: Género del individuo, sus posibles valores son \"Male\", \"Female\" or \"Other\"\n",
    "*  **Age**: Edad del individuo\n",
    "*  **hypertension**: 0 si el individuo no tiene hipertensión, 1 si el individuo si tiene hipertensión\n",
    "*  **heart_disease**: 0 si el individuo no ha tenido ningún ataque al corazón, 1 si el individuo ha tenido algún ataque al corazón\n",
    "*  **ever_married**: \"Yes\" si el individuo está casado, \"No\" si el individuo no está casado\n",
    "*  **work_type**: Tipo de oficio del individuo, sus posibles valores son \"children\", \"Govt_jov\", \"Never_worked\", \"Private\" o \"Self-employed\"\n",
    "*  **Residence_type**: Tipo de residencia del individuo, sus posibles valores son \"Rural\" o \"Urban\"\n",
    "*  **avg_glucose_level**: Nivel medio de glucosa en sangre del individuo\n",
    "*  **bmi**: Indice de masa corporal (IMC) del individuo\n",
    "*  **smoking_status**: Estatus de fumador del individuo, sus posibles valores son \"formerly smoked\", \"never smoked\", \"smokes\" or \"Unknown\"\n",
    "*  **stroke**: 1 si el individuo ha tenido derrame cerebral o 0 si no ha tenido derrame cerebral\n",
    "\n",
    "Una vez hemos tratado la base del problema proseguimos con la resolución de este. Para ello en este trabajo nos centraremos en las técnicas de preparación de datos para mejorar la calidad del dataset y no tanto en las técnicas de extracción de conocimiento de datos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524004af",
   "metadata": {},
   "source": [
    "## Lectura de Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e2b531",
   "metadata": {},
   "source": [
    " Comenzamos nuestro problema cargando el conjunto de datos desde el fichero *healthcare-dataset-stroke-data*, el cual modificaremos a lo largo del trabajo para lograr nuestro objetivo.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20dfe69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('./healthcare-dataset-stroke-data.csv',sep=',')#cargamos el conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d718800d",
   "metadata": {},
   "source": [
    "## Transformación de variables categóricas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1ff146",
   "metadata": {},
   "source": [
    " Una vez hemos cargado nuestro dataset, debemos tener en cuenta que algunas de las características que hemos tratado en el primer apartado no son numéricas. Por ende, nuestro algoritmo de clasificación no podra tratar este tipo de variables nominales. Por lo tanto, debemos transformar estar variables categóricas en variables numéricas para poder trabajar con el conjunto de datos.\n",
    " \n",
    "  Mostramos algunos de los datos de nuestro dataset para observar cuales son las variables categóricas y numéricas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "65e8f14f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
       "0   9046    Male  67.0             0              1          Yes   \n",
       "1  51676  Female  61.0             0              0          Yes   \n",
       "2  31112    Male  80.0             0              1          Yes   \n",
       "3  60182  Female  49.0             0              0          Yes   \n",
       "4   1665  Female  79.0             1              0          Yes   \n",
       "\n",
       "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
       "0        Private          Urban             228.69  36.6  formerly smoked   \n",
       "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
       "2        Private          Rural             105.92  32.5     never smoked   \n",
       "3        Private          Urban             171.23  34.4           smokes   \n",
       "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
       "\n",
       "   stroke  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()#visualizamos los primeros cinco datos del dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead58ec3",
   "metadata": {},
   "source": [
    "Es facil de observar que las variables *gender, ever_married, work_type, Residence_type* y *smoking_status* son variables categóricas con las que nuestro algoritmo de aprendizaje no podrá tratar el problema de clasificación.\n",
    "\n",
    "Por ello, a continuación hacemos uso del método de transformación de variables **One Hot** con el que crearemos nuevas variables binarias a partir de las variables categóricas dadas (una para cada valor de la variable categórica)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a486e92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensión del dataset incial: (5110, 12)\n",
      "Dimensión del dataset transformado: (5110, 21)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "## Transformación de variables categóricas\n",
    "\n",
    "#Separamos nuestro conjunto de datos en variables categoricas y numericas\n",
    "data_num=data.select_dtypes(exclude='object') #Variables numericas\n",
    "data_cat=data.select_dtypes(include='object') #Variables categoricas\n",
    "    \n",
    "data_cat_transform=[] #Lista en la que guardaremos las nuevas variables creadas con el metodo ONE HOT en base a las categoricas dadas\n",
    "\n",
    "for label in data_cat.columns:#procedemos a transformar las variables categoricas con ONE HOT\n",
    "    \n",
    "        lb = LabelBinarizer()        \n",
    "        lb_results = lb.fit_transform(data_cat[label])\n",
    "        \n",
    "        if lb_results.shape[1]>2:\n",
    "            nombres_nuevas_variables = list(map(lambda x: label + x, lb.classes_))#tenemos mas de dos etiquetas en la variable categorica\n",
    "        else:\n",
    "            clase=data_cat[label][0]\n",
    "            nombres_nuevas_variables=[label+clase]#tenemos solo dos etiquetas en la variable categorica\n",
    "        \n",
    "        \n",
    "        lb_results_train = pd.DataFrame(lb_results, columns=nombres_nuevas_variables)   \n",
    "    \n",
    "        data_cat_transform.append(lb_results_train)\n",
    "        \n",
    "data_cat=pd.concat(data_cat_transform,axis=1)#Concatenamos todas las variables creadas las cuales hacen referencia a todas las categoricas\n",
    "\n",
    "data_transform=pd.concat([data_cat.reset_index(drop=True),data_num.reset_index(drop=True)],axis=1) #Concatenamos con el resto de variables numericas y obtenemos nuestro conjunto de datos transformado\n",
    "print('Dimensión del dataset incial: {}\\nDimensión del dataset transformado: {}'.format(data.shape,data_transform.shape))#Imprimimos las dimensiones del nuevo dataset y el antiguo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c96672e",
   "metadata": {},
   "source": [
    "Observamos que hemos creado 9 variables mas de las que teniamos en nuestro dataset transformado tal y como se esperaba con **One Hot**. A continuación, mostramos el conjunto de datos transformado para poder visualizar que la transformación se ha evaluado correctamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "16a4829c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   genderFemale  genderMale  genderOther  ever_marriedYes  work_typeGovt_job  \\\n",
       "0             0           1            0                1                  0   \n",
       "1             1           0            0                1                  0   \n",
       "2             0           1            0                1                  0   \n",
       "3             1           0            0                1                  0   \n",
       "4             1           0            0                1                  0   \n",
       "\n",
       "   work_typeNever_worked  work_typePrivate  work_typeSelf-employed  \\\n",
       "0                      0                 1                       0   \n",
       "1                      0                 0                       1   \n",
       "2                      0                 1                       0   \n",
       "3                      0                 1                       0   \n",
       "4                      0                 0                       1   \n",
       "\n",
       "   work_typechildren  Residence_typeUrban  smoking_statusUnknown  \\\n",
       "0                  0                    1                      0   \n",
       "1                  0                    0                      0   \n",
       "2                  0                    0                      0   \n",
       "3                  0                    1                      0   \n",
       "4                  0                    0                      0   \n",
       "\n",
       "   smoking_statusformerly smoked  smoking_statusnever smoked  \\\n",
       "0                              1                           0   \n",
       "1                              0                           1   \n",
       "2                              0                           1   \n",
       "3                              0                           0   \n",
       "4                              0                           1   \n",
       "\n",
       "   smoking_statussmokes     id   age  hypertension  heart_disease  \\\n",
       "0                     0   9046  67.0             0              1   \n",
       "1                     0  51676  61.0             0              0   \n",
       "2                     0  31112  80.0             0              1   \n",
       "3                     1  60182  49.0             0              0   \n",
       "4                     0   1665  79.0             1              0   \n",
       "\n",
       "   avg_glucose_level   bmi  stroke  \n",
       "0             228.69  36.6       1  \n",
       "1             202.21   NaN       1  \n",
       "2             105.92  32.5       1  \n",
       "3             171.23  34.4       1  \n",
       "4             174.12  24.0       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_transform.head()#volvemos a visualizamos los primeros cinco datos del dataset para observar las nuesvas variavles"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0a496f1",
   "metadata": {},
   "source": [
    "<a name=\"item1\"></a>\n",
    "## Análisis exploratorio de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a74a3ff",
   "metadata": {},
   "source": [
    "Una vez hemos transformado las variables parece que podemos comenzar con la primera evaluación de nuestro dataset. Sin embargo, nos encontramos con otro obstáculo. Nuestro dataset tiene varios valores nulos en algunas de las características que hacen que esos datos no puedan ser manejados por nuestro algoritmo. Seguidamente, mostramos el número de valores nulos que nos encontramos en el dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "68ba8f13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de nulos totales del dataset: 201\n"
     ]
    }
   ],
   "source": [
    "Nulos=data_transform.isnull().sum()#tomamos los nulos del dataset en cada variable\n",
    "numNulos=Nulos.sum()#calculamos el numero de nulos totales\n",
    "print('Numero de nulos totales del dataset: {}'.format(numNulos))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf57047",
   "metadata": {},
   "source": [
    "Observamos que 201 datos contienen alguna característica con valor nulo, lo que hace que esos datos no puedan aportar a la resolución del problema. Una solución para poder continuar con el problema sería eliminar todos estos datos que no nos permiten continuar. Sin embargo, consideramos que estos datos tienen gran información para aportar por lo que esta opción la deshechamos. En consecuencia, procedemos hacer un análisis exhaustivo de los valores nulos de nuestro conjunto de dataset y aplicar alguna de las técnicas de imputación conocidas."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7f0241",
   "metadata": {},
   "source": [
    "Para resolver este problema, en esos valores nulos plantearemos el método de **imputación por media condicionada**. \n",
    "\n",
    "Para ello analizaremos la correlación las variables que contengan valores nulos con el resto de varibales para poder encontrar una imputación correcta correspondiente a cada dato. Sin embargo, antes de analizar la correlación entre variables eliminaremos de nuestros datasets la variable *id* debido a que esta variable hace referencia a la identificación de cada individuo y no aporta ninguna información a nuestro problema."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d37d0504",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(data.columns[0], axis=1, inplace=True)#eliminamos del conjunto de datos inicial la variable id\n",
    "data_transform.drop('id', axis=1, inplace=True)#eliminamos del conjunto de datos transformado la variable id (este sera el conjunto de datos que iremos debido a que es el utilizaremos)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce431a60",
   "metadata": {},
   "source": [
    "No obstante, al hacer uso de la técnica de **imputación por media condicionada** nos damos cuenta que el conjunto de datos puede contener valores anomalos en las variables, las cuales estaran alterando el valor de la media que imputamos y en consiguiente nuestra imputación no sera del todo correcta. Por lo tanto, es necesario detectar y eliminar estos datos, es decir, es procedemos con el análisis de **outliers** de nuestro dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c396d76",
   "metadata": {},
   "source": [
    "<a name=\"item2\"></a>\n",
    "## Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b7f5335",
   "metadata": {},
   "source": [
    "#### Reparto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b7b82f",
   "metadata": {},
   "source": [
    "Comenzamos haciendo la partición de los datos, modificamos únicamente nuestro conjunto de entrenamiento (solo modificamos el *train*) de cara obtener una mejor generalización en nuestro conjunto de testeo.\n",
    "\n",
    "Para ello detectamos y eliminamos los *outliers* de nuestros &#8220;datos de entrenamiento&#8221;. Estos datos contienen características considerablemente diferentes que provocan fallos en el proceso de aprendizaje y puede conllevar grandes problemas si los datos se normalizan en el futuro o se usan para hacer imputación por media en valores nulos tal y como hemos comentado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5e9b0a17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "\n",
    "X_train, X_test, y_train, y_test =model_selection.train_test_split(data_transform.iloc[:,:-1],data_transform.iloc[:,-1],\n",
    "                                                        train_size=0.75, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95f41e7a",
   "metadata": {},
   "source": [
    "#### Detección y eliminación de outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3800736",
   "metadata": {},
   "source": [
    "Una vez hemos hecho la particion de nuestro dataset en *train* y *test*, detectaremos y eliminaremos los outliers del conjunto de entrenamiento haciendo uso del metodo basado en el rango intercuartílico, dado que es el método mas robusto que conocemos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e11f098c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para calcular el IQR y los \"putnos de corte\" utilizamos el método describe y accedemos a los campos apropiados\n",
    "#Definimos la funcion para detectar outliers\n",
    "def deteccionOutilers_IQR(data, k=1.5):\n",
    "    resumen=data.describe()\n",
    "    IQR=resumen.loc['75%']-resumen.loc['25%']\n",
    "    l1=data>(k*IQR+resumen.loc['75%'])\n",
    "    l2=data<(-k*IQR+resumen.loc['25%'])\n",
    "    return np.logical_or(l1,l2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e038c22",
   "metadata": {},
   "source": [
    "A la hora de detectar outliers en nuestro conjunto de datos sabemos que no todas las variables de nuestros datos nos interesan, es decir, solo nos fijaremos en las variables númericas continuas del dataset y no las que hacen referencia a variables cualitativas.\n",
    "\n",
    "Por lo tanto, únicamente analizaremos las variables *bmi*, *age* y *avg_glucose_level*. Para ello, comenzaremos observando la distribución de estas variables a traves de diagramas de cajas y de histogramas y iremos detectando los outliers a eliminar."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb074bbc",
   "metadata": {},
   "source": [
    "**bmi**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b477358",
   "metadata": {},
   "source": [
    "Comenzamos analizando la variable **bmi** a través de su un diagrama de cajas y un histograma para visualizar su distribución. Sin emabargo, no nos podemos olvidar que hay valores nulos en esta variable con lo cual estos datos no se tendrán en cuenta en a la hora de analizar la distribución de la variable y detectar los posibles *outliers* (*comentar que python en las funciones utilizadas para mostrar las gráficas y hacer uso de funciones como describe no tiene en cuenta los valores nulos*)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9913efce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribución del bmi con un diagrama de cajas y un histograma\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.boxplot(data=X_train['bmi'])\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.histplot(data=X_train['bmi'], bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb66fd00",
   "metadata": {},
   "source": [
    "Observamos que hay unos cuantos valores que sobrepasan por bastante el nivel normal de ICM. Por lo que, ahora analizaremos cuantos de esos datos con aparencia anomala son *outliers* y los almacenaremos para eliminarlos una vez finalizado el análisis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4943d66b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de outliers detectados con la tecnica de IQR:  4\n",
      "Visualizamos caules son los outliers\n",
      "4209    92.0\n",
      "1559    66.8\n",
      "2128    97.6\n",
      "928     78.0\n",
      "Name: bmi, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "outliers_IQR_ibm=deteccionOutilers_IQR(X_train['bmi'],3.5)##detectamos los outliers MUY EXTREMOS en el ibm\n",
    "print('Numero de outliers detectados con la tecnica de IQR: ',outliers_IQR_ibm.sum())\n",
    "\n",
    "print('Visualizamos caules son los outliers\\n{}'.format(X_train['bmi'][outliers_IQR_ibm]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0b29a5",
   "metadata": {},
   "source": [
    "Detectamos una cantidad de 8 *outliers* en la variable **ibm** los cuales eliminaremos al final del proceso."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37db1edb",
   "metadata": {},
   "source": [
    "**avg_glucose_level**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be28aec6",
   "metadata": {},
   "source": [
    "Continuamos con la variable **avg_glucose_level** y pocedemos a hacer el mismo análisis que con la variable **ibm**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9faccbc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='avg_glucose_level', ylabel='Count'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribución del avg_glucose_level con un diagrama de cajas y un histograma\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.boxplot(data=X_train['avg_glucose_level'])\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.histplot(data=X_train['avg_glucose_level'], bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d335ffb",
   "metadata": {},
   "source": [
    "Observamos que tenemos algunos niveles de glucosa demasiado elevados en la distribución, por lo que es probable que encontremos algunos *outliers*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "21fe49de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de outliers detectados con la tecnica de IQR:  24\n",
      "Visualizamos caules son los outliers\n",
      "1113    251.46\n",
      "755     250.20\n",
      "1207    267.76\n",
      "785     254.60\n",
      "386     251.60\n",
      "193     271.74\n",
      "4447    247.87\n",
      "4283    253.93\n",
      "3215    260.85\n",
      "1067    254.63\n",
      "2293    247.48\n",
      "2751    255.17\n",
      "123     249.31\n",
      "243     247.51\n",
      "4983    267.60\n",
      "552     247.69\n",
      "1453    251.99\n",
      "4048    247.97\n",
      "3088    267.61\n",
      "4470    256.74\n",
      "4249    248.24\n",
      "1529    253.16\n",
      "2693    250.80\n",
      "3342    248.37\n",
      "Name: avg_glucose_level, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "outliers_IQR_avg_glucose_level=deteccionOutilers_IQR(X_train['avg_glucose_level'],3.5)##detectamos los outliers MUY EXTREMOS en el avg_glucose_level\n",
    "print('Numero de outliers detectados con la tecnica de IQR: ',outliers_IQR_avg_glucose_level.sum())\n",
    "\n",
    "print('Visualizamos caules son los outliers\\n{}'.format(X_train['avg_glucose_level'][outliers_IQR_avg_glucose_level]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98528515",
   "metadata": {},
   "source": [
    "Detectamos un total de 24 *outliers*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a6f27b3",
   "metadata": {},
   "source": [
    "**age**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d52fd4",
   "metadata": {},
   "source": [
    "Finalmente, analizamos la variable *edad* siguiendo el procedimiento anterior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "10d421b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#distribución del age con un diagrama de cajas y un histograma\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.boxplot(data=X_train['age'])\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.histplot(data=X_train['age'], bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fdd8e75",
   "metadata": {},
   "source": [
    "Observamos que ha simple vista no observamos en la distribución ningun dato que pueda considerarse *outlier*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "56a9c09f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de outliers detectados con la tecnica de IQR:  0\n",
      "Visualizamos caules son los outliers\n",
      "Series([], Name: age, dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "outliers_IQR_age=deteccionOutilers_IQR(X_train['age'],3)##detectamos los outliers MUY EXTREMOS en el age\n",
    "print('Numero de outliers detectados con la tecnica de IQR: ',outliers_IQR_age.sum())\n",
    "\n",
    "print('Visualizamos caules son los outliers\\n{}'.format(X_train['age'][outliers_IQR_age]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d887ccf7",
   "metadata": {},
   "source": [
    "Tal y como hemos predicho, no detectamos ningún *outlier* en esta característica."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23fd2587",
   "metadata": {},
   "source": [
    "Finalmente, tomamos todos los *outliers* detectados y procedemos a su eliminación del conjunto *train*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "355c9c21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de outliers detectados con la tecnica de IQR:  28\n",
      "Número de outliers de la clase negativa 25\n",
      "Número de outliers de la clase positiva 3\n",
      "Al detectar y eliminar con la tecnica de IQR los outliers en la base de datos tenemos 3804 datos. (Teniamos 3832 datos).\n"
     ]
    }
   ],
   "source": [
    "outliers_IQR=np.logical_or(outliers_IQR_ibm,\n",
    "                           np.logical_or(outliers_IQR_age,outliers_IQR_avg_glucose_level))##concatenamos todos los OUTLIERS detectados\n",
    "\n",
    "print('Numero de outliers detectados con la tecnica de IQR: ',outliers_IQR.sum())\n",
    "\n",
    "#Eliminamos los outliers de nuestros datos de entrenamiento\n",
    "X_train_sinOut=X_train[np.logical_not(outliers_IQR)]\n",
    "y_train_sinOut=y_train[np.logical_not(outliers_IQR)]\n",
    "\n",
    "print('Número de outliers de la clase negativa',y_train[np.logical_and(y_train==0,outliers_IQR)].shape[0])\n",
    "print('Número de outliers de la clase positiva',y_train[np.logical_and(y_train==1,outliers_IQR)].shape[0])\n",
    "\n",
    "print('Al detectar y eliminar con la tecnica de IQR los outliers en la base de datos tenemos', \n",
    "      '{} datos. (Teniamos {} datos).'.format(X_train.shape[0]-outliers_IQR.sum(),X_train.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed49474",
   "metadata": {},
   "source": [
    "Finalmente, observamos que detectamos un total de 28 individuos como posibles outliers de nuestros datos de entrenamiento y los eliminamos esperando una mejora de calidad de nuestro dataset. Además observamos que en la distribución de los datos no se muestran varios de los datos que se desviaban de la distribución de las variables y podían perjudicar el aprendizaje de nuestro algoritmo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8ee1bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots(ncols=3, figsize=(15,5))\n",
    "\n",
    "sns.boxplot(ax=axes[0],data=pd.DataFrame(X_train_sinOut['bmi']))\n",
    "axes[0].set_title('Bmi')\n",
    "\n",
    "sns.boxplot(ax=axes[1],data=pd.DataFrame(X_train_sinOut['avg_glucose_level']))\n",
    "axes[1].set_title('Avg_glucose_level')\n",
    "\n",
    "sns.boxplot(ax=axes[2],data=pd.DataFrame(X_train_sinOut['age']))\n",
    "axes[2].set_title('Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5400b4e",
   "metadata": {},
   "source": [
    "## Detección y modificación de valores nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41262768",
   "metadata": {},
   "source": [
    "Despues de detectar y eliminar los *outliers* somos capaces de resolver correctamente la imputación de valores nulos tal y como hemos explicado en el apartado [Análisis exploratorio de datos](#item1).\n",
    "\n",
    "A continuación, tal y como hemos explicabamos procedemos a imputar mediante la técnica de **imputación por media condicionada** y mostramos la matriz de correlaciones para visualizar las variables mas correlacionadas con las características que contenga los valores nulos.\n",
    "\n",
    "Comenzamos el análisis visualizando en que características del dataset se encuentran estos valores nulos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c070ac9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genderFemale                       0\n",
      "genderMale                         0\n",
      "genderOther                        0\n",
      "ever_marriedYes                    0\n",
      "work_typeGovt_job                  0\n",
      "work_typeNever_worked              0\n",
      "work_typePrivate                   0\n",
      "work_typeSelf-employed             0\n",
      "work_typechildren                  0\n",
      "Residence_typeUrban                0\n",
      "smoking_statusUnknown              0\n",
      "smoking_statusformerly smoked      0\n",
      "smoking_statusnever smoked         0\n",
      "smoking_statussmokes               0\n",
      "age                                0\n",
      "hypertension                       0\n",
      "heart_disease                      0\n",
      "avg_glucose_level                  0\n",
      "bmi                              199\n",
      "dtype: int64\n",
      "Numero de nulos totales del dataset: 199\n"
     ]
    }
   ],
   "source": [
    "Nulos=X_train_sinOut.isnull().sum()+X_test.isnull().sum()+y_train.isnull().sum()#observamos el numero de valores nulos en el total de nuestros datos\n",
    "print(Nulos)\n",
    "numNulos=Nulos.sum()\n",
    "print('Numero de nulos totales del dataset: {}'.format(numNulos))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa0f8240",
   "metadata": {},
   "source": [
    "Observamos que todos nuestros valores nulos se encuentran en la misma caracteristica, la correspondiente al IMC de los individuos. Por lo tanto, en esos valores nulos planteremos el método de **imputación por media condicionada** ya comentado y analizaremos la correlación de las demas variables con esta característica. \n",
    "\n",
    "*Comentar que en el apartado [Análisis exploratorio de datos](#item1) teniamos 201 valores nulos pero en la detección y eliminación de outliers algunos de estos los habremos eliminado*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961389aa",
   "metadata": {},
   "source": [
    "**Visualización de la matriz de correlaciones**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d772abf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlaciones_transform=pd.concat([X_train,X_test]).corr()#creamos la matriz de correlaciones\n",
    "fig, axes = plt.subplots(figsize=(14,14))\n",
    "sns.heatmap(correlaciones_transform, vmax=.8, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10},cmap=\"YlGnBu\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75aaff25",
   "metadata": {},
   "source": [
    "Gracias a la anterior matriz de correlación podemos observar que la variable *ibm* está correlacionada positivamente con la edad, con la hipertensión y con la media de glucosa en sangre del individuo. Sin embargo, no consideramos que la correlación con las variables *hypertension* y *avg_glucose_level* sea lo suficientemente alta como para considerar que hay depedencia entre ellas. \n",
    "\n",
    "Por otro lado, observamos que la variable *ibm* también esta altamente correlacionada positivamente con la variable *ever_marriedYes*, por lo que consideremos que hay una relación entre ellas. Sin embargo, observamos que existe una correlación positiva alta entre esta variable y la variable *age*, por lo tanto, no tiene sentido tener ambas en cuenta a la hora de hacer la imputación dada la correlación entre ambas.\n",
    "\n",
    "Finalmente, planteamos un análisis para las dos posibles variables y decidimos considerar únicamente uno de los dos factores con el fin de imputar los valores nulos del ICM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729e7112",
   "metadata": {},
   "source": [
    "Como ya hemos comentado observamos una correlación positiva entre la edad y el ICM por lo que vamos observar la variación de la media de la variable *ibm* entre 4 grupos de edades. Los grupos de edades se basan en:\n",
    "\n",
    "* Muy jóvenes: de 0 a 14 años\n",
    "* Jóvenes: de 15 a 30 años\n",
    "* Mayores: de 31 a 60 años\n",
    "* Muy mayores: a partir de 60 años "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bf186b79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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27ZzPEQCAOmZmVWfMLEpzhhSLxeNHgY+MjHA+RwAA6tiVV15ZMa9evTpMkIygNGdIqVTS8PCwJGl4eFg9PT2BEwEAgNP1/ve/v2L+wAc+EChJNlCaM+Stb31rxTx+UCAAAKg/X//61yvme++9N1CSbKA0Z8hLL71UdQYAAPVj69atFXOpVAqUJBsozRny8MMPV8zf/e53AyUBAABnamRkpOqMmUVpzhAuow0AAHB6KM0Zsnbt2oqZy2gDAABMD6U5QyaW5GuuuSZQEgAAgPpCac6Qu+66q2K+8847AyUBAACoL5TmDNm7d2/VGQAAAJNrCB0AtdPc3FxRlJubm4NlAQCgnnV2dqq/vz90jJNs3LgxyPO2tLQEe+5aYaU5Qz7xiU9UzJ/85CcDJQEAAGfqrLPOqjpjZrHSnCErV67UvHnzdOzYMc2bN08tLS2hIwEAUJfSsKr605/+VB/84AePz1/84hf53Z4gVpozpFwu69ixY5KkY8eOaXBwMHAiAABwulauXHl8dfmCCy6gMCeM0pwhnZ2dVWcAAFBfmpublcvldNttt4WOMutRmjPkwQcfrJgfeOCBMEEAAMCMmDdvnlpbW1llrgFKc4ZwGW0AAIDTU/VAQDO7191/z8x2SzqxYZkkd/fWRNNhRp1//vnat29fxQwAAIB4cWfP+KPo9tqkgyB5K1eurCjNF110UcA0AAAA9aNqaXb3/dHt05JkZufE3Qfp9f3vf79i/t73vhcoCQAAQH2ZVgE2s/WSPiPpRb28m4ZLenVCuZCAkZGRqjMAAAAmN91V4/8p6RJ3LycZBskaHh6uOgMAAGBy0z17xs8kHUsyCAAAAJBW011p/hNJ3zOzRyW9NL7R3cNfQxIAAABI2HRLc5ekf5a0W9JocnGQJDOrODezmQVMAwAAUD+mW5qH3f3m03kCM5sjqVfSL9z9WjNbJOkfJDVL2ivp99z9+dN5bJyaJUuW6MCBAxUzAAAA4k13n+btZnaTmS0zs0XjH9O87x9JeuqE+RZJ29x9haRt0YwaOHjwYNUZAAAAk5tuaf59jZXbf9HYqvH4R1VmtlzS2yV96YTN10kqRp8XJV0/zQw4Q1xGGwAA4PRMtzRfLOnzknZJ2ilpk6RLpnG/v5T0UVXuB730hIum7Jc06T4C0cp2r5n1Hjp0aJoxAQAAgJk33dJclPRbkjo1Vph/Sy+vFk/KzK6VdNDdd5xOMHe/293b3L2tqanpdB4CE8yfP7/qDAAAgMlN90DAi9z9shPm7Wa2K+Y+b5H0TjP7HUlnSzrHzL4q6YCZLXP3/Wa2TBI71tYIFzcBAAA4PdNdaX7czN40PpjZb2ts/+YpufufuPtyd2+W9D5J/+zu75e0WVIh+raCpPtOOTVOy7Jly6rOAAAAmFzVlWYz2y3JJTVK+q9m9kw0v0rSk6f5nHdIutfMbpT0jKT3nubj4BTt27ev6gwAAIDJxe2ece1MPIm7PyDpgejzQUlrZuJxcWpGRkaqzgAAAJhc1dLs7k/XKgiSR2kGAAA4PdPdpxkAAADILEozAAAAEIPSDAAAAMSgNGfI3Llzq84AAACYHKU5Q1588cWqMwAAACZHac6QCy64oOoMAACAyVGaM+Q1r3lNxdzS0hIoCQAAQH2hNGfIo48+WjE/8sgjgZIAAADUF0pzhixevLjqDAAAgMlRmjNk//79VWcAAABMjtKcIaOjo1VnAAAATK4hdACEY2ahIwBIqc7OTvX394eOkQp79uyRJG3cuDFwknRoaWnh3wKZRGnOEHevmFlpBjCV/v5+Pf7E49K5oZOkQPSj8vFfPB42RxocCR0ACIfSXCNpXbUJtVrASgVQB86VRlfzxzVelnuAvTqRXbz6AQAAgBisNNdIGlZVt27dqs985jPH59tuu01XXXVVwEQAAAD1gdKcIWvXrj1emhsaGijMKZDW3XZC4GCrSuzCBADpQmnOmAsvvFDPPPOMbr311tBRoLGDrX76ox/qwgUjoaMEd9bQ2N5iv977g8BJwnvm6JzQEQAAE1CaM2bRokVatGgRq8wpcuGCEX2i7WjoGEiRz/YuCB0BADABpRkAANQVdm17Gbu2VUpy1zZKMwAAqCv9/f368c6dOi90kBQYPw3akZ07Q8ZIhecSfnxKMwAAqDvnSbpRXNkWL7tHHv9NZ4DzNAMAAAAxKM0AAABADEozAAAAEIPSDAAAAMSgNAMAAAAxKM0AAABADEozAAAAEIPSDAAAAMRIrDSb2dlm9piZ7TKzJ8zstmj7IjMrmdme6HZhUhkAAACAmZDkFQFfknS1ux81s0ZJD5tZt6R3Sdrm7neY2S2SbpH0sQRzAABO0cDAgPRLKfcAb0jiBEekAR8InQIIIrGfhj7maDQ2Rh8u6TpJxWh7UdL1SWUAAAAAZkKSK80yszmSdkhqkfR5d3/UzJa6+35Jcvf9ZrZkivveJOkmSbrwwguTjAkAmGD58uU6ZIc0uno0dBSkSO6BnJa/cnnoGEAQib7v5u4j7r5K0nJJbzSzS0/hvne7e5u7tzU1NSWWEQAAAIhTk53V3P2IpAckvU3SATNbJknR7cFaZAAAAABOV2K7Z5hZk6Qhdz9iZnMlrZX0Z5I2SypIuiO6vS+pDAAAYPYZGBjQryTdIw8dBSmyX9LRgeQOVE1yn+ZlkorRfs05Sfe6+/1m9n1J95rZjZKekfTeBDMAAAAAZyyx0uzufZJeP8n2QUlrknpeAAAwuy1fvlxHymXdKAsdBSlyj1znLk/uQNVEz54BoLqBgQG98Ks5+mzvgtBRkCJP/2qO5if4FiMA4NRx1noAAAAgBivNQEDLly/Xr4f36xNtR+O/GZnx2d4FOjvBtxgBAKeOlWYAAAAgBqUZAAAAiEFpBgAAAGJQmgEAAIAYHAgIAJjcESn3AGsrGj9OlzNDSkckvTJ0CCAMSjMA4CQtLS2hI6TGnj17JEkrXrkicJIUeCWvDWQXpRkAcJKNGzeGjpAa4/8WnZ2dgZPgRM9p7ApwWTcY3S4OmiIdnpN0boKPT2kGAAB1hdXulx2K3gk5dwXvhJyrZF8blGYAAFBXeCfkZbwTUjsc4QEAAADEoDQDAAAAMTKxe0ZnZ6f6+/tDx0iF8aPAeWtrTEtLC/8WAAAgViZKc39/vx7f/aRG5y0KHSU4+83YkcY7fvZc4CTh5Y4dDh1BkvTM0Tn6bC8ngD1wbOyNr6XzRgMnCe+Zo3O0MnQIAECFTJRmSRqdt0i/vvja0DGQImc/eX/oCBwBfoLfRO+CnN3MEeArxWsDANImM6UZSCN2DXkZR4ADANKMAwEBAACAGJRmAAAAIAalGQAAAIhBaQYAAABiUJoBAACAGJRmAAAAIAalGQAAAIhBaQYAAABiUJoBAACAGJRmAAAAIAalGQAAAIhBaQYAAABiUJoBAACAGA1JPbCZXSDpbySdJ2lU0t3ufqeZLZL0D5KaJe2V9Hvu/nxSOSRpYGBAuWO/1NlP3p/k06DO5I4NamBgOHQMAABQB5JcaR6W9Mfu/luS3iTpQ2Z2saRbJG1z9xWStkUzAAAAkFqJrTS7+35J+6PPf2VmT0l6paTrJK2Ovq0o6QFJH0sqhyQtX75cB15q0K8vvjbJp0GdOfvJ+7V8+XmhYwAAgDpQk32azaxZ0uslPSppaVSox4v1klpkAAAAAE5X4qXZzBZI+kdJH3b3fzuF+91kZr1m1nvo0KHkAgIAAAAxEi3NZtaoscL8NXf/p2jzATNbFn19maSDk93X3e929zZ3b2tqakoyJgAAAFBVYqXZzEzSPZKecvf/fcKXNksqRJ8XJN2XVAYAAABgJiR2IKCkt0j6gKTdZrYz2vZxSXdIutfMbpT0jKT3JpgBAAAAOGNJnj3jYUk2xZfXJPW8AAAAwEzjioAAAABADEozAAAAECPJfZpTJXfsMJfRlmS/Hjvrn599TuAk4eWOHdbYVd4BAACqy0RpbmlpCR0hNfbs+ZUkacVrKIvSebw2AADAtGSiNG/cuDF0hNQY/7fo7OwMnAQAAKB+sE8zAAAAEIPSDAAAAMSgNAMAAAAxMrFPMwAAwEzq7OxUf39/6Bjas2ePpPDHb7W0tATPkDRKMwAAQJ2aO3du6AiZQWkGAAA4RbN9VRUnY59mAACAOlUul7VhwwYNDg6GjjLrUZoBAADqVLFYVF9fn4rFYugosx6lGQAAoA6Vy2V1d3fL3dXd3c1qc8IozQAAAHWoWCxqdHRUkjQyMsJqc8IozQAAAHWoVCppeHhYkjQ8PKyenp7AiWY3SjMAAEAdeutb31oxX3HFFYGSZAOnnAMApFYaLiCRlotHSNm4gASQVqw0AwBQxdy5c7mABFLpu9/9bsX80EMPBUqSDaw0AwBSi1VVYGrt7e361re+peHhYTU0NGjdunWhI81qrDQDAADUoUKhoFxurMrNmTNHhUIhcKLZjdIMAABQh/L5vDo6OmRm6ujo0OLFi0NHmtXYPQMAAKBOFQoF7d27l1XmGqA0AwAA1Kl8Pq9NmzaFjpEJ7J4BAAAAxKA0AwAA1KlyuawNGzZocHAwdJRZj9IMAABQp4rFovr6+lQsFkNHmfUozQAAAHWoXC6ru7tb7q7u7m5WmxPGgYAAuFTxBFyqGEA9KBaLcndJ0ujoqIrFom6++ebAqWYvVpoBpAKXKgaAU1MqlTQ0NCRJGhoaUk9PT+BEsxsrzQBYVQWAOtTe3q5vf/vbGhoaUmNjI5fRTlhiK81m9mUzO2hmPzph2yIzK5nZnuh2YVLPDwAAMJsVCgWZmSQpl8txgZOEJbnS/BVJd0n6mxO23SJpm7vfYWa3RPPHEsyQGmnYZ1RKz36j7DMKAMCZGb+M9ubNm7mMdg0kttLs7g9JOjxh83WSxs+JUpR0fVLPj8mx3ygAALNHoVBQa2srq8w1YONHXSby4GbNku5390uj+Yi7n3vC159399hdNNra2ry3tzexnAAAAIAkmdkOd2+buD21Z88ws5vMrNfMeg8dOhQ6DgAAADKs1qX5gJktk6To9uBU3+jud7t7m7u3NTU11SwgAAAAMFGtS/NmSeM73RQk3Vfj5wcAAABOWZKnnPs7Sd+XdJGZDZjZjZLukNRuZnsktUczAAAAkGqJnXLO3W+Y4ktrknpOAAAAIAmpPRAQAAAASAtKMwAAABCD0gwAAADEoDQDAAAAMSjNAAAAQAxKMwAAABDD3D10hlhmdkjS06FzzCJ5SeXQIYBJ8NpEWvHaRJrx+pxZr3L3ky5HXRelGTPLzHrdvS10DmAiXptIK16bSDNen7XB7hkAAABADEozAAAAEIPSnE13hw4ATIHXJtKK1ybSjNdnDbBPMwAAABCDlWYAAAAgBqU5Q8zsbWb2EzPrN7NbQudBtpnZl83soJn96IRtnzazX5jZzujjd0JmRDaZ2dlm9piZ7TKzJ8zstmj7e6N51Mw4UwGCMLNzzewbZvZjM3vKzN5sZovMrGRme6LbhaFzzkaU5owwszmSPi+pQ9LFkm4ws4vDpkLGfUXS2ybZ/hfuvir6+HaNMwGS9JKkq939MkmrJL3NzN4k6UeS3iXpoYDZgDslfcfdXyvpMklPSbpF0jZ3XyFpWzRjhlGas+ONkvrd/efu/htJfy/pusCZkGHu/pCkw6FzABP5mKPR2Bh9uLs/5e4/CRgNGWdm50i6QtI9kuTuv3H3Ixr7fV6Mvq0o6foQ+WY7SnN2vFLSsyfMA9E2IG3+0Mz6ot03eIsRQZjZHDPbKemgpJK7Pxo4EiBJr5Z0SNJfm9njZvYlM5svaam775ek6HZJyJCzFaU5O2ySbZw6BWnzBUmv0dhb4vsl/XnQNMgsdx9x91WSlkt6o5ldGjgSIEkNkt4g6Qvu/npJL4hdMWqG0pwdA5IuOGFeLmlfoCzApNz9QFRWRiX9H43tVgQEE731/YAm3/8eqLUBSQMnvPPxDY2V6ANmtkySotuDgfLNapTm7PiBpBVm9h/M7CxJ75O0OXAmoML4D/3I72rswCugpsysyczOjT6fK2mtpB8HDQVIcvfnJD1rZhdFm9ZIelJjv88L0baCpPsCxJv1GkIHQG24+7CZ/aGkLZLmSPqyuz8ROBYyzMz+TtJqSXkzG5D0KUmrzWyVxnYd2itpfah8yLRlkorRWYdyku519/vN7HclbZLUJOlbZrbT3a8JGRSZtEHS16IFsJ9L+m+KXqdmdqOkZyS9N2C+WYsrAgIAAAAx2D0DAAAAiEFpBgAAAGJQmgEAAIAYlGYAAAAgBqUZAAAAiEFpBoAaMrPfNTM3s9eGzgIAmD5KMwDU1g2SHtbYBYZqJjrnMADgNFGaAaBGzGyBpLdIulEnlGYzy5nZX5nZE2Z2v5l928zeE33tcjN70Mx2mNmWCVdNHL//a8zsETP7gZl9xsyORttXm9l2M/tbSbvN7Gwz+2sz221mj5vZVdH3/YGZ3XXC491vZqujz4+a2Z+b2Q/NbJuZNUXbN5rZk2bWZ2Z/n9S/GQCkBaUZAGrneknfcfefSjpsZm+Itr9LUrOk10n6oKQ3S5KZNWrsCnTvcffLJX1Z0p9O8rh3SrrT3f+jpH0TvvZGSf/L3S+W9CFJcvfXaWzFu2hmZ8dkni/ph+7+BkkPauzKjZJ0i6TXu3urpP8R/58OAPWN0gwAtXODpPFV2b+PZkn6T5K+7u6j7v6cpO3R9oskXSqpZGY7JX1C0vJJHvfNkr4eff63E772mLv/6wnP838lyd1/LOlpSStjMo9K+ofo869GjyFJfRq7lO/7JQ3HPAYA1L2G0AEAIAvMbLGkqyVdamYuaY4kN7OPSrKp7ibpCXd/8xk89QsTHm8yw6pcRKm2+uzR7dslXSHpnZJuNbNL3J3yDGDWYqUZAGrjPZL+xt1f5e7N7n6BpH/V2Mrtw5LeHe3bvFTS6ug+P5HUZGbHd9cws0smeexHJL07+rzaAYYPSfov0WOtlHRh9Bx7Ja2Knv8Cje3SMS4XZZek35f0sJnlJF3g7tslfVTSuZIWTOtfAQDqFCvNAFAbN0i6Y8K2f9RYEf2QpDWSfiTpp5IelfRLd/9NdEBgp5n9e439zP5LSU9MeJwPS/qqmf2xpG9J+uUUGf5K0hfNbLfGVpf/wN1fMrN/0ViB3x1l+OEJ93lB0iVmtiN63P+ssVXyr0aZTNJfuPuR6f9TAED9MXeP/y4AQKLMbIG7H41243hM0lui/Zunc995kl50dzez90m6wd2vm6FcR92dVWQAmcdKMwCkw/1mdq6ksyTdPt3CHLlc0l1mZpKOSPrvMx8PALKNlWYAAAAgBgcCAgAAADEozQAAAEAMSjMAAAAQg9IMAAAAxKA0AwAAADEozQAAAECM/w+BcG4k7BOs9wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_age=pd.concat([X_train_sinOut,X_test]).copy()#copiamos el conjunto de datos y definimos uno en el que crearemos la particion de edades\n",
    "data_age['Age groups']=pd.cut(data.age,4, labels=[0,15,31,60])#agrupamos por cada grupo de edad\n",
    "\n",
    "countplt, ax = plt.subplots(figsize = (10,7))\n",
    "ax =sns.countplot(x='Age groups', data=data_age);#mostramos la cantidad de individuos que contiene cada grupo de edad\n",
    "# show count (+ annotate)\n",
    "for rect in ax.patches:\n",
    "    ax.text (rect.get_x() + rect.get_width()  / 2,rect.get_height()+ 0.75,rect.get_height(),horizontalalignment='center', fontsize = 11)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.boxplot(y='bmi', x='Age groups' ,data=data_age)#mostramos la ditribucion del ICM en cada grupo de edad con diagramas de caja\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "sns.histplot(x='bmi', hue='Age groups', data=data_age, bins=50)#mostramos la ditribucion del ICM en cada grupo de edad a traves de un histograma"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f7feb55",
   "metadata": {},
   "source": [
    "Observamos que tenemos mayor cantidad de individuos *Mayores* en el conjunto de datos que en el resto grupo de edades aunque no consideramos que haya una gran diferencia, asimismo tenemos una cantidad suficiente de individuos en cada grupo por lo que no estaremos provocando ninún sesgo a la hora de imputar valores nulos.\n",
    "\n",
    "Además, es fácil de observar gracias al diagrama de cajas que los individuos de mayor edad tienen mayor ICM medio que los de edad mas temprana. Adicionalmente, analizando el histograma se observa que hay mayor cantidad de *Mayores* en niveles altos de ICM que cualquier otro grupo de edad y que los *Muy jóvenes* toman mayoritariamente niveles bajos de ICM excepto alguna excepción.\n",
    "\n",
    "En consecuencia, planteamos que no es correcto imputar una media del ICM sobre todos los valores perdidos y habría que diferenciar entre grupos de edades la dada la diferencia de valores que toman en esta carcaterística y la diferencia de número de individuos que hay para cada grupo de edad."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ea8b7f",
   "metadata": {},
   "source": [
    "Por otro lado, hemos comentado que la variable *ibm* también estaba correlacionada positivamente con la variable *ever_marriedYes* por lo que recreamos el análisis anterior en este caso para obtener información."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83560e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='Count'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tSW444dQAADBGS640V9VdSVqS05JcVVVfGcwXJvnCkJ95XZIPVtVbknwlyRuGPA4AAIxF1+kZrzsZH9JauyXJLYPHC0kuPRnHBQCAcViyNLfW7h1XEAAAmFTLPacZAAC+ZynNAADQQWkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6DDVdwAAVqbt27dnfn6+7xhMmMN/J7Zt29ZzEibNzMxMtm7d2neM41KaARiJ+fn53PP5v8sFaw/2HYUJcvoTiz/kfuze3T0nYZJ85Zur+47QSWkGYGQuWHswv/bSB/uOAUy4d99xVt8ROjmnGQAAOijNAADQQWkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0GFlprqozqurTVfWZqvp8Vf3GYP85VTVXVfcMtmePKgMAAJwMo1xpfizJT7bWXpTkxUkur6pXJLk2ya7W2sVJdg1mAACYWCMrzW3RNwfjaYOvluSKJLOD/bNJrhxVBgAAOBlGek5zVa2uqjuT7Esy11r7VJL1rbU9STLYnjfKDAAA8N2aGuXBW2sHk7y4qp6Z5ENV9aPLfW9VXZ3k6iS54IILRhMQgJG577778vBDq/PuO87qOwow4e59aHWeft99fcdY0liuntFa+0aSW5JcnmRvVW1IksF233Hes6O1trG1tnHdunXjiAkAAE9pZCvNVbUuyROttW9U1Zokm5L8xyQ3JtmS5LrB9oZRZQCgP+eff34eO7Anv/bSB/uOAky4d99xVp52/vl9x1jSKE/P2JBktqpWZ3FF+4OttT+rqtuSfLCq3pLkK0neMMIMJ8X27dszPz/fdwwmzOG/E9u2bes5CZNmZmYmW7du7TsGACfRyEpza+2zSV7yFPsXklw6qs8dhfn5+dz5ubtz8Mxz+o7CBFn1eEuS3P6lvT0nYZKsfuTrfUcAYARG+ouAK8nBM8/Jt37ktX3HACbcmi9+pO8IAIyA22gDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6ODmJgCMzFe+uTrvvuOsvmMwQfY+srhet/7MQz0nYZJ85Zurc3HfIToozQCMxMzMTN8RmECPz88nSZ52ob8ffNvFmfzvGUozACOxdevWviMwgbZt25Ykec973tNzEjgxzmkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADoozQAA0MHNTZbhvvvuy+pH/l/WfPEjfUcBJtzqRxZy330H+o4BwElmpRkAADpYaV6G888/Pw88NpVv/chr+44CTLg1X/xIzj9/fd8xADjJrDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6DCy0lxVz6qqv6yqu6vq81W1bbD/nKqaq6p7BtuzR5UBAABOhlGuNB9I8m9aa89N8ookb6uq5yW5Nsmu1trFSXYNZgAAmFgjK82ttT2ttTsGjx9KcneS85NckWR28LLZJFeOKgMAAJwMYzmnuaouSvKSJJ9Ksr61tidZLNZJzhtHBgAAGNbIS3NVrU3yJ0l+pbX24Am87+qq2l1Vu/fv3z+6gAAA0GFqlAevqtOyWJj/sLX2p4Pde6tqQ2ttT1VtSLLvqd7bWtuRZEeSbNy4sY0y53KsfuTrWfPFj/Qdgwmy6tHF/w946Iyzek7CJFn9yNeTrO87BgAn2chKc1VVkt9Pcndr7b8e8dSNSbYkuW6wvWFUGU6WmZmZviMwgebnH0qSzPyQgsSR1vueAbACjXKl+ZVJ/kWSu6rqzsG+X8tiWf5gVb0lyVeSvGGEGU6KrVu39h2BCbRt27YkyXve856ekwAAozay0txa+2SSOs7Tl47qcwEA4GRzR0AAAOigNAMAQAelGQAAOijNAADQQWkGAIAOSjMAAHRQmgEAoIPSDAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAAB2UZgAA6DDVdwAAWOm2b9+e+fn5vmNMhMP/HLZt29ZzkskwMzOTrVu39h2DZVCaAYCxWbNmTd8RYChKMwCMmJVEOPU5pxkAADoozQAA0EFpBgCADkozAAB0UJoBAKCD0gwAjM3CwkKuueaaLCws9B0FTojSDACMzY4dO/LZz342O3bs6DsKnBClGQAYi4WFhczNzSVJ5ubmrDZzSlGaAYCx2LFjRw4dOpQkOXTokNVmTilKMwAwFrt27VpyhkmmNAMAY9FaW3KGSTay0lxVf1BV+6rqc0fsO6eq5qrqnsH27FF9PgAwWS699NKj5k2bNvWUBE7cKFea35fk8mP2XZtkV2vt4iS7BjMA8D3grW99a1atWqweq1atytVXX91zIli+qVEduLV2a1VddMzuK5K8ZvB4NsktSd4xqgycfNu3b8/8/HzfMSbC4X8O27Zt6znJZJiZmcnWrVv7jgFMsOnp6WzatCk7d+7M5s2bMz093XckWLaRlebjWN9a25MkrbU9VXXemD8fTpo1a9b0HQHglPPWt741DzzwgFVmTjk1ypPwByvNf9Za+9HB/I3W2jOPeP4fW2tPeV5zVV2d5OokueCCC1527733jiwnAAAkSVXd3lrbeOz+cV89Y29VbRgE2pBk3/Fe2Frb0Vrb2FrbuG7durEFBACAY427NN+YZMvg8ZYkN4z58wEA4ISN8pJz709yW5LnVNVXq+otSa5Lsrmq7kmyeTADAMBEG+XVM958nKcuPc5+AACYSO4ICAAAHZRmAADooDQDAEAHpRkAADoozQAA0EFpBgCADiO9jfbJUlX7k7iPNpPo3CRf6zsEwCnG904m2YWtte+4HfUpUZphUlXV7qe6Pz0Ax+d7J6cip2cAAEAHpRkAADoozfDd2dF3AIBTkO+dnHKc0wwAAB2sNAMAQAelGYZUVZdX1f+pqvmqurbvPACTrqr+oKr2VdXn+s4CJ0pphiFU1eokv5Pkp5M8L8mbq+p5/aYCmHjvS3J53yFgGEozDOfHksy31r7UWns8yf9OckXPmQAmWmvt1iRf7zsHDENphuGcn+Qfjpi/OtgHAKxASjMMp55in0vRAMAKpTTDcL6a5FlHzD+Y5P6esgAAI6Y0w3D+NsnFVfXsqjo9yZuS3NhzJgBgRJRmGEJr7UCSf53kY0nuTvLB1trn+00FMNmq6v1JbkvynKr6alW9pe9MsFzuCAgAAB2sNAMAQAelGQAAOijNAADQQWkGAIAOSjMAAHRQmgEAoIPSDPA9rqo+UlXPPIHX/1JV/fequqyqbquqGuxfXVV3VtU/GVlYgJ4ozQAToqpWj/j4VVWrjp1ba69trX3jRI/XWtuZ5N4kh29QsTXJ37bW/vqkBAaYIEozwJCq6her6tOD1dXfraq3VdV/OuL5X6qq7cd57erB/m9W1W9W1aeS/PhxPufLVfXuwaru7qp6aVV9rKr+vqr+1eA1a6tqV1XdUVV3VdUVg/0XVdXdVfXeJHckedUx87MGxz+3I+e/rKr/W1UfT/LKI+K9Pck7q+r5WbxL5juOWIG+o6r+qKrWDo5xXVV9oao+W1X/+eT9LwEwekozwBCq6rlJfj7JK1trL05yMMk3k/yzI17280k+cJzX/sLgNU9P8rnW2stba59c4iP/obX240k+keR9SX4uySuS/Obg+UeT/Gxr7aVJLknyXw6fNpHkOUmub629JIsrw0/OrbV7O/5Mv1BVG5L8RhbL8uYkzzv8ntbaniS/ncVbI/+HLP535V1JNg2y7E7yq1V1TpKfTfL81toLB68FOGVM9R0A4BR1aZKXJfnbQTddk2Rfki9V1SuS3JPFcvpXSd52nNcmi8X0T5bxeTcOtnclWdtaeyjJQ1X16OB85IeTvLuqXp3kUJLzk6wfvOfe1trfHHGsY+euP9PLk9zSWtufJFX1gSQ/fMT7fifJda2191XV67JYqv9qcIzTs1ioH8xisf+9qvrzJH+2jD8zwMRQmgGGU0lmW2vvPGpn1VuSvDHJF5N8qLXWBiu+3/HagUdbaweX8XmPDbaHjnh8eJ7K4sr1uiQva609UVVfTnLG4DUPH3OsY+euP9OVSdrxgrXWDlXV4ecryVxr7c3fcfCqH8tiMX9TFk/l+MnjHRNg0jg9A2A4u5L8XFWdlyRVdU5VXZjkT5NcmeTNST7Q8dqT6fuS7BsU5kuSDHP84+X8VJLXVNV0VZ2W5A1LHONvkryyqmYGxzizqn54cF7z97XWPpLkV5K8eIh8AL2x0gwwhNbaF6rqXUl2Dq5I8USSt7XW7q2qLyR5Xmvt00u9NovnF58sf5jkw1W1O8mdWVzpPiFL/Jn+pqr+fRZPs9iTxV8gfMorfbTW9lfVLyV5f1U9bbD7XUkeSnJDVZ2RxdXot59oPoA+VWvH/YkbAAAQp2cAAEAnp2cATIiq+lCSZx+z+x2ttY/1kQeAb3N6BgAAdHB6BgAAdFCaAQCgg9IMAAAdlGYAAOigNAMAQIf/Dwoh2FPhVrp2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplt, ax = plt.subplots(figsize = (10,7))\n",
    "ax =sns.countplot(x='ever_marriedYes', data=data_age);#mostramos la cantidad de individuos que contiene cada estado civil\n",
    "# show count (+ annotate)\n",
    "for rect in ax.patches:\n",
    "    ax.text (rect.get_x() + rect.get_width()  / 2,rect.get_height()+ 0.75,rect.get_height(),horizontalalignment='center', fontsize = 11)\n",
    "\n",
    "plt.figure(figsize=(12,5))#mostramos la ditribucion del ICM en cada estado civil con diagramas de caja\n",
    "sns.boxplot(y='bmi', x='ever_marriedYes' ,data=data_age)\n",
    "\n",
    "plt.figure(figsize=(12,5))#mostramos la ditribucion del ICM en cada estado civil con un histograma\n",
    "sns.histplot(x='bmi', hue='ever_marriedYes', data=data_age, bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f2b63fe",
   "metadata": {},
   "source": [
    "Se observa que tenemos un caso similar a la relación entre el *ibm* y los grupos de edades. Tenemos una diferencia entre la cantidad de individuos casados y no casados significativa pero tenemos suficientes individuos en cada grupo por lo que no provocaremos sesgos ni obstaculos en la imputación. Observamos que la media y los valores de IMC que toman los individuos casados es mas alto que los que estan solteros. Por lo que consideramos que no sería correcto imputar un mismo valor para individuos casados y solteros."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "622b0807",
   "metadata": {},
   "source": [
    "Mostramos las medias agrupadas por grupos de edad y agrupadas por casado o soltero para corroborar nuestra hipótesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "246abfcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age groups\n",
      "0     22.128128\n",
      "15    30.043965\n",
      "31    31.531312\n",
      "60    29.833569\n",
      "Name: bmi, dtype: float64 \n",
      "\n",
      "ever_marriedYes\n",
      "0    25.142244\n",
      "1    30.807608\n",
      "Name: bmi, dtype: float64 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(data_age.groupby(['Age groups'])['bmi'].mean(),'\\n')\n",
    "print(data_age.groupby(['ever_marriedYes'])['bmi'].mean(),'\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e7f413",
   "metadata": {},
   "source": [
    "Observamos que lo concluido anteriormente tiene sentido y, por lo tanto, habría que imputar una media distinta para cada grupo de los analizados. Como ya hemos comentado solo haremos uso de una de las variables dada su correlación, en este consideramos que la variable *ever_marriedYes* es la correcta dado que observamos una clara diferencia de medias entre los individuos casados y los no casados (además que aunque la diferencia sea minima el estado civil tiene una mayor correlación con el ICM que la edad). Sin embargo, también se podría a llegar a imputar por la variable *age*.\n",
    "\n",
    "Por lo tanto, procedemos a introducir la media por el estado civil en los valores faltantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "fd3b8a87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unaig\\AppData\\Local\\Temp/ipykernel_9268/1477895642.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_sinOut['bmi'][np.logical_and(data_age['ever_marriedYes']==0,data_age['bmi'].isnull())]=medias[0]\n",
      "C:\\Users\\unaig\\anaconda3\\envs\\py38ml\\lib\\site-packages\\pandas\\core\\generic.py:8861: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return self._update_inplace(result)\n",
      "C:\\Users\\unaig\\AppData\\Local\\Temp/ipykernel_9268/1477895642.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_train_sinOut['bmi'][np.logical_and(data_age['ever_marriedYes']==1,data_age['bmi'].isnull())]=medias[1]\n",
      "C:\\Users\\unaig\\AppData\\Local\\Temp/ipykernel_9268/1477895642.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  X_test['bmi'][np.logical_and(data_age['ever_marriedYes']==1,data_age['bmi'].isnull())]=medias[1]\n"
     ]
    }
   ],
   "source": [
    "medias=data_age.groupby(['ever_marriedYes'])['bmi'].mean()#calculamos las medias agrupadas por el estado civil\n",
    "\n",
    "#imputamos las medias segun su estado civil en nuestros datos\n",
    "X_train_sinOut['bmi'][np.logical_and(data_age['ever_marriedYes']==0,data_age['bmi'].isnull())]=medias[0]\n",
    "X_test['bmi'][np.logical_and(data_age['ever_marriedYes']==0,data_age['bmi'].isnull())]=medias[0]\n",
    "X_train_sinOut['bmi'][np.logical_and(data_age['ever_marriedYes']==1,data_age['bmi'].isnull())]=medias[1]\n",
    "X_test['bmi'][np.logical_and(data_age['ever_marriedYes']==1,data_age['bmi'].isnull())]=medias[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82668650",
   "metadata": {},
   "source": [
    "Una vez hemos imputado los valores, nos aseguramos que hemos imputado todos los valores correctamente y no contenemos ningun valor nulo en nuestro conjunto de datos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea555277",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genderFemale                     0\n",
      "genderMale                       0\n",
      "genderOther                      0\n",
      "ever_marriedYes                  0\n",
      "work_typeGovt_job                0\n",
      "work_typeNever_worked            0\n",
      "work_typePrivate                 0\n",
      "work_typeSelf-employed           0\n",
      "work_typechildren                0\n",
      "Residence_typeUrban              0\n",
      "smoking_statusUnknown            0\n",
      "smoking_statusformerly smoked    0\n",
      "smoking_statusnever smoked       0\n",
      "smoking_statussmokes             0\n",
      "age                              0\n",
      "hypertension                     0\n",
      "heart_disease                    0\n",
      "avg_glucose_level                0\n",
      "bmi                              0\n",
      "dtype: int64\n",
      "Numero de nulos totales del dataset: 0\n"
     ]
    }
   ],
   "source": [
    "Nulos=X_train_sinOut.isnull().sum()+X_test.isnull().sum()\n",
    "numNulos=Nulos.sum()\n",
    "print(Nulos)\n",
    "print('Numero de nulos totales del dataset: {}'.format(numNulos))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f524e9",
   "metadata": {},
   "source": [
    "Efectivamente, tal y como hemos tratado actualmente no tenemos ningún valor nulo en nuestro conjunto de datos (ni en *train* ni en *test*).\n",
    "\n",
    "Además, comentar que tampoco tenemos ningún *outlier* en el conjunto de entrenamiento dado que los hemos eliminados en el apartado [Outliers](#item2). Es cierto, que se puede llegar a pensar que en la imputación de valores nulos hemos podido crear algún *outlier* en *train*; sin embargo, gracias a la técnica de imputación tratada en base a las medias no hemos introducido ningún valor anomalo que se salga de la distribución de la variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cb80f59",
   "metadata": {},
   "source": [
    "## 1.Generalización"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30b8e8fd",
   "metadata": {},
   "source": [
    "Una vez hemos repartido y adecuado nuestro dataset para poder entrenar y testear nuestro modelo de clasificación, iteramos por primera vez con el algoritmo de aprendizaje para analizar los resultados obtenidos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0eab9e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy obtenido con el conjunto de datos sin modificar: 93.81846635367762\n"
     ]
    }
   ],
   "source": [
    "# Se importa de la libreria scikit-learn de python (sklearn) el paquete correspondiente a la regresión lineal\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "LogReg = LogisticRegression(penalty='l2',max_iter=400,solver='newton-cg',C=10.e50)\n",
    "LogReg=LogReg.fit(X_train_sinOut,y_train_sinOut.ravel())\n",
    "\n",
    "# Cálculo de la media geométrica en test\n",
    "predicciones=np.round(LogReg.predict_proba(X_test.to_numpy())[:,1].reshape(-1,1),0)\n",
    "comparacion= predicciones==y_test.to_numpy().reshape(-1,1)   \n",
    "suma=comparacion.sum()\n",
    "accTest=100*suma/predicciones.size\n",
    "print('Accuracy obtenido con el conjunto de datos sin modificar: {}'.format(accTest))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b43db8bd",
   "metadata": {},
   "source": [
    "Comenzamos con un accuracy con un valor alto el cual podemos pensar que este conjunto de datos no hace falta modificarlo en gran medida para el problema de clasificación planteado. Sin embargo, a nosotros nos interesa saber cuantos accidentes cardiovasculares podemos predecir en base a nuestro algoritmo, observemos cuantos accidentes de estos somos capaces de predecir en nuestro conjunto *test* a generalizar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "db7cad76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de accidentes cerebrovasculares predecidos:  1.0\n",
      "Numero de accidentes cerebrovasculares \"reales\":  80\n"
     ]
    }
   ],
   "source": [
    "print('Numero de accidentes cerebrovasculares predecidos: ',predicciones.sum())\n",
    "print('Numero de accidentes cerebrovasculares \"reales\": ',y_test.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e09db1",
   "metadata": {},
   "source": [
    "Observamos que en nuestro conjunto de datos *test* hay un total de 80 inidividuos que sufrieron un accidente cardiovascular y entre estos solo hemos sido capaces de detectar **uno!** Entonces, ¿el accuracy nos sirve como parámetro de medida? En nuestro punto de vista no, debido a que tenemos una gran muestra de individuos que no han sufrido este tipo de accidentes (*clase negativa*) y una muestra muy pequeña de individuos que si han sufrido estos accidentes (*clase positiva*). En consecuencia, nuestro algoritmo de clasificación es sobre entrenado con individuos de la *clase negativa*, es decir, el dataset no esta balanceado. Por ende, predice casi siempre individuos que no van a sufrir un accidente y al ser la clase mayoritaria acierta en muchas ocasiones y por ello obtenemos un accuracy tan elevado.\n",
    "\n",
    "Sin embargo, a nosotros nos interesa que cualquier individuo sea consciente de que puede llegar a sufrir un accidente de este tipo aunque se de el caso de obtener un falso positivo para amparar el bienestar de los individuos. Por lo tanto, basamos la calidad de nuestro algorimto de clasificación en la mejor del **RECALL**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c427579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión de nuestro modelo 1.0\n",
      "Recall de nuestro modelo  0.0125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score,precision_score,confusion_matrix\n",
    "\n",
    "precision1 = precision_score(y_test, predicciones)##PRECISION\n",
    "recall1 = recall_score(y_test, predicciones)##RECALL\n",
    "matriz_de_decision=confusion_matrix(y_test, predicciones)##MATRIZ DE DECISION\n",
    "\n",
    "print('Precisión de nuestro modelo',precision1)\n",
    "print ('Recall de nuestro modelo ', recall1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8701c71d",
   "metadata": {},
   "source": [
    "Como ya hemos comentado nuestro modelo tiene un buen accuracy y una precisión muy buena; sin emabrgo, es facil de observar que obtenemos un **recall** muy bajo con el que no conseguimos detectar los individuos de la *clase positiva*. A continuación, mostramos la matriz de decisión donde se visualiza que no tenemos ningún falso positivo pero tenemos muchos falsos negativos y a nosotros nos interesa mas tener mas falsos positivos y pocos falsos negativos de cara a proteger la salud de los individuos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18ae1d4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OkiSpRQx3kiRJLWK4kyRJahHDnSRJUosY7iRJklrEcCdJktQihjtJkqQWMdxJkiS1iOFOkiSpRQx3kiRJLWK4kyRJahHDnSRJUosY7iRJklrEcCdJktQihjtJkqQWMdxJkiS1iOFOkiSpRQx3kiRJLWK4kyRJahHDnSRJUosY7iRJklrEcCdJktQihjtJkqQWMdxJkiS1iOFOkiSpRQx3kiRJLWK4kyRJahHDnSRJUosY7iRJklrEcCdJktQihjtJkqQWMdxJkiS1iOFOkiSpRQx3kiRJLWK4kyRJahHDnSRJUouklDLQbZBeKw9iSdLCJr1G2HMnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUGD3QDpDmpPHr/QDdBWihkuVVnLHzmyXnfEGlhs9gSs6xiz50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLDB7oBkhqt498/svcdNvYruN+cdKxbLLB+jz19CS+98OTuPTqa5n6wlQ23WhDvvXFz7HayivN49ZK7Xbf/Q/w45/+jNvGjuPueyaw2SYbc9ZpJw10szSHGe4kzVWHfPkgJk2ePF3ZcT8+kz/efQ8brvsmAL54yLe5e8JEvvEvn2H4sMU58cz/5mP/8jXO/+kpDFt88YFottRKd98zgSuvGcObN9yAqS+8MNDN0VxiuJM0V71xjdWme/381Knccdfd7PyO7Rk8eBC3jr+TMTf+gTOO/T5bbboJAButvx477vURzvnfi/jEvnsNRLOlVvrn7d/Gjm/fHoCDvvJ1Hn/iiYFtkOaKuf7MXZJJ/V6PSnL83F7u7EiyWZLjeoybmGTZVznf3ZOs/9pa9+ol2SHJBXX4fUm+PlBt6WV+Og40b11zw008+fTT7Lrj2wG46+57GDxoEJtvvNG0OssuvRRvWmsNrrzuhoFqptRKiyzio/YLg4W6566UcjNw81yY9e7ABcCdc2HeM0gyqJTyYrdxpZTzgfPnRTvmpiSDSyneQ2iBC39/BSsstyybvXlDAKY8/zyDBg1i0KBB09Ub8rohTLjv/oFooiQt0AY0wid5b5Ibktya5PdJVkiySJK7kyxX6yyS5C/9e9GSbJHk2jrttUneVMsHJTkqybgkY5N8oZZvXuvdnuTGJMP79XAtk2R0nd/JQDqW9eE6zW1JTk4yqJZPSvKfdZ7X1/a/FXgfcGStv1b9+12SPyS5Osm6XbbFsCQ/6Wj3HrX8xCQ3J7kjyWEd9ScmOTjJNcBeSd6d5K76+gMd9ab1kCVZLcmldf6XJlk1yRJ1XovUOosleSDJkCSfSnJTXb9zkyxW6+yVZHwtv2oW231aD2jtKb1ido6DWn5oklOSjAZ+OtsHluZbzz73HJePuZ6d/3l7kuYUW3WlkUx5/nn+dM+90+o9N2UKd0+YyJNPPT1QTZWkBda8CHeL1pBzW5LbgMM7xl0DbFVK2QQ4G/haKeUl4GfAfrXOjsDtpZS/95vvXcB2ddqDge/U8gOANYBNSikbAT9P8jrgHOBfSilvrvN8tt/8DgGuqfM7H1gVIMl6wAeBbUopGwMvdrRtceD6Os+rgE+VUq6t03+1lLJxKeUe4BTgC6WUTYGvACd02U7/DjxZStmwtvuyWv7NUspmwEbA9kk26pjmuVLKtsBvgFOB9wJvA97QZf4AxwM/7dsuwHGllCeB24Hta533AheXUqYC55VSNq/r90fgE7XOwcC7avn7atkM271HG7qZ4TjoGLcpsFspZd9XMD/Npy4fcz3PPPssu77z7dPKtt1yM1Ze8Q0ccuQxTLj/AR75+2MccuSxTJo82VtIkvQqzIsr57M15Gxcw9HBHeNWBi5OMg74KvBPtfx04KN1+OPAT7rMdwngV0nGA0d3TLsjcFLfLbxSyj+ANwEPl1JuqmVPdbnFtx1NqKSUciHweC1/B03AuKmG03cAa9Zxz9PcfgX4A7B6/0YmGQa8tbb1NuBkYMUu67Mj8KO+F6WUvuXvneQW4Na6jp3P8p1T/10XuLeUcncppfStRxdbA/9dh88Ctu2Yzwfr8D4d892g9jSOowm0fdt4DHBGkk8BfffSum332dXrOAA4v5TSP4iT5IDao3nzKaec8goWpYF00e+vYLWVR077lizA64YM4QeHfZPH/vE4u+z7cbbbfR8e/OvD7Paud7LM0ksNYGslacE00M/c/RD4QSnl/CQ7AIcClFIeSPK3JP8MbMnLPWWd/gO4vJTy/iSrA1fU8gClX91uZd10qxPgzFLK/+kybmoNU9D06HXbnosAT9RgOzMztDHJGjQ9fZuXUh5PcgYwtKNK5+9LzM769dc3zfnAd5MsTRNk+3oNzwB2L6XcnmQUsANAKeXAJFsCuwK3Jdm4W/urF3j5Q8TQLuOhx3FQTe42QSnlFJoe0c710Hzs6UmTueqGG/nkvnvPMG6j9ddl9Dlncu8DDzJ40CBWXWkkn/7at9j4n9YbgJZK0oJtoO95LAE8VIf37zfuNJoeqF/2+LJA57SjOspHAwcmGQxQA8tdwMgkm9ey4X3jO1xFDZFJdgb6ugwuBfZMsnzf/JKsxsw9DQyHppcQuDfJXnX6JHlzl2lGA5/ve5FkKWAETbh5sj6HtnOP5d0FrJFkrfr6Qz3qXUvTM0dd12tqGycBNwLHAhd0bO/hwMNJhtARsJOsVUq5oZRyMPB3YBW6b3eAiTSBEWCPHu2a2XGglrjkqmt4/vmp074l218S1lx1FVZdaSQTH3iQ626+hT3e8+553EpJWvANdM/doTS3Kx8Crqd5ZqvP+TS3Y7vdkgX4PnBmki/xck8TNKFwHWBskqnAqaWU45N8EPhhkkVpnrfbsd/8DgN+UW+BXgncD1BKuTPJt4DR9UsHU4HPAffNZL3OBk5NchCwJ00wOrHOZ0gdf3u/ab4N/KjeZn4ROKyUcl6SW4E7gAk0t0NnUEp5LskBwIVJ/k4T2jboUvUg4PQkXwUeBT7WMe4c4FfU3rnq34Eb6rqOowZWmi+LrE3TW3dpXZfx9NvuNM/4HQb8OMk36ry6OZTex4Fa4qLfX8G6b1yTtVaf8bPRCWf8jDVXXYUll1yCP99zLyee+XN2eccObLP5pl3mJOnVevbZ57jymuat5G+PPMKkyZP53SWXArD9ttuw6KK9brBoQZKX7yrOX5JsBhxdSnnbQLdF871pB3F51J/OmB89/sSTvG23D3LQJ0dxwEf2mWH8d449gd9dfhWPP/kUKy6/HHu9d2c+ts9eDB48qMvcND/IcqvOWPjMk/O+IXpFHvzrX3nHrrt3HXfphb9h5ZEj522D9MottkTfUHpVmS/DXZof3f0MsF8p5ZqBbo/me4Y7aR4z3EkDZEENd9IrZLiT5jHDnTRAZiPcDfQXKiRJkjQHGe4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIoY7SZKkFjHcSZIktYjhTpIkqUUMd5IkSS1iuJMkSWoRw50kSVKLGO4kSZJaxHAnSZLUIimlDHQbpNfKg1iStLBJrxH23EmSJLXI4IFugDQH9Pz0ovlfkgNKKacMdDukhY3nXnvZcydpoB0w0A2QFlKeey1luJMkSWoRw50kSVKLGO4kDTSf+ZEGhudeS/lTKJIkSS1iz50kSVKLGO60UEjyYpLbkoxP8qski72GeZ2RZM86fFqS9WdS99AkX3m1y3otBnLZs9JruyUZleT4VznPJZN89rW37tVLckWSzerwRUmWHMj2dJNkYpJlB7odfRbUczPJpFc77dyUZLMkx/UY96r3fZLdZ7Y957YkOyS5oA6/L8nXB6otvbyW69ecZrjTwuLZUsrGpZQNgOeBAztHJhn0amZaSvlkKeXOOdHAhclc2m5LAvMs3CWZ6e+EllJ2KaU8MY+aM9e82nPjFfDcnINKKTeXUg6aC7PeHZhn4W5m+72Ucn4p5Xvzqi1zy6yuIa+F4U4Lo6uBN9ZPgpcn+W9gXJJBSY5MclOSsUk+DZDG8UnuTHIhsHzfjPr11Lw7yS1Jbk9yacfy1q/1JiQ5qGPaL9XeivFJ/rVbQ5NMSvKfdZ7XJ1mhlq+W5NLazkuTrNpjXXst+zdJ/pDkjiQH1LJPJDm6o86nkvygS5tOTHJznfawjvLNk1xb23pjkuF1mx6VZFxt6xe6bLePJflzkiuBbTrmt1ySc+v+uCnJNrX80CSnd1mv7wFr1V6gI2vdr3bsz2lt7bc+M+y3JFvUdbm1/vumWj6q9i79FhidZNEkZ9f5nwMs2jHfab0k3fZ1kiPS0dNY1+vLSYbVfXpL3W671fGLJ7mwtnN8kg/OZLtP14OQ5IIkO3RZ9xmOg1o+KcnhSW4Atu623eaSBebcrPW6nZvvTXJDPXZ+n2SFJIskuTvJcrXOIkn+kn69aDM57nqdR932fWcP1zJJRtf5nUzHD74n+XCd5rYkJ6eGqXS55iR5K/A+4Mhaf63697t6/FydZN0u22dYkp90tHuPWt7rGjIxycFJrgH2qvvtrvr6Ax31ph3f6XItTLJEndcitc5iSR5IMiTNde2mun7npvYUJ9mr7u/bk1w1i+3eeW5vluSKLus+w3FQyw9NckqS0cBPex1br1kpxT//Wv8HTKr/Dgb+F/gMsAMwGVijjjsA+FYdfj1wM7AGzUXlEmAQMBJ4Atiz1rsC2AxYDnigY15L138PBa6t81sWeAwYAmwKjAMWB4YBdwCbdGl3Ad5bh7/f0b7fAvvX4Y8Dv+kybddl92vfosB4YJnalns66lwLbNhlvn3TDqrrvxHwOmACsHkdN6Ju688A5wKD+03bt91WBO6v2+91wBjg+Frnv4Ft6/CqwB9nsU1XB8Z3tHMnmm8DhuaD7AXAdv3Wpdd+G9HR5h2Bc+vwKODBjnpfAk6vwxsBLwCb1dcTa/u67uv6d2VHW+6s6zkYGFHLlgX+UtdhD+DUjvpLzGS7j+rbjrX8AmCHznb1Og46jru9PTdf1bm5FC9/WfGTwH/V4UOAf+04Ns/tMs9ex90M59FM9v0OwAW17Djg4Dq8a23zssB6NNeQvnP9BOCjs1ivM/q2bX19KbB2Hd4SuKzL+hwBHNPxeqle15COY/NrdXho3W9r0xz/v+xYr1G8fJ3oei2kOZbeXoc/CJxWh5fpaM+3gS/U4XHASnV4yV7bvcs5tBlwRZd29ToODgX+ACw6N88r//sxLSwWTXJbHb4a+DHwVuDGUsq9tXwnYKPUZ3Zo3jzXBrYDflFKeRH4a5LLusx/K+CqvnmVUv7RMe7CUsoUYEqSR4AVgG2BX5dSJgMkOQ94G3Brv/k+T/PGDM0F4Z11eGte/iR7Fs1FuJtuy34QOCjJ+2udVWgu0tfXdXtPkj/SXPjHdZnn3ml6eQbThLP1ad4QHi6l3FTX/6m6XjsCJ5VSXuiyXaB5U7iilPJorX8OsE4dtyNNz0pf3RFJhs9kvfrbqf71bdNhNPvzqo46vfbbEsCZSdau6zakY5pLOuptR/MGSillbJKxXdrRdV+XUo5LsnySkTQB5PFSyv1JhgDfSbId8BKwUl2/ccBRSY6geZO7OsmGdN/uXZrR1QzHAU3IeZHmTW1eaNu5uTJwTpIVaQJY3zqcThM4jqEJIT/p0tZex90M59Fs7vvtqNeJUsqFSR6v5e+gCbE31fqLAo/MYr2mSTKMZh/9qmN5r++yPjsC+/S9KKX0Lb/bNaTv3Dmn/rsucG8p5e66zJ/R/X/U6HUtPIcm1F1e23BCLd8gybdpHuMYBlxcy8cAZyT5JXBeR/tndv2amV7HAcD5pZRnX8G8XjHDnRYWz5ZSNu4sqBelyZ1FNJ/iLu5XbxeaC+3MZCZ1pnQMv0hz3s3uu+/UUj/udUzbzWwvO83tuR2BrUspz9RbCkNrndOAbwB30eXNJ8kawFdoegseT3JGnbbX+s9su8yq7YvUNk53Eaz7rds27bbs75ZSTp7Jsnu17z+Ay0sp70+yOk3vQp/J/erOzrHRy/8AewJvAM6uZfvRhL1NSylTk0wEhpZS/pxkU2AX4Lv1ts5veiz/BaZ/7GZo/wqzOA6eq4FpXmjbuflD4AellPPrNj4UoJTyQJK/Jflnmg81+3WZZ6/jrts6zM65RY86Ac4spfyfLuNm55qzCPBE//3WYznTLX8m15A+nft9dtavv75pzqc5T5amCbJ9wf8MYPdSyu1JRtH0dFJKOTDJljQ9nLcl2bhb+6vO82uGc6vqehxU/a8hc5zP3Ekvuxj4TO05Ick6SRan6enZpz5/sSLw9i7TXgdsXy9c1AvKzFwF7F6fBVkceD9Nr8XsupaXPxHvB1zzCqZdgqaX6Jk0z8ls1TeilHIDTQ/OvsAvukw7gubC9GR9hmTnWn4XMDLJ5gBpnv0ZDIwGDqzD3bbLDcAOaZ4NGgLs1TFuNPD5vhf1YjszTwPDO15fDHy89jKQZKUky/ebptd+WwJ4qA6Pmskyr6K+SSfZgObWbLc6vfb12TT7cU+aoNe37EdqsHs7sFqd/0jgmVLKz4CjgLfQe7tPBDZO82zXKsAWXdrV8ziYDy1I52bnsbN/v3GnAT8DftkjPPc67rqdR732ff916Ts+d6a5VQjNLdU9+86HJEsnWW0W6zXt/Kq9hPcm2atOnyRv7jJN/3N4KXpfQ/q7C1gjyVr19Yd61Ot6LSylTAJuBI6l6enu297DgYfrsTQtYCdZq5RyQynlYODvNNfBXteviTSBEZrHJbqZ2XEw1xnupJedRvPc0y1JxgMn03xq/TVwN81tsROBK/tPWG8rHgCcl+R2Xr610FUp5RaaT5A30gSc00op/W/7zMxBwMfS3Ab8CPAvr2Da39H04I2l6Sm4vt/4XwJjOm6hdLb7dprbU3fQ3GYaU8ufp7kF8sO6/pfQfKI9jeaZurG1fN9+83uY5hPtdcDvgVv6reNmaR5kvpN+36Ls0rbHgDFpHoo+spQymua5veuSjKMJT8P7TdNrv32f5lP/GJrngno5ERhWt+XXaPZn/3b13NellDtqmx6q2wLg53W9b6Z587mrlm8I3JjmFuY3gW/PZLuPobkNNI4mCHZu1z6zOg7mJwvSuXkoze3Kq2lCQqfzaW4FdrslC72PuxnOo5ns+06HAdsluYXm1vb9dR3vBL5F86WgsXXaFWexXmcDX03zBYG1aI7NT9Rl3wHs1mWabwNL1XPydppn4LpeQ/orpTxHs98uTPOFivt6tGtm18JzgA8z/T7/d5r9egkvn1vQfFlkXD2+rgJup/f16zDg2LqPe/VwH0rv42Cu83+okDSdNN+0O7qUcuksK0uabWm+vXt0KeVtA90WtZs9d5IASPMjwH+meQbKYCfNQWl+dPdcoNtzbtIcZc+dJElSi9hzJ0mS1CKGO0mSpBYx3EmSJLWI4U6SJKlFDHeSJEktYriTJElqkf8PqteTsXZIQn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.heatmap(matriz_de_decision, cmap = 'Reds', annot = True, fmt = 'd', linewidths = 5, cbar = False, annot_kws = {'fontsize': 15}, \n",
    "            yticklabels = ['No hay accidente cardiovascular', 'Hay accidente cardiovascular'],\n",
    "            xticklabels = ['Predicho no hay accidente cardiovascular', 'Predicho hay accidente cardiovascular'])\n",
    "plt.yticks(rotation = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2be6e59",
   "metadata": {},
   "source": [
    "Finalmente, antes de seguir modificando nuestro conjunto de datos para lograr una mejora en el recall, debemos tener cuidado dado que una gran mejora del recall nos puede llevar a una precisión muy baja y, en consecuencia, tener una gran cantidad de falsos positivos (algo que tampoco es apropiado poruq puede tener consecuencias como la saturación del servicio sanitario).\n",
    "\n",
    "Para ello, aparte de dirigir nuestro objetivo en la mejora del recall, también nos centraremos en mantener y lograr un **F Score** apto (teniendo en cuenta que favoreceremos el recall ante la precisión).\n",
    "\n",
    "A continuación, creamos la función correspondiente donde tomaremos el recall y la precisión para obtener el balance de ambos, donde tendremos que tener en cuenta el parametro B con el que favoreceremos el recall si lo aumentamos y lo perjudicaremos si lo reducimos. Si no seleccionamos ningún valor, B tendra el valor 1 predeterminado, es decir, el **F Score** tradicional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "36c95851",
   "metadata": {},
   "outputs": [],
   "source": [
    "#definimos la función F_Score\n",
    "def F_Score(recall, precision, B=1):\n",
    "    return (1+B**2)*(precision*recall)/((precision*B**2)+recall)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "529f8467",
   "metadata": {},
   "source": [
    "Una vez hemos definido el **F Score**, calculamos el balance entre la precisión y el recall con un *B=2* con esta primera iteración. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9d127de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F_Sore de nuestro modelo  0.01557632398753894\n"
     ]
    }
   ],
   "source": [
    "f1=F_Score(recall1,precision1,2)\n",
    "print('F_Sore de nuestro modelo ',f1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2a3819",
   "metadata": {},
   "source": [
    "Observamos que tenemos un **F Score** de mala calidad debido a al bajo valor del recall en nuestro modelo. Visto esto, nos centraremos en seguir modificando nuestro conjunto de datos en busca de obtener una mejora en el **F Score** (teniendo en cuenta que priorizamos el recall respecto la precisión)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5866c316",
   "metadata": {},
   "source": [
    "<a name=\"item3\"></a>\n",
    "## Selección de instancias y balanceo del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1cb6c0",
   "metadata": {},
   "source": [
    "Continuamos con la selección de instancias del modelo. Es necesario comentar que a parte de obtener un subconjunto de instacias del modelo para lograr una solución menos compleja y mas simple, tenemos un conjunto de datos desbalanceado; por lo tanto, a parte de hacer uso de técnicas de **undersampling** también haremos uso de técnicas de **oversampling** o **híbridas** con el objetivo de seleccionar las instancias relevantes (con la intención de eliminar instancias superfluas y el ruido del dataset) y creando si es necesario instancias artificiales (instancias que ayudaran a balancear el conjunto de datos de entrenamiento). \n",
    "\n",
    "*En este apartado solo modificamos train dado que es el conjunto de datos que usamos para entrenar, test no lo modificamos.*\n",
    "\n",
    "A continuación, mostramos que el obstáculo del desbalanceo del conjunto de datos es notorio:\n",
    "\n",
    "* *Clase positiva*: 35 individuos\n",
    "* *Clase negativa*: 1178 individuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bed7acbf",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unaig\\anaconda3\\envs\\py38ml\\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"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplt, ax = plt.subplots(figsize = (10,7))\n",
    "ax =sns.countplot(y_train_sinOut);\n",
    "\n",
    "# show count (+ annotate)\n",
    "for rect in ax.patches:\n",
    "    ax.text (rect.get_x() + rect.get_width()  / 2,rect.get_height()+ 0.75,rect.get_height(),horizontalalignment='center', fontsize = 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb99a39",
   "metadata": {},
   "source": [
    "Realizamos distintas técnicas de **undersampling**,**oversampling** y **híbridas** para modificar nuestro conjunto de entrenamiento y obtenemos el que mejor resultado nos de respecto a nuestro objetivo (la mejora del **F score priorizando el recall**). Haremos uso de las siguientes técnicas:\n",
    "\n",
    "*  Tomek Links\n",
    "*  Smote\n",
    "*  OSS\n",
    "*  NCL\n",
    "*  Smote con Tomek Links\n",
    "*  Smote con ENN\n",
    "\n",
    "\n",
    "Comentar que crearemos dos conjuntos de datos de entrenamiento, uno con los datos estandarizados y otros sin estandarizar, dado que la estandarización de los datos de entrenamiento puede hacer variar la selección y creación de instancias ya que normaliza los rangos de las distintas variables. Esta diferencia de rangos entre variables puede provocar que se les de mas importancia de la que merecen a algunas instancias que a otras, por ello creemos que la mejor opción la obtenndremos con la normalización de los datos."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fcb08e",
   "metadata": {},
   "source": [
    "Comenzamos evaluando todas las técnicas con los datos sin normalizar y tomaremos la mejor técnica. Posteriormente, repetiremos el proceso con los datos normalizados y finalmente compararemos los resultados y seleccionaremos el que mejor resultado nos ofrezca en base a nuestro objetivo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bde2a103",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USO DE DATOS SIN ESTANDARIZAR\n",
      "\n",
      "\n",
      "Resultado en train utilizando la Técnica OSS: Rendimiento: 0.0\n",
      "Resultado en test utilizando la Técnica OSS: Rendimiento: 0.11180339887498951\n",
      "Precision obtenido en test utilizando la Técnica OSS: Rendimiento: 1.0\n",
      "Recall obtenido en test utilizando la Técnica OSS: Rendimiento: 0.0125\n",
      "F Score obtenido en test utilizando la Técnica OSS: Rendimiento: 0.01557632398753894\n",
      "\n",
      "Resultado en train utilizando la Técnica NCL: Rendimiento: 0.20512421929813185\n",
      "Resultado en test utilizando la Técnica NCL: Rendimiento: 0.19340654975762475\n",
      "Precision obtenido en test utilizando la Técnica NCL: Rendimiento: 0.5\n",
      "Recall obtenido en test utilizando la Técnica NCL: Rendimiento: 0.0375\n",
      "F Score obtenido en test utilizando la Técnica NCL: Rendimiento: 0.046012269938650305\n",
      "\n",
      "Resultado en train utilizando la Técnica híbrida SMOTE con Tomek Links: Rendimiento: 0.203649710355409\n",
      "Resultado en test utilizando la Técnica híbrida SMOTE con Tomek Links: Rendimiento: 0.2936977140622804\n",
      "Precision obtenido en test utilizando la Técnica híbrida SMOTE con Tomek Links: Rendimiento: 0.2916666666666667\n",
      "Recall obtenido en test utilizando la Técnica híbrida SMOTE con Tomek Links: Rendimiento: 0.0875\n",
      "F Score obtenido en test utilizando la Técnica híbrida SMOTE con Tomek Links: Rendimiento: 0.10174418604651163\n",
      "\n",
      "Resultado en train utilizando la Técnica híbrida SMOTE con ENN: Rendimiento: 0.6393570957688458\n",
      "Resultado en test utilizando la Técnica híbrida SMOTE con ENN: Rendimiento: 0.5758785115804955\n",
      "Precision obtenido en test utilizando la Técnica híbrida SMOTE con ENN: Rendimiento: 0.22137404580152673\n",
      "Recall obtenido en test utilizando la Técnica híbrida SMOTE con ENN: Rendimiento: 0.3625\n",
      "F Score obtenido en test utilizando la Técnica híbrida SMOTE con ENN: Rendimiento: 0.3215077605321508\n",
      "\n",
      "Resultado en train utilizando la Técnica Tomek Links: Rendimiento: 0.0\n",
      "Resultado en test utilizando la Técnica Tomek Links: Rendimiento: 0.11180339887498951\n",
      "Precision obtenido en test utilizando la Técnica Tomek Links: Rendimiento: 1.0\n",
      "Recall obtenido en test utilizando la Técnica Tomek Links: Rendimiento: 0.0125\n",
      "F Score obtenido en test utilizando la Técnica Tomek Links: Rendimiento: 0.01557632398753894\n",
      "\n",
      "Resultado en train utilizando la Técnica SMOTE: Rendimiento: 0.203649710355409\n",
      "Resultado en test utilizando la Técnica SMOTE: Rendimiento: 0.2720263416489627\n",
      "Precision obtenido en test utilizando la Técnica SMOTE: Rendimiento: 0.2727272727272727\n",
      "Recall obtenido en test utilizando la Técnica SMOTE: Rendimiento: 0.075\n",
      "F Score obtenido en test utilizando la Técnica SMOTE: Rendimiento: 0.08771929824561403\n",
      "\n",
      "\n",
      "La mejor tecnica es : Técnica híbrida SMOTE con ENN.\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "from imblearn.metrics import geometric_mean_score\n",
    "from imblearn.pipeline import Pipeline\n",
    "import imblearn.combine as hib\n",
    "import imblearn.under_sampling as us\n",
    "import imblearn.over_sampling as os\n",
    "\n",
    "#tecnicas de muestreo\n",
    "tl = us.TomekLinks()\n",
    "smote = os.SMOTE(random_state = 42)\n",
    "oss = us.OneSidedSelection(random_state = 42)\n",
    "ncl = us.NeighbourhoodCleaningRule()\n",
    "smoteTomek = hib.SMOTETomek(random_state = 42)\n",
    "smoteEnn = hib.SMOTEENN(random_state = 42)\n",
    "\n",
    "\n",
    "listaMetodos = ['Técnica OSS', 'Técnica NCL', 'Técnica híbrida SMOTE con Tomek Links', 'Técnica híbrida SMOTE con ENN', 'Técnica Tomek Links', 'Técnica SMOTE']\n",
    "listaTecnicas = [oss, ncl, smoteTomek, smoteEnn, tl, smote]\n",
    "\n",
    "print('USO DE DATOS SIN ESTANDARIZAR\\n')\n",
    "# Se crea una lista para almacenar el resultado de test de cada técnica de\n",
    "#muestreo, almacenamos el F Score de las predicciones obtenidas con nuestro clasificador\n",
    "listaF_Score=[]\n",
    "\n",
    "# Por cada técnica se cogen los datos de entrada y de salida (muestreados para realizar el aprendizaje)\n",
    "for nombre, metodo in zip(listaMetodos, listaTecnicas):\n",
    "    # Generamos la pipeline con los componentes adecuados\n",
    "    pipe = Pipeline([(nombre,metodo),('Modelo Logistico', LogReg)])\n",
    "    # Entrenamiento de la pipeline\n",
    "    pipe = pipe.fit(X_train_sinOut,y_train_sinOut)\n",
    "    # Precicción de los datos de entrenamiento \n",
    "    prediccionesTrain = pipe.predict(X_train_sinOut)\n",
    "    # Cálculo de la media geométrica en entrenamiento\n",
    "    accTrain = geometric_mean_score(y_train_sinOut, prediccionesTrain)\n",
    "    # Se imprime el resultado en train: mostrad el nombre de la técnica y su rendimiento\n",
    "    print('\\nResultado en train utilizando la {}: Rendimiento: {}'.format(nombre,accTrain))\n",
    "    # Precicción de los datos de test \n",
    "    prediccionesTest = pipe.predict(X_test)\n",
    "    # Cálculo de la media geométrica en test\n",
    "    accTest = geometric_mean_score(y_test, prediccionesTest)\n",
    "    # Se imprime el resultado en test: mostrad el nombre de la técnica y su rendimiento\n",
    "    print('Resultado en test utilizando la {}: Rendimiento: {}'.format(nombre,accTest))        \n",
    "        \n",
    "    \n",
    "    precision = precision_score(y_test, prediccionesTest)\n",
    "    recall = recall_score(y_test, prediccionesTest)\n",
    "    \n",
    "    # Se añade a la lista el resultado de test a la lista listaF_Score\n",
    "    f_score=F_Score(recall,precision,2)\n",
    "    listaF_Score.append(f_score)\n",
    "    \n",
    "    print('Precision obtenido en test utilizando la {}: Rendimiento: {}'.format(nombre,precision))     \n",
    "    print('Recall obtenido en test utilizando la {}: Rendimiento: {}'.format(nombre,recall)) \n",
    "    print('F Score obtenido en test utilizando la {}: Rendimiento: {}'.format(nombre,f_score)) \n",
    "    \n",
    "#Se podria dar el caso de que en dos o mas técnicas se obtenga el mismo F Score, en ese caso se activa\n",
    "#el siguiente codigo y se desactiva la ultima linea. Despues habría que analizar con cual de las ténicas nos quedamos    \n",
    "#for i in np.where(listaF_Score==max(listaF_Score))[0]:\n",
    "#    print('\\n\\nLa mejor tecnica es : {}.'.format(listaMetodos[i]))\n",
    "print('\\n\\nLa mejor tecnica es : {}.'.format(listaMetodos[int(np.where(listaF_Score==max(listaF_Score))[0])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6605f371",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USO DE DATOS ESTANDARIZADOS\n",
      "\n",
      "\n",
      "Resultado en train utilizando la Técnica OSS: 0.07761505257063331\n",
      "Resultado en test utilizando la Técnica OSS: 0.11180339887498951\n",
      "Precision obtenido en test utilizando la Técnica OSS: 1.0\n",
      "Recall obtenido en test utilizando la Técnica OSS: 0.0125\n",
      "F Score obtenido en test utilizando la Técnica OSS: Rendimiento: 0.01557632398753894\n",
      "\n",
      "Resultado en train utilizando la Técnica NCL: 0.20506770346311556\n",
      "Resultado en test utilizando la Técnica NCL: 0.157981846339872\n",
      "Precision obtenido en test utilizando la Técnica NCL: 0.5\n",
      "Recall obtenido en test utilizando la Técnica NCL: 0.025\n",
      "F Score obtenido en test utilizando la Técnica NCL: Rendimiento: 0.0308641975308642\n",
      "\n",
      "Resultado en train utilizando la Técnica híbrida SMOTE con Tomek Links: 0.7747326171711871\n",
      "Resultado en test utilizando la Técnica híbrida SMOTE con Tomek Links: 0.7348639613807627\n",
      "Precision obtenido en test utilizando la Técnica híbrida SMOTE con Tomek Links: 0.1642651296829971\n",
      "Recall obtenido en test utilizando la Técnica híbrida SMOTE con Tomek Links: 0.7125\n",
      "F Score obtenido en test utilizando la Técnica híbrida SMOTE con Tomek Links: Rendimiento: 0.4272863568215892\n",
      "\n",
      "Resultado en train utilizando la Técnica híbrida SMOTE con ENN: 0.7772964112972509\n",
      "Resultado en test utilizando la Técnica híbrida SMOTE con ENN: 0.745415873662378\n",
      "Precision obtenido en test utilizando la Técnica híbrida SMOTE con ENN: 0.15803108808290156\n",
      "Recall obtenido en test utilizando la Técnica híbrida SMOTE con ENN: 0.7625\n",
      "F Score obtenido en test utilizando la Técnica híbrida SMOTE con ENN: Rendimiento: 0.4320113314447592\n",
      "\n",
      "Resultado en train utilizando la Técnica Tomek Links: 0.0\n",
      "Resultado en test utilizando la Técnica Tomek Links: 0.11180339887498951\n",
      "Precision obtenido en test utilizando la Técnica Tomek Links: 1.0\n",
      "Recall obtenido en test utilizando la Técnica Tomek Links: 0.0125\n",
      "F Score obtenido en test utilizando la Técnica Tomek Links: Rendimiento: 0.01557632398753894\n",
      "\n",
      "Resultado en train utilizando la Técnica SMOTE: 0.7750146971430942\n",
      "Resultado en test utilizando la Técnica SMOTE: 0.7356728377526329\n",
      "Precision obtenido en test utilizando la Técnica SMOTE: 0.16521739130434782\n",
      "Recall obtenido en test utilizando la Técnica SMOTE: 0.7125\n",
      "F Score obtenido en test utilizando la Técnica SMOTE: Rendimiento: 0.4285714285714286\n",
      "\n",
      "\n",
      "La mejor tecnica es : Técnica híbrida SMOTE con ENN.\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "from imblearn.metrics import geometric_mean_score\n",
    "from imblearn.pipeline import Pipeline\n",
    "import imblearn.combine as hib\n",
    "import imblearn.under_sampling as us\n",
    "\n",
    "print('USO DE DATOS ESTANDARIZADOS\\n')\n",
    "\n",
    "# Se crea una lista para almacenar el resultado de test de cada técnica de\n",
    "#muestreo, almacenamos el F Score de las predicciones obtenidas con nuestro clasificador\n",
    "listaF_Score_Norm = []\n",
    "\n",
    "# Por cada técnica se cogen los datos de entrada y de salida (muestreados para realizar el aprendizaje)\n",
    "for nombre, metodo in zip(listaMetodos, listaTecnicas):\n",
    "    # Generamos la pipeline con los componentes adecuados\n",
    "    pipe = Pipeline([('estandarizar',StandardScaler()),(nombre,metodo),('Modelo Logistico', LogReg)])\n",
    "    # Entrenamiento de la pipeline\n",
    "    pipe = pipe.fit(X_train_sinOut,y_train_sinOut)\n",
    "    # Precicción de los datos de entrenamiento \n",
    "    prediccionesTrain = pipe.predict(X_train_sinOut)\n",
    "    # Cálculo de la media geométrica en entrenamiento\n",
    "    accTrain = geometric_mean_score(y_train_sinOut, prediccionesTrain)\n",
    "    # Se imprime el resultado en train: mostrad el nombre de la técnica y su rendimiento\n",
    "    print('\\nResultado en train utilizando la {}: {}'.format(nombre,accTrain))\n",
    "    # Precicción de los datos de test \n",
    "    prediccionesTest = pipe.predict(X_test)\n",
    "    # Cálculo de la media geométrica en test\n",
    "    accTest = geometric_mean_score(y_test, prediccionesTest)\n",
    "    # Se imprime el resultado en test: mostrad el nombre de la técnica y su rendimiento\n",
    "    print('Resultado en test utilizando la {}: {}'.format(nombre,accTest))        \n",
    "        \n",
    "    \n",
    "    precision = precision_score(y_test, prediccionesTest)\n",
    "    recall = recall_score(y_test, prediccionesTest)\n",
    "    \n",
    "    # Se añade a la lista el resultado de test a la lista listaF_Score_Norm\n",
    "    f_score=F_Score(recall,precision,2)\n",
    "    listaF_Score_Norm.append(f_score)\n",
    "    \n",
    "    \n",
    "    print('Precision obtenido en test utilizando la {}: {}'.format(nombre,precision))     \n",
    "    print('Recall obtenido en test utilizando la {}: {}'.format(nombre,recall)) \n",
    "    print('F Score obtenido en test utilizando la {}: Rendimiento: {}'.format(nombre,f_score)) \n",
    "\n",
    "#Se podria dar el caso de que en dos o mas técnicas se obtenga el mismo F Score, en ese caso se activa\n",
    "#el siguiente codigo y se desactiva la ultima linea. Despues habría que analizar con cual de las ténicas nos quedamos\n",
    "#for i in np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0]:\n",
    "#    print('\\n\\nLa mejor tecnica es : {}.'.format(listaMetodos[i]))\n",
    "print('\\n\\nLa mejor tecnica es : {}.'.format(listaMetodos[int(np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0])]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84cd0947",
   "metadata": {},
   "source": [
    "Una vez hemos obtenido las mejores técnicas con los datos **sin normalizar** y los datos **normalizados**. Comparamos los resultados como ya hemos dicho para saber cual de las dos técnicas es mejor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "48ce1320",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La mejor técnica con los datos sin normalizar es Técnica híbrida SMOTE con ENN con un F Score de 0.3215077605321508.\n",
      "Con los datos normalizados es la Técnica híbrida SMOTE con ENN con un F Score 0.4320113314447592.\n",
      "Diferencia de F Score: 0.11050357091260837.\n",
      "La mejor configuracion es con los datos estandarizados y con la Técnica híbrida SMOTE con ENN.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#Mostramos la informacion de las mejores técnicas y sus F Score con los datos con o sin normalizar\n",
    "print('La mejor técnica con los datos sin normalizar es {} con un F Score de {}.\\nCon los datos normalizados es la {} con un F Score {}.\\nDiferencia de F Score: {}.'.format(listaMetodos[int(np.where(listaF_Score==max(listaF_Score))[0])],\n",
    "                                                                                                                                         listaF_Score[int(np.where(listaF_Score==max(listaF_Score))[0])],\n",
    "                                                                                                                                         listaMetodos[int(np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0])],\n",
    "                                                                                                                                         listaF_Score_Norm[int(np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0])],\n",
    "                                                                                                                                         abs(listaF_Score[int(np.where(listaF_Score==max(listaF_Score))[0])]-listaF_Score_Norm[int(np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0])])))\n",
    "#tomamos la mejor tecnica entre los datos sin o con normalizar (priorizamos que esten normalizados)\n",
    "if(max(listaF_Score)>max(listaF_Score_Norm)):\n",
    "    print('La mejor configuracion es con los datos sin estandarizar y con la {}.'.format(listaMetodos[int(np.where(listaF_Score==max(listaF_Score))[0])]\n",
    "))\n",
    "    tecnica=listaTecnicas[int(np.where(listaF_Score==max(listaF_Score))[0][0])]\n",
    "else:\n",
    "    print('La mejor configuracion es con los datos estandarizados y con la {}.'.format(listaMetodos[int(np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0])]\n",
    "))\n",
    "    tecnica=listaTecnicas[int(np.where(listaF_Score_Norm==max(listaF_Score_Norm))[0][0])]\n",
    "    #si tomamos la tecnica tratada con los datos normalizados, normalizamos los datos para continuar\n",
    "    scaler= StandardScaler()\n",
    "    X_train_sinOut=scaler.fit_transform(X_train_sinOut)\n",
    "    X_train_sinOut=pd.DataFrame(X_train_sinOut)\n",
    "    X_test=scaler.transform(X_test)\n",
    "    X_test=pd.DataFrame(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290c8a52",
   "metadata": {},
   "source": [
    "Observamos que la mejor técnica de las que hemos evaluado es *SMOTE con ENN*. Con el conjunto de datos normalizados y sin estandarizar obtenemos los mismos resultados pero hemos seleccionado la opción con los datos estandarizados dado que lo vemos mas adecuado, de esta forma el algoritmo de aprendizaje convergera antes y en el proximó apartado de selección de variables no tendremos el posible incombeniente de la diferencia de rangos.\n",
    "\n",
    "A continuación, muestreamos nuestros datos de entrenamiento con la técnica *SMOTE con ENN* y analizamos la distribución entre la *clase positiva y negativa*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a8ac4c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain_sampled, ytrain_sampled = tecnica.fit_resample(X_train_sinOut,y_train_sinOut) #muestreamos nuestros datos de entrenamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0224a90f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unaig\\anaconda3\\envs\\py38ml\\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"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countplt, ax = plt.subplots(figsize = (10,7))\n",
    "ax =sns.countplot(ytrain_sampled);\n",
    "\n",
    "# show count (+ annotate)\n",
    "for rect in ax.patches:\n",
    "    ax.text (rect.get_x() + rect.get_width()  / 2,rect.get_height()+ 0.75,rect.get_height(),horizontalalignment='center', fontsize = 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fcb8e0",
   "metadata": {},
   "source": [
    "Finalmente, gracias a este apartado en nuestro dataset tenemos suficientes instancias de la *clase positiva* y balanceamos el conjunto de datos resolviendo el problema del desbalanceo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be5ab16",
   "metadata": {},
   "source": [
    "<a name=\"item5\"></a>\n",
    "## 2.Generalización"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "955a08ba",
   "metadata": {},
   "source": [
    "Una vez hemos balanceado el conjunto de entrenamiento iteramos por segunda vez nuestro algoritmo y evaluamos los resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "78c7f869",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy obtenido con el conjunto de datos sin modificar: 73.0829420970266\n"
     ]
    }
   ],
   "source": [
    "LogReg=LogReg.fit(Xtrain_sampled,ytrain_sampled.ravel())\n",
    "\n",
    "# Cálculo de la media geométrica en test\n",
    "predicciones=np.round(LogReg.predict_proba(X_test)[:,1].reshape(-1,1),0)\n",
    "comparacion= predicciones==y_test.to_numpy().reshape(-1,1)   \n",
    "suma=comparacion.sum()\n",
    "accTest=100*suma/predicciones.size\n",
    "print('Accuracy obtenido con el conjunto de datos sin modificar: {}'.format(accTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9fcfb323",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión de nuestro modelo 0.15803108808290156\n",
      "Recall de nuestro modelo  0.7625\n",
      "F Sore de nuestro modelo  0.4320113314447592\n"
     ]
    }
   ],
   "source": [
    "precision = precision_score(y_test, predicciones)\n",
    "recall = recall_score(y_test, predicciones)\n",
    "matriz_de_decision=confusion_matrix(y_test, predicciones)\n",
    "\n",
    "print('Precisión de nuestro modelo',precision)\n",
    "print ('Recall de nuestro modelo ', recall)\n",
    "\n",
    "f2=F_Score(recall,precision,2)\n",
    "print('F Sore de nuestro modelo ',f2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c82b8a95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.heatmap(matriz_de_decision, cmap = 'Reds', annot = True, fmt = 'd', linewidths = 5, cbar = False, annot_kws = {'fontsize': 15}, \n",
    "            yticklabels = ['No hay accidente cardiovascular', 'Hay accidente cardiovascular'],\n",
    "            xticklabels = ['Predicho no hay accidente cardiovascular', 'Predicho hay accidente cardiovascular'])\n",
    "plt.yticks(rotation = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "045cab69",
   "metadata": {},
   "source": [
    "Observamos que después de aplicar la técnica de *SMOTE con ENN* para los datos estandarizados baja nuestro accuracy, esto nos podría confundir, ya que en principio que baje el accuracy no es una buena señal. Sin embargo, como estamos en un problema médico es preferible favorecer el **recall** que la **precisión** a la hora de predecir tal y como hemos explicado en . Por lo tanto, nos fijamos en si hemos logrado mejorar el valor del **F Score**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7e16d7bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diferencia del F Score: 0.41643500745722023\n"
     ]
    }
   ],
   "source": [
    "print('Diferencia del F Score: {}'.format(f2-f1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90a2274e",
   "metadata": {},
   "source": [
    "Observamos que gracias a nuestro muestreo en [Selección de instancias y balanceo del modelo](#item3), el conjunto de datos de entrenamiento que obtenemos provoca una mejora considerable en el **F Score**, por lo que logramos el objetivo."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "832c84e1",
   "metadata": {},
   "source": [
    "<a name=\"item4\"></a>\n",
    "## Selección de variables del modelo"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b46f696a",
   "metadata": {},
   "source": [
    "Por último, vamos a buscar las variables mas relevantes del modelo para intentar mejorar nuestro objetivo. Nuestra intención es eliminar las variables redundantes e irrelevantes, dado que estas pueden confundir al algoritmo de clasificación.\n",
    "\n",
    "Para ello comenzaremos analizando la correlación de todas las variables que tenemos en el conjunto de entrenamiento *train*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2841138a",
   "metadata": {},
   "source": [
    "Comenzaremos por calcular la matriz de correlación para observar la correlación entre las variables.\n",
    "\n",
    " *Comentar que la variable genderOther no la mostramos en las siguientes gráficas debido a que en el conjunto de entrenamiento no se encuentra ningún individuo con esa caracteristica.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3f4cfaf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "data_modificado=pd.concat((Xtrain_sampled,ytrain_sampled), axis = 1)\n",
    "data_modificado.columns = data_transform.columns\n",
    "data_modificado.drop('genderOther', axis=1, inplace=True)#eliminamos\n",
    "correlaciones_transform=data_modificado.corr()\n",
    "\n",
    "fig, axes = plt.subplots(figsize=(14,14))\n",
    "sns.heatmap(correlaciones_transform, vmax=.8, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10});"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f335e87",
   "metadata": {},
   "source": [
    "Dada la gran cantida de variables para facilitar la visualización, mostramos una gráfica con la última columna ya que indica la correlación de cada variable con la variable a predecir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3069f0e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimo:  -0.2910741582896458  Maximo:  0.6938963159431311\n"
     ]
    },
    {
     "data": {
      "image/png": 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c2qUxt74/gee+nxV1SCKSRkogiYiIiIiIJPhldQ63fziBfm3rcWa/VlGHU6ZUqpDFw2f05pAujbn5vfE8/8OsqEMSkTRRAklERERERCTk7tz0zjg25RVw10k9yMrK7KVrxalUIYtHwiTS394dz/+URBLJCEogiYiIiIiIhD4au4jPJvzC1Yd0om2D6lGHU2YVJpEO3rUxN707nv8NnR11SCKSYkogiaTA0rUb+WV1TtRhiIiIiMhvsGLdJm5+bxzdm9fm/P5tow6nzKtUIYv/nNmbg3dtxE3vjOMFJZFEYk0JJJEke2/0Ag649xsOvPcbPhq7MOpwRERERGQH3fbBBFauz+Xuk3tQIVuHSjuiUoUsHjmzNwd1bsRf3xnHiz8qiSQSV3pXFEmStRvzuOa10Vz+8s90aFSDXZrU5JIXR3Lb+xPIzS+IOjwRERERKcHXkxbz9s/zueR37dm1aa2owylXKlfI5j9n9ebAzo34y9vjeOnHOVGHJCIpUCHqAETi4Oc5K7jy1VHMXb6eyw/swGUHdcQd7vhoIs8MmcmYeSt5+IzeNKldJepQRURERKSINTm53Pj2WDo2qsGlB3aIOpxyqXKFbB49qzd/fGEkN749FoAz9tAEO5E4UQWSSCnkFzgPfzWVkx/7gbx859WL9uLqQ3ehYnYWlSpkccuxXXlowG5MWLiaox/6ju+nL406ZBEREREp4q5PJrFodQ53n9yDyhWyow6n3CpMIh2wS0NufHssL/+kSqRMU1DgzFuxnp/nrNAqjBhSBZLITpq/cgNXvTqKn2Yu55iezfj78d2oXbXir+53TM9m7Nq0Jhe/MJKznvqR/ztsFy7er71GwoqIiIiUAUNnLOOFoXM4v39bdmtVN+pwyr0gibQ7F78wghveGosBp/dTJVKcuDtL125i1rJ1zFyyjhlL1zFr6TpmLl3HrGXr2JgXJI6a1KrCH/ZuzRn9WlGnWqWIo5ZkMHePOobfrE+fPj58+PCow5AM9sGYBdz41ljyC5zbjuvGib2bY1ZyQmjdxjyuf2ss749ewMG7Nua+U3sWm3ASERERkfTIyc3n8H8NosDhkyv3pVolnV9PlpzcfC763wi+nbKEu07qzml9lUQqb1bn5G5ODM1YEiSHZi4NkkZrNuZtvl/FbKNVvWq0bVCDtg2C79UrZ/P68HkMnraUKhWzOKl3C87r35b2DWtE+IpkR5jZCHfvU+xtSiCJ7Li1G/O45b3xvDFiHr1a1uHB03vRun71HX68u/Pc97P4+4cTaVanKo+e1ZuuzWqnMGIRERER2ZZ/fjSRxwfN4KUL9mDvDg2iDid2CpNIg6Yu4a4Te3Bq35ZRhyRF5OTmM3vZemYuXcuMMDlUmChaunbT5vuZQfM6VWnboPpWX+0a1KBZnSrbnFo4adFqnhk8k3dGLWBTXgEH7NKQ8/u3Y58O9bd7Al6ioQSSSBKMmruSK175mbnL13PpAR24/KCOVNzJ8a4jZq/g0hdHsmL9Jm4/rps+TEVERETSbMy8lRz/yBBO69uSf57YI+pwYisnN5+B/xvBd1OXcNdJPTi1j/Z70y0vv4B5KzYElURL1zFz6VpmLV3PzKXrWLBqA4kpgYY1K4eJoeq02Zwkqk7LetWoUnHn+4MtXbuRF4fO4X9DZ7F07SZ2aVyT8/q34bhezUv1cyX5lEASKYX8Auexb6fzwOdTaFyrCg+c1ot+beuV+ucuW7uRK14ZxeBpSzmtT0tuPa6r3jxFRERE0mBTXgHHPjyYFes38fnV+1OritoKpFJObj4XPj+cwdOWcvdJPThFSaSkKyhwflmTs7kn0cyEvkRzlq8nr2DLcX+tKhVo27BGkCSqX522DYMkUev61aiZ4r+FjXn5vDdqAU8PnsmkRWuoX70SZ+7Zmt/v2ZqGNSun9Lllx6Q8gWRmhwMPAtnAU+5+Z5HbzwT+HG6uBf7o7qPD22YBa4B8IG9bgSZSAknSZUHYKPvHmcs5ukdT/nFC96T2LcovcP71xRQe+moaXZvV4tEzd6dV/WpJ+/kiIiIi8msPfjGVB76YwlN/6MPBXRpHHU5GSEwi3XNyT07evUXUIZU77s7ydUHz6hlLtjStLuxPlJO7ZepZlYpZtKlfnXYNwyRRgy2X61WvFPnyMXfnh+nLeGbITL6ctJiKWVkc07MZ5/dvS5dmtSKNLdOlNIFkZtnAFOAQYB4wDBjg7hMS7rM3MNHdV5jZEcAt7r5HeNssoI+77/B8cyWQJB0+HLOQG94aQ36Bc+tx3ThpBxpl76yvJv3CVa+Oxt25/9Re2pERERERSZEpv6zhqH9/x+HdmvLQgN2iDiej5OTmc8FzwxkyXUmkkqzdmMeswuVmYXIouLyW1TlbmldXyCpsXh0kiNqEy83aNqxO45pVys3U55lL1/HskJm8PnweG3Lz2atdfc7v35YDOzcqN68hTlKdQNqLICF0WLh9A4C7/3Mb968LjHP35uH2LJRAkjJkXdgo+/UR8+jZsg4PntaLNg12vFH2zpq7fD1/fHEE4+av5pLftefqQzptsxmdiIiIiPx2+QXOSY9+z+xl6/ji6v2pX0NLZtJtw6Z8Lnh+GN9PX8Y1h3SiVf3qVMgysrOMCllGVvg92M7afH12wn0Kb8vKYuv7ZBvZtvX9oq602ZaNefnMWbZ+q+VmhZeXrNm41X0Tm1dvThI1qE7zulV3uidrWbRqfS4vD5vDc9/PYuGqHNrUr8a5+7Tl5N1bUL2yJiSmS6oTSCcDh7v7BeH274E93P1P27j//wGdE+4/E1gBOPC4uz+xvedUAklSZXTYKHv28vVc+rsOXHHwzjfK3hk5ufnc+v4EXv5pDnu3r8+/B+xGA+3YiIiIiCTFU9/N4O8fTuTB03txXK/mUYeTsTZs2rKcLdWys4oklbITElJWuL0lCZVlRoVs+1XiKjsrq4RE19a3Zxe5LSt8rqVrN25OEs1fuXXz6gY1KhWTJKpB6/qla15dHuXmF/DJuEU8PXgmo+aupFaVCgzo14qz925DszpVow4v9lKdQDoFOKxIAqmfu19WzH0PAP4D9Hf3ZeF1zdx9gZk1Aj4HLnP3QcU8diAwEKBVq1a7z549u1RxiyRKbJTdqGZlHjitF3u0qx9ZPK8Pn8tf3xlHnWoVeeSM3vRpU/qm3SIiIiKZbPaydRz2r0Hs074BT53dp8xWpmSKggIPmzsXkF9A+N3JK3DyE76C7QLy8p0C33J7Xn7C7e7k5xdsua3AKUj4WYU/I7+A4Gdt834FRZ63hHiKfY7E+215PYWH3DUrV6BtwzBJFPYnKkwYqZF78UbMXsEzg2fy8biFmBlHdGvCef3b0rtV3ahDi60ysYTNzHoAbwNHuPuUbfysW4C17n5vSc+pCiRJpsRG2Uf1aModx3endrXo38AnLFjNH18cwfwVG7jxyF05d5822tERERER2QnuzhlP/si4+av47Or9aFpbVQySPoWJporZZXdJXVk3b8V6nv9hNi//NIc1OXns1qoO5/dvy+Fdm6jtR5KlOoFUgaCJ9kHAfIIm2me4+/iE+7QCvgL+4O7fJ1xfHchy9zXh5c+B29z9k5KeUwkkSZaPxi7khrfGkptfwC3HduWU3VuUqTf1VRty+b/XR/P5hF84qkdT7jqpBzW0/ldERETkN3n5pznc8NZY7jihO2fs0SrqcERkJ63dmMcbw+fy7PezmL1sPc3rVOXsvVtzWt9WSZ2WnclSmkAKn+BI4F9ANvCMu//DzC4GcPfHzOwp4CSgcN1Znrv3MbN2BFVJABWAl9z9H9t7PiWQpLTWbczj1vfH89rwefRsUZt/nb4bbdPQKHtnuDuPD5rB3Z9Mok2D6jx21u50alwz6rBEREREyoVFq3I45P5v6da8Ni9duEeZOlkoIjsnv8D5atJinh48g6EzllOtUjan7N6Cc/dpm5YBSHGW8gRSuimBJKUxZt5KrnhlFLOWreOP+7fnqkM6lYvpBT9MX8ZlL//Muo153HlSdzV+FBEREdkOd9/crPnTK/ejdX0dWIrEzbj5q3hmyEzeH72AvALnoM6NOb9/W/ZsV08J452gBJIIQZb68UHTuf+zKTSsWZn7T+3FXu2ja5S9MxavzuHSl0YybNYKzt6rNX85qguVKpT95JeIiIhIFN4dNZ8rXhnFX4/alQv2bRd1OCKSQotX5/DC0Nm88OMclq/bRJemtTivf1uO6dmUyhUya5JdaSiBJBlv4aqgUfbQGcs5snsT7jihO3WqVYo6rJ2Sm1/A3Z9M4snvZtKrZR3+c2ZvjbMUERERKWLZ2o0c8sAgWtarxlt/3JvsLFUiiGSCnNx83vl5Ps8MmcmUX9bSsGZlfr9na87coxX1a1SOOrwyTwkkyWgfj13I9YWNso/pyil9ylaj7J318diFXPvGGCpmG/8esBv7dmwYdUgiIiIiZcblL//Mx+MW8sFl+7JLE/WPFMk07s53U5fyzJCZfDN5CZUqZHFCr+ac17+t3hNKoASSZKT1m/K47f0JvDJsLj1a1ObBMtwoe2fNWLKWP74wkimL13DVwZ340wEdyNLZNREREclwX0z4hQueH86VB3fkyoM7RR2OiERs2uI1PDNkFm+NnEdObgH7dmzAef3bsn/Hhjp+KkIJJMk4Y+et4opXfmbmsnVcvH97rjq4U2x7Ba3flMdf3h7H2z/P53e7NOSBU3tRt3r5XJ4nIiIiUlqrc3I59P5B1K5akfcv6x/bfUAR+e1WrNvESz/N4bnvZ7F4zUbaN6zOufu05aTeLahaSX2SQAkkySAFBc4T383gvs8mU796Ze4/rSd7t28QdVgp5+68+OMcbnt/Ag1rVubRs3rTo0WdqMMSERERSbsb3hrLq8Pm8PYl+9CzZZ2owxGRMmhTXgEfjV3I04NnMnb+KupUq8gZ/Vrxh73a0KR2lajDi5QSSJIRFq3K4erXRvH99GUc0a0J/zyx/DbK3lmj567kkhdHsmTNRm4+tgtn9GsVi35PIumwMS+fyYvW0KlxTapU1BkoEZHy6PvpSznjyR8ZuF87bjxy16jDEZEyzt0ZNmsFTw+ewWcTfiHbjKN7NOW8/m0z9oS8EkgSe5+MW8T1b41hY24BtxzbhVP7tMzYxMmKdZu48tVRfDtlCSf2bs4/ju+uckyRYhQUOJMWrWHwtCV8N3Upw2YtJye3gJ4tavO/C/agVpWKUYcoIiK/wYZN+Rz2r0FkGXx8xX7a/xGR32TOsvX89/tZvDZ8Lms35tG3TV3O79+WQ7o0yagpjkogSWyt35TH7R9M4OWf5tK9eW0ePL0X7RrWiDqsyBUUOA99NY1/fTmFXRrX5NGzdo9dA3GRnbFw1Qa+m7qUwVOX8v30pSxduwmADo1q0L9DA1rUrcpdn0yiW/PaPH9eP2oqiSRS5g2asoQXf5xNr5Z1Oap7U1rVrxZ1SBKRv38wgacGz+SVgXuyZ7v6UYcjIuXUmpxcXh02l/9+P4t5KzbQsl5Vztm7Laf2aZER+4ZKIEksjZu/istf+ZmZS9cxcL92XHPILmqSWMS3U5ZwxSs/k5/v3HNKTw7v1iTqkETSak1OLkNnLGfw1CV8N20pM5asA6BBjcr071Cf/h0b0r9Dg63Wun8ybhGXvjSS3VrW4bnz+lG9coWowheREqzdmMc/PpzIyz/NoU61iqxcnwtAt+a1OLJ7U47q3pTW9XXyJFP8PGcFJz36PQP6teIfJ3SPOhwRiYH8AufzCYt4evBMhs1aQY3KFTi1T0vO3acNLevF92SFEkgSKwUFzpPfzeDezyZTr3olHji1F3t3iH+j7J01f+UGLnlxJKPnruSi/dpx7WG7UCFbiTaJp9z8AkbPXRlUGU1byqi5K8kvcKpUzGKPtvXZt2MD+ndswC6Na5a4zPXDMQu5/JWf6dO6Ls+e25dqlZREEilLvp+2lGvfGMPCVRu4cL92XHVwJ5au3cjHYxfxwdiFjJ67ElAyKVNszMvnmIcGsyYnj8+u2i8jKgREJL3GzFvJ04Nn8uGYhRS4c2iXJpy/b1v6tK4bu9YpSiBJbCxalcM1r49iyLRlHNa1MXee2EMj63fAxrx8/v7BRP43dDb92tbj4QG70ahWZk8XkHhwd6YvWcfgqUsYPG0pQ2csZ+3GPMygR/Pa9O/YgP4dGtK7dR0qV/htvTDeHTWfq14dxZ7t6vPMOX3VWFukDFi3MY+7PpnE8z/Mpm2D6tx7Sk92b133V/ebt2I9H49dxIdjFzIqTCZ1bVaLo3oomRRH938+hX9/OZVnzunDgZ0bRx2OiMTYolU5PP/DLF76aQ4r1+fSo0VtztunLUd2bxqb1TBKIEksfDp+EX9+M2iUffMxXTitb+Y2yt5Z7/w8nxveGkuNKhV4eMBu7KH+AFIOLVmzke+nL+W7qUsZMm0pC1flANCqXjX6d2zAvh0asFf7+kmZwvjWyHlc8/po+ndowJN/6KMkkkiEfpyxjGvfGMPcFes5b5+2/N+hu+xQk+RtJZMKK5PaqEdguTZx4WqOeWgwR/doyr9O3y3qcEQkQ2zYlM+bI+fxzJCZzFiyjia1qvDpVftRu2r5r4BUAknKtaBRdtDjoFvzWjx4+m60V6PsnTZ50Rr++MIIZi9fz58P34UL922nRJyUaRs25fPTrLCP0dSlTFq0BoDaVSuyT4f69O8Q9DFKVePc14bN5bo3x3DALg157Pe7/+ZKJhEpnQ2b8rn700n89/tZtKpXjXtO7km/tvV26mfNX7mBj8cu5IMxSibFQV5+ASc++j3zV2zg86v3p56q0kUkzQoKnG+nLmH4rOVce1jnqMNJCiWQpNxSo+zUWJOTy5/fHMNHYxdxeNcm3H1KD40slzIjv8AZv2DV5mlpI2avYFN+AZWys9i9dd2gyqhjA7o2q522kaov/TiHG98ey8G7NuY/Z/bW+5BImgyftZxr3xjDzKXrOHuv1vz5iM5J60lWmEz6cOxCfp6zEoAuTYNlbkd2b6rppeXAE4Omc8dHk3howG4c07NZ1OGIiMSCEkhS7hQUOE8Pnsndn05So+wUcQ/+jf/58SRa1q3Ko2ftzq5Na0UdlmSoOcvWM3jaUgZPW8L305dtnqbUuUnNsPF1Q/q1qbdDy1VS5fkfZvG3d8dzeNcmPHTGblRUM3qRlMnJzee+zybz1OCZNK9TlbtP7sHe7VO3H6BkUvkzc+k6Dv/XIPbr1JAnfr+7qqlFRJJECSQpV35ZncM1r41m8LSlapSdBsNmLefSF0eyOieXO07ozom9W0QdkmSAles38cP0ZXw3LagymrN8PQBNalXZXGG0d/sGNKxZOeJIt/bM4Jnc9sEEjurRlAdP66WJhiIp8POcFfzf66OZvmQdZ+7RihuO3JUaldM3CXHByg18NHYhH41dyMgwmbRr01oc1b0JR3ZvSjsto49cQYEz4MmhTFi4mi+u3p/GGgwiIpI0SiBJufFZ2Ch7Q24+fzu6KwP6qVF2OixZs5HLXh7J0BnLOWOPVvzt6C5qFixJtTEvn5GzVzJ42hIGT13K2PmrKHCoUbkCe7arT/8O9enfsSHtG1Yv83/zhUsmjuvVjPtP7ZW2ZXQicbcxL59/fTGVx7+dTpNaVbjr5B7s27FhpDEpmVQ2vTB0Nn99Zxx3ndSd0/q2ijocEZFYUQJJyrwNm/K5/cMJvPTjHLo2Cxpld2iknbJ0yssv4N7PpvDYt9Pp3rw2/zmzNy3rpaYpscSfuzNp0RqGTAumpf00czkbcvPJzjJ6taxD/w5BlVHPlnXK5VKwR76exj2fTuak3i245+QeZCmJJFIqY+et4prXRzHll7Wc3rclNx61a5nrzbdg5QY+HreID8cs2JxM6tykJkeHy9yUTEqPBSs3cOgDg+jZsjYvnL9HmT/pICJS3iiBJGXauPmruOKVn5m+JGyUfWgnTTmK0GfjF3HN66PJMuNfp/figF0aRR2SlBOLVuXw3dQlDJm2lMHTlrF07UYA2jesTv8OQR+jPdvVo2YZOyjcWQ9+MZUHvpjCaX1a8s8TuyuJJLITNuUV8PBXU3nkm+k0qFGJO0/qUS4+dwqTSR+NXciI2SuAIJl0VPemHNmjqabFpoi7c95/hzF0xnI+vXK/lE3fFBHJZEogSZmU2Ci7brVK3H9qL/p3VKPssmD2snVc/MJIJi1azWUHdOCKgztpmY78ytqNeQydvixsfr2UaYvXAtCgRiX26dCAfTo0oH+HBjSrUzXiSFPnvs8m89BX0zhzj1b8/fhuOhMu8huMX7CKa14bzaRFazipdwv+dnQXalcrfwlmJZPS5+2f53HVq6O56egunN+/bdThiIjEkhJIUuYsXp3DNa+P5rupSzmkS2PuOqkH9dQou0zJyc3npnfG8fqIeezbsQEPnr6bfkcZLi+/gNHzVjJ46jIGT1vCz3NWklfgVKmYRb+2YR+jDg3p3KRmxlTjuDt3fTKZx76dztl7teaWY7sqiVQGuTvPfT+LKYvXcs7ebejUuGbUIWW03PwC/vP1dB76aip1q1fijhO6c0iXxlGHlRQLV23g47FBMml4QjLpyO7BMjctz995S9du5OD7v6Vdg+q8fvHeOrElIpIiSiBJmfL5hF+47o3RbMjN56aju3BGv1Y64CrDXh02h5veHU+D6pV45Mze7NaqbtQhSZoUFDjTlqzlh7DKaOj0ZazZmIcZdG9em306NGDfDg3o3bpuRjddd3f+/uFEnh48k/P7t+WvR+2q97QyZE1OLte9MYaPxy0iO8vIL3B+t0tDBu7bjr3a19fvKs0mL1rDNa+PYtz81RzXqxm3HNM1tpNWlUxKrktfGsnn43/hw8v701FJYBGRlFECScqEDZvy+cdHE3hh6By6NK3Fvwf0okMj7QCUB+Pmr+LiF0bwy+ocbjq6C7/fs7UOumJoTU4uo+auZMTsFYycs5Kf56xgTU4eAC3rVaV/h4b079CAvdvXj+0B385yd259fwL//X4WF+3fjusP76y/kTJgyi9ruPiFEcxetp7rD+/Mybu34IWhs3nuh1ksXbuJbs1rceG+7Tiye9Ny2cy9PMnLL+DxQTP41xdTqFWlIv84oRuHd2sadVhps2hVDh+PW8iHY7Ykk3ZpXJOjeiiZtCM+Hb+Ii/43gmsO6cRlB3WMOhwRkVhTAknSblNeAYtW5TBv5Xrmr9jA/JUb+GDMQqYtXsuF+7bl/w7bRY2yy5lV63O5+rVRfDlpMcf2bMY/T+xO9coVog5LdpK7M3Ppuq2SRZN/WYM7mAUHNru1qsvurevSt01dWtevHnXIZZ6789d3xvHij3P40wEduObQTkoiRejdUfO5/s2x1KhSgYcH7MYe7epvvi0nN593fp7Pk9/NYPqSdTSvU5Vz92nD6f1aUUPva0k3bfEarnltNKPnreKo7k257biu1K9ROeqwIlOYTPpo7EKGzdqSTDqye1OO6tFEJ9eKWLUhl0Pu/5Z61Svx/mX9lewVEUmxlCeQzOxw4EEgG3jK3e8scruFtx8JrAfOcfeRO/LY4iiBFL31m/KYv2ID81Zu2JwgSvz+y5ocEv9rmUGb+tW57biu7NuxYXSBS6kUFDiPfjud+z6bTPuGNXj0rN111rScWL8pj9FzVzFyzgpGzl7ByDkrWLE+F4CaVSoEyaJWdendug49W9Ypc+Ozy4uCAufGt8fyyrC5XHlwR648uFPUIWWcTXkF3PHRRP77/Sz6tqnLI2f0plGtKsXet6DA+XryYh4fNIOfZi6nZpUKnLlHa87Zuw1Nahf/GNlx+QXOU9/N4L7Pp1C9Uja3H9+No3s0izqsMiUxmTR89grcoVPjGhzVvVlsk0kFBc6G3HzWbcpjw6Z81m/KZ/2mvPD7lssbNuWzbmM+P81axtAZy3nnkn3o3qJ21OGLiMReShNIZpYNTAEOAeYBw4AB7j4h4T5HApcRJJD2AB509z125LHFUQIptdydletzmb9yA/O2Sg6t33y58MCzUMVso2ntqjSvU5Xmdbd8bxF+b1K7iiqOYmTItKVc/vLP5OTmc/fJPTmqR+YsQygP3J15KzYwcs6KsMJoBRMXriG/IHi/b9+wOr3D6qLerevSoWGNjGl6nQ4FBc61b4zhzZHzuPawXbj0gA5Rh5QxFq7awKUvjmTknJVc0L8tfz6i8w5XK4yau5Inv5vBx2MXkp1lHNuzORfu15bOTWqlOOp4mr5kLde+PpqRc1ZyWNfG/P347jSsmblVRzvil9U5fDx2IR+NXcSw2cs3J5OO7N6Uo7o3TWvfH3dnY15BkODJzWf9xm0keIpc3m5CaFMeObkFvymWShWyuOrgTvzxd+1T9GpFRCRRqhNIewG3uPth4fYNAO7+z4T7PA584+4vh9uTgd8Bbbb32OIogVQ6BQXOkrUbf50cSthetyl/q8dUrZi9VWKoeZ2qtEjYblSziqZhZJjEA7Vz92nDH3/XnoY1KmvJTgRycvMZN39VQsJoJUvWbASgWqVserWsszlhtFurOtSppv5FqZZf4Fzz2ijeGbWAG4/szMD9dOCTat9PW8plSUhsz1m2nmeGzOTVYXPZkJvPfp0actF+7dhbDbd3SEGB88yQmdzz6WSqVMzmtuO6cmzPZvq3+412NJmUl1/A+tz8zYmbdRvz2JAbJm4Kkz4JCaANueF9Cu+fkPTZ6rbc/M0nHXZElkG1ShWoVimbapWyqVqpAtUrZVM13C7utl/fb8t9qlWqsPmxWrImIpJeqU4gnQwc7u4XhNu/B/Zw9z8l3OcD4E53Hxxufwn8mSCBVOJji6MEUsmK6z+0+fvKDSxcmcOm/K3P/tSpVjFIBv0qQVSN5nWrUrdaRe38ya9syivgnx9P5Nkhs4AgWdGqXjXa1K9O6/rVaF2/Om3qV6NV/Wo0rV1VScYkWbhqAyNnr9xcXTR+wSpy84P38lb1qgWVRa3q0Lt1XXZpXJMK2vmORF5+AVe+OooPxizkpqO7cH7/tlGHFEvuzmPfzuCeTyfRrmENHkvS0tqV6zfx4o9zeHbILJau3UiXprUYuF87juqhhtvbMmvpOq57Yww/zVrOQZ0b8c8Tu29z+aDsuF9W5/DJuEV8OGbh5mRSzSoV2JhXwKa831bNU6Vi1g4mc4pP+hQmeoomfSpXyNJ+oohITJSUQEpGp8jiPi2KZqW2dZ8deWzwA8wGAgMBWrVq9Vvii52d6T/UqGZlmtepSo8WdTii29bLy5rVqaqmobJTKlXI4uZjunJ0j2aMm7+KWcvWMXvZeqYuXsNXkxZvlaislJ1Fy3pVaR0ml9rUr06r8HuLulV1QLYNm/IKmLBwNSNnr2DEnBX8PHsFC1blAFC5QhY9W9ThvP5t2b1VXXZrVVdLRMqQCtlZPHBaL/ILnNs/mEDFbOMPe7WJOqxYWZ2TyzWvjebzCb9wdI+m3HVSj6Q1969TrRKXHtCB8/u35d1R83nyu5lc+eoo7vpkEuft05bT+7WkpnqFAUHV0f+GzubOjydRIdu475SenNi7uRIKSdK4VhXO3rsNZ+/dZnMyafqStUECp2IFqlcuPulTeLlqpWyqV6pA1YrZWq4sIiKloiVsZczO9B+qkGU0rVMlrCCqFlQOqf+QRCy/wFm4agNzlq1n1rL1zA6TS4VJpg25W5ZJZmcZzepU2VK5VC9MMjWoTqt61ahSMXP+/y5ZszFodB02ux4zbxUbwzPMzWpXoXfrupuXo+3atBaVKijxVtZtyivgkhdH8sXEX7jjhO6csUdmnwRJlokLV/PHF0Ywb8UG/nLUrpyzd5uUJiwKCpxvpizmiUEzGDpjOTUrV+CMPVpxzj5taFq7asqet6ybu3w9174xmqEzlrN/p4bceVL3jP73EBERKe9SvYStAkEj7IOA+QSNsM9w9/EJ9zkK+BNbmmj/29377chjixOHBNKGTfl8PvEX9R+SjOQe9OGavWw9s5auY87yLUmmWUvXsTonb6v7N6lVJVwSV7gsrvrm7fJcAZCXX8CkRWv4OaF30Zzl64GgMX3XZrXD5WjBdDQdlJVfG/Pyufh/I/h68hLuPqkHp/ZtGXVI5dpbI+dx49tjqV21Io+c0Zs+beql9fnHzFvJE4Nm8NHYhWSZcWzPZlywbzu6NMuchtvuzks/zeGODydiZtx09K6c2qelqo5ERETKuZQmkMInOBL4F5ANPOPu/zCziwHc/TEL9iYeBg4H1gPnuvvwbT12e88XhwTSqvW59LztM0D9h0SKWrl+06+qlgormZau3bjVfetXr7R5KVzRJFNZ+9tZsW4TP89dsbl/0eh5K1kfJowb1qxM71Z1NieMujWvnVGVV5kgJzefC58fzuBpS7n35J6ctHuLqEMqdzbm5XPb+xN48cc57NmuHg8N6B3pss25y7c03F6/KZ99OzZg4H7t6N+hQZl670m2+Ss38Oc3xjB42lL6d2jAXSf3oHkdJbhFRETiIOUJpHSLQwLJ3Zm6eK36D4n8Rms35jGnsFpp2XrmLF/HrKXB9sLVW/f/qlmlwlbNvBOXxjWqmdqJcQUFzrQla4PKorB/0Ywl64Bgyd6uTWtuXorWu1VdWtStGusDTgnk5OZz/nPD+GH6Mh44rRfH9WoedUjlxvyVG7jkxZGMnruSi/Zvx7WH7lJmGsSvWp/Liz/N5tkhs1iyZiOdm9Rk4H7tOLpHs1gtM3V3Xhs+l9s/mEiBOzceuStn7tFK710iIiIxogSSiGSEnNx85q1YHySUlickmZatY+6KDVuNJK5SMWtzQqno0rhmdX77xLg1ObmMmrty81K0n+esYE24FK9utYrhMrQgWdSzZW2qVVLiOFNt2JTPOc/+xLBZy3loQO+dHjefSQZNWcIVr/xMbr5z7yk9Obxbk6hDKtbGvHzeHbWAJwfNYOritTSpVYXz+rfh9H6tqFWOl9tCMP3x+jfH8u2UJezZrh73nNyTlvWqRR2WiIiIJJkSSCKS8fLyC1iwMids4l24NC5IMs1Zvn5zo2oI+g+1rFut2KVxLepWpVJ2FjOXrtucLBo5ewVTFq/BPZh6uEvjmuy2ubqoDm0bVNcZetnKuo15nPPsT4ycs5JHzuhdZhMiUSsocB75ehr3fzGFTo1q8uhZvWnXsEbUYW2Xu/PNlCU8OWgG309fRo3KFRjQryXn7tOWZuVsqZe78+bI+dz6/njy8p3rj+jM7/dsrWleIiIiMaUEkohICQoKnF/W5DBrabgkLqH/0uxl61m7cUtT7yyDapUqbL6uZpUK7Naq7ub+RT1b1in3lQaSHms35vH7p39k3PxVPHrm7hzcpXHUIZUpq9bnctVro/hq0mKO79WMO07sXi4r98bOW8WT383gw7ELMeCYns24YN+2dG1WO+rQtmvx6hxufHssX0xcTN82dbnn5J60aVA96rBEREQkhZRAEhHZSe7OsnWbwmRSkFxasW4TXZrVYvfWdenQsIbOxMtOW52Ty++f+pGJC9fw+B9254BdGkUdUpkwbv4q/vjiCBatyuFvR3fhrD1bl/sqvnkr1vPskFm88tMc1m3Kp3+HoOH2vh3LXsNtd+e90Qv427vjycnN57rDO3PO3m008VVERCQDKIEkIiJSRq1an8uZTw9lyi9reeoPfdivU8OoQ4rUa8Pm8td3x1G/eiUeObM3vVvVjTqkpFq1IZeXfpzDs0NmsjhsuH3hvu04pmfZaLi9dO1G/vL2WD4d/wu7tarDvaf0pH05WDYoIiIiyaEEkoiISBm2Yt0mznjqR2YsWcuz5/Rl7w4Nog4p7XJy87nlvfG8Mmwu/Ts04MHTe1G/RuWow0qZTXkFvDc6aLg9+Zc1NK5VmXP3acuAfq2oXTWaZbAfjlnITe+OY+3GPK45pBMX7NtOVUciIiIZRgkkERGRMm7Z2o0MeHIoc5dv4L/n9mWPdvWjDilt5i5fzx9fHMG4+av50wEduOqQThmTuHB3vp2yhCe/m8GQaUHD7dP7tuTc/m1pnqaG28vXbeKmd8fx4ZiF9GxRm3tP6UnHxjXT8twiIiJStiiBJCIiUg4sWbOR05/4gYWrcnj+vH70aVMv6pBS7uvJi7nylVEUuPPAqb0yupn4uPmreOq7Gbw/ZiEAR/doyoX7tqNb89Q13P5k3CL++s5YVm3I5cqDO3HRfu2okB39UjoRERGJhhJIIiIi5cTi1Tmc/sRQFq/ZyPPn94tdD6BC+QXOg19O5aGvptK5SS0eO6s3retrwhfA/JUbeHbwTF4OG27v3b4+A/drx/6dGiat4fbK9Zu45b3xvDNqAV2b1eK+U3vSuUmtpPxsERERKb+UQBIRESlHFq3K4bQnfmD52k28eOEe9GhRJ+qQkmrFuk1c8eooBk1Zwsm7t+Dvx3ejSsXsqMMqc1ZtyOWVn+bw7JBZLFqdwy6Na3LBvm05tlczKlfY+X+vLyb8wg1vj2XFuk1cdmBHLjmgPRVVdSQiIiIogSQiIlLuzF+5gdMe/4HVG3J56cI9U7qMKZ1Gz13JJS+OZMmajdx6XFdO79uyzI2xL2s25RXw/ugFPPndDCYtWkOjmpU5Z582nLlH69/UcHvVhlxue38Cb46cR+cmNbn3lJ6x+X8lIiIiyaEEkoiISDk0d/l6Tn9iKOs25fHyhXuya9Pyu8TI3Xn5p7nc8t54GtaszKNn9Y5dZVWquTvfTV3Kk9/N4LupS6leKZvT+rbivP5taFG3WomP/XryYm54cyxL1m7kkt+157IDO1KpgqqOREREZGtKIImIiJRTs5et47THh7Ipv4BXBu5Jp3I4HWvDpnz++s443hw5j/06NeTB03pRt3qlqMMq1yYsWM2T383g/dELcODI7k0ZuG87urfYuqJodU4u//hgIq8On0vHRjW479SeStyJiIjINimBJCIiUo7NXLqO0x7/gQKHVwbuSYdGNaIOaYfNXraOi18YycSFq7n8oI5ccVBHsrO0ZC1ZFqzcwH+/n8VLP85h7cY89mq3peH299OXcd0bo1m0OoeL9m/PFQd1VK8pERERKZESSCIiIuXctMVrOf2JH8gy49WL9qJtg7I/seyLCb9w1WujyDLjX6f14oDOjaIOKbZW5+Ty6k9zeWbITBauyqF5narMX7mBdg2rc98pPdktptP8REREJLmUQBIREYmBKb+s4fQnhlIpO4tXL9qzzI69zy9w7v98Mo98PZ1uzWvx6Jm707JeyT16JDly8wv4YMwCXhs2jx4ta3PVwZ1UdSQiIiI7TAkkERGRmJiwYDVnPDWU6pUq8MrAPctcYmbZ2o1c/srPDJm2jNP7tuSWY7sqgSEiIiJSTpSUQNL4DRERkXKkS7NavHD+HqzJyWXAk0OZv3JD1CFtNnLOCo5+aDDDZq3g7pN6cOdJPZQ8EhEREYkJJZBERETKmW7Na/O/8/dg1fpcznhyKItW5UQaj7vz/A+zOO3xH6iQbbz1x705tW/LSGMSERERkeRSAklERKQc6tmyDs+d349lazcx4MmhLF4dTRJp/aY8rnp1FH97dzz7dmzIB3/al27Na2//gSIiIiJSriiBJCIiUk71blWX/57bl19W5zDgyaEsWbMxrc8/Y8laTnjke94dvYBrDunEU3/oQ+1qFdMag4iIiIikhxJIIiIi5VifNvV45py+zF+5gTOfGsqytelJIn0ybhHHPTyExWtyeO7cflx2UEeysiwtzy0iIiIi6acEkoiISDm3Z7v6PHN2X2YvW8+ZT/3IinWbUvZcefkF/PPjiVz8wgjaNazOB5fvy36dGqbs+URERESkbFACSUREJAb27tCAJ//QhxlL1/H7Z35k1frcpD/HkjUbOevpH3n82xmctWcrXrt4L5rXqZr05xERERGRskcJJBERkZjYr1NDHj9rd6YsWssfnvmR1TnJSyINn7Wco/79HaPmruT+U3vy9+O7U7lCdtJ+voiIiIiUbaVKIJlZPTP73Mymht/rFnOflmb2tZlNNLPxZnZFwm23mNl8MxsVfh1ZmnhEREQy3QGdG/GfM3szfsFqznnmJ9ZuzCvVz3N3nhk8k9OfGEq1Stm8fck+nNi7RZKiFREREZHyorQVSNcDX7p7R+DLcLuoPOAad98V2BO41My6JNz+gLv3Cr8+KmU8IiIiGe/gLo15+IzdGD1vFec++xPrdjKJtG5jHpe9/DO3fTCBAzo34t0/9WfXprWSHK2IiIiIlAelTSAdBzwXXn4OOL7oHdx9obuPDC+vASYCzUv5vCIiIlKCw7s15cHTezFi9grOf24YGzbl/6bHT1u8luMeGcJHYxdy3eG78PhZu1O7asUURSsiIiIiZV1pE0iN3X0hBIkioFFJdzazNsBuwI8JV//JzMaY2TPFLYETERGRnXN0j2Y8cFovfpq5nAueH0ZO7o4lkT4cs5DjHh7MinWb+N/5e3DJ7zqQlWUpjlZEREREyrLtJpDM7AszG1fM13G/5YnMrAbwJnClu68Or34UaA/0AhYC95Xw+IFmNtzMhi9ZsuS3PLWIiEjGOq5Xc+45uSffT1/GwP+NKDGJlJtfwO0fTODSl0bSqUlNPri8P/t0aJDGaEVERESkrDJ33/kHm00GfufuC82sKfCNu+9SzP0qAh8An7r7/dv4WW2AD9y92/aet0+fPj58+PCdjltERCTTvDZsLte9OYYDOzfi0bN6/2qC2uLVOVz60kiGzVrBOXu34cYjd6VSBQ1rFREREckkZjbC3fsUd1tp9wzfA84OL58NvFvMkxvwNDCxaPIoTDoVOgEYV8p4REREpBin9m3JP07oxleTFvOnl34mN79g820/zljGkf8ezLj5q3nw9F7ccmxXJY9EREREZCul3Tu8EzjEzKYCh4TbmFkzMyucqLYP8HvgQDMbFX4dGd52t5mNNbMxwAHAVaWMR0RERLbhzD1ac9txXfl8wi9c/nKQRHpi0HTOeOpHalWpwDuX7sNxvTTnQkRERER+rVRL2KKiJWwiIiI77+nBM7n9gwk0r1OV+Ss3cHjXJtxzSg9qVtGUNREREZFMVtIStgrpDkZERESidX7/tuQXFHDvZ1O48cjOXLhvO4IV5yIiIiIixVMCSUREJAMN3K895+7TlorZ6nUkIiIiItunvUYREZEMpeSRiIiIiOwo7TmKiIiIiIiIiEiJlEASEREREREREZESKYEkIiIiIiIiIiIlUgJJRERERERERERKpASSiIiIiIiIiIiUSAkkEREREREREREpkRJIIiIiIiIiIiJSIiWQRERERERERESkREogiYiIiIiIiIhIiZRAEhERERERERGREimBJCIiIiIiIiIiJVICSURERERERERESqQEkoiIiIiIiIiIlEgJJBERERERERERKZESSCIiIiIiIiIiUiIlkEREREREREREpERKIImIiIiIiIiISImUQBIRERERERERkRIpgSQiIiIiIiIiIiVSAklEREREREREREqkBJKIiIiIiIiIiJRICSQRERERERERESlRqRJIZlbPzD43s6nh97rbuN8sMxtrZqPMbPhvfbyIiIiIiIiIiESntBVI1wNfuntH4Mtwe1sOcPde7t5nJx8vIiIiIiIiIiIRKG0C6TjgufDyc8DxaX68iIiIiIiIiIikWGkTSI3dfSFA+L3RNu7nwGdmNsLMBu7E40VEREREREREJCIVtncHM/sCaFLMTX/5Dc+zj7svMLNGwOdmNsndB/2GxxMmngYCtGrV6rc8VERERERERERESmG7CSR3P3hbt5nZL2bW1N0XmllTYPE2fsaC8PtiM3sb6AcMAnbo8eFjnwCeAOjTp49vL24REREREREREUmO0i5hew84O7x8NvBu0TuYWXUzq1l4GTgUGLejjxcRERERERERkWiVNoF0J3CImU0FDgm3MbNmZvZReJ/GwGAzGw38BHzo7p+U9HgRERERERERESk7truErSTuvgw4qJjrFwBHhpdnAD1/y+NFRERERERERKTsKG0FkoiIiIiIiIiIxJwSSCIiIiIiIiIiUiIlkEREREREREREpERKIImIiIiIiIiISImUQBIRERERERERkRIpgSQiIiIiIiIiIiVSAklEREREREREREqkBJKIiIiIiIiIiJRICSQRERERERERESmREkgiIiIiIiIiIlIiJZBERERERERERKRESiCJiIiIiIiIiEiJlEASEREREREREZESKYEkIiIiIiIiIiIlUgJJRERERERERERKpASSiIiIiIiIiIiUSAkkEREREREREREpkRJIIiIiIiIiIiJSIiWQRERERERERESkREogiYiIiIiIiIhIiZRAEhERERERERGREimBJCIiIiIiIiIiJVICSURERERERERESqQEkoiIiIiIiIiIlEgJJBERERERERERKVGpEkhmVs/MPjezqeH3usXcZxczG5XwtdrMrgxvu8XM5ifcdmRp4hERERERERERkeQrbQXS9cCX7t4R+DLc3oq7T3b3Xu7eC9gdWA+8nXCXBwpvd/ePShmPiIiIiIiIiIgkWWkTSMcBz4WXnwOO3879DwKmu/vsUj6viIiIiIiIiIikSWkTSI3dfSFA+L3Rdu5/OvBykev+ZGZjzOyZ4pbAiYiIiIiIiIhItLabQDKzL8xsXDFfx/2WJzKzSsCxwOsJVz8KtAd6AQuB+0p4/EAzG25mw5csWfJbnlpEREREREREREqhwvbu4O4Hb+s2M/vFzJq6+0IzawosLuFHHQGMdPdfEn725stm9iTwQQlxPAE8AdCnTx/fXtwiIiIiIiIiIpIcpV3C9h5wdnj5bODdEu47gCLL18KkU6ETgHGljEdERERERERERJKstAmkO4FDzGwqcEi4jZk1M7PNE9XMrFp4+1tFHn+3mY01szHAAcBVpYxHRERERERERESSbLtL2Eri7ssIJqsVvX4BcGTC9nqgfjH3+31pnl9ERERERERERFKvtBVIIiIiIiIiIiISc0ogiYiIiIiIiIhIiZRAEhERERERERGREimBJCIiIiIiIiIiJVICSURERERERERESqQEkoiIiIiIiIiIlEgJJBERERERERERKZESSCIiIiIiIiIiUiIlkEREREREREREpERKIImIiIiIiIiISImUQBIRERERERERkRIpgSQiIiIiIiIiIiVSAklEREREREREREqkBJKIiIiIiIiIiJRICSQRERERERERESmREkgiIiIiIiIiIlIiJZBERERERERERKRESiCJiIiIiIiIiEiJlEASEREREREREZESKYEkIiIiIiIiIiIlUgJJRERERERERERKpASSiIiIiIiIiIiUSAkkEREREREREREpkRJIIiIiIiIiIiJSIiWQRERERERERESkRKVKIJnZKWY23swKzKxPCfc73Mwmm9k0M7s+4fp6Zva5mU0Nv9ctTTwiIiIiIiIiIpJ8pa1AGgecCAza1h3MLBt4BDgC6AIMMLMu4c3XA1+6e0fgy3BbRERERERERETKkFIlkNx9ortP3s7d+gHT3H2Gu28CXgGOC287DnguvPwccHxp4hERERERERERkeRLRw+k5sDchO154XUAjd19IUD4vVEa4hERERERERERkd+gwvbuYGZfAE2Kuekv7v7uDjyHFXOd78DjisYxEBgYbq41s+1VPpUXDYClUQeRJpn0WkGvN84y6bWCXm+cZdJrBb3eOMuk1wp6vXGXSa83k14r6PXGWZxea+tt3bDdBJK7H1zKJ58HtEzYbgEsCC//YmZN3X2hmTUFFpcQxxPAE6WMpcwxs+Huvs0G5HGSSa8V9HrjLJNeK+j1xlkmvVbQ642zTHqtoNcbd5n0ejPptYJeb5xlymtNxxK2YUBHM2trZpWA04H3wtveA84OL58N7EhFk4iIiIiIiIiIpFGpEkhmdoKZzQP2Aj40s0/D65uZ2UcA7p4H/An4FJgIvObu48MfcSdwiJlNBQ4Jt0VEREREREREpAzZ7hK2krj728DbxVy/ADgyYfsj4KNi7rcMOKg0McRA7JbllSCTXivo9cZZJr1W0OuNs0x6raDXG2eZ9FpBrzfuMun1ZtJrBb3eOMuI12ruv7mftYiIiIiIiIiIZJB09EASEREREREREZFyTAkkEREREREREREpUal6IImIiIiIpJOZ9S7pdncfma5Y0sHMHgK22XPC3S9PYzgiIpLBlECKiJlVd/d1UceRamZWDbgGaOXuF5pZR2AXd/8g4tBSwszuBv4ObAA+AXoCV7r7C5EGlkJmVgnoTLBzO9ndN0UcUlKZWb2Sbnf35emKJd3MrD/Q0d2fNbOGQA13nxl1XMlkZleXdLu735+uWCR1wv+/FwJtSNj3cffzooopVcysMnASv36tt0UVUwrcF36vAvQBRgMG9AB+BPpHFFeqDA+/7wN0AV4Nt08BRkQSUZqZWRbBZ9DqqGNJpUw5PshEZlaV4HhoctSxiJSGEkhpZmZ7A08BNYBWZtYTuMjdL4k2spR5lmDnZq9wex7wOhDLBBJwqLtfZ2YnELzWU4CvgVgmkMzsKOAxYDrBzntbM7vI3T+ONrKkGkGQHDOgFbAivFwHmAO0jSyyFDKzmwkOzHYh+DuuSPD/eJ8o40qBmuH3XYC+wHvh9jHAoEgiSrFMSqYkeBf4DvgCyI84llR7F1hF8N61MeJYUsLdDwAws1eAge4+NtzuBvxflLGlgrs/B2Bm5wAHuHtuuP0Y8FmEoaWUmb0EXEzwNzsCqG1m97v7PdFGlnyZdnxgZp2Aa4HWbP05dGBkQaWQmR0D3AtUIthX7gXc5u7HRhpYkpnZWIqvljTA3b1HmkNKGTN7zd1PLeY1x+61FqUEUvo9ABxGeJDi7qPNbL9oQ0qp9u5+mpkNAHD3DWZmUQeVQhXD70cCL7v78ni/XO4j2JmdBmBm7YEPgdgkkNy9LWzeUX/P3T8Kt48ADo4ythQ7AdgNGAng7gvMrGbJDyl/3P1WADP7DOjt7mvC7VsIkt1xlEnJlELV3P3PUQeRJi3c/fCog0iTzoXJIwB3HxcemMVVM4Kkd2Hla43wurjq4u6rzexM4CPgzwSJpNglkMi844PXCU5APklmfA7dAvQDvgFw91Fm1ibCeFLl6KgDSKMrwu+Z9JoBJZAi4e5ziyQV4vzGuSks2XTYnGCI5RnR0PtmNolgCdsl4Zn+nIhjSqXFhcmj0AxgcVTBpFhfd7+4cMPdPzaz26MMKMU2ububWeHfbvWoA0qxVkDi8stNBBU6cZRJyZRCH5jZkYUJ4Jj73sy6JyZWYmyimT1FUB3pwFnAxGhDSqk7gZ/N7Otwe3+CA9O4qmhmFYHjgYfdPbfwMymOMuz4IM/dH406iDTKc/dVMT+pjLvPLrxsZq0J2iB8ER4Lxirv4O4Lw++zAcysFjF7jduSES+yjJkblql62DvmcuK9s3MzQS+glmb2IsHyl3MijSiF3P16M7sLWO3u+Wa2Hjgu6riSzcxODC+ON7OPgNcIdt5PAYZFFlhqLTWzv7L1gcqyaENKqdfM7HGgjpldCJxHUF4fV/8DfjKztwl+vycAz0cbUspkUjKl0BXAjWa2EchlS4l5rWjDSon+wDlmNpPghE2cy+nPBf7IljPBg4DYHpSG/eg+BvYIr7re3RdFGVOKPQ7MIuhxNSg8II1rD6RMOz5438wuAd4m4cRyjPtKjjOzM4DssB/s5cD3EceUMuF+40CgHtAeaEFQcXZQlHGlgpldBNxGUDxQmOB2oF1kQaWYucc2kV8mmVkD4EGCpS9GsHb9CneP7YGomdUH9iR4vUPdfWnEIaVM2DT8aoImeQPj2jTczJ4t4WaPYy+VsJn2zcB+BB8MgwjWr8d1ZwczOwQ4lOBv91N3/zzikFIqnOy0b7g5yN1/jjKeVDGzNUB1giqrTcQ7mZJxwoPsX0k8MxwnmdSYNmwBcCbQzt1vM7NWQBN3/yni0NLGzCq4e17UcSRbph0fhAnuotzdY3nQHR4f/IWEfSrgdneP5SoFMxtFsGTvR3ffLbxurLt3jzSwFDCzqcBecT6+LUoJJEmJTBuxW8jMXiVYn/8Hd+8W7tj+4O69oo1MksXMarj72qjjSDUzu6voMqfirouTTJg6l8nMrC7QkWByFwDuHrtG6WZ2G0GPq+/jPs3JzI4l6IdTyd1j25i2kJk9ChQAB7r7ruH/6c/cvW/EoaWEmTUG7gCaufsRZtaF4EDt6YhDE9lpZpYNVI/zREEz+9Hd9zCzn919NzOrAIyMYyWsmX0CnOju66OOJV2UQEoTM3uI4rvSA+Dul6cxnJRLWJ9fHI/xlIXh7t6n8A0zvG60u/eMOrZUMLMWwEMESxMdGExwxmxepIGlQOKEFHfPhAkpI929d5HrxsTxwx+2njrn7p3MrBnwurvHbepcYhVDW3e/3cxaAk3jXMVgZhcQLHNqAYwiqIr9IY6fRWZ2HsEytr2ANQTJpEHu/m6kgaWAmY0ADgS+SfjMjfP71Eh3751B+xgfE0wB/Yu79wwPQn+OaRXDv4u5ehUwPKZ/uxUJlp8WNgr/BnjcwwmDcWPFTBQEYjlREMDM7gZWAn8ALgMuASa4+1+ijCsVzGw3gvepH9l6OWasju0TqQdS+gyPOoB08nDEbgbKtKbhzwIvEfQ+gqAv0LPAIZFFlDoZMSHFzP5I8EHfzszGJNxUExgSTVRpkRFT50L/IaxiAG4H1gKPALGsYghdQfD6hrr7AWbWGbg14phSwt2fAZ4xsybAqQRj7QcS/A3HTUY0pk2QG1YvFO5jNCT4W46rBu7+mpndAODueWYW18bSVYDObJn+eRIwHjjfzA5w9yujCixFHiWYXPyfcPv34XUXRBZRamXSREGA64HzgbHARQSvOa59NB8HviJ4rXF+P95MCaQ0cffnoo4hKmbWDejC1ssGYtWc1sz2dPehBNNQMqZpONDQ3RP7If3XzK6MKphUy5AJKS8BHwP/JNgBKLQmzv2eyKypc3sUVjEAuPuKsGlrnOW4e46ZYWaV3X2Sme0SdVCpYMFUsi7ALwTVRycTJkZjKKMa0wL/Jmg63MjM/kHwu/1rtCGl1Lqwj2bh+/KeBFU5cdSBYGliHmxervgZwQm5OE5U7Fukcu4rMxsdWTSpl1ETBQkGCD3v7k9GHUga5Ln71VEHkU5KIKVZeLboz/w6oRK7MnrYvCzkdwSv9yPgCIJlTrFKIAGPmtlPBL/bE9nSNPyKmDdVW2pmZwEvh9sDiO9ksoyYkOLuqwh20AcAmFkjgveqGmH/pzlRxpdCxU2di+uOT6ZVMQDMM7M6wDvA52a2AlgQaUSpUx/IJlg+sBxYGsemw6HLCBrTbiRIfn9GMA0nrt4gqFo4iGAf43iCRGFcXU1Q9dvezIYADQmSZnHUnGC4QWGCrDpB76d8C6ZHxk2+mbV39+kAZtaOeJ6UK5RJEwUBjgX+ZWaDgFcIBrHE9XPoazMbCLxPZkwUVA+kdDOzz4BXCUrKLwbOBpbEtTGtmY0FehKsWe8ZNkR8yt2PiTi0pDKzLIKEwiUEUxX+F3FIaRFOgHmYoNeGE5z5vSKO0362MSHl8rh+QJjZMcD9QDNgMdAamOjuXSMNLIUsQ6bOhSX0pwG9gecIqxjc/fUSHxgTZrY/Qf+JT9x9U9TxpIqZ7Uqw7PYqINvdW0QcUtKZ2flFGyqb2Z3ufv22HlOemdmHwPGFfWLMrCnwgbvvHm1kqWFmbYB5wC4E78uTgV7uPizKuFLBzM4nqCb7huC17kfQQPxl4BZ3vza66JLPzA4iaHkwg+D1tgbOdfeSeqjGisV0omChsOLqCIL9jf7A5+4euyWKFkwU/FVCJa4TBUEJpLQzsxHuvntik0cz+9bd9486tlQws5/cvV/Y6PIAgoae4+J6EBpOCPkByCJ4M9F47JgwsyPc/eMi113s7o9FFVMqhaXkBwJfhBM0DgAGuPvAiENLCTP7E/Ciu6+IOpZUCpPdexJUphRWMXzp7rGrpivKgumg/Qnem4d4fKeBHg3sS3AAWpfgM+m7sDdSrIRNll9w9xfD7UeAKu5+frSRpUZYHXkUQX+clgTVOf/n7p9FGliKhPuOx7r7/HB7P+CRODbRBgiHN/wemERQgTTPYzgpspCZVWZLcnCSu8ex0mozMzsK6MrWK1DiXDFZmEQ6HDgX2NfdG0YcUtKFvW8vYcv+xXfAY+6+IdLAUkhL2NKvcLrAwvCNZAHBVJi4Gh4uG3iSoOx6LRDLST/h2aPrCcrpH/EYZ2fN7Dp3v9uKny7oBAenLxSWJsfETWa20d2/guDfgCApGssEEpDr7svMLMvMstz9azO7K+qgUqgJMMzMRgLPEFQgxe5v2N0LzOw+d9+L4CAlI5jZ3wia/b8VXvWsmb3u7n+PMKxUOQIYBDzo7nFdplfoROA9MysgeN3L3f3SiGNKGXd/MlxC/Q7QhmASaJx7Pl0MvBNWxPYmqMg5MtqQUsO2MSmS4ERObJjZge7+lZmdWOSm9maGu79V7APLOTN7DKhGsN/4FEHlbyyPhwDM7HDgdILX+w3Baz41yphS6DmC5YiFkxQHhNfF9fWqAindwjOD3xGcOXoIqAXc6u7vRRpYGoSlyLXcfcz27lvemNn3BGubr3b3RRGHk3Jmdoy7v29mZ2/jLvWBs+M0WjhcwvYBcC3B2ZTOwOkxHjn7BUF/jX8CDQiWsfV1972jjCuVLOiQfijBmbI+wGvA0zFLhGJmtwJjgLfimCQrjplNBHZz95xwuyow0t13jTay1AiXixdO1fvJ3RdHGU+ymVm9hM2aBAmVIcDfIH69J8wssUGrEVSpjAUKG+HfH0Vc6WBmexH0j8kBjnL3JRGHlBJhy4fCSZG9LJwU6e6nRRxaUpnZre5+s5k9W8zN7u7npT2oNChceZLwvQbBZ/ChUceWCmb2CkHvo48zoLJsdNHjneKuixMlkCQlwqUC2xS3pQNmdkhc+6XsLDO7GOjq7pdFHUuyhA2lvyCopjsvzgffFkwh20CwHPNMgp4xL7p7XJukA2BmPQkSSIcDXxOcBf7c3a+LNLAkMrM1BMsj8ggOymK/1DZc6jTA3VeG23UIqiSPjjKuVDCzU4B72dJLZV/gWnd/I8q4kimh54QlfC/kces9EQ4k2SZ3vzVdsaSDmb3P1tXNXYCFwAoAdz82irhSycyGuXtfMxtFMClzo5mNcvdeEYcmSWBmP7r7HmY2lKBychlBS4+OEYeWMmGj8I7u/kV40qaCu6+JOq5kM7P/EixZGxpu70FwEv2SSANLISWQ0szM2hJMDWlDwhLCuH0YhuXk44HCM0VFd+7iVpJb3FKuzdz98jSGU2aY2Uh3LzGZWNaFB9uJv9tKBAfeTowPus3sKuB1d58XdSzpYGaXEww1WEpQav2OB2N2s4Cp7t4+0gBlpyS8N7ciOLv/ebh9CDDY3U+PMLyUCPuXHVJYdWTBlL0v4nw2NNOYWU2Cz5+1UceSCmGj+21y92/TFUu6mNnbBCcvriRYtrYCqOjucV2ydwVBE+01BG0uegPXx7if100EK08OAh4h+Bx6yt1vijSwFAn7tQ0E6rl7ezPrSJBkOSji0JImrBp0oCJBL6854XZrYIK7d4swvJRSD6T0ewd4mmDUX5zHJl9D0ORxA0EJ49tx3dEJDQ+/70NwpuzVcPsUgmoVKafcvWa4tKmlx3eEfXFqAZ+a2XKCv+E33D3O46IbACd6kQmCYc+gWFSpZFplaKjwvXkE8HbC9d+kP5S0ySqyZG0ZQSVh7IQNWv9I0DAcgt/r4zFeWtwN+B9QL9xeCvzB3cdHGliSJSaI4r4cs5C7nxBevMXMviacFBlhSKl2nrs/aGaHAY0IkmfPEky4jR13vz28+KaZfUDQ7H9VlDGl2KVAP+BHAHefGlbxx0ks9g13hiqQ0qywhDHqONIlrLgaABwHzAbucPdRkQaVQuGH/qG+ZcRuReAzdz8g2siiEYcKpEIWTlCMOo50M7MeBCNYTyKYCHNwxCGlTLh8bd9w8zt3Hx1lPMkWvj9BMAGmDzCaoDq0B/Cju/ePKrZUMrNs4Dl3PyvqWNLBzO4h+J2+HF51GjDG3f8cXVSpYWZPEZz9fS686vdAvsdwVDRs7rf4Fw9HnZvZ7wj2q2LZm87MTgXuIcbLMTNVQi+gB4Fv3P1tM/vZ3XeLOrZUMLNqBCfXW7n7hWFFzi7u/kHEoaVEwpK9nz2Y5FuBoO9gj6hjk9JTBVL6PRiuZf8M2NxULKZnfnH3mWb2LlCVYMeuE8F0ibhqRtDQs7CBZ43wukxl279LuTHUzPq6+7CoA0mzxcAigiqGuJ092ixcwjaQLVO6XjCzJ9z9oQjDSqrCRHbY3HKgu48Nt7sB/xdlbKnk7vlm1tDMKrn7pqjjSTV3v9bMTiKoiDXgCXd/ezsPK6/6Flma91W4hC+uqhcmjwDc/ZuwX11c/YXgd7zVckxACaTyb4SZfQa0BW4Il2XGeWXGswSVsHuF2/OA1wmGs8TRt2Z2I1DVzA4hGHP/fsQxSZIogZR+3QkSKQey5Y3Sid+YznYE4xuPA+YSLIH5R+EEnBi7E/g54Uz//sAt0YWTWmZ2iru/XsJ1D0YQVqocAFxkZrOBdWxpPBzLsylm9keCyoWGBDvrF7r7hGijSqkLCBqXrgMws7sIRijHJoGUoHNh8gjA3ceZWa8I40mHWcAQM3uP4O8XiO/0Knd/E3gz6jjSIN/M2ns4KTHc98iPOKZUmhH2UvlfuH0WMDPCeFItY5ZjZqDzgV7ADHdfb2b1CZaxxVV7dz/NzAYAuPuGsD1CXF1P8DseC1wEfETQX1JiQAmk9DsBaJcBZ0GnEYyJfhdYTdDA9JLC98oY77Q/G077KVymeL27L4oyphS7geAMSrHXuft/0x1QCh0RdQBp1hq4Ms5LToswtj7wzCdeFXSJJoZLf14gOIFxFjAx2pBSbkH4lUVQJRpbZnYicBdBxaAR7yl71wJfm9kMgtfZmngfhJ4H3EpQKWnAIOL9ej8xs0/ZejnmxxHGI0kS9hf8BegSLm+Ku03hJDIHMLP2JKxEiRt3LyBojv5k1LFI8qkHUpqZ2avAZXFtAljIzG6h5KlksRo5Wyg8m3AmQZLwNjNrBTRx958iDi2pzOwI4EjgVLY0DIeg8XIXd+8XSWApFvceOQBmVsvdV5tZveJud/flxV1f3pnZ1QRT2AqX+hwP/Nfd/xVVTKliZlXYuvHwIODRDKgQzQhmNg04xt3jnhQEwMwqE0zAMWCSu8f2oCwTFVmOOSjGyzEzSljlexowgS0nbzxuU6kLhcu4/kowaOczgv/T57j7N1HGlWwJk8mKFdeq/UyjBFKamdk3BM0th7F1D6TYvWGGjUsvd/cHoo4lXczsUYKliQe6+65mVpegiXbf7Ty0XAkTKb2A24C/Jdy0Bvja3VdEEVcqhSNnL2RLj5wTCHqLxGqJk5l94O5Hm9lMgp2AxCocd/d2EYWWcuGUsv5sOVD5OeKQpJTM7H223pl1YCnB+9QL0USVWmY2xN33iTqOdAj3M44C2pBQVR/XKmcz6wPcyK9fb6wPysysFlu/3lieyMgkZjYZ6JFJCd9wmd6eBPsYQ919acQhJZ2ZtS7p9qKTbqV8UgIpzcxs/+KuTxxZGidm9nUmTSArnDqWOEnCzEYXafIZG+GElHczYQfAzMYAeyX0yKkO/BDHHfewkq6lu8+JOpZU21alVaE4Hahk4pnBbXzm1iNYtjfV3a9Pc0gpF041agK8w9Ynqt7a1mPKKzP7CMgh6LOxuQFvjKucJxMs2yv6emN5UGZmFxGcqNpA8HoLl2PG9kRGpgjbPZzi7mujjiUdzGwfYJS7rzOzs4DewINx/dvdHjP7wd332v49pSzKhDWnZYq7fxtmZzu6+xfhWMfsqONKoe/N7GGCZU6JjUtjOXUOyA3PiBaucW5IvKdKHAHcY2aDCBqlf+rueRHHlCoZ0yPH3d3M3gZ2jzqWNBjBryutCjkQpwOVo6MOIN22dXImbKY9gqDRZ9zUAtYDhyZc52ypnoyTFnFMfJZgibu/F3UQafR/QNc4VmoI64FRZvYlWye6L48upJR6FOgZVvBfCzwDPE8wbCcTVYk6ANl5SiClmZldSDAquh7QHmgOPAYcFGVcKbR3+P22hOtiN3Uuwb8Jeqg0MrN/ACcTrHmOJXc/18wqEiSSzgD+Y2afu/sFEYeWCs8CP4aJFQh65DwdXTgpN9TM+rr7sKgDSSV3bxt1DOmSqWc6i+Pu+TEegHNN0co5M4vr//OPzexQd/8s6kDS5OawAX7Rg+44JgcBphMkGiR+3gu/MkVeeHLuOODf7v60mZ0ddVAR0hKockxL2NLMzEYB/YAfE5Y4jXX37pEGJkljZp0JEoIGfJkJjUzDJNLhBNNg9nX3hhGHlBKZ1CPHzCYQNKadRVA9WLh0ILZn+8PpVf0Jdmy+c/d3oo0oucxssLv3N7M1bL3zFtspXdtYolgX+APQwd3PTHNIKWdmQ4Aj3H11uL0r8Lq7d4s2suQzsxMIpglmAbnE+P8ygJm9AHQGxrOlutnd/bzookodM9uN8OQNmVGlklHCqWSt3H1y1LGkmpl9C3xCsJ+8H7CEYElbRh7/Fbb8iDoO2TmqQEq/je6+qfDMZzi6MrZZPDNrDNwBNHP3I8ysC0EfmVhVbhSZXLWYLSNnMbN6ceqjksjMDgdOBw4AvgGeIpjMFjtmdhvwHfB0YR+kmDsi6gDSycz+A3Rgy9/uxWZ2iLtfGmFYSeXu/cPvsR5jX0TRJYqFTbS/IZhEF0d3AO+b2VEESeDnCaaDxtF9wF7AWM+MM6I9M+yA83HgK4r0fJLyz8yOAe4FKgFtzawXcFschwqFTiOo1D/f3ReFU5rviTimKMW2BDgTqAIpzczsbmAlwdnPy4BLgAnu/pco40qVsEnes8Bf3L1nmDD7OW47QMVMrtp8EzFu+GhmrxD0Pvo47o20zew8guqUvQimzX1HUIX0bqSBJZmZNSKY8tOBYKf9n4WVDHFmZuOBboUHoWaWRXBQ2jXayFIj7NXWmK0nG8W+aXqmMLPjgeuAmsCJ7j412ohSw8w+Jai2yojkgpk9CTzg7hOijiUdzOx7d997+/eU8sbMRhC0s/hGKzLiqUjP36pABXdfE97Wzd3HRRuh7CwlkNIsPCg5n6C5pQGfAk/F9cyZmQ1z975FppKNcvdeEYeWdJk0uSpRWGXWN9z8yd0XRxlPqplZE4Iqq/8D6satmsPMPiGo2hhE0HS5prufE2lQaWBmbwFXFfYJCnd87nT3AdFGlnxmdhlwM/ALWy+DifPyxGrA1QTLJQaaWUdgF3f/IOLQksbMHmLrExgHAjMIlqHGctmPmf2XoNH9x2y9xOn+qGJKJTObSNA/cybB64310uKwl+Rs4H22/v3Gsqo7k5jZj+6+R5HjgzFx+7+ciUvHYeuev+7ePvzMfczd49rzN6NoCVuamNkd7n6juxeY2Sx3PyXqmNJknZnVZ8tUsj2BVdGGlBoZNrkKADM7haAE+RuCD8OHzOxad38j0sBSIGxc2oXgoPs7ggbpcZwm2CShIvJTM4vjayxOfWCimf0UbvcFfgindRGzsvorCJIny6IOJI2eJUiMFlYzzANeB2KTQAKGF9keEUkU6TUz/KoUfsXd4VEHkGZnhN9vSLgubtMxM9U4MzsDyA6TC5cD30ccU9Jl6NJxgEsJe/4CuPvUsMJdYkAJpPQ5nGBZCMBdwOcRxpJOVxNMWWgfNvZsSHDgHVcZMbkqwV+BvoVVR2bWEPgCiF0CiSDBkE2wBHU5sNTd8yKNKDXMzOqyZX16duJ2jM/8/i3qANJoLjFN5JegvbufZmYDANx9g8VsDJu7Pxd1DOnm7rcWXg4rvGvEfMltBWCeu280s98BPQh6XMVSJk3JzECXAX8hqCx7iWBFxu2RRpQC2xjksFmM96kyqudvplECSVLK3Uea2f4EjTwNmOzuuRGHlUoHABeZ2WwyY3JVVpEla8sIpuHEjrufAJsnGh0GfG1m2e7eItrIkq42QeVC4sF1YRVSbM/8uvu3EDTEZ+u+QLHZuTOzq8OLM4BvzOxDMmDZT2hT2IOhsBq2PQmvPU7MbB/gFqA1wf/l2PbiM7OXgIuBfIL3rdpmdr+7x7U57ZtAHzPrADxNcILuJeDISKNKkbDK+RN3X2NmfwV6A7fHeQJqBjkqrHbe3AM2/H2/Hl1IKZE4yKEVsCK8XAeYA8Q1Sfqtmd0IVDWzQwh6/r4fcUySJEogpU+jcOfdEi5vFrcd93AcdnE6mRnu/lZaA0qD8Gz2xQTr9TPFJ2ET08LJVacBH0UYT8qY2dHAvgTjV+sSTIb5LtKgUsDd20QdQxTMbCDB2c8NBH2BjPglzApL6OeEX5my7AeCnk+fAC3N7EVgH+CcSCNKnaeBqwgOXPIjjiXVuoQTUM8k+Oz5M8HrjmsCqcDd88J9rH+5+0NmFudkyk3u/rqZ9Sc4cXMv8BiwR7RhSRLcwK+TRcVdV64VVtGZ2WPAe+7+Ubh9BHBwlLGl2PUEPX/HAhcRvD8/FWlEkjRqop0mZnZzSbcnlmHHgZk9G15sRNBz4qtw+wCCiQvbSjCVa2Y2wt0zpgcSbE4W9ic44B7k7m9HHFJKmNkjBI2lv3P3BVHHk2phQvRMoJ273xaOnG3i7j9t56HlkplNBfZy96VRxyKpEfbj25PgvWpoXH/Xhc1po44jHcLpib0IqnAedvdvzWy0u/eMNrLUMLMfgX8RVG0c4+4zzWycu3eLNrLUKGywbGb/JJiK+VJi02Upf8LEyZEEw0heTbipFkFCuF8kgaVYcccHZjbc3ftEFVO6hMv4Wrj7mKhjkeRQBVKauPut4djky939gajjSTV3PxeC8fYEHwgLw+2mwCNRxpZimdYDqbCaLHYVZUW5+6Ul3W5mP7j7XumKJw3+Q1CJcyBwG7CGYPlE35IeVI5NB9ZHHUQ6mNnnwCnuvjLcrgu84u6HRRpY6u1PkOx2oCIQy2Q3wfLaewjelxOXKMaxIf7jBFPmRgODwumJce6BdC5BpfM/wuRRW+CFiGNKpflm9jhBpcZdZlaZmC6TzyALCBr+H8vWjf7XEFROxtXScBnmCwSfQWcRtH2IJTP7huB3XAEYBSwxs2/d/eqSHiflgyqQ0szMvnb3A6KOI12KnhkLm1yOifHZsglAJ4JlbLHtgWRm5xOM5rwn3J5HcPbIgOvc/dEo44tC3M6KmtlId+9dZMRunM/s70YwqetHtj7ojuPo81Hu3qvIdbH6/1uUmf0H6MDWy22nby8xXB6Z2dfFXO3ufmDag0mzsHIyO6YDDjKOmVUjGEIzNpzi1BTo7u6fRRyalEJ4Qv15dz8z6ljSJazCuZmgDYITVLTfFqc+i4kSqgcvAFq6+81mNiZux0OZShVI6fe9mT1MULa5rvDKmJ4ZhKBRa2GPHAdOB4rbuY2LI6IOIE0uZutxwkvcvYWZVQE+AzIugUT8pkvkhjt5hU2HGxJUJMXV4wRLbccS79cJkG9mrdx9DkBYtRG3/79F7Q908/CsmZk9R/C7jp1MOklVVPj7VfIoPpoCH2bK1LlM4e75ZlbfzCq5+6ao40mHMFF0xbZuN7OH3P2yNIaUahXChO+pJDRKl3hQAin99g6/35ZwnRMsE4kdd/9T2CNn3/CqJ+LaIwfA3WcDmFkjoErE4aRSlrsnlt6+DuDuOeGkIyn//k2wxKexmf0DOBn4a7QhpVReBpVW/wUYbGbfhtv7AQMjjCcdJhNMwCkcctASiFU/hqLDOQj2LZYCg919ZgQhiZRWRk2dyzCzgSFm9h5bn1CP1VCh32CfqANIstuAT4Eh7j7MzNoBUyOOSZJES9hEksjMjgXuA5oBiwnGKE90966RBpZkZjbN3TsUc30WMC2O46K3J45LgMysM3BQuPmVu0+MMp5UCpNkswnGzCYuYYtreXkDtjSU/iGuDaULhcmyvkBhE/i+wA+Efa/c/diIQkuabQzrqEcwveoWd38lzSFJEoUVoXe6+7VRx5IuCUuprwM2FE6di9tnbSba1nChuA0V2lGF/9ejjkNkR6gCKc3MrDFwB9DM3Y8wsy4Ek3+ejji0lAirj+4imMZmbOkJVCvSwFLndoKDsi/Ctb8HAAMijikVPjOzv7t70YqU2wiWsMVKuOP+qbuXNHL19+mKJ42qAYXL2OJeWXZG+P2GhOsciF0yNOwTczgJE/bMrF9cJ+yF/hZ1AKm2rQOvsPfGF0DsEkhmNpygd9lL7r4i6nhSKVz2s7uZmWfO2d9cMxsA/AE4JryuYoTxSJIUvl+ZWXV3X7e9+0v5YmYtgIcIKqscGAxc4e7zIg1MkkKTDNLvvwQlfc3C7SnAlVEFkwZ3A8e6e213r+XuNWOcPALIDZd2ZZlZlrt/TTBiOG6uBdqb2TQzezP8mkbQpPb/Io4t6dw9H1hvZrVLuM+4NIaUcmb2N+A5ggqGBsCz4QSRWHL3tsV8xS55FPoPsBdbkttriPd0TIDuBAMcvi3uK+rgUimsorOo40iR0wn2p4aZ2StmdliYII2rn4F3zez3ZnZi4VfUQaXQuQTvVZkydS5jmNle4eCZieF2z3DYQaaK2/vWswRLTpsBzQmqu5+NNCJJGi1hSzMzG+bufYtMNvrVRJy4MLMh7h63db3bZGZfAMcD/yQ46F4M9HX3vUt6XHkVrmkuXJ43wd2nRxlPKpnZawTVZZ+z9Xr92E3pAjCzicBu7p4TblcFRrr7rtFGlhrhtJ+rgVbuPtDMOgK7uPsHEYeWdJk2YQ/AzP5OkGwYCTxDUFGYETtAZnYg8Nc4T2ELl08fTTDAoYDgd/xg3JagmllxB2Du7uelPRiRUjCzHwl6K76X8Dm01eTmODGzU9z99W1dZ2bnuPt/IwkuBbYx7TW2x7uZRkvY0m+dmdVny2SjPYFV0YaUUsPN7FXgHbbuK/JWZBGl1nFADnAVcCZQm60bpsfNTILy1Lbu/r6ZtQKaxHQpzIfhV6aYRdAIPifcrgzENkFIcGZsBFsGHcwjaA4fuwQSmTdhD3f/q5ndBBxKUNXwcJgUfjouiW8zG8uvp+nVAxYQLAGKJTPrQfA7PZKg6fKLQH+CqYq9ooss+dz93KhjSCczm0kxEyJjXB2aUdx9bpGCwfyoYkmDGwgHzhR3XZySR6GlZnYWwRRuCCqel5VwfylHlEBKv6sJSvram9kQoCFBBj6uahE0KT004ToHYplAKlzHbWa1CMo14+4/BAeeBxIkytYQ7MD3jTKoVHD358IqnFbuPjnqeNJgIzDezD4n+Js9hGBy178hlpVX7d39tLDfBu6+IcZLYTJtwh4QlGmY2SJgEcGo97rAG2b2ubtfF210SXF0kW0HlsW5v4iZjQBWEkzout7dC09U/Whmsat+NrNOBFVWjd29W5g8O9bd/x5xaKnSJ+FyFeAUgqSolH9zzWxvwM2sEnA54XK2ODGzIwiS280L959CtQg+h+LqPOBh4AGCz6Lvw+skBrSELQJmVgHYhWC962R3z404JEkSM7uIIJGygSCxUtg0PJZnyzJpKYyZHQPcC1Ry97Zm1gu4LQ7Tm4pjZmeXdLu7P5euWNLBzL4nmDg3JPw/3R542d37RRxaSmTShD0AM7scOJtgrP1TwDvunhsufZrq7u0jDTCJwv+789x9o5n9DugBPO/uK6OMKxXMrJ27z4g6jnQJpwleCzyeCct+imNmg929f9RxSOmEk0AfBA4m6Mn7KUGT5VhVqZhZT4JKyNvYepjDGuDruDf/l3hSBVKalNDksJOZxXZJVwaeLfs/oKvHfCR2gkxaCnML0A/4BsDdR4UNPeNqGfCRu8f191nUzcAnQEsze5FgaeY5kUaUWpk0YQ+CnnQnuvvsxCvdvcDMilbulHdvAn3MrANBZc57wEsEZ8FjwcyuTrj8q9vd/f60BpQ+1dz9pyKvObZVDGaWONY8i6AiqWZE4UgShfvJZ0YdR6q5+2gzGwccGrcTbyUxs+cIEoIrw+26wH3q1xYPSiClT+H40UYEPTa+CrcPIDggjWUCCXiS8GwZgLuPMbOXgLgmkKYTLNnLFJm0FCbP3VcV2XGPcwnn6cCDZvYm8GzcK1Tc/XMzG0nQKN0IdnximQgOJ+ydQpBoMIIJe6/HOLEPQZ+2rZJHZvY/d/99DP9vF7h7Xnji6l/u/pCZ/Rx1UEmWqUmEpWGFWeFJm5OBhdGGlFL3JVzOI+i7eGpEsUgShUNYHiT4zHXgB+CqOFYUunu+mdU3s0ruvinqeNKkR2LVq7uvMLPdIoxHkkgJpDQpbHxoZh8AXdx9YbjdlHiPT86os2UEDfG+D6dLJDYNj1u/GADc/cWwB0XhUpjjY3gwVmicmZ0BZIcTui4nWNMdS+5+VtjLawBBgsEJGk2/7O5roo0uNcLS+Q/N7JY4Tl9LMICtJ+zdSTCdLM4JpK6JG2Hl5O4RxZJquWEvrz+w5eRVxQjjSTp3vzX8HV7u7g9EHU8aXQo8AXQ2s/kECZU4V3GcXzShEPPK30zyEsHxzwnh9ukEDZf3iCyi1JoNDDGz99h6km9cqyWzzKxu4RI9M6uH8g6xkRV1ABmoTWHyKPQL0CmqYNIg086WPU5QXTaUYKJT4VecFS6FySLeS2EuIzgI3Uiw47MKuDLKgFLN3VcTVKm8AjQl2NEbaWaXRRpY6sWyr1WCWQQNaQvFdsKemd1gZmuAHma2OvxaAywG3o04vFQ5F9gL+Ie7zwwPuF+IOKakc/d84v+3WtRsdz+YYABLZ3fvX7SyLmbe2MHrpPwxd/+fu+eFXy8Q76ruBQRTXbMIKigLv+LqPoIT6reb2e0EJ1zvjjgmSRI10U4zM3sY6EiQZXeCjPs0d4/lAVlYovoEwbK9FYRny+K6w2Nm37v73tu/ZzwUsxTmeCCWS2HMbDd3j9sykG0ys2MJDkTbA/8DnnP3xWZWDZjo7q0jDTBJzKytu88sct3mpvBxZGbvEExK3GrCHkFSJZYVk2b2T3e/oYTbu7r7+HTGJKUXLp2uDbzK1mf1R0YWVAqZ2RyCXm2vEjS/j+VOfNjkvyvBAee1CTfVAq51967FPlDKjbDydSXBCSoHTiM4mfEIgLsvjyw4SQoz60IwpdmAL919QsQhSZIogRSBsC/BvuHmIHd/O8p4Uimh0WVVgqz7OoLKjRHuPiqquFIl3JmdDbzP1kvYYvlBaGYT2XopTFVgpLvvGm1kyWdmXxNU4bwOvBL3g00zex54yt0HFXPbQe7+ZQRhJZ2ZjXD33c3sS3c/KLwuK87NwzNtwt6OKJwoGXUcyWBmMynmTH4cp4GG78tFubsfmPZg0iD8jD2G4ORjb4KKhlfcfXCkgSWZmR1HcELqWIIm8IXWELze2C4fzxTh+9S2xG56cThk5jqCxOjmCuAYv1e1Ku56d5+T7lgk+ZRAkpQKG2b3IdgBMOAoYBjQmaBSJVbljNv4QIzdB2EhM/sYGJAwZaEO8IK7x22qEQBm1oSggedpBGdCX41jtRWAmd3l7n/e3nXlXdhc+B3gAuBXvVTi2J8gnDqWSRP2titOVWdmVj9hswpBlWg9d//bNh4i5VA41ehBgqru7KjjSQUz28vdf4g6DpHSMrPPCCoH/w+4GDgbWBK3fapCZjaWLScyqgJtgcmqHowHJZDSLKw+uotgGpuFX+7utSINLEXM7FPgJHdfG27XIFi/fgJBFVKXKONLNzM7xN0/jzqOZMnEpTAAZtad4EzSae5eKep4UqG4igwzG+PuPaKKKRXMbBeCM91XAo8Vvd3db01zSClnZi8Q9MjJiAl7OyJOFUjFMbPB7t4/6jiSzcwaA3cAzdz9iHDJxF7u/nTEoaWMme1PcBLjCIITcq+6+5vRRpUaZlYFOJ9fV21oFHg5Z2bDgWeAlxKndcVVQrXz5v0oM/vW3fePOrZ0MLPewEXuflHUsUjpqRt6+t0NHJNBO+ytgMSRlblAa3ffYGYbt/GYOLuLINkSF2+HX4W+iSiOlDOzXQl22k8BlhKs278m0qBSwMz+CFwCtDezMQk31QSGRBNV6rj7ZOCucKfu46jjSYdMnLCXScId9UJZBFXAcW3W+l+C/7t/CbenEJzlj2UCKaxyHgW8RtALaF3Jjyj3/gdMAg4DbiOYOJcp+89xdzpBn8XhYTLpWeCzuPb1Ijj+AVhoZkcRNNVuEWE8aeXuI82sb9RxSHIogZR+v2RQ8giCaVVDzaxw2s0xwMtmVh3IxGZqFnUASbaMzFkK8yxB8/tD3H1B1MGk0EvAx8A/gesTrl+T2MsrcTxreZbQp60wSbiVOC5hg2DCnpm9SVBafiVBVei1ZvZvd38o0uCisWn7dyk37ku4nEcwvOLUiGJJtQbu/pqZ3QDg7nlmlh91UCnUM5yOmSk6uPspZnacuz8XtkX4NOqgpPTcfRrwFzO7CTiaoBqpwMyeAR6MYe/Qv5tZbYITjw8RtEG4KtqQUidx34rgREZvYElE4UiSKYGUfsPN7FWCnhuJTZbfiiyiFHL3283sI6A/QfLkYncfHt58ZnSRRSZuZ1ZOBx4MD0RjvRTG3fcMG5gW2xgwLtx9FUGj+wHbueuXBDsE5V1cKzO2qZgJe/0SJ+wR7NzGipkZwWdOO3e/LWzw2cTdf4Lg7zvSAJPrfHefkXiFmbWNKpgUWxf2fHIAM9uT4P0rrpqY2dtAY3fvZmY9gGPj2ouPLVUbK82sG7AIaBNdOJJM4f/f8wiWY74JvEhwvPAV0Cu6yJLP3T8IL64CDogyljRJ3LfKAz4k+B1LDKgHUpqZ2bPFXO1az50Z4thnI2EpzLkEO/GxXApjZscA9wKV3L2tmfUCbnP3Y6ONLBpxajqcaTJlwl4iM3sUKAAOdPddwwbEn7l77Erqt9G/bIS77x5VTKkSLtd7COgGjAMaAie7+5gSH1hOmdm3BGPtHy98/zWzce7eLdrIUsPMLiA46OxOsFyxBnCTuz8eZVxSemY2AlgJPAW85e4bE257y91PjCq2VDCzTsCjZE7yV2JMFUhp5u7nRh2DpIaZZQF7bme87Kw0hZM2GbQU5hagH2GfJ3cfZWZtIownarE6+5BhO3cLiyaPCifsxTF5FNrD3XuHU/dw9xVmFqsG+GbWmaDZcO1wYEehWiQ0II6TsK/G/sAuBFXOk909dzsPK8+quftPQUHdZnlRBZMGX4ZLpQcB7SDW1XSZ5vfAbgTTuf5c+H/a3W+LW/Io9CRh8hfA3ceESzJjtY9hZu9Twv5hpp50jRslkNIsww5SMoq7F5jZfQTTjbZ1n1h9KGbYUpg8d19VZMdd4iMjdu5ChwBFRwcfUcx1cZJrZtlsWerUkKAiKU52IeglUoeg32ChNcCFUQSUauHv9EiCZU0VgEPNLLa9y4ClZtaeLf+PTwYWRhtSSr3Jr5dKvwHErpouA91PUIE0koSWHjGWKcnfe6MOQFJPCaT0y6SDlEz0mZmdRFCOG6sKjW04GXigaDWDu683s7gtyxxnZmcA2WbWEbgcKKnaLO7ilkmL/c5dpk3YK+LfBBMjG5nZPwjeu/4abUjJ5e7vAu+a2V7u/kPU8aTJ+0AOMJb4JQSLcynwBNDZzOYTNEg/K9qQki8Tq+kyUAt3PzzqINIoI5K/7v5t1DFI6imBlH6xP0jJcFcD1YF8M9tAcJDt7l4r2rBSJpOWwlxGMCp6I8E0tk+B2yONKIXM7Hx3f7rIdXe6e+FktoMiCCuVMmHnLqMm7CVy9xfDnhsHEbwvHx/jpv8/m9mlBAfgmw+2Y9prsYW794g6iHQJm6MfHE6yzYpbr8EEGVdNl4G+N7Pu7j426kDSpLjkb2yHCZnZWH69lG0VMBz4u7svS39Ukixqop1mZvYx8Cfg9bAfw8kEE1OOiDg0kd9sG81ax2TSDn1che9VL7j7i+H2f4DK7n5+tJGlhpm1I9i52xtYQbhz5+6zIw0sAjFt9r8nML7wgNvMagJd3P3HaCNLPjN7HZgEnAHcRnCQMtHdr4g0sBQws7sI+uR8FnUs6WBmlYGT2LJkDwj6xkQVUyplWDVdRkhILFQAOgIzCE7MFZ5wjeX+Y/i3ezLB3249YDXB643r3+7dQD7BiSsIpjYbQRKpv7sfs63HStmnCqT0y6gMdKZJGBXd1t1vN7OWQNPCUdFxkUlLYcLJidvKtHtcEyrAicB7ZlZA0B9nubtfEnFMKZNBZ/Z3RNyWJ0LQezAxKbaumOviooO7n2Jmx7n7c+Ey+U+jDipFhgJvh0Mscol/1e+7BAdgI8iMvjEnmNl4YAPwCdATuNLdX4g2LCmFo6MOICLvsqXn04JoQ0mLfdx9n4TtsWY2xN33MbPYLbvNNEogpd/xwEfA10AWwU7sweGI3VERxiXJ8R/CUdEEy5vWAo8AcRsVnUlLYT4o5rpWBBPnstMbSuqZWb2EzQuAdwiSgreZWb3E328cmNkxwJiESqNrgJPMbDZwhbvPjC66yMSxNNkS+9KFQw/iug9UOIVspZl1AxYRnPWOo8LBFWMzpO9gpvWNOdTdrzOzE4B5wCkE+89KIJVTmVjVG8q0v90aZrZHYZWvmfUDaoS3qXVLORfXnaeyrE/49R7BmbIzgWHAxWb2urvfHWVwUmqxHxUN4O6rCM6CDtjOXb+knJ/hd/c3Cy+Hy5xuBPYD7gSe3tbjyrERbJ1AMOCo8MsJRynHyD+APQHM7GiChrQDCMYLPwYcFl1okkQzzOxygqojCCooZ0QYTyo9YWZ1CZqEv0ew035TtCGlzFRgXIYkjyDz+sZUDL8fCbzs7ss1CVXKqUz7270AeMbMCpNGa4Dzwyrvf0YXliSDEkjpVx/o7e5rAczsZoKRpPsRHLgpgVS+ZcKo6N8iFnt6ZrYrQQPt3YB7gIvdPZZnUNy9bbgcZC93j9VyxG1wd18fXj4ReNrdRwAjzCy2S/a2IxZ/t0VcTDCJ7a8E789fAgMjjSh1vgwrPwcRJnzNrG20IaXMQuCbsGfb5iVd7n5/dCGlVH/gHDObSQb0jQHeN7NJBEvYLgn3qXIijklkhxXp+XSumWVEzyd3HwZ0N7PaBBXAKxNufs3Mznb356KJTkpLCaT0awVsStjOBVq7+wYzy4T17HEX+1HRv1G5PyscNqTtA9wLXEXQFLBW4VnQuC3pgs3Le+4lWBoSdxaeIVtPMKHrPwm3xXJcdAZO2MPdFxM08cwEb/Lrys83gN3/v717D7azru89/v5EEQEJiKPAeEohbRHlJmCOJwKK1qAFEwtBqGIvCCpeBigjVE/BS1CPgLYiPSB2jhxEpZSLhFAFFIPhEkQCAeaI56CCFqpG7kwJlMvn/PF7FqzsbkJM1rN/Wc/zec2s2ft5dvbMZ/Mb1t7ru36/77dClrbd0Txe0Dy6rlcDV2x/tGmU/pDtJyU9Arx98HVJs21/t17CiOfU155PwNMnFiZzJJAC0phKAWnqfRO4TtKC5noOcE6zpe/H9WLFKPRsVHRfzKQUwj5C6Y8zvDuji0e6Bi6XNA+4sOPHQ74ILKNMRLnN9g0Aknah7G7oogMkPTpxwt7gi10sijY7F97Lf55e1ZnR9pK2A7YHNpG0/9CXptPBYmiz2/ePbPepIevzgbtsPyZpL2An4GtVE7VsuI+i7X+n9A4dOBFIASnWWT3u+fRcurjTuTfU7dcF6yZJu1G2IQu4evCCJcafpFOAc21fWzvLukDSTbZ3qZ0jfneSHgY2ouy4WkGHpxtJejnwMuBm208197YE1rP9y6rhWiBpA0pvnK/yzIS9o6qGapmka4GrKEfFnxzcH+5xNu4kvZ0yqGMuZX0HHgb+qYu/lyRdBsyx/R/P+Y87QNIyyo7YrSmT9S4GXmF7n4qxqsnfGBHjSdKNtse6R2qfpYAUMUKS/hI4CNiWcpTt3C4XCJ/rKEyXpnapnFk7GNjG9gmStgK2sH195WgxAkPrO8P2/C6u74QJexvzzIS9j0M3dx4NSFpm+9W1c0wFSbNsL6mdYypIOoNyXO9ihnamdLUH0uBFl6RjgRW2T+1zESUvQiPGU5+ft7pgWu0AEV1i+6zmncD/Cvw/4ERJt1eO1aYDJB08uGiOwrx0cN2xF6SnUXoCvau5fhj4n/XitE/SXEmfbx5dP8c/WN/BZMEuru9S4IbmsQjYlDJdb3C/yy6R1JddGvtJmi5pPUlXSLpHUlePef0bcAnl79mNhx5d9bikdwJ/Qfm54ZlJZRER46IPQ1o6Kz2QItrxh8B2lG3mXe5ttT9wsaSneOYoTFcnV722eef3Jih9GSR1tmmrpM9R+j99o7l1pKQ9hhotd03n17eHE/aGHQn8d0n/QRlk0dkjmcDeto+VtB9wF/AOSsHw63VjjZ7tTwFI2rhclgm3HXYIZaLgZ2zf0UzX69y6/g7urB0gIv4zSUdPcvtBYKntZbY/PNWZYnRSQIoYoWZayH7Az4FzgRMmjK7shAlHYQ7jmaMw87t0bG2Cx5umrYanm/I+VTdSq/YBXj3UE+gs4CagqwWkXqxvzybsPc12l3elTDTYkbIPcI7t+wZTI7tG0g7A2cBmzfU9wF/Y/j9Vg7XE9o+BI4au7wA+Vy9RuyY0gx94ELjV9nLbk309Iup7TfNY2FzvC/wIOFzSebZPqpYs1loKSBGj9Qvg08DWts+UtJWkbbvUR6WxlOaFdkOUXw770t3JZF+i9LXaXNJngAOA4+pGat2mwKAYuEnFHFOhT+vblwl7T5ukh9nvAVt28LkZYKGkn1Ca33+wKYY+WjlTW74CHG17EUAzmewfgddVzNQaSXew8u9eAGx38XcuwKGUYvei5nov4DpgW0nzbZ9dK1hErNJLgF0Hu0IlfQI4H3g95TVECkhjLE20I0ZI0umUXQtvsv1KSS8GLrc9s3K0kevjUZhmTPYfN5fft31bzTxtkvRnlHe2r6QUCF8PfMz2P9XM1aa+rG+fJuwN9Om5GaD5+R6y/aSkjYCNbf+6+dps250YfS7pZts7P9e9rpD0kqHLF1KOJ25m++OVIrVK0kLgMNu/aa43B06n7HxebHuHmvkiYnKSbgN2HkzIlLQ+sKz5/ZsG2mMuO5AiRqvzfVQGenoUZkNgcMxpg8pZ2rYvZcz7/cAvgb8ZvADtsF6sb8+Ocw305rkZys839Pm/MzShDDgR6EQBCfi5pOMpx9gA3g3cUTFPq2zfO+HWFyVdTTNJsYO2HhSPGsuBbZtjmY/XChURz+mbwHWSFjTXc4Bzmjc0utwbthdSQIoYrV70URnSm6Mwkj5Oebf3AsqOjTObc9yfrpusNWcCewBzKUcSl0labPuUurHa0bf1lTSXsqsM4Erbl6zq33dA356bV6VLDZHeA3wKuLC5XkxpNN1JkoZH1k+j9BjpckH4KkmXAOc11/OAxc2L0AeqpYqIVWqOin+b8nekgMNtD6a9Hvzs3xnjIEfYIkaoGWl/ELArcBZNHxXb563yG8dUn47CNNtxd7H9aHO9AXCj7VfWTdae5gX3TOCNlMk/K2xvVzdVO/q0vpNM2HsnZTJKVxuk9+65eVUk3Wh71+f+l+suSWfb/nNJR3a1qD0ZSYuGLp+g7Lb6gu3/WylSq5reZfOA3Sl/X1wNXND1N6wixp2kU4BzbV9bO0uMXnYgRYyQ7W9IWkrpoyLgT7vaRwV6dxTmTkrPiUEz2vWBn1VL0zJJV1CKg0uAq4CZtpfXTdWqO+nP+vZtwl7vnpt7YDdJvw+8R9LXmLCrqqOTQAEOtf3z4RuStqkVpm1Noej85hER4+NG4DhJ21IGlJw7tAMpxlx2IEXEWunLURhJF1F2bXyXcgxmNuXd0OUAto941m8eQ5L+HtgNeAy4hnI0ZIntFVWDtaRP6yvpFmCvwYtsSZtR/t/dqW6y0Wt+tmfV4ULDs5J04biPP5d0BPAByvHau1m5gOSuTiWbbPeYpKW2d6uVqU2S9qf07HoZZY07u8s5ooua38HzgD8DtrL9R5UjxQhkB1JErLFJjsIcKWmPjh6F+VbzGLiyUo4pYfuvASS9iNJT5ExgC8rOnC7q0/p+FrhR0pUMTdirmqg9SykFQQFbUZrCC9iU0hy+c7s3mhfdEz0I3Gp7+bgXjwBsfwn4kqTTbX+gdp62NRMitwc2mbC+0yk7J7vqJGBOdgtGjK0/BLYDtibNszsjBaSIWBt9OgpzL/Dtwc/adZI+DOxJ2YX0C8pEtquqhmpXn9a3NxP2bG8DIOnLwMW2v91c/wnw5prZWnQoZTrmoF/OXsB1wLaS5ts++9m+cQx9XtL6th+TtBewE/A12w9UTTV6rwDeRil8zhm6/zDw3hqBpshvUjyKGD+STgT2p7QCOBc4oYPPy72VI2wRscZ6dhTm65QXZRcAZ3b9j1pJx1COrS21/UTtPG3r0/pKehNlMsqeNBP2gM5O2IPJj/lIusH2a2plaoukhcBhg/HnkjYHTgcOo6zzDjXzjZKkZZRJZFsDlwEXA6+wvU/FWK2RNMv2kto5pkrTiHcL4CLKcWoAbF/4bN8TEfVJOpzy99QMhnau215cLVSMTHYgRcTa6M1RGNvvljSdMrHqTEmmHOs6x/bDddONnu2Ta2eYSn1aX9vfl/QDVp6wtz3Q2QIScI+k44CvU460vZuy66yLth4UjxrLgW1t3yfp8VqhWvKU7Sck7Qd80fapkm6qHapFN0n6EOX/16ePrtl+T71IrZoOPALsPXTPQApIEeu2J4HvA/+F8ibVf6MMZXlTxUwxIikgRcTa6M1RGADbD0m6ANgAOArYDzhG0pdsn1o1XKy1vqxvDyfsQSkMfoJn+lwtbu510VWSLgHOa67nAYslbQQ8UC1VOx6X9E7gL3nmaNd6FfO07WzgJ8BbgPnAwUBnd0vaPqR2hohYI0dQ3qS6zvYbmz5un6qcKUYkR9giYo316ShMM23uEOAPKH/En2V7uaQNgdts/37VgLFW+rS+fZuw1zeSRCka7U7ZGXo1cIE7+AefpFdRdtAtsX1OM9L+INufqxytFZJusr2LpFts7yRpPeAy2516V1/SsbZPknQqZcfRSro0FTOiiyT9yPbM5pjxa5s+dctsv7pytBiB7ECKiDXWs6MwBwB/P/H8tu1HJHX1+ECf9GZ9+zRhT9IXbR/V9AWa7IXo3AqxWtUUis5vHp1m+8eUd7oH13cAnSweNQZHEB+QtAPwa0r/p64Z7Kq6oWqKiFhTd0nalNK/7LuS7gf+rWqiGJkUkCJijfXsKMyvJhYXJJ1o+29sX1ErVIxMb9a3ZxP2BhPHPl81xRRqxryfCLyMsgNJlLrS9KrBWiDpDiYvDM6oEGcqfEXSi4HjKA3DXwQcXzfS6Nle2Hz6A9t3Dn9N0sypTxQRvwvb+zWfflLSImAT4NKKkWKEcoQtItZYn47CSLrR9q4T7t3SxYlzfdSn9e3bhL2Jmhfgv2f7ltpZ2iDpp8CcLk8SHJD0kqHLFwLvADaz/fFKkVolaZtml9Uq73WFpKXAXNt3N9dvAP7B9o51k0VE9FcKSBGx1oaOwnwE2MJ2Z47CSPoA8EFKb5yfDn1pY+Aa2++uEixGIuvbD82kyLmUndfLgN9SdjccXTFWKyRdY3v32jlqkXS17T1q52jDsxS6l9rerVamNjW7jU6jNEjflTL5dY7tf60aLCKix3KELSLWWE+OwnwT+A7wP4CPDt1/2PZ9gwtJL7Z9/1SHi7WW9e2HTZope4cBZ9r+hKRO7kACbpB0LqX3xGODm7Y7N/pc0nAxZRrwGkrxt1OaCUbbA5s0RxQHplN2XnWS7R9JOgK4HHgUmG37t5VjRUT0WgpIEbE2NgD+jg4fhbH9IPAgzz3y+wrKO6QxRrK+vfF8SVsCBwJ/WztMy6YDjwB7D90z0LkCEvCFoc+fAO6grHHXvAJ4G7ApZTfOwMPAe2sEatMkTe83pDxP/y9JnWx+HxExLlJAiog1Zvvk2hnWIaodIFqV9R1v84HLKMcSfyRpBnB75UytsH1I7QxT6FDbPx++IWmbWmHaYnsBsEDSLNtLaueZAr1peh8RMW7SAykiYgQm600R3ZH1jXWdpGNtnyTpVCafTHbEJN821nrYE+gk4NPACspEo52Bo2x/vWqwFknaHBhMXru+w5NeIyLGQnYgRURERKdJ2hY4Hdjc9g6SdqJMd/p05WijNJi6dkPVFFOgrz2BgL1tHytpP+AuytS5RUAnC0iSDgROBq6k7AI9VdIxts+vGiwiosdSQIqIGI0cceq2rO94+0fgGOAMANu3SPomZTdHJ9he2Hz6A9t3Dn+tmWbVJb3qCTRkvebjPsA5tu+TOv3U9LfAzMGuI0kvBb4HpIAUEVHJtNoBIiLGgaRDJ7n3uaHLP57CODFiWd/O29D29RPudbLxP3CBpJcPLiS9gTIhszNsL2h6Pb3N9iFDjyNsX1s7X4sWSvoJZdrcFU1B5dHKmdo0bcKRtXvJa5eIiKqyAykiYvUcIOlR298AkHQasP7gi8Mj32MsZX277R5Jf0DTG0jSAcCv6kZqzeHARZLmUCYHfpayY6WLbpL0IcpxtqePrtl+T71I7bH9UUknAg/ZflLSI8DbB1+XNNv2d+slHLlLJV0GnNNcHwR8p2KeiIjeSxPtiIjVIGkD4GLKO/l/Atxn+6iqoWJksr7d1kxd+wrwOuB+yrj3g23/omqwlkiaRTmu9yiwr+3fVo7UCknnAT8B3kWZtHcwcJvtI6sGq6SLzf4lzQN2pxwjXmz7W5UjRUT0WgpIERGrIGmzocuNgYuAa4CPQ3amjLusb79I2ohyBGYFcNBgx1kXSFrIytPXXkXZZXU/gO25NXK1SdJNtneRdIvtnSStB1xm+021s9Uw+O9RO8eoSZrO0KmJPC9HRNSTI2wREau2lJVflAnYt3kYmFEjVIxM1rfDmheeHwJeDiygNOD9EPAR4GagMwUk4PO1A1TwePPxAUk7AL8Gtq4Xp7pOvSss6f2UnWUrgKcoz895Xo6IqCg7kCIinoOkacAs29fUzhKjl/XtLkkLKDtwllAaob8YeAFwpO1lFaO1StLmwGDy2vUTGhF3hqTDgAuAHYH/DbwION72GTVz1dK1I2ySbqc8N99TO0tERBQpIEVErAZJS2zPqp0j2pH17SZJt9resfn8ecA9wFa2H66brD2SDgROBq6k7NjYEzjGdudGn0vaxvYdz3WvLyRdaHv/2jlGRdKlwP62H6mdJSIiihSQIiJWg6RPAbcAFzpPnJ2T9e2miTsyurZDYzKSbgZmD3YdNaPev2d757rJRm+y9ZS01PZutTK1SdJkxaEHgVu7uMtM0i7AmcAPgccG920fUS1URETPpQdSRMTqORrYCHhS0gqaXgy2p9eNFSOS9e2mnSU91HwuYIPmusvrO21CMeFeSuPwzpC0HbA9sMmEosp04IV1Uk2JQ4FZwKLmei/gOmBbSfNtn10rWEvOAL4P3ErpgRQREZWlgBQRsRpsb1w7Q7Qn69tNtp9XO0MFl0q6DDinuT4I+E7FPG14BfA2YFNgztD9h4H31gg0RZ4CXmn7N/B0r6vTgdcCi4GuFZCesH107RAREfGMHGGLiFhNkuYCr28ur7R9Sc08MVpZ3+gKSfOA3Sk7rRbb/lblSK2QNMv2kto5pspwT6/mWpTjaztIusn2LhXjjZykzwC/ABay8hG2+6qFiojouRSQIiJWg6TPUaYaDcZ+vxNYavuj9VLFqGR9o2skTWdop3kXX3RLOgn4NGXM+6XAzsBRtr9eNVhLJJ0GbAWc19yaB9wFHANcYvuNtbK1QdJwM/SnX7DYnlEhTkREkAJSRMRqkXQL8GrbTzXXzwNusr1T3WQxClnf6ApJ7wfmU4oqT/FMv6fOveiWtMz2qyXtB/wp8NfAoi42DIendxwN7y67Grigq43/m4mCl9p+SNLxwK7ACbZvrBwtIqK30gMpImL1bQoM3sXfpGKOaMemZH1j/H0E2N72PbWDTIH1mo/7AOfYvq/UWLqpKRSd3zz64Djb/yxpD2A28AWe6fkUEREVpIAUEbF6PgvcKOlKyju/rwc+VjVRjFLWN7riZ8AjtUNMkYWSfkLZbfVBSS8FHq2cqTXNxLkTgZdRnqe6PE0Q4Mnm477Al20vkPTJinkiInovR9giIlaDpLOB24H7gV8CP7T967qpYlSyvtEVknYBzgR+yMqNh4+oFqpFkl4MPGT7SUkbARsP/t+VNNv2d+smHB1JPwXm2L6tdpapIOkS4G7gzcBulELh9V09ohgRMQ5SQIqIWA2S3gTsAewJzACWUaYbnVIzV4xG1je6QtL1lN44t1J6IAFg+6xqoSqRdKPtXWvnGBVJ19jevXaOqSJpQ+CtlElzt0vaEtjR9uWVo0VE9FYKSBERq6lprDwTeCNwOLDC9nZ1U8WoZH2jCyRda/t1tXOsC7o22l7SKcAWwEWsvLvswlqZIiKiX9IDKSJiNUi6AtgIWAJcBcy0vbxuqhiVrG90yCJJ7wMWsnKR4b5n/5bO6tq7pNMp/a32HrpnIAWkiIiYEikgRUSsnlsoPRh2AB4EHpC0xPaKurFiRLK+0RXvaj5+jJULKDMqZIkRsn1I7QwREdFvOcIWEfE7kPQi4BDKqOwtbK9fOVKMUNY3xp2kA4FLbT8k6XhgV+AE2zdWjjblJF1oe//aOdaWpGNtnyTpVCbZVdXVBukREbHuyQ6kiIjVIOnDlAbLuwG/AL5KOeoUHZD1jQ45zvY/S9oDmA18ATgdeG3dWKPXjLWf6EFK0+XlXSgeNQZT126omiIiInovBaSIiNWzAfB3wFLbT9QOEyOX9Y2ueLL5uC/wZdsLJH2yYp42HQrMAhY113sB1wHbSppv++xawUbJ9sLm0x/YvnP4a5JmTn2iiIjoqxxhi4iIiOgISZcAdwNvpuyoWwFcb3vnqsFaIGkhcJjt3zTXm1N2Wx0GLLa9Q818oyZpKTDX9t3N9RuAf7C9Y91kERHRF9NqB4iIiIiIkTkQuAx4q+0HgM2AY6omas/Wg+JRYzmwbTNx7vFKmdp0OHCRpC0k7QOcAuxTOVNERPRIdiBFRERExNiRdBqwFXBec2secBelYHaJ7TfWytYWSbOAM4BHgX1t/7ZypIiI6JEUkCIiIiJi7EgSpWi0OyDgauACd+yP2+ao3vDP9CrgV8D9ALbn1sgVERH9kwJSRERERMQ6qul19Kxs/2CqskRERL+lgBQRERERY0fS/sCJwMsoO5AE2Pb0qsFa1DQKH0xeu9728pp5IiKiX1JAioiIiIixI+mnwBzbt9XOMhUkHQicDFxJKZbtCRxj+/yauSIioj9SQIqIiIiIsSPpGtu7184xVSTdDMwe7DqS9FLge7Z3rpssIiL64vm1A0RERERErIEbJJ0LXAQ8Nrhp+8Jqido1bcKRtXuBabXCRERE/6SAFBERERHjaDrwCLD30D0DXS0gXSrpMuCc5vog4DsV80RERM/kCFtERERExBiQNA/YndIDabHtb1WOFBERPZICUkRERESMDUnH2j5J0qmUHUcrsX1EhVhTRtJ0hk4R2L6vYpyIiOiRHGGLiIiIiHEymLp2Q9UUU0zS+4H5wArgKcouJAMzauaKiIj+yA6kiIiIiBg7kra2feeEezNt/6hSpFZJuh2YZfue2lkiIqKfMrkhIiIiIsbRBZJePriQ9AbgqxXztO1nlKbhERERVWQHUkRERESMHUkzgdOAOcCuwGeBObb/tWqwlkjaBTgT+CHw2OB+13s+RUTEuiMFpIiIiIgYS5JmAWcAjwL72v5t5UitkXQ9cDVwK6UHEgC2z6oWKiIieiUFpIiIiIgYG5IWsvL0tVcBvwLuB7A9t0autkm61vbraueIiIj+yhS2iIiIiBgnn68doJJFkt4HLGTlI2z31YsUERF9kh1IERERETGWJG0OzGwur7e9vGaeNkm6Y5Lbtj1jysNEREQvpYAUEREREWNH0oHAycCVgIA9gWNsn18zV0RERFelgBQRERERY0fSzcDswa4jSS8Fvmd757rJ2iHpHcClth+WdBxl8twJtm+qHC0iInpiWu0AERERERFrYNqEI2v30u2/bY9vikd7AG8BzgK+XDlTRET0SJd/yUZEREREd10q6TJJfyXpr4B/Ab5TOVObnmw+7gucbnsB8IKKeSIiomdyhC0iIiIixpKkecDulB5Ii21/q3Kk1ki6BLgbeDOwG7CC0ji8k0f2IiJi3ZMCUkRERESMLUnTgecPrrs61l7ShsBbgVtt3y5pS2BH25dXjhYRET2RAlJEREREjB1J7wfmU3biPEXZhZSx9hERES1JASkiIiIixo6k24FZtu+pnSUiIqIP0kQ7IiIiIsbRz4BHaoeIiIjoi+xAioiIiIixI2kX4Ezgh8Bjg/u2j6gWKiIiosOe/9z/JCIiIiJinXMG8H3gVkoPpIiIiGhRCkgRERERMY6esH107RARERF9kR5IERERETGOFkl6n6QtJW02eNQOFRER0VXpgRQRERERY0fSHZPctu0ZUx4mIiKiB1JAioiIiIiIiIiIVcoRtoiIiIgYO5LeIWnj5vPjJF3YTGaLiIiIFqSAFBERERHj6HjbD0vaA3gLcBbw5cqZIiIiOisFpIiIiIgYR082H/cFTre9AHhBxTwRERGdlgJSRERERIyjuyWdARwIfFvS+uRv24iIiNakiXZEREREjB1JGwJvBW61fbukLYEdbV9eOVpEREQnpYAUERERERERERGrlG2+ERERERERERGxSikgRURERERERETEKqWAFBERERERERERq5QCUkRERERERERErFIKSBERERERERERsUr/H5ZgWnHUCkFKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nos quedamos con la última columna del DataFrame \n",
    "    # (todas las filas de correlaciones menos la última porque es la correlación de la variable a predecir consigo misma)\n",
    "correlacionConClase =correlaciones_transform.iloc[:-1,-1]\n",
    "# Mostramos el valor de correlación mínimo y máximo\n",
    "descripcion= correlacionConClase.describe()\n",
    "minimo,maximo=descripcion.loc['min'],descripcion.loc['max']\n",
    "print('Minimo: ',minimo,' Maximo: ',maximo)\n",
    "# Mostramos una figura que muestre la correlación entre las variables de entrada y de salida\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "ax = fig.gca()\n",
    "ax.plot(range(correlacionConClase.shape[0]), correlacionConClase)\n",
    "ax.set_xticks(range(correlacionConClase.shape[0]))\n",
    "ax.set_xticklabels(data_modificado.columns[:-1], rotation='90')\n",
    "plt.ylim(-1,1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909df183",
   "metadata": {},
   "source": [
    "A la hora de analizar las gráficas anteriores hay que tener en cuenta que nos interesan variables que esten correlacionadas positivamente con la variable a predecir y que al mismo tiempo sea una variable no redundante. Por ejemplo, la variable *age* puede ser una variable con gran importancia con la variable respuesta dada la correlación entre ambas, sin embargo al seleccionar esta caracteristica habría variables como el estado civil o poseer hipertensión que quedarían en un segundo plano como caracteristicas redundantes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb6c4b0a",
   "metadata": {},
   "source": [
    "Para hacer esta selección de variables aplicaremos varios *filtros* y *wrappers* con los que iremos evalueando nuestro algoritmo y finalmente tomaremos la técnica que mejor resultado nos proporcione en el **F score**. Hacemos uso de las siguientes técnicas:\n",
    "\n",
    "*  Filtro T test\n",
    "*  PCA\n",
    "*  Filtro ANOVA\n",
    "*  Wrapper SBS\n",
    "\n",
    "*Para hacer uso del filtro T test sera necesaria la construcción del filtro tal y como hacemos en el siguiente apartado de código.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "534e93a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.base import TransformerMixin\n",
    "\n",
    "class t_test(TransformerMixin):\n",
    "\n",
    "    def __init__(self, tipoSeleccion = 'Kbest', K = None, percentil = None):\n",
    "        self.tipoSeleccion = tipoSeleccion\n",
    "        self.K = K\n",
    "        self.percentil = percentil\n",
    "        \n",
    "    def fit(self, X_train, y_train):\n",
    "        # Variables para almacenar el número de ejemplos, las medias, desviaciones estándar y variazas\n",
    "            # de los ejemplos según su clase (clase 0: C0 y clase 1: C1)\n",
    "        N0 = len(X_train[y_train == 0])\n",
    "        N1 = len(X_train[y_train == 1])\n",
    "        mediasC0 = X_train[y_train == 0].describe().loc['mean']\n",
    "        mediasC1 = X_train[y_train == 1].describe().loc['mean']\n",
    "        stdsC0 = X_train[y_train == 0].describe().loc['std']\n",
    "        stdsC1 = X_train[y_train == 1].describe().loc['std']\n",
    "        varsC0 = stdsC0**2\n",
    "        varsC1 = stdsC1**2\n",
    "        # Propiedad para almacenar el valor t de cada variable\n",
    "        self.t = np.abs(mediasC0 - mediasC1)/np.sqrt(np.abs(varsC0/N0 + varsC1/N1))\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        # Variable para almacenar los índices de las variables ordenadas de acuerdo al valor t\n",
    "        indices = np.argsort(self.t).values[::-1]\n",
    "        # Variable para almacenar True en caso de que la variable se seleccione y False en caso contrario\n",
    "            # de aceurdo al tipo de selección especificdo en el constructor\n",
    "        booleanos = np.zeros(X.shape[1],).astype('bool')\n",
    "        if self.tipoSeleccion == 'Kbest':\n",
    "            booleanos[:self.K] = True\n",
    "        elif self.tipoSeleccion == 'percentil':\n",
    "            booleanos[:int(np.round(X.shape[1]*(self.percentil),0))] = True\n",
    "        self.var=indices[booleanos]\n",
    "        return X.iloc[:,indices[booleanos]]\n",
    "    \n",
    "    # Método para asignar los valores de los híper-parámetros y que, de este modo, \n",
    "    # podamos aplicar GridSearchCV sobre un objeto de esta clase\n",
    "    def set_params(self, **parameters):\n",
    "        for parameter, value in parameters.items():\n",
    "            setattr(self, parameter, value)\n",
    "        return self\n",
    "    \n",
    "    def get_var(self):\n",
    "        return self.var"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81c3bf04",
   "metadata": {},
   "source": [
    "Una vez hemos cargado  la técnica *T test*, procedemos a evaluar todas las posibles técnicas y seleccionar la que mejor resultado nos de en el **F Score**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4963ab22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Resultado en train utilizando la Técnica PCA: 0.7782797070320335\n",
      "Resultado en test utilizando la Técnica PCA: 0.7317763736974817\n",
      "Precision obtenido en test utilizando la Técnica PCA: 0.1342685370741483\n",
      "Recall obtenido en test utilizando la Técnica PCA: 0.8375\n",
      "F Score obtenido en test utilizando la Técnica PCA: Rendimiento: 0.4090354090354091\n",
      "\n",
      "Resultado en train utilizando la Wrapper SBS: 0.8494683719123307\n",
      "Resultado en test utilizando la Wrapper SBS: 0.7489140719369893\n",
      "Precision obtenido en test utilizando la Wrapper SBS: 0.15776081424936386\n",
      "Recall obtenido en test utilizando la Wrapper SBS: 0.775\n",
      "F Score obtenido en test utilizando la Wrapper SBS: Rendimiento: 0.43478260869565216\n",
      "\n",
      "Resultado en train utilizando la Filtro T test: 0.8533986188550386\n",
      "Resultado en test utilizando la Filtro T test: 0.7663765752499353\n",
      "Precision obtenido en test utilizando la Filtro T test: 0.163727959697733\n",
      "Recall obtenido en test utilizando la Filtro T test: 0.8125\n",
      "F Score obtenido en test utilizando la Filtro T test: Rendimiento: 0.4532775453277545\n",
      "\n",
      "Resultado en train utilizando la Filtro Anova: 0.8447214069167714\n",
      "Resultado en test utilizando la Filtro Anova: 0.7497773737474607\n",
      "Precision obtenido en test utilizando la Filtro Anova: 0.1585677749360614\n",
      "Recall obtenido en test utilizando la Filtro Anova: 0.775\n",
      "F Score obtenido en test utilizando la Filtro Anova: Rendimiento: 0.43600562587904357\n",
      "\n",
      "\n",
      "La mejor tecnica es : Filtro T test.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\unaig\\anaconda3\\envs\\py38ml\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:114: UserWarning: Features [2] are constant.\n",
      "  warnings.warn(\"Features %s are constant.\" % constant_features_idx,\n",
      "C:\\Users\\unaig\\anaconda3\\envs\\py38ml\\lib\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:116: RuntimeWarning: invalid value encountered in true_divide\n",
      "  f = msb / msw\n"
     ]
    }
   ],
   "source": [
    "from sklearn import neighbors\n",
    "from imblearn.metrics import geometric_mean_score\n",
    "from imblearn.pipeline import Pipeline\n",
    "import imblearn.combine as hib\n",
    "import imblearn.under_sampling as us\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import feature_selection\n",
    "from sklearn import linear_model\n",
    "\n",
    "pca = PCA(n_components=int(np.round(Xtrain_sampled.shape[1]*0.5,0)),svd_solver='full') \n",
    "sbs = feature_selection.RFE(linear_model.LogisticRegression(solver='lbfgs'), n_features_to_select=10, step=1)\n",
    "ttest = t_test(K = 10)\n",
    "anova = feature_selection.SelectPercentile(feature_selection.f_classif, percentile = 30)\n",
    "\n",
    "listaMetodosSV = ['Técnica PCA', 'Wrapper SBS', 'Filtro T test', 'Filtro Anova']\n",
    "listaTecnicasSV = [pca,sbs,ttest, anova]\n",
    "# Se crea una lista para almacenar el resultado de test de cada técnica de\n",
    "#muestreo con KNN, almacenamos el recall de las predicciones obtenidas con KNN!!\n",
    "listaF_ScoreSV = []\n",
    "# Por cada técnica se cogen los datos de entrada y de salida (muestreados para realizar el aprendizaje)\n",
    "\n",
    "for nombre, metodo in zip(listaMetodosSV, listaTecnicasSV):\n",
    "    # Generamos la pipeline con los componentes adecuados\n",
    "    pipe = Pipeline([(nombre,metodo),('Modelo Logistico', LogReg)])\n",
    "    # Entrenamiento de la pipeline\n",
    "    pipe = pipe.fit(Xtrain_sampled,ytrain_sampled)\n",
    "    # Precicción de los datos de entrenamiento \n",
    "    prediccionesTrain = pipe.predict(Xtrain_sampled)\n",
    "    # Cálculo de la media geométrica en entrenamiento\n",
    "    accTrain = geometric_mean_score(ytrain_sampled, prediccionesTrain)\n",
    "    # Se imprime el resultado en train: mostrad el nombre de la técnica y su rendimiento\n",
    "    print('\\nResultado en train utilizando la {}: {}'.format(nombre,accTrain))\n",
    "    # Precicción de los datos de test \n",
    "    prediccionesTest = pipe.predict(X_test)\n",
    "    # Cálculo de la media geométrica en test\n",
    "    accTest = geometric_mean_score(y_test, prediccionesTest)\n",
    "    # Se imprime el resultado en test: mostrad el nombre de la técnica y su rendimiento\n",
    "    print('Resultado en test utilizando la {}: {}'.format(nombre,accTest))        \n",
    "        \n",
    "    # Se añade a la lista el resultado de test a la lista listaRecall\n",
    "    precision = precision_score(y_test, prediccionesTest)\n",
    "    recall = recall_score(y_test, prediccionesTest)\n",
    "    \n",
    "    f_score=F_Score(recall,precision,2)\n",
    "    listaF_ScoreSV.append(f_score)\n",
    "    \n",
    "    print('Precision obtenido en test utilizando la {}: {}'.format(nombre,precision))     \n",
    "    print('Recall obtenido en test utilizando la {}: {}'.format(nombre,recall)) \n",
    "    print('F Score obtenido en test utilizando la {}: Rendimiento: {}'.format(nombre,f_score)) \n",
    "\n",
    "#for i in np.where(listaF_ScoreSV==max(listaF_ScoreSV))[0]:    \n",
    "#    print('\\n\\nLa mejor tecnica es : {}.'.format(listaMetodosSV[i]))\n",
    "print('\\n\\nLa mejor tecnica es : {}.'.format(listaMetodosSV[int(np.where(listaF_ScoreSV==max(listaF_ScoreSV))[0])]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a19268cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numero de variables iniciales: 19\n",
      "Numero de variables actuales: 10\n"
     ]
    }
   ],
   "source": [
    "tecnica=listaTecnicasSV[int(np.where(listaF_ScoreSV==max(listaF_ScoreSV))[0][0])]\n",
    "\n",
    "tecnica.fit(Xtrain_sampled,ytrain_sampled)\n",
    "Xtrain_finall = tecnica.transform(Xtrain_sampled)\n",
    "ytrain_finall = ytrain_sampled.copy()\n",
    "Xtest_finall = tecnica.transform(X_test)\n",
    "ytest_finall=y_test.copy()\n",
    "print('Numero de variables iniciales: {}\\nNumero de variables actuales: {}'.format(Xtrain_sampled.shape[1],Xtrain_finall.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b89cc191",
   "metadata": {},
   "source": [
    "Observamos que la técnica de selección de variables que mejor resultado nos proporciona es el filtro *T test* con el que reducimos a 10 las variables a utilizar las cuales mostramos a continuación."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "70c797bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['smoking_statussmokes', 'genderOther', 'heart_disease',\n",
      "       'work_typeSelf-employed', 'age', 'hypertension', 'work_typePrivate',\n",
      "       'Residence_typeUrban', 'smoking_statusUnknown', 'avg_glucose_level'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data_transform.columns[tecnica.get_var()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cfdad0d",
   "metadata": {},
   "source": [
    "Observar que con el filtro T test nos quedamos con las siguientes caracteristicas de cada individuo:\n",
    "\n",
    "* El individuo fuma o no.\n",
    "* Si es de genero no binario.\n",
    "* Si el individuo ha tenido o no un ataque al corazón.\n",
    "* Si el individuo esta trabajando o no.\n",
    "* La edad del individuo.\n",
    "* Si sufre o no hipertensión.\n",
    "* Si el individuo trabajo o no en el ambito privado.\n",
    "* Si vive en zona urbana o no.\n",
    "* Si no se sabe la situación de fumar del individuo.\n",
    "* El nivel medio de glucosa del individuo.\n",
    "\n",
    "*Cabe comentar que variables que estaban correlacionadas con la variable respuesta como el estado civil no se han incluido dada su alta correlación con la edad.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5859493a",
   "metadata": {},
   "source": [
    "## 3.Generalización"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f045291e",
   "metadata": {},
   "source": [
    "Para acabar, despues de hacer la última modificación en el conjunto de entrenamiento, itermos por última vez nuestro algoritmo y analizamos los resultados finales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "fbf442a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuamos con la configuracion de los datos creada con el Técnica híbrida SMOTE con ENN\n",
      "Accuracy obtenido con el conjunto de datos sin modificar: 72.84820031298905\n"
     ]
    }
   ],
   "source": [
    "LogReg=LogReg.fit(Xtrain_finall,ytrain_finall.ravel())\n",
    "print('Continuamos con la configuracion de los datos creada con el {}'.format(listaMetodos[int(np.where(listaF_Score==max(listaF_Score))[0][0])]\n",
    "))\n",
    "# Cálculo de la media geométrica en test\n",
    "predicciones=np.round(LogReg.predict_proba(Xtest_finall)[:,1].reshape(-1,1),0)\n",
    "comparacion= predicciones==ytest_finall.to_numpy().reshape(-1,1)   \n",
    "suma=comparacion.sum()\n",
    "accTest=100*suma/predicciones.size\n",
    "print('Accuracy obtenido con el conjunto de datos sin modificar: {}'.format(accTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6cae23c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión de nuestro modelo 0.163727959697733\n",
      "Recall de nuestro modelo  0.8125\n",
      "F Sore de nuestro modelo  0.43600562587904357\n"
     ]
    }
   ],
   "source": [
    "precision3 = precision_score(ytest_finall, predicciones)\n",
    "recall3 = recall_score(ytest_finall, predicciones)\n",
    "matriz_de_decision=confusion_matrix(ytest_finall, predicciones)\n",
    "\n",
    "print('Precisión de nuestro modelo',precision3)\n",
    "print ('Recall de nuestro modelo ', recall3)\n",
    "f3=F_Score(recall,precision,2)\n",
    "print('F Sore de nuestro modelo ',f3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ba8689aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D2bTXbvxxz/0W1HXZlezV7wS+f8sN/Oc359KqdWsO+d2ZPH3XPTw/3MNEqnT8vx9h504d2HStNWjeLBj52jtcOep59ttsfTZquzq3TXyVBya/ye4bd6Dj6q2YOutDrh0zmakzP+Bb+TfuAH5931iGvfgmp/XamukfzmH06wvegrfs0IZVmvvDBSuCqPvF+OVNRNxK8UWGoU3dFi335h/EJ0SbpmyHgF4nHEevE4+jQ5fOvD9tOs89+DD/Ou1M3po0eaFy+/zo++z1g+/SboP1eeOZZ7nll79h1M23LFSmQ5fPcsQFv2ezPXdj7uzZjPn37dz4o9OYNW1a462QqrokvbvIuHkXLZc/abpSuOCRCdz9/Ou8+t4sWkSwYdvVOGTLjTii28a0bN6Mp96cxgWPPMP4qdN594M5dFj9M2y7zpqc+LmubLb2gptNfa68m9feq/59uLu+tU+D36xV42h+0rl1gzUft1ruwl0UP7j7GDA6pXT4YopLYLiTGp3hTmoaSxLulrunJPMP7nZt6nZIkiStiJb4CxWSJEla/hnuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKRUmrqNkiflgexJGllE7Um2HMnSZJUIoY7SZKkEjHcSZIklYjhTpIkqURaNHUDpKVq1vSmboG0clit7aLj3nq58dshrWzad1psEXvuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSqRFUzdAUrm9+NLL/PXqaxg1ZizPPv8CPbfvzt8GXrJQmb33P4hXp0xZaFz7tddi+N13NGZTpdKZO3cel197A/+89Q5ee+NN1mrXli/u1Yuf/c/35pfZ+9CjePX1Nxaar/1aazL8lhsbu7laSgx3kpapZ59/gfsfHM5223Rjzty5Nct9ab8v8I0jvzr/dcuWLRujeVKpnfabc3j48Sc56dhv8NmNOzHlzak8P+nFRcp96fN7843DDp7/2vNvxWa4k7RM7b3nHvTZa08A+p38U96ZNq1quY7t29N9220asWVSuQ175DFuv/te/n3VZWzaeeMGy3Zsvzbdu23VSC3TsrbMn7mLiBn1XveNiIuW9XKXRET0jIgLakybHBHtP2G9B0dEk50lEdE7Im7NwwdGxE+bqi21LE/HgZatZs18tFdqCoNuu4Ode3RfbLBT+azUV92U0uMppX7LoOqDgUYLdxHRvNa0lNLglNJvG6sty0pE2MtccoP+fQvddtyVHnvsRb+Tf8qrr01Z/EySahozfgKbdNqQX/3hQnb4/IFst/cBnHRaf96Y+tYiZQfdegfd9vwiPfY9kH4/P3ORZ/C0YmnScBcRX46IRyPiyYi4OyLWiYhmEfFsRHTIZZpFxHP1e9Ei4nMR8VCe96GI2DyPbx4R50bE2IgYExE/yON3zOVGR8RjEdG6Xg/X2hExJNd3KRAVyzo6zzMqIi6tC1MRMSMi/i/X+Uhu/67AgcA5uXyX/HdHRDwREQ9ExBZVtsUaEXFFRbsPzeMHRMTjETE+Is6sKD85Is6IiAeBwyPiixExIb/+SkW5+T1kEbFxRAzN9Q+NiI0iom2uq1kus1pEvBwRLSPiOxExIq/foIhYLZc5PCLG5fHDFrPd5/eA5p7S+5bkOMjj+0fEZRExBLh6iQ8srXD27t2LM356CldeejGn/rAfT44Zy1HHHc97781Y/MySqpr69jvcdPsQnn7uOc478+ec/bOTGf/MRE76WX9SSvPL7b3Hrpzxkx9w5fnncOr3j+fJcU9x1Pd+yHszPP9WVI3RG7JqRIyqeL0WMDgPPwjsnFJKEfFt4NSU0k8i4hrgKOBPQB9gdEqp/keNCUCvlNLciOgD/AY4FDge6Axsn6etFRGrANcDR6SURkREG+D9evX9EngwpfSriDgg10NEbAkcAeyWUpoTEX/ObbsaWB14JKX084j4PfCdlNJZETEYuDWl9M9cx1DghJTSsxGxE/BnYO96y/8FMD2ltE2eZ808/ucppbdzoBwaEdumlMbkaR+klHaPiFbAs7nO5/K6VnMRcHVK6aqIOBa4IKV0cESMBvYE7gW+DNyZ1/WmlNJfcnvOAo4DLgTOAL6QUno1ItrluhfZ7jXaUM0ixwHwkzytB7B7Sqn+/lKJnH7qT+YP99xhe7bfblsOPvJoBg2+hb5Hfa0JWyatwFICEn/+7a9Ys21bADq0X5ujv/9jHnniSXbpuQMAp//w+/Nn6dl9G7bfZmsO7vtdBt12J32POLQpWq5PqTF67t5PKXWv+6MIBnU2BO6MiLHAKcDWefzlwDfz8LHAFVXqbQvcGBHjgPMq5u0DXJJSmguQUnob2ByYklIakce9Wze9Qi/gmjz9NuCdPH4fioAxIofUfYDP5mmzgVvz8BPAJvUbGRFrALvmto4CLgXWq7I+fYCL616klOqW/9WIGAk8mdex8nZvXYjbApiUUno2FR/HrqlSP8AuwD/y8N+A3SvqOSIPH1lRb7fc0ziWItDWbePhwJUR8R2g7pZwte2+pGodBwCDqwW7iDg+92g+ftlll32MRWlF0HXTLnTeeCOeenpCUzdFWmG1ab0GXT/beX6wA+ixbTdatmzJc1W+MVun62c707lTJ56a+GxjNFPLQFM/x3Qh8MeU0uCI6A30B0gpvRwRb0TE3sBOFMGivl8D96aUDomITYD78vgAUr2y1cZVU61MAFellE6rMm1OWtC3PY/q27MZMC0H24Ys0saI6AycDOyYUnonIq4EWlUUmbmYti9O3TyDgbNzb1sP4J48/krg4JTS6IjoC/QGSCmdkHsgDwBGRUT3au3P5rLgQ0SrKtOhxnGQzaw2Q0rpMqAu1X2SddcKICIWX0hSVV022YjZs+csOiGlJfqiU+D5t6Jq6i9UtAVezcPfqjdtIEUP1A0ppXmLmbdvxfghwAmRH8DPgWUCsH5E7JjHtY5FH9AfRg6REbEfUHdbdChwWER0rKsvIhb31aP3gNZQ9BICkyLi8Dx/RMR2VeYZApxU9yLflm1DEW6m5+fQ9quxvAlA54jokl/Xuo/1EEXPHHldH8xtnAE8BpxPcTu5bnu3BqZEREsqAnZEdEkpPZpSOgN4C+hE9e0OMJkiMEJx27yaho4DrYQmPvc8k158ia233LKpmyKtsHrvujPPPD+Jt6dNnz9uxKgxzJk7l803/WzN+Sa+MIlJL7/M1lt0bYxmahlo6p67/hS3K18FHqF4ZqvOYIrbsdVuyQL8HrgqIn7Mgp4mKEJhV2BMRMwB/pJSuigijgAujIhVKZ6361OvvjOBa/Mt0PuBlwBSSk9FxOnAkPylgznA94HafdpwHfCXiOgHHEYRjAbkelrm6aPrzXMWcHG+zTwPODOldFNEPAmMB16guB26iJTSBxFxPHBbRLxFEdq6VSnaD7g8Ik4BpgLHVEy7HriR3DuX/QJ4NK/rWHJgpfiyyGYUvXVD87qMo952p3jG70zgrxHxs1xXNf2pfRxoBff++x9w/4PFofvGm28yY+ZM7rhrKAB77r4bjz7+OINvu4PevXanY4f2vDBpMgMGXsF6667DVw48oCmbLq3QjjjoAP72z5s58dTT+e43v87MWbM4d8BAdu25Az23K35T8r6HHmHwnUPpvevOdGy/Ni+89BIDrvw7663Tka/sv28Tr4E+qaj8xszyJCJ6AuellPZo6rZoubfgIJ41vYFiagqvvPYa+xxwcNVpQ2/7FzNmzOTsP5zHMxOf470Z79GubVv22HUXfnTS91inY4fGbayW3GptFx331suN3w416MVXXuWs8y5mxKgxtGzZgn1235XT+p1I2zbFZ/UJz73A2RcM4JnnX+C992bQrm0b9thpR3703WNZp8Mn+qlXLWvtO9UN1bxvvlyGuyh+dPdE4KiU0oNN3R4t9wx3UmMz3ElNY0UNd9LHZLiTGpvhTmoaSxDumvoLFZIkSVqKDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqEcOdJElSiRjuJEmSSsRwJ0mSVCKGO0mSpBIx3EmSJJWI4U6SJKlEDHeSJEklYriTJEkqkUgpNXUbpE/Lg1iStLKJWhPsuZMkSSqRFk3dAGkpqPnpRcu/iDg+pXRZU7dDWtl47pWXPXeSmtrxTd0AaSXluVdShjtJkqQSMdxJkiSViOFOUlPzmR+paXjulZQ/hSJJklQi9txJkiSViOFOK4WImBcRoyJiXETcGBGrfYq6royIw/LwwIjYqoGy/SPi5E+6rE+jKZe9OLW2W0T0jYiLPmGd7SLie5++dZ9cRNwXET3z8O0R0a4p21NNREyOiPZN3Y46K+q5GREzPum8y1JE9IyIC2pM+8T7PiIObmh7LmsR0Tsibs3DB0bET5uqLbV8muvX0ma408ri/ZRS95RSN2A2cELlxIho/kkqTSl9O6X01NJo4MpkGW23dkCjhbuIaPB3QlNK+6eUpjVSc5aZT3pufAyem0tRSunxlFK/ZVD1wUCjhbuG9ntKaXBK6beN1ZZlZXHXkE/DcKeV0QPApvmT4L0R8Q9gbEQ0j4hzImJERIyJiO8CROGiiHgqIm4DOtZVVK+n5osRMTIiRkfE0IrlbZXLvRAR/Srm/XHurRgXET+s1tCImBER/5frfCQi1snjN46IobmdQyNioxrrWmvZ/4qIJyJifEQcn8cdFxHnVZT5TkT8sUqbBkTE43neMyvG7xgRD+W2PhYRrfM2PTcixua2/qDKdjsmIiZGxP3AbhX1dYiIQXl/jIiI3fL4/hFxeZX1+i3QJfcCnZPLnlKxP+e3td76LLLfIuJzeV2ezP9unsf3zb1LtwBDImLViLgu1389sGpFvfN7Sart64j4XVT0NOb1+klErJH36ci83Q7K01ePiNtyO8dFxBENbPeFehAi4taI6F1l3Rc5DvL4GRHxq4h4FNil2nZbRlaYczOXq3ZufjkiHs3Hzt0RsU5ENIuIZyOiQy7TLCKei3q9aA0cd7XOo2r7vrKHa+2IGJLru5SKH3yPiKPzPKMi4tLIYSqqXHMiYlfgQOCcXL5L/rsjHz8PRMQWVbbPGhFxRUW7D83ja11DJkfEGRHxIHB43m8T8uuvVJSbf3xHlWthRLTNdTXLZVaLiJcjomUU17URef0GRe4pjojD8/4eHRHDFrPdK8/tnhFxX5V1X+Q4yOP7R8RlETEEuLrWsfWppZT886/0f8CM/G8L4N/AiUBvYCbQOU87Hjg9D38GeBzoTHFRuQtoDqwPTAMOy+XuA3oCHYCXK+paK//bH3go19ce+C/QEugBjAVWB9YAxgPbV2l3Ar6ch39f0b5bgG/l4WOBf1WZt+qy67VvVWAcsHZuy/MVZR4CtqlSb928zfP6bwusArwA7Jintcnb+kRgENCi3rx122094KW8/VYBhgMX5TL/AHbPwxsBTy9mm24CjKto574U3wYMig+ytwK96q1Lrf3WpqLNfYBBebgv8EpFuR8Dl+fhbYG5QM/8enJuX9V9nf/ur2jLU3k9WwBt8rj2wHN5HQ4F/lJRvm0D271v3XbM428Fele2q9ZxUHHcfdVz8xOdm2uy4MuK3wb+kId/Cfyw4tgcVKXOWsfdIudRA/u+N3BrHncBcEYePiC3uT2wJcU1pO5c/zPwzcWs15V12za/Hgpslod3Au6psj6/A/5U8XrNWteQimPz1DzcKu+3zSiO/xsq1qsvC64TVa+FFMfSXnn4CGBgHl67oj1nAT/Iw2OBDfJwu1rbvco51BO4r0q7ah0H/YEngFWX5Xnlfz+mlcWqETEqDz8A/BXYFXgspTQpj98X2DbyMzsUb56bAb2Aa1NK84DXIuKeKvXvDAyrqyul9HbFtNtSSh8CH0bEm8A6wO7AzSmlmQARcROwB/BkvXpnU7wxQ3FB+Hwe3oUFn2T/RnERrqbasl8B+kXEIblMJ4qL9CN53b4UEU9TXPjHVqnzq1H08rSgCGdbUbwhTEkpjcjr/25erz7AJSmluVW2CxRvCvellKbm8tcDXfO0PhQ9K3Vl20RE6wbWq75981/dNl2DYn8OqyhTa7+1Ba6KiM3yurWsmOeuinK9KN5ASSmNiYgxVdpRdV+nlC6IiI4RsT5FAHknpfRSRLQEfhMRvYCPgA3y+o0Fzo2I31G8yT0QEdtQfbtXaUZVixwHFCFnHsWbWmMo27m5IXB9RKxHEcDq1uFyisDxJ4oQckWVttY67hY5j5Zw3/ciXydSSrdFxDt5/D4UIXZELr8q8OZi1mu+iFiDYh/dWLG8z1RZnz7AkXUvUkp1y692Dak7d67P/24BTEopPZuXeQ3V/0eNWtfC6ylC3b25DX/O47tFxFkUj3GsAdyZxw8HroyIG4CbKtrf0PWrIbWOA4DBKaX3P0ZdH5vhTiuL91NK3StH5IvSzMpRFJ/i7qxXbn+KC21DooEyH1YMz6M475b03XdOyh/3KuatZomXHcXtuT7ALimlWfmWQqtcZiDwM2ACVd58IqIzcDJFb8E7EXFlnrfW+je0XRbX9ma5jQtdBPN+q7ZNqy377JTSpQ0su1b7fg3cm1I6JCI2oehdqDOzXtklOTZq+SdwGLAucF0edxRF2OuRUpoTEZOBVimliRHRA9gfODvf1vlXjeXPZeHHblrVL7CY4+CDHJgaQ9nOzQuBP6aUBudt3B8gpfRyRLwREXtTfKg5qkqdtY67auuwJOcWNcoEcFVK6bQq05bkmtMMmFZ/v9VYzkLLb+AaUqdyvy/J+tVXN89givNkLYogWxf8rwQOTimNjoi+FD2dpJROiIidKHo4R0VE92rtzyrPr0XOrazqcZDVv4YsdT5zJy1wJ3Bi7jkhIrpGxOoUPT1H5ucv1gP2qjLvw8Ce+cJFvqA0ZBhwcH4WZHXgEIpeiyX1EAs+ER8FPPgx5m1L0Us0K4rnZHaum5BSepSiB+frwLVV5m1DcWGanp8h2S+PnwCsHxE7AkTx7E8LYAhwQh6utl0eBXpH8WxQS+DwimlDgJPqXuSLbUPeA1pXvL4TODb3MhARG0REx3rz1NpvbYFX83DfBpY5jPwmHRHdKG7NVitTa19fR7EfD6MIenXLfjMHu72AjXP96wOzUkrXAOcCO1B7u08GukfxbFcn4HNV2lXzOFgOrUjnZuWx86160wYC1wA31AjPtY67audRrX1ff13qjs/9KG4VQnFL9bC68yEi1oqIjRezXvPPr9xLOCkiDs/zR0RsV2We+ufwmtS+htQ3AegcEV3y66/VKFf1WphSmgE8BpxP0dNdt71bA1PysTQ/YEdEl5TSoymlM4C3KK6Dta5fkykCIxSPS1TT0HGwzBnupAUGUjz3NDIixgGXUnxqvRl4luK22ADg/voz5tuKxwM3RcRoFtxaqCqlNJLiE+RjFAFnYEqp/m2fhvQDjoniNuA3gP/5GPPeQdGDN4aip+CRetNvAIZX3EKpbPdoittT4yluMw3P42dT3AK5MK//XRSfaAdSPFM3Jo//er36plB8on0YuBsYWW8de0bxIPNT1PsWZZW2/RcYHsVD0eeklIZQPLf3cESMpQhPrevNU2u//Z7iU/9wiueCahkArJG35akU+7N+u2ru65TS+NymV/O2APh7Xu/HKd58JuTx2wCPRXEL8+fAWQ1s9+EUt4HGUgTByu1aZ3HHwfJkRTo3+1PcrnyAIiRUGkxxK7DaLVmofdwtch41sO8rnQn0ioiRFLe2X8rr+BRwOsWXgsbkeddbzHpdB5wSxRcEulAcm8flZY8HDqoyz1nAmvmcHE3xDFzVa0h9KaUPKPbbbVF8oeLFGu1q6Fp4PXA0C+/zX1Ds17tYcG5B8WWRsfn4GgaMpvb160zg/LyPa/Vw96f2cbDM+T9USFpIFN+0Oy+lNHSxhSUtsSi+vXteSmmPpm6Lys2eO0kARPEjwBMpnoEy2ElLURQ/ujsIqPacm7RU2XMnSZJUIvbcSZIklYjhTpIkqUQMd5IkSSViuJMkSSoRw50kSVKJGO4kSZJK5P8B3eG1u5rpPTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.heatmap(matriz_de_decision, cmap = 'Reds', annot = True, fmt = 'd', linewidths = 5, cbar = False, annot_kws = {'fontsize': 15}, \n",
    "            yticklabels = ['No hay accidente cardiovascular', 'Hay accidente cardiovascular'],\n",
    "            xticklabels = ['Predicho no hay accidente cardiovascular', 'Predicho hay accidente cardiovascular'])\n",
    "plt.yticks(rotation = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0c121dae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diferencia del F Score: 0.003994294434284396\n"
     ]
    }
   ],
   "source": [
    "print('Diferencia del F Score: {}'.format(f3-f2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5839eda",
   "metadata": {},
   "source": [
    "Observamos que gracias a nuestra selección de variables en [Selección de variables del modelo](#item4), el conjunto de datos de entrenamiento que obtenemos provoca una mejora pequeña en el **F Score**, sin embargo logramos cumplir conn el objetivo y mejoramos el **F Score**. Además, si nos fijamos en los resultados de [2.Generalización](#item5) obtenemos no solo una mejora del **recall** si no que también mejoramos la **pracisión**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cccc5475",
   "metadata": {},
   "source": [
    "## Conclusiones"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080bde20",
   "metadata": {},
   "source": [
    "Finalmente, concluimos con una breve conclusión en base a todos los procedimientos que hemos seguido."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "29cfdeee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diferencia entre el resultado final e inicial en la precisión: -0.836272040302267\n",
      "Diferencia entre el resultado final e inicial en la recall: 0.8\n",
      "Diferencia entre el resultado final e inicial en la F score: 0.4204293018915046\n"
     ]
    }
   ],
   "source": [
    "print('Diferencia entre el resultado final e inicial en la precisión: {}'.format(precision3-precision1))\n",
    "print('Diferencia entre el resultado final e inicial en la recall: {}'.format(recall3-recall1))\n",
    "print('Diferencia entre el resultado final e inicial en la F score: {}'.format(f3-f1))\n",
    "\n",
    "#imprimir tambien los finales y hablar de las tecnicas que se han seleccionado y lo que hacen...\n",
    "#explicar porque primero outliers y luego nulos..."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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 },
 "nbformat": 4,
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