diff --git a/parte_2/#6 - Boosting.ipynb b/parte_2/#6 - Boosting.ipynb index 4306b07..27e1afd 100644 --- a/parte_2/#6 - Boosting.ipynb +++ b/parte_2/#6 - Boosting.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "be3ff293", + "id": "0fc599d8", "metadata": {}, "source": [ "# Modelo: Boosting" @@ -10,23 +10,25 @@ }, { "cell_type": "markdown", - "id": "917877b3", + "id": "e5d203c2", "metadata": {}, "source": [ - "El modelo a entrenar en el siguiente notebook será un ensamble. En particular buscaremos hacer un **Boosting**. Boosting es un ensamble de tipo homogéneo que consiste en entrenar muchas instancias de un modelo de forma iterativa." + "El modelo a entrenar en el siguiente notebook será un ensamble. En particular buscaremos hacer un **Boosting**. Boosting es un ensamble de tipo homogéneo que consiste en entrenar diferentes instancias con un modelo de forma iterativa." ] }, { "cell_type": "markdown", - "id": "3ef07e14", + "id": "890bc33b", "metadata": {}, "source": [ - "La particularidad de Boosting se da en como se realiza ese entrenamiento. En primer lugar, se entrena un modelo como lo veniamos haciendo hasta ahora. A continuación, a las instancias en las que el primer modelo falló se les asigna un peso mayor para realizar el entrenamiento del segundo modelo. Y de esta manera se continua de forma iterativa hasta obtener un score deseado pero buscando evitar overfittear." + "La particularidad de Boosting se da en como se realiza ese entrenamiento. En primer lugar, se entrena un modelo como lo veniamos haciendo hasta ahora. A continuación, a las instancias en las que el primer modelo falló se les asigna un peso mayor para realizar el entrenamiento del segundo modelo. Es decir, un indicativo para comunicarle al segundo modelo en que instancias se equivocó el primer modelo, las mal clasificadas. En definitiva Boosting, tendras distintos modelos donde cada uno proriza los mal clasificados del anterior modelo.\n", + "\n", + "De esta manera se continua de forma iterativa hasta obtener un score deseado. El sesgo y la varianza bajarían, ya que en cada estimador se ven los datos de forma diferente por el peso asignado." ] }, { "cell_type": "markdown", - "id": "9d7c8b5b", + "id": "b3ea744e", "metadata": {}, "source": [ "A la hora de evaluar instancias, el modelo realiza una votación ponderada (según que tan bien fue la performance de ese modelo durante el entrenamiento) entre todos sus estimadores y arroja una predicción." @@ -34,15 +36,15 @@ }, { "cell_type": "markdown", - "id": "621a1b43", + "id": "6088db75", "metadata": {}, "source": [ - "Para implementar este ensamble utilizaremos **GradientBoostingClassifier** de la libreria sklearn. Esta implementación utiliza árboles de decisión como algoritmo base. Además, utiliza una función de perdida para calcular el error en las predicciones arrojadas por cada árbol y a partir de ella optimiza los parámetros de los siguientes árboles mediante descenso por gradiente" + "Para implementar este ensamble utilizaremos **GradientBoostingClassifier** de la libreria sklearn. Esta implementación utiliza árboles de decisión como algoritmo base. Además, utiliza una función de perdida para calcular el error en las predicciones arrojadas por cada árbol y a partir de ella optimiza los parámetros de los siguientes árboles mediante descenso por gradiente." ] }, { "cell_type": "markdown", - "id": "1a44ae0f", + "id": "3d8dfc1a", "metadata": {}, "source": [ "## Librerias y funciones necesarias" @@ -50,7 +52,7 @@ }, { "cell_type": "markdown", - "id": "396b98f5", + "id": "ed89dec0", "metadata": {}, "source": [ "Comenzamos importando las librerias y funciones que serán necesarias para preprocesar nuestros datos, realizar nuestro entrenamiento y obtener metricas " @@ -59,7 +61,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "302b1d22", + "id": "986d3ecb", "metadata": {}, "outputs": [], "source": [ @@ -76,13 +78,13 @@ "from sklearn.metrics import accuracy_score\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.preprocessing import StandardScaler\n", - "from sklearn import tree\n" + "from sklearn import tree" ] }, { "cell_type": "code", "execution_count": 2, - "id": "18b25633", + "id": "2e280c00", "metadata": {}, "outputs": [], "source": [ @@ -100,7 +102,7 @@ }, { "cell_type": "markdown", - "id": "09061d26", + "id": "6b814da8", "metadata": {}, "source": [ "## Primer preprocesamiento" @@ -108,7 +110,7 @@ }, { "cell_type": "markdown", - "id": "a29d6843", + "id": "1223160e", "metadata": {}, "source": [ "En primer lugar obtenemos el dataset para entrenar y el holdout. En segundo lugar, aplicamos una función que trabaja sobre las features, generalizando algunas y dejando de lado otras según lo observado en la primer parte de este trabajo práctico. También separamos a la variable target del resto del dataset. Por último, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo" @@ -117,7 +119,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "be2cb57a", + "id": "b2d7832d", "metadata": {}, "outputs": [ { @@ -136,7 +138,7 @@ }, { "cell_type": "markdown", - "id": "68f4960d", + "id": "643312d8", "metadata": {}, "source": [ "Luego vamos a realizar un split del dataset para dividir en train y test. Como observamos en la primer parte de este trabajo práctico, la variable target no esta distribuida uniformente por lo cual realizamos una división estratificada" @@ -145,7 +147,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "d30209dd", + "id": "1c0d86e3", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +156,7 @@ }, { "cell_type": "markdown", - "id": "859695ab", + "id": "ca0beb15", "metadata": {}, "source": [ "### Entrenamiento" @@ -162,7 +164,7 @@ }, { "cell_type": "markdown", - "id": "21e06657", + "id": "4f3b1ca5", "metadata": {}, "source": [ "Vamos a realizar un entrenamiento con 5 folds. Para ello utilizaremos StratifiedKFold para asegurarnos de obtener folds balanceados. " @@ -170,28 +172,34 @@ }, { "cell_type": "markdown", - "id": "15ea6f8f", + "id": "f0872fc9", "metadata": {}, "source": [ - "En cuanto a la busqueda de hiperparámetros, vamos a buscar distitnas variaciones de altura de los árboles y mínimas instancias por hoja tal y como realizamos en el modelo de árbol de decisión. También, buscaremos el parámetros *n_estimators*, el cual define la cantidad de aŕboles que entrenaremos en cada modelo. Si este número fuese muy grande, el modelo podria estar overfitteando. Aún así, según su propia documentación, **GradientBoostingClassifier** es un modelo muy robusto al overfitting por lo que deberia arrojar mejores resultados con más estimadores. Otro hiperparámetro importante será el *learning rate*, el cual determinará cuanto contribuye cada árbol con el siguiente" + "En cuanto a la búsqueda de hiperparámetros, vamos a buscar distintas variaciones de altura de los árboles y mínimas instancias por hoja tal y como realizamos en el modelo de árbol de decisión. \n", + "\n", + "También, buscaremos el hiperparámetro *n_estimators*, el cual define la cantidad de modelos que se usarán uno al otro en la cadena de boosting. En nuestro caso seria *n* arboles usados. Si este número fuese muy grande, el modelo podría estar overfitteando. Aún así, según su propia documentación, **GradientBoostingClassifier** es un modelo muy robusto al overfitting por lo que debería arrojar mejores resultados con más estimadores." ] }, { "cell_type": "code", "execution_count": 5, - "id": "224ca367", + "id": "a0613564", "metadata": {}, "outputs": [], "source": [ - "cv = StratifiedKFold(n_splits=5,random_state=10, shuffle=True).split(X_train, y_train)\n", + "cv = StratifiedKFold(n_splits=5, random_state=10, shuffle=True).split(X_train, y_train)\n", "clf = GradientBoostingClassifier(random_state=10)\n", - "params = {\"max_depth\":np.arange(3,8),\"min_samples_leaf\":np.arange(50,150,20),\"n_estimators\":np.arange(50,1000,50),\"learning_rate\":np.arange(0.1,0.9,0.1)}\n", - "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1)\n" + "\n", + "params = {\"max_depth\":np.arange(3,8),\n", + " \"min_samples_leaf\":np.arange(50,150,20),\n", + " \"n_estimators\":np.arange(50,350,50)}\n", + "\n", + "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose = True)" ] }, { "cell_type": "markdown", - "id": "df5b88c2", + "id": "b88d3c74", "metadata": {}, "source": [ "Ahora sí, entrenamos nuestro modelo" @@ -200,17 +208,36 @@ { "cell_type": "code", "execution_count": 6, - "id": "dd479f3a", + "id": "155b80e3", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 47.4s\n", + "[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 3.9min\n", + "[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 10.9min\n", + "[Parallel(n_jobs=-1)]: Done 750 out of 750 | elapsed: 23.2min finished\n" + ] + }, { "data": { "text/plain": [ - "GridSearchCV(cv=,\n", + "GridSearchCV(cv=,\n", " estimator=GradientBoostingClassifier(random_state=10), n_jobs=-1,\n", " param_grid={'max_depth': array([3, 4, 5, 6, 7]),\n", - " 'min_samples_leaf': array([ 50, 70, 90, 110, 130])},\n", - " scoring='roc_auc')" + " 'min_samples_leaf': array([ 50, 70, 90, 110, 130]),\n", + " 'n_estimators': array([ 50, 100, 150, 200, 250, 300])},\n", + " scoring='roc_auc', verbose=True)" ] }, "execution_count": 6, @@ -224,16 +251,70 @@ }, { "cell_type": "markdown", - "id": "cd0f7c2b", + "id": "ffb69a0d", "metadata": {}, "source": [ - "Realizamos nuestras predicciones para una análisis más amplio" + "Algo que resulta interesante observar es que valor se tiene en la función de pérdida (Deviance) en cada iteración. Graficamos " ] }, { "cell_type": "code", "execution_count": 7, - "id": "500c882f", + "id": "56e42b82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Deviance')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "best = clf.best_estimator_\n", + "score_estimadores = best.train_score_\n", + "plt.figure(dpi=125)\n", + "plt.plot(score_estimadores)\n", + "plt.xlabel(\"Cantidad de estimadores\")\n", + "plt.ylabel(\"Deviance\")" + ] + }, + { + "cell_type": "markdown", + "id": "64667cbf", + "metadata": {}, + "source": [ + "Lo que se observa es que a partir de cierto numero de estimadores, la pérdida se reduce muy lentamente y comienza a comportarse de forma asintótica. Por lo que aumentar más el número de estimadores generaria poca ganancia. " + ] + }, + { + "cell_type": "markdown", + "id": "2a5acca5", + "metadata": {}, + "source": [ + "Realizamos nuestras predicciones para una análisis más amplio" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "a186384c", "metadata": {}, "outputs": [], "source": [ @@ -242,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "e3a403a3", + "id": "5b1d423a", "metadata": {}, "source": [ "### Metricas" @@ -250,18 +331,18 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "1fbc2aa9", + "execution_count": 9, + "id": "27658f9f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AUC-ROC score sobre test: 0.9296294958832876\n", - "AUC-ROC score sobre train: 0.9408900908373998\n", - "Accuracy sobre test: 0.8719484108705665\n", - "Accuracy sobre train: 0.8802595208845209\n", + "AUC-ROC score sobre test: 0.9292791497286477\n", + "AUC-ROC score sobre train: 0.9407463366669064\n", + "Accuracy sobre test: 0.8713342545677875\n", + "Accuracy sobre train: 0.8812960687960688\n", " precision recall f1-score support\n", "\n", " Bajo valor 0.89 0.94 0.92 4945\n", @@ -271,12 +352,12 @@ " macro avg 0.84 0.80 0.81 6513\n", "weighted avg 0.87 0.87 0.87 6513\n", "\n", - "Los mejores hiperpametros elegidos: {'max_depth': 7, 'min_samples_leaf': 50}\n" + "Los mejores hiperpametros elegidos: {'max_depth': 5, 'min_samples_leaf': 70, 'n_estimators': 300}\n" ] }, { "data": { - "image/png": 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ALgW7x0XEuGrO6QBcDpwWEeOqGK/2AnA78BmwGKlv/WlJW0bEa1ma7qSaeqHRQI/s527Zeq50ETFN0qS8dLXioG5mZuWr7gPljgfOLth3LnBONeecAfwE/KuqBBExV56SHgU+Jg2s26noSQ3AQd3MzMpX3QfKXQ7cVLCvulr60qSBcXsCXbNaeqfscCdJnSJiQuF5ETFR0mPAPnm7R5Oa1gt1B0ZlP4/J1l0LytEG6JCXrlYc1M3MrMnKmtmrDOJFLAu0AR4rcuw54A1g41rmNQTYVpIK+tX7AB9m5Zso6Vvm7TtfGRDz9rVXywPlzMysfDX8QLlBwNYFy3HZsSOAo4oWMw142wV4K2/3QFKtfJu8dCsB6wCPF6TbXVLrvH39SLX4V0spvGvqZmZWvhr4PvWIGAM8P3cRZpfhnYh4V9LmpHvLHyRNNrMYqcl+UWDfvLxek/QEcIukE5gz+cwHQP+8S1wCHAjcJela0qj7k4DTC25zq5GDupmZla/Gn1GumGGkJvoLgJ7ARFKN+oiIeLMgbT9Sv/4NpJj7JHBMbjY5gIj4XNJ2WbrHgeGkwX2XlVowB3UzM7NqRMTzpP7t3PbnwA61PHcscFi2VJfuVWrfV18lB3UzMytf5VlTL1sO6mZmVr5aNN7c75XIQd3MzMqXa+ol8btlZmbWRLimbmZm5asRH71aiRzUzcysfLn5vSQO6mZmVr5cUy+Jg7qZmZUv19RL4nfLzMysiXBN3czMypeb30vioG5mZuXLze8lcVA3M7Py5Zp6SfwVyMzMrIlwTd3MzMqXm99L4qBuZmbly83vJXFQNzOz8uWaekn8bpmZmTURrqmbmVn5ck29JA7qZmZWvtynXhIHdTMzK1+uqZfEQd3MzMqXa+ol8VcgMzOzJsI1dTMzK19ufi+Jg7qZmZUvN7+XxEHdzMzKlhzUS+J2DTMzsypI6iTpO0khaf2CY4dJ+lTSFEnvS9qlyPldJd0saZSk8ZLul9S7SLpNJL0mabKkryWdovn4RuOgbmZmZUtSnZZ6cCZFWrUl7QfcCNwD7Ai8BjwoaeOCpPcA2wFHAAcCKwMDJbXKy2sF4AlgGLALcAVwHnBCqYV187uZmZWvRmx9l9QH+AMpuF5fcPhc4O6IODPbfk7SmsBZwE7Z+X2B7YHtI+LJbN9QYDCwF3Bvdu5JwEhgv4iYBjwjaSHgdElXR8TU2pbZNXUzMytbjVxTv5oUzIcWlGk5YCXmBOWcu4FtJLXNtncExgBP5RJExFBgEFngz0v3UBbQ8/PqBvQtpcAO6mZmZgUk7QOsQWoGL9QnWw8p2D8YaAMsm5duaEREkXR9sut0BJYsktcQIPKuVStufjczs7JV19q2pC5Al4Ld4yJiXDXndAAuB06LiHFFytA9W48p2D86W/fIS1eYJpcul6ZbsbwiYpqkSXnpasU1dTMzK1v10Px+PPBtwXJ8DZc9A/gJ+NeCfG0LgmvqZmZWtuqhX/xy4KaCfdXV0pcmDYzbE+iaXb9TdriTpE7MqZF3BX7MOz1Xgx+VrUeTmtYLdc9LMyYvr/xytAE65KWrFQd1MzMrX3WM6Vkze5VBvIhlSf3ijxU59hzwBnBAtt2HuQfR9QGmAV9m20OAbSWpoF+9D/BhVr6Jkr5l3r7zlUmvvrCvvVpufjczM5tjELB1wXJcduwI4KiI+BL4FNi34Nx+wDN5o9gHkmrl2+QSSFoJWAd4PO+8gcDukloX5DUGeLWUwrumbmZmZauhp4mNiDHA81WU4Z2IeDf7+RzgP5K+INXg+wEbAVvk5fWapCeAWySdAEwBzgc+APrnXeIS0sQ0d0m6ljTq/iTg9ILb3GrkoG5mZmWrXOd+j4i7slHyp2bLUGDPiHitIGk/Ur/+DaSY+yRwTETMyMvrc0nbZekeB4YDZwOXlVouB3UzMytb5RDUI+J5ivTuR8TNwM01nDsWOCxbqkv3KlA4xWzJ3KduZmbWRLimbmZmZascauqVxEHdzMzKl2N6SRzUzcysbLmmXhoHdTMzK1sO6qXxQDkzM7MmwjV1MzMrW66pl8ZB3czMypdjekkc1M3MrGy5pl4a96mbmZk1Ea6pm5lZ2XJNvTQO6mZmVrYc1EvjoG5mZmXLQb007lM3MzNrIlxTNzOz8uWKekkc1Jux/lcdwY6brw7Avwe8zuFn31Ft+k3XXZ7f7rkpm667PIv07MLEydP4/ufRvPrel/yr/yt88On3c6XffL0VefKmY2ssx6YHXsy7n3xT9FjXTu056oAt2XWrtVh+yV60bNmCb4aN4vEXPuLvtz/NyDETa/lqrdL8OGwYzzzzFG+9+QZDhwxmxPDhtGjZkkUWWYT1N9iI/Q88iBVXXKnK80ePHsWdd/ybl158gW++/oqpU6fSuXNnll9hRbbfYUf23GsfWrdpU20ZJk6cwN133slzzz7NN19/zeTJk+jZsxfLr7ACG228Cb8+9Df1/bKtgJvfS+Og3kz9aof1Zgf0mrRoIa46bT8O23vT2ftGj5tE545tWXOlJVhzpSX4ccTYeYJ6vh9HjKvy2PQZM4vu77Pcojx8zVEs1bsHABMnT2XmzFmsslxvVlmuNwftthG7HHkNH332Q61eh1WOH4cNY4dfbk1EzN7XoUMHZsyYwddffcXXX33Fww8+wAknn8oBBx48z/mDP/mYI3//O0aPGgVAixYt6NixI6NHj+btt97k7bfe5P777uX6G2+hR48eRcvwwfuDOP5PxzD8558BaN26Ne3atWPYsB8YNuwHXnv1FQf1BuCgXhoH9Waoe5cOXHzi3owZP4lhw8eyynK9q01/7ZkHcMgefRk9bhLn/uNR7v3v24weNwlJLLloN7bfbDV+qiZoAyz7y9NKKmP7dq3pf+URLNW7B19+O5wjz7uTF9/+DIA1V1qcf5y5P+uvvgwPXX0ka+/1VyZMmlpS/lbeZs6aSUTQd5PN2G33Pdho47707NWLmTNnMmTwJ1x68YW8+87bXHTBX1l66WXYdLPNZ587Y8YMTjjuj4weNYrevRfjtDPPpu8mm9K6dWsmTpzAQ/0f4PJLL2bokMFc/LfzufCSy+a5/meffcqRhx/GhAkT2GzzLTjiqGNYfY01kMSkiRP55JOPefbppxryLWm2HNRL44FyzdBFJ+zFIj27cNbVAxg+akK1aXfZag0O2aMvU6ZOZ8fDr+Kf977I6HGTAIgIvhk2mhvve5kBz31Qr2U8dI9NWHaJXsycOYt+J9w4O6ADfPDp9+z1x+sZO34yiy/SnWMP3qZer22Nr0uXrtx9/4Ncf+PN7LTLrvTs1QuAli1bstrqa3DDTf9ipZVWBuDWW26a69xB773L9999B8BfLriQLbbcitatWwPQsWMnDjz4EH5z2P8B8MzTTzJr1qy5zp81axZnnnYqEyZMYPsdd+Ka625gjTXXnB1cOnTsyPobbMjJfz59wb0BZvPJQb2Z2XqjlTl4t41584P/cdP9r9SY/tT/2xGAa+96nveHfregizfb9putCsAzrw8p2rw+fPQE/vPoGwActOuGDVYuaxidO3dmlVVWrfJ46zZt2HnX3QD45OOP5jo2fPjw2T+vtlrxLqbV11gTgOnTpzN9+vS5jr304gsM/uRj2rZty2lnnOWaYmNTHZdmxkG9GWnXtjXXnL4f06fP5Ojz756rv7KYlZZZhPVWXQqAewa+3RBFnC3Xj/7pVz9VmWbI/34EYJnFe7HCUgs3SLmsfLRp2xaAmTPnrmkvvsQSs3/+uCDg53z0YWpZWm75FWib5ZPz2KMDANhk083o1q17vZXX5o+kOi3NTcX0qUtqB9wLXBoRLzZ2eSrRWUfuzHJLLsTfb3uaD6sZ1Jaz8VrLAjB12nQ+/mIYv9phPY7otwWrr7g4AJ9/M5z+T7/HtXc+z6Qp06rN6/nbTmCV5RaldauW/DRyHK8N+pKb7n+ZVwd9We15LVtW/b2zZYs5x1ZdflE+/+bnGl+TNR1vv/kmACuuNPcI+NVXX4M+q6zKkMGfcNbpf+a0M89m476bzNWn/q+bb6JVq9accNIp8+T7/nvvAdBnlVUZPvxnrr/2Gl568QVGjRxJt27dWHuddTn4kN+w1trrLPgXac0yMNdFxQT1iJgiaUvg741dlkq0dp8lOObArflm2Cj+ev3jtTonV/sdPW4yF5+wF0ftv1W2PYkO7VqzzipLss4qS7Lfjuuzy5HXVDvCfaM1l2X0uEm0kVhm8V4ss3gv9t95Q675z3OcdOkD86T/ZtiobJT7olXmueoKi83+ufdC3Wr1mqxp+OCD93nu2acB2HOvfeY61qJFCy6/8mqOO+YPDB06hKOPPHz26Pfx48fTokULNty4L0cc+QfWWXe9uc6dNm0aP/44DIDx48ez7567MXr06DTyvX17hg8fzlNPPsHTTz3JSaf8mQMPPqRhXrBZLVVa8/uTwHaNXYhK06KF+MeZB9CqVUuOu/DeGmvVOd26dABgoe6dOGr/rXjshQ/ps/PZLLblySy06Yn87qx/M3HyVFZbYTFu+suv5zl/7PhJXH7rU2x64MV03/g4FtvyZHr0PZ7ND7qE/778MQBHH7g1J/123v/Sp14ZDMBWG67MhmssM8/xJRbpxoE7z+lL79ypXa1ek1W+sWPG8OeTT2DWrFmsseZa7L7nXvOkWXzxJfjnzf9im23TZ2vWrFmMHz9+9s9TJk2avZ1v3Nixs3++847bmTp1KhdcdAmvvfkuL7/2Fo8OfIpNNt2MiJg9At8WLDe/l6bSgvq/gIMkXSNpJ0nrSVo3f2nsApajYw/6BeuuuhQPPzOIx18s3sdYTIvsF6JlyxZ89f0IDjjpZr7+YSSQ7i3/zyNvcOZVqf9xm437zO5/z/ng0+85/cqHefeTb5gyNQ1Gigje/vhr9jzmOh58OjVznnzY9nTt1H6uc2996FW+HZbuMb77sv9jn+3WpXPHdrRr25ptNu7DI9ceTatWcz6+UTCC2ZqmKVOmcNyxR/Pdt9/SvXt3Lrr0clq2bDlPupdfeoFddvglb77xGiefehqP/fdpXn97EPc/9Aj99j+QDz54nz/+4Qj633/fXOfNijmfo1mzZnHCSaey8y67zZ6kZsmlluLvV/2DhRdZhFmzZnHLTTcs2BdsDuolqrSg/iiwOHBU9vObwFvZ8na2rhVJXSQtkb/EzNrVYCvJMov35PQjdmLchMmccPH9JZ07YdKU2T/fcO9LTJs+Y540N97/EhMnp3vEt964T0n5n37lwwB06tCWrTdaea5jEydPY+8//ZMffh5D74W68u+LfsvPL1/K6Nf/zqPXHc1yS/bi+Ivm/EEePW5ySde2yjNt2jSOP/Zo3nn7LTp37sx1N9zM4osvMU+677//juP+eDQTJ07ksiuu5sCDD2GJJZekffv2rLjiSpx2xln87vAjstr23xiVTVAD0LFjx9k/d+rUiT333mee/Nu1a0e//Q4A4K233mTmzOKTJ1k9aeDR71mF8QVJwyVNlfSlpMsldc1Lc6ukKLLsUJBXG0mXSPpR0kRJT0laucg1+2THJmZpL5ZU/XSHVaiYPvXM1vWY1/HA2fk7Zvw8iNa9m9btURefsBcd27flrKsHMGb8JDq2n/tz0rJF+tS3atli9rFJU6YTEQwbPqcpcmgVo9BnzJjFl9+OYI2VFmeJRbqVVLb/fTeC4aPHs1D3ziy7eM95jn/46fesu/f5/N+vNmf7TVdlyUW7M3nKdN755GuuvuM5xoyfE8g/+9qD5Jqy6dOmccKf/sgrL79Ehw4d+Mf1N7LKqqsVTXvv3Xcxbdo0Vll1NTbauG/RNIf85jBuuP5aJk6cyBuvv8aOO+0MpPvYO3bsyMSJE1lyqaWLtgIALLPscgBMmTyZMWPG0LPnvJ9fq1g9gDeAq4CRwOrAOdk6v6/wS+DAgnMHF2xfBexHijffA6cDz0haLSLGAkjqDjwLfAbsRaq4Xg50AI4utfAVFdQj4oV6zO5yYK5ZK1otvPa39Zh/WVhqsfTH5rxjduO8Y3arMt3+O2/I/lkf9Ub9/sYHn37Px58PK+laNdwhN1/GTpjMpbc8yaW3PDnPsT23XRuAadNn8O4nX9f/xa0sTJ8+nROPP5YXX3iOdu3bc811N1Q78vzLL74AYMkll6wyTadOnejZsxcjR47gh+/nvhNk+RVW5IP3B9W6fM2xibchNfT7GxGFD8F4XtJU4AZJi0VEbuKMyRHxelX5SFoC+B1wVETcku17C/gG+D1wcZb0CKALsGdEjMrStQKulXRB3vVqpdKa3wGQtJqk30v6c7Yu/pW9GhExLiK+y1/Ucr5aO5qsV9/7gkmTU5fEysssUjRNq1YtWG7JNNvXN1l/e20ts3hPFureGYCvSjwXYL8dNwBgwLPvM3Fy0+s6sRTQTzrhTzz/3LO0a9eOq665jvXW36Dac1pkrU8//FD138Jp06Yxfny6W6NTp45zHdu47yYAfPvN18yYMW+XE8BX/0u3Ynbs2JFu3brV6rXY/CmTPvXcH6hSgsR2pBg7u58wC9pPAjvlpdsReDoX0DP3ZueWPDC8ooK6pLaS7gU+AK4DzsrWH0i6Z377IJqyjfe7kPbrHF3lkpt+9d8DXp+9L/dglklTpvHQM4MAOPxXm9O61bxNkYfvuzkd26fJO3Ij2mvrr3/cHUgPannujaElnbvLVmuw2y/WYsaMmVx2q+fgbopmzJjBqSedwHPPPE2bNm34+1X/qLI5PV+fbCa6wZ98zIcfFJ+++KEHH2DatPRFcM211p7r2M677ErLli2ZMGECD/afdxzKlClTuOfuOwHYdLPNadGiov6MVhypbsv8X1ctJbXLBmCfBQyIiK/ykqwgaaykaZLekbRHQRZ9gJ8jYnTB/sHZsfx0Q/ITRMQYYFhBulqptE/jBcDOpOaKbhHRHuiWbe+cHbd6dN51jzFh0lSWWbwXd176O5bOmvNbt2rJAbtsOLtJ/56BbzP4yx/nOvfdB07n6AO2YsWlF57rG/O6qy7FfVf8nr23SzcrXHzzk3P1j+ec+Jtfste269Cz25ya1GILdeW0w3fkjot+C8D5Nwxk0JCGm77WGsbMmTM57dSTePqpJ7KAfg2bbLpZrc7dY8+9adu2LTNnzuS4Y//Afwc+zuTJ6fM1evQo/nndP7j4b+cDsN76G8zTN7/Mssux9z6/AuDySy7isUcHzJ5K9rtvv+X4Y4/m559+ok2bNvzf74+sr5dsVahrTb3YoGhJXWpx6a+BycA7pAB7QN6x94ATgN2BXwEjgAcl5Y+s7A6MKZLvaFK/fanpakU1TRVaTiR9D1wUEVcVOXYscHJELD6/+bdf5+jKeTPqyRM3HssW669Y7fPUt99sVf5z8WGza+Sjxk6kY/s2tG2THpLxwlufss+f/jnPk9Imv3fN7J+nTpvOuIlT6NS+Le3bpQaVWbNmcdUdz/Hnvz9YbdkAJk2exoyZM+mS3fo2ffpMLrhxIBfe+N86vPryN/qta2pO1AS98/Zb/PaQg4D0yNMuXbpWm/7Oe+5n0d5znjb49FNPcvqpJzFlSrqDQxIdOnRg4sSJs9Mst9zy/PPmf7HwwvN2LU2bNo1jjz6SV195GYA2bdrQrl17xo1Lg0fbtm3LX/92Edttv2PdXmgFa9eqYWZWX/Gk/9bp7/Lnl+54LgWDooFzI+Kc6s6TtCbQEVgNOIM0MO6XETHP7Q6SWgCvAl0iYtVs343A5hHRpyDticAFEdEm254OnBkRFxak+wh4NSIOr+1rhQobKEf61jKkimNDmI9vNVazJ17+hA1+9TeOP2Rbtu3bh94LdWXSlOm89dHX3Pnom9w+4PV55t8GOOq8O+m79nKss8pSLNyzM907d2DKtOl88sUwXn3vC27p/wrvDa56bOI/7nyOYcPHsu6qS7Fory60bNmCz77+mWffGMKN973Mx5/7OepNVf6T06ZPn87IkSOqTT9z1tx/Z7f95Xb06bMKd915B2++/hrfffctU6ZMoWvXbqyw4opss+0v2XvffrRrV3zSojZt2nDtP2/iwf7388jDD/H5Z58xefIkFl9iCTbaqC+H/Oa3s0fA24JVD93i8wyKBqp/VjQQEbm+m9eyAW6DgD2BefpkImKWpAeAiyW1j4jJpJp2sW+j3YH8/vPapquVSqupvwd8FBEHFzn2b2D1iJjvCZmbY03dyltzralb+WuomvrKpzxRp7/LQy/avs7lVOo/nAqcVVijzktzEmlEe4eImCzpt6QvEz3z+9Wz4N8zIrbKtl8ERkbEnnlpupKC/W8j4tZSylppNfW/APdJWgZ4APgJWBjYB+gL7Nt4RTMzs/pWJncMbgS0JjXBzyNrft8X+DirpUMa5T4L2JuspSC7J307UizLGQicJqlbNkCOLK9ZWR4lqaigHhH9Je1J6h+5jDRfUJA1i0TEI41YPDMzq3CS+pNmKP2ANFBuLeCkbPshSUsDtwF3AZ+TmsmPBNYnBXAAIuI7STcBl0iaSZp85jRgLPDPvEteDxyT5X0BafKZS4DrS71HHSosqANExABggKSOpJHvYyJiYvVnmZlZJcrNO9CA3gT6AaeS7hD7CriR9NjvaZLGkwLzGaSW4mmkLwE7RsQTBXkdC0wALgQ6A68A2+ZmkwOIiNGStgGuBh4CxpNq9qfPT+ErLqjnZIHcwdzMrAlr6Ob3rM+8aL95dnwU6Va22uQ1FTgxW6pLNxjYtoRiVqnsg7qkeW5fq0ZExLELrDBmZtagPA1vaco+qAO7lpA2SM0dZmZmzU7ZB/WIWLaxy2BmZo3DFfXSlH1QNzOz5svN76WpyKAuaQVgJWCe6aAion/Dl8jMzBYEB/XSVFRQzybhfxDYKrcrW+fPODTvo8TMzKwiOaaXptKe0nYRsCiwOSmg70kK8DcD/wM2brSSmZmZNbJKC+o7AOcDb2TbP0TEi9lTbB4mPQrPzMyaiLo+erW5qajmd9LsPd9GxExJE4GeecceJ80Hb2ZmTUQzjMt1Umk19W+BXtnPnwG75R3rC0xp8BKZmdkC45p6aSqtpv4UaSq9B4G/A7dJ2og09+6GpIe8mJmZNUuVFtRPAToARMS/JU0gPXa1PXA0cz/5xszMKlwzrGzXSUUF9YiYBEzK236QVGs3M7MmqDk2oddFRfWpS3pF0lGSFmrsspiZ2YIn1W1pbioqqAPDgEuB7yU9IenXkjo3dqHMzGzB8EC50lRUUI+IfUi3tf0OmEF6kPxPkh6QtLekto1aQDMzs0ZUUUEdICImRMTtEbEz0Bs4DugB3A381KiFMzOzeuXm99JU1EC5QhExUtIrwNLAysAijVwkMzOrR82xCb0uKjKoS1oe2C9bViXV0O8F7mrMcpmZWf1yTC9NRQV1SceTAvl6wFjStLDHAs9HxKzGLJuZmVljq6igDpwHDAD+Avw3IqY3cnnMzGwBcvN7aSotqC+cTUBjZmbNgGN6aSoqqDugm5k1L66pl6bibmkzMzOz4iqqpm5mZs2La+qlcU3dzMzKVkNPPiNpJ0kvSBouaaqkLyVdLqlrQbpdJb0vaYqkTyX9pkhebSRdIulHSRMlPSVp5SLp+mTHJmZpL5bUpvTSO6ibmVkZa4S533sAbwBHANsDlwO/Bu7LK9NmpCeEvgbsCNwD3Cxpn4K8rgL+DzgN2AtoCzyT/wVBUnfgWaBNluY04PDsuiWruOZ3pf+lnYDNSG/+KOAlYGBERGOWzczM6ldDt75HxB0Fu56XNBW4QdJiEfEDcCbwRkQckaV5LpsU7TzgfgBJS5CeU3JURNyS7XsL+Ab4PXBxdu4RQBdgz4gYlaVrBVwr6YLserVWUTX17BvNq8AjpDdli2z9KPCKpG6NVzozM2uiRmbrNtmDw7Ymr+aeuRtYRdIy2fZ2pBg7O10WtJ8kVUxzdgSezgX0zL3ZuduVWtCKCuqkx64uD2wfET0iYpWI6EFqIlk+O25mZk1EYz16VVJLSe0krQucBQyIiK9IsaY1MKTglMHZuk/e+ueIGF0kXZ+87T6FeUXEGNKjxvPT1UqlBfXdgFMi4qn8ndn2n4HdG6VUZma2QNR1oJykLpKWKFi61OLSXwOTgXdIAfaAbH/3bD2mIH0uePfIS1eYJpeuR952bdPVSqUF9Y5U/XjVH7PjZmbWRLSQ6rQAxwPfFizH1+LSOwGbkAa6rQI8IqnlgnmV9afSBsq9Bxwt6YmImJnbKakFcAzwbqOVzMzMytHlwE0F+8bVdFJEfJD9+Fo2wG0QsCfwSba/a8EpuRp8rm98dJE0uXT5/ee1TVcrlRbU/0waZPC5pIdJtfaFgT2ARZmPQQVmZla+6jr6PSLGUYsgXoMPgOnACqSB2tNJ/d1P5KXJ9X8PyVsvIql7Qb96YR/6EAr6zrNb3nozb799jSqq+T0iXgQ2JdXYDyDdPnAAqYa+aUS81IjFMzOzetZYA+UKbEQaHPdlREwFngMK70nvBwzOBtNBqoDOAvbOey3dSZXPx/POGwhsW3D31r7ZuU+WWtBKq6kTEe+QbtA3M7MmrkUD36cuqT/wNql2PhlYCzgp234oS/YX0v3r15JuP9uaVMHsl8snIr6TdBNwiaSZwPekiWXGAv/Mu+T1pO7jhyRdACwOXAJcX+o96lCBQd3MzGwBepMUnE8ltWZ/BdwIXBoR0wAi4mVJewF/BQ4jTSjzu4govHf9WGACcCHQGXgF2DYixuYSRMRoSdsAV5O+NIwnjQE4fX4KX/ZBXdIA4ISI+Cz7uTpBGljwJnBL1kxiZmYVqqEf6BIRF5KCcE3pBgDVxqQsBp2YLdWlGwxsW0Ixq1T2QZ307SZ3G0EXUuCuzuLAgaQ+kEMXXLHMzGxB80PaSlP2QT0its77eavanCPpAFJThpmZVTDhqF6Ksg/q8+kZ0sh4MzOrYA09UK7SVVxQzyaa+QWwEtCu8HhEXB4RPwFXNnTZzMzMGlNFBXVJiwLPkwJ6wOx2mfx+9vl6Bq2ZmZWfhh4oV+kqavIZUsAeCSxJCugbAcuQnm37GSnYm5lZE1HXB7o0NxVVUyc9P/2PpCfmACgivgEuUPo6dw3p2bRmZtYEtGiOkbkOKq2m3hUYHhGzSHP5Lpx37DVgs0YplZmZWRmotKD+P9Ik9wAfAwfnHduT+XiijZmZlS83v5em0prfHyNNhn8vaXq+hyX9THpizqLAKY1YNjMzq2ceKFeaigrqEfHnvJ8HStqE9HCXdsBTETGw0QpnZmb1zjG9NBUV1AtFxNukp+mYmZk1exUd1CWtCqwGjABejIiZjVwkMzOrRx79XpqSg7qks+rr4hFR41Su2a1qJ5Ga2VsD9wEXkR5NdyjpfvUAPpb0i4gYUV/lMzOzxuWQXpr5qamfQ81PSqut2szPfiLwN+Bh0nNmzwDWJN2PfiIwGFiD9OzZs0j3sZuZWRPggXKlmZ+g/iL1F9Rr4zfAXyLiHABJDwAPAsdGxDVZmv9KmgH8AQd1M7Mmww90KU3JQb22jz+tR8sCz+VtP0tqkXmnIN3bpOljzczMmqVKGCjXFpict537eWpBumlUxusxM7NacvN7aSolCBZr7m/ILgAzM2sEjumlqZSg/pykWQX7XirYV2lT3pqZWQ1cUy9NvQZ1Se2AfUgPVlkM6EjVdyRERGxTi2zPrafimZmZNWn1FtQlbQncBSzCnHvHYU5Qz28uF7VsPo8IB3Uzs2bKo99LUy9BXdKywCNAJ+AT4CngWGACcAUp0P8CWJ40+9s/gRn1cW0zM2u63PxemvqqqZ9ACuj/BXaPiOmSjgUmRMTsGegkHQ5cA6wTEbvU07XNzKyJckgvTX0NLtuW1Jx+RkRMrypRRNxAmvltR0lH1tO1zczMjPoL6ksAM4H38vYF6R7zQtdnx35dT9c2M7MmqoVUp6VUkvaV9LCk7yRNlDRI0m+V1w8g6XlJUWTpU5BXV0k3Sxolabyk+yX1LnLNTSS9JmmypK8lnZJ/vVLUV/P7LGBMROQPfpsIdJHUMv/paRExXtI4YOV6uraZmTVRjdClfjzwFalbeTjwS+BG0oyl+QO3XyE9fyTfVwXb95CeJHoEMAU4Hxgoaf2ImAEgaQXgCdJYtNyzTS4kVZQvLbXw9RXUvweWlaS8wP4t0Ccr4OwavKSuQDfmnRHOzMxsLo0wUG7Xgqd9PiupJ3C8pL9ERG5+lDER8XpVmUjqC2wPbB8RT2b7hpIeQrYXcG+W9CRgJLBfREwDnpG0EHC6pKsjoqRYWV/N75+SHouaX/t+JVsXfpP5S7b+rJ6ubWZmTZRUt6VUVTy++z2gC2nuldraERhDqoHn8h4KDAJ2Kkj3UBbQc+4mVX77lnA9oP6C+jOkQYo75u27jtR3vp+kDyX9R9L7pCepBfCverq2mZnZgrQZ8H1EjM/bt2XW5z5F0guStig4pw8wtKBbGlJNvQ+ApI6kZv0hBWmGkOJkH0pUX83v9wLrAO1yOyLiPUnHA5eR+hRWK0h/RT1d28zMmqj5GeyWT1IXUi0737iIGFfL8zcD9iP1see8ANxOanFejNQi/bSkLSPitSxNd1JNvdBooEf2c7dsPVe6iJgmaVJeulqrl6AeET+SnnteuP8qSU+Spo5dEhgLPBERz9THdc3MrGmrhy7144GzC/adC5xT87W1BGmw23PAVbn9EXF2QbpHgY+BM5m7ab3BLfAHukTEEOCvC/o6ZmbW9NTDQLnLgZsK9tVYS5fUDRhIGsS2d94AuXlExERJj5EqsDmjSZXZQt2BUdnPY7J114JrtwE65KWrtUp5SluD+PalKxq7CGZz+d/wiY1dBLOiVuldypixxpM1s9eqqT1HUnvgUVKw7RsRY+fj0kOAbQvuCoPUT/5hVraJknJ3iuVbmTROrbCvvUb1/rhSSWtLOlnSNZJuLjjWWtJixW6+NzMzK9SijkupJLUijftaBdghIr6vxTkdgV2At/J2DyTVyrfJS7cSafzZ4wXpdpfUOm9fP1It/tVSy1+fT2nrAdwK7JzbRRq9d1hestbAu0AvSetExIf1dX0zM2t6GuE+9WtJAfoE0gRqG+cdew/YkHRv+YOkyWYWy9IuCuybSxgRr0l6ArhF0gnMmXzmA6B/Xp6XAAcCd0m6Flgjy//0gtvcaqW+ntLWFniS9A1kMvA6sAkF08RGxCRJN5Lmf9+XrAnCzMysmEZ49Op22fqyIseWBYYBbYALgJ6k2VNfBY6IiDcL0vcj9enfQIq3TwLH5GaTA4iIzyVtl6V7nDSL3dlVXL9G9VVTPwJYlzQJzY4R8T9Jw4CFi6R9gBTUC+/pMzMzm0tDB/WIWKYWyXaoZV5jSa3Vh9WQ7lVg4+rS1FZ99anvR2pq/2NE/K+GtB+S5rQt+aZ6MzMzq1p91dRXIQXqZ2tKGBEzJY0lDSAwMzOrUiP0qVe0+grqbYFJ+f0ENWhPGjRgZmZWpUboU69o9dX8/iPQObtZv1qS1iAF9W/q6dpmZtZENfQDXSpdfQX1F7P1gbVIewap/91TxZqZmdWj+grqV2brcyRtWCyBpC6SriPdyjYTuKaerm1mZk1UC6lOS3NTXw90eVfSeaR7616S9ArZU3Ek3QAsBWxKmssW4JSI+Lw+rm1mZk1XvU972sTV24xyEXGupJ+BvwFb5R06jDS7HMB44OSI+Gd9XdfMzJquZljZrpN6faBLRFwn6Q7Sk2o2AXoDLYGfSDPu3BcRoyE1x9f2ebZmZmZWs3p/SltEjAf+lS3zyB5YfxzwR9IUe2ZmZkU1x37xumiwR6/mBfNjKXh2rJmZWTGO6aWpU1CX9EvgUGA10niGL4HbIuLBvDTtSMH8JFIwFzCJeR9ab2ZmNhdPPlOa+Q7qki4ATsltZuvVgF0lXRcRR2cTzdwHrJilGUO6le3KiBg536U2M7Nmwc3vpZmvoC5pC+DUbHME8CYpaG9I6ic/UtJLwNVAL+Bn4FLg+oiYUNdCm5mZ2bzmt6Z+eLZ+EdgjIsYASOoBPARsBtwOtAauAk6LiEl1KqmZmTU7rqiXZn6D+sakqV6PywV0gIgYJek44K0s72si4k91LaSZmTVP7lMvzfwG9UWBGcCgIsfey461JDW/m5mZzRfhqF6K+Z2BrwMwMiKi8EBEzAJyg+C+nN+CmZmZWWkW6H3qETFzQeZvZmZNm5vfS9Ngk8+YmZmVykG9NHUJ6j0kPVvVMYBqjgNERGxTh+ubmVkTJw9/L0ldgnob5n4aWzHVHZ+nP97MzMzm3/wG9dvqtRRmZmZFuPm9NPMV1CPiN/VdEDMzs0JufS+NB8qZmVnZ8tzvpZnf+9TNzMwWuBaq21IqSftKeljSd5ImShok6bcqGLEn6TBJn0qaIul9SbsUyaurpJsljZI0XtL9knoXSbeJpNckTZb0taRTCq9XWw7qZmZmcxxPejz4CcCuwEDgRuCsXAJJ+2X77gF2BF4DHpS0cUFe9wDbAUcABwIrAwMltcrLawXgCWAYsAtwBXBedv2SqcikcM3WiAkz/GZYWRk+fmpjF8GsqFV6d2yQdvGrX/lfnf4uH7PpsiWVU1KviBhRsO8GoB/QPSJmSRoKvBMRB+SleRUYExE7Zdt9gVeB7SPiyWzfysBgYL+IuDfb909ge2CliJiW7bsAOBJYNCJK+iPgmrqZmZWtFqhOS6kKA3rmPaAL0FHScsBKwL0Fae4GtpHUNtveERgDPJWX91DSM1N2yjtvR+ChXEDPy6sb0LfU8juom5lZ2ZLqttSTzYDvI2I80CfbN6QgzWDS/C3LZtt9gKFFnpEyOJeHpI7AkkXyGkKay6UPJfLodzMza7IkdSHVsvONi4hxtTx/M2A/5vRxd8/WYwqSjs7WPfLSFabJpcul6VYsr4iYJmlSXrpac03dzMzKVj2Mfj8e+LZgOb4215a0BGmw23PAVQvkBdYz19TNzKxs1cN96pcDNxXsq7GWLqkbaeT7SGDv7LHiMKdG3hX4Me+UXA1+VF66JYtk3T0vzZi8vPKv3Yb0iPNRlMhB3czMylZdY3rWzF6rpvY511R74FFSsO0bEWPzDuf6v/sAQ/P29wGmAV/mpdtWkgr61fsAH2ZlmyjpW+btO18ZEPP2tdfIze9mZmaZ7B7ye4FVgB0i4vv84xHxJfApsG/Bqf2AZ/JGsQ8k1cpnP41U0krAOsDjeecNBHaX1LogrzGkW+JK4pq6mZmVrUaYJvZa0iQwJwBdCiaUeS+7b/wc4D+SviD1t/cDNgK2yCWMiNckPQHcIukEYApwPvAB0D8vz0tIE9PcJelaYA3gJOD0gtvcasVB3czMylYjTP2+Xba+rMixZYGvIuIuSR2AU7NlKLBnRLxWkL4fqU//BlK8fRI4JiJm5BJExOeStsvSPQ4MB86u4vo18oxyeTyjnJUbzyhn5aqhZpS79a1v6vR3+dANlmpWT4RxTd3MzMrWfD7XpNnyQDkzM7MmwjV1MzMrW66nl8ZB3czMylYjjH6vaA7qZmZWthzSS+M+dTMzsybCNXUzMytbbn0vjYO6mZmVLd/SVhoHdTMzK1vuIy6Ng7qZmZUt19RL4y9BZmZmTYRr6mZmVrZcTy+Ng7qZmZUtN7+XxkHdzMzKlvuIS+P3y8zMrIlwTd3MzMqWm99L46BuZmZlyyG9NA7qZmZWtlxRL4371M3MzJoI19TNzKxstXADfEkc1M3MrGy5+b00DupmZla25Jp6SRzUzcysbLmmXhoPlDMzM2siXFM3M7Oy5YFypXFN3czMypZUt2X+rqkVJF0vaZCkGZI+KpLmeUlRZOlTkK6rpJsljZI0XtL9knoXyW8TSa9Jmizpa0mnaD6m03NN3czMylYj9amvBuwMvEGq/FZVAX4FOLFg31cF2/dk+R0BTAHOBwZKWj8iZkD6EgE8ATwFnAGsCVwIzAQuLaXgDupmZmZzeyQiHgaQdCuwfhXpxkTE61VlIqkvsD2wfUQ8me0bCgwG9gLuzZKeBIwE9ouIacAzkhYCTpd0dURMrW3B3fxuZmZlS3X8Nz8iYlY9FX9HYAypBp7LeygwCNipIN1DWUDPuRvoBvQt5YKuqRuPDXiQC849o+Z0T79Mt+7da0z30Yfvc+RvD2LWrPR7cf8jT9J7scWrTP/aKy/xyEP388lHHzJm9ChatmzFQosswjrrrs/e/Q5ghRVXrv2LsYoxdcpkPnr/Xb4YOpgvPxvMF58OZvhPPwJw6BF/Yo/9fl1jHoM/HMSA+/7DkI/eZ/z4sXTt1oM1192APfc7hKWWXb7oOZMnTeTNV15g0Ntv8MXQT/j5xx+YMXMGXbv1YMVVVuOXO+/JehttWuO1h/80jAfuvJX33nyVUSOG06FjR5ZfeVV22uNXrN9389LeDKtSi/IeJ7elpIlAS1JT/ZkR8WLe8T7A0IiIgvMGZ8eQ1BFYEhhSkGYIEFm652tbIAd1m61FixbVBu0WtfjtmjF9Ohf95ezZAb06EcFFfz2bRx56YPa+9h06MGP6dL79+iu+/forHhvwIMeddBp77rtf7V6EVYxPB3/MX045Zr7PH3DfHdx63RXMmjULSbTv0JGRw3/iuSce5eVnn+S4M85nky23mee84//vAIZ9/+3s7dat29CqVWtGDv+JkcN/4vUXn2XLbXfkj6eeS8tWxf9EfvzBu5z/5z8xaeIEADp07MT4cWN5941XePeNV9j9Vwfzm6OOm+/XZnPUdfIZSV2ALgW7x0XEuDplDC8AtwOfAYuR+taflrRlRLyWpelOqqkXGg30yH7ulq3nShcR0yRNyktXKw7qNtvCiyzKA48+VXPCavz71pv48ovPWG2Ntfj4w/erTfvYgAdnB/S99t2PX//2cBZaeBFmzZrFp0MHc+WlF/LBoHf5+yUXsPZ667PscivUqWxWfjp17sJyK/ZhuZX6sPyKq3DLPy5j9KgRNZ73/jtv8K9r/05EsP2ue3Pg7/5Al67dGPHzT9x41cW88fJzXHH+GSy93AosvuTSc507Y8YMllp2BbbdaXfW23iz2ceH/zSMe2+/iacee5AXnh5Ir0UW5eD/m/dLx7gxo7nwjBOYNHECq6y+NkefcjaLL7k0kydN4qF7buOe227k4Xv/zTIrrMjW2+1SP2+U1cXxwNkF+84FzqlLphExV56SHgU+Bs5k7qb1BuU+das3X3/1P26/5QZ6L7Y4h/7uiBrT//exAQCss94GnHDqmSy08CJAajHos8pqXHLFtbTv0IGZM2fy4nPPLNCyW8Nbdc11uOOR5znv8us59Ig/sfk229OqdetanfvvG64mIlh3w0048oTT6dK1GwC9Fl6EE8++kKWWXYFp06Zy1y3XzXPusX8+jytvuYfd9j1wroC/0CK9+cNJZ7L9rnsD8NgDdzNt6rzjk/rfdSvjx42le49enP63K2fn0b5DB/b/zZFst+teANxx4z+YOWNGKW+JFVEPt7RdTmrezl8ur+9yRsRE4DFgvbzdo4GuRZJ3B0ZlP4/J1nOlk9QG6JCXrlYc1K1e5JrSp02bxvGnnEG7du1qPGfkiFQj67PKakWPd+rcmaWWWgaASZMm1VtZrTy0bNlyvs77/puv+HzoJwDsfeBv5jneunVr9uh3MABvvvICkws+O6uvvR7V3f677c57ADBlymS+++Z/cx2LCF54eiAAO+y+D506d57n/L0P+C0AI4f/xEeD3qnlq7Kq1HWgXESMi4jvCpa6Nr3X1hBg5SL3m/fJjuW+DHyb7cu3MiDm7WuvloO61YuH+9/H+++9w9bbbMcmm21Rq3MWWzwNnhsy+OOixyeMH88333wFQJ9VVq2Xclrle//dNwFo36EjfVZfu2iadbOBbtOmTWXwh++VlH/rNm1m/zxr5txjQ777+n+MHjlirmsUWqT3Yiyx9LKprO+8UdK1bV4tVLeloWQD3nYB3srbPZBUK98mL91KwDrA4wXpdpeU31TVj1SLf7WUclRMUJfUStK62b17tgCMGT2a3xywD9tsuj7bbLo+++25Exf99Wy++OzTas8bPvxnrrvqcjp27MSxJ/251tfbfa9fAfDeO29x2YV/YfjPPwGpNjR08Cec9KejmDxpEuustwFb/uKX8//CrEn57qsvAVhi6WWrrO13696Drt3SoM9vsvS1latdt2rdmsWWXGquY9/m5VXV6Pp0bIXs2l+UdG2bV2Pc0iapg6R9JO0DLA10yW1LWkjS5pIGSPqNpK0lHQi8BCwKnJfLJxsw9wRwi6R9Je0K3A98APTPu+QlwMLAXZJ+IelY0r3r5xfc5lajShooNwt4nTQA4elGLkuTNGXKZD77dAidOndm8qRJfPvN13z7zdc8NuBBjjj6OA749bxNnQCXX3Q+EyaM57iTT2OhhRau9fW22Hobjjr2BP55zZX0v+9u+t939+zR79OnT6dnr4U45LDDOfR3R9KiRcV8/7QFbNTI4QD07FX99/sevRZm7JjRs2vWtTFp4gQe+M8tAGy65bZ06Nip6LU7de5C27ZVdzHlylbKta2sLAzcV7Avt7018B3QBrgA6AlMJNWoj4iINwvO60fqw7+BFHOfBI7JzSYHEBGfS9ouS/c4MJw0uO+yUgteMUE9ImZJ+pLUlGH1qNdCC3PY7//AVr/4JUsuvTStW7dhxvTpvD/oXa6/5go++egD/nHlpfRaaCG223Hu0bwvPPs0Lz73NKustjp77bt/ydc+8Ne/ZfElluTCv5zF+HHj5ur/nDZtKhMnTGTa1Km0yWsSteZt8uTJANUGVYC22biOyZMn1irfiOCqC89m9MgRdOrchYMOn3fk++TJk0q8tseC1FVjTBMbEV9BjdX8HWqZ11jgsGypLt2rwMa1ybM6lVb9uQA4U9JijV2QpmSjvpvy28OPYrkVVqR16xQ8W7VuzXobbMS1N93GamusBcB1V/99rvvPJ4wfz+UXn0/Lli05+bRzSq5NT548iT+feCynn/Qn+qyyOtfd/G+eeP51BjzxPBdcciXdunXn/nv+w5GHHcyE8ePr7wWbFXHbP6/k9Zeeo0WLFvzx1HNZaOFFG7tIRoqsdVmam0oL6vsCCwFfSnpT0iNZv0Zuebi2GUnqImmJ/GX8uIYaEFk5Wrduw++P+iMAP//0I58OHTz72LVXX86I4T+zd78DWKnPKiXnffXfL+HF555mnfU24O//uIE1116XTp0707PXQmz5i2257uZ/061bd7784jP+fetN9faarLK1b98egKlTp1SbbuqUKVn6jjXmec9tN/DQ3bcjiSOPP50NN92yimt3KPHaHWq8tlWvhVSnpbmptKDeiTS8/zVSH0YnoHPeUjhrUHWOJ91GMHu57por6rOsTcaqa6w5++cfvvsOgE+HDGZA//vo2bMXBx1yGJMmTZxrmZp3f++UKZPn2Tdx4kQezSae2e+gQ4reYtS9R0922Hk3AN+nbrP16Jn6q0eOGF5tulEjfgage89e1aa7746buetf1wPwu2NO4pe77FnjtSeMH1dtYM+VraZrm9W3iulTB4iIresxu8uBuap/Rx79p2+rSGsFfhz2AxHByJEj2G37rapNe9C+uwOw+Za/4MLLrwbg22++YubMmQAsvsSSVZ67xFJLZ9f7vh5KbU3BEsssB6Tby2bOnFl0BPyY0aMYO2Y0AEtl6Yvpf+et/OemfwBw6JHHsfNe1U9HvGReXt/87wtW7FN8joVv/vd5du2qR8hb7TS/unbdVFpNvd4Um5Cgc5dSKvrNxycffjD7596LV/1gllK00JyP3o/DhlWZbvSokQDzjEK25mutdTcE0oNZhnxcfCri995Mt/a2adOWVdZYp2iah+6+ndtvuAqAg/7v6NkT1lRniaWXpUc2sv3dN4vfPvzzjz/w3ddp0pq11tuoxjytBu5UL0lF1dQBJK0DnAZsRprofhTp/sC/RURps0wYEVHt7Fozpk/nxutT7XqhhRdh5T5pEpgttt6GV94pPmkMwLtvv8kxv0+3wBV7StvSyyxLm7ZtmTZ1Kg/efzd9N533qVaTJ09i4KNpKtnV8roArHlbfKllWGHlVfl86Cf0v/NWVltz3bmOz5gxnYfv/TcAG222Fe07zNuvPeC+O7j1+isA2P83R7DPgb+t1bUlscU2O/DQPf/mvw/fxy577UfHTnPPKtf/rtsA6LnQIqy+9nrFsrES1PWBLs1NRdXUJW1O6k/fALgLOCtbbwC8KmmzRixeRfpx2A/836/3Y0D/+xj2w5wm7hkzZvDeO29x9O8P5cP3BwFw5DHH19v94m3btWOX3VLf5SsvPs95Z57Kd99+Q0QwY/p0Pnz/PY4+/FB+yJ6m1W//mmtRVnkmjB/HuDGjZy+5J1ROnTplrv3Tp809/8bBhx+DJN55/WWu//vfGD9uLAAjh//Mpef9ma+++Iw2bdqy32/mfQbBY/3v5pZ/pKm/+x3yf/Q75PCSyrzX/ofSuUtXRo8cwfmn/YkfvvsGgCmTJ3PPbTfwxID7ATjo//5Q5VPezBYUzfuY1/Il6RVgPLBL/o37klqSJtLvFBHzHdhHTJhROW9GPRn2w/fss+t2s7fbtG1Lh/YdmDhxAtOnTwegVatWHHXsCfQ7oObnW+fUVFOH9Efw5OP+wDtvzZlKs1279kyfPp2ZM9N/b4sWLTjymOOrnPimqRs+ft4HijQl/9dvZ4b/VHX3S84xp5zDNjvuNte+h++9g1uv+/vs1qYOHTsxcUK69bF16zZVPnp1z63Xm/3loVv3ntVe93fHnMhmv9h+nv0fv/8O55923FyPXp0yeTKzZqVxIs3h0aur9O7YIFXoN78cW6e/yxsu17VZVfUr7WvkOsA++QEdICJmSrqKNP2elaBHj5786aTT+OiD9/hs6FDGjB7F+PHjadeuHcsstzzrrr8he+zdj6WWXqber92ufXuuuPYmnvrv4zz138f4dMgnjB07hpatWtJ7scVYa5312OtX+1f5wBdr3nb/1UGs2Gc1Btz/H4Z+9AHjx4+l50KLsMY667PX/odWOY1rfkVmzOiR1V5japGntAGsttZ6XHHz3fS/6zbefeOVbMKazqyw8qrstGc/1u87b3eSzZ9mFZHrQaXV1IcDJ0XErUWO/Qa4OCLme2745lhTt/LW1GvqVrkaqqb+1v/qVlPfYNnmVVOvqD514BHgIknb5u/Mtv8GDGiUUpmZ2QLRGA90qWSV1vx+ArAa8ISkccDPpIn3u5Aed3diI5bNzMysUVVUUI+I0ZL6kp5Zuxnp4S6jgJeBxyJiVnXnm5lZZWmGM73WSUUFdUhPayM1s7up3cysiXNML03ZB3VJPUpJHxGjFlRZzMysgTmql6TsgzowAihl9OO8E0GbmZk1A5UQ1H9LaUHdzMyaiOY4gr0uyj6oF7sn3czMmgcPlCtN2Qd1MzNrvhzTS1NxQV3SFsDhwEpAu8LjEeHHeZmZWbNUUTPKSdoeeBboBawPfEsaSLcy0BF4u/FKZ2Zm9c7PUy9JRQV14FzgCmDnbPvMiPgFqdY+nRTwzcysifA0saWptKC+CjAQmEUaEd8RICK+Bs4Bzmi0kpmZWb2T6rY0N5UW1KcALSI9Wm4YkP9sxfHAko1SKjMzWyDc+l6aShso9z6p//wp4BngdEkjSE3vfwU+bMSymZmZNapKq6lfwZyJaE4j1c4HkJrkewJ/aJximZnZAtEIVXVJK0i6XtIgSTMkfVRFusMkfSppiqT3Je1SJE1XSTdLGiVpvKT7JfUukm4TSa9Jmizpa0mnSKV3IFRUTT0iHs/7+XtJ6wErAO2BIRExrdEKZ2Zm9a6RBrutRhqQ/Qap8jtPBVjSfsCNwPmkQdr9gAclbR4Rr+clvSfL7whSF/L5wEBJ60fEjCyvFYAnSK3QZwBrAhcCM4FLSym4Uvd0ZZD0S+DpWECFHjFhRuW8GdYsDB8/tbGLYFbUKr07Nki0/eSHiXX6u7zqYqWXU1KL3KO8Jd0KrB8RqxekGQq8ExEH5O17FRgTETtl232BV4HtI+LJbN/KwGBgv4i4N9v3T2B7YKVc5VTSBcCRwKIRUes/BJXW/P4E8IOkKyVt3NiFMTOzpicX0KsiaTnSrdT3Fhy6G9hGUttse0dgDKkGnst7KDAI2CnvvB2Bhwpam+8GugF9Syl7pQX1NYFbSM0ir0r6UtL5ktZo5HKZmdkCUKaj3/tk6yEF+wcDbYBl89INLdK6PDiXh6SOpDu3CvMaQhpD1ocSVFRQj4iPIuL0iFgB2Bh4GDgEGCTpQ0l/btwSmplZvapjVJfURdISBUuXOpaqe7YeU7B/dLbukZeuME0uXS5Nt2J5ZbX2SXnpaqWignq+iHgzIo4jfcPZg/Tm/bVRC2VmZvWqHmaUO540pXj+cnwjvqQFqqJGv+fL+ix2BfYj9U20Ap5s1EKZmVm9qodZ4S4HbirYN66OeeZq5F2BH/P252rwo/LSFZsUrXtemjF5ec0mqQ3QIS9drVRUUJfUkjRCcH9gN6AT8ApwAnBfRIxoxOKZmVmZiYhx1D2IF8r1f/cBhubt7wNMA77MS7etJBX0q/chmywtIiZK+pZ5+85XJnUiFPa1V6vSmt9/Bh4BVgX+AiwdEVtExHUO6GZmTU85DpSLiC+BT4F9Cw71A57JG8U+kFQr3yaXQNJKwDrA43nnDQR2l9S6IK8xpFviaq2iaurAVcBdEfFpYxfEzMwaQCPMPSOpA3NuOVsa6CJpn2z7hYgYTnqI2H8kfQE8RwrCGwFb5PKJiNckPQHcIukE5kw+8wHQP++SlwAHAndJuhZYAzgJOL3USdUqavKZBc2Tz1i58eQzVq4aavKZz36aXKe/yysu0n5+Jp9ZBvhfFYe3jojns3SHAacCS5Ga4U+LiEcL8upK6tffizljv46JiB8K0m2SpVsbGA78A7io1MnWHNTzOKhbuXFQt3LVlIN6Jau05nczM2tGmuMz0evCQd3MzMqWY3ppHNTNzKx8OaqXpNJuaZtNUntJvSW1b+yymJmZlYOKC+qSdpH0FjAe+A4YL+ktSTvVcKqZmVWYepgmtlmpqKAuaQ/SQ1ymkebuPYA0m9xUYICk3RuvdGZmVt+kui3NTUXd0ibpPeDjiDioyLE7gNUiYp35zd+3tFm58S1tVq4a6pa2r0ZMqdPf5WV6tWtWob2iauqkuXFvr+LYvynxubNmZlbmynGe2DJWaUF9FGmS+2JWpsSn2ZiZmTUllXZL2z3ABZImA/dHxJhsCr59Sc9Sv7FRS2dmZvWqOQ52q4tKC+p/Jk2ufwPwT0nTgdakRpb+wGmNWDYzM6tnzXGwW11UVFCPiKnA3pLWADZnzoPmX46IDxu1cGZmVu8c00tTUUFd0hbAu1kA/7DgWEdgvYh4sVEKZ2Zm1sgqbaDcc8CqVRzrkx03M7Mmwvepl6aiaupU3xLTEZjcUAUxM7OG0Awjcx2UfVCXtDGwSd6uAyRtVpCsHbA7MLjBCmZmZgtcc6xt10XZB3Vge+Ds7OcA/lgkzXRSQD+qoQplZmZWbsq+Tz0izo2IFhHRgtQOs3FuO29pGxFrR8SrjV1eMzOrP55QrjSVUFOfLQvsZmbWTLj5vTRlH9QlrVtK+oh4d0GVxczMGpZnlCtN2Qd14G1SX3pNlKVruWCLY2ZmDcYxvSSVENS3buwCmJmZVYKyD+oR8UJt00padkGWxczMGpYr6qUp+6BeE0m9gH7AAcDGuPndzKzJ8EC50lRkUJfUAdiTFMi3JT2p7T3guMYsl5mZ1S8PlCtNxdwiJqmlpJ0l/Qf4CbgdWJv0xWS/iFgvIq5qzDKamVllk3SopCiyXFiQ7jBJn0qaIul9SbsUyaurpJsljZI0XtL9knovyPKXfU1d0qakGvm+QC9gJHAHcCfwUbb9Y6MV0MzMFpzGq6jvAIzN2/4+94Ok/YAbgfOBZ0ldwA9K2jwiXs875x5gNeAIYEqWfqCk9SNixoIodNkHdeAl0q1qzwGXA0/m3gxJXRuzYGZmtmA1YuP7OxExoopj5wJ3R8SZ2fZzktYEzgJ2ApDUlzTN+fYR8WS2byhpSvO9gHsXRKErofn9Q9L/65bAsaQHunRu3CKZmVlDKLdHr0paDliJeYPy3cA2ktpm2zsCY4CncgkiYigwiCzwLwhlH9QjYi1gdeASYEXgVuBHSfeSnsxWm4lpzMzMSvGxpJmSvpT0Z0m5O6v6ZOshBekHA22AZfPSDY2Iwhg1OC+PelcJze9ExCfAacBpeX3s+2RLAMdKIiJebMRimplZPavr6HdJXYAuBbvHRcS4Kk4ZRnoy6Buk+LIb8FdgceBooHuWbkzBeaOzdY9s3b1Imly6HkX214uKCOr5IuIV4BVJfyT1V+xPqrHvIenriFiuUQtoZmb1ph6a0I9nzuO7c84FzimWOCKeAJ7I2/WkpMnAcZLOr3NpFrCyb36vSkTMjIjHI+JgYBHgINJoeDMzs5zLgSULlstLzONe0sRmazOnRl44UDtXgx+VrUcXSZNLN6rI/npRcTX1YiJiMnBXtpiZWRNR15p61sxeVVP7/Mj1pfcBhubt7wNMA77MS7etJBX0q/chDQBfICq2pm5mZtZA9gNmAu9FxJfAp6S5U/L1A56JiGnZ9kBSrXybXAJJKwHrAI8vqII2iZq6mZk1TQ09TaykJ0gTyuRq07sBhwNXRkRuorNzgP9I+oI0h0o/YCNgi1w+EfFaltctkk5gzuQzHwD9F1T5HdTNzKxsNcIDXYYAhwFLkFqzPwX+BFydSxARd2XPIDk1W4YCe0bEawV59SP1399AirdPAscsqNnkADTvLXTN14gJM/xmWFkZPn5qYxfBrKhVendskHA7fsqsOv1d7tyuRbN6Ioz71M3MzJoIN7+bmVn5alb17LpzUDczs7Ll56mXxkHdzMzKViMMlKto7lM3MzNrIlxTNzOzsuWKemkc1M3MrHw5qpfEQd3MzMqWB8qVxkHdzMzKlgfKlcYzylm9k9SF9Azjy7MnJJk1On8urTlwULd6J2kJ4FtgyYj4rrHLYwb+XFrz4FvazMzMmggHdTMzsybCQd3MzKyJcFC3BWEccG62NisX/lxak+eBcmZmZk2Ea+pmZmZNhIO6mZlZE+GgbmZm1kQ4qJuZmTURDuoVTtI5kiJvmSJpsKSTJZX8/yvpeUmPLoiyzi9JX0m6prHLYXUj6f3sM7p5kWNbZcfWz9t3jqRNGraUNZN0aFbWXo1dFrNCfqBL0zAZ+EX2c3tga+BC0pe2C0vM6yhgZv0VzQwkrQasmW0eALxUi9POBiYAry6ocpk1NQ7qTcOsiHg9b/s5SWsAe1FiUI+IT+q1ZGVEUvuImNzY5WimDgRmAS8A+0r6Y0RMb+QyNSpJLYEWzf19sPrl5vemazzQOn+HpAslfShpgqTvJd0lqXdBmnma3yVtIelVSZMljZB0i6QeVV1YUkdJEyWdWOTY/ZJey0t3jaShkiZlzezXS+pa04uTtJekQVl3ww+SLpfULu94rjl35+ya44D7asrX6p8kAfsDzwKXAz2BHWo4JzeBxiV5XUtbZcfaZf/fP2T//4Mk7VlDfudIGiWp8Hdi9Szv7bPtnSU9JelnSeMkvSGp2rJm5/XIfi9GZL8nr0raoiDN85IelXSIpKHAVGCtmvI2K4WDehMhqVW2dJa0G7A3cH9BsoWBC4CdgWOBZYAXJFXZYiNpPeAp0peEfYFTgF2BgVlNYx4RMREYAOxXkFfn7Np3Zrs6AC2B04EdgTOALYGHanitu2Wv7RNgD+Bi4AjgjiLJbwC+APYELq0uX1tgNiF91u4EngBGkprgq9M3W1+d/dwXeDfb9x/g96T/9z1In4MHss9FVe4CugPbF+zfH/gZeDrbXhZ4BDiY9Dv0CvB47gtFMdnvwUDS78UppN+TCcBT2e9PvvWBk4CzgJ1IT40zqz8R4aWCF+AcIIosdwMtqzmvJbB4lna7vP3PA4/mbfcHvgZa5+3bLjtv12ry3y1Ls2Levl8DM4BFqjinFbBpdt5Kefu/Aq7J234XeLXg3MOz89bItrfKtq9r7P+j5r4A/yCN++iabV8PTAQ65aXJ/X+tn7cvgBML8loz2//7gv2vAu/UUI53gf8U7Psi/7NVcKxF9pl8Argzb/+hWRl6Zdu5z/r2eWlaZ783D+Ttex6YRnr0a6P/v3hpmotr6k3DZGCDbNmMVAvfAbgxP5GkHbNmwbGk4Jp7pvRK1eS9OfBw5PX7RcSTwJjsWlX5b5Ymv7a+H/BcRPyUV6aDJb0naQIwHXi5ujJJ6gSszbytEPdk68IyPVZNGW0By1qB9gUej4ix2e47Sa001TaZVyE3cr6wK+UeYB1JHas59y5gN0nts7JtCCyX7c+VdwlJt0n6nvQ7Mp30Jbam35FxEfFEbkf2+9KfeT+PH0SEa+e2wDioNw2zIuLtbHklIq4CzgN+I2l1AEkbkJrEfyA1LfYFNs7Ob1cs00x34Kci+38CquxXj4hpwANkQV1ST+CXzGl6J+sHvR14E/hVVp7cH/qqytQNUGGZsoAxtUiZipXdGs52wELAI5K6SeoGfAgMo+Ym+GK6A9MjYlTB/p9In4tu1Zx7N9CR1EwOqen9a7LR9Uq3gA4gBeKzSHeRbEBqWq/pd+TnIvuL/Y7482gLlEe/N12Ds/VqwEekYDkW+FVEzAKQtHQt8hlF6osvtEh2rDp3AYdJWpP0JWImqfaSsy8wKCJ+n9shacsa8hxDauqcq0zZ4Lq2RcrkJxY1rlzg/le25FtI0sIRUSwgVmUU0FpS94gYnbd/EdL/9ZiqToyIbyW9Auwn6X7SF8l/R0TuM7ICsA6wR0Q8nDsvV7OvoUy1/R3x59EWKNfUm67Vs/WIbN2e1JSY/0flwFrk8zKwR/5gOkm/JNWIXq7qpMzzwI+kGtH+wMC8JthcmaYVnFNtmSJiAjAI2Kfg0K/yymtlQFIHYHfSwMetC5b9SZWKftVkMZ15a8i5/999C/bvC7wXaZBmde4iDVDbBViMvKZ30ucR8j6T2RffTWvI82Wgi6Tt8s5rRfoi7c+jNSjX1JuGFpJyTeltgPVII8k/AV7M9j8F/Am4WtKDpJrzwbXI+3xS8+Sjkq4m1T4uJDWZP17diRExU9K9pIFFC1MwGj4r0z8knQm8Rvpju00tynQO8JCkO0gj3lcmjep/ICI+rMX51jB2BzoBV0XE84UHJZ1MqslfXcX5g4HdJb1EGlg3NCI+kNQfuDyrQQ8FDiKNsN+9FmW6D7gSuA74JCLezzs2hDTO5MJsRHsn0vPXv68hz8dIvw93SDqV1MR+DNCb9Lk0azCuqTcN7UlB8TXgGdIflDuArXMD3CLicdLtNruT+g23INVWipldm4+Id0j9ol1IfeSXkP6I7RgRtZl57i5gUWASUDj97D+By7Ly9geWpBb9rBExgFQzWwN4GDiVdOvaQbUojzWcA4BvSC02xdwGbCxp+SqO/4H0N2og8Bbpyyqk/+cbSf/vD5M+B/tExCM1FSgihpN+Rwpr6UTEVNKETVNJwf880pfaF2rIcybpC+ljpN+PB0i/L9tlvz9mDUZzupPMQNI7wEcRcUhjl8XMzErjmroBIGlRSQeQ7gN+q7HLY2ZmpXNQt5z9SJOE3A3c3MhlMTOz+eDmdzMzsybCNXUzM7MmwkHdzMysiXBQNzMzayIc1M3MzJoIB3UzM7MmwkHdrAxJ+kpSSDq0YP8y2f6QtMyCvJaZVR4HdWuyJN2aFwDzlwmShki6UdJajV1OM7P64qBuzcF00kM2cks70kNgfge8LemIRixbqaaTHmIyNPvZzGw2B3VrDl6NiEVzC9CB9JCaz0lPKvxHpdTYI+L7iOiTLTU9PczMmhkHdWt2ImJaRDxFemLddNLvQSXV1s3MinJQt2YrIj4B3s421weQdGjW7/5Vtr2jpIGSfpY0S9Kf8vOQtKKk6yR9KmmSpPGSBkk6W1LXqq6t5PeS3pY0UdJISU9L2qG6MtdmoJykdpKOlvScpOGSpkr6Nts+VlLPavJvK+kMSYMlTZY0QtJDktauoVw9JJ0v6YNszMJESR9LuljSwtWda2b1p1VjF8CskX2XrecJwJJOAC4lPV9+LDCr4PhhwHVA62zXJKAtsFa2HCLplxHxRcF5LUnP8t432zWT9AzvXwC/kHTs/L4YSSuSnlu/UrZrFjAGWAhYAtgqey23Fjm9M/Ay6QvO1OzcnqQWjV9K2joi3ixyzbWB/wKLZLsmZ+eumi2/lbRzRLwxv6/LzGrHNXVr7pbO1qML9i8CXARcC/SOiO5AJ+B+AEk7ATcCM4CzgcUioiOpv35TUgvAskB/SYW/ZycxJ6CfC/SIiB7A4qRgfxkpCJdEUjfgCVJA/wk4GOgSET2zcq0BnF/kteacC/QCdgA6Zq93C9IXnw7AVUWu2RUYQHq//gdsC3SMiE6k92Ew6YvBANfYzRpARHjx0iQXUm00gOerOL4BqZYcwJXZvkOz7QDurOK8lsAXWZp9q0jTA/ghS7NX3v4OpJpyAH8vcp6AZ/LKcGjB8WXyji1TcOxv2f7xwEolvE9fZedNAlYocnzvvGsuVXDstBrOXYzUUhDARY39mfDipakvrqlbsyNpMUkHk2qYLYBppGfJF7qkiiy2BJYDvo6I+4oliIhRwMBsc7u8Q9sBXUjN0xcVOS+AC2rxMoo5JFtfHRGfzsf590fE50X2DyAFZYDVC479KlvfUezciPgBuD7b3H8+ymRmJXCfujUHW0qKKo5NItWGC4PgZOD9Ks7ZJFv3lvRjNdftlK2Xytu3XrYeEhFVnfsyqVm/1r+f2aC53tnmY7U9r8BbxXZGxHRJP5Oa2LvnXbMNc4L809Xk+zRwCrCkpIUiYvh8ls/MauCgbs3BdGBU9nOuqfg74CXghoj4psg5IyNiVpH9MCd4tmHO4LDqdMj7OddXXuU95hExVdIIYNFa5J2TX46vSzgv3/hqjk3J1q3z9vUgdUVANa+HOYMRARYGHNTNFhAHdWsOXo2IrUo8Z2Y1x3KB7JmI2Hb+imRmVv/cp25Wup+y9VLVpiouV0tdrKoEWbN2rxLzzW/KX7rKVPVrFHO+/CxeTbol8n7+ecEVx8wc1M1K92q2XlHSStWmnNc72bqPpKqa7jejxFa0iPiaNNoeYJcSyzRfImIa8GG2uU01SXOtGd+4P91swXJQNyvds8zpt74im0ymKEmtJXXK2/Uk6Za2lsDJRdIL+PN8luvWbH30fHzZmF/3ZuuDJC1beFBSb+D32eZdDVQms2bLQd2sRBExHTiKdFvajsCTkvrmJpmR1ELSqpJOBT4F1s47dxJwYbZ5nKSzJHXOzlsUuI10y9yk+SjaJaQJYDoBL0g6UFKHvDKtKekKSXvOR95VuRb4ljQY8ClJW2dfTJDUl3TPfTdSs/tl9XhdMyvCA+XM5kNEPC7pIOBm0vSurwJTJU0g3YeeP0q88Ha6S0i3tu1DmsXtLEnjSMEP4FjgBErsG4+IMdnc8Y8BKwB3ADMljSEF+rZZ0kGl5FvDNcdK2p10T/7ypFaMSdkthB2zZKOA3d30brbguaZuNp8i4i5gRVLNexBpvvRupFvD3gAuBzaLiFcKzptJmrTlCOBd0uQ3kALizhFxdR3K9CmwJnAc8AowjjSn+8/Ac8AfSZPJ1JuIeA9YjTRpzsfZ7hbAENLc+atGxOv1eU0zK05pAiszMzOrdK6pm5mZNREO6mZmZk2Eg7qZmVkT4aBuZmbWRDiom5mZNREO6mZmZk2Eg7qZmVkT4aBuZmbWRDiom5mZNREO6mZmZk2Eg7qZmVkT4aBuZmbWRDiom5mZNREO6mZmZk2Eg7qZmVkT8f/xCNXJUmU1RAAAAABJRU5ErkJggg==\n", 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\n", 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CqVJw61bCix46BI0bG0mTZe79dSiEEMm5ezeISZN+IywsklWr+qVdAfDx8aFcuXIA5bTW2TpAS/6UFCIPCIkI4W7QXa77X8froRfBEcEEhAVwO/A2h30OExgeyOm7ya+ZXs6pHK52rlR2qUzLci0xaROVilWirFNZqrlWo0bxGlioeN1pkZGwbx8ERS+y4B8KnDT2FyyAs2fB2zvhTdzcoHx56NABHjyAb76BMmWy/esghBCZYTJpvvnmb6ZM2cWjR2GMG9cck0ljYWHeh9okyRIiF2mt2X1tN3uu7eGy72V2XtnJw7CHKZ5voSywt7KnuH1xXqj3Ao7WjtRyr0WN4jWo5V6LMo4pJDp//gnf/QqO0Wut37gBv/xiTMx4/XragfbrB66u8MEH4OQUO6GjEELkNefP3+eVV37h0CFvmjUry7JlPahfP2/MLy5JlhA55MbDG1z3v866s+u4H3yfXy/+SmB4YOxxJxsnqrtVx9XOlRIOJWhSugnlnMtRwqEEzjbOlHYsjauda8JWqJRERMCPP8LixfDXXymf16ULNGsGN2/CrFnJzybdqJExn5EQQuQDWmsuX/ZlyZLuDB3a2OytV/FJkiVEFkVERXDyv5PcDbrLLxd+4cKDC5z87yQPQh4kOK+ck5FAda7cmWltp+HhlIWxf3/9BWPHwp07xpN1ly4lPF6sGHz7LbRuHffkna2t8RJCiHzu118vcPToTd5993Fq1nTHy2sctrZ5L6XJexEJkcdd8b3Crxd/5cSdE6w9u5bgiOAEx8s7l6dBqQY0KdOEhqUaUsW1Co1LN87ahLd+fnD4MBw8aHThxde3L9SpA48ewcKFULdu5u8jhBB5mLf3Q8aM2c7GjeepXt2NqVNb4+BgnScTLJAkS4g0/XrhV1b8u4K/b//N/eD7sV1+DlYONC3TFBc7FxqWakidEnWo7V6b6m7VM59QeXnBhAlgZwcbN0K5cnD+fPLnrltnLFXi7p65ewkhRD4RGWni00+P8M47e4mIMDFzZjumTGmdZ5OrGHk7OiFymdaaQ96HOH33NF/+/SV/3/479piTjRN1StSha9Wu9Kjeg0alG2XuJiYTREUZT/n98w8cOACXLxtJk1+85Tbd3CAgwJiU89YtY36ptm2NZVycnLL4SYUQIv84e/Yekyb9RocOFfnii+5Ur+5m7pDSRZIsUehprTly8wgrTqxg55WdXPO/luD4s3We5bOun1Hcvnj6LhgWBj/9ZEyFcPOmMQ7q2DEjudLamHcqJTVrwpQpMHhw8oPShRCikPD3D2XXrqv061eLevVKcvToqzRqVDpfrTUsSZYotPxD/fnyry+ZsmtKbFmdEnWY12kePav3pFTRUjjbZvApu61bjRan+JydjYk6fX2ha1coXdpopfL0hKAgo3WqUSMoXtzoJhRCiEJMa83KlacZP34Hvr4hNG/ugYeHE40b57+5+STJEoXKo7BHnLt3jrVn1/LpkU+JMEVQqVglelTvwYDaA2hVvlX6LnTxIuzaBcePG3NIbd6ccDb0mCf+iheXrj0hhEinixcfMGLEFnbvvkb9+iX55Zdn8fDIvz9DJckSBd4RnyN8evRTVp9enWCB457VezK8yXAer/Q4NkVs0r5QVBT07m0kVIk5OBitUNWrw3vvQY8e2fcBhBCiELh58xH16y/F0lIxf35nxoxpRpEi+Xvxd0myRIGitebEnRMcu3WM371+5+CNg3g99ALAzc6NvjX7Ut2tOi08WqS/1QqM8VTVqsG16PFa7u7w0UfGxJ5Vq8qM6EIIkUnXr/tTsWIxypZ1YsGCznTvXt0siznnBEmyRIFxyPsQU3ZN4cCNA7Fl5ZzKMaPdDJ6t8yyexT0zftGoKGPeqfiD1R89AkfHbIhYCCEKr//+C2TChJ2sXXuGf/8dRs2a7gwf3tTcYWUrSbJEvhYQFsC7f7zLsVvH2Hd9HwBznphDu4rtqO1eG0ebDCZDp05Bz57gET0b+8GDcccGD4bPPpMESwghssBk0nz55V9MnbqLwMBwJkxoQblyBaPlKjFJskS+FGWKYs6BOSw6soj7wfcBGFx/MMMaD6NFuRaZu+jatfDMM8a+l5exJE3r1hAYCNu2Qam8seCoEELkV0FB4XTs+D1//ulDy5blWLq0O3XrljR3WDlGkiyRr/iH+rP69GrmH57PJd9LWCpLfuz7IwPrDMzc3Cm7dkGnTgnL3nsPpk3LnoCFEEJgMmksLBQODtbUr1+Sl19uwCuvNMpTiznnBEmyRL4QGB7IsuPLmPjbxNiyCc0n8FGnj7C0sMzYxR4+hCeegNu3E067MGgQjBljDGYXQgiRLTZuPM/UqbvYtu05KlVyYenSwvP0tSRZIk/bcXkHS/9aysbzG2PLFnZZyNDGQ7G3sk/fRSIi4N9/4coVWLAAjh6NO1a3LsybB507ywzrQgiRjby8/BkzZju//HKBGjXc8PUNoVIlF3OHlaskyRJ50p3AO1T+pDIhkSFYW1rTtExTXmrwEkMbD01/y5XJZCxrU7580mNPPw1r1hiThgohhMg2JpNm/vxDzJz5O1FRJt59twOTJrXExqbwpRyF7xOLPO2a3zVe2vQSh30OEx4VTnW36vw99G8crB3Sf5Hz5+HCBXjzzYRTL/zvf9C0KdSuLa1WQgiRQ5SCffu8aN26PJ9/3o2qVV3NHZLZSJIl8oSjN4/y/v73+eXCL7Fl+1/aT+vyrdN/kY0bYfRo8PGJK3NwMLoI+/cHl8LVTC2EELnF1zeEGTP28uabbShTxpHVq/vh4GCVrxZzzgmSZAmz+v3678w7NI9tl7dh0iY8i3syr9M8elRPx8DIkBDo0wesrY21BC9cMMqLFzeeEGzaFBo0kC5BIYTIIVprfvjhJG+8sZP794Np1Kg0L73UkKJFrc0dWp4gSZYwi8u+l3lr91usPbsWgPYV2/Nljy+p5lYtfRfw8oKKFePee3qCszN8/TX065f9AQshhEjg/Pn7DB++hX37rtOwYSk2bx7EY4+VNXdYeYokWSLXLTu+jGFbhgEwoPYAPnjiAyq7VE674tmzMH06/PxzXJmjozElQyFvkhZCiNw2dux2jh+/xaJFXRg58rF8v5hzTpAkS+QaL38vFhxewKdHP8W2iC1HXj1CvZL10q4YEgJDhhhPA8b3zTfw8ss5EqsQQoikdu68QuPGpXFzs2fJku5YW1vi4eFk7rDyLEk7RY67E3iHxl82puInFfn06Kf0qN4Dn/E+aSdYV6/C7Nlgbx+XYI0YYcx7pbUkWEIIkUtu3w7g2WfX0aXLD8yZcwCAypVdJMFKg7RkiRz1y4VfeGrVUwCUcCjBN72+Sd+g9qAgqFIl7r2HhzE1g0MGpnIQQgiRJVFRJpYt+4s339xNcHAEb77ZmrffbmvusPINSbJEjhm/fTyLjizCwcqBn57+iV41eqW/sru7sS1fHk6dAif5a0kIIXLb8OFb+Oqrv2ndujxLl3andu0S5g4pX5EkS2Q7rTWfHPmERUcW4WbnxqFXDlHdrXrqlUwm+Ocf+PNPGDUqrvzKFSgi/0yFECK3BASEoZSiaFFrhg1rQvPmHgwZ0qDAL+acE+S3l8hWkaZI2i5vy2Gfw9gWseWvoX9RoViF1CsdOQLNmyctv3NHEiwhhMglWmt+/vkcY8du5+mna/LJJ11p1Kg0jRqVNndo+ZYMfBfZJsoURbOvm3HY5zDNPZrzcOrD1BOskBBjFvaYBKtCBfjlF6N7UGsoWTJ3AhdCiELu2jU/evRYSb9+a3FysuHpp2uZO6QCQZoJRJb9F/gfnx/7nDkH5hBhiqC3Z29+fubn1JdT+OEHGDsW/P2N9xs2QO/euRGuEEKIeFasOMGIEVvQGj744HHeeKMl1taW5g6rQJAkS2TJw9CHPP7d45y9d5Z6JesxvMlwXm/8euoJ1ty5MHWqsd++PezeLUvfCCFELtNao5SialVX2revyOLF3ahcWdZ4zU6SZIlMu+x7meZfN+dByAM+7vQxE1pMSD25CguDZ581FnIGuHQJqlbNlViFEEIY7t8PZsqU3yhTxpF3332c1q3Ls3Xrc+YOq0CS5gORKZceXKLV/1rhH+rPh098yBst30g5wfL2hnffBVvbuATrp58kwRJCiFyktWbFihN4ei5mxYp/CQmJNHdIBZ60ZIkMO333NE+teoqwyDCOvHqExmUap3zyxIkwf37c+169jLUHLaW/Xwghcsv58/d5/fXN/PGHF02alGHHju40blzG3GEVeJJkiQz5/OjnjNpmzGO1ZdCW1BOsRo2Mua8APv8cXnsNrKxyIUohhBDxeXn5c+LEHT77rCvDhzfB0lI6snKDJFkiXSKiIpj9+2ze2/8eTco0YX7n+bStkMrSCoMGxSVY//4L9dKxELQQQohss337ZXx8HvHqq43o0qUq16+PxcXFztxhFSqSZIk0aa1ps7wNR24e4akaT7G2/1qsLFNokTp9Gnr0AC8v4/2VK1C5cu4FK4QQhdytWwGMG7edtWvPUqdOCYYMaUCRIhaSYJmBtBeKVEWZomj5v5YcuXmEp2s+zYYBG5JPsM6eNSYWrVvXSLCsrODyZUmwhBAil0RFmfjssyN4ei5m48bzTJvWhqNHX6VIEflVby7ylRepGrt9LH/6/EmvGr1Y239t8k8QzpsHtWvHTSz63XfGdA1VquRqrEIIUZjt2nWVMWO206hRaf79dxjvvfc4dnYyDtacJMkSKVr+z3I+P/Y5A2oPYOOAjcknWEeOwOTJxv7EiRAUBC+8AKnNlyWEECJbPHwYyu+/Xwegc+cqbN/+HHv3vkjNmu7mDUwAMiZLpGDKb1P46NBHFLUuysIuC5MmWMOGwcWLsG+f8f6bb+Dll3M9TiGEKIy01qxde5Zx47YTHBzBjRvjcXKyoUsXmX8wL5GWLJHE0uNL+ejQR3Sv1p1bE25R2jHRCuzDh8OyZbB3L4webUw2KgmWEELkiqtX/ejW7ScGDFiHq6sdmzcPwsnJxtxhiWRIS5ZIYMHhBbyx8w1c7VxZ+fRKHG0cE57w+++wdKmx7+cHxYrleoxCCFFYnT59l6ZNv0IpmDu3I+PHN8fKSiZ3zqskyRKAMQ9Wn9V92HJpC5WKVWLX4F0JEywvL6hYMe79pEmSYAkhRC558CAYNzd7atd25403WvDqq42oWLGYucMSaZAkS3D67mn6r+3P+fvnaVCqAXsG78HFLt5K7AEB0DjezO579kC7drkfqBBCFDL37gUxadJvbNlyiXPnRlK8uD3vvfe4ucMS6SRJViF36r9T1FtqzMb+WdfPGNl0ZMJB7hUqwI0bxn7jxnD8uBmiFEKIwsVk0ixf/g+TJ+/C3z+UsWObYWMj3YL5jSRZhVR4VDgTd07k82Of42jtyKp+q+hWrVvCk1q3jkuwvvpKBrcLIUQuuH8/mD59VnPgwA2aNi3DsmU9aNiwdNoVRZ4jSVYhdPTmUXqu7MndoLuUKlqKgy8fpLJLvJnZAwLAySnu/bVrCcdjCSGEyDEuLrbY21vxxRfdGDq0sSzmnI/lme+cUspTKfWbUipIKXVHKfWRUso6HfXclFJLlVI3ouueVkoNy42Y86NjN4/RbkU77gbdZUa7GdwYdyNhgvXoUcIEy8dHEiwhhMhhW7ZcpFWr//HoURiWlhZs3/4cw4c3lQQrn8sTLVlKKRdgD3AJ6AuUBRYA9sCoNKqvBTyBt4AbQDdgiVIqSmv9VY4FnQ+duHOCXqt6YW9lz5+v/En9UvWTntSyZdy+ySQztwshRA7y8XnE2LHb+fnnc1Sp4oK390Nq1y6R/AobIt/JE0kWMAxwAvporX0BlFJFgC+UUh9orW8lV0kpVQroALyktV4RXbxHKdUUeBaQJCva5N8mM+/QPEoVLcXO53cmn2ABnDljbCXBEkKIHBMZaWLx4qNMn76XsLBIpk9vy5tvtpa1BguYvNIO2RXYFZNgRVuDEV/nVOrF/Gt8mKj8ISAZQrSDNw4y79A8AI6+epTGZRonf+KMGca2d29JsIQQIgdprfnmm39o2rQMJ08OZ/bsDpJgFUB5JcnyBM7HL9Ba+wO3o48lS2vtDewE3lJK1VJKOSqlnsFIzD7PuXDzD601w7YMo7h9cW6Mu0E553LJn3j/Psyebex/8knuBSiEEIWEv38ob7+9h+DgCKysLNm9ezC7dw/G07O4uUMTOSSvdBe6AP7JlPsBrmnU7QusBqL7uYgCRmut16dWSSnlhNFFGaNUuiLNZ4ZvGc7pu6eZ23FuygkWQK9exrZ3byhfPldiE0KIwkBrzerVZxg/fgd37gTSpEkZevf2pEQJB3OHJnJYXkmyMkUZIwOXA9WAQRgtX52ARUopP631qlSqTwBm5HyU5jPnwByW/bWMMo5leLlhCnNcXbhgzId1/77xfsOG3AtQCCEKuMuXfRk5cis7d16hbt0S/PzzM7RokcofvKJAyStJlh/gnEy5C+CbTHmM7kB/oJ7W+lR02T6lVAlgPpBakrUA+Dre+1LAsXRHnMdtOLeBN3e/SdeqXdn07CasLFPo63/xRSPBKlsWVq7M3SCFEKIA01rTu/cqrl3zZ968Towd20wWcy5k8kqSdZ5EY6+UUs5AaRKN1UqkFkb34OlE5f8Aryql7LXWwclV1Fo/Ah7Fu18mws6bjt48yjPrnqFR6Ub80PeHlBOsmTPhyBFwcDDmwxJCCJFlBw7c4LHHymJtbcmKFb0pUcKB8uWTa0cQBV1eGfi+DeiolCoWr6w/YMIY2J4SL8ASqJeovDFwN6UEqyDTWjNx50ScbJzY+fxOXO1SGNL24AHMmmXsb9mSewEKIUQBdfduEIMHb6BNm+V8/vlRAJo0KSMJViGWV1qylgKjgY1KqQ8wJiOdByyNP0eWUmo3UEFrXTW6aCvGBKTrlFKzMMZkdQaGUMDHW6Xko4Mfsf/GfuZ2nIubvVvyJ2kNJUsa+599Bu3a5V6AQghRwJhMmm+++ZspU3bx6FEYb7zRgtdeS2GqHFGo5IkkS2vtp5R6AvgM2AgEYIyXmpboVEvixay1Doiu9z4wFygGXMMY1L44xwPPY87eO8uiI4sAGNF0RPInRUZC27YQFWW8H5HCeUIIIdLl2WfXsXbtWZo392Dp0u7Ur18gH1YXmZAnkiwArfU5oGMa57RPpuwyMCCHwso3HgQ/oP/a/twJvMOmZzdR1Lpo0pO0hubN4a+/jPfXr4NFXukxFkKI/CMoKBxb2yJYWlrwwgv1eOKJSrz2WmMsLArO+F6RdfIbtgB4EPyANsvbcPbeWb7v8z29avRKetLx40ZC9ddf8NRTEBgIFSrkfrBCCJHP/fLLBWrV+oLPPzceSO/Zswavv95EEiyRhCRZ+dytgFs0+7oZ5+6f47Oun/F8vecTnhAZCS+8AE2bxpWtWmU8USiEECLdbtx4SO/eq3jqqVXY2FhSt24Jc4ck8rg8010oMu7YzWM8+eOTBEcEs/yp5QxpMCThCRER8NxzsHYtDBsGfftCp05miVUIIfKzL7/8iwkTdhAZaWLWrPZMntwKW1v5FSpSJ/9C8qnjt47T5Ycu2FvZs+25bTxW9rGkJ730kpFgvfsuvP127gcphBAFhK1tEVq0KMcXX3SjWrUUntwWIpFMdxcqpayVUsOVUmuVUruiy9oopdoqpZIZdS2yy95re2n6VVNM2sTuwbuTJlgmE3z8Mfz4IwwZIgmWEEJkkJ9fCMOHb2bFihMAvPBCPXbufF4SLJEhmWrJUko5AHsxJv1UgI4+NBHoAYwBPs+OAEVC/975lz6r+2BXxI5ven1DjeI1kp7UuTPs3h23L4QQIl201vz00ykmTNjJvXtBuLjYAQVrVRCRezLbXTgTaJJM+VdAT6A3kmRlO5M28covrxBhiuDPV/+kXsnEE90Dy5fHJVg3bkA5WYhUCCHS4+LFB4wYsYXdu6/RoEEpfvnlWZo18zB3WCIfy2yS9TRG69Vg4Pt45Qejt8k0r4isGrR+EH/d/otFXRYln2B16gS7dhn78+dLgiWEEBmwa9dV/vzThwULOjN6dDOKFJEH8EXWKK112mclrqRUGEaCZgeEAlprbamUsgOCgHCttW22RprDlFIegLe3tzceHnnvL5ffr/9O+2/b07BUQ/4a+lfSpuuwMLCN/pJfuQKVK+d6jEIIkd/s2nWViIgounatRlSUif/+C6JMGUdzhyVykY+PD+WMRolyWmuf7Lx2ZtP0h9HbxNlIl+itfyavK5LxKOwRT616CoCfnv4paYL188/QJLr3dsQISbCEECINd+4E8txzP9Op0/fMnv0HWmssLS0kwRLZKrNJ1oHo7eqYAqXUF8CPGN2I+7MYl4jnvT/e42HYQzYP3Ixncc+EB5ctg6efhtOnjRnd33nHPEEKIUQ+YDJpliw5hqfnYtasOcPkyS3ZtesFGdguckRmk6z3gQigEXFPFr6O0X0YAXyQ9dAEwGXfy8w7NI+uVbvSvXr3hAd37DAmGS1a1EiyoqKgZEnzBCqEEPnAd9/9y4gRW6lduwR//z2UuXM74eBgbe6wRAGVqYHvWuu/lFI9gS+AKvEOXQGGa63/yY7gCrubj25S7bNqALzU4KWEBy9cgCefNPaXLYPatXM5OiGEyB8CAsLw8npInToleO65utjYWDJgQB1Za1DkuEzP+K61/g2oppSqBrgD97TWl7ItMsGbu98E4P3H36d/7f4JD/bsaWw//xwGDcrlyIQQIu/TWrNx43nGjNmOlZUFFy6MwsrKkoED65o7NFFIZKq7UCm1Rym1G0BrfUlrfSgmwVJKzVZKzcrOIAujHZd38P3J7xnRZARvtXkr4cErV+BSdD77+uu5H5wQQuRxXl7+9Oq1ir591+DgYMX//vcUVlaW5g5LFDKZbclqT9xYrMTejj42I5PXLvS01ry3/z0slAUz2if6Ml6+DNWMLkQ++wws5YeGEELEd+DADbp0+QGTSfPeex2YOLElNjayVK/Ifdn6r04pJQODssG2y9s4cOMACzovoIRDibgDAQFQq5ax37AhjBxpngCFECIPCg6OwN7eisaNSzNwYB3efLM1Vaq4mjssUYilu7tQKTVDKRWllIoiuhUr5n288pPRx27nTLgFn0mbeGv3W5RzKsfwpsMTHpw+HSIioHFj+OsvkEeOhRACX98QXnvtF5o2/Yrw8Cjs7Kz4+utekmAJs8toS1Z6f6uvyWggwrD0+FL+/e9fvu75NbZFomdw1xqGDIHvvjPe//abJFhCiEJPa83335/kjTd28uBBMCNHNiUiIgpraxlGIfKGjCRZJ4Bvo/dfxGix+i7ecQ34AceQJCtTrvtfZ+TWkbQq14ohDYbEHfDxiUuwPvwQXFzMEp8QQuQVN28+4vnnN7Bv33UaNSrNtm3P0aRJGXOHJUQC6U6ytNabgE0ASqkXo8teSrWSSDetNbN/nw3Ax50/xtIi3l9iMYs+r1kD/fsnU1sIIQoXJycb7t4N4pNPnmTEiKaymLPIkzI7Gan8a85m/df2Z/259bzc4GWaezSPO/D55zBqlLHfsaN5ghNCiDxg584rfPXV36xa9TSOjjacPDkMS0v5dSTyrkw/XaiUsgK6AjUwltNJQGs9OwtxFSq3Am6x4fwGPIt78lWvr+IObN8el2BNmybdhEKIQun27QDGj9/B6tVnqFixGDduPKRSJRdJsESel6kkSynlAewFKqdymiRZ6fTdv99h0iZWPr0SCxX9Q+Pff6FrV2O/Tx947z3zBSiEEGYQFWVi6dLjvPXWHoKDI3jzzda8/XZb7O2tzB2aEOmS2Zasd0m4ZmFiKU1UKpJx5OYRyjuXp0GpBnGFMTO5f/IJjBljlriEEMKcHj0KY9as36lfvyRLlnSndu0SaVcSIg/JbFvrExiJVMzyORroCRwELgPdsx5a4WDSJo7ePErTMk3jCv394cgRY18SLCFEIfLoURiLFv2JyaRxcbHj8OFX2LdviCRYIl/KbJJVMnq7MKZAa70FGAhUBXplMa5C46u/vuJWwC26V4uXl/76q7EdN84sMQkhRG7TWrN+/Vlq1vyc8eN3cODADQCqVHHFwkLmBRT5U2aTrNDobXDMvlKqGmCKLn8mi3EVClprhm0ZRqVilXixwYtG4dWrMHiwsf/EE+YLTgghcsm1a3706LGSfv3W4uxsw++/D6Ft2wrmDkuILMvsmKy7QFHAFbgGeAL7gKjo4zImKx1+OPkDAF2qdIkb8P5idLI1Ywb06GGmyIQQIneEh0fRqtX/8PML5YMPHueNN1rKjO2iwMhsknUCY+B7I+BnYBpQirhldzZnObJC4NOjnwLw3uPxnhw8dw4qVICZM80TlBBC5IJ//71DvXolsba25Ouve+HpWZzKlWWaGlGwZLa7cDLQATiFMVXDZ8AdwBdj6Z1x2RFcQXYn8A7Hbx1netvpuNm7GYX//AMPHkDTpqlXFkKIfOr+/WBeeWUTDRosY/36cwB061ZNEixRIGV2xvdrGN2EMcZGv0Q6LTu+DIAe1aO7BCMjoUsXY3/gQDNFJYQQOUNrzbff/svEiTvx8wtlzJjH6Nw5tZmAhMj/Mj3je0qUUh2Bd7XWLbL72gXFYe/DzP5jNo1KN4qbuuHHH+HePWO/Tx/zBSeEENlMa0337j+xbdtlmjQpw44d3WncWBZzFgVfhpIspVQF4HmgHMbg95+11ieijz0GzANaZ3OMBc7rm1/HpE1senYTSinQGt5+2zh4/z4oeVxZCJH/hYdHYW1tiVKKHj2q061bNYYPbyLL4YhCI91JllKqIcYThEXjFb+plBoC2ANfYIzxUsjThSnyDfHl1N1T9KvVDw8nD6Nw6FDw8YFatcDNzbwBCiFENti+/TIjRmzhs8+60r17dUaMkLGmovDJyJ8TMwBHjCQq5mUJLAI+jt5XwDHgyWyNsgD56dRPAIxvPj6u8MQJY/vHH7kfkBBCZKNbtwJ45pm1dO36I0opHByszR2SEGaTke7CFhgtVJuBrzASqlcxltMB8AHGaK03ZmeABcl1/+uM3jYaz+KetPCIHrIWGAjHj0O5ctKKJYTI15YuPc7kyb8RGhrJ22+34a232mBnJ4s5i8IrI0lWTAbwotbaH0ApdRC4T/TahVrrf7M3vIJl8m+TARjZdKQxFgvg3XeN7auvmikqIYTIHrdvB9CoUWmWLOlOzZru5g5HCLNTWqdv+JRSygRorbVlesrzG6WUB+Dt7e2Nh4dHtl8/0hSJ1bvGX3RR70TFzfBevjx4e0NwMNjZZft9hRAipzx8GMr06Xvp27cm7dtXJDLShKWlivsjUoh8wMfHh3LlygGU01r7ZOe1MzyFg1LqajrKtdZaJkCJ55D3IQBeb/x6XIL14IGRYBUpIgmWECLf0Fqzdu1Zxo3bzu3bgbi729O+fUWKFJGnBoWILzPzZCVetVMnKpenCxMJCg+i+0/dsbeyT7iETs/o4WwTJpgnMCGEyKArV3wZNWob27dfpnZtd9as6U/r1uXNHZYQeVJGkyxpA86ERX8uIjA8kC+6fUFx++JxB27eNLZz5pgnMCGEyKBly/7i99+vM3duR8aPb46VVb4eKSJEjkr3mKyCLqfGZIVGhmL3vh3lnMrhNc4rbqxCcDA4OMCAAbBqVbbdTwghstvvv1/HycmGhg1LExgYzv37wVSsWMzcYQmRLXJyTJZ0oOewT498CsCUVlMSDgaNeZqwQuLeVyGEyBvu3QtiyJCNtG//Le+8sw+AokWtJcESIp2yfe1CkdDS40txsXXh9SavxxVqDdu2GfszZpgnMCGESIHJpFm+/B8mT96Fv38o48c3Z9as9uYOS4h8R5KsHPTtiW+55n+NN1q8QRGLeF/qDz4Af38YPx7s7c0WnxBCJOfjjw8xZcouHnusLEuXdqdhw9LmDkmIfEmSrBy0/MRyAN7t8G5c4Z07xmLQlpbw5ptmikwIIRIKCgrHzy8UDw8nXnutEcWK2fLKKw1lMWchskD+9+QQrTUHvQ/SsXJH7Kyi58AKCYHS0X8RLl4M7jIjshDC/LZsuUjt2l/w7LPr0Frj4mLH0KGNJcESIovkf1AOOXX3FJGmSDzdPOMKDx82tt26wbBh5glMCCGi+fg84umn19Cjx0qsrCyZMaOdzNYuRDbKUnehUqor8DjgorV+VSkVMyPdLa11ZJajy6e01rRZ3gZHa0fGNBsTd2DKFGMr82IJIcxs+/bL9O+/lvDwKN55py1vvtkGW1sZQSJEdsrU/yilVBHgZ6B7vOJXge+B1tH7y7McXT518r+TPAp7xPP1nqeaWzWj0McHjh+Hhg2hbl3zBiiEKLQiI00UKWJBgwal6NixMnPmPEGNGsXTriiEyLDMdhdOAXpgzAAfv2358+j3fTN6QaWUp1LqN6VUkFLqjlLqI6WUdTrrllVKfauUuqeUClFKnVNKPZfRGLLLkuNLAHj/8ffjCs+cMbaDB5shIiFEYefvH8qIEVvo1WslWmtKlSrKhg0DJMESIgdlNsl6AWN9wrcSle+N3tbJyMWUUi7AHsAaI0F7CxgKLEhH3dLAYaBMdJ0ewBLAJiMxZBetNavPrKaqa1XKO8dbz+tTY1JSnnjCHGEJIQoprTUrV57C03MxS5Ycp1w5J8LDo8wdlhCFQmY74CtGbxcBH8Qrfxi9LZXB6w0DnIA+WmtfiO2S/EIp9YHW+lYqdT8CvIEntdYxPzl2Z/D+2eaXC7/gH+rP6MdGxxWaTLB1q7Ffu7Z5AhNCFDrXr/szdOiv/PbbVerWLcGGDQNo0aKcucMSotDIbEtWcPTWNVF5y+htUAav1xXYFZNgRVuDEV/nlCoppZyAZ4Av4iVYZjXz95kACQe8d+1qbHv3Bgt5oFMIkTssLBT//vsfH3/cib/+GioJlhC5LLO/8Y9Fb7+MKVBKTQZWYXQjHs3g9TyB8/ELtNb+wO3oYylphNHFGKGU+l0pFRE9nmuuUsoqtRsqpZyUUh4xLzLe+pbErxd+5cSdE7zR4g2K28cb53As+sv1ww9ZvYUQQqRqz55rTJy4E4Dy5Z25fn0sb7zREisrSzNHJkThk9kk66PobVeMpArgQ6BE9Pt5GbyeC+CfTLkfSVvL4otJjL4GjmO0ei0ExgGz07jnBIxuxpjXsdRPT9sXx7/AxtKGme1nJjzg5wdNm4KDQ1ZvIYQQybp7N4gXXtjAE098x5o1Z7h3z+hQsLNL9e9NIUQOylSSpbXeDbwCPCLuCUOFMSbrVa313lSqZ6eY+Hdprd/QWu/VWs/FSPLGK6XsUqm7ACgX79U0K4HceHiDHZd3MKbZGIpaF407EBTdc2opf0UKIbKfyaT58su/qFFjMStXnmLixBacPTsSd3f5o04Ic8v0zHNa6xVKqbUY47DcgXvAIa11RsdjgdFi5ZxMuQvgm0x5/HpgPJkY325gGlAVOJVcRa31I4wkESDLsxx/9+93aDSvNno14YGo6KFiPXpk6fpCCJGc69f9GT16G40alWbp0u7Ur5/lkQ9CiGyS2clIPwS+1VqfB37LhjjOk2jslVLKGShNorFaiZxN47q2WYwr3X469RO13GtR3a16wgOTJhlbGfAuhMgmgYHhbN58kWefrUPlyi4cOvQyDRuWxsJClsQRIi/JymSkZ5RSx5VSY5RSWV3peBvQUSlVLF5Zf8AE7EypktbaC6OlqmOiQ52AENJOwrLF9svbOXf/HJ0rJ3oQ8soV+DL62YCYJwyFECILNm06T61anzNw4HrOnbsHQOPGZSTBEiIPykrzisJ4um8hcFMptVkpNUAplZlJQJcCAcBGpVRnpdRLGOOqlsafI0sptVspdTlR3WlAL6XUIqVUJ6XUW8BEYEEmuy4z7NKDSwAMbzo84YGqVY3tunXQoEFuhCKEKKBu3HhI796r6N17NXZ2VuzePZiaNbP6960QIidlZTLSZzBam5pGX6cbxtOGAUqptVrr19J7Ma21n1LqCeAzYCNGwvU1RgIVn2XimLXWvyqlBgLTgeEY0z7MAHJtFeatl7dSzLYYlV0qxxUePx63//TTuRWKEKIA8vcPpV69JYSGRjJrVnumTGmFjY0s5ixEXpep/6Va6xvAx8DHSqkKGMnWM0ATjJnbXwbSnWRFX/McSbv9Ep/TPoXy1cDqjNwvu2it2XNtDzXcalDEIt6Xc98+Y/vnn+YISwhRAHh5+VOhQjGKFbNl/vzOtG1bgWrV3MwdlhAinbJjNPYjjCcA/YDIbLhevrLv+j7Co8JpVa5VwgN//21spZtQCJFBfn4hDBu2mSpVPuXo0ZsAvPJKI0mwhMhnMvt0oQvQB6MF6/F411FAGPBLtkSXD7y3/z0A3n383YQHDh0ytjZmWadaCJEPaa356adTTJiwk3v3ghg+vAnVq0tiJUR+ldlO/TskTKw0cBD4DlirtX6YUsWCJDgimD+8/qC5R/OEy+hcvgxeXtC4sfmCE0LkK5GRJrp1+5HffrtKgwal+OWXZ2nWzMPcYQkhsiCzSVbMOg2XgO+BH7TW17MlonzkD68/iDRF8kytZxIemB29os+MGbkflBAiXzGZNBYWiiJFLKhXryTdulVj1KjHKFJE5tYTIr/LbJL1BfC91vpIdgaT33z999cAvNjgxYQHfvrJ2HbrlssRCSHyk99+u8LYsdtZs6Y/deqU4OOPO6ddSQiRb2R27cJRhT3BCo8K55cLv9C6fGtc7eKtYW0yxS2lI+sVCiGScedOIIMGradz5x8IDo7Azy/E3CEJIXJAuluylFJ7AK21fiJ6PzVaa/1E1kLL27Zf3k6EKYLn6j6X8IC3t7GVrkIhRCJaa5Yt+4upU3cRFBTBlCmtmD69LQ4O1uYOTQiRAzLSXdgeY4B74v3EVCrHCow/fYz5r9qUb5PwQEySVUoWaRVCJKSU4sCBG9SpU4IlS7pTt25Jc4ckhMhBGUmybmCsJRizX+ATqdQcu3WMyi6VqV2idsIDS5YY23r1cj8oIUSeExAQxqxZvzNsWBOqVnVl2bIe2NlZyVqDQhQC6U6ytNYVk9svjEIiQth1dRf9avVLeMBkgo0bjf1mzXI9LiFE3qG1ZuPG84wZsx0fn0d4eDgxblxz6RoUohDJ7GSk72CMu3o3mWOPYxxMa9xWvvXLBWOu1WZlEyVSFy9CcDCMGiWD3oUoxLy8/Bk1ahubN1/E07M4e/e+SPv2Fc0dlhAil2V2CoeZGN2FSZIsYBdGt2KBXb3079vGkjkvN3w5rlBrqFnT2H+iQI/5F0KkYcqUXezadZX33uvApEmtsLaWP7qEKIyyNRFSSjnF7GbndfMSrTXrz62nfcX2CadumDUrbr9Xr9wPTAhhVgcP3qBChWJ4eDjx8cedef/9x6lSxTXtikKIAisjUzi8CLyYqCxxl2D56K1/1sLKu07fPc0VvyuMbTY24YH1643to0dgITM1C1FY+PqGMGXKb3z99T+88kpDvv66Fx4eTmlXFEIUeBlpyapIwqkbFNAu0TkxLVh/ZCmqPCxmlvenPJ+KKwwLg9OnoXJlcHQ0U2RCiNykteb770/yxhs7efAgmFGjmvLee4+bOywhRB6SkSTLH/CK3q+AkWzdiHdcA37AMaDAzsS5+dJmHK0dKe9cPq7w2DFj27+/eYISQuS6N9/czdy5B2nUqDTbtj1HkyZlzB2SECKPycgUDp8AnwAopUzRZZVyKK48S2uNu4N7wsIDB4ztwIG5H5AQIteEhEQQFhZFsWK2vPxyQ0qXLsrIkbKYsxAieZn9ydABKHTt4lprfB750LVq14QHvvrK2Hp45H5QQohcsWPHZerUWcLYsdsBqF7djbFjm0uCJYRIUUYGvrcF0Fr/QfS4rJiy5ESfV6A8DHtIhCmC4vbF4wq1hqtXjekb3NzMF5wQIkfcvh3A+PE7WL36DBUrFmPAgNppVxJCCDI2JmsfcfNf7SP1ZXV0Bq+dL7z7uzEtWN0SdeMKd+0ytm1TzDeFEPnUzz+f46WXNhEcHMGbb7bm7bfbYm9vZe6whBD5REYTIZXCfqFw7v45APrW7BtX+MknxnbECDNEJITICVprlFJUrepKkyZl+PTTJ6ldu4S5wxJC5DMZSbJeSmG/UNBas+3yNjpX6YxS8fLLq1eNrSwILUS+9+hRGO+8sxeTSfPpp12pV68ku3cPNndYQoh8KiNPF36b3H5hcebeGQDKOpaNKwwLg3PnZDFoIfI5rTXr159j7Njt3LoVwKuvNoxtzRJCiMzK7ALRNoADEKa1DopeTmck4A5s11rvzMYY84Qdl3cAMLzJ8LjCJUuMbdOmZohICJEdrl/3Z+TIrWzdeolatdxZtepp2rSpYO6whBAFQGafPV4M3AMmRr//DXgPGAtsU0r1y4bY8pR7wfcAqOxSOa7w00+N7RtvmCEiIUR2uHMnkN9/v86HHz7BP/+8LgmWECLbZPYJwJj+sc1KqZpAUyAKCMVo4RoHrMtydHnIzYCblHQoiZt99DQNWsO1a8Z+xYpmi0sIkXH793tx4sQdRo9uRvPmHnh7j8fFxc7cYQkhCpjMtmSVi95eAhpF788GHover5GVoPIi74felHWKNx7r+nVjO3KkWeIRQmTc/fvBvPzyJtq2XcH8+YcJCYkAkARLCJEjMptk2URvI4DaGPNi/QVcji4vmsW48pSHoQ85dusYtdxrxRVOjO4pbdQo+UpCiDxDa83y5f/g6bmYb7/9l7Fjm3Hy5HDs7GTOKyFEzslsd+EtoBKwHGgdXXYWKBW9fz+LceUpR24eITgimGdqPRNX+Ef0hPaD5fFuIfK6Y8du8fLLv9CkSRl27OhO48aymLMQIudltiVrE8ZkpP2BMsAprfV1oHH08dNZDy3v+OvWXwA0LRvvKcLAQCPBKlLgJrYXokAIDo5gzx5j3ORjj5Vlx47n+fPPVyTBEkLkmsxmCNMBe6AV4EXcU4bVgN+BlVkPLe9Yf249ZR3LUqpoqbjCyEgoXjzlSkIIs9m69RIjR27l9u0Arl8fR6lSRencuYq5wxJCFDKZSrK01sHA8GTK5wHzshpUXhJliuLU3VM0Kh1v7FV4uJFk2dikXFEIketu3nzEuHE7WLfuLJUru7Bp07OUKlWghogKIfKRTPd1KaWKAC8CT2JMQnof2AZ8q7WOzJ7wzO/03dOER4UzsM7AuMJjx4xtpUrmCUoIkcSNGw+pU+cLQkMjefvtNrz1VhsZ2C6EMKvMzvhuC+zE6C6Mrw/wklKqo9Y6NKvB5QUxy+l4OHnEFQYEGNtq1cwQkRAiPl/fEFxd7Shf3pmJE1vyzDO18fSUrnwhhPllduD7WxhPFapkXi2ijxcI3g+9AWhaJt6g97+MgfA4OJghIiEEwMOHoYwevZVKlT7B2/shAO+8004SLCFEnpHZJOsZjLmx1mIMdreN3q7BSLSeSblq/nLs1jGcbZwp51wurvDgQWNbq1bylYQQOUZrzerVp/H0/JzFi4/xzDO1cHCwNndYQgiRRGbHZFWM3r6utfaP3r+ilBqGkWBVTKZOvrT+3Hrql6yfsPCh8VeztGQJkbsCAsLo338tO3ZcoU6dEqxb159WrcqbOywhhEhWZluyQqK3iZ+JrpLoeL729+2/AahdonbCA76+0K2bGSISonArWtQaW9sizJ3bkb//HioJlhAiT8tsS9Zx4Algi1LqW8Ab8MB42jBmiZ18b9fVXQC83vj1uMLff4fz56FvXzNFJUThsm/fdaZN28OGDQMoUcKBDRsGoJQyd1hCCJGmzCZZHwOPY0zdMDFeucJIsj7OYlx5wu2A2wC08GgRV/juu8b2mQIz7EyIPOnevSAmTvyN7777Fw8PJ65d86NECQdJsIQQ+Uamugu11juA14EAEj5ZGAAM01pvz7YIzeiy32VquNXAyjLeXDu7d0OZMlC/fsoVhRCZZjJpvvnmbzw9P+fHH08yYUJzzp4dQbNmHmlXFkKIPCTTk5Fqrb9WSq0CWgLFMSYjPaS1Dsyu4MzNy9+L8s7xxnysXWtse/QwT0BCFBLffPMPVau6smxZDxo0KJV2BSGEyIMynGQppSoStxD031rrndkaUR6hteaq31ValmsZVzh1qrEdNco8QQlRQAUFhTNv3iHGjWtOsWK2bNr0LK6udlhaZvbZHCGEML90J1nKGAixBHgVo2swpnw58JrWWmd/eOZzP/g+QRFBVCoWb+mce/eMbZ065glKiAJo8+aLjBq1FS+vh1So4MxLLzXE3V2mRxFC5H8Z+TNxNDCUpDO8vwSMy/bIzOya/zUAKrtUjisMD4dx40AG3gqRZT4+j+jbdzU9e67EysqS3357gZdeamjusIQQIttkJMl6OXobDvwC/AqEYSRaQ7I3LPO76ncVgEou0S1ZJhOEhYGtrRmjEqLgGDhwPVu2XGLGjHacOjWcjh0rp11JCCHykYyMyaqOMT1DV631PgClVAdgN8aSOgXKvuv7gHgtWdeMli3CwswTkBAFwNGjN6ld2x0HB2s+/7wbNjaW1Kghaw0KIQqmjLRk2QLEJFjRYvZtsimePCMw3HhI0sXWxSjw9TW2jz1mpoiEyL/8/UMZMWILzZt/zdy5xtqf9eqVlARLCFGgZebpwnLEG/ieUrnW+kbWQjMv3xBf6paoa0x8qHVccuXqat7AhMhHtNasXHmaCRN28N9/Qbz+emPGj29u7rCEECJXZGaerOuJ3utkynUmr51nPAh5gItddCvWhg1xBzp2NE9AQuRDI0ZsYenSv6hXryQbNgygRYty5g5JCCFyTWYmoUn8dGFKr4xdVClPpdRvSqkgpdQdpdRHSinrDF5jnFJKK6U2Z/T+id18dJNSRaMnQbx82djeuAEWMm+PEKkJC4skLCwSgGeeqc3HH3fir7+GSoIlhCh0MtLa9AdxrVbZSinlAuwBLgF9gbLAAsAeSNfMn0qpUsAM4G52xOQf6o+7vbvx5vp1cHAAD1nWQ4jU7NlzjeHDt/Dcc3V55512dOhQiQ4dKqVdUQghCqB0J1la6/Y5GMcwwAnoo7X2BVBKFQG+UEp9oLW+lY5rfIQxtUSFrAZzJ/AOQRFBcS1ZV69CxYoyP5YQKbh7N4g33tjJDz+cpFw5Jxo1Km3ukIQQwuzySt9XV2BXTIIVbQ1GfJ3TqqyUag30BqZmRzAxc2TFJln37kFp+aUhRHJWrz5NjRqLWbnyFJMmteTs2ZH06FHd3GEJIYTZ5ZXB6Z7A/+IXaK39lVK3o4+lSCllCSwG3tda31bZ0Np046HxYGS9kvWMgqAgKCfjSYRIjr29FTVrFmfp0h7Uq1fS3OEIIUSekVeSLBfAP5lyPyCtORNGAA7AwozcUCnlhNFFGaNUzM7FBxcBqO5WHe7ehQsXoGVLhBAQGBjOzJn7KF/emTFjmtGzZw169KhOdvyBI4QQBUle6S7MFKVUCWA2MEFrHZ7B6hMA73ivYzEHrvtfx83OjWK2xeDzz43C+vWzI2Qh8rVNm85Tq9bnzJ9/mHPn7sWWS4IlhBBJ5ZWWLD/AOZlyF8A3mfIYs4GTwH6lVLHosiJAkej3gVrryBTqLgC+jve+FNGJ1o2HNyjnHN09OG+esX3hhTQ/hBAF1Y0bDxk9ehu//HKB6tXd2L17MI8/Lk8NCiFEavJKknWeRGOvlFLOQOnoYynxBNpiJGmJ+WEMqN+eXEWt9SPgUbz7xR7zfuSNZ3FPY6b3kBCjUGZ6F4XYwYM32LHjMrNnt2fy5FbY2OSVHx1CCJF3ZfonpVLKDZgMPA64aK2rKqUGRV9zu9Y6I/NVbQPeUkoV01r7R5f1B0zAzlTqjQOKJSpbBIQAb2K0cmWI1hrvh950qtwJzkfndy+/nNHLCJHvHT7sze3bgfTtW5Nnn61D69blKVcuuQZnIYQQyclUkhU9FupPjDmpFHGTlD4JPIeR4HyUgUsuBUYDG5VSH2BMRjoPWBp/jiyl1G6ggta6KoDW+kQysfljdBPuy9CHiuYX6kdIZAjlnMrB5uiJ47t0ycylhMiX/PxCmDp1F19++Tc1axand29PLCyUJFhCCJFBmR34/i5QEYhKVL4CI+nqmZGLaa39gCeASGAjMAdjvNSERKdaksNdnLceGTldOedycOWKUfjkkzl5SyHyBK01P/xwkho1FvPVV38zYkQTDh16BQsLGdQuhBCZkdmEpTtG61UXYHe88qPR2yoZvaDW+hyQ6urL6Zl1Pqsz0/uGGOPsSziUgH/+gXr1wMkpjVpC5H+//nqRF17YQIMGpdi8eRCPPVbW3CEJIUS+ltmWrOhF/TiYwnG3TF7X7G4G3ATAxdYFbt6EOnXMHJEQOSc0NJJ//70DQI8e1fnhhz4cO/aaJFhCCJENMptk3Y/eJp6NfWD0NlsWaTaHU3dPAVBFuRpJlq2tmSMSImf89tsV6tZdQufOPxAUFI6FheK55+pRpEi+nj5PCCHyjMz+NI3pItwYU6CU2goswehG3J1MnXzB66EXAMX+95NRIMvpiALmzp1ABg1aT+fOPxAREcX//tcLBwdrc4clhBAFTmbHZM0GemEMfo95srALxqD3hxgD4/OlkIgQKharCL/uNQqGDzdrPEJkp5Mn/6Nt2+UEBUUwZUorpk9vKwmWEELkkEy1ZGmtLwNtgD0Yc1mp6O0eoK3W+kq2RZjLrvheoU35NrBvH5QuDSVlwVuR/4WERABQq5Y7zzxTm7//HsqcOR0lwRJCiByU6ekQtNangI5KKTuil7/RWodmW2Rm4hviS+mipcHFBapWNXc4QmRJQEAYM2bsY+PG85w8OZyiRa358ssMzbAihBAik7I855TWOgRjhvUCw9HGESIioHZtc4ciRKZordmw4Txjxmzj5s0AhgxpQERE4mnthBBC5KTMzvie1k9rrbXOt4ubRZoi4eFDsLExdyhCZNj9+8EMGbKRLVsu4elZnH37+tKuXUVzhyWEEIVOZhOhAj0FdNUiJYydwEDzBiJEJjg6WnPzZgDvvdeBSZNaYW1tae6QhBCiUMpskvVtoveWQCWgJRAMrM1KUOZW3KKosdO4sXkDESKdDh68wccfH2blyqextS3C8eOvYWkp810JIYQ5ZSrJ0lq/lFy5UqoLsA34OytBmZubhYOxYy1PXom87cGDYKZO3cXXX/9D6dJFuXTpAXXrlpQESwgh8oBs/Umstd4BBAJjsvO6ua0YdsaOJFkij9Ja8+23J/D0/JxvvvmHUaOacu7cSOrWlSlHhBAir8jswPe2yRTbAl2BokDprARlbo7nrxo7RfLt2H1RwIWERDJjxj7Kl3dm27bnaNKkjLlDEkIIkUhms4h9xM30npgGTmTyunmCy7lrxk6TJuYNRIh4QkIi+PLLvxg58jHs7a3Ys+dFypd3lrUGhRAij8pKU01KTxjeAEZk4bpmVcSiCDaR0W+qVTNrLELE2LHjMiNGbOXqVT8qVizGU095Urmyi7nDEkIIkYrMJlnJDXwPA7yBI1rryGSO5wsWygJWroKiRc0dihDcuhXA+PE7WLPmDJUqFWPLlkF06ybJvxBC5AcZTrKUUjaAX/Tbw1rre9kbknlVdqkMt84b6xYKYUYmk+bxx7/l6lU/3nqrNdOmtcXe3srcYQkhhEinDCdZWuswpdQ6jCcTC9xoWx0VPZn9U0+ZNxBRaJ05c5eaNd2xsFAsXtyNMmUcqVXL3dxhCSGEyKDMjpi9jDEmq8AthlbBys3Y8fQ0byCi0Hn0KIyxY7dRr95SvvnGmGquY8fKkmAJIUQ+ldkka2b09n2lVIGaTKpMaHR3TIkS5g1EFBpaa9atO0vNmp/z6adHGTKkPn371jR3WEIIIbIoswPfhwMPgdeA/kqpi0BIvONaa/1EVoMzh0qnfIyd5s3NG4goNAYP3sgPP5ykVi13Vq16mjZtKpg7JCGEENkgs0lWO4z5sBTgAjwW75gi5Tm08jzns9FzZJUpcMPNRB4SERFFkSIWKKXo1KkytWu7M2FCC1nMWQghCpB0J1lKqcEYLVTfY8yFlW8TqdS4hQBdu4KNjblDEQXUH394MWzYZqZNa8Nzz9Vj8OD65g5JCCFEDshIS9YKwAR8r7WumCPR5AEuoUDDhuYOQxRA9+8HM3nybyxffoIyZRxxdrY1d0hCCCFyUEa7C1Oa5b3AsIkEbOWXn8heP/54kjFjtuPvH8rYsc2YPbsDTk7SWiqEEAWZrICcSBETsjC0yHZ37wZRubILy5b1oFEjmehWCCEKg8zM+L4nHafl26cLi5iAevXMHYbI54KDI3j33d9p0aIcvXrVYMyYZowZ0wxLS1nMWQghCovMNNm0S+N4vn66sIgJqFHD3GGIfGzr1kuMHLmV69f9GT++Ob161ZDkSgghCqHMJFkFelyWTSRQqZK5wxD50M2bjxg7djvr15+jShUXtm9/ji5dqpo7LCGEEGaSmSSrQGcgzmUqgqXMVSQy7ttv/+WXXy4wfXpb3nyzNXZ2spizEEIUZplZINorJwLJK6zdZDkdkX7Hjt0kIsJEy5bleOONFjz9dE1q1Chu7rCEEELkATJQJDEnJ3NHIPKBhw9DGTVqK82afc3kyb8BYGNTRBIsIYQQsTLSknUDYzLSgi0szNwRiDxMa82aNWcYN24Hd+4E8tprjZgzp6O5wxJCCJEHpTvJKsizvCcgs72LVHzzzT+89tqv1KlTgvXrn6Fly3LmDkkIIUQeJbNuJuboaO4IRB4TFhbJnTuBVKhQjIED6xAaGsnrrzfGykoekBBCCJEyGZOVWKNG5o5A5CF7916jfv2l9Oq1ishIEw4O1owa9ZgkWEIIIdIkSVZiNrKenDCWwXnxxY08/vh3BAVFMGtWeywtC/QUcUIIIbKZdBcmVqaMuSMQZnbw4A169lzJo0dhTJjQnFmzOlC0qLW5wxJCCJHPSJKVmLRkFVpRUSYsLS2oU6cEbdpUYNas9jRoUMrcYQkhhMinpLswMTs7c0cgcllQUDhTpvxGhw7fYjJpnJ1t2bTpWUmwhBBCZIkkWYlZyVIohcnmzRepXfsLPvroEOXLOxMcHGHukIQQQhQQ0l0oCqU7dwIZMWILGzacp1o1V3bteoEnnqhs7rCEEEIUIJJkiULJ0lLx558+zJzZjilTWmNrK/8VhBBCZC/5zSIKjSNHfPjhh5N8+mlX3N0duHJlDHZ20j0shBAiZ8iYLFHg+fuHMmLEFlq0+IY1a85y48ZDAEmwhBBC5ChpyRIFltaalStPM2HCDv77L4jXX2/Mhx8+gYuLPEEqhBAi50mSJQqse/eCef31zVSu7MKGDQNo0UIWcxZCCJF7JMkSBUpYWCTr159j0KC6lCjhwO+/D6FevZIUKSI940IIIXKXJFmiwNi9+yojRmzl4sUHVKpUjBYtytGoUWlzhyWEEKKQkj/vRb7333+BPP/8z3Ts+D2hoZFs2vSsdA0KIYQwuzyTZCmlPJVSvymlgpRSd5RSHymlUl2VVylVOvq8E0qpAKWUj1LqJ6VUhdyKW5hXaGgkDRsuY9Wq00ya1JKzZ0fQq1cNc4clhBBC5I3uQqWUC7AHuAT0BcoCCwB7YFQqVRtHn/8/4E+gODAdOKqUqqO1vpeTcQvzuXHjIeXLO2NrW4SPPupEvXolqVevpLnDEkIIIWLliSQLGAY4AX201r4ASqkiwBdKqQ+01rdSqHcA8NRaR8YUKKUOATeAwcD8nA1b5LbAwHBmztzHokV/sn3783TsWJnnn69n7rCEEEKIJPJKd2FXYFdMghVtDUZ8nVOqpLX2j59gRZf5APeAMjkRqDCfTZvOU6vW58yff5hBg+pKy5UQQog8La+0ZHlidPnF0lr7K6VuRx9LN6VUdaAEcC77whPmpLWmf/+1rF9/jurV3di9ezCPP17J3GEJIYQQqcorSZYL4J9MuR/gmt6LKKUU8ClwC1iZxrlOGF2UMUql9z4id5hMGgsLhVKKOnVKUL9+SSZPboWNTV75ZyuEEEKkLK90F2aXmcATwGCtdVAa504AvOO9juVsaCIjDh3yplGjZRw65A3AzJntmT69nSRYQggh8o28kmT5Ac7JlLsAvsmUJ6GUeg14B3hda707HVUWAOXivZqmL1SRk3x9Q3j99V9p1ep/3LkTyMOHoeYOSQghhMiUvNIscJ5EY6+UUs5A6ehjqVJK9QGWAO9orf+X1vkAWutHwKN418hIvCIH/PTTKcaN2879+8GMGNGE999/gmLFbM0dlhBCCJEpeSXJ2ga8pZQqprX2jy7rD5iAnalVVEq1xxh/9ZXW+t0cjFHksEOHvClb1onNmwfx2GNlzR2OEEIIkSVKa23uGGImIz0DXAQ+IG4y0h+11qPinbcbqKC1rhr9viZwGGNM1esYSVmMe1rrKxmIwQPw9vb2xsPDI4ufSKRHaGgkH364nz59atKgQSmCgyOwtraUxZyFEELkGh8fH8qVKwdQLnoaqGyTJ1qytNZ+SqkngM+AjUAA8DUwLdGpliSMuRnGWC5n4GCic78FhuRAuCIb/PbbFUaM2Mrly75oDQ0alMLe3srcYRV6Wmvu379PaGgoUVFR5g5HCCEyzdLSEltbW4oXL262IUF5IskC0FqfAzqmcU77RO9XACtyLCiR7e7cCWTChB2sXHmaChWc2bx5IN27Vzd3WAIjwbp58yYBAQFYW1tjaWlp7pCEECLTwsPDCQwMJCwsjLJly5ol0cozSZYoHGbN2sfatWeZMqUV06e3xcEh1TXARS66f/8+AQEBlChRAjc3N3OHI4QQWfbgwQPu3r3L/fv3cXd3z/X7S5Ilctw//9zG0dGGqlVdmT27AyNHPkadOiXMHZZIJDQ0FGtra0mwhBAFhpubG/7+/oSGmmc6IBlhLHJMQEAY48dvp0mTr5gyZRcA7u4OkmDlUVFRUdJFKIQocCwtLc02xlRaskS201rz88/nGDt2OzdvBjBkSAM++ijV4XZCCCFEgSNJlsh2H310kKlTd1OzZnF+/LEv7dpVNHdIQgghRK6T7kKRLSIiovD1DQHg+efr8eGHT3DixDBJsIRZzJw5E6VU7MvNzY3WrVuzdevWZM/38/Nj0qRJVKlSBRsbG0qWLMnAgQM5d+5csucHBgYya9Ys6tSpg729PQ4ODjz22GMsWLDAbGM/csvChQspX748lpaW9O7dO9uvH//7ltJrxYoVWbrHiRMnmDlzJsHBwemu079/fyZNmpSl++ZHv/76K/Xr18fW1pbq1auzfPnydNU7d+4c3bp1w8HBARcXF1544QXu37+f4JytW7fSrl073N3dsbGxoXLlykyYMIGHDx/GnmMymahRowY//vhjtn6u3CItWSLLDhy4wbBhm6lc2YVNm56lbFknpk5tbe6wRCFnZ2fHnj17ALh16xYffPABPXv2ZP/+/bRs2TL2vDt37tC2bVv8/PyYNm0aDRs2xMfHh48//pimTZuydetW2rZtG3v+/fv36dChA97e3owbN47WrY1/64cPH2bOnDlYWloyduzY3P2wueTSpUu88cYbTJkyhZ49e1K8ePFsv8fhw4cTvG/RogWjR49m0KBBsWVVqlTJ0j1OnDjBrFmzGDVqFPb29mme//fff/Prr79y9erVLN03vzlw4AB9+vTh1VdfZdGiRezZs4dXXnkFR0dH+vXrl2K9R48e8fjjj+Ph4cFPP/1EcHAwb775Jt27d+fw4cNYWBjtO76+vjRr1owxY8bg5ubG6dOnmTlzJqdPn2bnTmOxFwsLC6ZOncqMGTMYMGAARYrks7RFay0vY9Z7D0B7e3trkT737wfpV17ZpGGmLl36Y71mzWltMpnMHZbIpGvXrulr166ZO4xsMWPGDO3g4JCgzMfHRyul9NChQxOU9+nTR9vY2Ohz584lKA8MDNQ1a9bUZcuW1SEhIbHl/fv31/b29vrUqVNJ7vvgwQN98ODBbPwk6RccHJzj9/j11181oK9cuZLla4WGhuqoqKg0zwP0vHnzsny/+JYvX64Bfe/evXSdP3jwYN2rV69suXdufJ+yS+fOnXXLli0TlA0cOFDXrFkz1XoffvihtrOz03fu3IktO3bsmAb0zz//nGrdL7/8UgP65s2bsWVBQUHawcFBb9iwIeMfQqf9s83b21sDGvDQ2ZxbSHehyJTffruCp+fnLF9+gtGjH+P8+VH0719bFtoWeVbZsmVxd3fnxo0bsWVeXl5s3LiRwYMH4+mZYI16HBwcmDZtGjdv3mTt2rWx569bt45hw4ZRp06dJPdwdXVN0EqWnHPnztG3b19cXV2xt7enfv36rFy5EoDr16+jlGLdunUJ6owbN46KFSvGvl+xYgVKKQ4fPkynTp1wcHBg0qRJtG/fnh49eiS55+LFi7Gzs4vthtFa8/HHH1O9evXYbpqFCxemGveQIUPo2bMnYLQkxe+28/Lyol+/fjg7O+Pg4ECXLl04depUgvoVK1Zk1KhRfPTRR1SoUAE7Ozt8fX1TvWdKVqxYQb169bC1taVs2bJMmzYtwdNj/v7+vPbaa5QtWxZbW1vKlSvHs88+G1v3pZdeAsDd3R2lVIKvbWJBQUGsX78+ScvN4cOH6dWrF2XKlMHBwYEGDRrw/fffJzhn3759KKXYsmUL/fr1w8nJif79+8fGOGLECEqXLo2NjQ2NGzeObb2JsWXLFjp16kSJEiVwcnKiWbNmbN++PVNfs4wKCwtj7969sfHGePbZZzl37hzXr19Pse4///xD/fr1KVmyZGxZkyZNcHNz49dff031vjFTyISHh8eW2dvb0717d7799ttMfBLzymftbsLctNYopahSxRVPz+IsXNiFJk3KmDssIdIUGBiIr68vlSpVii37448/0FrHJg+JxZT/8ccfvPDCC+zfvx+tNU8++WSmYrh06RItWrSgXLlyfPrpp5QqVYrTp08nSPwyYtCgQQwdOpS33noLe3t7Tpw4wejRo/H19cXV1TX2vJUrV9KtWzecnZ0BGDt2LF9//TXTpk2jWbNmHDp0iClTpmBnZ8ewYcOSvdf06dOpVasWU6ZM4eeff6Z06dJUqVKFgIAA2rdvj4WFBUuXLsXW1pb333+ftm3bcvLkyZg14QBYv3491apV45NPPsHS0hIHB4cMf+YFCxYwefJkxo8fz/z58zl37lxskjVnzhwAJkyYwLZt25gzZw4VK1bk9u3bbNu2DYDu3bvz9ttv895777F9+3acnZ2xsbFJ8X6HDx8mKCiIVq1aJSj38vKiVatWDBs2DFtbWw4ePMgrr7yCyWTixRdfTHDu0KFDef7559mwYQOWlpaEh4fTqVMn/vvvP95//33Kli3LDz/8QPfu3fn777+pW7cuANeuXaNnz55MnDgRCwsLtm3bRrdu3dizZw/t27dPMWatdbqmLLC0tEzxD+MrV64QERGR5I+PmjVrAnD+/PkUk9PQ0NBkv6Y2NjbJjnOMiooiIiKCs2fPMnv2bHr16pXk2i1btuSdd97BZDLFdjfmB5JkiXQJCYng/ff34+PziBUrelO5sgv7979k7rBEbhg+HBK1SuSqunVhyZJMVY2MjASMMVmTJ0/G0dExwXipmzdvAlC+fPlk6zs5OVGsWDF8fHzSdX5aZs6cibW1NQcPHsTJyQmAjh0zP73JsGHDmDJlSuz7qlWrMnr0aNavX89rr70GGMnA4cOHWbNmDWD88ly8eDFLly5l6NChsTEEBwcza9Yshg4dmuwvsSpVqlC9urEEVsOGDWN/CX766ad4eXlx5syZ2F/A7dq1o3z58ixatIj58+fHXiMiIoJt27ZlKrkCCAgIYMaMGUyePJkPPvgAgE6dOmFtbc2ECROYNGkSbm5uHD16lEGDBiVIdmJastzd3WPHdDVu3DjNcWXHjh2jaNGiVK5cOUF5zPXASGratm2Lj48Py5YtS5Jk9erVi7lz58a+X758OSdOnODff/+lVq1aAHTp0oVLly7x7rvvxn6vRo0aFVvHZDLRoUMHzpw5w5dffplqkvX777/ToUOHVD8XwN69e1O8jp+fHwDFihVLUO7i4gKQaitktWrVWL58OSEhIdjZ2QFw48YNbt++TdGiRZOcX6FChdj/W08++SQ//fRTknPq16/Po0ePOHfuHLVr107zs+UVkmSJNG3ffpmRI7dy9aof/fvXIiIiCisrmbRS5G1BQUFYWcUtOm5pacmmTZuoUaNGlq+d2W7x3bt3x3YbZYfu3bsneO/m5kanTp1YtWpVbJK1evVqihYtGtuNuGuXMTHw008/HZuEgpFozZ07F29vbypUqJDuGPbv30+dOnViEywwuk07derEgQMHEpzbvn37TCdYAIcOHSIwMJD+/fsniT0kJITTp0/Trl07GjVqxIoVKyhdujRPPvlksl276XX79u1kEzE/Pz9mzJjBpk2buHnzZmzLUXIrJiT+Pu3cuZO6detSvXr1BJ+jU6dO/PDDD7HvfXx8mDZtGrt27eL27dsx44dp3LhxqjE3btyYY8eOpfnZsuP/QnJee+01PvnkE15//XXmzJlDcHBwbPKe3P+drVu3EhQUxJkzZ3jvvffo2bMnv/32W4LJkWO+B7dv35YkSxQMt28HMG7cDtasOUOlSsXYunUQXbtWM3dYIrdlshXJ3Ozs7Pjjjz8wmUxcunSJqVOnMnjwYE6fPk3p0qUBY5wWGH9l169fP8k1AgIC8Pf3x8PDI8n5Ma06GfHgwQPKlMm+7vX4Y15iDBw4kBdffJE7d+5QqlQpVq5cSZ8+fbC1tQWMpyO11im24GQ0yfLz80s2jpIlS3L69Ok0482ImCkAGjVqlOxxb29vAD777DNcXV2ZP38+kyZNoly5crz55psMHz48w/dMqetryJAhHDp0iHfeeYfatWvj5OTEkiVLWL16dZJzE3/u+/fv888//yT4IyBGTGJhMpno1asXDx8+ZPbs2VStWhUHBwfeeeedNLuXixYtSoMGDdL8bKmt8BDTYhV/OgWIa+GK3x2dWI0aNfjmm28YO3Zs7Di1vn370q1bNwICApKcX69ePcB4krRp06Y0aNCADRs2JBgHF/M9CAkJSfNz5SWSZIkU+fqGsHnzRd56qzXTprXF3j7pDwQh8ioLCwuaNGkCwGOPPUaNGjVo1qwZs2fPZkl04ti2bdvYgcnJjcvavHlz7Hnxz9+xY0emuvnc3Ny4detWisdjEqH4g34h7hdbYsm1Cjz11FPY2NiwZs0aunTpwokTJ/jwww9jj7u6uqKU4sCBA1hbJ12gPaOtG66urly4cCFJ+X///ZfkF3FWH4yJud7PP/+cYKxXjJjxds7OzixatIhFixZx6tQpPvnkE0aMGEGdOnVo06ZNhu/p7++foCw0NJTNmzezYMECRo8eHVtuMpmSvUbiz+3q6kq9evX45ptvUrzv5cuX+eeff9i4cSNPPfVUbHl6kozs6C6sUqUKVlZWnD9/ni5dusSWnz9/HiDJWK3EBg8ezLPPPsvFixdxcXGhbNmy1K5dm169eqVar169elhZWXH58uUE5THfg/y2tqokWSKBv/66xZ4915g0qRW1a5fA23s8rq525g5LiCxr0qQJAwcOZPny5cyYMYNSpUpRoUIFevfuzbfffsuECRMStE4FBwfz/vvv4+HhEfuEVfny5enXrx9LlizhpZdeih1PE8Pf359z587RokWLZGPo2LEj69atY+7cuTg6OiY5XqJECaysrBIMDg4PD+f3339P9+d0dHSkR48erFy5El9fX9zd3RMkhE888QRgtKqlNOA/I1q3bs26deu4cOFCbILm5+fHrl27Ysd8ZZcWLVpgb2+Pj48Pffr0SVedunXrsnDhQr755hvOnTtHmzZtYpPL9EwcW6NGDe7du0dQUFBsV2dYWBgmkylBkhoQEMAvv/ySrpg6duzI1q1bKVOmTIotmzHJVPx7eHl5cfDgwTRbUbOju9DGxoYOHTqwbt26BOMYV69eTc2aNVN9IjOGtbV1bFftnj17uHjxIkOGDEm1zpEjR4iIiEgyBi7macbMtCCbkyRZAoBHj8J4++09fP75Mdzc7Hj11Ua4uNhJgiUKlOnTp7Nq1SoWLVoU+yTaF198Qdu2bWnTpg1vvfUWDRs25ObNm3z88cdcv36drVu3xrYwxZzfvn17WrVqxfjx42OfOjty5AifffYZU6dOTTHJmjFjBps3b6Z169ZMnjyZ0qVLc/bsWYKDg5k8eTIWFhb07duXxYsXU7VqVYoXL87ixYtjn+pNr4EDB9K3b1+8vLzo379/ggkcq1evzsiRI3nhhReYNGkSzZo1IyIigosXL7J37142btyYoa/pSy+9xMKFC+nevTvvvfde7NOFRYoUYdy4cRm6VlqKFSvG7NmzmTx5Mj4+PrRv3x5LS0uuXr3Kpk2bWL9+Pfb29rRq1Yo+ffpQp04dLC0t+e6777C2to5txYoZP/b555/Tu3dv7O3tY5/oS6xVq1aYTCb++eef2IlnnZ2dadq0KXPmzMHd3Z0iRYowZ84cnJ2duXv3bpqfY/DgwSxbtoz27dszceJEqlevjr+/P//88w/h4eF8+OGHeHp64uHhwdSpU4mKiiIwMJAZM2bEdlmnxtHRMbYVNyumT59O+/btGTFiBM888wx79+7lp59+StIlWqRIEV588cXYlrmgoCBmzpxJ27ZtsbW15c8//+TDDz9k5syZCRK7vn370qRJE+rVq4ednR3//vsv8+bNo169eklWEzh+/Dg1a9bMkQlwc1R2T7yVX18U0slITSaTXrPmtC5d+mMNM/Urr2zS9+8HmTssYQYFfTLSGM8995x2cnLS/v7+sWW+vr564sSJulKlStrKykq7u7vrAQMG6LNnzyZ7jUePHumZM2fqWrVqaVtbW21vb6+bNm2qFy5cmGDi0uScOXNG9+rVSzs5OWl7e3vdoEEDvWrVqtjjd+/e1b1799ZOTk66bNmyetGiRXrs2LG6QoUKseekNZlmaGiodnZ21oDev39/kuMmk0l/9tlnuk6dOtra2lq7urrqFi1a6AULFqQa+4YNGzSQ5N/J9evXdd++fbWjo6O2t7fXnTp10idPnkxwToUKFfTIkSNTvX5ySGYy0pUrV+qmTZtqOzs77eTkpBs2bKinT5+uIyIitNZaT5o0SdetW1cXLVpUOzk56VatWukdO3YkuMbMmTO1h4eHtrCwSPC1TU7dunX1W2+9laDs0qVL+vHHH9f29va6XLlyet68eUn+3e3du1cD+tixY0mu+fDhQz1+/Hhdvnx5bWVlpUuXLq27deumN2/eHHvO0aNHddOmTbWtra2uVq2a/vbbb/WLL76oa9euna6vXXbYtGmTrlu3rra2ttZVq1bV33zzTZJzAP3iiy/Gvg8ODtZdunTRbm5u2sbGRtevX18vX748Sb0PP/xQN2jQQDs6OmoHBwddu3ZtPX36dP3w4cMk59atW1dPnz49U5/BnJORKh39tEJhp5TyALy9vb1jB7kWBhcu3Kdmzc+pVcudpUt70Lp15h5NF/lfTHN8eroBhChMPvvsMz755BMuXbokEy6bwZkzZ6hfvz6XLl1KMM9deqX1s83HxydmjF85rbVPpgNNRv6Z0Utkm/DwKHbtMtbgqlGjODt2PM/ff78uCZYQQiTj1VdfJSQkJM3ZykXOmD9/PoMHD85UgmVukmQVMn/84UWDBkvp0uUHLl16AECnTlWwtpZ5r4QQIjl2dnasWLEiyVOfIueZTCaqVq3K7NmzzR1KpsjA90Li/v1gJk36jRUrTlC2rCNr1/anatWU5zkRQggRp1OnTuYOoVCysLDgrbfeMncYmSZJViHw4EEwnp6L8fMLZdy4Zsye3QFHx5TX6hJCCCFE1kmSVYD5+obg6mqHm5s9Eye2pHPnKjRqVNrcYQkhhBCFgozJKoCCgyN4881dlC+/kAsXjGUopk5tLQmWEEIIkYukJauA2br1EiNHbuX6dX+efbYOzs62aVcSQgghRLaTJKuACA+PYtCg9axff44qVVzYseN5OneuYu6whBBCiEJLkqwCwtraEhubIkyf3pY332yNnZ0s5iyEEEKYk4zJyseOHbtJ27bLuX7dH4AffujD7NkdJMESQggh8gBJsvKhhw9DGTVqK82afc358/e5etUPQJZ7ECLazJkzUUrFvtzc3GjdujVbt25N9nw/Pz8mTZpElSpVsLGxoWTJkgwcOJBz584le35gYCCzZs2iTp062Nvb4+DgwGOPPcaCBQsIDQ3NyY9mdgsXLqR8+fJYWlomWcQ3O8T/vqX0WrFiRaav3759e3r06JFt8Z46dQpHR0fu3buXbdfMDx4+fMgrr7yCq6srjo6O9OvXj9u3b6dZT2vNRx99RKVKlbCxsaFOnTpJFpxObNGiRSilknzf3n///Tw/f5l0F+YjWmvWrDnDuHE7uHMnkNdea8ScOR1xdbUzd2hC5Dl2dnbs2bMHgFu3bvHBBx/Qs2dP9u/fT8uWLWPPu3PnDm3btsXPz49p06bRsGFDfHx8+Pjjj2natClbt26lbdu2seffv3+fDh064O3tzbhx42jdujUAhw8fZs6cOVhaWjJ27Njc/bC55NKlS7zxxhtMmTKFnj17Urx48Wy/x+HDhxO8b9GiBaNHj2bQoEGxZVWqZH686RdffIGlZfatcPH2228zZMgQ3N3ds+2a+cGAAQM4c+YMS5cuxdbWlmnTptG1a1eOHz9OkSIppxbz5s1j2rRpvP3227Ro0YJffvmFgQMHYm9vT8+ePZOcf+fOHWbNmkWJEiWSHBs5ciQfffQRe/fupUOHDtn6+bJNdq84nV9fgAegvb29U1yp29xMJpN+8skfdJ06X+iDB2+YOxxRwKS1Un1+MmPGDO3g4JCgzMfHRyul9NChQxOU9+nTR9vY2Ohz584lKA8MDNQ1a9bUZcuW1SEhIbHl/fv31/b29vrUqVNJ7vvgwQN98ODBbPwk6RccHJzj9/j11181oK9cuZLla4WGhuqoqKg0zwP0vHnzUj0nNz57cq5cuaKVUvrvv//O8rUiIyN1eHh4NkSV8w4dOqQBvWPHjtiy8+fPa6WUXr16dYr1wsLCtKOjo54wYUKC8h49euh69eolW+eFF17QgwcP1u3atdPdu3dPcvyll17STz31VKrxpvWzzdvbWwMa8NDZnFtId2EeFxYWyYcf7uf27QCUUnz/fR/+/nsoLVuWM3doQuQrZcuWxd3dnRs3bsSWeXl5sXHjRgYPHoynp2eC8x0cHJg2bRo3b95k7dq1seevW7eOYcOGUadOnST3cHV1TdBKlpxz587Rt29fXF1dsbe3p379+qxcuRKA69evo5Ri3bp1CeqMGzeOihUrxr5fsWIFSikOHz5Mp06dcHBwYNKkSSl2hS1evBg7OzsePnwIGH9cf/zxx1SvXh0bGxsqV67MwoULU417yJAhsS0NVapUSdBt5+XlRb9+/XB2dsbBwYEuXbpw6tSpBPUrVqzIqFGj+Oijj6hQoQJ2dnb4+vqmes/kzJw5k6JFi3L06FFatGiBra0tn3/+OQBTp06lbt26FC1alLJlyzJw4MAkXViJv0Yx1zt16hStW7fG3t6eOnXqsGPHjjRj+e6776hcuTINGzZMUJ6ROL799ltq1KiBjY0N//77LwBbtmyhWbNm2NnZ4e7uzvDhwwkKCoqtGxQUxKhRo6hRowb29vZUrFiRYcOGxX5/c9q2bdsoVqxYgq66GjVq0KBBgxS75AGuXLlCQEAAnTt3TlDepUsXTp48meD/JsCBAwfYuHEjc+bMSfGa/fv3Z8uWLdy/fz+TnyZnSXdhHrZ37zWGD9/ChQsPsLa25I03WlK8uL25wxIiXwoMDMTX15dKlSrFlv3xxx9orZPtpgBiy//44w9eeOEF9u/fj9aaJ598MlMxXLp0iRYtWlCuXDk+/fRTSpUqxenTp5P8ckmvQYMGMXToUN566y3s7e05ceIEo0ePxtfXF1fXuLVJV65cSbdu3XB2dgZg7NixfP3110ybNo1mzZpx6NAhpkyZgp2dHcOGDUv2XtOnT6dWrVpMmTKFn3/+mdKlS1OlShUCAgJo3749FhYWsV1H77//Pm3btuXkyZOUKxf3B+H69eupVq0an3zyCZaWljg4OGTqc4eHhzNo0CDGjx/PBx98gJubGwB3797lrbfeokyZMty7d4/58+fTrl07zp49m2oXVkREBM899xxjxoxh+vTpzJ07l6effhovL6/Yaydn165dySbV6Y3j+PHjXL9+ndmzZ+Pi4kK5cuVYt24dAwYM4KWXXmLWrFncvn2bqVOn4ufnx6pVqwAIDg4mKiqK999/H3d3d7y9vXn//ffp3bs3e/fuTfVrFxUVFdN7kyKlVKpdqufPn6dGjRpJxgHXrFmT8+fPp1gvZryijU3CZd1i3p87d47y5cvHxjlq1CimTZtG6dIpT6TdokULoqKi2LdvH/369Uv1c5mDJFl50N27QUycuJPvvz+Jh4cTGzYMoHdvz7QrCpEDhm8ezqm7p9I+MYfULVGXJT2WZKpuZGQkYIzJmjx5Mo6OjgnGS928eRMg9gd7Yk5OThQrVgwfH590nZ+WmTNnYm1tzcGDB3FycgKgY8eOmboWwLBhw5gyZUrs+6pVqzJ69GjWr1/Pa6+9BhitTIcPH2bNmjWA0ZqwePFili5dytChQ2NjCA4OZtasWQwdOhQLi6SdHFWqVKF69eoANGzYMLZl7dNPP8XLy4szZ85Qs2ZNANq1a0f58uVZtGgR8+fPj71GREQE27Zty3RyFf8677//PgMGDEhQ/r///S92PyoqihYtWuDh4cGePXuStJ7EFx4ezpw5c+jWrRtgtMpUqlSJbdu28fzzzydbR2vN8ePHkx38n944fH19OXbsWGwiqrVm4sSJDBgwgK+//jr2vNKlS9OtWzemT59O7dq1cXd3Z8mSuP8TkZGRVKpUidatW3Px4sXY71NynnjiCX7//fcUj4Px/du3b1+Kx/38/ChWrFiSchcXl1RbJ2NaQI8ePUr79u1jy//880+ABHW/+OILgoKCGD9+fKqxFitWjPLly3PkyBFJskT6vPbar2zZcpEJE5oza1YHiha1NndIQuQ7QUFBWFnFTWdiaWnJpk2bqFGjRpavndkneXfv3k2/fv1iE6ys6t69e4L3bm5udOrUiVWrVsUmWatXr6Zo0aKxXWS7du0C4Omnn45NQsFItObOnYu3tzcVKlRIdwz79++nTp06sQkWGN2mnTp14sCBAwnObd++fZYTrBiJPzsY3VjvvvsuZ86c4dGjR7HlFy9eTDXJsrCwSJDsVqxYETs7u9jkOjl+fn6EhYUlO+A9vXHUq1cvQUvfxYsX8fLyYtGiRQm+N+3atcPCwoLjx49Tu3ZtAL7//nsWLFjApUuXEnQlppVkLVu2jICAgBSPAzg6OqZ6PLOcnJx4/vnnmTt3LnXr1qV58+b8+uuvsd3lMf+v7t69yzvvvMN3332HtXXav/+KFy+ericbzUGSrDzi1Kn/KFvWCVdXOz76qCOzZrWnQYNS5g5LiEy3IpmbnZ0df/zxByaTiUuXLjF16lQGDx7M6dOnY7sfypYtC8CNGzeoX79+kmsEBATg7++Ph4dHkvNT+0WWkgcPHlCmTJnMfqQkSpYsmaRs4MCBvPjii9y5c4dSpUqxcuVK+vTpg62tscTW/fv30Vqn+GRgRpMsPz+/ZOMoWbIkp0+fTjPezLC3t6do0aIJyo4dO0avXr146qmnmDp1KiVKlEApRfPmzdOcVsPOzi7JL3Nra+tU66XU9ZWROBJ/PWLGFfXp0yfZe3p7ewOwYcMGBg8ezNChQ3n//fdxc3Pj9u3b9OnTJ83PWrVq1XR1F6bGxcUlNpb4/Pz8EnRTJ2fhwoXcuXMnttWwePHivPvuu0ycODH2/+U777xDvXr1aNOmDf7+/oDRWhcZGYm/vz9FixZN0O1qY2NDSEhIqvc1F0myzCwoKJzZs39nwYI/GTasMZ991o0aNbL/sWghChsLCwuaNGkCwGOPPUaNGjVo1qwZs2fPju1qadu2LUoptmzZkuy4rM2bN8eeF//8HTt2ZKqbz83NjVu3bqV4PCYRCg8PT1Du5+eX7PnJ/TJ86qmnsLGxYc2aNXTp0oUTJ07w4Ycfxh53dXVFKcWBAweSbSXIaEufq6srFy5cSFL+33//JfmFm11z+SV3nQ0bNuDs7MyaNWtiuzu9vLyy5X7JiflsMUlAZuJI/Dlirrl48WKaNWuW5PyYBH3t2rU0aNCAZcuWxR5LqwswRnZ0F3p6erJr1y601gk+w/nz56lbt26q13Zzc2Pnzp3cunULX19fqlWrxi+//IK1tTWNGjWKvc4ff/yBi4tLkvouLi5s27YtwbhIf3//2Ba+vEaSLDP69dcLjBq1jRs3HvLcc3V5++22aVcSQmRKkyZNGDhwIMuXL2fGjBmUKlWKChUq0Lt3b7799lsmTJiQoHUqODiY999/Hw8PD/r37w8YY7H69evHkiVLeOmll6hVq1aCe/j7+3Pu3DlatGiRbAwdO3Zk3bp1zJ07N9kumRIlSmBlZZVgEtTw8PB0/wIFo6unR48erFy5El9fX9zd3RMkhE888QRgtKqlNOA/I1q3bs26deu4cOFCbILm5+fHrl27Ysd85YaQkBCsrKwS/NL/8ccfc+x+tra2lC9fnmvXrmVbHJ6ennh4eHD16lVGjhyZ4nkhISFJEuT03iM7ugu7du3Ku+++y+7du2P/bV28eJF//vknwRjB1JQpU4YyZcoQFRXFkiVLGDBgQOx9Fy1alCR5HTduHHZ2dnz44YfUq1cvttxkMnHjxg1efvnldN03t0mSZSbTpu3mgw8OUK2aK7t2vcATT1Q2d0hCFHjTp09n1apVLFq0KPax8C+++IK2bdvSpk0b3nrrLRo2bMjNmzf5+OOPuX79Olu3bo1tYYo5v3379rRq1Yrx48fTqlUrAI4cOcJnn33G1KlTU0yyZsyYwebNm2ndujWTJ0+mdOnSnD17luDgYCZPnoyFhQV9+/Zl8eLFVK1aleLFi7N48eIkLQZpGThwIH379sXLy4v+/fsn6FqpXr06I0eO5IUXXmDSpEk0a9aMiIgILl68yN69e9m4cWOGvqYvvfQSCxcupHv37rz33nuxTxcWKVKEcePGZehaWdGpUycWLVrE6NGj6dOnD4cPH+b777/P0Xu2atWKv/76K9viUEqxYMECBg0aRFBQEN27d8fBwQEvLy+2bNnCBx98QPXq1enUqRMjR47k3XffpUWLFmzdupXdu3en6x7ZMSaxRYsWdOnShZdffpn58+fHTkZar149+vbtG3ve7NmzmT17NleuXIntgv7xxx8JCQmhatWq3Lp1i2XLlnHt2rUESWKDBg2S3LNYsWIULVo0wYB5gAsXLhAYGEibNm2y/LlyRHZPvJVfX+TCZKQREVE6ONiYbO748Zt65sy9OiQkIsfuJ0RGFPTJSGM899xz2snJSfv7+8eW+fr66okTJ+pKlSppKysr7e7urgcMGKDPnj2b7DUePXqkZ86cqWvVqqVtbW21vb29btq0qV64cGGCiUuTc+bMGd2rVy/t5OSk7e3tdYMGDfSqVatij9+9e1f37t1bOzk56bJly+pFixbpsWPH6goVKsSes3z5cg3oe/fuJXuP0NBQ7ezsrAG9f//+JMdNJpP+7LPPdJ06dbS1tbV2dXXVLVq00AsWLEg19g0bNmggyb+T69ev6759+2pHR0dtb2+vO3XqpE+ePJngnAoVKuiRI0emev3kkGgy0tS+t3PnztUeHh6xMVy8eDFJ/cSTWqZ0PWdnZz1jxoxUY1u/fr22tbXVjx49ynIc8e3cuVO3a9dOOzg4aAcHB127dm39xhtvxP6bjYyM1G+88YZ2d3fXjo6Oul+/fvrPP//UgF67dm2qMWcXf39//fLLL+tixYrpokWL6r59++qbN28mOGfGjBlJ/r18//332tPTU9vY2Gg3Nzf9wgsvpOv3bkpfr/nz5+sKFSpok8mUYl1zTkaqdBoD4AoLpZQH4O3t7R07yDU7HTniw+uvb6ZDh4osXJi5OXaEyEnXr18HSDDppRAiZREREZQvX565c+cyePBgc4dTKDVt2pSePXvyzjvvpHhOWj/bfHx8Yp7yLKe1TvmR0kyQGd9zmJ9fCMOHb6ZFi2+4fTuQpk3LmjskIYQQ2cDKyoqpU6fyySefmDuUQumPP/7gypUrjBkzxtyhpEjGZOWgLVsu8vLLv3DvXhCvv96YDz54AhcXWcxZCCEKimHDhvHo0SPu37+fIwtmi5Q9evSI7777LtmJUfMKSbJykKOjDWXKOLJp07M0b579XZBCCCHMy8bGhunTp5s7jEIpuXU68xpJsrJRaGgkc+cewNLSgrffbkvbthX466+hWFhkz9wwQgghhMg/JMnKJrt2XWXEiC1cuuTLM8/Ujn3kWhIsIYQQonCSge9Z9N9/gTz//M906vQ9YWFRbNr0LKtX98u2mY2FyC2WlpZERUWZOwwhhMhWUVFRWFpamuXekmRl0T//3GHVqtNMmtSSs2dH0KtX1id6E8IcbG1tCQ8P58GDB+YORQghssWDBw8IDw9PMKFwbpLuwkz49987nDlzj0GD6vLkk1W5cmUMFSoUM3dYQmRJ8eLFCQsL4+7du/j7+5vtLz8hhMgOUVFRhIeH4+joaLYnP6UlKwMCA8OZOHEnjRt/yeTJvxEWFgkgCZYoEJRSlC1bluLFiye7cLAQQuQn1tbWFC9enLJly5ptCE+eaclSSnkCnwEtgQDgO+BtrXV4GvUUMAUYAbgDJ4DxWus/szO+jRvPM3r0Nnx8HjF4cH3mzeuEjU2e+fIJkS2UUri7u5s7DCGEKBDyRJaglHIB9gCXgL5AWWABYA+MSqP6FGAWMBU4CYwEdiqlGmitr2ZHfPv3e9Gnz2pq1HBjz57BdOhQKTsuK4QQQogCLE8kWcAwwAnoo7X2BVBKFQG+UEp9oLW+lVwlpZQt8CYwX2u9MLpsP3ARmIjRupUpERFRnD59l4YNS9O6dXm+/74P/fvXktYrIYQQQqRLXhmT1RXYFZNgRVuDEV/nVOq1xEjO1sQURHcv/gx0y2wwhw5507jxl7Rv/y337wejlOL55+tJgiWEEEKIdMsrSZYncD5+gdbaH7gdfSy1eiSuC5wDyiulMrxQ4OTJv9Gq1f+4dy+YL7/sgZubrDUohBBCiIzLK00zLoB/MuV+gGsa9cK01qHJ1FPRx0OSq6iUcsJoBYtRFmDlykO8+GIbJk1qibOzLTdv3kzfJxBCCCFEvnP79u2Y3WyftyavJFnmMAGYkbT4a7799mu+/TbX4xFCCCGE+VQEvLLzgnklyfIDnJMpdwF8kymPX89GKWWbqDXLBdDRx1OyAPg63vvywEGgOSDNV3lHKeAY0BS4Y+ZYRELyvcmb5PuSN8n3Je8qC/wJeGf3hfNKknWeRGOvlFLOQGmSjrdKXA+gBvBvvHJP4IbWOtmuQgCt9SPgUbz7xeze1Fr7pDtykaPifV/uyPclb5HvTd4k35e8Sb4veVe8702q83JmRl4Z+L4N6KiUKhavrD9gAnamUu8QRqLUP6ZAKWWFMdfW1uwPUwghhBAiffJKkrUUY5b3jUqpzkqpl4B5wNL4c2QppXYrpS7HvI/uIvwQmKiUGquUehxYCbgBH+fqJxBCCCGEiCdPdBdqrf2UUk9gLKuzESPh+hqYluhUS5LGPBfjScKJxC2r0yUTs70/wpg5/lFaJ4pcJd+XvEu+N3mTfF/yJvm+5F059r1RWuvsvqYQQgghRKGXV7oLhRBCCCEKFEmyhBBCCCFygCRZQgghhBA5QJIsIYQQQogcUCiSLKWUp1LqN6VUkFLqjlLqI6WUdTrqKaXUVKXUDaVUiFLqsFKqeW7EXBhk5vuilCodfd4JpVSAUspHKfWTUqpCbsVdGGT2/0yia4xTSmml1OacirOwycr3RSlVVin1rVLqXvTPs3NKqedyOubCIAu/Y9yUUkujf8cEKaVOK6WG5UbMhYFSqmr01/eEUipSKXU6nfWy7Xd/npjCIScppVyAPcAljElKy2IsqWMPjEqj+hSMxzqnAieBkcBOpVSDTEwRIeLJwvelcfT5/8NYBqE4MB04qpSqo7W+l5NxFwZZ/D8Tc41SGGuD3s2hMAudrHxflFKlgcPABWAoxqPqtQGbHAy5UMji/5e1GCuUvAXcALr9v737D7ajrO84/v5AIIlICFEC0vDTAAlaikhbEKsJBiplbFEbHEFLQLFqbQlCa0EqAUTKOAhjFTuDBWIqSgexVioFwSTSUlSmKSNgWmxNDJEEtEkQ8pPw7R/Pc7jL5tx7z91zNkfOfl4zO3fPc/bHd/eZc/d7nufZPcAXJG2PiBtqC7o5XgOcCnyP1KjUacNS7679ETHQE3AR8AwwpVD2AeA5YP8R1psAbAA+VSjbHVgBXN/v43qpT13Uy2RgXKlsGunXAS7o93ENwlS1bkrb+BKwEFgC3NHvYxqEqZt6ARaRfpt1134fx6BNXfwv24/0G7vzSuVLgXv7fVyDMAG7FOZvBh7uYJ2eXvub0F14CnBPRBR/aPofSBntySOs9wZgUl4WgIjYCtxO+rZh3alULxGxPiKeK5U9DjwF7F9HoA1U9TMDgKQ3AqeRvgVa71SqF0mTgNNJF4jt9YbYSFU/L7vlvxtK5RtID9i2LkXE8xVW6+m1vwlJ1gxKPzIdEeuBJyj9KHWb9SivC/wIOFDSxF4F2FBV62UHkg4HppLqxrpXuW4k7Qp8DrgyIp6oK8CGqlovx5C+iW+TtFTStjxu6Or8W6/WnUr1EhGrSL/Ne7GkIyXtKel0UmL2+frCtVH09NrfhCRrb2B9m/J1wJRR1tsS6fcRy+spv2/VVa2XF1H6+fTPAj8j/W6lda+buvkwsAdwbY9jsur1sl/++0XgQdJF/FpgPnB578JrrG4+L+8A1gKPkMbJ3QKcHxFf62WANiY9vfYP/MB3G3gLgLcAb42IZ/scS6NJmkq6aP9Rbl63Xw2tL9P3RMQFeX6xpD2BCyVdHhGb+hRbY+UviDcBhwFnkFq+TgKuk7QuIr7az/isN5qQZK0D9mpTvjfwf23Ki+uNlzShlNHuTRqsuK53ITZS1Xp5gaRzgU8A74uIe3sYW9NVrZvLSXfi3Cdpci4bB4zLr58pj6ezMenmfxmkO+CK7gU+DkwHfth1dM1VtV5OBeYCR0VE6/wvyV9WrgGcZPVHT6/9TeguXE6pX1zSXsCr2LHPtbwewBGl8hnAT/3Nr2tV66W17NuBLwCfiIgba4mwuarWzQzgTaR/Qq3pBOB38/ycOoJtkKr18ugo253QZVxNV7VejgS2A+VnNy0D9pf0sl4GaR3r6bW/CUnWncCcwjdrSN8enicNOhzO/aQ+8rmtgjxI9B3At3ofZuNUrRckzSKNv7ohIq6oKb4mq1o384HZpekh0vPMZgPfryHWJqlULxGxktRSVU5yTwI2MXoSZiOr+nlZCewKHFUqfz3wZERs7GWQ1rHeXvv7/RyLnfCcjL1Jg6KXkAZ8nk36Vv250nL3Aj8ulf0lsBk4DzgRuC2f/EP7fVwv9alqvQAzSYNMf0i61fa4wvTqfh/XIEzdfGbabGsJfk5W3+sFeBvpon8dKbm6GNgKfLLfx/VSn7r4X7YnKdF6DHgPaWzp1aTWrUv6fVyDMJEeCPuHeVpMeuBr6/U+7eoll/Xs2t/3k7CTTvRM4B5gI+lOjk8Du5eWWQKsKJWJ9KC5VfmEPwAc3+/jGZSpSr0A80j94u2mm/t9TIMyVf3MtNmOk6xfkXoB3kXqmtpCerDiRYD6fUyDMHVxjZkO3AqsBp7N9XMefmhsr+rl4BGuF7NGqJeeXfuVN2hmZmZmPdSEMVlmZmZmO52TLDMzM7MaOMkyMzMzq4GTLDMzM7MaOMkyMzMzq4GTLDMzM7MaOMkyMzMzq4GTLDMzM7MaOMkyGwCSbpYUI0wHj3F7K/J6S+qJeNj9tot9g6TFkn6vxv2+cP4KZZMlLcjTrNLyBxfiW1BXXMPEOqvNOdqa6+xvJU3tYtvz8/HO62HIZo01rt8BmJmNYhIwC5gl6cyIuGUn7XcycGnh9ZKdtN8qdgMOAv4YOF7SMRGxvcJ25uftLAVu7ll0Zg3lliyzwTM7IlSaVvQ7qDFaGRECJgAXFsqvqmNnETGvda46XH5F4dwuqCOmDi3MMR8O/CSXHUX68XQz6zMnWWYNkbu/vizpR5LWS9omaY2k2yS9poP1Xy/pjrzOlvx3saT3l5Z7i6S7JK3Ly/2XpEsk7TbWmCNiC/AZYEMuOlDSPnk/u0j6iKRlkjZKelbS98tdXZKmS/qqpNU5nqck3S/posIyL+ouzF2APyls5tJC19ysdt2Fkh7Jr/+jtP+zCsuekssk6YOSHsxxb5T0gKTTx3qO8nl6DPh6oeiAwv7fmuvjcUmbJG2WtFzSFZIm5mVm5WM/KK/25nbdoZLmSrpP0tN5Ow9J+pCkjpJTs6Zxd6FZc0wGziiV7Qu8E5gtaWZEPNluRUl7AHcBryituy/wLPDFvNw84EbSr9i3HA5cARwn6W1R7Vfp213EFwLvKZX9JnCTpCMj4i9y2TeBGYVlXpmnSfS2ZWxR3t7rJE2PiB/n8lbitAa4O8/fCMwrrf/bwK2SDoqIT1fYf/EcFevxOODk0rJHAJcAh7DjOWy/celSYEGp+CjgeuC1wJ+MIVazRnBLltngWVwaFP2fuXwdKaE6gNQN93Lg3PzeFHZMwIpmMJRgvRPYHZgG/AEpiUHSy4HrSBf7O/N+XgZcnNc7FRjT4HVJ44ELSAkRwKqIeErSmxhKDv49x3IYsDyXXSjpCEmvYCjB+igwHtiPlHR8abj95i7AQwpFlxW6B5cMs9qXgefz/Nwc/2RgTi77SkRsl/RGhhKsK4G9SElfqyXq8hx3xyQdBrw9v1wL/Gvh7X8mdR/uQxq79SrgW/m9MyRNiYgludtxZS5fWuwOVbpx4q/yezcBU0l18vlc9mFJrx1LzGZN4JYss4aIiA35YnkJqXVpj9IiR4yw+mpgO7ArqcViOvAI8G8R8Yu8zBtICQPAKcCqNts5kXTRH81BKtzpV9C60J9SKLsyIlYDSLoGuIGU6J1MamV5mpQQnEE65keAByLi2x3E0bGIWKV0N+aJpNarq4DTSAkppJYueHGi+fE8FU0gnctvdrDbsySdVXi9HHhvRGwulK0GPklK9vYjJVotIiWn3xtlPyeT6h7g7DyVzQYe7iBms8ZwS5bZ4CkPfD8aQNL5wDXA69gxwQKYONwGI2IN8GeksVEnAlcDdwBrcjcSpJaS0Uzp+CiG/BL4LnBaRCzMZa8svL9qmPl98h12Z5Nad44ldVveDqyWdEOFWEbTSqSOljSd3KIFPBwRy1pxdbCdKucJUsvhC1+eJe1CqqezSS2L7cbFDVvvBXXGbDawnGSZNUfrgr+ZNP5nHPDrna4cEdeTLra/BZxJapEaRxoUPg14qrD4RW3ucBRwToe7W1lYb1JEvDkivlF4/+eF+WmF+QPKy0TE7cD+wNGkFqZFpBac90s6YaRD7jDWoq8BG/P8B4CT8vyiwjLF83R8m3O0SyGZHM1CUuI0F3gOOBD4uqRW9+p0UlINcA+wb97HNcNsb7hjLsb87mFivqzDmM0aw0mWWXOMz3+D1Do0mR0HMrclaV9Jfw0cA/wvKZm4v/U2qWXpfobuAvyopNmSxkuaKul0Sd9l6O61bv1LYf5iSb8m6VDSuCtIx3h3jv1vgN8BngC+wdDgcxi5hWZdYX5GJ3dHRsQvgX/ML88nJUDPk8ZrtdxZmP+MpJmSdpd0qKQ/JSVDHYuI5yLiNlLXKKQuwT/P8+MLi24BNkk6FnjvMJtrHfOBkvYqlN9N6i4GuEzSsTnmaZLOAZZhZjtwkmXWHK0xPhOBR0ktPUd3uO5E4GPAA3m9zaRB25C66B6NiGdIiUWQkpfv5OXWAreSEp2eiIilwFfyyxOAx4H/AWbmsmsjojUI/iOkB4muJSUarValDfl4htvH08B/55fvArbmGwlGG8va2n5rue+0xozl7d7H0IM+jyfVxZYc/2eBV4+y/eF8inSnJ8B5efD8cqB1l+OppPFpPyAl2e38IP89BFifj3dOfs7aFfm9w/NyW0h1/3fAb1SM2WygOckya46rSHf/PQk8A9wGvLvDdX9BSgCWkVo7tpEGVN8CzImIrQARcROpi+yuvNxW4Keku9nOBX7Wm0MB0t2F84GHSMncJuBB4H0RcUFhuatJA7t/nuNeA/xTjnvNKPs4i5RQbBpDXN/O+2hZ1GaZc4AP5m1vzNNjedkPjWFfL4iItaQ6AtgT+FhEbAN+H1hMSsBWke7W/PthNnMp6dysb7P9y0jdrfeRkrTNDLVqnlklZrNBp2qPrDEzMzOzkbgly8zMzKwGTrLMzMzMauAky8zMzKwGTrLMzMzMauAky8zMzKwGTrLMzMzMauAky8zMzKwGTrLMzMzMauAky8zMzKwGTrLMzMzMauAky8zMzKwGTrLMzMzMauAky8zMzKwG/w9MOafSPRCKoAAAAABJRU5ErkJggg==\n", 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\n", 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" ] @@ -312,15 +393,15 @@ }, { "cell_type": "markdown", - "id": "012e6b22", + "id": "477db433", "metadata": {}, "source": [ - "Obtenemos buenas métricas a nivel general y además la brecha entre el test y el train parece ser lógica por lo que entendemos no estariamos overfitteado (algo que al usar aŕboles lógicamente hay que cuidar). Probemos igualmente otro preprocesamiento apra ver si obtenemos algo diferente" + "Obtenemos buenas métricas a nivel general y además la brecha entre el test y el train parece ser lógica por lo que entendemos no estariamos overfitteado. Probemos igualmente otro preprocesamiento para ver si obtenemos algo diferente" ] }, { "cell_type": "markdown", - "id": "16bf1081", + "id": "b19d5228", "metadata": {}, "source": [ "## Segundo preprocesamiento" @@ -328,16 +409,16 @@ }, { "cell_type": "markdown", - "id": "4f60168c", + "id": "2c3863f4", "metadata": {}, "source": [ - "Realizamos nuesttro nuevo preprocesado. En este caso se trata de una modificación mas leve a nuestras features en donde no agruparemos como lo hicimos en el primer preprocesado (por ejemplo en la educación). Además, tendremos en cuenta a la feature barrio, generalizando entre los residente en Palermo y los no residentes en Palermo. " + "Realizamos nuestro nuevo preprocesado. En este caso se trata de una modificación mas leve a nuestras features en donde no agruparemos como lo hicimos en el primer preprocesado (por ejemplo en la educación). Además, tendremos en cuenta a la feature barrio, generalizando entre los residente en Palermo y los no residentes en Palermo. " ] }, { "cell_type": "code", - "execution_count": 4, - "id": "34758a0c", + "execution_count": 10, + "id": "2da9aa1b", "metadata": {}, "outputs": [ { @@ -356,19 +437,19 @@ }, { "cell_type": "markdown", - "id": "91905254", + "id": "b86e5a66", "metadata": {}, "source": [ - "También, utilizaremos el algoritmo de RFECV para que seleccione solo las features que resultan mas importantes con un único árbol de decisión a la hora de entrenar con gradient boosting donde internamente utiliza 'n' arboles de decisiones (estimadores) donde el calculo de gini o entropía es el mismo que un árbol único.\n", + "También, utilizaremos el algoritmo de RFECV para que seleccione solo las features que resultan más importantes al entrenar con un único árbol de decisión, tomando los hiperparámetros que habiamos encontrado como óptimos en el modelo *Árbol de decisión*.\n", "\n", - "Creemos que ésto puede favorecer a gradient boosting dándole de \"comer\" features que fueron útiles a partir de un primer árbol de decisión con la importancia de features, pero también podríamos perder información necesaria y por lo tanto más score porque esto termina tratándose de un **método de reducción de dimensionalidad**.\n", + "Creemos que ésto podria llegar a favorecer a gradient boosting para que solo entrene con features que fueron útiles a partir de un primer árbol de decisión, además podría terminar siendo una posible ventaja a la hora de entrenar ya que seguramente bajaria el tiempo. Aunque también podríamos perder información necesaria y por lo tanto más score porque esto termina tratándose de un **método de reducción de dimensionalidad**.\n", "\n", - "Además de que solo estaríamos sesgados a ver solo features importances de un único y primer árbol solo. No explotaríamos la idea de éste modelo de ensamble 'boosting' donde un estimador le dice a otro en cual instancias se equivocó asignándoles un peso. O sea, no explotaríamos ésta idea porque estaríamos quitándole features a estas instancias mal clasificadas el cual un estimador podría aprovechar clasificar mejor." + "Otro punto a destacar es que el sesgo del modelo podría aumentar al ver solo features importantes de un único y primer árbol solo y podría aprovecharse la idea de éste modelo de ensamble 'boosting' donde un estimador le dice a otro en cual instancias se equivocó asignándoles un peso, dado que estaríamos quitándole features a estas instancias mal clasificadas el cual otro estimador podría aprovechar clasificar mejor." ] }, { "cell_type": "markdown", - "id": "3fdd5570", + "id": "71a79026", "metadata": {}, "source": [ "Veamos más detalle de qué hace el algoritmo RFECV:" @@ -376,8 +457,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "859560c1", + "execution_count": 11, + "id": "b5454995", "metadata": {}, "outputs": [ { @@ -386,7 +467,7 @@ "" ] }, - "execution_count": 13, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -397,8 +478,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "338bdda3", + "execution_count": 12, + "id": "8e6bc359", "metadata": {}, "outputs": [ { @@ -435,10 +516,10 @@ "\n", "Parametros recibidos\n", "--------\n", - " * clf -> El clasificador que se utilizará para obtener las importancias de faetures.\n", + " * clf -> El clasificador que se utilizará para obtener las importancias de features y que se usará para validar diferentes scores con cross-validation.\n", " * X_df -> El dataset a cual reducir la features, sin la feature de validación.\n", " * Y_df -> La feature de validación utilizada para encontrar el número optimo de features según el scoring del clasificador recibido.\n", - " * min_features_to_select -> Mínimo de features a selección, por default es 1.\n", + " * min_features_to_select -> Mínimo de features a seleccionar, por default es 1.\n", " * n_jobs -> Número de núcleos para correr en paralelo mientras se entrena cada fold de cross-validation, por default es -1 (todos los núcleos).\n", " * scoring -> Un string indicando el tipo de métrica a utilizar para calcular el score vía cross-validation, por default es 'roc_auc'.\n", " * cv -> Número que indica la cantidad de divisiones realizadas por cross-validation.\n", @@ -460,16 +541,16 @@ }, { "cell_type": "markdown", - "id": "24ab09a6", + "id": "5b467ddc", "metadata": {}, "source": [ - "Usamos rfecv" + "Aplicamos RFECV" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "c43e0c2a", + "execution_count": 13, + "id": "220beb7e", "metadata": {}, "outputs": [], "source": [ @@ -483,8 +564,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "fb685a83", + "execution_count": 14, + "id": "06a90237", "metadata": {}, "outputs": [ { @@ -910,7 +991,7 @@ "[32561 rows x 20 columns]" ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -921,7 +1002,7 @@ }, { "cell_type": "markdown", - "id": "3bb44e6e", + "id": "3536ab02", "metadata": {}, "source": [ "Realizamos nuevamente el split" @@ -929,8 +1010,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "49a45f5d", + "execution_count": 15, + "id": "fb46309a", "metadata": {}, "outputs": [], "source": [ @@ -939,7 +1020,7 @@ }, { "cell_type": "markdown", - "id": "b182bf6c", + "id": "d92ebca1", "metadata": {}, "source": [ "### Entrenamiento" @@ -947,7 +1028,7 @@ }, { "cell_type": "markdown", - "id": "12644f17", + "id": "b11741fc", "metadata": {}, "source": [ "Volvemos a realizar un entrenamiento con 5 folds, utilizando las mismas librerias y funciones utilizadas anteriormente" @@ -955,20 +1036,24 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "8ea5e885", + "execution_count": 16, + "id": "b5982159", "metadata": {}, "outputs": [], "source": [ - "cv = StratifiedKFold(n_splits=5,random_state=10, shuffle=True).split(X_train, y_train)\n", + "cv = StratifiedKFold(n_splits=5, random_state=10, shuffle=True).split(X_train, y_train)\n", "clf_2 = GradientBoostingClassifier(random_state=10)\n", - "params = {\"max_depth\":np.arange(3,8),\"min_samples_leaf\":np.arange(50,150,20)}\n", - "clf_2 = GridSearchCV(clf_2, params, scoring='roc_auc', cv=cv, n_jobs = -1)\n" + "\n", + "params = {\"max_depth\":np.arange(3,8),\n", + " \"min_samples_leaf\":np.arange(50,150,20),\n", + " \"n_estimators\":np.arange(50,350,50)}\n", + "\n", + "clf_2 = GridSearchCV(clf_2, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose = True)" ] }, { "cell_type": "markdown", - "id": "13a2db91", + "id": "e0167eca", "metadata": {}, "source": [ "Entrenamos nuestro modelo" @@ -976,21 +1061,40 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "1a7c6d98", + "execution_count": 17, + "id": "04102c41", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", + "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 25.3s\n", + "[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 2.5min\n", + "[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 7.6min\n", + "[Parallel(n_jobs=-1)]: Done 750 out of 750 | elapsed: 14.9min finished\n" + ] + }, { "data": { "text/plain": [ - "GridSearchCV(cv=,\n", + "GridSearchCV(cv=,\n", " estimator=GradientBoostingClassifier(random_state=10), n_jobs=-1,\n", " param_grid={'max_depth': array([3, 4, 5, 6, 7]),\n", - " 'min_samples_leaf': array([ 50, 70, 90, 110, 130])},\n", - " scoring='roc_auc')" + " 'min_samples_leaf': array([ 50, 70, 90, 110, 130]),\n", + " 'n_estimators': array([ 50, 100, 150, 200, 250, 300])},\n", + " scoring='roc_auc', verbose=True)" ] }, - "execution_count": 13, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1001,7 +1105,61 @@ }, { "cell_type": "markdown", - "id": "cdc07672", + "id": "38ecb56f", + "metadata": {}, + "source": [ + "De nuevo, graficamos pérdida en función de estimadores" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4e41afe9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Deviance')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "best = clf_2.best_estimator_\n", + "score_estimadores = best.train_score_\n", + "plt.figure(dpi=125)\n", + "plt.plot(score_estimadores)\n", + "plt.xlabel(\"Cantidad de estimadores\")\n", + "plt.ylabel(\"Deviance\")" + ] + }, + { + "cell_type": "markdown", + "id": "0551d002", + "metadata": {}, + "source": [ + "Y observamos un comportamiento similar al caso anterior" + ] + }, + { + "cell_type": "markdown", + "id": "ac677ed0", "metadata": {}, "source": [ "Realizamos nuestras predicciones para una análisis más amplio" @@ -1009,8 +1167,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "f983d3b4", + "execution_count": 19, + "id": "cc58a0a2", "metadata": {}, "outputs": [], "source": [ @@ -1019,7 +1177,7 @@ }, { "cell_type": "markdown", - "id": "f8725e08", + "id": "ee9d7402", "metadata": {}, "source": [ "### Metricas" @@ -1027,33 +1185,33 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "9a2b0dac", + "execution_count": 20, + "id": "aa1ed14e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "AUC-ROC score sobre test: 0.9256305972844143\n", - "AUC-ROC score sobre train: 0.9352436938513804\n", - "Accuracy sobre test: 0.8714877936434823\n", - "Accuracy sobre train: 0.8762285012285013\n", + "AUC-ROC score sobre test: 0.9220030024143127\n", + "AUC-ROC score sobre train: 0.9301482100862505\n", + "Accuracy sobre test: 0.8661139259941655\n", + "Accuracy sobre train: 0.8713528869778869\n", " precision recall f1-score support\n", "\n", - " Bajo valor 0.90 0.94 0.92 4945\n", - " Alto valor 0.78 0.66 0.71 1568\n", + " Bajo valor 0.89 0.94 0.91 4945\n", + " Alto valor 0.76 0.65 0.70 1568\n", "\n", " accuracy 0.87 6513\n", - " macro avg 0.84 0.80 0.81 6513\n", - "weighted avg 0.87 0.87 0.87 6513\n", + " macro avg 0.83 0.79 0.81 6513\n", + "weighted avg 0.86 0.87 0.86 6513\n", "\n", - "Los mejores hiperpametros elegidos: {'max_depth': 6, 'min_samples_leaf': 50}\n" + "Los mejores hiperpametros elegidos: {'max_depth': 4, 'min_samples_leaf': 50, 'n_estimators': 300}\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -1089,15 +1247,17 @@ }, { "cell_type": "markdown", - "id": "58bb5481", + "id": "44d2734f", "metadata": {}, "source": [ - "Nuevamente obtenemos buenas métricas a nivel general y bastante parecidas al primer preprocesamiento. Se redujo la brecha entre el test y train y además mejoro levemente el recall para la clase de altos ingresos. Para finalizar pasamos a testear en el holdout" + "La métrica AUC-ROC empeoró algunos decimales respecto al anterior preprocesado, algo que habiamos mencionado que podría ocurrirnos. El tiempo de entrenamiento efectivamente disminuyó. Aún así no notamos grandes diferencias de un preprocesado a otro.\n", + "\n", + "Para finalizar pasamos a testear en el holdout con el primer preprocesamiento." ] }, { "cell_type": "markdown", - "id": "f3380103", + "id": "7f4d1108", "metadata": {}, "source": [ "## Holdouts" @@ -1105,7 +1265,7 @@ }, { "cell_type": "markdown", - "id": "2bbae39b", + "id": "554454c4", "metadata": {}, "source": [ "Realizamos los testeos requeridos en el holdout. Como el primer preprocesamiento dio levemente mejor la métrica AUC-ROC para el test, utilizamos el primer modelo" @@ -1113,8 +1273,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "ddad53ff", + "execution_count": 21, + "id": "a8fafacc", "metadata": {}, "outputs": [], "source": [ @@ -1124,7 +1284,7 @@ }, { "cell_type": "markdown", - "id": "05d9b48a", + "id": "b55dae1f", "metadata": {}, "source": [ "Apliquemos el procesado con el que obtuvimos el mejor score AUC-ROC:" @@ -1132,8 +1292,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "387b704e", + "execution_count": 22, + "id": "f84f626e", "metadata": {}, "outputs": [ { @@ -1150,7 +1310,7 @@ }, { "cell_type": "markdown", - "id": "9345f9f6", + "id": "491131da", "metadata": {}, "source": [ "Hagamos **.predict()** sobre este holdout para luego agregarlo como nueva columna en este dataset para así exportar el **.csv** con facilidad mediante Pandas. " @@ -1158,8 +1318,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "cc8e4525", + "execution_count": 23, + "id": "388a4015", "metadata": {}, "outputs": [], "source": [ @@ -1169,8 +1329,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "45df1c27", + "execution_count": 24, + "id": "052f079f", "metadata": {}, "outputs": [], "source": [ @@ -1198,7 +1358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.8.5" } }, "nbformat": 4, diff --git a/parte_2/#6 [+comentarios] - Boosting.ipynb b/parte_2/#6 [+comentarios] - Boosting.ipynb deleted file mode 100644 index 8367fbf..0000000 --- a/parte_2/#6 [+comentarios] - Boosting.ipynb +++ /dev/null @@ -1,1343 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0fc599d8", - "metadata": {}, - "source": [ - "# Modelo: Boosting" - ] - }, - { - "cell_type": "markdown", - "id": "e5d203c2", - "metadata": {}, - "source": [ - "El modelo a entrenar en el siguiente notebook será un ensamble. En particular buscaremos hacer un **Boosting**. Boosting es un ensamble de tipo homogéneo que consiste en entrenar diferentes instancias con un modelo de forma iterativa." - ] - }, - { - "cell_type": "markdown", - "id": "890bc33b", - "metadata": {}, - "source": [ - "La particularidad de Boosting se da en como se realiza ese entrenamiento. En primer lugar, se entrena un modelo como lo veniamos haciendo hasta ahora. A continuación, a las instancias en las que el primer modelo falló se les asigna un peso mayor para realizar el entrenamiento del segundo modelo. Es decir, un indicativo para comunicarle al segundo modelo en que instancias se equivocó el primer modelo, las mal clasificadas. En definitiva Boosting, tendras distintos modelos donde cada uno proriza los mal clasificados del anterior modelo.\n", - "\n", - "De esta manera se continua de forma iterativa hasta obtener un score deseado. El sesgo y la varianza, ya que en cada estimador se ven los datos de forma diferente por el peso asignado." - ] - }, - { - "cell_type": "markdown", - "id": "b3ea744e", - "metadata": {}, - "source": [ - "A la hora de evaluar instancias, el modelo realiza una votación ponderada (según que tan bien fue la performance de ese modelo durante el entrenamiento) entre todos sus estimadores y arroja una predicción." - ] - }, - { - "cell_type": "markdown", - "id": "6088db75", - "metadata": {}, - "source": [ - "Para implementar este ensamble utilizaremos **GradientBoostingClassifier** de la libreria sklearn. Esta implementación utiliza árboles de decisión como algoritmo base. Además, utiliza una función de perdida para calcular el error en las predicciones arrojadas por cada árbol y a partir de ella optimiza los parámetros de los siguientes árboles mediante descenso por gradiente." - ] - }, - { - "cell_type": "markdown", - "id": "3d8dfc1a", - "metadata": {}, - "source": [ - "## Librerias y funciones necesarias" - ] - }, - { - "cell_type": "markdown", - "id": "ed89dec0", - "metadata": {}, - "source": [ - "Comenzamos importando las librerias y funciones que serán necesarias para preprocesar nuestros datos, realizar nuestro entrenamiento y obtener metricas " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "986d3ecb", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.metrics import confusion_matrix\n", - "from sklearn.metrics import roc_curve, auc\n", - "from sklearn.metrics import roc_auc_score\n", - "from sklearn.metrics import accuracy_score\n", - "from sklearn.ensemble import GradientBoostingClassifier\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn import tree\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "2e280c00", - "metadata": {}, - "outputs": [], - "source": [ - "from preprocessing import obtener_datasets\n", - "from preprocessing import aplicar_preparacion\n", - "from preprocessing import conversion_numerica\n", - "from preprocessing import plot_roc_curves\n", - "from preprocessing import graficar_matriz_confusion\n", - "from preprocessing import aplicar_preparacion_generalizado\n", - "from preprocessing import conversion_numerica_generalizada\n", - "from preprocessing import get_dataframe_scaled\n", - "from preprocessing import reduccion_rfecv\n", - "from preprocessing import get_dataframe_polynomial" - ] - }, - { - "cell_type": "markdown", - "id": "6b814da8", - "metadata": {}, - "source": [ - "## Primer preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "1223160e", - "metadata": {}, - "source": [ - "En primer lugar obtenemos el dataset para entrenar y el holdout. En segundo lugar, aplicamos una función que trabaja sobre las features, generalizando algunas y dejando de lado otras según lo observado en la primer parte de este trabajo práctico. También separamos a la variable target del resto del dataset. Por último, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "b2d7832d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_for_prediction = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df = conversion_numerica(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "643312d8", - "metadata": {}, - "source": [ - "Luego vamos a realizar un split del dataset para dividir en train y test. Como observamos en la primer parte de este trabajo práctico, la variable target no esta distribuida uniformente por lo cual realizamos una división estratificada" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "1c0d86e3", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.2, random_state=30,stratify=y_df)" - ] - }, - { - "cell_type": "markdown", - "id": "ca0beb15", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "4f3b1ca5", - "metadata": {}, - "source": [ - "Vamos a realizar un entrenamiento con 5 folds. Para ello utilizaremos StratifiedKFold para asegurarnos de obtener folds balanceados. " - ] - }, - { - "cell_type": "markdown", - "id": "f0872fc9", - "metadata": {}, - "source": [ - "En cuanto a la busqueda de hiperparámetros, vamos a buscar distitnas variaciones de altura de los árboles y mínimas instancias por hoja tal y como realizamos en el modelo de árbol de decisión. \n", - "\n", - "También, buscaremos el hiperparámetro *n_estimators*, el cual define la cantidad de modelos que se usarán uno al otro en la cadena de boosting. En nuestro caso seria *n* arboles usados. Si este número fuese muy grande, el modelo podria estar overfitteando. Aún así, según su propia documentación, **GradientBoostingClassifier** es un modelo muy robusto al overfitting por lo que deberia arrojar mejores resultados con más estimadores. \n", - "\n", - "Otro hiperparámetro importante será el *learning rate*, el cual determinará cuanto contribuye cada árbol con el siguiente en la función de perdida utilizada. En este caso tomaremos el default" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "a0613564", - "metadata": {}, - "outputs": [], - "source": [ - "cv = StratifiedKFold(n_splits=5,random_state=10, shuffle=True).split(X_train, y_train)\n", - "clf = GradientBoostingClassifier(random_state=10)\n", - "params = {\"max_depth\":np.arange(3,8),\n", - " \"min_samples_leaf\":np.arange(50,150,20),\n", - " \"n_estimators\":np.arange(50,350,50)}\n", - " #\"learning_rate\":np.arange(0.1,0.9,0.1)}\n", - "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose = True)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b88d3c74", - "metadata": {}, - "source": [ - "Ahora sí, entrenamos nuestro modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "155b80e3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 1.1min\n", - "[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 5.7min\n", - "[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 16.2min\n", - "[Parallel(n_jobs=-1)]: Done 750 out of 750 | elapsed: 33.7min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=GradientBoostingClassifier(random_state=10), n_jobs=-1,\n", - " param_grid={'max_depth': array([3, 4, 5, 6, 7]),\n", - " 'min_samples_leaf': array([ 50, 70, 90, 110, 130]),\n", - " 'n_estimators': array([ 50, 100, 150, 200, 250, 300])},\n", - " scoring='roc_auc', verbose=True)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "ffb69a0d", - "metadata": {}, - "source": [ - "Algo que resulta interesante observar es que valor se tiene en la función de pérdida (Deviance) en cada iteración. Graficamos " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "56e42b82", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Deviance')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "best = clf.best_estimator_\n", - "score_estimadores = best.train_score_\n", - "plt.figure(dpi=125)\n", - "plt.plot(score_estimadores)\n", - "plt.xlabel(\"Cantidad de estimadores\")\n", - "plt.ylabel(\"Deviance\")" - ] - }, - { - "cell_type": "markdown", - "id": "64667cbf", - "metadata": {}, - "source": [ - "Lo que se observa es que a partir de cierto numero de estimadores, la pérdida se reduce muy lentamente y comienza a comportarse de forma asintótica. Por lo que aumentar más el número de estimadores generaria poca ganancia. " - ] - }, - { - "cell_type": "markdown", - "id": "2a5acca5", - "metadata": {}, - "source": [ - "Realizamos nuestras predicciones para una análisis más amplio" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a186384c", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "5b1d423a", - "metadata": {}, - "source": [ - "### Metricas" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "27658f9f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.9292791497286477\n", - "AUC-ROC score sobre train: 0.9407463366669064\n", - "Accuracy sobre test: 0.8713342545677875\n", - "Accuracy sobre train: 0.8812960687960688\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.89 0.94 0.92 4945\n", - " Alto valor 0.78 0.65 0.71 1568\n", - "\n", - " accuracy 0.87 6513\n", - " macro avg 0.84 0.80 0.81 6513\n", - "weighted avg 0.87 0.87 0.87 6513\n", - "\n", - "Los mejores hiperpametros elegidos: {'max_depth': 5, 'min_samples_leaf': 70, 'n_estimators': 300}\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(\"Accuracy sobre train: \", \"%0.16f\" % accuracy_score(clf.predict(X_train), y_train))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf, X_test, y_test, X_train, y_train) # 9296" - ] - }, - { - "cell_type": "markdown", - "id": "477db433", - "metadata": {}, - "source": [ - "Obtenemos buenas métricas a nivel general y además la brecha entre el test y el train parece ser lógica por lo que entendemos no estariamos overfitteado. Probemos igualmente otro preprocesamiento para ver si obtenemos algo diferente" - ] - }, - { - "cell_type": "markdown", - "id": "b19d5228", - "metadata": {}, - "source": [ - "## Segundo preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "2c3863f4", - "metadata": {}, - "source": [ - "Realizamos nuesttro nuevo preprocesado. En este caso se trata de una modificación mas leve a nuestras features en donde no agruparemos como lo hicimos en el primer preprocesado (por ejemplo en la educación). Además, tendremos en cuenta a la feature barrio, generalizando entre los residente en Palermo y los no residentes en Palermo. " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "2da9aa1b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica_generalizada' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_holdout = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion_generalizado(df)\n", - "X_df = conversion_numerica_generalizada(X_df)\n" - ] - }, - { - "cell_type": "markdown", - "id": "b86e5a66", - "metadata": {}, - "source": [ - "También, utilizaremos el algoritmo de RFECV para que seleccione solo las features que resultan más importantes al entrenar con un único árbol de decisión, tomando los hiperparámetros que habiamos encontrado como óptimos en el modelo *Árbol de decisión*\n", - "\n", - "Creemos que ésto podria llegar a favorecer a gradient boosting para que solo entrene con features que fueron útiles a partir de un primer árbol de decisión, aunque también podríamos perder información necesaria y por lo tanto más score porque esto termina tratándose de un **método de reducción de dimensionalidad**. Esto podria terminar siendo una posible ventaja a la hora de entrenar ya que seguramente bajaria el tiempo\n", - "\n", - "Otro punto a destacar es que el sesgo del modelo podría aumentar al ver solo features importantes de un único y primer árbol solo y podira aprovecharse la idea de éste modelo de ensamble 'boosting' donde un estimador le dice a otro en cual instancias se equivocó asignándoles un peso, dado que estaríamos quitándole features a estas instancias mal clasificadas el cual un estimador podría aprovechar clasificar mejor." - ] - }, - { - "cell_type": "markdown", - "id": "71a79026", - "metadata": {}, - "source": [ - "Veamos más detalle de qué hace el algoritmo RFECV:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "b5454995", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reduccion_rfecv" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "8e6bc359", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mreduccion_rfecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mX_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mY_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mmin_features_to_select\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'roc_auc'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Es una técnica de selección de tipo embedded, encargándose de rankear variables según métodos internos de cada algoritmo.\n", - "\n", - "La idea principal de RFECV es igual a la del RFE. Es decir:\n", - "\n", - "1. Entrenar un modelo con un clasificador recibido ('clf')\n", - "2. Obtener importancias a partir de un modelo. \n", - "3. Eliminar la/las variables menos importantes\n", - "4. Repetir \n", - "\n", - "Esto se repite hasta que converga y mi modelo deje de mejorar. Un hiperparámetro importante de la implementación RFE \n", - "en sklearn es la **cantidad de features a seleccionar** pero ese numero no suele conocerse.\n", - "\n", - "Para buscar el numero de optimo de features a seleccionar, el método de RFECV utiliza cross-validation (por eso el acrónimo RFECV: RFE-CV).\n", - "Es decir, realiza la validación (sobre el conjunto validación 'Y_df' recibido aplicándole el clasificador 'clf') con cross-validation usando\n", - "diferentes números de features en distintos folds, y seleccionando el mejor numero de features que mejor score ('score'='roc_auc' por default) nos de.\n", - "\n", - "Internamente se utiliza cross-validation de forma estratificada (StratifiedKFold) para mantener la proporción de clases (de 'Y_df') en cada fold.\n", - "\n", - "Parametros recibidos\n", - "--------\n", - " * clf -> El clasificador que se utilizará para obtener las importancias de features y que se usará para validar diferentes scores con cross-validation.\n", - " * X_df -> El dataset a cual reducir la features, sin la feature de validación.\n", - " * Y_df -> La feature de validación utilizada para encontrar el número optimo de features según el scoring del clasificador recibido.\n", - " * min_features_to_select -> Mínimo de features a seleccionar, por default es 1.\n", - " * n_jobs -> Número de núcleos para correr en paralelo mientras se entrena cada fold de cross-validation, por default es -1 (todos los núcleos).\n", - " * scoring -> Un string indicando el tipo de métrica a utilizar para calcular el score vía cross-validation, por default es 'roc_auc'.\n", - " * cv -> Número que indica la cantidad de divisiones realizadas por cross-validation.\n", - "\n", - "Retorno\n", - "--------\n", - " * X_reduced -> pd.DataFrame: retorna el dataset reducido con RFECV.\n", - "\u001b[0;31mFile:\u001b[0m ~/Escritorio/TP-FIUFIP-Organizacion-de-Datos/parte_2/preprocessing.py\n", - "\u001b[0;31mType:\u001b[0m function\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "reduccion_rfecv?" - ] - }, - { - "cell_type": "markdown", - "id": "5b467ddc", - "metadata": {}, - "source": [ - "Aplicamos RFECV" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "220beb7e", - "metadata": {}, - "outputs": [], - "source": [ - "clf_2 = tree.DecisionTreeClassifier(random_state=10, criterion = 'gini', max_depth = 5, min_samples_leaf = 500)\n", - "X_reducido = reduccion_rfecv(\n", - " clf = clf_2,\n", - " X_df = X_df,\n", - " Y_df = y_df,\n", - " min_features_to_select=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "06a90237", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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"d92ebca1", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "b11741fc", - "metadata": {}, - "source": [ - "Volvemos a realizar un entrenamiento con 5 folds, utilizando las mismas librerias y funciones utilizadas anteriormente" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b5982159", - "metadata": {}, - "outputs": [], - "source": [ - "cv = StratifiedKFold(n_splits=5,random_state=10, shuffle=True).split(X_train, y_train)\n", - "clf_2 = GradientBoostingClassifier(random_state=10)\n", - "params = {\"max_depth\":np.arange(3,8),\"min_samples_leaf\":np.arange(50,150,20),\"n_estimators\":np.arange(50,350,50)}\n", - "clf_2 = GridSearchCV(clf_2, params, scoring='roc_auc', cv=cv, n_jobs = -1)\n" - ] - }, - { - "cell_type": "markdown", - "id": "e0167eca", - "metadata": {}, - "source": [ - "Entrenamos nuestro modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "04102c41", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=GradientBoostingClassifier(random_state=10), n_jobs=-1,\n", - " param_grid={'max_depth': array([3, 4, 5, 6, 7]),\n", - " 'min_samples_leaf': array([ 50, 70, 90, 110, 130]),\n", - " 'n_estimators': array([ 50, 100, 150, 200, 250, 300])},\n", - " scoring='roc_auc')" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf_2.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "38ecb56f", - "metadata": {}, - "source": [ - "De nuevo, graficamos pérdida en función de estimadores" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "4e41afe9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Deviance')" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "best = clf_2.best_estimator_\n", - "score_estimadores = best.train_score_\n", - "plt.figure(dpi=125)\n", - "plt.plot(score_estimadores)\n", - "plt.xlabel(\"Cantidad de estimadores\")\n", - "plt.ylabel(\"Deviance\")" - ] - }, - { - "cell_type": "markdown", - "id": "0551d002", - "metadata": {}, - "source": [ - "Y observamos un comportamiento similar al caso anterior" - ] - }, - { - "cell_type": "markdown", - "id": "ac677ed0", - "metadata": {}, - "source": [ - "Realizamos nuestras predicciones para una análisis más amplio" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "cc58a0a2", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf_2.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "ee9d7402", - "metadata": {}, - "source": [ - "### Metricas" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "aa1ed14e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.9220030024143127\n", - "AUC-ROC score sobre train: 0.9301482100862505\n", - "Accuracy sobre test: 0.8661139259941655\n", - "Accuracy sobre train: 0.8713528869778869\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.89 0.94 0.91 4945\n", - " Alto valor 0.76 0.65 0.70 1568\n", - "\n", - " accuracy 0.87 6513\n", - " macro avg 0.83 0.79 0.81 6513\n", - "weighted avg 0.86 0.87 0.86 6513\n", - "\n", - "Los mejores hiperpametros elegidos: {'max_depth': 4, 'min_samples_leaf': 50, 'n_estimators': 300}\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_2.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_2.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(\"Accuracy sobre train: \", \"%0.16f\" % accuracy_score(clf_2.predict(X_train), y_train))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_2.best_params_)\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_2, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "44d2734f", - "metadata": {}, - "source": [ - "Se redujo la brecha entre el test y train y además mejoro levemente el recall para la clase de altos ingresos. Para finalizar pasamos a testear en el holdout" - ] - }, - { - "cell_type": "markdown", - "id": "7f4d1108", - "metadata": {}, - "source": [ - "## Holdouts" - ] - }, - { - "cell_type": "markdown", - "id": "554454c4", - "metadata": {}, - "source": [ - "Realizamos los testeos requeridos en el holdout. Como el primer preprocesamiento dio levemente mejor la métrica AUC-ROC para el test, utilizamos el primer modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "a8fafacc", - "metadata": {}, - "outputs": [], - "source": [ - "from preprocessing import aplicar_preparacion_holdout\n", - "X_holdout = aplicar_preparacion_holdout(df_for_prediction, generalizada=False)" - ] - }, - { - "cell_type": "markdown", - "id": "b55dae1f", - "metadata": {}, - "source": [ - "Apliquemos el procesado con el que obtuvimos el mejor score AUC-ROC:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "f84f626e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "X_holdout_numerico = conversion_numerica(X_holdout) " - ] - }, - { - "cell_type": "markdown", - "id": "491131da", - "metadata": {}, - "source": [ - "Hagamos **.predict()** sobre este holdout para luego agregarlo como nueva columna en este dataset para así exportar el **.csv** con facilidad mediante Pandas. " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "388a4015", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred_holdout = clf.predict(X_holdout_numerico)\n", - "X_holdout['tiene_alto_valor_adquisitivo'] = y_pred_holdout" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "052f079f", - "metadata": {}, - "outputs": [], - "source": [ - "name_model = '#6 - Boosting'\n", - "\n", - "if X_holdout.index[0] == 0:\n", - " X_holdout.index += 1 \n", - "X_holdout['tiene_alto_valor_adquisitivo'].to_csv('predicciones/' + name_model + '.csv', index=True, index_label = 'id')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git "a/parte_2/#EXTRAS - An\303\241lisis Exploratorio [Parte 2].ipynb" "b/parte_2/#EXTRAS - An\303\241lisis Exploratorio [Parte 2].ipynb" index 58fc3c7..e04b4c7 100644 --- "a/parte_2/#EXTRAS - An\303\241lisis Exploratorio [Parte 2].ipynb" +++ "b/parte_2/#EXTRAS - An\303\241lisis Exploratorio [Parte 2].ipynb" @@ -834,7 +834,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Aquella transformación con la que logramos mantener un 95% de varianza es la MinMaxScaler() con un numero de componentes menor, de 22~23." + "Vemos que el preprocesado de StandarScaler() y PowerTransformer() tuvieron la misma varianza explicada en función de la cantidad de componentes, esto se puede reflejar en el grafico con la curva superpuesta. Pero aquella transformación con la que logramos mantener un 95% de varianza es la MinMaxScaler() con un numero de componentes menor, de 22~23." ] }, { diff --git a/parte_2/TP_ORGA_DATOS.pdf b/parte_2/TP_ORGA_DATOS.pdf index 6e0593d..d0a67ce 100644 Binary files a/parte_2/TP_ORGA_DATOS.pdf and b/parte_2/TP_ORGA_DATOS.pdf differ diff --git a/parte_2/predicciones/#6 - Boosting.csv b/parte_2/predicciones/#6 - Boosting.csv index 8bd0152..6cd5adc 100644 --- a/parte_2/predicciones/#6 - Boosting.csv +++ b/parte_2/predicciones/#6 - Boosting.csv @@ -79,7 +79,7 @@ id,tiene_alto_valor_adquisitivo 78,0 79,0 80,0 -81,1 +81,0 82,0 83,0 84,1 @@ -222,7 +222,7 @@ id,tiene_alto_valor_adquisitivo 221,0 222,0 223,0 -224,0 +224,1 225,1 226,0 227,1 @@ -312,7 +312,7 @@ id,tiene_alto_valor_adquisitivo 311,1 312,0 313,0 -314,1 +314,0 315,0 316,0 317,0 @@ -372,9 +372,9 @@ id,tiene_alto_valor_adquisitivo 371,0 372,0 373,0 -374,0 +374,1 375,0 -376,1 +376,0 377,1 378,0 379,0 @@ -429,7 +429,7 @@ id,tiene_alto_valor_adquisitivo 428,0 429,0 430,0 -431,0 +431,1 432,0 433,0 434,0 @@ -500,8 +500,8 @@ id,tiene_alto_valor_adquisitivo 499,0 500,0 501,0 -502,1 -503,1 +502,0 +503,0 504,0 505,0 506,1 @@ -569,7 +569,7 @@ id,tiene_alto_valor_adquisitivo 568,0 569,0 570,0 -571,0 +571,1 572,0 573,0 574,0 @@ -713,7 +713,7 @@ id,tiene_alto_valor_adquisitivo 712,1 713,0 714,0 -715,0 +715,1 716,0 717,0 718,0 @@ -774,7 +774,7 @@ id,tiene_alto_valor_adquisitivo 773,0 774,0 775,0 -776,1 +776,0 777,1 778,0 779,0 @@ -787,7 +787,7 @@ id,tiene_alto_valor_adquisitivo 786,1 787,1 788,0 -789,0 +789,1 790,0 791,0 792,0 @@ -809,7 +809,7 @@ id,tiene_alto_valor_adquisitivo 808,1 809,0 810,0 -811,1 +811,0 812,0 813,0 814,0 @@ -1313,13 +1313,13 @@ id,tiene_alto_valor_adquisitivo 1312,0 1313,0 1314,0 -1315,1 +1315,0 1316,0 1317,0 1318,0 1319,0 1320,1 -1321,0 +1321,1 1322,0 1323,0 1324,1 @@ -1495,7 +1495,7 @@ id,tiene_alto_valor_adquisitivo 1494,0 1495,0 1496,0 -1497,1 +1497,0 1498,1 1499,0 1500,0 @@ -1515,7 +1515,7 @@ id,tiene_alto_valor_adquisitivo 1514,1 1515,0 1516,0 -1517,0 +1517,1 1518,0 1519,0 1520,0 @@ -1631,7 +1631,7 @@ id,tiene_alto_valor_adquisitivo 1630,1 1631,0 1632,1 -1633,0 +1633,1 1634,0 1635,0 1636,0 @@ -1649,7 +1649,7 @@ id,tiene_alto_valor_adquisitivo 1648,0 1649,0 1650,0 -1651,1 +1651,0 1652,0 1653,0 1654,0 @@ -1679,7 +1679,7 @@ id,tiene_alto_valor_adquisitivo 1678,0 1679,0 1680,0 -1681,1 +1681,0 1682,0 1683,0 1684,0 @@ -1769,7 +1769,7 @@ id,tiene_alto_valor_adquisitivo 1768,1 1769,0 1770,1 -1771,1 +1771,0 1772,0 1773,0 1774,0 @@ -1903,7 +1903,7 @@ id,tiene_alto_valor_adquisitivo 1902,0 1903,1 1904,0 -1905,0 +1905,1 1906,0 1907,0 1908,1 @@ -2039,7 +2039,7 @@ id,tiene_alto_valor_adquisitivo 2038,0 2039,1 2040,0 -2041,0 +2041,1 2042,0 2043,0 2044,0 @@ -2214,7 +2214,7 @@ id,tiene_alto_valor_adquisitivo 2213,0 2214,0 2215,0 -2216,0 +2216,1 2217,1 2218,0 2219,1 @@ -2314,7 +2314,7 @@ id,tiene_alto_valor_adquisitivo 2313,0 2314,0 2315,0 -2316,0 +2316,1 2317,1 2318,0 2319,0 @@ -2394,7 +2394,7 @@ id,tiene_alto_valor_adquisitivo 2393,0 2394,0 2395,0 -2396,1 +2396,0 2397,0 2398,0 2399,0 @@ -2431,14 +2431,14 @@ id,tiene_alto_valor_adquisitivo 2430,0 2431,0 2432,0 -2433,1 +2433,0 2434,0 -2435,1 +2435,0 2436,0 2437,0 2438,1 2439,0 -2440,0 +2440,1 2441,0 2442,1 2443,0 @@ -2465,7 +2465,7 @@ id,tiene_alto_valor_adquisitivo 2464,0 2465,1 2466,0 -2467,0 +2467,1 2468,0 2469,0 2470,1 @@ -2520,11 +2520,11 @@ id,tiene_alto_valor_adquisitivo 2519,0 2520,0 2521,0 -2522,0 +2522,1 2523,0 2524,0 2525,1 -2526,0 +2526,1 2527,0 2528,0 2529,0 @@ -2551,7 +2551,7 @@ id,tiene_alto_valor_adquisitivo 2550,1 2551,0 2552,0 -2553,1 +2553,0 2554,0 2555,0 2556,0 @@ -2585,7 +2585,7 @@ id,tiene_alto_valor_adquisitivo 2584,1 2585,0 2586,1 -2587,0 +2587,1 2588,0 2589,0 2590,0 @@ -2652,7 +2652,7 @@ id,tiene_alto_valor_adquisitivo 2651,0 2652,0 2653,1 -2654,0 +2654,1 2655,0 2656,0 2657,0 @@ -2689,7 +2689,7 @@ id,tiene_alto_valor_adquisitivo 2688,0 2689,0 2690,0 -2691,0 +2691,1 2692,0 2693,0 2694,0 @@ -2783,7 +2783,7 @@ id,tiene_alto_valor_adquisitivo 2782,1 2783,1 2784,1 -2785,1 +2785,0 2786,0 2787,0 2788,1 @@ -2987,7 +2987,7 @@ id,tiene_alto_valor_adquisitivo 2986,1 2987,0 2988,1 -2989,0 +2989,1 2990,0 2991,0 2992,0 @@ -3077,7 +3077,7 @@ id,tiene_alto_valor_adquisitivo 3076,0 3077,1 3078,1 -3079,0 +3079,1 3080,0 3081,0 3082,0 @@ -3163,7 +3163,7 @@ id,tiene_alto_valor_adquisitivo 3162,0 3163,0 3164,1 -3165,0 +3165,1 3166,0 3167,0 3168,0 @@ -3188,7 +3188,7 @@ id,tiene_alto_valor_adquisitivo 3187,0 3188,0 3189,0 -3190,0 +3190,1 3191,0 3192,1 3193,0 @@ -3239,9 +3239,9 @@ id,tiene_alto_valor_adquisitivo 3238,0 3239,0 3240,1 -3241,1 +3241,0 3242,0 -3243,0 +3243,1 3244,0 3245,0 3246,0 @@ -3298,7 +3298,7 @@ id,tiene_alto_valor_adquisitivo 3297,0 3298,0 3299,1 -3300,1 +3300,0 3301,0 3302,0 3303,0 @@ -3423,7 +3423,7 @@ id,tiene_alto_valor_adquisitivo 3422,0 3423,0 3424,0 -3425,1 +3425,0 3426,0 3427,0 3428,1 @@ -3486,7 +3486,7 @@ id,tiene_alto_valor_adquisitivo 3485,0 3486,0 3487,0 -3488,0 +3488,1 3489,0 3490,0 3491,0 @@ -3641,7 +3641,7 @@ id,tiene_alto_valor_adquisitivo 3640,0 3641,0 3642,0 -3643,0 +3643,1 3644,0 3645,0 3646,1 @@ -3735,7 +3735,7 @@ id,tiene_alto_valor_adquisitivo 3734,0 3735,1 3736,0 -3737,0 +3737,1 3738,0 3739,0 3740,0 @@ -3764,7 +3764,7 @@ id,tiene_alto_valor_adquisitivo 3763,1 3764,1 3765,0 -3766,0 +3766,1 3767,1 3768,1 3769,0 @@ -3814,7 +3814,7 @@ id,tiene_alto_valor_adquisitivo 3813,0 3814,0 3815,1 -3816,0 +3816,1 3817,0 3818,0 3819,0 @@ -3831,7 +3831,7 @@ id,tiene_alto_valor_adquisitivo 3830,0 3831,0 3832,0 -3833,0 +3833,1 3834,0 3835,1 3836,0 @@ -3895,7 +3895,7 @@ id,tiene_alto_valor_adquisitivo 3894,0 3895,0 3896,0 -3897,0 +3897,1 3898,1 3899,0 3900,0 @@ -3960,7 +3960,7 @@ id,tiene_alto_valor_adquisitivo 3959,0 3960,0 3961,0 -3962,0 +3962,1 3963,0 3964,0 3965,0 @@ -3978,7 +3978,7 @@ id,tiene_alto_valor_adquisitivo 3977,0 3978,0 3979,0 -3980,0 +3980,1 3981,0 3982,0 3983,0 @@ -3996,7 +3996,7 @@ id,tiene_alto_valor_adquisitivo 3995,0 3996,0 3997,0 -3998,1 +3998,0 3999,1 4000,0 4001,0 @@ -4007,7 +4007,7 @@ id,tiene_alto_valor_adquisitivo 4006,0 4007,0 4008,0 -4009,1 +4009,0 4010,0 4011,0 4012,1 @@ -4097,7 +4097,7 @@ id,tiene_alto_valor_adquisitivo 4096,0 4097,1 4098,0 -4099,1 +4099,0 4100,0 4101,0 4102,0 @@ -4183,7 +4183,7 @@ id,tiene_alto_valor_adquisitivo 4182,0 4183,0 4184,0 -4185,1 +4185,0 4186,0 4187,1 4188,0 @@ -4305,7 +4305,7 @@ id,tiene_alto_valor_adquisitivo 4304,0 4305,0 4306,0 -4307,1 +4307,0 4308,0 4309,0 4310,0 @@ -4330,7 +4330,7 @@ id,tiene_alto_valor_adquisitivo 4329,0 4330,0 4331,0 -4332,1 +4332,0 4333,0 4334,0 4335,0 @@ -4423,13 +4423,13 @@ id,tiene_alto_valor_adquisitivo 4422,1 4423,0 4424,0 -4425,0 +4425,1 4426,0 4427,0 4428,0 4429,0 4430,0 -4431,1 +4431,0 4432,0 4433,0 4434,0 @@ -4539,7 +4539,7 @@ id,tiene_alto_valor_adquisitivo 4538,0 4539,0 4540,1 -4541,1 +4541,0 4542,0 4543,0 4544,1 @@ -4559,12 +4559,12 @@ id,tiene_alto_valor_adquisitivo 4558,0 4559,0 4560,1 -4561,1 +4561,0 4562,0 4563,0 4564,0 4565,0 -4566,0 +4566,1 4567,0 4568,0 4569,0 @@ -4612,7 +4612,7 @@ id,tiene_alto_valor_adquisitivo 4611,1 4612,0 4613,0 -4614,1 +4614,0 4615,0 4616,1 4617,0 @@ -4723,7 +4723,7 @@ id,tiene_alto_valor_adquisitivo 4722,0 4723,0 4724,0 -4725,0 +4725,1 4726,1 4727,0 4728,0 @@ -4943,7 +4943,7 @@ id,tiene_alto_valor_adquisitivo 4942,0 4943,1 4944,0 -4945,1 +4945,0 4946,0 4947,0 4948,0 @@ -5129,7 +5129,7 @@ id,tiene_alto_valor_adquisitivo 5128,0 5129,1 5130,1 -5131,0 +5131,1 5132,0 5133,1 5134,1 @@ -5139,7 +5139,7 @@ id,tiene_alto_valor_adquisitivo 5138,1 5139,0 5140,1 -5141,0 +5141,1 5142,1 5143,1 5144,0 @@ -5339,7 +5339,7 @@ id,tiene_alto_valor_adquisitivo 5338,0 5339,0 5340,0 -5341,1 +5341,0 5342,0 5343,0 5344,1 @@ -5431,7 +5431,7 @@ id,tiene_alto_valor_adquisitivo 5430,1 5431,1 5432,0 -5433,0 +5433,1 5434,0 5435,0 5436,1 @@ -5498,7 +5498,7 @@ id,tiene_alto_valor_adquisitivo 5497,0 5498,0 5499,0 -5500,1 +5500,0 5501,0 5502,0 5503,0 @@ -5521,11 +5521,11 @@ id,tiene_alto_valor_adquisitivo 5520,0 5521,0 5522,0 -5523,0 +5523,1 5524,1 5525,1 5526,0 -5527,0 +5527,1 5528,0 5529,0 5530,0 @@ -5663,7 +5663,7 @@ id,tiene_alto_valor_adquisitivo 5662,0 5663,0 5664,1 -5665,0 +5665,1 5666,0 5667,0 5668,0 @@ -5881,7 +5881,7 @@ id,tiene_alto_valor_adquisitivo 5880,0 5881,0 5882,1 -5883,1 +5883,0 5884,0 5885,0 5886,0 @@ -5959,7 +5959,7 @@ id,tiene_alto_valor_adquisitivo 5958,0 5959,1 5960,0 -5961,0 +5961,1 5962,1 5963,0 5964,0 @@ -6033,7 +6033,7 @@ id,tiene_alto_valor_adquisitivo 6032,0 6033,0 6034,0 -6035,1 +6035,0 6036,0 6037,0 6038,1 @@ -6069,7 +6069,7 @@ id,tiene_alto_valor_adquisitivo 6068,0 6069,0 6070,0 -6071,0 +6071,1 6072,0 6073,1 6074,0 @@ -6137,7 +6137,7 @@ id,tiene_alto_valor_adquisitivo 6136,0 6137,0 6138,0 -6139,0 +6139,1 6140,1 6141,1 6142,0 @@ -6332,7 +6332,7 @@ id,tiene_alto_valor_adquisitivo 6331,0 6332,0 6333,0 -6334,1 +6334,0 6335,0 6336,0 6337,0 @@ -6342,7 +6342,7 @@ id,tiene_alto_valor_adquisitivo 6341,0 6342,0 6343,1 -6344,0 +6344,1 6345,1 6346,0 6347,0 @@ -6390,12 +6390,12 @@ id,tiene_alto_valor_adquisitivo 6389,0 6390,0 6391,0 -6392,1 +6392,0 6393,0 6394,0 6395,0 6396,0 -6397,1 +6397,0 6398,1 6399,0 6400,0 @@ -6408,7 +6408,7 @@ id,tiene_alto_valor_adquisitivo 6407,0 6408,0 6409,0 -6410,0 +6410,1 6411,1 6412,0 6413,0 @@ -6480,7 +6480,7 @@ id,tiene_alto_valor_adquisitivo 6479,1 6480,1 6481,0 -6482,0 +6482,1 6483,1 6484,0 6485,1 @@ -6515,7 +6515,7 @@ id,tiene_alto_valor_adquisitivo 6514,1 6515,0 6516,1 -6517,0 +6517,1 6518,1 6519,0 6520,0 @@ -6697,7 +6697,7 @@ id,tiene_alto_valor_adquisitivo 6696,1 6697,1 6698,0 -6699,0 +6699,1 6700,0 6701,0 6702,0 @@ -6712,7 +6712,7 @@ id,tiene_alto_valor_adquisitivo 6711,0 6712,0 6713,1 -6714,0 +6714,1 6715,0 6716,0 6717,0 @@ -6863,7 +6863,7 @@ id,tiene_alto_valor_adquisitivo 6862,1 6863,1 6864,0 -6865,0 +6865,1 6866,0 6867,0 6868,0 @@ -6973,7 +6973,7 @@ id,tiene_alto_valor_adquisitivo 6972,0 6973,0 6974,1 -6975,1 +6975,0 6976,1 6977,0 6978,0 @@ -6984,7 +6984,7 @@ id,tiene_alto_valor_adquisitivo 6983,0 6984,1 6985,0 -6986,0 +6986,1 6987,1 6988,0 6989,0 @@ -7158,7 +7158,7 @@ id,tiene_alto_valor_adquisitivo 7157,0 7158,1 7159,0 -7160,0 +7160,1 7161,0 7162,1 7163,0 @@ -7331,7 +7331,7 @@ id,tiene_alto_valor_adquisitivo 7330,0 7331,0 7332,0 -7333,1 +7333,0 7334,0 7335,0 7336,1 @@ -7341,7 +7341,7 @@ id,tiene_alto_valor_adquisitivo 7340,0 7341,0 7342,0 -7343,1 +7343,0 7344,0 7345,0 7346,0 @@ -7381,7 +7381,7 @@ id,tiene_alto_valor_adquisitivo 7380,0 7381,0 7382,0 -7383,0 +7383,1 7384,1 7385,1 7386,1 @@ -7422,7 +7422,7 @@ id,tiene_alto_valor_adquisitivo 7421,0 7422,0 7423,0 -7424,0 +7424,1 7425,1 7426,0 7427,1 @@ -7467,7 +7467,7 @@ id,tiene_alto_valor_adquisitivo 7466,0 7467,0 7468,0 -7469,1 +7469,0 7470,1 7471,0 7472,0 @@ -7596,7 +7596,7 @@ id,tiene_alto_valor_adquisitivo 7595,0 7596,0 7597,0 -7598,0 +7598,1 7599,0 7600,0 7601,0 @@ -7680,7 +7680,7 @@ id,tiene_alto_valor_adquisitivo 7679,0 7680,0 7681,0 -7682,0 +7682,1 7683,1 7684,0 7685,0 @@ -7721,7 +7721,7 @@ id,tiene_alto_valor_adquisitivo 7720,0 7721,0 7722,0 -7723,0 +7723,1 7724,1 7725,0 7726,1 @@ -7892,7 +7892,7 @@ id,tiene_alto_valor_adquisitivo 7891,0 7892,1 7893,0 -7894,0 +7894,1 7895,0 7896,0 7897,0 @@ -7971,7 +7971,7 @@ id,tiene_alto_valor_adquisitivo 7970,0 7971,0 7972,1 -7973,0 +7973,1 7974,0 7975,0 7976,0 @@ -8044,12 +8044,12 @@ id,tiene_alto_valor_adquisitivo 8043,0 8044,0 8045,1 -8046,0 +8046,1 8047,0 8048,0 8049,1 8050,1 -8051,0 +8051,1 8052,1 8053,0 8054,0 @@ -8095,7 +8095,7 @@ id,tiene_alto_valor_adquisitivo 8094,0 8095,0 8096,0 -8097,0 +8097,1 8098,0 8099,0 8100,0 @@ -8200,7 +8200,7 @@ id,tiene_alto_valor_adquisitivo 8199,0 8200,0 8201,0 -8202,0 +8202,1 8203,0 8204,0 8205,0 @@ -8296,7 +8296,7 @@ id,tiene_alto_valor_adquisitivo 8295,0 8296,0 8297,0 -8298,0 +8298,1 8299,0 8300,1 8301,0 @@ -8328,7 +8328,7 @@ id,tiene_alto_valor_adquisitivo 8327,0 8328,1 8329,0 -8330,0 +8330,1 8331,0 8332,1 8333,0 @@ -8337,8 +8337,8 @@ id,tiene_alto_valor_adquisitivo 8336,0 8337,0 8338,0 -8339,0 -8340,1 +8339,1 +8340,0 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id,tiene_alto_valor_adquisitivo 13531,0 13532,0 13533,0 -13534,0 +13534,1 13535,0 13536,0 13537,0 @@ -13559,7 +13559,7 @@ id,tiene_alto_valor_adquisitivo 13558,0 13559,0 13560,1 -13561,1 +13561,0 13562,0 13563,0 13564,0 @@ -13576,7 +13576,7 @@ id,tiene_alto_valor_adquisitivo 13575,1 13576,0 13577,0 -13578,0 +13578,1 13579,0 13580,1 13581,1 @@ -13671,7 +13671,7 @@ id,tiene_alto_valor_adquisitivo 13670,1 13671,1 13672,0 -13673,0 +13673,1 13674,0 13675,0 13676,1 @@ -13862,7 +13862,7 @@ id,tiene_alto_valor_adquisitivo 13861,0 13862,0 13863,0 -13864,1 +13864,0 13865,0 13866,0 13867,0 @@ -13876,18 +13876,18 @@ id,tiene_alto_valor_adquisitivo 13875,0 13876,0 13877,0 -13878,1 +13878,0 13879,0 13880,0 13881,1 13882,0 13883,0 13884,0 -13885,1 +13885,0 13886,0 13887,0 13888,0 -13889,1 +13889,0 13890,0 13891,0 13892,0 @@ -14076,7 +14076,7 @@ id,tiene_alto_valor_adquisitivo 14075,1 14076,0 14077,1 -14078,1 +14078,0 14079,0 14080,0 14081,0 @@ -14097,7 +14097,7 @@ id,tiene_alto_valor_adquisitivo 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14920,0 14921,0 @@ -14989,7 +14989,7 @@ id,tiene_alto_valor_adquisitivo 14988,1 14989,0 14990,0 -14991,1 +14991,0 14992,0 14993,0 14994,0 @@ -15047,7 +15047,7 @@ id,tiene_alto_valor_adquisitivo 15046,0 15047,1 15048,0 -15049,0 +15049,1 15050,0 15051,0 15052,1 @@ -15071,7 +15071,7 @@ id,tiene_alto_valor_adquisitivo 15070,0 15071,0 15072,0 -15073,0 +15073,1 15074,1 15075,0 15076,0 @@ -15135,7 +15135,7 @@ id,tiene_alto_valor_adquisitivo 15134,0 15135,0 15136,1 -15137,0 +15137,1 15138,0 15139,0 15140,0 @@ -15153,9 +15153,9 @@ id,tiene_alto_valor_adquisitivo 15152,1 15153,0 15154,0 -15155,0 +15155,1 15156,0 -15157,1 +15157,0 15158,1 15159,0 15160,0 @@ -15198,7 +15198,7 @@ id,tiene_alto_valor_adquisitivo 15197,0 15198,0 15199,0 -15200,1 +15200,0 15201,0 15202,0 15203,0 @@ -15211,7 +15211,7 @@ id,tiene_alto_valor_adquisitivo 15210,0 15211,0 15212,0 -15213,1 +15213,0 15214,1 15215,0 15216,0 @@ -15266,7 +15266,7 @@ id,tiene_alto_valor_adquisitivo 15265,0 15266,0 15267,0 -15268,0 +15268,1 15269,1 15270,0 15271,0 @@ -15277,7 +15277,7 @@ id,tiene_alto_valor_adquisitivo 15276,0 15277,0 15278,0 -15279,0 +15279,1 15280,0 15281,0 15282,0 @@ -15288,7 +15288,7 @@ id,tiene_alto_valor_adquisitivo 15287,0 15288,0 15289,1 -15290,0 +15290,1 15291,0 15292,1 15293,0 @@ -15353,7 +15353,7 @@ id,tiene_alto_valor_adquisitivo 15352,0 15353,0 15354,0 -15355,0 +15355,1 15356,0 15357,0 15358,0 @@ -15479,7 +15479,7 @@ id,tiene_alto_valor_adquisitivo 15478,0 15479,0 15480,0 -15481,0 +15481,1 15482,1 15483,1 15484,0 @@ -15540,7 +15540,7 @@ id,tiene_alto_valor_adquisitivo 15539,0 15540,1 15541,0 -15542,0 +15542,1 15543,0 15544,0 15545,0 @@ -15568,7 +15568,7 @@ id,tiene_alto_valor_adquisitivo 15567,0 15568,0 15569,0 -15570,1 +15570,0 15571,0 15572,0 15573,0 @@ -15579,14 +15579,14 @@ id,tiene_alto_valor_adquisitivo 15578,0 15579,0 15580,1 -15581,0 +15581,1 15582,0 15583,0 15584,0 15585,1 15586,1 15587,0 -15588,0 +15588,1 15589,0 15590,0 15591,1 @@ -15595,7 +15595,7 @@ id,tiene_alto_valor_adquisitivo 15594,0 15595,0 15596,1 -15597,1 +15597,0 15598,0 15599,0 15600,0 @@ -15639,14 +15639,14 @@ id,tiene_alto_valor_adquisitivo 15638,0 15639,0 15640,0 -15641,0 +15641,1 15642,0 15643,0 15644,0 15645,0 15646,1 15647,0 -15648,1 +15648,0 15649,1 15650,0 15651,0 @@ -15658,7 +15658,7 @@ id,tiene_alto_valor_adquisitivo 15657,0 15658,0 15659,0 -15660,0 +15660,1 15661,0 15662,1 15663,0 @@ -15729,7 +15729,7 @@ id,tiene_alto_valor_adquisitivo 15728,0 15729,1 15730,0 -15731,1 +15731,0 15732,0 15733,0 15734,0 @@ -15835,7 +15835,7 @@ id,tiene_alto_valor_adquisitivo 15834,1 15835,1 15836,0 -15837,1 +15837,0 15838,0 15839,0 15840,0 @@ -15984,7 +15984,7 @@ id,tiene_alto_valor_adquisitivo 15983,0 15984,0 15985,0 -15986,1 +15986,0 15987,0 15988,0 15989,0 @@ -16043,7 +16043,7 @@ id,tiene_alto_valor_adquisitivo 16042,0 16043,0 16044,0 -16045,0 +16045,1 16046,0 16047,0 16048,0 @@ -16129,7 +16129,7 @@ id,tiene_alto_valor_adquisitivo 16128,0 16129,0 16130,0 -16131,1 +16131,0 16132,0 16133,0 16134,0 @@ -16171,7 +16171,7 @@ id,tiene_alto_valor_adquisitivo 16170,0 16171,0 16172,0 -16173,1 +16173,0 16174,0 16175,1 16176,1 @@ -16224,7 +16224,7 @@ id,tiene_alto_valor_adquisitivo 16223,0 16224,0 16225,1 -16226,0 +16226,1 16227,1 16228,0 16229,0 @@ -16242,7 +16242,7 @@ id,tiene_alto_valor_adquisitivo 16241,1 16242,0 16243,0 -16244,0 +16244,1 16245,1 16246,1 16247,0