diff --git "a/parte_2/#1 - \303\201rbol de decisi\303\263n.ipynb" "b/parte_2/#1 - \303\201rbol de decisi\303\263n.ipynb" index 2235c37..b22ad8c 100644 --- "a/parte_2/#1 - \303\201rbol de decisi\303\263n.ipynb" +++ "b/parte_2/#1 - \303\201rbol de decisi\303\263n.ipynb" @@ -1482,7 +1482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/parte_2/#2 - KNN.ipynb b/parte_2/#2 - KNN.ipynb index 7132517..38a1dc4 100644 --- a/parte_2/#2 - KNN.ipynb +++ b/parte_2/#2 - KNN.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a3faf75e", + "id": "8f9972d4", "metadata": {}, "source": [ "# Modelo: KNN" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "25bf4558", + "id": "75528424", "metadata": {}, "source": [ "El siguiente modelo que vamos a entrenar se trata de KNN. Utilizaremos KNeighborsClassifier, distintos scalers y métricas de la libreria sklearn" @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "885bab5d", + "id": "1c782c62", "metadata": {}, "source": [ "## Librerias y funciones necesarias" @@ -26,7 +26,7 @@ }, { "cell_type": "markdown", - "id": "b39b9481", + "id": "cff34f8c", "metadata": {}, "source": [ "Comenzamos importando todas las librerias y funciones necesarias" @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "0b336df3", + "id": "cac81f6f", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "22ac65af", + "id": "832c71c0", "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "markdown", - "id": "6e8de06c", + "id": "1a5f5cbd", "metadata": {}, "source": [ "## Primer preprocesamiento" @@ -84,7 +84,7 @@ }, { "cell_type": "markdown", - "id": "6c44f992", + "id": "34f41e41", "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. También, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo. Por último, vamos a escalar nuestro dataset con StandarScaler de sklearn" @@ -93,7 +93,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "80c3b20c", + "id": "41abf772", "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ }, { "cell_type": "markdown", - "id": "9d7b7c06", + "id": "d828c212", "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" @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "8e3a8131", + "id": "bfd44bb7", "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "markdown", - "id": "58d6f097", + "id": "4b8a9e90", "metadata": {}, "source": [ "Finalemte escalamos los datos" @@ -139,7 +139,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "3b061101", + "id": "a770246a", "metadata": {}, "outputs": [], "source": [ @@ -150,7 +150,7 @@ }, { "cell_type": "markdown", - "id": "87329dae", + "id": "adcb7277", "metadata": {}, "source": [ "### Entrenamiento" @@ -158,7 +158,7 @@ }, { "cell_type": "markdown", - "id": "1b7c15a6", + "id": "986f8d05", "metadata": {}, "source": [ "Vamos a realizar el entrenamiento de KNN, buscando hiperparámetros adecuados y utilizando K folds. Utilizamos Gridsearch y StratifiedKfold de sklearn. Variaremos la cantidad de vecinos asi como también si se ponderan las distancias o no de los mismos" @@ -167,7 +167,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "f4ff555f", + "id": "df355929", "metadata": {}, "outputs": [ { @@ -182,14 +182,14 @@ "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 1.7min\n", - "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 7.4min finished\n" + "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 1.3min\n", + "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 5.6min finished\n" ] }, { "data": { "text/plain": [ - "GridSearchCV(cv=,\n", + "GridSearchCV(cv=,\n", " estimator=KNeighborsClassifier(), n_jobs=-1,\n", " param_grid={'n_neighbors': array([20, 25, 30, 35, 40, 45, 50, 55]),\n", " 'weights': ['uniform', 'distance']},\n", @@ -211,7 +211,7 @@ }, { "cell_type": "markdown", - "id": "a1d8f1f1", + "id": "38cbecc5", "metadata": {}, "source": [ "Veamos que hiperprámetros resultaron óptimos" @@ -220,7 +220,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "5885c362", + "id": "a2798f08", "metadata": {}, "outputs": [ { @@ -240,7 +240,7 @@ }, { "cell_type": "markdown", - "id": "3e0afcd2", + "id": "73b101ba", "metadata": {}, "source": [ "### Métricas" @@ -248,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "8564f03c", + "id": "a54f091d", "metadata": {}, "source": [ "Evaluamos nuestro modelo en base a las métricas" @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "926ab997", + "id": "c1d751b5", "metadata": {}, "outputs": [], "source": [ @@ -267,7 +267,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "1b99a399", + "id": "eae924f9", "metadata": {}, "outputs": [ { @@ -328,7 +328,7 @@ }, { "cell_type": "markdown", - "id": "69f36667", + "id": "a78b8836", "metadata": {}, "source": [ "Obtuvimos 0.88 de AUC-ROC sobre test. Veamos si on otro preprocesamiento podemos mejorar estos datos" @@ -336,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "24d81c67", + "id": "a3b24e1a", "metadata": {}, "source": [ "## Segundo preprocesamiento" @@ -344,7 +344,7 @@ }, { "cell_type": "markdown", - "id": "d508c6a1", + "id": "7407a6b7", "metadata": {}, "source": [ "En primer lugar obtenemos el dataset para entrenar. 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. También, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo. Por último, vamos a escalar nuestro dataset con Normalizer de sklearn" @@ -353,7 +353,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "73f46ba9", + "id": "a4ec3dad", "metadata": {}, "outputs": [ { @@ -372,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "958175b1", + "id": "015056cd", "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" @@ -381,7 +381,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "125342ea", + "id": "d07f3429", "metadata": {}, "outputs": [], "source": [ @@ -390,7 +390,7 @@ }, { "cell_type": "markdown", - "id": "2fd268e5", + "id": "bfcfe5a8", "metadata": {}, "source": [ "Luego escalamos" @@ -399,7 +399,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "7e293577", + "id": "60e02931", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "9c6a1e58", + "id": "b4ce45f0", "metadata": {}, "source": [ "### Entrenamiento" @@ -418,7 +418,7 @@ }, { "cell_type": "markdown", - "id": "c1369b64", + "id": "1383f833", "metadata": {}, "source": [ "Vamos a realizar el entrenamiento de KNN, buscando hiperparámetros adecuados y utilizando K folds. Utilizamos Gridsearch y StratifiedKfold de sklearn. Variaremos la cantidad de vecinos asi como también si se ponderan las distancias o no de los mismos" @@ -427,7 +427,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "f34b7e76", + "id": "aeeb6466", "metadata": {}, "outputs": [ { @@ -442,14 +442,14 @@ "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 32.0s\n", - "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 2.3min finished\n" + "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 19.4s\n", + "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 1.4min finished\n" ] }, { "data": { "text/plain": [ - "GridSearchCV(cv=,\n", + "GridSearchCV(cv=,\n", " estimator=KNeighborsClassifier(), n_jobs=-1,\n", " param_grid={'n_neighbors': array([20, 25, 30, 35, 40, 45, 50, 55]),\n", " 'weights': ['uniform', 'distance']},\n", @@ -471,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "21f88330", + "id": "867243b5", "metadata": {}, "source": [ "Veamos que hiperprámetros usar" @@ -480,7 +480,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "f780fd17", + "id": "f597eefb", "metadata": {}, "outputs": [ { @@ -500,7 +500,7 @@ }, { "cell_type": "markdown", - "id": "4b9215d2", + "id": "058a8a60", "metadata": {}, "source": [ "### Métricas" @@ -509,7 +509,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "916afbab", + "id": "2128e12b", "metadata": {}, "outputs": [], "source": [ @@ -519,7 +519,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "ef61f292", + "id": "e12ad2cc", "metadata": {}, "outputs": [ { @@ -578,7 +578,7 @@ }, { "cell_type": "markdown", - "id": "c27699e1", + "id": "628f3ab8", "metadata": {}, "source": [ "Vemos que nuesto score empeoro respecto del preprocesamiento anterior. Sigamos con otro preprocesado más" @@ -586,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "da84169f", + "id": "ebdb6775", "metadata": {}, "source": [ "## Tercer preprocesamiento" @@ -594,7 +594,7 @@ }, { "cell_type": "markdown", - "id": "28cea177", + "id": "492316fe", "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. También, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo. Por último, vamos a escalar nuestro dataset con MinMaxScaler de sklearn" @@ -602,8 +602,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "a7ab1cb7", + "execution_count": 18, + "id": "7d91e5df", "metadata": {}, "outputs": [ { @@ -622,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "333e5829", + "id": "3e26c262", "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" @@ -630,8 +630,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "68411e70", + "execution_count": 19, + "id": "2c5c95c0", "metadata": {}, "outputs": [], "source": [ @@ -640,7 +640,7 @@ }, { "cell_type": "markdown", - "id": "a01c0c20", + "id": "abf41c87", "metadata": {}, "source": [ "Luego escalamos" @@ -648,8 +648,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "c8bbb937", + "execution_count": 20, + "id": "2116620c", "metadata": {}, "outputs": [], "source": [ @@ -660,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "1c245462", + "id": "0594ee72", "metadata": {}, "source": [ "### Entrenamiento" @@ -668,7 +668,7 @@ }, { "cell_type": "markdown", - "id": "cae6190a", + "id": "172cd000", "metadata": {}, "source": [ "Vamos a realizar el entrenamiento de KNN, buscando hiperparámetros adecuados y utilizando K folds. Utilizamos Gridsearch y StratifiedKfold de sklearn. Variaremos la cantidad de vecinos asi como también si se ponderan las distancias o no de los mismos" @@ -676,8 +676,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "65280eee", + "execution_count": 21, + "id": "1c457f96", "metadata": {}, "outputs": [ { @@ -692,21 +692,21 @@ "output_type": "stream", "text": [ "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 54.0s\n", - "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 4.5min finished\n" + "[Parallel(n_jobs=-1)]: Done 17 tasks | elapsed: 39.5s\n", + "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 3.1min finished\n" ] }, { "data": { "text/plain": [ - "GridSearchCV(cv=,\n", + "GridSearchCV(cv=,\n", " estimator=KNeighborsClassifier(), n_jobs=-1,\n", " param_grid={'n_neighbors': array([20, 25, 30, 35, 40, 45, 50, 55]),\n", " 'weights': ['uniform', 'distance']},\n", " scoring='roc_auc', verbose=4)" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "ce87def6", + "id": "d4cf0f77", "metadata": {}, "source": [ "Veamos que hiperprámetros usar" @@ -729,8 +729,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "bb53e3e9", + "execution_count": 22, + "id": "d5b1f866", "metadata": {}, "outputs": [ { @@ -739,7 +739,7 @@ "{'n_neighbors': 40, 'weights': 'uniform'}" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -750,7 +750,7 @@ }, { "cell_type": "markdown", - "id": "ee6a81cf", + "id": "b87daae6", "metadata": {}, "source": [ "### Métricas" @@ -758,8 +758,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "b5be24a6", + "execution_count": 23, + "id": "2f151abb", "metadata": {}, "outputs": [], "source": [ @@ -768,8 +768,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "27395483", + "execution_count": 24, + "id": "d4a1e730", "metadata": {}, "outputs": [ { @@ -802,6 +802,18 @@ "needs_background": "light" }, "output_type": "display_data" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ @@ -816,7 +828,7 @@ }, { "cell_type": "markdown", - "id": "9b5f70dd", + "id": "332a2d75", "metadata": {}, "source": [ "Mejoramos respecto del preprocesado anterior, practicamente obtuvimos algo similar al primer preprocesamiento. Para finalizar, testiemos en los holdouts" @@ -824,19 +836,118 @@ }, { "cell_type": "markdown", - "id": "754f147e", + "id": "1bf3d882", "metadata": {}, "source": [ "## Holdout" ] }, + { + "cell_type": "markdown", + "id": "3331a3aa", + "metadata": {}, + "source": [ + "El mejor modelo en cuanto a score resultó ser el primero y por ende testearemos el holdout con él" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "da752f17", + "metadata": {}, + "outputs": [], + "source": [ + "df, df_for_prediction = obtener_datasets()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "ec597fd1", + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessing import aplicar_preparacion_holdout\n", + "\n", + "X_holdout = aplicar_preparacion_holdout(df_for_prediction, generalizada=False)" + ] + }, + { + "cell_type": "markdown", + "id": "e65fbac0", + "metadata": {}, + "source": [ + "Luego aplicamos el preprocesado correspondiente" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "c19359d4", + "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": "b5992dad", + "metadata": {}, + "source": [ + "Y escalamos" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "4c150995", + "execution_count": 42, + "id": "6cc5376e", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "X_holdout_numerico = get_dataframe_scaled(X_holdout_numerico,StandardScaler())" + ] + }, + { + "cell_type": "markdown", + "id": "ba24a0f7", + "metadata": {}, + "source": [ + "Predecimos" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b5b2f94f", + "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": 44, + "id": "59eade46", + "metadata": {}, + "outputs": [], + "source": [ + "name_model = '#2 - KNN'\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": { diff --git a/parte_2/#2 - Naive Bayes.ipynb b/parte_2/#2 - Naive Bayes.ipynb deleted file mode 100644 index 0b103ba..0000000 --- a/parte_2/#2 - Naive Bayes.ipynb +++ /dev/null @@ -1,2435 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Modelo: Naive Bayes\n", - "\n", - "https://scikit-learn.org/stable/modules/naive_bayes.html\n", - "\n", - "Trabajaremos con el modelo de **Naive Bayes**, se conocen 3 diferentes formas de implementar: \n", - "* Cuando trabajamos con features continuos -> (GaussianNB) \n", - "* Cuando trabajamos con features discretos -> (MultinomialNB) \n", - "* Cuando trabajamos con features categóricos -> (CategoricalNB)\n", - "\n", - "Estaremos utilizando los 3 tipos de técnicas de Naive Bayes. Esto implica tener que dividir nuestro dataset de 3 formas distintas." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Importación de librerias y datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "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 accuracy_score, roc_auc_score\n", - "from sklearn.metrics import classification_report" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Importamos diferentes funciones del preprocessing.py que utilizaremos y el dataset a trabajar" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [] - }, - "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", - "\n", - "df, df_for_prediction = obtener_datasets()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# GaussianNB" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.naive_bayes import GaussianNB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solo para valores continuos. Es decir nos quedaremos con las 2 columnas numéricas continuas de 'edad' y 'suma_declarada_bolsa_argentina'" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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anios_estudiadosedadganancia_perdida_declarada_bolsa_argentinahoras_trabajo_registradastiene_alto_valor_adquisitivo
count32561.00000032561.00000032561.00000032561.00000032561.000000
mean14.05386838.581647990.34501440.4374560.240810
std2.66448813.6404337408.98695112.3474290.427581
min1.00000017.000000-4356.0000001.0000000.000000
25%13.00000028.0000000.00000040.0000000.000000
50%14.00000037.0000000.00000040.0000000.000000
75%16.00000048.0000000.00000045.0000000.000000
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" - ], - "text/plain": [ - " anios_estudiados edad \\\n", - "count 32561.000000 32561.000000 \n", - "mean 14.053868 38.581647 \n", - "std 2.664488 13.640433 \n", - "min 1.000000 17.000000 \n", - "25% 13.000000 28.000000 \n", - "50% 14.000000 37.000000 \n", - "75% 16.000000 48.000000 \n", - "max 20.000000 90.000000 \n", - "\n", - " ganancia_perdida_declarada_bolsa_argentina horas_trabajo_registradas \\\n", - "count 32561.000000 32561.000000 \n", - "mean 990.345014 40.437456 \n", - "std 7408.986951 12.347429 \n", - "min -4356.000000 1.000000 \n", - "25% 0.000000 40.000000 \n", - "50% 0.000000 40.000000 \n", - "75% 0.000000 45.000000 \n", - "max 99999.000000 99.000000 \n", - "\n", - " tiene_alto_valor_adquisitivo \n", - "count 32561.000000 \n", - "mean 0.240810 \n", - "std 0.427581 \n", - "min 0.000000 \n", - "25% 0.000000 \n", - "50% 0.000000 \n", - "75% 0.000000 \n", - "max 1.000000 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Primer Preprocesamiento: obtener_features_continuas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Empezamos primero importando la función creada en el preprocessing.py que nos da el dataset con las features continuas mencionadas" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from preprocessing import obtener_features_continuas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ademas aplicamos la preparación al dataset que ya venimos comentando en otros notebooks y luego aplicamos esta función importada para obtener las features consideradas como variables continuas." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "df, df_for_prediction = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df_c = obtener_features_continuas(X_df) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entre hiperparametros importantes está el 'var_smoothing' es aquel factor que se le asigna cuando viene eventos con probabilidades igual a 0 de elementos que nunca vio. Este hiperparametro es el único que se tendrá en cuenta al utilizar GridSearchCV. Recordemos también que inicialmente haremos una partición del dataset de manera estratificada, además al utilizar cross-validation con GridSearch le estaremos indicando que haga folds estratificados con StatifiedKFold manteniendo las diferentes proporciones de la clase a predecir en cada fold.\n", - "\n", - "Entrenemos con todo lo mencionado:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 10 folds for each of 7 candidates, totalling 70 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 17 tasks | elapsed: 2.8s\n", - "[Parallel(n_jobs=-1)]: Done 70 out of 70 | elapsed: 3.2s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=GaussianNB(), n_jobs=-1,\n", - " param_grid={'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09,\n", - " 1e-08, 1e-07]},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_df_c, y_df, test_size=0.20, random_state=10, stratify=y_df)\n", - "\n", - "params = {\n", - " 'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09, 1e-08, 1e-07],\n", - "}\n", - "\n", - "clf = GaussianNB()\n", - "cv = StratifiedKFold(n_splits=10).split(X_train, y_train)\n", - "clf_1 = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "\n", - "clf_1.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Vamos a predecir ahora con X_test y mostrar diferentes métricas:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.7608417722498504\n", - "AUC-ROC score sobre train: 0.7643356120777687\n", - "Accuracy sobre test: 0.7930293259634577\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.80 0.98 0.88 4945\n", - " Alto valor 0.74 0.21 0.33 1568\n", - "\n", - " accuracy 0.79 6513\n", - " macro avg 0.77 0.60 0.61 6513\n", - "weighted avg 0.78 0.79 0.75 6513\n", - "\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": [ - "y_pred = clf_1.predict(X_test)\n", - "\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_1.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_1.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_test, y_pred))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_1.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_1, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lo cual se puede observar que a pesar de haber solo utilizado 2 features, no fue un muy mal resultado. Es mucho mejor que un modelo random." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Guardamos la predicción de probabilidad de cada clase para usarlo en un futuro ensamble gaussiano. Por ahora nos quedamos como mejor preprocesamiento a éste ultimo aplicado." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "proba_gauss_train = clf_1.predict_proba(X_train)\n", - "proba_gauss_test = clf_1.predict_proba(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Segundo Preprocesamiento: get_dataframe_scaled()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "En este segundo preprocesamiento, utilizaremos 5 escalados guardados en un vector al cual aplicaremos con nuestra función get_dataframe_scaled(). Es decir GaussianNB entrenará 5 preprocesamientos distintos, uno por uno. Solamente consideraremos la métrica de AUC-ROC por tratarse de 5 escalados distintos. Importamos nuestra función y los 5 escalados: " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from preprocessing import get_dataframe_scaled\n", - "from sklearn.preprocessing import (\n", - " MinMaxScaler,\n", - " PowerTransformer,\n", - " RobustScaler,\n", - " StandardScaler,\n", - " Normalizer\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entrenamiento y AUC-ROC score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entrenemos entonces el modelo con estos 5 preprocesamientos (escalados). Recordemos otra vez escalar después de dividir para evitar un posible data leak." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 1 con scaler de StandardScaler()\n", - " AUC-ROC score sobre test: 0.7613218619095766\n", - "AUC-ROC score sobre train: 0.7643349026782453\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 2 con scaler de MinMaxScaler()\n", - " AUC-ROC score sobre test: 0.7618572795650111\n", - "AUC-ROC score sobre train: 0.7643346366534238\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 3 con scaler de RobustScaler()\n", - " AUC-ROC score sobre test: 0.7593918950289923\n", - "AUC-ROC score sobre train: 0.7643356120777689\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 4 con scaler de PowerTransformer()\n", - " AUC-ROC score sobre test: 0.7648258393347227\n", - "AUC-ROC score sobre train: 0.7674680583795501\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 5 con scaler de Normalizer()\n", - " AUC-ROC score sobre test: 0.6293316403912425\n", - "AUC-ROC score sobre train: 0.6238155698281903\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n" - ] - } - ], - "source": [ - "X_df_c = obtener_features_continuas(X_df) \n", - "scalers = [StandardScaler(), MinMaxScaler(), RobustScaler(), PowerTransformer(), Normalizer()]\n", - "for count, scaler in enumerate(scalers):\n", - " print(\"---------------------------------------------------------------------\")\n", - " print(\"Aplicando preprocesamiento #\",count+1, \"con scaler de\", scaler)\n", - " X_train, X_test, y_train, y_test = train_test_split(X_df_c, y_df, test_size=0.20, random_state=10, stratify=y_df) \n", - " X_train = get_dataframe_scaled(X_train, scaler)\n", - " X_test = get_dataframe_scaled(X_test, scaler)\n", - "\n", - " params = {'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09, 1e-08, 1e-07]}\n", - "\n", - " cv_e = StratifiedKFold(n_splits=10).split(X_train, y_train)\n", - " clf = GridSearchCV(GaussianNB(), params, scoring='roc_auc', cv=cv_e, n_jobs = -1)\n", - " \n", - " clf.fit(X_train, y_train)\n", - " y_pred = clf.predict(X_test)\n", - "\n", - " 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(\"Los mejores hiperpametros elegidos: \", clf.best_params_) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Se pudo observar que el escalado \"PowerTransformer()\" es superior al resto en cuanto el AUC-ROC ya demás superó al primer preprocesamiento realizado." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Eligiendo al mejor: PowerTransformer()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Repliquemos la situación ya con el mejor hiperparametro encontrado con este escalado:\n", - "\n", - " Los mejores hiperparámetros elegidos: {'var_smoothing': 1e-13} con el preprocesamiento PowerTransformer()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " AUC-ROC score sobre test: 0.7648258393347227\n", - "AUC-ROC score sobre train: 0.7674680583795501\n", - "Accuracy sobre test: 0.7850452940273299\n" - ] - } - ], - "source": [ - "X_df_c = obtener_features_continuas(X_df) \n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X_df_c, y_df, test_size=0.20, random_state=10, stratify=y_df) \n", - "X_train = get_dataframe_scaled(X_train, PowerTransformer())\n", - "X_test = get_dataframe_scaled(X_test, PowerTransformer())\n", - "\n", - "clf_2 = GaussianNB(var_smoothing=1e-13)\n", - " \n", - "clf_2.fit(X_train, y_train)\n", - "y_pred = clf_2.predict(X_test)\n", - "\n", - "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_test, y_pred))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Veamos la matriz de confusión y diferentes métricas:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ei4K6iIjkL/Wpx6KgLiIi+Us19VgU1EVEJH+pph6LLoFERESKhGrqIiKSv9T8HouCuoiI5C81v8eioC4iInnLFNRjUbuGiIhIkVBNXURE8pZq6vEoqIuISP5STI9FQV1ERPKWaurxqE9dRESkSKimLiIieUs19XgU1EVEJG8pqMej5ncREclbZtagJQvnb2lmY8zMzWyzlH0nmNkPZjbHzL42s73T5G9jZg+Y2WQzm2Fmz5lZpzTptjKzwWY228x+NbMLrB5vQEFdRETylzVwabhLSdOqbWZ9gPuAp4E9gMHAi2a2ZUrSp4FdgZOBI4HuQD8za5J0rDWBt4CxwN7AbcBVwDlxC6vmdxERkTTMrAdwGiG43pOy+0rgKXe/NHrd38w2AC4D9ozy9wJ2A3Zz97ejbSOAYcCBwDNR3vOASUAfd58HvGtmywMXm9kd7j63rmVWTV1ERPJWjpvf7yAE8xEpZeoKdGNxUE54CtjJzJpHr/cApgLvJBK4+wjgK6LAn5TupSigJx+rLdArToEV1EVEJG/lKqib2cHA+oRm8FQ9ovXwlO3DgGbA6knpRri7p0nXIzpPBdA5zbGGA550rjpR87uIiOSthta2zaw10Dpl83R3n15DnnLgVuAid5+epgztovXUlO1TonX7pHSpaRLpEmnapjuWu88zs8qkdHWimrqIiBSzs4HRKcvZteS5BPgTeGjpFi37VFMXEZG8lYV+8VuB+1O21VRLX40wMO4AoE10/pbR7pZm1pLFNfI2wLik7Ika/ORoPYXQtJ6qXVKaqUnHSi5HM6A8KV2dKKiLiEj+amBMj5rZMwbxNFYn9Iu/nmZff+AT4IjodQ+WHETXA5gH/By9Hg7sbGaW0q/eA/g2Kt8sMxtN9b7z7oR3n9rXXiM1v4uISN7KwUC5r4AdUpazon0nA6e6+8/AD8AhKXkPA95NGsXej1Ar3ynp/XQDNgbeSMrXD9jPzJqmHGsqMChO4VVTFxGRvLWsp4l196nA+xnK8Lm7fxH9fAXwhJmNJNTgDwO2ALZLOtZgM3sLeNDMzgHmANcA3wAvJJ3iJsLENH3N7G7CqPvzgItTbnOrlYK6iIhITO7eNxolf2G0jAAOcPfBKUkPI/Tr/5cQc98GznD3BUnH+snMdo3SvQFMAC4HbolbLgV1ERHJW/nwQBd3f580vfvu/gDwQC15pwEnREtN6QYBqVPMxqagLiIi+Sv3Mb2gKKiLiEjeyoeaeiHR6HcREZEioZq6iIjkLdXU41FQFxGRvKWgHo+CuoiI5C0F9XjUpy4iIlIkVFMXEZH8pYp6LArqjdgL/z6ZPbZdD4DHXvmYEy9/PG265dpWcOrhvdl9m3VZc9XlKWvejKkzKxn601ief+cLHn5xMPMXLMx4nrIWTfn7UTty4C4b03WV5Vm4cBE/jPqTvm98yr3PfMiiRZ4233+vPIqj9615Lobvf/qDzQ65to7vWArJ7Nmz+fyzIQz9/nuGDRvKsO+/Z+zYPwA4+9zzOfb4GufyqOb338dw0H77MHt2JQD3P/QoPTffImP6BQsW8MzTfXntlZf5ddQvLFy4iM6dO7Pr7ntw9LHH06JFi/q/OakzNb/Ho6DeSB26+6ZVAb0mG/VYhZfvOo2O7VsBsHDhImZUzmH5dq3Yvmcrtu/Zjb8cuDX7nHoXE6fMrJa/Y/tWvH3/mXRffUUAZs2eS/NmTei5fhd6rt+FA3fZmH1OvYs5c+dnLMPsOfOYNnNO2n2Tps6qy9uVAvTdt99w2sknZu14V19xeVVAr01lZSWnnHgCX30Zpvlu1qwZJaWl/PDDCH74YQRvvPYqDzzyOO3bt89a+SQ9BfV4FNQboXaty7nx3IOYOqOSsROmsXbXTmnTlZaW8ORNf6Vj+1b8NnYyZ177NP/7eBgLFiyiZXlzjt2vF9eddQAb9ejMzecdzHEXPVztGI/f+Be6r74iYydM44RLH6X/JyMwMw7aZWPuuvRwttlkTf514SGccuWTGcv73NtfZGxFkOLWunUb1l5nHdZeex16rLMuN99wHRMnToh9nFdfeYnBgz5igw034puvv6o1/bVXX8lXX35By5YtuezKq9ll190pKSlh8KCBXHrRBfz880guOPcs7nvwkXq8K4lDQT0eDZRrhG4450BWWK41l93xChMmV69dJ/TaqCurr9IBgL9e+hhvfvQ9CxYsAmBm5Vzu6vs+Nz/8NgD77bhhtV++3bdZl203XQuAw8+9n/6fhMcOuzvPvf0FZ1zzFABH77MlPbqumN03KQVvk003Y8DgIfz3gYc569zz2WPPvWjWrFns40yZMplbbryeVq1ace75F9aa/scfRvDaqy8DcMnlV7Hb7ntSUhL+VPbaamtuue0OAIZ88jEDB3wYuzwiS5OCeiOzwxbdOXrfLRnyzS/c/9zAGtN26tCm6ucvhv6aNs1n34XtzZqW0qxp6RL7jth7cwDeHzKCT775pVreZ978nF/GTKS0tITDdt8s1vuQ4ldaWlp7ojq48fprmTJlCmeceRYdll++1vSvv/Yq7s4qnTuz+x57Vtu/4UYbs1nP8N1+7bVXslJGqYE1cGlkFNQbkRbNm3LnxX2YP38hp1/zFO7pB6gljPp9YtXPm6yzWto0m60Xtg/7eRxz5y1YYt/2PbsB8PbAoRnP8fagsG/HLbrX/gZEYhr40QDeeO1V1t9gQw457PA65RnyyccAbL31thmbfrfZNjwy+5PBqU/ZlGwzswYtjU3BBHUza2Fmr5jZdrWnlnQuO2UvunZenjuf7M+3P/xea/rPvv+NL4eNBuC+q45it23WoUmT8JVpWd6c0w7vzTnH7cK8+Qu48NYXl8i7XNuKqsF1348cm/EcQ38K+2pqft9h8+58+/JlTP3kX4z78CYGPnE+l526V9XxRdKprKzkn1ddTpMmTbj08quqmtBr4u788vNIANZca62M6dZcK1ywTpo0kalTp2SnwJKWgno8BTNQzt3nmNn2wL9yXZZCtFGPVTjjyB34bexk/nnPG3XK4+70Oec+nvnXiWzYfRVeuuPUqtHvbVuVs3DhIvoPGcG19/Zj8Nc/L5G30/KLm+7/GD814zn+mBD2tW5ZRkVZM2bNnlctzSortmP+/IXMqJxDm5ZlbLLOqmyyzqqceMh2HHXBA7w/5Ic6vR9pXO6643b++P13jjnuL3Tv0aNOeWbNmkVlZRgh37HjChnTJe+bMGECbdu2a1hhRbKkYGrqkbeBXXNdiEJTUmLcdekRNGlSylnXP0PlnOqBM5Pfxk5mr5Pv4KV3vwLCiPi2rcqrfq4oa0abVmXV8rUsb171c03nq5y9+Fa2VhVL3vf71fDR/P3ap+i2x6W02eIfrNz7Albc7jyOv+hhxk6YxnJtK3jm1hNZc9WOdX4/0jh8/9239H3iMTp1WolTTjujzvkqKxffItmirPr3evG+xd/Vylm6rXJpUk09noKpqUceAu41s1bAG8CfwBIdw+7+RS4Kls/OPGpHNllnVV5+9yve+PC7WHl33XodHr3ueBzn3Buf4/UPv+XPSdNZfeUO/O3gbfjbIdvy/O0ncdo/+/Lwi9ntX7y77wfVts2snMtT/T5j0FcjGdz3Qtq3qeCSk/dMezudNE4LFizgyssuYeHChVx40SWUl5fnukjSAI0xMDdEodXUXwNWBk6Nfh4CfBotn0XrOjGz1ma2SvLiC+tegy0UXVZejotP3pPpM2dzzo3Pxcq7aqf2PH3L32hV0Zw+59zPXX3fZ9Tvk5g9Zz5DR47lrBue5YYH3qKkpIQbzj6QDu1aVuWdWTm36ufyFplvQyova1r184xZ6SeYSee3sVO45+lwO9Hu26yrX3yp8shDDzJixHB22Glneu+4U6y85eUVVT/PmT07Y7o5sxd/V8srKjKmkyzQ6PdYCi2o75Cy7Ji0JF7X1dnA6ORlwfivslnWvHDjOQdSUdacmx96h6kzKqkoa7bEUloSvvVNoqb0irJmVQHyxEO2pUXzpnw1fAwffJq+3/q2R98FQp/4DpsvHsE+dsK0qp9X6tg2Y/lWWj7smz5zdtr+9Jp8+u0oANq0KmO5tvrDKjBhwnju/c+dtGjRgjP/cTaVs2YtsSQH47lz54RtcxZvq6ioqKrZjx//Z8bzJO9bvg63yYksKwXV/O7u1dtj6+9W4P7kDU06bjQ6i8fPC6uutBwAV52xL1edsW/GdIfvtTmH7xXuvd3isOv45offq0ak/zJmYsZ8M2bNYdzE6azYoTWrrbR4ysxJU2cxfvIMOrZvxbprdMp4W9s6a4bZ7Ib/PC7eGxNJY9LEScydG1qJ9t+n+j3myRJT0Hbv3oNnXgiTzZgZq3ddg++/+5affvwxY96ffgwXucst10GD5JYytcLFU2g1dQDMbF0zO8nM/i9arxv3GO4+3d3HJC9WGn+2qmK2KLqPfdVOmee3bta0CW2jgXLTU+Znf39ImEFul63WyZh/l15rA/BeNNtcHD3X7xKdd7bmgJes2WLLXgAMGvhRxrkcBn40IKTt1WuZlaux0kC5eAqqpm5mzYHHgIMIvSVzgeaAm9lzwNHuXnwd4w2wZZ/ra9z/1n1nst1ma6V9StvXw8ewT+8N2Hjtzmy27mp89n31WeWO2W9LWjQP/eJDvl1y1ri+r3/KobtvxvY916Lneqvx6XdL5j9ol43p2jk8te3pNz+L9b46r9iOkw4NUxa8+dH3tU6kI41Dj7XX5uvvM18g/v77GPbcNfSzZ3pK255778NDD9zH6NG/8fZb/dht9yVr/N988zWfDvkEgL332S+LpZd0GmFcbpBCq6lfC+wFnAy0dfcyoG30eq9ov2TJoy8PZvaceTRpUsrTt/6Ng3fdhLIWIYAv17aCC/+2OzefdxAAAz7/ka+Gj1ki/5sffc+Az3+kpKSEvjf/ld6bhwk7zIwDd96Yuy49AoDHXv24WvP74Xv1pO/Nf2Wf3hss0V9eUdaMQ3fflPceOpvl2lYwY9acOt93L4Vn+rRpTJkyuWpZtCg8e2D27NlLbJ83L3vX8mut1a0qWF99xWW8/Va/qvN+8vFgzj7zdAA232JLtt5m26ydV9JTTT2egqqpA32A/3P3+xIb3H06cJ+ZlQPnA+fmqnDFZvS4KRx/8SM8+M9jWaljWx674S8sWrSImZVzad1y8T28w34ey7H/93DaYxx1/oNVj17td+/fmTV7LiVmlEUj4j/64ifOuv7ZavlKS0rYf6eN2H+njYDQdz933gLatS6ntDRci/45aTrHXPgQP/46PrtvXPLGYQcfwB9/VJ/98D933cF/7rqj6vVV/7yO/Q44MGvnvejSyxk9+je++vILzjv7HzRv3hwrKakaEd+16xrccLPmwZL8U2hBvT0wPMO+4dF+yaKX3/uaTQ+5hlP6bE/vzbux+sodKG/RjElTZzF05B+8/O7XPPDCwIzPQx8/eQa9jriBvx+1Iwftsgmrr9KB+QsX8v13o3jy9U+595kPWbSoetP5h5/9wOV3vkqvjbrSbbWOLNe2JW1aljFleiXDfxnHmwO+48EXBjFlet2ejy0SR3l5OQ88/BhPP/Ukr7/6CqN++ZlFi5y1unVj19324Jjj/kKLFi1qP5A0WCOsbDeIFVJfpJl9CXzn7ken2fcYsJ67b1zf45dtfHrhfBjSKEz59M5cF0EkrRZNls1d4N0veKtBf5dH3LBbo7osKLSa+tXAs2bWBXieMKNcR+BgoBdwSO6KJiIi2aaaejwFFdTd/QUzOwC4HLiFMALega+AA9z91RwWT0REJKcKKqgDuPsrwCtmVkEY+T7V3XWTsohIESopUVU9joIL6glRIFcwFxEpYmp+jyfvg7qZ/TtGcnf3M5daYUREZJlqjPeaN0TeB3VgnxhpHVBQFxGRRinvg7q7r57rMoiISG6ooh5P3gd1ERFpvNT8Hk9BBnUzWxPoBlSb0sndX1j2JRIRkaVBQT2eggrqZtYaeBHondgUrZNnHCpdlmUSEZGlRzE9nkJ7StsNwIrAtoSAfgAhwD8A/AJsmbOSiYiI5FihBfXdgWuAT6LXf7j7h+5+IvAycE7OSiYiIlmnR6/GU1DN74R53ke7+0IzmwUsl7TvDcJ88CIiUiQaYVxukEKrqY8GOkQ//wjsm7SvFzBnmZdIRESWGtXU4ym0mvo7wM6EwXL/Ah4xsy2AecDmhIe8iIiINEqFFtQvAMoB3P0xM5tJeOxqGXA6cG8OyyYiIlnWCCvbDVJQQd3dK4HKpNcvEmrtIiJShBpjE3pDFFSfupkNNLNTzWz5XJdFRESWPrOGLY1NQQV1YCxwM/C7mb1lZseYWatcF0pERJYODZSLp6CCursfTLit7a/AAuB+4E8ze97MDjKz5jktoIiISA4VVFAHcPeZ7v6ou+8FdALOAtoDTwF/5rRwIiKSVWp+j6egBsqlcvdJZjYQWA3oDqyQ4yKJiEgWNcYm9IYoyKBuZmsAfaJlHUIN/Rmgby7LJSIi2aWYHk9BBXUzO5sQyDcFphGmhT0TeN/dF+WybCIiIrlWUEEduAp4BbgaeNPd5+e4PCIishSp+T2eQgvqHaMJaEREpBFQTI+noIK6ArqISOOimno8BXdLm4iIiKRXUDV1ERFpXFRTj0dBXURE8pZiejwK6iIikrdUU4+n4IK6hf/hPYFtCNPDTgYGAP3c3XNZNhERyS7F9HgKaqCcmbUDBgGvAicB20Xr14CBZtY2d6UTEZFCZ2Z7mtkHZjbBzOaa2c9mdquZtUlJt4+ZfW1mc8zsBzM7Ps2xmpnZTWY2zsxmmdk7ZtY9Tboe0b5ZUdobzaxZfcpfUEGd8NjVNYDd3L29u6/t7u2B3aLtN+e0dCIiklU5ePRqe+AT4GRCbLkVOAZ4NqlM2wAvAoOBPYCngQfM7OCUY/0b+BtwEXAg0Bx4N/kCIaqsvgc0i9JcBJwYnTe2Qmt+3xc4393fSd7o7u+Y2f8BNxAeyyoiIkVgWTe/u/vjKZveN7O5wH/NbCV3/wO4FPjE3U+O0vSPnklyFfAcgJmtQohHp7r7g9G2T4HfCC3MN0Z5TwZaAwe4++QoXRPgbjO7NjpfnRVaTb2CzI9XHRftFxGRIlFi1qAlSyZF62Zm1hzYgaSae+QpYG0z6xK93pUQY6vSRUH7bcK4sIQ9gP8lAnrkmSjvrnELWmhB/UvgdDMrTd5oZiXAGcAXOSmViIgUFTMrNbMWZrYJcBnwiruPInT1NgWGp2QZFq17JK3Hu/uUNOl6JL3ukXosd58KjE1JVyeF1vz+f4SrnJ/M7GVCrb0jsD+wIvW4qhERkfzV0Mq2mbUmNG8nm+7u02vJ+iuwcvTzm8AR0c/tovXUlPSJ4N0+KV1qmkS69kmv65quTgqqpu7uHwJbE2rsRxD6L44g1NC3dvcBOSyeiIhkWRYGyp0NjE5Zzq7DqfcEtiIMdFsbeDW1lTgfFVpNHXf/nDBCUEREilxJw7vFbwXuT9lWWy0dd/8m+nFwNMDtK+AAYGi0vU1KlkQNPtE3PiVNmkS65P7zuqark4KqqYuIiMTh7tPdfUzKUmtQT/ENMB9YExgZ/Zza3514PTxpvUJ0y1pquuQ+9OGpx4pueetE9X77WuV9Td3MXgHOcfcfo59r4oQrmyHAg+4+d6kXUERElpo8mSZ2C8LguJ/dfa6Z9QcOBm5PSnMYMCwaTAdh/Nci4CCiloIowO8KXJ2Urx9wkZm1jQbIARwS5X07bkHzPqgDrYBEP0ZrQuCuycrAkYT/hOOWXrFERGRpW9Yx3cxeAD4j1M5nAxsC50WvX4qSXU24f/1uwu1nOxDGdx2WOI67jzGz+4GbzGwh8DthYplpwL1Jp7yHcPfWS2Z2LSGG3QTcE/cedSiAoO7uOyT93LsueczsCOCOpVUmERFZNoxlXlMfQgjOFxK6qEcB9wE3u/s8AHf/yMwOBP4JnECYUOav7p567/qZwEzgekIFdSCws7tPSyRw9ylmthMhZr0EzCDU7C+uT+HzPqjX07uEkfEiIlLAsjBQLhZ3v54QhGtL9wpQY5dw1AV8brTUlG4YsHOMYmZUcEE9mmhmR6Ab0CJ1v7vf6u5/smRfh4iISNErqKBuZisC7xMCukNVu0xyP3u9JsEXEZH8kycD5QpGod3SdithDt7OhIC+BdCFMLn+j4RgLyIiRcKsYUtjU1A1dcLz0/9OmBMXwNz9N+BaC5dzdxImxxcRkSKQxYeyNAqFVlNvA0xw90WEGYE6Ju0bDGyTk1KJiIjkgUIL6r8QZtkB+B44OmnfAdRjSj0REclfan6Pp9Ca318nzMbzDOH+wJfNbDxhyr4VgQtyWDYREckyDZSLp6CCurv/X9LP/cxsK8LDXVoA77h7v5wVTkREsk4xPZ6CCuqp3P0zwnR+IiIijV5BB3UzWwdYF5gIfOjuC3NcJBERySKNfo8ndlA3s8uydXJ3r3Uq1+hWtfMIzexNgWeBGwhz4x5HuF/dge/NbEd3n5it8omISG4ppMdTn5r6FdT+pLS6qsv87OcC1wEvEya6vwTYgHA/+rnAMGB9wuT3lxHuYxcRkSKggXLx1Ceof0j2gnpdHA9c7e5XAJjZ88CLwJnufmeU5k0zWwCchoK6iEjRWNYPdCl0sYN6XR9/mkWrA/2TXr9HaJH5PCXdZ4TpY0VERBqlQhgo15zwoPqExM9zU9LNozDej4iI1JGa3+MplCCYrrl/WXYBiIhIDiimx1MoQb2/mS1K2TYgZVuhTXkrIiK1UE09nqwGdTNrARxMeLDKSkAFme9IcHffqQ6HvTJLxRMRESlqWQvqZrY90BdYgcX3jsPioJ7cXG7Usfnc3RXURUQaKY1+jycrQd3MVgdeBVoCQ4F3gDOBmcBthEC/I7AGYfa3e4EF2Ti3iIgULzW/x5Otmvo5hID+JrCfu883szOBme5eNQOdmZ0I3Als7O57Z+ncIiJSpBTS48nW4LKdCc3pl7j7/EyJ3P2/hJnf9jCzU7J0bhERESF7QX0VYCHwZdI2J9xjnuqeaN8xWTq3iIgUqRKzBi2NTbaa3xcBU909efDbLKC1mZUmPz3N3WeY2XSge5bOLSIiRaoRxuUGyVZN/XdCAE/++EdHx98gOaGZtQHaAmVZOreIiBQpM2vQ0thkK6j/QHgsanLte2C0Pjcl7dXR+scsnVtERIqUWcOWxiZbQf1dwiDFPZK2/YfQd97HzL41syfM7GvCk9QceChL5xYRERGy16f+DLAx0CKxwd2/NLOzgVuAdaMlOf1tWTq3iIgUqcY42K0hshLU3X0c4bnnqdv/bWZvE6aO7QxMA95y93ezcV4RESluiunxLPUHurj7cOCfS/s8IiJSfBrjYLeGKJSntC0T7z93Ta6LILKEufNTH04okh9aNNGDMfNR1oO6mW0E7AqsCpS5+wlJ+5oCyxOe0DY22+cWEZHiokuHeLL5lLb2wMPAXolNhFHuJyQlawp8AXQws43d/dtsnV9ERIqPmt/jycpFkJk1B94mBPTZQH9gTmo6d68E7ovOe0g2zi0iIsWrxBq2NDbZatk4GdiEMKHM+u6+M2GkezrPR+vtsnRuEREpUgrq8WQrqPchNLX/3d1/qSXtt4SHv/TI0rlFRESE7PWpr00I1O/VltDdF5rZNKBdls4tIiJFSn3q8WQrqDcHKt19QR3Tl5Gmz11ERCRZY2xCb4hsNb+PA1qZWdvaEprZ+oSg/luWzi0iIkVKD3SJJ1tB/cNofWQd0l5C6H/XVLEiIiJZlK2gfnu0vsLMNk+XwMxam9l/CLeyLQTuzNK5RUSkSJWYNWhpbLL1QJcvzOwq4HJggJkNBFoDmNl/CbPLbQ2UR1kucPefsnFuEREpXppRLp6szSjn7lea2XjgOqB30q4TCLPLAcwAznf3e7N1XhERKV6NsLLdIFmd+93d/2NmjxMetboV0AkoBf4EBgHPuvsUCM3x7j49m+cXERFpzLL+QBd3nwE8FC3VmFlr4Czg78By2T6/iIgUj8bYL94Qy+zRq0nB/EygzbI6r4iIFC7F9HgaFNTNbBfgOGBdwniGn4FH3P3FpDQtCMH8PEIwN6ASuL8h5xYRkeKnyWfiqXdQN7NrgQsSL6P1usA+ZvYfdz89mmjmWWCtKM1Uwq1st7v7pHqXWkREGgU1v8dTr6BuZtsBF0YvJwJDCEF7c0I/+SlmNgC4A+gAjAduBu5x95kNLbSIiIhUV9+a+onR+kNgf3efCmBm7YGXgG2AR4GmwL+Bi6JnqYuIiNSZKurx1Deob0mY6vWsREAHcPfJZnYW8Gl07Dvd/R8NLaSIiDRO6lOPp75BfUVgAfBVmn1fRvtKCc3vIiIi9WIoqsdR3xn4yoFJ7u6pO9x9EZAYBPdzfQsmIiIi8SzV+9TdfeHSPL6IiBQ3Nb/Hs8wmnxEREYlLQT2ehgT19mb2XqZ9ADXsB3B336kB5xcRkSJnGv4eS0OCejOWfBpbOjXtr9YfLyIiIvVX36D+SFZLISIikoaa3+OpV1B39+OzXRAREZFUy7r13cwOAY4CNgXaAT8SJlF7KPmOLzM7gTBV+qrACOBid38t5VhtgFuBAwiTsb0FnOHuY1PSbQXcAmxEmIH1buDGdHeY1aa+t7SJiIgsdSVmDVrq4WzCQ8fOAfYB+gH3AZclEphZn2jb08AewGDgRTPbMuVYTwO7AicDRwLdgX5m1iTpWGsSgv1YYG/gNuCq6PyxafS7iIjkrRw0v+/j7hOTXr9nZssBZ5vZ1dFcLFcCT7n7pVGa/ma2ASHw7wlgZr2A3YDd3P3taNsIYBhwIPBMlPc8wtwufdx9HvCumS0PXGxmd7j73DiFV01dREQkkhLQE74EWgMVZtYV6MbioJzwFLCTmTWPXu9BeDLpO0nHHkGYiXXPpHx7AC9FAT35WG2BXnHLr6AuIiJ5y6xhS5ZsA/zu7jOAHtG24SlphhHuCls9et0DGJGmX3xY4hhmVgF0TnOs4YQ7xHoQk5rfRUQkb5U0cO53M2tNqGUnm+7u0+uYfxugD4v7uNtF66kpSadE6/ZJ6VLTJNIl0rRNdyx3n2dmlUnp6kw1dRERyVtZqKmfDYxOWc6u27ltFcJgt/6EEfB5TzV1EREpZrcC96dsq7WWbmZtCSPfJwEHRQPkYHGNvA0wLilLogY/OSld5zSHbpeUZmrSsZLP3Yzw4LTJxKSgLiIieauho9+jZvY6NbUnmFkZ8Boh2PZy92lJuxP93z0I96eT9Hoei59OOhzY2cwspV+9B/BtVLZZZjaa6n3n3QGjel97rdT8LiIieWtZ36ce3UP+DLA2sLu7/568391/Bn4ADknJehjwbtIo9n6EWnnVM07MrBuwMfBGUr5+wH5m1jTlWFOBQXHLr5q6iIjkrRw8z+VuwiQw5wCtUyaU+TK6b/wK4AkzG0nobz8M2ALYLpHQ3Qeb2VvAg2Z2DjAHuAb4Bngh6Zg3ESam6WtmdwPrE+5dvzjlNrc6UVAXERFZbNdofUuafasDo9y9r5mVAxdGywjgAHcfnJL+MEKf/n8J8fZtwjSxCxIJ3P0nM9s1SvcGMAG4PMP5a6WgLiIieaueU73Wm7t3qWO6B4AHakkzDTghWmpKNwhInWK2XhTURUQkb+lx6vEoqIuISN7SaO54FNRFRCRvmarqsegiSEREpEiopi4iInlL9fR4FNRFRCRvLevR74VOQV1ERPKWQno86lMXEREpEqqpi4hI3lLrezwK6iIikrd0S1s8CuoiIpK31Eccj4K6iIjkLdXU49FFkIiISJFQTV1ERPKW6unxKKiLiEjeUvN7PArqIiKSt9RHHI8+LxERkSKhmrqIiOQtNb/Ho6AuIiJ5SyE9HgV1ERHJW6qox6M+dRERkSKhmrqIiOStEjXAx6KgLiIieUvN7/EoqIuISN4y1dRjUVAXEZG8pZp6PBooJyIiUiRUUxcRkbylgXLxKKiLiEjeUvN7PArqIiKStxTU41GfuoiISJFQTV1ERPKWbmmLR0G9EZg7Zw7Dv/2CUT8NY9TIEYz6cTiTJowDoM8Jf2fPg45Km8/d+WHo13w15CN+HPo1f4wexexZM2lRVsFKnbuwSa/t2XHPAykrr6i1DAvmz+f9N19iyEfv8vtvvzB71kxatWnHiit3Zp0NN2OPA4+kWfMWafOOGTWS159/jGFff8b0qVOoaNWabutsyG77H063dTes/wcjee2br75k4EcfMPT77/hjzGgmT5nM3DlzaduuLd27r81ue+7DbnvsVeNTvAZ82J/nnu7L8KHfM2vWTDos35Gttt6WY//yN1ZYsVON5x/00Ye8/OJzfP/tN0yZMpnS0iZ0XGEFNtm0J4f0OZK1unXP9luWNEoU02Mxd891GfLGJyOnFeWHMeybz7nuwlPS7qspqL/y1IM89+g9Va+tpISy8gpmz5pJ4nvTYYVOnHvV7azUuUvG84/7/Tf+deU5jB3zKwClpaU0LyuncuaMqjS3Pfoq7TusUC3vJx++w703X8GCBfMBKK9oyezKWbg7VlLCkX87i133O6zmD6CA9VipVa6LkDMXX3A277zVr+p1eXk5ixY5c+bMrtq2ac8tuPm2u6ioqH5heetN1/HUE48CUFJSQll5ObNmzgSgZctW/OuOe9hw402q5XN3rr3qMl5+8bklzj1//nzmzw/fw9LSUs654GIOPvTw7LzZAtSmbNmE2/eGT2rQ3+UdeyzXqC4LVFNvJCpatma1NbvTZY3udFmzB0/8919MmzKpxjwLFiygvKIlW+2wB5tvuxNr9FiPpk2bMXfObD758H889eC/mfjnWG69/CyuvecpmjVrXu0YUydP5LoLT2HKpAms0WM9DjnmFHqsvwklpaXMmzuH0aNG8tnA92jatHre33/7hf/eciULFsxn017bc/Qp59K+wwrMmD6V5x75D/37vcgT9/2LVbuuRY/1q/9xlsK2ac8t6Ln5lqy7/oasvMoqlEctQhMnTuCVF5/nvnvu5PNPP+H2W27gosuuWiLvqy+9UBXQ/3rSaRx17PGUl1fw66hfuPryi/nm6y857+zTefalN2jTpu2SeV9+oSqgH3zo4Rx3wkl0XGEFFi1axIjhw7j1puv4+svPueWGa9hk0550XWPNpf9hiNSRaupJirWmvmjhQkpKS5fYdvZx+zFx/Ngaa+q/jvyBDit0oqJl+trij8O+4epz/grASedcwdY77Vktze1Xn8fngz+g+7obcf61d9K0abM6l/uOay/k04/eo3OXNbny34/SpMmS16A3Xfp3vv38Y9bovh6X/+vBOh+3kDTmmnpt7r7jNh5+4F6at2hB/wFDaNK0KRC6evbbc2cmTBjPAQcfxv9dcsUS+aZPn8ZhB+7NpIkTOeb4v3L6mecssf+Uvx7L558NYZNNe3LPA49WO+/MGTPYe7feVFZWcvJpZ/KXv5281N5jPltWNfX+IxpWU9+he+OqqWv0eyOQGtDrarU1umUM6ABrrb0BK6+6OgC//Dis2v4xo0by+eAPADj29AtiBfTZlTP58uMBAOxx0JHVAjrAPoceB8DIEd/x5x+j63xsKQ7rrb8BEMaMTJs+rWr7p0M+ZsKE8QAc95e/VcvXunUbDjy4DwBvvvEaqRWbiRMnALD2uuulPW/LVq1YdbUuAMyeXdmwNyG1sgb+a2wU1KVBmkZN7osWLaq2b2D/0B+6ate1WGW1NWId94fvv67qR99g015p03RbZ0NalIUm2e+/HBLr+FL4vvrycyD0d7dvv1zV9k+HfAzA6l3XoNNKK6fNu9XW2wIw/s9x/DrqlyX2rbTyKgAMH/p92rwzZ8zgt19HAdBj7XXr/wakTkqsYUtjUzB96mbWBNgAGO3uE3JdHoEZ06YyZtRIAFbpUj1o/zT0GwBWW6MHlbNm8spTD/LZwP5MnjiesoqWdO22DjvvfTAb9ty6Wt7ffwt/aFu3bU/rtu3Tnr+ktJSVOq/Gzz8MZcxvP2frbUkemz27knFjx/LGay/zxKMPAXDo4UcvMQL+l5/Dd3KNNdfKeJzkfT+P/Ikuq3eten3AQYcyeOAAPv9sCDdee1VVn7q7M2L4UG658ToqKyvZZNOe7LDTLtl+i5KiMda2G6JggjqwCPgY2BP4X47LIsCLT9zHggXzKSuvYPNtd6q2f1xSk/hlfz+G8WPHUFpaSouyCmZOn8rXnw7k608HsvsBR3DE3/6xRN4pk8J1W7vllq+xDIn90ybXPOhPCtfEiRPYc+ftqm1v0qQphx5+JCedesaS6aOm9+U7Vr+bIqFFWRmtWrVmxozpVc3tCb133Jkz/nEud995G88905fnnum7xOj3Dssvz/F/PYkTTjyVkhI1dkp+KZig7u6LzOxnoF2uyyLw2aD+vPt6NEL42FNo2apNtTSJW9Y+evd1Ssw48sSz6b37/jRv0YKpkyfy9IN3MPC9frz54pN0WbMHW+2we1XeudFtS5nuXU9I7J89e1ZW3pfkn9KSUtov1wGAmTOmM2/ePMyMPkcezZHHHE9pypiRysrQz92iRc3fnRYtWjBjxnQqZ1X/7hx93Ams0rkz11x5GdOnT6s6JsC8ufOYNWsW8+bOpVmzuo8TkfrRNLHxFNpl5rXApWa2Uq4L0piNHPE9/73lStydntvsyC77HJo23SIP/ey+aBF7HnQ0u+3fh+bRH9q27Ttw4jlX0GXNHgC8+vRDy6bwUnDatW/Pm+8O4M13B/Dhx1/y4uvvcGifo+j7+CMcfvC+VX3r2TJ7diXnn30GF5xzJmuvsy73PfQE7w0Ywhv/+5Abbv03bdq25Zm+j/O3449k5owZtR9QGsQauDQ2hRbUDwGWB342syFm9qqZvZK0vFzXA5lZazNbJXmZNWP60it5kfh15A/cfOmZzJldybobbc7J512VMW2LsvKqn3c7oPokHWbG7gccAYQ+9KmTJ1bta96iDIB5c+fUWJ7E/rKy2me1k8JXUlLCyiuvwjkXXMQZ/ziXqVOmcMkF5zBn9uIJacrLw/duzpyavzuJ/eUpE9fcdsuNvP/e/9hk0578+z/3s+HGm9CyVSs6dFieHXbchfsefoK27dox8qcfeeSh+7L8DiVViVmDlsam0IJ6S2A4MBiYFb1ulbS0jnGss4HRycuTD96V1cIWm99++ZEbLj6NWTOn0329jfnHZTfXeJtau/ahv7uiVWtat0nfa5I8E92kCX8uzhv1lSf61jNJ7G+TNPpZGoeDDj2cZs2aMX78nwwaOKBqe4flOwIwYfyfmbIyZ/ZsZkQX8R06LB63MWvWLF6JJp458pjj005B2779cuy5934AvP+ehvdIfimYPnUAd98hi4e7Fbg/ecMRfzlNNztnMGbUSG646HRmTp/GmmuvzzlX/quqKT2TVbqswZhfR9b5HMl/QBP3v0+fOpnp06akvShYtHAhf4wOU8+usmrXavuluDVv3pw2bdoyYcJ4xoz+rWr76l3XYOCADxj5048Z8ybvS54R7rdfR7Fw4UIAVl6lc8b8nVddDYCxf/xe7/JL3TS+unbDFFpNPWvcfbq7j0leKlrFqeg3HmN+Hcn1F53GjGlT6NptHc67+vYlmtYzWXfjzQGYNWM606dNSZvmj9Gjqn7u0HHFqp+7rbthVSvAt58NTpv3h6FfMycaIJc4lzQelZWzmDIlfK8STe4APbcI8xqM+uVnxo39I23ewYNCzb7jCiuyWpfVq7aXJN3YnCkvwORJ4W6LioqW9Sy91Jk61WMpuKBuZhub2bNmNtbM5kbrZ8xs41yXrRj9MXoUN1x0GtOnTqbLmj047593UFZetz9km/XqXTU5zJsvPFltv7vz5othe9du6yxxP3pZeUs22mIbAPq9+AQLFiyolv+1Z8MUnmv0WI8VVspcq5LCk+7/O1Xfxx+pmqBo4003q9res+cWLL98R9ydRx6s3uc9Y/p0XnjuaQD22GufJVqIVuvSlebNw4RKzz/7VNrzzp5dyRuvheE7622gpwQubZpRLp6CCupmti2hP70n0Be4LFr3BAaZ2TY5LF5emzVjOjOmTa1aEiPT582ds8T2+fPnVeUZ9/tvXP9/pzJtymRW7dqN86+5o8ZpY1NVtGrNfn2OB6DfC4/z9stPVw1smzp5Iv+99UpG/TQcM+PAo06qlv/Ao06iadNm/Pbzj9x9/cVMnhjuP545YxoP33UD33w2CDPjsONPr/fnIvnp559+5C/H9OG1V17izz/HVW13d34e+RM3XXc1//3PnQDsuPNurLFmt6o0TZo25eTTzgTgheee5v5776qazvXXX3/h7DNPZeKECbRt146jjv3LEudt0aIF++x3IAADPujP5Refz5jRv+HuLJg/n2+++pKTTziW38eEnro+Rx6z9D4EkXooqAe6mNlAYAawt7svSNpeCrwOtHT3egf2Yn2gCyx+gEtt/nbWZWy7y94A3PevqxnwzqtAeORp0xruyV1z7Q0485Ibq213d+6/7Z9VxyktLaVFeQWVM2dUPT71iL/+g93275P2uNUevdqyVdWjX/Xo1eL1w/BhHNXnwKrXzZs3p6y8nNmVlcydO7dq+3a9d+Sf191Mi7Kyase49cZreerJx4DwvSsrK2dmNHdCRcuW3HbHvWkfvTpn9mzOPvNUPoummwVo0aKM+QvmszBqQSgpKeH0M8+pdlHQmCyrB7oM+blhf5c379qmUVXXC2qgHLAxcHByQAdw94Vm9m/gufTZpD7cF8/nXjlrZrjfIINMtwOaGX8761I22nwb+vd7gV9/GkFl5Uzatl+e7uttxO4HHEHXbutkPO4W2+3Cyqt25fXnHmXo158zY9oUWrdtz1prb8DuBxxBt3XV/FmMVu2yOldfdzOfDfmYod9/x6RJE5k2bSrNmzWjy+pdWWfd9dljr33Yolf1KYYTzj7/Inpu2Yvnnu7LsKHfUTlrFp1WWpmttt6WY//yN1bslH66ixZlZdx5zwO83e913ur3GsOHD2Xa1Gk0KS1lpU4rsdEmm3HwYUew9jqa931ZaFQROQsKraY+ATjP3R9Os+944EZ3r3le0RoUc01dClNjralL/ltWNfVPf2nY3+WeqzeumnpB9akDrwI3mNnOyRuj19cBr+SkVCIislRooFw8hdb8fg6wLvCWmU0HxgMdCZPOfAqcm8OyiYiI5FRBBXV3n2JmvYC9gW0ID3eZDHwEvO7JncAiIlLwGuFMrw1SUEEdwtPaCM3samoXESlyiunx5H1QN7P2tadazN0nL62yiIjIMqaoHkveB3VgIhBn9GNp7UlERESKTyEE9b8QL6iLiEiRaIwj2Bsi74N6unvSRUSkcdBAuXgK7T51ERFpRHLxkDYzW9PM7jGzr8xsgZl9lyHdCWb2g5nNMbOvzWzvNGnamNkDZjbZzGaY2XNm1ilNuq3MbLCZzTazX83sArP4lzR5X1NPZWbbAScC3YBqD/R29w2WeaFERKSYrAvsBXxCqPxWqwCbWR/gPuAa4D3gMOBFM9vW3T9OSvp0dLyTgTlR+n5mtlliynMzWxN4C3gHuATYALgeWAjcHKfgBRXUzWw3woNb/gdsBvQDyoCtgTHAB7krnYiIZF1umt9fdfeXAczsYUK8SXUl8JS7Xxq97m9mGxCeHrpnlLcXsBuwm7u/HW0bAQwDDgSeifKeB0wC+rj7POBdM1seuNjM7nD3xU8xqkWhNb9fCdxGuIICuNTddyTU2ucTrpZERKRI5GKa2NomMjOzroS480zKrqeAncysefR6D2AqoQaeOPYI4CuiwJ+U7qUooCcfqy3QK07ZCy2or02onS8ijIivAHD3X4ErCM0WIiJSJMwatiwlPaL18JTtw4BmwOpJ6UZ49SenDUscw8wqgM5pjjWcEOd6EENBNb8T+iNK3N3NbCywBjAg2jeD8MGIiEiRaGhcNrPWhOeDJJvu7umfF1037aL11JTtU6J1+6R0qWkS6RJp2qY7lrvPM7PKpHR1Umg19a+B7tHP7xL6G/aO+tr/CXybs5KJiEg+OhsYnbKcndMSLUWFVlO/jcXNGhcRHsWamAN+DHBADsokIiJLS8Ob0G8F7k/Z1pBaOiyukbcBxiVtT9TgJyelS9eC3C4pzdSkY1Uxs2ZAeVK6OimooO7ubyT9/LuZbQqsSRgBPzxlkIGIiBS4hs4oFzWzNzSIp0r0f/cARiRt7wHMA35OSrezmVlKv3oPopZld59lZqOp3nfenXBJk9rXXqOCan43s12Sb8b34Ed3/0YBXUSk+OTjQDl3/xn4ATgkZddhwLtJ8agfoVa+0+L3Y92AjYE3kvL1A/Yzs6Ypx5oKDIpTtoKqqRNuzv/TzJ4B+qbc4C8iItJgZlbO4lvOVgNam9nB0esP3H0C4Y6rJ8xsJNCfEIS3ALZLHMfdB5vZW8CDZnYOiyef+QZ4IemUNwFHAn3N7G5gfcK96xfHrbBa9ZH2+cvM1gMOJ3x4XYFRQF/CBAANHiT3ychphfNhSKPQY6VWuS6CSFptykqWybQww/6Y1aC/y2uvVBG7nGbWBfglw+4d3P39KN0JwIXAqoRm+Ivc/bWUY7Uh9OsfSKhIvw2c4e5/pKTbKkq3ETABuAu4Ic3tcDWXvZCCejIz25wQ4A8BOgFDgSfd/br6HlNBXfKNgrrkq2UW1Mc2MKh3ih/UC1lB9aknc/ch7n4WYWTh/oR+i3/mtFAiIpJVuZhRrpAVWp96lWgavn2APoS+j0SzhoiIFAk9ejWegqqpm1mpme1pZo8B4wlPv+kInAOs5O575LSAIiIiOVRoNfXxhCn1vgKuJgyQG5PLAomIyNKjino8hRbU/024le2HXBdERESWAUX1WAoqqLv7lbkug4iILDuNcbBbQxRUn7qIiIhkVlA1dRERaVw0+j0eBXUREclbiunxKKiLiEj+UlSPpWD71M2szMw6mVlZrssiIiKSDwouqJvZ3mb2KTADGAPMMLNPzWzPWrKKiEiB0TSx8RRUUDez/YGXCQ+hPxs4gjCb3FzgFTPbL3elExGRbMvH56nns4J6SpuZfQl87+5Hpdn3OLCuu29c3+PrKW2Sb/SUNslXy+opbaMmzmnQ3+UuHVo0qtBeUDV1oAfwaIZ9j0X7RUSkWFgDl0am0IL6ZKB7hn3do/0iIiKNUqHd0vY0cK2ZzQaec/epZtYGOITwLPX7clo6ERHJqsY42K0hCi2o/x+wGvBf4F4zmw80JTSyvABclMOyiYhIljXGwW4NUVBB3d3nAgeZ2frAtkA7QpP7R+7+bU4LJyIiWaeYHk9BBXUz2w74Igrg36bsqwA2dfcPc1I4ERGRHCu0gXL9gXUy7OsR7RcRkSKh+9TjKaiaOjW3xFQAs5dVQUREZFlohJG5AfI+qJvZlsBWSZuOMLNtUpK1APYDhi2zgomIyFLXGGvbDZH3QR3YDbg8+tmBv6dJM58Q0E9dVoUSERHJN3nfp+7uV7p7ibuXENphtky8Tlqau/tG7j4o1+UVEZHs0YRy8RRCTb1KFNhFRKSRUPN7PHkf1M1skzjp3f2LpVUWERFZtjSjXDx5H9SBzwh96bWxKF3p0i2OiIgsM4rpsRRCUN8h1wUQEREpBHkf1N39g7qmNbPVl2ZZRERk2VJFPZ68D+q1MbMOwGHAEcCWqPldRKRoaKBcPAUZ1M2sHDiAEMh3Jjyp7UvgrFyWS0REsksD5eIpmKBuZqXA7oRAvi9QDowjvIc+7v5MDosnIiKSc3kf1M1sa0IgPwToAEwCHgeeBL6LXo/LWQFFRGTpUUU9lrwP6sAAwq1q/YFbgbfdfQGAmbXJZcFERGTpUkyPpxCC+rfA+sD2wEKgg5m96O4zclssERFZ2jRQLp68n3bV3TcE1gNuAtYCHgbGmdkzhCez1WViGhERkaKX90EdwN2HuvtF7t4V2JYQ2LeP1gBnmtl2OSqeiIgsJdbAf41NQQT1ZO4+0N1PA1YC9iYMmNsF6G9mP+e0cCIiklVmDVsam0LoU0/L3RcCbwBvmFkZsD9weE4LJSIikkMFG9STuftsoG+0iIhIkWiMte2GKLjmdxEREUmvKGrqIiJSnBrjYLeGUFAXEZG8peb3eBTURUQkbymmx6M+dRERkSKhmrqIiOQvVdVjUVAXEZG8pYFy8Sioi4hI3tJAuXjUpy4iIlIkVFMXEZG8pYp6PArqIiKSvxTVY1FQFxGRvKWBcvEoqIuISN7SQLl4zN1zXQYpMmbWGjgbuNXdp+e6PCKg76U0DgrqknVmtgowGujs7mNyXR4R0PdSGgfd0iYiIlIkFNRFRESKhIK6iIhIkVBQl6VhOnBltBbJF/peStHTQDkREZEioZq6iIhIkVBQFxERKRIK6iIiIkVCQV1ERKRIKKgXODO7wsw8aZljZsPM7Hwzi/3/a2bvm9lrS6Os9WVmo8zszlyXQxrGzL6OvqPbptnXO9q3WdK2K8xsq2VbytqZ2XFRWTvkuiwiqfRAl+IwG9gx+rkM2AG4nnDRdn3MY50KLMxe0UTAzNYFNoheHgEMqEO2y4GZwKClVS6RYqOgXhwWufvHSa/7m9n6wIHEDOruPjSrJcsjZlbm7rNzXY5G6khgEfABcIiZ/d3d5+e4TDllZqVASWP/HCS71PxevGYATZM3mNn1Zvatmc00s9/NrK+ZdUpJU6353cy2M7NBZjbbzCaa2YNm1j7Tic2swsxmmdm5afY9Z2aDk9LdaWYjzKwyama/x8za1PbmzOxAM/sq6m74w8xuNbMWSfsTzbl7ReecDjxb23El+8zMgMOB94BbgeWA3WvJk5hA46akrqXe0b4W0f/3H9H//1dmdkAtx7vCzCabWervxHrRsXeLXu9lZu+Y2Xgzm25mn5hZjWWN8rWPfi8mRr8ng8xsu5Q075vZa2Z2rJmNAOYCG9Z2bJE4FNSLhJk1iZZWZrYvcBDwXEqyjsC1wF7AmUAX4AMzy9hiY2abAu8QLhIOAS4A9gH6RTWNatx9FvAK0CflWK2icz8ZbSoHSoGLgT2AS4DtgZdqea/7Ru9tKLA/cCNwMvB4muT/BUYCBwA313RcWWq2InzXngTeAiYRmuBr0ita3xH93Av4Itr2BHAS4f99f8L34Pnoe5FJX6AdsFvK9sOB8cD/oterA68CRxN+hwYCbyQuKNKJfg/6EX4vLiD8nswE3ol+f5JtBpwHXAbsSXhqnEj2uLuWAl6AKwBPszwFlNaQrxRYOUq7a9L294HXkl6/APwKNE3atmuUb58ajr9vlGatpG3HAAuAFTLkaQJsHeXrlrR9FHBn0usvgEEpeU+M8q0fve4dvf5Prv+PGvsC3EUY99Emen0PMAtomZQm8f+1WdI2B85NOdYG0faTUrYPAj6vpRxfAE+kbBuZ/N1K2VcSfSffAp5M2n5cVIYO0evEd323pDRNo9+b55O2vQ/MIzz6Nef/L1qKc1FNvTjMBnpGyzaEWvjuwH3Jicxsj6hZcBohuCaeKd2thmNvC7zsSf1+7v42MDU6VyZvRmmSa+t9gP7u/mdSmY42sy/NbCYwH/iopjKZWUtgI6q3QjwdrVPL9HoNZZSlLGoFOgR4w92nRZufJLTS1NhknkFi5HxqV8rTwMZmVlFD3r7AvmZWFpVtc6BrtD1R3lXM7BEz+53wOzKfcBFb2+/IdHd/K7Eh+n15gerfx2/cXbVzWWoU1IvDInf/LFoGuvu/gauA481sPQAz60loEv+D0LTYC9gyyt8i3UEj7YA/02z/E8jYr+7u84DniYK6mS0H7MLipneiftBHgSHAoVF5En/oM5WpLWCpZYoCxtw0ZUpXdll2dgWWB141s7Zm1hb4FhhL7U3w6bQD5rv75JTtfxK+F21ryPsUUEFoJofQ9P4r0eh6C7eAvkIIxJcR7iLpSWhar+13ZHya7el+R/R9lKVKo9+L17BovS7wHSFYTgMOdfdFAGa2Wh2OM5nQF59qhWhfTfoCJ5jZBoSLiIWE2kvCIcBX7n5SYoOZbV/LMacSmjqXKFM0uK55mjLpiUW5lQjcD0VLsuXNrKO7pwuImUwGmppZO3efkrR9BcL/9dRMGd19tJkNBPqY2XOEC8nH3D3xHVkT2BjY391fTuRL1OxrKVNdf0f0fZSlSjX14rVetJ4YrcsITYnJf1SOrMNxPgL2Tx5MZ2a7EGpEH2XKFHkfGEeoER0O9Etqgk2UaV5KnhrL5O4zga+Ag1N2HZpUXskDZlYO7EcY+LhDynI4oVJxWA2HmE/1GnLi//eQlO2HAF96GKRZk76EAWp7AyuR1PRO+D5C0ncyuvDdupZjfgS0NrNdk/I1IVxI6/soy5Rq6sWhxMwSTenNgE0JI8mHAh9G298B/gHcYWYvEmrOR9fh2NcQmidfM7M7CLWP6wlN5m/UlNHdF5rZM4SBRR1JGQ0flekuM7sUGEz4Y7tTHcp0BfCSmT1OGPHenTCq/3l3/7YO+WXZ2A9oCfzb3d9P3Wlm5xNq8ndkyD8M2M/MBhAG1o1w92/M7AXg1qgGPQI4ijDCfr86lOlZ4HbgP8BQd/86ad9wwjiT66MR7S0Jz1//vZZjvk74fXjczC4kNLGfAXQifC9FlhnV1ItDGSEoDgbeJfxBeRzYITHAzd3fINxusx+h33A7Qm0lnaravLt/TugXbU3oI7+J8EdsD3evy8xzfYEVgUogdfrZe4FbovK+AHSmDv2s7v4KoWa2PvAycCHh1rWj6lAeWXaOAH4jtNik8wiwpZmtkWH/aYS/Uf2ATwkXqxD+n+8j/L+/TPgeHOzur9ZWIHefQPgdSa2l4+5zCRM2zSUE/6sIF7Uf1HLMhYQL0tcJvx/PE35fdo1+f0SWGVvcnSQCZvY58J27H5vrsoiISDyqqQsAZraimR1BuA/401yXR0RE4lNQl4Q+hElCngIeyHFZRESkHtT8LiIiUiRUUxcRESkSCuoiIiJFQkFdRESkSCioi4iIFAkFdRERkSKhoC6Sh8xslJm5mR2Xsr1LtN3NrMvSPJeIFB4FdSlaZvZwUgBMXmaa2XAzu8/MNsx1OUVEskVBXRqD+YSHbCSWFoSHwPwV+MzMTs5h2eKaT3iIyYjoZxGRKgrq0hgMcvcVEwtQTnhIzU+EJxXeVSg1dnf/3d17REttTw8TkUZGQV0aHXef5+7vEJ5YN5/we1BItXURkbQU1KXRcvehwGfRy80AzOy4qN99VPR6DzPrZ2bjzWyRmf0j+RhmtpaZ/cfMfjCzSjObYWZfmdnlZtYm07ktOMnMPjOzWWY2ycz+Z2a711TmugyUM7MWZna6mfU3swlmNtfMRkevzzSz5Wo4fnMzu8TMhpnZbDObaGYvmdlGtZSrvZldY2bfRGMWZpnZ92Z2o5l1rCmviGRPk1wXQCTHxkTragHYzM4BbiY8X34asChl/wnAf4Cm0aZKoDmwYbQca2a7uPvIlHylhGd5HxJtWkh4hveOwI5mdmZ934yZrUV4bn23aNMiYCqwPLAK0Dt6Lw+nyd4K+IhwgTM3yrscoUVjFzPbwd2HpDnnRsCbwArRptlR3nWi5S9mtpe7f1Lf9yUidaOaujR2q0XrKSnbVwBuAO4GOrl7O6Al8ByAme0J3AcsAC4HVnL3CkJ//daEFoDVgRfMLPX37DwWB/Qrgfbu3h5YmRDsbyEE4VjMrC3wFiGg/wkcDbR29+Wicq0PXJPmvSZcCXQAdgcqove7HeHCpxz4d5pztgFeIXxevwA7AxXu3pLwOQwjXBi8ohq7yDLg7lq0FOVCqI068H6G/T0JtWQHbo+2HRe9duDJDPlKgZFRmkMypGkP/BGlOTBpezmhpuzAv9LkM+DdpDIcl7K/S9K+Lin7rou2zwC6xficRkX5KoE10+w/KOmcq6bsu6iWvCsRWgocuCHX3wktWop9UU1dGh0zW8nMjibUMEuAeYRnyae6KcMhtge6Ar+6+7PpErj7ZKBf9HLXpF27Aq0JzdM3pMnnwLV1eBvpHBut73D3H+qR/zl3/ynN9lcIQRlgvZR9h0brx9Pldfc/gHuil4fXo0wiEoP61KUx2N7MPMO+SkJtODUIzga+zpBnq2jdyczG1XDeltF61aRtm0br4e6eKe9HhGb9Ov9+RoPmOkUvX69rvhSfptvo7vPNbDyhib1d0jmbsTjI/6+G4/4PuADobGbLu/uEepZPRGqhoC6NwXxgcvRzoql4DDAA+K+7/5YmzyR3X5RmOywOns1YPDisJuVJPyf6yjPeY+7uc81sIrBiHY6dkFyOX2PkSzajhn1zonXTpG3tCV0RUMP7YfFgRICOgIK6yFKioC6NwSB37x0zz8Ia9iUC2bvuvnP9iiQikn3qUxeJ789ovWqNqdJL1FJXypQgatbuEPO4yU35q2VMlV2TWXzxs3IN6VZJ+nn80iuOiCioi8Q3KFqvZWbdakxZ3efRuoeZZWq634aYrWju/ithtD3A3jHLVC/uPg/4Nnq5Uw1JE60Zv6k/XWTpUlAXie89Fvdb3xZNJpOWmTU1s5ZJm94m3NJWCpyfJr0B/1fPcj0crU+vx8VGfT0TrY8ys9VTd5pZJ+Ck6GXfZVQmkUZLQV0kJnefD5xKuC1tD+BtM+uVmGTGzErMbB0zuxD4AdgoKW8lcH308iwzu8zMWkX5VgQeIdwyV1mPot1EmACmJfCBmR1pZuVJZdrAzG4zswPqcexM7gZGEwYDvmNmO0QXJphZL8I9920Jze63ZPG8IpKGBsqJ1IO7v2FmRwEPEKZ3HQTMNbOZhPvQk0eJp95OdxPh1raDCbO4XWZm0wnBD+BM4Bxi9o27+9Ro7vjXgTWBx4GFZjaVEOibR0m/inPcWs45zcz2I9yTvwahFaMyuoWwIko2GdhPTe8iS59q6iL15O59gbUINe+vCPOltyXcGvYJcCuwjbsPTMm3kDBpy8nAF4TJbyAExL3c/Y4GlOkHYAPgLGAgMJ0wp/t4oD/wd8JkMlnj7l8C6xImzfk+2lwCDCfMnb+Ou3+czXOKSHoWJrASERGRQqeauoiISJFQUBcRESkSCuoiIiJFQkFdRESkSCioi4iIFAkFdRERkSKhoC4iIlIkFNRFRESKhIK6iIhIkVBQFxERKRIK6iIiIkVCQV1ERKRIKKiLiIgUCQV1ERGRIqGgLiIiUiT+H4jSsl68e4OdAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "graficar_matriz_confusion(y_test, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.79 0.97 0.87 4945\n", - " Alto valor 0.69 0.20 0.31 1568\n", - "\n", - " accuracy 0.79 6513\n", - " macro avg 0.74 0.58 0.59 6513\n", - "weighted avg 0.77 0.79 0.74 6513\n", - "\n" - ] - } - ], - "source": [ - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plot_roc_curves(clf_2, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Vemos que tiene graves problemas la precisión de los altos valores como nos suele ocurrir con los diferentes modelos probados. Aún así nos quedaremos con éste como \"mejor modelo\" para utilizarlo después en un futuro ensamble:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "proba_gauss_train = clf_2.predict_proba(X_train)\n", - "proba_gauss_test = clf_2.predict_proba(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# MultinomialNB\n", - "\n", - "Solo para valores discretos." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.naive_bayes import MultinomialNB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Primer Preprocesamiento: obtener_features_discretas()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Algo similar como hicimos en el TP1 al discretizar la edad: importaremos nuestra función que discretizará todas nuestras features. A las categóricas las convierte a binarias tal como venia haciendo la \"conversion_numerica\" que veníamos trabajando en otros notebooks, pero en adición a esto, la función de \"obtener_features_discretas\" también discretiza la \"suma_declarada_bolsa_argentina\" con valores entre 0 a 8. Además de forma similar ocurrirá con las horas de trabajo registradas y la edad alcanzada. Es decir, dejamos como discreta-pura a la de años estudiados sin ser alterada.\n", - "\n", - "Consideramos a esto mencionado como el *primer preprocesamiento* a aplicar:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - }, - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " edad anios_estudiados educacion_alcanzada \\\n", - "count 32561.000000 32561.000000 32561.000000 \n", - "mean 2.494641 14.053868 4.204539 \n", - "std 1.925783 2.664488 0.956610 \n", - "min 0.000000 1.000000 0.000000 \n", - "25% 1.000000 13.000000 4.000000 \n", - "50% 2.000000 14.000000 4.000000 \n", - "75% 4.000000 16.000000 5.000000 \n", - "max 9.000000 20.000000 6.000000 \n", - "\n", - " suma_declarada_bolsa_argentina horas_trabajo_registradas \\\n", - "count 32561.000000 32561.000000 \n", - "mean 0.124597 2.035748 \n", - "std 0.613312 0.821068 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 2.000000 \n", - "50% 0.000000 2.000000 \n", - "75% 0.000000 2.000000 \n", - "max 7.000000 5.000000 \n", - "\n", - " genero_mujer estado_marital_matrimonio_civil \\\n", - "count 32561.000000 32561.000000 \n", - "mean 0.330795 0.459937 \n", - "std 0.470506 0.498400 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 1.000000 1.000000 \n", - "max 1.000000 1.000000 \n", - "\n", - " estado_marital_matrimonio_militar estado_marital_pareja_no_presente \\\n", - "count 32561.000000 32561.000000 \n", - "mean 0.000706 0.012837 \n", - "std 0.026569 0.112575 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 0.000000 0.000000 \n", - "max 1.000000 1.000000 \n", - "\n", - " estado_marital_separado_a ... categoria_de_trabajo_sin_trabajo \\\n", - "count 32561.000000 ... 32561.000000 \n", - "mean 0.031479 ... 0.000215 \n", - "std 0.174612 ... 0.014661 \n", - "min 0.000000 ... 0.000000 \n", - "25% 0.000000 ... 0.000000 \n", - "50% 0.000000 ... 0.000000 \n", - "75% 0.000000 ... 0.000000 \n", - "max 1.000000 ... 1.000000 \n", - "\n", - " categoria_de_trabajo_trabajo_voluntariado religion_budismo \\\n", - "count 32561.000000 32561.000000 \n", - "mean 0.000430 0.031909 \n", - "std 0.020731 0.175761 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 0.000000 0.000000 \n", - "max 1.000000 1.000000 \n", - "\n", - " religion_cristianismo religion_judaismo religion_otro \\\n", - "count 32561.000000 32561.000000 32561.000000 \n", - "mean 0.854274 0.095943 0.008323 \n", - "std 0.352837 0.294518 0.090851 \n", - "min 0.000000 0.000000 0.000000 \n", - "25% 1.000000 0.000000 0.000000 \n", - "50% 1.000000 0.000000 0.000000 \n", - "75% 1.000000 0.000000 0.000000 \n", - "max 1.000000 1.000000 1.000000 \n", - "\n", - " rol_familiar_registrado_con_hijos rol_familiar_registrado_otro \\\n", - "count 32561.000000 32561.000000 \n", - "mean 0.155646 0.030128 \n", - "std 0.362525 0.170942 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 0.000000 0.000000 \n", - "max 1.000000 1.000000 \n", - "\n", - " rol_familiar_registrado_sin_familia rol_familiar_registrado_soltero_a \n", - "count 32561.000000 32561.000000 \n", - "mean 0.255060 0.105832 \n", - "std 0.435901 0.307627 \n", - "min 0.000000 0.000000 \n", - "25% 0.000000 0.000000 \n", - "50% 0.000000 0.000000 \n", - "75% 1.000000 0.000000 \n", - "max 1.000000 1.000000 \n", - "\n", - "[8 rows x 40 columns]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from preprocessing import obtener_features_discretas\n", - "X_df_d_n = obtener_features_discretas(X_df)\n", - "X_df_d_n.describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dividíamos como veníamos haciendo con el GaussianNB recordando que es de manera estratificada tanto en el train_test_split y en GridSearchCV con StratifiedKFold.\n", - "\n", - "Lo que tenemos en cuenta ahora con GridSearchCV es el hiperparametro de 'alpha' que actúa de forma similar al 'var_smoothing' (es decir,evitar eventos con probabilidad nulas) pero según entendemos en la bibliografía (https://scikit-learn.org/stable/modules/naive_bayes.html#multinomial-naive-bayes) lo podemos ir probando 0 y 1. \n", - "\n", - "Entrenemos entonces:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 10 folds for each of 7 candidates, totalling 70 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 17 tasks | elapsed: 1.4s\n", - "[Parallel(n_jobs=-1)]: Done 70 out of 70 | elapsed: 2.8s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=MultinomialNB(), n_jobs=-1,\n", - " param_grid={'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1]},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_df_d_n, y_df, test_size=0.20, random_state=10, stratify=y_df)\n", - "\n", - "params = {\n", - " 'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1],\n", - "}\n", - "\n", - "clf = MultinomialNB()\n", - "cv = StratifiedKFold(n_splits=10).split(X_train, y_train)\n", - "clf_3 = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "\n", - "clf_3.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Veamos diferentes métricas y gráficos:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8650272255009184\n", - "AUC-ROC score sobre train: 0.8678716381868958\n", - "Accuracy sobre test: 0.7990173499155535\n", - "Los mejores hiperpametros elegidos: {'alpha': 0.3}\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.90 0.83 0.86 4945\n", - " Alto valor 0.57 0.69 0.62 1568\n", - "\n", - " accuracy 0.80 6513\n", - " macro avg 0.73 0.76 0.74 6513\n", - "weighted avg 0.82 0.80 0.81 6513\n", - "\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": [ - "y_pred = clf_3.predict(X_test)\n", - "\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_3.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_3.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_test, y_pred))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_3.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_3, X_test, y_test, X_train, y_train)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "La verdad, un buen modelo en general con tan solo ver la diagonal de la matriz de confusión. El AUC-ROC también se ve una buena mejora en comparación al modelo GaussianoNB. Mantendremos a este como el mejor modelo para un futuro ensamble gaussiano:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "proba_multi_train = clf_3.predict_proba(X_train)\n", - "proba_multi_test = clf_3.predict_proba(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Probemos un segundo preprocesamiento:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Segundo Preprocesamiento: Scalers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "En este segundo preprocesamiento probaremos solamente 2 escalados: MinMaxScaler() y Normalizer() con nuestra función get_dataframe_scaled(), pues los demás preprocesamientos nos dan valores negativos y MultinomialNB solo trata con valores discretos mayores a cero. Veamos si esto mejora un poco el anterior resultado." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n", - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 1 con scaler de MinMaxScaler()\n", - " AUC-ROC score sobre test: 0.8563973478673572\n", - "AUC-ROC score sobre train: 0.8596401611223669\n", - "Los mejores hiperpametros elegidos: {'alpha': 0.1}\n", - "---------------------------------------------------------------------\n", - "Aplicando preprocesamiento # 2 con scaler de Normalizer()\n", - " AUC-ROC score sobre test: 0.8545626251005963\n", - "AUC-ROC score sobre train: 0.8590594611828472\n", - "Los mejores hiperpametros elegidos: {'alpha': 0.001}\n" - ] - } - ], - "source": [ - "scalers = [MinMaxScaler(), Normalizer()]\n", - "X_df_d = obtener_features_discretas(X_df)\n", - "\n", - "for count, scaler in enumerate(scalers):\n", - " print(\"---------------------------------------------------------------------\")\n", - " print(\"Aplicando preprocesamiento #\",count+1, \"con scaler de\", scaler)\n", - " X_train, X_test, y_train, y_test = train_test_split(X_df_d, y_df, test_size=0.20, random_state=10, stratify=y_df) \n", - " X_train = get_dataframe_scaled(X_train, scaler)\n", - " X_test = get_dataframe_scaled(X_test, scaler)\n", - " \n", - " params = {\n", - " 'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1],\n", - " }\n", - " cv_e = StratifiedKFold(n_splits=10).split(X_train, y_train)\n", - " clf = GridSearchCV(MultinomialNB(), params, scoring='roc_auc', cv=cv_e, n_jobs = -1)\n", - " \n", - " clf.fit(X_train, y_train)\n", - " y_pred = clf.predict(X_test)\n", - "\n", - " 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(\"Los mejores hiperpametros elegidos: \", clf.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Empeoró rotundamente ambos preprocesamiento con escalados la métrica de AUC-ROC sobre test.\n", - "Nos quedaremos entonces con el primer preprocesamiento simple." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# CategorialNB" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Primer Preprocesameitno: conversion_numerica()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Este método de NB considera solo las variables categóricas, nosotros teníamos " - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(df.columns) - len(df.describe().columns)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Un total de 7 variables categóricas. Según la bibliografía (https://scikit-learn.org/stable/modules/naive_bayes.html#categorical-naive-bayes) recomienda que el frame **X** que usaremos para entrenar esté encodeado. Seguiremos manteniendo la esencia de convertir a binaria aquellas variables categóricas que no representan un valor de orden, y luego con orden a las que sí (la educación alcanzada por ejemplo)." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - }, - { - "data": { - "text/plain": [ - "(32561, 36)" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_df_cat = conversion_numerica(X_df[['categoria_de_trabajo', 'educacion_alcanzada', 'estado_marital', 'genero', 'religion', 'rol_familiar_registrado', 'trabajo']])\n", - "X_df_cat.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Quedándonos así 36 features encodeadas, provenientes de variables categóricas. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Apliquemos otra vez como venimos haciendo la división en train y test, luego GridSearchCV con StratifiedKFold y entrenemos.\n", - "\n", - "Tanto CategorialNB como MultinomialNB consideraremos como hiperparámetro importante al mismo 'alpha'" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 20 folds for each of 7 candidates, totalling 140 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 26 tasks | elapsed: 0.6s\n", - "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed: 2.4s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=CategoricalNB(), n_jobs=-1,\n", - " param_grid={'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1]},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.naive_bayes import CategoricalNB\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X_df_cat, y_df, test_size=0.20, random_state=10, stratify=y_df)\n", - "\n", - "params = {\n", - " 'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1],\n", - "}\n", - "\n", - "clf = CategoricalNB()\n", - "cv = StratifiedKFold(n_splits=20).split(X_train, y_train)\n", - "clf_4 = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "\n", - "clf_4.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Vamos a predecir y mostrar algunas métricas:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8602924903530673\n", - "AUC-ROC score sobre train: 0.8630706398682936\n", - "Accuracy sobre test: 0.7813603562106556\n", - "Los mejores hiperpametros elegidos: {'alpha': 0.1}\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.79 0.85 4945\n", - " Alto valor 0.53 0.75 0.62 1568\n", - "\n", - " accuracy 0.78 6513\n", - " macro avg 0.72 0.77 0.73 6513\n", - "weighted avg 0.82 0.78 0.79 6513\n", - "\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": [ - "y_pred = clf_4.predict(X_test)\n", - "\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_4.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_4.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_test, y_pred))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_4.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_4, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Un modelo bastante bueno otra vez con buen AUC-ROC aunque empeoró la precisión de los de bajo valor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Guardemos entonces las probabilidades de este modelo como las mejores por ahora:" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "proba_catego_train = clf_4.predict_proba(X_train)\n", - "proba_catego_test = clf_4.predict_proba(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Segundo Preprocesamietno: conversion_numerica_generalizada()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Aplicaremos un segundo preprocesamiento donde se incluye una generalización de features, agregando la categoría de barrios indicando si se pertenece a Palermo o no, como también ahora la educación alcanzada se vuelve mas general tomando cada nivel alcanzado. Importemos y apliquemos entonces esta función:" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "from preprocessing import aplicar_preparacion_generalizado\n", - "from preprocessing import conversion_numerica_generalizada" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "df_, df_for_prediction = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion_generalizado(df_)" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica_generalizada' en las variables categóricas.\n" - ] - }, - { - "data": { - "text/plain": [ - "(32561, 39)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_df_cat = conversion_numerica_generalizada(X_df[['barrio','categoria_de_trabajo', 'educacion_alcanzada', 'estado_marital', 'genero', 'religion', 'rol_familiar_registrado', 'trabajo']])\n", - "X_df_cat.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 20 folds for each of 7 candidates, totalling 140 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 26 tasks | elapsed: 0.6s\n", - "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed: 2.4s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=CategoricalNB(), n_jobs=-1,\n", - " param_grid={'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1]},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.naive_bayes import CategoricalNB\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X_df_cat, y_df, test_size=0.20, random_state=10, stratify=y_df)\n", - "\n", - "params = {\n", - " 'alpha': [0.0001, 0.001, 0.1, 0.3, 0.6, 0.9, 1],\n", - "}\n", - "\n", - "\n", - "clf = CategoricalNB()\n", - "cv = StratifiedKFold(n_splits=20).split(X_train, y_train)\n", - "clf_5 = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "\n", - "clf_5.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8628155243391593\n", - "AUC-ROC score sobre train: 0.8653079771371819\n", - "Accuracy sobre test: 0.7799785045294028\n", - "Los mejores hiperpametros elegidos: {'alpha': 0.001}\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.79 0.85 4945\n", - " Alto valor 0.53 0.75 0.62 1568\n", - "\n", - " accuracy 0.78 6513\n", - " macro avg 0.72 0.77 0.73 6513\n", - "weighted avg 0.82 0.78 0.79 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = clf_5.predict(X_test)\n", - "\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_5.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_5.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_test, y_pred))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_5.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_5, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Vemos una mejora respecto al AUC-ROC anterior. Nos quedaremos entonces este como mejor modelo para usar en el ensamble." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "proba_catego_train = clf_5.predict_proba(X_train)\n", - "proba_catego_test = clf_5.predict_proba(X_test)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Aplicando ensamble Gaussiano" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Teniendo en cuenta lo visto en la clase práctica de la materia:\n", - "\n", - " \"...en sklearn no tiene la funcionalidad de trabajar al mismo tiempo con variables categóricas y variables continuas. Se podría hacer un ensamble agarramos las probabilidades de GaussianNB, MultinomialNB y CategoricalNB y le metemos un GaussianNB al final juntando lo que devuelven los anteriores\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Nos guiaremos ademas en la idea de utiliza las funciones de np.hstack tal como lo mencionado en la siguiente fuente: \n", - "\n", - "https://stackoverflow.com/questions/14254203/mixing-categorial-and-continuous-data-in-naive-bayes-classifier-using-scikit-lea" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "X_train_assemble = np.hstack((proba_gauss_train, proba_multi_train, proba_catego_train))\n", - "X_test_assemble = np.hstack((proba_gauss_test, proba_multi_test, proba_catego_test))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Podríamos intentar unir este *X_train* y *X_test* de la siguiente forma: \n", - "\n", - "X_df_new = pd.DataFrame(np.vstack((X_train_assemble, X_test_assemble)))\n", - "\n", - "Para después agarrar train_test_split() y dividir (otra vez) en train y test.\n", - "\n", - "Pero no seria lo correcto por que estamos **asumiendo** que la división realizada por **train_test_split()** haya sido **consecutiva**." - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(26048, 6)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_assemble.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(6513, 6)" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_test_assemble.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Es decir, asumiríamos que agarró los 26048 elementos primero, y luego 6513 siguientes: eso no realiza *train_test_split* pues, la misma lo divide teniendo en cuenta un factor random (lo cual al indicar el random_state=10 mantenemos siempre el mismo split) y ademas tiene en cuenta la división con respecto a la proporción de la clase (stratify) a predecir como veníamos aplicando en diferentes splits.\n", - "\n", - "Al aplicar ese *np.vstack* asumimos que esto no haya pasado y por consiguiente el modelo nos podría dar cualquier cosa.\n", - "\n", - "Vamos a demostrarlo:" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 20 folds for each of 7 candidates, totalling 140 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 28 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed: 0.8s finished\n", - "/home/feduntu/.local/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", - " _warn_prf(average, modifier, msg_start, len(result))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.4884594699861745\n", - "AUC-ROC score sobre train: 0.5066819187564227\n", - "Accuracy sobre test: 0.7592507293106096\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.76 1.00 0.86 4945\n", - " Alto valor 0.00 0.00 0.00 1568\n", - "\n", - " accuracy 0.76 6513\n", - " macro avg 0.38 0.50 0.43 6513\n", - "weighted avg 0.58 0.76 0.66 6513\n", - "\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": [ - "X_df_new = pd.DataFrame(np.vstack((X_test_assemble, X_train_assemble)))\n", - "X_train, X_test, y_train, y_test = train_test_split(X_df_new, y_df, test_size=0.20, random_state=0, stratify=y_df)\n", - "\n", - "params = {\n", - " 'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09, 1e-08, 1e-07],\n", - "}\n", - "\n", - "clf = GaussianNB()\n", - "cv = StratifiedKFold(n_splits=20).split(X_train, y_train)\n", - "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "\n", - "clf.fit(X_train, y_train)\n", - "y_pred = clf.predict(X_test)\n", - "\n", - "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_test, y_pred))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf, X_test, y_test, X_train, y_train)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Es decir, nos dió peor que un modelo RANDOM." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Primer Preprocesamiento directo" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Para esto, entonces hagamos un split con 'train_test_split' tal como veníamos haciendo en cada calculo de probabilidad de clase realizado con los distintos modelos. Mantenemos el mismo random_state pero solamente usaremos el *y_test* e *y_train* que nos dará el split, pues el *X_train* como *X_test* serán el del ensamble. " - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 20 folds for each of 7 candidates, totalling 140 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 28 tasks | elapsed: 0.2s\n", - "[Parallel(n_jobs=-1)]: Done 140 out of 140 | elapsed: 0.7s finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=GaussianNB(), n_jobs=-1,\n", - " param_grid={'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09,\n", - " 1e-08, 1e-07]},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_s, X_test_s, y_train, y_test = train_test_split(X_df, y_df, test_size=0.20, random_state=10, stratify=y_df)\n", - "X_train, X_test = X_train_assemble, X_test_assemble\n", - "\n", - "params = {\n", - " 'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09, 1e-08, 1e-07],\n", - "}\n", - "\n", - "clf = GaussianNB()\n", - "cv = StratifiedKFold(n_splits=20).split(X_train, y_train)\n", - "clf_6 = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "\n", - "clf_6.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8705184968325046\n", - "AUC-ROC score sobre train: 0.8725916319474045\n", - "Accuracy sobre test: 0.7967142637801320\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.81 0.86 4945\n", - " Alto valor 0.56 0.75 0.64 1568\n", - "\n", - " accuracy 0.80 6513\n", - " macro avg 0.73 0.78 0.75 6513\n", - "weighted avg 0.83 0.80 0.81 6513\n", - "\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": [ - "y_pred = clf_6.predict(X_test)\n", - "\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_6.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_6.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_test, y_pred))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_6.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_6, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lo cual se puede observar una mejora increíble con un ensamble en comparación a haber trabajo individualmente cada modelo." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Segundo Preprocesamiento: get_dataframe_polynomial_all()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Para este caso, buscaremos realizar una expansión del dataset mediante PolynomialFeatures. Ésto solo se aplicará para TODAS las features de nuestro dataset. En este caso al trabajar con un ensamble haremos uso de la función del preprocessing.py que aplica PolynomialFeature a todas las features." - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "from preprocessing import get_dataframe_polynomial_all" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Grado 2, interaction_only = true" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* **Grado 2, True**: veamos primero con una expansión de grado 2 con interaction_only=True para las potencias:" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "X_train_ensamble, X_test_ensamble = pd.DataFrame(X_train_assemble), pd.DataFrame(X_test_assemble)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset inicial con 6 features...\n", - "Dataset nuevo con PolynomialFeature con 21 features...\n", - "Dataset inicial con 6 features...\n", - "Dataset nuevo con PolynomialFeature con 21 features...\n", - " AUC-ROC score sobre test: 0.8772469743711439\n", - "AUC-ROC score sobre train: 0.8796718341988208\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n" - ] - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.20, random_state=10, stratify=y_df) \n", - "X_train = get_dataframe_polynomial_all(X_train_ensamble, 2, True)\n", - "X_test = get_dataframe_polynomial_all(X_test_ensamble, 2, True)\n", - "\n", - "params = {'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09, 1e-08, 1e-07]}\n", - "cv_e = StratifiedKFold(n_splits=10).split(X_train, y_train)\n", - "clf_7 = GridSearchCV(GaussianNB(), params, scoring='roc_auc', cv=cv_e, n_jobs = -1)\n", - " \n", - "clf_7.fit(X_train, y_train)\n", - "y_pred = clf_7.predict(X_test)\n", - "\n", - "print(\" AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_7.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_7.predict_proba(X_train)[:, 1]))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_7.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Grado 2, interaction_only = false" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "* **Grado 2, False**: lo mismo con intercation_only = False" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset inicial con 6 features...\n", - "Dataset nuevo con PolynomialFeature con 27 features...\n", - "Dataset inicial con 6 features...\n", - "Dataset nuevo con PolynomialFeature con 27 features...\n", - " AUC-ROC score sobre test: 0.8751515393821838\n", - "AUC-ROC score sobre train: 0.8771551748982203\n", - "Los mejores hiperpametros elegidos: {'var_smoothing': 1e-13}\n" - ] - } - ], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_df, y_df, test_size=0.20, random_state=10, stratify=y_df) \n", - "X_train = get_dataframe_polynomial_all(X_train_ensamble, 2, False)\n", - "X_test = get_dataframe_polynomial_all(X_test_ensamble, 2, False)\n", - "\n", - "params = {'var_smoothing': [1e-13, 1e-12, 1e-11, 1e-10, 1e-09, 1e-08, 1e-07]}\n", - "cv_e = StratifiedKFold(n_splits=10).split(X_train, y_train)\n", - "clf_8 = GridSearchCV(GaussianNB(), params, scoring='roc_auc', cv=cv_e, n_jobs = -1)\n", - " \n", - "clf_8.fit(X_train, y_train)\n", - "y_pred = clf_8.predict(X_test)\n", - "\n", - "print(\" AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_8.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_8.predict_proba(X_train)[:, 1]))\n", - "print(\"Los mejores hiperpametros elegidos: \", clf_8.best_params_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Eligiendo al mejor" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Entonces como mejor preprocesamiento realizado al ensamble gaussiano es aquel donde expandimos con PolynomialFeatures con grado 2 e intercation_only=True para las potencias con un 'var_smoothing' de 1e-13.\n", - "\n", - "Presentaremos este como mejor modelo para el ensamble. Entrenemos dicho modelo para ver sus métricas después." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Entrenamiento" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset inicial con 6 features...\n", - "Dataset nuevo con PolynomialFeature con 27 features...\n", - "Dataset inicial con 6 features...\n", - "Dataset nuevo con PolynomialFeature con 27 features...\n" - ] - }, - { - "data": { - "text/plain": [ - "GaussianNB(var_smoothing=1e-13)" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_train_s, X_test, y_train_s, y_test = train_test_split(X_df, y_df, test_size=0.20, random_state=10, stratify=y_df) \n", - "X_train, X_test = pd.DataFrame(X_train_assemble), pd.DataFrame(X_test_assemble)\n", - "X_train = get_dataframe_polynomial_all(X_train, 2, False)\n", - "X_test = get_dataframe_polynomial_all(X_test, 2, False)\n", - "\n", - "clf_9 = GaussianNB(var_smoothing = 1e-13)\n", - "\n", - "clf_9.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Veamos diferentes métricas para el mismo al predecir" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " AUC-ROC score sobre test: 0.8751515393821838\n", - "AUC-ROC score sobre train: 0.8771551748982203\n", - "Accuracy sobre test: 0.7798249654537079\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.92 0.77 0.84 4945\n", - " Alto valor 0.53 0.80 0.64 1568\n", - "\n", - " accuracy 0.78 6513\n", - " macro avg 0.73 0.79 0.74 6513\n", - "weighted avg 0.83 0.78 0.79 6513\n", - "\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": [ - "y_pred = clf_9.predict(X_test)\n", - "\n", - "\n", - "print(\" AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, clf_9.predict_proba(X_test)[:, 1]))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_9.predict_proba(X_train)[:, 1]))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_test, y_pred))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_9, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Un buen ensamble si miramos la diagonal, no es perfecto pues accuracy de 0.77 pero esto por tratar de optimizar el AUC-ROC al máximo que resultó 0.88 sobre Test." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Predicciones holdout" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Presentar los 2 modelos ¿¿¿¿¿¿¿?????????\n", - " \n", - "* Ensamble Gaussiano\n", - "* Alguno de estas 3 elegir: CategorialNB, MultinomialNB ó GaussianNB¿¿¿¿¿¿¿¿¿¿¿¿?????????????" - ] - } - ], - "metadata": { - "interpreter": { - "hash": "63fd5069d213b44bf678585dea6b12cceca9941eaf7f819626cde1f2670de90d" - }, - "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": "3.8.5" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/parte_2/#3 - Red neuronal definitiva.ipynb b/parte_2/#3 - Red neuronal definitiva.ipynb deleted file mode 100644 index a335ce0..0000000 --- a/parte_2/#3 - Red neuronal definitiva.ipynb +++ /dev/null @@ -1,5475 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6dd0b718", - "metadata": {}, - "source": [ - "# Modelo: Red Neuronal" - ] - }, - { - "cell_type": "markdown", - "id": "9f5598c5", - "metadata": {}, - "source": [ - "El modelo a entrenar en el siguiente notebook se tratará de una red neuronal. Por el tipo de modelo del que se trata, iremos mostrando la serie de pasos y decisiones hasta llegar a los hiperparámetros y estructura de la red final, en lugar de realizar dicha busqueda con GridSearch o técnicas similares. Por una cuestión de no extenderse demasiado, buscaremos sintetizar el camino pero intentando mostrar los problemas que surgieron" - ] - }, - { - "cell_type": "markdown", - "id": "2bfca43e", - "metadata": {}, - "source": [ - "## Librerias y funciones necesarias" - ] - }, - { - "cell_type": "markdown", - "id": "fafe7886", - "metadata": {}, - "source": [ - "Para comenzar importamos las librerias que utilizaremos. En este caso para la construcción de la red utilizaremos la libreria Keras y para evaluar las metricas utilizaremos Sklearn. Luego importamos las funciones necesarias para los preprocesamientos" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "5c8ac750", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], - "source": [ - "import pandas\n", - "import matplotlib.pyplot as plt\n", - "import keras\n", - "import tensorflow as tf\n", - "import numpy as np\n", - "import tensorflow.keras.optimizers\n", - "import tensorflow.keras.metrics\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense , Dropout\n", - "from keras.regularizers import l2,l1\n", - "from keras.callbacks import EarlyStopping, ModelCheckpoint\n", - "from sklearn.model_selection import train_test_split\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, roc_auc_score\n", - "from preprocessing import obtener_datasets\n", - "from preprocessing import aplicar_preparacion\n", - "from preprocessing import aplicar_preparacion_generalizado\n", - "from preprocessing import conversion_numerica\n", - "from preprocessing import conversion_numerica_generalizada\n", - "from preprocessing import plot_roc_curves_red\n", - "from preprocessing import graficar_matriz_confusion\n", - "from preprocessing import get_dataframe_scaled\n", - "from sklearn.preprocessing import StandardScaler" - ] - }, - { - "cell_type": "markdown", - "id": "59f9bc37", - "metadata": {}, - "source": [ - "Para lograr tener la misma salida realizamos lo siguiente. Es valido aclarar que esto genera la misma salida en la misma cpu, al cambiar, en nuestra experiencia genero salidas parecidas pero podria no suceder. La idea de esto es poder reproducir siempre los mismos resultados si se corre de nuevo el notebook. En ocasiones observamos bastante diferencia según se elija una seed u otra. También lo ejecutaremos previo a cada entrenamiento ya que en algunas ocasiones encontramos error al entrenar si no lo haciamos " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a1001240", - "metadata": {}, - "outputs": [], - "source": [ - "from numpy.random import seed\n", - "seed(0)\n", - "import tensorflow.random\n", - "tensorflow.random.set_seed(0)\n" - ] - }, - { - "cell_type": "markdown", - "id": "4195794b", - "metadata": {}, - "source": [ - "## Primer preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "9af5d982", - "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": "ab8a92a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_holdout = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df = conversion_numerica(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "aa05d4b3", - "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": "c75b05c0", - "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": "8160b288", - "metadata": {}, - "source": [ - "### Primer diseño de la red" - ] - }, - { - "cell_type": "markdown", - "id": "3f04a07e", - "metadata": {}, - "source": [ - "#### Diseño y entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "15b720f5", - "metadata": {}, - "source": [ - "Comenzaremos con una red simple de una capa de 4 neuronas con función de activación Tanh y una ultima capa de una neurona con función de activación Sigmoidea. Esta última capa se repetirá en todas nuestras redes a construir. También se repetirá nuestra función de perdida (binary_crossentropy) y las métricas para evaluar que serán AUC y accuracy. Comenzaremos con SGD como primer optimizador, cuyo larning rate por default es 0.01" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "7fb0aca4", - "metadata": {}, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Dense(4,input_shape = (40,),activation='tanh'))\n", - "model.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "a8be541e", - "metadata": {}, - "source": [ - "Compilamos nuestro primer modelo y observamos un resumen de su composición" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9191e827", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense (Dense) (None, 4) 164 \n", - "_________________________________________________________________\n", - "dense_1 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 169\n", - "Trainable params: 169\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "auc_ = tensorflow.keras.metrics.AUC(num_thresholds=200)\n", - "model.compile(loss='binary_crossentropy', optimizer='SGD',metrics=[auc_,'accuracy'])\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "f5e27efb", - "metadata": {}, - "source": [ - "Ahora si, realicemos nuestro primer entrenamiento. Primeramente entrenaremos 100 epochs" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "3b5349f4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5590 - auc: 0.5937 - accuracy: 0.7493 - val_loss: 0.5148 - val_auc: 0.6521 - val_accuracy: 0.7778\n", - "Epoch 2/100\n", - "814/814 [==============================] - 1s 847us/step - loss: 0.5077 - auc: 0.6510 - accuracy: 0.7823 - val_loss: 0.5110 - val_auc: 0.6514 - val_accuracy: 0.7778\n", - "Epoch 3/100\n", - "814/814 [==============================] - 1s 852us/step - loss: 0.4987 - auc: 0.6892 - accuracy: 0.7817 - val_loss: 0.4892 - val_auc: 0.7241 - val_accuracy: 0.7778\n", - "Epoch 4/100\n", - "814/814 [==============================] - 1s 823us/step - loss: 0.4874 - auc: 0.7122 - accuracy: 0.7836 - val_loss: 0.4821 - val_auc: 0.7274 - val_accuracy: 0.7778\n", - "Epoch 5/100\n", - "814/814 [==============================] - 1s 824us/step - loss: 0.4896 - auc: 0.7167 - accuracy: 0.7774 - val_loss: 0.4902 - val_auc: 0.7357 - val_accuracy: 0.7778\n", - "Epoch 6/100\n", - "814/814 [==============================] - 1s 849us/step - loss: 0.4741 - auc: 0.7324 - accuracy: 0.7846 - val_loss: 0.4878 - val_auc: 0.7367 - val_accuracy: 0.7778\n", - "Epoch 7/100\n", - "814/814 [==============================] - 1s 839us/step - loss: 0.4778 - auc: 0.7375 - accuracy: 0.7812 - val_loss: 0.4866 - val_auc: 0.7599 - val_accuracy: 0.7778\n", - "Epoch 8/100\n", - "814/814 [==============================] - 1s 827us/step - loss: 0.4774 - auc: 0.7376 - accuracy: 0.7801 - val_loss: 0.4693 - val_auc: 0.7997 - val_accuracy: 0.7778\n", - "Epoch 9/100\n", - "814/814 [==============================] - 1s 846us/step - loss: 0.4743 - auc: 0.7361 - accuracy: 0.7813 - val_loss: 0.5216 - val_auc: 0.6573 - val_accuracy: 0.7778\n", - "Epoch 10/100\n", - "814/814 [==============================] - 1s 850us/step - loss: 0.4759 - auc: 0.7414 - accuracy: 0.7802 - val_loss: 0.5006 - val_auc: 0.7818 - val_accuracy: 0.7778\n", - "Epoch 11/100\n", - "814/814 [==============================] - 1s 857us/step - loss: 0.4636 - auc: 0.7580 - accuracy: 0.7792 - val_loss: 0.4765 - val_auc: 0.8103 - val_accuracy: 0.7778\n", - "Epoch 12/100\n", - "814/814 [==============================] - 1s 853us/step - loss: 0.4664 - auc: 0.7431 - accuracy: 0.7855 - val_loss: 0.4307 - val_auc: 0.8273 - val_accuracy: 0.7778\n", - "Epoch 13/100\n", - "814/814 [==============================] - 1s 844us/step - loss: 0.4548 - auc: 0.7693 - accuracy: 0.7814 - val_loss: 0.5369 - val_auc: 0.6785 - val_accuracy: 0.7807\n", - "Epoch 14/100\n", - "814/814 [==============================] - 1s 857us/step - loss: 0.4678 - auc: 0.7534 - accuracy: 0.7788 - val_loss: 0.4231 - val_auc: 0.8347 - val_accuracy: 0.7807\n", - "Epoch 15/100\n", - "814/814 [==============================] - 1s 826us/step - loss: 0.4540 - auc: 0.7767 - accuracy: 0.7779 - val_loss: 0.4194 - val_auc: 0.8340 - val_accuracy: 0.7807\n", - "Epoch 16/100\n", - "814/814 [==============================] - 1s 855us/step - loss: 0.4559 - auc: 0.7712 - accuracy: 0.7788 - val_loss: 0.4402 - val_auc: 0.8310 - val_accuracy: 0.7807\n", - "Epoch 17/100\n", - "814/814 [==============================] - 1s 855us/step - loss: 0.4484 - auc: 0.7842 - accuracy: 0.7754 - val_loss: 0.4493 - val_auc: 0.8241 - val_accuracy: 0.7807\n", - "Epoch 18/100\n", - "814/814 [==============================] - 1s 840us/step - loss: 0.4383 - auc: 0.7945 - accuracy: 0.7836 - val_loss: 0.5185 - val_auc: 0.7580 - val_accuracy: 0.7807\n", - "Epoch 19/100\n", - "814/814 [==============================] - 1s 841us/step - loss: 0.4534 - auc: 0.7806 - accuracy: 0.7738 - val_loss: 0.4162 - val_auc: 0.8397 - val_accuracy: 0.7807\n", - "Epoch 20/100\n", - "814/814 [==============================] - 1s 961us/step - loss: 0.4334 - auc: 0.8049 - accuracy: 0.7809 - val_loss: 0.4140 - val_auc: 0.8415 - val_accuracy: 0.7807\n", - "Epoch 21/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4333 - auc: 0.8059 - accuracy: 0.7821 - val_loss: 0.4124 - val_auc: 0.8380 - val_accuracy: 0.7807\n", - "Epoch 22/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4375 - auc: 0.7985 - accuracy: 0.7806 - val_loss: 0.4178 - val_auc: 0.8418 - val_accuracy: 0.7807\n", - "Epoch 23/100\n", - "814/814 [==============================] - 1s 928us/step - loss: 0.4298 - auc: 0.8083 - accuracy: 0.7872 - val_loss: 0.4183 - val_auc: 0.8380 - val_accuracy: 0.7807\n", - "Epoch 24/100\n", - "814/814 [==============================] - 1s 858us/step - loss: 0.4275 - auc: 0.8076 - accuracy: 0.7836 - val_loss: 0.4092 - val_auc: 0.8407 - val_accuracy: 0.7807\n", - "Epoch 25/100\n", - "814/814 [==============================] - 1s 854us/step - loss: 0.4397 - auc: 0.7911 - accuracy: 0.7824 - val_loss: 0.4141 - val_auc: 0.8380 - val_accuracy: 0.7807\n", - "Epoch 26/100\n", - "814/814 [==============================] - 1s 861us/step - loss: 0.4302 - auc: 0.8098 - accuracy: 0.7804 - val_loss: 0.4892 - val_auc: 0.8225 - val_accuracy: 0.7807\n", - "Epoch 27/100\n", - "814/814 [==============================] - 1s 891us/step - loss: 0.4337 - auc: 0.8106 - accuracy: 0.7795 - val_loss: 0.4123 - val_auc: 0.8386 - val_accuracy: 0.7807\n", - "Epoch 28/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4330 - auc: 0.8041 - accuracy: 0.7791 - val_loss: 0.4570 - val_auc: 0.8223 - val_accuracy: 0.7807\n", - "Epoch 29/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4310 - auc: 0.8072 - accuracy: 0.7791 - val_loss: 0.4455 - val_auc: 0.8199 - val_accuracy: 0.7807\n", - "Epoch 30/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4282 - auc: 0.8131 - accuracy: 0.7784 - val_loss: 0.4091 - val_auc: 0.8395 - val_accuracy: 0.7807\n", - "Epoch 31/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4345 - auc: 0.8072 - accuracy: 0.7763 - val_loss: 0.4495 - val_auc: 0.8336 - val_accuracy: 0.7778\n", - "Epoch 32/100\n", - "814/814 [==============================] - 1s 910us/step - loss: 0.4314 - auc: 0.8073 - accuracy: 0.7777 - val_loss: 0.4111 - val_auc: 0.8462 - val_accuracy: 0.7809\n", - "Epoch 33/100\n", - "814/814 [==============================] - 1s 848us/step - loss: 0.4291 - auc: 0.8169 - accuracy: 0.7778 - val_loss: 0.4206 - val_auc: 0.8331 - val_accuracy: 0.7809\n", - "Epoch 34/100\n", - "814/814 [==============================] - 1s 847us/step - loss: 0.4276 - auc: 0.8123 - accuracy: 0.7754 - val_loss: 0.4120 - val_auc: 0.8450 - val_accuracy: 0.7809\n", - "Epoch 35/100\n", - "814/814 [==============================] - 1s 857us/step - loss: 0.4251 - auc: 0.8178 - accuracy: 0.7786 - val_loss: 0.4306 - val_auc: 0.8237 - val_accuracy: 0.7780\n", - "Epoch 36/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4224 - auc: 0.8142 - accuracy: 0.7816 - val_loss: 0.4879 - val_auc: 0.8178 - val_accuracy: 0.7809\n", - "Epoch 37/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4278 - auc: 0.8115 - accuracy: 0.7815 - val_loss: 0.4117 - val_auc: 0.8440 - val_accuracy: 0.7809\n", - "Epoch 38/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4285 - auc: 0.8171 - accuracy: 0.7807 - val_loss: 0.4073 - val_auc: 0.8377 - val_accuracy: 0.7809\n", - "Epoch 39/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4236 - auc: 0.8151 - accuracy: 0.7820 - val_loss: 0.4085 - val_auc: 0.8411 - val_accuracy: 0.7809\n", - "Epoch 40/100\n", - "814/814 [==============================] - 1s 840us/step - loss: 0.4207 - auc: 0.8178 - accuracy: 0.7814 - val_loss: 0.4104 - val_auc: 0.8310 - val_accuracy: 0.7809\n", - "Epoch 41/100\n", - "814/814 [==============================] - 1s 850us/step - loss: 0.4228 - auc: 0.8184 - accuracy: 0.7831 - val_loss: 0.4079 - val_auc: 0.8427 - val_accuracy: 0.7809\n", - "Epoch 42/100\n", - "814/814 [==============================] - 1s 853us/step - loss: 0.4276 - auc: 0.8158 - accuracy: 0.7799 - val_loss: 0.4195 - val_auc: 0.8269 - val_accuracy: 0.7809\n", - "Epoch 43/100\n", - "814/814 [==============================] - 1s 867us/step - loss: 0.4262 - auc: 0.8138 - accuracy: 0.7832 - val_loss: 0.4258 - val_auc: 0.8392 - val_accuracy: 0.7807\n", - "Epoch 44/100\n", - "814/814 [==============================] - 1s 929us/step - loss: 0.4250 - auc: 0.8199 - accuracy: 0.7813 - val_loss: 0.4109 - val_auc: 0.8401 - val_accuracy: 0.7807\n", - "Epoch 45/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4289 - auc: 0.8132 - accuracy: 0.7739 - val_loss: 0.4071 - val_auc: 0.8348 - val_accuracy: 0.7807\n", - "Epoch 46/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4197 - auc: 0.8194 - accuracy: 0.7823 - val_loss: 0.4085 - val_auc: 0.8473 - val_accuracy: 0.7807\n", - "Epoch 47/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4171 - auc: 0.8251 - accuracy: 0.7842 - val_loss: 0.4071 - val_auc: 0.8468 - val_accuracy: 0.7807\n", - "Epoch 48/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4207 - auc: 0.8206 - accuracy: 0.7788 - val_loss: 0.4139 - val_auc: 0.8374 - val_accuracy: 0.7809\n", - "Epoch 49/100\n", - "814/814 [==============================] - 1s 857us/step - loss: 0.4278 - auc: 0.8157 - accuracy: 0.7784 - val_loss: 0.4086 - val_auc: 0.8479 - val_accuracy: 0.7809\n", - "Epoch 50/100\n", - "814/814 [==============================] - 1s 872us/step - loss: 0.4209 - auc: 0.8215 - accuracy: 0.7796 - val_loss: 0.4290 - val_auc: 0.8271 - val_accuracy: 0.7809\n", - "Epoch 51/100\n", - "814/814 [==============================] - 1s 966us/step - loss: 0.4235 - auc: 0.8176 - accuracy: 0.7809 - val_loss: 0.4099 - val_auc: 0.8318 - val_accuracy: 0.7809\n", - "Epoch 52/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4179 - auc: 0.8182 - accuracy: 0.7877 - val_loss: 0.4692 - val_auc: 0.8242 - val_accuracy: 0.7809\n", - "Epoch 53/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4226 - auc: 0.8173 - accuracy: 0.7796 - val_loss: 0.4101 - val_auc: 0.8290 - val_accuracy: 0.7809\n", - "Epoch 54/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4259 - auc: 0.8170 - accuracy: 0.7793 - val_loss: 0.4062 - val_auc: 0.8405 - val_accuracy: 0.7809\n", - "Epoch 55/100\n", - "814/814 [==============================] - 1s 940us/step - loss: 0.4230 - auc: 0.8164 - accuracy: 0.7748 - val_loss: 0.4066 - val_auc: 0.8424 - val_accuracy: 0.7780\n", - "Epoch 56/100\n", - "814/814 [==============================] - 1s 841us/step - loss: 0.4186 - auc: 0.8214 - accuracy: 0.7835 - val_loss: 0.4083 - val_auc: 0.8465 - val_accuracy: 0.7809\n", - "Epoch 57/100\n", - "814/814 [==============================] - 1s 828us/step - loss: 0.4202 - auc: 0.8221 - accuracy: 0.7805 - val_loss: 0.4089 - val_auc: 0.8480 - val_accuracy: 0.7809\n", - "Epoch 58/100\n", - "814/814 [==============================] - 1s 888us/step - loss: 0.4200 - auc: 0.8238 - accuracy: 0.7798 - val_loss: 0.5224 - val_auc: 0.8270 - val_accuracy: 0.7809\n", - "Epoch 59/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4177 - auc: 0.8267 - accuracy: 0.7826 - val_loss: 0.4139 - val_auc: 0.8464 - val_accuracy: 0.7809\n", - "Epoch 60/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4250 - auc: 0.8174 - accuracy: 0.7768 - val_loss: 0.4056 - val_auc: 0.8355 - val_accuracy: 0.7809\n", - "Epoch 61/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4219 - auc: 0.8156 - accuracy: 0.7759 - val_loss: 0.4067 - val_auc: 0.8438 - val_accuracy: 0.7809\n", - "Epoch 62/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4253 - auc: 0.8176 - accuracy: 0.7734 - val_loss: 0.4061 - val_auc: 0.8443 - val_accuracy: 0.7809\n", - "Epoch 63/100\n", - "814/814 [==============================] - 1s 844us/step - loss: 0.4145 - auc: 0.8254 - accuracy: 0.7828 - val_loss: 0.4052 - val_auc: 0.8463 - val_accuracy: 0.7809\n", - "Epoch 64/100\n", - "814/814 [==============================] - 1s 835us/step - loss: 0.4166 - auc: 0.8260 - accuracy: 0.7811 - val_loss: 0.4061 - val_auc: 0.8457 - val_accuracy: 0.7809\n", - "Epoch 65/100\n", - "814/814 [==============================] - 1s 891us/step - loss: 0.4190 - auc: 0.8210 - accuracy: 0.7804 - val_loss: 0.4067 - val_auc: 0.8484 - val_accuracy: 0.7809\n", - "Epoch 66/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4146 - auc: 0.8262 - accuracy: 0.7820 - val_loss: 0.4073 - val_auc: 0.8433 - val_accuracy: 0.7809\n", - "Epoch 67/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4216 - auc: 0.8173 - accuracy: 0.7830 - val_loss: 0.4059 - val_auc: 0.8460 - val_accuracy: 0.7809\n", - "Epoch 68/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4185 - auc: 0.8256 - accuracy: 0.7790 - val_loss: 0.4070 - val_auc: 0.8367 - val_accuracy: 0.7809\n", - "Epoch 69/100\n", - "814/814 [==============================] - 1s 953us/step - loss: 0.4233 - auc: 0.8183 - accuracy: 0.7796 - val_loss: 0.4116 - val_auc: 0.8398 - val_accuracy: 0.7809\n", - "Epoch 70/100\n", - "814/814 [==============================] - 1s 917us/step - loss: 0.4224 - auc: 0.8180 - accuracy: 0.7760 - val_loss: 0.4141 - val_auc: 0.8395 - val_accuracy: 0.7809\n", - "Epoch 71/100\n", - "814/814 [==============================] - 1s 922us/step - loss: 0.4145 - auc: 0.8274 - accuracy: 0.7807 - val_loss: 0.4081 - val_auc: 0.8428 - val_accuracy: 0.7809\n", - "Epoch 72/100\n", - "814/814 [==============================] - 1s 745us/step - loss: 0.4130 - auc: 0.8277 - accuracy: 0.7807 - val_loss: 0.4071 - val_auc: 0.8423 - val_accuracy: 0.7809\n", - "Epoch 73/100\n", - "814/814 [==============================] - 1s 712us/step - loss: 0.4167 - auc: 0.8254 - accuracy: 0.7807 - val_loss: 0.4189 - val_auc: 0.8252 - val_accuracy: 0.7809\n", - "Epoch 74/100\n", - "814/814 [==============================] - 1s 722us/step - loss: 0.4194 - auc: 0.8232 - accuracy: 0.7798 - val_loss: 0.4042 - val_auc: 0.8399 - val_accuracy: 0.7809\n", - "Epoch 75/100\n", - "814/814 [==============================] - 1s 715us/step - loss: 0.4214 - auc: 0.8210 - accuracy: 0.7788 - val_loss: 0.4046 - val_auc: 0.8412 - val_accuracy: 0.7809\n", - "Epoch 76/100\n", - "814/814 [==============================] - 1s 909us/step - loss: 0.4152 - auc: 0.8244 - accuracy: 0.7829 - val_loss: 0.4126 - val_auc: 0.8334 - val_accuracy: 0.7809\n", - "Epoch 77/100\n", - "814/814 [==============================] - 1s 910us/step - loss: 0.4164 - auc: 0.8233 - accuracy: 0.7824 - val_loss: 0.4039 - val_auc: 0.8473 - val_accuracy: 0.7809\n", - "Epoch 78/100\n", - "814/814 [==============================] - 1s 926us/step - loss: 0.4182 - auc: 0.8252 - accuracy: 0.7782 - val_loss: 0.4039 - val_auc: 0.8453 - val_accuracy: 0.7809\n", - "Epoch 79/100\n", - "814/814 [==============================] - 1s 921us/step - loss: 0.4208 - auc: 0.8203 - accuracy: 0.7800 - val_loss: 0.4195 - val_auc: 0.8250 - val_accuracy: 0.7809\n", - "Epoch 80/100\n", - "814/814 [==============================] - 1s 842us/step - loss: 0.4163 - auc: 0.8194 - accuracy: 0.7849 - val_loss: 0.4041 - val_auc: 0.8456 - val_accuracy: 0.7809\n", - "Epoch 81/100\n", - "814/814 [==============================] - 1s 734us/step - loss: 0.4171 - auc: 0.8201 - accuracy: 0.7775 - val_loss: 0.4043 - val_auc: 0.8452 - val_accuracy: 0.7780\n", - "Epoch 82/100\n", - "814/814 [==============================] - 1s 728us/step - loss: 0.4201 - auc: 0.8251 - accuracy: 0.7801 - val_loss: 0.4147 - val_auc: 0.8437 - val_accuracy: 0.7809\n", - "Epoch 83/100\n", - "814/814 [==============================] - 1s 727us/step - loss: 0.4425 - auc: 0.8009 - accuracy: 0.7678 - val_loss: 0.4289 - val_auc: 0.7964 - val_accuracy: 0.7622\n", - "Epoch 84/100\n", - "814/814 [==============================] - 1s 818us/step - loss: 0.4290 - auc: 0.8145 - accuracy: 0.7657 - val_loss: 0.4060 - val_auc: 0.8465 - val_accuracy: 0.7778\n", - "Epoch 85/100\n", - "814/814 [==============================] - 1s 951us/step - loss: 0.4165 - auc: 0.8256 - accuracy: 0.7814 - val_loss: 0.4072 - val_auc: 0.8431 - val_accuracy: 0.7778\n", - "Epoch 86/100\n", - "814/814 [==============================] - 1s 926us/step - loss: 0.4186 - auc: 0.8243 - accuracy: 0.7804 - val_loss: 0.4067 - val_auc: 0.8494 - val_accuracy: 0.7778\n", - "Epoch 87/100\n", - "814/814 [==============================] - 1s 925us/step - loss: 0.4183 - auc: 0.8266 - accuracy: 0.7777 - val_loss: 0.5333 - val_auc: 0.8080 - val_accuracy: 0.7778\n", - "Epoch 88/100\n", - "814/814 [==============================] - 1s 921us/step - loss: 0.4175 - auc: 0.8226 - accuracy: 0.7800 - val_loss: 0.4095 - val_auc: 0.8325 - val_accuracy: 0.7778\n", - "Epoch 89/100\n", - "814/814 [==============================] - 1s 713us/step - loss: 0.4150 - auc: 0.8246 - accuracy: 0.7782 - val_loss: 0.4113 - val_auc: 0.8418 - val_accuracy: 0.7778\n", - "Epoch 90/100\n", - "814/814 [==============================] - 1s 721us/step - loss: 0.4196 - auc: 0.8217 - accuracy: 0.7817 - val_loss: 0.4053 - val_auc: 0.8459 - val_accuracy: 0.7807\n", - "Epoch 91/100\n", - "814/814 [==============================] - 1s 716us/step - loss: 0.4105 - auc: 0.8299 - accuracy: 0.7837 - val_loss: 0.4293 - val_auc: 0.8365 - val_accuracy: 0.7807\n", - "Epoch 92/100\n", - "814/814 [==============================] - 1s 742us/step - loss: 0.4143 - auc: 0.8283 - accuracy: 0.7808 - val_loss: 0.4032 - val_auc: 0.8467 - val_accuracy: 0.7807\n", - "Epoch 93/100\n", - "814/814 [==============================] - 1s 917us/step - loss: 0.4191 - auc: 0.8282 - accuracy: 0.7778 - val_loss: 0.4044 - val_auc: 0.8389 - val_accuracy: 0.7778\n", - "Epoch 94/100\n", - "814/814 [==============================] - 1s 924us/step - loss: 0.4139 - auc: 0.8265 - accuracy: 0.7827 - val_loss: 0.4036 - val_auc: 0.8446 - val_accuracy: 0.7807\n", - "Epoch 95/100\n", - "814/814 [==============================] - 1s 921us/step - loss: 0.4196 - auc: 0.8228 - accuracy: 0.7780 - val_loss: 0.4046 - val_auc: 0.8440 - val_accuracy: 0.7807\n", - "Epoch 96/100\n", - "814/814 [==============================] - 1s 925us/step - loss: 0.4110 - auc: 0.8321 - accuracy: 0.7811 - val_loss: 0.4076 - val_auc: 0.8443 - val_accuracy: 0.7807\n", - "Epoch 97/100\n", - "814/814 [==============================] - 1s 803us/step - loss: 0.4211 - auc: 0.8257 - accuracy: 0.7777 - val_loss: 0.4051 - val_auc: 0.8416 - val_accuracy: 0.7778\n", - "Epoch 98/100\n", - "814/814 [==============================] - 1s 729us/step - loss: 0.4191 - auc: 0.8211 - accuracy: 0.7766 - val_loss: 0.4035 - val_auc: 0.8457 - val_accuracy: 0.7807\n", - "Epoch 99/100\n", - "814/814 [==============================] - 1s 711us/step - loss: 0.4165 - auc: 0.8289 - accuracy: 0.7818 - val_loss: 0.4028 - val_auc: 0.8498 - val_accuracy: 0.7807\n", - "Epoch 100/100\n", - "814/814 [==============================] - 1s 714us/step - loss: 0.4178 - auc: 0.8275 - accuracy: 0.7811 - val_loss: 0.4019 - val_auc: 0.8465 - val_accuracy: 0.7807\n" - ] - } - ], - "source": [ - "history = model.fit(X_train.values, y_train,epochs=100,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "fa1ce383", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "968b42b5", - "metadata": {}, - "source": [ - "Para evaluar los resultados obtenidos, observaremos la curva de aprendizaje tanto de la accuracy como del AUC" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "9619335a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "083ef659", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "aa0b8246", - "metadata": {}, - "source": [ - "Llegado este punto es importante destacar algunas cosas. En primer lugar, el calculo de AUC provisto por keras tomo una cierta cantidad de samples a la hora de calcular esta metrica (en este caso tomamos 200, que además es lo que toma la función por default) y no es del todo representativa e incluso su valor es distinto al real. Por esto, vamos a calcularlo con la función de sklearn.metrics y de paso aprovecharemos para obtener otras metricas que resultan interesantes para evaluar el modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "caa94a79", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8535609175419410\n", - "AUC-ROC score sobre train: 0.8524914212033472\n", - "Accuracy sobre test: 0.7807461999078765\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.93 0.81 0.87 5709\n", - " Alto valor 0.30 0.59 0.40 804\n", - "\n", - " accuracy 0.78 6513\n", - " macro avg 0.62 0.70 0.63 6513\n", - "weighted avg 0.85 0.78 0.81 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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v9gJwG/ApsCypb/0ZSVtHxKAsTXdSTb3QOKBH9nO37HGBdBExU9LUvHS14pq6mZk1ZScCIwu2E2vIcybwPfDvqhJExNkRcXNEvBQR9wLbAN+QBtY1GtfUzcysfNV99PsVwE0F+6qrpa9AGhi3D9A1q6V3yg53ktQpIiYX5ouIKZKeAPbL2z2O1LReqDswNvt5fPbYtaAcbYAOeelqxUHdzMzKVx2b37Nm9iqDeBErAW2AJ4ocex54HdislucaCuwgSQX96n2BD7LyTZE0koX7ztcAxMJ97dVy87uZmZWvhh8oNxjYtmA7ITt2DPDbosVMA952B97M292fVCvfPi/d6sAGwJMF6faS1Dpv3wGkWvyrpRTeNXUzMytf9TArXCkiYjwwcMEizCvD2xHxjqSfkO4tf4g02cyypCb7ZYD98841SNIA4GZJJzF/8pn3gQfzLnEpcAhwt6RrSaPuTwbOKLjNrUYO6mZmZqX5ltREfyHQE5hCqlEfExFvFKQ9gNSvfwMp5j4FHJebTQ4gIj6TtGOW7klgFHA2cHmpBXNQNzOz8tX408QSEQNh/lKzEfEZsHMt804Ajsy26tK9Su376qvkoG5mZuWrgZvfK52DupmZla8yqKlXEr9bZmZmTYRr6mZmVr5cUy+Jg7qZmZWvFu5TL4WDupmZlS/X1Evid8vMzKyJcE3dzMzKl29pK4mDupmZlS83v5fEQd3MzMqXa+olcVA3M7Py5Zp6SfxumZmZNRGuqZuZWfly83tJHNTNzKx8ufm9JA7qZmZWvlxTL4m/ApmZmTURrqmbmVn5cvN7SRzUzcysfLn5vSQO6mZmVr5cUy+J3y0zM7MmwjV1MzMrX66pl8RB3czMypf71EvioG5mZuXLNfWSOKibmVn5ck29JP4KZGZm1kS4pm5mZuXLze8l8btlZmblS6rbVufLq5OkrySFpI0Kjh0p6RNJ0yW9J2n3Ivm7SvqXpLGSJkm6X1KvIuk2lzRI0jRJX0g6VSr9BTiom5lZ2ZJUp60e/JkirdqSDgRuBO4FdgEGAQ9J2qwg6b3AjsAxwCHAGkB/Sa3yzrUqMAD4Ftgd+DtwHnBSqYV187uZmVkRkvoCvyMF1+sLDp8L3BMRf86ePy9pPeAsYNcsfz9gJ2CniHgq2zcMGALsC9yX5T0ZGAMcGBEzgWclLQmcIenqiJhR2zK7pm5mZmWrkWvqV5OC+bCCMq0MrM78oJxzD7C9pLbZ812A8cDTuQQRMQwYTBb489I9nAX0/HN1A/qVUmAHdTMzK1+q47aol5X2A9YlNYMX6ps9Di3YPwRoA6yUl25YRESRdH2z63QE+hQ511Ag8q5VK25+NzOzslXX2rakLkCXgt0TI2JiNXk6AFcAp0fExCJl6J49ji/YPy577JGXrjBNLl0uTbdi54qImZKm5qWrFdfUzcysKTsRGFmwnVhDnjOB74F/L96i1T/X1M3MrGzVQ7/4FcBNBfuqq6WvQBoYtw/QNbt+p+xwJ0mdmF8j7wp8l5c9V4Mfmz2OIzWtF+qel2Z83rnyy9EG6JCXrlYc1M3MrGzVNahnzexVBvEiViL1iz9R5NjzwOvAwdnzviw4iK4vMBMYnj0fCuwgSQX96n2BD7LyTZE0koX7ztcgjQoo7GuvlpvfzcysbDXC6PfBwLYF2wnZsWOA30bEcOATYP+CvAcAz+aNYu9PqpVvn/d6Vgc2AJ7My9cf2EtS64JzjQdeLaXwrqmbmVn5auD1XCJiPDBwgSLM/3LwdkS8k/18DnCnpM9JNfgDgE2BrfLONUjSAOBmSScB04ELgPeBB/MucSlpYpq7JV1LGnV/MnBGwW1uNXJQNzMzK1FE3J2Nkv9Ttg0D9omIQQVJDyD1699AirlPAcdFxOy8c30maccs3ZPAKOBs4PJSy+WgbmZmZauepnqtk4gYSJE2g4j4F/CvGvJOAI7MturSvQoUTjFbMgd1MzMrW+UQ1CuJg7qZmZUtB/XSePS7mZlZE+GaupmZlS3X1EvjoG5mZuXLMb0kDupmZla2XFMvjYO6mZmVLQf10nignJmZWRPhmrqZmZUt19RL46BuZmblyzG9JA7qZmZWtlxTL4371M3MzJoI19TNzKxsuaZeGgd1MzMrWw7qpXFQNzOzsuWgXhr3qZuZmTURrqmbmVn5ckW9JA7qzdiDVx3DLj9ZB4DbH32No86+o9r0W/x4FX61zxZs8eNVWLpnF6ZMm8nXP4zj1XeH8+8HX+H9T75eIH3Lli34yYarskHfPmyw1vKs37cPq/RZghYtWnD9PS9wwsX/qfZ6P/vpBvRbf2U2WHN5ei/TnSW6dUKCb0dN4LX3/sdN97/MoPeG1+1NsLI1+N13ePnFF/jwww/5+quRjB07hunTZ9C9ezf6rrkWu+62B7vstvtCzbNTp07lsUce4qOPPmTY0KGMGT2KcePG06ZNa3r37sMmm/XjoEMOpXfvPkWv+9233/Lss0/z5huvM2zoEEaPGkWLli1Zeuml2WjjTTnokENZbbXVG+ItMNz8XioH9Wbq5ztvOC+g16RFC3HV6Qdy5M+2mLdv3MSpdO7YlvVW7816q/fmu9ETFgrqyy3Vjf7//P0il/GyU/ZnmSW6LHDNTu3bsnKfJVm5z5IcvPsm/P22Zzntbw8t8jWsfN115+0M6P/kvOcdOnSgVetWjBo1ilGjXuClF1/gwQf+w5XXXEvHjp3mpfv+u2+58C/nzXvesmVLOnbsxKRJE/nkk2F88skw7r/vHs6/8CJ23GmXBa753bffsvNPtyUiFrju7Nmz+WLECL4YMYJHHnqAk075EwcfcthifPWW46BeGgf1Zqh7lw5c8sefMX7SVL4dNYE1V+5Vbfpr/3wwh+/dj3ETp3LuPx7nvv++xbiJU5FEn2W6sdOWa/P96IlF806cPI0PPvmad4eM5N2hIznukG1Zv2/xGlKh2x99jfeGjuSdISP5+vvxzJw1G0msvuJSnHrkThy02yb84RfbM3jISO7971slvw9W3jbeZFM23bQf6663Hr1796FDx44AjB41iocevJ/r/nE1b77xOpdfcjFnnXv+vHwdOnbiV78+io022pjV1liDnj2XoGXLlsyaNYvB777D3y6/lI8+/IAzTzuVtdZah9595n8e58ydQ0TQb/Mt2XOvvdl0s370XGIJ5syZw9AhH3PZJRfxzttvcfGFf2GFFVZkiy1/0uDvS3PjoF4a5X8jbe7ab3Bss3gzbjj3UA7bczN+f+E97Lfjhmy10WpVNr/vvs26/OdvRzN9xiy2Ofxy3hv2Va2vI4nCz9eAG49nq41Wq1Xze02evfkENt9gFZ57fSi7HXNNnc5Vrsa92TRfV324+sq/cdMN19OuXTtefu0tWrduXat8EydOZMfttmbatKn87rjjOeqY3847NmnSJL76aiRrrrlW0byzZs7k4AP245NPhrHJpptx48231strqUTtWjVMb3ef3z1Sp7/LI/+xV7P6VuDR783MtpuuwWF7bsYb7/+Pm+5/pcb0f/q/1Dx57d0DSwrowEIBvb699dEXACy7ZLfFeh0rT+uuux4A06dPZ+KECbXO16VLF1ZYcUUAfvjh+wWOde7cucqADtC6TRt222NPAD7+6MMSS2yLRHXcmhk3vzcj7dq25pozDmTWrDkce8E9NQbd1Vdcmg3XWh6Ae/uXV/O2JDZdbyUA/vf16EYujTWGd995G0h93j169qx1vnHjxjJixP8AqhwsV502bdsCMGfO3JLzWunc/F6aignqktoB9wGXRcSLjV2eSnTWb3Zj5T5L8rdbn+GDgkFtxWz2oxQ0Z8ycxUeff8vPd96QYw7YinVWWw6Az74cxYPPvMu1dw1k6vSZi7XsOd06t6fvysvwh8O2nxfUr7lzYINc2xrf1KlT+e7bb3ns0Ye59ZabATj40F/U+Id/7ty5jB0zhg8+eI9rr76K6dOm0blLF/bce9+Sy/DWG28AsNrqHgHfEBzUS1MxQT0ipkvaGvhbY5elEq3ftzfHHbItX347lr9c/2TNGYBVl18KgHETp3HJSfvy24O2yZ5PpUO71mywZh82WLMPB+6yEbv/5hq+q2KwXF3tv9OG3HbRLxfaP3bCFE665H6ee33oYrmulYfRo0ax/TZbLrS/VavWHHTIofz22KrvsDj/3LO4/757F9q/woorcvFlf6NHjx4lleX999/j+eeeAWCfffcrKa9ZQ6i0PvWngB0buxCVpkUL8Y8/H0yrVi054aL7al2r7talAwBLdu/Ebw/ahide+IC+u53NslufwpJb/JFfn3U7U6bNYO1Vl+Wm83+x2Mo/bcZMvhs9ke/HTJzX5Dlh0jTOvPIRHnrm3cV2XSsPLVq2pGfPJejZcwnatGkDpNrboYf9gl/+6te0bNmyyrydOnWmZ88l6Nq127x9K660Eqeedma1fefFTBg/ntNOOYm5c+ey7no/Yq99Sq/lW+kk1WlrbiotqP8bOFTSNZJ2lbShpB/nb41dwHJ0/KHb8eO1lueRZwfz5Iu1H9zTIvuFaNmyBSO+Hs3BJ/+LL74ZA8Cs2XO487HX+fNVjwKw/WZ95/W/17fHB37ASj89nRV3OJ3um53AFodcwqD3hnPtWQfz3L9PXOBedmt6evTowXMvvsJzL77C62+/xxMDnuGgQw7l9ttu4Wd77847b1c93uOEk07muRdf4cVXX2fQG+9w5dXX0kIt+O3Rv+aM005h9uzZtSrD9OnTOeH4Y/lq5Ei6d+/OxZddUe2XCas/DR3Us9jygqRRkmZIGi7pCkld89LcIimKbDsXnKuNpEslfSdpiqSnJa1R5Jp9s2NTsrSXSGqzKO9XpQX1x4HlgN9mP78BvJltb2WPtSKpi6Te+VvMaZh+4Ya04nI9OeOYXZk4eRonXXJ/SXknT50+7+cb7nuJmbMW/gN44/0vMWXaDAC23axv3QpbC7Nmz+Gdj79kn+Ou47Hn3+PHay3PlacdsNiva+WhRYsW9O7dh1NPO5MTTjqFcePGcerJJzJt2rQa83bo2JFtttue2+66l169luXxRx/h3rvvrDHfzJkzOfH4Y3n7rTfp3Lkz193wL5Zbrnd9vByrjYYf/d4DeB04BtgJuAL4BVB4D+5woF/BNqggzVXA/wGnA/sCbYFnC74gdAeeA9pkaU4HjsquW7JKC+rbFmzb5W2557V1IjAyf5v9w+D6LGtZuOSkfenYvi2X/ftpxk+aSsf2bRbYWrZIn/pWLVvM25f7dvvtqPm3CQ0b8X3R88+ePZfhI9Po895Ld1u8L6bA1dkAuT23+xE9unZs0Gtb4/v5gQfRpk0bfvj+e155qfZjZzt37jyv6fzBB6r/ojtr5kxO+sPveeXll+jQoQP/uP5G1lxr7TqV28pbRNwREadExAMRMTAirgFOA34qadm8pNMi4rWCbd4fTUm9gV8Dp0TEzRExANgb6AYcnXeeY4AuwD4RMSAibgZOAY4puF6tVMxAOYCIeKEeT3cFcFP+jlZLrT+yHs9fFpZfNt3qc95xe3LecXtWme6g3TbhoN02AWDTA/7K+598zUeffVvStRp6HqNvRo2f9/PKvZdg7IQpDVsAa1Rt27ala7dujPrhB0aO/LKkvEsttTQAX1WTb9asWfzxxON58YXnade+PddcdwM/Wn+DOpXZSlcm/eJjssdSmsR3JFWc59XwI2KspKeAXYFLst27AM9ExNi8vPcB12fnuKWUglZaTR0ASWtLOlrSadljyV+dI2JiRHyVv6nlInVhNFmvvvs5U6elLok1Vly6aJpWrVqwcp8lAPjymzFF0ywuKy23xLyfJ2ddANZ8TJ0yhXFjxwHpXvVSfPVV+v7evop8s2bN4uST/sDA55+jXbt2XHXNdWy40cZ1K7Atkrr2qRfrapVU40AcSS0ltcvGap0FPBoRI/KSrCppgqSZkt6WtHfBKfoCP0TEuIL9Q7Jj+ekWuIUnIsYD3xakq5WKqqlLagvcDvyM1Fsyg9RHEZLuBw6LiKbXMV4Hmx14UbXHc9O2Fpsmdur0mTz87GAO3n0Tjvr5T7jmroHMmj1ngTRH7f8TOrZPk3H89+WP6q3cLVu2qHZyjxYtxPGHpd6W78dMZNj/incPWGWaPXs2rVpV/+fp9ttuYfbsWQD8OC/g1pR37NixPPLQgwBsVCRQz549mz+dfBLPP/sMbdq04W9X/YNNN+u3KC/D6kE9VNRPBM4u2HcucE4N+b4gjeEC+C9wcN6xd0ljuD4iNaf/BnhI0v4RkevT6Q6ML3LecaR+e0pMVyuVVlO/ENiN1AfRLSLak97QY7L9FzZe0Zqm8657gslTZ7Dicktw12W/ZoWsOb91q5YcvPsm85r07+3/FkOGf7dQ/i6d2tGzW8d5W+tW6SPXtm3rBfa3b7fgvN1H//wn3H7RL9lpy7Xo1rn9vP2tW7Vkix+vwqP/+B079FsTgL/e8N/FPiWtNazPPv2Uww4+gEcffojvv5v/uYoIPv/sMy78y3lc94+rAfjpjjstsBTqWWeexqUX/5X33xvMjBnzW3CmTJnMgP8+yS8OPoAxY0bTqlVrfn3Ubxa47pw5czj9TyfzzNMDsoB+DZtvsfA98tZw6mH0+xVAn4KtNoPQdgU2Jw10WxN4TFJLgIi4MiL+kfW5P0xqQn8dOK+qkzWUiqqpAwcCp0XEjbkdETERuFFSB9Lggj82VuGaoi++GcOhp/6LOy85kt23Xpfdt16XsROm0LF9G9q2SYH4hTc/4di/3F00/3/+djRbbbTaQvt/uc/m/HKfzec9/8v1T3LBP+dPitNCYr+dNmS/nTYE0mpvM2fNoWun9rRunW4lmj17Dn+98b/88z5PMNgUvf/eYN5/bzCQ+s87dOjA1KlTFwjU22y7HedfePEC+aZOmcITjz3KHbfdQosWLejYKS3LOnnSpHlf/rp06cr5F15E3zXXXCDv4Hffmbfca0Rw1hmnV1vGu+69n2V6Vb/KoTWuLEaUPDNWRLyf/ThI0pvAYGAfYKHRlRExV9IDwCWS2kfENFJNu2thWlLNPL//vLbpaqXSgnoPCvoe8gxlEZoqrGYDXv6YjX/+V048fAd26NeXXkt2Zer0Wbz54Rfc9fgb3Pboa/U+D/b9T73DzFlz2Hrj1VhrlWVZqmdnunZqz+RpMxj+6WhefvtTbnl4EEOLtA5Y5VtxpZW46JLLeeP11/joww8YPXo0EyaMp02bNqy08sqss8567LbHnvTbfIuF8v722N+zzrrr8eabb/DVyC8ZM3oMs2fPonuPHqy88ipsseVP2Hvf/YrOJjd37vzP8axZsxgzpvp1BebMnVPtcau78hgnx/vALGDVEvIMBZaW1L2gX72wD30oBX3n2S1vvag63lWpopZelfQu8GFEHFbk2O3AOhGxyMNTm8vSq1Y5vPSqlauGWnp1jVMH1Onv8rCLd6pzOSVtRroH/YCIuK/I8RbAa0CHiFgn29cbGAEcExE3Zfu6A18C50fEJdm+00j3pvfJBsgh6dek0e/LR8Q3pZS10mrq5wP/kbQi8ADwPbAUsB/pxv/9G69oZmZW3xq6pi7pQdJkZu8D04AfASdnzx+WtAJwK3A38Bmpmfw3wEakQdwARMRXkm4CLpU0B/iaFLwnAP/Mu+T1wHHZuS8kDc67FLi+1IAOFRbUI+JBSfuQRjJeThoBH2R9HRHxWCMWz8zMKt8bwAHAn0iDyUcAN5JWCJ0paRIpMJ9JqlTOJH0J2CWbYCbf8cBk4CKgM/AKsEP+JDURMU7S9sDVwMPAJNIcKmcsSuErqvk9n6SOpJHv4yOiXmYdcfO7lRs3v1u5aqjm97VOf6pOf5c/vnDH8uiVbyAVVVPPlwVyTyFmZtaElclAuYpR9kFd0lUlJI+IOH6xFcbMzBpUmUwTWzHKPqgDe5SQNkh9GGZmZs1O2Qf1iFipsctgZmaNwxX10pR9UDczs+bLze+lqcigLmlVYHWgXeGxiHiw4UtkZmaLg4N6aSoqqGfL5T0EbJPblT3m3/LQsiHLZGZmi49jemkqbZW2i4FlgJ+QAvo+pAD/L+B/wGaNVjIzM7NGVmlBfWfgAtISdwDfRMSLEXEU8AhwUqOVzMzM6l09LL3arFRU8ztpSr6RETFH0hSgZ96xJ0nzwZuZWRPRDONynVRaTX0ksET286fAnnnH+gHTG7xEZma22LimXppKq6k/DexAGiz3N+BWSZuSJtTfhLTIi5mZWbNUaUH9VKADQETcLmkyadnV9sCxLLicnZmZVbhmWNmuk4oK6hExFZia9/whUq3dzMyaoObYhF4XFdWnLukVSb+VtGRjl8XMzBY/qW5bc1NRQR34FrgM+FrSAEm/kNS5sQtlZmaLhwfKlaaignpE7Ee6re3XwGzgJuB7SQ9I+pmkto1aQDMzs0ZUUUEdICImR8RtEbEb0As4AegB3AN836iFMzOzeuXm99JU1EC5QhExRtIrwArAGsDSjVwkMzOrR82xCb0uKjKoS1oFODDb1iLV0O8D7m7McpmZWf1yTC9NRQV1SSeSAvmGwATStLDHAwMjYm5jls3MzKyxVVRQB84DHgXOB/4bEbMauTxmZrYYufm9NJUW1JfKJqAxM7NmwDG9NBUV1B3QzcyaF9fUS1Nxt7SZmZlZcQ7qZmZWthp6RjlJu0p6QdIoSTMkDZd0haSuBen2kPSepOmSPpH0yyLnaiPpUknfSZoi6WlJaxRJ1zc7NiVLe4mkNiUXHgd1MzMrY40w+UwP4HXgGGAn4ArgF8B/5pdJW5IWExsE7ALcC/xL0n4F57oK+D/gdGBfoC3wbP4XBEndgeeANlma04GjsuuWrKL61M3MrHlp6D71iLijYNdASTOAGyQtGxHfAH8GXo+IY7I0z2fzp5wH3A8gqTdpSvPfRsTN2b43gS+Bo4FLsrzHAF2AfSJibJauFXCtpAuz69VaxdXUlewm6a+S/pk97iqPpjAza3LKZJrYMdljm2yNkW3Jq7ln7gHWlLRi9nxHUoydly4L2k8Bu+bl2wV4JhfQM/dleXcstaAVFdSzZopXgcdI33S2yh4fB16R1K3xSmdmZk2FpJaS2kn6MXAW8GhEjABWAVoDQwuyDMke++Y9/hAR44qk65v3vG/huSJiPGlV0vx0tVJRQZ207OoqwE4R0SMi1oyIHqR+j1Wy42Zm1kTUdaCcpC6SehdsXWpx6S+AacDbpAB7cLa/e/Y4viB9Lnj3yEtXmCaXrkfe89qmq5VKC+p7AqdGxNP5O7PnpwF7NUqpzMxssaiH5vcTgZEF24m1uPSuwOakgW5rAo9JarlYXmQ9qrSBch2pennV77LjZmbWRLSoe8f4FcBNBfsm1pQpIt7PfhyUDXAbDOwDfJzt71qQJVeDz/WNjyuSJpcuv/+8tulqpdJq6u8CxxZ+W5LUAjgOeKdRSmVmZmUpIiZGxFcFW41BvcD7wCxgVeDz7OfC/u7c86F5j0tnY8EK0+X3oQ8tPFd2y1svFu63r1GlBfXTgO2AzyT9XdJpkv5GepO3yY6bmVkTUSaj3zclDY4bHhEzgOeBwnvSDwCGZIPpII1ynwv8bP5rUXfSiPYn8/L1B3YoGOi9f5b3qVILWlHN7xHxoqQtgDNIgxZyzRMvAxdEhGvqZmZNSEPfrSzpQeAtUu18GvAj4OTs+cNZsvNJ969fS7r9bFtSTDogd56I+ErSTcClkuYAX5MmlpkA/DPvkteTWpoflnQhsBxwKXB9qfeoQ4UFdYCIeJs0646ZmTVxLRp+BpI3SMH5T6TW7BHAjcBlETETICJelrQv8BfgSNKEMr+OiMJ7148HJgMXAZ2BV4AdImJCLkFEjJO0PXA16UvDJNIYgDMWpfAVF9TNzMwWl4i4iBSEa0r3KPBoDWlmAH/MturSDQF2KKGYVSr7oC7pUeCkiPg0+7k6QWqOfwO4OXtDzcysQnmy0NKUfVAnNVnkRrt3IQXu6iwHHEIa2HDE4iuWmZktbo7ppSn7oB4R2+b9vE1t8kg6mNQ/YWZmFUw4qpei7IP6InqWtFqOmZlVsEYYKFfRKi6oZxPNbAesDrQrPB4RV0TE98CVDV02MzOzxlRRQV3SMsBAUkAPmNcuk9/PvkgLy5uZWfnxQLnSVNqMcleQ1rXtQwromwIrkhas/5QU7M3MrIkokxnlKkZF1dRJ66f/nrQMHoAi4kvgQqWvc9eQFpw3M7MmoB4WdGlWKq2m3hUYFRFzSavsLJV3bBCwZaOUyszMrAxUWlD/H2nlGoCPgMPyju3DIixTZ2Zm5cvN76WptOb3J0gr3NxHmnP3EUk/kJbBWwY4tRHLZmZm9cwD5UpTUUE9Ik7L+7m/pM1Ji7u0A56OiP6NVjgzM6t3jumlqaigXigi3iItkWdmZtbsVXRQl7QWsDYwGngxIuY0cpHMzKweefR7aUoO6pLOqq+LR0SNU7lmt6qdTGpmbw38B7iYtN7sEaT71QP4SNJ2ETG6vspnZmaNyyG9NItSUz+HmldKq63azM/+R+CvwCOkxePPBNYj3Y/+R2AIsC5pQfmzSPexm5lZE+CBcqVZlKD+IvUX1Gvjl8D5EXEOgKQHgIeA4yPimizNfyXNBn6Hg7qZWZPhBV1KU3JQr+3yp/VoJeD5vOfPkVpk3i5I9xZp+lgzM7NmqRIGyrUFpuU9z/08oyDdTCrj9ZiZWS25+b00lRIEizX3N2QXgJmZNQLH9NJUSlB/XtLcgn0vFeyrtClvzcysBq6pl6Zeg7qkdsB+pIVVlgU6UvUdCRER29fitOfWU/HMzMyatHoL6pK2Bu4Glmb+veMwP6jnN5eLWjafR4SDuplZM+XR76Wpl6AuaSXgMaAT8DHwNHA8MBn4OynQbwesQpr97Z/A7Pq4tpmZNV1ufi9NfdXUTyIF9P8Ce0XELEnHA5MjYt4MdJKOAq4BNoiI3evp2mZm1kQ5pJemvgaX7UBqTj8zImZVlSgibiDN/LaLpN/U07XNzMyM+gvqvYE5wLt5+4J0j3mh67Njv6ina5uZWRPVQqrTVipJ+0t6RNJXkqZIGizpV8rrB5A0UFIU2foWnKurpH9JGitpkqT7JfUqcs3NJQ2SNE3SF5JOzb9eKeqr+X0uMD4i8ge/TQG6SGqZv3paREySNBFYo56ubWZmTVQjdKmfCIwgdSuPAn4K3EiasTR/4PYrpPVH8o0oeH4vaSXRY4DpwAVAf0kbRcRsAEmrAgNIY9Fya5tcRKooX1Zq4esrqH8NrCRJeYF9JNA3K+C8GrykrkA3Fp4RzszMbAGNMFBuj4LVPp+T1BM4UdL5EZGbH2V8RLxW1Ukk9QN2AnaKiKeyfcNIi5DtC9yXJT0ZGAMcGBEzgWclLQmcIenqiCgpVtZX8/snpGVR82vfr2SPhd9kzs8eP62na5uZWRMl1W0rVRXLd78LdCHNvVJbuwDjSTXw3LmHAYOBXQvSPZwF9Jx7SJXffiVcD6i/oP4saZDiLnn7riP1nR8o6QNJd0p6j7SSWgD/rqdrm5mZLU5bAl9HxKS8fVtnfe7TJb0gaauCPH2BYQXd0pBq6n0BJHUkNesPLUgzlBQn+1Ki+mp+vw/YAGiX2xER70o6Ebic1KewdkH6v9fTtc3MrIlalMFu+SR1IdWy802MiIm1zL8lcCCpjz3nBeA2UovzsqQW6WckbR0Rg7I03Uk19ULjgB7Zz92yxwXSRcRMSVPz0tVavQT1iPiOtO554f6rJD1Fmjq2DzABGBARz9bHdc3MrGmrhy71E4GzC/adC5xT87XVmzTY7Xngqtz+iDi7IN3jwEfAn1mwab3BLfYFXSJiKPCXxX0dMzNreuphoNwVwE0F+2qspUvqBvQnDWL7Wd4AuYVExBRJT5AqsDnjSJXZQt2BsdnP47PHrgXXbgN0yEtXa5WySluDeOvxixu7CGYLmDm7yr8jZo2qXavKWBgza2avVVN7jqT2wOOkYNsvIiYswqWHAjsU3BUGqZ/8g6xsUyTl7hTLtwZpnFphX3uN6v1/RdL6kk6RdI2kfxUcay1p2WI335uZmRVqUcetVJJakcZ9rQnsHBFf1yJPR2B34M283f1JtfLt89KtThp/9mRBur0ktc7bdwCpFv9qqeWvz1XaegC3ALvldpFG7x2Zl6w18A6whKQNIuKD+rq+mZk1PY1wn/q1pAB9EmkCtc3yjr0LbEK6t/wh0mQzy2ZplwH2zyWMiEGSBgA3SzqJ+ZPPvA88mHfOS4FDgLslXQusm53/jILb3GqlvlZpaws8RfoGMg14DdicgmliI2KqpBtJ87/vT9YEYWZmVkwjLL26Y/Z4eZFjKwHfAm2AC4GepNlTXwWOiYg3CtIfQOrTv4EUb58CjsvNJgcQEZ9J2jFL9yRpFruzq7h+jeqrpn4M8GPSJDS7RMT/JH0LLFUk7QOkoF54T5+ZmdkCGjqoR8SKtUi2cy3PNYHUWn1kDeleBTarLk1t1Vef+oGkpvbfR8T/akj7AWlO25JvqjczM7Oq1VdNfU1SoH6upoQRMUfSBNIAAjMzsyo1Qp96RauvoN4WmJrfT1CD9qRBA2ZmZlVqhD71ilZfze/fAZ2zm/WrJWldUlD/sp6ubWZmTVRDL+hS6eorqL+YPR5Si7RnkvrfPVWsmZlZPaqvoH5l9niOpE2KJZDURdJ1pFvZ5gDX1NO1zcysiWoh1WlrbuprQZd3JJ1HurfuJUmvkK2KI+kGYHlgC9JctgCnRsRn9XFtMzNruipjMtryUW8zykXEuZJ+AP4KbJN36EjS7HIAk4BTIuKf9XVdMzNrupphZbtO6nVBl4i4TtIdpJVqNgd6AS2B70kz7vwnIsZBao6v7Xq2ZmZmVrN6X6UtIiYB/862hWQL1p8A/J40xZ6ZmVlRzbFfvC4abOnVvGB+PAVrx5qZmRXjmF6aOgV1ST8FjgDWJo1nGA7cGhEP5aVpRwrmJ5OCuYCpLLxovZmZ2QI8+UxpFjmoS7oQODX3NHtcG9hD0nURcWw20cx/gNWyNONJt7JdGRFjFrnUZmbWLLj5vTSLFNQlbQX8KXs6GniDFLQ3IfWT/0bSS8DVwBLAD8BlwPURMbmuhTYzM7OFLWpN/ajs8UVg74gYDyCpB/AwsCVwG9AauAo4PSKm1qmkZmbW7LiiXppFDeqbkaZ6PSEX0AEiYqykE4A3s3NfExF/qGshzcyseXKfemkWNagvA8wGBhc59m52rCWp+d3MzGyRCEf1UizqDHwdgDEREYUHImIukBsEN3xRC2ZmZmalWaz3qUfEnMV5fjMza9rc/F6aBpt8xszMrFQO6qWpS1DvIem5qo4BVHMcICJi+zpc38zMmjh5+HtJ6hLU27DgamzFVHd8of54MzMzW3SLGtRvrddSmJmZFeHm99IsUlCPiF/Wd0HMzMwKufW9NB4oZ2ZmZctzv5fGQd3MzMqWm99Ls6iTz5iZmTU5kvaX9IikryRNkTRY0q9UMAxf0pGSPpE0XdJ7knYvcq6ukv4laaykSZLul9SrSLrNJQ2SNE3SF5JOLbxebTmom5lZ2ZLqti2CE4GpwEnAHkB/4EbgrPll0oHZvnuBXYBBwEOSNis4173AjsAxwCHAGkB/Sa3yzrUqMAD4Ftgd+DtwXnb9kqnITK/N1kdfT/GbYWWlT8/2jV0Es6K6tGuYhvF/vDKiTn+Xf7fFiiWVU9ISETG6YN8NwAFA94iYK2kY8HZEHJyX5lVgfETsmj3vB7wK7BQRT2X71gCGAAdGxH3Zvn8COwGrR8TMbN+FwG+AZSJiRinld03dzMzKVkPX1AsDeuZdoAvQUdLKwOrAfQVp7gG2l9Q2e74LMB54Ou/cw0gLoe2al28X4OFcQM87VzegX6nld1A3MzOr3pbA1xExCeib7RtakGYIaVK2lbLnfYFhRRY+G5I7h6SOQJ8i5xpKmqCtLyXy6HczMytbdW3kl9SFVMvONzEiJtYy/5bAgczv4+6ePY4vSDoue+yRl64wTS5dLk23YueKiJmSpualqzXX1M3MrGy1kOq0kQa+jSzYTqzNtSX1Jg12ex64avG8wvrlmrqZmZWteph75grgpoJ9NdbSJXUjjXwfA/wsIuZmh3I18q7Ad3lZcjX4sXnp+hQ5dfe8NOPzzpV/7TZAh7x0teagbmZmTVbWzF6rpvYcSe2Bx0nBtl9ETMg7nOv/7gsMy9vfF5gJDM9Lt4MkFfSr9wU+yMo2RdJIFu47XwMQC/e118jN72ZmVrbqofm9JNk95PcBawI7R8TX+ccjYjjwCbB/QdYDgGfzRrH3J9XK5y0xLml1YAPgybx8/YG9JLUuONd40i1xJXFN3czMylYjTP1+LWkSmJOALgUTyryb3Td+DnCnpM9J/e0HAJsCW+USRsQgSQOAmyWdBEwHLgDeBx7MO+elpIlp7pZ0LbAucDJwRsFtbrXioG5mZmWrEZqTd8weLy9ybCVgRETcLakD8KdsGwbsExGDCtIfQOrTv4EUb58CjouI2bkEEfGZpB2zdE8Co4Czq7h+jTyjXB7PKGflxjPKWblqqBnlbn1rZJ3+Lh++UZ9mtSSM+9TNzMyaCDe/m5lZ2WpW1ex64KBuZmZla1FGsDdnDupmZla2HNJL4z51MzOzJsI1dTMzK1tufS+Ng7qZmZUtOaqXxEHdzMzKlvuIS+OgbmZmZcs19dL4S5CZmVkT4Zq6mZmVLdfTS+OgbmZmZcvN76VxUDczs7LlPuLS+P0yMzNrIlxTNzOzsuXm99I4qJuZWdlySC+Ng7qZmZUtV9RL4z51MzOzJsI1dTMzK1st3ABfEgd1MzMrW25+L42DupmZlS25pl4SB3UzMytbrqmXxgPlzMzMmgjX1M3MrGx5oFxpHNTNzKxsufm9NA7qZmZWthzUS+M+dTMzszySVpV0vaTBkmZL+rBImoGSosjWtyBdV0n/kjRW0iRJ90vqVeR8m0saJGmapC8knapFmPjeNXUzMytbjXRL29rAbsDrpMpvVRXgV4A/FuwbUfD83ux8xwDTgQuA/pI2iojZkL5EAAOAp4EzgfWAi4A5wGWlFNxBvRmYMX0aH733Dp9/MoThnw7h80+GMPqH7wD4xdF/YO8DflHjOYZ8OJjH7r+ToR++x+RJE+jarQfrbrAxex9wOMuvtEq1eYd/OpTH7r+Tj957m/HjxtC+fUdWWHlVtt15T7b56W61WoXpu2++ov/D9/Lum68y+ofvAejecwlWXWNtttjmp2yyxTY1vxHWJPzh2KN55aUXAdhtz7055/y/LnD8sUce4ryzTq/1+R7t/wy9ll1u3vMpU6bw4sDneP21Vxny0Yd8+803zJ49i+49erL2Ouuy1777scWWW9XPi7EatWic5vfHIuIRAEm3ABtVkW58RLxW1Ukk9QN2AnaKiKeyfcOAIcC+wH1Z0pOBMcCBETETeFbSksAZkq6OiBm1LbiDejPw6dCP+Mtpxy1y/sf+cwe3/vPvzJ07F0m079CRMaO+Z+BTj/PK80/xhzMuoN9W2xfN+/gDd3HLdVcwd+5cADp26sy0qZP5cPBbfDj4LQa98AynnHsprVq1rvL6Tz3+ADdfcxkzZ6bPddt27QD49qsv+farL5kwboyDejMxoP8T8wJ6Vdq2bUuPnktUm2bChPHMmT2bnksswZJLLb3AscMO3JeRX34573mbNm1o3bo1P3z/HT98/x3PP/s0O++6B2effyGtWvlP6OLWGDX1iJhbT6faBRhPqoHnzj1M0mBgV+YH9V2AB7OAnnMPcBrQDxhY2wv6E9lMdOrchZVX65tta3LztZczfuzoGvO9/87r3HL934gIdtzjZxzyq9/RuWs3Ro/6npuuuoQ3XnmeKy88kxVWWpVl+6ywQN63X3uZf197ORHBVjvswmH/dzw9l1yKWTNn8vLzA7jxyot4a9CL3HLd3/j1cacUvf7zAx7j+isuQBJ77H8ou+59AEv3SrWqiRPG8eHgtxn1/bd1f4Os7E2YMJ4rLvkrnTp3Zskll+J/wz8vmm7HnXdlx513rfI8U6ZMYeftfsKc2bPZdfc9FwrMs2fPZpVVV2PPfX7GFltuxQorrgTAd99+w003XMcjD97Pf598jGV69eJ3vz+h/l6gVaKtJU0BWpKa6v8cEfnfOvsCwyIiCvINyY4hqSPQBxhakGYoEFm6gbUtkIN6M7Dmuhtw2yMDF9h3+41X1Srv7TdeTUSwwSabc8wJZ8zbv8SSS3PSWRdx8jGH8OX/PuPuW67jpD9ftEDeu/99LRHBqmuszfGn/WVeM3vrNm3Ydqc9mDlzBv/824UMePR+dtv3QHott/wC+Uf/8B03XX0JAP/3+z+x8177L3C8S9fubL71DrV6HVb5/nbZxYwdO4ZTzziLpwf0rzKo1+SZAf2ZPn0aAHvste9Cx88+/6/8eMONF+oWWqbXspx59vm0bNGSB++/l3vvuoNfH/1b2rZtu0jlsNqp6+h3SV2ALgW7J0bExLqdmReA24BPgWVJfevPSNo6IgZlabqTauqFxgE9sp+7ZY8LpIuImZKm5qWrFY9+bwZatmy5SPm+/nIEnw/7GIB9D/rlQsdbt27NXj8/DIA3X3mBadOmzjs2fuwYhn+avnjuuf+hRfvNd9h1Hzp26sycObN54eknFzr++AN3M23qFFZdY+2FAro1L6+/9ipPPPow66y7Hvvud0CdzvXYIw8BsO56P2KllRceD7LhRptUO85jz31+BsC0aVMZsYhfLKz2VMd/wInAyILtxLqWKyLOjoibI+KliLgX2Ab4BvhzXc9dFw7qVqX3330DgPYdOtJ3nfWLptlgky0AmDlzBkM+eHfe/vwm8eWWX7Fo3pYtW7Js79Rk/95bC481efGZFOh/sv3OJZfdmo7p06fz1/PPoWWrVpz253Np0WLR/2x9+cUI3hv8DgC7F6ml10Z+zXzO3PrqerWqtFDdNuAKUvN2/nZFfZczIqYATwAb5u0eB3Qtkrw7MDb7eXz2uEA6SW2ADnnpaqVimt8ltSIN8x8ZEaMauzzNwVcjhgPQe/mVqqztd+vegy7dujNx/DhGjhjOj7Mgn2/u3DlVXiN3bOQXwxfY/903XzF+3BgAVl6tL58O+ZAH7rqZIR8MZvq0qfRccik22Hhz9j7wcJZceqFbPq0J+ee1V/P1VyM59PBfsfoafWvOUI1HH34QSIMtq+t3r87bb6Yvu61bt2b5FVasU3msZnUdKJc1s9e1qX1RDQV2kKSCfvW+wAeQvgxIGpnty7cGIBbua69WJdXU5wKvAT9q7II0F2PHpO9OPZZYstp0PZdYCoBxY+YPvMsPtF+OGL5QHoBZs2bx7dcjAZg6ZTLTp02bd+zbr+ePPv7ovbc5/fe/4o1XBjJr5gxatmyZbnF75D5O+PUBfPz+OyW+MqsUQ4d8xN133MoyvXpx1DG/q9O55syZw5OPPQLAdtv/lE6dOpV8jsmTJ3PLzTcAsP1Pd16kc1jTlA142x14M293f1KtfPu8dKsDGwBPFqTbS1L+bUAHkGrxr5ZSjooJ6tktBsNJb5A1gFyQzd1CVpU2bdPxadOmzNvXrUdPVlp1DQAevudW5sxZuLb+5EP3MHXK5HnPp06d//OUSZPm/XzfbTewzLK9ueCqm7nryVe484mX+cvfb2KZZXszdcpkLjnnZCZPaqwv4ra4zJkzhwvOPYs5c+Zw8p/+TPsOHep0vtcGvcKoUT8AsOfePys5f0Rw7p9PY/SoUXTp0pXfHe+R7w1Bqtu2aNdUB0n7SdoPWAHoknsuaUlJP5H0qKRfStpW0iHAS8AywHm582QD5gYAN0vaX9IewP3A+8CDeZe8FFgKuFvSdpKOJ927fkHBbW41qpignrkQ+LOkZRu7IFazAw4/GoCRIz7nr2f8geGfDmXWrFmMGzuah++5lTtvunqB24laaP7Hce4Ct4mKU8+7jDWzfn1JrLXejzn53Mto0aIFE8eP45knHmqIl2QN6K7bb2HokI/ZZrsd2Gqbbet8vsezAXLLLtebDTfepOT8V//9MgY+9wwtWrTgrPMvZJll3O3TEFTHbREtBfwn27Yh9cPnnq8NfAu0IcWkAcA12b6fRMQbBec6gHSf+g3AXaTR8rvmZpMDiIjPgB2B3qQa/B+Bs4HLSy14xfSpZ/YHlgSGS3of+J50H19ORMRetTlRsdscXhvyDZ06F9750Hy1a98egBnTp1ebbuaMdLx9+44L7N9ki2047Kjfc8eNV/POG6/wzhuvLHB8mWV7s8W2O/LAnTcD0DHvvW/ffn6tbP2N+9F7hZUXuu5Kq6zOuhtswntvv8b777zO3gceXsKrs3L21VcjueG6f9CxY0f++Kczas5QgwkTxvPiwOcA2H3PvWs1i2G+G6//B7ffcjOSOO3P57D1NtvVuUxWOy0aYUWXiBhBzd8JajWCNyImAEdmW3XpXgU2q805q1NpNfVOpEEDg4Ap2fPOeVspEXmh2xxuveHqei1spevRM/Wljx1d/bjEMaNTk2b3IrN47XPgEVx6/Z38dLd9WGHlVVliqWVYdY21OfCIY7j8hrvnDcBbculetG49vzspvx+/qtHz+cdGj/q+Vq/JKsPfL7uY6dOncfiv/o/OnTszdeqUBba5c1JLzpzZc+bvq2Yk+oAnn2DmzJm0aNGC3ffcu6Sy/Pumf3LDddcA8MdTz2DvfX17pZWviqqpR0Td2+DmuwK4KX/H4UcdN7Iez1/xeq+Yasdfffk/5syZU3QE/PhxY5k4fhwAfVZcuDYNafT6b04qfuvm558MAWCNtdZb8NrLr0SLFi2q/UOdr5EWfbDF5Jtvvgbg2qv/zrVX/73KdP998jH+++RjANxx74Os0XfNoukeffgBADbaZNMF5nmvya033zjv+sefeAo/P+iQWue1+uHf7NJUWk293kTExIj4Kn9z0/uC1tsg9TtOmzqFYR+9VzTNu2+mgZlt2rRlzXU3KOn848eN5b23Xwdgmx13X+BY23bt6bt2utHh6y9HVHmO3LEll/EwCyvuk2FDGTY0fXksNoNcVW6/9WauuTLdzvzb35/AoYcvPAGTNYBG6lSvVBVVUweQtAFwOrAlafq8saRRh3+NiHery2ulWW75FVlljbX4fNjHPHj3Lay13o8XOD579iweve92IPWf5/eD12Tu3LnccOVfmT1rFqussRbrb9xvoTTb7LQHH3/wLoPfHMTIEcMXagn432fD+CCbIGfDzbYs9eVZGbvrvuoHPh595C945603i67SVuixR9Ig486du7Dt9j+t3fVvv4WrrrgUgKN+cyy/PPKoWuWz+udWuNJUVE1d0k9I/ekbA3cDZ2WPGwOvSvJf9ipMnjSRiRPGzdty8yDMnDF9gf2zZi5498Rh/3ccknjn9Zf559//yqSJEwAYM+oHrjj/NL4Y/ilt2rTlwCOOWeia48eO4bZ//p1PhnzAjGww3dy5cxn20Xucf+qxvPbis3To2IljTzmn6Cxh2+60OyusvCpz587hkrP/yNAP57cWDPngXS4952Tmzp3L0r16s93Oe9bbe2VNx+xZs/jvk48DsOPOu9Rqnvb77r6Tv112MQC/Pvq3/F8d7483a0haePGY8iXpFWASsHv+7QCSWpKm5+sUEYsc2D/6ekrlvBklOvqg3Wq1mtmxp5yzUIB89D93cGu2UpskOnTsxJTJ6T7y1q3bVLn06g/ffcMxB89vVu/YqTMzpk9j9uz0X9dzyaU55dzLWK3v2lWW54fvvuGsE4/ih+++AaBd1howPZtnfomlluHPF11TZX9+pevTs31jF6Es1bam/twzT3HqSccDcMud97H2OuvWeO5N1l9r3pfempZwPemU0xZ5ZrpK16Vdw6x0/sbwCXX6u7zJyl2bVVW/0prfNwD2yw/oABExR9JVpJv6rZ7tuf+hrNZ3bR67/06GfvQ+kydNoOeSS7PO+huxz4FHsPxKCy+KAWkVtQN/+Rs+fPdNvvnqSyZOGEe7Dh1ZrvcKbLrltuy01/41Ntkvtcyy/O2me3nkvtt57aXn+OHbrwmCFVZelU222JY99jvEtyFalXKLt6y8yqq1CugA+RWdsWOqX554xowZi144q5VmFZHrQaXV1EcBJ0fELUWO/RK4JCKqn9O0Gk25pm6VyTV1K1cNVVN/8391q6lvvFLzqqlXVJ868BhwsaQFFtHOnv8VeLRRSmVmZotFPSy92qxUWvP7SaQp+gZImgj8QJrOrwtpEv0/NmLZzMzMGlVFBfWIGCepH2klnC2Zvybty8AT2aIvZmbWRDTCLLEVraKCOsxbre1R3NRuZtbkOaaXpuyDuqQepaSPiLGLqyxmZtbAHNVLUvZBHRjNgiux1WThCcrNzMyagUoI6r+itKBuZmZNRHMcwV4XZR/Ui92TbmZmzYMHypWm7IO6mZk1X47ppam4oC5pK+AoYHWgXeHxiFhvoUxmZmbNQEXNKCdpJ+A5YAlgI2AkaSDdGkBH4K3GK52ZmdU7r6dekooK6sC5wN+B3bLnf46I7Ui19lmkgG9mZk2Ep4ktTaUF9TWB/sBc0oj4jgAR8QVwDnBmo5XMzMzqnVS3rbmptKA+HWgRaWm5b4H8NT8nAX0apVRmZrZYuPW9NJU2UO49Uv/508CzwBmSRpOa3v8CfNCIZTMzM2tUlRbU/w6slP18Omkp1twc8F8B+zRCmczMbHFpjtXtOqiooB4RT+b9/LWkDYFVgfbA0IiY2WiFMzOzetccB7vVRUUFdUk/BZ7J+tTJHj9t3FKZmdni0hwHu9VFpQ2UGwB8I+lKSZs1dmHMzKzpkbSqpOslDZY0W9KHVaQ7UtInkqZLek/S7kXSdJX0L0ljJU2SdL+kXkXSbS5pkKRpkr6QdKpU+leaSgvq6wE3k+5Tf1XScEkXSFq3kctlZmaLQSONfl+bFGc+Az4uWi7pQOBG4F5gF2AQ8FCRCue9wI7AMcAhpMHe/SW1yjvXqqRK67fA7qTxY+cBJ5VacGUt2RVH0ibAQcD+QC/SG39XRPx1Uc/50ddTKvPNsCarT8/2jV0Es6K6tGvRIA3jQ76t29/lNXt1LLmcklpExNzs51uAjSJinYI0w4C3I+LgvH2vAuMjYtfseT/gVWCniHgq27cGMAQ4MCLuy/b9E9gJWD03NkzShcBvgGUiYkZty15pNfV5IuKNiDiBdG/63kB30m1tZmbWRDTGjHK5gF5lmaSVSTOZ3ldw6B5ge0lts+e7AONJt2Hnzj0MGAzsmpdvF+DhgsHe9wDdgH6llL2iBsrly960PYADSW9OK+CpRi2UmZnVq7oOlJPUBehSsHtiREysw2n7Zo9DC/YPAdqQbr0emqUbFgs3iQ/JnUNSR1LltPBcQ0kzp/YFBta2YBVVU5fUUtKukm4HfiD1VSxF6ndYNiJ2adQCmplZuTmRtPhX/nZiHc/ZPXscX7B/XPbYIy9dYZpculyabsXOldXap+alq5VKq6n/QHoDBgPnA/dExFeNWSAzM1t86qHj/grgpoJ9damll7VKC+pXAXdHxCeNXRAzM2sAdYzqWTN7fQfxXI28K/Bd3v5cDX5sXrpia5J0z0szPu9c80hqA3TIS1crFdX8HhHnOqCbmTUfZbr0aq7/u2/B/r7ATGB4Xro1itxv3jd3joiYQuoSKDzXGqSvNIV97dWqqKBuZmbW2CJiOPAJ6ZbqfAcAz+aNYu9PqpVvn0sgaXVgA+DJvHz9gb0ktS4413jSLXG1VmnN72Zm1ow0xjSxkjow/5azFYAukvbLnr8QEaOAc4A7JX0OPE8KwpsCW+XOExGDJA0AbpZ0Emn58AuA94EH8y55KWlimrslXQusC5wMnFHqmiYVO/nM4uDJZ6zcePIZK1cNNfnM5z9Mq9Pf5VWWar8ok8+sCPyvisPbRsTALN2RwJ+A5YFhwOkR8XjBubqSBuvty/xbr4+LiG8K0m2epVsfGAX8A7i4yO1w1ZfdQX0+B3UrNw7qVq4aLKiPqmNQX7L0oF7JKrZPXVJ7Sb0k+a+emZkZFRjUJe0u6U1gEvAVMEnSm5J2rSGrmZlVmDId/V62KiqoS9obeIR0y8CJwMGk2eRmAI9K2qvxSmdmZvVNqtvW3FRUn7qkd4GPIuLQIsfuANaOiA0W9fzuU7dy4z51K1cN1ac+YvT0Ov1dXnGJds0qtFdUTZ10c/5tVRy7nYVv3jczs0rWSAuqV6pKC+pjSbPsFLMGJU6nZ2Zm1pRU2uQz9wIXSpoG3B8R47N7APcnraV+Y6OWzszM6lVzHOxWF5UW1E8jze5zA/BPSbOA1qRGlgeB0xuxbGZmVs+a42C3uqiooB4RM4CfSVoX+AnzV7p5OSI+aNTCmZlZvXNML01FBXVJWwHvZAH8g4JjHYENI+LFRimcmZlZI6u0gXLPA2tVcaxvdtzMzJoI36demoqqqVN9S0xHYFpDFcTMzBpCM4zMdVD2QV3SZsDmebsOlrRlQbJ2wF7AkAYrmJmZLXbNsbZdF2Uf1IGdgLOznwP4fZE0s0gB/bcNVSgzM7NyU/Z96hFxbkS0iIgWpHaYzXLP87a2EbF+RLza2OU1M7P64wnlSlMJNfV5ssBuZmbNhJvfS1P2QV3Sj0tJHxHvLK6ymJlZw/KMcqUp+6AOvEXqS6+JsnQtF29xzMyswTiml6QSgvq2jV0AMzOzSlD2QT0iXqhtWkkrLc6ymJlZw3JFvTRlH9RrImkJ4ADgYGAz3PxuZtZkeKBcaSoyqEvqAOxDCuQ7kFZqexc4oTHLZWZm9csD5UpTMUFdUktgZ1Ig3xPoAHxHeg0HRsR9jVg8MzOzRlf2QV3SFqRAvj+wBDAGuAO4C/gwe/5doxXQzMwWH1fUS1L2QR14iXSr2vPAFcBTETEbQFLXxiyYmZktXo7ppamEGdo+IP2/bg0cT1rQpXPjFsnMzBpCQy+9KukISVFku6gg3ZGSPpE0XdJ7knYvcq6ukv4laaykSZLul9Rr0d+NmpV9UI+IHwHrAJcCqwG3AN9Juo+0MlttJqYxMzMrxc5Av7ztH7kDkg4EbgTuBXYBBgEPZauK5rsX2BE4BjgEWAPoL2mxtZIrorJiYl4f+37AkqSg/jBwZUS8WJdzf/T1lMp6M6zJ69OzfWMXwayoLu1aNEjL+Ngpc+r0d7lHx5YllVPSEcC/gSUjYnQVaYYBb0fEwXn7XgXGR8Su2fN+wKvAThHxVLZvDdKKoottcHfZ19QLRcQrEfE7YFlgd9KAuZ8Cz0sa3qiFMzOzetXQze81l0crA6sDhUH5HmB7SW2z57sA44GncwkiYhgwGNi1/kuWVFxQz4mIORHxZEQcBiwNHEoaDW9mZlZXH0maI2m4pNOy26oB+maPQwvSDwHaACvlpRsWCzeHD8k7R72rhNHvNYqIacDd2WZmZk1EXWvbkroAXQp2T4yIiVVk+RY4G3id1L27J/AXYDngWKB7lm58Qb5x2WOP7LF7kTS5dD2K7K8XTSKom5mZVeFEUpDOdy5wTrHEETEAGJC36ylJ04ATJF2wWEpYjyq2+d3MzJo+1fEfaX6TPgXbFSUW4z7SuiLrM79GXjhPSq4GPzZ7HFckTS7d2CL764Vr6mZmVrbq2vyeNbNX1dS+KHJ96X2BYXn7+wIzgeF56XaQpIJ+9b6k+VcWC9fUzcysbKmOWz05EJgDvBsRw4FPSFOX5zsAeDYiZmbP+5Nq5dvPey3S6sAGwJP1V7QFuaZuZmaWkTQAeI75tek9gaNIc6Hk1hk5B7hT0uekKcwPADYFtsqdJyIGZee6WdJJwHTgAuB94MHFVX4HdTMzK18NP/n7UOBIoDepNfsT4A/A1bkEEXF3tgT4n7JtGLBPRAwqONcBpP77G0jx9inguNz6JYtDxc0otzh5RjkrN55RzspVQ80oN3lG3YJUp7aLYwqa8uWaupmZla3mFZLrzgPlzMzMmgjX1M3MrGy5ol4aB3UzMytfjuolcVA3M7OyJUf1kjiom5lZ2fJAudL4ljard9mqSCcCV1SzEpJZg/Ln0poDB3Wrd5J6AyOBPhHxVWOXxwz8ubTmwbe0mZmZNREO6mZmZk2Eg7qZmVkT4aBui8NE4Fzqdw1js7ry59KaPA+UMzMzayJcUzczM2siHNTNzMyaCAd1MzOzJsJB3czMrIlwUK9wks6RFHnbdElDJJ0iqeT/X0kDJT2+OMq6qCSNkHRNY5fD6kbSe9ln9CdFjm2THdsob985kjZv2FLWTNIRWVmXaOyymBXygi5NwzRgu+zn9sC2wEWkL20XlXiu3wJz6q9oZiBpbWC97OnBwEu1yHY2MBl4dXGVy6ypcVBvGuZGxGt5z5+XtC6wLyUG9Yj4uF5LVkYktY+IaY1djmbqEGAu8AKwv6TfR8SsRi5To5LUEmjR3N8Hq19ufm+6JgGt83dIukjSB5ImS/pa0t2SehWkWaj5XdJWkl6VNE3SaEk3S+pR1YUldZQ0RdIfixy7X9KgvHTXSBomaWrWzH69pK41vThJ+0oanHU3fCPpCknt8o7nmnN3y645EfhPTee1+idJwEHAc8AVQE9g5xry5CbQuDSva2mb7Fi77P/7m+z/f7CkfWo43zmSxkoq/J1YJzv3Ttnz3SQ9LekHSRMlvS6p2rJm+Xpkvxejs9+TVyVtVZBmoKTHJR0uaRgwA/hRTec2K4WDehMhqVW2dZa0J/Az4P6CZEsBFwK7AccDKwIvSKqyxUbShsDTpC8J+wOnAnsA/bOaxkIiYgrwKHBgwbk6Z9e+K9vVAWgJnAHsApwJbA08XMNr3TN7bR8DewOXAMcAdxRJfgPwObAPcFl157XFZnPSZ+0uYAAwhtQEX51+2ePV2c/9gHeyfXcCR5P+3/cmfQ4eyD4XVbkb6A7sVLD/IOAH4Jns+UrAY8BhpN+hV4Anc18oisl+D/qTfi9OJf2eTAaezn5/8m0EnAycBexKWjXOrP5EhLcK3oBzgCiy3QO0rCZfS2C5LO2OefsHAo/nPX8Q+AJonbdvxyzfHtWcf88szWp5+34BzAaWriJPK2CLLN/qeftHANfkPX8HeLUg71FZvnWz59tkz69r7P+j5r4B/yCN++iaPb8emAJ0ykuT+//aKG9fAH8sONd62f6jC/a/CrxdQzneAe4s2Pd5/mer4FiL7DM5ALgrb/8RWRmWyJ7nPus75aVpnf3ePJC3byAwk7T0a6P/v3hrmptr6k3DNGDjbNuSVAvfGbgxP5GkXbJmwQmk4JpbU3r1as79E+CRyOv3i4ingPHZtary3yxNfm39QOD5iPg+r0yHSXpX0mRgFvBydWWS1AlYn4VbIe7NHgvL9EQ1ZbTFLGsF2h94MiImZLvvIrXSVNtkXoXcyPnCrpR7gQ0kdawm793AnpLaZ2XbBFg5258rb29Jt0r6mvQ7Mov0Jbam35GJETEgtyP7fXmQhT+P70eEa+e22DioNw1zI+KtbHslIq4CzgN+KWkdAEkbk5rEvyE1LfYDNsvytyt20kx34Psi+78HquxXj4iZwANkQV1ST+CnzG96J+sHvQ14A/h5Vp7cH/qqytQNUGGZsoAxo0iZipXdGs6OwJLAY5K6SeoGfAB8S81N8MV0B2ZFxNiC/d+TPhfdqsl7D9CR1EwOqen9C7LR9Uq3gD5KCsRnke4i2ZjUtF7T78gPRfYX+x3x59EWK49+b7qGZI9rAx+SguUE4OcRMRdA0gq1OM9YUl98oaWzY9W5GzhS0nqkLxFzSLWXnP2BwRFxdG6HpK1rOOd4UlPnAmXKBte1LVImr1jUuHKB+9/Zlm9JSUtFRLGAWJWxQGtJ3SNiXN7+pUn/1+OryhgRIyW9Ahwo6X7SF8nbIyL3GVkV2ADYOyIeyeXL1exrKFNtf0f8ebTFyjX1pmud7HF09tie1JSY/0flkFqc52Vg7/zBdJJ+SqoRvVxVpsxA4DtSjeggoH9eE2yuTDML8lRbpoiYDAwG9is49PO88loZkNQB2Is08HHbgu0gUqXigGpOMYuFa8i5/9/9C/bvD7wbaZBmde4mDVDbHViWvKZ30ucR8j6T2RffLWo458tAF0k75uVrRfoi7c+jNSjX1JuGFpJyTeltgA1JI8k/Bl7M9j8N/AG4WtJDpJrzYbU49wWk5snHJV1Nqn1cRGoyf7K6jBExR9J9pIFFS1EwGj4r0z8k/RkYRPpju30tynQO8LCkO0gj3tcgjep/ICI+qEV+axh7AZ2AqyJiYOFBSaeQavJXV5F/CLCXpJdIA+uGRcT7kh4Ershq0MOAQ0kj7PeqRZn+A1wJXAd8HBHv5R0bShpnclE2or0Taf31r2s45xOk34c7JP2J1MR+HNCL9Lk0azCuqTcN7UlBcRDwLOkPyh3AtrkBbhHxJOl2m71I/YZbkWorxcyrzUfE26R+0S6kPvJLSX/EdomI2sw8dzewDDAVKJx+9p/A5Vl5HwT6UIt+1oh4lFQzWxd4BPgT6da1Q2tRHms4BwNfklpsirkV2EzSKlUc/x3pb1R/4E3Sl1VI/883kv7fHyF9DvaLiMdqKlBEjCL9jhTW0omIGaQJm2aQgv95pC+1L9RwzjmkL6RPkH4/HiD9vuyY/f6YNRjN704yA0lvAx9GxOGNXRYzMyuNa+oGgKRlJB1Mug/4zcYuj5mZlc5B3XIOJE0Scg/wr0Yui5mZLQI3v5uZmTURrqmbmZk1EQ7qZmZmTYSDupmZWRPhoG5mZtZEOKibmZk1EQ7qZmVI0ghJIemIgv0rZvtD0oqL81pmVnkc1K3JknRLXgDM3yZLGirpRkk/auxympnVFwd1aw5mkRbZyG3tSIvA/Bp4S9IxjVi2Us0iLWIyLPvZzGweB3VrDl6NiGVyG9CBtEjNZ6SVCv9RKTX2iPg6IvpmW02rh5lZM+Ogbs1ORMyMiKdJK9bNIv0eVFJt3cysKAd1a7Yi4mPgrezpRgCSjsj63Udkz3eR1F/SD5LmSvpD/jkkrSbpOkmfSJoqaZKkwZLOltS1qmsrOVrSW5KmSBoj6RlJO1dX5toMlJPUTtKxkp6XNErSDEkjs+fHS+pZzfnbSjpT0hBJ0ySNlvSwpPVrKFcPSRdIej8bszBF0keSLpG0VHV5zaz+tGrsApg1sq+yx4UCsKSTgMtI68tPAOYWHD8SuA5one2aCrQFfpRth0v6aUR8XpCvJWkt7/2zXXNIa3hvB2wn6fhFfTGSViOtW796tmsuMB5YEugNbJO9lluKZO8MvEz6gjMjy9uT1KLxU0nbRsQbRa65PvBfYOls17Qs71rZ9itJu0XE64v6usysdlxTt+ZuhexxXMH+pYGLgWuBXhHRHegE3A8gaVfgRmA2cDawbER0JPXXb0FqAVgJeFBS4e/ZycwP6OcCPSKiB7AcKdhfTgrCJZHUDRhACujfA4cBXSKiZ1audYELirzWnHOBJYCdgY7Z692K9MWnA3BVkWt2BR4lvV//A3YAOkZEJ9L7MIT0xeBR19jNGkBEePPWJDdSbTSAgVUc35hUSw7gymzfEdnzAO6qIl9L4PMszf5VpOkBfJOl2TdvfwdSTTmAvxXJJ+DZvDIcUXB8xbxjKxYc+2u2fxKwegnv04gs31Rg1SLHf5Z3zeULjp1eQ95lSS0FAVzc2J8Jb96a+uaaujU7kpaVdBiphtkCmElaS77QpVWcYmtgZeCLiPhPsQQRMRbonz3dMe/QjkAXUvP0xUXyBXBhLV5GMYdnj1dHxCeLkP/+iPisyP5HSUEZYJ2CYz/PHu8oljcivgGuz54etAhlMrMSuE/dmoOtJUUVx6aSasOFQXAa8F4VeTbPHntJ+q6a63bKHpfP27dh9jg0IqrK+zKpWb/Wv5/ZoLle2dMnapuvwJvFdkbELEk/kJrYu+ddsw3zg/wz1Zz3GeBUoI+kJSNi1CKWz8xq4KBuzcEsYGz2c66p+CvgJeCGiPiySJ4xETG3yH6YHzzbMH9wWHU65P2c6yuv8h7ziJghaTSwTC3OnZNfji9KyJdvUjXHpmePrfP29SB1RUA1r4f5gxEBlgIc1M0WEwd1aw5ejYhtSswzp5pjuUD2bETssGhFMjOrf+5TNyvd99nj8tWmKi5XS122qgRZs/YSJZ43vyl/hSpT1a+xzP/ys1w16Xrn/fzD4iuOmTmom5Xu1exxNUmrV5tyYW9nj30lVdV0vyUltqJFxBek0fYAu5dYpkUSETOBD7Kn21eTNNea8aX7080WLwd1s9I9x/x+679nk8kUJam1pE55u54i3dLWEjilSHoBpy1iuW7JHo9dhC8bi+q+7PFQSSsVHpTUCzg6e3p3A5XJrNlyUDcrUUTMAn5Lui1tF+ApSf1yk8xIaiFpLUl/Aj4B1s/LOxW4KHt6gqSzJHXO8i0D3Eq6ZW7qIhTtUtIEMJ2AFyQdIqlDXpnWk/R3Sfsswrmrci0wkjQY8GlJ22ZfTJDUj3TPfTdSs/vl9XhdMyvCA+XMFkFEPCnpUOBfpOldXwVmSJpMug89f5R44e10l5JubduPNIvbWZImkoIfwPHASZTYNx4R47O5458AVgXuAOZIGk8K9G2zpINLOW8N15wgaS/SPfmrkFoxpma3EHbMko0F9nLTu9ni55q62SKKiLuB1Ug178Gk+dK7kW4Nex24AtgyIl4pyDeHNGnLMcA7pMlvIAXE3SLi6jqU6RNgPeAE4BVgImlO9x+A54HfkyaTqTcR8S6wNmnSnI+y3S2AoaS589eKiNfq85pmVpzSBFZmZmZW6VxTNzMzayIc1M3MzJoIB3UzM7MmwkHdzMysiXBQNzMzayIc1M3MzJoIB3UzM7MmwkHdzMysiXBQNzMzayIc1M3MzJoIB3UzM7MmwkHdzMysiXBQNzMzayIc1M3MzJoIB3UzM7Mm4v8BXmermwWYlKAAAAAASUVORK5CYII=\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = model.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "8604c41d", - "metadata": {}, - "source": [ - "Obtuvimos un AUC-ROC de 0.85 tanto para el set de train como para el set de test. Aún así, en otros modelos obtuvimos mejores valores y además la precision con respecto a las instancias de alto valor adquisitivo es bastante baja. Busquemos complejizar más la red" - ] - }, - { - "cell_type": "markdown", - "id": "8e6a21e5", - "metadata": {}, - "source": [ - "### Segundo diseño de la red" - ] - }, - { - "cell_type": "markdown", - "id": "04e6310b", - "metadata": {}, - "source": [ - "#### Diseño y entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "540da704", - "metadata": {}, - "source": [ - "Para complejizar la red agregaremos más capas. Como la red se volverá más compleja utilizaremos relu como función de activación" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "2ed6a287", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "251a457d", - "metadata": {}, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Dense(16,input_shape = (40,),activation='relu'))\n", - "model.add(Dense(8,activation='relu'))\n", - "model.add(Dense(4,activation='relu'))\n", - "model.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "39bee9c8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_1\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_2 (Dense) (None, 16) 656 \n", - "_________________________________________________________________\n", - "dense_3 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_4 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_5 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 833\n", - "Trainable params: 833\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.SGD()\n", - "model.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "5691a219", - "metadata": {}, - "source": [ - "Vemos que pasamos de alrededor de 100 params a 800. Finalmente, entrenamos nuestra red" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "5b2d4a70", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/100\n", - "814/814 [==============================] - 1s 980us/step - loss: 2.7978 - auc: 0.5132 - accuracy: 0.7431 - val_loss: 0.5308 - val_auc: 0.6194 - val_accuracy: 0.7593\n", - "Epoch 2/100\n", - "814/814 [==============================] - 1s 749us/step - loss: 0.5255 - auc: 0.6169 - accuracy: 0.7607 - val_loss: 0.5099 - val_auc: 0.6823 - val_accuracy: 0.7591\n", - "Epoch 3/100\n", - "814/814 [==============================] - 1s 759us/step - loss: 0.5070 - auc: 0.6890 - accuracy: 0.7622 - val_loss: 0.4888 - val_auc: 0.7753 - val_accuracy: 0.7593\n", - "Epoch 4/100\n", - "814/814 [==============================] - 1s 954us/step - loss: 0.4976 - auc: 0.7145 - accuracy: 0.7614 - val_loss: 0.4662 - val_auc: 0.7853 - val_accuracy: 0.7593\n", - "Epoch 5/100\n", - "814/814 [==============================] - 1s 994us/step - loss: 0.4878 - auc: 0.7420 - accuracy: 0.7559 - val_loss: 0.4836 - val_auc: 0.7997 - val_accuracy: 0.7593\n", - "Epoch 6/100\n", - "814/814 [==============================] - 1s 970us/step - loss: 0.4679 - auc: 0.7610 - accuracy: 0.7630 - val_loss: 0.4409 - val_auc: 0.8068 - val_accuracy: 0.7593\n", - "Epoch 7/100\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.4650 - auc: 0.7637 - accuracy: 0.7592 - val_loss: 0.4748 - val_auc: 0.7894 - val_accuracy: 0.7593\n", - "Epoch 8/100\n", - "814/814 [==============================] - 1s 857us/step - loss: 0.4539 - auc: 0.7796 - accuracy: 0.7610 - val_loss: 0.4213 - val_auc: 0.8372 - val_accuracy: 0.7593\n", - "Epoch 9/100\n", - "814/814 [==============================] - 1s 749us/step - loss: 0.4441 - auc: 0.7912 - accuracy: 0.7617 - val_loss: 0.4950 - val_auc: 0.7727 - val_accuracy: 0.7593\n", - "Epoch 10/100\n", - "814/814 [==============================] - 1s 753us/step - loss: 0.4410 - auc: 0.7987 - accuracy: 0.7587 - val_loss: 0.4165 - val_auc: 0.8502 - val_accuracy: 0.7593\n", - "Epoch 11/100\n", - "814/814 [==============================] - 1s 753us/step - loss: 0.4308 - auc: 0.8082 - accuracy: 0.7620 - val_loss: 0.4167 - val_auc: 0.8316 - val_accuracy: 0.7593\n", - "Epoch 12/100\n", - "814/814 [==============================] - 1s 956us/step - loss: 0.4274 - auc: 0.8103 - accuracy: 0.7636 - val_loss: 0.4383 - val_auc: 0.8332 - val_accuracy: 0.7775\n", - "Epoch 13/100\n", - "814/814 [==============================] - 1s 964us/step - loss: 0.4183 - auc: 0.8186 - accuracy: 0.7847 - val_loss: 0.3974 - val_auc: 0.8538 - val_accuracy: 0.7992\n", - "Epoch 14/100\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.4226 - auc: 0.8195 - accuracy: 0.7839 - val_loss: 0.3960 - val_auc: 0.8533 - val_accuracy: 0.7970\n", - "Epoch 15/100\n", - "814/814 [==============================] - 1s 974us/step - loss: 0.4189 - auc: 0.8237 - accuracy: 0.7872 - val_loss: 0.4080 - val_auc: 0.8492 - val_accuracy: 0.8144\n", - "Epoch 16/100\n", - "814/814 [==============================] - 1s 856us/step - loss: 0.4167 - auc: 0.8256 - accuracy: 0.7893 - val_loss: 0.3863 - val_auc: 0.8586 - val_accuracy: 0.8024\n", - "Epoch 17/100\n", - "814/814 [==============================] - 1s 765us/step - loss: 0.4135 - auc: 0.8290 - accuracy: 0.7890 - val_loss: 0.4556 - val_auc: 0.8384 - val_accuracy: 0.7579\n", - "Epoch 18/100\n", - "814/814 [==============================] - 1s 776us/step - loss: 0.4088 - auc: 0.8294 - accuracy: 0.7907 - val_loss: 0.3883 - val_auc: 0.8592 - val_accuracy: 0.7996\n", - "Epoch 19/100\n", - "814/814 [==============================] - 1s 765us/step - loss: 0.4136 - auc: 0.8298 - accuracy: 0.7862 - val_loss: 0.3911 - val_auc: 0.8562 - val_accuracy: 0.8019\n", - "Epoch 20/100\n", - "814/814 [==============================] - 1s 953us/step - loss: 0.4027 - auc: 0.8396 - accuracy: 0.7964 - val_loss: 0.3835 - val_auc: 0.8603 - val_accuracy: 0.7986\n", - "Epoch 21/100\n", - "814/814 [==============================] - 1s 969us/step - loss: 0.4000 - auc: 0.8400 - accuracy: 0.7957 - val_loss: 0.3904 - val_auc: 0.8642 - val_accuracy: 0.8142\n", - "Epoch 22/100\n", - "814/814 [==============================] - 1s 976us/step - loss: 0.4013 - auc: 0.8397 - accuracy: 0.7980 - val_loss: 0.3888 - val_auc: 0.8653 - val_accuracy: 0.8101\n", - "Epoch 23/100\n", - "814/814 [==============================] - 1s 979us/step - loss: 0.3988 - auc: 0.8429 - accuracy: 0.8047 - val_loss: 0.4214 - val_auc: 0.8397 - val_accuracy: 0.7507\n", - "Epoch 24/100\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.3928 - auc: 0.8438 - accuracy: 0.7993 - val_loss: 0.4117 - val_auc: 0.8621 - val_accuracy: 0.7857\n", - "Epoch 25/100\n", - "814/814 [==============================] - 1s 989us/step - loss: 0.3959 - auc: 0.8446 - accuracy: 0.8019 - val_loss: 0.3796 - val_auc: 0.8626 - val_accuracy: 0.8099\n", - "Epoch 26/100\n", - "814/814 [==============================] - 1s 970us/step - loss: 0.4773 - auc: 0.8424 - accuracy: 0.7969 - val_loss: 0.4006 - val_auc: 0.8473 - val_accuracy: 0.8108\n", - "Epoch 27/100\n", - "814/814 [==============================] - 1s 816us/step - loss: 0.4067 - auc: 0.8379 - accuracy: 0.7954 - val_loss: 0.3879 - val_auc: 0.8564 - val_accuracy: 0.8147\n", - "Epoch 28/100\n", - "814/814 [==============================] - 1s 755us/step - loss: 0.4004 - auc: 0.8421 - accuracy: 0.8014 - val_loss: 0.4101 - val_auc: 0.8561 - val_accuracy: 0.7844\n", - "Epoch 29/100\n", - "814/814 [==============================] - 1s 759us/step - loss: 0.3992 - auc: 0.8376 - accuracy: 0.7924 - val_loss: 0.3733 - val_auc: 0.8668 - val_accuracy: 0.8131\n", - "Epoch 30/100\n", - "814/814 [==============================] - 1s 763us/step - loss: 0.3980 - auc: 0.8438 - accuracy: 0.7971 - val_loss: 0.3924 - val_auc: 0.8572 - val_accuracy: 0.7949\n", - "Epoch 31/100\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.3963 - auc: 0.8441 - accuracy: 0.7945 - val_loss: 0.4160 - val_auc: 0.8544 - val_accuracy: 0.7946\n", - "Epoch 32/100\n", - "814/814 [==============================] - 1s 989us/step - loss: 0.3995 - auc: 0.8413 - accuracy: 0.7990 - val_loss: 0.3738 - val_auc: 0.8657 - val_accuracy: 0.8105\n", - "Epoch 33/100\n", - "814/814 [==============================] - 1s 985us/step - loss: 0.3943 - auc: 0.8492 - accuracy: 0.8002 - val_loss: 0.3743 - val_auc: 0.8673 - val_accuracy: 0.8142\n", - "Epoch 34/100\n", - "814/814 [==============================] - 1s 966us/step - loss: 0.3896 - auc: 0.8514 - accuracy: 0.8000 - val_loss: 0.4011 - val_auc: 0.8604 - val_accuracy: 0.7992\n", - "Epoch 35/100\n", - "814/814 [==============================] - 1s 978us/step - loss: 0.3934 - auc: 0.8469 - accuracy: 0.7987 - val_loss: 0.3724 - val_auc: 0.8680 - val_accuracy: 0.8107\n", - "Epoch 36/100\n", - "814/814 [==============================] - 1s 921us/step - loss: 0.3874 - auc: 0.8481 - accuracy: 0.8013 - val_loss: 0.4254 - val_auc: 0.8610 - val_accuracy: 0.7838\n", - "Epoch 37/100\n", - "814/814 [==============================] - 1s 778us/step - loss: 0.3966 - auc: 0.8444 - accuracy: 0.7988 - val_loss: 0.3733 - val_auc: 0.8658 - val_accuracy: 0.8128\n", - "Epoch 38/100\n", - "814/814 [==============================] - 1s 754us/step - loss: 0.3872 - auc: 0.8538 - accuracy: 0.8050 - val_loss: 0.3738 - val_auc: 0.8661 - val_accuracy: 0.8076\n", - "Epoch 39/100\n", - "814/814 [==============================] - 1s 753us/step - loss: 0.3853 - auc: 0.8516 - accuracy: 0.8046 - val_loss: 0.4397 - val_auc: 0.8360 - val_accuracy: 0.7411\n", - "Epoch 40/100\n", - "814/814 [==============================] - 1s 897us/step - loss: 0.3859 - auc: 0.8490 - accuracy: 0.8014 - val_loss: 0.3905 - val_auc: 0.8624 - val_accuracy: 0.7935\n", - "Epoch 41/100\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.3839 - auc: 0.8526 - accuracy: 0.8057 - val_loss: 0.3714 - val_auc: 0.8674 - val_accuracy: 0.8130\n", - "Epoch 42/100\n", - "814/814 [==============================] - 1s 970us/step - loss: 0.3886 - auc: 0.8521 - accuracy: 0.8037 - val_loss: 0.3717 - val_auc: 0.8692 - val_accuracy: 0.8136\n", - "Epoch 43/100\n", - "814/814 [==============================] - 1s 969us/step - loss: 0.3853 - auc: 0.8520 - accuracy: 0.8051 - val_loss: 0.3756 - val_auc: 0.8673 - val_accuracy: 0.8102\n", - "Epoch 44/100\n", - "814/814 [==============================] - 1s 974us/step - loss: 0.3841 - auc: 0.8563 - accuracy: 0.8091 - val_loss: 0.3717 - val_auc: 0.8674 - val_accuracy: 0.8130\n", - "Epoch 45/100\n", - "814/814 [==============================] - 1s 979us/step - loss: 0.3945 - auc: 0.8465 - accuracy: 0.7968 - val_loss: 0.3723 - val_auc: 0.8680 - val_accuracy: 0.8099\n", - "Epoch 46/100\n", - "814/814 [==============================] - 1s 996us/step - loss: 0.3827 - auc: 0.8532 - accuracy: 0.8058 - val_loss: 0.3722 - val_auc: 0.8668 - val_accuracy: 0.8156\n", - "Epoch 47/100\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.3820 - auc: 0.8587 - accuracy: 0.8080 - val_loss: 0.3691 - val_auc: 0.8692 - val_accuracy: 0.8125\n", - "Epoch 48/100\n", - "814/814 [==============================] - 1s 975us/step - loss: 0.3809 - auc: 0.8556 - accuracy: 0.8078 - val_loss: 0.3779 - val_auc: 0.8674 - val_accuracy: 0.8052\n", - "Epoch 49/100\n", - "814/814 [==============================] - 1s 976us/step - loss: 0.3918 - auc: 0.8501 - accuracy: 0.7995 - val_loss: 0.3699 - val_auc: 0.8706 - val_accuracy: 0.8082\n", - "Epoch 50/100\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.3795 - auc: 0.8584 - accuracy: 0.8084 - val_loss: 0.3869 - val_auc: 0.8686 - val_accuracy: 0.8118\n", - "Epoch 51/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3830 - auc: 0.8562 - accuracy: 0.8072 - val_loss: 0.3797 - val_auc: 0.8673 - val_accuracy: 0.8079\n", - "Epoch 52/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3795 - auc: 0.8544 - accuracy: 0.8111 - val_loss: 0.3816 - val_auc: 0.8665 - val_accuracy: 0.8061\n", - "Epoch 53/100\n", - "814/814 [==============================] - 1s 806us/step - loss: 0.3817 - auc: 0.8563 - accuracy: 0.8027 - val_loss: 0.3708 - val_auc: 0.8678 - val_accuracy: 0.8167\n", - "Epoch 54/100\n", - "814/814 [==============================] - 1s 751us/step - loss: 0.3847 - auc: 0.8536 - accuracy: 0.8075 - val_loss: 0.4218 - val_auc: 0.8625 - val_accuracy: 0.7843\n", - "Epoch 55/100\n", - "814/814 [==============================] - 1s 749us/step - loss: 0.3850 - auc: 0.8529 - accuracy: 0.8036 - val_loss: 0.3683 - val_auc: 0.8685 - val_accuracy: 0.8154\n", - "Epoch 56/100\n", - "814/814 [==============================] - 1s 767us/step - loss: 0.3778 - auc: 0.8575 - accuracy: 0.8073 - val_loss: 0.3915 - val_auc: 0.8649 - val_accuracy: 0.8052\n", - "Epoch 57/100\n", - "814/814 [==============================] - 1s 988us/step - loss: 0.3815 - auc: 0.8571 - accuracy: 0.8070 - val_loss: 0.3849 - val_auc: 0.8663 - val_accuracy: 0.8021\n", - "Epoch 58/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3840 - auc: 0.8553 - accuracy: 0.8043 - val_loss: 0.3719 - val_auc: 0.8683 - val_accuracy: 0.8124\n", - "Epoch 59/100\n", - "814/814 [==============================] - 1s 985us/step - loss: 0.3758 - auc: 0.8630 - accuracy: 0.8098 - val_loss: 0.3682 - val_auc: 0.8692 - val_accuracy: 0.8156\n", - "Epoch 60/100\n", - "814/814 [==============================] - 1s 965us/step - loss: 0.3837 - auc: 0.8561 - accuracy: 0.8054 - val_loss: 0.3740 - val_auc: 0.8704 - val_accuracy: 0.8070\n", - "Epoch 61/100\n", - "814/814 [==============================] - 1s 984us/step - loss: 0.3783 - auc: 0.8560 - accuracy: 0.8075 - val_loss: 0.3715 - val_auc: 0.8684 - val_accuracy: 0.8116\n", - "Epoch 62/100\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.3880 - auc: 0.8538 - accuracy: 0.8018 - val_loss: 0.3832 - val_auc: 0.8639 - val_accuracy: 0.7943\n", - "Epoch 63/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3764 - auc: 0.8592 - accuracy: 0.8109 - val_loss: 0.3768 - val_auc: 0.8682 - val_accuracy: 0.8093\n", - "Epoch 64/100\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.3771 - auc: 0.8607 - accuracy: 0.8096 - val_loss: 0.3762 - val_auc: 0.8688 - val_accuracy: 0.8073\n", - "Epoch 65/100\n", - "814/814 [==============================] - 1s 858us/step - loss: 0.3749 - auc: 0.8593 - accuracy: 0.8099 - val_loss: 0.4339 - val_auc: 0.8406 - val_accuracy: 0.7428\n", - "Epoch 66/100\n", - "814/814 [==============================] - 1s 753us/step - loss: 0.3779 - auc: 0.8589 - accuracy: 0.8100 - val_loss: 0.3681 - val_auc: 0.8705 - val_accuracy: 0.8118\n", - "Epoch 67/100\n", - "814/814 [==============================] - 1s 754us/step - loss: 0.3754 - auc: 0.8589 - accuracy: 0.8109 - val_loss: 0.3726 - val_auc: 0.8694 - val_accuracy: 0.8148\n", - "Epoch 68/100\n", - "814/814 [==============================] - 1s 761us/step - loss: 0.3790 - auc: 0.8604 - accuracy: 0.8052 - val_loss: 0.3666 - val_auc: 0.8716 - val_accuracy: 0.8165\n", - "Epoch 69/100\n", - "814/814 [==============================] - 1s 948us/step - loss: 0.3835 - auc: 0.8565 - accuracy: 0.8071 - val_loss: 0.3645 - val_auc: 0.8713 - val_accuracy: 0.8159\n", - "Epoch 70/100\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.3825 - auc: 0.8560 - accuracy: 0.8048 - val_loss: 0.3703 - val_auc: 0.8701 - val_accuracy: 0.8145\n", - "Epoch 71/100\n", - "814/814 [==============================] - 1s 966us/step - loss: 0.3782 - auc: 0.8602 - accuracy: 0.8104 - val_loss: 0.3677 - val_auc: 0.8676 - val_accuracy: 0.8165\n", - "Epoch 72/100\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.3736 - auc: 0.8630 - accuracy: 0.8099 - val_loss: 0.3911 - val_auc: 0.8652 - val_accuracy: 0.8015\n", - "Epoch 73/100\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.3780 - auc: 0.8589 - accuracy: 0.8102 - val_loss: 0.3829 - val_auc: 0.8670 - val_accuracy: 0.8009\n", - "Epoch 74/100\n", - "814/814 [==============================] - 1s 961us/step - loss: 0.3780 - auc: 0.8608 - accuracy: 0.8105 - val_loss: 0.3749 - val_auc: 0.8680 - val_accuracy: 0.8105\n", - "Epoch 75/100\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.3783 - auc: 0.8598 - accuracy: 0.8067 - val_loss: 0.3858 - val_auc: 0.8688 - val_accuracy: 0.8019\n", - "Epoch 76/100\n", - "814/814 [==============================] - 1s 977us/step - loss: 0.3762 - auc: 0.8572 - accuracy: 0.8088 - val_loss: 0.3646 - val_auc: 0.8720 - val_accuracy: 0.8131\n", - "Epoch 77/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3757 - auc: 0.8605 - accuracy: 0.8094 - val_loss: 0.4029 - val_auc: 0.8671 - val_accuracy: 0.7995\n", - "Epoch 78/100\n", - "814/814 [==============================] - 1s 960us/step - loss: 0.3831 - auc: 0.8565 - accuracy: 0.8073 - val_loss: 0.3642 - val_auc: 0.8712 - val_accuracy: 0.8179\n", - "Epoch 79/100\n", - "814/814 [==============================] - 1s 810us/step - loss: 0.3792 - auc: 0.8574 - accuracy: 0.8096 - val_loss: 0.3701 - val_auc: 0.8703 - val_accuracy: 0.8118\n", - "Epoch 80/100\n", - "814/814 [==============================] - 1s 757us/step - loss: 0.3766 - auc: 0.8566 - accuracy: 0.8091 - val_loss: 0.3714 - val_auc: 0.8713 - val_accuracy: 0.8084\n", - "Epoch 81/100\n", - "814/814 [==============================] - 1s 749us/step - loss: 0.3768 - auc: 0.8583 - accuracy: 0.8072 - val_loss: 0.3857 - val_auc: 0.8697 - val_accuracy: 0.8047\n", - "Epoch 82/100\n", - "814/814 [==============================] - 1s 767us/step - loss: 0.3740 - auc: 0.8645 - accuracy: 0.8151 - val_loss: 0.3641 - val_auc: 0.8723 - val_accuracy: 0.8139\n", - "Epoch 83/100\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.3805 - auc: 0.8580 - accuracy: 0.8058 - val_loss: 0.3655 - val_auc: 0.8698 - val_accuracy: 0.8174\n", - "Epoch 84/100\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.3757 - auc: 0.8616 - accuracy: 0.8132 - val_loss: 0.3653 - val_auc: 0.8699 - val_accuracy: 0.8171\n", - "Epoch 85/100\n", - "814/814 [==============================] - 1s 981us/step - loss: 0.3731 - auc: 0.8604 - accuracy: 0.8073 - val_loss: 0.3641 - val_auc: 0.8728 - val_accuracy: 0.8121\n", - "Epoch 86/100\n", - "814/814 [==============================] - 1s 979us/step - loss: 0.3761 - auc: 0.8596 - accuracy: 0.8074 - val_loss: 0.3736 - val_auc: 0.8705 - val_accuracy: 0.8102\n", - "Epoch 87/100\n", - "814/814 [==============================] - 1s 965us/step - loss: 0.3755 - auc: 0.8628 - accuracy: 0.8124 - val_loss: 0.3773 - val_auc: 0.8705 - val_accuracy: 0.8061\n", - "Epoch 88/100\n", - "814/814 [==============================] - 1s 978us/step - loss: 0.3718 - auc: 0.8650 - accuracy: 0.8115 - val_loss: 0.3660 - val_auc: 0.8719 - val_accuracy: 0.8130\n", - "Epoch 89/100\n", - "814/814 [==============================] - 1s 977us/step - loss: 0.3756 - auc: 0.8611 - accuracy: 0.8105 - val_loss: 0.3658 - val_auc: 0.8708 - val_accuracy: 0.8164\n", - "Epoch 90/100\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.3767 - auc: 0.8595 - accuracy: 0.8112 - val_loss: 0.3650 - val_auc: 0.8711 - val_accuracy: 0.8170\n", - "Epoch 91/100\n", - "814/814 [==============================] - 1s 879us/step - loss: 0.3731 - auc: 0.8627 - accuracy: 0.8099 - val_loss: 0.3771 - val_auc: 0.8679 - val_accuracy: 0.8085\n", - "Epoch 92/100\n", - "814/814 [==============================] - 1s 746us/step - loss: 0.3732 - auc: 0.8631 - accuracy: 0.8139 - val_loss: 0.3650 - val_auc: 0.8700 - val_accuracy: 0.8133\n", - "Epoch 93/100\n", - "814/814 [==============================] - 1s 757us/step - loss: 0.3778 - auc: 0.8611 - accuracy: 0.8068 - val_loss: 0.3657 - val_auc: 0.8729 - val_accuracy: 0.8116\n", - "Epoch 94/100\n", - "814/814 [==============================] - 1s 754us/step - loss: 0.3736 - auc: 0.8625 - accuracy: 0.8106 - val_loss: 0.3652 - val_auc: 0.8706 - val_accuracy: 0.8168\n", - "Epoch 95/100\n", - "814/814 [==============================] - 1s 907us/step - loss: 0.3834 - auc: 0.8558 - accuracy: 0.8036 - val_loss: 0.3615 - val_auc: 0.8731 - val_accuracy: 0.8191\n", - "Epoch 96/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3707 - auc: 0.8661 - accuracy: 0.8129 - val_loss: 0.3686 - val_auc: 0.8715 - val_accuracy: 0.8162\n", - "Epoch 97/100\n", - "814/814 [==============================] - 1s 992us/step - loss: 0.3772 - auc: 0.8633 - accuracy: 0.8107 - val_loss: 0.3640 - val_auc: 0.8727 - val_accuracy: 0.8142\n", - "Epoch 98/100\n", - "814/814 [==============================] - 1s 981us/step - loss: 0.3796 - auc: 0.8580 - accuracy: 0.8077 - val_loss: 0.3702 - val_auc: 0.8697 - val_accuracy: 0.8096\n", - "Epoch 99/100\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.3736 - auc: 0.8641 - accuracy: 0.8146 - val_loss: 0.3636 - val_auc: 0.8722 - val_accuracy: 0.8153\n", - "Epoch 100/100\n", - "814/814 [==============================] - 1s 978us/step - loss: 0.3778 - auc: 0.8612 - accuracy: 0.8104 - val_loss: 0.3715 - val_auc: 0.8721 - val_accuracy: 0.8090\n" - ] - } - ], - "source": [ - "history = model.fit(X_train.values, y_train,epochs=100,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "7b0c4b93", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "acbd36d7", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "13211f7c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "cd1f077a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "970ea10f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8722387590020841\n", - "AUC-ROC score sobre train: 0.8696085021532897\n", - "Accuracy sobre test: 0.8089973898357132\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.85 0.88 5311\n", - " Alto valor 0.49 0.63 0.55 1202\n", - "\n", - " accuracy 0.81 6513\n", - " macro avg 0.70 0.74 0.71 6513\n", - "weighted avg 0.83 0.81 0.82 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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KrUOuDHxjQghLuXMnkNGjtxISEs7SpV2S3wC4fv06RYoUASiitbboAK3M1JMlhBAAPH54n2PLv2D7zh8IuX2d48XssQsI5ogHXHKLWqnB09u5h9tR1KUIpT1K0cS5MLlsc1GlQBXsre2pUqAKZfOWlR4mIbKhyEjNggVHGDNmGw8fhjB8+HNERmqsrIwdiyhFlhDCOA8f8mjNcvZd3sUW07/svbaPQ7HnOiprfljpYCIV1LYqQo38ZalRpjFOdrkoU6AC7o7ulMlbBhc7l8SOIoTIxs6e9eWtt35n/34f6tYtzLfftqdq1cwxv7gUWUKIdBcSHsK5f7Zz+NxOtvkf4XjgZWwfh3JU33yyUgRQGDqfhgL5S+ChnWj00lDqNX0NO5uERp0LIYR5eoaLF+8zb147+vWraXjvVWxSZAkhLCc4GB0WxuUty9h3bgvbQ86yL+QilxyenrLO6y68dA+sHJ14rtWbNK/WmUruFbFzczcguBAiK1m79hx//XWDDz54nvLl3bl6dTj29pmvpMl8iYQQmd+dO3DuHIHhQezY9zM7bh/kz+CLXHSDu06x1rMCkx14+ULXU1C9zZvUKd+Cwnb5zMtLloRSpQx5C0KIrMfH5wFDh27C2/ssZcvmZezYhjg52WbKAgukyBJCJCckxPzx5k04d45rX3/EHz57+LQBnMoftU5U51ORB9BBl8VTO9OqTBtaNu+Lo7Mb5JKr9YQQaRceHsmXX/7JpEk7CQuLZMqUJowZ0zDTFlfRMnc6IUTGiIiAyEjz8+BgWLoUZs+GM2cACLCFoW1gaSV4XAuo9WTT9z1epctzb1KxQhOsrWVCTSGE5Z0+fZfRo7fSrFlxvvmmHWXL5jU6UopIkSVETqQ1nDwJe/bA2rWwaVPcxcDq8rCmI1wu7Mhe9yAAcmHH6wWa8mLDN2nn1QF7axmQLoRIH/7+wWzbdpkuXSpQpUoB/vrrbWrU8MhSt4iSIkuInCA0FE6cgHXr4Phx+O03QkzwdyHYVhLOd4ZrxfMQ7OxAIKGcVr6xNg6iZJ6SdPLqxGctP8tSv+CEEFmP1polS04yYsRm7t9/zHPPeeLp6ULNmoWMjpZqUmQJkV1pDY0awb595pfARTf4oAksnpLQBn64OShqFapFORsnCjsXZlT9URTLXSwDQwshcrLz5+8xcOB6tm//l6pVC/D776/i6Zl158CTIkuI7EZr2LsXGjfmqivMbw7nKhVkdZ7bcVYr6lqUMQ3G0KBIA8q7l5cbFAshDHXjxkOqVp2PyaT4/PNWDB1aF2trK6NjPRMpsoTI6oKD4Y034MIF0JrIo0dY7QWjhsGVPNEr3aZp8aYUcCpAt4rdaFe2nRRVQohM4coVf4oXz03hwi7MmtWKdu3KGnIz5/QgRZYQWdGGDebeqo8/BiDcCubVguX1Xdnb4clqxV2LM6v1LNqUaSOD1IUQmcp//wUwcuQWVqw4xT//9Kd8eXcGDKhtdCyLkiJLiMxu82a4exd27IAff4yz6KorLGjizPTqAUQqDTygS4UuuNm7Mey5YVRwr2BMZiGESERkpOa77/5m7NhtBASEMnJkPYoUyR49V/FJkSVEZhEaCh98AJcvw6+/glLm8VUJ8Hm9Ix/XDWPenfXAI6oVrEbzEs2Z1GSS3ChZCJFpBQaG0qLFYg4evE79+kWYP78dlSsXMDpWupEiSwijHT1qnl7hjTeeTAgKYGcHffuCry9+PTpx3OUxJ+0e8v2xBRy77Q13oH6R+gysNZCeVXoaFl8IIZITGamxslI4OdlStWoB3nyzGm+9VSNT3cw5PUiRJYRRfvwR3nwzbpu7O5w/D7lzExYRxrzD8/ju7x2c+ntJzCql8pRiUO1BvFz+ZZqVaJbBoYUQInW8vc8yduw2Nm7sSYkSeZg/v73RkTKMFFlCZJRHj6BHD/PVgNu2PWmvUQOGDYM6daBcOS76XWLG76P56fhPhEaEYmNlw1vV36KGRw1qFapF7UK1ZUJQIUSmd/WqP0OHbuL3389Rrlxe7t9/TIkSeZLfMBuRIkuIjHDiBFSp8uR19epw8aJ57FX79vg99mPRP4uY+1V7LvldAqBdmXb0qtKLl8u/jI3JxqDgQgiROpGRms8/38+UKbuIiIjkgw+aMXp0fezscl7JkfPesRAZqUED2L8/bltwsHm8FXDZ7zLT17zFD8d+AKBE7hK0KNmC2a1nUyl/pYxOK4QQz0wp+OOPqzRsWJSvv25L6dJuRkcyjBRZQljazJkwezbcvPmkrUMH6N0bXn4ZgO+PfM8Huz/g2oNrABRwKsC4huMYWneonAoUQmQ59+8/ZvLknYwb14hChZxZtqwLTk42Of73mRRZQljS2LEwY8aT15Urw5YtULAgABfvX2Tg+oFsvbyViu4V+ej5j2hbpi1VC1Y1KLAQQqSd1pqffz7Ou+9uwdc3iBo1PHjjjerkyiV3lAApsoSwjAcPYOhQ+Okn8+uDB6Fu3ZjF/sH+zDk4h6m7pgLQu2pvFnRYgLWV/AgKIbKms2d9GTBgPX/8cYXq1Quybl0P6tQpbHSsTEV+wwuRVmFh8MsvMHUqXLnypH3uXKhbF601f974k6m7prLp4iYASuYpycKXFtKoWCNjMgshhIUMG7aJw4dvMmdOawYNqpPlb+acHqTIEiItzp6F8uWfvC5YEMaMMU8e6uTEilMrmLZ7GifvnASgjFsZBtcZzOA6g7FS8otICJE1bdlyiZo1Pcib15F589pha2vC01PuMpEYKbKESK2QkCcFVunS5p6r1q3RWnPs9jH6/tKXv2/9DcDg2oMZ23AshV2kC10IkXXduvWIESM2s2zZKUaNqsdnn7WiZMmcNedVWkiRJURqhIRA2bLm588/D9u3A3D8v+M0+KEBAaEBgHlW9kN9D5HHQX4JCSGyroiISL799m/GjdtOUFAY48Y15P33GxsdK8uQIkuIlFq82DwNQ5TQ31bw89EfGL99PP8F/gdA3xp96V+rPzU8ahiVUgghLGbAgPX8739HaNiwKPPnt6NixfxGR8pSpMgSIiXOnXtSYLm5cf7oNrzm5EOjcXNwo3fV3gytM5SahWoam1MIIZ7Ro0chKKXIlcuW/v1r8dxznvTpUy3b38w5PcgIXCGS88UX4OUFwL0R79Bv4cuU+7EGGs2nLT7l9ru3WdRxkRRYQogsTWvNqlWnKV/+ayZMMA+FqFHDgzffrC4FVhpJT5YQCVm2DJYsgTVrALjnAF2GFWSv4wLCj4RT0b0io+qPok+1PsbmFEIIC/j3Xz8GD97Ihg0XKF8+Hy+/XMHoSNmCFFlCxPfXX/DqqwAEW8PiKtCvA8BtnK2d+fXlX2lftr2hEYUQwlIWLjzGwIHr0Ro++uh53n23Pra2JqNjZQtSZAkRTWtz71XPngCc/34GlW9NJDQilPxO+elVuRczW83M8ffiEkJkD1prlFKULu1G06bFmTu3rUzLYGFSZAnx77/w888waVJM08+DG/Pa9TEADKkzhM9bfY6NycaohEIIYTG+vkGMGbOVQoWc+eCD52nYsCgbNvQ0Ola2JEWWyNmWL4du3WJe3nCGEbNaseLGFnLZ5mJxp8V09OpoXD4hhLAQrTWLFv3DqFFb8PMLZsSI54yOlO1JkSVypkePwOXJrSDCBr7DC9VPsePGXrixBU8XT84NPoejjaOBIYUQwjLOnvXlnXfWsXv3VWrVKsTmze2oWbOQ0bGyPZnCQeQsn3wCSsUpsI6vW0BFrx3suLGXPPZ52PraVi4NvSQFlhAi27h61Z9jx27z1VdtOHjwLSmwMoj0ZImcYd06ePtt+M88Mzv588PAgax+pTKdl78MwFvV3+K7F7+TGzgLIbKFTZsucv36Q95+uwatW5fmypVh5MnjYHSsHEX+mojszdcXOnWCF180F1heXnD4ML7/nqJJsR0xBdba7mv5vsP3UmAJIbK8mzcf8corK2jT5he++OJPwsMjAaTAMoD8RRHZ16VL5ps5e3tDy5awZAlBx/9mgv9vuH/mzu6ru+lQrgPXhl+Tea+EEFleREQkX331J15ec/H2PsuECY3466+3sbaWP/VGkdOFIvsJDYWtW6F9VOHk7g4bN3IvxJ/631bn/L3zVHCvQO8qvXmvwXsy75UQIlvYtu0yQ4duokmTYsyb147y5d2NjpTjSZElso/wcNi+HV544UlbhQpw+DCn75+j4jcVAZjTeg6D6gzC2kq+/YUQWduDB8EcO3abJk2K06pVKTZt6kmrVqXkn8dMQv7KiOzDJt5koX/9BVWqsOPmPlr/3BqA6c9PZ9hzwwwIJ4QQlqO1ZsWK0wwfvomgoDCuXRuBi4sdrVuXNjqaiEWKLJE9tG795PnBg1CrFphMnL57mpeWvkR4ZDibe22mValWxmUUQggLuHzZj0GDNrBp00UqVnRn+fKuuLjYGR1LJECKLJH1XbsGW7aYn9+5Yx6DBSw9uZTuq7oDsLvPbhoVa2RUQiGEsIiTJ+9Qu/b/UApmzGjBiBHPYWMjN3POrKTIElnb6dNQ0TzWik8+AXd3tNaM3DySOX/OwcbKhs29NkuBJYTI0u7dCyJvXkcqVnTn3Xfr8fbbNShePLfRsUQy5LpOkXVdvfqkwHJ0hKFDAfhw94fM+XMOtQrVwmeED81KNDMwpBBCpN3du4H06eONl9fX+PoGoZTiww+flwIri5AiS2RNAQFQqpT5+ahREBjIhaDrvLLiFSb9MYkSuUuw9429FMhVwNicQgiRBpGRmgULjuDl9TWLFx/ntdeqYGcnpwWzGjldKLKeyEgoWhQiImD8eCI+mMbXf37Ju1veJTwynL41+vJpy0+xs5aBoEKIrMfXN4hOnZaxd+81atcuxLfftqd6dQ+jY4k0kCJLZD1z5oCfHxQowJHBL/P2/2pz9PZRyucrzw8v/cBzns8ZnVAIIdIsTx57HB1t+OabtvTrVxOTSU46ZVWZ5iunlPJSSm1VSgUqpW4rpT5VStmmYLu8Sqn5SqlrUdueVEr1z4jMwgDt2sG77wJwfdda6n5fl6O3jzKg1gCOvHNECiwhRJa0fv15GjT4gYcPQzCZrNi0qScDBtSWAiuLyxQ9WUqpPMAO4ALQGSgMzAIcgcHJbL4C8ALGA9eAtsA8pVSE1vp/6RZaZLwjR2DDBgA2ftqPl5Y3QKE40u8I1T2qGxxOCCFS7/r1hwwbtonffjtDqVJ58PF5QMWK+WXG9mwiUxRZQH/ABeiktb4PoJSyBr5RSn2ktb6Z0EZKqYJAM+ANrfXCqOYdSqnawKuAFFnZxe7d0KQJmEz8uHMWb+4wz9q+qOMiKbCEEFlOeHgkc+f+xcSJOwkJCWfixMaMG9cQBweb5DcWWUZm6YdsA2yLLrCiLMecL6kpuqO/Gx/Ea38AyL8B2cXCheYCC/h0SsuYAmt+u/n0rtrbwGBCCJE2WmsWLDhK7dqFOH58ANOmNZMCKxvKLD1ZXsAPsRu01v5KqVtRyxKktfZRSm0BxiulzgE+mAu2VkDPdMwrMkpkJLzxBv85QfvRhTgcsQmPXB4cH3CcfI75jE4nhBAp5u8fzMyZ+xk/vhGOjjZs394bd3dHOTWYjWWWIisP4J9Aux/glsy2nYFlwKmo1xHAEK31qqQ2Ukq5YD5FGa1gipKKjPXTT8yvBQPaA9ykhkcNvLt5S4ElhMgytNYsW3aKESM2c/t2ALVqFaJjRy/y53cyOppIZ5mlyEoTZS7/fwTKAD2AW0BLYI5Syk9rvTSJzUcCk9M/pUiTgABo1YoZVgcY2x5cbJ0Z23AcYxuOlf/6hBBZxsWL9xk0aANbtlyicuX8/PbbK9SrV8ToWCKDZJYiyw9wTaA9D3A/gfZo7YCuQBWt9Ymotj+UUvmBz4GkiqxZwPexXhcEDqU4sUg/a9dChw6sKQdjzfd35vLQy+R1kt4rIUTWobWmY8el/PuvP5991pJhw+rKzZxzmMxSZJ0l3tgrpZQr4BG1LDEVMJ8ePBmv/SjwtlLKUWsdlNCGWuuHwMNYx0tDbGFx778P06ezogK88grktsvNPwP+kQJLCJFl7N17jTp1CmNra2Lhwo7kz+9E0aIJ9SOI7C6zXF24EWihlModq60rEAlsSWK7q4AJqBKvvSZwJ7ECS2RC4eHQvj1Mn87WkuYCC2BTr00UdS1qbDYhhEiBO3cC6d17NY0a/cjXX/8FQK1ahaTAysEyS0/WfGAI4K2U+gjzZKSfAfNjz5GllNoOFNNal45q2oB5AtKVSqmpmMdktQL6IOOtspbXXoP16zmfF7q95YJ1RBDH3jlGxfwVjU4mhBBJir6Z85gx23j4MIR3361H3741jY4lMoFMUWRprf2UUs2BrwBv4BHm8VIT4q1qIlZmrfWjqO2mAzOA3MC/mAe1z0334MIyrlyBpUsJtobOU7x45HeRw30PS4ElhMgSXn11JStWnOa55zyZP78dVavKxerCLFMUWQBa6zNAi2TWaZpA20WgWzrFEhmhd2/87aHUWEfu3zvLDx1+oGrBqkanEkKIRAUGhmJvb43JZMVrr1WhefMS9O1bEysrGd8rnsgsY7JETnX/Pr5/76FuPyvuE0TPyj15o/obRqcSQohE/f77OSpU+IavvzZfkP7ii+V4551aUmCJp0iRJYzVvj0jW8N5t0hmtpzJz51/NjqREEIk6Nq1B3TsuJSXXlqKnZ2JypXzGx1JZHKZ5nShyIFOn2Z+2AEWV4XXq77Ou/XfNTqREEIk6Lvv/mbkyM2Eh0cydWpT3nuvAfb28idUJE2+Q4RhvpjalpFtwRk7Pnz+Q6PjCCFEouztralXrwjffNOWMmXyGh1HZBFpPl2olLJVSg1QSq1QSm2LamuklGqslMpluYgiO9qy+0eGV7iKUxicGXEJTxdPoyMJIUQMP7/HDBiwjoULjwHw2mtV2LKllxRYIlXS1JOllHICdmKe9FMBOmrRKKA9MBT42hIBRTa0cyfDV70J7uDtOYrCLoWNTiSEEID5Vji//nqCkSO3cPduIHnyOAByVxCRNmntyZoC1MJcYMX2v6i2jmmPJLIzvX8/Yz96njPuMP1YXp4f+JnRkYQQAoDz5+/RsuVievVaTaFCzhw48BYffdTc6FgiC0trkfUy5t6r1+K174v6WC7NiUS2FeFzjcHTGzCjIeRSdoxaccPoSEIIEWPbtsscPHidWbNacehQX+rWlWEM4tkorXXya8XfSKkQzKcaHYBgQGutTUopByAQCNVa21s0aTpTSnkCPj4+Pnh6yg+WpWmtsZpmrunLh+Xm2JTb2FrbGZxKCJHTbdt2mbCwCNq0KUNERCT//RdIoULORscSGej69esUKVIEoIjW+rol953WnqwHUR/jVyOtoz76p3G/IpuqMassANYRcGzqf1JgCSEMdft2AD17/kbLlouZNm03WmtMJispsIRFpbXI2hv1cVl0g1LqG+AXzKcR9zxjLpFdbNjAuI+bcyzgItVuQXDnv7E12RqdSgiRQ0VGaubNO4SX11yWLz/Fe+/VZ9u212Rgu0gXaS2ypgNhQA2eXFn4DubTh2HAR88eTWR5H3zAhdfa8UnoDgA2uQzAVK2GwaGEEDnZTz/9w8CBG6hYMT9HjvRjxoyWODnJP34ifaRpCget9d9KqReBb4BSsRZdAgZorY9aIpzIwo4ehUmT+Pgl80vvNj9RoE786ySEECL9PXoUwtWrD6hUKT89e1bGzs5Et26V5F6DIt2lecZ3rfVWoIxSqgzgDtzVWl+wWDKRdf32G7z8Mn8Vhh+rw9vV3+YlKbCEEBlMa42391mGDt2EjY0V584NxsbGRPfulY2OJnKINJ0uVErtUEptB9BaX9Ba748usJRS05RSUy0ZUmQht2/Dyy/zaQOo29fcNKbhGGMzCSFynKtX/enQYSmdOy/HycmGH354CRsbk9GxRA6T1p6spjwZixXf+1HLJqdx3yKruncPPDxYUB3GtDQ3/fbKb5R2K21sLiFEjrJ37zVat/6ZyEjNhx82Y9So+tjZya16Rcaz6HedUqqiJfcnspgiRbjmCm9HjcO6PuK63DJHCJFhgoLCcHS0oWZND7p3r8S4cQ0pVcrN6FgiB0vx6UKl1GSlVIRSKoKoXqzo17Haj0ctu5U+cUWmdOcOtG1LZPBj2vY0N/3S+RcpsIQQGeL+/cf07fs7tWv/j9DQCBwcbPj++w5SYAnDpbYnK6WXYixPbRCRRT16BAUKAND/RTiVHwbUGkCPyj0MDiaEyO601ixefJx3393CvXtBDBpUm7CwCGxtZeyVyBxSU2QdAxZFPX8dc4/VT7GWa8APOIQUWTnHzJkATG1ly/9qhtKkWBO+bvu1waGEENndjRsP6dVrNX/8cYUaNTzYuLEntWoVMjqWEHGk9d6FkUTdr9DykYwh9y5MIysrPm6gGd8CirkW41DfQ7g7uRudSgiRzT16FMJzzy3gnXdqMnBgbayt0zq3tsjp0vPehWmdjFS+mwUcOcIHjTSTnod6nvXY3GszznZy3y8hRPrYsuUS//vfEZYufRlnZzuOH++PySR/jkTmlearC5VSNkAboBzm2+nEobWe9gy5RBbwxw8TmfQ81HIqw47Xd2BvbW90JCFENnTr1iNGjNjMsmWnKF48N9euPaBEiTxSYIlML01FVtSptZ1AySRWkyIrG4vUkXR13ADAL33WSoElhLC4iIhI5s8/zPjxOwgKCmPcuIa8/35jHB1tjI4mRIqktSfrA+LeszC+1A/0ElnKL7P64OsEg8+4UDZfOaPjCCGyoYcPQ5g6dRdVqxZg3rx2VKyY3+hIQqRKWvtam2MupKJvn6OBF4F9wEWg3bNHE5mV7+3LvO2/GK+7MOOTw0bHEUJkIw8fhjBnzkEiIzV58jhw4MBb/PFHHymwRJaU1iKrQNTH2dENWuv1QHegNNDhGXOJzCo4mJcmlibUGj7xr4lj8TJGJxJCZANaa1atOk358l8zYsRm9u69BkCpUm5YWaV0ikYhMpe0FlnBUR+Dop8rpcoAkVHtrzxjLpFJHWpfg/2emnK+8NJPfxkdRwiRDfz7rx/t2y+hS5cVuLrasWtXHxo3LmZ0LCGeWVrHZN0BcgFuwL+AF/AHEBG1XMZkZUPnfM9Rp9EZAFaM/Rus5MoeIcSzCQ2NoEGDH/DzC+ajj57n3Xfry4ztIttIa5F1DPPA9xrAb8AEoCBPbruz7pmTiUwlODyY1rOqgx18cb8ulQvXMDqSECIL++ef21SpUgBbWxPff98BL698lCyZx+hYQlhUWrsi3gOaAScwT9XwFXAbuI/51jvDLRFOZB59lnTjqt1jpm+HoeO8jY4jhMiifH2DeOutNVSr9i2rVpl7xtu2LSMFlsiW0jrj+7+YTxNGGxb1ENnQpn2LWHb5d+pfg/FVB0PBgkZHEkJkMVprFi36h1GjtuDnF8zQoXVo1SqpmYCEyPrSPON7YpRSLYAPtNb1LL1vkfFC1vzG4B19wA1WnaoIB78yOpIQIovRWtOu3a9s3HiRWrUKsXlzO2rWlJs5i+wvVUWWUqoY0Asognnw+29a62NRy+oAnwENLZxRGKjijpe55Ab/86lOwYNHjI4jhMhCQkMjsLU1oZSiffuytG1bhgEDasntcESOkeIiSylVHfMVhLliNY9TSvUBHIFvMI/xUsjVhdnC9ml9uOQGeYOtePt7KbCEECm3adNFBg5cz1dftaFdu7IMHFjb6EhCZLjU/DsxGXDGXERFP0zAHGBm1HMFHAJesGhKkfFCQxnvswiAE70PGhxGCJFV3Lz5iFdeWUGbNr+glMLJydboSEIYJjWnC+th7qFaB/wPc0H1Nubb6QBcB4Zqrb0tGVAYICKCix52/DUUmgQXwKO8/AcqhEje/PmHee+9rQQHh/P++40YP74RDg5yM2eRc6WmyMob9fF1rbU/gFJqH+BL1L0Ltdb/WDaeMESHDoxtYX76/us/GJtFCJFl3Lr1iBo1PJg3rx3ly7sbHUcIwymtUzZ8SikVCWittSkl7VmNUsoT8PHx8cHT09PoOMYJDyfC1ob8oyHA2ZbgCcEoJfcNE0I87cGDYCZO3EnnzuVp2rQ44eGRmExKfmeILOX69esUKVIEoIjW+rol953qKRyUUpdT0K611jIBSlbUogVjW8B9R/ix/bfyy1II8RStNStWnGb48E3cuhWAu7sjTZsWx9parhoUIra0zJMV/66dOl67XF2YVZ0+zao7u5jZDfI65KVbxW5GJxJCZDKXLt1n8OCNbNp0kYoV3Vm+vCsNGxY1OpYQmVJqiyzp1sjOxoyhW1fz04NvH8TBxsHYPEKITOfbb/9m164rzJjRghEjnsPGJkuPFBEiXaW4yNJaSz9wdnbtGoGb10FNhZtDHkq7lTY6kRAik9i16wouLnZUr+7BpElNGDiwNsWL5zY6lhCZnhROwuzHH+nfHiKUZnGnxUanEUJkAnfvBtKnjzdNmy5i0qQ/AMiVy1YKLCFSSIosAatWce6rKfxSBVqVbEXbMm2NTiSEMFBkpGbBgiN4eX3N4sXHGTHiOX79tbPRsYTIcix+g2iRxfj4QJcu9OsD9trEDy/JvFhC5HQzZ+5nzJht1KlTmPnz21G9uofRkYTIkqTIyun27+e7mrC7OPSr8RaFXQobnUgIYYDAwFD8/ILx9HShb98a5M5tz1tvVZebOQvxDOSnJ6dbuZLhL4C1smZGyxlGpxFCGGD9+vNUrPgNr766Eq01efI40K9fTSmwhHhG8hOUk/n6MuvGSh7bwJvV3yC3fW6jEwkhMtD16w95+eXltG+/BBsbE5MnN5EJiIWwoGc6XaiUagM8D+TRWr+tlIqeke6m1jr8mdOJ9BMUREjPV5nS1PxyevOPDI0jhMhYmzZdpGvXFYSGRjBpUmPGjWuEvb2MIBHCktL0E6WUsgZ+A9rFan4bWAw0jHr+4zOnE+ln0iS2XtnOo/qwqMkc8jnmMzqRECIDhIdHYm1tRbVqBWnRoiSffNKccuXk51+I9JDW04VjgPaYZ4CP3bf8ddTrVF/rq5TyUkptVUoFKqVuK6U+VUrZpnDbwkqpRUqpu0qpx0qpM0qpnqnNkGMEBsLnn/N9Dcht50qXBn2NTiSESGf+/sEMHLieDh2WoLWmYMFcrF7dTQosIdJRWous1zDfn3B8vPadUR8rpWZnSqk8wA7AFnOBNh7oB8xKwbYewAGgUNQ27YF5gF1qMuQo3btzzRXWeMGrlbrjaONodCIhRDrRWrNkyQm8vOYyb95hihRxITQ0wuhYQuQIaT0BXzzq4xwg9mCeB1EfC6Zyf/0BF6CT1vo+xJyS/EYp9ZHW+mYS234K+AAvaK2jf3NsT+Xxc45Vq2DtWoZ3AxsrG4Y/N9zoREKIdHLlij/9+q1l69bLVK6cn9Wru1GvXhGjYwmRY6S1Jyso6qNbvPb6UR8DU7m/NsC26AIrynLM+VoltpFSygV4BfgmVoElkuLtzdqysLo8vF71dcrlK2d0IiFEOrGyUvzzz3/MnNmSv//uJwWWEBksrUXWoaiP30U3KKXeA5ZiPo34Vyr35wWcjd2gtfYHbkUtS0wNzKcYw5RSu5RSYVHjuWYopWySOqBSykUp5Rn9IPW9b1lS5C8/06GH+XnfmjIWS4jsZseOfxk1agsARYu6cuXKMN59tz42NiaDkwmR86S1yPo06mMbzEUVwMdA/qjXn6Vyf3kA/wTa/Xi6tyy26MLoe+Aw5l6v2cBwYFoyxxyJ+TRj9ONQ0qtnA35+zK1jftqzck/qFK5jbB4hhMXcuRPIa6+tpnnzn1i+/BR375pPKDg4JPn/phAiHaWpyNJabwfeAh7y5ApDhXlM1tta651JbG5J0fm3aa3f1Vrv1FrPwFzkjVBKOSSx7SygSKxH7fSNajz/W/8yrI35+ddtvzY2jBDCIiIjNd999zflys1lyZITjBpVj9OnB+Hu7mR0NCFyvDTPPKe1XqiUWoF5HJY7cBfYr7VO7XgsMPdYuSbQnge4n0B77O3AfGVibNuBCUBp4ERCG2qtH2IuEgFyxCzHMzdNAuCb/G/gap/Qp1sIkdVcueLPkCEbqVHDg/nz21G1ao4Y+SBElpDWyUg/BhZprc8CWy2Q4yzxxl4ppVwBD+KN1YrndDL7tX/GXNmG1prV//2BrS30e+F9o+MIIZ5BQEAo69ad59VXK1GyZB7273+T6tU9sLLK/v8sCpGVPMtkpKeUUoeVUkOVUu7PmGMj0EIplTtWW1cgEtiS2EZa66uYe6paxFvUEnhM8kVYjrH94K+cdgxk+g4wFStudBwhRBqtWXOWChW+pnv3VZw5cxeAmjULSYElRCb0LDeIVpiv7psN3FBKrVNKdVNKpWUS0PnAI8BbKdVKKfUG5nFV82PPkaWU2q6Uuhhv2wlAB6XUHKVUS6XUeGAUMCuNpy6zHa01E/Z9QMFHMPD5MWAl9wUXIqu5du0BHTsupWPHZTg42LB9e2/Kl3/W/2+FEOnpWSYjfQVzb1PtqP20xXy14SOl1AqtdYrnB9Ba+ymlmgNfAd6YC67vMRdQsZniZ9Zar1VKdQcmAgMwT/swGfgk1e8qm/pg9wf8FXiOTw+A49T4nX5CiMzO3z+YKlXmERwcztSpTRkzpgF2dnIzZyEyO6W1Tn6tpHagVDHMxdYrQK2oZq21zlKTskTNleXj4+ODp6en0XEsJjwynAKf5edRoB/3ZoDzvUeQK5fRsYQQKXD1qj/FiuUGYMGCIzRuXIwyZfIaG0qIbOb69esUKVIEoIjW+rol922J80YPMV8B6AeEW2B/woJWnV7F/WA/Fq0G546vSIElRBbg5/eY/v3XUarUl/z11w0A3nqrhhRYQmQxab26MA/QCXMP1vOx9qOAEOB3i6QTz2zmgZnkD4Qup4FjvxodRwiRBK01v/56gpEjt3D3biADBtSibFkprITIqtJ6Uv82cQsrDewDfgJWaK0fJLahyDj3H9/n8M3D9D8NNlWrgylLncEVIkcJD4+kbdtf2Lr1MtWqFeT331+lbt3sM3RBiJworUVW9H0aLgCLgZ+11lcskkhYzCsrXgGg3QWgT1djwwghEhQZqbGyUlhbW1GlSgHati3D4MF1sLaWq4CFyOrSWmR9AyzWWv9pyTDCsq4/NI/fa3ceqFrV2DBCiKds3XqJYcM2sXx5VypVys/Mma2MjiSEsKC03rtwsBRYmduhG4c4d+8cn+53QgG0bm10JCFElNu3A+jRYxWtWv1MUFAYfn6PjY4khEgHKe7JUkrtwDw1Q/Oo50nRWuvmzxZNPIt159cB8PyJQPDykvFYQmQCWmu+/fZvxo7dRmBgGGPGNGDixMY4OdkaHU0IkQ5Sc7qwKeYB7vGfx6eSWCYyQEBoANN2T6PGbUWNWxo2LDE6khAC843o9+69RqVK+Zk3rx2VKxcwOpIQIh2lpsi6hvlegtHPpZDKpOb+NReAMXs0qmJFqFbN2EBC5GCPHoUwdeou+vevRenSbnz7bXscHGzkXoNC5AApLrK01sUTei4ynz3X9gBRc2Pd+cPQLELkVFprvL3PMnToJq5ff4inpwvDhz8npwaFyEHSOhnpJMzjrj5IYNnzmBcmN25LpJOb18/g8QismjSFfPmMjiNEjnP1qj+DB29k3brzeHnlY+fO12natLjRsYQQGSytUzhMwXy68KkiC9iG+bSi3L3UAP/4HObY43/56E9g7Cij4wiRI40Zs41t2y7z4YfNGD26Aba2cuGJEDmRRQshpZRL9FNL7lek3JKjiwBo6dUW2rUzOI0QOce+fdcoViw3np4uzJzZiunTn6dUKTejYwkhDJSaKRxeB16P1xb/lGDRqI/+zxZLpNWC4+Yiq1bVNgYnESJnuH//MWPGbOX774/y1lvV+f77Dnh6uiS/oRAi20tNT1Zx4k7doIAm8daJ7sHa/UypRJo8DHmIb8Qjyt8Fesjko0KkJ601ixcf5913t3DvXhCDB9fmww+fNzqWECITSU2R5Q9cjXpeDHOxdS3Wcg34AYeAyZYIJ1IneuqGCbuBL0sZG0aIbG7cuO3MmLGPGjU82LixJ7VqFTI6khAik0nNFA5fAF8AKKUio9pKpFMukQYL/5gDwCtlOoKV3FxWCEt7/DiMkJAIcue25803q+PhkYtBg+RmzkKIhKV14Hszi6YQz0xrzaPQABreAJvpHxsdR4hsZ/PmiwwcuIGGDYuyaFFHypbNS9myeY2OJYTIxFIz8L0xgNZ6N1HjsqLbEhK1nsggp279w23rx4w9jflehUIIi7h16xEjRmxm2bJTFC+em27dKhodSQiRRaSmJ+sPnsx/9QdJ31ZHp3Lf4hn9ProDlIU2jpWNjiJEtvHbb2d44401BAWFMW5cQ95/vzGOjjZGxxJCZBGpLYRUIs+FwRYXvkeBACizeo/RUYTI8rTWKKUoXdqNWrUK8eWXL1CxYn6jYwkhspjUFFlvJPJcGCzowmnOOgXR404BlKur0XGEyLIePgxh0qSdREZqvvyyDVWqFGD79t5GxxJCZFGpubpwUULPhfF+/nYQOEOtgjWMjiJElqS1ZtWqMwwbtombNx/x9tvVY3qzhBAirdJ6g2g7wAkI0VoHRt1OZxDgDmzSWm+xYEaRjMPn/4Ca0HP4D0ZHESLLuXLFn0GDNrBhwwUqVHBn6dKXadSomNGxhBDZQFond5kL3AWi70C8FfgQGAZsVEp1sUA2kRK7dnHQ0/w0v3NBY7MIkQXdvh3Arl1X+Pjj5hw9+o4UWEIIi0nrFYB1oz6uU0qVB2oDEUAw5h6u4cDKZ04nkhX03gjOt4KKuUoaHUWILGPPnqscO3abIUPq8txznvj4jCBPHgejYwkhspm09mQVifp4AYgeCDQNqBP1vNyzhBIpFB7OR7mOEmINU9t8anQaITI9X98g3nxzDY0bL+Tzzw/w+HEYgBRYQoh0kdYiyy7qYxhQEfO8WH8DF6Pacz1jLpESq1axL6rc7ejV0dAoQmRmWmt+/PEoXl5zWbToH4YNq8vx4wNwcJA5r4QQ6SetpwtvAiWAH4GGUW2ngehBQb7PmEukxJ49XHSDzp6tMFmZjE4jRKZ16NBN3nzzd2rVKsTmze2oWVNu5iyESH9p7clag3ky0q5AIeCE1voKUDNq+clnjyaSc/P2Ba67Qp2yTY2OIkSmExQUxo4d/wJQp05hNm/uxcGDb0mBJYTIMGntyZoIOAINgKs8ucqwDLALWPLs0URylt39A4D6RRsmvaIQOcyGDRcYNGgDt2494sqV4RQsmItWrUoZHUsIkcOkqcjSWgcBAxJo/wz47FlDiRQ4epT1xUMBqF+kvsFhhMgcbtx4yPDhm1m58jQlS+ZhzZpXKVhQhogKIYyR5ps4K6WsgdeBFzBPQuoLbAQWaa3DLRNPJGrxYvYXgWK2+WU8lhDAtWsPqFTpG4KDw3n//UaMH99IBrYLIQyV1hnf7YEtmE8XxtYJeEMp1UJrHfys4UTiwk+f5HE9KF24stFRhDDU/fuPcXNzoGhRV0aNqs8rr1TEyyuf0bGEECLNA9/HY76qUCXwqBe1XKSXrVs58c9WABoVbWRwGCGM8eBBMEOGbKBEiS/w8XkAwKRJTaTAEkJkGmktsl7BPDfWCsyD3e2jPi7HXGi9YpF0ImE//8yCqClg36j+hrFZhMhgWmuWLTuJl9fXzJ17iFdeqYCTk63RsYQQ4ilpHZNVPOrjO1pr/6jnl5RS/TEXWMUT2EZYio0Nf3tAfqf8FHUtanQaITLMo0chdO26gs2bL1GpUn5WruxKgwbyMyCEyJzS2pP1OOpj/GuiS8VbLtJB5P59HPWArhW6Gh1FiAyVK5ct9vbWzJjRgiNH+kmBJYTI1NLak3UYaA6sV0otAnwAT8xXG0bfYkekhwsX+MfvLCHWUNG9otFphEh3f/xxhQkTdrB6dTfy53di9epuKKWMjiWEEMlKa5E1E3ge89QNo2K1K8xF1sxnzCUSs2MHm0ubn1b3qG5sFiHS0d27gYwatZWffvoHT08X/v3Xj/z5naTAEkJkGWk6Xai13gy8Azwi7pWFj4D+WutNFkso4po7l41RRVYNjxrGZhEiHURGahYsOIKX19f88stxRo58jtOnB1K3rqfR0YQQIlXSPBmp1vp7pdRSoD6QD/NkpPu11gGWCifiCQmBkye52wTK5imDrUmuqBLZ04IFRyld2o1vv21PtWoFk99ACCEyoVQXWUqp4jy5EfQRrfUWiyYSifv7bzRwxh36lWhmdBohLCYwMJTPPtvP8OHPkTu3PWvWvIqbmwMmU1qvzRFCCOOluMhS5oEQ84C3MZ8ajG7/EeirtdaWjyfi2LmTa67mp442jsZmEcJC1q07z+DBG7h69QHFirnyxhvVcXd3MjqWEEI8s9T8mzgE6MfTM7y/AQy3eDLxtJAQ/ok6c1K1YFVjswjxjK5ff0jnzst48cUl2NiY2Lr1Nd54Qy7mEEJkH6kpst6M+hgK/A6sBUIwF1p9LBtLJOjgQQ5Gjf0tn6+8sVmEeEbdu69i/foLTJ7chBMnBtCiRUmjIwkhhEWlZkxWWczTM7TRWv8BoJRqBmzHfEsdkd7CwzlTyAREUK1gNaPTCJFqf/11g4oV3XFysuXrr9tiZ2eiXDm516AQIntKTU+WPUB0gRUl+rmdhfKIJPgf2Il3mQhal2qNnbV8ykXW4e8fzMCB63nuue+ZMWMfAFWqFJACSwiRraXl6sIixBr4nli71vras0UT8a0ra/74SkW5/7bIGrTWLFlykpEjN/Pff4G8805NRox4zuhYQgiRIdIyT9aVeK91Au06jfsWiQkO5mzUP/0dvToaGkWIlBo4cD3z5/9NlSoFWL26G/XqFTE6khBCZJi0TEIT/+rCxB6p26lSXkqprUqpQKXUbaXUp0qpVM22qZQarpTSSql1qT1+pufvz96iUDEyH24ObkanESJRISHhhISEA/DKKxWZObMlf//dTwosIUSOk5rept086bWyKKVUHmAHcAHoDBQGZgGOwOAU7qMgMBm4kx4ZjRb2yJ+DnvBWLrkptMi8duz4lwED1tOzZ2UmTWpCs2YlaNashNGxhBDCECkusrTWTdMxR3/ABeiktb4PoJSyBr5RSn2ktb6Zgn18inlqiWLpF9M4JzYsJMQaathmy7cnsrg7dwJ5990t/PzzcYoUcaFGDQ+jIwkhhOEyyz0r2gDbogusKMsx52uV3MZKqYZAR2BsuqTLBH7f/g0ArZu/Y3ASIeJatuwk5crNZcmSE4weXZ/TpwfRvn1Zo2MJIYThMsvgdC/gh9gNWmt/pdStqGWJUkqZgLnAdK31LfPdf7KZixfxLvyI2mH58axU3+g0QsTh6GhD+fL5mD+/PVWqFDA6jhBCZBqZpcjKA/gn0O4HJDfKeyDgBMxOzQGVUi6YT1FGK5ia7TPSv6t/4J+CML1KT6OjCEFAQChTpvxB0aKuDB1alxdfLEf79mXJlv/gCCHEM8gspwvTRCmVH5gGjNRah6Zy85GAT6zHIQvHs5g1R5cC0LHh2wYnETndmjVnqVDhaz7//ABnztyNaZcCSwghnpZZerL8ANcE2vMA9xNojzYNOA7sUUrljmqzBqyjXgdorcMT2XYW8H2s1wXJjIXWnTustv+XspFucr9CYZhr1x4wZMhGfv/9HGXL5mX79t48/7xcNSiEEEnJLEXWWeKNvVJKuQIeUcsS4wU0xlykxeeHeUD9poQ21Fo/BB7GOl7qEmeQu2uWsLcIjCrZNtNmFNnfvn3X2Lz5ItOmNeW99xpgZ5dZfnUIIUTmlebflEqpvMB7wPNAHq11aaVUj6h9btJap2a+qo3AeKVUbq21f1RbVyAS2JLEdsOB3PHa5gCPgXGYe7mytHX7FxJZHDo26290FJHDHDjgw61bAXTuXJ5XX61Ew4ZFKVIkoQ5nIYQQCUlTkRU1Fuog5jmpFE8mKX0B6Im5wPk0FbucDwwBvJVSH2GejPQzYH7sObKUUtuBYlrr0gBa62MJZPPHfJrwj1S9qcwoKAjvsOMUiHCgbpF6RqcROYSf32PGjt3Gd98doXz5fHTs6IWVlZICSwghUimtA98/AIoDEfHaF2Iuul5Mzc601n5AcyAc8AY+wTxeamS8VU1knlOc6S5w4+9sKR7JSwWbYqWy9DUKIgvQWvPzz8cpV24u//vfEQYOrMX+/W9hZSWnqYUQIi3SWrC0w9x71RrYHqv9r6iPpVK7Q631GaBFMus0TcF+kl0nq9iy4zuC80PHpnKqUKS/tWvP89prq6lWrSDr1vWgTp3CRkcSQogsLa3dI+5RH/clsjxvGvcrooWH431vP7kirHm+bGuj04hsKjg4nH/+uQ1A+/Zl+fnnThw61FcKLCGEsIC0Flm+UR/jz8bePepjtrxJc0YK37ubtcVDaOtaEztrO6PjiGxo69ZLVK48j1atfiYwMBQrK0XPnlWwtpZT00IIYQlp/W0afYrQO7pBKbUBmIf5NOL2BLYRqbBnwzz8HKBjA5mAVFjW7dsB9OixilatfiYsLIIffuiAk5Ot0bGEECLbSeuYrGlAB8yD36OvLGyNedD7A8wD40VaaY33tS3YlFO0rdbV6DQiGzl+/D8aN/6RwMAwxoxpwMSJjaXAEkKIdJKmniyt9UWgEbAD81xWKurjDqCx1vqSxRLmQPrkSbw9HtLMthyu9nLZvHh2jx+HAVChgjuvvFKRI0f68cknLaTAEkKIdJTmwRda6xNa6xaYb7LsCThrrVtorU9YLF0Odcx7HtdyQ8dackNo8WwePQph5MjNVKz4DQEBoVhbW/Hddy9SuXIBo6MJIUS298xzTmmtH2OeYV1YiPdZbygLHeq9YXQUkUVprVm9+ixDh27kxo1H9OlTjbCw+NPaCSGESE9pnfE9ud/WWmudYyYNtajr1/F2uUUdClPYRS6jF6nn6xtEnz7erF9/AS+vfPzxR2eaNCludCwhhMhx0loIyRTQ6eTy6h84XhA+qtDZ6Cgii3J2tuXGjUd8+GEzRo9ugK2tyehIQgiRI6W1yFoU77UJKAHUB4KAFc8SKidb8/evUAI6NpFZ3kXK7dt3jZkzD7BkycvY21tz+HBfTCaZ70oIIYyUpiJLa53gYCGlVGtgI3DkWULlWA8e4G11nrIRufFyL290GpEF3LsXxNix2/j++6N4eOTiwoV7VK5cQAosIYTIBCz6m1hrvRkIAIZacr85xd21y9hbRNOx2AsoJWdkReK01ixadAwvr69ZsOAogwfX5syZQXLVoBBCZCJpHfjeOIFme6ANkAvweJZQOdXavQuI9IBOLQYZHUVkco8fhzN58h8ULerKxo09qVWrkNGRhBBCxJPWMVl/8GSm9/g0cCyN+825QkPxDjpCwXAH6hStb3QakQk9fhzGd9/9zaBBdXB0tGHHjtcpWtRV7jUohBCZ1LNMs5DY+axrwMBn2G+OFLB9I1uKhdPHvRlWSv5oirg2b77IwIEbuHzZj+LFc/PSS16ULJnH6FhCCCGSkNYiK6GB7yGAD/Cn1jo87ZFypi1b5hGSGzo2G2B0FJGJ3Lz5iBEjNrN8+SlKlMjN+vU9aNu2jNGxhBBCpECqiyyllB3gF/XygNb6rmUj5UBa4313N87O1jQr39boNCKTiIzUPP/8Ii5f9mP8+IZMmNAYR0cbo2MJIYRIoVQXWVrrEKXUSsxXJspoWwsIO/Qn6zwf09apFnbWdkbHEQY7deoO5cu7Y2WlmDu3LYUKOVOhgrvRsYQQQqRSWgf/XMQ8JktuhmYBe9bOxc8BOjZ4y+gowkAPH4YwbNhGqlSZz4IF5qnmWrQoKQWWEEJkUWktsqZEfZyulLK1UJYcy/vfjdhEKtrU6m50FGEArTUrV56mfPmv+fLLv+jTpyqdO8tktEIIkdWldeD7AOAB0BfoqpQ6DzyOtVxrrZs/a7icQF+8iHf++zxvXRZXe1ej4wgD9O7tzc8/H6dCBXeWLn2ZRo2KGR1JCCGEBaS1yGqCeT4sBeQB6sRapkh8Di0Rz9HV3+DjCuNr9DA6ishAYWERWFtboZSiZcuSVKzozsiR9eRmzkIIkY2kuMhSSvXG3EO1GPNcWFJIWYD3qZVQAjo07mt0FJFBdu++Sv/+65gwoRE9e1ahd++qRkcSQgiRDlLTk7UQiAQWa62Lp0uanMbXF29HH+pGelDIWS7UzO58fYN4772t/PjjMQoVcsbV1d7oSEIIIdJRak8Xyl2LLejS6h84UQA+LvOS0VFEOvvll+MMHboJf/9ghg2ry7RpzXBxkek6hBAiO3uW2+qIZ7Tmr5/AEzq2GGJ0FJHO7twJpGTJPHz7bXtq1JD7pwshRE6Qlhnfd6RgNbm6MDlBQXjrM5QLc8UrfwWj0wgLCwoK44MPdlGvXhE6dCjH0KF1GTq0LiaT3JdSCCFyirT0ZDVJZrlcXZgCdzasYF/hSEYXaml0FGFhGzZcYNCgDVy54s+IEc/RoUM5Ka6EECIHSkuRJeOyLGDdrv8RmQ86thhsdBRhITduPGTYsE2sWnWGUqXysGlTT1q3Lm10LCGEEAZJS5FVwuIpcpqICFY/+gsPV3vqlGxkdBphIYsW/cPvv59j4sTGjBvXEAcHuZmzEELkZGm5QfTV9AiSkwTs3sbWImG8kacRVkpOI2Vlhw7dICwskvr1i/Duu/V4+eXylCuXz+hYQgghMgH5C2+AzZu/JsQaOjYbYHQUkUYPHgQzePAG6tb9nvfe2wqAnZ21FFhCCCFipKYn6xrmyUjFs9Aa71s7cSlqolmVDkanEamktWb58lMMH76Z27cD6Nu3Bp980sLoWEIIITKhFBdZMsu7ZYSd/Id1HgG0daiBrcnW6DgilRYsOErfvmupVCk/q1a9Qv36RYyOJIQQIpOSyUgz2O41X+HvAB3rv2F0FJFCISHh3L4dQLFiuenevRLBweG8805NbGzkZs5CCCESJ2OyMpj3pXXYRCra1OttdBSRAjt3/kvVqvPp0GEp4eGRODnZMnhwHSmwhBBCJEuKrAykr1/HO88dmqtSuNi5GB1HJOHOnUBef92b55//icDAMKZObYrJJFPECSGESDk5XZiBjvz2Nddd4f0qrxgdRSRh375rvPjiEh4+DGHkyOeYOrUZuXLJ+DkhhBCpI0VWBvI+sRw8oUPzQUZHEQmIiIjEZLKiUqX8NGpUjKlTm1KtWkGjYwkhhMii5HRhRnn4EG/byzwXVhAPl0JGpxGxBAaGMmbMVpo1W0RkpMbV1Z41a16VAksIIcQzkSIrg1xc8yMn80PHMu2NjiJiWbfuPBUrfsOnn+6naFFXgoLCjI4khBAim5DThRlkzcGFkB86th5mdBQB3L4dwMCB61m9+ixlyrixbdtrNG9e0uhYQgghshEpsjJCaCjeoSfwCnWhXMFKRqcRgMmkOHjwOlOmNGHMmIbY28uPghBCCMuSvywZ4M5Wb/YVjmCM+/NGR8nR/vzzOj//fJwvv2yDu7sTly4NxcHBxuhYQgghsikZk5UB1u6cj1bQseUQo6PkSP7+wQwcuJ569RawfPlprl17ACAFlhBCiHQlPVnpTWu8/Q7gYWdH7TJNjU6To2itWbLkJCNHbua//wJ5552afPxxc/LkcTA6mhBCiBxAiqx0FvDXXrYWCuYNl8ZYKek4zEh37wbxzjvrKFkyD6tXd6NePbmZsxBCiIwjRVY627T+C0KsoWOT/kZHyRFCQsJZteoMPXpUJn9+J3bt6kOVKgWwtpYCVwghRMaSvzzpzPvGdlzCTDSr+bLRUbK97dsvU6XKfHr2/I0DB3wAqFHDQwosIYQQhpC/Puko7OI51uX3p51dJWxNcu+79PLffwH06vUbLVosJjg4nDVrXpVTg0IIIQyXaYospZSXUmqrUipQKXVbKfWpUirJykQp5RG13jGl1COl1HWl1K9KqWIZlTspu1bP4YE9dKzb2+go2VZwcDjVq3/L0qUnGT26PqdPD6RDh3JGxxJCCCEyx5gspVQeYAdwAegMFAZmAY7A4CQ2rRm1/g/AQSAfMBH4SylVSWt9Nz1zJ8f7/O/YeiheaPyWkTGypWvXHlC0qCv29tZ8+mlLqlQpQJUqBYyOJYQQQsTIFEUW0B9wATppre8DKKWsgW+UUh9prW8mst1ewEtrHR7doJTaD1wDegOfp2/sxOm7d/F2uUlzXQIXe1ejYmQ7AQGhTJnyB3PmHGTTpl60aFGSXr2qGB1LCCGEeEpmOV3YBtgWXWBFWY45X6vENtJa+8cusKLargN3gULpETSl/l79NTdcoGPlrkbGyFbWrDlLhQpf8/nnB+jRo7L0XAkhhMjUMktPlhfmU34xtNb+SqlbUctSTClVFsgPnLFcvNTzPrYUlR86tB5qZIxsQWtN164rWLXqDGXL5mX79t48/3wJo2MJIYQQScosRVYewD+Bdj/ALaU7UUop4EvgJrAkmXVdMJ+ijFYwpcdJ1uPHeFud57nQ/BR0LWyx3eY0kZEaKyuFUopKlfJTtWoB3nuvAXZ2meXbVgghhEhcZjldaClTgOZAb611YDLrjgR8Yj0OWSrEhXU/ccpd07FkW0vtMsfZv9+HGjW+Zf9+83xXU6Y0ZeLEJlJgCSGEyDIyS5HlByQ0OjwPcD+B9qcopfoCk4B3tNbbU7DJLKBIrEftlEVN3pp9CwDo2GaEpXaZY9y//5h33llLgwY/cPt2AA8eBBsdSQghhEiTzNItcJZ4Y6+UUq6AR9SyJCmlOgHzgEla6x+SWx9Aa/0QeBhrH6nJm7iICLwfH6W8gzNlC8tVb6nx668nGD58E76+QQwcWIvp05uTO7e90bGEEEKINMksRdZGYLxSKrfW2j+qrSsQCWxJakOlVFPM46/+p7X+IB0zpsh/uzaw3yOcsbmbGB0ly9m/34fChV1Yt64HderIWDYhhBBZW2YpsuYDQwBvpdRHmCcj/QyYH3uOLKXUdqCY1rp01OvygDfmSUwXK6Wei7XPu1rrSxmUP8barXPR9tCxlVxVmJzg4HA+/ngPnTqVp1q1gnz6aUtsbU1yr0EhhBDZQqYosrTWfkqp5sBXmIumR8D3wIR4q5qIm7ku5rFcrsC+eOsuAvqkQ9zEaY23714KudtSq3zzDD10VrN16yUGDtzAxYv30RqqVSuIo6ON0bFyPK01vr6+BAcHExERYXQcIYRIM5PJhL29Pfny5bPckKBUyhRFFoDW+gzQIpl1msZ7vRBYmG6hUunR8cNsKxjEm44NsFLSG5OQ27cDGDlyM0uWnKRYMVfWretOu3ZljY4lMBdYN27c4NGjR9ja2mIymYyOJIQQaRYaGkpAQAAhISEULlzYkEIr0xRZ2cHmdbMJsYaOjfsZHSXTmjr1D1asOM2YMQ2YOLExTk5J3gNcZCBfX18ePXpE/vz5yZs3r9FxhBDimd27d487d+7g6+uLu7t7hh9fiiwLWn11Cy75rWj63KtGR8lUjh69hbOzHaVLuzFtWjMGDapDpUr5jY4l4gkODsbW1lYKLCFEtpE3b178/f0JDjZmOiA5p2UhoT5XWJ/3Hu1M5bE1Se8MwKNHIYwYsYlatf7HmDHbAHB3d5ICK5OKiIiQU4RCiGzHZDIZNsZUerIsZNfq2Tywh041XzM6iuG01vz22xmGDdvEjRuP6NOnGp9+muRwOyGEECLbkZ4sC/E+441tBLzQYoDRUQz36af76NJlBS4udvzxx+v8+ONLuLs7GR1LCCGEyFBSZFlA5AN/1jheo0VEMZztXZLfIBsKC4vg/v3HAPTqVYWPP27OsWP9adKkuLHBRI40ZcoUlFIxj7x589KwYUM2bNiQ4Pp+fn6MHj2aUqVKYWdnR4ECBejevTtnzpxJcP2AgACmTp1KpUqVcHR0xMnJiTp16jBr1izDxn5klNmzZ1O0aFFMJhMdO3a0+P5jf90SeyxcuPCZjnHs2DGmTJlCUFBQirfp2rUro0ePfqbjZkVr166latWq2NvbU7ZsWX788ccUbXfy5Enat2+Pu7s7uXPnpnHjxuzcuTPOOsWLF0/0a3zw4EEArly5gpOTE1euXLH0W8sQcrrQAv72/oYbLjC5VGejoxhi795r9O+/jpIl87BmzasULuzC2LENjY4lcjgHBwd27NgBwM2bN/noo4948cUX2bNnD/Xr149Z7/bt2zRu3Bg/Pz8mTJhA9erVuX79OjNnzqR27dps2LCBxo0bx6zv6+tLs2bN8PHxYfjw4TRsaP5eP3DgAJ988gkmk4lhw4Zl7JvNIBcuXODdd99lzJgxvPjii+TLl8/ixzhw4ECc1/Xq1WPIkCH06NEjpq1UqVLPdIxjx44xdepUBg8ejKOjY7LrHzlyhLVr13L58uVnOm5Ws3fvXjp16sTbb7/NnDlz2LFjB2+99RbOzs506dIl0e18fX1p3rw5JUuWZMGCBdja2vLll1/Spk0bDh06ROXKlQFYvXo1ISEhcbYdM2YMZ86coVatWoC5EOvSpQuTJ09m0aJF6fdm04vWWh5aA3gC2sfHR6fW+CEVtZqMvnX/Wqq3zcp8fQP1W2+t0TBFe3jM1MuXn9SRkZFGxxJp9O+//+p///3X6BgWMXnyZO3k5BSn7fr161oppfv16xenvVOnTtrOzk6fOXMmTntAQIAuX768Lly4sH78+HFMe9euXbWjo6M+ceLEU8e9d++e3rdvnwXfScoFBQWl+zHWrl2rAX3p0qVn3ldwcLCOiIhIdj1Af/bZZ898vNh+/PFHDei7d++maP3evXvrDh06WOTYGfF1spRWrVrp+vXrx2nr3r27Ll++fJLbLVmyRANxfp8EBQVpe3t7PW3atES3CwgI0E5OTnrgwIFx2nft2qVtbGz0nTt3Uv8mdPK/23x8fDSgAU9t4dpCThc+q7AwvDlDveB8FMxTxOg0GWbr1kt4eX3Njz8eY8iQOpw9O5iuXSsaNquuEMkpXLgw7u7uXLt2Labt6tWreHt707t3b7y84tyjHicnJyZMmMCNGzdYsWJFzPorV66kf//+VKpU6aljuLm5xeklS8iZM2fo3Lkzbm5uODo6UrVqVZYsWQKYT40opVi5cmWcbYYPH07x4sVjXi9cuBClFAcOHKBly5Y4OTkxevRomjZtSvv27Z865ty5c3FwcODBgweA+Z/rmTNnUrZsWezs7ChZsiSzZ89OMnefPn148cUXAXNPUuzTdlevXqVLly64urri5ORE69atOXHiRJztixcvzuDBg/n0008pVqwYDg4O3L9/P8ljJmbhwoVUqVIFe3t7ChcuzIQJE+JcPebv70/fvn0pXLgw9vb2FClShFdffTVm2zfeeAMAd3d3lFJxPrfxBQYGsmrVqqd6bg4cOECHDh0oVKgQTk5OVKtWjcWLF8dZ548//kApxfr16+nSpQsuLi507do1JuPAgQPx8PDAzs6OmjVrsmVL3Fv1rl+/npYtW5I/f35cXFyoW7cumzZtStPnLLVCQkLYuXNnTN5or776KmfOnEny9F1YWBgArq6uMW329vbY2tpGd2okaM2aNQQGBtKzZ8847Q0bNiRv3rz8+uuvaXgnxpLThc/o/KZfOJ03kk/ztzY6SobQWqOUolQpN7y88jF7dmtq1SpkdCwhkhUQEMD9+/cpUaJETNvu3bvRWscUD/FFt+/evZvXXnuNPXv2oLXmhRdeSFOGCxcuUK9ePYoUKcKXX35JwYIFOXnyZJzCLzV69OhBv379GD9+PI6Ojhw7dowhQ4Zw//593NzcYtZbsmQJbdu2jfmjN2zYML7//nsmTJhA3bp12b9/P2PGjMHBwYH+/fsneKyJEydSoUIFxowZw2+//YaHhwelSpXi0aNHNG3aFCsrK+bPn4+9vT3Tp0+ncePGHD9+nCJFnvzzuWrVKsqUKcMXX3yByWTCySn1F8TMmjWL9957jxEjRvD5559z5syZmCLrk08+AWDkyJFs3LiRTz75hOLFi3Pr1i02btwIQLt27Xj//ff58MMP2bRpE66urtjZ2SV6vAMHDhAYGEiDBg3itF+9epUGDRrQv39/7O3t2bdvH2+99RaRkZG8/vrrcdbt168fvXr1YvXq1ZhMJkJDQ2nZsiX//fcf06dPp3Dhwvz888+0a9eOI0eOxJxO+/fff3nxxRcZNWoUVlZWbNy4kbZt27Jjxw6aNm2aaGatdYqmLDCZTIn+Y3zp0iXCwsKe+uejfPnyAJw9ezbR4rR9+/YUKFCAd999l+nTp2NjY8PMmTNRStGrV69E8/z6668UL178qX9UrKyseO6559i6dWvWOxVv6a6xrPogjacLPx1dXzMFff7KkVRtl9UEBYXqCRO269dfX210FJFOEu1S799f6wYNjHv075/q9xJ9ujAsLEyHhYXpq1ev6m7duuk8efLos2fPxqz38ccfa0AfO3Ys0X3lzp1bv/DCC1prrT/55BMNxNlHavTo0UO7u7vrBw8eJLj833//1YBesWJFnPZhw4bpYsWKxbyOPt31ySefxFnP19dX29jY6O+++y6m7cqVK1opFbPPixcvaqWU/vbbb+NsO2bMGF2wYMEkT+GtXr36qdNAX3zxhVZK6dOnT8e03bt3Tzs5OemRI0fGtBUrVkznzZtXBwQEJLr/hBDrdOHDhw91rly59Lhx4+KsM2/ePO3g4KB9fX211lpXrFgxzrHjS83pwo8++kjnypUryXUiIyN1WFiY7tevn65Xr15M+86dOzWg+8f7Hv7hhx+0tbW1PnXqVJz2unXr6q5duyZ4jIiICB0WFqZbtWqlu3fvnmSe6OMm99i5c2ei+9i7d68G9IEDB+K03717VwP6l19+STLD+fPnddmyZWOOlTdvXr1///5E14/+3o3/tY02efJknS9fviSPmRgjTxdKT9az0BrvgMNUULkoU6y60WnSzaZNFxk0aAOXL/vRtWsFwsIisLGRSStF5hYYGIiNzZObjptMJtasWUO5cuWeed9pPS2+ffv2mNNGltCuXbs4r/PmzUvLli1ZunQpffv2BWDZsmXkypUr5jTitm3miYFffvllwsPDY7Zt0aIFM2bMwMfHh2LFiqU4w549e6hUqVJMDweYT5u2bNmSvXv3xlm3adOmaeq9irZ//34CAgLo2rXrU9kfP37MyZMnadKkCTVq1GDhwoV4eHjwwgsvJHhqN6Vu3bqV4AB/Pz8/Jk+ezJo1a7hx40ZMz1FCd0yI/3XasmULlStXpmzZsnHeR8uWLfn5559jXl+/fp0JEyawbds2bt26FXOqrWbNmklmrlmzJocOHUr2vVniZyEhd+7coVOnTpQqVYo5c+ZgMpn47rvv6NChA7t3747zvRJt+fLlhIWFxbnAIbZ8+fLh6+tLWFhYnJ/rzE6KrGdw+8BWDuQPZVyu542Oki5u3XrE8OGbWb78FCVK5GbDhh60aVPG6Fgio82bZ3SCNHFwcGD37t1ERkZy4cIFxo4dS+/evTl58iQeHh6AeZwWwLVr16hatepT+3j06BH+/v54eno+tX7Zsqm/sfm9e/coVMhyp9cLFCjwVFv37t15/fXXuX37NgULFmTJkiV06tQJe3t7wHzll9Y60SsDU1tk+fn5JZijQIECnDx5Mtm8qeHr6wtAjRo1Elzu4+MDwFdffYWbmxuff/45o0ePpkiRIowbN44BA1I/j2FwcHCCpxP79OnD/v37mTRpEhUrVsTFxYV58+axbNmyp9aN/759fX05evRogsVC9F0XIiMj6dChAw8ePGDatGmULl0aJycnJk2alOzp5Vy5clGtWrVk31tSd3jIkycPQMw4vmh+fn4AcU5Hx/fpp5/i5+fH33//HfO5a968ORUrVuSDDz5IcGzVr7/+SpUqVRItiKP3ExwcLEVWTrF281doK+jYcojRUdLF/fuPWbfuPOPHN2TChMY4Omadb2whrKysYi4Dr1OnDuXKlaNu3bpMmzaNeVGFY+PGjWMGJic0LmvdunUx68Vef/PmzbRokfq7GOTNm5ebN28mujy6EAoNDY3THv2HLb6EetReeukl7OzsWL58Oa1bt+bYsWN8/PHHMcvd3NxQSrF3715sbZ++BVhqezfc3Nw4d+7cU+3//fffU3+In/XCmOj9/fbbb3HGekWLHm/n6urKnDlzmDNnDidOnOCLL75g4MCBVKpUiUaNGqX6mP7+/nHagoODWbduHbNmzWLIkCe//yMjIxPcR/z37ebmRpUqVViwYEGix7148SJHjx7F29ubl156Kab98ePHyWbetWsXzZo1S3a9nTt3Jjq2q1SpUtjY2HD27Flat34y5vjs2bMAT43Viu306dN4eXnFKU5NJhNVqlTh0qVLT61/7do19u3bF+f7ND5/f39sbW1xdnZO7m1lKlJkPQPvO7so7GpDzSppGwSbGf3990127PiX0aMbULFifnx8RuDm5mB0LCGeWa1atejevTs//vgjkydPpmDBghQrVoyOHTuyaNEiRo4cGad3KigoiOnTp+Pp6RlzhVXRokXp0qUL8+bN44033qBChQpxjuHv78+ZM2eoV69eghlatGjBypUrmTFjRoJ/LPLnz4+NjU2cSVBDQ0PZtWtXit+ns7Mz7du3Z8mSJdy/fx93d/c4BWHz5s0Bc69aYgP+U6Nhw4asXLmSc+fOxRRofn5+bNu2jX79+j3z/mOrV68ejo6OXL9+nU6dOqVom8qVKzN79mwWLFjAmTNnaNSoUUxxmZKJY8uVK8fdu3cJDAyMOdUZEhJCZGRknCL10aNH/P777ynK1KJFCzZs2EChQoUS7dmMLqZiH+Pq1avs27cv2V5US5wutLOzo1mzZqxcuTLOYPNly5ZRvnz5JK/ILFasGGvWrCE4ODjmH4eIiAj++eefBHvYoq+u7d69e6L7vHLlSpp6jw1n6UFeWfVBKge+Pzx3XNu+jx44rW6K1s/sHjwI1kOGbNBWVlO1u/un+v79rDOXi7CM7D5PltZanz17VptMJj1mzJiYtlu3bukyZcro/Pnz6zlz5uhdu3bpX3/9VdeoUUM7OTnpXbt2xdnH3bt3dcWKFXXu3Ln11KlT9bZt2/S2bdv09OnTdcGCBfWcOXMSzXX+/Hnt6uqqq1Spon/++We9fft2/dVXX+kZM2bErNOtWzedO3duvXDhQr1u3Tr9wgsv6KJFiyY48D2xgdu//fabBrSHh8dTcw5prfXgwYO1q6ur/vDDD/XWrVv1hg0b9Jw5c/RLL72UaHatEx74/vDhQ128eHFdqlQpvWTJEr169Wpdq1YtnTt3bn3t2pO5A4sVK6YHDRqU5P4TQrx5smbOnKnt7e31e++9pzds2KA3b96s582bp1944QUdGBiotda6fv36+rPPPtMbN27UW7Zs0b169dK2trYxg/OPHDmiAT127Fh98OBBffz48USPf+7cOQ3oPXv2xGmvXbu2Llq0qF6xYoVevXq1rlu3ri5RokSc77voAeiHDh2Ks21wcLCuWbOmLlOmjP7222/1zp079erVq/WkSZP02LFjY9bx9PTUVapU0WvXrtVLlizRZcuW1cWLF9cVK1ZM9ecxLfbs2aNNJpMeMGCA3rlzp540aZJWSunly5fHWc9kMuk333wz5vXhw4e1tbW1btWqlf7999/1+vXr9UsvvaSVUk/9PGmtdZUqVXTDhg2TzFKnTh09YMCANL0PIwe+G17cZJZHaous5Z++rpmC3rLjfylaP7OKjIzUy5ef1B4eMzVM0W+9tUb7+gYaHUsYICcUWVpr3bNnT+3i4qL9/f1j2u7fv69HjRqlS5QooW1sbLS7u7vu1q1bnCvmYnv48KGeMmWKrlChgra3t9eOjo66du3aevbs2XEmLk3IqVOndIcOHbSLi4t2dHTU1apV00uXLo1ZfufOHd2xY0ft4uKiCxcurOfMmZPo1YWJFVnBwcHa1dU1weJAa/PP/VdffaUrVaqkbW1ttZubm65Xr56eNWtWktkTKrK0Nl/B2LlzZ+3s7KwdHR11y5YtnypcLFVkaW2e7LJ27drawcFBu7i46OrVq+uJEyfqsLAwrbXWo0eP1pUrV9a5cuXSLi4uukGDBnrz5s1x9jFlyhTt6emprays4nxuE1K5cmU9fvz4OG0XLlzQzz//vHZ0dNRFihTRn3322VPfd4kVWVpr/eDBAz1ixAhdtGhRbWNjoz08PHTbtm31unXrYtb566+/dO3atbW9vb0uU6aMXrRokX799dczrMjSWus1a9boypUra1tbW126dGm9YMGCp9YB9Ouvvx6nbfv27bpJkybazc1N586dWzdo0EBv3LjxqW1PnTqlAf3NN98kmuG///7TJpNJb9++PU3vwcgiS2md+MRgOYlSyhPw8fHxiRnkmpSeAwuwPo8vd6YGYWud+Bwrmd25c76UL/81FSq4M39+exo2LGp0JGGQ6MkFkzoNIERO9NVXX/HFF19w4cIFmXDZAF9//TWzZ89O8+c/ud9t169fjx7jV0RrfT3NQRMgM76nQeidW6x3vUM7VS5LFlihoRFs22a+B1e5cvnYvLkXR468IwWWEEIk4O233+bx48esXbvW6Cg5TmRkJF988QWTJk3KkgWuFFlp8Mdvs3hgDx2rJz5IL7Pavfsq1arNp3Xrn7lw4R4ALVuWwtZW5r0SQoiEODg4sHDhwqeu+hTp7+bNm/Tp0yfJmeIzM7m6MA28T63CLje80CbrTN3g6xvE6NFbWbjwGIULO7NiRVdKl058nhMhhBBPtGzZ0ugIOZKnpyfjx483OkaaSZGVSpFBgayxu0KLsCI4O+Y2Ok6K3LsXhJfXXPz8ghk+vC7TpjXD2TnrneYUQgghshIpslLp8NpvuemsmVqkg9FRknX//mPc3BzIm9eRUaPq06pVKWrU8DA6lhBCCJEjyJisVPL+6yeUhhc7jDI6SqKCgsIYN24bRYvO5tw5820oxo5tKAWWEEIIkYGkJys1IiLwDj9J/UA3CuQrbnSaBG3YcIFBgzZw5Yo/r75aCVdXe6MjCSGEEDmSFFmpcG7Hcs64RfBZnsw3ADI0NIIePVaxatUZSpXKw+bNvWjVqpTRsYQQQogcS4qsVFjzx3ywhZfajjA6ylNsbU3Y2VkzcWJjxo1riIOD3MxZCCGEMJKMyUoF7wd/UTHAkTKl6xodBYBDh27QuPGPXLniD8DPP3di2rRmUmAJIYQQmYAUWSl06+huDuYLpqNbfaOj8OBBMIMHb6Bu3e85e9aXy5f9ALLkbLhCpIcpU6aglIp55M2bl4YNG7Jhw4YE1/fz82P06NGUKlUKOzs7ChQoQPfu3Tlz5kyC6wcEBDB16lQqVaqEo6MjTk5O1KlTh1mzZhEcHJyeb81ws2fPpmjRophMJjp27Gjx/cf+uiX2WLhwYZr337RpU9q3b2+xvCdOnMDZ2Zm7d+9abJ9ZwYMHD3jrrbdwc3PD2dmZLl26cOvWrWS3i4iI4NNPP8XLywtHR0dKlizJ6NGjCQgIiFln4cKFiX7tX3jhhZj1+vbtS9++fdPl/VmKnC5MobUb5qAVdGwxyLAMWmuWLz/F8OGbuX07gL59a/DJJy1wc3MwLJMQmZWDgwM7duwAzLNGf/TRR7z44ovs2bOH+vWf/LN0+/ZtGjdujJ+fHxMmTKB69epcv36dmTNnUrt2bTZs2EDjxo1j1vf19aVZs2b4+PgwfPhwGjZsCMCBAwf45JNPMJlMDBs2LGPfbAa5cOEC7777LmPGjOHFF18kX758Fj/GgQMH4ryuV68eQ4YMoUePHjFtpUqlfbzpN998g8lkuTtcvP/++/Tp0wd3d3eL7TMr6NatG6dOnWL+/PnY29szYcIE2rRpw+HDh7G2Try0mD59Oh988AEffPABdevW5eTJk4wfP56bN2/yyy+/ANCuXbunvg8uXLhA7969adOmTUzbmDFjqFixIu+99x5lypRJnzf6rCx9x+ms+gA8Ae3j45PgXbrbDMqtC79nrSMjIxO+jXcGiIyM1C+88LOuVOkbvW/fNcNyiOwpuTvVZyWTJ0/WTk5OcdquX7+ulVK6X79+cdo7deqk7ezs9JkzZ+K0BwQE6PLly+vChQvrx48fx7R37dpVOzo66hMnTjx13Hv37ul9+/ZZ8J2kXFBQULofY+3atRrQly5deuZ9BQcH64iIiGTXA/Rnn32W5DoZ8d4TcunSJa2U0keOHHnmfYWHh+vQ0FALpEp/+/fv14DevHlzTNvZs2e1UkovW7YsyW3LlSunX3/99ThtkyZN0nZ2djosLCzR7SZPnqxNJpO+detWnPZmzZrpYcOGJXnM5H63+fj4aEADntrCtYWcLkyBh1fPsz2PPx3tq2X4KbmQkHA+/ngPt249QinF4sWdOHKkH/XrF8nQHEJkdYULF8bd3Z1r167FtF29ehVvb2969+6Nl5dXnPWdnJyYMGECN27cYMWKFTHrr1y5kv79+1OpUqWnjuHm5hanlywhZ86coXPnzri5ueHo6EjVqlVZsmQJAFeuXEEpxcqVK+NsM3z4cIoXLx7zOvp0yoEDB2jZsiVOTk6MHj060VNhc+fOxcHBgQcPHgDmf65nzpxJ2bJlsbOzo2TJksyePTvJ3H369OHFF18EzD1JsU/bXb16lS5duuDq6oqTkxOtW7fmxIkTcbYvXrw4gwcP5tNPP6VYsWI4ODhw//79JI+ZkClTppArVy7++usv6tWrh729PV9//TUAY8eOpXLlyuTKlYvChQvTvXv3p05hxf8cRe/vxIkTNGzYEEdHRypVqsTmzZuTzfLTTz9RsmRJqlevHqc9NTkWLVpEuXLlsLOz459//gFg/fr11K1bFwcHB9zd3RkwYACBgYEx2wYGBjJ48GDKlSuHo6MjxYsXp3///jFf3/S2ceNGcufOHedWQ+XKlaNatWqJnpKPFhYWhqura5w2V1dXIiMjk9xuyZIlPP/88xQsWDBOe9euXfnll18IDw9P5bvIGHK6MAU2ec8k1Bo61u2TocfdufNfBgxYz7lz97C1NfHuu/XJl88xQzMIkV0EBARw//59SpQoEdO2e/dutNYxxUN80e27d+/mtddeY8+ePWit44wLSY0LFy5Qr149ihQpwpdffknBggU5efJknMIvNXr06EG/fv0YP348jo6OHDt2jCFDhnD//n3c3J7cm3TJkiW0bds25o/bsGHD+P7775kwYQJ169Zl//79jBkzBgcHB/r375/gsSZOnEiFChUYM2YMv/32Gx4eHpQqVYpHjx7RtGlTrKysYk4dTZ8+ncaNG3P8+HGKFHnyD+GqVasoU6YMX3zxBSaTCScnpzS979DQUHr06MGIESP46KOPyJs3LwB37txh/PjxFCpUiLt37/L555/TpEkTTp8+neQprLCwMHr27MnQoUOZOHEiM2bM4OWXX+bq1asx+07Itm3bEiyqU5rj8OHDXLlyhWnTppEnTx6KFCnCypUr6datG2+88QZTp07l1q1bjB07Fj8/P5YuXQpAUFAQERERTJ8+HXd3d3x8fJg+fTodO3Zk586dSX7uIiIios/eJEopleQp1bNnz1KuXLmnOh3Kly/P2bNnk9z322+/zWeffcZLL71EnTp1OH36NF999RX9+/dP9Gt0+PBhzp8/z7hx455aVr9+fXx9fTl27Bi1atVK8thGkCIrBbwvrcM1l6JJi4wZYHfnTiCjRm1h8eLjeHq6sHp1Nzp29Ep+QyHSwYB1Azhx50TyK6aTyvkrM6/9vDRtG/3f7c2bN3nvvfdwdnaOM17qxo0bABQtWjTB7V1cXMidOzfXr19P0frJmTJlCra2tuzbtw8XFxcAWrRokaZ9AfTv358xY8bEvC5dujRDhgxh1apVMQOCr169yoEDB1i+fDkAly5dYu7cucyfP59+/frFZAgKCmLq1Kn069cPK6unT3KUKlWKsmXLAlC9evWYnrUvv/ySq1evcurUKcqXLw9AkyZNKFq0KHPmzOHzzz+P2UdYWBgbN25Mc3EVez/Tp0+nW7ducdp/+OGHmOcRERHUq1cPT09PduzYQatWrRLdX2hoKJ988glt27YFzL0yJUqUYOPGjfTq1SvBbbTWHD58OMHB/ynNcf/+fQ4dOhRTiGqtGTVqFN26deP777+PWc/Dw4O2bdsyceJEKlasiLu7O/PmPfmZCA8Pp0SJEjRs2JDz58/HfJ0S0rx5c3bt2pXocjB//f74449El/v5+ZE7d+6n2vPkyZNs7+S4ceMICQmhRYsWMcVer169mDNnTqLb/Prrr9jb29O5c+enllWsWBGTycSff/6ZKYssOV2YjFD/e6x3ukV7XRoba9sMOWbfvmv59dcTjBz5HGfODJICS4g0CAwMxMbGBhsbG4oVK8bKlStZvHgx5cqVe+Z9p3XYwPbt2+nSpUtMgfWs2rVrF+d13rx5admyZUyPB8CyZcvIlStXzCmybdu2AfDyyy8THh4e82jRogW3b9/Gx8cnVRn27NlDpUqVYgosMJ82bdmyJXv37o2zbtOmTZ+5wIoW/72D+TRW/fr1cXV1xdraGk9PTwDOnz+f5L6srKziFLvFixfHwcEhprhOiJ+fHyEhIQkOeE9pjipVqsTp6Tt//jxXr17llVdeifO1adKkCVZWVhw+fDhm3cWLF1O9enVy5cqFjY1NzAUYyb3Xb7/9lkOHDiX5+Pbbb5Pcx7OYO3cuX3zxBbNnz2bXrl188803bNy4kSFDhiS4fmRkJEuXLqVdu3YJ/txYW1uTO3fuFF3ZaATpyUrGH95zeGgPHct2S37lZ3DixH8ULuyCm5sDn37agqlTm1KtWsFktxMivaW1F8loDg4O7N69m8jISC5cuMDYsWPp3bs3J0+exMPDfB/PwoULA3Dt2jWqVq361D4ePXqEv79/zB/J2Osn1VuQmHv37lGoUKG0vqWnFChQ4Km27t278/rrr3P79m0KFizIkiVL6NSpE/b25lts+fr6orVO9MpAHx8fihUrluIMfn5+CeYoUKAAJ0+eTDZvWjg6OpIrV644bYcOHaJDhw689NJLjB07lvz586OU4rnnnkt2Wg0HBwdsbeP+E21ra5vkdtHL7Ozs0pwj/ufD19d8r9lOnToleMzoAnj16tX07t2bfv36MX36dPLmzcutW7fo1KlTsu+1dOnSKTpdmJQ8efIkWIz7+fnFOU0d37179xg1ahSfffZZTFHVuHFjXFxc6NWrF8OGDXvq52rnzp3cunWLnj17JrpfOzs7Hj9+nGRmo0iRlYzV/yzDLhe0bj88XfYfGBjKtGm7mDXrIP371+Srr9pSrpzlL4sWIqexsrKKOX1Qp04dypUrR926dZk2bVrMqZbGjRujlGL9+vUJjstat25dzHqx19+8eXOaTvPlzZuXmzdvJro8uhAKDQ2N0+7n55fg+gn9MXzppZews7Nj+fLltG7dmmPHjvHxxx/HLHdzc0Mpxd69e58qLIBU9/S5ublx7ty5p9r/+++/p/7gWurCoYT2s3r1alxdXVm+fHnM6c6rV69a5HgJiX5v/v7+ac4R/31E73Pu3LnUrfv0pNfRBfqKFSuoVq1anB6n5E4BRrPE6UIvLy+2bduG1jrOezh79iyVK1dOdLtLly4REhJCtWrV4rRHXzhw6dKlp4qsX3/9ldy5c8ecyk2Iv79/kmPnjCRFVhIiQ0NYY32RFsGFcHa2/Bdw7dpzDB68kWvXHtCzZ2Xef79x8hsJIdKkVq1adO/enR9//JHJkydTsGBBihUrRseOHVm0aBEjR46M8ws+KCiI6dOn4+npSdeuXQHzWKwuXbowb9483njjDSpUqBDnGP7+/pw5c4Z69eolmKFFixasXLmSGTNm4Ozs/NTy/PnzY2NjE2cS1NDQ0BT/AQVwdnamffv2LFmyhPv37+Pu7h6nIGzevDlg7lVIbMB/ajRs2JCVK1dy7ty5mALNz8+Pbdu2xYz5ygiPHz/GxsYmzh/96HmX0oO9vT1Fixbl33//tVgOLy8vPD09uXz5MoMGJT4n4+PHj58qkFN6jG+//ZZHjx4luU5C35uxtWnThg8++IDt27fHfG+dP3+eo0ePxhkjGF90D+mRI0do1KhRTPvff/8NEOcKWoCQkBB+++03Onfu/FSPYbS7d+8SFBRkkWEA6UGKrCQc2vA9t3JpOha03OzA0SZM2M5HH+2lTBk3tm17jebNS1r8GEKIuCZOnMjSpUuZM2cOn3zyCWCenLJx48Y0atSI8ePHU716dW7cuMHMmTO5cuUKGzZsiOlhil6/adOmNGjQgBEjRtCgQQMA/vzzT7766ivGjh2baJE1efJk1q1bR8OGDXnvvffw8PDg9OnTBAUF8d5772FlZUXnzp2ZO3cupUuXJl++fMydO/epHoPkdO/enc6dO3P16lW6du0a56qtsmXLMmjQIF577TVGjx5N3bp1CQsL4/z58+zcuRNvb+9UfU7feOMNZs+eTbt27fjwww9jri60trZm+PDhqdrXs2jZsiVz5sxhyJAhdOrUiQMHDrB48eJ0PWaDBg1iCgRL5FBKMWvWLHr06EFgYCDt2rXDycmJq1evsn79ej766CPKli1Ly5YtGTRoEB988AH16tVjw4YNbN++PUXHsEQxUq9ePVq3bs2bb77J559/HjMZaZUqVeIMTp82bRrTpk3j0qVLFCtWjAIFCtCxY0cmTpxIeHg4NWrU4NSpU0yePJkWLVrEGdcHsGHDBvz9/eNMRBtf9Di16DFpmY6lJ97Kqg8SmIx07JiaWk1G/3frYqKTmKVGWFiEDgoyTzZ3+PANPWXKTv34ceKTrwmRkbL7ZKTRevbsqV1cXLS/v39M2/379/WoUaN0iRIltI2NjXZ3d9fdunXTp0+fTnAfDx8+1FOmTNEVKlTQ9vb22tHRUdeuXVvPnj07zsSlCTl16pTu0KGDdnFx0Y6OjrpatWp66dKlMcvv3LmjO3bsqF1cXHThwoX1nDlz9LBhw3SxYsVi1vnxxx81oO/evZvgMYKDg7Wrq6sG9J49e55aHhkZqb/66itdqVIlbWtrq93c3HS9evX0rFmzksy+evVqDTz1fXLlyhXduXNn7ezsrB0dHXXLli318ePH46xTrFgxPWjQoCT3nxDiTUaa1Nd2xowZ2tPTMybD+fPnn9q+SZMmul27dsnuz9XVVU+ePDnJbKtWrdL29vb64cOHz5wjti1btugmTZpoJycn7eTkpCtWrKjffffdmO/Z8PBw/e6772p3d3ft7Oysu3Tpog8ePKgBvWLFiiQzW4q/v79+8803de7cuXWuXLl0586d9Y0bN+KsM3ny5Ke+Xx48eKBHjRqlS5Uqpe3t7XWJEiX0kCFD9P379586RpcuXbSHh0eSk9YOGTJEN2rUKMmsRk5GqnQyA+ByCqWUJ+Dj4+NjHuSqNeVH2pLPKhd7Pk94PERq/Pnndd55Zx3NmhVn9uy0zbEjRHq6cuUK8HSXvRAiYWFhYRQtWpQZM2bQu3dvo+PkOOHh4RQtWpRPPvkkyc9/cr/brl+/Hn2VZxGtdeKXlKaBTOGQiLN7VnM2dzgdCz3/TPvx83vMgAHrqFdvAbduBVC7dmELJRRCCGEkGxsbxo4dyxdffGF0lBzp119/JVeuXEmeTjSajMlKxJod34CCl9qMSPM+1q8/z5tv/s7du4G8805NPvqoOXnyyM2chRAiu+jfvz8PHz7E19c3XW6YLRJnZWXFDz/8kORs/kbLvMkM5u13gErWDpSukPbBdM7OdhQq5MyaNa/y3HOeFkwnhBAiM7Czs2PixIlGx8iREpuNPzORIisBt07/xUG3IN5XTVO1XXBwODNm7MVksuL99xvTuHEx/v67H1ZWGXtTaSGEEEIYT4qsBPy+3nyvrY7PD0jxNtu2XWbgwPVcuHCfV16pGHPJtRRYQgghRM4kA98T4H19G56BJmrU75Lsuv/9F0CvXr/RsuViQkIiWLPmVZYt62KxmY2FyCgmk4mIiAijYwghhEVFRERgMpkMObYUWfE8uuPDdtf7dLSpjErgTvTxHT16m6VLTzJ6dH1Onx5Ihw6Zc9ZZIZJjb29PaGgo9+7dMzqKEEJYxL179wgNDY0zoXBGktOF8ezcNJ8wE3Ss+Xqi6/zzz21OnbpLjx6VeeGF0ly6NJRixXJnXEgh0kG+fPkICQnhzp07+Pv7G/afnxBCWEJERAShoaE4OzsbduWn9GTFs/nyFnIHKxq36f/UsoCAUEaN2kLNmt/x3ntbCQkJB5ACS2QLSikKFy5Mvnz5ErxxsBBCZCW2trbky5ePwoULGzaEJ9P0ZCmlvICvgPrAI+An4H2tdWgy2ylgDDAQcAeOASO01gfTkmO7/W06hZfCxjZu16K391mGDNnI9esP6d27Kp991hI7u0zz6RPCIpRSuLu7Gx1DCCGyhUxRJSil8gA7gAtAZ6AwMAtwBAYns/kYYCowFjgODAK2KKWqaa0vpzZLoC10rPRynLY9e67SqdMyypXLy44dvWnWrERqdyuEEEKIHCZTFFlAf8AF6KS1vg+glLIGvlFKfaS1vpnQRkope2Ac8LnWenZU2x7gPDAKc+9WqthGQOuX3iUsLIKTJ+9QvboHDRsWZfHiTnTtWkF6r4QQQgiRIpllTFYbYFt0gRVlOeZ8rZLYrj7m4mx5dEPU6cXfgLZpCdIoKB/HT4dQs+Z3NG26CF/fIJRS9OpVRQosIYQQQqRYZimyvICzsRu01v7ArahlSW1H/G2BM0BRpVSqbxQYcLQzDRr8wN27QXz3XXvy5pV7DQohhBAi9TJL10wewD+Bdj/ALZntQrTWwQlsp6KWP05oQ6WUC+ZesGiFAf78O5LXXy/B6NH1cXW158aNGyl7B0IIIYTIcm7duhX91OLz1mSWIssII4HJTzd/z6JF37NoUYbnEUIIIYRxigNXLbnDzFJk+QGuCbTnAe4n0B57OzullH283qw8gI5anphZwPexXhcF9gHPAdJ9lXkUBA4BtYHbBmcRccnXJnOSr0vmJF+XzKswcBDwsfSOM0uRdZZ4Y6+UUq6AB0+Pt4q/HUA54J9Y7V7ANa11gqcKAbTWD4GHsY4X/fSG1vp6ipOLdBXr63Jbvi6Zi3xtMif5umRO8nXJvGJ9bZKclzMtMsvA941AC6VU7lhtXYFIYEsS2+3HXCh1jW5QStlgnmtrg+VjCiGEEEKkTGYpsuZjnuXdWynVSin1BvAZMD/2HFlKqe1KqYvRr6NOEX4MjFJKDVNKPQ8sAfICMzP0HQghhBBCxJIpThdqrf2UUs0x31bHG3PB9T0wId6qJp7OPAPzlYSjeHJbndZpmO39IeaZ4x8mt6LIUPJ1ybzka5M5ydclc5KvS+aVbl8bpbW29D6FEEIIIXK8zHK6UAghhBAiW5EiSwghhBAiHUiRJYQQQgiRDqTIEkIIIf7f3t1Hy1WVdxz//iDkBSQkAUKAQAADJEBpBNqCWE0wUJFli9rgErQEFIpKSxBaIKIEECl1ISyr6FpYIKaitIi1UimvSaQFRFYDNUAstiaESEK0SYC8Jzz9Y+8hh8nc3LkzczJwz++z1ll3Zs95ec7Za+55Zu99zjErQSWSLEnjJN0vabWkpZL+VtLAJpaTpEslPS9praRHJR27PWKuglbqRdLeeb4nJb0i6QVJt0sas73iroJWvzN165gmKSTdXVacVdNOvUjaV9JMScvz/7NnJZ1RdsxV0MY5ZndJ38znmNWS5ks6b3vEXAWSxubj+6SkTZLmN7lcx879b4pbOJRJ0nDgIeA50k1K9yU9Umdn4PxeFr+EdFnnpcB/AZ8B7pM0oYVbRFhBG/VydJ7/FtJjEPYAPg88LumIiFheZtxV0OZ3praOUaRng75UUpiV0069SNobeBT4BXAu6VL1w4FBJYZcCW1+X/6J9ISS6cDzwPuBb0jaHBE3lxZ0dRwOnAL8lNSo1GzDUufO/RHRryfgMuBVYESh7FxgE7DPNpYbDKwCvlQoGwgsBG7q9n691ac26mUYMKCubDTp6QAXdXu/+sPUat3UrePbwExgDnB3t/epP0zt1Aswi/Rs1h27vR/9bWrjf9ko0jN2p9aVzwUe7PZ+9YcJ2KHw+jZgfhPLdPTcX4XuwpOBByKi+KDpfyRltCdtY7l3AkPzvABExAbgLtKvDWtPS/USESsjYlNd2QvAcmCfMgKtoFa/MwBIehdwKulXoHVOS/UiaShwGukEsbncECup1e/LTvnvqrryVaQbbFubIuK1Fhbr6Lm/CknWOOoeMh0RK4EXqXsodYPlqF8WeBbYX9KQTgVYUa3Wy1YkHQKMJNWNta/lupG0I/A14JqIeLGsACuq1Xo5ivRLfKOkuZI25nFD1+VnvVp7WqqXiFhMejbvdEmHSdpV0mmkxOzr5YVrvejoub8KSdZwYGWD8hXAiF6WWx/p+Yj1yyl/bq1rtV7eQOnx6V8Ffk16bqW1r526+TSwC3BDh2Oy1utlVP77LeAJ0kn8BmAacFXnwqusdr4vHwKWAU+TxsndDlwYEd/vZIDWJx099/f7ge/W780A3gu8LyJWdzmWSpM0knTS/rPcvG5vDrUf0w9ExEX59WxJuwIXS7oqItZ2KbbKyj8QbwUOBk4ntXydCNwoaUVEfK+b8VlnVCHJWgHs1qB8OPB/DcqLyw2SNLguox1OGqy4onMhVlKr9fI6SecAXwA+EREPdjC2qmu1bq4iXYnzsKRhuWwAMCC/f7V+PJ31STv/yyBdAVf0IPA5YCzw87ajq65W6+UUYApwZETUjv+c/GPlesBJVnd09Nxfhe7CBdT1i0vaDdibrftc65cDOLSufBzwvH/5ta3VeqnN+0HgG8AXIuKWUiKsrlbrZhzwbtI/odp0PPBH+fXkMoKtkFbr5Zle1ju4zbiqrtV6OQzYDNTfu2kesI+knTsZpDWto+f+KiRZ9wCTC7+sIf16eI006LAnj5D6yKfUCvIg0Q8BP+58mJXTar0gaSJp/NXNEXF1SfFVWat1Mw2YVDc9Rbqf2STg8RJirZKW6iUiFpFaquqT3BOBtfSehNm2tfp9WQTsCBxZV3408FJErOlkkNa0zp77u30fi+1wn4zhpEHRc0gDPs8i/ar+Wt18DwK/rCu7FFgHXACcANyZD/5B3d6vt/rUar0A40mDTH9OutT22ML09m7vV3+Y2vnONFjXHHyfrK7XC/AB0kn/RlJyNR3YAHyx2/v1Vp/a+F+2KynReg74GGls6XWk1q3Lu71f/WEi3RD2T/M0m3TD19r7PRvVSy7r2Lm/6wdhOx3o8cADwBrSlRxfBgbWzTMHWFhXJtKN5hbnA/4YcFy396e/TK3UCzCV1C/eaLqt2/vUX6ZWvzMN1uMk601SL8BHSF1T60k3VrwMULf3qT9MbZxjxgJ3AEuA1bl+LsA3je1UvRywjfPFxG3US8fO/corNDMzM7MOqsKYLDMzM7PtzkmWmZmZWQmcZJmZmZmVwEmWmZmZWQmcZJmZmZmVwEmWmZmZWQmcZJmZmZmVwEmWmZmZWQmcZJn1A5JukxTbmA7o4/oW5uXmlBNxj9ttFPsqSbMlvb/E7b5+/AplwyTNyNPEuvkPKMQ3o6y4eoh1YoNjtCHX2TcljWxj3dPy/k7tYMhmlTWg2wGYmfViKDARmCjpjIi4fTttdxhwReH9nO203VbsBIwB/hw4TtJREbG5hfVMy+uZC9zWsejMKsotWWb9z6SIUN20sNtB9dGiiBAwGLi4UH5tGRuLiKm1Y9Xk/AsLx3ZGGTE1aWaO+RDgV7nsSNLD082sy5xkmVVE7v76jqRnJa2UtFHSUkl3Sjq8ieWPlnR3XmZ9/jtb0ifr5nuvpHslrcjz/ULS5ZJ26mvMEbEe+AqwKhftL2nPvJ0dJJ0vaZ6kNZJWS3q8vqtL0lhJ35O0JMezXNIjki4rzPOG7sLcBfirwmquKHTNTWzUXSjp6fz+P+u2f2Zh3pNzmSSdJ+mJHPcaSY9JOq2vxygfp+eAHxSK9its/325Pl6QtFbSOkkLJF0taUieZ2Le9zF5sfc06g6VNEXSw5Jezut5StKnJDWVnJpVjbsLzapjGHB6XdlewIeBSZLGR8RLjRaUtAtwL7B73bJ7AauBb+X5pgK3kJ5iX3MIcDVwrKQPRGtPpW90Ep8JfKyu7PeAWyUdFhF/nct+BIwrzLNHnobS2ZaxWXl975A0NiJ+mctridNS4L78+hZgat3yfwDcIWlMRHy5he0Xj1GxHo8FTqqb91DgcuBAtj6GjVcuXQHMqCs+ErgJOAL4TB9iNasEt2SZ9T+z6wZFP5nLV5ASqv1I3XBvA87Jn41g6wSsaBxbEqwPAwOB0cCfkJIYJL0NuJF0sr8nb2dnYHpe7hSgT4PXJQ0CLiIlRACLI2K5pHezJTl4NMdyMLAgl10s6VBJu7MlwfosMAgYRUo6vt3TdnMX4IGFoisL3YNzeljsO8Br+fWUHP8wYHIu+25EbJb0LrYkWNcAu5GSvlpL1FU57qZJOhj4YH67DPj3wsf/Suo+3JM0dmtv4Mf5s9MljYiIObnbcVEun1vsDlW6cOLz+bNbgZGkOvl6Lvu0pCP6ErNZFbgly6wiImJVPlleTmpd2qVulkO3sfgSYDOwI6nFYizwNPAfEfHbPM87SQkDwMnA4gbrOYF00u/NGBWu9CuonehPLpRdExFLACRdD9xMSvROIrWyvExKCE4n7fPTwGMRcX8TcTQtIhYrXY15Aqn16lrgVFJCCqmlC96YaH4uT0WDScfyR01s9kxJZxbeLwA+HhHrCmVLgC+Skr1RpESrRqTk9Ke9bOckUt0DnJWnepOA+U3EbFYZbsky63/qB75PAJB0IXA98A62TrAAhvS0wohYCvwlaWzUCcB1wN3A0tyNBKmlpDcjmt6LLV4BfgKcGhEzc9kehc8X9/B6z3yF3Vmk1p1jSN2WdwFLJN3cQiy9qSVSEySNJbdoAfMjYl4tribW08pxgtRy+PqPZ0k7kOrpLFLLYqNxcT3We0GZMZv1W06yzKqjdsJfRxr/MwD4nWYXjoibSCfb3wfOILVIDSANCh8NLC/MflmDKxwFnN3k5hYVlhsaEe+JiB8WPv9N4fXowuv96ueJiLuAfYAJpBamWaQWnE9KOn5bu9xkrEXfB9bk1+cCJ+bXswrzFI/TcQ2O0Q6FZLI3M0mJ0xRgE7A/8ANJte7VsaSkGuABYK+8jet7WF9P+1yM+aM9xHxlkzGbVYaTLLPqGJT/Bql1aBhbD2RuSNJekv4GOAr4X1Iy8UjtY1LL0iNsuQrws5ImSRokaaSk0yT9hC1Xr7Xr3wqvp0vaV9JBpHFXkPbxvhz73wF/CLwI/JAtg89h2y00KwqvxzVzdWREvAL8c357ISkBeo00XqvmnsLrr0gaL2mgpIMk/QUpGWpaRGyKiDtJXaOQugT/Kr8eVJh1PbBW0jHAx3tYXW2f95e0W6H8PlJ3McCVko7JMY+WdDYwDzPbipMss+qojfEZAjxDaumZ0OSyQ4BLgMfycutIg7YhddE9ExGvkhKLICUvD+X5lgF3kBKdjoiIucB389vjgReA/wHG57IbIqI2CP580o1El5ESjVqr0qq8Pz1t42Xgv/PbjwAb8oUEvY1lra2/Nt9DtTFjeb0Ps+VGn8eR6mJ9jv+rwNt7WX9PvkS60hPggjx4fgFQu8rxFNL4tJ+RkuxGfpb/HgiszPs7Od9n7er82SF5vvWkuv974HdbjNmsX3OSZVYd15Ku/nsJeBW4E/hok8v+lpQAzCO1dmwkDai+HZgcERsAIuJWUhfZvXm+DcDzpKvZzgF+3ZldAdLVhdOAp0jJ3FrgCeATEXFRYb7rSAO7f5PjXgr8S457aS/bOJOUUKztQ1z3523UzGowz9nAeXnda/L0XJ73U33Y1usiYhmpjgB2BS6JiI3AHwOzSQnYYtLVmv/Qw2quIB2blQ3WfyWpu/VhUpK2ji2tmme0ErNZf6fWblljZmZmZtviliwzMzOzEjjJMjMzMyuBkywzMzOzEjjJMjMzMyuBkywzMzOzEjjJMjMzMyuBkywzMzOzEjjJMjMzMyuBkywzMzOzEjjJMjMzMyuBkywzMzOzEjjJMjMzMyuBkywzMzOzEvw/tXCxyXmqdpUAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = model.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "308c6d60", - "metadata": {}, - "source": [ - "Visualizando lo obtenido vemos una mejora interesante. No solo obtuvimos un mejor score de AUC-ROC sino que mejoro mucho la precision de la clase de altos ingresos. Aun así notamos algunos problemas en la curva de aprendizaje de la metrica accuracy. Por lo que probaremos bajar el learning rate " - ] - }, - { - "cell_type": "markdown", - "id": "df38f636", - "metadata": {}, - "source": [ - "### Tercer diseño de la red" - ] - }, - { - "cell_type": "markdown", - "id": "94682c6b", - "metadata": {}, - "source": [ - "#### Diseño y entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "856c15bf", - "metadata": {}, - "source": [ - "Realizaremos el mismo entrenamiento que antes pero bajando el learning rate" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1c9addac", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "1e2d2da1", - "metadata": {}, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Dense(16,input_shape = (40,),activation='relu'))\n", - "model.add(Dense(8,activation='relu'))\n", - "model.add(Dense(4,activation='relu'))\n", - "model.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "50385f73", - "metadata": {}, - "source": [ - "Compilamos y mostramos un resumen de la red" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "79b344a1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_2\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_6 (Dense) (None, 16) 656 \n", - "_________________________________________________________________\n", - "dense_7 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_8 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_9 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 833\n", - "Trainable params: 833\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.SGD(0.0001)\n", - "model.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "7adbd9bd", - "metadata": {}, - "source": [ - "Entrenamos aumentando también la cantidad de epochs. Sino aumentasemos los epochs parecia que la red podia seguir aprendiendo (esperable al haber disminuido el learning rate)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "6e0440ea", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/350\n", - "814/814 [==============================] - 1s 970us/step - loss: 3.2669 - auc: 0.5280 - accuracy: 0.5703 - val_loss: 0.5560 - val_auc: 0.5480 - val_accuracy: 0.7591\n", - "Epoch 2/350\n", - "814/814 [==============================] - 1s 759us/step - loss: 0.5410 - auc: 0.5771 - accuracy: 0.7603 - val_loss: 0.5392 - val_auc: 0.5671 - val_accuracy: 0.7591\n", - "Epoch 3/350\n", - "814/814 [==============================] - 1s 806us/step - loss: 0.5286 - auc: 0.5870 - accuracy: 0.7619 - val_loss: 0.5287 - val_auc: 0.5898 - val_accuracy: 0.7591\n", - "Epoch 4/350\n", - "814/814 [==============================] - 1s 965us/step - loss: 0.5241 - auc: 0.6012 - accuracy: 0.7612 - val_loss: 0.5240 - val_auc: 0.6077 - val_accuracy: 0.7591\n", - "Epoch 5/350\n", - "814/814 [==============================] - 1s 962us/step - loss: 0.5277 - auc: 0.6152 - accuracy: 0.7557 - val_loss: 0.5221 - val_auc: 0.6147 - val_accuracy: 0.7591\n", - "Epoch 6/350\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.5162 - auc: 0.6276 - accuracy: 0.7629 - val_loss: 0.5200 - val_auc: 0.6304 - val_accuracy: 0.7591\n", - "Epoch 7/350\n", - "814/814 [==============================] - 1s 977us/step - loss: 0.5205 - auc: 0.6329 - accuracy: 0.7589 - val_loss: 0.5185 - val_auc: 0.6355 - val_accuracy: 0.7591\n", - "Epoch 8/350\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.5166 - auc: 0.6436 - accuracy: 0.7608 - val_loss: 0.5176 - val_auc: 0.6454 - val_accuracy: 0.7591\n", - "Epoch 9/350\n", - "814/814 [==============================] - 1s 990us/step - loss: 0.5144 - auc: 0.6473 - accuracy: 0.7615 - val_loss: 0.5173 - val_auc: 0.6463 - val_accuracy: 0.7591\n", - "Epoch 10/350\n", - "814/814 [==============================] - 1s 959us/step - loss: 0.5167 - auc: 0.6516 - accuracy: 0.7586 - val_loss: 0.5162 - val_auc: 0.6450 - val_accuracy: 0.7593\n", - "Epoch 11/350\n", - "814/814 [==============================] - 1s 970us/step - loss: 0.5663 - auc: 0.6540 - accuracy: 0.7631 - val_loss: 0.5159 - val_auc: 0.6488 - val_accuracy: 0.7593\n", - "Epoch 12/350\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.5105 - auc: 0.6561 - accuracy: 0.7633 - val_loss: 0.5148 - val_auc: 0.6539 - val_accuracy: 0.7593\n", - "Epoch 13/350\n", - "814/814 [==============================] - 1s 988us/step - loss: 0.5109 - auc: 0.6566 - accuracy: 0.7629 - val_loss: 0.5139 - val_auc: 0.6620 - val_accuracy: 0.7593\n", - "Epoch 14/350\n", - "814/814 [==============================] - 1s 964us/step - loss: 0.5149 - auc: 0.6674 - accuracy: 0.7566 - val_loss: 0.5134 - val_auc: 0.6578 - val_accuracy: 0.7593\n", - "Epoch 15/350\n", - "814/814 [==============================] - 1s 964us/step - loss: 0.5172 - auc: 0.6585 - accuracy: 0.7577 - val_loss: 0.5140 - val_auc: 0.6533 - val_accuracy: 0.7593\n", - "Epoch 16/350\n", - "814/814 [==============================] - 1s 914us/step - loss: 0.5154 - auc: 0.6597 - accuracy: 0.7589 - val_loss: 0.5123 - val_auc: 0.6622 - val_accuracy: 0.7593\n", - "Epoch 17/350\n", - "814/814 [==============================] - 1s 753us/step - loss: 0.5157 - auc: 0.6657 - accuracy: 0.7575 - val_loss: 0.5120 - val_auc: 0.6652 - val_accuracy: 0.7593\n", - "Epoch 18/350\n", - "814/814 [==============================] - 1s 744us/step - loss: 0.5069 - auc: 0.6662 - accuracy: 0.7642 - val_loss: 0.5113 - val_auc: 0.6710 - val_accuracy: 0.7593\n", - "Epoch 19/350\n", - "814/814 [==============================] - 1s 758us/step - loss: 0.5188 - auc: 0.6654 - accuracy: 0.7554 - val_loss: 0.5111 - val_auc: 0.6673 - val_accuracy: 0.7593\n", - "Epoch 20/350\n", - "814/814 [==============================] - 1s 877us/step - loss: 0.5099 - auc: 0.6803 - accuracy: 0.7576 - val_loss: 0.5096 - val_auc: 0.6786 - val_accuracy: 0.7593\n", - "Epoch 21/350\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.5089 - auc: 0.6775 - accuracy: 0.7605 - val_loss: 0.5092 - val_auc: 0.6791 - val_accuracy: 0.7593\n", - "Epoch 22/350\n", - "814/814 [==============================] - 1s 991us/step - loss: 0.5081 - auc: 0.6817 - accuracy: 0.7598 - val_loss: 0.5086 - val_auc: 0.6820 - val_accuracy: 0.7593\n", - "Epoch 23/350\n", - "814/814 [==============================] - 1s 966us/step - loss: 0.5028 - auc: 0.6859 - accuracy: 0.7630 - val_loss: 0.5084 - val_auc: 0.6870 - val_accuracy: 0.7593\n", - "Epoch 24/350\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.5045 - auc: 0.6823 - accuracy: 0.7638 - val_loss: 0.5075 - val_auc: 0.6862 - val_accuracy: 0.7593\n", - "Epoch 25/350\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.5050 - auc: 0.6883 - accuracy: 0.7608 - val_loss: 0.5067 - val_auc: 0.6871 - val_accuracy: 0.7593\n", - "Epoch 26/350\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.5084 - auc: 0.6903 - accuracy: 0.7568 - val_loss: 0.5065 - val_auc: 0.6867 - val_accuracy: 0.7593\n", - "Epoch 27/350\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.5084 - auc: 0.6943 - accuracy: 0.7559 - val_loss: 0.5065 - val_auc: 0.6845 - val_accuracy: 0.7593\n", - "Epoch 28/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5073 - auc: 0.6883 - accuracy: 0.7583 - val_loss: 0.5057 - val_auc: 0.6905 - val_accuracy: 0.7593\n", - "Epoch 29/350\n", - "814/814 [==============================] - 1s 981us/step - loss: 0.5060 - auc: 0.6795 - accuracy: 0.7637 - val_loss: 0.5050 - val_auc: 0.6918 - val_accuracy: 0.7593\n", - "Epoch 30/350\n", - "814/814 [==============================] - 1s 854us/step - loss: 0.5088 - auc: 0.6892 - accuracy: 0.7571 - val_loss: 0.5052 - val_auc: 0.6922 - val_accuracy: 0.7593\n", - "Epoch 31/350\n", - "814/814 [==============================] - 1s 756us/step - loss: 0.5089 - auc: 0.6881 - accuracy: 0.7580 - val_loss: 0.5041 - val_auc: 0.6913 - val_accuracy: 0.7593\n", - "Epoch 32/350\n", - "814/814 [==============================] - 1s 754us/step - loss: 0.5086 - auc: 0.6861 - accuracy: 0.7593 - val_loss: 0.5034 - val_auc: 0.6942 - val_accuracy: 0.7593\n", - "Epoch 33/350\n", - "814/814 [==============================] - 1s 758us/step - loss: 0.5090 - auc: 0.6986 - accuracy: 0.7536 - val_loss: 0.5029 - val_auc: 0.6967 - val_accuracy: 0.7593\n", - "Epoch 34/350\n", - "814/814 [==============================] - 1s 954us/step - loss: 0.5095 - auc: 0.6929 - accuracy: 0.7563 - val_loss: 0.5027 - val_auc: 0.6969 - val_accuracy: 0.7593\n", - "Epoch 35/350\n", - "814/814 [==============================] - 1s 965us/step - loss: 0.5066 - auc: 0.6923 - accuracy: 0.7585 - val_loss: 0.5025 - val_auc: 0.6993 - val_accuracy: 0.7593\n", - "Epoch 36/350\n", - "814/814 [==============================] - 1s 982us/step - loss: 0.4995 - auc: 0.7019 - accuracy: 0.7628 - val_loss: 0.5020 - val_auc: 0.6991 - val_accuracy: 0.7593\n", - "Epoch 37/350\n", - "814/814 [==============================] - 1s 970us/step - loss: 0.5044 - auc: 0.6968 - accuracy: 0.7583 - val_loss: 0.5010 - val_auc: 0.7041 - val_accuracy: 0.7593\n", - "Epoch 38/350\n", - "814/814 [==============================] - 1s 986us/step - loss: 0.5031 - auc: 0.7064 - accuracy: 0.7567 - val_loss: 0.5000 - val_auc: 0.7066 - val_accuracy: 0.7593\n", - "Epoch 39/350\n", - "814/814 [==============================] - 1s 976us/step - loss: 0.5002 - auc: 0.6993 - accuracy: 0.7620 - val_loss: 0.5005 - val_auc: 0.7067 - val_accuracy: 0.7593\n", - "Epoch 40/350\n", - "814/814 [==============================] - 1s 969us/step - loss: 0.4986 - auc: 0.6986 - accuracy: 0.7641 - val_loss: 0.4997 - val_auc: 0.7086 - val_accuracy: 0.7593\n", - "Epoch 41/350\n", - "814/814 [==============================] - 1s 977us/step - loss: 0.4982 - auc: 0.7083 - accuracy: 0.7604 - val_loss: 0.4991 - val_auc: 0.7082 - val_accuracy: 0.7593\n", - "Epoch 42/350\n", - "814/814 [==============================] - 1s 963us/step - loss: 0.5037 - auc: 0.7059 - accuracy: 0.7563 - val_loss: 0.4984 - val_auc: 0.7108 - val_accuracy: 0.7593\n", - "Epoch 43/350\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.4984 - auc: 0.7040 - accuracy: 0.7619 - val_loss: 0.4988 - val_auc: 0.7111 - val_accuracy: 0.7593\n", - "Epoch 44/350\n", - "814/814 [==============================] - 1s 958us/step - loss: 0.5017 - auc: 0.7072 - accuracy: 0.7579 - val_loss: 0.4984 - val_auc: 0.7097 - val_accuracy: 0.7593\n", - "Epoch 45/350\n", - "814/814 [==============================] - 1s 965us/step - loss: 0.5048 - auc: 0.7072 - accuracy: 0.7556 - val_loss: 0.4981 - val_auc: 0.7118 - val_accuracy: 0.7593\n", - "Epoch 46/350\n", - "814/814 [==============================] - 1s 958us/step - loss: 0.4943 - auc: 0.7113 - accuracy: 0.7627 - val_loss: 0.4972 - val_auc: 0.7142 - val_accuracy: 0.7593\n", - "Epoch 47/350\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.4967 - auc: 0.7200 - accuracy: 0.7573 - val_loss: 0.4969 - val_auc: 0.7173 - val_accuracy: 0.7593\n", - "Epoch 48/350\n", - "814/814 [==============================] - 1s 891us/step - loss: 0.4973 - auc: 0.7152 - accuracy: 0.7604 - val_loss: 0.4965 - val_auc: 0.7197 - val_accuracy: 0.7593\n", - "Epoch 49/350\n", - "814/814 [==============================] - 1s 762us/step - loss: 0.5018 - auc: 0.7160 - accuracy: 0.7553 - val_loss: 0.4961 - val_auc: 0.7171 - val_accuracy: 0.7593\n", - "Epoch 50/350\n", - "814/814 [==============================] - 1s 747us/step - loss: 0.4977 - auc: 0.7154 - accuracy: 0.7587 - val_loss: 0.4951 - val_auc: 0.7197 - val_accuracy: 0.7593\n", - "Epoch 51/350\n", - "814/814 [==============================] - 1s 751us/step - loss: 0.4976 - auc: 0.7168 - accuracy: 0.7581 - val_loss: 0.4944 - val_auc: 0.7219 - val_accuracy: 0.7593\n", - "Epoch 52/350\n", - "814/814 [==============================] - 1s 895us/step - loss: 0.4859 - auc: 0.7256 - accuracy: 0.7666 - val_loss: 0.4940 - val_auc: 0.7250 - val_accuracy: 0.7593\n", - "Epoch 53/350\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.4971 - auc: 0.7198 - accuracy: 0.7579 - val_loss: 0.4940 - val_auc: 0.7249 - val_accuracy: 0.7593\n", - "Epoch 54/350\n", - "814/814 [==============================] - 1s 980us/step - loss: 0.4968 - auc: 0.7185 - accuracy: 0.7593 - val_loss: 0.4935 - val_auc: 0.7241 - val_accuracy: 0.7593\n", - "Epoch 55/350\n", - "814/814 [==============================] - 1s 959us/step - loss: 0.4985 - auc: 0.7165 - accuracy: 0.7581 - val_loss: 0.4924 - val_auc: 0.7263 - val_accuracy: 0.7593\n", - "Epoch 56/350\n", - "814/814 [==============================] - 1s 966us/step - loss: 0.4935 - auc: 0.7150 - accuracy: 0.7631 - val_loss: 0.4928 - val_auc: 0.7276 - val_accuracy: 0.7593\n", - "Epoch 57/350\n", - "814/814 [==============================] - 1s 962us/step - loss: 0.4966 - auc: 0.7211 - accuracy: 0.7579 - val_loss: 0.4927 - val_auc: 0.7266 - val_accuracy: 0.7593\n", - "Epoch 58/350\n", - "814/814 [==============================] - 1s 964us/step - loss: 0.4936 - auc: 0.7256 - accuracy: 0.7586 - val_loss: 0.4925 - val_auc: 0.7258 - val_accuracy: 0.7593\n", - "Epoch 59/350\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.4917 - auc: 0.7319 - accuracy: 0.7579 - val_loss: 0.4920 - val_auc: 0.7271 - val_accuracy: 0.7593\n", - "Epoch 60/350\n", - "814/814 [==============================] - 1s 970us/step - loss: 0.4968 - auc: 0.7244 - accuracy: 0.7566 - val_loss: 0.4908 - val_auc: 0.7302 - val_accuracy: 0.7593\n", - "Epoch 61/350\n", - "814/814 [==============================] - 1s 978us/step - loss: 0.4941 - auc: 0.7210 - accuracy: 0.7613 - val_loss: 0.4900 - val_auc: 0.7336 - val_accuracy: 0.7593\n", - "Epoch 62/350\n", - "814/814 [==============================] - 1s 986us/step - loss: 0.4986 - auc: 0.7280 - accuracy: 0.7535 - val_loss: 0.4901 - val_auc: 0.7319 - val_accuracy: 0.7593\n", - "Epoch 63/350\n", - "814/814 [==============================] - 1s 968us/step - loss: 0.4888 - auc: 0.7284 - accuracy: 0.7623 - val_loss: 0.4891 - val_auc: 0.7360 - val_accuracy: 0.7593\n", - "Epoch 64/350\n", - "814/814 [==============================] - 1s 956us/step - loss: 0.4930 - auc: 0.7264 - accuracy: 0.7593 - val_loss: 0.4889 - val_auc: 0.7352 - val_accuracy: 0.7593\n", - "Epoch 65/350\n", - "814/814 [==============================] - 1s 962us/step - loss: 0.4871 - auc: 0.7290 - accuracy: 0.7640 - val_loss: 0.4889 - val_auc: 0.7359 - val_accuracy: 0.7593\n", - "Epoch 66/350\n", - "814/814 [==============================] - 1s 963us/step - loss: 0.4900 - auc: 0.7303 - accuracy: 0.7608 - val_loss: 0.4878 - val_auc: 0.7378 - val_accuracy: 0.7593\n", - "Epoch 67/350\n", - "814/814 [==============================] - 1s 965us/step - loss: 0.4850 - auc: 0.7349 - accuracy: 0.7638 - val_loss: 0.4882 - val_auc: 0.7401 - val_accuracy: 0.7593\n", - "Epoch 68/350\n", - "814/814 [==============================] - 1s 816us/step - loss: 0.4916 - auc: 0.7378 - accuracy: 0.7557 - val_loss: 0.4866 - val_auc: 0.7396 - val_accuracy: 0.7593\n", - "Epoch 69/350\n", - "814/814 [==============================] - 1s 747us/step - loss: 0.4897 - auc: 0.7392 - accuracy: 0.7567 - val_loss: 0.4869 - val_auc: 0.7388 - val_accuracy: 0.7593\n", - "Epoch 70/350\n", - "814/814 [==============================] - 1s 757us/step - loss: 0.4934 - auc: 0.7308 - accuracy: 0.7570 - val_loss: 0.4868 - val_auc: 0.7398 - val_accuracy: 0.7593\n", - "Epoch 71/350\n", - "814/814 [==============================] - 1s 747us/step - loss: 0.4869 - auc: 0.7414 - accuracy: 0.7589 - val_loss: 0.4863 - val_auc: 0.7410 - val_accuracy: 0.7593\n", - "Epoch 72/350\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.4878 - auc: 0.7375 - accuracy: 0.7591 - val_loss: 0.4852 - val_auc: 0.7437 - val_accuracy: 0.7593\n", - "Epoch 73/350\n", - "814/814 [==============================] - 1s 989us/step - loss: 0.4872 - auc: 0.7394 - accuracy: 0.7594 - val_loss: 0.4843 - val_auc: 0.7459 - val_accuracy: 0.7593\n", - "Epoch 74/350\n", - "814/814 [==============================] - 1s 969us/step - loss: 0.4900 - auc: 0.7366 - accuracy: 0.7576 - val_loss: 0.4846 - val_auc: 0.7442 - val_accuracy: 0.7593\n", - "Epoch 75/350\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.4894 - auc: 0.7396 - accuracy: 0.7563 - val_loss: 0.4843 - val_auc: 0.7456 - val_accuracy: 0.7593\n", - "Epoch 76/350\n", - "814/814 [==============================] - 1s 964us/step - loss: 0.4824 - auc: 0.7430 - accuracy: 0.7626 - val_loss: 0.4831 - val_auc: 0.7480 - val_accuracy: 0.7593\n", - "Epoch 77/350\n", - "814/814 [==============================] - 1s 959us/step - loss: 0.4851 - auc: 0.7374 - accuracy: 0.7614 - val_loss: 0.4834 - val_auc: 0.7472 - val_accuracy: 0.7593\n", - "Epoch 78/350\n", - "814/814 [==============================] - 1s 976us/step - loss: 0.4886 - auc: 0.7434 - accuracy: 0.7557 - val_loss: 0.4825 - val_auc: 0.7488 - val_accuracy: 0.7593\n", - "Epoch 79/350\n", - "814/814 [==============================] - 1s 960us/step - loss: 0.4852 - auc: 0.7415 - accuracy: 0.7601 - val_loss: 0.4827 - val_auc: 0.7485 - val_accuracy: 0.7593\n", - "Epoch 80/350\n", - "814/814 [==============================] - 1s 961us/step - loss: 0.4757 - auc: 0.7469 - accuracy: 0.7670 - val_loss: 0.4820 - val_auc: 0.7512 - val_accuracy: 0.7593\n", - "Epoch 81/350\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.4819 - auc: 0.7473 - accuracy: 0.7618 - val_loss: 0.4819 - val_auc: 0.7494 - val_accuracy: 0.7593\n", - "Epoch 82/350\n", - "814/814 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0.8419 - accuracy: 0.7932 - val_loss: 0.4011 - val_auc: 0.8471 - val_accuracy: 0.7895\n", - "Epoch 305/350\n", - "814/814 [==============================] - 1s 984us/step - loss: 0.3995 - auc: 0.8442 - accuracy: 0.7956 - val_loss: 0.3942 - val_auc: 0.8502 - val_accuracy: 0.7972\n", - "Epoch 306/350\n", - "814/814 [==============================] - 1s 991us/step - loss: 0.3986 - auc: 0.8424 - accuracy: 0.7951 - val_loss: 0.3943 - val_auc: 0.8493 - val_accuracy: 0.7955\n", - "Epoch 307/350\n", - "814/814 [==============================] - 1s 974us/step - loss: 0.3962 - auc: 0.8441 - accuracy: 0.7981 - val_loss: 0.3939 - val_auc: 0.8494 - val_accuracy: 0.7966\n", - "Epoch 308/350\n", - "814/814 [==============================] - 1s 984us/step - loss: 0.3927 - auc: 0.8474 - accuracy: 0.7981 - val_loss: 0.3947 - val_auc: 0.8492 - val_accuracy: 0.7947\n", - "Epoch 309/350\n", - "814/814 [==============================] - 1s 985us/step - loss: 0.4008 - auc: 0.8448 - accuracy: 0.7933 - 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- val_accuracy: 0.7987\n", - "Epoch 315/350\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.3940 - auc: 0.8481 - accuracy: 0.8023 - val_loss: 0.3934 - val_auc: 0.8499 - val_accuracy: 0.7967\n", - "Epoch 316/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3968 - auc: 0.8442 - accuracy: 0.7959 - val_loss: 0.3926 - val_auc: 0.8508 - val_accuracy: 0.7984\n", - "Epoch 317/350\n", - "814/814 [==============================] - 1s 983us/step - loss: 0.3931 - auc: 0.8444 - accuracy: 0.7988 - val_loss: 0.3927 - val_auc: 0.8510 - val_accuracy: 0.7996\n", - "Epoch 318/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3995 - auc: 0.8435 - accuracy: 0.7965 - val_loss: 0.3926 - val_auc: 0.8507 - val_accuracy: 0.7959\n", - "Epoch 319/350\n", - "814/814 [==============================] - 1s 983us/step - loss: 0.3987 - auc: 0.8451 - accuracy: 0.7980 - val_loss: 0.3929 - val_auc: 0.8513 - val_accuracy: 0.7992\n", - "Epoch 320/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3986 - auc: 0.8454 - accuracy: 0.7987 - val_loss: 0.3925 - val_auc: 0.8517 - val_accuracy: 0.8001\n", - "Epoch 321/350\n", - "814/814 [==============================] - 1s 971us/step - loss: 0.3983 - auc: 0.8455 - accuracy: 0.7971 - val_loss: 0.3931 - val_auc: 0.8505 - val_accuracy: 0.7969\n", - "Epoch 322/350\n", - "814/814 [==============================] - 1s 982us/step - loss: 0.3973 - auc: 0.8421 - accuracy: 0.7953 - val_loss: 0.3923 - val_auc: 0.8511 - val_accuracy: 0.7976\n", - "Epoch 323/350\n", - "814/814 [==============================] - 1s 969us/step - loss: 0.3961 - auc: 0.8449 - accuracy: 0.7964 - val_loss: 0.3921 - val_auc: 0.8514 - val_accuracy: 0.7982\n", - "Epoch 324/350\n", - "814/814 [==============================] - 1s 867us/step - loss: 0.3950 - auc: 0.8492 - accuracy: 0.7996 - val_loss: 0.3925 - val_auc: 0.8512 - val_accuracy: 0.7972\n", - "Epoch 325/350\n", - "814/814 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1s 981us/step - loss: 0.3986 - auc: 0.8443 - accuracy: 0.7992 - val_loss: 0.3913 - val_auc: 0.8520 - val_accuracy: 0.8012\n", - "Epoch 331/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3920 - auc: 0.8479 - accuracy: 0.8019 - val_loss: 0.3912 - val_auc: 0.8524 - val_accuracy: 0.8001\n", - "Epoch 332/350\n", - "814/814 [==============================] - 1s 980us/step - loss: 0.3947 - auc: 0.8478 - accuracy: 0.8043 - val_loss: 0.3915 - val_auc: 0.8519 - val_accuracy: 0.7984\n", - "Epoch 333/350\n", - "814/814 [==============================] - 1s 976us/step - loss: 0.3910 - auc: 0.8506 - accuracy: 0.8002 - val_loss: 0.3907 - val_auc: 0.8528 - val_accuracy: 0.8001\n", - "Epoch 334/350\n", - "814/814 [==============================] - 1s 974us/step - loss: 0.3923 - auc: 0.8507 - accuracy: 0.8025 - val_loss: 0.3910 - val_auc: 0.8523 - val_accuracy: 0.8015\n", - "Epoch 335/350\n", - "814/814 [==============================] - 1s 974us/step - loss: 0.3938 - auc: 0.8470 - accuracy: 0.7991 - val_loss: 0.3911 - val_auc: 0.8524 - val_accuracy: 0.7982\n", - "Epoch 336/350\n", - "814/814 [==============================] - 1s 980us/step - loss: 0.3950 - auc: 0.8449 - accuracy: 0.7960 - val_loss: 0.3963 - val_auc: 0.8507 - val_accuracy: 0.7929\n", - "Epoch 337/350\n", - "814/814 [==============================] - 1s 975us/step - loss: 0.3966 - auc: 0.8445 - accuracy: 0.7957 - val_loss: 0.3912 - val_auc: 0.8522 - val_accuracy: 0.8025\n", - "Epoch 338/350\n", - "814/814 [==============================] - 1s 988us/step - loss: 0.3998 - auc: 0.8441 - accuracy: 0.7981 - val_loss: 0.3906 - val_auc: 0.8530 - val_accuracy: 0.8015\n", - "Epoch 339/350\n", - "814/814 [==============================] - 1s 976us/step - loss: 0.3972 - auc: 0.8471 - accuracy: 0.7983 - val_loss: 0.3918 - val_auc: 0.8518 - val_accuracy: 0.7995\n", - "Epoch 340/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3922 - auc: 0.8491 - accuracy: 0.7998 - val_loss: 0.3908 - val_auc: 0.8524 - val_accuracy: 0.8010\n", - "Epoch 341/350\n", - "814/814 [==============================] - 1s 988us/step - loss: 0.4013 - auc: 0.8430 - accuracy: 0.7971 - val_loss: 0.3900 - val_auc: 0.8530 - val_accuracy: 0.7996\n", - "Epoch 342/350\n", - "814/814 [==============================] - 1s 1000us/step - loss: 0.3937 - auc: 0.8468 - accuracy: 0.8003 - val_loss: 0.3953 - val_auc: 0.8512 - val_accuracy: 0.7930\n", - "Epoch 343/350\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.3968 - auc: 0.8493 - accuracy: 0.7999 - val_loss: 0.3900 - val_auc: 0.8530 - val_accuracy: 0.8018\n", - "Epoch 344/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4002 - auc: 0.8435 - accuracy: 0.7964 - val_loss: 0.3921 - val_auc: 0.8533 - val_accuracy: 0.8022\n", - "Epoch 345/350\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3952 - auc: 0.8498 - accuracy: 0.8017 - val_loss: 0.3932 - val_auc: 0.8519 - val_accuracy: 0.7956\n", - "Epoch 346/350\n", - "814/814 [==============================] - 1s 973us/step - loss: 0.3953 - auc: 0.8461 - accuracy: 0.8004 - val_loss: 0.3895 - val_auc: 0.8538 - val_accuracy: 0.8030\n", - "Epoch 347/350\n", - "814/814 [==============================] - 1s 985us/step - loss: 0.3903 - auc: 0.8503 - accuracy: 0.8038 - val_loss: 0.3928 - val_auc: 0.8539 - val_accuracy: 0.8009\n", - "Epoch 348/350\n", - "814/814 [==============================] - 1s 972us/step - loss: 0.3957 - auc: 0.8463 - accuracy: 0.7992 - val_loss: 0.3906 - val_auc: 0.8539 - val_accuracy: 0.8015\n", - "Epoch 349/350\n", - "814/814 [==============================] - 1s 974us/step - loss: 0.3922 - auc: 0.8468 - accuracy: 0.7980 - val_loss: 0.3892 - val_auc: 0.8540 - val_accuracy: 0.8022\n", - "Epoch 350/350\n", - "814/814 [==============================] - 1s 983us/step - loss: 0.3924 - auc: 0.8495 - accuracy: 0.8023 - val_loss: 0.3906 - val_auc: 0.8530 - val_accuracy: 0.8029\n" - ] - } - ], - "source": [ - "history = model.fit(X_train.values, y_train,epochs=350,verbose=1,validation_data=(X_test.values, y_test))\n" - ] - }, - { - "cell_type": "markdown", - "id": "a73233d1", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "24678365", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "fb98bf37", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b545cf9a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "4ed9d5a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8528542281422176\n", - "AUC-ROC score sobre train: 0.8483672182449495\n", - "Accuracy sobre test: 0.8028558268079227\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.84 0.88 5335\n", - " Alto valor 0.47 0.62 0.53 1178\n", - "\n", - " accuracy 0.80 6513\n", - " macro avg 0.69 0.73 0.70 6513\n", - "weighted avg 0.83 0.80 0.81 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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w7x689FLsdACxbG2hQoXEL+LjA87Oie8TIgu4dy+QkSN3ERoawfLlnZI+ALhx4wZeXl4AXlrrVB2glZFasoQQIuO4dg22boX798EUPzsK15H8ix+H9VU2Pf6DG49uEGiK4rxHdAV7iNt7lzMCij+GOu4V8a7WAu/CVcntmp+8Tnmxs7Yjh20O3B3c0+2tCZHVREVpFi48zujRu3n0KJShQ2sRFaWxsjL2pjZJsoQQIq5Fi4j88AN+tr7NX3nhmgsc9wS7SDiaH3wT3pzmCIXDoGKoKz6+dnjkK0Zx16JUe+kNCrsUorBzIfPdy7a2YGNjyFsSIis7f96XPn028ssv16lZswALFrShYsWMMb+4JFlCiKwhLAxCou980xpOnoTw8GfX/+03Hpsi+PPcPn619+Wq/2X8bKPYViCI8DchKM5NbjlszPMaVfDwobgyUcylCEVcClPbswaV81SgYK6iT3fbCSHShdaaf/7xY9681vTtW9Xw1qu4JMkSQmRujx7BxIkwa1aiuzXm1qjrLrCrGOwqbp7g8o/o8d8UiX7OB14PwZOcROXIwcCXxlA5X2XKeJQht2PutH8fQohk27Tpb37//SYff/wSZcp4cPXqUOztM15Kk/EiEkKIxISHQ0QErF4Nv/4Kd+/CuXNw9ux/ddq3R1evzl59id9DLrHB5Q6/Bf791KmcTY60da5AwXylKOZenOpF61GrcF3srKU1SoiM7Pr1h7z33nbWrz9PqVK5GDOmHk5OthkywQJJsoQQGZnW5m7Azz6DyZOf3u2Rmz894WjTMux9uTQbL28jJHy9eac1EAj5cuSjTck21ChQg7IeZamWv5okU0JkMhERUXz11W9MmLCP8PAoJk1qyOjR9TJschUjY0cnhMge/P3NiwAfOQK7d4OvLzx4AP/+N91dlIJ9vRpxNp+Jo0VtOfjkDFcfXYveew4unqOQSyEq5atEXqe8vF3lbSrkrYC9tb0x70kIkWrOnr3PyJG7aNy4CN9805pSpXIZHVKySJIlhDDO/fvmVqqZMwHzgsC+jnDGA266QFCPUvyVM5C/c8Eh65uY5yQGboGbvRsNCjegmFsx3qjwBuU8ypE3R17D3ooQInUFBISwe/e/dOpUlgoV8vL7729TpYpnplprWJIsIUTaO3DAPH4K4PhxOHiQm7f+Zm0ZuOcEv71hHpD+tAuxW965vOlevjuvl3+dvDnyksM2R7qELoRIX1prli07zbBhO/DzC6ZWrYIULOhM1ar5kz44g5EkSwiRdoKDwcWFsKhwZtWCncXh9wIQ1hlC4/z2yWXvTudiTbA12VLFswq2Jlsq56tMEdci5LDNgYt9+i+HIYRIfxcuPGDAgC3s2XOZihXzsnHjaxQsmHlXKZAkSwiROgICzFMpHDjAnUt/8VVNuOIKW96HR3GGRSkUDQvWpXmpVnjn9qZ58ebSKiWE4ObNR1SsOB+TSfHll815772aWFtbGR3WC5EkSwhhuchIaN+e4O2b2VkcDheCZU3gZgfzbjttompkXpyLlKV9uU50KNOBPE55jI1ZCJGhXLkSQJEirhQo4MyMGc1p3bqUIYs5pwVJsoQQKRcSQsTUT/lp3cdMagRXPvxvVzGXInQqUI0hNYdQr1A9w0IUQmRsd+8+YfjwnaxadYa//upHmTIe9O9f3eiwUpUkWUKIpEVFwbvvErl7J3+FXmNVWfi8PhDdYlU2V2m6V+hB9/LdKepW1NBQhRAZW1SU5ttv/2DMmN08eRLG8OG18fLKGi1XCUmSJYT4j9Zw+TKsWQObN0NEBA/+/IWJjeFYfvitV/zq75V+k4ntZuDu4G5IuEKIzCUwMIymTZfw6683qFPHi/nzW1O+fNadekWSLCGyu6gomDsXjh6FH3+MLb7vCB++BN+O/q/q62W6UjR3SdqUakMVzyrYmGwMCFgIkdlERWmsrBROTrZUrJiXt96qRJ8+VTLUYs5pQZIsIbKrkBA4fBiaNQPMCylfdYV1ddz4X11HToffBKC4W3FG1BlB36p9sVKZ+04fIUT6W7/+PGPG7GbbttcpWtSN+fPbGB1SupEkS4jsZNUq2LIFFi+OLfozH/xQGZbVd8U3LADwh3B/8jjlYV7reXQs09GwcIUQmdfVqwG89952Nm78G2/vXPj5BVO0qJvRYaUrSbKEyA5u3IC2beHECaIUHCwCu6u4sqRsBNesn5jrhAXQskRLOpbpSMcyHWWclRDCIlFRmi+//IVJkw4QGRnFxx83ZuTIOtjZZb+UI/u9YyGym5490UuWcNYDhr4Bu2OXrwkA4J0q7zCyzkhKuJfIVGuCCSEyJqVg//6r1KtXiK+/bkWJEtn3DzZJsoTIKoKCYMcOOHQInM3LUDxZ9C3jS9/mp5Fw38lczUpZMabuGFqVbEXV/FWxt7Z/zkmFECJpfn7BTJy4j7Fj65M/f05WrOiEk5NNtv/DTZIsITK7v/+G2rXB3z+2KErBF3VgTG/z6wImN6Y0HEGbUm2okLeCQYEKIbIarTU//niS99/fia9vEFWqeNK7d2Vy5LA1OrQMQZIsITKzyEgoXdq8XbYs1KrFvZ6v0u/WAtZd2IibvRtTm37OO1X7GhunECLLOX/el/79t7B//xUqV87H5s3dqVGjgNFhZSiSZAmRWT18GDv9At7ehP11nM8Of86k/a0BqF+oPnvf3Iu1lfw3F0KkviFDtnPs2C1mzWrBwIE1Mv1izmlBfvsKkZk8fGi+U7B5c7h1Cw2czw0/fdGSb2d6cT/oPgCzX57NezXfMzZWIUSWs3PnJapW9SRXLkfmzWuNra2JggWdjQ4rw5K0U4iM7vp16NHDfMuOqyv4+ODnf4tXu4DVJCg7CKYcn4W7gzvftPqGsA/DJMESQqSq27cf89prq2nR4kc+//wwAMWKuUmClQRpyRIiI5s6FcaMiX15oV4ZpjV3YmHUsdiyzmU783aVt2levLkREQohsrDIyCgWLPiDsWP3EBQUztix9fjwwwZGh5VpSJIlREajNXzzDQwaFFt0e+qHDC94huUX10EUVMpXifdqvEfvyr0NDFQIkdX177+F7747Tr16hZg/vzXlyuUxOqRMRZIsITKSOnXgyJHYl1EK/m/xcHr/+wlchDal2jC4xmBptRJCpJnHj0NRSpEjhy39+lWjVq2C9OpVKcsv5pwWZEyWEEYLDYVt28zjraITLF2qJMv3zKbsV970/ncGznbOrO+6nk3dNkmCJYRIE1pr1qw5S5kyXzNu3B4AqlTx5K23KkuCZSFpyRLCSPfumee5ijOR6MUTe6i6tT2PDw0BYFD1QUxpMgVnOxlgKoRIG5cv+zNo0Da2br1ImTK5efXVskaHlCVIkiWEUd5/H2bMMG+bTBxfPYf+9xfx+/omsVWuDr1KIZdCBgUohMgOFi06wYABW9AaPv30Jd5/vw62tiajw8oSJMkSIr2FhcHnn/+XYE2bxpgK95j66wAAmhZryojaI2hUpBF21nYGBiqEyMq01iilKFHCnUaNijB3biuKFXMzOqwsRZIsIdLLwoWwejVs3x5bdGPup3SxX8eRX49Q3K0423tsp4R7CQODFEJkdb6+QYwevYv8+XPy8ccvUa9eIbZufd3osLIkGfguRFqbORPy5YO3345NsKJKe/PZplHUCZvHrzd+ZWSdkZzqf0oSLCFEmtFas2jRCUqXnsuiRX8RHBxhdEhZnrRkCZEW7t41J1YJbdjA3Zdq0mppK47/MQ2Aw70PU7dQ3XQOUAiRnZw/78u7727m4MGrVKuWnx07WlO1an6jw8rypCVLiNQWFhY/wRo71nz3oNZ84nqS0l+X5vjt4/Sp3Icbw25IgiWESHNXrwZw4sQd5sxpya+/9pEEK51IS5YQqUVr6NcPvv3W/LpcOTh5kodhj9l6cSsL1i/gwNUDAOztuZfGRRsbGKwQIqvbvv0fbtx4xNtvV6FFixJcuTIENzcHo8PKViTJEuJFPXoEs2bBxIn/ldWpA/v28bffRZotacb1R9fJ65SXSQ0n8V7N93BzkDt4hBBp49atxwwdup1Vq87i45OHXr0qYW1tJQmWASTJEuJFPHxonqk9rosXCS3ixaxfZzJmj3lx5+/bfs/rFV7H3to+/WMUQmQLkZFRfPPNUcaN20tISATjxtVn3Lj6WFvLyCCjSJIlhCUiI+Hdd83TMgCUKQPr13M9rwODtg1i76q9PAl7gpWyYuNrG2ldqrWx8Qohsrzdu//lvfe207BhYebNa02ZMh5Gh5TtSZIlREppDdZx/ut06QJLl/LLrd9oMLsBkToSgAkNJjCh4QRMVjJzshAibTx8GMKJE3do2LAIzZsXZ/v212nevDhKyVqDGYEkWUKkRGgoeHn999rPD9zcWPLXEnqu7wnAgjYLeKfKO/JLTgiRZrTWrFp1lqFDtxMUFM61a8NwdrajRQuZay8jkSRLiJSoWRPu3zdv+/mhXV15Z+PbLPzT3G24pfsWWpVsZWCAQois7t9//Rk4cCvbt/9DuXIerFzZGWdnWYIrI5IkS4jkWrsW/vrLvP3kCf+E3ubtxR04cPUAVTyrsLbLWgq7FjY2RiFElnb69D2qV/8OpWDq1KYMG1YLGxsZkpBRSZIlRFL8/KBBAzhzBoDzH/ZjyLqO7Ly0E4B23u1Y02UN1lby30kIkTYePAgiVy5HypXz4P33a/P221UoUsTV6LBEEuRTQYjnuXQJSpjHOPziBT0HFeBS8Hy4BC1LtOSjxh9RLX81g4MUQmRV9+8HMnLkLrZsuci5cwPJnduRTz55yeiwRDJJkiVEYo4dg1GjYN8+AE7WL0XdJhcg+CadynZiQLUBMmO7ECLNREVpfvjhT0aN2k1AQAhDhtTEzk66BTMbSbKESEzz5uDvz31H6DO8OJusL+Bs58ySDkto593O6OiEEFmYr28QHTqs4PDha1Svnp8FC9pQubKn0WEJC0iSJUSMsDAICQEXFwC+fKsMH5e8RWD4Vd7weYMhNYdQNX9Vg4MUQmR1bm72ODra8M03rejbtyomk8zYnlllmO+cUqq0UmqXUipQKXVHKTVNKWWbjONyKaXmK6WuRR97WinVLz1iFlnE+fNQvjw4OoKLC5EK+rWBEYXO8TD0IVu7b+X/OvyfJFhCiDSzZcsF6tb9H48ehWIyWbF9++v0719dEqxMLkO0ZCml3IC9wEWgI1AAmAE4AoOSOHwVUBr4ALgGtALmKaUitdbfpVnQImto0gT27o19ueH91vTOsQd/FUI5j3Ls6bmHvDnyGhigECIru3HjEUOGbGft2nMUL+7G9esPKVcuj0xmnEVkiCQL6Ac4Ax201n4ASilr4Bul1Kda61uJHaSUygc0BnprrRdFF+9VSlUHXgMkyRKJe/TIvPZgdIIVPHs6zXOs5/D1LQBMazqNEXVGyC86IUSaiIiIYu7c3xk/fh+hoRGMH9+AsWPr4eBgY3RoIhVllCSrJbA7JsGKthKYDzQHFj3juJifxocJyh8COVIzQJGFPHoE+fJBcDAA57YupvqfAwj0DySPUx5+eesXirsXNzhIIURWprVm4cI/qV49P99805rSpXMbHZJIAxmls7c0cD5ugdY6ALgdvS9RWuvrwE7gA6VUWaVUTqVUF8yJ2ddpF67IlCIi4NNPzQPboxOs9Qe/pcaJgQSGBzK23ljuvH9HEiwhRJoICAjhww/3EhQUjo2NiT17erJnT09JsLKwjNKS5QYEJFLuD7gncWxHYAVwJvp1JDBYa73meQcppZwxd1HGyJesSEXm9cUXMG6cebtSJXpNrMjivX0B+Orlrxhcc7CBwQkhsiqtNStWnGHYsB3cufOEatXy0759afLkcTI6NJHGMkqSZRFlHjDzA1AS6I655asZMEsp5a+1Xv6cw4cDE9M+SpEhBAbCBx8AEOn3gIE/f8DiPxYA8GjMI3La5TQyOiFEFvXPP34MHLiVnTsvUb58Htau7ULt2l5GhyXSSUZJsvwBl0TK3QC/RMpjtAY6AxW01qeiy/YrpfIAXwLPS7JmAN/HeZ0POJrsiEXmsXs3dOoEwLGOtaj+Va7YXVeGXJEESwiRJrTWtG+/nMuXA/jii2YMGVJTFnPOZjJKknWeBGOvlFIugCcJxmolUBZz9+DpBOV/Am8rpRy11kGJHai1fgQ8inM9C8IWGd6VK9CsGQDX3U00q3EeQmBE7RF83vRzTFbyC08IkboOH75GjRoFsLU1sWhRe/LkcaJQocTaEURWl1EGvm8DmiqlXOOUdQaiMA9sf5argAmokKC8KnDvWQmWyAbeew/KloWiRQGYP+t1Ko1yISAkgG/bfMsXzb+QBEsIkaru3QukZ8911K//A19//TsA1arllwQrG8soLVnzgcHAeqXUp5gnI/0CmB93jiyl1B6gsNa6RHTRVswTkK5WSk3GPCarOdALGW+VPUVGwooVMGcOAA+qluGtznZsDPiJHLY5ONT7EPUK1TM4SCFEVhIVpVm48DijR+/m0aNQ3n+/Nu+8IytEiAySZGmt/ZVSTYA5wHrgMebxUuMSVDURJ2at9ePo46YAUwFX4DLmQe1z0zxwkfGMHg1ffgnA8v8Np9u1GRACHUp3YPbLs/FykQGnQojU9dprq1m16iy1ahVk/vzWVKwoN6sLM6W1NjqGDEEpVRC4fv36dQoWLGh0OMISgYGQwzwH7YcbhjDlz9mUylWKL5p9QTvvdgYHJ4TISgIDw7C3t8ZksmLTpr+5desx77xTFSsrGd+b2dy4cQMvLy8AL631jdQ8d0YZkyXEizl1KjbBWvpaOab8OZs6XnU42OugJFhCiFS1cePflC37DV9/bb4hvW1bb959t5okWOIpkmSJzG3XLlAKKpjvfThd2p3e5S5So0AN9vbcK4s7CyFSzbVrD2nffjmvvLIcOzsT5cvnMTokkcFJkiUyr9u3oXlz83bDhoRsXk+XQXkJjwxnVedV2FnbGRufECLL+PbbPyhb9mu2b/+HyZMbcfJkfxo3Lmp0WCKDkyRLZE7r1kH+/Obtzz4jdPcOat+YxDnfcyxst5BCLoWMjU8IkaXY21tTu7YXp071Z8KEhtjbZ4j7xkQGZ3GSpZSyVUr1V0qtUkrtji6rr5RqoJTKkXohChFHeDiMGAEdO5pfN2tG8PD3qLygMifunOC9Gu/Ru3JvY2MUQmR6/v7B9O+/mUWLTgDwxhsV2LmzByVL5nr+gULEYVEqrpRyAvZhnvRTATG3KI4A2gDvAV+nRoBCxAoOBkfH/17PmYMeOJA2S5pyzvcc71R5h1kvzzIsPCFE5qe1ZunSUwwfvpP79wNxc3MAZFUQYRlLW7ImAdUwJ1hxfRdd1t7ykIRIQGtYtCh+gnXiBAwaxPh949l7eS/NizdnQZsF8otQCGGxCxce0KzZEnr0WEf+/Dk5cqQPn37axOiwRCZmaafyq5hbr3oCS+KU/xz97P0iQQkRzzffwKBB5m1HR7h/H+3gwPSfv2DKoSlUz1+drd23SoIlhHghu3f/y6+/3mDGjOYMHlwTa2sZtixejEWTkSqlQjEnaA5ACKC11iallAMQCIRpre1TNdI0JpORZlBxuwj37UM3bMgXv3zBN0e/4erDq+RxysMfff+goLN8z4QQKbd797+Eh0fSsmVJIiOjuHs3kPz5cxodlkhHaTkZqaUtWQ+BXEDCT7YW0c8BlgYkRDxvv21+rlyZyxULU/FzFx6HPQbg3arv8mXzL3GydTIwQCFEZnTnzhPef38nS5eeolatgrz8cglMJitJsESqsrQt9HD084qYAqXUN8BPmLsRD71gXCK7u30b+vSBpUsB2L7oQ4p9VYzHYY/pWbEn/qP9md9mviRYQogUiYrSzJt3lNKl57Jy5RlGjarD7t1vyHADkSYsbcmaArQCqvDfnYXvYh70HgZ8+uKhiWzr3r3/5sAC+n1WlwXrXgVgfuv5vFvtXaMiE0Jkcv/3f38xYMBW6tTxYv781pQvL6tCiLRjUZKltf5DKdUW+AYoHmfXJaC/1vrP1AhOZEOnT0P58gA8rFia3iNLsu6fTXjm8ORArwOUzFXS4ACFEJnN48ehXL36EB+fPLz+enns7Ex07eojaw2KNGfxlLVa611ASaVUScADuK+1vphqkYnsZ9Uq6NIFgPvv9+Olooc4/c8mupTrwsJ2C8lhK3PcCiGST2vN+vXnee+97djYWPH334OwsTHRrVt5o0MT2YSlk5HuxXxHYZPoxOpinH0fRe+bmEoxiuzgzz9jE6ztyz6m5d/jMT0w8VPHn+hevrvBwQkhMpurVwMYNGgbmzdfwNs7F/Pnt8HGxmR0WCKbsbQlqxH/jcVK6MPofZJkieT76isA7s76hJZ/fwjAnJZzJMESQqTY4cPXaNHiR6KiNJ980pgRI+pgZydrDYr0l6o/dUqpcql5PpENBARAvXpw5gwa6GC/AZMysanbJlqWbGl0dEKITCQoKBxHRxuqVvWkWzcfxo6tR/Hi7kaHJbKxZE/hoJSaqJSKVEpFEt2KFfM6TvnJ6H230yZckaWEh5vvIjxzBoBWX1TiyJ2jTG06VRIsIUSy+fkF8847G6le/TvCwiJxcLDh++/bSYIlDJfSebJUMh8rUzFGkRU9eGBeLic4GJ3Lnc8Pfsr2wBNUzleZYbWHGR2dECIT0Frzf//3F97ec1m48E9eeqkI4eGRRoclRKyUdBeeABZHb7+JucXq/+Ls14A/cBRJssTzhIZC7twARCmoONqZ03s/wDuXN1u6b8FKyXphQojnu3nzET16rGP//itUqeLJtm2vU61a/qQPFCIdJTvJ0lpvADYAKKXejC7rnUZxiaxKa3jpJQBCSxaj6TB3Tt87RoviLdjw2gbsrO0MDlAIkRk4O9tx714gs2e/zIAB1WUxZ5EhWToZqfw0C8u0bQu//MJxT+jQN4Rr947RumRrNnbbKC1YQojn2rnzEt99d5zly18lZ047Tp7sh8kkvzdExmXx3YVKKRugJeANOCTcr7X+6AXiElnNjh3w8ssA/OwF9foAgbcYUnMIs16eZWhoQoiM7fbtxwwbtoMVK85QpIgr1649pGhRN0mwRIZn6WSkBYF9QLHnVJMkS5hNmwajR8e+/HJCU0y39rG6y2ral25vXFxCiAwtMjKK+fOP8cEHewkKCmfs2Hp8+GEDHB1tjA5NiGSxtCXrY+KvWZjQsyYqFdnNtm3/JVjjxrH8NR/WrelG13JdJcESQjzXo0ehTJ58gIoV8zJvXmvKlctjdEhCpIilba1NMCdSk6Nfa6At8DPwD9D6xUMTmV5UFLRqZd4+coRLw3szaOsgyuQuw8wWM42NTQiRIT16FMqsWb8SFaVxc3PgyJE+7N/fSxIskSlZmmTljX6O/aTUWm8BugElgHYvGJfI7O7fB1P0OmF58vCHlzWVF1QmPCqcHzv+iGdOT2PjE0JkKFpr1qw5S5kyXzNs2A4OH74GQPHi7lhZKYOjE8IyliZZIdHPQTHbSqmSQFR0eZcXjEtkdjVrxm7+eng51b6vzuOwx6ztspYqnlUMDEwIkdFcvuxPmzbL6NRpFS4udhw40IsGDQobHZYQL8zSMVn3gByAO3AZKA3sB2Km2pUxWdlZeDhcvgzA//74jj5LX8JKWbHt9W00KdbE4OCEEBlJWFgkdev+D3//ED799CXef78OtrYmo8MSIlVYmmSdwDzwvQqwFhgH5MO8pA7A5heOTGROWkOvXgDsGdmJPpveAeBQ70PU8apjYGBCiIzkr7/uUKFCXmxtTXz/fTtKl85NsWJuRoclRKqytLtwFNAYOIV5qoY5wB3AD/PSO0NTIziRCVWoAEuXsrUkNHVaDcDh3oclwRJCAODrG0SfPhuoVGkBa9acA6BVq5KSYIksydIZ3y9j7iaMMST6IbKznTvh9GmCraH16+aiw70PU7dQXWPjEkIYTmvN4sV/MWLETvz9Q3jvvRo0b/68mYCEyPwsnvH9WZRSTYGPtda1U/vcIgN78gRatADgzVkNwfcA3ct3lwRLCIHWmtatl7Jt2z9Uq5afHTtaU7WqLOYssr4UJVlKqcJAD8AL8+D3tVrrE9H7agBfAPVSOUaRGfTrB8D+IrDK9wAl3UvyY4cfjY1JCGGosLBIbG1NKKVo06YUrVqVpH//arIcjsg2lNbJuxFQKVUZ8x2EOeIURwG9AEfgG8xjvBSgtdaZ6vaQ6KWCrl+/fp2CBQsaHU7mYzIRqqLI+4kLD0Mf8tvbv1GjQA2joxJCGGT79n8YMGALc+a0pHXrUkaHI8Qz3bhxAy8vLwAvrfWN1Dx3Sv6cmAjkxJxExTxMwCxgevS2Ao4CL6dmkCKD++svInUU9Qc78TD0IXNazpEES4hs6tatx3TpsoqWLX9CKYWTk63RIQlhmJR0F9bGPP/VZuA7zAnV25iX0wG4AbyntV6fmgGKDO76dahUieY94ahrIL0q9WJg9YFGRyWEMMD8+ccYNWoXISERfPhhfT74oD4ODrKYs8i+UpJk5Yp+flNrHQCglPoZ8CV67UKt9V+pG57I0CIioFAhdhaHvcXMRfNbz0cpWQJDiOzo9u3HVKniybx5rSlTxsPocIQwXEq6C60AYhKs6G2/ONuSYGUnd++CjQ2hJmjxhrnoQK8D2FnbGRuXECLdPHwYwnvvbWP//isAjB/fkH373pQES4hoKZ7CQSn1bzLKtdZaJkDJykaPBmBm9EQdQ2oOoUHhBgYGJIRIL1prVq06y9Ch27l9+wkeHo40alQEa2u5a1CIuCyZJyvhqp06QblC1i7M+hYv5lQeGNvU/PKzJp8ZG48QIl1cuuTHoEHb2L79H8qV82Dlys7Uq1fI6LCEyJBSmmTJYBsBixdzIh806W0FRLGu6zocbByMjkoIkQ4WLPiDAweuMHVqU4YNq4WNTaaarUeIdJXsebKyOpknK/miWrWkutd2ThawZkXnFXQs09HokIQQaejAgSs4O9tRubInT56E4esbRJEirkaHJUSqyCjzZAkBwcEsvruD4/lhTqs5kmAJkYXdvx9Ir17radRoMRMm7AcgRw5bSbCESCZJskTyzZjB+dY1Gd/I3PrZq1IvY+MRQqSJqCjNwoXHKV36a5YsOcmwYbVYulT+oBIipVJ9gWiRRTVrxi9/76bV6xBoA+trzMDe2t7oqIQQaWD69F8YPXo3NWoUYP781lSu7Gl0SEJkSpJkiaRpzYOfd9PmPXhoD0s6LOGVCj2MjkoIkYoCA8Pw9w+hYEFn3nmnCq6u9vTpU1kWcxbiBcj/HvF84eFQrRp1+sBjO8W+N/fRQxIsIbKULVsuUK7cN7z22mq01ri5OdC3b1VJsIR4QfI/SDzbsWNga8vAfMe5kBu6lutCoyKNjI5KCJFKbtx4xKuvrqRNm2XY2JiYOLGhLIslRCp6oe5CpVRL4CXATWv9tlIqZka6W1rriBeOThjnwQOoXp1bOWFedaiYqxwLOy42OiohRCrZvv0fOndeRVhYJBMmNGDs2PrY28sIEiFSk0X/o5RS1sBaoHWc4reBJUC96O0fXjg6YZzBgwFo388VrQJY0H6hrEsoRBYQERGFtbUVlSrlo2nTYnz+eRO8vXMbHZYQWZKl3YWjgTaYZ4CP27b8dfTrFN/rq5QqrZTapZQKVErdUUpNU0rZJvPYAkqpxUqp+0qpYKXUOaXU6ymNQcRx9y4P7eCoUwC1CtaiZsGaRkckhHgBAQEhDBiwhXbtlqG1Jl++HKxb11USLCHSkKVJ1huY1yf8IEH5vuhnn5ScTCnlBuwFbDEnaB8AfYEZyTjWEzgC5I8+pg0wD5BmF0uNHw979zL2naIAjK031uCAhBCW0lqzbNkpSpeey7x5x/DyciYsLNLosITIFixaVkcpFQLYAE5AEKC11qbolqcQIFRrnezF7JRSY4FxQCGttV90WV/gm+iyW885dglQDGigtbb4N4csqxOHUgTZgPOHJiJ1JMHjgmVOLCEyoStXAujbdxO7dv1L+fJ5WLCgDbVrexkdlhAZSkZcVico+tk9QXmd6OfAFJ6vJbA7JsGKthJzfM2fdZBSyhnoAnzzIgmWiGP0aAC+H9aQSB3J6s6rJcESIpOyslL89dddpk9vxh9/9JUES4h0ZmmSdTT6+duYAqXUKGA55m7E31N4vtLA+bgFWusA4Hb0vmepgrmLMVwpdUApFR49nmuqUsrmeRdUSjkrpQrGPIB8KYw563nyBKZNw88BhjkdophbMVmbUIhMZu/ey4wYsROAQoVcuHJlCO+/XwcbG5PBkQmR/ViaZE2Lfm6JOakC+AzIE/36ixSezw0ISKTcn6dby+KKSYy+B45hbvWaCQwFPkrimsOB63EeR59fPRv47jsA+g4vQZSOYkKDCTJnjhCZxL17gbzxxjqaNPk/Vq48w/375g4FB4fn/r0phEhDFiVZWus9QB/gEf/dYaiAh8DbWut9zzk8NcXEv1tr/b7Wep/WeirmJG+YUup548JmAF5xHtXTNtQMTmsYPpydxWGNzT+MqjOKNyu9aXRUQogkREVpvv32D7y957Js2SlGjKjN2bMD8fBwMjo0IbI9i2ee01ovUkqtwjwOywO4D/yitU7peCwwt1i5JFLuBvglUh73ODDfmRjXHswD6UsApxI7UGv9CHOSCCAtNr//zjUXaPEG5M+Znw8bfGh0REKIZLhyJYDBg7dRpYon8+e3pmJFGfkgREZh6WSknwGLtdbngV2pEMd5Eoy9Ukq5AJ4kGKuVwNkkzisjtpOrVy/6tjVvjqs/jpx2OY2NRwjxTE+ehLF58wVee82HYsXc+OWXt6hc2RMrq2z+x6IQGcyLTEZ6Ril1TCn1nlLK4wXj2AY0VUq5xinrDEQBO591kNb6KuaWqqYJdjUDgkk6CRMAR45wMPg8O0pAu1JtGVB9gNERCSGeYcOG85Qt+zXduq3h3Ln7AFStml8SLCEyoBdZIFphvrtvJnBTKbVZKdVVKWXJJKDzgcfAeqVUc6VUb8zjqubHnSNLKbVHKfVPgmPHAe2UUrOUUs2UUh8AI4AZFnZdZj+//86M2ubNGS1mGhuLECJR1649pH375bRvvwIHBxv27OlJmTIv+vetECItWTomqwjm+ak6Yx4wbg20wny34WOl1Cqt9TvJPZnW2l8p1QSYA6zHnHB9jzmBisuUMGat9SalVDdgPNAf87QPE4HPU/yusqlLq75lQzOolrcKxd2LGx2OECKBgIAQKlSYR0hIBJMnN2L06LrY2clizkJkdBbN+B7vBEoVxpxsdQGqRRdrrXWmmpQl2874HhrKuLaOfFo3ik2vbaSNd1ujIxJCRLt6NYDChV0BWLjwOA0aFKZkyVzGBiVEFpMRZ3yP6xHmOwD9gYhUOJ9IT2vXsqNoFAAtS7YyOBghBIC/fzD9+m2mePGv+P33mwD06VNFEiwhMhlL7y50AzpgbsF6Kc55FBAKbEyV6ESau/XhEP7oCW8VexWTVaZqfBQiy9Fas3TpKYYP38n9+4H071+NUqUksRIis7K0U/8O8RMrDfwM/B+wSmv9MBViE2ntyBGGVTHfndSy6msGByNE9hYREUWrVj+xa9e/VKqUj40bX6NmzWw0dEGILMjSJCtmnYaLwBLgR631lVSJSKSb3z4byMqqUM2xBJ3KdjI6HCGypagojZWVwtraigoV8tKqVUkGDaqBtXVqjOYQQhjJ0iTrG2CJ1vq31AxGpK+vnE4DsPStrQZHIkT2tGvXJYYM2c7KlZ3x8cnD9OnNjQ5JCJGKLF27cJAkWJnb7VO/sKpkOG19c1EyV0mjwxEiW7lz5wndu6+hefMfCQoKx98/2OiQhBBpINktWUqpvZinZmgSvf08Wmvd5MVCE2lpzk9DCHeAD6oONToUIbINrTULFvzBmDG7CQwMZ/Touowf3wAnJ1ujQxNCpIGUdBc2wjzAPeF2Quo5+4TRwsII+PMInzkco9wDE7UmykLQQqQXpRSHD1/DxycP8+a1pnz5vEaHJIRIQylJsq5hXkswZlsSqcyoQQOaVPwN8kOTPLWMjkaILO/x41AmTz5Av37VKFHCnQUL2uDgYCNrDQqRDSQ7ydJaF0lsW2Qu187/xvGW4GlyZfa4Q0aHI0SWpbVm/frzvPfedm7ceETBgs4MHVpLugaFyEYsnYx0AuZxVx8nsu8lzDuTGrcl0ltgIEvLmze/7bIElPwlLURauHo1gEGDtrF58wVKl87Nvn1v0qhREaPDEkKkM0uncJiEubvwqSQL2I25W1FWL81IbtwALy/mDodyJk/alGpjdERCZFmjR+9m9+5/+eSTxowcWRdbW1lNQYjsKFUTIaWUc8xmap5XvKCHD8HLi8OF4KYztK/U3uiIhMhyfv75GoULu1KwoDPTpzdnypSXKF7c3eiwhBAGSskUDm8CbyYoS9glWCj6OeDFwhKpqkABAKbVNb98r/ZQ42IRIovx8wtm9OhdfP/9n/TpU5nvv29HwYLOSR8ohMjyUtKSVYT4UzcooGGCOjEtWAdfKCqRenbvhsBArrjCZm/FgGr9KZWrlNFRCZHpaa1ZsuQk77+/kwcPghg0qDqffPKS0WEJITKQlCRZAcDV6O3CmJOta3H2a8AfOApMTI3gRCp45RUAZn/WHn13PW9XedvggITIGsaO3cPUqT9TpYon27a9TrVq+Y0OSQiRwaRkCofZwGwApVRUdFnRNIpLpIa334agIPwcYNbd9bQt1ZbKnpWNjkqITCs4OJzQ0EhcXe15663KeHrmYOBAWcxZCJE4S38zNAakXTwji4yEhQsBmDm7GwCvl3/dyIiEyNR27PgHH595DBmyHYBSpXIxZEgtSbCEEM+UkoHvDQC01geJHpcVU5aY6HrCKCdOAPDg3TeY7buel0u8TFefrsbGJEQmdPv2Y4YN28GKFWcoUsSVrl3LGR2SECKTSMmYrP38N//Vfp6/rI5O4blFatIaqlUDYLLPAx4/eMynL31qcFBCZD5r156jd+8NBAWFM3ZsPT78sAGOjjZGhyWEyCRSmgipZ2yLjGT6dAAu5II5D7bSqmQrGYslRAporVFKUaKEO9Wq5eerr16mXLk8RoclhMhkUpJk9X7GtshoRo0C4Ptv34VTCxhWa5jBAQmROTx6FMqECfuIitJ89VVLKlTIy549PY0OSwiRSaXk7sLFiW2LDObYMQCC8+bi6/NLaF68OU2LNTU4KCEyNq01a9acY8iQ7dy69Zi3364c25olhBCWsui2GKWUnVLKXSnlFP3aWSk1Vik1QynVPHVDFClSvToA6z/vRVB4EH0q9zE4ICEytitXAmjTZhmdO6/C1dWegwd78d137STBEkK8MEvvPZ4L3AdGRL/eBXwCDAG2KaU6pUJsIqXu3o3dnPRkE042TrQq2crAgITI+O7cecKBA1f47LMm/Pnnu9SvX9jokIQQWYSlSVbN6OfNSqkyQHXMdx4GYR4QP/TFQxMpduoUAOFTPubCgwv45PEhh20Og4MSIuM5dOgqc+b8BkCtWgW5fn0YY8bUw9bWZHBkQoisxNIkyyv6+SJQJXr7I6BG9Lb3iwQlLDRzJgC7Spk/KF4u8bKR0QiR4fj6BvHWWxto0GARX355hODgcADc3BwMjkwIkRVZmmTZRT+HA+Uwz4v1B/BPdLk0n6Q3rWHrVgA2WV8CkPFYQkTTWvPDD39SuvRcFi/+iyFDanLyZH8cHGTOKyFE2rF0wtBbQFHgB6BedNlZIF/0tu8LxiVSat4883P58uy8to8aBWrg5eL1/GOEyCaOHr3FW29tpFq1/OzY0ZqqVWUxZyFE2rO0JWsD5rFXnYH8wCmt9RWgavT+0y8emkiRnTsBeLR9A//6/0uZ3GUMDkgIYwUFhbN372UAatQowI4dPfj11z6SYAkh0o2lLVnjAUegLnCV/+4yLAkcAJa9eGgi2QICYMMGcHVlweXVAHQs09HYmIQw0NatFxk4cCu3bz/mypWh5MuXg+bNixsdlhAim7EoydJaBwH9Eyn/AvjiRYMSKaA1uLmZtzt3ZvPFzTjZONG2VFtj4xLCADdvPmLo0B2sXn2WYsXc2LDhNfLlkyGiQghjWLyIs1LKGngTeBnwwDwOaxuwWGsdkTrhiSRFT9sAEDHtc45940XTYk1lIkWR7Vy79hAfn28ICYngww/r88EH9WVguxDCUBYlWUope2An5u7CuDoAvZVSTbXWIS8anEiGn382P8+axarrOwgKD6JL2S7GxiREOvLzC8bd3YFChVwYMaIOXbqUo3Tp3EaHJYQQFg98/wDzXYUqkUft6P0iPYSFmZ87d+Z/J/6Hh6MHXX26GhuTEOng4cMQBg/eStGis7l+/SEAEyY0lARLCJFhWJpkdcE8N9YqzIPd7aOfV2JOtKQpJb0cOQLAbVMwu//dTZ/KfbC2srgXWIgMT2vNihWnKV36a+bOPUqXLmVxcrI1OiwhhHiKpZ/GRaKf39VaB0RvX1JK9cOcYBVJ5BiR2qKiYMUKyJmT5f9uBJC1CkWW9vhxKJ07r2LHjkv4+ORh9erO1K1byOiwhBAiUZa2ZAVHPye8J7p4gv0iLa1ZA4AuUpgZv84AoI5XHSMjEiJN5chhi729NVOnNuX48b6SYAkhMjRLk6xjmLsFtyilpiqlBimlPgc2898SOyKt3bgBwKAxFbnx6AZj643FZCUL3IqsZf/+K9St+z/u3QtEKcW6dV0ZNaouNjbysy6EyNgs7S6cDryEeeqGEXHKFeYka/oLxiWS4/59AG6rJwBMaDjByGiESFX37wcyYsQu/u///qJgQWcuX/YnTx4nmZ5ECJFpWNSSpbXeAbwLPCb+nYWPgX5a6+2pFqF4tnPnoGhR/n14hSZFm2BvbW90REK8sKgozcKFxyld+mt++ukkw4fX4uzZAdSsWdDo0IQQIkUsvg1Na/29Umo5UAfIjXky0l+01k9SKzjxHFrD+vVEtm3DJf/9VM9f3eiIhEg1Cxf+SYkS7ixY0IZKlfIlfYAQQmRAKU6ylFJF+G8h6ONa652pGpFInps3ATiXz8STsCfUKFDD4ICEsFxgYBhffPELQ4fWwtXVng0bXsPd3QGTydJho0IIYbxkJ1nKPBBiHvA25q7BmPIfgHe01jr1wxPPNGwYAGvKaHgkdxWKzGvz5gsMGrSVq1cfUriwC717V8bDw8nosIQQ4oWl5M/EwUBfnp7hvTcwNNUjE88XPX3DYnWSgs4FKetR1uCAhEiZGzce0bHjCtq2XYaNjYldu96gd+/KRoclhBCpJiVJ1lvRz2HARmATEIo50eqVumGJ5woLA60JblCbyw+vUDFvRbnjSmQ63bqtYcuWi0yc2JBTp/rTtGkxo0MSQohUlZIxWaUwT8/QUmu9H0Ap1RjYg3lJHZFeduwAYGM9DwDalGpjZDRCJNvvv9+kXDkPnJxs+frrVtjZmfD2lrUGhRBZU0pasuwBYhKsaDHbdqkUj0iO/fsB2F3CPBnj6+VfNzAYIZIWEBDCgAFbqFXre6ZO/RmAChXySoIlhMjSLLm70Is4A9+fVa61vvZioYlnip6E9Ptr62hYuCE57XIaHJAQidNas2zZaYYP38Hdu4G8+25Vhg2rZXRYQgiRLiyZJ+tKgtc6kXJt4blFchw5wlmfvMBd3B3cjY5GiGcaMGAL8+f/QYUKeVm3riu1a3sZHZIQQqQbSyahSXh34bMeKTupUqWVUruUUoFKqTtKqWlKKdsUnmOoUkorpTan9PqZyo0bbKxg7qGd3GiywcEIEV9oaAShoREAdOlSjunTm/HHH30lwRJCZDspaW06yH+tVqlKKeUG7AUuAh2BAsAMwBEYlMxz5AMmAvfSIsYMY9o0CAlhfMnrFMhZgPJ5yxsdkRCx9u69TP/+W3j99fJMmNCQxo2L0rhxUaPDEkIIQyQ7ydJaN0rDOPoBzkAHrbUfgFLKGvhGKfWp1vpWMs4xDfPUEoXTLkyD+fvD6NFowN7GARd7F6MjEgKAe/cCef/9nfz440m8vJypUsXT6JCEEMJwGWXNipbA7pgEK9pKzPE1T+pgpVQ9oD0wJk2iyyjWrgXgbIV8PIkIYlD1ZDXyCZGmVqw4jbf3XJYtO8XIkXU4e3YgbdqUMjosIYQwXEZJskoD5+MWaK0DgNvR+55JKWUC5gJTtNa30yrADGH3bgDOfzURgMqeMju2MJ6jow1lyuTm+PF3mTatGTlypGgopRBCZFkZ5Q5ANyAgkXJ/IKnb5wYATsDMlFxQKeWMuYsyRr6UHG+I6CTrH1MAAMXcZIZskf6ePAlj0qT9FCrkwnvv1aRtW2/atCklqw4IIUQCGaUlyyJKqTzAR8BwrXVYCg8fDlyP8ziayuGlrv37wdcXypRh1dnV2FjZ4OHoYXRUIpvZsOE8Zct+zZdfHuHcufux5ZJgCSHE0zJKkuUPJDaK2w3wS6Q8xkfASeCQUspVKeWKuXXOOvr181rqZgBecR7VLQk83WyOnpVi4kR8g3zJaZdTPthEurl27SGvvLKc9u1X4OBgw549PZk3T5ZzEkKI58ko3YXnSTD2SinlAniSYKxWAqWBBpiTtIT8MQ+o357YgVrrR8CjONdLWcTpzdY8ziWiY3uufvoancp2MjggkZ38/PM1duz4h48+asSoUXWxs8sovzqEECLjsvg3pVIqFzAKeAlw01qXUEp1jz7ndq11Suar2gZ8oJRyjR7wDtAZiAJ2Pue4oYBrgrJZQDAwFnMrV9awfDlYW3M7yPxlLe5W3OCARFZ35Mh1bt9+QseOZXjtNR/q1SuEl5dMGyKEEMllUZIVPRbqV8xzUin+m6T0ZeB1zAnOtBSccj4wGFivlPoU82SkXwDz486RpZTaAxTWWpcA0FqfSCS2AOBJgoWsM7fQULh8GWxsuPDgAgA+eXwMDkpkVf7+wYwZs5tvvz1OmTK5ad++NFZWShIsIYRIIUvHZH0MFAEiE5Qvwpx0tU3JybTW/kATIAJYD3wOfI95cHpcJjJOF2f6WbrU/PzGG3yw9wMAGhZuaGBAIivSWvPjjyfx9p7Ld98dZ8CAavzySx+srDJ4V7oQQmRQliYsrTG3XrUA9sQp/z36OcV9WVrrc0DTJOo0SsZ5kqyT6Rw8aH4eMYKb25pha7LFy0XWgROpa9OmC7zxxjoqVcrH5s3dqVGjgNEhCSFEpmZpkhUzd8DPz9ify8LzioS0hkWLADjnFsHNxzcZV3+csTGJLCMkJIK///alYsV8tGlTih9/7EDXrj5YW2eUG4+FECLzsvQ3qW/0c8LZ2LtFP2ftRZrT0/Hj5ucWLVh2bhUA71R5x8CARFaxa9clypefR/PmPxIYGIaVleL11ytIgiWEEKnE0t+mMV2E62MKlFJbgXmYuxH3JHKMsMS+fQDosWNZemop9QrVo7Br1l0DW6S9O3ee0L37Gpo3/5Hw8Ej+9792ODnJUjhCCJHaLO0u/Ahoh3nwe8ydhS0wD3p/iHlgvEgN4eEAHPMI45L/JYbXTngvgBDJd/LkXRo0+IHAwHBGj67L+PENJMESQog0YlFLltb6H6A+sBfzXFYq+nkv0EBrfSnVIszuAgMBWHplMyZlonPZzgYHJDKj4GBzsl62rAddupTj+PG+fP55U0mwhBAiDVk8HYLW+hTQVCnlQPTyN1rrkFSLTJidPk1kXg+Wn1tJ8+LN8XCS9QpF8j1+HMrEiftZv/48J0/2J0cOW779NkUzrAghhLDQC49w1VoHa61vSYKVBh49gm3b2N+1Jnee3KF7+e5GRyQyCa01a9eeo0yZr5k581caNixCeHjCae2EEEKkJUtnfE/qt7XWWme/SUNT28aNEBbGsjKR2D+w5xXvV4yOSGQCvr5B9Oq1ni1bLlK6dG727+9Iw4ZFjA5LCCGyHUsTIZkCOj2sWkVoPg9WB/xCO+925LTLaXREIhPImdOWmzcf88knjRk5si62tiajQxJCiGzJ0iRrcYLXJqAoUAcIAla9SFACePgQtm9n24CXeBi6ne4+0lUonu3nn68xffoRli17FXt7a44deweTSea7EkIII1mUZGmteydWrpRqAWwDjr9IUILYrsKlJYNxfezKyyVeNjoikQE9eBDEmDG7+f77P/H0zMHFiw8oXz6vJFhCCJEBpOpvYq31DuAJ8F5qnjdbWrWKxwU92OT/G6+WeRU7azujIxIZiNaaxYtPULr01yxc+CeDBlXn3LmBlC+f1+jQhBBCRLN04HuDRIrtgZZADsDzRYLK9gICYMcO1g+qT0jEHrmrUDwlODiCiRP3U6iQC9u2vU61avmNDkkIIUQClo7J2s9/M70npIETFp5XwH9dhUUe4xnmScPCDY2OSGQAwcHhfPvtHwwcWANHRxv27n2TQoVcZK1BIYTIoF7kt7N6xuM6MODFQ8vGVq7kfmEPdvn/QddyXTFZyd1h2d2OHf/g4zOPoUN3sGXLBQCKFXOTBEsIITIwS1uyEhv4Hoo5wfpNax1heUjZXEAA7NzJqvdqE6kPSldhNnfr1mOGDdvBypVnKFrUlS1butOqVUmjwxJCCJEMKU6ylFJ2gH/0yyNa6/upG1I2t2EDhIeztKA/JUwlqJa/mtERCYNERWleemkx//7rzwcf1GPcuAY4OtoYHZYQQohkSnGSpbUOVUqtxtzVKKNtU9vKlVwt6cHPD08xvsF4lJJ5X7ObM2fuUaaMB1ZWirlzW5E/f07KlpU1K4UQIrOxdEDHP5jHX8liaKnJ3x927WJ5+xIAdPPpZnBAIj09ehTKkCHbqFBhPgsXmqeaa9q0mCRYQgiRSVmaZE2Kfp6ilLJNpVhETFdh3ntUzleZMh5ljI5IpAOtNatXn6VMma/56qvf6dWrIh07yvdeCCEyO0sHvvcHHgLvAJ2VUheA4Dj7tda6yYsGl+2sXMmZsrk5+eQSX9T+wuhoRDrp2XM9P/54krJlPVi+/FXq1y9sdEhCCCFSgaVJVkPM82EpwA2oEWef4tlzaIlnie4qXDa8EuBL13JdjY5IpKHw8Eisra1QStGsWTHKlfNg+PDaspizEEJkIclOspRSPTG3UC0BriGJVOpavx4dEcFS95s0yNsALxcvoyMSaeTgwav067eZcePq8/rrFejZs6LRIQkhhEgDKWnJWgREAUu01kXSJJrsbOVKfquUm8shtxntM9HoaEQa8PUNYtSoXfzwwwny58+Ji4u90SEJIYRIQyntLpT5BNKCnx/s3s3SEeWwtgqgU9lORkckUtlPP53kvfe2ExAQwpAhNfnoo8Y4O8ui30IIkZVZOiZLpKb164mIimCl83VaFG5BLsdcRkckUtm9e4EUK+bGggVtqFJF1k8XQojswJIZ3/cmo5rcXZgSK1eyr1ou7oY9kGV0soigoHA+/vgAtWt70a6dN++9V5P33quJySRrDQohRHZhSUtWwyT2y92FKfHggbmrcJQ3jjbBtPNuZ3RE4gVt3XqRgQO3cuVKAMOG1aJdO29JroQQIhuyJMmScVmpaf16QlQka52u0s67HTlscxgdkbDQzZuPGDJkO2vWnKN4cTe2b3+dFi1KGB2WEEIIg1iSZBVN9Siys5Ur2VorF48iHtDdR7oKM7PFi/9i48a/GT++AWPH1sPBQRZzFkKI7MySBaKvpkUg2ZKvL+zZw9IxRXGzj6JFiRZGRyRS6OjRm4SHR1Gnjhfvv1+bV18tg7d3bqPDEkIIkQHIQBEjrVvHQ+tINttdo3PZztiaZBnIzOLhwxAGDdpKzZrfM2rULgDs7KwlwRJCCBErJS1Z1zBPRipSy6pVrK/jTmiUH93KdzM6GpEMWmtWrjzD0KE7uHPnCe+8U4XPP29qdFhCCCEyoGQnWTLLeyq7fx/27mXp2IIUyOlA/UL1jY5IJMPChX/yzjub8PHJw5o1XahTR5Y/EkIIkTiZjNQo69Zx1z6S3dbXGeYzDJOVLAycUYWGRnDnzhMKF3alWzcfQkIiePfdqtjYyPdMCCHEs8mYLKOsWsXKem5EEUU3H+kqzKj27btMxYrzadduORERUTg52TJoUA1JsIQQQiRJkiwjRHcVLqthT6lcpajiWcXoiEQC9+4F8uab63nppf8jMDCcyZMbYTLJFHFCCCGST7oLjbB2Lf+6RHHEdJtJPu+ilHx4ZyQ//3yNtm2X8ehRKMOH12Ly5MbkyCF3fgohhEgZSbKMsHIly+u5AgFyV2EGEhkZhclkhY9PHurXL8zkyY2oVCmf0WEJIYTIpKS7ML3duwf797OskjVVPatSKlcpoyPK9gIDwxg9eheNGy8mKkrj4mLPhg2vSYIlhBDihUiSld7WruVU7ihOm3zpXl6W0THa5s0XKFfuG6ZN+4VChVwICgo3OiQhhBBZhHQXpreVK1lazwXFI7qW62p0NNnWnTtPGDBgC+vWnadkSXd2736DJk2KGR2WEEKILERastLT3bvoA/tZ5qNpVKQRBZwLGB1RtmUyKX799QaTJjXk5Mn+kmAJIYRIddKSlZ7WruVIAc1Vq0eMk7mx0t1vv93gxx9P8tVXLfHwcOLSpfdwcLAxOiwhhBBZlLRkpaeVK1laNyc2Vja8WvZVo6PJNgICQhgwYAu1ay9k5cqzXLv2EEASLCGEEGlKkqz0cucOEYcOsNI7kpYlW+Lu4G50RFme1pqlS09RuvRc5s07Rt++VTl/fiCFC7saHZoQQohsQLoL08vatewpormvgmQZnXRy/34Q7767mWLF3Fi3riu1a8tizkIIIdKPJFnpZeVKltbJgZONpm2ptkZHk2WFhkawZs05uncvT548Thw40IsKFfJibS2NtkIIIdKXfPKkh9u3Cf7lAGuLhdG+dHucbJ2MjihL2rPnXypUmM/rr6/lyJHrAFSp4ikJlhBCCEPIp096WLuWzSXhiQqTCUjTwN27T+jRYy1Nmy4hJCSCDRtek65BIYQQhsswSZZSqrRSapdSKlApdUcpNU0p9dxVeZVSntH1TiilHiulbiilliqlCqdX3MmyciXLajmRyyEXzYo1MzqaLCUkJILKlRewfPlpRo6sw9mzA2jXztvosIQQQoiMMSZLKeUG7AUuAh2BAsAMwBEY9JxDq0bX/x/wK5AbGA/8rpTy0VrfT8u4k+X2bQJ+P8iWxibeKvsGNiaZNiA1XLv2kEKFXLC3t2batGZUqJCXChXyGh2WEEIIEStDJFlAP8AZ6KC19gNQSlkD3yilPtVa33rGcYeB0lrriJgCpdQvwDWgJ/Bl2oadDGvWsLYMhKlI6SpMBU+ehDFp0n5mzfqV7dt70LRpMXr0qGB0WEIIIcRTMkp3YUtgd0yCFW0l5viaP+sgrXVA3AQruuwGcB/InxaBptjKlSyt4YCXsxd1C9U1OppMbcOG85Qt+zVffnmE7t3LS8uVEEKIDC2jtGSVxtzlF0trHaCUuh29L9mUUqWAPMC51AvPQjdvcvvEIfa9pHjf5zWsVEbJaTMXrTWdO69izZpzlCqViz17evLSS0WNDksIIYR4roySZLkBAYmU+wPJnhpdKaWAr4BbwLIk6jpj7qKMkS+510m2NWtYWRailJauQgtERWmsrBRKKXx88lCxYl5GjaqLnV1G+bEVQgghni2rfVpNApoAL2utA5OoOxyYmKbRrFrF0up2lMldjIp5K6bppbKaX365zoABW/jmm9bUqePFpEmNjA5JCCGESJGM0n/lD7gkUu4G+CVS/hSl1DvABOBdrfWeZBwyA/CK86ievFCT6eZNLp05zO+5Q+nm0w1zI5tIip9fMO++u4m6df/HnTtPePgwxOiQhBBCCItklJas8yQYe6WUcgE8o/c9l1KqAzAPmKC1/l9S9QG01o+AR3HOkZJ4k7Z6NcvKmze7lZe1CpNj6dJTDB26HV/fIAYMqMaUKU1wdbU3OiwhhBDCIhklydoGfKCUctVaB0SXdQaigJ3PO1Ap1Qjz+KvvtNYfp2GMKaJXreSnGrbUyF+JEu4ljA4nU/jll+sUKODM5s3dqVGjgNHhCCGEEC8koyRZ84HBwHql1KeYJyP9Apgfd44spdQeoLDWukT06zLAesyTmC5RStWKc877WutL6RR/fDdu8NelXzjfDGbJgPdnCgmJ4LPPDtGhQxkqVcrHtGnNsLU1yVqDQgghsoQMkWRprf2VUk2AOZiTpsfA98C4BFVNxI+5JuaxXC7AzwnqLgZ6pUG4SVu9mmU+YIUVXcp1MSSEjG7XrksMGLCVf/7xQ2uoVCkfjo4yG77RtNb4+voSEhJCZGSk0eEIIYTFTCYT9vb25M6d27Bx0RkiyQLQWp8DmiZRp1GC14uARWkWlIWiVq5gWT1rGhdtiGdOT6PDyVDu3HnC8OE7WLbsNIULu7B5czdaty5ldFgCc4J18+ZNHj9+jK2tLSaTyeiQhBDCYmFhYTx58oTQ0FAKFChgSKKVYZKsLOP6dX6++SvXnWCSdBU+ZfLk/axadZbRo+syfnwDnJyeuwa4SEe+vr48fvyYPHnykCtXLqPDEUKIF/bgwQPu3buHr68vHh4e6X59SbJSW/RdhbZWNnQs09HoaDKEP/+8Tc6cdpQo4c5HHzVm4MAa+PjkMToskUBISAi2traSYAkhsoxcuXIREBBASIgx0wHJCONUFr5qOSsrmGhVsjWu9q5Gh2Oox49DGTZsO9Wqfcfo0bsB8PBwkgQrg4qMjJQuQiFElmMymQwbYyotWanp2jV2+f7OAzuy9TI6WmvWrj3HkCHbuXnzMb16VWLatOcOtxNCCCGyHEmyUtPq1SwtDzmsHWlTqo3R0Rhm2rSfGTNmD2XK5OannzrSsGERo0MSQggh0p10F6aioNXLWF9G0aHsqzjYOBgdTroKD4/Ezy8YgB49KvDZZ004caKfJFjCEJMmTUIpFfvIlSsX9erVY+vWrYnW9/f3Z+TIkRQvXhw7Ozvy5s1Lt27dOHfuXKL1nzx5wuTJk/Hx8cHR0REnJydq1KjBjBkzDBv7kV5mzpxJoUKFMJlMtG/fPtXPH/f79qzHokWLXugaJ06cYNKkSQQFBSX7mM6dOzNy5MgXum5mtGnTJipWrIi9vT2lSpXihx9+SNZxp0+fpk2bNnh4eODq6kqDBg3Yt29fvDoJ/5/GPObPnx9b58qVKzg5OXHlypXUfFvpRlqyUsvVq2x6dIxAm+zXVXj48DX69dtMsWJubNjwGgUKODNmTD2jwxLZnIODA3v37gXg1q1bfPrpp7Rt25ZDhw5Rp06d2Hp37tyhQYMG+Pv7M27cOCpXrsyNGzeYPn061atXZ+vWrTRo0CC2vq+vL40bN+b69esMHTqUevXMP+tHjhzh888/x2QyMWTIkPR9s+nk4sWLvP/++4wePZq2bduSO3fuVL/GkSNH4r2uXbs2gwcPpnv3/36vFi9e/IWuceLECSZPnsygQYNwdHRMsv7x48fZtGkT//777wtdN7M5fPgwHTp04O2332bWrFns3buXPn36kDNnTjp16vTM43x9fWnSpAnFihVj4cKF2Nra8tVXX9GyZUuOHj1K+fLlY+vG/X8ao1ixYrHbRYoUoVOnTkycOJHFixen/ptMa1preWgNUBDQ169f1xaZPl23ew3t8Zm7DosIs+wcmYyvb6Du02eDhkna03O6XrnytI6KijI6LGGhy5cv68uXLxsdRqqYOHGidnJyild248YNrZTSffv2jVfeoUMHbWdnp8+dOxev/MmTJ7pMmTK6QIECOjg4OLa8c+fO2tHRUZ86deqp6z548ED//PPPqfhOki8oKCjNr7Fp0yYN6EuXLr3wuUJCQnRkZGSS9QD9xRdfvPD14vrhhx80oO/fv5+s+j179tTt2rVLlWunx/cptTRv3lzXqVMnXlm3bt10mTJlnnvcsmXLNBDv90lQUJC2t7fXH330UWxZYv9PE3PgwAFtY2Oj7927l7I3EC2p323Xr1/XgAYK6lTOLaS7MJX4r13KtlKKLhW6YWPK+jOX79p1idKlv+aHH04weHANzp8fROfO5QybVVeIpBQoUAAPDw+uXbsWW3b16lXWr19Pz549KV063hr1ODk5MW7cOG7evMmqVati669evZp+/frh4+Pz1DXc3d3jtZIl5ty5c3Ts2BF3d3ccHR2pWLEiy5YtA8xdI0opVq9eHe+YoUOHUqRIkdjXixYtQinFkSNHaNasGU5OTowcOZJGjRrRps3T40Hnzp2Lg4MDDx8+BMx/XE+fPp1SpUphZ2dHsWLFmDlz5nPj7tWrF23btgXMLUlxu+2uXr1Kp06dcHFxwcnJiRYtWnDq1Kl4xxcpUoRBgwYxbdo0ChcujIODA35+fs+95rMsWrSIChUqYG9vT4ECBRg3bly8u8cCAgJ45513KFCgAPb29nh5efHaa6/FHtu7d28APDw8UErF+9omFBgYyJo1a55quTly5Ajt2rUjf/78ODk5UalSJZYsWRKvzv79+1FKsWXLFjp16oSzszOdO3eOjXHAgAF4enpiZ2dH1apV2bkz/lK9W7ZsoVmzZuTJkwdnZ2dq1qzJ9u3bLfqapVRoaCj79u2LjTfGa6+9xrlz557bfRceHg6Ai4tLbJm9vT22trYxjRopUq9ePXLlysXSpUtTfKzRpLswNVy5wpqQ44RbQTefbkZHk6a01iilKF7cndKlczNzZguqVctvdFhCJOnJkyf4+flRtGjR2LKDBw+itY5NHhKKKT948CBvvPEGhw4dQmvNyy+/bFEMFy9epHbt2nh5efHVV1+RL18+Tp8+HS/xS4nu3bvTt29fPvjgAxwdHTlx4gSDBw/Gz88Pd3f32HrLli2jVatWsR96Q4YM4fvvv2fcuHHUrFmTX375hdGjR+Pg4EC/fv0Svdb48eMpW7Yso0ePZu3atXh6elK8eHEeP35Mo0aNsLKyYv78+djb2zNlyhQaNGjAyZMn8fLyij3HmjVrKFmyJLNnz8ZkMuHk5JTi9zxjxgxGjRrFsGHD+PLLLzl37lxskvX5558DMHz4cLZt28bnn39OkSJFuH37Ntu2bQOgdevWfPjhh3zyySds374dFxcX7Ozsnnm9I0eOEBgYSN26deOVX716lbp169KvXz/s7e35+eef6dOnD1FRUbz55pvx6vbt25cePXqwbt06TCYTYWFhNGvWjLt37zJlyhQKFCjAjz/+SOvWrTl+/Hhsd9rly5dp27YtI0aMwMrKim3bttGqVSv27t1Lo0aNnhmz1jpZUxaYTKZn/mF86dIlwsPDn/rjo0yZMgCcP3/+mclpmzZtyJs3L++//z5TpkzBxsaG6dOno5SiR48e8eoGBwfj4eGBv78/pUqVYtiwYbzzzjvx6lhZWVGrVi127dqV6briJclKDdF3FRZ2zE9tr9pGR5MmgoPDmTLlEDduPGLRovYUK+bGoUO9jQ5LpIf+/SFBq0S6Kl8e5s2z6NCIiAjAPCZr1KhR5MyZM94v6Zs3bwJQqFChRI93dnbG1dWVGzduJKt+UiZNmoStrS0///wzzs7OADRtavn0Jv369WP06NGxr0uUKMHgwYNZs2ZN7AfV1atXOXLkCCtXrgTMH55z585l/vz59O3bNzaGoKAgJk+eTN++fbGyerqTo3jx4pQqZV4Cq3LlyrEfsF999RVXr17lzJkzsR/ADRs2pFChQsyaNYsvv/wy9hzh4eFs27bNouQK4PHjx0ycOJFRo0bx6aefAtCsWTNsbW0ZPnw4I0eOJFeuXPz+++907949XrIT05Ll4eERO6aratWqSY4rO3r0KDly5Ig3Tiju+cCc1DRo0IAbN26wYMGCp5Ksdu3aMXXq1NjXP/zwAydOnOCvv/6ibNmyALRo0YKLFy/y8ccfx36vBg0aFHtMVFQUjRs35syZM3z77bfPTbIOHDhA48aNn/u+APbt2/fM8/j7+wPg6uoar9zNzQ3gua2Qbm5uHDp0iDZt2pA/v/mP8Fy5crFt27Z4X8cSJUowdepUKleuTEhICEuXLqVv3748fPiQESNGxDtnxYoV+frrr5N8TxmNJFmp4OaGH9nfBEZX7omVyno9sNu3/8PAgVv5919/OncuS3h4JDY2MmmlyNgCAwOxsfmv695kMrFhwwa8vb1f+NyWdovv2bMnttsoNbRu3Tre61y5ctGsWTOWL18em2StWLGCHDlyxHYj7t5tnhj41VdfjU1CwZxoTZ06levXr1O4cOFkx3Do0CF8fHxiEywwd5s2a9aMw4cPx6vbqFEjixMsgF9++YUnT57QuXPnp2IPDg7m9OnTNGzYkCpVqrBo0SI8PT15+eWXE+3aTa7bt28nmoj5+/szceJENmzYwM2bN2NbjhJbMSHh92nnzp2UL1+eUqVKxXsfzZo148cff4x9fePGDcaNG8fu3bu5fft2bFdb1apVnxtz1apVOXr0aJLvLTX+LyTm3r17dOjQgeLFizNr1ixMJhPffvst7dq14+DBg7E/KwlbtVq3bk1YWBiffPIJQ4YMiff/N3fu3Pj6+hIeHh6vPKOTJOtFXb7Myoi/0Aq6lc9aXYW3bz9m6NAdrFx5hqJFXdm6tTstW5Y0OiyR3ixsRTKag4MDBw8eJCoqiosXLzJmzBh69uzJ6dOn8fQ0L9xeoEABAK5du0bFihWfOsfjx48JCAigYMGCT9WPadVJiQcPHsT+ZZ8a8ubN+1RZt27dePPNN7lz5w758uVj2bJldOjQAXt7e8B855fW+pktOClNsvz9/RONI2/evJw+fTrJeFPC19cXgCpVqiS6//r16wDMmTMHd3d3vvzyS0aOHImXlxdjx46lf//+Kb5mSEhIot2JvXr14pdffmHChAmUK1cOZ2dn5s2bx4oVK56qm/B9+/r68ueffyaaLMSsuhAVFUW7du14+PAhH330ESVKlMDJyYkJEyYk2b2cI0cOKlWqlOR7e94KDzEtVjHj+GLEtHDF7Y5OaNq0afj7+/PHH3/Efu2aNGlCuXLl+Pjjj587tqpLly6sXr2af/75J17iHnOekJAQSbKyleiuwnLOJSifp3zS9TMRP79gNm++wAcf1GPcuAY4OmaeH2whrKysqFatGgA1atTA29ubmjVr8tFHHzEvOnFs0KBB7MDkxMZlbd68ObZe3Po7duywqJsvV65c3Lp165n7YxKhsLCweOUxH2wJJdai9sorr2BnZ8fKlStp0aIFJ06c4LPPPovd7+7ujlKKw4cPY2v79ALtKW3dcHd35++//36q/O7du099EL/ojTEx51u7dm28sV4xYsbbubi4MGvWLGbNmsWpU6eYPXs2AwYMwMfHh/r166f4mgEBAfHKQkJC2Lx5MzNmzGDw4MGx5VFRUYmeI+H7dnd3p0KFCixcuPCZ1/3nn3/4888/Wb9+Pa+88kpseXBwcJIxp0Z3YfHixbGxseH8+fO0aNEitvz8+fMAT43Viuvs2bOULl06XnJqMpmoUKECly5dSjKuxAQEBGBra0vOnDktOt4okmS9oAtb/o9jjWFKtd5Z4s66P/64xd69lxk5si7lyuXh+vVhuLtnr4lVRdZUrVo1unXrxg8//MDEiRPJly8fhQsXpn379ixevJjhw4fHa50KCgpiypQpFCxYMPYOq0KFCtGpUyfmzZtH7969Y8fTxAgICODcuXPUrp342MymTZuyevVqpk6dmuiHRZ48ebCxsYk3CWpYWBgHDhxI9vvMmTMnbdq0YdmyZfj5+eHh4REvIWzSpAlgblV71oD/lKhXrx6rV6/m77//jk3Q/P392b17d+yYr9RSu3ZtHB0duXHjBh06dEjWMeXLl2fmzJksXLiQc+fOUb9+/djkMjkTx3p7e3P//n0CAwNjuzpDQ0OJioqKl6Q+fvyYjRs3Jiumpk2bsnXrVvLnz//Mls2YZCruNa5evcrPP/+cZCtqanQX2tnZ0bhxY1avXh1vHOOKFSsoU6bMc+/ILFy4MBs2bCAkJCT2D4fIyEj++uuvJFvYli9fjqurKyVKlIhXfuXKFYtajw2X2nNCZNYHlsyTdemSntQQzST0v37/Jv+4DOjhwxA9ePBWbWU1WXt4TNN+fplnLheROrL6PFlaa33+/HltMpn06NGjY8tu376tS5YsqfPkyaNnzZqlDxw4oJcuXaqrVKminZyc9IEDB+Kd4/79+7pcuXLa1dVVT548We/evVvv3r1bT5kyRefLl0/PmjXrmXFduHBBu7i46AoVKugff/xR79mzR8+ZM0dPnTo1tk7Xrl21q6urXrRokd68ebN++eWXdaFChXThwoVj6yQ1z9PatWs1oD09PfWAAQOe2j9o0CDt4uKiP/nkE71r1y69detWPWvWLP3KK688M3attV63bt1T8x89evRIFylSRBcvXlwvW7ZMr1u3TlerVk27urrqa9euxdYrXLiwHjhw4HPPnxgSzJM1ffp0bW9vr0eNGqW3bt2qd+zYoefNm6dffvllHRgYqLXWuk6dOvqLL77Q27Zt0zt37tQ9evTQtra2+uzZs1prrY8fP64BPWbMGP3rr7/qkydPPvP6f//9twb0oUOH4pVXr15dFypUSK9atUqvW7dO16xZUxctWjTez92+ffs0oI8ePRrv2JCQEF21alVdsmRJvWDBAr1v3z69bt06PWHCBD1mzJjYOgULFtQVKlTQmzZt0suWLdOlSpXSRYoU0eXKlUvx19EShw4d0iaTSffv31/v27dPT5gwQSul9MqVK+PVM5lM+q233op9fezYMW1tba2bN2+uN27cqLds2aJfeeUVrZSK9/+pSpUqevbs2XrHjh16/fr1unPnzhpI9P9QjRo1dP/+/S16H0bOk2V4cpNRHpYkWVGffaa9B6FrzamY7GMymqioKL1y5Wnt6TldwyTdp88G7esbaHRYwgDZIcnSWuvXX39dOzs764CAgNgyPz8/PWLECF20aFFtY2OjPTw8dNeuXWM/lBN69OiRnjRpki5btqy2t7fXjo6Ounr16nrmzJnxJi5NzJkzZ3S7du20s7OzdnR01JUqVdLLly+P3X/v3j3dvn177ezsrAsUKKBnzZqlhwwZkqIkKyQkRLu4uCSaHGht/n8/Z84c7ePjo21tbbW7u7uuXbu2njFjxnNjTyzJ0lrrK1eu6I4dO+qcOXNqR0dH3axZs6cSl9RKsrQ2T3ZZvXp17eDgoJ2dnXXlypX1+PHjdXh4uNZa65EjR+ry5cvrHDlyaGdnZ123bl29Y8eOeOeYNGmSLliwoLaysor3tU1M+fLl9QcffBCv7OLFi/qll17Sjo6O2svLS3/xxRdP/dw9K8nSWuuHDx/qYcOG6UKFCmkbGxvt6empW7VqpTdv3hxb5/fff9fVq1fX9vb2umTJknrx4sX6zTffTLckS2utN2zYoMuXL69tbW11iRIl9MKFC5+qA+g333wzXtmePXt0w4YNtbu7u3Z1ddV169bV27Zti1enS5cuukiRItre3l47ODjoGjVq6B9//PGp89+9e1ebTCa9Z88ei96DkUmW0jrlE4NlRUqpgsD169evxw5yTcrxJmWp2uAcX738FYNrDk76gAzo7799KVPma8qW9WD+/DbUq2fZreki84uZXPB53QBCZEdz5sxh9uzZXLx4MUsMC8lsvv76a2bOnGnx1z+p3203btyIGePnpbW+YXGgich68w2kl0uXWGp9DisUXcp1MTqaFAkLi2T3bvMaXN7eudmxowfHj78rCZYQQiTi7bffJjg4mE2bNhkdSrYTFRXF7NmzmTBhQqZMcCXJslDUyhUs94Em+eqQN8eL3Zacng4evEqlSvNp0eJHLl58AECzZsWxtZV5r4QQIjEODg4sWrToqbs+Rdq7desWvXr1empOrcxC7i600KF9i7hZFz6p+bbRoSSLr28QI0fuYtGiExQokJNVqzpTosSz5zkRQgjxn2bNmhkdQrZUsGBBPvjgA6PDsJgkWZb45x+W2l3EDms6lE7ebcRGevAgiNKl5+LvH8LQoTX56KPG5Mz57LW6hBBCCPHiJMmyQNjKZawqB228muBi75L0AQbx8wvG3d2BXLkcGTGiDs2bF6dKFU+jwxJCCCGyBRmTZYGdB3/A3wG61Xon6coGCAoKZ+zY3RQqNJO//zYvQzFmTD1JsIQQQoh0JC1ZKXXxIktzXMYZO1qVbGV0NE/ZuvUiAwdu5cqVAF57zQcXF3ujQxJCCCGyJUmyUihw5U9s8IYuxVrjYJNxlpsJC4uke/c1rFlzjuLF3dixowfNmxc3OiwhhBAi25LuwhTaeOQHgmyhe51+RocSj62tCTs7a8aPb8CpU/0lwRJCCCEMJklWSly4wFLna+QhB42LJr3CeVo7evQmDRr8wJUrAQD8+GMHPvqoMQ4ONsYGJoQQQghJslLiwcrFbC8BXb07Ym1lXE/rw4chDBq0lZo1v+f8eV/+/dcfIFPOhitEWpg0aRJKqdhHrly5qFevHlu3bk20vr+/PyNHjqR48eLY2dmRN29eunXrxrlz5xKt/+TJEyZPnoyPjw+Ojo44OTlRo0YNZsyYQUhISFq+NcPNnDmTQoUKYTKZaN++faqfP+737VmPRYsWWXz+Ro0a0aZNm1SL99SpU+TMmZP79++n2jkzg4cPH9KnTx/c3d3JmTMnnTp14vbt20keFxkZybRp0yhdujSOjo4UK1aMkSNH8uTJk3j1Evu+58uXL16dd955h3feyZg3oMWQMVkpsPrYYiIqQ/d6/Q25vtaalSvPMHToDu7cecI771Th88+b4u6eccaGCZFRODg4sHfvXsA8a/Snn35K27ZtOXToEHXq1Imtd+fOHRo0aIC/vz/jxo2jcuXK3Lhxg+nTp1O9enW2bt1KgwYNYuv7+vrSuHFjrl+/ztChQ6lXrx4AR44c4fPPP8dkMjFkyJD0fbPp5OLFi7z//vuMHj2atm3bkjt37lS/xpEjR+K9rl27NoMHD6Z79+6xZcWLWz4c4ptvvsFkSr0VLj788EN69eqFh4dHqp0zM+jatStnzpxh/vz52NvbM27cOFq2bMmxY8ewtn52ajFlyhQ+/vhjPv74Y2rWrMnp06f54IMPuHXrFj/99FO8ugm/77a2tvH2jx49mnLlyjFq1ChKliyZum8wtaT2itOZ9QEUBPT169cTX6b7/HndsBe66GR3HRUVlXidNBYVFaVffvlH7ePzjf7552uGxCCyrqRWqs9MJk6cqJ2cnOKV3bhxQyuldN++feOVd+jQQdvZ2elz587FK3/y5IkuU6aMLlCggA4ODo4t79y5s3Z0dNSnTp166roPHjzQP//8cyq+k+QLCgpK82ts2rRJA/rSpUsvfK6QkBAdGRmZZD1Af/HFF8+tkx7vPTGXLl3SSil9/PjxFz5XRESEDgsLS4Wo0t4vv/yiAb1jx47YsvPnz2ullF6xYsVzj/X29tZvvvlmvLIJEyZoOzs7HR4eHluWnO+71lo3btxYDxky5Ll1kvrddv36dQ1ooKBO5dxCuguT6frK7zlYGLr5vJau3XKhoRF89tkhbt9+jFKKJUs6cPx4X+rU8Uq3GITICgoUKICHhwfXrl2LLbt69Srr16+nZ8+elC5dOl59Jycnxo0bx82bN1m1alVs/dWrV9OvXz98fHyeuoa7u3u8VrLEnDt3jo4dO+Lu7o6joyMVK1Zk2bJlAFy5cgWlFKtXr453zNChQylSpEjs60WLFqGU4siRIzRr1gwnJydGjhz5zK6wuXPn4uDgwMOHDwHzH9fTp0+nVKlS2NnZUaxYMWbOnPncuHv16kXbtm0Bc0tS3G67q1ev0qlTJ1xcXHBycqJFixacOnUq3vFFihRh0KBBTJs2jcKFC+Pg4ICfn99zr5mYSZMmkSNHDn7//Xdq166Nvb09X3/9NQBjxoyhfPny5MiRgwIFCtCtW7enurASfo1iznfq1Cnq1auHo6MjPj4+7NixI8lY/u///o9ixYpRuXLleOUpiWPx4sV4e3tjZ2fHX3/9BcCWLVuoWbMmDg4OeHh40L9/fwIDA2OPDQwMZNCgQXh7e+Po6EiRIkXo169f7Pc3rW3btg1XV9d4Sw15e3tTqVKlZ3bJxwgPD8fFJf4k3i4uLkRFRVkUS+fOnfnpp5+IiIiw6Pi0JklWMq048SNaQfd6A9Ltmvv2XaZixfl88MFeli41/8LKndsRGxtZzFmIlHry5Al+fn4ULVo0tuzgwYNorWOTh4Riyg8ePAjAoUOH0Frz8ssvWxTDxYsXqV27NhcvXuSrr75i48aN9O7dO17ilxLdu3fnpZdeYvPmzbzxxht069aNnTt3PpW8LFu2jFatWsV+uA0ZMoQJEybw5ptvsmXLFnr16sXo0aOZP3/+M681fvx4pk6dCsDatWs5cuQIrVu35vHjxzRq1Ig///yT+fPn8+OPP/LgwQMaNGjA9evX451jzZo1bN68mdmzZ7NhwwacnJwset9hYWF0796dHj16sG3bNpo3bw7AvXv3+OCDD9iyZQuzZ8/mypUrNGzYMMkP4PDwcF5//XV69erFunXryJMnD6+++ioPHjx47nG7d+9ONKlObhzHjh3jiy++4KOPPmLr1q14eXmxevVq2rVrR/ny5Vm3bh3Tpk1j7dq19OnTJ/a4oKAgIiMjmTJlCtu2beOTTz7hwIEDyRojFxkZSURExHMfkZGRzz3H+fPn8fb2fqrBoUyZMpw/f/65x7799tssWbKEvXv38uTJE37//XfmzJlDv379nupm/Oyzz7CxscHV1ZWuXbsm+v+kTp06+Pr6cuLEiSTfuxFkTFZynD/PUo87VFCelMtTLs0vd+9eICNG7GTJkpMULOjMunVdad++dNIHCpEG+m/uz6l7p5KumEbK5ynPvDbzLDo25kPt1q1bjBo1ipw5c8YbL3Xz5k0AChUqlOjxzs7OuLq6cuPGjWTVT8qkSZOwtbXl559/xtnZGYCmTZtadC6Afv36MXr06NjXJUqUYPDgwaxZsyZ2QPDVq1c5cuQIK1euBODSpUvMnTuX+fPn07dv39gYgoKCmDx5Mn379sXK6um/v4sXL06pUqUAqFy5cmzL2ldffcXVq1c5c+YMZcqUAaBhw4YUKlSIWbNm8eWXX8aeIzw8nG3btlmcXMU9z5QpU+jatWu88v/973+x25GRkdSuXZuCBQuyd+/e2EQsMWFhYXz++ee0amWeYNrb25uiRYuybds2evTokegxWmuOHTuWaGKT3Dj8/Pw4evQoXl5eseccMWIEXbt25fvvv4+t5+npSatWrRg/fjzlypXDw8ODefP++z8RERFB0aJFqVevHhcuXIj9PiWmSZMmHDhw4Jn7wfz9279//zP3+/v74+rq+lS5m5tbkq2TY8eOJTQ0lKZNm8YM1aFHjx7MmjUrXr2ePXvSpk0b8ubNy+nTp/n444+pV68ef/31F25ubrH1ypUrh8lk4rfffqNatWrPvbYRpCUrGc6v/IY/PaFb5TfS5XrvvLOJpUtPMXx4Lc6dGygJlhAWCAwMxMbGBhsbGwoXLszq1atZsmQJ3t7eL3xuS4cM7Nmzh06dOsUmWC+qdevW8V7nypWLZs2asXz58tiyFStWkCNHjtgust27dwPw6quvxmu9aNq0KXfu3Hmq9Skphw4dwsfHJzbBAnO3abNmzTh8+HC8uo0aNXrhBCtGwvcO5m6sOnXq4OLigrW1NQULFgTgwoULzz2XlZVVvGS3SJEiODg4xCbXifH39yc0NDTRAe/JjaNChQqxCVbM/qtXr9KlS5d435uGDRtiZWXFsWPHYusuWbKEypUrkyNHDmxsbGJvwEjqvS5YsICjR48+97FgwYLnnuNFzJ07l9mzZzNz5kwOHDjAN998w7Zt2xg8eHC8eosXL6Zz5840aNCAAQMGsGPHDm7dusV3330Xr561tTWurq7JurPRCNKSlQzLziyHsvBa/bS7q/DUqbsUKOCMu7sD06Y1ZfLkRlSqlC/J44RIa5a2IhnNwcGBgwcPEhUVxcWLFxkzZgw9e/bk9OnTeHqa1/EsUKAAANeuXaNixYpPnePx48cEBATEfkjGrf+81oJnefDgAfnz57f0LT0lb968T5V169aNN998kzt37pAvXz6WLVtGhw4dsLc3L7Hl6+uL1vqZdwZev36dwoULJzsGf3//ROOIaYFIKl5LODo6kiNHjnhlR48epV27drzyyiuMGTOGPHnyoJSiVq1aSU6r4eDg8NSda7a2ts89LmafnZ2dxXEk/Hr4+prXmu3QoUOi14xJgNetW0fPnj3p27cvU6ZMIVeuXNy+fZsOHTok+V5LlCgR24L0LEn9EeHm5pZoMu7v74+7u/szj3vw4AEjRozgiy++iE2qGjRogLOzMz169GDIkCHP/H9VoUIFvL29+eOPP57aZ2dnR3Bw8HNjNookWUnQZ8+yNO996loVoYhrkVQ/f2BgGB99dIAZM36lX7+qzJnTCm/v1L8tWojsxsrKKrb7oEaNGnh7e1OzZk0++uij2K6WBg0aoJRiy5YtiY7L2rx5c2y9uPV37NhhUTdfrly5uHXr1jP3xyRCYWFh8cr9/f0TrZ/Yh+Err7yCnZ0dK1eupEWLFpw4cYLPPvssdr+7uztKKQ4fPvxUYgGkuKXP3d2dv//++6nyu3fvPvWBm1o3DSV2nnXr1uHi4sLKlStjuzuvXr2aKtdLTMx7CwgIsDiOhO8j5pxz586lZs2aT9WPSdBXrVpFpUqV4rU4JdUFGCM1ugtLly7N7t270VrHew/nz5+nfPnyzzzu0qVLhIaGUqlSpXjlMTcOXLp0yaI/XgICAsiVK1eKj0sPkmQl4Y9Vs/knFwyt/laqn3vTpr8ZNGgb16495PXXy/Phhw2SPkgIYZFq1arRrVs3fvjhByZOnEi+fPkoXLgw7du3Z/HixQwfPjzeL/igoCCmTJlCwYIF6dy5M2Aei9WpUyfmzZtH7969KVu2bLxrBAQEcO7cOWrXrp1oDE2bNmX16tVMnTqVnDlzPrU/T5482NjYxJsENSwsLNkfoAA5c+akTZs2LFu2DD8/Pzw8POIlhE2aNAHMrQrPGvCfEvXq1WP16tX8/fffsQmav78/u3fvjh3zlR6Cg4OxsbGJ96GfcN6l1GRvb0+hQoW4fPlyqsVRunRpChYsyL///svAgQOfWS84OPipBDm511iwYAGPHz9+bp3EfjbjatmyJR9//DF79uyJ/dm6cOECf/75Z7wxggnFtJAeP36c+vXrx5bHtE7FvYM2oRMnTvD333/Tu3fveOX3798nKCgoVYYBpAVJspKw9MIaTCWgc/13U/W848bt4dNPD1OypDu7d79BkybFUvX8QoinjR8/nuXLlzNr1iw+//xzwDw5ZYMGDahfvz4ffPABlStX5ubNm0yfPp0rV66wdevW2BammPqNGjWibt26DBs2jLp16wLw22+/MWfOHMaMGfPMJGvixIls3ryZevXqMWrUKDw9PTl79ixBQUGMGjUKKysrOnbsyNy5cylRogS5c+dm7ty5T7UYJKVbt2507NiRq1ev0rlz53h3bZUqVYqBAwfyxhtvMHLkSGrWrEl4eDgXLlxg3759rF+/PkVf0969ezNz5kxat27NJ598gr29PVOmTMHa2pqhQ4em6FwvolmzZsyaNYvBgwfToUMHjhw5wpIlS9L0mnXr1n2q++pF4lBKMWPGDLp3705gYCCtW7fGycmJq1evsmXLFj799FNKlSpFs2bNGDhwIB9//DG1a9dm69at7NmzJ1nXSI1kpHbt2rRo0YK33nqLL7/8MnYy0goVKtCxY8fYeh999BEfffQRly5donDhwuTNm5f27dszfvx4IiIiqFKlCmfOnGHixIk0bdo0dlzf9OnTuXTpEo0aNSJPnjycPn2aKVOm4OXlxdtvvx0vlphxajFj0jKc1J54K7M+SGQy0ohTf2nP99Evf+z9zEnMUiI8PFIHBZknmzt27KaeNGmfDg4OT+IoIdJHVp+MNMbrr7+unZ2ddUBAQGyZn5+fHjFihC5atKi2sbHRHh4eumvXrvrs2bOJnuPRo0d60qRJumzZstre3l47Ojrq6tWr65kzZ8abuDQxZ86c0e3atdPOzs7a0dFRV6pUSS9fvjx2/71793T79u21s7OzLlCggJ41a5YeMmSILly4cGydH374QQP6/v37iV4jJCREu7i4aEAfOnToqf1RUVF6zpw52sfHR9va2mp3d3ddu3ZtPWPGjOfGvm7dOg089XNy5coV3bFjR50zZ07t6OiomzVrpk+ePBmvTuHChfXAgQOfe/7EkGBSyud9b6dOnaoLFiwYG8OFCxeeOr5hw4a6devWSZ7PxcVFT5w48bmxrVmzRtvb2+tHjx69cBxx7dy5Uzds2FA7OTlpJycnXa5cOf3+++/H/sxGRETo999/X3t4eOicOXPqTp066V9//VUDetWqVc+NObUEBATot956S7u6uuocOXLojh076ps3b8arM3HixKd+Xh4+fKhHjBihixcvru3t7XXRokX14MGDtZ+fX2ydjRs36lq1amk3NzdtbW2tPT099VtvvaVv3br1VByDBw/W9evXf26sRk5GqnQSA+CyC6VUQeD69evXYwe57p30Jk3U//F/9WfwxkvDXuj8v/12g3ff3UzjxkWYOdOyOXaESEtXrlwBnt9kL4T4T3h4OIUKFWLq1Kn07NnT6HCynYiICAoVKsTnn3/+3K9/Ur/bbty4EXOXp5fW+tm3lFpApnB4jmWXN2IfqWhf9+2kKz+Dv38w/ftvpnbthdy+/YTq1QukYoRCCCGMYmNjw5gxY5g9e7bRoWRLS5cuJUeOHPHWN8xoZEzWM4Se/JPVngG0tatITrvnDwJ8li1bLvDWWxu5fz+Qd9+tyqefNsHNTRZzFkKIrKJfv348evQIX1/fNFkwWzyblZUV//vf/567ILXRMm5kBtu+ZioBDtC9wbPv8EhKzpx25M+fkw0bXqNWrYKpGJ0QQoiMwM7OjvHjxxsdRrb0rNn4MxJJshKjNctubMPF00TL2snvZw8JiWDq1MOYTFZ8+GEDGjQozB9/9MXKKv0WlBZCCCFExiBJViIen/idjZ6P6OZYAztru6QPAHbv/pcBA7Zw8aIfXbqUi73lWhIsIYQQInuSge+J2LD+M4JtoHuToUnWvXv3CT16rKVZsyWEhkayYcNrrFjRKdVmNhYivZhMJiIjI40OQwghUlVkZCQmk8mQa0uSlZDWLL2zm3yhNjSq3iXJ6n/+eYfly08zcmQdzp4dQLt2GXPWWSGSYm9vT1hYGA/+v707j5ajLPM4/v1BSIKYfSEQTEAiBFQEozNsIwEBWXQGdILHgCwuDCoKSJRFlE1lGIbluCAHGCBmRHEQcUQZwxbMiBE8A2iQKC6EJCYkxCyQ7YbwzB/v26TS6Zvbt293urn9+5xT53a/XW/VU/WevvX0W29VLV3a7FDMzOpi6dKldHR0bHJD4a3JpwvLLP3NLKaPWsWZ/d/FtttUznyffHIRTz21hMmT38pRR43jT3/6DGPHDt66gZrV2fDhw1m3bh2LFy9m+fLlTfvlZ2ZWDxs2bKCjo4MBAwY07cpP92SV+cl917NhG5h81JTNPnvppQ6mTJnOhAk38vnP38e6dS8DOMGyXkESo0ePZvjw4RUfHGxm9lrSt29fhg8fzujRo5s2hKdlerIkjQe+DhwIvAh8G7goIjq6qCfgPOCTwAjgCeCciJhVSxx3L5vF7v36885937tp+d1z+PSn72X+/JWcfPLbuOqqI+jXr2V2n1ldSGLEiBHNDsPMrFdoiZ4sSUOAB4G+wPuBC4HTgWuqqH4ecClwLfBeYCEwXVJNT1x+bNhaJg89ZJOsd+bMuRx//B3ssMN2PPjgyUydehwjR+5Qy+LNzMysTbRKV8wZwEDg+Ij4G4CkPsD1kr4aEX+tVElSf+AC4OqIuDaXzQT+AEwh9W5124eOPZ/16zcwe/Zi9ttvJw4+eAzTph3PpEl7u/fKzMzMqtISPVnA0cD9pQQr+z4pviO3UO9AUnL2/VJBPr14F3BMLYHsvWp7lq3cnQkTbmTixKm88MJqJHHSSfs4wTIzM7OqtUqSNR6YUyyIiOWkU3/ju6hHeV3gaWCMpG4/KLDvox/koINuYcmS1dx443sZNszPGjQzM7Pua5WumSHA8grly4ChXdRbFxFrK9RT/nxNpYqSBpJ6wUpGAzzxRB9OOWU3Pve5Axk0qD8LFiyobgvMzMzsNWfhwoWll3W/b02rJFnN8Fng4s2Lb2bq1JuZOnWrx2NmZmbNsyswt54LbJUkaxkwqEL5EOBvFcqL9fpJ6l/WmzUEiPx5Z64Bbi68HwP8AtgfcPdV6xgFPAa8E1jU5FhsU26b1uR2aU1ul9Y1GpgFzKv3glslyZpD2dgrSYOAndh8vFV5PYA9gScL5eOB5yKi4qlCgIhYCawsrK/0ckFEzK86cmuoQrsscru0FrdNa3K7tCa3S+sqtM0W78tZi1YZ+H4vcLikwYWyScArwPQt1HuElChNKhVI2o50r62f1j9MMzMzs+q0SpJ1A+ku73dLOlLSacBVwA3Fe2RJekDSH0vv8ynCK4Apks6SdBjwXWAY8O9bdQvMzMzMClridGFELJP0btJjde4mJVw3A18om3VbNo/5StKVhFPY+Fid90TEn7sZxkrSneNXdjWjbVVul9bltmlNbpfW5HZpXQ1rG0VEvZdpZmZm1vZa5XShmZmZWa/iJMvMzMysAZxkmZmZmTWAkywzMzOzBmiLJEvSeEn3SVolaZGkf5PUt4p6knS+pOckrZH0S0n7b42Y20Et7SJppzzfE5JelDRf0u2Sxm6tuNtBrd+ZsmWcLSkk3dOoONtNT9pF0mhJUyUtyf/PnpZ0YqNjbgc9OMYMk3RDPsaskjRb0hlbI+Z2IGlc3r9PSHpZ0uwq69Xt2N8St3BoJElDgAeBZ0g3KR1NeqTO64Azu6h+HumyzvOB3wCfAqZL2reGW0RYQQ/aZUKe/xbSYxCGA18EHpX0lohY0si420EPvzOlZYwiPRt0cYPCbDs9aRdJOwG/BH4PnE66VP3NQL8GhtwWevh9+S/SE0ouBJ4DjgG+JWlDRNzUsKDbx5uBY4FfkTqVqu1Yqt+xPyJ69QRcALwEDC2UnQ68DOy8hXr9gRXAVwtlfYFngeubvV2v9akH7TIY6FNWtgvp6QDnNnu7esNUa9uULePbwFRgBnBPs7epN0w9aRdgGunZrNs2ezt629SD/2WjSM/YPbWs/GHggWZvV2+YgG0Kr28DZldRp67H/nY4XXg0cH9EFB80/X1SRnvkFuodCAzM8wIQER3AXaRfG9YzNbVLRCyPiJfLyuYDS4CdGxFoG6r1OwOApIOB40i/Aq1+amoXSQOBE0gHiA2NDbEt1fp92S7/XVFWvoJ0g23roYh4pYZqdT32t0OSNZ6yh0xHxHJgIWUPpa5Qj/K6wNPAGEnb1yvANlVru2xG0h7ASFLbWM/V3DaStgW+AXwlIhY2KsA2VWu7vJ30S3y9pIclrc/jhq7Mz3q1nqmpXSJiHunZvBdK2lvSAEknkBKzbzYuXOtCXY/97ZBkDQGWVyhfBgztot66SM9HLK+n/LnVrtZ22YTS49O/BvyV9NxK67metM0ngR2Aa+sck9XeLqPy35uBX5MO4tcCZwOX1S+8ttWT78v7geeBp0jj5G4HzomIH9QzQOuWuh77e/3Ad+v1LgHeDRwVEauaHEtbkzSSdNA+OXevW2so/Zi+PyLOza8fkjQAmCLpsohY06TY2lb+gXgr8CZgMqnn6wjgOknLIuJ7zYzP6qMdkqxlwKAK5UOAv1UoL9brJ6l/WUY7hDRYcVn9QmxLtbbLqyR9HPgS8NGIeKCOsbW7WtvmMtKVODMlDc5lfYA++f1L5ePprFt68r8M0hVwRQ8AXwDGAb/tcXTtq9Z2ORaYBOwTEaX9PyP/WLkacJLVHHU99rfD6cI5lJ0XlzQI2InNz7mW1wPYs6x8PPCcf/n1WK3tUpr3eOBbwJci4paGRNi+am2b8cC7SP+EStNBwHvy68MbEWwbqbVdftfFcvv3MK52V2u77A1sAMrv3fQ4sLOk19UzSKtaXY/97ZBk3QscXvhlDenXwyukQYedeYR0jnxSqSAPEn0/8NP6h9l2am0XJE0kjb+6KSIub1B87azWtjkbOLRsepJ0P7NDgUcbEGs7qaldImIuqaeqPMk9AlhD10mYbVmt35e5wLbAPmXlE4DFEbG6nkFa1ep77G/2fSy2wn0yhpAGRc8gDfg8jfSr+htl8z0A/LGs7HxgLXAWcBhwZ975b2z2dr3Wp1rbBdiLNMj0t6RLbfcvTLs3e7t6w9ST70yFZc3A98lqersA7yMd9K8jJVcXAh3Al5u9Xa/1qQf/ywaQEq1ngJNIY0uvJPVuXdTs7eoNE+mGsP+cp4dIN3wtvR9RqV1yWd2O/U3fCVtpR+8F3A+sJl3JcRXQt2yeGcCzZWUi3WhuXt7hs4ADmr09vWWqpV2AU0nnxStNtzV7m3rLVOt3psJynGS1SLsAHySdmlpHurHiBYCavU29YerBMWYccAewAFiV2+csfNPYerXLrls4XkzcQrvU7divvEAzMzMzq6N2GJNlZmZmttU5yTIzMzNrACdZZmZmZg3gJMvMzMysAZxkmZmZmTWAkywzMzOzBnCSZWZmZtYATrLMzMzMGsBJllkvIOk2SbGFadduLu/ZXG9GYyLudL2VYl8h6SFJxzRwva/uv0LZYEmX5Gli2fy7FuK7pFFxdRLrxAr7qCO32Q2SRvZg2Wfn7T21jiGbta0+zQ7AzKwLA4GJwERJJ0bE7VtpvYOBiwvvZ2yl9dZiO2As8C/AAZLeHhEbaljO2Xk5DwO31S06szblniyz3ufQiFDZ9Gyzg+qmuREhoD8wpVB+RSNWFhGnlvZVlfM/W9i3lzQipipNzTHvAfwll+1Deni6mTWZkyyzNpFPf31H0tOSlktaL2mRpDslvbmK+hMk3ZPrrMt/H5L0sbL53i3pZ5KW5fl+L+kiSdt1N+aIWAdcA6zIRWMkjcjr2UbSmZIel7Ra0ipJj5af6pI0TtL3JC3I8SyR9IikCwrzbHK6MJ8C/EthMRcXTs1NrHS6UNJT+f3/la3/lMK8R+cySTpD0q9z3KslzZJ0Qnf3Ud5PzwA/LBS9obD+o3J7zJe0RtJaSXMkXS5p+zzPxLztY3O1QyqdDpU0SdJMSSvzcp6U9AlJVSWnZu3GpwvN2sdgYHJZ2Y7AB4BDJe0VEYsrVZS0A/AzYFhZ3R2BVcDNeb5TgVtIT7Ev2QO4HNhf0vuitqfSVzqITwVOKit7J3CrpL0j4vO57MfA+MI8w/M0kPr2jE3Ly9tP0riI+GMuLyVOi4Dp+fUtwKll9f8euEPS2Ii4qob1F/dRsR33B44sm3dP4CJgNzbfh5UXLl0MXFJWvA9wPfAW4FPdiNWsLbgny6z3eahsUPQTuXwZKaF6A+k03OuBj+fPhrJ5AlY0no0J1geAvsAuwD+RkhgkvR64jnSwvzev53XAhbnesUC3Bq9L6gecS0qIAOZFxBJJ72JjcvDLHMubgDm5bIqkPSUNY2OC9VmgHzCKlHR8u7P15lOAuxWKLi2cHpzRSbXvAK/k15Ny/IOBw3PZdyNig6SD2ZhgfQUYREr6Sj1Rl+W4qybpTcDx+e3zwP8WPv4J6fThCNLYrZ2An+bPJksaGhEz8mnHubn84eLpUKULJ76YP7sVGElqk2/msk9Kekt3YjZrB+7JMmsTEbEiHywvIvUu7VA2y55bqL4A2ABsS+qxGAc8BfwiIpbmeQ4kJQwARwPzKiznMNJBvytjVbjSr6B0oD+6UPaViFgAIOlq4CZSonckqZdlJSkhmEza5qeAWRFxXxVxVC0i5ildjXkYqffqCuA4UkIKqacLNk00v5Cnov6kffnjKlZ7iqRTCu/nAB+OiLWFsgXAl0nJ3ihSolUiUnL6qy7WcySp7QFOy1O5Q4HZVcRs1jbck2XW+5QPfN8XQNI5wNXAfmyeYAFs39kCI2IR8BnS2KjDgCuBe4BF+TQSpJ6Srgyteis2ehH4OXBcREzNZcMLn8/r5PWIfIXdaaTenXeQTlveBSyQdFMNsXSllEjtK2kcuUcLmB0Rj5fiqmI5tewnSD2Hr/54lrQNqZ1OI/UsVhoX12m7FzQyZrNey0mWWfsoHfDXksb/9AHeWm3liLiedLD9O+BEUo9UH9Kg8F2AJYXZL6hwhaOAj1S5urmFegMj4pCI+FHh8xcKr3cpvH5D+TwRcRewM7AvqYdpGqkH52OSDtrSJlcZa9EPgNX59enAEfn1tMI8xf10QIV9tE0hmezKVFLiNAl4GRgD/FBS6fTqOFJSDXA/sGNex9WdLK+zbS7G/KFOYr60ypjN2oaTLLP20S//DVLv0GA2H8hckaQdJf0r8Hbgz6Rk4pHSx6SepUfYeBXgZyUdKqmfpJGSTpD0czZevdZT/1N4faGk0ZLeSBp3BWkbp+fYvw78A7AQ+BEbB5/DlntolhVej6/m6siIeBG4O789h5QAvUIar1Vyb+H1NZL2ktRX0hslfZqUDFUtIl6OiDtJp0YhnRL8XH7drzDrOmCNpHcAH+5kcaVtHiNpUKF8Oul0McClkt6RY95F0keAxzGzzTjJMmsfpTE+2wO/I/X07Ftl3e2B84BZud5a0qBtSKfofhcRL5ESiyAlLw/m+Z4H7iAlOnUREQ8D381vDwLmA38C9spl10ZEaRD8maQbiT5PSjRKvUor8vZ0to6VwB/y2w8CHflCgq7GspaWX5rvwdKYsbzcmWy80ecBpLZYl+P/GrB7F8vvzFdJV3oCnJUHz88BSlc5Hksan/YYKcmu5LH8dzdged7ew/N91i7Pn+2R51tHavv/AN5WY8xmvZqTLLP2cQXp6r/FwEvAncCHqqy7lJQAPE7q7VhPGlB9O3B4RHQARMStpFNkP8vzdQDPka5m+zjw1/psCpCuLjwbeJKUzK0Bfg18NCLOLcx3JWlg9ws57kXAf+e4F3WxjlNICcWabsR1X15HybQK83wEOCMve3WensnzfqIb63pVRDxPaiOAAcB5EbEe+EfgIVICNo90teZ/drKYi0n7ZnmF5V9KOt06k5SkrWVjr+aJtcRs1tuptlvWmJmZmdmWuCfLzMzMrAGcZJmZmZk1gJMsMzMzswZwkmVmZmbWAE6yzMzMzBrASZaZmZlZAzjJMjMzM2sAJ1lmZmZmDeAky8zMzKwBnGSZmZmZNYCTLDMzM7MGcJJlZmZm1gBOsszMzMwa4P8BU8beDdP7MugAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = model.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "803d532b", - "metadata": {}, - "source": [ - "Si bien se estabilizo un poco mas el entrenamiento, perdimos score en metricas. Es importante destacar que la curva de aprendizaje viendo la métrica accuracy se estanca durante muchos epochs. Busquemos usar un mejor optimizador (en cuanto a complejidad del mismo)" - ] - }, - { - "cell_type": "markdown", - "id": "972372da", - "metadata": {}, - "source": [ - "### Cuarto entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "469f45aa", - "metadata": {}, - "source": [ - "#### Diseño" - ] - }, - { - "cell_type": "markdown", - "id": "e2bcf87a", - "metadata": {}, - "source": [ - "Ahora cambiamos el optimizador por RMSprop, agregamos algo de regularización ya que sino corremos riesgo de overfittear" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "3b93573d", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "3acc99d1", - "metadata": {}, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Dense(16,input_shape = (40,),activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model.add(Dense(8,activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model.add(Dense(4,activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "09da4edf", - "metadata": {}, - "source": [ - "Compilamos y mostramos un resumen de la red" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "ec019c1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_3\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_10 (Dense) (None, 16) 656 \n", - "_________________________________________________________________\n", - "dense_11 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_12 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_13 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 833\n", - "Trainable params: 833\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001)\n", - "model.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "ec6efaaf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/200\n", - "814/814 [==============================] - 2s 1ms/step - loss: 4.2603 - auc: 0.5244 - accuracy: 0.3538 - val_loss: 0.5725 - val_auc: 0.6612 - val_accuracy: 0.7777\n", - "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5125 - auc: 0.7287 - accuracy: 0.7936 - val_loss: 0.4975 - val_auc: 0.8027 - val_accuracy: 0.7927\n", - "Epoch 3/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4676 - auc: 0.8049 - accuracy: 0.7963 - val_loss: 0.4607 - val_auc: 0.8283 - val_accuracy: 0.7952\n", - "Epoch 4/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4373 - auc: 0.8290 - accuracy: 0.8023 - val_loss: 0.4404 - val_auc: 0.8477 - val_accuracy: 0.7990\n", - "Epoch 5/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4392 - auc: 0.8427 - accuracy: 0.7999 - val_loss: 0.4247 - val_auc: 0.8581 - val_accuracy: 0.8013\n", - "Epoch 6/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4191 - auc: 0.8537 - accuracy: 0.8140 - val_loss: 0.4180 - val_auc: 0.8686 - val_accuracy: 0.8099\n", - "Epoch 7/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4249 - auc: 0.8548 - accuracy: 0.8148 - val_loss: 0.4166 - val_auc: 0.8592 - val_accuracy: 0.8151\n", - "Epoch 8/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3931 - auc: 0.8627 - accuracy: 0.8218 - val_loss: 0.4092 - val_auc: 0.8766 - val_accuracy: 0.8207\n", - "Epoch 9/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3907 - auc: 0.8678 - accuracy: 0.8261 - val_loss: 0.3907 - val_auc: 0.8723 - val_accuracy: 0.8256\n", - "Epoch 10/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3878 - auc: 0.8656 - accuracy: 0.8208 - val_loss: 0.3952 - val_auc: 0.8787 - val_accuracy: 0.8244\n", - "Epoch 11/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3905 - auc: 0.8685 - accuracy: 0.8263 - val_loss: 0.3983 - val_auc: 0.8804 - val_accuracy: 0.8244\n", - "Epoch 12/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3864 - auc: 0.8670 - accuracy: 0.8270 - val_loss: 0.3806 - val_auc: 0.8785 - val_accuracy: 0.8268\n", - "Epoch 13/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3731 - auc: 0.8760 - accuracy: 0.8319 - val_loss: 0.3779 - val_auc: 0.8806 - val_accuracy: 0.8268\n", - "Epoch 14/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3835 - auc: 0.8757 - accuracy: 0.8303 - val_loss: 0.3875 - val_auc: 0.8814 - val_accuracy: 0.8253\n", - "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3821 - auc: 0.8738 - accuracy: 0.8305 - val_loss: 0.3769 - val_auc: 0.8779 - val_accuracy: 0.8253\n", - "Epoch 16/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3841 - auc: 0.8693 - accuracy: 0.8255 - val_loss: 0.3786 - val_auc: 0.8858 - val_accuracy: 0.8270\n", - "Epoch 17/200\n", - "814/814 [==============================] - 1s 816us/step - loss: 0.3703 - auc: 0.8783 - accuracy: 0.8301 - val_loss: 0.3884 - val_auc: 0.8682 - val_accuracy: 0.8197\n", - "Epoch 18/200\n", - "814/814 [==============================] - 1s 819us/step - loss: 0.3756 - auc: 0.8783 - accuracy: 0.8328 - val_loss: 0.3694 - val_auc: 0.8854 - val_accuracy: 0.8276\n", - "Epoch 19/200\n", - "814/814 [==============================] - 1s 815us/step - loss: 0.3842 - auc: 0.8740 - accuracy: 0.8235 - val_loss: 0.3959 - val_auc: 0.8846 - val_accuracy: 0.8251\n", - "Epoch 20/200\n", - "814/814 [==============================] - 1s 958us/step - loss: 0.3616 - auc: 0.8839 - accuracy: 0.8339 - val_loss: 0.4069 - val_auc: 0.8834 - val_accuracy: 0.8237\n", - "Epoch 21/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3736 - auc: 0.8796 - accuracy: 0.8301 - val_loss: 0.3669 - val_auc: 0.8872 - val_accuracy: 0.8299\n", - "Epoch 22/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3660 - auc: 0.8823 - accuracy: 0.8291 - val_loss: 0.3668 - val_auc: 0.8882 - val_accuracy: 0.8303\n", - "Epoch 23/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3623 - auc: 0.8835 - accuracy: 0.8329 - val_loss: 0.3843 - val_auc: 0.8860 - val_accuracy: 0.8271\n", - "Epoch 24/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3616 - auc: 0.8839 - accuracy: 0.8333 - val_loss: 0.3649 - val_auc: 0.8870 - val_accuracy: 0.8287\n", - "Epoch 25/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3651 - auc: 0.8828 - accuracy: 0.8316 - val_loss: 0.3699 - val_auc: 0.8896 - val_accuracy: 0.8271\n", - "Epoch 26/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3670 - auc: 0.8847 - accuracy: 0.8313 - val_loss: 0.3731 - val_auc: 0.8899 - val_accuracy: 0.8267\n", - "Epoch 27/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3704 - auc: 0.8826 - accuracy: 0.8304 - val_loss: 0.3617 - val_auc: 0.8883 - val_accuracy: 0.8299\n", - "Epoch 28/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3697 - auc: 0.8819 - accuracy: 0.8291 - val_loss: 0.3865 - val_auc: 0.8881 - val_accuracy: 0.8259\n", - "Epoch 29/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3648 - auc: 0.8817 - accuracy: 0.8341 - val_loss: 0.3721 - val_auc: 0.8900 - val_accuracy: 0.8287\n", - "Epoch 30/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3753 - auc: 0.8839 - accuracy: 0.8333 - val_loss: 0.3950 - val_auc: 0.8871 - val_accuracy: 0.8268\n", - "Epoch 31/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3637 - auc: 0.8835 - accuracy: 0.8341 - val_loss: 0.3661 - val_auc: 0.8915 - val_accuracy: 0.8290\n", - "Epoch 32/200\n", - "814/814 [==============================] - 1s 927us/step - loss: 0.3616 - auc: 0.8834 - accuracy: 0.8337 - val_loss: 0.3699 - val_auc: 0.8829 - val_accuracy: 0.8253\n", - "Epoch 33/200\n", - "814/814 [==============================] - 1s 816us/step - loss: 0.3608 - auc: 0.8877 - accuracy: 0.8322 - val_loss: 0.3987 - val_auc: 0.8599 - val_accuracy: 0.8113\n", - "Epoch 34/200\n", - "814/814 [==============================] - 1s 829us/step - loss: 0.3690 - auc: 0.8900 - accuracy: 0.8341 - val_loss: 0.3799 - val_auc: 0.8773 - val_accuracy: 0.8323\n", - "Epoch 35/200\n", - "814/814 [==============================] - 1s 875us/step - loss: 0.3581 - auc: 0.8872 - accuracy: 0.8313 - val_loss: 0.4458 - val_auc: 0.8290 - val_accuracy: 0.8027\n", - "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3655 - auc: 0.8829 - accuracy: 0.8273 - val_loss: 0.3577 - val_auc: 0.8926 - val_accuracy: 0.8326\n", - "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3618 - auc: 0.8847 - accuracy: 0.8322 - val_loss: 0.3574 - val_auc: 0.8915 - val_accuracy: 0.8325\n", - "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3575 - auc: 0.8892 - accuracy: 0.8317 - val_loss: 0.3590 - val_auc: 0.8909 - val_accuracy: 0.8326\n", - "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3630 - auc: 0.8857 - accuracy: 0.8330 - val_loss: 0.3802 - val_auc: 0.8910 - val_accuracy: 0.8296\n", - "Epoch 40/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3567 - auc: 0.8874 - accuracy: 0.8340 - val_loss: 0.3571 - val_auc: 0.8921 - val_accuracy: 0.8319\n", - "Epoch 41/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3521 - auc: 0.8909 - accuracy: 0.8303 - val_loss: 0.3561 - val_auc: 0.8929 - val_accuracy: 0.8320\n", - "Epoch 42/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3655 - auc: 0.8834 - accuracy: 0.8299 - val_loss: 0.3574 - val_auc: 0.8915 - val_accuracy: 0.8325\n", - "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3684 - auc: 0.8877 - accuracy: 0.8370 - val_loss: 0.3924 - val_auc: 0.8709 - val_accuracy: 0.8145\n", - "Epoch 44/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3584 - auc: 0.8887 - accuracy: 0.8336 - val_loss: 0.3771 - val_auc: 0.8904 - val_accuracy: 0.8299\n", - "Epoch 45/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3618 - auc: 0.8875 - accuracy: 0.8306 - val_loss: 0.3551 - val_auc: 0.8944 - val_accuracy: 0.8343\n", - "Epoch 46/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3536 - auc: 0.8892 - accuracy: 0.8369 - val_loss: 0.3628 - val_auc: 0.8891 - val_accuracy: 0.8308\n", - "Epoch 47/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.8949 - accuracy: 0.8371 - val_loss: 0.3745 - val_auc: 0.8782 - val_accuracy: 0.8222\n", - "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8903 - accuracy: 0.8348 - val_loss: 0.3681 - val_auc: 0.8943 - val_accuracy: 0.8308\n", - "Epoch 49/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3522 - auc: 0.8909 - accuracy: 0.8309 - val_loss: 0.3618 - val_auc: 0.8935 - val_accuracy: 0.8323\n", - "Epoch 50/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3536 - auc: 0.8909 - accuracy: 0.8345 - val_loss: 0.3675 - val_auc: 0.8935 - val_accuracy: 0.8316\n", - "Epoch 51/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3564 - auc: 0.8891 - accuracy: 0.8345 - val_loss: 0.4143 - val_auc: 0.8873 - val_accuracy: 0.8270\n", - "Epoch 52/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3589 - auc: 0.8865 - accuracy: 0.8345 - val_loss: 0.3551 - val_auc: 0.8930 - val_accuracy: 0.8336\n", - "Epoch 53/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3600 - auc: 0.8880 - accuracy: 0.8286 - val_loss: 0.3700 - val_auc: 0.8807 - val_accuracy: 0.8242\n", - "Epoch 54/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3565 - auc: 0.8871 - accuracy: 0.8342 - val_loss: 0.3563 - val_auc: 0.8962 - val_accuracy: 0.8348\n", - "Epoch 55/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3589 - auc: 0.8875 - accuracy: 0.8320 - val_loss: 0.3820 - val_auc: 0.8917 - val_accuracy: 0.8293\n", - "Epoch 56/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3592 - auc: 0.8892 - accuracy: 0.8354 - val_loss: 0.3539 - val_auc: 0.8961 - val_accuracy: 0.8353\n", - "Epoch 57/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3556 - auc: 0.8912 - accuracy: 0.8351 - val_loss: 0.3561 - val_auc: 0.8966 - val_accuracy: 0.8337\n", - "Epoch 58/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3550 - auc: 0.8909 - accuracy: 0.8378 - val_loss: 0.3621 - val_auc: 0.8948 - val_accuracy: 0.8334\n", - "Epoch 59/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3491 - auc: 0.8946 - accuracy: 0.8357 - val_loss: 0.3562 - val_auc: 0.8957 - val_accuracy: 0.8340\n", - "Epoch 60/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3658 - auc: 0.8887 - accuracy: 0.8329 - val_loss: 0.3575 - val_auc: 0.8933 - val_accuracy: 0.8334\n", - "Epoch 61/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3535 - auc: 0.8906 - accuracy: 0.8363 - val_loss: 0.3546 - val_auc: 0.8965 - val_accuracy: 0.8340\n", - "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3554 - auc: 0.8896 - accuracy: 0.8325 - val_loss: 0.3601 - val_auc: 0.8949 - val_accuracy: 0.8319\n", - "Epoch 63/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3539 - auc: 0.8909 - accuracy: 0.8365 - val_loss: 0.3569 - val_auc: 0.8963 - val_accuracy: 0.8322\n", - "Epoch 64/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3538 - auc: 0.8916 - accuracy: 0.8346 - val_loss: 0.3536 - val_auc: 0.8948 - val_accuracy: 0.8354\n", - "Epoch 65/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3515 - auc: 0.8889 - accuracy: 0.8352 - val_loss: 0.3786 - val_auc: 0.8936 - val_accuracy: 0.8308\n", - "Epoch 66/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3501 - auc: 0.8914 - accuracy: 0.8350 - val_loss: 0.3804 - val_auc: 0.8925 - val_accuracy: 0.8317\n", - "Epoch 67/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3467 - auc: 0.8926 - accuracy: 0.8350 - val_loss: 0.3627 - val_auc: 0.8954 - val_accuracy: 0.8317\n", - "Epoch 68/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8949 - accuracy: 0.8349 - val_loss: 0.3798 - val_auc: 0.8747 - val_accuracy: 0.8187\n", - "Epoch 69/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3526 - auc: 0.8913 - accuracy: 0.8329 - val_loss: 0.3506 - val_auc: 0.8971 - val_accuracy: 0.8345\n", - "Epoch 70/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3574 - auc: 0.8914 - accuracy: 0.8342 - val_loss: 0.3567 - val_auc: 0.8969 - val_accuracy: 0.8345\n", - "Epoch 71/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3503 - auc: 0.8943 - accuracy: 0.8369 - val_loss: 0.3703 - val_auc: 0.8944 - val_accuracy: 0.8311\n", - "Epoch 72/200\n", - "814/814 [==============================] - 1s 817us/step - loss: 0.3538 - auc: 0.8937 - accuracy: 0.8364 - val_loss: 0.3710 - val_auc: 0.8944 - val_accuracy: 0.8300\n", - "Epoch 73/200\n", - "814/814 [==============================] - 1s 818us/step - loss: 0.3558 - auc: 0.8929 - accuracy: 0.8351 - val_loss: 0.3554 - val_auc: 0.8933 - val_accuracy: 0.8339\n", - "Epoch 74/200\n", - "814/814 [==============================] - 1s 834us/step - loss: 0.3565 - auc: 0.8894 - accuracy: 0.8313 - val_loss: 0.3646 - val_auc: 0.8942 - val_accuracy: 0.8322\n", - "Epoch 75/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3579 - auc: 0.8921 - accuracy: 0.8308 - val_loss: 0.3542 - val_auc: 0.8943 - val_accuracy: 0.8343\n", - "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3471 - auc: 0.8918 - accuracy: 0.8353 - val_loss: 0.3701 - val_auc: 0.8952 - val_accuracy: 0.8303\n", - "Epoch 77/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3525 - auc: 0.8913 - accuracy: 0.8355 - val_loss: 0.3642 - val_auc: 0.8949 - val_accuracy: 0.8308\n", - "Epoch 78/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8910 - accuracy: 0.8317 - val_loss: 0.3879 - val_auc: 0.8910 - val_accuracy: 0.8290\n", - "Epoch 79/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3589 - auc: 0.8890 - accuracy: 0.8318 - val_loss: 0.3837 - val_auc: 0.8698 - val_accuracy: 0.8145\n", - "Epoch 80/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3585 - auc: 0.8906 - accuracy: 0.8384 - val_loss: 0.3847 - val_auc: 0.8940 - val_accuracy: 0.8300\n", - "Epoch 81/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.8943 - accuracy: 0.8349 - val_loss: 0.3696 - val_auc: 0.8966 - val_accuracy: 0.8279\n", - "Epoch 82/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3502 - auc: 0.8955 - accuracy: 0.8338 - val_loss: 0.3555 - val_auc: 0.8966 - val_accuracy: 0.8339\n", - "Epoch 83/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3644 - auc: 0.8887 - accuracy: 0.8325 - val_loss: 0.3526 - val_auc: 0.8967 - val_accuracy: 0.8353\n", - "Epoch 84/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3537 - auc: 0.8938 - accuracy: 0.8364 - val_loss: 0.3560 - val_auc: 0.8942 - val_accuracy: 0.8359\n", - "Epoch 85/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3414 - auc: 0.8966 - accuracy: 0.8451 - val_loss: 0.3684 - val_auc: 0.8966 - val_accuracy: 0.8411\n", - "Epoch 86/200\n", - "814/814 [==============================] - 1s 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"Epoch 149/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3456 - auc: 0.8977 - accuracy: 0.8483 - val_loss: 0.3458 - val_auc: 0.9012 - val_accuracy: 0.8480\n", - "Epoch 150/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3435 - auc: 0.8974 - accuracy: 0.8469 - val_loss: 0.3505 - val_auc: 0.9006 - val_accuracy: 0.8472\n", - "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3551 - auc: 0.8946 - accuracy: 0.8480 - val_loss: 0.3620 - val_auc: 0.8981 - val_accuracy: 0.8440\n", - "Epoch 152/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3512 - auc: 0.8960 - accuracy: 0.8469 - val_loss: 0.3534 - val_auc: 0.8989 - val_accuracy: 0.8435\n", - "Epoch 153/200\n", - "814/814 [==============================] - 1s 843us/step - loss: 0.3447 - auc: 0.8972 - accuracy: 0.8463 - val_loss: 0.3537 - val_auc: 0.9001 - val_accuracy: 0.8458\n", - "Epoch 154/200\n", - "814/814 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"814/814 [==============================] - 1s 1ms/step - loss: 0.3533 - auc: 0.8962 - accuracy: 0.8465 - val_loss: 0.3470 - val_auc: 0.9021 - val_accuracy: 0.8481\n", - "Epoch 160/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3431 - auc: 0.8986 - accuracy: 0.8474 - val_loss: 0.3734 - val_auc: 0.8846 - val_accuracy: 0.8360\n", - "Epoch 161/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.9002 - accuracy: 0.8440 - val_loss: 0.3496 - val_auc: 0.8969 - val_accuracy: 0.8420\n", - "Epoch 162/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3566 - auc: 0.8968 - accuracy: 0.8459 - val_loss: 0.3815 - val_auc: 0.8724 - val_accuracy: 0.8251\n", - "Epoch 163/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3414 - auc: 0.8978 - accuracy: 0.8474 - val_loss: 0.3612 - val_auc: 0.8996 - val_accuracy: 0.8446\n", - "Epoch 164/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3456 - auc: 0.9012 - accuracy: 0.8472 - val_loss: 0.3471 - val_auc: 0.9011 - val_accuracy: 0.8474\n", - "Epoch 165/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.8969 - accuracy: 0.8474 - val_loss: 0.3468 - val_auc: 0.9012 - val_accuracy: 0.8472\n", - "Epoch 166/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3449 - auc: 0.8996 - accuracy: 0.8505 - val_loss: 0.3605 - val_auc: 0.9001 - val_accuracy: 0.8445\n", - "Epoch 167/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3465 - auc: 0.8981 - accuracy: 0.8461 - val_loss: 0.3442 - val_auc: 0.9018 - val_accuracy: 0.8489\n", - "Epoch 168/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.8973 - accuracy: 0.8445 - val_loss: 0.3570 - val_auc: 0.8989 - val_accuracy: 0.8440\n", - "Epoch 169/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3461 - auc: 0.8978 - accuracy: 0.8471 - val_loss: 0.3518 - val_auc: 0.9025 - val_accuracy: 0.8481\n", - "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3496 - auc: 0.8946 - accuracy: 0.8469 - val_loss: 0.3587 - val_auc: 0.8991 - val_accuracy: 0.8417\n", - "Epoch 171/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3443 - auc: 0.9001 - accuracy: 0.8520 - val_loss: 0.3443 - val_auc: 0.9006 - val_accuracy: 0.8503\n", - "Epoch 172/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8978 - accuracy: 0.8477 - val_loss: 0.3468 - val_auc: 0.9017 - val_accuracy: 0.8458\n", - "Epoch 173/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3488 - auc: 0.8963 - accuracy: 0.8470 - val_loss: 0.3511 - val_auc: 0.9012 - val_accuracy: 0.8455\n", - "Epoch 174/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3496 - auc: 0.8967 - accuracy: 0.8449 - val_loss: 0.3525 - val_auc: 0.9003 - val_accuracy: 0.8462\n", - "Epoch 175/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3411 - auc: 0.8982 - accuracy: 0.8480 - val_loss: 0.3419 - val_auc: 0.9023 - val_accuracy: 0.8478\n", - "Epoch 176/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3469 - auc: 0.8985 - accuracy: 0.8468 - val_loss: 0.3506 - val_auc: 0.9006 - val_accuracy: 0.8442\n", - "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3422 - auc: 0.8975 - accuracy: 0.8508 - val_loss: 0.3573 - val_auc: 0.8902 - val_accuracy: 0.8369\n", - "Epoch 178/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3451 - auc: 0.8988 - accuracy: 0.8471 - val_loss: 0.3439 - val_auc: 0.9032 - val_accuracy: 0.8500\n", - "Epoch 179/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3466 - auc: 0.8951 - accuracy: 0.8469 - val_loss: 0.3456 - val_auc: 0.8996 - val_accuracy: 0.8452\n", - "Epoch 180/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3479 - auc: 0.8986 - accuracy: 0.8457 - val_loss: 0.3442 - val_auc: 0.9023 - val_accuracy: 0.8491\n", - "Epoch 181/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3440 - auc: 0.8985 - accuracy: 0.8453 - val_loss: 0.3528 - val_auc: 0.8983 - val_accuracy: 0.8452\n", - "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3389 - auc: 0.9004 - accuracy: 0.8489 - val_loss: 0.3559 - val_auc: 0.9004 - val_accuracy: 0.8429\n", - "Epoch 183/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3433 - auc: 0.9008 - accuracy: 0.8491 - val_loss: 0.3677 - val_auc: 0.8982 - val_accuracy: 0.8408\n", - "Epoch 184/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3427 - auc: 0.8999 - accuracy: 0.8491 - val_loss: 0.3573 - val_auc: 0.8924 - val_accuracy: 0.8354\n", - "Epoch 185/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3446 - auc: 0.8980 - accuracy: 0.8504 - val_loss: 0.3460 - val_auc: 0.9024 - val_accuracy: 0.8481\n", - "Epoch 186/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3486 - auc: 0.8951 - accuracy: 0.8467 - val_loss: 0.3451 - val_auc: 0.9020 - val_accuracy: 0.8471\n", - "Epoch 187/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3325 - auc: 0.9052 - accuracy: 0.8524 - val_loss: 0.3673 - val_auc: 0.8994 - val_accuracy: 0.8442\n", - "Epoch 188/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3408 - auc: 0.8978 - accuracy: 0.8494 - val_loss: 0.3396 - val_auc: 0.9040 - val_accuracy: 0.8492\n", - "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3447 - auc: 0.8982 - accuracy: 0.8481 - val_loss: 0.3640 - val_auc: 0.9016 - val_accuracy: 0.8446\n", - "Epoch 190/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3472 - auc: 0.8989 - accuracy: 0.8500 - val_loss: 0.3530 - val_auc: 0.9009 - val_accuracy: 0.8432\n", - "Epoch 191/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3515 - auc: 0.8982 - accuracy: 0.8474 - val_loss: 0.3439 - val_auc: 0.9020 - val_accuracy: 0.8492\n", - "Epoch 192/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3514 - auc: 0.8985 - accuracy: 0.8453 - val_loss: 0.3437 - val_auc: 0.9021 - val_accuracy: 0.8478\n", - "Epoch 193/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3428 - auc: 0.8985 - accuracy: 0.8466 - val_loss: 0.3447 - val_auc: 0.9013 - val_accuracy: 0.8458\n", - "Epoch 194/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3429 - auc: 0.8994 - accuracy: 0.8482 - val_loss: 0.3414 - val_auc: 0.9032 - val_accuracy: 0.8483\n", - "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8952 - accuracy: 0.8418 - val_loss: 0.3433 - val_auc: 0.9015 - val_accuracy: 0.8478\n", - "Epoch 196/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3379 - auc: 0.8994 - accuracy: 0.8509 - val_loss: 0.3418 - val_auc: 0.9013 - val_accuracy: 0.8488\n", - "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3394 - auc: 0.8981 - accuracy: 0.8490 - val_loss: 0.3455 - val_auc: 0.9013 - val_accuracy: 0.8458\n", - "Epoch 198/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3450 - auc: 0.8966 - accuracy: 0.8479 - val_loss: 0.3628 - val_auc: 0.8992 - val_accuracy: 0.8446\n", - "Epoch 199/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3435 - auc: 0.8985 - accuracy: 0.8503 - val_loss: 0.3413 - val_auc: 0.9002 - val_accuracy: 0.8435\n", - "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3447 - auc: 0.8987 - accuracy: 0.8452 - val_loss: 0.3509 - val_auc: 0.8984 - val_accuracy: 0.8406\n" - ] - } - ], - "source": [ - "history = model.fit(X_train.values, y_train,epochs=200,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "503adec9", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "b6723a03", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "1e493515", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "4953c99a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "862896f5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8984583608468667\n", - "AUC-ROC score sobre train: 0.8994050838552559\n", - "Accuracy sobre test: 0.8406264394288346\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.88 0.90 5135\n", - " Alto valor 0.61 0.69 0.65 1378\n", - "\n", - " accuracy 0.84 6513\n", - " macro avg 0.76 0.79 0.77 6513\n", - "weighted avg 0.85 0.84 0.84 6513\n", - "\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": [ - "y_pred = model.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "d553f8ff", - "metadata": {}, - "source": [ - "Observamos que mejoro considerablemente el AUC-ROC. También se observan mejoras significativas en la medición de precision y recall de la clase con altos ingresos. Por último, destacar la estabilización de la curva de aprendizaje" - ] - }, - { - "cell_type": "markdown", - "id": "3efcb419", - "metadata": {}, - "source": [ - "### Quinto entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "a08e443a", - "metadata": {}, - "source": [ - "#### Diseño" - ] - }, - { - "cell_type": "markdown", - "id": "e48d1b47", - "metadata": {}, - "source": [ - "Realizamos un retoque final a la red, modificando la regularización a l1 y agregando una capa más de 16 neuronas. No agrandamos más al red en este caso porque no encontramos mejora alguna e incluso empeoraba en algunas configuraciones" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "9f9b6541", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "8052df04", - "metadata": {}, - "outputs": [], - "source": [ - "model = Sequential()\n", - "model.add(Dense(16,input_shape = (40,),activation='relu', kernel_regularizer=l1(0.0001)))\n", - "model.add(Dense(16,activation='relu', kernel_regularizer=l1(0.0001)))\n", - "model.add(Dense(8,activation='relu', kernel_regularizer=l1(0.0001)))\n", - "model.add(Dense(4,activation='relu', kernel_regularizer=l1(0.0001)))\n", - "model.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "26e010a0", - "metadata": {}, - "source": [ - "Compilamos y mostramos un resumen de la red" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "39f59cbd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_4\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_14 (Dense) (None, 16) 656 \n", - "_________________________________________________________________\n", - "dense_15 (Dense) (None, 16) 272 \n", - "_________________________________________________________________\n", - "dense_16 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_17 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_18 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 1,105\n", - "Trainable params: 1,105\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001)\n", - "model.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "2aa0ad62", - "metadata": {}, - "source": [ - "Tenemos 1200 params, mientrás que anteriormente teniamos aproximadamente 800" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "e76845b1", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/200\n", - "814/814 [==============================] - 2s 1ms/step - loss: 2.1281 - auc: 0.3829 - accuracy: 0.7333 - val_loss: 0.5417 - val_auc: 0.5710 - val_accuracy: 0.7792\n", - "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5233 - auc: 0.6245 - accuracy: 0.7791 - val_loss: 0.4984 - val_auc: 0.7327 - val_accuracy: 0.7778\n", - "Epoch 3/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4870 - auc: 0.7412 - accuracy: 0.7815 - val_loss: 0.4661 - val_auc: 0.7939 - val_accuracy: 0.7781\n", - "Epoch 4/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4573 - auc: 0.7933 - accuracy: 0.7834 - val_loss: 0.4377 - val_auc: 0.8270 - val_accuracy: 0.7795\n", - "Epoch 5/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4365 - auc: 0.8226 - accuracy: 0.7805 - val_loss: 0.4144 - val_auc: 0.8450 - val_accuracy: 0.7841\n", - "Epoch 6/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4049 - auc: 0.8480 - accuracy: 0.8007 - val_loss: 0.3985 - val_auc: 0.8576 - val_accuracy: 0.8025\n", - "Epoch 7/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3990 - auc: 0.8537 - accuracy: 0.8075 - val_loss: 0.3898 - val_auc: 0.8648 - val_accuracy: 0.8085\n", - "Epoch 8/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3866 - auc: 0.8646 - accuracy: 0.8199 - val_loss: 0.3785 - val_auc: 0.8769 - val_accuracy: 0.8228\n", - "Epoch 9/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3782 - auc: 0.8710 - accuracy: 0.8265 - val_loss: 0.3722 - val_auc: 0.8805 - val_accuracy: 0.8282\n", - "Epoch 10/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3766 - auc: 0.8732 - accuracy: 0.8239 - val_loss: 0.3701 - val_auc: 0.8819 - val_accuracy: 0.8291\n", - "Epoch 11/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3751 - auc: 0.8739 - accuracy: 0.8250 - val_loss: 0.3726 - val_auc: 0.8815 - val_accuracy: 0.8231\n", - "Epoch 12/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3721 - auc: 0.8746 - accuracy: 0.8291 - val_loss: 0.3691 - val_auc: 0.8837 - val_accuracy: 0.8299\n", - "Epoch 13/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3620 - auc: 0.8833 - accuracy: 0.8330 - val_loss: 0.3680 - val_auc: 0.8839 - val_accuracy: 0.8296\n", - "Epoch 14/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3689 - auc: 0.8820 - accuracy: 0.8288 - val_loss: 0.3701 - val_auc: 0.8843 - val_accuracy: 0.8313\n", - "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3680 - auc: 0.8821 - accuracy: 0.8321 - val_loss: 0.3677 - val_auc: 0.8842 - val_accuracy: 0.8259\n", - "Epoch 16/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3709 - auc: 0.8783 - accuracy: 0.8276 - val_loss: 0.3672 - val_auc: 0.8854 - val_accuracy: 0.8296\n", - "Epoch 17/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3651 - auc: 0.8830 - accuracy: 0.8304 - val_loss: 0.3656 - val_auc: 0.8862 - val_accuracy: 0.8294\n", - "Epoch 18/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3652 - auc: 0.8805 - accuracy: 0.8328 - val_loss: 0.3688 - val_auc: 0.8842 - val_accuracy: 0.8277\n", - "Epoch 19/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3738 - auc: 0.8785 - accuracy: 0.8248 - val_loss: 0.3697 - val_auc: 0.8853 - val_accuracy: 0.8270\n", - "Epoch 20/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3594 - auc: 0.8871 - accuracy: 0.8337 - val_loss: 0.3788 - val_auc: 0.8830 - val_accuracy: 0.8227\n", - "Epoch 21/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3627 - auc: 0.8853 - accuracy: 0.8312 - val_loss: 0.3659 - val_auc: 0.8871 - val_accuracy: 0.8276\n", - "Epoch 22/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3618 - auc: 0.8851 - accuracy: 0.8311 - val_loss: 0.3647 - val_auc: 0.8883 - val_accuracy: 0.8296\n", - "Epoch 23/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3572 - auc: 0.8877 - accuracy: 0.8356 - val_loss: 0.3714 - val_auc: 0.8877 - val_accuracy: 0.8260\n", - "Epoch 24/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3579 - auc: 0.8869 - accuracy: 0.8358 - val_loss: 0.3642 - val_auc: 0.8885 - val_accuracy: 0.8311\n", - "Epoch 25/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3606 - auc: 0.8859 - accuracy: 0.8358 - val_loss: 0.3659 - val_auc: 0.8879 - val_accuracy: 0.8294\n", - "Epoch 26/200\n", - "814/814 [==============================] - 2s 3ms/step - loss: 0.3614 - auc: 0.8873 - accuracy: 0.8323 - val_loss: 0.3657 - val_auc: 0.8881 - val_accuracy: 0.8296\n", - "Epoch 27/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3651 - auc: 0.8855 - accuracy: 0.8304 - val_loss: 0.3648 - val_auc: 0.8883 - val_accuracy: 0.8322\n", - "Epoch 28/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3644 - auc: 0.8855 - accuracy: 0.8350 - val_loss: 0.3832 - val_auc: 0.8787 - val_accuracy: 0.8334\n", - "Epoch 29/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3623 - auc: 0.8840 - accuracy: 0.8361 - val_loss: 0.3662 - val_auc: 0.8881 - val_accuracy: 0.8311\n", - "Epoch 30/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3639 - auc: 0.8889 - accuracy: 0.8353 - val_loss: 0.3711 - val_auc: 0.8875 - val_accuracy: 0.8270\n", - "Epoch 31/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3631 - auc: 0.8860 - accuracy: 0.8346 - val_loss: 0.3676 - val_auc: 0.8892 - val_accuracy: 0.8316\n", - "Epoch 32/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3615 - auc: 0.8858 - accuracy: 0.8355 - val_loss: 0.3666 - val_auc: 0.8886 - val_accuracy: 0.8337\n", - "Epoch 33/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3626 - auc: 0.8885 - accuracy: 0.8333 - val_loss: 0.3636 - val_auc: 0.8902 - val_accuracy: 0.8310\n", - "Epoch 34/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3594 - auc: 0.8921 - accuracy: 0.8362 - val_loss: 0.3648 - val_auc: 0.8904 - val_accuracy: 0.8351\n", - "Epoch 35/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3598 - auc: 0.8878 - accuracy: 0.8340 - val_loss: 0.4243 - val_auc: 0.8499 - val_accuracy: 0.8173\n", - "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3596 - auc: 0.8867 - accuracy: 0.8321 - val_loss: 0.3958 - val_auc: 0.8753 - val_accuracy: 0.8211\n", - "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3620 - auc: 0.8871 - accuracy: 0.8338 - val_loss: 0.3635 - val_auc: 0.8905 - val_accuracy: 0.8326\n", - "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3566 - auc: 0.8911 - accuracy: 0.8347 - val_loss: 0.3645 - val_auc: 0.8902 - val_accuracy: 0.8326\n", - "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3607 - auc: 0.8879 - accuracy: 0.8378 - val_loss: 0.3653 - val_auc: 0.8902 - val_accuracy: 0.8305\n", - "Epoch 40/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3555 - auc: 0.8896 - accuracy: 0.8370 - val_loss: 0.3650 - val_auc: 0.8909 - val_accuracy: 0.8331\n", - "Epoch 41/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3541 - auc: 0.8911 - accuracy: 0.8333 - val_loss: 0.3702 - val_auc: 0.8860 - val_accuracy: 0.8359\n", - "Epoch 42/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3630 - auc: 0.8870 - accuracy: 0.8325 - val_loss: 0.3675 - val_auc: 0.8888 - val_accuracy: 0.8336\n", - "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3609 - auc: 0.8899 - accuracy: 0.8376 - val_loss: 0.3688 - val_auc: 0.8919 - val_accuracy: 0.8300\n", - "Epoch 44/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3550 - auc: 0.8912 - accuracy: 0.8355 - val_loss: 0.3700 - val_auc: 0.8889 - val_accuracy: 0.8306\n", - "Epoch 45/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3638 - auc: 0.8875 - accuracy: 0.8312 - val_loss: 0.3669 - val_auc: 0.8904 - val_accuracy: 0.8316\n", - "Epoch 46/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3550 - auc: 0.8901 - accuracy: 0.8406 - val_loss: 0.3657 - val_auc: 0.8919 - val_accuracy: 0.8353\n", - "Epoch 47/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3513 - auc: 0.8942 - accuracy: 0.8389 - val_loss: 0.3635 - val_auc: 0.8916 - val_accuracy: 0.8299\n", - "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3534 - auc: 0.8928 - accuracy: 0.8369 - val_loss: 0.3656 - val_auc: 0.8907 - val_accuracy: 0.8337\n", - "Epoch 49/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3569 - auc: 0.8909 - accuracy: 0.8324 - val_loss: 0.3685 - val_auc: 0.8898 - val_accuracy: 0.8331\n", - "Epoch 50/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3538 - auc: 0.8927 - accuracy: 0.8357 - val_loss: 0.3712 - val_auc: 0.8901 - val_accuracy: 0.8316\n", - "Epoch 51/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3577 - auc: 0.8901 - accuracy: 0.8368 - val_loss: 0.3683 - val_auc: 0.8910 - val_accuracy: 0.8336\n", - "Epoch 52/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3566 - auc: 0.8894 - accuracy: 0.8377 - val_loss: 0.3632 - val_auc: 0.8923 - val_accuracy: 0.8353\n", - "Epoch 53/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3603 - auc: 0.8894 - accuracy: 0.8306 - val_loss: 0.4313 - val_auc: 0.8413 - val_accuracy: 0.8111\n", - "Epoch 54/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3598 - auc: 0.8886 - accuracy: 0.8360 - val_loss: 0.3672 - val_auc: 0.8921 - val_accuracy: 0.8331\n", - "Epoch 55/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3601 - auc: 0.8885 - accuracy: 0.8321 - val_loss: 0.3709 - val_auc: 0.8896 - val_accuracy: 0.8325\n", - "Epoch 56/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3585 - auc: 0.8900 - accuracy: 0.8383 - val_loss: 0.3630 - val_auc: 0.8928 - val_accuracy: 0.8346\n", - "Epoch 57/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3588 - auc: 0.8898 - accuracy: 0.8342 - val_loss: 0.3637 - val_auc: 0.8917 - val_accuracy: 0.8345\n", - "Epoch 58/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3575 - auc: 0.8912 - accuracy: 0.8386 - val_loss: 0.3635 - val_auc: 0.8920 - val_accuracy: 0.8349\n", - "Epoch 59/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3523 - auc: 0.8945 - accuracy: 0.8371 - val_loss: 0.3640 - val_auc: 0.8926 - val_accuracy: 0.8320\n", - "Epoch 60/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3654 - auc: 0.8894 - accuracy: 0.8339 - val_loss: 0.3666 - val_auc: 0.8929 - val_accuracy: 0.8311\n", - "Epoch 61/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3553 - auc: 0.8916 - accuracy: 0.8378 - val_loss: 0.3627 - val_auc: 0.8930 - val_accuracy: 0.8334\n", - "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3598 - auc: 0.8898 - accuracy: 0.8352 - val_loss: 0.3610 - val_auc: 0.8929 - val_accuracy: 0.8320\n", - "Epoch 63/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8929 - accuracy: 0.8381 - val_loss: 0.3592 - val_auc: 0.8934 - val_accuracy: 0.8351\n", - "Epoch 64/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3551 - auc: 0.8919 - accuracy: 0.8367 - val_loss: 0.4057 - val_auc: 0.8647 - val_accuracy: 0.8187\n", - "Epoch 65/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3572 - auc: 0.8887 - accuracy: 0.8372 - val_loss: 0.3675 - val_auc: 0.8901 - val_accuracy: 0.8308\n", - "Epoch 66/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3550 - auc: 0.8903 - accuracy: 0.8369 - val_loss: 0.3625 - val_auc: 0.8920 - val_accuracy: 0.8340\n", - "Epoch 67/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3498 - auc: 0.8928 - accuracy: 0.8377 - val_loss: 0.3590 - val_auc: 0.8931 - val_accuracy: 0.8323\n", - "Epoch 68/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3514 - auc: 0.8954 - accuracy: 0.8375 - val_loss: 0.3584 - val_auc: 0.8929 - val_accuracy: 0.8359\n", - "Epoch 69/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3561 - auc: 0.8915 - accuracy: 0.8348 - val_loss: 0.3586 - val_auc: 0.8941 - val_accuracy: 0.8342\n", - "Epoch 70/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3587 - auc: 0.8907 - accuracy: 0.8353 - val_loss: 0.3568 - val_auc: 0.8939 - val_accuracy: 0.8348\n", - "Epoch 71/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3537 - auc: 0.8932 - accuracy: 0.8395 - val_loss: 0.3570 - val_auc: 0.8940 - val_accuracy: 0.8349\n", - "Epoch 72/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8939 - accuracy: 0.8381 - val_loss: 0.3653 - val_auc: 0.8909 - val_accuracy: 0.8310\n", - "Epoch 73/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3559 - auc: 0.8922 - accuracy: 0.8381 - val_loss: 0.3575 - val_auc: 0.8941 - val_accuracy: 0.8343\n", - "Epoch 74/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3549 - auc: 0.8918 - accuracy: 0.8347 - val_loss: 0.3610 - val_auc: 0.8925 - val_accuracy: 0.8322\n", - "Epoch 75/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3590 - auc: 0.8913 - accuracy: 0.8321 - val_loss: 0.3756 - val_auc: 0.8821 - val_accuracy: 0.8277\n", - "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3536 - auc: 0.8906 - accuracy: 0.8364 - val_loss: 0.3589 - val_auc: 0.8935 - val_accuracy: 0.8337\n", - "Epoch 77/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3547 - auc: 0.8913 - accuracy: 0.8361 - val_loss: 0.3624 - val_auc: 0.8925 - val_accuracy: 0.8322\n", - "Epoch 78/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3581 - auc: 0.8903 - accuracy: 0.8335 - val_loss: 0.3554 - val_auc: 0.8946 - val_accuracy: 0.8334\n", - "Epoch 79/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3614 - auc: 0.8894 - accuracy: 0.8357 - val_loss: 0.3570 - val_auc: 0.8948 - val_accuracy: 0.8343\n", - "Epoch 80/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3513 - auc: 0.8939 - accuracy: 0.8418 - val_loss: 0.3558 - val_auc: 0.8955 - val_accuracy: 0.8376\n", - "Epoch 81/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8928 - accuracy: 0.8360 - val_loss: 0.3807 - val_auc: 0.8897 - val_accuracy: 0.8311\n", - "Epoch 82/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3542 - auc: 0.8942 - accuracy: 0.8369 - val_loss: 0.3541 - val_auc: 0.8962 - val_accuracy: 0.8379\n", - "Epoch 83/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3594 - auc: 0.8923 - accuracy: 0.8337 - val_loss: 0.3542 - val_auc: 0.8968 - val_accuracy: 0.8376\n", - "Epoch 84/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8957 - accuracy: 0.8384 - val_loss: 0.3760 - val_auc: 0.8880 - val_accuracy: 0.8279\n", - "Epoch 85/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3461 - auc: 0.8965 - accuracy: 0.8410 - val_loss: 0.3598 - val_auc: 0.8944 - val_accuracy: 0.8345\n", - "Epoch 86/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3519 - auc: 0.8936 - accuracy: 0.8389 - val_loss: 0.3657 - val_auc: 0.8919 - val_accuracy: 0.8325\n", - "Epoch 87/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8968 - accuracy: 0.8371 - val_loss: 0.3557 - val_auc: 0.8976 - val_accuracy: 0.8345\n", - "Epoch 88/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3523 - auc: 0.8977 - accuracy: 0.8386 - val_loss: 0.3565 - val_auc: 0.8963 - val_accuracy: 0.8372\n", - "Epoch 89/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3607 - auc: 0.8921 - accuracy: 0.8381 - val_loss: 0.3532 - val_auc: 0.8977 - val_accuracy: 0.8362\n", - "Epoch 90/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3546 - auc: 0.8919 - accuracy: 0.8379 - val_loss: 0.3550 - val_auc: 0.8964 - val_accuracy: 0.8340\n", - "Epoch 91/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8953 - accuracy: 0.8402 - val_loss: 0.3596 - val_auc: 0.8943 - val_accuracy: 0.8340\n", - "Epoch 92/200\n", - 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1ms/step - loss: 0.3524 - auc: 0.8968 - accuracy: 0.8373 - val_loss: 0.3535 - val_auc: 0.8979 - val_accuracy: 0.8359\n", - "Epoch 98/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3553 - auc: 0.8930 - accuracy: 0.8358 - val_loss: 0.3548 - val_auc: 0.8976 - val_accuracy: 0.8369\n", - "Epoch 99/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8961 - accuracy: 0.8376 - val_loss: 0.3549 - val_auc: 0.8976 - val_accuracy: 0.8351\n", - "Epoch 100/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3547 - auc: 0.8955 - accuracy: 0.8392 - val_loss: 0.3534 - val_auc: 0.8984 - val_accuracy: 0.8374\n", - "Epoch 101/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3605 - auc: 0.8911 - accuracy: 0.8339 - val_loss: 0.3533 - val_auc: 0.8985 - val_accuracy: 0.8362\n", - "Epoch 102/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3530 - auc: 0.8955 - accuracy: 0.8367 - val_loss: 0.3520 - val_auc: 0.8982 - val_accuracy: 0.8391\n", - "Epoch 103/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3524 - auc: 0.8950 - accuracy: 0.8367 - val_loss: 0.3526 - val_auc: 0.8986 - val_accuracy: 0.8356\n", - "Epoch 104/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3563 - auc: 0.8924 - accuracy: 0.8370 - val_loss: 0.3555 - val_auc: 0.8990 - val_accuracy: 0.8343\n", - "Epoch 105/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.8970 - accuracy: 0.8385 - val_loss: 0.3522 - val_auc: 0.8982 - val_accuracy: 0.8353\n", - "Epoch 106/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3514 - auc: 0.8956 - accuracy: 0.8374 - val_loss: 0.3568 - val_auc: 0.8947 - val_accuracy: 0.8362\n", - "Epoch 107/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3578 - auc: 0.8920 - accuracy: 0.8339 - val_loss: 0.3517 - val_auc: 0.8993 - val_accuracy: 0.8366\n", - "Epoch 108/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3528 - auc: 0.8937 - accuracy: 0.8374 - val_loss: 0.3565 - val_auc: 0.8987 - val_accuracy: 0.8340\n", - "Epoch 109/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3547 - auc: 0.8944 - accuracy: 0.8371 - val_loss: 0.3600 - val_auc: 0.8973 - val_accuracy: 0.8320\n", - "Epoch 110/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3542 - auc: 0.8935 - accuracy: 0.8381 - val_loss: 0.3616 - val_auc: 0.8967 - val_accuracy: 0.8343\n", - "Epoch 111/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3541 - auc: 0.8937 - accuracy: 0.8386 - val_loss: 0.3573 - val_auc: 0.8974 - val_accuracy: 0.8334\n", - "Epoch 112/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3516 - auc: 0.8988 - accuracy: 0.8388 - val_loss: 0.3559 - val_auc: 0.8981 - val_accuracy: 0.8371\n", - "Epoch 113/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3556 - auc: 0.8927 - accuracy: 0.8368 - val_loss: 0.3556 - val_auc: 0.8963 - val_accuracy: 0.8329\n", - "Epoch 114/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8941 - accuracy: 0.8384 - val_loss: 0.4212 - val_auc: 0.8546 - val_accuracy: 0.8062\n", - "Epoch 115/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3425 - auc: 0.8975 - accuracy: 0.8417 - val_loss: 0.3558 - val_auc: 0.8987 - val_accuracy: 0.8331\n", - "Epoch 116/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.8993 - accuracy: 0.8412 - val_loss: 0.3630 - val_auc: 0.8968 - val_accuracy: 0.8357\n", - "Epoch 117/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8960 - accuracy: 0.8330 - val_loss: 0.3534 - val_auc: 0.8982 - val_accuracy: 0.8339\n", - "Epoch 118/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3514 - auc: 0.8987 - accuracy: 0.8391 - val_loss: 0.3509 - val_auc: 0.8987 - val_accuracy: 0.8360\n", - "Epoch 119/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.8967 - accuracy: 0.8401 - val_loss: 0.3517 - val_auc: 0.8992 - val_accuracy: 0.8340\n", - "Epoch 120/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3500 - auc: 0.8950 - accuracy: 0.8405 - val_loss: 0.3504 - val_auc: 0.8995 - val_accuracy: 0.8357\n", - "Epoch 121/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3569 - auc: 0.8914 - accuracy: 0.8342 - val_loss: 0.3622 - val_auc: 0.8957 - val_accuracy: 0.8337\n", - "Epoch 122/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3603 - auc: 0.8969 - accuracy: 0.8358 - val_loss: 0.3684 - val_auc: 0.8961 - val_accuracy: 0.8337\n", - "Epoch 123/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3501 - auc: 0.8985 - accuracy: 0.8379 - val_loss: 0.3551 - val_auc: 0.9000 - val_accuracy: 0.8351\n", - "Epoch 124/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8980 - accuracy: 0.8380 - val_loss: 0.4591 - val_auc: 0.8297 - val_accuracy: 0.8033\n", - "Epoch 125/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3520 - auc: 0.8984 - accuracy: 0.8393 - val_loss: 0.3535 - val_auc: 0.8995 - val_accuracy: 0.8342\n", - "Epoch 126/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3426 - auc: 0.9026 - accuracy: 0.8397 - val_loss: 0.3506 - val_auc: 0.9007 - val_accuracy: 0.8365\n", - "Epoch 127/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3433 - auc: 0.9022 - accuracy: 0.8420 - val_loss: 0.3497 - val_auc: 0.9008 - val_accuracy: 0.8380\n", - "Epoch 128/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3490 - auc: 0.8970 - accuracy: 0.8390 - val_loss: 0.3493 - val_auc: 0.9006 - val_accuracy: 0.8385\n", - "Epoch 129/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3497 - auc: 0.8972 - accuracy: 0.8402 - val_loss: 0.3529 - val_auc: 0.9004 - val_accuracy: 0.8342\n", - "Epoch 130/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3525 - auc: 0.8966 - accuracy: 0.8377 - val_loss: 0.3647 - val_auc: 0.8955 - val_accuracy: 0.8339\n", - "Epoch 131/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3507 - auc: 0.8982 - accuracy: 0.8401 - val_loss: 0.3542 - val_auc: 0.8978 - val_accuracy: 0.8386\n", - "Epoch 132/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3522 - auc: 0.8969 - accuracy: 0.8374 - val_loss: 0.3620 - val_auc: 0.8973 - val_accuracy: 0.8334\n", - "Epoch 133/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3563 - auc: 0.8929 - accuracy: 0.8364 - val_loss: 0.3542 - val_auc: 0.9003 - val_accuracy: 0.8368\n", - "Epoch 134/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8993 - accuracy: 0.8400 - val_loss: 0.3482 - val_auc: 0.9021 - val_accuracy: 0.8372\n", - "Epoch 135/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3519 - auc: 0.8961 - accuracy: 0.8400 - val_loss: 0.3498 - val_auc: 0.9000 - val_accuracy: 0.8360\n", - "Epoch 136/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8975 - accuracy: 0.8380 - val_loss: 0.3515 - val_auc: 0.8991 - val_accuracy: 0.8368\n", - "Epoch 137/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3568 - auc: 0.8966 - accuracy: 0.8361 - val_loss: 0.3563 - val_auc: 0.8993 - val_accuracy: 0.8366\n", - "Epoch 138/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3436 - auc: 0.9009 - accuracy: 0.8421 - val_loss: 0.3584 - val_auc: 0.8978 - val_accuracy: 0.8368\n", - "Epoch 139/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3522 - auc: 0.8962 - accuracy: 0.8386 - val_loss: 0.3539 - val_auc: 0.8992 - val_accuracy: 0.8368\n", - "Epoch 140/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3547 - auc: 0.8964 - accuracy: 0.8379 - val_loss: 0.3617 - val_auc: 0.8979 - val_accuracy: 0.8334\n", - "Epoch 141/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3487 - auc: 0.8971 - accuracy: 0.8378 - val_loss: 0.3585 - val_auc: 0.8980 - val_accuracy: 0.8322\n", - "Epoch 142/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8988 - accuracy: 0.8377 - val_loss: 0.3506 - val_auc: 0.9015 - val_accuracy: 0.8356\n", - "Epoch 143/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3468 - auc: 0.8980 - accuracy: 0.8397 - val_loss: 0.3543 - val_auc: 0.8992 - val_accuracy: 0.8380\n", - "Epoch 144/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3440 - auc: 0.9000 - accuracy: 0.8413 - val_loss: 0.3509 - val_auc: 0.9000 - val_accuracy: 0.8379\n", - "Epoch 145/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3542 - auc: 0.8968 - accuracy: 0.8348 - val_loss: 0.3979 - val_auc: 0.8724 - val_accuracy: 0.8182\n", - "Epoch 146/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8984 - accuracy: 0.8418 - val_loss: 0.3482 - val_auc: 0.9014 - val_accuracy: 0.8369\n", - "Epoch 147/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8985 - accuracy: 0.8388 - val_loss: 0.3601 - val_auc: 0.8972 - val_accuracy: 0.8343\n", - "Epoch 148/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3479 - auc: 0.8991 - accuracy: 0.8409 - val_loss: 0.3506 - val_auc: 0.9011 - val_accuracy: 0.8371\n", - "Epoch 149/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.8994 - accuracy: 0.8382 - val_loss: 0.3501 - val_auc: 0.9015 - val_accuracy: 0.8345\n", - "Epoch 150/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3449 - auc: 0.9002 - accuracy: 0.8396 - val_loss: 0.3541 - val_auc: 0.8991 - val_accuracy: 0.8374\n", - "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3526 - auc: 0.8982 - accuracy: 0.8384 - val_loss: 0.3546 - val_auc: 0.8996 - val_accuracy: 0.8379\n", - "Epoch 152/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3545 - auc: 0.8988 - accuracy: 0.8376 - val_loss: 0.3499 - val_auc: 0.9024 - val_accuracy: 0.8392\n", - "Epoch 153/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3487 - auc: 0.8991 - accuracy: 0.8378 - val_loss: 0.3523 - val_auc: 0.9009 - val_accuracy: 0.8365\n", - "Epoch 154/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8974 - accuracy: 0.8392 - val_loss: 0.3504 - val_auc: 0.9008 - val_accuracy: 0.8391\n", - "Epoch 155/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3461 - auc: 0.9010 - accuracy: 0.8423 - val_loss: 0.3524 - val_auc: 0.9006 - val_accuracy: 0.8425\n", - "Epoch 156/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3483 - auc: 0.8994 - accuracy: 0.8486 - val_loss: 0.3478 - val_auc: 0.9026 - val_accuracy: 0.8491\n", - "Epoch 157/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3459 - auc: 0.8992 - accuracy: 0.8478 - val_loss: 0.3511 - val_auc: 0.9010 - val_accuracy: 0.8443\n", - "Epoch 158/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.9011 - accuracy: 0.8481 - val_loss: 0.3501 - val_auc: 0.9007 - val_accuracy: 0.8486\n", - "Epoch 159/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8996 - accuracy: 0.8452 - val_loss: 0.3535 - val_auc: 0.9003 - val_accuracy: 0.8486\n", - "Epoch 160/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3485 - auc: 0.8996 - accuracy: 0.8452 - val_loss: 0.3683 - val_auc: 0.8900 - val_accuracy: 0.8379\n", - "Epoch 161/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3480 - auc: 0.9020 - accuracy: 0.8495 - val_loss: 0.3519 - val_auc: 0.9018 - val_accuracy: 0.8455\n", - "Epoch 162/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3506 - auc: 0.9001 - accuracy: 0.8446 - val_loss: 0.3490 - val_auc: 0.9009 - val_accuracy: 0.8505\n", - "Epoch 163/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3464 - auc: 0.8998 - accuracy: 0.8462 - val_loss: 0.3527 - val_auc: 0.9002 - val_accuracy: 0.8419\n", - "Epoch 164/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3472 - auc: 0.9029 - accuracy: 0.8476 - val_loss: 0.3533 - val_auc: 0.9024 - val_accuracy: 0.8485\n", - "Epoch 165/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3532 - auc: 0.8978 - accuracy: 0.8464 - val_loss: 0.3498 - val_auc: 0.9026 - val_accuracy: 0.8532\n", - "Epoch 166/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3469 - auc: 0.9008 - accuracy: 0.8442 - val_loss: 0.3556 - val_auc: 0.8997 - val_accuracy: 0.8469\n", - "Epoch 167/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3516 - auc: 0.8984 - accuracy: 0.8453 - val_loss: 0.3560 - val_auc: 0.9021 - val_accuracy: 0.8494\n", - "Epoch 168/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3511 - auc: 0.8991 - accuracy: 0.8442 - val_loss: 0.3636 - val_auc: 0.8982 - val_accuracy: 0.8369\n", - "Epoch 169/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3523 - auc: 0.8981 - accuracy: 0.8449 - val_loss: 0.3574 - val_auc: 0.9010 - val_accuracy: 0.8440\n", - "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3526 - auc: 0.8986 - accuracy: 0.8443 - val_loss: 0.3588 - val_auc: 0.8994 - val_accuracy: 0.8457\n", - "Epoch 171/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.9021 - accuracy: 0.8490 - val_loss: 0.3559 - val_auc: 0.9021 - val_accuracy: 0.8460\n", - "Epoch 172/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3531 - auc: 0.8990 - accuracy: 0.8466 - val_loss: 0.3546 - val_auc: 0.9009 - val_accuracy: 0.8445\n", - "Epoch 173/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3517 - auc: 0.8986 - accuracy: 0.8446 - val_loss: 0.3557 - val_auc: 0.9029 - val_accuracy: 0.8468\n", - "Epoch 174/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3564 - auc: 0.8969 - accuracy: 0.8440 - val_loss: 0.3644 - val_auc: 0.8992 - val_accuracy: 0.8443\n", - "Epoch 175/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3452 - auc: 0.9005 - accuracy: 0.8462 - val_loss: 0.3540 - val_auc: 0.9007 - val_accuracy: 0.8478\n", - "Epoch 176/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8993 - accuracy: 0.8447 - val_loss: 0.3567 - val_auc: 0.9015 - val_accuracy: 0.8466\n", - "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3501 - auc: 0.8976 - accuracy: 0.8464 - val_loss: 0.3534 - val_auc: 0.9004 - val_accuracy: 0.8442\n", - "Epoch 178/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.9013 - accuracy: 0.8493 - val_loss: 0.3558 - val_auc: 0.9009 - val_accuracy: 0.8437\n", - "Epoch 179/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8965 - accuracy: 0.8451 - val_loss: 0.3536 - val_auc: 0.9015 - val_accuracy: 0.8431\n", - "Epoch 180/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3523 - auc: 0.8997 - accuracy: 0.8448 - val_loss: 0.3602 - val_auc: 0.9000 - val_accuracy: 0.8412\n", - "Epoch 181/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3487 - auc: 0.9000 - accuracy: 0.8458 - val_loss: 0.3571 - val_auc: 0.9011 - val_accuracy: 0.8469\n", - "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8997 - accuracy: 0.8458 - val_loss: 0.3611 - val_auc: 0.9006 - val_accuracy: 0.8445\n", - "Epoch 183/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3483 - auc: 0.9019 - accuracy: 0.8469 - val_loss: 0.3622 - val_auc: 0.9005 - val_accuracy: 0.8455\n", - "Epoch 184/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.9009 - accuracy: 0.8492 - val_loss: 0.3724 - val_auc: 0.8897 - val_accuracy: 0.8345\n", - "Epoch 185/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3485 - auc: 0.9000 - accuracy: 0.8475 - val_loss: 0.3511 - val_auc: 0.9023 - val_accuracy: 0.8508\n", - "Epoch 186/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8966 - accuracy: 0.8471 - val_loss: 0.3514 - val_auc: 0.9026 - val_accuracy: 0.8492\n", - "Epoch 187/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3406 - auc: 0.9043 - accuracy: 0.8495 - val_loss: 0.3597 - val_auc: 0.8999 - val_accuracy: 0.8419\n", - "Epoch 188/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3497 - auc: 0.8970 - accuracy: 0.8491 - val_loss: 0.3519 - val_auc: 0.9015 - val_accuracy: 0.8497\n", - "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3468 - auc: 0.9007 - accuracy: 0.8494 - val_loss: 0.3554 - val_auc: 0.9020 - val_accuracy: 0.8455\n", - "Epoch 190/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3491 - auc: 0.8998 - accuracy: 0.8498 - val_loss: 0.3608 - val_auc: 0.9002 - val_accuracy: 0.8412\n", - "Epoch 191/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3562 - auc: 0.8986 - accuracy: 0.8453 - val_loss: 0.3547 - val_auc: 0.9011 - val_accuracy: 0.8523\n", - "Epoch 192/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3536 - auc: 0.9002 - accuracy: 0.8452 - val_loss: 0.3588 - val_auc: 0.9007 - val_accuracy: 0.8506\n", - "Epoch 193/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3490 - auc: 0.8988 - accuracy: 0.8441 - val_loss: 0.3561 - val_auc: 0.9004 - val_accuracy: 0.8439\n", - "Epoch 194/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3514 - auc: 0.8992 - accuracy: 0.8466 - val_loss: 0.3494 - val_auc: 0.9029 - val_accuracy: 0.8485\n", - "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3558 - auc: 0.8958 - accuracy: 0.8434 - val_loss: 0.3509 - val_auc: 0.9034 - val_accuracy: 0.8488\n", - "Epoch 196/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3477 - auc: 0.8992 - accuracy: 0.8471 - val_loss: 0.3617 - val_auc: 0.8964 - val_accuracy: 0.8497\n", - "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8983 - accuracy: 0.8512 - val_loss: 0.3587 - val_auc: 0.9002 - val_accuracy: 0.8465\n", - "Epoch 198/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3490 - auc: 0.8990 - accuracy: 0.8465 - val_loss: 0.3607 - val_auc: 0.9016 - val_accuracy: 0.8483\n", - "Epoch 199/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3487 - auc: 0.9014 - accuracy: 0.8491 - val_loss: 0.3519 - val_auc: 0.9022 - val_accuracy: 0.8508\n", - "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3511 - auc: 0.9000 - accuracy: 0.8438 - val_loss: 0.3594 - val_auc: 0.9009 - val_accuracy: 0.8437\n" - ] - } - ], - "source": [ - "history = model.fit(X_train.values, y_train,epochs=200,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "09ef61ad", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "4058106a", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "88012835", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "e762b2b7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "b49f4454", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.9009191669589980\n", - "AUC-ROC score sobre train: 0.9019667376267725\n", - "Accuracy sobre test: 0.8436972209427299\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.89 0.90 5063\n", - " Alto valor 0.64 0.69 0.66 1450\n", - "\n", - " accuracy 0.84 6513\n", - " macro avg 0.77 0.79 0.78 6513\n", - "weighted avg 0.85 0.84 0.85 6513\n", - "\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": [ - "y_pred = model.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "981ae1a0", - "metadata": {}, - "source": [ - "Observamos que mejoro levemente el AUC-ROC y también mejoro la precision en la clase de altos ingresos. Pasamos al siguiente preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "255d4874", - "metadata": {}, - "source": [ - "## Segundo preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "9dc3db7c", - "metadata": {}, - "source": [ - "Volveremos a entrenar una red, pero en este caso realizaremos otro preprocesamiento a nuestros datos. Volvemos a cargar el dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "4999c3d1", - "metadata": {}, - "outputs": [], - "source": [ - "df, df_holdout = obtener_datasets()" - ] - }, - { - "cell_type": "markdown", - "id": "11a103eb", - "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. Luego, escalamos nuestro datos con StandardScaler de sklearn" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "d3539acd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica_generalizada' en las variables categóricas.\n" - ] - } - ], - "source": [ - "X_df, y_df = aplicar_preparacion_generalizado(df)\n", - "X_df = conversion_numerica_generalizada(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "d0cf99cf", - "metadata": {}, - "source": [ - "Observemos cuantas features tenemos" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "f102b44f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(32561, 43)" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "X_df.shape" - ] - }, - { - "cell_type": "markdown", - "id": "9836a37a", - "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": 42, - "id": "4e0e700b", - "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": "740a7b62", - "metadata": {}, - "source": [ - "Finalemente, escalamos los datos" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "8623c1b3", - "metadata": {}, - "outputs": [], - "source": [ - "X_train = get_dataframe_scaled(X_train, StandardScaler())\n", - "X_test = get_dataframe_scaled(X_test, StandardScaler())" - ] - }, - { - "cell_type": "markdown", - "id": "430e0a1d", - "metadata": {}, - "source": [ - "### Primer diseño de la red" - ] - }, - { - "cell_type": "markdown", - "id": "6d206010", - "metadata": {}, - "source": [ - "Como ya fuimos de menos a más en el anterior preprocesado, comenzemos con una red algo más compleja. Usaremos función de activación relu en las capas, optimizador SGD (learning rate 0.01) y obviamente sigmoidea como función de activación en la última capa" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "f9426465", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "8ee4f9d7", - "metadata": {}, - "outputs": [], - "source": [ - "model_2 = Sequential()\n", - "model_2.add(Dense(16,input_shape = (43,),activation='relu'))\n", - "model_2.add(Dense(8,activation='relu'))\n", - "model_2.add(Dense(4,activation='relu'))\n", - "model_2.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "239430e6", - "metadata": {}, - "source": [ - "Vemos un resumen de nuestra red" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "49ce64fb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_5\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_19 (Dense) (None, 16) 704 \n", - "_________________________________________________________________\n", - "dense_20 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_21 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_22 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 881\n", - "Trainable params: 881\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.SGD(lr=0.001)\n", - "model_2.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model_2.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "63730f1e", - "metadata": {}, - "source": [ - "Finalmente entrenamos" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "96d25169", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.6538 - auc: 0.6197 - accuracy: 0.6581 - val_loss: 0.5826 - val_auc: 0.6542 - val_accuracy: 0.7382\n", - "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5671 - auc: 0.6664 - accuracy: 0.7483 - val_loss: 0.5361 - val_auc: 0.6975 - val_accuracy: 0.7546\n", - "Epoch 3/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5241 - auc: 0.7092 - accuracy: 0.7617 - val_loss: 0.5047 - val_auc: 0.7412 - val_accuracy: 0.7623\n", - "Epoch 4/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4950 - auc: 0.7536 - accuracy: 0.7665 - val_loss: 0.4789 - val_auc: 0.7739 - val_accuracy: 0.7700\n", - "Epoch 5/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4743 - auc: 0.7839 - accuracy: 0.7702 - val_loss: 0.4565 - val_auc: 0.7986 - val_accuracy: 0.7855\n", - "Epoch 6/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4443 - auc: 0.8097 - accuracy: 0.7977 - val_loss: 0.4373 - val_auc: 0.8173 - val_accuracy: 0.8041\n", - "Epoch 7/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4296 - auc: 0.8253 - accuracy: 0.8079 - val_loss: 0.4213 - val_auc: 0.8307 - val_accuracy: 0.8159\n", - "Epoch 8/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4149 - auc: 0.8362 - accuracy: 0.8168 - val_loss: 0.4080 - val_auc: 0.8418 - val_accuracy: 0.8205\n", - "Epoch 9/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3990 - auc: 0.8484 - accuracy: 0.8217 - val_loss: 0.3972 - val_auc: 0.8503 - val_accuracy: 0.8217\n", - "Epoch 10/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3962 - auc: 0.8513 - accuracy: 0.8202 - val_loss: 0.3886 - val_auc: 0.8569 - val_accuracy: 0.8242\n", - "Epoch 11/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3834 - auc: 0.8603 - accuracy: 0.8253 - val_loss: 0.3817 - val_auc: 0.8622 - val_accuracy: 0.8280\n", - "Epoch 12/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3756 - auc: 0.8653 - accuracy: 0.8318 - val_loss: 0.3763 - val_auc: 0.8662 - val_accuracy: 0.8296\n", - "Epoch 13/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3651 - auc: 0.8741 - accuracy: 0.8384 - val_loss: 0.3721 - val_auc: 0.8692 - val_accuracy: 0.8294\n", - "Epoch 14/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3685 - auc: 0.8739 - accuracy: 0.8333 - val_loss: 0.3687 - val_auc: 0.8719 - val_accuracy: 0.8302\n", - "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3673 - auc: 0.8740 - accuracy: 0.8307 - val_loss: 0.3659 - val_auc: 0.8742 - val_accuracy: 0.8320\n", - "Epoch 16/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3679 - auc: 0.8726 - accuracy: 0.8303 - val_loss: 0.3635 - val_auc: 0.8760 - val_accuracy: 0.8333\n", - "Epoch 17/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3607 - auc: 0.8793 - accuracy: 0.8340 - val_loss: 0.3614 - val_auc: 0.8776 - val_accuracy: 0.8333\n", - "Epoch 18/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3620 - auc: 0.8743 - accuracy: 0.8340 - val_loss: 0.3596 - val_auc: 0.8791 - val_accuracy: 0.8334\n", - "Epoch 19/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3655 - auc: 0.8762 - accuracy: 0.8315 - val_loss: 0.3581 - val_auc: 0.8802 - val_accuracy: 0.8342\n", - "Epoch 20/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3562 - auc: 0.8822 - accuracy: 0.8361 - val_loss: 0.3567 - val_auc: 0.8812 - val_accuracy: 0.8343\n", - "Epoch 21/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3560 - auc: 0.8812 - accuracy: 0.8344 - val_loss: 0.3555 - val_auc: 0.8822 - val_accuracy: 0.8342\n", - "Epoch 22/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3532 - auc: 0.8835 - accuracy: 0.8375 - val_loss: 0.3544 - val_auc: 0.8831 - val_accuracy: 0.8343\n", - "Epoch 23/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3501 - auc: 0.8843 - accuracy: 0.8395 - val_loss: 0.3533 - val_auc: 0.8840 - val_accuracy: 0.8353\n", - "Epoch 24/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3472 - auc: 0.8863 - accuracy: 0.8386 - val_loss: 0.3524 - val_auc: 0.8847 - val_accuracy: 0.8359\n", - "Epoch 25/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8832 - accuracy: 0.8382 - val_loss: 0.3515 - val_auc: 0.8852 - val_accuracy: 0.8369\n", - "Epoch 26/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8886 - accuracy: 0.8372 - val_loss: 0.3507 - val_auc: 0.8858 - val_accuracy: 0.8369\n", - "Epoch 27/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8849 - accuracy: 0.8348 - val_loss: 0.3500 - val_auc: 0.8863 - val_accuracy: 0.8366\n", - "Epoch 28/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3506 - auc: 0.8862 - accuracy: 0.8386 - val_loss: 0.3492 - val_auc: 0.8869 - val_accuracy: 0.8371\n", - "Epoch 29/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3426 - auc: 0.8897 - accuracy: 0.8430 - val_loss: 0.3485 - val_auc: 0.8873 - val_accuracy: 0.8377\n", - "Epoch 30/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3484 - auc: 0.8885 - accuracy: 0.8379 - val_loss: 0.3479 - val_auc: 0.8878 - val_accuracy: 0.8374\n", - "Epoch 31/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3480 - auc: 0.8881 - accuracy: 0.8374 - val_loss: 0.3472 - val_auc: 0.8883 - val_accuracy: 0.8383\n", - "Epoch 32/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3484 - auc: 0.8874 - accuracy: 0.8407 - val_loss: 0.3467 - val_auc: 0.8887 - val_accuracy: 0.8382\n", - "Epoch 33/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8899 - accuracy: 0.8396 - val_loss: 0.3461 - val_auc: 0.8891 - val_accuracy: 0.8392\n", - "Epoch 34/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3416 - auc: 0.8936 - accuracy: 0.8392 - val_loss: 0.3455 - val_auc: 0.8896 - val_accuracy: 0.8391\n", - "Epoch 35/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3438 - auc: 0.8910 - accuracy: 0.8401 - val_loss: 0.3450 - val_auc: 0.8899 - val_accuracy: 0.8392\n", - "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8903 - accuracy: 0.8380 - val_loss: 0.3445 - val_auc: 0.8903 - val_accuracy: 0.8392\n", - "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3454 - auc: 0.8898 - accuracy: 0.8403 - val_loss: 0.3440 - val_auc: 0.8905 - val_accuracy: 0.8388\n", - "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3434 - auc: 0.8922 - accuracy: 0.8408 - val_loss: 0.3435 - val_auc: 0.8909 - val_accuracy: 0.8392\n", - "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3399 - auc: 0.8921 - accuracy: 0.8427 - val_loss: 0.3431 - val_auc: 0.8912 - val_accuracy: 0.8396\n", - "Epoch 40/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3400 - auc: 0.8913 - accuracy: 0.8426 - val_loss: 0.3426 - val_auc: 0.8916 - val_accuracy: 0.8403\n", - "Epoch 41/200\n", - "814/814 [==============================] - 1s 989us/step - loss: 0.3408 - auc: 0.8923 - accuracy: 0.8378 - val_loss: 0.3422 - val_auc: 0.8918 - val_accuracy: 0.8409\n", - "Epoch 42/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3452 - auc: 0.8908 - accuracy: 0.8368 - val_loss: 0.3418 - val_auc: 0.8921 - val_accuracy: 0.8409\n", - "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3389 - auc: 0.8930 - accuracy: 0.8426 - val_loss: 0.3414 - val_auc: 0.8923 - val_accuracy: 0.8412\n", - "Epoch 44/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3392 - auc: 0.8945 - accuracy: 0.8425 - val_loss: 0.3410 - val_auc: 0.8925 - val_accuracy: 0.8412\n", - "Epoch 45/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3444 - auc: 0.8917 - accuracy: 0.8386 - val_loss: 0.3407 - val_auc: 0.8928 - val_accuracy: 0.8409\n", - "Epoch 46/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3365 - auc: 0.8941 - accuracy: 0.8442 - val_loss: 0.3403 - val_auc: 0.8931 - val_accuracy: 0.8415\n", - "Epoch 47/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3345 - auc: 0.8980 - accuracy: 0.8446 - val_loss: 0.3400 - val_auc: 0.8933 - val_accuracy: 0.8411\n", - "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3365 - auc: 0.8953 - accuracy: 0.8424 - val_loss: 0.3396 - val_auc: 0.8936 - val_accuracy: 0.8417\n", - "Epoch 49/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3395 - auc: 0.8951 - accuracy: 0.8427 - val_loss: 0.3393 - val_auc: 0.8938 - val_accuracy: 0.8417\n", - "Epoch 50/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3356 - auc: 0.8965 - accuracy: 0.8407 - val_loss: 0.3390 - val_auc: 0.8939 - val_accuracy: 0.8422\n", - "Epoch 51/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3396 - auc: 0.8939 - accuracy: 0.8404 - val_loss: 0.3388 - val_auc: 0.8941 - val_accuracy: 0.8431\n", - "Epoch 52/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3396 - auc: 0.8902 - accuracy: 0.8393 - val_loss: 0.3385 - val_auc: 0.8944 - val_accuracy: 0.8431\n", - "Epoch 53/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3373 - auc: 0.8956 - accuracy: 0.8371 - val_loss: 0.3382 - val_auc: 0.8945 - val_accuracy: 0.8434\n", - "Epoch 54/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3423 - auc: 0.8911 - accuracy: 0.8391 - val_loss: 0.3379 - val_auc: 0.8948 - val_accuracy: 0.8439\n", - "Epoch 55/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3389 - auc: 0.8942 - accuracy: 0.8399 - val_loss: 0.3376 - val_auc: 0.8949 - val_accuracy: 0.8442\n", - "Epoch 56/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3307 - auc: 0.8980 - accuracy: 0.8454 - val_loss: 0.3374 - val_auc: 0.8951 - val_accuracy: 0.8443\n", - "Epoch 57/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3355 - auc: 0.8966 - accuracy: 0.8453 - val_loss: 0.3371 - val_auc: 0.8953 - val_accuracy: 0.8446\n", - "Epoch 58/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3354 - auc: 0.8967 - accuracy: 0.8446 - val_loss: 0.3369 - val_auc: 0.8955 - val_accuracy: 0.8457\n", - "Epoch 59/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3327 - auc: 0.8988 - accuracy: 0.8455 - val_loss: 0.3366 - val_auc: 0.8956 - val_accuracy: 0.8457\n", - "Epoch 60/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3398 - auc: 0.8944 - accuracy: 0.8406 - val_loss: 0.3364 - val_auc: 0.8959 - val_accuracy: 0.8458\n", - "Epoch 61/200\n", - "814/814 [==============================] - 1s 996us/step - loss: 0.3344 - auc: 0.8963 - accuracy: 0.8443 - val_loss: 0.3362 - val_auc: 0.8960 - val_accuracy: 0.8460\n", - "Epoch 62/200\n", - "814/814 [==============================] - 1s 993us/step - loss: 0.3368 - auc: 0.8976 - accuracy: 0.8423 - val_loss: 0.3359 - val_auc: 0.8962 - val_accuracy: 0.8462\n", - "Epoch 63/200\n", - "814/814 [==============================] - 1s 988us/step - loss: 0.3324 - auc: 0.8971 - accuracy: 0.8452 - val_loss: 0.3357 - val_auc: 0.8963 - val_accuracy: 0.8455\n", - "Epoch 64/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3343 - auc: 0.8972 - accuracy: 0.8439 - val_loss: 0.3355 - val_auc: 0.8965 - val_accuracy: 0.8457\n", - "Epoch 65/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3356 - auc: 0.8940 - accuracy: 0.8423 - val_loss: 0.3353 - val_auc: 0.8966 - val_accuracy: 0.8462\n", - "Epoch 66/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3316 - auc: 0.8986 - accuracy: 0.8426 - val_loss: 0.3351 - val_auc: 0.8967 - val_accuracy: 0.8466\n", - "Epoch 67/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3279 - auc: 0.8993 - accuracy: 0.8437 - val_loss: 0.3349 - val_auc: 0.8969 - val_accuracy: 0.8469\n", - "Epoch 68/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3297 - auc: 0.9018 - accuracy: 0.8465 - val_loss: 0.3347 - val_auc: 0.8970 - val_accuracy: 0.8466\n", - "Epoch 69/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3365 - auc: 0.8963 - accuracy: 0.8402 - val_loss: 0.3346 - val_auc: 0.8970 - val_accuracy: 0.8462\n", - "Epoch 70/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3347 - auc: 0.8976 - accuracy: 0.8414 - val_loss: 0.3344 - val_auc: 0.8971 - val_accuracy: 0.8466\n", - "Epoch 71/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3323 - auc: 0.8986 - accuracy: 0.8470 - val_loss: 0.3342 - val_auc: 0.8973 - val_accuracy: 0.8468\n", - "Epoch 72/200\n", - "814/814 [==============================] - 1s 996us/step - loss: 0.3276 - auc: 0.9017 - accuracy: 0.8475 - val_loss: 0.3341 - val_auc: 0.8974 - val_accuracy: 0.8465\n", - "Epoch 73/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3318 - auc: 0.8984 - accuracy: 0.8444 - val_loss: 0.3339 - val_auc: 0.8975 - val_accuracy: 0.8465\n", - "Epoch 74/200\n", - "814/814 [==============================] - 1s 995us/step - loss: 0.3298 - auc: 0.9005 - accuracy: 0.8473 - val_loss: 0.3337 - val_auc: 0.8976 - val_accuracy: 0.8462\n", - "Epoch 75/200\n", - "814/814 [==============================] - 1s 987us/step - loss: 0.3320 - auc: 0.8998 - accuracy: 0.8446 - val_loss: 0.3336 - val_auc: 0.8977 - val_accuracy: 0.8463\n", - "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3305 - auc: 0.8979 - accuracy: 0.8429 - val_loss: 0.3335 - val_auc: 0.8978 - val_accuracy: 0.8465\n", - "Epoch 77/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3295 - auc: 0.8994 - accuracy: 0.8464 - val_loss: 0.3333 - val_auc: 0.8978 - val_accuracy: 0.8462\n", - "Epoch 78/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3303 - auc: 0.9010 - accuracy: 0.8448 - val_loss: 0.3331 - val_auc: 0.8979 - val_accuracy: 0.8462\n", - "Epoch 79/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3322 - auc: 0.8981 - accuracy: 0.8432 - val_loss: 0.3330 - val_auc: 0.8980 - val_accuracy: 0.8462\n", - "Epoch 80/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3246 - auc: 0.9004 - accuracy: 0.8491 - val_loss: 0.3329 - val_auc: 0.8980 - val_accuracy: 0.8463\n", - "Epoch 81/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3270 - auc: 0.9007 - accuracy: 0.8463 - val_loss: 0.3328 - val_auc: 0.8982 - val_accuracy: 0.8468\n", - "Epoch 82/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3261 - auc: 0.9034 - accuracy: 0.8462 - val_loss: 0.3326 - val_auc: 0.8983 - val_accuracy: 0.8466\n", - "Epoch 83/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3333 - auc: 0.8984 - accuracy: 0.8422 - val_loss: 0.3325 - val_auc: 0.8983 - val_accuracy: 0.8466\n", - "Epoch 84/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3299 - auc: 0.8996 - accuracy: 0.8438 - val_loss: 0.3324 - val_auc: 0.8984 - val_accuracy: 0.8466\n", - "Epoch 85/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3196 - auc: 0.9055 - accuracy: 0.8507 - val_loss: 0.3323 - val_auc: 0.8986 - val_accuracy: 0.8469\n", - "Epoch 86/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3291 - auc: 0.8992 - accuracy: 0.8451 - val_loss: 0.3322 - val_auc: 0.8985 - val_accuracy: 0.8463\n", - "Epoch 87/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3273 - auc: 0.9021 - accuracy: 0.8436 - val_loss: 0.3321 - val_auc: 0.8986 - val_accuracy: 0.8465\n", - "Epoch 88/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3249 - auc: 0.9034 - accuracy: 0.8472 - val_loss: 0.3320 - val_auc: 0.8987 - val_accuracy: 0.8468\n", - "Epoch 89/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3233 - auc: 0.9038 - accuracy: 0.8482 - val_loss: 0.3318 - val_auc: 0.8988 - val_accuracy: 0.8469\n", - "Epoch 90/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3291 - auc: 0.8991 - accuracy: 0.8472 - val_loss: 0.3317 - val_auc: 0.8989 - val_accuracy: 0.8474\n", - "Epoch 91/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3242 - auc: 0.9025 - accuracy: 0.8467 - val_loss: 0.3315 - val_auc: 0.8990 - val_accuracy: 0.8475\n", - "Epoch 92/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3300 - auc: 0.8995 - accuracy: 0.8445 - val_loss: 0.3315 - val_auc: 0.8991 - val_accuracy: 0.8478\n", - "Epoch 93/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3361 - auc: 0.8969 - accuracy: 0.8402 - val_loss: 0.3314 - val_auc: 0.8991 - val_accuracy: 0.8474\n", - "Epoch 94/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3269 - auc: 0.9011 - accuracy: 0.8455 - val_loss: 0.3312 - val_auc: 0.8992 - val_accuracy: 0.8472\n", - "Epoch 95/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3325 - auc: 0.8987 - accuracy: 0.8427 - val_loss: 0.3311 - val_auc: 0.8993 - val_accuracy: 0.8475\n", - "Epoch 96/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3250 - auc: 0.9032 - accuracy: 0.8474 - val_loss: 0.3310 - val_auc: 0.8994 - val_accuracy: 0.8475\n", - "Epoch 97/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3278 - auc: 0.9032 - accuracy: 0.8463 - val_loss: 0.3309 - val_auc: 0.8994 - val_accuracy: 0.8475\n", - "Epoch 98/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3305 - auc: 0.8995 - accuracy: 0.8445 - val_loss: 0.3309 - val_auc: 0.8995 - val_accuracy: 0.8477\n", - "Epoch 99/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3248 - auc: 0.9039 - accuracy: 0.8490 - val_loss: 0.3307 - val_auc: 0.8995 - val_accuracy: 0.8477\n", - "Epoch 100/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3257 - auc: 0.9034 - accuracy: 0.8496 - val_loss: 0.3307 - val_auc: 0.8997 - val_accuracy: 0.8475\n", - "Epoch 101/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3326 - auc: 0.8994 - accuracy: 0.8420 - val_loss: 0.3306 - val_auc: 0.8997 - val_accuracy: 0.8474\n", - "Epoch 102/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3238 - auc: 0.9046 - accuracy: 0.8470 - val_loss: 0.3305 - val_auc: 0.8997 - val_accuracy: 0.8475\n", - "Epoch 103/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3262 - auc: 0.9027 - accuracy: 0.8436 - val_loss: 0.3304 - val_auc: 0.8997 - val_accuracy: 0.8474\n", - "Epoch 104/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3257 - auc: 0.9020 - accuracy: 0.8474 - val_loss: 0.3303 - val_auc: 0.8999 - val_accuracy: 0.8480\n", - "Epoch 105/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3234 - auc: 0.9032 - accuracy: 0.8464 - val_loss: 0.3303 - val_auc: 0.8999 - val_accuracy: 0.8480\n", - "Epoch 106/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3278 - auc: 0.9020 - accuracy: 0.8460 - val_loss: 0.3302 - val_auc: 0.9000 - val_accuracy: 0.8478\n", - "Epoch 107/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3298 - auc: 0.9011 - accuracy: 0.8455 - val_loss: 0.3301 - val_auc: 0.9000 - val_accuracy: 0.8478\n", - "Epoch 108/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3232 - auc: 0.9026 - accuracy: 0.8479 - val_loss: 0.3300 - val_auc: 0.9001 - val_accuracy: 0.8478\n", - "Epoch 109/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3256 - auc: 0.9021 - accuracy: 0.8455 - val_loss: 0.3299 - val_auc: 0.9001 - val_accuracy: 0.8478\n", - "Epoch 110/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3277 - auc: 0.8999 - accuracy: 0.8456 - val_loss: 0.3298 - val_auc: 0.9002 - val_accuracy: 0.8480\n", - "Epoch 111/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3241 - auc: 0.9022 - accuracy: 0.8451 - val_loss: 0.3298 - val_auc: 0.9003 - val_accuracy: 0.8480\n", - "Epoch 112/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3208 - auc: 0.9069 - accuracy: 0.8500 - val_loss: 0.3297 - val_auc: 0.9003 - val_accuracy: 0.8478\n", - "Epoch 113/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3281 - auc: 0.9015 - accuracy: 0.8476 - val_loss: 0.3296 - val_auc: 0.9004 - val_accuracy: 0.8480\n", - "Epoch 114/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3280 - auc: 0.9001 - accuracy: 0.8450 - val_loss: 0.3296 - val_auc: 0.9004 - val_accuracy: 0.8485\n", - "Epoch 115/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3198 - auc: 0.9035 - accuracy: 0.8493 - val_loss: 0.3295 - val_auc: 0.9005 - val_accuracy: 0.8477\n", - "Epoch 116/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3202 - auc: 0.9057 - accuracy: 0.8503 - val_loss: 0.3294 - val_auc: 0.9005 - val_accuracy: 0.8478\n", - "Epoch 117/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3280 - auc: 0.9023 - accuracy: 0.8463 - val_loss: 0.3293 - val_auc: 0.9006 - val_accuracy: 0.8478\n", - "Epoch 118/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3274 - auc: 0.9026 - accuracy: 0.8485 - val_loss: 0.3293 - val_auc: 0.9007 - val_accuracy: 0.8478\n", - "Epoch 119/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3219 - auc: 0.9039 - accuracy: 0.8487 - val_loss: 0.3292 - val_auc: 0.9007 - val_accuracy: 0.8481\n", - "Epoch 120/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3252 - auc: 0.9013 - accuracy: 0.8466 - val_loss: 0.3291 - val_auc: 0.9007 - val_accuracy: 0.8483\n", - "Epoch 121/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3265 - auc: 0.9011 - accuracy: 0.8469 - val_loss: 0.3291 - val_auc: 0.9008 - val_accuracy: 0.8483\n", - "Epoch 122/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3280 - auc: 0.9025 - accuracy: 0.8482 - val_loss: 0.3290 - val_auc: 0.9009 - val_accuracy: 0.8478\n", - "Epoch 123/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3236 - auc: 0.9045 - accuracy: 0.8477 - val_loss: 0.3290 - val_auc: 0.9009 - val_accuracy: 0.8475\n", - "Epoch 124/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3187 - auc: 0.9069 - accuracy: 0.8482 - val_loss: 0.3289 - val_auc: 0.9010 - val_accuracy: 0.8481\n", - "Epoch 125/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3239 - auc: 0.9045 - accuracy: 0.8486 - val_loss: 0.3288 - val_auc: 0.9010 - val_accuracy: 0.8480\n", - "Epoch 126/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3217 - auc: 0.9056 - accuracy: 0.8499 - val_loss: 0.3287 - val_auc: 0.9010 - val_accuracy: 0.8480\n", - "Epoch 127/200\n", - "814/814 [==============================] - 1s 990us/step - loss: 0.3177 - auc: 0.9077 - accuracy: 0.8514 - val_loss: 0.3286 - val_auc: 0.9011 - val_accuracy: 0.8477\n", - "Epoch 128/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3228 - auc: 0.9035 - accuracy: 0.8486 - val_loss: 0.3286 - val_auc: 0.9011 - val_accuracy: 0.8478\n", - "Epoch 129/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3223 - auc: 0.9043 - accuracy: 0.8486 - val_loss: 0.3286 - val_auc: 0.9012 - val_accuracy: 0.8488\n", - "Epoch 130/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3269 - auc: 0.9019 - accuracy: 0.8469 - val_loss: 0.3285 - val_auc: 0.9012 - val_accuracy: 0.8481\n", - "Epoch 131/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3221 - auc: 0.9034 - accuracy: 0.8503 - val_loss: 0.3284 - val_auc: 0.9013 - val_accuracy: 0.8485\n", - "Epoch 132/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3252 - auc: 0.9033 - accuracy: 0.8466 - val_loss: 0.3284 - val_auc: 0.9013 - val_accuracy: 0.8483\n", - "Epoch 133/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3296 - auc: 0.8994 - accuracy: 0.8470 - val_loss: 0.3283 - val_auc: 0.9013 - val_accuracy: 0.8483\n", - "Epoch 134/200\n", - "814/814 [==============================] - 1s 992us/step - loss: 0.3237 - auc: 0.9038 - accuracy: 0.8490 - val_loss: 0.3283 - val_auc: 0.9014 - val_accuracy: 0.8480\n", - "Epoch 135/200\n", - "814/814 [==============================] - 1s 996us/step - loss: 0.3244 - auc: 0.9039 - accuracy: 0.8481 - val_loss: 0.3282 - val_auc: 0.9014 - val_accuracy: 0.8480\n", - "Epoch 136/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3258 - auc: 0.9040 - accuracy: 0.8481 - val_loss: 0.3281 - val_auc: 0.9014 - val_accuracy: 0.8481\n", - "Epoch 137/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3224 - auc: 0.9052 - accuracy: 0.8503 - val_loss: 0.3281 - val_auc: 0.9015 - val_accuracy: 0.8481\n", - "Epoch 138/200\n", - "814/814 [==============================] - 1s 997us/step - loss: 0.3186 - auc: 0.9060 - accuracy: 0.8490 - val_loss: 0.3280 - val_auc: 0.9015 - val_accuracy: 0.8477\n", - "Epoch 139/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3240 - auc: 0.9035 - accuracy: 0.8498 - val_loss: 0.3280 - val_auc: 0.9016 - val_accuracy: 0.8480\n", - "Epoch 140/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3257 - auc: 0.9033 - accuracy: 0.8462 - val_loss: 0.3279 - val_auc: 0.9016 - val_accuracy: 0.8480\n", - "Epoch 141/200\n", - "814/814 [==============================] - 1s 998us/step - loss: 0.3177 - auc: 0.9053 - accuracy: 0.8499 - val_loss: 0.3279 - val_auc: 0.9016 - val_accuracy: 0.8480\n", - "Epoch 142/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3192 - auc: 0.9068 - accuracy: 0.8463 - val_loss: 0.3278 - val_auc: 0.9016 - val_accuracy: 0.8480\n", - "Epoch 143/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3191 - auc: 0.9058 - accuracy: 0.8497 - val_loss: 0.3277 - val_auc: 0.9017 - val_accuracy: 0.8480\n", - "Epoch 144/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3202 - auc: 0.9053 - accuracy: 0.8507 - val_loss: 0.3277 - val_auc: 0.9017 - val_accuracy: 0.8478\n", - "Epoch 145/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3290 - auc: 0.9020 - accuracy: 0.8464 - val_loss: 0.3276 - val_auc: 0.9019 - val_accuracy: 0.8478\n", - "Epoch 146/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9054 - accuracy: 0.8490 - val_loss: 0.3276 - val_auc: 0.9018 - val_accuracy: 0.8478\n", - "Epoch 147/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3212 - auc: 0.9060 - accuracy: 0.8477 - val_loss: 0.3275 - val_auc: 0.9019 - val_accuracy: 0.8480\n", - "Epoch 148/200\n", - "814/814 [==============================] - 1s 985us/step - loss: 0.3190 - auc: 0.9059 - accuracy: 0.8501 - val_loss: 0.3275 - val_auc: 0.9019 - val_accuracy: 0.8483\n", - "Epoch 149/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3187 - auc: 0.9069 - accuracy: 0.8497 - val_loss: 0.3274 - val_auc: 0.9019 - val_accuracy: 0.8483\n", - "Epoch 150/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3201 - auc: 0.9048 - accuracy: 0.8499 - val_loss: 0.3274 - val_auc: 0.9020 - val_accuracy: 0.8481\n", - "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3219 - auc: 0.9048 - accuracy: 0.8496 - val_loss: 0.3273 - val_auc: 0.9020 - val_accuracy: 0.8480\n", - "Epoch 152/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3229 - auc: 0.9042 - accuracy: 0.8485 - val_loss: 0.3272 - val_auc: 0.9020 - val_accuracy: 0.8486\n", - "Epoch 153/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9047 - accuracy: 0.8472 - val_loss: 0.3272 - val_auc: 0.9021 - val_accuracy: 0.8488\n", - "Epoch 154/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9057 - accuracy: 0.8490 - val_loss: 0.3271 - val_auc: 0.9021 - val_accuracy: 0.8486\n", - "Epoch 155/200\n", - "814/814 [==============================] - 1s 989us/step - loss: 0.3210 - auc: 0.9064 - accuracy: 0.8496 - val_loss: 0.3271 - val_auc: 0.9021 - val_accuracy: 0.8483\n", - "Epoch 156/200\n", - "814/814 [==============================] - 1s 993us/step - loss: 0.3232 - auc: 0.9038 - accuracy: 0.8500 - val_loss: 0.3270 - val_auc: 0.9022 - val_accuracy: 0.8494\n", - "Epoch 157/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3180 - auc: 0.9062 - accuracy: 0.8519 - val_loss: 0.3270 - val_auc: 0.9022 - val_accuracy: 0.8485\n", - "Epoch 158/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - auc: 0.9078 - accuracy: 0.8498 - val_loss: 0.3269 - val_auc: 0.9023 - val_accuracy: 0.8486\n", - "Epoch 159/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9056 - accuracy: 0.8500 - val_loss: 0.3269 - val_auc: 0.9023 - val_accuracy: 0.8486\n", - "Epoch 160/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3218 - auc: 0.9055 - accuracy: 0.8488 - val_loss: 0.3269 - val_auc: 0.9023 - val_accuracy: 0.8483\n", - "Epoch 161/200\n", - "814/814 [==============================] - 1s 989us/step - loss: 0.3186 - auc: 0.9085 - accuracy: 0.8515 - val_loss: 0.3268 - val_auc: 0.9024 - val_accuracy: 0.8485\n", - "Epoch 162/200\n", - "814/814 [==============================] - 1s 998us/step - loss: 0.3223 - auc: 0.9057 - accuracy: 0.8505 - val_loss: 0.3268 - val_auc: 0.9024 - val_accuracy: 0.8483\n", - "Epoch 163/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3141 - auc: 0.9092 - accuracy: 0.8498 - val_loss: 0.3267 - val_auc: 0.9025 - val_accuracy: 0.8483\n", - "Epoch 164/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3166 - auc: 0.9094 - accuracy: 0.8514 - val_loss: 0.3267 - val_auc: 0.9025 - val_accuracy: 0.8483\n", - "Epoch 165/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3223 - auc: 0.9044 - accuracy: 0.8508 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8475\n", - "Epoch 166/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3150 - auc: 0.9083 - accuracy: 0.8508 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8481\n", - "Epoch 167/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3217 - auc: 0.9047 - accuracy: 0.8485 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8481\n", - "Epoch 168/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3217 - auc: 0.9057 - accuracy: 0.8478 - val_loss: 0.3265 - val_auc: 0.9025 - val_accuracy: 0.8481\n", - "Epoch 169/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3229 - auc: 0.9056 - accuracy: 0.8488 - val_loss: 0.3265 - val_auc: 0.9026 - val_accuracy: 0.8475\n", - "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3186 - auc: 0.9061 - accuracy: 0.8504 - val_loss: 0.3264 - val_auc: 0.9026 - val_accuracy: 0.8483\n", - "Epoch 171/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3182 - auc: 0.9073 - accuracy: 0.8512 - val_loss: 0.3264 - val_auc: 0.9027 - val_accuracy: 0.8480\n", - "Epoch 172/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3211 - auc: 0.9064 - accuracy: 0.8511 - val_loss: 0.3264 - val_auc: 0.9027 - val_accuracy: 0.8474\n", - "Epoch 173/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3223 - auc: 0.9055 - accuracy: 0.8470 - val_loss: 0.3263 - val_auc: 0.9027 - val_accuracy: 0.8480\n", - "Epoch 174/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3254 - auc: 0.9033 - accuracy: 0.8471 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", - "Epoch 175/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3177 - auc: 0.9073 - accuracy: 0.8482 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", - "Epoch 176/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3182 - auc: 0.9067 - accuracy: 0.8492 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", - "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3205 - auc: 0.9046 - accuracy: 0.8495 - val_loss: 0.3262 - val_auc: 0.9028 - val_accuracy: 0.8478\n", - "Epoch 178/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3191 - auc: 0.9064 - accuracy: 0.8501 - val_loss: 0.3262 - val_auc: 0.9028 - val_accuracy: 0.8480\n", - "Epoch 179/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9048 - accuracy: 0.8493 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8475\n", - "Epoch 180/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3180 - auc: 0.9073 - accuracy: 0.8513 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8477\n", - "Epoch 181/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3191 - auc: 0.9072 - accuracy: 0.8490 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8466\n", - "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3188 - auc: 0.9061 - accuracy: 0.8494 - val_loss: 0.3260 - val_auc: 0.9030 - val_accuracy: 0.8481\n", - "Epoch 183/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3157 - auc: 0.9094 - accuracy: 0.8527 - val_loss: 0.3260 - val_auc: 0.9029 - val_accuracy: 0.8475\n", - "Epoch 184/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3150 - auc: 0.9087 - accuracy: 0.8507 - val_loss: 0.3260 - val_auc: 0.9030 - val_accuracy: 0.8480\n", - "Epoch 185/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3181 - auc: 0.9067 - accuracy: 0.8519 - val_loss: 0.3259 - val_auc: 0.9029 - val_accuracy: 0.8480\n", - "Epoch 186/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9045 - accuracy: 0.8486 - val_loss: 0.3259 - val_auc: 0.9030 - val_accuracy: 0.8481\n", - "Epoch 187/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3094 - auc: 0.9126 - accuracy: 0.8550 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8468\n", - "Epoch 188/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3193 - auc: 0.9056 - accuracy: 0.8518 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8477\n", - "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3144 - auc: 0.9089 - accuracy: 0.8499 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8477\n", - "Epoch 190/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3192 - auc: 0.9063 - accuracy: 0.8511 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8472\n", - "Epoch 191/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3212 - auc: 0.9078 - accuracy: 0.8474 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8465\n", - "Epoch 192/200\n", - "814/814 [==============================] - 1s 999us/step - loss: 0.3245 - auc: 0.9053 - accuracy: 0.8467 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8466\n", - "Epoch 193/200\n", - "814/814 [==============================] - 1s 993us/step - loss: 0.3173 - auc: 0.9071 - accuracy: 0.8504 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8465\n", - "Epoch 194/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9057 - accuracy: 0.8498 - val_loss: 0.3257 - val_auc: 0.9032 - val_accuracy: 0.8466\n", - "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3264 - auc: 0.9019 - accuracy: 0.8464 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8468\n", - "Epoch 196/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3180 - auc: 0.9062 - accuracy: 0.8493 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8471\n", - "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3166 - auc: 0.9060 - accuracy: 0.8503 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8471\n", - "Epoch 198/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3164 - auc: 0.9078 - accuracy: 0.8510 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8481\n", - "Epoch 199/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3169 - auc: 0.9082 - accuracy: 0.8531 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8474\n", - "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3167 - auc: 0.9084 - accuracy: 0.8486 - val_loss: 0.3255 - val_auc: 0.9032 - val_accuracy: 0.8462\n" - ] - } - ], - "source": [ - "history = model_2.fit(X_train.values, y_train,epochs=200,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "5cfcf350", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "269dac2b", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "9048387c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "8bb11060", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "0478b998", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.9032321093250243\n", - "AUC-ROC score sobre train: 0.9077897388180396\n", - "Accuracy sobre test: 0.8461538461538461\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.92 0.88 0.90 5141\n", - " Alto valor 0.62 0.71 0.66 1372\n", - "\n", - " accuracy 0.85 6513\n", - " macro avg 0.77 0.79 0.78 6513\n", - "weighted avg 0.86 0.85 0.85 6513\n", - "\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": [ - "y_pred = model_2.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model_2.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model_2.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model_2, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "ebb3a401", - "metadata": {}, - "source": [ - "Lo obtenido es muy prometedor, practicamente empato los resultados del último modelo del primer preprocesado. Veamos que sucede si mejoramos el optimizador" - ] - }, - { - "cell_type": "markdown", - "id": "41e44801", - "metadata": {}, - "source": [ - "### Segundo diseño de la red" - ] - }, - { - "cell_type": "markdown", - "id": "0b9a6dfe", - "metadata": {}, - "source": [ - "Seguiremos con la misma estrucctura pero modificando el learnin rate a 0.0001 y cambiando el optimziador" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "5a33eb2b", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "b20bc13f", - "metadata": {}, - "outputs": [], - "source": [ - "model_2 = Sequential()\n", - "model_2.add(Dense(16,input_shape = (43,),activation='relu'))\n", - "model_2.add(Dense(8,activation='relu'))\n", - "model_2.add(Dense(4,activation='relu'))\n", - "model_2.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "3576204e", - "metadata": {}, - "source": [ - "Vemos un resumen de nuestra red" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "e50c637a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_6\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_23 (Dense) (None, 16) 704 \n", - "_________________________________________________________________\n", - "dense_24 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_25 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_26 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 881\n", - "Trainable params: 881\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001)\n", - "model_2.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model_2.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "e065c6c0", - "metadata": {}, - "source": [ - "Finalmente entrenamos" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "1e285930", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/200\n", - "814/814 [==============================] - 2s 1ms/step - loss: 0.6239 - auc: 0.6590 - accuracy: 0.6945 - val_loss: 0.4875 - val_auc: 0.7859 - val_accuracy: 0.7926\n", - "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4576 - auc: 0.8078 - accuracy: 0.8054 - val_loss: 0.4019 - val_auc: 0.8489 - val_accuracy: 0.8188\n", - "Epoch 3/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3878 - auc: 0.8578 - accuracy: 0.8271 - val_loss: 0.3730 - val_auc: 0.8694 - val_accuracy: 0.8308\n", - "Epoch 4/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3644 - auc: 0.8746 - accuracy: 0.8346 - val_loss: 0.3614 - val_auc: 0.8778 - val_accuracy: 0.8333\n", - "Epoch 5/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3615 - auc: 0.8797 - accuracy: 0.8335 - val_loss: 0.3553 - val_auc: 0.8825 - val_accuracy: 0.8356\n", - "Epoch 6/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3459 - auc: 0.8875 - accuracy: 0.8427 - val_loss: 0.3511 - val_auc: 0.8858 - val_accuracy: 0.8363\n", - "Epoch 7/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3480 - auc: 0.8877 - accuracy: 0.8396 - val_loss: 0.3482 - val_auc: 0.8878 - val_accuracy: 0.8372\n", - "Epoch 8/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3439 - auc: 0.8901 - accuracy: 0.8422 - val_loss: 0.3459 - val_auc: 0.8894 - val_accuracy: 0.8377\n", - "Epoch 9/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8904 - accuracy: 0.8417 - val_loss: 0.3443 - val_auc: 0.8903 - val_accuracy: 0.8391\n", - "Epoch 10/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3490 - auc: 0.8871 - accuracy: 0.8360 - val_loss: 0.3428 - val_auc: 0.8913 - val_accuracy: 0.8405\n", - "Epoch 11/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3411 - auc: 0.8914 - accuracy: 0.8407 - val_loss: 0.3415 - val_auc: 0.8922 - val_accuracy: 0.8405\n", - "Epoch 12/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3394 - auc: 0.8917 - accuracy: 0.8427 - val_loss: 0.3404 - val_auc: 0.8929 - val_accuracy: 0.8414\n", - "Epoch 13/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3315 - auc: 0.8976 - accuracy: 0.8492 - val_loss: 0.3393 - val_auc: 0.8935 - val_accuracy: 0.8419\n", - "Epoch 14/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3374 - auc: 0.8956 - accuracy: 0.8443 - val_loss: 0.3385 - val_auc: 0.8940 - val_accuracy: 0.8415\n", - "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3387 - auc: 0.8947 - accuracy: 0.8413 - val_loss: 0.3377 - val_auc: 0.8946 - val_accuracy: 0.8425\n", - "Epoch 16/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3387 - auc: 0.8937 - accuracy: 0.8412 - val_loss: 0.3369 - val_auc: 0.8951 - val_accuracy: 0.8435\n", - "Epoch 17/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3327 - auc: 0.8984 - accuracy: 0.8463 - val_loss: 0.3361 - val_auc: 0.8956 - val_accuracy: 0.8435\n", - "Epoch 18/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3350 - auc: 0.8940 - accuracy: 0.8456 - val_loss: 0.3354 - val_auc: 0.8961 - val_accuracy: 0.8449\n", - "Epoch 19/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3403 - auc: 0.8941 - accuracy: 0.8419 - val_loss: 0.3349 - val_auc: 0.8964 - val_accuracy: 0.8462\n", - "Epoch 20/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3298 - auc: 0.9000 - accuracy: 0.8485 - val_loss: 0.3343 - val_auc: 0.8968 - val_accuracy: 0.8466\n", - "Epoch 21/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3302 - auc: 0.8989 - accuracy: 0.8477 - val_loss: 0.3339 - val_auc: 0.8971 - val_accuracy: 0.8481\n", - "Epoch 22/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3291 - auc: 0.8996 - accuracy: 0.8488 - val_loss: 0.3334 - val_auc: 0.8975 - val_accuracy: 0.8494\n", - "Epoch 23/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3258 - auc: 0.9007 - accuracy: 0.8507 - val_loss: 0.3329 - val_auc: 0.8977 - val_accuracy: 0.8481\n", - "Epoch 24/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3240 - auc: 0.9017 - accuracy: 0.8497 - val_loss: 0.3325 - val_auc: 0.8980 - val_accuracy: 0.8480\n", - "Epoch 25/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3288 - auc: 0.8994 - accuracy: 0.8483 - val_loss: 0.3323 - val_auc: 0.8983 - val_accuracy: 0.8474\n", - "Epoch 26/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3254 - auc: 0.9030 - accuracy: 0.8492 - val_loss: 0.3317 - val_auc: 0.8984 - val_accuracy: 0.8483\n", - "Epoch 27/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3305 - auc: 0.9002 - accuracy: 0.8476 - val_loss: 0.3313 - val_auc: 0.8987 - val_accuracy: 0.8474\n", - "Epoch 28/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3306 - auc: 0.8990 - accuracy: 0.8508 - val_loss: 0.3310 - val_auc: 0.8989 - val_accuracy: 0.8480\n", - "Epoch 29/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3223 - auc: 0.9027 - accuracy: 0.8540 - val_loss: 0.3307 - val_auc: 0.8991 - val_accuracy: 0.8491\n", - "Epoch 30/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3260 - auc: 0.9031 - accuracy: 0.8510 - val_loss: 0.3306 - val_auc: 0.8993 - val_accuracy: 0.8495\n", - "Epoch 31/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3252 - auc: 0.9027 - accuracy: 0.8500 - val_loss: 0.3300 - val_auc: 0.8996 - val_accuracy: 0.8498\n", - "Epoch 32/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3261 - auc: 0.9015 - accuracy: 0.8536 - val_loss: 0.3299 - val_auc: 0.8997 - val_accuracy: 0.8503\n", - "Epoch 33/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3257 - auc: 0.9038 - accuracy: 0.8519 - val_loss: 0.3296 - val_auc: 0.9000 - val_accuracy: 0.8501\n", - "Epoch 34/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9061 - accuracy: 0.8497 - val_loss: 0.3294 - val_auc: 0.9001 - val_accuracy: 0.8506\n", - "Epoch 35/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3227 - auc: 0.9040 - accuracy: 0.8513 - val_loss: 0.3293 - val_auc: 0.9002 - val_accuracy: 0.8505\n", - "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9035 - accuracy: 0.8491 - val_loss: 0.3293 - val_auc: 0.9004 - val_accuracy: 0.8505\n", - "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3246 - auc: 0.9032 - accuracy: 0.8516 - val_loss: 0.3291 - val_auc: 0.9004 - val_accuracy: 0.8506\n", - "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3208 - auc: 0.9063 - accuracy: 0.8517 - val_loss: 0.3291 - val_auc: 0.9005 - val_accuracy: 0.8506\n", - "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9048 - accuracy: 0.8543 - val_loss: 0.3291 - val_auc: 0.9006 - val_accuracy: 0.8509\n", - "Epoch 40/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3216 - auc: 0.9029 - accuracy: 0.8514 - val_loss: 0.3289 - val_auc: 0.9008 - val_accuracy: 0.8505\n", - "Epoch 41/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3202 - auc: 0.9052 - accuracy: 0.8506 - val_loss: 0.3288 - val_auc: 0.9008 - val_accuracy: 0.8506\n", - "Epoch 42/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3259 - auc: 0.9027 - accuracy: 0.8485 - val_loss: 0.3288 - val_auc: 0.9009 - val_accuracy: 0.8512\n", - "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3226 - auc: 0.9039 - accuracy: 0.8522 - val_loss: 0.3287 - val_auc: 0.9009 - val_accuracy: 0.8503\n", - "Epoch 44/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3207 - auc: 0.9052 - accuracy: 0.8513 - val_loss: 0.3288 - val_auc: 0.9009 - val_accuracy: 0.8498\n", - "Epoch 45/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3260 - auc: 0.9030 - accuracy: 0.8475 - val_loss: 0.3289 - val_auc: 0.9010 - val_accuracy: 0.8500\n", - "Epoch 46/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3179 - auc: 0.9057 - accuracy: 0.8564 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8500\n", - "Epoch 47/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3165 - auc: 0.9086 - accuracy: 0.8530 - val_loss: 0.3287 - val_auc: 0.9010 - val_accuracy: 0.8498\n", - "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3186 - auc: 0.9066 - accuracy: 0.8522 - val_loss: 0.3287 - val_auc: 0.9011 - val_accuracy: 0.8500\n", - "Epoch 49/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9053 - accuracy: 0.8516 - val_loss: 0.3287 - val_auc: 0.9011 - val_accuracy: 0.8500\n", - "Epoch 50/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3186 - auc: 0.9068 - accuracy: 0.8492 - val_loss: 0.3287 - val_auc: 0.9011 - val_accuracy: 0.8498\n", - "Epoch 51/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3209 - auc: 0.9052 - accuracy: 0.8524 - val_loss: 0.3287 - val_auc: 0.9012 - val_accuracy: 0.8503\n", - "Epoch 52/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3225 - auc: 0.9011 - accuracy: 0.8507 - val_loss: 0.3291 - val_auc: 0.9012 - val_accuracy: 0.8506\n", - "Epoch 53/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3228 - auc: 0.9048 - accuracy: 0.8476 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8501\n", - "Epoch 54/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3231 - auc: 0.9031 - accuracy: 0.8499 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8503\n", - "Epoch 55/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9046 - accuracy: 0.8496 - val_loss: 0.3287 - val_auc: 0.9012 - val_accuracy: 0.8497\n", - "Epoch 56/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3140 - auc: 0.9082 - accuracy: 0.8554 - val_loss: 0.3287 - val_auc: 0.9012 - val_accuracy: 0.8498\n", - "Epoch 57/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3197 - auc: 0.9063 - accuracy: 0.8554 - val_loss: 0.3286 - val_auc: 0.9013 - val_accuracy: 0.8495\n", - "Epoch 58/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3187 - auc: 0.9072 - accuracy: 0.8581 - val_loss: 0.3286 - val_auc: 0.9013 - val_accuracy: 0.8497\n", - "Epoch 59/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3167 - auc: 0.9085 - accuracy: 0.8560 - val_loss: 0.3285 - val_auc: 0.9015 - val_accuracy: 0.8498\n", - "Epoch 60/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3231 - auc: 0.9052 - accuracy: 0.8523 - val_loss: 0.3285 - val_auc: 0.9015 - val_accuracy: 0.8498\n", - "Epoch 61/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3172 - auc: 0.9070 - accuracy: 0.8535 - val_loss: 0.3284 - val_auc: 0.9014 - val_accuracy: 0.8497\n", - "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3207 - auc: 0.9071 - accuracy: 0.8529 - val_loss: 0.3283 - val_auc: 0.9016 - val_accuracy: 0.8503\n", - "Epoch 63/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3157 - auc: 0.9069 - accuracy: 0.8571 - val_loss: 0.3284 - val_auc: 0.9016 - val_accuracy: 0.8497\n", - "Epoch 64/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3189 - auc: 0.9066 - accuracy: 0.8551 - val_loss: 0.3283 - val_auc: 0.9017 - val_accuracy: 0.8500\n", - "Epoch 65/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3174 - auc: 0.9059 - accuracy: 0.8546 - val_loss: 0.3284 - val_auc: 0.9018 - val_accuracy: 0.8489\n", - "Epoch 66/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3166 - auc: 0.9074 - accuracy: 0.8558 - val_loss: 0.3283 - val_auc: 0.9018 - val_accuracy: 0.8494\n", - "Epoch 67/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3126 - auc: 0.9088 - accuracy: 0.8554 - val_loss: 0.3285 - val_auc: 0.9020 - val_accuracy: 0.8483\n", - "Epoch 68/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3127 - auc: 0.9114 - accuracy: 0.8563 - val_loss: 0.3279 - val_auc: 0.9020 - val_accuracy: 0.8498\n", - "Epoch 69/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3193 - auc: 0.9069 - accuracy: 0.8518 - val_loss: 0.3280 - val_auc: 0.9020 - val_accuracy: 0.8498\n", - "Epoch 70/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9068 - accuracy: 0.8538 - val_loss: 0.3279 - val_auc: 0.9021 - val_accuracy: 0.8498\n", - "Epoch 71/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3167 - auc: 0.9080 - accuracy: 0.8559 - val_loss: 0.3280 - val_auc: 0.9020 - val_accuracy: 0.8503\n", - "Epoch 72/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3135 - auc: 0.9100 - accuracy: 0.8581 - val_loss: 0.3280 - val_auc: 0.9021 - val_accuracy: 0.8503\n", - "Epoch 73/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3168 - auc: 0.9078 - accuracy: 0.8551 - val_loss: 0.3280 - val_auc: 0.9021 - val_accuracy: 0.8503\n", - "Epoch 74/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3154 - auc: 0.9093 - accuracy: 0.8546 - val_loss: 0.3281 - val_auc: 0.9022 - val_accuracy: 0.8506\n", - "Epoch 75/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3174 - auc: 0.9087 - accuracy: 0.8542 - val_loss: 0.3279 - val_auc: 0.9024 - val_accuracy: 0.8501\n", - "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3152 - auc: 0.9073 - accuracy: 0.8531 - val_loss: 0.3282 - val_auc: 0.9023 - val_accuracy: 0.8503\n", - "Epoch 77/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3137 - auc: 0.9093 - accuracy: 0.8560 - val_loss: 0.3280 - val_auc: 0.9023 - val_accuracy: 0.8498\n", - "Epoch 78/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3167 - auc: 0.9092 - accuracy: 0.8551 - val_loss: 0.3281 - val_auc: 0.9024 - val_accuracy: 0.8501\n", - "Epoch 79/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3176 - auc: 0.9074 - accuracy: 0.8544 - val_loss: 0.3279 - val_auc: 0.9025 - val_accuracy: 0.8500\n", - "Epoch 80/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3135 - auc: 0.9079 - accuracy: 0.8582 - val_loss: 0.3280 - val_auc: 0.9025 - val_accuracy: 0.8500\n", - "Epoch 81/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9084 - accuracy: 0.8553 - val_loss: 0.3277 - val_auc: 0.9028 - val_accuracy: 0.8500\n", - "Epoch 82/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3126 - auc: 0.9114 - accuracy: 0.8557 - val_loss: 0.3277 - val_auc: 0.9027 - val_accuracy: 0.8508\n", - "Epoch 83/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3204 - auc: 0.9074 - accuracy: 0.8532 - val_loss: 0.3278 - val_auc: 0.9029 - val_accuracy: 0.8501\n", - "Epoch 84/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3140 - auc: 0.9097 - accuracy: 0.8550 - val_loss: 0.3276 - val_auc: 0.9030 - val_accuracy: 0.8509\n", - "Epoch 85/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3054 - auc: 0.9138 - accuracy: 0.8608 - val_loss: 0.3274 - val_auc: 0.9030 - val_accuracy: 0.8501\n", - "Epoch 86/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9084 - accuracy: 0.8579 - val_loss: 0.3275 - val_auc: 0.9031 - val_accuracy: 0.8511\n", - "Epoch 87/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3129 - auc: 0.9112 - accuracy: 0.8573 - val_loss: 0.3274 - val_auc: 0.9032 - val_accuracy: 0.8512\n", - "Epoch 88/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3116 - auc: 0.9117 - accuracy: 0.8608 - val_loss: 0.3273 - val_auc: 0.9033 - val_accuracy: 0.8505\n", - "Epoch 89/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3123 - auc: 0.9106 - accuracy: 0.8589 - val_loss: 0.3272 - val_auc: 0.9034 - val_accuracy: 0.8517\n", - "Epoch 90/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - auc: 0.9070 - accuracy: 0.8587 - val_loss: 0.3273 - val_auc: 0.9035 - val_accuracy: 0.8511\n", - "Epoch 91/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3096 - auc: 0.9109 - accuracy: 0.8603 - val_loss: 0.3270 - val_auc: 0.9036 - val_accuracy: 0.8512\n", - "Epoch 92/200\n", - "814/814 [==============================] - 1s 1ms/step - 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val_loss: 0.3267 - val_auc: 0.9040 - val_accuracy: 0.8505\n", - "Epoch 98/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3178 - auc: 0.9085 - accuracy: 0.8542 - val_loss: 0.3268 - val_auc: 0.9040 - val_accuracy: 0.8508\n", - "Epoch 99/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3110 - auc: 0.9125 - accuracy: 0.8596 - val_loss: 0.3268 - val_auc: 0.9040 - val_accuracy: 0.8506\n", - "Epoch 100/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3119 - auc: 0.9121 - accuracy: 0.8599 - val_loss: 0.3269 - val_auc: 0.9041 - val_accuracy: 0.8509\n", - "Epoch 101/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3203 - auc: 0.9073 - accuracy: 0.8546 - val_loss: 0.3270 - val_auc: 0.9041 - val_accuracy: 0.8509\n", - "Epoch 102/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3090 - auc: 0.9139 - accuracy: 0.8574 - val_loss: 0.3268 - val_auc: 0.9042 - val_accuracy: 0.8509\n", - "Epoch 103/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3128 - auc: 0.9107 - accuracy: 0.8551 - val_loss: 0.3267 - val_auc: 0.9044 - val_accuracy: 0.8511\n", - "Epoch 104/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3134 - auc: 0.9099 - accuracy: 0.8583 - val_loss: 0.3268 - val_auc: 0.9044 - val_accuracy: 0.8514\n", - "Epoch 105/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3091 - auc: 0.9121 - accuracy: 0.8577 - val_loss: 0.3265 - val_auc: 0.9046 - val_accuracy: 0.8515\n", - "Epoch 106/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3104 - auc: 0.9127 - accuracy: 0.8587 - val_loss: 0.3264 - val_auc: 0.9046 - val_accuracy: 0.8511\n", - "Epoch 107/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3163 - auc: 0.9093 - accuracy: 0.8560 - val_loss: 0.3268 - val_auc: 0.9045 - val_accuracy: 0.8515\n", - "Epoch 108/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3148 - auc: 0.9093 - accuracy: 0.8589 - val_loss: 0.3266 - val_auc: 0.9046 - val_accuracy: 0.8512\n", - "Epoch 109/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9099 - accuracy: 0.8552 - val_loss: 0.3265 - val_auc: 0.9046 - val_accuracy: 0.8506\n", - "Epoch 110/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3114 - auc: 0.9103 - accuracy: 0.8600 - val_loss: 0.3267 - val_auc: 0.9047 - val_accuracy: 0.8518\n", - "Epoch 111/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3105 - auc: 0.9108 - accuracy: 0.8572 - val_loss: 0.3264 - val_auc: 0.9050 - val_accuracy: 0.8524\n", - "Epoch 112/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3093 - auc: 0.9148 - accuracy: 0.8591 - val_loss: 0.3265 - val_auc: 0.9049 - val_accuracy: 0.8520\n", - "Epoch 113/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3152 - auc: 0.9096 - accuracy: 0.8577 - val_loss: 0.3264 - val_auc: 0.9049 - val_accuracy: 0.8515\n", - "Epoch 114/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3157 - auc: 0.9081 - accuracy: 0.8569 - val_loss: 0.3264 - val_auc: 0.9051 - val_accuracy: 0.8508\n", - "Epoch 115/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3061 - auc: 0.9116 - accuracy: 0.8606 - val_loss: 0.3265 - val_auc: 0.9051 - val_accuracy: 0.8515\n", - "Epoch 116/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3059 - auc: 0.9145 - accuracy: 0.8629 - val_loss: 0.3264 - val_auc: 0.9051 - val_accuracy: 0.8515\n", - "Epoch 117/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3147 - auc: 0.9102 - accuracy: 0.8559 - val_loss: 0.3268 - val_auc: 0.9051 - val_accuracy: 0.8521\n", - "Epoch 118/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9114 - accuracy: 0.8591 - val_loss: 0.3265 - val_auc: 0.9050 - val_accuracy: 0.8512\n", - "Epoch 119/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3080 - auc: 0.9132 - accuracy: 0.8601 - val_loss: 0.3265 - val_auc: 0.9052 - val_accuracy: 0.8521\n", - "Epoch 120/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3095 - auc: 0.9111 - accuracy: 0.8587 - val_loss: 0.3264 - val_auc: 0.9052 - val_accuracy: 0.8524\n", - "Epoch 121/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3160 - auc: 0.9088 - accuracy: 0.8571 - val_loss: 0.3266 - val_auc: 0.9052 - val_accuracy: 0.8523\n", - "Epoch 122/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3171 - auc: 0.9114 - accuracy: 0.8598 - val_loss: 0.3266 - val_auc: 0.9052 - val_accuracy: 0.8523\n", - "Epoch 123/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3085 - auc: 0.9136 - accuracy: 0.8602 - val_loss: 0.3263 - val_auc: 0.9052 - val_accuracy: 0.8511\n", - "Epoch 124/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3057 - auc: 0.9145 - accuracy: 0.8617 - val_loss: 0.3266 - val_auc: 0.9054 - val_accuracy: 0.8524\n", - "Epoch 125/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9120 - accuracy: 0.8611 - val_loss: 0.3264 - val_auc: 0.9054 - val_accuracy: 0.8518\n", - "Epoch 126/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3050 - auc: 0.9156 - accuracy: 0.8618 - val_loss: 0.3262 - val_auc: 0.9054 - val_accuracy: 0.8518\n", - "Epoch 127/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3045 - auc: 0.9158 - accuracy: 0.8627 - val_loss: 0.3262 - val_auc: 0.9053 - val_accuracy: 0.8512\n", - "Epoch 128/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3099 - auc: 0.9114 - accuracy: 0.8596 - val_loss: 0.3267 - val_auc: 0.9054 - val_accuracy: 0.8526\n", - "Epoch 129/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3075 - auc: 0.9133 - accuracy: 0.8606 - val_loss: 0.3267 - val_auc: 0.9055 - val_accuracy: 0.8524\n", - "Epoch 130/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9100 - accuracy: 0.8609 - val_loss: 0.3266 - val_auc: 0.9055 - val_accuracy: 0.8524\n", - "Epoch 131/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3113 - auc: 0.9113 - accuracy: 0.8610 - val_loss: 0.3266 - val_auc: 0.9054 - val_accuracy: 0.8521\n", - "Epoch 132/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3133 - auc: 0.9111 - accuracy: 0.8582 - val_loss: 0.3269 - val_auc: 0.9055 - val_accuracy: 0.8531\n", - "Epoch 133/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3152 - auc: 0.9086 - accuracy: 0.8593 - val_loss: 0.3268 - val_auc: 0.9055 - val_accuracy: 0.8521\n", - "Epoch 134/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3114 - auc: 0.9115 - accuracy: 0.8605 - val_loss: 0.3268 - val_auc: 0.9054 - val_accuracy: 0.8523\n", - "Epoch 135/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3111 - auc: 0.9118 - accuracy: 0.8605 - val_loss: 0.3266 - val_auc: 0.9053 - val_accuracy: 0.8521\n", - "Epoch 136/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3112 - auc: 0.9128 - accuracy: 0.8598 - val_loss: 0.3266 - val_auc: 0.9054 - val_accuracy: 0.8524\n", - "Epoch 137/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3125 - auc: 0.9125 - accuracy: 0.8599 - val_loss: 0.3269 - val_auc: 0.9055 - val_accuracy: 0.8528\n", - "Epoch 138/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3044 - auc: 0.9149 - accuracy: 0.8641 - val_loss: 0.3267 - val_auc: 0.9055 - val_accuracy: 0.8521\n", - "Epoch 139/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3125 - auc: 0.9109 - accuracy: 0.8583 - val_loss: 0.3267 - val_auc: 0.9054 - val_accuracy: 0.8523\n", - "Epoch 140/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3144 - auc: 0.9105 - accuracy: 0.8587 - val_loss: 0.3270 - val_auc: 0.9054 - val_accuracy: 0.8523\n", - "Epoch 141/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3055 - auc: 0.9136 - accuracy: 0.8618 - val_loss: 0.3270 - val_auc: 0.9055 - val_accuracy: 0.8529\n", - "Epoch 142/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3056 - auc: 0.9157 - accuracy: 0.8607 - val_loss: 0.3270 - val_auc: 0.9056 - val_accuracy: 0.8529\n", - "Epoch 143/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3058 - auc: 0.9137 - accuracy: 0.8625 - val_loss: 0.3269 - val_auc: 0.9056 - val_accuracy: 0.8523\n", - "Epoch 144/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3043 - auc: 0.9148 - accuracy: 0.8640 - val_loss: 0.3270 - val_auc: 0.9056 - val_accuracy: 0.8524\n", - "Epoch 145/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3163 - auc: 0.9109 - accuracy: 0.8589 - val_loss: 0.3268 - val_auc: 0.9057 - val_accuracy: 0.8529\n", - "Epoch 146/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3073 - auc: 0.9143 - accuracy: 0.8620 - val_loss: 0.3273 - val_auc: 0.9056 - val_accuracy: 0.8521\n", - "Epoch 147/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3106 - auc: 0.9142 - accuracy: 0.8608 - val_loss: 0.3273 - val_auc: 0.9055 - val_accuracy: 0.8515\n", - "Epoch 148/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3058 - auc: 0.9145 - accuracy: 0.8618 - val_loss: 0.3270 - val_auc: 0.9057 - val_accuracy: 0.8521\n", - "Epoch 149/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3066 - auc: 0.9145 - accuracy: 0.8609 - val_loss: 0.3273 - val_auc: 0.9057 - val_accuracy: 0.8523\n", - "Epoch 150/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3046 - auc: 0.9142 - accuracy: 0.8625 - val_loss: 0.3276 - val_auc: 0.9056 - val_accuracy: 0.8526\n", - "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3126 - auc: 0.9115 - accuracy: 0.8629 - val_loss: 0.3276 - val_auc: 0.9058 - val_accuracy: 0.8532\n", - "Epoch 152/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3108 - auc: 0.9129 - accuracy: 0.8620 - val_loss: 0.3272 - val_auc: 0.9057 - val_accuracy: 0.8531\n", - "Epoch 153/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3103 - auc: 0.9122 - accuracy: 0.8588 - val_loss: 0.3271 - val_auc: 0.9058 - val_accuracy: 0.8532\n", - "Epoch 154/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3104 - auc: 0.9118 - accuracy: 0.8578 - val_loss: 0.3272 - val_auc: 0.9057 - val_accuracy: 0.8528\n", - "Epoch 155/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3057 - auc: 0.9152 - accuracy: 0.8625 - val_loss: 0.3273 - val_auc: 0.9059 - val_accuracy: 0.8532\n", - "Epoch 156/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3104 - auc: 0.9117 - accuracy: 0.8622 - val_loss: 0.3270 - val_auc: 0.9059 - val_accuracy: 0.8523\n", - "Epoch 157/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3048 - auc: 0.9139 - accuracy: 0.8636 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8535\n", - "Epoch 158/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3033 - auc: 0.9157 - accuracy: 0.8621 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8528\n", - "Epoch 159/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3101 - auc: 0.9131 - accuracy: 0.8612 - val_loss: 0.3269 - val_auc: 0.9058 - val_accuracy: 0.8531\n", - "Epoch 160/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3078 - auc: 0.9141 - accuracy: 0.8610 - val_loss: 0.3275 - val_auc: 0.9059 - val_accuracy: 0.8528\n", - "Epoch 161/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3079 - auc: 0.9157 - accuracy: 0.8621 - val_loss: 0.3270 - val_auc: 0.9059 - val_accuracy: 0.8540\n", - "Epoch 162/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3100 - auc: 0.9139 - accuracy: 0.8614 - val_loss: 0.3271 - val_auc: 0.9060 - val_accuracy: 0.8537\n", - "Epoch 163/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3030 - auc: 0.9157 - accuracy: 0.8613 - val_loss: 0.3272 - val_auc: 0.9059 - val_accuracy: 0.8532\n", - "Epoch 164/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3050 - auc: 0.9165 - accuracy: 0.8628 - val_loss: 0.3269 - val_auc: 0.9059 - val_accuracy: 0.8535\n", - "Epoch 165/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3094 - auc: 0.9130 - accuracy: 0.8608 - val_loss: 0.3270 - val_auc: 0.9060 - val_accuracy: 0.8534\n", - "Epoch 166/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3047 - auc: 0.9150 - accuracy: 0.8627 - val_loss: 0.3273 - val_auc: 0.9060 - val_accuracy: 0.8534\n", - "Epoch 167/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3098 - auc: 0.9124 - accuracy: 0.8618 - val_loss: 0.3274 - val_auc: 0.9061 - val_accuracy: 0.8537\n", - "Epoch 168/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3099 - auc: 0.9129 - accuracy: 0.8565 - val_loss: 0.3273 - val_auc: 0.9059 - val_accuracy: 0.8544\n", - "Epoch 169/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3071 - auc: 0.9151 - accuracy: 0.8618 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8528\n", - "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3081 - auc: 0.9133 - accuracy: 0.8590 - val_loss: 0.3274 - val_auc: 0.9060 - val_accuracy: 0.8524\n", - "Epoch 171/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3041 - auc: 0.9163 - accuracy: 0.8632 - val_loss: 0.3270 - val_auc: 0.9060 - val_accuracy: 0.8540\n", - "Epoch 172/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3096 - auc: 0.9137 - accuracy: 0.8613 - val_loss: 0.3273 - val_auc: 0.9061 - val_accuracy: 0.8526\n", - "Epoch 173/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3075 - auc: 0.9146 - accuracy: 0.8592 - val_loss: 0.3273 - val_auc: 0.9060 - val_accuracy: 0.8526\n", - "Epoch 174/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3130 - auc: 0.9112 - accuracy: 0.8589 - val_loss: 0.3275 - val_auc: 0.9060 - val_accuracy: 0.8532\n", - "Epoch 175/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3057 - auc: 0.9145 - accuracy: 0.8610 - val_loss: 0.3272 - val_auc: 0.9060 - val_accuracy: 0.8532\n", - "Epoch 176/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3091 - auc: 0.9124 - accuracy: 0.8601 - val_loss: 0.3277 - val_auc: 0.9061 - val_accuracy: 0.8529\n", - "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3082 - auc: 0.9118 - accuracy: 0.8631 - val_loss: 0.3273 - val_auc: 0.9060 - val_accuracy: 0.8538\n", - "Epoch 178/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3078 - auc: 0.9136 - accuracy: 0.8617 - val_loss: 0.3279 - val_auc: 0.9062 - val_accuracy: 0.8531\n", - "Epoch 179/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3083 - auc: 0.9118 - accuracy: 0.8600 - val_loss: 0.3271 - val_auc: 0.9061 - val_accuracy: 0.8534\n", - "Epoch 180/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3075 - auc: 0.9145 - accuracy: 0.8609 - val_loss: 0.3276 - val_auc: 0.9061 - val_accuracy: 0.8526\n", - "Epoch 181/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3073 - auc: 0.9144 - accuracy: 0.8613 - val_loss: 0.3275 - val_auc: 0.9062 - val_accuracy: 0.8531\n", - "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3038 - auc: 0.9152 - accuracy: 0.8601 - val_loss: 0.3279 - val_auc: 0.9062 - val_accuracy: 0.8528\n", - "Epoch 183/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3052 - auc: 0.9161 - accuracy: 0.8615 - val_loss: 0.3272 - val_auc: 0.9062 - val_accuracy: 0.8528\n", - "Epoch 184/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3040 - auc: 0.9156 - accuracy: 0.8609 - val_loss: 0.3275 - val_auc: 0.9062 - val_accuracy: 0.8528\n", - "Epoch 185/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3062 - auc: 0.9146 - accuracy: 0.8629 - val_loss: 0.3275 - val_auc: 0.9063 - val_accuracy: 0.8531\n", - "Epoch 186/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3115 - auc: 0.9110 - accuracy: 0.8598 - val_loss: 0.3278 - val_auc: 0.9063 - val_accuracy: 0.8531\n", - "Epoch 187/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.2982 - auc: 0.9188 - accuracy: 0.8663 - val_loss: 0.3271 - val_auc: 0.9062 - val_accuracy: 0.8534\n", - "Epoch 188/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3072 - auc: 0.9128 - accuracy: 0.8636 - val_loss: 0.3277 - val_auc: 0.9064 - val_accuracy: 0.8534\n", - "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3048 - auc: 0.9154 - accuracy: 0.8629 - val_loss: 0.3278 - val_auc: 0.9063 - val_accuracy: 0.8538\n", - "Epoch 190/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3070 - auc: 0.9145 - accuracy: 0.8622 - val_loss: 0.3276 - val_auc: 0.9064 - val_accuracy: 0.8535\n", - "Epoch 191/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3120 - auc: 0.9143 - accuracy: 0.8604 - val_loss: 0.3272 - val_auc: 0.9062 - val_accuracy: 0.8531\n", - "Epoch 192/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3118 - auc: 0.9135 - accuracy: 0.8612 - val_loss: 0.3270 - val_auc: 0.9063 - val_accuracy: 0.8535\n", - "Epoch 193/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3064 - auc: 0.9138 - accuracy: 0.8604 - val_loss: 0.3271 - val_auc: 0.9064 - val_accuracy: 0.8534\n", - "Epoch 194/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3090 - auc: 0.9138 - accuracy: 0.8623 - val_loss: 0.3270 - val_auc: 0.9065 - val_accuracy: 0.8524\n", - "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3140 - auc: 0.9098 - accuracy: 0.8569 - val_loss: 0.3271 - val_auc: 0.9063 - val_accuracy: 0.8535\n", - "Epoch 196/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3058 - auc: 0.9134 - accuracy: 0.8624 - val_loss: 0.3269 - val_auc: 0.9064 - val_accuracy: 0.8535\n", - "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3036 - auc: 0.9137 - accuracy: 0.8621 - val_loss: 0.3272 - val_auc: 0.9066 - val_accuracy: 0.8538\n", - "Epoch 198/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3055 - auc: 0.9146 - accuracy: 0.8625 - val_loss: 0.3274 - val_auc: 0.9066 - val_accuracy: 0.8546\n", - "Epoch 199/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3068 - auc: 0.9148 - accuracy: 0.8637 - val_loss: 0.3271 - val_auc: 0.9065 - val_accuracy: 0.8537\n", - "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3053 - auc: 0.9160 - accuracy: 0.8609 - val_loss: 0.3270 - val_auc: 0.9065 - val_accuracy: 0.8537\n" - ] - } - ], - "source": [ - "history = model_2.fit(X_train.values, y_train,epochs=200,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "ecc6c01f", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "9f550992", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "a080b88b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "32e0ef2b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "fa919109", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.9067663301417634\n", - "AUC-ROC score sobre train: 0.9154927212988944\n", - "Accuracy sobre test: 0.8536772608628896\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.93 0.88 0.91 5196\n", - " Alto valor 0.62 0.73 0.67 1317\n", - "\n", - " accuracy 0.85 6513\n", - " macro avg 0.77 0.81 0.79 6513\n", - "weighted avg 0.87 0.85 0.86 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = model_2.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model_2.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model_2.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model_2, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "6539b050", - "metadata": {}, - "source": [ - "Observamos un incrememento en la métrica AUC-ROC. Busquemos seguir complejizando la red" - ] - }, - { - "cell_type": "markdown", - "id": "30cac619", - "metadata": {}, - "source": [ - "### Tercer diseño de la red" - ] - }, - { - "cell_type": "markdown", - "id": "ab276d18", - "metadata": {}, - "source": [ - "Buscaremos agrandar la red para ver si obtenemos mejores resultados. Como corremos riesgo de overfittear, utilizaremos regularizaión l1" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "45ddb2c0", - "metadata": {}, - "outputs": [], - "source": [ - "seed(0)\n", - "tensorflow.random.set_seed(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "cd49fdab", - "metadata": {}, - "outputs": [], - "source": [ - "model_2 = Sequential()\n", - "model_2.add(Dense(16,input_shape = (43,),activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model_2.add(Dense(16,activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model_2.add(Dense(16,activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model_2.add(Dense(8,activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model_2.add(Dense(4,activation='relu', kernel_regularizer=l2(0.0001)))\n", - "model_2.add(Dense(1, activation=\"sigmoid\"))" - ] - }, - { - "cell_type": "markdown", - "id": "9190944c", - "metadata": {}, - "source": [ - "Vemos un resumen de nuestra red" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "3cb00537", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model: \"sequential_7\"\n", - "_________________________________________________________________\n", - "Layer (type) Output Shape Param # \n", - "=================================================================\n", - "dense_27 (Dense) (None, 16) 704 \n", - "_________________________________________________________________\n", - "dense_28 (Dense) (None, 16) 272 \n", - "_________________________________________________________________\n", - "dense_29 (Dense) (None, 16) 272 \n", - "_________________________________________________________________\n", - "dense_30 (Dense) (None, 8) 136 \n", - "_________________________________________________________________\n", - "dense_31 (Dense) (None, 4) 36 \n", - "_________________________________________________________________\n", - "dense_32 (Dense) (None, 1) 5 \n", - "=================================================================\n", - "Total params: 1,425\n", - "Trainable params: 1,425\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ], - "source": [ - "opt = tensorflow.keras.optimizers.RMSprop(lr=0.0001)\n", - "model_2.compile(loss='binary_crossentropy', optimizer=opt,metrics=['AUC','accuracy'])\n", - "model_2.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "ea746358", - "metadata": {}, - "source": [ - "Finalmente entrenamos" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "41c39ac5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/200\n", - "814/814 [==============================] - 2s 1ms/step - loss: 0.6365 - auc: 0.4833 - accuracy: 0.7509 - val_loss: 0.4872 - val_auc: 0.7732 - val_accuracy: 0.7593\n", - "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4643 - auc: 0.8002 - accuracy: 0.7607 - val_loss: 0.4135 - val_auc: 0.8524 - val_accuracy: 0.7593\n", - "Epoch 3/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4082 - auc: 0.8532 - accuracy: 0.7670 - val_loss: 0.3889 - val_auc: 0.8702 - val_accuracy: 0.8202\n", - "Epoch 4/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3861 - auc: 0.8709 - accuracy: 0.8257 - val_loss: 0.3751 - val_auc: 0.8786 - val_accuracy: 0.8294\n", - "Epoch 5/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3805 - auc: 0.8754 - accuracy: 0.8281 - val_loss: 0.3655 - val_auc: 0.8834 - val_accuracy: 0.8345\n", - "Epoch 6/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3611 - auc: 0.8849 - accuracy: 0.8381 - val_loss: 0.3588 - val_auc: 0.8868 - val_accuracy: 0.8377\n", - "Epoch 7/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3583 - auc: 0.8874 - accuracy: 0.8364 - val_loss: 0.3543 - val_auc: 0.8897 - val_accuracy: 0.8392\n", - "Epoch 8/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3519 - auc: 0.8905 - accuracy: 0.8404 - val_loss: 0.3514 - val_auc: 0.8914 - val_accuracy: 0.8415\n", - "Epoch 9/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8915 - accuracy: 0.8408 - val_loss: 0.3490 - val_auc: 0.8928 - val_accuracy: 0.8434\n", - "Epoch 10/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3558 - auc: 0.8877 - accuracy: 0.8356 - val_loss: 0.3469 - val_auc: 0.8940 - val_accuracy: 0.8428\n", - "Epoch 11/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3502 - auc: 0.8909 - accuracy: 0.8413 - val_loss: 0.3453 - val_auc: 0.8952 - val_accuracy: 0.8443\n", - "Epoch 12/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3466 - auc: 0.8920 - accuracy: 0.8427 - val_loss: 0.3436 - val_auc: 0.8960 - val_accuracy: 0.8443\n", - "Epoch 13/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3372 - auc: 0.8995 - accuracy: 0.8493 - val_loss: 0.3423 - val_auc: 0.8968 - val_accuracy: 0.8442\n", - "Epoch 14/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3427 - auc: 0.8973 - accuracy: 0.8453 - val_loss: 0.3414 - val_auc: 0.8973 - val_accuracy: 0.8469\n", - "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3425 - auc: 0.8974 - accuracy: 0.8457 - val_loss: 0.3408 - val_auc: 0.8978 - val_accuracy: 0.8477\n", - "Epoch 16/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8963 - accuracy: 0.8442 - val_loss: 0.3398 - val_auc: 0.8983 - val_accuracy: 0.8463\n", - "Epoch 17/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3390 - auc: 0.8997 - accuracy: 0.8453 - val_loss: 0.3390 - val_auc: 0.8987 - val_accuracy: 0.8475\n", - "Epoch 18/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3406 - auc: 0.8956 - accuracy: 0.8469 - val_loss: 0.3386 - val_auc: 0.8989 - val_accuracy: 0.8477\n", - "Epoch 19/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3431 - auc: 0.8974 - accuracy: 0.8452 - val_loss: 0.3381 - val_auc: 0.8993 - val_accuracy: 0.8465\n", - "Epoch 20/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3333 - auc: 0.9033 - accuracy: 0.8486 - val_loss: 0.3375 - val_auc: 0.8994 - val_accuracy: 0.8462\n", - "Epoch 21/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3340 - auc: 0.9014 - accuracy: 0.8494 - val_loss: 0.3371 - val_auc: 0.8997 - val_accuracy: 0.8471\n", - "Epoch 22/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3330 - auc: 0.9023 - accuracy: 0.8509 - val_loss: 0.3371 - val_auc: 0.8999 - val_accuracy: 0.8469\n", - "Epoch 23/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3304 - auc: 0.9026 - accuracy: 0.8525 - val_loss: 0.3364 - val_auc: 0.9002 - val_accuracy: 0.8460\n", - "Epoch 24/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3290 - auc: 0.9031 - accuracy: 0.8517 - val_loss: 0.3363 - val_auc: 0.9000 - val_accuracy: 0.8475\n", - "Epoch 25/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3323 - auc: 0.9024 - accuracy: 0.8500 - val_loss: 0.3362 - val_auc: 0.9003 - val_accuracy: 0.8463\n", - "Epoch 26/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3300 - auc: 0.9052 - accuracy: 0.8504 - val_loss: 0.3359 - val_auc: 0.9003 - val_accuracy: 0.8465\n", - "Epoch 27/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3352 - auc: 0.9018 - accuracy: 0.8483 - val_loss: 0.3356 - val_auc: 0.9005 - val_accuracy: 0.8474\n", - "Epoch 28/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3354 - auc: 0.9009 - accuracy: 0.8524 - val_loss: 0.3356 - val_auc: 0.9004 - val_accuracy: 0.8480\n", - "Epoch 29/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3269 - auc: 0.9048 - accuracy: 0.8535 - val_loss: 0.3352 - val_auc: 0.9008 - val_accuracy: 0.8466\n", - "Epoch 30/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3313 - auc: 0.9047 - accuracy: 0.8513 - val_loss: 0.3353 - val_auc: 0.9008 - val_accuracy: 0.8471\n", - "Epoch 31/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3280 - auc: 0.9059 - accuracy: 0.8529 - val_loss: 0.3353 - val_auc: 0.9008 - val_accuracy: 0.8475\n", - "Epoch 32/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3293 - auc: 0.9043 - accuracy: 0.8537 - val_loss: 0.3350 - val_auc: 0.9009 - val_accuracy: 0.8475\n", - "Epoch 33/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3301 - auc: 0.9061 - accuracy: 0.8518 - val_loss: 0.3349 - val_auc: 0.9011 - val_accuracy: 0.8471\n", - "Epoch 34/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3255 - auc: 0.9083 - accuracy: 0.8534 - val_loss: 0.3345 - val_auc: 0.9012 - val_accuracy: 0.8477\n", - "Epoch 35/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3274 - auc: 0.9061 - accuracy: 0.8513 - val_loss: 0.3345 - val_auc: 0.9013 - val_accuracy: 0.8485\n", - "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3251 - auc: 0.9061 - accuracy: 0.8526 - val_loss: 0.3342 - val_auc: 0.9015 - val_accuracy: 0.8466\n", - "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3304 - auc: 0.9041 - accuracy: 0.8524 - val_loss: 0.3342 - val_auc: 0.9015 - val_accuracy: 0.8471\n", - "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3273 - auc: 0.9066 - accuracy: 0.8524 - val_loss: 0.3340 - val_auc: 0.9017 - val_accuracy: 0.8481\n", - "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3245 - auc: 0.9064 - accuracy: 0.8573 - val_loss: 0.3340 - val_auc: 0.9018 - val_accuracy: 0.8472\n", - "Epoch 40/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3259 - auc: 0.9052 - accuracy: 0.8526 - val_loss: 0.3337 - val_auc: 0.9019 - val_accuracy: 0.8475\n", - "Epoch 41/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3250 - auc: 0.9069 - accuracy: 0.8532 - val_loss: 0.3337 - val_auc: 0.9019 - val_accuracy: 0.8478\n", - "Epoch 42/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3295 - auc: 0.9055 - accuracy: 0.8496 - val_loss: 0.3337 - val_auc: 0.9020 - val_accuracy: 0.8471\n", - "Epoch 43/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3264 - auc: 0.9059 - accuracy: 0.8542 - val_loss: 0.3335 - val_auc: 0.9019 - val_accuracy: 0.8478\n", - "Epoch 44/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3238 - auc: 0.9079 - accuracy: 0.8542 - val_loss: 0.3342 - val_auc: 0.9018 - val_accuracy: 0.8468\n", - "Epoch 45/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3279 - auc: 0.9069 - accuracy: 0.8504 - val_loss: 0.3339 - val_auc: 0.9020 - val_accuracy: 0.8478\n", - "Epoch 46/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3220 - auc: 0.9078 - accuracy: 0.8576 - val_loss: 0.3333 - val_auc: 0.9023 - val_accuracy: 0.8471\n", - "Epoch 47/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3208 - auc: 0.9106 - accuracy: 0.8553 - val_loss: 0.3330 - val_auc: 0.9025 - val_accuracy: 0.8475\n", - "Epoch 48/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3216 - auc: 0.9093 - accuracy: 0.8546 - val_loss: 0.3330 - val_auc: 0.9027 - val_accuracy: 0.8474\n", - "Epoch 49/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3249 - auc: 0.9085 - accuracy: 0.8545 - val_loss: 0.3329 - val_auc: 0.9026 - val_accuracy: 0.8480\n", - "Epoch 50/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3218 - auc: 0.9094 - accuracy: 0.8527 - val_loss: 0.3330 - val_auc: 0.9025 - val_accuracy: 0.8469\n", - "Epoch 51/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3267 - auc: 0.9064 - accuracy: 0.8534 - val_loss: 0.3330 - val_auc: 0.9026 - val_accuracy: 0.8472\n", - "Epoch 52/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3269 - auc: 0.9032 - accuracy: 0.8526 - val_loss: 0.3334 - val_auc: 0.9027 - val_accuracy: 0.8472\n", - "Epoch 53/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3270 - auc: 0.9071 - accuracy: 0.8496 - val_loss: 0.3330 - val_auc: 0.9027 - val_accuracy: 0.8471\n", - "Epoch 54/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3281 - auc: 0.9049 - accuracy: 0.8539 - val_loss: 0.3327 - val_auc: 0.9029 - val_accuracy: 0.8469\n", - "Epoch 55/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3267 - auc: 0.9067 - accuracy: 0.8493 - val_loss: 0.3328 - val_auc: 0.9030 - val_accuracy: 0.8469\n", - "Epoch 56/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3212 - auc: 0.9084 - accuracy: 0.8569 - val_loss: 0.3328 - val_auc: 0.9032 - val_accuracy: 0.8472\n", - "Epoch 57/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3244 - auc: 0.9080 - accuracy: 0.8525 - val_loss: 0.3328 - val_auc: 0.9030 - val_accuracy: 0.8488\n", - "Epoch 58/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3227 - auc: 0.9092 - accuracy: 0.8602 - val_loss: 0.3324 - val_auc: 0.9033 - val_accuracy: 0.8474\n", - "Epoch 59/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3219 - auc: 0.9103 - accuracy: 0.8561 - val_loss: 0.3323 - val_auc: 0.9033 - val_accuracy: 0.8488\n", - "Epoch 60/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3265 - auc: 0.9080 - accuracy: 0.8527 - val_loss: 0.3321 - val_auc: 0.9035 - val_accuracy: 0.8491\n", - "Epoch 61/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3236 - auc: 0.9076 - accuracy: 0.8532 - val_loss: 0.3321 - val_auc: 0.9035 - val_accuracy: 0.8492\n", - "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3272 - auc: 0.9080 - accuracy: 0.8527 - val_loss: 0.3320 - val_auc: 0.9036 - val_accuracy: 0.8492\n", - "Epoch 63/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3206 - auc: 0.9088 - accuracy: 0.8581 - val_loss: 0.3321 - val_auc: 0.9037 - val_accuracy: 0.8489\n", - "Epoch 64/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3258 - auc: 0.9073 - accuracy: 0.8527 - val_loss: 0.3320 - val_auc: 0.9039 - val_accuracy: 0.8492\n", - "Epoch 65/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9080 - accuracy: 0.8550 - val_loss: 0.3320 - val_auc: 0.9039 - val_accuracy: 0.8514\n", - "Epoch 66/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3219 - auc: 0.9094 - accuracy: 0.8577 - val_loss: 0.3321 - val_auc: 0.9040 - val_accuracy: 0.8508\n", - "Epoch 67/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3185 - auc: 0.9100 - accuracy: 0.8536 - val_loss: 0.3324 - val_auc: 0.9041 - val_accuracy: 0.8495\n", - "Epoch 68/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3180 - auc: 0.9131 - accuracy: 0.8569 - val_loss: 0.3315 - val_auc: 0.9040 - val_accuracy: 0.8505\n", - "Epoch 69/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3252 - auc: 0.9081 - accuracy: 0.8534 - val_loss: 0.3319 - val_auc: 0.9041 - val_accuracy: 0.8521\n", - "Epoch 70/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3251 - auc: 0.9088 - accuracy: 0.8542 - val_loss: 0.3316 - val_auc: 0.9042 - val_accuracy: 0.8500\n", - "Epoch 71/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3216 - auc: 0.9101 - accuracy: 0.8566 - val_loss: 0.3315 - val_auc: 0.9043 - val_accuracy: 0.8494\n", - "Epoch 72/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3195 - auc: 0.9113 - accuracy: 0.8573 - val_loss: 0.3315 - val_auc: 0.9043 - val_accuracy: 0.8494\n", - "Epoch 73/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9095 - accuracy: 0.8553 - val_loss: 0.3315 - val_auc: 0.9044 - val_accuracy: 0.8495\n", - "Epoch 74/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3221 - auc: 0.9098 - accuracy: 0.8557 - val_loss: 0.3317 - val_auc: 0.9045 - val_accuracy: 0.8506\n", - "Epoch 75/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3242 - auc: 0.9096 - accuracy: 0.8553 - val_loss: 0.3316 - val_auc: 0.9044 - val_accuracy: 0.8508\n", - "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3224 - auc: 0.9084 - accuracy: 0.8544 - val_loss: 0.3319 - val_auc: 0.9045 - val_accuracy: 0.8520\n", - "Epoch 77/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3211 - auc: 0.9096 - accuracy: 0.8563 - val_loss: 0.3322 - val_auc: 0.9042 - val_accuracy: 0.8506\n", - "Epoch 78/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3224 - auc: 0.9109 - accuracy: 0.8546 - val_loss: 0.3320 - val_auc: 0.9048 - val_accuracy: 0.8500\n", - "Epoch 79/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3243 - auc: 0.9084 - accuracy: 0.8516 - val_loss: 0.3314 - val_auc: 0.9047 - val_accuracy: 0.8514\n", - "Epoch 80/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3196 - auc: 0.9091 - accuracy: 0.8590 - val_loss: 0.3315 - val_auc: 0.9047 - val_accuracy: 0.8509\n", - "Epoch 81/200\n", - "814/814 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val_loss: 0.3317 - val_auc: 0.9052 - val_accuracy: 0.8505\n", - "Epoch 92/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3213 - auc: 0.9101 - accuracy: 0.8581 - val_loss: 0.3324 - val_auc: 0.9051 - val_accuracy: 0.8498\n", - "Epoch 93/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3283 - auc: 0.9079 - accuracy: 0.8517 - val_loss: 0.3319 - val_auc: 0.9050 - val_accuracy: 0.8511\n", - "Epoch 94/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3210 - auc: 0.9102 - accuracy: 0.8559 - val_loss: 0.3319 - val_auc: 0.9052 - val_accuracy: 0.8500\n", - "Epoch 95/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3232 - auc: 0.9099 - accuracy: 0.8547 - val_loss: 0.3321 - val_auc: 0.9048 - val_accuracy: 0.8517\n", - "Epoch 96/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - auc: 0.9134 - accuracy: 0.8593 - val_loss: 0.3316 - val_auc: 0.9051 - val_accuracy: 0.8492\n", - "Epoch 97/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3211 - auc: 0.9118 - accuracy: 0.8561 - val_loss: 0.3319 - val_auc: 0.9050 - val_accuracy: 0.8491\n", - "Epoch 98/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3248 - auc: 0.9092 - accuracy: 0.8531 - val_loss: 0.3319 - val_auc: 0.9051 - val_accuracy: 0.8500\n", - "Epoch 99/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3183 - auc: 0.9133 - accuracy: 0.8587 - val_loss: 0.3319 - val_auc: 0.9052 - val_accuracy: 0.8500\n", - "Epoch 100/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3190 - auc: 0.9131 - accuracy: 0.8612 - val_loss: 0.3322 - val_auc: 0.9052 - val_accuracy: 0.8498\n", - "Epoch 101/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3261 - auc: 0.9092 - accuracy: 0.8561 - val_loss: 0.3324 - val_auc: 0.9051 - val_accuracy: 0.8506\n", - "Epoch 102/200\n", - "814/814 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2ms/step - loss: 0.3227 - auc: 0.9111 - accuracy: 0.8557 - val_loss: 0.3326 - val_auc: 0.9051 - val_accuracy: 0.8500\n", - "Epoch 108/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3213 - auc: 0.9103 - accuracy: 0.8588 - val_loss: 0.3325 - val_auc: 0.9051 - val_accuracy: 0.8501\n", - "Epoch 109/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3214 - auc: 0.9107 - accuracy: 0.8561 - val_loss: 0.3324 - val_auc: 0.9053 - val_accuracy: 0.8500\n", - "Epoch 110/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3179 - auc: 0.9119 - accuracy: 0.8594 - val_loss: 0.3329 - val_auc: 0.9052 - val_accuracy: 0.8501\n", - "Epoch 111/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3174 - auc: 0.9124 - accuracy: 0.8589 - val_loss: 0.3341 - val_auc: 0.9051 - val_accuracy: 0.8529\n", - "Epoch 112/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3156 - auc: 0.9158 - accuracy: 0.8602 - val_loss: 0.3329 - val_auc: 0.9051 - val_accuracy: 0.8501\n", - "Epoch 113/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3216 - auc: 0.9109 - accuracy: 0.8582 - val_loss: 0.3329 - val_auc: 0.9050 - val_accuracy: 0.8509\n", - "Epoch 114/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3214 - auc: 0.9099 - accuracy: 0.8586 - val_loss: 0.3329 - val_auc: 0.9052 - val_accuracy: 0.8491\n", - "Epoch 115/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3124 - auc: 0.9136 - accuracy: 0.8622 - val_loss: 0.3330 - val_auc: 0.9051 - val_accuracy: 0.8505\n", - "Epoch 116/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3127 - auc: 0.9160 - accuracy: 0.8638 - val_loss: 0.3332 - val_auc: 0.9048 - val_accuracy: 0.8511\n", - "Epoch 117/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3218 - auc: 0.9118 - accuracy: 0.8576 - val_loss: 0.3333 - val_auc: 0.9050 - val_accuracy: 0.8512\n", - "Epoch 118/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3209 - auc: 0.9125 - accuracy: 0.8586 - val_loss: 0.3340 - val_auc: 0.9049 - val_accuracy: 0.8500\n", - "Epoch 119/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3152 - auc: 0.9143 - accuracy: 0.8628 - val_loss: 0.3334 - val_auc: 0.9050 - val_accuracy: 0.8508\n", - "Epoch 120/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3179 - auc: 0.9117 - accuracy: 0.8596 - val_loss: 0.3335 - val_auc: 0.9049 - val_accuracy: 0.8503\n", - "Epoch 121/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3232 - auc: 0.9101 - accuracy: 0.8581 - val_loss: 0.3340 - val_auc: 0.9049 - val_accuracy: 0.8512\n", - "Epoch 122/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3218 - auc: 0.9131 - accuracy: 0.8612 - val_loss: 0.3342 - val_auc: 0.9049 - val_accuracy: 0.8520\n", - "Epoch 123/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3164 - auc: 0.9147 - accuracy: 0.8603 - val_loss: 0.3343 - val_auc: 0.9048 - val_accuracy: 0.8503\n", - "Epoch 124/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3139 - auc: 0.9156 - accuracy: 0.8604 - val_loss: 0.3346 - val_auc: 0.9049 - val_accuracy: 0.8514\n", - "Epoch 125/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3199 - auc: 0.9134 - accuracy: 0.8606 - val_loss: 0.3348 - val_auc: 0.9046 - val_accuracy: 0.8520\n", - "Epoch 126/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3119 - auc: 0.9172 - accuracy: 0.8628 - val_loss: 0.3343 - val_auc: 0.9045 - val_accuracy: 0.8508\n", - "Epoch 127/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9167 - accuracy: 0.8626 - val_loss: 0.3345 - val_auc: 0.9045 - val_accuracy: 0.8503\n", - "Epoch 128/200\n", - "814/814 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1ms/step - loss: 0.3217 - auc: 0.9104 - accuracy: 0.8588 - val_loss: 0.3358 - val_auc: 0.9043 - val_accuracy: 0.8495\n", - "Epoch 134/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3179 - auc: 0.9134 - accuracy: 0.8591 - val_loss: 0.3353 - val_auc: 0.9048 - val_accuracy: 0.8515\n", - "Epoch 135/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3163 - auc: 0.9145 - accuracy: 0.8618 - val_loss: 0.3351 - val_auc: 0.9045 - val_accuracy: 0.8492\n", - "Epoch 136/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3178 - auc: 0.9148 - accuracy: 0.8599 - val_loss: 0.3353 - val_auc: 0.9045 - val_accuracy: 0.8498\n", - "Epoch 137/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3182 - auc: 0.9141 - accuracy: 0.8627 - val_loss: 0.3360 - val_auc: 0.9046 - val_accuracy: 0.8511\n", - "Epoch 138/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3113 - auc: 0.9164 - accuracy: 0.8648 - val_loss: 0.3355 - val_auc: 0.9045 - val_accuracy: 0.8506\n", - "Epoch 139/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3169 - auc: 0.9138 - accuracy: 0.8604 - val_loss: 0.3357 - val_auc: 0.9044 - val_accuracy: 0.8503\n", - "Epoch 140/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3194 - auc: 0.9135 - accuracy: 0.8589 - val_loss: 0.3359 - val_auc: 0.9045 - val_accuracy: 0.8498\n", - "Epoch 141/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3110 - auc: 0.9158 - accuracy: 0.8638 - val_loss: 0.3364 - val_auc: 0.9045 - val_accuracy: 0.8518\n", - "Epoch 142/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3113 - auc: 0.9181 - accuracy: 0.8629 - val_loss: 0.3360 - val_auc: 0.9043 - val_accuracy: 0.8495\n", - "Epoch 143/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3134 - auc: 0.9149 - accuracy: 0.8634 - val_loss: 0.3357 - val_auc: 0.9047 - val_accuracy: 0.8508\n", - "Epoch 144/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3131 - auc: 0.9154 - accuracy: 0.8628 - val_loss: 0.3357 - val_auc: 0.9049 - val_accuracy: 0.8512\n", - "Epoch 145/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3218 - auc: 0.9127 - accuracy: 0.8584 - val_loss: 0.3360 - val_auc: 0.9044 - val_accuracy: 0.8491\n", - "Epoch 146/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3153 - auc: 0.9159 - accuracy: 0.8615 - val_loss: 0.3366 - val_auc: 0.9048 - val_accuracy: 0.8524\n", - "Epoch 147/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3166 - auc: 0.9159 - accuracy: 0.8610 - val_loss: 0.3362 - val_auc: 0.9048 - val_accuracy: 0.8515\n", - "Epoch 148/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3119 - auc: 0.9165 - accuracy: 0.8640 - val_loss: 0.3360 - val_auc: 0.9047 - val_accuracy: 0.8500\n", - "Epoch 149/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9169 - accuracy: 0.8616 - val_loss: 0.3366 - val_auc: 0.9048 - val_accuracy: 0.8523\n", - "Epoch 150/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3131 - auc: 0.9154 - accuracy: 0.8635 - val_loss: 0.3367 - val_auc: 0.9049 - val_accuracy: 0.8517\n", - "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3181 - auc: 0.9133 - accuracy: 0.8643 - val_loss: 0.3368 - val_auc: 0.9048 - val_accuracy: 0.8528\n", - "Epoch 152/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3159 - auc: 0.9154 - accuracy: 0.8633 - val_loss: 0.3364 - val_auc: 0.9046 - val_accuracy: 0.8503\n", - "Epoch 153/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3163 - auc: 0.9144 - accuracy: 0.8599 - val_loss: 0.3364 - val_auc: 0.9051 - val_accuracy: 0.8517\n", - "Epoch 154/200\n", - "814/814 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1ms/step - loss: 0.3163 - auc: 0.9152 - accuracy: 0.8621 - val_loss: 0.3361 - val_auc: 0.9047 - val_accuracy: 0.8506\n", - "Epoch 160/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3131 - auc: 0.9169 - accuracy: 0.8634 - val_loss: 0.3370 - val_auc: 0.9054 - val_accuracy: 0.8526\n", - "Epoch 161/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3158 - auc: 0.9168 - accuracy: 0.8635 - val_loss: 0.3362 - val_auc: 0.9048 - val_accuracy: 0.8497\n", - "Epoch 162/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3165 - auc: 0.9158 - accuracy: 0.8636 - val_loss: 0.3367 - val_auc: 0.9053 - val_accuracy: 0.8508\n", - "Epoch 163/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3112 - auc: 0.9168 - accuracy: 0.8635 - val_loss: 0.3364 - val_auc: 0.9048 - val_accuracy: 0.8495\n", - "Epoch 164/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3121 - auc: 0.9184 - accuracy: 0.8648 - val_loss: 0.3365 - val_auc: 0.9051 - val_accuracy: 0.8517\n", - "Epoch 165/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3138 - auc: 0.9164 - accuracy: 0.8654 - val_loss: 0.3364 - val_auc: 0.9050 - val_accuracy: 0.8501\n", - "Epoch 166/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3102 - auc: 0.9176 - accuracy: 0.8645 - val_loss: 0.3370 - val_auc: 0.9053 - val_accuracy: 0.8505\n", - "Epoch 167/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3139 - auc: 0.9161 - accuracy: 0.8641 - val_loss: 0.3371 - val_auc: 0.9054 - val_accuracy: 0.8521\n", - "Epoch 168/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3163 - auc: 0.9152 - accuracy: 0.8597 - val_loss: 0.3364 - val_auc: 0.9052 - val_accuracy: 0.8495\n", - "Epoch 169/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3150 - auc: 0.9166 - accuracy: 0.8642 - val_loss: 0.3363 - val_auc: 0.9053 - val_accuracy: 0.8498\n", - "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3130 - auc: 0.9162 - accuracy: 0.8631 - val_loss: 0.3361 - val_auc: 0.9056 - val_accuracy: 0.8512\n", - "Epoch 171/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3110 - auc: 0.9180 - accuracy: 0.8658 - val_loss: 0.3364 - val_auc: 0.9053 - val_accuracy: 0.8515\n", - "Epoch 172/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3170 - auc: 0.9153 - accuracy: 0.8622 - val_loss: 0.3363 - val_auc: 0.9051 - val_accuracy: 0.8500\n", - "Epoch 173/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3141 - auc: 0.9167 - accuracy: 0.8641 - val_loss: 0.3368 - val_auc: 0.9054 - val_accuracy: 0.8505\n", - "Epoch 174/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - auc: 0.9150 - accuracy: 0.8614 - val_loss: 0.3372 - val_auc: 0.9057 - val_accuracy: 0.8514\n", - "Epoch 175/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3121 - auc: 0.9168 - accuracy: 0.8635 - val_loss: 0.3364 - val_auc: 0.9052 - val_accuracy: 0.8498\n", - "Epoch 176/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3134 - auc: 0.9158 - accuracy: 0.8629 - val_loss: 0.3371 - val_auc: 0.9055 - val_accuracy: 0.8511\n", - "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9145 - accuracy: 0.8634 - val_loss: 0.3370 - val_auc: 0.9053 - val_accuracy: 0.8514\n", - "Epoch 178/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3120 - auc: 0.9172 - accuracy: 0.8659 - val_loss: 0.3374 - val_auc: 0.9056 - val_accuracy: 0.8503\n", - "Epoch 179/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3139 - auc: 0.9143 - accuracy: 0.8637 - val_loss: 0.3369 - val_auc: 0.9053 - val_accuracy: 0.8512\n", - "Epoch 180/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9158 - accuracy: 0.8614 - val_loss: 0.3375 - val_auc: 0.9057 - val_accuracy: 0.8508\n", - "Epoch 181/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9173 - accuracy: 0.8634 - val_loss: 0.3383 - val_auc: 0.9056 - val_accuracy: 0.8529\n", - "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3103 - auc: 0.9175 - accuracy: 0.8628 - val_loss: 0.3376 - val_auc: 0.9052 - val_accuracy: 0.8500\n", - "Epoch 183/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3109 - auc: 0.9186 - accuracy: 0.8648 - val_loss: 0.3370 - val_auc: 0.9052 - val_accuracy: 0.8520\n", - "Epoch 184/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3118 - auc: 0.9169 - accuracy: 0.8642 - val_loss: 0.3385 - val_auc: 0.9057 - val_accuracy: 0.8508\n", - "Epoch 185/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9171 - accuracy: 0.8655 - val_loss: 0.3374 - val_auc: 0.9055 - val_accuracy: 0.8518\n", - "Epoch 186/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3151 - auc: 0.9149 - accuracy: 0.8624 - val_loss: 0.3390 - val_auc: 0.9053 - val_accuracy: 0.8501\n", - "Epoch 187/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3041 - auc: 0.9212 - accuracy: 0.8694 - val_loss: 0.3377 - val_auc: 0.9052 - val_accuracy: 0.8509\n", - "Epoch 188/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3118 - auc: 0.9162 - accuracy: 0.8653 - val_loss: 0.3373 - val_auc: 0.9055 - val_accuracy: 0.8508\n", - "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3092 - auc: 0.9186 - accuracy: 0.8651 - val_loss: 0.3383 - val_auc: 0.9053 - val_accuracy: 0.8518\n", - "Epoch 190/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9172 - accuracy: 0.8670 - val_loss: 0.3382 - val_auc: 0.9058 - val_accuracy: 0.8524\n", - "Epoch 191/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3165 - auc: 0.9174 - accuracy: 0.8625 - val_loss: 0.3381 - val_auc: 0.9054 - val_accuracy: 0.8515\n", - "Epoch 192/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3161 - auc: 0.9168 - accuracy: 0.8622 - val_loss: 0.3382 - val_auc: 0.9051 - val_accuracy: 0.8514\n", - "Epoch 193/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3121 - auc: 0.9166 - accuracy: 0.8636 - val_loss: 0.3384 - val_auc: 0.9055 - val_accuracy: 0.8520\n", - "Epoch 194/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3146 - auc: 0.9169 - accuracy: 0.8659 - val_loss: 0.3384 - val_auc: 0.9056 - val_accuracy: 0.8526\n", - "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3198 - auc: 0.9129 - accuracy: 0.8597 - val_loss: 0.3401 - val_auc: 0.9054 - val_accuracy: 0.8529\n", - "Epoch 196/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3118 - auc: 0.9167 - accuracy: 0.8638 - val_loss: 0.3387 - val_auc: 0.9055 - val_accuracy: 0.8532\n", - "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3110 - auc: 0.9159 - accuracy: 0.8640 - val_loss: 0.3395 - val_auc: 0.9055 - val_accuracy: 0.8532\n", - "Epoch 198/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3120 - auc: 0.9172 - accuracy: 0.8659 - val_loss: 0.3393 - val_auc: 0.9055 - val_accuracy: 0.8518\n", - "Epoch 199/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3102 - auc: 0.9185 - accuracy: 0.8683 - val_loss: 0.3398 - val_auc: 0.9048 - val_accuracy: 0.8535\n", - "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3095 - auc: 0.9198 - accuracy: 0.8658 - val_loss: 0.3393 - val_auc: 0.9050 - val_accuracy: 0.8508\n" - ] - } - ], - "source": [ - "history = model_2.fit(X_train.values, y_train,epochs=200,verbose=1,validation_data=(X_test.values, y_test))" - ] - }, - { - "cell_type": "markdown", - "id": "312d6ae8", - "metadata": {}, - "source": [ - "#### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "c26cf24c", - "metadata": {}, - "source": [ - "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "28015da0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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lW7duREREtHRzpJmEh4cTHBzMxo0bKSkpISwsrKWbJCIiIiIiUit1OzaSemwPPHpORURERESkNVDkIiIiIiIiItKKKJAXERERERERaUUUyEuj9OzZk8cee6ze6//4448YY8jKytprbRIREREREWkLVOyuDZk4cSIjRoxoUABel7lz5xIZGVnv9cePH8+2bduIjY1t8rFFRERERETaMgXyUs5ai8fjIShozy+LDh06NGjfISEhJCUlNbZpIiIiIiIi4qPU+mZgraWgpGyf/1hr693GKVOm8NNPP/H4449jjMEYw8svv4wxhi+//JJRo0YRGhrKjBkzWLduHaeeeiqJiYlERUUxZswYvv322yr7q55ab4zh+eef5/TTTyciIoK+ffvyySeflN9fPbX+5ZdfJi4ujmnTpjFw4ECioqI47rjj2LZtW/k2ZWVl/P73vycuLo727dtz6623cumll3Laaac16nkSERERERE5EKhHvhkUlnoY9Ndp+/y4y+87loiQ+j2Fjz/+OKtXr2bIkCHcd999ACxbtgyA2267jYcffphevXrRrl07Nm/ezAknnMA//vEPQkNDefXVVzn55JNZtWoV3bt3r/MY9957Lw899BD/+te/eOKJJ7jwwgvZuHEj8fHxta5fUFDAww8/zGuvvUZAQAAXXXQRt9xyC2+88QYADz74IG+88QYvvfQSAwcO5PHHH2fq1KkceeSRDTlNIiIiIiIiBxT1yLcRsbGxhISEEBERQVJSEklJSQQGBgJw3333MXnyZHr37k18fDzDhw/nmmuuYciQIfTt25e//e1v9O7du0oPe22mTJnC+eefT58+fbj//vvJy8tjzpw5da5fWlrK//73P0aPHs3IkSO54YYb+O6778rvf+KJJ7j99ts5/fTTGTBgAE8++SRxcXHNcj5ERERERERaK/XIN4Pw4ECW33dsixy3OYwePbrK7by8PO655x4+//xztm3bRllZGYWFhWzatGm3+xk2bFj535GRkcTExLB9+/Y614+IiKB3797ltzt16lS+fnZ2Nunp6YwdO7b8/sDAQEaNGoXX623Q4xMRERERkQNbUamHRZuzmJOSybKt2fRLjGbyoESGdonFGNPSzWt2CuSbgTGm3inu+6Pq1edvueUWvvnmGx5++GH69OlDeHg4Z511FiUlJbvdT3BwcJXbxpjdBt21rd+Qcf8iIiIiItI6lXm8fLhwC7+u28l5Y7pxcK/2Dd6HtZaPF23l9Vkb+S01mxJPRewxbVk6T3y/lqSYMCYPSmTyoETG925PUOCBkZTeeqNPabCQkBA8Hs8e15s5cyZTpkzh9NNPB1wP/YYNG/Zy66qKjY0lMTGRuXPnMmHCBAA8Hg8LFixgxIgR+7QtIiIiIiLScKm7Cigu89IjPqI8gPZ6LZ/+tpXHvl1Dyo58AD5auIWjByZy2/ED6NMxCoD1GXm8Nz+VL5ZsIzY8mPPHdufUEZ3LO1CXpGZzz6fLmL9xV/nxOkaHMjY5niFdYlm8OYufVmeQllPEa7M28s68zSy8a7ICeWl9evbsyezZs9mwYQNRUVF19pb37duXDz/8kJNPPhljDHfddVeLpLPfeOONPPDAA/Tp04cBAwbwxBNPsGvXrgMyNUZEREREDmzWWp76cR1vzNrIMYOTuOKwZLrFR+z1Yy5OzeaNWRvZVVDKLcf2Y0BSTK3rbdhZQPuoEGLCgmvZU8Os3Z7Ho9+u5vPf3IxUIUEB9OkQRf+kaJZvzWFVei4A8ZEhjOvdnq+WpvHtinR+WLWdU0d0ZuPOgioBOsBvqUu4//MVnDmqK0WlHt6Ztxlr3XDj6yb25pQRnekeH1ElVigq9fDLuh18szydMo8lMvTACX8PnEcie3TLLbdw6aWXMmjQIAoLC3nppZdqXe+RRx7h8ssvZ/z48SQkJHDrrbeSk5Ozj1sLt956K2lpaVxyySUEBgZy9dVXc+yxx5YX6RMRERER2Z9k5pcQEhRAVLWAsczj5a6Pl/HWHFdz6uVfNvDqrxs4dnASVx7ei1E92jVrOwpKyvh08VZem7WRpVsqvsf/uGo7VxyezB+O6ktESBDWWr5bsZ0nfljL4s1ZBBgY2CmGscnxjO0ZT2JsGEWlHopLvRSWejBAbEQwseHBxEWEEB0WREClwHl7ThFP/rCWqQu34PWNmA0PDqSw1MPybTks3+baEhMWxNUTejHl0GSiQoNYuz2PB79ayTfL0/lwwRYAAgxM7N+RM0Z2IS27iNdnbWTDzgJe/mVD+fFOHdGZ244fQKfY8FrPQ1hwIJMGJDJpQGKznt/9gdGY5JqMMTFAdnZ2NjExVa9YFRUVkZKSQnJyMmFhYS3TwDbK6/UycOBAzjnnHP72t781+/713IqIiIhIQxWWePhq2Tbem5fKL+t2Eh4cyIUHd+fqCb3oGBNGYYmHG99ayLcr0jEGrp/Yh9+2ZPPz6ozyfRzUPY6rDu/FsYOTCAyoCIyttaxKz2V7TjFd24XTtV0EIUEB5fdtyy5iVXoua9Jz2bCzgI0789m4s4CtWYXlgXRIUAAnDe1EfkkZ05alA9AlLpxLxvXgo4VbWJnmescDAwweb/PFhpMHJXLz5H70T4xmS1Yhq9JyWZWeS2hQAGeP7kZseM2e/9nrd/Lhgi0kd4jkjIO60DGm4ju512uZsXYHb83ZRHGZl99N7M2YnrVPc91a5eTkEBsbCxBrrd1tT6oC+VookN8/bNy4ka+//pojjjiC4uJinnzySV566SUWL17MwIEDm/14em5FREREmqbM4yW/2ENsRO3p2QUlZXy1NI1RPdrRo31kreu0hO05RcxOySQ5IZIhXWLrtc3WrEKe/GEtny7aSm5xWY37Q4ICOGd0V5ZvzWHBpixCggL4z3kHcdyQJABWpeXywoz1TF24tbxIW7f4cC4bn0x8ZAg/r8lg+podZOQWl+8zwEDnuHDaR4awfkc+uUU1j+vXPT6CCw/uztmjuxEfGQLAdyvS+evHy9iSVVi+XmRIIBeP68kVhyXjtZY5KZnMSclk7oZM8kvKCAsKJCw4kLDgALwWsgtL3U9BaZXicn6H903glmP6M7xbXL3Oo1RQIN9ECuT3D5s3b+a8885j6dKlWGsZMmQI//znP8uL3zU3PbciIiLSVmzNKuTuT5YRExbMPacMIrqe46JXp+fStV14rTM2bc0q5PKX57Jmex6nDO/MdRN70zcxGgCP1/LevM088s1qtucWExYcwB0nDOSig3sQELDn+kcer2VHXjHpOUWk5xSTmV/MsK5xDOxUc7x3fRSUlDF/4y6mr9nBz6szynuljYEbjuzDTUf3q9IzXllRqYdnf17PUz+upai0IgA/a2Q3zhjZhXUZeTz5/VrmVRrjHRMWxAtTxtTag5yRW8xrv27gNd849urCgwPp0i6cLbsKKSytWrg6KMDQq0MkfROj6ZUQSff4CHomRNIjPoIO0aG11pYqKCnj8e/W8M2ydE4Z0Zkp43sSFxFS/5PnY62lxOOlcjgZYEx5xoA0nAL5JlIg3zbpuRUREZH9kbWW1el5dG0X3izFun5YtZ2b31lUHjT27RjFC5eOoXv7uguvWWt58KtV/O+ndXSODeNfZw/n0D4J5fevSsvl0hfnkJZTVL7MGDhucBKTBnTkuenrWZ2eB7ge4PwSF5Ae3jeBh84aVusY5+zCUqYtS+PTxVv5dd1OympJ+x7cOYazR3Xl1BFdaBdZdzCaXVDK3A2ul3l2SiZLt2RX2Z8x0CshknUZror6Ib3i+c95B1VJ7bbWMm1ZGn//fAWpu1yP9tjkeG46ui+HJLevckHCWsvslEye+nEdmfnFPHrOiPKLGnUpLPHw4cJU3pqzCWvh8L4dmNA3gVE92xEaFIi1lozcYjZmFrAzr4TkhEiSEyIVOB9AFMg3kQL5tknPrYiIiOwL6TlFBAUY2keF7nHddRl53Pfpcn5anUFseDBTxvfkskMb14Na5vHy6Ler+e8P6wAY1CmGnfnFpOcUExcRzFMXjmR874Qa23m9lvs+W16lyBjAZYf25NbjBrB4cxZXvTqPnKIy+nSM4s4TBvL23E3l47H94iKCuXFSXy48uDvvzN3MA1+uoKjUS3RYEOeP7U5wYEUgvCotj59XZ1RJ3Q4w0CE6lKSYMCJDg5i7IZNSj4tlggMNI7u3o2f7SHokRNAjPhKLZW6KC9xXpedSPezpHBvG+D4JTOjXgcP6JBAfGcIni7dy+we/kV/iISEqlJuO7suWrEJ+S83it9Ts8lT2TrFh3HHCQE4a1kkzKkmzUSDfRArk2yY9tyIiIrI37cwr5vHv1vDm7E0EBRqum9iHqyf0Iiy45ow8uUWlPPH9Wl6ckVKjJzoyJJCLxvXgoG7tWJPuCoitTs8lNCiQv548qNb07W3Zhdz09iJmp2QCcNEh3fnLiYPILizl6lfnsTg1m6AAw+0nDOSc0V3LU+09XsudHy3h7bmbAfjrSYNYl5HHG7Nd9fUe7SPYll1ESZmX0T3a8fylo8svMqxKy+WpH9cyfc0Ozh7Vlesm9qkydn5dRh43v7OIxanZdZ6z/onRnDy8E8cP7UTP9pFV0t135Zfw8aItvDc/lWVb9zzDUq8OkRycHM+YnvGMTY6na7vaMxDWZeRx/RsLytPtK4sICeTKw5K5dmLvWocXiDSFAvkmUiDfNum5FRERaduWbslm6sItnDCsEyO715wObN6GTB76ahUhQQHcfEy/WtepTWGJhxdnpvD0j+vIq1YUrWu7cP5y4kCOHZzEzvyS8h7kz37bxo48V+Rs0oCO3HHCQFam5fDfH9axYlvd3+8DAwx/PrY/Vx3ei4AAg7WWjxdt5a6Pl5JbVEZkSCAPnDmMU4Z3Lt+mqNTDrR/8xseLtgJu3PXI7u2Y0C+B1el5fLJ4KwEGHjprOGeN6gq49Pxb3/+N7b5CbMcMSuQ/5x9U60WJ3SnzeHlr7mbWbc+rsjw+MoRjByfRP2n36eh+q9JyWb4tm407C3w/+ZR5LSO7t+Pg5HhG94ynQ/SeMyD8Cks8/GvaKhZs2sWApGiGdY1jWNdY+idFExyoVHbZOxTIN5EC+bZJz62IiEjb5PVaXpyZwoNfrSxP1Z40oCM3T+7HkC6xpOcU8c8vV/LRwi1VtjtxaCf+fFz/3VZf/2HVdu74cAnbst3Y8SFdYrjj+IFkFpTwj89XlC/vGB1aHhT79UqI5K6TBnHkgI7ly6y1fL9yOy/OTCG7sJR+idH0T4ymX2I0UxdtKQ/Gjx7YkTtPHMS/pq3kiyVpAAzvGsuj546gV4eoGu201vLizA289usGNuwsqHJfUIDhsfNGcNKwzlWWZxWU8Ni3a2gXEcINk/rUWRxOROpHgXwTKZBvm/TcioiItD0ZucX833uLy+f0HtQphlXpueXzaR/eN4H5G3dRUOLBGDhnVDcslvfmp2KtG5t9wdjuXDyuB306VvQe5xeX8Y8vVvCmLwW9S1w4fzq2P6cM71xeFK2gpIz//biO//28npIyL8a4VPKxyfEcnNyeyYMSG1TIzFrLm3M2ce+nyykpqxhbHhRg+P1RfbluYm+C6tGbvGlnAT+vyeDn1Rmszcjj9uMHMnlQYr3bISKNo0C+iRTIt016bkVERA4cqbsKeOK7tXRtF85JwzuTnFC11zwzv4Svl6Xx8Ner2JFXQmhQAHedNIgLD+5Oyo58Hv9uDZ8s3lpeIG1k9zjuOWUww7rGAbAyLYcHvljJT74LAAAjusVx9uiudI+P4C9Tl7LR17N92aE9+fOxAwgPqT3tfFt2Iesz8hncOaZRReyqW7olm+vfXMDGnQX06RjFo+eMYGjX+s2NLiItR4F8EymQr13Pnj256aabuOmmmwAwxvDRRx9x2mmn1br+hg0bSE5OZuHChYwYMaLRx22u/exJW35uRUREDiSfLt7KHR8tKa8wDjC0SywnD++EwfDN8nTmbczEX0NuQFI0/zn/IPpVmx5sVVoub8zeyMju7ar0pFc2Y80OXv4lhR9WZZT34vt1jg3j4bOHM75PzUrwe1tecRlzN2Qyrlf7Bo9bF5GW0ZBAXqUWpdG2bdtGu3b1K/JSX1OmTCErK4upU6eWL+vWrRvbtm0jIWHf/xMUERGR/Y/Ha9mWXUh+safK3Or5xWXc88ky3pufCsDwbnHEhQczY+0OlmzJZsmWqtXRB3eO4YShnbjisORag93+SdHcd+qQ3bblsL4JHNY3gYzcYqYu3MJ78zezOj2PM0Z24Z5TBhMTFrzb7feWqNAgjuzfcc8rikirpEBeGi0pKWmfHCcwMHCfHUtERETqZq0lI7eY7bnFFJZ6KCr1UFzqxeJSz2ubF93jtSzanEVBSRnd4yPoEhe+x3HaCzbt4q6pS8kpKiU2PJi48BBiw4PJLylj084CNu8qKC9KB5AQFUL3+Agy8orZnFmIMXD9xD784ei+BAcGkJlfwpdLt/HVUlf07agBHTl6UGKd0481RofoUK6a0IsrD08mp6iM2PCWCeBFpG1o8UDeGHM98CcgCVgM3GitnbOb9W8Cfgd0B3YA7wO3W2uLKq3TBXgQOB6IANYCl1lr5+2VB2EtlBbseb3mFhwBpn7VQZ999lnuueceUlNTCQio+Od56qmn0r59e+68805uvvlmZs2aRX5+PgMHDuSBBx7g6KOPrnOf1VPr58yZwzXXXMOKFSsYMmQId955Z5X1PR4PV199Nd9//z1paWl0796d6667jj/84Q8A3HPPPbzyyivl+wb44Ycf6NmzZ43U+p9++ok//elPLF68mPj4eC699FL+/ve/ExTkXtITJ05k2LBhhIWF8fzzzxMSEsK1117LPffcU6/zJSIiIq6a++uzNzJjzQ42ZbppvQpLPbWuG2BgVI92TB6UyJH9O5KyI59vlqfz/crt7MwvKV8vKMDQpV04gzrF8MfJ/Wqks3+7PJ0b3lpAUakr1raZwlqPFxIYQHhIINmFpezIK2FHnjtGp9gwHj13BIf0al++bnxkCBce3IMLD+7RpPNRH8YYBfEiste1aCBvjDkXeAS4FpgN3ARMM8b0t9Zur2X9C4B/ApcDvwD9gJcBC9zsW6cdMBP4ARfIZwB9gV177YGUFsD9nfe8XnO7YyuE1D3dSWVnn302N954Iz/88ANHHXUUAJmZmXz11Vd88cUX5OXlccIJJ/CPf/yD0NBQXn31VU4++WRWrVpF9+7d97j/vLw8TjrpJCZPnszrr79OSkpKeYDu5/V66dq1K++99x7t27fnl19+4eqrr6ZTp06cc8453HLLLaxYsYKcnBxeeuklAOLj49m6dWuV/WzZsoUTTjiBKVOm8Oqrr7Jy5UquuuoqwsLCqgTqr7zyCjfffDOzZ8/m119/ZcqUKRx66KFMnjy5XudMRERkf7ZsazZfLNnG5EFJjOgW1+z7zy8u4+Z3FzFtWXqV5QHG9T6HBwcSFhxIaHAghSVlrE7PY+6GXczdsIv7v1hZZZuYsCA6xoSxObOA4jJv+Vzf3yxP56oJvfj9pL6EhwTy1pxN3PnRErwWJvbvwI2T+pBTWEZWYQlZBaWEBQfSIz6C7u0j6BQbTmCAIaeolE07C9iwM5/84jKOHZzULAXjRET2Zy3dI38z8Jy19iUAY8y1wIm4QP2ftaw/HphprX3Td3uDMeYt4OBK69wKbLbWXlZpWUqzt7yVadeuHccffzxvvvlmeSD//vvvk5CQwJFHHklAQADDhw8vX/9vf/sbH330EZ988gk33HDDHvf/5ptv4vV6eeGFFwgLC2Pw4MGkpqbyu9/9rnyd4OBg7r333vLbycnJ/Prrr7z77rucc845REVFER4eTnFx8W5T6Z966im6devGk08+iTGGAQMGsHXrVm699Vb++te/lmccDBs2jLvvvhuAvn378uSTT/Ldd98pkBcRkVZta1YhD3+9io8WbsFa+O8P6zi8bwLXH9mHg5Pjy7Pa6qPU42VNeh492keUjzMHV/H9ylfmsTItl5DAAG6c1IchXWPp2T6SLnHhtU6JlrqrgG+Xp/PNinRmr88kMSaMyYMSOWZQImOS4wkODMDrtaTnFrFhRwEvzEjh2xXpPP3jOj5dvJXD+3bgrTluqrazR3Xl/jOGElyPqdJiwoIZ0iWWIV1UlV1E2o4WC+SNMSHAKOAB/zJrrdcY8y0wro7NfgEuMsaMtdbOMcb0Ak4AXqu0zim4Xv33gCOALcBT1trndtOWUKDyoK7outatVXCE6x3f14IbNq7rwgsv5KqrruKpp54iNDSUN954g/POO4+AgADy8vK45557+Pzzz9m2bRtlZWUUFhayadOmeu17xYoV5ansfuPG1Xwa//vf//Liiy+yadMmCgsLKSkpaXAl+hUrVjBu3LgqX1QOPfRQ8vLySE1NLc8gGDZsWJXtOnXqxPbtNRI9REREWoWcolKe/nEdL85Iodg3R/ioHu1YtDmL6Wt2MH3NDkb3aEdyQiTpucWkZxeRnltEp9hwrp6QzMnDOpePTbfWMm1ZOg99tZL1O/IJDjSM7N6OCf060C0+gns/WcbO/BISokJ55uJRjOqx5+K2XdtFMOXQZKYcmkyZx0tggKlxUSEgwNApNpxOseGM692er5elcc8ny0jdVVgexN9wZB/+75h+DbogISLS1rRkj3wCEAikV1ueDgyobQNr7ZvGmARghnGf7kHA/6y191darRduDP0jwP3AGOA/xpgSa+0rdbTlduDuRj8SY+qd4t6STj75ZKy1fP7554wZM4bp06fz6KOPAnDLLbfwzTff8PDDD9OnTx/Cw8M566yzKCkp2cNe6+/tt9/mlltu4d///jfjxo0jOjqaf/3rX8yePbvZjlFZcHDV8WnGGLxe7145loiISGMVlXr4dPFWvlyaRue4MM4a1Y3hXWPLA9nM/BJempnCy79sKJ9ObWxyPHeeMJDh3eLYnFnAMz+v4915qczbuIt5G6uOJswqKOWP7yzm0W/WcO0RvenTMYqHvlpZvl5IYAAlHi+zUzKZnZJZvt3gzjE8d8loOseFN/gx7amYnd8xg5M4tE8Cj327ms9+28b1R/bhokP2/jh2EZHWrqVT6xvEGDMRuAO4Djemvg/wuDHmLmvt33yrBQDzrLV3+G4vNMYMwY3DryuQfwAX+PtFA6nN2/qWFxYWxhlnnMEbb7zB2rVr6d+/PyNHjgRg5syZTJkyhdNPPx1wY943bNhQ730PHDiQ1157jaKiovJe+VmzZlVZZ+bMmYwfP57rrruufNm6deuqrBMSEoLHU3sRncrH+uCDD7DWln/JmTlzJtHR0XTt2rXebRYREamPkjIvuwpK6BgdWmcvscdrCTDUuN/rtczftItPF28lZUc+yQmR9EuMpn9SNNFhQXwwP5V356WSXVhavs3rszbRt2MUZ4/uyvacYt6Yvam8wFzfjlH8+bgBHD2wY/mxusVH8PfThvL7SX15f0EqXq8lMSaMxJgwEqJC+WHVdl6YkcKmzALu+GhJ+XHCggO48rBeXHNEL3bmlTB9TQY/r9nBnJRMJvbvwANnDCUiZO9/VYwMDeLOEwdx54mD9vqxREQOFC0ZyO8APEBiteWJQFod2/wNeM1a+7zv9hJjTCTwrDHmH9ZaL7ANWF5tuxXAmXU1xFpbDBT7bx/IqVwXXnghJ510EsuWLeOiiy4qX963b18+/PBDTj75ZIwx3HXXXQ3qvb7gggu48847ueqqq7j99tvZsGEDDz/8cJV1+vbty6uvvsq0adNITk7mtddeY+7cuSQnJ5ev07NnT6ZNm8aqVato3749sbE1x7tdd911PPbYY9x4443ccMMNrFq1irvvvpubb765SkV+ERFp3co8Xnbml5AYE7bnlYFd+SWs2Z5HYkwoPdo3PVPOWssXS9L4x+fL2ZpdRI/2ERwzKJHJg5IY2T2OlWm5TF+zg59XZzBvYybhwYH0T4qmX6L7Sd1VwGe/bWNbdvnEOkxfs6PWY3WJC+fs0V3ZsCOfL5emsWZ7XpWCcUO6xHDDkX04ZlASAQG1f0/pGBPGdRP71Fg+qHMMlx3ak7fmbObZn9exPbeYs0d15ebJ/UmKdec2OiyYngmRXDyuZxPOmIiI7CstFshba0uMMfOBo4CpAMaYAN/tJ+vYLAKoHl36u2/9/9VmAv2rrdMP2NjEJh8QJk2aRHx8PKtWreKCCy4oX/7II49w+eWXM378eBISErj11lvJycmp936joqL49NNPufbaaznooIMYNGgQDz74IGeeWXH95JprrmHhwoWce+65GGM4//zzue666/jyyy/L17nqqqv48ccfGT16NHl5eeXTz1XWpUsXvvjiC/70pz8xfPhw4uPjueKKK/jLX/7S+BMjIiL7lbziMqa8OId5G3fRu0MkxwxOYvKgREZ0jaOw1MOa7XmsTstlVXouq9NzWZWWy/Zcd00+wMDvJvbmD0f1q1GULT2niK+WprEzr5iswlKyCkrJLSqla7sIxiTHM7ZnPEmxYaxMy+GeT5Yxa31FqvnGnQU8Nz2F56anEBRgKPPaKvsu9ZSVV22vLDo0iGOHJDGqRzs27MxndVouq9PzSMspYkLfBC46pAcT+3ck0Beg31dUymeLt/HJ4i0EBwZwxWHJHNGvQ5M6GiJCgrjisGQuPqQHuUWltc73LiIirYex1u55rb11cDf93CvANcAc3PRz5wADrLXpxphXgS3W2tt969+Dq3R/NRWp9U8D86215/rWGYMrinc38C4wFngOuNpa+0Y92xUDZGdnZxMTE1PlvqKiIlJSUkhOTq5S2E1aPz23IiLNZ11GHmvS85g8KLE8QK2vwhIPl740hzmVxmv7RYUGkVdcVue2iTGhpOe4gH5QpxgePXcE/ZOiK8aRz02lxLP7jLOu7cLZmlWI10JoUADXHtGbi8f1YG5KJt8sT+e7ldvJLiwlMiSQcb3bc3jfDhzaJ4FSj7f8osLq9FwiQ4M4YWgnjujXgbDgwBrH8Xptnb3rIiLS9uTk5PgzkmOttbvtVW3RMfLW2neMMR2A+4AkYBFwnLXWXwCvO1V74P+OmzP+70AX3BzxnwJ3VtrnXGPM6bhx73/FTT13U32DeBEREWm8TTsLeOy71UxduAWvhcP7JvCf8w6iXWTNeb3LPF6MMVUC/aJSD1e9Oo85KZlEhwbxzMWj2JFfwjfL0/lx5XZyfUF8h+hQ+vtS2PsnRdEvMZq+idFEhQbxxZJt3PnREpZvy+HkJ2YwoV8CP67KKO9BP6h7HEO7xBIbHkxseDBRoUGsTs9jzoadLN+aQ+quQgBOGJrEHScMpGs7N0vM8UM7cfzQTpR6vGzKLKBbu4gaPf4DO1XtANgdBfEiItJYLdojv79Sj3zbpOdWRKRxPF7L+ow8Xpy5gffmbS4PmP3V0Lu2C+eZi0cxuLOre7JxZz5P/7iODxakEhMWzKQBHTlmcBIH94rnD28t5IdVGUSEBPLaFWMZ1SO+/DglZV7WbM+lU2w48bVcGKhse24Rt3+whO9WVkw7elifBG6YtPu51nOLSlm4KYv4yBDNSy4iIvtUQ3rkFcjXQoF826TnVkSkfrILSvloYSq/pWazKj2Xtdvzyuc1BziiXwf+75h+BAcGcO3r89m4s4DQoABuPW4Av6Vm8cnirXhr+fphDFjrqqm/fNlYDunVvknttNbywYItzE3J5Lyx3Tio+57nQhcREWkpCuSbSIF826TnVkRao5yiUkICA2odgw2wPiOPd+Zu5tghSYysI5DNLixl+dYcNu7MZ2NmAZt2FlBQUsbY5PYc3jeBQZ1iCAgwbNyZz4szUnh3Xmr5dGh+YcEBjOkZz++P6suYnhW96NkFpdz0jutlr2xi/w5cN7EPZR4vXy9P55vl6WzJKiQkKIDnLxnNhH4dmnhmREREWhcF8k1Un0C+Z8+ehIeHt0wDZa8oLCxkw4YNCuRFZL+3aWcBXy9P45vl6czdkElYcCB/PrY/l4zrWWXc9aeLt3LbB7+RX+KC7jNGduG24wbQ0TedW3pOEc/+vJ43K81TXpuEqBB6d4hizoZM/F8bBiRFc+LQTvRPcnOid20XUWdRO6/X8th3a3j253VM7NeR64/sw9CuVdPWrbWsSs8lLCiQnglNnzpORESktVEg30S7C+Q9Hg+rV6+mY8eOtG/ftJQ/2b/s3LmT7du3069fPwIDa+/ZEhFpSUu3ZHPHR0v4LTW71vvH9GzHP88cRtd24fz9sxW8NsvNvJqcEMmGnflYC5EhgVx3ZB+2ZhXy3ryKCu5d24XTu0MUPdpH0D0+ggBjmLl2B7+u30lBSUWQP7F/B646vBfje7dv8HRo1tomTaEmIiJyIFMg30S7C+QBtm3bRlZWFh07diQiIkJfSlo5ay0FBQVs376duLg4OnXq1NJNEpEDWJnHy7yNu/hmeTrfr9yOx2s5bkgSJw/rzJAuMbX+T/F6LS/MSOGhaSsp9VgCAwxje8YzeVAiRw9M5KfV2/nnlyvJL/EQEhRAt3bhrMvIB+D6I3vzx6P7sWxrDvd8uoyFm7Kq7HtMz3bcMKkvE/om1HrskjIv8zfuYmVaDof1SaBvYvReOS8iIiJtnQL5JtpTIG+tJS0tjaysrH3eNtl74uLiSEpK0oUZkTam1OPl/i9W8PPqDP519vA6x5HvTlGph2d/Xs8v63bQu0MUw7vGMaxbLH06RJGeW8zqtFxWpeeyfGsO09dksKugtNb99GwfwXFDOjG0Syz9k6Lo2T6SzIIS/u/dxUxfswOAYwcn8o/Th5IQFVpl29RdBdz+4ZLy9eIignn03BEc2b9j+Tper+WjhVt44vs1dG8fyfUTe3NwEwvKiYiISPNQIN9Eewrk/TweD6WltX8Zk9YlODhY6fQibVBOUSnXv7GgPPhtFxHMh9cdSnIDxmhPX5PBXVOXsmFnQY37Agy1VmePi/BNuTYoEYBPf9vGdyvSKSr1VlkvJDCA4EBDfomHsOAA/nrSYM4f263OC47WWqYu2sK8Dbu4/sg+dI5TLRcREZHWQoF8E9U3kBcRkf2D12urFHmrjy1ZhVz+0lxWpecSHhxIl3bhrN2eR4/2EXzwu/FVerynr8ng4WmrKPFY+idG0S8pmr4do/l08VY+WbwVgMSYUK6Z0Jv0nCIWp2axdEsOecVlBAUYendw2/TrGMXonvGM6dmOoMCAKu3JLy7j2xXpzFy7g9XpeaxOzy0fmz4gKZonzj9Iae0iIiIHMAXyTaRAXkT2FWsty7bm0C8xmpCggD1vIOWKSj18ungrr8/ayG9bsmkXEULH6FCSYsPoFBvOmSO7MLrSNGiVLdqcxVWvziMjt5iO0aG8cOkYkmLDOOPpmWzOLGR4tzjeuupgSj2Wf3y+nHfnpdbZjgADl4zryf8d04/osODy5V6vJS2niISo0EY9t16vZUtWIRl5xQzuHENokLKGREREDmQK5JtIgbyI7Ct/+2w5L8xIYUS3OF65bCyxEcF73ugAUubxsi4jnw0789m0s4CNmflszSoiwBjCgt3c6GHBAUSFBhMXEUxceDCx4cEs2LSLd+elkl24++FNxw5O5NbjBtCrQxTgqr7/94e1fLUsDWtdT/eLU8aUp6Cvy8jjzKd/IauglLHJ8WzcmU96TjEAl47rwaF9ElizPY9VabmsTs8lISqUPx/Xn2Fd4/bqeRIREZEDnwL5JlIgLyL7wgfzU/m/9xaX3x7UKYbXrzyY+MiQeu/DWssPq7YzdeFWAgMMsb5ANzY8mEGdYxjdo2oKt7WW6Wt28PyMFFZsy2F87/acPKwzh/dLaHCPr9dr2VVQQlxESJ3zh9dmc2YBP6/JYPrqHcxct4PcorIGHbeyru3CueDg7pw0tDMFpWWkZRexPaeYuRsy+WBBKl4LQQGGc8d0Y2tWIT+syijf9qRhnXjgjKFVetEB5m3I5ILnZ1NS5sar90qI5MGzhjGmjt59ERERkeagQL6JFMiLSG3mbshk5todRIQEEhceQmxEMO0iQhjSJYaIkKAG7Wvx5izOfuZXSsq8nDO6K9+v3M6OvBL6JUbx+pUH0zE6bLfbe72Wr5al8eT3a1m+re7P+cpF1XKLynhhRgor03JrrBcTFsTRgxKJDQ+mqNRLcZmH4jIvXeLCGdszntE92xEXEYK1liVbsvl08VY++20b27KLCDDQITqUpJgwOkSHElzlwgHkl5SRXVhKVkEpWQUl5FQL3KNCg0hOiKR7+wh6to+gS1wExrjU+aJSL0WlHnKL3D6yC0vYVVBK+8gQzhvbjSP6dazzIsLq9Fwe/HIl363cXr4swMDJwztz3cQ+9E+qe7z518vS+McXKzhuSBJ/PLofYcFKaxcREZG9S4F8EymQF2l7NmcW8Ls35hMSGMBJwzpz4rBOJMaElfdgP/nDWuakZNa6bWhQAIf3TWDyoESOGphI+8gQikq9LngtLCEyJIhu8RHl62/PLeKUJ2aSllPE0QMTefbiUazfkc+Fz88iPaeY5IRIHjt3BEmxYcSGBxMWHEhxmYf1GfmsTs9lVVou05allc8THhESyPlju5MUE0ZWYQlZBaXszCthdsrOWqc5iwgJ5Nwx3Zg0oCM/rsrgs9+2lqeP786ApGiKSj21VmdviMAAw8jucRzetwMT+nVgaJfYBvXoN9Qv63bw9I/r6Nougmsm9KJnAyrSi4iIiOwrCuSbSIG8SNuyPaeIs5/5lY2VAlRjYGzPeIpKPSxOzQbcVGDHDE4kODCArIISsgpL2ZZVRFpOUZXtggMDytOy/brHR3B43wQm9OvAcz+vZ97GXfTuEMnU6w8tT+3euDOfC56bzZaswirbhgYFUOa1eKrNYxYTFsSUQ5O5bHxP2tWSjl/m8TJ/4y6+Xp7OdyvSscD5Y7tz/tjuxIZXLco2Z0MmP6/OwAJhQW5cenBgAGu25zInJbP8ogFAWHAARw1M5ORhnZnQL4HcojLSc4pIzykmI7cYT7X/K5EhgcSGuzHuseHBJMWGExXasAwGERERkQOdAvkmUiAvsn9L3VXAjDU76N4+gtE94utVEXzTzgJ+Xb+DYwcnERdREfRmFZRw7jOzWJWeS7f4cC4d15Mvl6Yxf+Ou8nXCggO4YGwPrp7Qi6TYqinv1lpWpuXyzfJ0vlmezpIt2eX3+ces5xaVUuqp+lkbHRbEx9cfWl6EzW9LViF/em8xK7blkF1YWmUO8piwIPonRdMvMZpBnWM4ZXjnGuO795aM3GLmbXAZCRP6dSBSgbiIiIhIs1Ig30QK5EX2P4UlHqYtS+O9+Zv5Zd1O/B9dESGBjOvVnsP7JnD0oES6touosp3Ha3lxRgr//mYVRaVeIkMCueiQHlxxeDKRIUFc+PxsFm3OomN0KO9fO57u7d32qbsK+HJJGh5rOWtU1ypziu9ORm4xxWUeYsODiQoNwhhDXnEZs9fv5OfVGUxfs4Nt2UU8fdFIJvbvuNt9eb2WvJIysgtKCQ4MIDEmFGP2Xgq6iIiIiLQcBfJNpEBepH6KyzyuAFlBKVmVfgcYl0revX0EHaLqH3yu3Z7Lp4u38dlvW9m4s4DQIDf9WGhQANmFpeSXeMrXPah7HJszC9iRV1JlH+N7t+fs0V05bnAnNu8q4E/v/8bizVkAtI8MYWe+Wz80KICu7cJZl5FPXEQw714zjn6JdRc/a07WWgXkIiIiIntDZgpMuxPGXA59jm7p1jSIAvkmUiAvUtOu/BLen5/KZ79tZXtuMVkFpRSWeva4XURIID3bRzKyRxxjk9sztmc8SbFheLyWTZkFrErLZfm2HL5Zns6K3VRfBzfV2FmjunLmyK50i4/A63Vp7T+vyeCHlduZsyGzvKc+KjSI4jIPpR5LdFgQfzlxIGeP6saPq7fz5PdrWbApC3Djt9+86hCGd4tr4hkSERERkWZTUgAbf4GUH6FgV9X7EgfDwddAQLVZZbweePFYSJ0L8b3hxvmugFEroUC+iRTIizjWWhanZvParxv59LetNQq4gZvOKyY8mDj//OURIZR5vGzcWcC27EK8tXzEdIoNIzO/hOJq+wsKMEzo14GTh3dibHJ7yjze8unHggINA5NiCNhNdfPUXQV8uGAL789PZVOmK1x39MCO/P20oVXGtltr+XX9Tj5dvJWzR3djZPd2jTxDItLqWQurvoTEQdCuZ0u3RkSkpoxVkLUZ+rau3uUGsxbSl8G672Hdd7DxV/DsZladQ/8Ak++ruuyXJ+HrOytuX/IJ9Dpi77R3L1Ag30QK5OVA9OWSbTz41UqSEyKZPCiJowd1rDJXeWZ+CavSct30Zum5rE5zv3Mrzfk9qFMMFx3Sg6FdYn1BezDRoUF1BtclZV5Sd7le9zkbMpm7IZPlW3PKg/vQoAD6JbribWN6tuO4IVUL0TWW12uZt3EXxWUeDuuToDR2Eanbis/gnQuhwwC4blar6rmRNiRtCXx+C4w4H0ZNaenWVJW1GYJCIWr3dV+kkYpz4fERULADLp8G3Q9p6RY1r5J8WPmFC9zXfQ956VXvj+kCvSdBfK+Kz+f8HfDrk+7vM1+AoWe5v3eug6fHQ1kRtEuGXSkw+HQ4++V99nCaqiGBvMoOixzgPF7Lv6at4n8/rQNgw84CfliVwZ1TYUS3OCJCAlmVlseOvNqveIYEBXDSsE5cdEgPDuoW16CgOCQogF4doujVIYrjh3YCIKeolJXbcukYHUq3+Ii9Mn94QIBhbHJ8s+9XRA5Ac59zvzNWwoYZkHx4y7ZHWr/05TDrvzD2Gug0rH7beD2uNzKwlq/mqfPh9dOhKBu2L4fBZ0DYftLRlLkenj4MwuPgxgUQHLbHTaSBZj3tgniABa/uP4G81wtLP4D1P7qe8Q79Gri9Bxa+Dj/cD3lpFcuDwqHnYdDnKBfAJ/Sr/QJrQBDMfAw+vgES+kLiUPd3WRH0muh66p+Z4C7W5m0/IC80qUe+FuqRl/1NSZmXL5duY2CnmAYVZMvML+H3by1kxlr3D+DyQ5NpHxXC18vSyudGr6x7fAT9EqPpnxTl+x1NckIkoUGBNdYVYec6WPstjLoMgpqeSSGyz+1cB0+MrLg95Ew468WWa48cGN66AFZ9DsERcNrTMPi0PW/z7iVuiMfYq2HCLRDuG/K18Vd442woya1Y95i/w/gb90rTG+zdS2D5x+7vs16CIWfsm+OWFbugtvckaN973xyzJRRkwuPDodjXMRscAbeshtB9U5y3Tik/w9d/gW2L3e3QWPfZWZ/Uf2thzTfwzV8hY4VbFtsdhpzuns/u41yGx554PfDmubD2G4jtBiMugJ8ehOBIuO5XaNcDnjsKtsyDo+6Gw29u/OPdh5Ra30QK5GV/Yq3l5ncX89HCLQCMTY7n4kN6cOzgJEKCAtx4dF/RuPScIgpLPRSVeiku8/DZ4m1sySokPDiQh84axsnDO5fvNy27iJ9Wbwegf1IMfTtGaW5wqb/iPHh6HGRtgol3wMRb981xSwth6u/cl5tzXqn4stsSMlZD7lbodjAEh9e8PzsVdqyGrmNa/kuX1O7ru+CX/0D7vrBzDQQEw80rIKpDS7dszzxlMPNRWPwOHPsP6HfsnrfZsQa+u89dwDjzeVcXYE9Ki+C9S8FbBqc8ATGd97xNa5Kb7j7Huo1pnv0V5cC/+lQd1zvxdpjwZwgIqH2bTbNccS6/sDiY8Cfo0N8FyqUF0PNw6H8CTLvdpRr/YTEEBjdPm3enrMQVDet2cM1sgert7n0UXPzh3m8TwJe3weynIWkYXPPzgTsk5tt7YMajkDjE9TTvXAunPAkjL666XtoS91o56OK6A9bcNMjPgKShjW/PjjWuGvyaae52SLQLmNOXgglwF5kOua7u52PrIvjmLnchANxr/Yg/w5gr6xe8V1eYBc9Ngsx1FctOeBjGXuX+Xvg6fHy9q39y48K634P7EQXyTaRAXvYnT/24loe+WkWAAWMMHt8A84SoUDpGh7I2I6/WInR+PdpH8OzFo+mfpEBCmtHnt1SkJIfGuC+VEXt5OIOnDN65CFZ/6W73ORoueLdmxdp9YdMsePVU98UqKAx6jK8Yw7dhBqz9DnascuvGdofTn3apgpVlb4HpD7vCPpVFtIeJt0Gn4Xun7UXZ8MWfIXkCHHTh3jlGa1BWDI8MhIKdcN6b8PPDsHUBHH0vHHZTS7du93ashY+ucT1NAOHxcP2cui9A5GXAjw/A/JfB+mYbie4EV3wNcd13fyx/IAEQleTOVddRDW9zUbb7ol/XRS1rIXfbvr1QUFbixtPuXAMXfdA801T99h58eCW07wN9j3Up9gCDTnW98yGRNbd5/UyX3dTrSDc+ePvyqvf3ORrOfR0w8NhQyN8OZzwHw86put6C1yB1jgumwmJrHidnG3x1m8s8GXTKnh+LpwxePwNSfoKBJ8PZr1R83nq98MLRsGW+a9/ab137/rgUYrvued+VeT3w1e0uPX/i7XsOyjfNghePA3wxTGPHjVvrLuLEdKl9SENLy02H/4xwF3LOe8v9T/n2Huh2CFwxrWI9a9352DzL3T7+IVfNvbJNs+GNs9x4+4veb9xrfdMslx1SnOPS2kddBkfc6oZ5fH6zC5oBRlwEk+91/8v8z2XWJvjub7DkXXc7MNS18fCbm35BfvtKeP4oKMmDHofBpZ9WBOwlBfDvAVCcDRd96NL193MNCeT3/8sSIm3Y18vS+Nc0Fwzce8pgZt46iT8c1ZeO0aHsyCtm+bYcSsq8hAcHMrxrLCcO68RZo7py0SHdufKwZG47fgCfXH+YgnipKX2ZG5eWmdLwbTfMqAjiozu5f+q//Kd521edtfDZH1wQHxTmxtCt/db1Lu6JpxSmPwLzXnJfYppq+wp48xwXxAdHuN/rvndphm9fALOecl+4TID7Mp29CV4+yfVilBa5Hrvv7oMnRsG8F2Hz7Ko/q75w6YA/P+y+SDe3r+6A396Gz//PZTa0VSs+dUF8dGcXcI2+3C2f/5ILUvaGpnaeWAtznoP/HeaC+NAY19NUmAlf/F/t6//yBPznIJj3ggvi+x3vCvvlboPXzoD8nXUfb8sCmOl7b8d0deNYXzreBasNkbcdnhgN/z3EBfS1mf6wu7Ay57mG7bsp5r3ggniAHx9s+vMDsHyq+z3oNDjuftd7GhDs0s/fvrDma2vLAvdZZgLhpEfh2hku8yEqyd3f/0R38SQ43I0/P/hqt/yX/1Rt77KP4JMbXLr5pzfVfCxeD3xwpWvfx9e7YmF78s1fXRAP7v3y+f9V7HfZhy6ID4mCU59yARQWFr1V71NVbsl7MOcZlxa98LXdr1ta6NqPdSnUALP/V/9j5WXAb+/CR9fCv/vD48Ncxkl9n/u0pe61srv3jZ/XC9t+gxmPuc/zspK6163t+DMecUF8l1HQ/3gYfr57nWye5XrG/VZ84gvifUHzl7e6set+KdPhtdN96fkWPvm9+z/UECk/u8+L4hx3IeG62XDiw+7iYVCoe50f+4D7v7fodfhXb3igm/useuMc9/73B/FDz4Eb58Exf2uerLqOA+D8t2DIWXD6/6r2uodEwPDz3N/zDrxhU+qRr4V65GVf83otHmsJDqz48FmxLYczn/6FghIPFx/Sg7+dNqT8vlKPlxlrdlDq8TIgKYau7cJ3Oy2bSBWeUnhyNOza4L5g+sdk1qdHvaTA9WDtSoGRl0K/4+Dt811A+/tFEJ24d9r87b3uS40JcD1TZUXwvi/wqlyxtjaf/x/Mfd79HRLt/qmPuQI6Dqy5bmmROy+7Ulzw0fOwquMvszbDC8e4lPquY+GSqW6Zf6qcrM3Q/WCXYpo8waW+TrvDfbkGl8JduKuicFH3cS4FMNCfUmjht3fcl2ZwafmnP9OwMaA529wX7H7H1exlWvMtvHFmxe0j73RpjW3RSyfCxhlwxG1w5O2ucvK/B7gvqhd/5DIsGsta93rYNMsVA8tMca8pDJz/tnuNNFRRDnx4Faz+yt1OPgJOe8pdjHhukkt9P/uVivHYXq/rfZ3zjLvd+SCY/DdXzC97i3sd56S6IOGSTyA0qurxyorh2Ymud3jImXDy4/DBVRUZMeNucJ8d7Xrsue0fXuMuHkHt00Vlp7oLW2VFENkB/vCb+wK+JzvXwdaFblhBQ4evFGS6CxxFWRXLLv3UvW/rkrMV5r8Ci950j/viqVXfY8V5LoApK3IBuT+FeeOvrme7tKBmxsfbF8LKz2DYeXDGMxXLS/JdunTXMVWzjgoy4dHBbl8XT4XeR7rg8oXJbplf9fTrn/4FP/y94vaYq1wgVpdFb8HUayvWnfs8YF2P+aE3wZNj3EXKI/8CR/ypYv2GpjBX/n8E7n/J1T+6YQW1+fov7uJUdCc3POTlE11we9MSiO2y+2PN/I+7OEEtsU9tvdjVLf0Qpl4HZYVuqMMlH9eeEbbmG3dxYt0PLnvCr99x7j1avSDgojfde7XjYPe/aeApLjPjiZHgKal4nsGNCV/9lXsOJt/rLg78d6z7fJnwJ3ehbM6z7n/7he+5i3dvX1hRAG7XRrfuqCnuPV0fa751M3uUFbmskfPerPv9ufZbl/FVOdXdr+fhLnjvfFD9jttctq+Apw5xr5M/LoOYTvv2+A2k1PomUiAve1tGbjFzN2SyODWL3zZns3RLNgWlHpITIunvm47t3Xmb2ZJVyKF92vPyZWOrBPkiTTL/Zfj0Dy41zuvr8Q2LhcP/Dw6+dvfj1L663fU4x3RxxWRCY1xK25b5btvjH2y+dlrrvswsehO+u9ctO/k/MOpS9/c3d7uKtUHhLs2wtlT0+a/Ap78HjPvi7f+yCO4LZ0ClL+ElBS5Ar673UW78Xtcxrjdy5xrXo3nZl/UfTrDqK/jkxoovde37ui9h/U+omUZqfcH8F39yQWVwhDv+6Mtc6v7u7NroArS8NDflzhnPVYyjLcpxX2ZytrjqvulLICLBfbFpa5WmM1bDf8e4C0M3La0IAPxDRgae7Etlxl3c+elBNwb0lCcgOmn3+06d78aAbpxZ+/1hcS4VuOOAmvelL3f7r/66ylwPb53vKusHhbmAfMyVFcHS93+Hn//lns/r57gU5c9uqriAdNyDLuiuHFxlrHLjmwt3udf4+W9Vfe9//w/4+aGKfUa2dxcHvr+vItUeIL63S1ftczT0mVwzgNswE14+oeJ2YAjcMK/qBYAPr3aveb/jHoRDrq39/NXW/pBoGH4ujL6ifuP+wWWmzPovdBzkxn/Pf8kF8Zd+WnPdlOnugsjKLyqGJkDNYHnJ+/DBFe59euOCqu/tBa+5HvOAILj8azc8IX2ZuzCKgetn1x28VvfFn1yw1vsoF8w+OxGyNrqLTz3Gu9dDcARc/ZOrJL5pFrx0gmv76Mtdz6QJdNMt1lZpfMt8ePF4N85/wp9h0p0ukP/cl/XR83DYMN1ls9w43wV1JfnwcH9XlG/K51WHEnk97qJLQt+an3fzXoTP/ugu4HQY4PabOBSu/Lbm59LmufDiMWC9cP470P8497g2zoTDb4Gj7qr7nK383GVMgdt/n0nu/KUvdRdbA0Pgqu9rHz/u9bqhKT8/VHX5pLvcRfAqj+cl997zC46EHuNcJlv1QNjrcRcW/NOo+UV2cP9jty1ymQ5TPqs4bys+dUPMohLhj8vd63LaHe72jQtc5sYHV7gMjeBI8Ja6iwF9j4VzXnX1Dl45ye3rko9dcO+35huY/m/3Gm3X072OA4PdhXRvqcvmOfvl+v2/KC1yr8nM9e7/Uof+7lgtVcvgxeNg068VF572Ywrkm0iBvOwt1lrenruZuz9Ztttx7X7JCZFMve5QYiP2QUEbaRtKi9xV/pwtcNw/3bQu3/zVfZkB92X8/LdrL6JUeVzihR9UVKdd9wO8dpr7InTjAojr1vj27VznUv63r3BBd2l+xX3VvzR5PS7Ffe23rmLt+W9V/RK2eY77kuctdf+8J9zi0kTnPl/zC3ll/nTlkEj3mP29NwHBbl8xXd3Y4j31/lSXv9MVJ2vfB0ZcuOdCVVmbXQqpP7UV4wKmMVdC32Nq9gTl73CBzc61Fcv6n+C+eAWFuos38192j+2an+HpQyF7s+uVae55qa11QU3qXDeGMrJ98+27tLD24oIN4Q/i+h0PF7xdsdwfWJlAuHm5Kw710TUugIbdz0ecmeKGTCzzFfsKCnOpnh36Q3yye41+cYs7JzFdfK8h31jiomyXDrv4LZedMeRM1zPXZZRLaX3vUhewRndyQUCXkVWPXVYMzxzhKkAPPsO9tn57x12oOPW/rppzbTbPhVdPcT25kR1g5CXutVC4C5490r1HKvfy+y2b6tKZN8+p+j4aeDKc+WLFLBaeUvjf4a5do6a4L/UpP1edHWDLAnjO19s46jIXUEd3hj8sqvuiYnaqL6NgizvPZUUV93Uf79rRe5I797UFDjvXwX8Pdu/niz6AhP5uLLK3zAXZlTMmfv2vC5T8ehzq6gosfss9pzfOr2jnOxe7NOfDboaj7656TGtdFtGyD33vweku4Fv6gUvDP+eV2h9rbTJT3Oe49bpib2m/uX1e9YO7UPTaae5zI3Goe3zPH+Xe68POhTOehTfPc5kV1V//4DKRnjnCXdTsfwKc+0bFxZkf7ncXtfxO+5+b197vk9/DgldcCvjpvnT3knx46zz3vI++HE58pOI5KS1yWRG5W93Fm8Gnuc+lgh01LwyXFsEzh7sCov7HAe61+N6lu78oWXkM9dhr4IRKAbm17iLZ6i/d/8Orf6xax6Ak330G+LOkxt3gXlef3Og+Jy7/CrqNdfct/8SXpu+F4Re49123se71kTLd9aaX5rvX0BnPuc/ktd+4bQ+72a03/2U37MWv+vj/shJfbY8d7vx/dZvLKql8kbus2I1l9//vGHiKy1zzvy/9WWpx3eF3v7plX9/pjl2XQafCGc+33hlqFr8DH13t3rN/WNwytXXqSYF8EymQl70hv7iMv0xdWl59vl9iFKN6xDO8ayzDusYRFxHMmu15rE7LZVV6LlkFJdxxwkB6dYjaw55FGmDW0+4ff0yXijl/vR5Y/LYLMkoL3Jek06qNM9u+0hXKyd7sgtDTnqq4z1p45WTXkzLyUjilkePl05e5AnL5GRXLTIALdkZe6jIGqn8pL9zlq1i7HjDuC+Skv7j1np3oevQHnuJ6Iipvm5tWsz5AYIjrJaxcoCdzvethWfi6G4cc3s59sapvz1lTeb2uOvDc533FpHwS+rkU3f7Hu7YW57lelq0LXXG9ibe54kNlRS6gGXu1+zINcOlnLr3aH6C07+t6XJurmm9ehgtQVn7mbncY6Hp+GjvsoijHvbbWfe+KCO5KcdkRY650AVBDswlKC10KfVEWXPAe9Dum6v0vHONqFXQ72PVMestckFuQ6YLW2tLuN8xwBcvKiqh4Hd5Zs+hXQaa7GLZjlQseL//KBWFTr3dp7tV1HOwuIliPC+rPfaPutNAt8+H5o10QAa5X7Yzn9jwd2LrvXbqwP3jw13Yo3OW+vJ/zat3b+p+btd+5sc2eEneR6ZzX3PPyyxMuFTo83gW82aluXmcsXPmde0wvn+h6VIed5z47Hh/u2nLSYy4Lpboq57AfXPaVyy6p7QJdTBeXkjz4DNcT6n+N+wPuylXWP77BPYa+x7iUZKj48g+ugNe4612Pf2mhLwDdBsf/y41bL8mHh3q7tOurf4LOI2q2vTDLBaNZm1zv5Pqf3LmonIZfX+9eWjEePzjS9WD7sxFytsH/DnXDLiLau9/tkt0FvLAYl5Hy1CHuXFUeTpC21I2jz1jhzu2V31Wdr95a13s+/yXoNMJdOKj8ubF5riuAFxTupkgDF1D6i7CBS82feJv72/8ZFNMVfr/ABbKrv4Y3z3b3n/G8uyi17jtY+717j0R2dNkL/qwVT5kb556zpeaFBf85f+5I91ne83D3/q1+ETV/pztfudvcxaxTnnDncMErFYF1YIi76DniAncePrgSlr7vPm+vne4uhr92hstiGHmpW7f6/6vNc9znhL9YnLfMnavTnqp4n3pKXZ2UxW+7SvWT7qz53E+70/XiB4a491zHwa4NlYPT4lz47Gb3eTHprzWHgDw1zg2NGHCSa7s/W+3ga13qu384UNYmNwTsyDv3z4KA9VVa5D5beh7qqtrv7eK8TaBAvokUyEtjebyWn9dksHDjLjrGhNGzfSQ92keQV1zGjW8tZO32PAIDDH86tj9XH95L49pl3yrJd//I8jNq74Vd843rMbAe1+twzN/dF5HVX7uepJJc92Xw6h9qFqjZNNulPJpAuGFuw+f13brI9SIV7nJfaCf91aX1xXXfcw9A9hYXLFTuCY1Ocl9MOg6CK76pOf63oUqL3JfJjgP3nN6+t/gvKix4tWJcb49D4ai/ul6ydd+7L+2XT3MprOt/csF75XGzY66EE//t/i7OhUcGu2q+570FA06occhyZSWw/gfXo1VZZAf3mojp4r7Qr/zc9TLlZ7gMhrAYF0TE94ZLP9l9NeviPDfGdsOMqsuLcurOngiPh4Mucu3YlVLx5dNaX2posmtfeFzF2NCMVW7cd109M5XHBoMLZk981KWuz37aPZbrfq3ohc3a5C4aFex0vcHHPwidhtX9OCv3Jsd0rQjg2yW7eggBgS4oXfphxRRmQ89xQe6eMhH8FeYDQ1zmwIATd7++nz94mPtCRS/enirhV7fuezd/elmhG79/4iPw7BHuNXPKEy5AAnfRYNEb7kLJ+BtdmnBQmAv0Y7tWXGyM6+4uNlYOukoK3MW+1Dmu1/6Kr6tmAOVsdT3ca7+Djb9UnQKuXbLLcmjfx70vTABcO7Mi+N25zo3Vtl4XiOdnuPW8ZW46rWPvrxqY+VPNoxJdfZDVX8H7l7nX3e8X1Z1CvHmOuxDhf03X1iteH6nz4XnfBaVzXqtZhX71NJexBC5ovOJrd+HEzz+MJGmYSyn/5QnX4+4tde+ny76ChD41j+v1wJqvofPImhfnrHWZDjtWwdH3uB7qrQvchaERF1VU8D/xEXfB+PHhrme5cm8yVGTMVBcc6S4sVZ+rfPq/XTZMpxGuR91/7qtnbV39I0Qm1H4+U36GV04BrLuwsWFm1Rkezn6laqZGUbbLNsna6C7KbFngAvQBJ7l16wp6ty50hecKd7nX8PlvNnzMePpyN/2rX2Oqsa//0b2X/GK6utlVdlcjorUrK27cFHf7mAL5JlIgLw21PiOP9+an8uGCVNJziutcLzEmlCfOH8nY5P33SqAcwPxfdtolu2C7ttTuxW+7NEJwPb4moKI4UI/D3JeoutKk3zjbfcELj3fp1KMvr18a3uY58PpZLqDsMsqlgjamkm3qfBfQb/rF3Q6LcxcdWirw3luKsl2wNuvpqinFwZGud63y1GCbZrlzW5JbkUZZ+aKGv85A9/Fw+Ze1Hy833RU6Sp1bd5sCQ9y0Yf5enY6DXFAaGgWvnOp6fuK6u/a161lz+7ISFzSt+672/cf3cr3gvSe5iylLP3QXNWrrxa6vYx+AcdfVXF5a6L6g5293PTdDz3aBQVG2K/CVl+6yPib8yQWWLx7retWThrmLKPUp0rZ9pdvOf0Fm9BWuAFzl56Yg06XHh0a7LJj6jC0tK3E9iF1GNW6KOHC9tcs/dmOIuzRwHxtmuIuBJXkVKe9dx7rz4u+5zdnqCtuVFrix7SW5Vcc3lxS4KdYKdrjp2vzDAnZtdOnMKT/56gx8VXvBSr/SQtfTv+ordx6Lq30fri176IOrXGXtLqPc8J7SAvf8n/5szYyVshJ4cpS7kDP5PhfILZ9aUYRsd37+lxvHDq7Xu+vo3a9fl8Vvu/NcfeiDn78wXG2v9fwdLqugOAfieriAFFyV/JMfr/8FnOpm/sfViPALj3dFQTsNr5Sa7xsmtPZb996+fk7V/0dlxW5Y1JZ5btx8b9949h7ja39/5e906eaeYnfhtn0fF6gueR9Wfb77OiqVffc3N3uCX/dx7uLnwJNrDwBT57n3sb/WTPfxLsNjTxfcdqxxr5WDLt5zzY26PHuku0jS52j3P7Mx/DVvhl8Ax/+z9ikLZZ9TIN9ECuTFb+rCLTz67WpKy7zEhAcTFxFMbHgwHi9kF5aQXVhKVkEp23Mrgve4iGAm9e9IVmEpG3fms3lXISVlXib068Aj5wwnIWr/vxooB6DCLJd+WJTtvpQOP7fudf3psJWNmuJSSHcXmGeud2Mv/fOnx/eCo+52PZqVg5CCzIqe0x1r3PFK892XpgverZrK2VDWwqov3Zfxg3/XuOrgrUV2qgsGFr/tenEveKf2uYG3LoTZz8Ahv6v5RTZnmwuavKW1BxTbFrvxozlbIDS2ak+zf97vrE1uewAMHPp7l4ZZ3mO92Y3DzlzveqDOe71qgOj1uotHS951BbrOfqVqIbSQqNrrEXg9rtdxybvuglO7ZPeai0927diVUlExvijb7bNdsru/fd/ai3z5lRa6jILqvWr+OcKDwuH6We6L/9L33fjcq39sWH2ILQtceuzwC2r2MLZmm+f60oez3fNy9U81MxQqj7WO7OjSqitXnZ/xqMsuaN/HBWYzHnGvYU+JC1wv+aRh7+2SfFdFfO7zrhJ8aKy7mFm9R3n7Sniq0n57H+VqhtT1ubfoTZj6O3fhsazYBf5X/VCzhkF1Xo97fOFxbsjQ3mKtyyyI6lj7/TMeg299Y/lDol02yYgLmlaQLG+7G7piPe65vfSTigsulVPz/c54DoadU3M/ZcUua6iuHvTqpl7vpj0Lj3e93ZUr0+9pZhM/T5m7COEtcxd6kobseRv/a7XjYLjsC/ec7gub57pCd0f91V0kbQxr3Wfjvmqz1IsC+SZSIC+5RaXc/fEyPvSNZ9+TAAMT+3fk7FFdmTSwI6FBFamaXq8lt6hMBeuk6UryKwLg3LSq95kANyaz00E1e46K81xP/JxnXO/G737Zc6EXf0+OCXRF8cZeVb8vd54yWPgq/PBA1Wl39iT5CFesrnKRIamfHWsB69LpG8Of6tx7kpuKLT7ZpdYu/9gFKaUFLvA9/+26U22zU93rMqZL7e3ITXNpq/6LPEPOqvgCOu1Ol0ZrfBcj+k5u3OPYFyrXg4ju7Ip0BQS5GgCVq3S3dVsXucrqQ85wF5CqK85zvfJ5abWPhS/OhUeHuIyFysXskie4FPeGjif3s9YF8mExtWeGQMX4+c4jXQbJ7obleD1urPkO33jwuO5u6ryWqszdUKVFbjiACYDjHmh8QFjd9393RVBPf6bmZ4bX4wrCrfjU1c/43czmKTy2bbGv/oJPx0HuM23QqRXF6PaWrYtc3ZSmFuEUQYF8kymQb9sWbc7i928tZFNmAQEGbpzUl0kDXA97VkEJOYWlBAYEEFuph75LXDjtIltpJU/ZP1nrvhyu+979bFvsUnr3JDzejdfrfaTriVn3g0uv9veY1jaWsjZerxtzHp/c8PRacF/Ef3myore9uuhOFb2jSUNdteq2NgXa/qL6eEtwveD+8fB9jnY9Wk3ttcnf6SojL34bsC4dP/mIiqrNpz8Dw89r2jH2he0rXWEsfzrtCQ+7C13SMNtXuM+1oefUXmjxxwfhx/vd3x0Guvmn+xy994Pk/J3us2/oWfUb4rPsI3hvivt7/O9dO2X3yopd1f/kCc079GnVV64oaa+JbqiPSCukQL6JFMgfmApLPNz0zkIALh3fk3G92mMqfSHYtLOAF2em8PqsjZR5LV3iwnnsvBGM6anx7NKMtsx3BYDG/772seZlxb4prKbWPv43LM4FvzFdqvZilBS4StvVx4H6xfVw8x0ffsu+7S0qLapZIC0kUj0X+5vZz7gq85kprnfdn5Y67gY3/rc5p+rZttjVXVj/Y8WyY/7uCp+1Ft/d52pO1FWdWpqupMDN292+j6tov79WzPZ6XaHPrQvdMIL6pGOLiNRBgXwTKZA/MP3nuzU88s3q8tuDO8dw5eHJdG0XwQvTU/h6eRpe39vhxKGduP/0oQd2OrzX41Jn45MbXjFVGic71c2RW5TliuJc8nHVsZfWuorfC3zzCQeFueI+vSe59dv32n0PkafUXShY970rOhUW53rm+xx14BV8k72nrNiNew8I3HuvG2tdZfFfn3C9Z4f9ce8cZ2+x1k2XmDhYQby4oQKFmc2Xmi4ibZYC+SZSIH/gScsu4siHf6Sw1MOR/Tvw6/qdFJV6a6x3RL8OXHl4Mof1SajSW3/AyUxxY183/eoqXd8wZ/fTQvl5va5yddIQjWVuKK/Hja3dOLNiWeWpwMBN/fT5zYBx1ZoHn6aeaxEREZE2oiGBfC2DkkQOPA9NW0lhqYdRPdrx4pQx/HrbUdxyTD86RIcSEhjAuaO78fUfJ/DK5WM5vG+HfRvEF+5yhWE+v8VVSt6brHVTEz19qAviwY1fnnbHnrf1lMFHV7sUwmcm+Aps1YOn1I2X3p2iHLfe7tpdkFm/49XVhjnPwftXuLmCW8L0f7sgPiQKjn8IMK6C8nxf7/vGX+DLP7u/j74bRpyvIF5EREREaqUe+VqoR34/VFbiKmHHdod+x+xmPV9KqL+yd85WVnU4lmPf3gXAx9cfyvBuceWre7wWr7UEB7bANa2yYhfI/fwv31QpuHGAp/9v76Rq7trgqgiv+drd7nEYjL8B3r7QTRNz0YcuBbvWtpbAB5e7KrN+YbFw1ku1b5OZ4uaDXvcDpPzsxm2PvNRVG65cAdjrhdlPw7f3upTES6bWzAwoLXSFhFZ/5aZAO+Yfu68iXJm1rs3f3gOZvgA+KtFNXdRxQP320Rw2/govnwDWWzH1288Pw/d/c1NcnfEMfHmrK043+Aw460Wl64qIiIi0MUqtbyIF8vuZ9OWuJzhtiZsi5cL3ap8refojbm5ab9We3VwTzWlFf2X4iLE8cu6I5m2b1+Ombtq+DM55tf7jSZdNdcWesja62+37uODXelywO+76urfN3uLGUC98w83hXFl0Eoy40AW8/nmXCzJdb/CcZ90cvIGhbtqnQ65zlYK/uh1mPQXxveG6XyvmfvYrLYJ3L4E101yV6ZMec8ffPNs9H8c+4Oad3TDdjc1e+527iFKbdj3htP9Bj3Gwa6M7dxtnVNwf190F2fHJ7nZJPrx1nrsYUHkfpz8D3Q/xnY9Ul2Ww6C3wFFdUQm+X7IppbZ7l1ovs4C4+7FwLEe3h4qk15zbeGwp3wdOHucJ1w86FM551y611U/As/7hi3cShcMU0DVsQERERaYMUyDeRAvn9hNcDv/7X9Vp6Stwcw9bjgrGrfoD2vSvWnfMcfHGL+zs40hfI9SRn62piclaz1bYn8KpvSOzau/ZjVT7mjtVuCq+ojnvuFZ3+b1e9GFyAefnXEJ1Y9/qeMpfGPucZdzsqEY68A0Zc5Hrnv7rVBccXfeiKlJW3ywspP8G8F2DlF+487I4JhP7Hu2m9Zj0FRdluea+JcNyDVXuji3LgyTFuPt8j/wJH/KnivuI8eOdCFxAHhcP5b7rCa2XF8NnNsOh13/ECXG+zX0AQdDvYPYbeR7nU+o+vh+zNgIFh57jHUZLrnq+Jt7lgPHOdm5v50k/cuXnjbBeIh0TBpLvcVGY5qe54Y65yQfzqL6seu7qgcJd5cOgfXIr9a6fDtkXudXTxR9BphJsDdt337hz7syP8QiLdc9su2V2o8V8kiEyo+/VhLWSudxc1Fr8FWxe4ba752c1fXPn8vnCMuxAUHg9X/wjtetT9WERERETkgKVAvokUyO8HCrPg7QsqCoP1O87N1fv+5ZA6BzoMgCu/hdBo3xyulwEWjrjNBYXGkF9cxlmPfMYThbfTJ2Cr2+ayLyGijunkctPhnYvc/qHigkB8Lxh7NSQfXnX9zXPhxWN9FxfiXCXypKEw5XMXJNZ4TLtcirh/yqXD/uimAvOniVvreqgXv+kqk1/1g5u3edGbMO9F15Ps1+MwGHO5q2TuDyatdePe575QtZcboONgN4VUn6NqDz6XvA8fXOGqpF8/213QmPciLHzdPa6QKLjgHeh5WMU21roLLd/c5QLp+F4uyO99lFsvrNp7pyjb9f4veqNiWbdD4PSn3ba5afDqqZCxEiI7QkwnN01VWKy7sNF1tNvHl7e5c1RZz8NhzBUuWN6V4rIbMte7bcddX3U+2cIsd4EgdY57XIHBNYP3+giJcsdr18OdNz9vmQvcszZVLAsMhcu/rH0+9uxUN9/68POg84iGt0NEREREDggK5JtIgfx+4Pu/u7HjIVFw3ANw0MUuAM1Ng2cnQu42bP8TKR11BSHvnOd67EdfASf+G4+FDxak8sjXq0nLKWJETC4fhd6Dyd3meokvngohEVWPt20xvHU+5GxxY5a9ZZTPowyuh/u4B1xAb4wLKP93uEuNH3KW61V/8Vg3xrnn4XDh+xBcKbjLWO1SxDPXQXCESw0fdErNx11a5MZSb5kP0Z1c0FnmK4AXEu0KoI2+HDoO3P35277CBfQZK12AOPz83c8Dba2rqL5hugui87dX3BfXHc58EbqNqX3bHWt901Ql775Nfis/h5mPQ/8T3LzRlduVvwNeO80NowDXS33JVOg0vOo+VnwGMx6BLqNdAN+hf/2O7Vec556PDdPd7dBY6DXBXYhoV+1xFGVV1FzI9P3kbKHK66M2AcEu/b/3JBh0atUMEhERERGRahTIN5EC+RbmKYVHh7hU77NegiFnVL0/dR72peMxnhI81hBoLMvjJrLpqP8SFBjMw1+vYmWaq5LeJS6cx84bwZiIdBdoF2W73ulBp7re6c4HwYpP4KPfuYC5fV/X8xzbtaJo3pL3YMm77tgjL3WZAVOvhaUfQFwPuHa66/ndughePhFK8lwGQdcxvuBvA2xd6KrDx3aD899yPfd1ydkKzxxREUwnDnHB6tBz6l/krTEyVrlq9t5SwEDfye7iSN/Ju78I0NwKd8G7l7qe6nNfh8RBe+c4pYWuVkF8L9dTHhjUgG2L3OtjV4r7Xb3ifvve0OPQvft8iYiIiMgBRYF8EymQb2HLP4F3L3bFyf64HIJCaqwy9aWHOG3jPwD41TOIKaV/ppiK9WLCgrhhUh8uGdeTsGBfELppFrx2hguo/cJiK8aP9zkaznzBpbNXZq0bm/3NXwHrisJlrnPjwC+f5lK+/db/6NK2PSU1H1f3cXDOaxDVYc/nIH05/PY29D8Ruo3ddxXMV34B6Uth6Nn172HfW6xV5XYRERERaTMUyDeRAvkW9trprvDYYTe7+bSrmbYsjWtem88lgdO4rHceK4bdyq+ppcxJyWRbdiFnj+7GjZP6EBdR8wIAuWluGrO138H6n6DYF8SPu8GNId9dz/PqaW4e8hLfnOhH3Q2H31xzvVVfwqynXWq8vzhafG83/nlf9myLiIiIiEiroUC+iRTIt6DMFPjPCMDAHxa5auGVbM4s4MT/TCenqIwrD0vmLyc1Ie3aU+ZS3r1lbjq0+ti+Ej650aVOn/qUm75NRERERESkiRoSyDdgUKjIPjD/Zfe796QaQXypx8uNby0kp6iM4d3i+PNxA2ps3iCBQXUXcKtLxwFw5TdNO66IiIiIiEgTKJCX/UdZScXUZKMvr3JXUamHf365kkWbs4gJC+LJ8w8iJEi94SIiIiIi0vYokJf9x8rP3PRt0Z2g33GUerzMWLuDTxdv5etl6eQVlwHwr7OH0y0+Yg87ExEREREROTApkJf9x7wXAcgacC5PT1vDe/NTycyvqP7eOTaM303szbGDk1qqhSIiIiIiIi1OgbzsfZ4y+PZu2LG66vKgMFfRvV0y3pBoAjZMx0sAJ0zvxVbWA5AQFcKJQztx8vDOjOzejoAATUcmIiIiIiJtmwJ52ft+/hf8+uRuV/GPdv/eM5xtJoGJ/Tpw0cE9mNi/A0GBGgsvIiIiIiLip0Be9q4NM+Hnh9zfE/5ctRJ9cQ42cz2rVvxGcPZGIk0xW4Zez49HTaRH+8gWaa6IiIiIiMj+ToG8NJ21kLMFYrqAqZT6XpAJH14F1gsjLoRJd9bY9F9freSpjHUEGPjvBSO5dGinfdhwERERERGR1kc5y9J0X90Ojw6Gl0+CLQvcMmvhkxtdgB/fG45/qMZmz/28nqd+XAfAP04fyvEK4kVERERERPZIPfLSNKnzYfb/3N8bZ8BzR8KQMyGhn5tOLiAYznoRQqOqbPbevM3844sVANx63ADOH9t9X7dcRERERESkVVIgL43n9cDnfwQsDDwZgiPht3dg6QcV60y+FzqPqLLZ18vSuO3DJQBcPaEX1x7Ra9+1WUREREREpJVTar003rwXYdtiCIuFEx+FM56Ba36C5CPc/f2Oh4N/V2WTX9ft5Ia3FuLxWs4e1ZXbjx+AMZpSTkREREREpL7UIy+Nk7cdvvub+3vSXRDVwf3daThc8jHs2gBx3SGg4lrR0i3ZXPXqPErKvEwelMgDZwxVEC8iIiIiItJACuSlcb75KxRnu8B99OVV7zMG4pOrLFqfkcelL84hr7iMg5PjeeL8gzQ/vIiIiIiISCMokpKG2/gLLH4LMHDiIxAQuNvVd+YVc/ELc9iZX8KQLjE8f+lowoJ3v42IiIiIiIjUToG8NIzXC1/+2f098hLoOnq3q3u8lpveWcSWrEJ6to/g5cvGEh0WvA8aKiIiIiIicmBSIC8Ns/orSFsCIdFw1N17XP2J79cwfc0OwoIDeObi0SREhe6DRoqIiIiIiBy4FMhL/VkLP//L/T32Sohsv9vVp6/J4PHv1gBw/+lD6Z8UvbdbKCIiIiIicsDbLwJ5Y8z1xpgNxpgiY8xsY8zYPax/kzFmlTGm0Biz2RjzqDEmrI51bzPGWGPMY3ul8W3Juu9h6wIICodDrt/tqtuyC/nD24uwFs4f250zRnbdR40UERERERE5sLV4IG+MORd4BLgXGAksBqYZYzrWsf4FwD996w8ErgDOBe6vZd0xwDXAb3ul8W3Nzw+736Mvq5hurhYlZV5ueHMhmfklDO4cw90nD9pHDRQRERERETnwtXggD9wMPGetfclauxy4FigALq9j/fHATGvtm9baDdbar4G3gCq9+MaYKOAN4Cpg115rfVuxYSZs+gUCQ2D8jXWutiWrkHOe+ZX5G3cRHRbEUxeOVIV6ERERERGRZtSigbwxJgQYBXzrX2at9fpuj6tjs1+AUf70e2NML+AE4Itq6/0X+Nxa+y17YIwJNcbE+H8ADeauzj82/qCLIKZzrat8vzKdE/8znUWbs4gJC+LpC0fRo33kPmykiIiIiIjIgS+ohY+fAAQC6dWWpwMDatvAWvumMSYBmGGMMbjH8D9rbXlqvTHmPFya/ph6tuN2YM8l2Nuq1Hmw/gcwgXDoTTXuLvN4+fc3q3n6x3UADO8ay5MXjKRbfMQ+bqiIiIiIiMiBb39IrW8QY8xE4A7gOlywfgZwojHmLt/93YDHgQuttUX13O0DQGylH1Vmq8w/Nn74edCuR427H/xqZXkQP2V8T969dpyCeBERERERkb2kpXvkdwAeILHa8kQgrY5t/ga8Zq193nd7iTEmEnjWGPMPXKp+R2CB67AHXK//BGPMDUCotdZTeYfW2mKg2H+70nay+mtY/SVg4LCba9ydVVDC67M2AfDQWcM4Z3S3fdxAERERERGRtqVFe+SttSXAfOAo/zJjTIDv9q91bBYBeKst8wfmBvgOGAqMqPQzD1f4bkT1IF52IzcNpv7O/T32akjoU2OVN2ZvorDUw8BOMZw9SokMIiIiIiIie1tL98iDm3ruFWPMPGAOcBMQCbwEYIx5Fdhirb3dt/6nwM3GmIXAbKAPrpf+U1+QngssrXwAY0w+sNNaW2W57IbXCx9dAwU7IHEoTL6vxiolZV5e+WUDAFcelqxMBhERERERkX2gxQN5a+07xpgOwH1AErAIOM5a6y+A152qPfB/B6zvdxcgAxfc37mv2twm/PIfWP8jBEfAWS9CcFiNVT5dvJXtucV0jA7l5OG1V7IXERERERGR5tXigTyAtfZJ4Mk67ptY7XYZcK/vp777n7jHlaRC6nz4/m/u7+MfhA79aqxireX5GSkAXDq+JyFBra5uooiIiIiISKuk6EuqKs6FDy4HbxkMPh0OurjW1X5dt5MV23IIDw7kwoO77+NGioiIiIiItF0K5KWq+a/Arg0Q2x1OegzqGPf+3PT1AJw9uitxESH7rn0iIiIiIiJtnAJ5qWrp++73ob+H8LhaV1m7PZcfVmVgDFx+aPK+a5uIiIiIiIgokJdKdq6DrQvBBMKg0+pc7QXf2PjJAxPpmRC5jxonIiIiIiIioEBeKlv6ofvd6wiI6lDrKgs37eKduZsBuPLwXvuqZSIiIiIiIuKjQF4cayvS6oecVesqRaUe/u+9xXgtnDaiM2OT4/dhA0VERERERAQUyIvf9uWQsRICQ2DAibWu8vC0VazPyKdjdCj3njJkHzdQREREREREQIG8+C3x9cb3PabWIndzUjJ5YaYbG//gmcOIjQjeh40TERERERERPwXy4kur/8D9PeTMGncXlJTxp/cXYy2cM7orRw7ouI8bKCIiIiIiIn4K5AW2zIesjRAcCf2Oq3H3P79cycadBXSODeMvJw1qgQaKiIiIiIiInwJ5qUirH3AChERUuWtVWi6v/roRgIfOGk5MmFLqRUREREREWpIC+bbO64FlH7m/a6lW/9z09QAcPySJw/om7MuWiYiIiIiISC0UyLd1G2dCXhqExUHvSVXuSssu4uNFWwC4eoLmjBcREREREdkfKJBv65Z/7H4POgWCQqrc9fIvGyj1WMb2jOeg7u1aoHEiIiIiIiJSnQL5tm7TbPe7z+Qqi/OKy3hjthsbr954ERERERGR/YcC+basOBe2L3N/dx1T5a535m4mt6iMXh0imaTp5kRERERERPYbCuTbsq0LwXohthvEdCpfXObx8uKMFACuOrwXAQGmpVooIiIiIiIi1SiQb8s2z3G/q/XGf75kG1uyCkmICuH0g7q0QMNERERERESkLgrk27LUee53pUDeWls+5dyl43oSFhzYEi0TERERERGROiiQb6ushdS57u9Kgfys9Zks3ZJDWHAAFx3So4UaJyIiIiIiInVRIN9W7UqBgh0QGAKdhpUv9leqP3NkV9pFhtS1tYiIiIiIiLQQBfJtlT+tvtNwCAoFYGdeMdOWpQFw4cHqjRcREREREdkfKZBvq2pJq/9gQSqlHsvwrrEM6hzTQg0TERERERGR3VEg31ZVq1hvreWtOZsBOH9s95ZqlYiIiIiIiOyBAvm2qKQA0pe6v32B/Kz1maTsyCcyJJCTh3duwcaJiIiIiIjI7iiQb4u2LQJvGUQlQWxXAN6aswmAUw/qQmRoUAs2TkRERERERHZHgXxb5B8f320MGENmfglfLXVF7i5QWr2IiIiIiMh+TYF8W1St0N2HC1Ip8XgZ0iWGIV1iW7BhIiIiIiIisicK5Nsaa2FzRSDvity5tHoVuRMREREREdn/KZBva7JTIS8NAoKg0wjmbtjFuox8IkICOUVF7kRERERERPZ7CuTbmlTftHOJQyAkgo8WpgJw8rDORIcFt2DDREREREREpD4UyLc1qfPcb9/4+NnrMwE4dkhiS7VIREREREREGkCBfFtTXrF+LDvyilm/Ix+AUd3jW7BRIiIiIiIiUl8K5NuajNXud9JQ5m3YBUD/xGhiI5RWLyIiIiIi0hookG9LirKhONv9HdedeRtcWv3onu1asFEiIiIiIiLSEArk25Ksze53eDyERDJ3o+uRH9NTafUiIiIiIiKthQL5tiTbF8jHdaOgpIxlW1zvvHrkRUREREREWg8F8m2Jv0c+thuLNmdR5rV0ig2jS1x4y7ZLRERERERE6k2BfFuSvcn9juteXuhudM94jDEt2CgRERERERFpCAXybUmlHvm5vkJ3Y5RWLyIiIiIi0qookG9LfGPkPTFdWeArdDe6hwrdiYiIiIiItCYK5NsSX498Slk8+SUeokOD6J8U3cKNEhERERERkYZQIN9WlBZB/nYA5mZGAjCyRzsCAzQ+XkREREREpDVRIN9WZKe638GRzNjqBTQ+XkREREREpDVSIN9W+CrW27huzNtYUbFeREREREREWhcF8m2Fb3x8UURn0nOKCQ40DO8a17JtEhERERERkQZTIN9W+CrWbyMBgCFdYgkPCWzJFomIiIiIiEgjKJBvK3w98quL3bj4MUqrFxERERERaZUUyLcVvh75RTlRAIzqoUJ3IiIiIiIirZEC+bbC1yO/vsT1xHeJC2/J1oiIiIiIiEgjKZBvCzxlkLMFgM1eN0Y+OFBPvYiIiIiISGukaK4tyN0G1gMBQWz1xgIQHGhauFEiIiIiIiLSGArk2wLf+HhiulDscX+qR15ERERERKR1UjTXFvjGxxPXnVKPBSAkSE+9iIiIiIhIa6Rori3I3gSAN7YrHq8L5NUjLyIiIiIi0jopmmsLfD3y3uiu5YvUIy8iIiIiItI6KZprC3xj5EsrBfIqdiciIiIiItI6KZBvC3w98iVRXcoXBQfoqRcREREREWmNFM0d6KyF7FQASqJcj3xQgCEgQD3yIiIiIiIirZEC+QNd/g4oKwSgKDwJUKE7ERERERGR1kwR3YHOV7GeqCRKTDCg8fEiIiIiIiKtmQL5A135HPLdKPV4AVWsFxERERERac0U0R3ofBXrie1GaZnmkBcREREREWntFNEd6Cr1yJf4euQVyIuIiIiIiLReiugOdJV75MsDeY2RFxERERERaa32i0DeGHO9MWaDMabIGDPbGDN2D+vfZIxZZYwpNMZsNsY8aowJq3T/7caYucaYXGPMdmPMVGNM/73/SPZD5T3y3Skp84+RD2zBBomIiIiIiEhTtHggb4w5F3gEuBcYCSwGphljOtax/gXAP33rDwSuAM4F7q+02hHAf4FDgMlAMPC1MSZyLz2M/Ze/an2lHvkQ9ciLiIiIiIi0WkEt3QDgZuA5a+1LAMaYa4ETgctxAXt144GZ1to3fbc3GGPeAg72r2CtPa7yBsaYKcB2YBTwc3M/gP1WUQ4UZbu/47pRmpEHaIy8iIiIiIhIa9aiEZ0xJgQXXH/rX2at9fpuj6tjs1+AUf70e2NML+AE4IvdHCrW9zuzjnaEGmNi/D9AdIMeyP6q0Pdwg8IhNJoSj6rWi4iIiIiItHYt3SOfAAQC6dWWpwMDatvAWvumMSYBmGGMMbjH8D9r7f21rW+MCQAew/XiL62jHbcDdze8+fs5r8f9DgwGoNQ3Rj5Y88iLiIiIiIi0Wq0uojPGTATuAK7Djak/AzjRGHNXHZv8FxgCnLeb3T6A67X3/3Rtpua2LE+p+x3grtdojLyIiIiIiEjr19I98jsAD5BYbXkikFbHNn8DXrPWPu+7vcRXxO5ZY8w/fKn5ABhjngROAiZYa1PraoS1thgorrRdgx/IfsnrC+T9PfKaR15ERERERKTVa9GIzlpbAswHjvIv86XCHwX8WsdmEYC32jKPf3PfPowviD8dmGStTWnOdrca3jL329cjrzHyIiIiIiIirV9L98iDm3ruFWPMPGAOcBMQCfir2L8KbLHW3u5b/1PgZmPMQmA20AfXS/+ptdYf0P8XuAA4Fcg1xiT5lmdbawv3/kPaT3iqBvLqkRcREREREWn9GhzIG2M2AC8CL1trNzW1Adbad4wxHYD7gCRgEXCctdZfAK87VXvg/w5Y3+8uQAYuuL+z0jq/8/3+sdrhLgNebmqbW43qqfW+YnchQQfI0AEREREREZE2qDE98o8BU4C/GmN+AF4APvKNM28Ua+2TwJN13Dex2u0y4F7fT137U6QKtaTW+4vdqUdeRERERESktWpwRGetfcxaOwIYC6wAngC2GWOeNMaMbOb2SVOUV613PfIlSq0XERERERFp9Rod0VlrF1hrfw90xvWOXwnMNcYsMsZcbg6Y0u+tmL9HPtA3Rr7MV+xO88iLiIiIiIi0Wo0udmeMCcZVhb8MmAzMwqXZdwXuB47GFZyTllLHPPLqkRcREREREWm9GlPsbiQueD8fV4TuVeCP1tqVldb5CJjbXI2URiofI191HvmQQCVLiIiIiIiItFaN6ZGfC3yDqww/1VpbWss6KcDbTWmYNINqqfUaIy8iIiIiItL6NSaQ72Wt3bi7Fay1+bhee2lJ1YrdlXp8Y+QVyIuIiIiIiLRajYnoOhpjDq6+0BhzsDFmdDO0SZpLtenn/PPIq9idiIiIiIhI69WYiO6/QLdalnfx3Sf7C6+vRz5QY+RFREREREQOFI0J5AcBC2pZvtB3n+wvPFV75DVGXkREREREpPVrTERXDCTWsrwTUNa05kizqp5ar0BeRERERESk1WtMRPc18IAxJta/wBgTh5s7/ptmapc0h2qp9SW+MfIhGiMvIiIiIiLSajWmav0twM/ARmPMQt+yEUA6cHEztUuaQ3nVen+PvKtaH6IeeRERERERkVarwYG8tXaLMWYYcCEwHCgEXgLeqmNOeWkpXo/7rdR6ERERERGRA0ZjeuT988Q/28xtkeZWPbW+PJBX1XoREREREZHWqlGBPIAxZhDQHQipvNxa+0lTGyXNpDy1vur0c5pHXkREREREpPVqcCBvjOkFfAQMBSzg7961vt+BzdM0abLyqvXuKSkt0xh5ERERERGR1q4xEd3jQArQESgABgMTgHnAxGZrmTSdP5APrNYjr0BeRERERESk1WpMav04YJK1docxxgt4rbUzjDG3A/8BDmrWFkrjVUut1xh5ERERERGR1q8xXbOBQK7v7x1AZ9/fG4H+zdEoaSbe6tPPqUdeRERERESktWtMj/xS3LRzKcBs4M/GmBLgamB9M7ZNmso//VxgtXnkVexORERERESk1WpMIP93INL391+Bz4DpwE7g3GZqlzSHSqn1Hq/F41WxOxERERERkdauwYG8tXZapb/XAgOMMfHALmutrXtL2ecqzSPvT6sHTT8nIiIiIiLSmjUoojPGBBtjyowxQyovt9ZmKojfD1Wafq6kciCvYnciIiIiIiKtVoMCeWttKbAJzRXfOnj8gXwwpWWVAvkA9ciLiIiIiIi0Vo2J6P4B3O9Lp5f9WZXUepcwERRgCAhQj7yIiIiIiEhr1ZhidzcAfYCtxpiNQH7lO621I5ujYdIMylPrgzT1nIiIiIiIyAGiMYH81OZuhOwlnopAvqQ8kFdvvIiIiIiISGvWmKr19+6NhsheUEvVes0hLyIiIiIi0ropqjuQVU6tL3Nj5JVaLyIiIiIi0ro1uEfeGOMF6pxqzlqrivb7C4+vRz4guFJqvQJ5ERERERGR1qwxY+RPr3Y7GDgIuBS4u8ktkubj75EPDFJqvYiIiIiIyAGiMWPkP65l8fvGmGXAucALTW6VNI9KqfUlJeqRFxERERERORA0Z1Q3CziqGfcnTVUptb68R15V60VERERERFq1ZgnkjTHhwO+BLc2xP2kmtaTWq0deRERERESkdWtMsbtdVC12Z4BooAC4qJnaJc2hvEc+iBKPqtaLiIiIiIgcCBpT7O6PVA3kvUAGMNtau6tZWiXNo3yMfDClZb4eeRW7ExERERERadUaU+zu5b3QDtkbvL4e+UCNkRcRERERETlQNLh71hhzmTHm7FqWn22MubR5miXNwlNRtV5j5EVERERERA4MjYnqbgd21LJ8O3BH05ojzary9HMaIy8iIiIiInJAaExU1x1IqWX5Rt99sr+oJbVegbyIiIiIiEjr1piobjswrJblw4GdTWuONBuvF6wL3isXuwtRsTsREREREZFWrTFV698C/mOMyQV+9i07AngceLu5GiZN5E+rBwgIpETF7kRERERERA4IjQnk7wJ6At8B/mgxAHgVjZHff/jT6gECg8sDeaXWi4iIiIiItG6NmX6uBDjXGPMXYARQCCyx1m5s5rZJU3gqBfIBwZSW+YrdKbVeRERERESkVWtMjzwA1to1wJpmbIs0J6+n4m9NPyciIiIiInLAaMw88h8YY26tZfmfjTHvNU+zpMn8qfUmAAICygN5jZEXERERERFp3RrTPTsB+KKW5V/67pP9gT+1PiAYQGPkRUREREREDhCNieqigJJalpcCMU1rjjQbf9X6ADd6otTjGyOvQF5ERERERKRVa0xUtwQ4t5bl5wHLm9YcaTb+QD7QF8j75pFXsTsREREREZHWrTHF7v4GfGiM6Q1871t2FHABcFZzNUyaqFpqvcbIi4iIiIiIHBgaM/3cp8aY03Bzxp+Fm35uMTAJyGzW1knjVUut94+RD1GPvIiIiIiISKvWqOnnrLWfA58DGGNigPOBh4FRQGCztU4az1+1PrBqj7zGyIuIiIiIiLRujY7qjDETjDGvAFuB/8Ol2R/SXA2TJvJU65EvUyAvIiIiIiJyIGhQj7wxJgmYAlyBq1D/LhAKnGatVaG7/UmNHnlXtT5EgbyIiIiIiEirVu+ozhjzKbAKGAbcBHS21t64l9olTVVj+jn1yIuIiIiIiBwIGtIjfzzwH+Bpa+2avdQeaS7VU+vLA3lVrRcREREREWnNGtI9exgQDcw3xsw2xtxgjEnYS+2Spqqr2J2q1ouIiIiIiLRq9Y7qrLWzrLVXAZ2AZ4DzcIXuAoDJxpjovdNEaZTqqfVlGiMvIiIiIiJyIGhwVGetzbfWvmitPQwYCvwbuA3Yboz5pLkbKI3k8fXIB2j6ORERERERkQNJk6I6a+0qa+2fga64ueRlf+HvkQ/UGHkREREREZEDSYOmn6uLtdYDTPX9yP5AVetFREREREQOSIrqDlQ1UuvdGPlQFbsTERERERFp1RTVHajKq9YH4fFaPF4XyKtHXkREREREpHVTVHeg8nrc74Cg8rR60PRzIiIiIiIirZ2iugNVpdT6ksqBvIrdiYiIiIiItGr7RSBvjLneGLPBGFNkjJltjBm7h/VvMsasMsYUGmM2G2MeNcaENWWfB5zy1PpgSssqBfIB+8VTLiIiIiIiIo3U4lGdMeZc4BHgXmAksBiYZozpWMf6FwD/9K0/ELgCOBe4v7H7PCBVqlrvL3QXFGAICFCPvIiIiIiISGvW4oE8cDPwnLX2JWvtcuBaoAC4vI71xwMzrbVvWms3WGu/Bt4CKve4N3SfBx5P5UBeU8+JiIiIiIgcKFo0sjPGhACjgG/9y6y1Xt/tcXVs9gswyp8qb4zpBZzw/+3de5QtV10n8O+v+z4CSW4AgQSMQYyAaNQMLJSgI1EeixmUQRxHdFyKZM0sFHUYHBkzjiggL0FgaZhxAOUhKKBglOGlcQkMTgwPIYkS8QEBAiSBZEgiSe6je88fVafvuU3fRyDdp2vfz2etWt2nqk5lF0XdPt+zf3tXkrd9ucesqt1VtWe2JDn5Kzy1xZsrrd+3FuT1xgMAAEzdjgX/9++aZDnJNevWX5PkGzZ6Q2vt96rqrkneW1WV4Rx+q7U2K62/zcdMcn6SX77tzd/G1ia7O9gjv8uM9QAAAJM3uWRXVecm+W9JfirD+PfHJXl0Vf3SV3DY5yY5ZW45/Str5TYw//i5A54hDwAA0ItF98h/PslKklPXrT81ydWHec+zkvxua+0V4+vLq+rEJC+rqmd/Ocdsre1Nsnf2eujon7gNSuv1yAMAAEzfQpNda21fkg8medhsXVUtja8vPszb7phkdd26ldnbv8xj9mfuOfImuwMAAOjHonvkk+Exca+uqg8keV+SpyQ5Mckrk6SqXpPk062188f935LkqVX1oSSXJPn6DL30b2mtrRzLMY8La4+fW86+A4I8AABALxYe5Ftrb6iquyV5ZpLTknw4yaNaa7PJ6s7IoT3wv5qkjT+/OsnnMoT7X7wNx+zfLMgvH+yR32XWegAAgMlbeJBPktbaBUkuOMy2c9e9PpDkGePyZR3zuKC0HgAAoEuSXa/WSut3ZN+KWesBAAB6Idn1aq20fkf2z8bIm7UeAABg8iS7Xm1QWm+MPAAAwPQJ8r2aK603Rh4AAKAfkl2vVsce+eWdxsgDAAB0RLLr1cqX9sjvMkYeAABg8iS7Xs0/R/6A0noAAIBeSHa9mpXWL+3IPpPdAQAAdEOQ79XK/HPk9cgDAAD0QrLr1dxkd/sPjJPdGSMPAAAweZJdrzx+DgAAoEuSXa9WZmPkdx6ctd4YeQAAgMkT5Hu1Nmu9MfIAAAA9kex6dUhp/ThGXpAHAACYPMmuV/Ol9bPnyJvsDgAAYPIku16tldYfHCO/W488AADA5El2vVorrV8+OEZ+h8nuAAAApk6Q79UGs9YbIw8AADB9kl2PWktWxyC/vDP7DgjyAAAAvZDsetRWD/4+N2v9LkEeAABg8iS7Hs3K6pMxyOuRBwAA6IVk16PVuSC/vPPgZHfLJrsDAACYOkG+R7MZ65NDJ7vzHHkAAIDJk+x6tDIf5Jez/4Ax8gAAAL2Q7Ho0K61f2pFUGSMPAADQEcmuR3PPkE9ijDwAAEBHBPkezcbIL+1IkrUe+V3GyAMAAEyeZNejWZBfngV5Y+QBAAB6Idn1aK60fmW1ZWV1CPLGyAMAAEyfZNejudL6WVl94vFzAAAAPZDsejRXWr9vPsib7A4AAGDyBPkezZXW7z8wF+SXXG4AAICpk+x6dEhp/TA+fsdSZWlJjzwAAMDUCfI9Wh175Jd3ro2RN9EdAABAH6S7Hq0c7JHftxbk9cYDAAD0QJDv0Qaz1u8yYz0AAEAXpLsezZfWHxjGyO9SWg8AANAF6a5Ha7PWz5XW65EHAADognTXo9WV4afJ7gAAALoj3fVoda5H/oAgDwAA0BPprkdrpfUHe+R3mbUeAACgC4J8j2az1i/vUFoPAADQGemuR6vzz5EfZq0X5AEAAPog3fVovrT+gFnrAQAAeiLd9WjtOfI7jJEHAADojCDfo9nj55aMkQcAAOiNdNejudL62Rj5XUrrAQAAuiDd9WittH6nHnkAAIDOSHc9Wpu1fvngZHeCPAAAQBekux6tzIL8zuwz2R0AAEBXBPkezZXW71NaDwAA0BXprkdrpfU7sv/AMNmd58gDAAD0Qbrr0cpckNcjDwAA0BXprkcbzFpvjDwAAEAfBPkerX7pZHd65AEAAPog3fVoZeyRX1rO/pVxjLwgDwAA0AXprkezHvnlnWvPkd9lsjsAAIAuSHc9miutPzhG3qUGAADogXTXo7XS+h0Hx8jvMNkdAABADwT5Hq2V1nv8HAAAQG+kux6t9cjvzL4DgjwAAEBPpLserY2R37E2a70x8gAAAH2Q7nq0OvbIL+9UWg8AANAZ6a5HKwd75Ncmu1s22R0AAEAPBPkeHVJaP5u13qUGAADowbZId1X15Kq6sqpurapLqurbjrDvu6qqbbC8dW6fk6rqgqq6qqpuqaqPVNWTtuZstoH50voDxsgDAAD0ZOHprqp+KMmLkjwjyQOSXJrknVV198O85XFJ7jG3nJVkJckfzO3zoiSPSvKjSe6f5CVJLqiqx2zCKWw/a6X1B8fI79IjDwAA0IXtkO6emuTlrbVXttY+kuRJSW5O8sSNdm6tXd9au3q2JHnEuP98kH9Ikle31t7VWruytfayDF8QHLanvytzz5HfZ7I7AACAriw03VXVriQPTHLRbF1rbXV8fc4xHua8JK9vrX1xbt3/TfKYqvrqGnx3kvsm+dPDtGN3Ve2ZLUlO/jJOZ/uYldbPj5E32R0AAEAXFt1Ne9cky0muWbf+miSnHe3N41j6s5K8Yt2mn0nykSRXJdmX5B1Jntxae89hDnV+khvmlquOsf3b08osyO/0HHkAAIDOTD3dnZfk8tba+9at/5kkD07ymAw9/j+X5KVV9fDDHOe5SU6ZW07fnOZukdWVJMlK7cjK6hDkldYDAAD0YceC//ufzzBR3anr1p+a5OojvbGqTkzy+CRPX7f+Dkmek+T7W2uzmewvq6qzk/yXzJXxz7TW9ibZO3eM23QS285YWr9/7nsaj58DAADow0LTXWttX5IPJnnYbF1VLY2vLz7K238wye4kr123fue4rK5bv5LpVyAcm7G0fn+bC/LGyAMAAHRh0T3yyfCouFdX1QeSvC/JU5KcmOSVSVJVr0ny6dba+eved16SC1tr182vbK3dWFXvTvKCqrolySeSPDTJj2WYIb9vrSVtKK3f35bXVu9cOj6+wwAAAOjdwoN8a+0NVXW3JM/MMMHdh5M8qrU2mwDvjKzrXa+q+yX5ziSPPMxhH59h3PvrktwlQ5j/xSS/dXu3f9uZPXouB4P8jqXK0pIeeQAAgB4sPMgnSWvtgiQXHGbbuRus+2iSwybT8fnyP3F7tW9SZjPWJ7llZeiFP2Hn8uH2BgAAYGLUW/dm9WCQv2n/MGP9ySdsi+9rAAAAuB0I8r0ZHz2XJDeO8/CftFuQBwAA6IUg35u10vrKTfv0yAMAAPRGkO/NrLR+eWduunX4/eQTdi6wQQAAANyeBPnezGatX9qRm24dftcjDwAA0A9BvjcrsyC/cy7I65EHAADohSDfm7XS+h1rpfV79MgDAAB0Q5DvzerBHvl/3jv8btZ6AACAfgjyvZnNWm+MPAAAQJcE+d7MeuSXd+RGs9YDAAB0R5DvzepGk93pkQcAAOiFIN+bQ0rr9cgDAAD0RpDvzSGz1uuRBwAA6I0g35vVleHn3Kz1gjwAAEA/BPnejKX1bWk5N+8bQr3SegAAgH4I8r0ZS+tXcrAXXo88AABAPwT53oyl9QdqCO8n7FzKzmWXGQAAoBcSXm/G0vr9bbi0yuoBAAD6Isj3ZiytP5DlJMnJu5XVAwAA9ESQ781aj/wY5I2PBwAA6Iog35txjPw+pfUAAABdEuR7M5bW79MjDwAA0CVBvjdjaf3e1VmPvCAPAADQE0G+N6sHkhwM8iftVloPAADQE0G+N+uCvB55AACAvgjyvRlL629dEeQBAAB6JMj3ZuyRv2WlkiR7zFoPAADQFUG+N2OQv3UM8nrkAQAA+iLI92Ysrb95xXPkAQAAeiTI92bskb/5wNAjf5IeeQAAgK4I8r1ZnfXIK60HAADokSDfm5VDe+QFeQAAgL4I8r0ZS+v3t+UkZq0HAADojSDfm7G0/kCWs3O5snuHSwwAANATKa83KweD/Mkn7ExVLbhBAAAA3J4E+d7MSuuznJN2Gx8PAADQG0G+N2OQX2nLJroDAADokCDfm7G0fn8EeQAAgB4J8r0Ze+RnY+QBAADoiyDfm0OCvB55AACA3gjyvZmftd5kdwAAAN0R5Huzeujj5wAAAOiLIN+b1ZUkyf7sUFoPAADQIUG+N2Np/Upb0iMPAADQIUG+N4eU1uuRBwAA6I0g3xul9QAAAF0T5HszK63PkiAPAADQIUG+N2Np/dAjb4w8AABAbwT5zrTVA0mMkQcAAOiVIN+blVmQN2s9AABAjwT53oyl9avZkRN3LS+4MQAAANzeBPnejJPd7dq9O1W14MYAAABwexPke7K6mkpLkpywe/eCGwMAAMBmEOR7MpbVJ8kdThDkAQAAeiTI92RFkAcAAOidIN+T8dFzSXLHE+6wwIYAAACwWQT5nhwS5PXIAwAA9EiQ78lYWn+gLeUkz5AHAADokiDfk7FH/kCWc7IgDwAA0CVBvifjrPVDkN+x4MYAAACwGQT5nqwc7JHfI8gDAAB0SZDvyVhav19pPQAAQLcE+Z6MpfUrSusBAAC6Jcj3ZK60/qTdgjwAAECPBPmezErrm9J6AACAXgnyPVFaDwAA0L1tEeSr6slVdWVV3VpVl1TVtx1h33dVVdtgeeu6/e5fVX9SVTdU1Rer6v1Vdcbmn83i7N+3b/iZ5ezRIw8AANClhQf5qvqhJC9K8owkD0hyaZJ3VtXdD/OWxyW5x9xyVpKVJH8wd8wzk7w3yd8lOTfJtyR5VpJbN+Uktolbbh1O70CWc5IeeQAAgC5th7T31CQvb629Mkmq6klJHp3kiUmet37n1tr186+r6vFJbs5ckE/y7CRva609bW7dPx2uAVW1O8nuuVUn38Zz2BZu2bs3e5Ks1o4sL9WimwMAAMAmWGiPfFXtSvLAJBfN1rXWVsfX5xzjYc5L8vrW2hfHYy5l+CLg76vqnVV17Viu/9gjHOP8JDfMLVfd1nPZDvbu3Tv8srQdvp8BAABgMyy6tP6uSZaTXLNu/TVJTjvam8ex9GclecXc6rsnOSnJLyR5R5JHJvmjJG+uqoce5lDPTXLK3HL6sZ/C9nHL3qG0vi0ZHw8AANCrqXfdnpfk8tba++bWzb6c+OPW2ovH3z9cVQ9J8qQk715/kNba3iR7Z6+rJlKW/q7nJR9929rLr7nh88MveuQBAAC6tejE9/kME9Wdum79qUmuPtIbq+rEJI9P8vQNjnkgyUfWrb8iyXd+2S3djr7wqeSzl669vONs9e57LqY9AAAAbLqFBvnW2r6q+mCShyW5MFkb4/6wJBcc5e0/mGGCutducMz3J7nfuv3vm+QTt0Ozt48H/2TyTY9Nklz/xb35r2+6LDevLOXHHv7Di20XAAAAm2bRPfLJ8Oi5V1fVB5K8L8lTkpyYZDaL/WuSfLq1dv66952X5MLW2nUbHPMFSd5QVe9J8hdJHpXk+zI8iq4fp501LEme/cZL82f7Kw/62jvnkd96xoIbBgAAwGZZeJBvrb2hqu6W5JkZJrj7cJJHtdZmE+CdkWR1/j1Vdb8MZfKPPMwx/2h8jN35SX4jyUeT/EBr7b2bchIL9jefviFv/tAw0f4vPvobpzPGHwAAgNusWmuLbsO2U1V7ktxwww03ZM+ePYtuzhG11vIjL78kF3/sujzmW++Z3/jhf7HoJgEAAHAb3XjjjTnllFOS5JTW2o1H2nfRj5/jK/TnV1ybiz92XXbtWMrTHrV+WgAAAAB6I8hP2P6V1Tzn7VckSZ74HffO6Xe+41HeAQAAwNQJ8hP2++/7ZD72uS/mLifuyk9995mLbg4AAABbQJCfsIuuuDZJ8p8ffp/sOWHnglsDAADAVlj4rPV8+V71hAflHX97dR7xjacuuikAAABsEUF+wpaWKv/6m++x6GYAAACwhZTWAwAAwIQI8gAAADAhgjwAAABMiCAPAAAAEyLIAwAAwIQI8gAAADAhgjwAAABMiCAPAAAAEyLIAwAAwIQI8gAAADAhgjwAAABMiCAPAAAAEyLIAwAAwIQI8gAAADAhgjwAAABMiCAPAAAAEyLIAwAAwITsWHQDtrMbb7xx0U0AAADgOHBb8me11jaxKdNUVV+d5KpFtwMAAIDjzumttU8faQdBfgNVVUnumeSmRbflKE7O8IXD6dn+bT3euVbT4DpNg+s0Da7TNLhO0+FaTYPrNA3b+TqdnOQz7ShBXWn9Bsb/0Y74Dch2MHzfkCS5qbVmHMA25lpNg+s0Da7TNLhO0+A6TYdrNQ2u0zRs8+t0TO0x2R0AAABMiCAPAAAAEyLIT9veJM8Yf7K9uVbT4DpNg+s0Da7TNLhO0+FaTYPrNA2Tv04muwMAAIAJ0SMPAAAAEyLIAwAAwIQI8gAAADAhgjwAAABMiCA/YVX15Kq6sqpurapLqurbFt2m41lVnV9V76+qm6rq2qq6sKrut26fd1VVW7f81qLafDyqql/Z4Br83dz2E6rqpVV1XVX9c1W9qapOXWSbj0fjv23rr1OrqpeO291LC1JV31VVb6mqz4z/uz923faqqmdW1Wer6paquqiq7rNun7tU1euq6saq+kJV/XZVnbSlJ9K5I12nqtpZVc+vqsur6ovjPq+pqnuuO8ZG9+EvbPnJdOwY7qdXbXAN3rFuH/fTJjuG67TR36tWVT8/t4/7aZMd42fxo37Oq6ozquqtVXXzeJwXVNWOrT2boxPkJ6qqfijJizI8NuEBSS5N8s6quvtCG3Z8e2iSlyZ5cJJHJNmZ5E+r6sR1+708yT3mlqdtZSNJkvxtDr0G3zm37cVJvi/JD2a4pvdM8uatbiB5UA69Ro8Y1//B3D7upcU4McPfnCcfZvvTkvxskicl+fYkX8zw9+mEuX1el+SbMlzX703yXUletlkNPk4d6TrdMcNnh2eNPx+X5H5J/mSDfZ+eQ++z39yMxh7HjnY/Jck7cug1+OF1291Pm+9o1+ke65YnJmlJ3rRuP/fT5jqWz+JH/JxXVctJ3ppkV5KHJPnxJE9I8szNb/5t4/FzE1VVlyR5f2vtp8fXS0k+leQ3W2vPW2jjSJJU1d2SXJvkoa2194zr3pXkw621pyywace1qvqVJI9trZ29wbZTknwuyY+01v5wXPcNSa5Ick5r7a+2sKnMqaqXZPiAep/WWnMvbQ9V1ZJ8f2vtwvF1JflMkl9vrb1wXHdKkmuSPKG19vqqun+SjyR5UGvtA+M+j0rytiSnt9Y+s/Vn0rf11+kw+zwoyfuS3Ku19slx3ZVJXtJae8kWNPO4t9F1qqpXJblTa+2xh3mP+2mLHeP9dGGSk1trD5tbd2XcT1tq/WfxY/mcV1X/Ksn/TnLP1to14z5PSvL8JHdrre1bxLlsRI/8BFXVriQPTHLRbF1rbXV8fc6i2sWXOGX8ef269f++qj5fVX9TVc+tqjtudcPIfcbyuI+N5YhnjOsfmOHb2/l76++SfDLurYUZ/8370SS/0w799tm9tP3cO8lpOfQeuiHJJTl4D52T5Auz0DG6KMlqhh58FuOUDD2IX1i3/hfGEtQPVdXPb8fy0uPAuWN570er6n9W1VfNbXM/bTNjmfajk/z2BpvdT1tr/WfxY/mcd06Sy2chfvTOJHsyVL5sG/7PM013TbKcoYdj3jVJvmHrm8N6Y4XES5L8ZWvtb+Y2/V6ST2TosfqWDN/u3S9DWSNb45IMJVIfzVDW9stJ/k9VnZUhgOxrrX1h3XuuGbexGI9Ncqckr5pb517anmb3yUZ/n06b2+fa+Y2ttQNVdX3cZwsxDnt4fpLfb63dOLfpN5L8dYYPwQ9J8twM/24+dcsbefx6R4ay348nOTPJc5K8varOaa2txP20Hf14kpvypcPy3E9b6DCfxY/lc95p2fhvWLLN7ilBHjbHS5OclUPHXqe1Nj9m7fKq+mySP6+qM1tr/7SVDTxetdbePvfysnGYyieS/LsktyymVRzFeUnePl8i6l6C20dV7UzyxiSV5Cfnt7XWXjT38rKq2pfkf1XV+a21vVvYzONWa+31cy8vr6rLkvxTknOT/PlCGsXRPDHJ61prt86vdD9tuQ0/i/dEaf00fT7JSpL1M2mfmuTqrW8O86rqggxjeb+7tXbVUXa/ZPz59ZvbKg5n/Fb27zNcg6uT7KqqO63bzb21IFV1ryQPT/KKo+zqXtoeZvfJkf4+XZ3kkIlZx/LSu8R9tqXmQvy9kjxiXW/8Ri7J0An0tZvcNA6jtfaxDJ8DZ//WuZ+2kar6lxmqw472NytxP22aI3wWP5bPeVdn479hyTa7pwT5CRonWfhgkvkJNJbG1xcvql3HuxpckOT7k3xPa+3jx/C2s8efn920hnFENTyi58wM1+CDSfbn0HvrfknOiHtrUX4iQ9noW4+y39njT/fSYn08wwed+XtoT4axurN76OIkd6qqB86973syfCa5JGyJuRB/nyQPb61ddwxvOzvD2Otrj7Ifm6SqTk/yVTn4b537aXs5L8kHW2uXHsO+Z8f9dLs6hs/ix/I57+Ik37zuSWCPSHJjhokltw2l9dP1oiSvrqoPZJhl9ikZHo3xykU26jj30iQ/kuTfJLmpqmbjaG5ord1SVWeO29+W5LoM43pfnOQ9rbXLFtHg41FVvTDJWzKU098zwyMcVzKMDb2hqn47yYvG8YU3Zng0zMVmrN964xeUP5Hk1a21A3Pr3UsLNH75NV/5cO+qOjvJ9a21T45PGPjvVfUPGYL9szLMZXBhkrTWrqjhOdgvH2cC3pnkgiSvN8P27edI1ylDCPzDDI+e+94ky3N/s65vre2rqnMyfAHzFxnG+56T4T57bWvt/23NWfTvKNfp+gzzuLwpwxdkZyb5tST/mGHyLffTFjnav3vjPnsyPNLs5zZ4v/tpaxzxs/gxfs770wyB/Xer6mkZxsX/apKXbrshEK01y0SXJD+dIYzszfCt67cvuk3H85Jhtt+NlieM278mybszBI9bk/xDhj/Iexbd9uNpSfL6DKFib5Krxtdnzm0/IcMfguszPP/6zUlOW3S7j8clySPHe+i+69a7lxZ7Xc49zL91rxq3V4bn7V49Xp+LNriGd8kwYeFNSW5I8jtJTlr0ufW0HOk6ZSjlPdzfrHPH9z8gyV9lmMX+lgwfbM9PsnvR59bTcpTrdIcMgf3aJPuSXJnh+fCnrjuG+2mB12lun/+Y5OYkp2zwfvfT1lynI34WH/c56ue8DMON3jZez88leWGSHYs+v/WL58gDAADAhBgjDwAAABMiyAMAAMCECPIAAAAwIYI8AAAATIggDwAAABMiyAMAAMCECPIAAAAwIYI8AAAATIggDwAAABMiyAMAAMCECPIAAAAwIYI8APAlqmqpqs6vqo9X1S1VdWlV/dtx27lV1arq0VV1WVXdWlV/VVVnrTvGD1TV31bV3qq6sqp+bt323VX1/Kr61LjPP1bVeVt5ngAwRTsW3QAAYFs6P8mPJnlSkn9I8l1JXltVn5vb5wVJ/lOSq5M8J8lbquq+rbX9VfXAJG9M8itJ3pDkIUn+R1Vd11p71fj+1yQ5J8nPJrk0yb2T3HWTzwsAJq9aa4tuAwCwjVTV7iTXJ3l4a+3iufWvSHLHJC9L8hdJHt9ae8O47S5JrkryhNbaG6vqdUnu1lp75Nz7fy3Jo1tr31RV903y0SSPaK1dtFXnBgA90CMPAKz39RkC+59V1fz6XUk+NPd6LeS31q6vqo8muf+46v5J/njdcf8yyVOqajnJ2UlWkrz7dm05ABwHBHkAYL2Txp+PTvLpddv2Jjnzdvhv3HI7HAMAjksmuwMA1vtIhsB+RmvtH9ctn5rb78GzX6rqzknum+SKcdUVSb5j3XG/I8nft9ZWklye4XPIQzfrJACgV3rkAYBDtNZuqqoXJnlxVS0leW+SUzIE8RuTfGLc9elVdV2Sa5I8O8nnk1w4bvv1JO+vql/KMNndOUl+OslPjf+NK6vq1Ul+p6pmk93dK8ndW2tv3PyzBIDpMtkdAPAlahgc/7NJfjLJ1yX5QpK/zjA7/VKGye6+L8nzktwnyYeT/IfW2mVzx/iBJM8ct382yW+21l44t/2E8XiPT/JVST6Z5DmttVdu7tkBwLQJ8gDAbVJV52YI8ndurX1hoY0BgOOQMfIAAAAwIYI8AAAATIjSegAAAJgQPfIAAAAwIYI8AAAATIggDwAAABMiyAMAAMCECPIAAAAwIYI8AAAATIggDwAAABMiyAMAAMCE/H/Dg6IV3qKaPwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"Accuracy\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"accuracy\"], label=\"training\")\n", - "plt.plot(history.history[\"val_accuracy\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje Accuracy\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "4175ccbf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(12, 6), dpi=100)\n", - "plt.ylabel(\"AUC\")\n", - "plt.xlabel(\"epoc\")\n", - "plt.plot(history.history[\"auc\"], label=\"training\")\n", - "plt.plot(history.history[\"val_auc\"], label=\"validation\")\n", - "plt.title(\"Curva de aprendizaje AUC\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "63c5aa62", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.9050429082148531\n", - "AUC-ROC score sobre train: 0.9189147194959716\n", - "Accuracy sobre test: 0.8507600184246891\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.93 0.88 0.90 5179\n", - " Alto valor 0.62 0.72 0.67 1334\n", - "\n", - " accuracy 0.85 6513\n", - " macro avg 0.77 0.80 0.78 6513\n", - "weighted avg 0.86 0.85 0.86 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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\n", 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RIl8xaROn753m6K2jnL5/musPr3M78DaXfC9xJ/BOmq/TpkwbHG0cCQ5+SNOiDVFaU/foTZysHCi9cAU2UUYXnENEwkfOY5UuDWFh0L49VKsGLVsarUn16oGtbeZ9YCFElrh3L4iJE3cSFhbJqlW9Uq8AeHt7U7JkSYCSWutMHaCVk1qyhBB5VGhkKF7+XviH+nM78Da7r+zm/IPz3Aq4RWhkKDce3SA8KvyxenWK1aG+R30crB2o71EfSwtLqhauioeTB7YRJmwiTBQNt8Zx5x9YTpgIrmfg7t3o2vsfD6R4cXj3XahUKWF5kSJQrhxYyY9EIXIjk0mzbNkxJk/exaNHYYwd+xQmk8bCwrwtzfITRQjxRILCg7gbdJfgiGBuPrrJw7CH7Pfaz983/+asz1kCwwOTrWtnZUepgqXoW6MvlVwrUbpQaToWqo/L+etYqUTjJi5ch3//hfWfQWAg+CUxQ8vdu9Cnj3GsY8foAINg+HBjnJO9DPgWIq85d86HV1/9lYMHb9C4cQm++qoLtWvnjPnFJckSQiTpUdgjDlw/wN83/+bY7WOc9TmLraVt7ADy6w+vpzruqUyhMjQu0RhPZ0+qFK6CRwEPijsVp3qR6hR3ijcz5KNHRvfczZtpD7BTJ2jQAAoUMFqgunaFChUy+GmFELmV1ppLl3xZvLgzQ4bUN3vrVXySZAkh0FpzJ/AOP5/5mR9P/sjhm4cfO8fB2oEijkWoW6wuNpY2tCjVgodhD6lZpCYAVQpXwdbSFld7V6oUrkJxp+IpDwr384OPP4YvvzSSrBht20KLFtA8icesCxc2uvqkRUqIfO23385z+PBN3n33aapWdcfLayx2djkvpcl5EQkhslyUKYrjd45z7PYxPvzzQ7z8vRJMW2ChLGhUohGDag2inkc9aherjZ2VXcZv6O0NmzYZ68+9887jx5WCdu1g82awts74fYQQedqNGw8ZPXobGzaco1IlN6ZMaY6jo02OTLBAkiwh8oWwyDCO3jrK18e+5sajG+y5uifB8ZalW9KqdCsqulakf83+WFqkYx6ZyEiIijL2g4Nh7lw4dw4cHeH4cTh1Kul6jRsbT/G9/LIx6FwIIZIRGWnis8/+5p139hIRYWLmzFZMntw8xyZXMXJ2dEKINIsyRXH+wXku+V7ivM95rvpf5citI5y+d5qQyIQrTHWu2Jnq7tVpWbolTUo2wdXeNe5gWBgcP2Ksj3f3Lly9arQu/fgjBAQkvOmdO/DgQdIBOTvHdevVqwf9+sEzz0CpUsYxIYRIozNn7jNx4k7atCnDl192plIlN3OHlCaSZAmRC4VGhvLvnX855H0In2Affjn7C2d9ziZ5rpu9Gx0rdKSGew2alWpGx/IdjbFSJpMxLmriO0bL08WLxgDyKynM42tra3TrxShTBnx9jSSqcGGjLDIS3ngDjHlnhBAiQ/z9Q9m16wq9elWjVq2iHD78GvXqeeSqCYAlyRIiF7gTeIfT907z791/WXVqFUduHUlwXKGoUrgK3Sp1o3OlzrjYuVC0QFHcHdwf/4EUHAxOTkaSlVijRuDpacwbNWCAUebkBJUrG+OmPDzilnoRQogsoLVm5cpTjBu3HV/fEJ56yhNPT2fq1y+eeuUcRpIsIXKgWwG32HdtH/u99rPl0hauP7ye4Hi3yt3oWqkrpQqWom6xuhR2KJz8X3f378OxY9C3L/j7JzzWvz9UrAivviotT0IIs7tw4QHDh29m9+6r1K5dlF9/7YunZ+4dXiBJlhA5wGXfy2y+uJmdV3ay6cKmx463K9eOV+q8QpXCVahepDo2ljZJXyg4GH75xRg/degQPHz4+DnNm0OzZvDBB7LwsBAix7h58xG1ay/B0lIxb14HRo9ujJVV7v4ZJUmWEGYQZYriyyNf8tOpnzhx5wShkaGxx56p8AyF7ArRo0oP2pZti4u9CxYqiR80Fy/C3r3w119xA8y//DLhOe7u0KYN1KwZ9zSfEELkINeu+VOmTCFKlHBm/vwOdO5cySyLOWcFSbKEyCb3gu4x7+A8frvwW4JB6mULlaVZqWZ0qdiFzpU6U8CmQNIXiIyEN9+Ef/6BAwceP16gANjYGNMpnDljdAPK+CkhRA51924g48fv4OefT/Pvv0OpWtWdYcMamjusTCVJlhBZ5Pjt46w/tx7vR978ffNvztw/E3usnEs5elXtxftt38fKIt5/w6AguBydgD18CN9/D//9BwcPJhyobmUFr7xiDEjv0gXKlwfLdMxtJYQQZmIyaf73v3+YMmUXgYHhjB/fhJIl80bLVWKSZAmRSR6FPeKb499w8MZBDtw4wK2AWwmO96jSg1fqvkLnip0TDlK/d89YRubChZRvULasMc/UggXGVApCCJHLBAWF067d9/z1lzdNm5ZkyZLO1KxZ1NxhZRlJsoTIoNDIUPZe3ctPp37i+O3jnPU5m2DB5EG1BzGm8RhqFa2VsLUqxv37xhN9YWFxZS+8AMWLQ926xvuCBaFbN+n2E0LkaiaTxsJC4ehoQ+3aRXnllTq8+mq9HLWYc1aQJEuIdNpzdQ8rT65k6fGlCcp7VOnBgFoDaFm6JYUdCiddWWtjwHr//sbYKjASqTffhFGjoFChrA1eCCGy2YYN55gyZRdbt75I2bIuLFnSxdwhZRtJsoRIgyhTFMuOL2PZ8WUcvnkYgKc8n2Jw7cF0rtQZT2fP1C8SHv54N9/gwbBsmUylIITIc7y8/Bk9ehu//nqeypXd8PUNoWxZF3OHla0kyRIiBaGRoUzYMYEvjnwRWza0/lBGNx5NVfeqyVf87z/43//g0iXYvt1orYo/Z9WaNdCrl3QDCiHyHJNJM2/eQWbO/J2oKBPvvtuGiRObYmub/1KO/PeJhUgDkzax5OgSpu2Zhn+oP9Xdq9OvRj9GNx6Nk61T8hWPHYP69ROW1apltGL17m10F37xhQxcF0LkWUrBvn1eNG9eii++6ESFCq6pV8qjJMkSIlpYZBjrzq7jp5M/cfjmYe4H38fG0oalXZfySt1XUl6UVGvo1Am2bYsr++Yb6N4dXPJX87gQIv/x9Q1hxoy9TJ3aguLFnVi9uheOjta5ajHnrCBJlsj3bgXc4q3db/Htv9/GlhV1LMon7T9hdOPRyS9hA8bg9RYtICQkrmzWLJg+XboChRB5ntaaH374jzff3IGPTzD16nnw8st1KVAghZ+b+YgkWSLfCQwP5OfTP/O71+/sv76fK35XACjpXJL+NfszoemElJ8O3LED3nkHDh9OeGzoUJg7Fxwds/gTCCGE+Z0758OwYZvZt+8adesWY9Om/jRqVMLcYeUokmSJfCHSFMmyY8tY+PdCzvmcS3Csvkd9Pnv2M5qWbJr8BUJDYckSGDcuYXmHDjBoEPTpY8zCLoQQ+cSYMds4evQWCxd2ZMSIRrl+MeesIL8VRJ4WERXBvEPzmLp7amxZfY/6vFjzRfrW6IuHk0fKF7h5EypUMJKsGDVqGAsxN2kiiZUQIl/ZseMy9et74ObmwOLFnbGxscTT09ncYeVY8htC5EkhESGsOLGC4VuGx5bNaDWD4Q2HU8SxSNoucuJE3MzrAMOGwfDhRpIlhBD5yO3bAYwbt53Vq08zYUITPvmkA+XKyUM9qZEkS+QZdwLvsPLkStafW89/d//jYdhDShUsRb8a/ZjZeiZ2VnZpu9APP8DAgXHva9eGo0el1UoIke9ERZn46qt/mDp1N8HBEUyd2py3325p7rByDfmtIXK9IzeP8NPJn1j498IE5d92/5YXa76IpYVl2i+2Y0fCBGvFCujbVxIsIUS+NGzYZr7++hjNm5diyZLOVK+exp4AAUiSJXKxd39/l88Of4ZPsA8AFVwr8E7Ld+hfs3/6Eiut4fvv4euv4c8/jbItW+DZZ7MgaiGEyNkCAsJQSlGggA1Dhzbgqac8GTy4Tp5fzDkrSJIlchWtNX/f/JsBvwzgst9lAF6p8wrDGg6jQfEGGbkglCgBt2/HlY0eLQmWECLf0Vrzyy9nGTNmG88/X5VPP32WevU8qFcvlQeERLIkyRK5QlhkGFsvbaXfun6ERhpP+lUtXJXjbxzH1iqdS9Tcvm1MFhoZCd/GTUDKn39Cs2aZGLUQQuQOV6/6MXLkVrZsuUjVqoV5/vlq5g4pT5AkS+RofiF+jNo6itWnVxNpigSgTZk2fNn5S6oUrpK+i2ltjK9asyauzMXFSLZu3gSnFNYkFEKIPGrFihMMH74ZreGDD57mzTebYmOTjiEXIlmSZIkc6YrfFQZvGMz+6/sBY26rwXUG07J0S2oVrZW+i504YQxonzw5rqxlS9i6FRwcMi9oIYTIRbTWKKWoUMGV1q3LsGhRJ5mWIZNJkiVyFK01H/75IdP2TIstW/HcCl6q81L6LxYYCC+8YCRT8fn7Q8GCTxaoEELkUj4+wUyevJPixZ14992nad68FFu2vGjusPIkmQNf5BihkaF0+qlTbIK1fcB29AydsQRr3Dij+y8mwXr7bbh3z+gylARLCJEPaa1ZseIEVaosYsWKfwkJiTR3SHmetGQJs4s0RfLxgY/56p+vuP7wOl0qdeGnnj/hZJvOMVJ+fvDLL/DRR3DpklFWvTqcPAlKHj0WQuRf58758MYbm/jjDy8aNCjO9u2dqV+/uLnDyvMkyRJmdd7nPG2/a8vNgJsAbOm/hWcqPINKb1J04AA0b56w7MwZqFo1kyIVQojcy8vLnxMn7vD5588ybFgDLC2lIys7yFdZmEWUKYp5B+dR73/1uBlwkzGNx2B6x8SzFZ9Nf4J15kxcgtWjB5w7ZzwxKAmWECIf27btEkuXHgOgY8cKXLs2hpEjG0mClY2kJUtkuzWn1zDnwByO3T6GjaUN+1/eT/NSzVOvmJTffoNu3Yz9evWM7kIhhMjHbt0KYOzYbfz88xlq1CjC4MF1sLKywMXF3tyh5TuSZIls9cXhLxi5dSQAU5tPZWbrmdhY2qTvIufPw9SpxtxWhw8bZWXKGIs4CyFEPhUVZeLLL48wbdoeQkMjmTatBdOmtcDKSlquzEWSLJEtLvleYvCGwRy4cYAyhcpw4JUDFHfKwKDLr76CoUPj3j/9NLz2GvTrl3nBCiFELrRr1xVGj95Gq1alWby4M1Wrups7pHxPkiyRpUzaxJvb32Th3wsBaFi8IXtf2oujjWP6LxYVFZdgvfceTJuW8vlCCJHHPXwYyokTd2jVqgwdOpRn27YX6dChfPrHtoosIW2IIsv4hvjyzA/PsPDvhZRzKcfOgTs5/Prh9CdY+/cbUzFYRf9N0LixJFhCiHxNa82aNaepWvULnntuFY8ehaGUomPHCpJg5SDSkiUy3eYLm1l5aiU/nvwRgG6Vu7G+z3osVDpzeq1hxAhYvDiurHx52Ls3E6MVQojc5coVP0aM2MK2bZeoXt2dNWt64+xsa+6wRBIkyRKZJjA8kObfNOffu/8CULNITQbWGsjEZhPTfhGtwccHbtyA+vXjyqdNM7oIhRAiHzt16h4NG36NUjBnTjvGjXsKa2tZzDmnkiRLZIq7gXdp820bzvqcpVTBUhx5/QhFHIuk7yJaG/NdHTwYV9a1K6xbB9bWmRuwEELkIg8eBOPm5kD16u68+WYTXnutHmXKFDJ3WCIVMiZLPJHwqHDGbhtL+c/Kc/7BecY/NZ5rY66lP8ECmDUrLsGaPx+2bIFff5UESwiRb92/H8TgwRuoUuULfHyCUUrx3ntPS4KVS0hLlsgw3xBf+q7ty84rOyniWITdg3bT2LNx+i6iNSxZYizg7OtrlF29asx7JYQQ+ZTJpFm+/DiTJu3C3z+UMWMaY2sr3YK5jSRZIkNO3zvN0989jU+wD5+0/4Q3m7yZ/idaAgJgwACjtQrgqafg+eclwRJC5Gs+PsH06LGaP/+8TsOGxfnqqy7Ureth7rBEBkiSJdLlQfADpu6eyprTa3gY9pDve3zPgFoD0n+hF16An3+Oe3/qlDFNgxBC5HMuLnY4OFjz5ZedGDKkvqw1mIvlmO+cUqqKUmqnUipIKXVHKfWxUirV9VaUUm5KqSVKqevRdU8ppYamVk+k36OwRxT+pDBfH/sapRQb+mzIWILl4xOXYE2YYLyXBEsIkY9t3nyBZs2+4dGjMCwtLdi27UWGDWsoCVYulyO+e0opF2APYAP0BN4ChgDz01D9Z6Ab8A7QFdgGLFZKvZ410eZPXv5euH3sBsDCjgvxm+zHc1WeS/+FNm0C9+ilHubPh08+ATe3TIxUCCFyD2/vRzz//Bq6dFnJ3buB3LjxEEAmFM0jckp34VDAGeihtfYFUEpZAV8qpT7QWt9KqpJSqhjQBnhZa70iuniPUqoh0Bf4Ossjzwc2nttIn7V9iDRFMqrRKMY8NSb9FzGZoFMn2L7deF+/Powdm6lxCiFEbhEZaWLRosNMn76XsLBIpk9vydSpzbG3l6ep85Ic0ZIFPAvsikmwoq3BiK9DCvVi/jU+TFT+EJA/AzLBJwc+ofvq7oRHhbOl/xY+e/azjF1oyJC4BGvBAjh6FOQvNSFEPqW1Ztmy4zRsWJz//hvG7NltJMHKg3JKS1YV4Jv4BVprf6XU7ehjSdJa31BK7QDeUkqdB25gJGwdgBezMN58Yc/VPUzaNYnyLuU58MoBihYomv6LmEzw44+wbJnx3sdHugeFEPmSv38oc+ce5K23WuDgYM3u3YNwd3eQrsE8LKckWS6AfxLlfoBrKnV7AquB09Hvo4BRWut1KVVSSjljdFHGKJamSPOJXVd28cwPz2ChLPjp+Z/Sn2D99hu8+y4cORJXtmaNJFhCiHxHa83q1acZN247d+4E0qBBcbp3r0KRIo7mDk1ksZySZGWIMtL/5UBFoD9wG2gPLFRK+WmtV6VQfTwwI+ujzH0CwwPp8lMX3Bzc+GPwH1QuXDntlS9dgvXrYdIk432tWlC1qrHQc4sWWROwEELkUJcu+TJixBZ27LhMzZpF+OWXF2jSpKS5wxLZJKckWX5AwSTKXQDfJMpjdAZ6A7W01iejy/YppYoA84CUkqz5wNJ474sBR5I5N9/wfuRN6xWtCYsKY1qLaWlPsLSGhg3hn3/iyj77DEaNyppAhRAih9Na0737Kq5e9eeTT9ozZkxjWcw5n8kpSdY5Eo29UkoVBDyijyWnGkb34KlE5ceB15RSDlrr4KQqaq0fAY/i3S8DYectUaYoeqzuwWW/yzxT4RlGNUpjgrRqFfTrF/d+9GiYMwfs7LImUCGEyMH+/PM6jRqVwMbGkhUrulOkiCOlSiXVjiDyupzydOFWoJ1SqlC8st6ACdiRQj0vwBKolai8PnAvuQRLPO7H/36k6hdVOXrrKG+3eJutL25NW+L5yitxCZaHB9y+DZ9+KgmWECLfuXcviEGD1tOixXK++OIwAA0aFJcEKx/LKUnWEiAA2KCU6qCUehn4BFgSf44spdRupdSlePW2ANeBtUqpAUqptkqpOcBg4PPsCz93m/PnHAasH8BF34tMajqJ2W1mp63i2rWwfLmxv3o13LoFxeT5ASFE/mIyab7++h+qVFnETz+d5M03m/D66/XNHZbIAXJEd6HW2k8p1RYjMdqAkXAtBaYlOtWSeDFrrQOi670PzAEKAVcxBrUvyvLA84BvT3zLlN1TKFuoLH+/9jfuju5pr/zGG8Z22zbo2DFrAhRCiByub9+1/PzzGZ56ypMlSzpTu7b8sSkMSmtt7hhyBKWUJ3Djxo0beHp6mjucbHH94XVKLywNQMDUAArYFEh75aVL4fXolYvk35AQIp8JCgrHzs4KS0sLfvvtPLduBfD66/WxsJDxvbmNt7c3JUuWBCiptfbOzGvnlO5Ckc201rz666sA/Nbvt7QnWBcuwDPPxCVYe/ZkUYRCCJEz/frreapV+5IvvjAeSO/atTJvvNFAEizxmBzRXSiy38SdE9l1ZRcTmkygS6UuaatUrRqcPRv3fv16aNMmawIUQogc5vr1h4wevZWNG89TsaIrNWsWMXdIIoeTJCsfemv3W8w7NI+aRWqmfZB7585xCdbatdCzp6w9KITIN/73v38YP347kZEmZs1qzaRJzbCzk1+hImXyLySfmb5nOh/++SHV3auz96W92Fvbp14pIgK2bDH2r12D0qWzNEYhhMhp7OysaNKkJF9+2YmKFWV5MJE2GR6TpZSyUUoNU0r9rJTaFV3WQinVUimVjhHUIrvsvLyT9/a/B8C+wftwc0jDD4qQEKhQwdifNEkSLCFEvuDnF8KwYZtYseIEAAMH1mLHjgGSYIl0yVBLllLKEdiLMemnAmIeL5sAdAFGA19kRoAic9wPuk+HHzpQtlBZDrxygMIOhVOvdOsWlCgR937WrKwLUAghcgCtNT/9dJLx43dw/34QLi5Ga7+sCiIyIqMtWTOBBhgJVnxfR5d1z3hIIrNprRm+ZTgAH7X7CA8nj9QrffttwgTL319mcRdC5GkXLjygffvvGTBgPcWLO3Ho0Kt88EFbc4clcrGMJlnPY7ReDUxUfiB6m8ZVhUVW01oz6/dZrD2zll7VevFC9RdSr3T4MAwebOw/9xyYTFBQloUQQuRtu3Zd4a+/vJk/vwNHjrxO48b5Y85EkXUyNBmpUioMo6vRHggFtNbaUillDwQB4VrrXNXskVcnI11waAHjd4ynQfEG/Pnyn9ha2aZcITISrK2N/SVL4mZ1F0KIPGjXritERETx7LMViYoycfduEMWLO5k7LJGNcuJkpA+jt4mzkZi1VfwzeF2Rif68/ifjd4ynTrE6/PXqX6knWAMHxiVYhQrBkCFZHqMQQpjDnTuBvPjiL7Rv/z2zZ/+B1hpLSwtJsESmymiS9Wf0dnVMgVLqS+BHjG7E/U8Yl3hCJm1i/PbxAHzb/VssLSxTruDtDT/8YOz36AHnzsk8WEKIPMdk0ixefIQqVRaxZs1pJk1qyq5dA2Vgu8gSGZ0n632gE1CPuCcL38AY9B4OfPDkoYknMWzTMI7cOsKCjguoVbRWyidrDR06GPtr1kDv3lkfoBBCmMF33/3L8OFbaNq0JEuWdKZmzaLmDknkYRlKsrTW/yilugJfAuXjHboMDNNaH8+M4ETGhEeF88NJo1VqTOMxKZ/s4wPu7sa+i4uxLqEQQuQhAQFheHk9pEaNIrz4Yk1sbS3p06eGrDUoslyGZ3zXWu8EKiqlKgLuwH2t9cVMi0xk2IQdEwiOCOabbt+k3AQeEhKXYFlYwM2bYJ+GGeCFECIX0FqzYcM5Ro/ehrW1BefPj8Ta2pJ+/WqaOzSRT2RoTJZSao9SajeA1vqi1vpgTIKllJqtlJJZK81Aa8347eP5/PDn9K/Zn8F1BqdcoXlzY+vqaiydIwmWECKP8PLyp1u3VfTsuQZHR2u++eY5rK1TGZsqRCbLaEtWa+LGYiX2dvSxGRm8tsigSTsnseCvBbQo1YLlzy1PuRVr0SI4dszYv3/faMkSQog84M8/r9Ox4w+YTJr33mvDhAlNsbWVpXpF9svUf3VKqeqZeT2RdveD7vPFkS+wtbRl70t7U36a8K+/YNQoY/+PPyTBEkLkCcHBETg4WFO/vgf9+tVg6tTmlC/vau6wRD6W5t+uSqkZSqkopVQU0a1YMe/jlf8Xfex21oQrkjN8y3BCIkPYN3hfygnWunXQpImxP306tGiRPQEKIUQW8fUN4fXXf6Vhw68JD4/C3t6apUu7SYIlzC69TRgqja81mRijSMWXR75k7Zm1DK0/lKc8n0r+xH//hV69jP3Jk2H27OwJUAghsoDWmu+++5fKlRexbNlxnn66DBERUeYOS4hY6ekuPAF8G73/EkaL1XfxjmvADziCJFnZ5l7QPabunkrTkk1Z8MyC5E9s3x527TL2GzSAjz7KngCFECIL3Lz5iAED1rNv3zXq1fNg69YXadCguLnDEiKBNCdZWuuNwEYApdRL0WUvZ1FcIo3m/DmHR2GPmNdhHnZWySwX2agRHDli7H/4IUyZkn0BCiFEFnB2tuXevSA+/fQZhg9viJWVjC0VOU9GJyOVf805wKl7p5j/13zc7N1oXKJx0ie9915cgnXxIlSokH0BCiFEJtqx4zJff32MVauex8nJlv/+G4qlpfw6EjlXhp8uVEpZA88ClYHHJljSWsuAnyyktWbwhsEALO68OOnpGurVg+PRk+/v2ycJlhAiV7p9O4Bx47azevVpypQpxPXrDylb1kUSLJHjZSjJUkp5AnuBcimcJklWFpq6eyr/3P6HsY3H0rt6EmsNdu0al2D9+CO0apW9AQohxBOKijKxZMlR3nprD8HBEUyd2py3326Jg4O1uUMTIk0y2pL1LgnXLEwsuYlKRSbwDfFl7sG5FHcqzryO8x4/4coV2LTJ2L99G4oVy94AhRAiEzx6FMasWb9Tu3ZRFi/uTPXqRcwdkhDpktG21rYYiVTM8jka6AocAC4BnZ88NJGc1359jSgdxdKuS7FQib6FFy5A+ej895tvJMESQuQqjx6FsXDhX5hMGhcXew4depV9+wZLgiVypYwmWUWjt7FzBmitNwP9gApAtyeMSyTj8M3DrD+3nm6Vu/FsxWcfP6FxvAHwgwZlX2BCCPEEtNasW3eGqlW/YNy47fz553UAypd3xcIihSXChMjBMppkhUZvg2P2lVIVAVN0+QtPGJdIwq2AWzy19CkK2BRgSeclj58QFQX+/kZLlskElrIYqhAi57t61Y8uXVbSq9fPFCxoy++/D6Zly9LmDkuIJ5bRMVn3gAKAK3AVqALsA2Km2pUxWVmg7Xdt0Wi+7vo1Hk4eCQ9euRLXTThpEqS0OLQQQuQQ4eFRNGv2DX5+oXzwwdO8+WZTbGzkD0SRN2Q0yTqBMfC9HvALMA0ohrGkDsCmJ45MJHDZ9zLnfM5Rya0SfWv0ffyEzvGGwQ0cmH2BCSFEBvz77x1q1SqKjY0lS5d2o0qVwpQr52LusITIVBntLpwEtAFOYkzV8DlwB/DFWHpnbGYEJwyRpkgGbTDGV617Yd3jJxw4AOfOGftag/1j05YJIUSO4OMTzKuvbqROna9Yt+4sAJ06VZQES+RJGZ3x/SpGN2GMMdEvkcm01rT7rh0Hbxxk/FPjqVGkRsIToqKge3djf+nSbI9PCCHSQmvNt9/+y4QJO/DzC2X06EZ06JDSTEBC5H4ZnvE9OUqpdsC7WusmmX3t/CbSFMkzPzzD716/079mf+Z2mPv4SePHg48PuLvDq69mf5BCCJEKrTWdO//E1q2XaNCgONu3d6Z+fVnMWeR96UqylFKlgQFASYzB779orU9EH2sEfAI0z+QY862XN77M7qu7qeZejRXPrUh66ZwffjC2p09nb3BCCJGK8PAobGwsUUrRpUslOnWqyLBhDWQ5HJFvKK3T9iCgUqouxhOEBeIVm4DBgAPwJcYYLwVorXWuejwkeqmgGzdu3MDT09Pc4XDm/hlqL6lNCacSXB1zNekES2uwsABHRwgMzP4ghRAiGdu2XWL48M18/vmzdO5cydzhCJEsb29vSpYsCVBSa+2dmddOz58TMwAnjCQq5mUJLATmRu8r4AjwTGYGmR+9svEVIk2RbOy7MekECyAszNj26pV9gQkhRApu3QrghRd+5tlnf0QphaOjjblDEsJs0tNd2ARj/qtNwNcYCdVrGMvpAHgDo7XWGzIzwPzo1L1T/H3zb5qXak7tYrWTP/G994xt7RTOEUKIbLJkyVEmTdpJaGgkb7/dgrfeaoG9vSzmLPKv9CRZbtHbl7TW/gBKqQOAD9FrF2qt/83c8PIfrTWv/foaAEu7pvC04HffwfvvG/svyAT7Qgjzu307gHr1PFi8uDNVq7qbOxwhzC493YUWADEJVvS+b7x9SbAywWW/y/x982/GNB5D5cKVkz7pzh146SVj/48/oESJ7AtQCCGiPXwYyujRW9m37xoA06e3Yu/elyTBEiJauqdwUEpdSUO51lrLBCgZMG77OABeqv1S8ifNjZ7KoUMHaNEiG6ISQog4Wmt+/vkMY8du4/btQNzdHWjdugxWVvLUoBDxZWSerMSrdupE5QpZuzBDokxR7Lqyiw7lO1DXo27SJz18CAsXgoMDbNuWrfEJIcTly76MHLmVbdsuUb26O2vW9KZ581LmDkuIHCm9SZasOpyFVp9eTWhkaMqtWG+/bczy/s47sgi0ECLbffXVP/z++zXmzGnHuHFPYW2dq2brESJbpXmerLwuJ8yTVePLGjwIecCNcTewskgi/w0PB1tbYz80NG5fCCGy0O+/X8PZ2Za6dT0IDAzHxyeYMmUKmTssITJFTpknS2ShH//7kdP3T1O3WN2kEyyIS6ratZMESwiR5e7fD2Lw4A20bv0t77yzD4ACBWwkwRIijTJ97UKRfmGRYQzZNISCtgVZ3Wt10id9803c/qZN2ROYECJfMpk0y5cfZ9KkXfj7hzJu3FPMmtXa3GEJketIkpUDjNwykuCIYH7s+SNOtk5JnxSz+POtW9KKJYTIUnPnHmTy5F00alSCJUs6U7euh7lDEiJXkiTLzCKiIlh6fCkVXSvSv2b/pE86d87Y1qoFHvLDTgiR+YKCwvHzC8XT05nXX69HoUJ2vPpqXVnMWYgnIP97zGzElhEATGw6MfmTqlY1tu++mw0RCSHym82bL1C9+pf07bsWrTUuLvYMGVJfEiwhnpD8DzKj8Khwvj72NUDyrVgxLCygW7dsiEoIkV94ez/i+efX0KXLSqytLZkxo1XyC9ILIdLtiboLlVLPAk8DLlrr15RSMTPS3dJaRz5xdHnctye+BWBBxwU42jgmfdL588b2zTezKSohRH6wbdslevf+mfDwKN55pyVTp7bAzk5GkAiRmTL0P0opZQX8AnSOV/wa8D3QPHp/+RNHl8ftuLIDdwd3RjcenfxJu3YZ20aNsicoIUSeFhlpwsrKgjp1itGuXTk++qgtlSsXNndYQuRJGe0unAx0wZgBPn7b8hfR73um94JKqSpKqZ1KqSCl1B2l1MdKKZs01i2hlPpWKXVfKRWilDqrlHoxvTFkpyhTFLuv7KZduXZYqBS+DV98YWybNMmewIQQeZK/fyjDh2+mW7eVaK0pVqwA69f3kQRLiCyU0SRrIMb6hG8lKt8bva2RnosppVyAPYANRoL2FjAEmJ+Guh7AIaB4dJ0uwGIgR89zsO/aPvxC/Whfrn3SJ0RFGa1XZ89CmTJQokS2xieEyBu01qxceZIqVRaxePFRSpZ0Jjw8ytxhCZEvZLQDvkz0diHwQbzyh9HbYum83lDAGeihtfaF2C7JL5VSH2itb6VQ92PgBvCM1jrmJ8fudN4/2609sxYLZUGXSl2SPqFPHzhyxNjfuDH7AhNC5BnXrvkzZMhv7Nx5hZo1i7B+fR+aNClp7rCEyDcy2pIVHL11TVTeNHoblM7rPQvsikmwoq3BiK9DcpWUUs7AC8CX8RKsHC8iKoKN5zfSukxr3B3dHz/ho49g3TpjPyTEmB9LCCHSycJC8e+/d5k7tz3//DNEEiwhsllGk6zoJhb+F1OglJoErMLoRjyczutVAc7FL9Ba+wO3o48lpx5GF2OEUup3pVRE9HiuOUop65RuqJRyVkp5xrxIf+tbhn1x5AtuB95mRMMRjx9cuRKmTjX2160DO7vsCksIkQfs2XOVCRN2AFCqVEGuXRvDm282xdra0syRCZH/ZDTJ+jh6+yxGUgXwIVAk+v0n6byeC+CfRLkfj7eWxReTGC0FjmK0ei0AxgKzU7nneIxuxpjXkZRPzxwmbeKzvz/DQlnQvUr3hAcjI6F/9HxZ770HPdP9/IAQIp+6dy+IgQPX07btd6xZc5r7940OBXv7FP/eFEJkoQwlWVrr3cCrwCPinjBUGGOyXtNa702hemaKiX+X1vpNrfVerfUcjCRvnFLKPoW684GS8V4NszZUw5RdU7jqf5UZrWYkfKowMhKso38YjhsH06ZlRzhCiFzOZNL873//ULnyIlauPMmECU04c2YE7u7JzL0nhMg2GZ55Tmu9Qin1M8Y4LHfgPnBQa53e8VhgtFgVTKLcBfBNojx+PTCeTIxvNzANqACcTKqi1voRRpIIkC2zHJu0iY3nN+Js68xbLRI9mBl/yZwPPkAIIdLi2jV/Ro3aSr16HixZ0pnatbNt5IMQIhUZnYz0Q+BbrfU5YGcmxHGORGOvlFIFAQ8SjdVK5Ewq181RA5q2XNzChQcXWNZtGVYW8b70S5bA7OjezZAQGYclhEhRYGA4mzZdoG/fGpQr58LBg69Qt64HFhayJI4QOcmTTEZ6Wil1VCk1WimVxCNy6bIVaKeUKhSvrDdgAnYkV0lr7YXRUtUu0aH2QAipJ2HZRmvN1N1TKWBTgL41+sYduHgRhg0z9nv3lgRLCJGijRvPUa3aF/Trt46zZ+8DUL9+cUmwhMiBnmSBaIXxdN8C4KZSapNSqo9SKiOTgC4BAoANSqkOSqmXMcZVLYk/R5ZSardS6lKiutOAbkqphUqp9kqpt4AJwPwMdl1miX3X9nHq3ikmNJmAg7VD3IHNm43tZ5/BmjXmCU4IkeNdv/6Q7t1X0b37auztrdm9exBVqz7p37dCiKz0JJORvoDR2tQw+jqdMJ42DFBK/ay1fj2tF9Na+yml2gKfAxswEq6lGAlUfJaJY9Za/6aU6gdMB4ZhTPswA/go3Z8qC43YYkzX8FKdlxIe2BHdUNe3L0IIkRR//1Bq1VpMaGgks2a1ZvLkZtjaymLOQuR0Smud+lkpXUCp0hjJ1gtAg+hirbXOVZOyRM+VdePGjRt4enpm6rVN2oTlbEsqulbkwqgLcQcCA8HJydh/wu+DECLv8fLyp3TpQgAsW3aMli1LU7Gim3mDEiKP8fb2pmTJkgAltdbemXntJ+kujPEI4wlAPyAyE66X56w7Y8ze/mrdVxMeuG+Mp6BHj2yOSAiRk/n5hTB06CbKl/+Mw4dvAvDqq/UkwRIil8no04UuQA+MFqyn411HAWHAr5kSXR6x/tx6AEY3Hp3wwNWrxrZly2yOSAiRE2mt+emnk4wfv4P794MYNqwBlSpJYiVEbpXRTv07JEysNHAA+A74WWv9MLmK+c29oHusPLWSHlV6YG+daG7UkSONbePG2R+YECJHiYw00anTj+zceYU6dYrx6699adw4c4cuCCGyV0aTrJh1Gi4C3wM/aK2vZUpEecz6s0YrVq9qvR4/ePassW3Q4PFjQoh8wWTSWFgorKwsqFWrKJ06VWTkyEZYWWXGaA4hhDllNMn6Evhea/13ZgaTFx2+aayV3bVS14QHNm40toMHxy2nI4TIV3buvMyYMdtYs6Y3NWoUYe7cDuYOSQiRiTK6duFISbBSZ9ImvjnxDW3LtsXJ1inhwVejB8GPGZP9gQkhzOrOnUD6919Hhw4/EBwcgZ9fiLlDEkJkgTS3ZCml9mBMzdA2ej8lWmvd9slCy/2O3T4GQO2itRMeOHcOHjww9uvUyd6ghBBmo7Xmq6/+YcqUXQQFRTB5cjOmT2+Jo6ONuUMTQmSB9HQXtsYY4J54PzGVwrF85dfzxkOWr9R9JeGBhQuN7ccfZ29AQgizUkrx55/XqVGjCIsXd6ZmzaLmDkkIkYXSk2Rdx1hLMGZfEqlUfP/f99ha2lLNvVrCA9u3G9vx47M/KCFEtgoICGPWrN8ZOrQBFSq48tVXXbC3t5a1BoXIB9KcZGmtyyS1L5L2l/dfXPO/xqt1X0WpeD9Md+2Ca9egXDmwzFWT4gsh0kFrzYYN5xg9ehve3o/w9HRm7NinpGtQiHwko5ORvoMx7urdJI49jXEwtXFbedqOy8aahANqDUh4oE8fYztxYjZHJITILl5e/owcuZVNmy5QpUph9u59idaty5g7LCFENsvoFA4zMboLH0uygF0Y3Yr5evXS371+p6RzSVqVbhVXGBYGvr5QrRoMHWq+4IQQWWry5F3s2nWF995rw8SJzbCxkVZrIfKjTJ3tTinlHLObmdfNbUzaxOGbh+lQvkPCrsI90Y17nTubJzAhRJY5cOA63t6PAJg7twOnTg1j2rSWkmAJkY+lZwqHl4CXEpUl7hIsFb31f7KwcrdDNw4RGB5IfY/6cYUmE3TqZOwPHGiewIQQmc7XN4TJk3eydOlxXn21LkuXdsPT0zn1ikKIPC89XXplSDh1gwJaJTonptnmjyeKKpd7f//7FLIrRP+a/eMKt2wxtmXKQM2aZolLCJF5tNZ8//1/vPnmDh48CGbkyIa8997T5g5LCJGDpCfJ8ge8ovdLYyRb1+Md14AfcASYkRnB5UZHbh5h66WtzGg1g4J2BY3C4GDoGr2sztat5gtOCJFppk7dzZw5B6hXz4OtW1+kQYPi5g5JCJHDpGcKh0+BTwGUUqbosrJZFFeuNXHnRJxsnBjTON5yObNnG1s3N6hSxTyBCSGeWEhIBGFhURQqZMcrr9TFw6MAI0bIYs5CiKRl9AnANpkaRR6x5+oefvf6nQlNJuBi72IUnj0Lc+YY+5cumS84IcQT2b79EsOHb6F581J8+213KlVyo1IlN3OHJYTIwdIz8L0lgNb6D6LHZcWUJSX6vHzlf//8D4BhDYfFFVaLnu19wQIoVCj7gxJCPJHbtwMYN247q1efpkyZQvTpU93cIQkhcon0tGTtI27+q32kvKyOTue184Sr/lcBKOdSzigYOTLu4Nix2R+QEOKJ/PLLWV5+eSPBwRFMndqct99uiYODtbnDEkLkEulNhFQy+/leWGQYh28epkeVHkZBeDh88YWxf+WK+QITQqSb1hqlFBUquNKgQXE+++wZqlcvYu6whBC5THqSrJeT2RfA4qOLAXi2wrNGwfnzxnbwYCgrzwcIkRs8ehTGO+/sxWTSfPbZs9SqVZTduweZOywhRC6VnqcLv01qXxiO3T4GQL+a/YyCu3eNbfv2ZopICJFWWmvWrTvLmDHbuHUrgNdeqxvbmiWEEBmV0QWibQFHIExrHRS9nM4IwB3YprXekYkx5nhaa47cOoKzrTMFbAoYhd98Y2zLlTNfYEKIVF275s+IEVvYsuUi1aq5s2rV87RoUdrcYQkh8oCMTu6yCLgPTIh+vxN4DxgDbFVK9cqE2HKN8w/Oc87nHEPrRy/6bDLBypXGfnV5EkmInOzOnUB+//0aH37YluPH35AESwiRaTL6BGDj6O0mpVRVoCEQBYRitHCNBdY+cXS5xMEbBwFoV66dUXD7trFt1QqcnMwUlRAiOfv3e3HixB1GjWrMU095cuPGOFxc7M0dlhAij8loS1bJ6O1FoF70/mygUfR+5ScJKrf52/tvFIqmJZsaBSdPGts+fcwXlBDiMT4+wbzyykZatlzBvHmHCAmJAJAESwiRJTKaZNlGbyOA6hjzYv0DxExpXuAJ48pVdl7ZSeXClXG0cTQK/vzT2DZoYL6ghBCxtNYsX36cKlUW8e23/zJmTGP++28Y9vYy55UQIutktLvwFlAWWA40jy47AxSL3vd5wrhyjfCocK76X6VRiUZxhcePG1uZukGIHOHIkVu88sqvNGhQnO3bO1O/vizmLITIehltydqIMRlpb6A4cFJrfQ2oH3381JOHljvsurILgNalWxsFUVGwZQs0bgyFC5svMCHyueDgCPbsMVZhaNSoBNu3D+Cvv16VBEsIkW0y2pI1HXAAmgFexD1lWBH4HVj55KHlDstPLAdgXJNxRsGkSca2aFEzRSSE2LLlIiNGbOH27QCuXRtLsWIF6NChvLnDEkLkMxlKsrTWwcCwJMo/AT550qByC601Z+6foXTB0hQrEN1TunSpsf3f/8wXmBD51M2bjxg7djtr156hXDkXNm7sS7Fi+WqIqBAiB8nwIs5KKSvgJeAZjElIfYCtwLda68jMCS9n23F5B2fun2F+h/lGgdbw6JHRVSgtWUJkq+vXH1KjxpeEhkby9tsteOutFjKwXQhhVhmd8d0O2IHRXRhfD+BlpVQ7rXXokwaX0805MAc7Kzteq/eaUXD4sLFt0cJ8QQmRz/j6huDqak+pUgWZMKEpL7xQnSpVZDykEML8Mjrw/S2MpwpVEq8m0cfzNK01e6/tpU/1PjjZRk842qmTsR0wwHyBCZFPPHwYyqhRWyhb9lNu3HgIwDvvtJIESwiRY2Q0yXoBY26snzEGu9tFb9dgJFovZEp0Odjuq7sB4tYqvHYNfH3BzQ1q1zZfYELkcVprVq8+RZUqX7Bo0RFeeKEajo425g5LCCEek9ExWWWit29orf2j9y8rpYZiJFhlkqiTp2y5uAWAqc2nGgUxc2N9/rmZIhIi7wsICKN375/Zvv0yNWoUYe3a3jRrVsrcYQkhRJIy2pIVEr1N/Ex0+UTH8yStNVsubqFpyaaUcC5hFPr6GtvSsrisEFmlQAEb7OysmDOnHceODZEESwiRo2W0Jeso0BbYrJT6FrgBeGI8bRizxE6e9ffNvzn/4Dxz682NK4xJssqUMUtMQuRV+/ZdY9q0Paxf34ciRRxZv74PSilzhyWEEKnKaJI1F3gaY+qGCfHKFUaSNTepSnnFihMrABhYe2Bc4bFjxtbZOfsDEiIPun8/iAkTdvLdd//i6enM1at+FCniKAmWECLXyFB3odZ6O/AGEEDCJwsDgKFa622ZFmEOtOHcBsoUKkMRxyJxhZs3g4UFFJCJD4V4EiaTZtmyY1Sp8gU//vgf48c/xZkzw2nc2NPcoQkhRLpkeDJSrfVSpdQqoClQGGMy0oNa68DMCi4n2ndtH3eD7jK60ei4wgsXICAASsn4ECEyw7Jlx6lQwZWvvupCnTrFUq8ghBA5ULqTLKVUGeIWgj6mtd6RqRHlcF/98xUAr9R9Ja7w+++N7cKF2R+QEHlAUFA4n3xykLFjn6JQITs2buyLq6s9lpYZfTZHCCHML81JljIGQiwGXsPoGowpXw68rrXWmR9eznPizgmsLKyoVbRWXGHMeKyOHc0TlBC52KZNFxg5cgteXg8pXbogL79cF3d3R3OHJYQQTyw9fyaOAobw+AzvLwNjMz2yHCgsMoxzPucYXHtwwsG3zs5gaQkODuYLTohcxtv7ET17rqZr15VYW1uyc+dAXn65rrnDEkKITJOeJCumfywc+BX4DQjDSLQGZ25YOdOuK7sAqORWKeGBVaugYkUzRCRE7tWv3zo2b77IjBmtOHlyGO3alTN3SEIIkanSMyarEsb0DM9qrfcBKKXaALsxltTJ807fPw1Au3Lt4gojIoxtzDxZQohkHT58k+rV3XF0tOGLLzpha2tJ5cqy1qAQIm9KT0uWHUBMghUtZt82k+LJ0X49/ysA1YtUjyu8etXYvvqqGSISInfw9w9l+PDNPPXUUubMOQBArVpFJcESQuRpGXm6sCTxBr4nV661vv5koeUsWmu8H3lTya0SNpbxFqP91Ui8ZFFoIR6ntWblylOMH7+du3eDeOON+owb95S5wxJCiGyRkXmyriV6r5Mo1xm8do51+OZhvB56Mb/D/IQHwsON7bPPZn9QQuRww4dvZsmSf6hVqyjr1/ehSZOS5g5JCCGyTUYmoUn8dGFyr/RdVKkqSqmdSqkgpdQdpdTHSimb1GsmuMZYpZRWSm1K7/1Tc8j7EAB1PRI9/bR7t7F1lEfOhQAIC4skLCwSgBdeqM7cue35558hkmAJIfKd9LQ2/UFcq1WmUkq5AHuAi0BPoAQwH3AARqbxGsWAGcC9rIjx4I2DAFR3r57wwKlTxtbSMituK0SusmfPVYYN28yLL9bknXda0aZNWdq0KWvusIQQwizSnGRprVtnYRxDAWegh9baF0ApZQV8qZT6QGt9Kw3X+BhjaonSWRHgNf9ruNm74e7onvCAyQQtWmTFLYXINe7dC+LNN3fwww//UbKkM/XqeZg7JCGEMLucsmbFs8CumAQr2hqM+DqkVlkp1RzoDkzJiuAiTZEcuXWE9uXbJzwQGgo+PlC5clbcVohcYfXqU1SuvIiVK08ycWJTzpwZQZculVKvKIQQeVxOGZxeBfgmfoHW2l8pdTv6WLKUUpbAIuB9rfXtBDOxZ5ID141Hziu7JUqmNm82th7yV7vIvxwcrKlatTBLlnShVq2i5g5HCCFyjJySZLkA/kmU+wGuqdQdDjgCC9JzQ6WUM0YXZYxiyZ3r9dALgJalWyY88PbbxrZz5/TcWohcLTAwnJkz91GqVEFGj25M166V6dKlElnxB44QQuRmOaW7MEOUUkWA2cB4rXV4OquPB27Eex1J7sQoUxQAbvZuCQ/cvWtsGzdO562FyJ02bjxHtWpfMG/eIc6evR9bLgmWEEI8Lqe0ZPkBBZModwFSWq9mNvAfsF8pVSi6zAqwin4fqLWOTKbufGBpvPfFSCbROnXPeIKwaIF4XSFhYeDnB1OyZBiYEDnK9esPGTVqK7/+ep5KldzYvXsQTz8tTw0KIURKckqSdY5EY6+UUgUBj+hjyakCtMRI0hLzwxhQvy2pilrrR8CjePdL9iY+IT4AFHWMl2T9+6+xLSzLgoi878CB62zffonZs1szaVIzbG1zyo8OIYTIuTL8k1Ip5QZMAp4GXLTWFZRS/aOvuU1rnZ75qrYCbymlCmmt/aPLegMmYEcK9cYChRKVLQRCgKkYrVxP7NjtY9T3qJ8wEYsZ9N6+fdKVhMjlDh26we3bgfTsWZW+fWvQvHkpSpZMqsFZCCFEUjKUZEWPhfoLY04qRdwkpc8AL2IkOB+n45JLgFHABqXUBxiTkX4CLIk/R5ZSajdQWmtdAUBrfSKJ2Pwxugn3petDJSNmzcJulbslPHDlirGtVSszbiNEjuHnF8KUKbv43/+OUbVqYbp3r4KFhZIESwgh0imjA9/fBcoAUYnKV2AkXV3TczGttR/QFogENgAfYYyXGp/oVEuyuYvzVsAt/EP9qVmkZsIDP/wADg7ZGYoQWUprzQ8//Eflyov4+utjDB/egIMHX8XCQga1CyFERmQ0YemM0XrVEdgdr/xw9LZ8ei+otT4LtEvlnNZpuE6q56THyXsnAajoWjGu8LPPjG3t2pl5KyHM6rffLjBw4Hrq1CnGpk39adSohLlDEkKIXC2jSVbM2jIHkjnulkx5rnPm/hkAijgWMQru3IExY4z9ZcvMFJUQmSM0NJLz532oXbsYXbpU4ocfetCnTw2srHL17C5CCJEjZPQnqU/0NvFs7P2it1mySLM5BIUHAVC9SPTC0KdPG9uRI6FqVTNFJcST27nzMjVrLqZDhx8ICgrHwkLx4ou1JMESQohMktGfpjFdhBtiCpRSW4DFGN2Iu5Ookysd9D5IQduCONk4GQX3ovPH554zX1BCPIE7dwLp338dHTr8QEREFN980w1HRxtzhyWEEHlORrsLZwPdMAa/xzxZ2BFj0PtDjIHxecKD4Ac4WDvETd9w0hijRYEC5gtKiAz677+7tGy5nKCgCCZPbsb06S0lwRJCiCySoZYsrfUloAWwB2MuKxW93QO01FpfzrQIzezIrSO0KxdvPH5MkiVTN4hcJCQkAoBq1dx54YXqHDs2hI8+aicJlhBCZKEMT4egtT4JtFNK2RO9/I3WOjTTIssBIqIiEhaYTHD2LFSqJNM3iFwhICCMGTP2sWHDOf77bxgFCtjwv/+la4YVIYQQGfTEc05prUMwZljPc/xCjdV6YufI+uEHuHw5bgoHIXIorTXr159j9Oit3LwZwODBdYiISDytnRBCiKyU0RnfU/tprbXWuX5xs7uBdwEoVqCYUbBwobHt2dM8AQmRBj4+wQwevIHNmy9SpUph9u3rSatWZcwdlhBC5DsZTYTyxRTQ3o+8AShoF72cyLlz4OQEJWSSRpFzOTnZcPNmAO+914aJE5thY2Np7pCEECJfymiS9W2i95ZAWaApEAz8/CRB5RRhUWEA2FvZGwVWVjI3lsiRDhy4zty5h1i58nns7Kw4evR1LC1lvishhDCnDCVZWuuXkypXSnUEtgLHniSonOKS7yUAyhQqYxSEhUHTpuYLSIhEHjwIZsqUXSxdehwPjwJcvPiAmjWLSoIlhBA5QKb+JNZabwcCgdGZeV1z+cv7L5xtnSnvWh5u3YLwcLC1NXdYQqC15ttvT1ClyhcsW3ackSMbcvbsCGrWLGru0IQQQkTL6MD3lkkU2wHPAgUAjycJKqf45/Y/VHKrhIWygGvXjMLChc0akxAAISGRzJixj1KlCrJ164s0aFDc3CEJIYRIJKNjsvYRN9N7Yho4kcHr5ihe/l642LkYby5eNLaVK5svIJGvhYRE8L///cOIEY1wcLBmz56XKFWqoKw1KIQQOdSTTLOQ3BOG14HhT3DdHCHKFIVGU9alrFGwO3o5xnLlzBeUyLe2b7/E8OFbuHLFjzJlCvHcc1UoV87F3GEJIYRIQUaTrKQGvocBN4C/tdaRGQ8pZ9hzdQ8ALUtF94yePWtsq1c3U0QiP7p1K4Bx47azZs1pypYtxObN/enUqaK5wxJCCJEG6U6ylFK2gF/020Na6/uZG1LOcPKesUZh23JtjYJz56B+fTNGJPIbk0nz9NPfcuWKH2+91Zxp01ri4GBt7rCEEEKkUbqTLK11mFJqLcaTiXl2tO2pe6cAqOZeDbSGwECwtzdzVCI/OH36HlWrumNhoVi0qBPFiztRrZq7ucMSQgiRThkdMXsJY0xWnl0M7ar/VQraFjSeLDxwwChs0cK8QYk87dGjMMaM2UqtWktYtsyYaq5du3KSYAkhRC6V0SRrZvT2faWUTSbFkqNcfHCRIo5FjDe3bhnbjh3NF5DIs7TWrF17hqpVv+Czzw4zeHBtevaUlQWEECK3y+jA92HAQ+B1oLdS6gIQEu+41lq3fdLgzMnWyhZXe1fjzfr1xlbWLBRZYNCgDfzww39Uq+bOqlXP06JFaXOHJIQQIhNkNMlqhTEflgJcgEbxjimSn0MrV9Ba4/3Im2fKP2MUBAUZ2/LlzReUyFMiIqKwsrJAKUX79uWoXt2d8eObyGLOQgiRh6Q5yVJKDcJoofoeYy6sXJ1IpeRByAPCo8LxdPY0Cvbtg9q1QSU3NZgQaffHH14MHbqJadNa8OKLtRg0qLa5QxJCCJEF0tOStQIwAd9rrctkSTQ5xN6rewGoXqQ6XL8OAQEQHGzmqERu5+MTzKRJO1m+/ATFiztRsKCduUMSQgiRhdLbXZgvmnIO3zwMQOMSjeGMl1H4+utmjEjkdj/++B+jR2/D3z+UMWMaM3t2G5ydZbFxIYTIy55kWZ08y8bSeGDSzcENwqPXLKwqT3uJjLt3L4hy5Vz46qsu1KuXJ9ZPF0IIkYqMzPi+Jw2n5eqnC0MiQ7C2sMbKwgrCwoxCW2l1EGkXHBzBu+/+TpMmJenWrTKjRzdm9OjGWFrKYs5CCJFfZKQlq1Uqx3P904XnfM7FtmZxMbolS5IskUZbtlxkxIgtXLvmz7hxT9GtW2VJroQQIh/KSJKV58dlXfK9hIp5kvD6dWPrIV08ImU3bz5izJhtrFt3lvLlXdi27UU6dqxg7rCEEEKYSUaSrLKZHkUOcyfwDiWdSxpvrl0ztmXKmCsckUt8++2//PrreaZPb8nUqc2xt5fFnIUQIj/LyALRXlkRSE5T2KGwsRMUBO7uYC2/MMXjjhy5SUSEiaZNS/Lmm014/vmqVK5c2NxhCSGEyAFkoEgikaZIAsIDaFi8oVGweze4uJg3KJHjPHwYysiRW2jceCmTJu0EwNbWShIsIYQQsdLTknUdYzLSPO1+0H0AY7Z3f3+jJcvZ2bxBiRxDa82aNacZO3Y7d+4E8vrr9fjoo3bmDksIIUQOlOYkK6/P8h7DL8QPABd7l7gnC595xowRiZxk2bLjvP76b9SoUYR1616gadOS5g5JCCFEDiWTkSZyJ+gOAM62zvDhXKOwcWMzRiTMLSwskjt3AilduhD9+tUgNDSSN96oj7W1LOYshBAieTImK5GIqAgAPAp4xE3f0LmzGSMS5rR371Vq115Ct26riIw04ehow8iRjSTBEkIIkSpJshK5G3gXAEsLS2P6hrJlQeX5qcFEIvfuBfHSSxt4+unvCAqKYNas1lhayr8DIYQQaSfdhYlYWRhfEicbJ7CykjUL86EDB67TtetKHj0KY/z4p5g1qw0FCtiYOywhhBC5jCRZiVz2uwxED3wPDIRixcwckcguUVEmLC0tqFGjCC1alGbWrNbUqSPffyGEEBkj3YWJ2FnZAeAWpI0pHHSuXoZRpEFQUDiTJ++kTZtvMZk0BQvasXFjX0mwhBBCPBFJshK5HXgbC2WB9akzRoGnp3kDEllq06YLVK/+JR9/fJBSpQoSHBxh7pCEEELkEdJdmEhgeCAmbTImIQVo2tS8AYkscedOIMOHb2b9+nNUrOjKrl0Dadu2nLnDEkIIkYdIkpXIVb+rlC5YGg4fNgpKlDBvQCJLWFoq/vrLm5kzWzF5cnPs7OS/ghBCiMwlv1kSeRj2EFtHW/CKXge7ShXzBiQyzd9/e/PDD//x2WfP4u7uyOXLo7G3l4W/hRBCZA0Zk5VIWGQYxZ2Kw5noMVnW8ks4t/P3D2X48M00abKMNWvOcP36QwBJsIQQQmQpSbIS8Q3xpbZTRTh2DMrJGJ3cTGvNTz+dpEqVRSxefJQhQ+pz7twISpcuZO7QhBBC5APSXZgEm9BwY6dLF/MGIp7I/fvBvPHGJsqVc2H9+j40aSKLOQshhMg+kmQloZxD9LQNzZqZNxCRbmFhkaxbd5b+/WtSpIgjv/8+mFq1imJlJY22QgghspckWUmo6FjK3CGIDNi9+wrDh2/hwoUHlC1biCZNSlKvnoe5wxJCCJFPyZ/3SXC1cDB3CCId7t4NZMCAX2jX7ntCQyPZuLGvdA0KIYQwuxyTZCmlqiildiqlgpRSd5RSHyulUlyVVynlEX3eCaVUgFLKWyn1k1Kq9JPE4nDH19iJjHySy4hsEBoaSd26X7Fq1SkmTmzKmTPD6datsrnDEkIIIXJGd6FSygXYA1wEegIlgPmAAzAyhar1o8//BvgLKAxMBw4rpWpore9nJB67G7eMndq1M1JdZIPr1x9SqlRB7Oys+Pjj9tSqVZRatYqaOywhhBAiVo5IsoChgDPQQ2vtC6CUsgK+VEp9oLW+lUy9P4EqWuvYJiel1EHgOjAImJeRYAr/fsTYKSldTjlNYGA4M2fuY+HCv9i2bQDt2pVjwIBa5g5LCCGEeExO6S58FtgVk2BFW4MRX4fkKmmt/eMnWNFl3sB9oHhGg3G69cDYcXbO6CVEFti48RzVqn3BvHmH6N+/prRcCSGEyNFySktWFYwuv1haa3+l1O3oY2mmlKoEFAHOZiQQG0sbrC5cgj59MlJdZAGtNb17/8y6dWepVMmN3bsH8fTTZc0dlhBCCJGinJJkuQD+SZT7Aa5pvYhSSgGfAbeAlamc64zRRRmjGIC1pTVEBEFwcFpvK7KIyaSxsFAopahRowi1axdl0qRm2NrmlH+2QgghRPJySndhZpkJtAUGaa2DUjl3PHAj3usIgLVF9Hp2jRtnVYwiDQ4evEG9el9x8OANAGbObM306a0kwRJCCJFr5JQkyw8omES5C+CbRPljlFKvA+8Ab2itd6ehynygZLxXQwCrSJNx1EHmyjIHX98Q3njjN5o1+4Y7dwJ5+DDU3CEJIYQQGZJTmgXOkWjslVKqIOARfSxFSqkewGLgHa31N6mdD6C1fgQ8incNAALCA4yCjh3TFLjIPD/9dJKxY7fh4xPM8OENeP/9thQqZGfusIQQQogMySlJ1lbgLaVUIa21f3RZb8AE7EipolKqNcb4q6+11u8+aSBlIwoAAVCt2pNeSqTTwYM3KFHCmU2b+tOoUQlzhyOEEEI8kZySZC0BRgEblFIfYExG+gmwJP4cWUqp3UBprXWF6PdVgQ0Yk5h+r5R6Kt4172utL6c7Eq2hatWMfg6RDqGhkXz44X569KhKnTrF+Pjj9tjYWMpizkIIIfKEHJFkaa39lFJtgc8xkqYAYCkwLdGpliSMuTHGWK6CwIFE534LDE5vLEV8gsHePr3VRDrt3HmZ4cO3cOmSL1pDnTrFcHCwNndY+Z7WGh8fH0JDQ4mKijJ3OEIIkWGWlpbY2dlRuHDh2CFB2S1HJFkAWuuzQLtUzmmd6P0KYEVmxuHhbwKrHPNlyXPu3Alk/PjtrFx5itKlC7JpUz86d65k7rAERoJ18+ZNAgICsLGxwdLS0twhCSFEhoWHhxMYGEhYWBglSpQwS6Il2UQi7kFAkybmDiPPmjVrHz//fIbJk5sxfXpLHB1TXANcZCMfHx8CAgIoUqQIbm5u5g5HCCGe2IMHD7h37x4+Pj64u7tn+/0lyUrENgro29fcYeQpx4/fxsnJlgoVXJk9uw0jRjSiRo0i5g5LJBIaGoqNjY0kWEKIPMPNzQ1/f39CQ80zHZCMME4kwBZZszCTBASEMW7cNho0+JrJk3cB4O7uKAlWDhUVFSVdhEKIPMfS0tJsY0ylJSuR0v5AhQrmDiNX01rzyy9nGTNmGzdvBjB4cB0+/jjF4XZCCCFEniNJViI2jZuAjYwTehIff3yAKVN2U7VqYX78sSetWpUxd0hCCCFEtpPuwkRskO6SjIiIiMLXNwSAAQNq8eGHbTlxYqgkWMIsZs6ciVIq9uXm5kbz5s3ZsmVLkuf7+fkxceJEypcvj62tLUWLFqVfv36cPXs2yfMDAwOZNWsWNWrUwMHBAUdHRxo1asT8+fPNNvYjuyxYsIBSpUphaWlJ9+7dM/368b9vyb1WrFjxRPc4ceIEM2fOJDg4OM11evfuzcSJE5/ovrnRb7/9Ru3atbGzs6NSpUosX748TfXOnj1Lp06dcHR0xMXFhYEDB+Lj45PgnEuXLjF06FDq1KmDlZUVNWrUeOw6AQEBuLq6cuBA4lmacgdpyUrEqXBxc4eQ6/z553WGDt1EuXIubNzYlxIlnJkypbm5wxL5nL29PXv27AHg1q1bfPDBB3Tt2pX9+/fTtGnT2PPu3LlDy5Yt8fPzY9q0adStWxdvb2/mzp1Lw4YN2bJlCy1btow938fHhzZt2nDjxg3Gjh1L8+bGv/VDhw7x0UcfYWlpyZgxY7L3w2aTixcv8uabbzJ58mS6du1K4cKFM/0ehw4dSvC+SZMmjBo1iv79+8eWlS9f/onuceLECWbNmsXIkSNxSMM6tceOHeO3337jypUrT3Tf3ObPP/+kR48evPbaayxcuJA9e/bw6quv4uTkRK9evZKt9+jRI55++mk8PT356aefCA4OZurUqXTu3JlDhw5hYWG075w+fZrNmzfTuHFjTCYTJpPpsWs5OTkxatQo3nrrLX7//fcs+6xZRmstL60BPAG9+n/vaJE2Pj5B+tVXN2qYqT085uo1a05pk8lk7rBEBl29elVfvXrV3GFkihkzZmhHR8cEZd7e3loppYcMGZKgvEePHtrW1lafPXs2QXlgYKCuWrWqLlGihA4JCYkt7927t3ZwcNAnT5587L4PHjzQBw4cyMRPknbBwcFZfo/ffvtNA/ry5ctPfK3Q0FAdFRWV6nmA/uSTT574fvEtX75cA/r+/ftpOn/QoEG6W7dumXLv7Pg+ZZYOHTropk2bJijr16+frlq1aor1PvzwQ21vb6/v3LkTW3bkyBEN6F9++SW2LP73/6WXXtLVq1dP8nrXrl3TgD5x4kRGPkaqP9tu3LihAQ146kzOLaS7MBEbG1mQOC127rxMlSpfsHz5CUaNasS5cyPp3bu62WbVFSI1JUqUwN3dnevXr8eWeXl5sWHDBgYNGkSVKgnWqMfR0ZFp06Zx8+ZNfv7559jz165dy9ChQ5Ps2nB1dU3QSpaUs2fP0rNnT1xdXXFwcKB27dqsXLkSgGvXrqGUYu3atQnqjB07ljJlysS+X7FiBUopDh06RPv27XF0dGTixIm0bt2aLl26PHbPRYsWYW9vz8OHDwHjj+u5c+dSqVIlbG1tKVeuHAsWLEgx7sGDB9O1a1fAaEmK323n5eVFr169KFiwII6OjnTs2JGTJ08mqF+mTBlGjhzJxx9/TOnSpbG3t8fX1zfFeyZnxYoV1KpVCzs7O0qUKMG0adMSPD3m7+/P66+/TokSJbCzs6NkyZL0jZ6aZ8WKFbz88ssAuLu7o5RK8LVNLCgoiHXr1j3WcnPo0CG6detG8eLFcXR0pE6dOnz//fcJztm3bx9KKTZv3kyvXr1wdnamd+/esTEOHz4cDw8PbG1tqV+/Pjt2JFyqd/PmzbRv354iRYrg7OxM48aN2bZtW4a+ZukVFhbG3r17Y+ON0bdvX86ePcu1a9eSrXv8+HFq165N0aJFY8saNGiAm5sbv/32W2xZTItWakqXLk2jRo2euJvYHKS7MBEbC1naJSVaa5RSlC/vSpUqhVmwoCMNGkgXq8j5AgMD8fX1pWzZsrFlf/zxB1rr2OQhsZjyP/74g4EDB7J//3601jzzzDMZiuHixYs0adKEkiVL8tlnn1GsWDFOnTqVIPFLj/79+zNkyBDeeustHBwcOHHiBKNGjcLX1xdXV9fY81auXEmnTp0oWLAgAGPGjGHp0qVMmzaNxo0bc/DgQSZPnoy9vT1Dhw5N8l7Tp0+nWrVqTJ48mV9++QUPDw/Kly9PQEAArVu3xsLCgiVLlmBnZ8f7779Py5Yt+e+//yhZsmTsNdatW0fFihX59NNPsbS0xNHRMd2fef78+UyaNIlx48Yxb948zp49G5tkffTRRwCMHz+erVu38tFHH1GmTBlu377N1q1bAejcuTNvv/027733Htu2baNgwYLY2tome79Dhw4RFBREs2bNEpR7eXnRrFkzhg4dip2dHQcOHODVV1/FZDLx0ksvJTh3yJAhDBgwgPXr12NpaUl4eDjt27fn7t27vP/++5QoUYIffviBzp07c+zYMWrWrAnA1atX6dq1KxMmTMDCwoKtW7fSqVMn9uzZQ+vWrZONWWudpikLLC0tk/3D+PLly0RERDz2x0fV6LV9z507l2xyGhoamuTX1NbWNtlxjqlp2rQpO3fuzFBdc5IkKxELJY17SQkJieD99/fj7f2IFSu6U66cC/v3v2zusER2GDYMErVKZKuaNWHx4gxVjYyMBIwxWZMmTcLJySnBeKmbN28CUKpUqSTrOzs7U6hQIby9vdN0fmpmzpyJjY0NBw4cwDl6Pr527TI+vcnQoUOZPHly7PsKFSowatQo1q1bx+uvvw4YycChQ4dYs2YNYPzyXLRoEUuWLGHIkCGxMQQHBzNr1iyGDBmSZAtD+fLlqVTJWAKrbt26sb9gP/vsM7y8vDh9+nTsL+BWrVpRqlQpFi5cyLx582KvERERwdatWzOUXIExCHrGjBlMmjSJDz74AID27dtjY2PD+PHjmThxIm5ubhw+fJj+/fsnSHZiWrLc3d1jx3TVr18/1XFlR44coUCBApQrVy5Bed94k1ZrrWnZsiXe3t589dVXjyVZ3bp1Y86cObHvly9fzokTJ/j333+pVq0aAB07duTixYu8++67sd+rkSNHxtYxmUy0adOG06dP87///S/FJOv333+nTZs2KX4ugL179yZ7HT8/PwAKFSqUoNzFxQUgxVbIihUrsnz5ckJCQrCPXgv4+vXr3L59mwIFCqQaV1Jq167Np59+SkBAAE5OThm6hjlIkpWIo6UsDp3Ytm2XGDFiC1eu+NG7dzUiIqKwtpanMEXOFhQUhLV1XMu0paUlGzdupHLlyk987Yx2i+/evTu22ygzdO7cOcF7Nzc32rdvz6pVq2KTrNWrV1OgQIHYbsRdu4yJgZ9//vnYJBSMRGvOnDncuHGD0qVLpzmG/fv3U6NGjdgEC4xu0/bt2/Pnn38mOLd169YZTrAADh48SGBgIL17934s9pCQEE6dOkWrVq2oV68eK1aswMPDg2eeeSbJrt20un37dpKJmJ+fHzNmzGDjxo3cvHkztuUoqRUTEn+fduzYQc2aNalUqVKCz9G+fXt++OGH2Pfe3t5MmzaNXbt2cfv27Zjxw9SvXz/FmOvXr8+RI0dS/WyZ8X8hKa+//jqffvopb7zxBh999BHBwcGxyXtG/+8ULlwYrTV3796VJCs3U2nsI84Pbt8OYOzY7axZc5qyZQuxZUt/nn22ornDEtktg61I5mZvb88ff/yByWTi4sWLTJkyhUGDBnHq1Ck8PDwAY5wWGH9l165d+7FrBAQE4O/vj6en52Pnx7TqpMeDBw8oXjzzutfjj3mJ0a9fP1566SXu3LlDsWLFWLlyJT169MDOzhhv6uPjg9Y62Rac9CZZfn5+ScZRtGhRTp06lWq86REzBUC9evWSPH7jxg0APv/8c1xdXZk3bx4TJ06kZMmSTJ06lWHDhqX7nsl1fQ0ePJiDBw/yzjvvUL16dZydnVm8eDGrV69+7NzEn9vHx4fjx48n+CMgRsyqCyaTiW7duvHw4UNmz55NhQoVcHR05J133km1e7lAgQLUqVMn1c+W0goPMS1WMeP4YsS0cMXvjk6scuXKLFu2jDFjxsSOU+vZsyedOnUiICAg1biSEvM9CAkJyVB9c5EkKxELeRYglq9vCJs2XeCtt5ozbVpLHBxkvJrIPSwsLGjQoAEAjRo1onLlyjRu3JjZs2ezODpxbNmyZezA5KTGZW3atCn2vPjnb9++PUPdfG5ubty6dSvZ4zGJUHh4eILymF9siSXVKvDcc89ha2vLmjVr6NixIydOnODDDz+MPe7q6opSij///BObJCZeTm/rhqurK+fPn3+s/O7du4/9In7SB2NirvfLL78kGOsVI2a8XcGCBVm4cCELFy7k5MmTfPrppwwfPpwaNWrQokWLdN/T398/QVloaCibNm1i/vz5jBo1KrY8qSkI4PHP7erqSq1atVi2bFmy97106RLHjx9nw4YNPPfcc7HlaUkyMqO7sHz58lhbW3Pu3Dk6duwYW37u3DmAx8ZqJTZo0CD69u3LhQsXcHFxoUSJElSvXp1u3bqlGldSYr4HuW1tVUmyErGwyN9Px/3zzy327LnKxInNqF69CDdujMPVVbpQRe7XoEED+vXrx/Lly5kxYwbFihWjdOnSdO/enW+//Zbx48cnaJ0KDg7m/fffx9PTM/YJq1KlStGrVy8WL17Myy+/HDueJoa/vz9nz56lSZMmScbQrl071q5dy5w5c5Ls8ihSpAjW1tYJBgeHh4ena34gJycnunTpwsqVK/H19cXd3T1BQti2bVvAaFVLbsB/ejRv3py1a9dy/vz52ATNz8+PXbt2xY75yixNmjTBwcEBb29vevTokaY6NWvWZMGCBSxbtoyzZ8/SokWL2OQyLRPHVq5cmfv37xMUFBTb1RkWFobJZEqQpAYEBPDrr7+mKaZ27dqxZcsWihcvnmzLZkwyFf8eXl5eHDhwINVW1MzoLrS1taVNmzasXbs2wTjG1atXU7Vq1RSfyIxhY2MT21W7Z88eLly4wODBg1Otl5Rr165RsGBBihUrlqH65iJJViL5tSXr0aMw3n57D198cQQ3N3tee60eLi72kmCJPGX69OmsWrWKhQsXxj6J9uWXX9KyZUtatGjBW2+9Rd26dbl58yZz587l2rVrbNmyJbaFKeb81q1b06xZM8aNGxf71Nnff//N559/zpQpU5JNsmbMmMGmTZto3rw5kyZNwsPDgzNnzhAcHMykSZOwsLCgZ8+eLFq0iAoVKlC4cGEWLVoU+1RvWvXr14+ePXvi5eVF7969sbKK+1FfqVIlRowYwcCBA5k4cSKNGzcmIiKCCxcusHfvXjZs2JCur+nLL7/MggUL6Ny5M++9917s04VWVlaMHTs2XddKTaFChZg9ezaTJk3C29ub1q1bY2lpyZUrV9i4cSPr1q3DwcGBZs2a0aNHD2rUqIGlpSXfffcdNjY2sa1YMePHvvjiC7p3746Dg0PsE32JNWvWDJPJxPHjx2Mnni1YsCANGzbko48+wt3dHSsrKz766CMKFizIvXv3Uv0cgwYN4quvvqJ169ZMmDCBSpUq4e/vz/HjxwkPD+fDDz+kSpUqeHp6MmXKFKKioggMDGTGjBmxXdYpcXJyim3FfRLTp0+ndevWDB8+nBdeeIG9e/fy008/PdYlamVlxUsvvRTbMhcUFMTMmTNp2bIldnZ2/PXXX3z44YfMnDkzQWIXHBwcuwqDl5cXjx49ip2+pFWrVri7u8eee/ToUZo2bZrmaR9yjMyeeCu3voiejPTwz8uTnbAsLzKZTHrNmlPaw2Ouhpn61Vc3ah+fIHOHJcwgr09GGuPFF1/Uzs7O2t/fP7bM19dXT5gwQZctW1ZbW1trd3d33adPH33mzJkkr/Ho0SM9c+ZMXa1aNW1nZ6cdHBx0w4YN9YIFCxJMXJqU06dP627dumlnZ2ft4OCg69Spo1etWhV7/N69e7p79+7a2dlZlyhRQi9cuFCPGTNGly5dOvac1CbTDA0N1QULFtSA3r9//2PHTSaT/vzzz3WNGjW0jY2NdnV11U2aNNHz589PMfb169dr4LF/J9euXdM9e/bUTk5O2sHBQbdv317/999/Cc4pXbq0HjFiRIrXTwpJTEa6cuVK3bBhQ21vb6+dnZ113bp19fTp03VERITWWuuJEyfqmjVr6gIFCmhnZ2fdrFkzvX379gTXmDlzpvb09NQWFhYJvrZJqVmzpn7rrbcSlF28eFE//fTT2sHBQZcsWVJ/8sknj/2727t3rwb0kSNHHrvmw4cP9bhx43SpUqW0tbW19vDw0J06ddKbNm2KPefw4cO6YcOG2s7OTlesWFF/++23KU7amRU2btyoa9asqW1sbHSFChX0smXLHjsH0C+99FLs++DgYN2xY0ft5uambW1tde3atfXy5csfq3f16tWYSUAfe+3duzf2vPDwcO3q6prkvdPCnJORKh39tEJ+p5TyBG4cXfsd9Z8faO5wss358z5UrfoF1aq5s2RJF5o3z9ij6SL3i5lcMC3dAELkJ59//jmffvopFy9elAmXzWDz5s3079+fmzdvZmgKiNR+tnl7e8eM8SuptfbOcKBJyGXtblkvPwzJCg+PYtcuYw2uypULs337AI4de0MSLCGESMJrr71GSEhIgtnKRfaZN28eb775Zobn2DInSbISsVB5e/6nP/7wok6dJXTs+AMXLz4AoH378tjY5O3PLYQQGWVvb8+KFSsee+pTZL3AwEBatWrFuHHjzB1KhsjA90Qs8mhTsI9PMBMn7mTFihOUKOHEzz/3pkKF5Oc5EUIIEad9+/bmDiFfKlCgADNmzDB3GBkmSVYiKg+2ZD14EEyVKovw8wtl7NjGzJ7dBien5NfqEkIIIcSTkyQrkbw0T5avbwiurva4uTkwYUJTOnQoT716HuYOSwghhMgXZExWIlZ5IO8MDo5g6tRdlCq1gPPnjWUopkxpLgmWEEIIkY1yf0aRyawtc/eXZMuWi4wYsYVr1/zp27cGBQvapV5JCCGEEJkud2cUWcBC5c7GvfDwKPr3X8e6dWcpX96F7dsH0KFDeXOHJYQQQuRbkmQlonJpkmVjY4mtrRXTp7dk6tTm2NvLYs5CCCGEOeXOjCIL5aYk68iRm7RsuZxr1/wB+OGHHsye3UYSLCGEECIHyD0ZRTbJDUsmPHwYysiRW2jceCnnzvlw5YofkDtiFyI7zJw5E6VU7MvNzY3mzZvHLkabmJ+fHxMnTqR8+fLY2tpStGhR+vXrx9mzZ5M8PzAwkFmzZlGjRg0cHBxwdHSkUaNGzJ8/n9DQ0Kz8aGa3YMECSpUqhaWlJd27d8/068f/viX3WrFiRYav37p1a7p06ZJp8Z48eRInJyfu37+fadfMDR4+fMirr76Kq6srTk5O9OrVi9u3b6daT2vNxx9/TNmyZbG1taVGjRqPLTh9+/ZtJk2aRJ06dXBycsLT05P+/fvj5eWV4Lz3338/x89fJt2FieXgREVrzZo1pxk7djt37gTy+uv1+Oijdri62ps7NCFyHHt7e/bs2QPArVu3+OCDD+jatSv79++nadOmsefduXOHli1b4ufnx7Rp06hbty7e3t7MnTuXhg0bsmXLFlq2bBl7vo+PD23atOHGjRuMHTuW5s2bA3Do0CE++ugjLC0tGTNmTPZ+2Gxy8eJF3nzzTSZPnkzXrl0pXLhwpt/j0KFDCd43adKEUaNG0b9//9iy8uUzPt70yy+/xNIy8+ZDfPvttxk8eDDu7u6Zds3coE+fPpw+fZolS5ZgZ2fHtGnTePbZZzl69ChWVsmnFp988gnTpk3j7bffpkmTJvz666/069cPBwcHunbtCsA///zDL7/8wiuvvMJTTz2Fj48P7777Lo0aNeLUqVOxX+sRI0bw8ccfs3fvXtq0aZMtnzvdMnvF6dz6AjwBfWPHjmRX6jY3k8mkn3nmB12jxpf6wIHr5g5H5DGprVSfm8yYMUM7OjomKPP29tZKKT1kyJAE5T169NC2trb67NmzCcoDAwN11apVdYkSJXRISEhsee/evbWDg4M+efLkY/d98OCBPnDgQCZ+krQLDg7O8nv89ttvGtCXL19+4muFhobqqKioVM8D9CeffJLiOdnx2ZNy+fJlrZTSx44de+JrRUZG6vDw8EyIKusdPHhQA3r79u2xZefOndNKKb169epk64WFhWknJyc9fvz4BOVdunTRtWrVin3v5+enIyIiEpxz48YNrZTSc+fOTVD+8ssv6+eeey7FeFP72Xbjxg0NaMBTZ3JuId2FOVxYWCQffrif27cDUErx/fc9OHZsCE2bljR3aELkKiVKlMDd3Z3r16/Hlnl5ebFhwwYGDRpElSpVEpzv6OjItGnTuHnzJj///HPs+WvXrmXo0KHUqFHjsXu4uromaCVLytmzZ+nZsyeurq44ODhQu3ZtVq5cCcC1a9dQSrF27doEdcaOHUuZMmVi369YsQKlFIcOHaJ9+/Y4OjoyceLEZLvCFi1ahL29PQ8fPgSMP67nzp1LpUqVsLW1pVy5cixYsCDFuAcPHhzb0lC+fPkE3XZeXl706tWLggUL4ujoSMeOHTl58mSC+mXKlGHkyJF8/PHHlC5dGnt7e3x9fVO8Z1JmzpxJgQIFOHz4ME2aNMHOzo4vvvgCgClTplCzZk0KFChAiRIl6Nev32NdWIm/RjHXO3nyJM2bN8fBwYEaNWqwffv2VGP57rvvKFeuHHXr1k1Qnp44vv32WypXroytrS3//vsvAJs3b6Zx48bY29vj7u7OsGHDCAoKiq0bFBTEyJEjqVy5Mg4ODpQpU4ahQ4fGfn+z2tatWylUqFCCrrrKlStTp06dZLvkAS5fvkxAQAAdOnRIUN6xY0f++++/2P+bhQoVeqw1zNPTE3d3d27dupWgvHfv3mzevBkfH58n/VhZQroLE8tB3YV7915l2LDNnD//ABsbS958symFCzuYOywhcqXAwEB8fX0pW7ZsbNkff/yB1jo2eUgspvyPP/5g4MCB7N+/H601zzzzTIZiuHjxIk2aNKFkyZJ89tlnFCtWjFOnTiVI/NKjf//+DBkyhLfeegsHBwdOnDjBqFGj8PX1xdU1bm3SlStX0qlTJwoWLAjAmDFjWLp0KdOmTaNx48YcPHiQyZMnY29vz9ChQ5O81/Tp06lWrRqTJ0/ml19+wcPDg/LlyxMQEEDr1q2xsLCI7Tp6//33admyJf/99x8lS8b9Qbhu3ToqVqzIp59+iqWlJY6Ojhn63OHh4fTv359x48bxwQcf4ObmBsC9e/d46623KF68OPfv32fevHm0atWKM2fOpNiFFRERwYsvvsjo0aOZPn06c+bM4fnnn8fLyyv22knZtWtXkkl1WuM4evQo165dY/bs2bi4uFCyZEnWrl1Lnz59ePnll5k1axa3b99mypQp+Pn5sWrVKgCCg4OJiori/fffx93dnRs3bvD+++/TvXt39u7dm+LXLioqKqb3JllKqRS7VM+dO0flypUfGwdctWpVzp07l2y9mPGKtrYJl3WLeX/27FlKlSqVZN0LFy5w7949qlatmqC8SZMmREVFsW/fPnr16pX8hzITSbJyoHv3gpgwYQfff/8fnp7OrF/fh+7dq6ReUYgsMGzTME7eO5n6iVmkZpGaLO6yOEN1IyMjAWNM1qRJk3ByckowXurmzZsAyf5gd3Z2plChQnh7e6fp/NTMnDkTGxsbDhw4gLOzMwDt2rXL0LUAhg4dyuTJk2PfV6hQgVGjRrFu3Tpef/11wGhlOnToEGvWrAGM1oRFixaxZMkShgwZEhtDcHAws2bNYsiQIVhYPN7JUb58eSpVqgRA3bp1Y1vWPvvsM7y8vDh9+nTsL8BWrVpRqlQpFi5cyLx582KvERERwdatWzOcXMW/zvvvv0+fPn0SlH/zzTex+1FRUTRp0gRPT0/27NnzWOtJfOHh4Xz00Ud06tQJMFplypYty9atWxkwYECSdbTWHD16NMnB/2mNw9fXlyNHjsQmolprJkyYQJ8+fVi6dGnseR4eHnTq1Inp06dTvXp13N3dWbw47v9EZGQkZcuWpXnz5ly4cCH2+5SUtm3b8vvvvyd7HIzv3759+5I97ufnR6FChR4rd3FxSbF1MqYF9PDhw7Ru3Tq2/K+//gJItq7WmtGjR1O8eHH69euX4FihQoUoVaoUf//9tyRZuUIOaMl6/fXf2Lz5AuPHP8WsWW0oUMDG3CEJkesEBQVhbR03nYmlpSUbN26kcuXKT3ztjD7Ju3v3bnr16hWbYD2pzp07J3jv5uZG+/btWbVqVWyStXr1agoUKBDbRbZr1y4Ann/++dgkFIxEa86cOdy4cYPSpUunOYb9+/dTo0aNBC0Mrq6utG/fnj///DPBua1bt37iBCtG4s8ORjfWu+++y+nTp3n06FFs+YULF1JMsiwsLBIku2XKlMHe3j42uU6Kn58fYWFhSQ54T2sctWrVStDSd+HCBby8vFi4cGGC702rVq2wsLDg6NGjVK9eHYDvv/+e+fPnc/HixQRdiaklWV999RUBAQHJHgdwcnJK8XhGOTs7M2DAAObMmUPNmjV56qmn+O2332K7y5P7fzVz5kx2797Ntm3bkvz3U7hw4TQ92WgOkmTlECdP3qVECWdcXe35+ON2zJrVmjp1ipk7LCEy3Ipkbvb29vzxxx+YTCYuXrzIlClTGDRoEKdOncLDw1jHs0SJEgBcv36d2rVrP3aNgIAA/P398fT0fOz8lH6RJefBgwcUL148ox/pMUWLFn2srF+/frz00kvcuXOHYsWKsXLlSnr06IGdnbHElo+PD1rrZJ8MTG+S5efnl2QcRYsW5dSpU6nGmxEODg4UKFAgQdmRI0fo1q0bzz33HFOmTKFIkSIopXjqqadSnVbD3t4eG5uEf8za2NikWC+5rq/0xJH46xEzrqhHjx5J3vPGjRsArF+/nkGDBjFkyBDef/993NzcuH37Nj169Ej1s1aoUCFN3YUpcXFxiY0lPj8/vwTd1ElZsGABd+7ciW01LFy4MO+++y4TJkyI/X8Z39dff83s2bNZtmwZbdu2TfKatra2hISEpHhfc5EkK7FsbskKCgpn9uzfmT//L4YOrc/nn3eicuXMfyxaiPzGwsKCBg0aANCoUSMqV65M48aNmT17dmxXS8uWLVFKsXnz5iTHZW3atCn2vPjnb9++PUPdfG5ubo8N3I0vJhEKDw9PUO7n55fk+Un9MnzuueewtbVlzZo1dOzYkRMnTvDhhx/GHnd1dUUpxZ9//vlYYgGku6XP1dWV8+fPP1Z+9+7dx37hZtZcfkldZ/369RQsWJA1a9bEdncmnlcpM8V8Nn9//wzHkfhzxFxz0aJFNG7c+LHzYxL0n3/+mTp16vDVV1/FHkutCzBGZnQXVqlShV27dqG1TvAZzp07R82aNVO8tpubGzt27ODWrVv4+vpSsWJFfv31V2xsbKhXr16Cc9evX8+wYcOYPXs2r7zySrLX9Pf3j23hy2kkyUosG5Os3347z8iRW7l+/SEvvliTt99umXolIUSGNGjQgH79+rF8+XJmzJhBsWLFKF26NN27d+fbb79l/PjxCVqngoODef/99/H09KR3796AMRarV69eLF68mJdffplq1aoluIe/vz9nz56lSZMmScbQrl071q5dy5w5c5LskilSpAjW1tYJJkENDw9P8y9QMLp6unTpwsqVK/H19cXd3T1BQhjTGvDgwYNkB/ynR/PmzVm7di3nz5+PTdD8/PzYtWtX7Jiv7BASEoK1tXWCX/o//vhjlt3Pzs6OUqVKcfXq1UyLo0qVKnh6enLlyhVGjBiR7HkhISGPJchpvUdmdBc+++yzvPvuu+zevTv239aFCxc4fvx4gjGCKSlevDjFixcnKiqKxYsX06dPnwT33bdvH/369eP1119n+vTpyV7HZDJx/fr1FJMwc5Iky0ymTdvNBx/8ScWKruzaNZC2bcuZOyQh8rzp06ezatUqFi5cyEcffQQYk1O2bNmSFi1a8NZbb1G3bl1u3rzJ3LlzuXbtGlu2bIltYYo5v3Xr1jRr1oxx48bRrFkzAP7++28+//xzpkyZkmySNWPGDDZt2kTz5s2ZNGkSHh4enDlzhuDgYCZNmoSFhQU9e/Zk0aJFVKhQgcKFC7No0aLHWgxS069fP3r27ImXlxe9e/dO8ERbpUqVGDFiBAMHDmTixIk0btyYiIgILly4wN69e9mwYUO6vqYvv/wyCxYsoHPnzrz33nuxTxdaWVkxduzYdF3rSbRv356FCxcyatQoevTowaFDh/j++++z9J7NmjXjn3/+ybQ4lFLMnz+f/v37ExQUROfOnXF0dMTLy4vNmzfzwQcfUKlSJdq3b8+IESN49913adKkCVu2bGH37t1pukdmjEls0qQJHTt25JVXXmHevHmxk5HWqlWLnj17xp43e/ZsZs+ezeXLl2O7oH/88UdCQkKoUKECt27d4quvvuLq1asJksSzZ8/SvXt3KlasyMCBA2MHxgO4u7snmIz2/PnzBAYG0qJFiyf+XFkisyfeyq0vYiYj3bs32QnLnlRERJQODjYmmzt69KaeOXOvDgmJSKWWENkjr09GGuPFF1/Uzs7O2t/fP7bM19dXT5gwQZctW1ZbW1trd3d33adPH33mzJkkr/Ho0SM9c+ZMXa1aNW1nZ6cdHBx0w4YN9YIFCxJMXJqU06dP627dumlnZ2ft4OCg69Spo1etWhV7/N69e7p79+7a2dlZlyhRQi9cuFCPGTNGly5dOvac5cuXa0Dfv38/yXuEhobqggULakDv37//seMmk0l//vnnukaNGtrGxka7urrqJk2a6Pnz56cY+/r16zXw2L+Ta9eu6Z49e2onJyft4OCg27dvr//7778E55QuXVqPGDEixesnhUSTkab0vZ0zZ4729PSMjeHChQuP1W/VqpXu3LlzqtcrWLCgnjFjRoqxrVu3TtvZ2elHjx49cRzx7dixQ7dq1Uo7OjpqR0dHXb16df3mm2/G/puNjIzUb775pnZ3d9dOTk66V69e+q+//tKA/vnnn1OMObP4+/vrV155RRcqVEgXKFBA9+zZU9+8eTPBOTNmzHjs38v333+vq1Spom1tbbWbm5seOHCgvnHjRoJ6Mf++k3q99NJLCc6dN2+eLl26tDaZTMnGas7JSJVOZQBcfqGU8gRu3Ni7F894j5Zmlr//9uaNNzbRpk0ZFizI2Bw7QmSla9euASSY9FIIkbyIiAhKlSrFnDlzGDRokLnDyZcaNmxI165deeedd5I9J7Wfbd7e3jFPeZbUWif/SGkGyIzviWXymCw/vxCGDdtEkybLuH07kIYNS2Tq9YUQQpiHtbU1U6ZM4dNPPzV3KPnSH3/8weXLlxk9erS5Q0mWjMnKQps3X+CVV37l/v0g3nijPh980BYXF1nMWQgh8oqhQ4fy6NEjfHx8smTBbJG8R48e8d133yU5MWpOIUlWYpnYkuXkZEvx4k5s3NiXp57yzLTrCiGEyBlsbW1TfPpNZJ2k1unMaSTJSuwJkqzQ0EjmzPkTS0sL3n67JS1bluaff4ZgYWH+WeSFEEIIkb0kycoku3ZdYfjwzVy86MsLL1SPfeRaEiwhhBAif5KB70/o7t1ABgz4hfbtvycsLIqNG/uyenWvTJvZWIjsYmlpSVRUlLnDEEKITBUVFYWlpaVZ7i1J1hM6fvwOq1adYuLEppw5M5xu3Z58ojchzMHOzo7w8HAePHhg7lCEECJTPHjwgPDw8AQTCmcn6S5MLA0tUP/+e4fTp+/Tv39NnnmmApcvj6Z06UJZH9v/27vzaDnKMo/j3x+EJIDZSMIWDKCBBHQQjM6wqQkCCowzgBM8ArK4DSoOW2QTZVMZhmE5LsgBBggZQRxEHBkZwxbMGFE8A2iQKI4SkpCQoFmAJDchPPPH+zYpOn3v7dtLurn9+5xT53a/XW/VU/WevvX0W29VmTXRqFGj6OrqYsmSJSxfvrxlv/zMzBph/fr1rF27liFDhrTsyk/3ZJXrIcl66aW1TJ06g4kTr+fss++jq+sVACdY1i9IYsyYMYwaNarig4PNzN5IBg4cyKhRoxgzZkzLhvC0TU+WpAnAN4D9gReBW4ELImJtL/UEnAN8FhgNPA6cERGP9FSvr+6+ey6f//y9LFiwkhNOeAdXXHEIgwa1ze4zawhJjB49utVhmJn1C22RJUgaATwIPA0cDYwBrgK2Ak7tpfo5wMXAucCvgc8BMyTtHRF/rCGYjYpmzZrHUUfdwfjxI3nwwROYPHnXPi/WzMzMOktbJFnAKcBQ4KiI+AuApAHAtZK+FhHPVaokaTBwHnBlRFydy2YBvwemknq3+maLLQBYt249c+YsYZ99duDAA8cyffpRTJmyp3uvzMzMrCrtMibrMOD+UoKVfY8U36E91NuflJx9r1SQTy/eBRxeUyQjRjB79nwmTryeSZOm8cILq5DE8cfv5QTLzMzMqtYuSdYEYG6xICKWA4vyZz3Vo7wu8BQwVlKfHxR49pdnccABN7F06Squv/5vGTnSzxo0MzOzvmuXrpkRwPIK5cuAbXqp1xURayrUU/58daWKkoaSesFKxgDcfvtsTjzxPXzhC/szbNhgFi5cWN0WmJmZ2RvOokWLSi8bft+adkmyWuFM4MKNi29k2rQbmTZtk8djZmZmrbMLMK+RC2yXJGsZMKxC+QjgLxXKi/UGSRpc1ps1Aoj8eXeuAm4svB8L/AzYF3D3VfvYHngUeDewuMWx2Ou5bdqT26U9uV3a1xjgEWB+oxfcLknWXMrGXkkaBuzAxuOtyusBjAeeKJRPAJ6NiIqnCgEiYiWwsrC+0suFEbGg6sitqQrtstjt0l7cNu3J7dKe3C7tq9A2Pd6XsxbtMvD9XuBgScMLZVOAV4EZPdSbTUqUppQKJG1ButfWjxsfppmZmVl12iXJuo50l/e7JR0q6WTgCuC64j2yJD0g6Q+l9/kU4WXAVEmnSToIuB0YCfzrJt0CMzMzs4K2OF0YEcskvZ/0WJ27SQnXjcAXy2bdnI1jvpx0JeFUNjxW5wM13O19JenO8St7m9E2KbdL+3LbtCe3S3tyu7SvprWNIqLRyzQzMzPreO1yutDMzMysX3GSZWZmZtYETrLMzMzMmsBJlpmZmVkTdESSJWmCpPskvSxpsaR/kTSwinqSdK6kZyWtlvRzSftuipg7QS3tImmHPN/jkl6UtEDSbZJ23lRxd4JavzNlyzhdUki6p1lxdpp62kXSGEnTJC3N/8+eknRcs2PuBHUcY0ZKui4fY16WNEfSKZsi5k4gaVzev49LekXSnCrrNezY3xa3cGgmSSOAB4GnSTcpHUN6pM5WwKm9VD+HdFnnucCvgc8BMyTtXcMtIqygjnaZmOe/ifQYhFHAl4BfSnp7RCxtZtydoM7vTGkZ25OeDbqkSWF2nHraRdIOwM+B3wGfJl2q/jZgUBND7gh1fl/+g/SEkvOBZ4HDgW9LWh8RNzQt6M7xNuAI4BekTqVqO5Yad+yPiH49AecBLwHbFMo+DbwC7NhDvcHACuBrhbKBwDPAta3erjf6VEe7DAcGlJXtRHo6wFmt3q7+MNXaNmXLuBWYBswE7mn1NvWHqZ52AaaTns26eau3o79Ndfwv2570jN2TysofBh5o9Xb1hwnYrPD6FmBOFXUaeuzvhNOFhwH3R0TxQdPfI2W0h/ZQb39gaJ4XgIhYC9xF+rVh9ampXSJieUS8Ula2AFgK7NiMQDtQrd8ZACQdCBxJ+hVojVNTu0gaChxDOkCsb26IHanW78sW+e+KsvIVpBtsW50i4tUaqjX02N8JSdYEyh4yHRHLgUWUPZS6Qj3K6wJPAWMlbdmoADtUre2yEUm7A9uS2sbqV3PbSNoc+Cbw1YhY1KwAO1St7fJO0i/xdZIelrQujxu6PD/r1epTU7tExHzSs3nPl7SnpCGSjiElZt9qXrjWi4Ye+zshyRoBLK9QvgzYppd6XZGej1heT/lzq12t7fI6So9P/zrwHOm5lVa/etrms8DWwNUNjslqb5ft898bgV+RDuJXA6cDlzQuvI5Vz/flaOB54EnSOLnbgDMi4vuNDND6pKHH/n4/8N36vYuA9wMfjIiXWxxLR5O0LemgfULuXrf2UPoxfX9EnJVfPyRpCDBV0iURsbpFsXWs/APxZmA34FhSz9chwDWSlkXEd1sZnzVGJyRZy4BhFcpHAH+pUF6sN0jS4LKMdgRpsOKyxoXYkWptl9dI+hTwZeATEfFAA2PrdLW2zSWkK3FmSRqeywYAA/L7l8rH01mf1PO/DNIVcEUPAF8ExgG/qTu6zlVruxwBTAH2iojS/p+Zf6xcCTjJao2GHvs74XThXMrOi0saBuzAxudcy+sBjC8rnwA8619+dau1XUrzHgV8G/hyRNzUlAg7V61tMwF4L+mfUGk6APhAfn1wM4LtILW2y297We7gOuPqdLW2y57AeqD83k2PATtK2qqRQVrVGnrs74Qk617g4MIva0i/Hl4lDTrszmzSOfIppYI8SPRo4MeND7Pj1NouSJpEGn91Q0Rc2qT4OlmtbXM6MLlseoJ0P7PJwC+bEGsnqaldImIeqaeqPMk9BFhN70mY9azW78s8YHNgr7LyicCSiFjVyCCtao099rf6Phab4D4ZI0iDomeSBnyeTPpV/c2y+R4A/lBWdi6wBjgNOAi4M+/8t7R6u97oU63tAuxBGmT6G9KltvsWpre2erv6w1TPd6bCsmbi+2S1vF2AD5EO+teQkqvzgbXAV1q9XW/0qY7/ZUNIidbTwPGksaWXk3q3Lmj1dvWHiXRD2H/I00OkG76W3o+u1C65rGHH/pbvhE20o/cA7gdWka7kuAIYWDbPTOCZsjKRbjQ3P+/wR4D9Wr09/WWqpV2Ak0jnxStNt7R6m/rLVOt3psJynGS1SbsAHyGdmuoi3VjxPECt3qb+MNVxjBkH3AEsBF7O7XMavmlso9pllx6OF5N6aJeGHfuVF2hmZmZmDdQJY7LMzMzMNjknWWZmZmZN4CTLzMzMrAmcZJmZmZk1gZMsMzMzsyZwkmVmZmbWBE6yzMzMzJrASZaZmZlZEzjJMusHJN0iKXqYdunj8p7J9WY2J+Ju11sp9hWSHpJ0eBPX+9r+K5QNl3RRniaVzb9LIb6LmhVXN7FOqrCP1uY2u07StnUs+/S8vSc1MGSzjjWg1QGYmfViKDAJmCTpuIi4bROtdzhwYeH9zE203lpsAewM/COwn6R3RsT6GpZzel7Ow8AtDYvOrEO5J8us/5kcESqbnml1UH00LyIEDAamFsova8bKIuKk0r6qcv5nCvv2ombEVKVpOebdgT/lsr1ID083sxZzkmXWIfLpr+9IekrScknrJC2WdKekt1VRf6Kke3Kdrvz3IUmfLJvv/ZJ+ImlZnu93ki6QtEVfY46ILuAqYEUuGitpdF7PZpJOlfSYpFWSXpb0y/JTXZLGSfqupIU5nqWSZks6rzDP604X5lOAfyos5sLCqblJlU4XSnoyv//fsvWfWJj3sFwmSadI+lWOe5WkRyQd09d9lPfT08APCkVvLqz/g7k9FkhaLWmNpLmSLpW0ZZ5nUt72nXO191U6HSppiqRZklbm5Twh6TOSqkpOzTqNTxeadY7hwLFlZdsBHwYmS9ojIpZUqihpa+AnwMiyutsBLwM35vlOAm4iPcW+ZHfgUmBfSR+K2p5KX+kgPg04vqzs3cDNkvaMiLNz2Y+ACYV5RuVpKI3tGZuel7ePpHER8YdcXkqcFgMz8uubgJPK6v8NcIeknSPiihrWX9xHxXbcFzi0bN7xwAXArmy8DysvXLoQuKiseC/gWuDtwOf6EKtZR3BPlln/81DZoOjHc/kyUkL1ZtJpuDcBn8qfbcPGCVjRBDYkWB8GBgI7AX9PSmKQ9CbgGtLB/t68nq2A83O9I4A+DV6XNAg4i5QQAcyPiKWS3suG5ODnOZbdgLm5bKqk8ZJGsiHBOhMYBGxPSjpu7W69+RTgroWiiwunB2d2U+07wKv59ZQc/3Dg4Fx2e0Ssl3QgGxKsrwLDSElfqSfqkhx31STtBhyV3z4P/E/h4/8inT4cTRq7tQPw4/zZsZK2iYiZ+bTjvFz+cPF0qNKFE1/Kn90MbEtqk2/lss9KentfYjbrBO7JMusQEbEiHywvIPUubV02y/geqi8E1gObk3osxgFPAj+LiD/nefYnJQwAhwHzKyznINJBvzc7q3ClX0HpQH9YoeyrEbEQQNKVwA2kRO9QUi/LSlJCcCxpm58EHomI+6qIo2oRMV/pasyDSL1XlwFHkhJSSD1d8PpE84t5KhpM2pc/qmK1J0o6sfB+LvCxiFhTKFsIfIWU7G1PSrRKREpOf9HLeg4ltT3AyXkqNxmYU0XMZh3DPVlm/U/5wPe9ASSdAVwJ7MPGCRbAlt0tMCIWA/9EGht1EHA5cA+wOJ9GgtRT0pttqt6KDV4EfgocGRHTctmowufzu3k9Ol9hdzKpd+ddpNOWdwELJd1QQyy9KSVSe0saR+7RAuZExGOluKpYTi37CVLP4Ws/niVtRmqnk0k9i5XGxXXb7gXNjNms33KSZdY5Sgf8NaTxPwOAv6q2ckRcSzrY/jVwHKlHagBpUPhOwNLC7OdVuMJRwMerXN28Qr2hEfG+iPhh4fMXCq93Krx+c/k8EXEXsCOwN6mHaTqpB+eTkg7oaZOrjLXo+8Cq/PrTwCH59fTCPMX9tF+FfbRZIZnszTRS4jQFeAUYC/xAUun06jhSUg1wP7BdXseV3Syvu20uxvzRbmK+uMqYzTqGkyyzzjEo/w1S79BwNh7IXJGk7ST9M/BO4I+kZGJ26WNSz9JsNlwFeKakyZIGSdpW0jGSfsqGq9fq9d+F1+dLGiPpLaRxV5C2cUaO/RvAe4BFwA/ZMPgceu6hWVZ4PaGaqyMj4kXg7vz2DFIC9CppvFbJvYXXV0naQ9JASW+R9HlSMlS1iHglIu4knRqFdErwC/n1oMKsXcBqSe8CPtbN4krbPFbSsEL5DNLpYoCLJb0rx7yTpI8Dj2FmG3GSZdY5SmN8tgR+S+rp2bvKulsC5wCP5HprSIO2IZ2i+21EvERKLIKUvDyY53seuIOU6DRERDwM3J7fHgAsAP4P2COXXR0RpUHwp5JuJPo8KdEo9SqtyNvT3TpWAr/Pbz8CrM0XEvQ2lrW0/NJ8D5bGjOXlzmLDjT73I7VFV47/68Bbe1l+d75GutIT4LQ8eH4uULrK8QjS+LRHSUl2JY/mv7sCy/P2Hpzvs3Zp/mz3PF8Xqe3/DXhHjTGb9WtOssw6x2Wkq/+WAC8BdwIfrbLun0kJwGOk3o51pAHVtwEHR8RagIi4mXSK7Cd5vrXAs6Sr2T4FPNeYTQHS1YWnA0+QkrnVwK+AT0TEWYX5LicN7H4hx70Y+M8c9+Je1nEiKaFY3Ye47svrKJleYZ6PA6fkZa/K09N53s/0YV2viYjnSW0EMAQ4JyLWAX8HPERKwOaTrtb8924WcyFp3yyvsPyLSadbZ5GStDVs6NU8rpaYzfo71XbLGjMzMzPriXuyzMzMzJrASZaZmZlZEzjJMjMzM2sCJ1lmZmZmTeAky8zMzKwJnGSZmZmZNYGTLDMzM7MmcJJlZmZm1gROsszMzMyawEmWmZmZWRM4yTIzMzNrAidZZmZmZk3gJMvMzMysCf4fc2WskZ7rrxwAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "y_pred = model_2.predict(X_test).round()\n", - "print(\"AUC-ROC score sobre test: \", \"%0.16f\" % roc_auc_score(y_test, model_2.predict(X_test)))\n", - "print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, model_2.predict(X_train)))\n", - "print(\"Accuracy sobre test: \", \"%0.16f\" % accuracy_score(y_pred, y_test))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves_red(model_2, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "491241d7", - "metadata": {}, - "source": [ - "Observamos que practicamente obtuvimos los mismo resultados que en el entramiento anterior por lo que pararemos aquí" - ] - }, - { - "cell_type": "markdown", - "id": "bac3073f", - "metadata": {}, - "source": [ - "## Holdouts" - ] - }, - { - "cell_type": "markdown", - "id": "321a2543", - "metadata": {}, - "source": [ - "### Primer modelo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "febd5173", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "f217ebef", - "metadata": {}, - "source": [ - "### Segundo modelo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3bb1078d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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" - }, - "toc-autonumbering": true - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/parte_2/#5 - Red neuronal.ipynb b/parte_2/#5 - Red neuronal.ipynb index 96fc5f1..2b662e8 100644 --- a/parte_2/#5 - Red neuronal.ipynb +++ b/parte_2/#5 - Red neuronal.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "203a0354", + "id": "cb2e3c8d", "metadata": {}, "source": [ "# Modelo: Red Neuronal" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "e379b729", + "id": "a0a1e585", "metadata": {}, "source": [ "El modelo a entrenar en el siguiente notebook se tratará de una red neuronal. Por el tipo de modelo del que se trata, iremos mostrando la serie de pasos y decisiones hasta llegar a los hiperparámetros y estructura de la red final, en lugar de realizar dicha busqueda con GridSearch o técnicas similares. Por una cuestión de no extenderse demasiado, buscaremos sintetizar el camino pero intentando mostrar los problemas que surgieron" @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "8aaae8a2", + "id": "ceae10f3", "metadata": {}, "source": [ "## Librerias y funciones necesarias" @@ -26,7 +26,7 @@ }, { "cell_type": "markdown", - "id": "ee47ef01", + "id": "5949ca39", "metadata": {}, "source": [ "Para comenzar importamos las librerias que utilizaremos. En este caso para la construcción de la red utilizaremos la libreria Keras y para evaluar las metricas utilizaremos Sklearn. Luego importamos las funciones necesarias para los preprocesamientos" @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "9136bbd5", + "id": "2042a90c", "metadata": {}, "outputs": [ { @@ -76,7 +76,7 @@ }, { "cell_type": "markdown", - "id": "615e4ab3", + "id": "b5965668", "metadata": {}, "source": [ "Para lograr tener la misma salida realizamos lo siguiente. Es válido aclarar que esto genera la misma salida en la misma cpu, al cambiar, en nuestra experiencia genero salidas parecidas pero podria no suceder. La idea de esto es poder reproducir siempre los mismos resultados si se corre de nuevo el notebook. En ocasiones observamos bastante diferencia según se elija una seed u otra. También lo ejecutaremos previo a cada entrenamiento ya que en algunas ocasiones encontramos error al entrenar si no lo haciamos " @@ -85,7 +85,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "6cb39fa1", + "id": "60128447", "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "markdown", - "id": "98b94de8", + "id": "15e9795c", "metadata": {}, "source": [ "## Primer preprocesamiento" @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "c4278873", + "id": "316861c8", "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" @@ -114,7 +114,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "97a0dc88", + "id": "db946612", "metadata": {}, "outputs": [ { @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "a4b5f113", + "id": "bd46dcb6", "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" @@ -142,7 +142,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "96c1c62c", + "id": "834819e1", "metadata": {}, "outputs": [], "source": [ @@ -151,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "d6f7abc4", + "id": "3082a00a", "metadata": {}, "source": [ "### Primer diseño de la red" @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "880a3b7c", + "id": "67c88483", "metadata": {}, "source": [ "#### Diseño y entrenamiento" @@ -167,7 +167,7 @@ }, { "cell_type": "markdown", - "id": "2d0d4646", + "id": "8c440ffe", "metadata": {}, "source": [ "Comenzaremos con una red simple de una capa de 4 neuronas con función de activación Tanh y una ultima capa de una neurona con función de activación Sigmoidea. Esta última capa se repetirá en todas nuestras redes a construir. También se repetirá nuestra función de perdida (binary_crossentropy) y las métricas para evaluar que serán AUC y accuracy. Comenzaremos con SGD como primer optimizador, cuyo larning rate por default es 0.01" @@ -176,7 +176,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "636a7a5a", + "id": "8bfcd9fa", "metadata": {}, "outputs": [], "source": [ @@ -187,7 +187,7 @@ }, { "cell_type": "markdown", - "id": "efc42979", + "id": "6ea732d8", "metadata": {}, "source": [ "Compilamos nuestro primer modelo y observamos un resumen de su composición" @@ -196,7 +196,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "63d7e0f7", + "id": "9f328f87", "metadata": {}, "outputs": [ { @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "91d904f4", + "id": "1b39dfdf", "metadata": {}, "source": [ "Ahora si, realicemos nuestro primer entrenamiento. Primeramente entrenaremos 100 epochs" @@ -235,7 +235,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "4d93ac87", + "id": "df98448b", "metadata": {}, "outputs": [ { @@ -243,149 +243,149 @@ "output_type": "stream", "text": [ "Epoch 1/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.5590 - auc: 0.5937 - accuracy: 0.7493 - val_loss: 0.5148 - val_auc: 0.6521 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 2s 1ms/step - loss: 0.5590 - auc: 0.5937 - accuracy: 0.7493 - val_loss: 0.5148 - val_auc: 0.6521 - val_accuracy: 0.7778\n", "Epoch 2/100\n", - "814/814 [==============================] - 1s 909us/step - loss: 0.5077 - auc: 0.6510 - accuracy: 0.7823 - val_loss: 0.5110 - val_auc: 0.6514 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 866us/step - loss: 0.5077 - auc: 0.6510 - accuracy: 0.7823 - val_loss: 0.5110 - val_auc: 0.6514 - val_accuracy: 0.7778\n", "Epoch 3/100\n", - "814/814 [==============================] - 1s 979us/step - loss: 0.4987 - auc: 0.6892 - accuracy: 0.7817 - val_loss: 0.4892 - val_auc: 0.7241 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 827us/step - loss: 0.4987 - auc: 0.6892 - accuracy: 0.7817 - val_loss: 0.4892 - val_auc: 0.7241 - val_accuracy: 0.7778\n", "Epoch 4/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4874 - auc: 0.7122 - accuracy: 0.7836 - val_loss: 0.4821 - val_auc: 0.7274 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 800us/step - loss: 0.4874 - auc: 0.7122 - accuracy: 0.7836 - val_loss: 0.4821 - val_auc: 0.7274 - val_accuracy: 0.7778\n", "Epoch 5/100\n", - "814/814 [==============================] - 1s 932us/step - loss: 0.4896 - auc: 0.7167 - accuracy: 0.7774 - val_loss: 0.4902 - val_auc: 0.7357 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 919us/step - loss: 0.4896 - auc: 0.7167 - accuracy: 0.7774 - val_loss: 0.4902 - val_auc: 0.7357 - val_accuracy: 0.7778\n", "Epoch 6/100\n", - "814/814 [==============================] - 1s 913us/step - loss: 0.4741 - auc: 0.7324 - accuracy: 0.7846 - val_loss: 0.4878 - val_auc: 0.7367 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 890us/step - loss: 0.4741 - auc: 0.7324 - accuracy: 0.7846 - val_loss: 0.4878 - val_auc: 0.7367 - val_accuracy: 0.7778\n", "Epoch 7/100\n", - "814/814 [==============================] - 1s 967us/step - loss: 0.4778 - auc: 0.7375 - accuracy: 0.7812 - val_loss: 0.4866 - val_auc: 0.7599 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 851us/step - loss: 0.4778 - auc: 0.7375 - accuracy: 0.7812 - val_loss: 0.4866 - val_auc: 0.7599 - val_accuracy: 0.7778\n", "Epoch 8/100\n", - "814/814 [==============================] - 1s 926us/step - loss: 0.4774 - auc: 0.7376 - accuracy: 0.7801 - val_loss: 0.4693 - val_auc: 0.7997 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 885us/step - loss: 0.4774 - auc: 0.7376 - accuracy: 0.7801 - val_loss: 0.4693 - val_auc: 0.7997 - val_accuracy: 0.7778\n", "Epoch 9/100\n", - "814/814 [==============================] - 1s 916us/step - loss: 0.4743 - auc: 0.7361 - accuracy: 0.7813 - val_loss: 0.5216 - val_auc: 0.6573 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 835us/step - loss: 0.4743 - auc: 0.7361 - accuracy: 0.7813 - val_loss: 0.5216 - val_auc: 0.6573 - val_accuracy: 0.7778\n", "Epoch 10/100\n", - "814/814 [==============================] - 1s 953us/step - loss: 0.4759 - auc: 0.7414 - accuracy: 0.7802 - val_loss: 0.5006 - val_auc: 0.7818 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 842us/step - loss: 0.4759 - auc: 0.7414 - accuracy: 0.7802 - val_loss: 0.5006 - val_auc: 0.7818 - val_accuracy: 0.7778\n", "Epoch 11/100\n", - "814/814 [==============================] - 1s 906us/step - loss: 0.4636 - auc: 0.7580 - accuracy: 0.7792 - val_loss: 0.4765 - val_auc: 0.8103 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 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val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 870us/step - loss: 0.4678 - auc: 0.7534 - accuracy: 0.7788 - val_loss: 0.4231 - val_auc: 0.8347 - val_accuracy: 0.7807\n", "Epoch 15/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4540 - auc: 0.7767 - accuracy: 0.7779 - val_loss: 0.4194 - val_auc: 0.8340 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 810us/step - loss: 0.4540 - auc: 0.7767 - accuracy: 0.7779 - val_loss: 0.4194 - val_auc: 0.8340 - val_accuracy: 0.7807\n", "Epoch 16/100\n", - "814/814 [==============================] - 1s 945us/step - loss: 0.4559 - auc: 0.7712 - accuracy: 0.7788 - val_loss: 0.4402 - val_auc: 0.8310 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 801us/step - loss: 0.4559 - auc: 0.7712 - accuracy: 0.7788 - val_loss: 0.4402 - val_auc: 0.8310 - val_accuracy: 0.7807\n", "Epoch 17/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4484 - 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"814/814 [==============================] - 1s 1ms/step - loss: 0.4334 - auc: 0.8049 - accuracy: 0.7809 - val_loss: 0.4140 - val_auc: 0.8415 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 796us/step - loss: 0.4334 - auc: 0.8049 - accuracy: 0.7809 - val_loss: 0.4140 - val_auc: 0.8415 - val_accuracy: 0.7807\n", "Epoch 21/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4333 - auc: 0.8059 - accuracy: 0.7821 - val_loss: 0.4124 - val_auc: 0.8380 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 807us/step - loss: 0.4333 - auc: 0.8059 - accuracy: 0.7821 - val_loss: 0.4124 - val_auc: 0.8380 - val_accuracy: 0.7807\n", "Epoch 22/100\n", - "814/814 [==============================] - 1s 951us/step - loss: 0.4375 - auc: 0.7985 - accuracy: 0.7806 - val_loss: 0.4178 - val_auc: 0.8418 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 780us/step - loss: 0.4375 - auc: 0.7985 - accuracy: 0.7806 - val_loss: 0.4178 - val_auc: 0.8418 - val_accuracy: 0.7807\n", "Epoch 23/100\n", - "814/814 [==============================] - 1s 911us/step - loss: 0.4298 - auc: 0.8083 - accuracy: 0.7872 - val_loss: 0.4183 - val_auc: 0.8380 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 791us/step - loss: 0.4298 - auc: 0.8083 - accuracy: 0.7872 - val_loss: 0.4183 - val_auc: 0.8380 - val_accuracy: 0.7807\n", "Epoch 24/100\n", - "814/814 [==============================] - 1s 940us/step - loss: 0.4275 - auc: 0.8076 - accuracy: 0.7836 - val_loss: 0.4092 - val_auc: 0.8407 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 784us/step - loss: 0.4275 - auc: 0.8076 - accuracy: 0.7836 - val_loss: 0.4092 - val_auc: 0.8407 - val_accuracy: 0.7807\n", "Epoch 25/100\n", - "814/814 [==============================] - 1s 908us/step - loss: 0.4397 - auc: 0.7911 - accuracy: 0.7824 - val_loss: 0.4141 - val_auc: 0.8380 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 792us/step - loss: 0.4397 - auc: 0.7911 - accuracy: 0.7824 - val_loss: 0.4141 - val_auc: 0.8380 - val_accuracy: 0.7807\n", "Epoch 26/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4302 - auc: 0.8098 - accuracy: 0.7804 - val_loss: 0.4892 - val_auc: 0.8225 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 790us/step - loss: 0.4302 - auc: 0.8098 - accuracy: 0.7804 - val_loss: 0.4892 - val_auc: 0.8225 - val_accuracy: 0.7807\n", "Epoch 27/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4337 - auc: 0.8106 - accuracy: 0.7795 - val_loss: 0.4123 - val_auc: 0.8386 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 790us/step - loss: 0.4337 - auc: 0.8106 - accuracy: 0.7795 - val_loss: 0.4123 - val_auc: 0.8386 - val_accuracy: 0.7807\n", "Epoch 28/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4330 - auc: 0.8041 - accuracy: 0.7791 - val_loss: 0.4570 - val_auc: 0.8223 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 812us/step - loss: 0.4330 - auc: 0.8041 - accuracy: 0.7791 - val_loss: 0.4570 - val_auc: 0.8223 - val_accuracy: 0.7807\n", "Epoch 29/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4310 - auc: 0.8072 - accuracy: 0.7791 - val_loss: 0.4455 - val_auc: 0.8199 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 793us/step - loss: 0.4310 - auc: 0.8072 - accuracy: 0.7791 - val_loss: 0.4455 - val_auc: 0.8199 - val_accuracy: 0.7807\n", "Epoch 30/100\n", - "814/814 [==============================] - 1s 937us/step - loss: 0.4282 - auc: 0.8131 - accuracy: 0.7784 - val_loss: 0.4091 - val_auc: 0.8395 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 783us/step - loss: 0.4282 - auc: 0.8131 - accuracy: 0.7784 - val_loss: 0.4091 - val_auc: 0.8395 - val_accuracy: 0.7807\n", "Epoch 31/100\n", - "814/814 [==============================] - 1s 952us/step - loss: 0.4345 - auc: 0.8072 - accuracy: 0.7763 - val_loss: 0.4495 - val_auc: 0.8336 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 914us/step - loss: 0.4345 - auc: 0.8072 - accuracy: 0.7763 - val_loss: 0.4495 - val_auc: 0.8336 - val_accuracy: 0.7778\n", "Epoch 32/100\n", - "814/814 [==============================] - 1s 882us/step - loss: 0.4314 - auc: 0.8073 - accuracy: 0.7777 - val_loss: 0.4111 - val_auc: 0.8462 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4314 - auc: 0.8073 - accuracy: 0.7777 - val_loss: 0.4111 - val_auc: 0.8462 - val_accuracy: 0.7809\n", "Epoch 33/100\n", - "814/814 [==============================] - 1s 881us/step - loss: 0.4291 - auc: 0.8169 - accuracy: 0.7778 - val_loss: 0.4206 - val_auc: 0.8331 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 999us/step - loss: 0.4291 - auc: 0.8169 - accuracy: 0.7778 - val_loss: 0.4206 - val_auc: 0.8331 - val_accuracy: 0.7809\n", "Epoch 34/100\n", - 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1s 978us/step - loss: 0.4276 - auc: 0.8158 - accuracy: 0.7799 - val_loss: 0.4195 - val_auc: 0.8269 - val_accuracy: 0.7809\n", "Epoch 43/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4262 - auc: 0.8138 - accuracy: 0.7832 - val_loss: 0.4258 - val_auc: 0.8392 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 790us/step - loss: 0.4262 - auc: 0.8138 - accuracy: 0.7832 - val_loss: 0.4258 - val_auc: 0.8392 - val_accuracy: 0.7807\n", "Epoch 44/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4250 - auc: 0.8199 - accuracy: 0.7813 - val_loss: 0.4109 - val_auc: 0.8401 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 795us/step - loss: 0.4250 - auc: 0.8199 - accuracy: 0.7813 - val_loss: 0.4109 - val_auc: 0.8401 - val_accuracy: 0.7807\n", "Epoch 45/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4289 - auc: 0.8132 - accuracy: 0.7739 - val_loss: 0.4071 - val_auc: 0.8348 - 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loss: 0.4207 - auc: 0.8206 - accuracy: 0.7788 - val_loss: 0.4139 - val_auc: 0.8374 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 805us/step - loss: 0.4207 - auc: 0.8206 - accuracy: 0.7788 - val_loss: 0.4139 - val_auc: 0.8374 - val_accuracy: 0.7809\n", "Epoch 49/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4278 - auc: 0.8157 - accuracy: 0.7784 - val_loss: 0.4086 - val_auc: 0.8479 - val_accuracy: 0.7809\n", "Epoch 50/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4209 - auc: 0.8215 - accuracy: 0.7796 - val_loss: 0.4290 - val_auc: 0.8271 - val_accuracy: 0.7809\n", "Epoch 51/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4235 - auc: 0.8176 - accuracy: 0.7809 - val_loss: 0.4099 - val_auc: 0.8318 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 969us/step - loss: 0.4235 - auc: 0.8176 - accuracy: 0.7809 - val_loss: 0.4099 - val_auc: 0.8318 - val_accuracy: 0.7809\n", "Epoch 52/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4179 - auc: 0.8182 - accuracy: 0.7877 - val_loss: 0.4692 - val_auc: 0.8242 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 794us/step - loss: 0.4179 - auc: 0.8182 - accuracy: 0.7877 - val_loss: 0.4692 - val_auc: 0.8242 - val_accuracy: 0.7809\n", "Epoch 53/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4226 - auc: 0.8173 - accuracy: 0.7796 - val_loss: 0.4101 - val_auc: 0.8290 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 788us/step - loss: 0.4226 - auc: 0.8173 - accuracy: 0.7796 - val_loss: 0.4101 - val_auc: 0.8290 - val_accuracy: 0.7809\n", "Epoch 54/100\n", - "814/814 [==============================] - 1s 893us/step - loss: 0.4259 - auc: 0.8170 - accuracy: 0.7793 - val_loss: 0.4062 - val_auc: 0.8405 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 793us/step - loss: 0.4259 - auc: 0.8170 - accuracy: 0.7793 - val_loss: 0.4062 - val_auc: 0.8405 - val_accuracy: 0.7809\n", "Epoch 55/100\n", - "814/814 [==============================] - 1s 917us/step - loss: 0.4230 - auc: 0.8164 - accuracy: 0.7748 - val_loss: 0.4066 - val_auc: 0.8424 - val_accuracy: 0.7780\n", + "814/814 [==============================] - 1s 801us/step - loss: 0.4230 - auc: 0.8164 - accuracy: 0.7748 - val_loss: 0.4066 - val_auc: 0.8424 - val_accuracy: 0.7780\n", "Epoch 56/100\n", - "814/814 [==============================] - 1s 956us/step - loss: 0.4186 - auc: 0.8214 - accuracy: 0.7835 - val_loss: 0.4083 - val_auc: 0.8465 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 803us/step - loss: 0.4186 - auc: 0.8214 - accuracy: 0.7835 - val_loss: 0.4083 - val_auc: 0.8465 - val_accuracy: 0.7809\n", "Epoch 57/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4202 - auc: 0.8221 - accuracy: 0.7805 - val_loss: 0.4089 - val_auc: 0.8480 - val_accuracy: 0.7809\n", "Epoch 58/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4200 - auc: 0.8238 - accuracy: 0.7798 - val_loss: 0.5224 - val_auc: 0.8270 - val_accuracy: 0.7809\n", "Epoch 59/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4177 - auc: 0.8267 - accuracy: 0.7826 - val_loss: 0.4139 - val_auc: 0.8464 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 1000us/step - loss: 0.4177 - auc: 0.8267 - accuracy: 0.7826 - val_loss: 0.4139 - val_auc: 0.8464 - val_accuracy: 0.7809\n", "Epoch 60/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4250 - auc: 0.8174 - accuracy: 0.7768 - val_loss: 0.4056 - val_auc: 0.8355 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 796us/step - loss: 0.4250 - auc: 0.8174 - accuracy: 0.7768 - val_loss: 0.4056 - val_auc: 0.8355 - val_accuracy: 0.7809\n", "Epoch 61/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4219 - auc: 0.8156 - accuracy: 0.7759 - val_loss: 0.4067 - val_auc: 0.8438 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 792us/step - loss: 0.4219 - auc: 0.8156 - accuracy: 0.7759 - val_loss: 0.4067 - val_auc: 0.8438 - val_accuracy: 0.7809\n", "Epoch 62/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4253 - auc: 0.8176 - accuracy: 0.7734 - val_loss: 0.4061 - val_auc: 0.8443 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 787us/step - loss: 0.4253 - auc: 0.8176 - accuracy: 0.7734 - val_loss: 0.4061 - val_auc: 0.8443 - val_accuracy: 0.7809\n", "Epoch 63/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4145 - auc: 0.8254 - accuracy: 0.7828 - val_loss: 0.4052 - val_auc: 0.8463 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 806us/step - loss: 0.4145 - auc: 0.8254 - accuracy: 0.7828 - val_loss: 0.4052 - val_auc: 0.8463 - val_accuracy: 0.7809\n", "Epoch 64/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4166 - auc: 0.8260 - accuracy: 0.7811 - val_loss: 0.4061 - val_auc: 0.8457 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 814us/step - loss: 0.4166 - auc: 0.8260 - accuracy: 0.7811 - val_loss: 0.4061 - val_auc: 0.8457 - val_accuracy: 0.7809\n", "Epoch 65/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4190 - auc: 0.8210 - accuracy: 0.7804 - val_loss: 0.4067 - val_auc: 0.8484 - val_accuracy: 0.7809\n", "Epoch 66/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4146 - auc: 0.8262 - accuracy: 0.7820 - val_loss: 0.4073 - val_auc: 0.8433 - val_accuracy: 0.7809\n", "Epoch 67/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4216 - auc: 0.8173 - accuracy: 0.7830 - val_loss: 0.4059 - val_auc: 0.8460 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 978us/step - loss: 0.4216 - auc: 0.8173 - accuracy: 0.7830 - val_loss: 0.4059 - val_auc: 0.8460 - val_accuracy: 0.7809\n", "Epoch 68/100\n", - "814/814 [==============================] - 1s 909us/step - loss: 0.4185 - auc: 0.8256 - accuracy: 0.7790 - val_loss: 0.4070 - val_auc: 0.8367 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 810us/step - loss: 0.4185 - auc: 0.8256 - accuracy: 0.7790 - val_loss: 0.4070 - val_auc: 0.8367 - val_accuracy: 0.7809\n", "Epoch 69/100\n", - "814/814 [==============================] - 1s 941us/step - loss: 0.4233 - auc: 0.8183 - accuracy: 0.7796 - val_loss: 0.4116 - val_auc: 0.8398 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 802us/step - loss: 0.4233 - auc: 0.8183 - accuracy: 0.7796 - val_loss: 0.4116 - val_auc: 0.8398 - val_accuracy: 0.7809\n", "Epoch 70/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4224 - auc: 0.8180 - accuracy: 0.7760 - val_loss: 0.4141 - val_auc: 0.8395 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 814us/step - loss: 0.4224 - auc: 0.8180 - accuracy: 0.7760 - val_loss: 0.4141 - val_auc: 0.8395 - val_accuracy: 0.7809\n", "Epoch 71/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4145 - auc: 0.8274 - accuracy: 0.7807 - val_loss: 0.4081 - val_auc: 0.8428 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 794us/step - loss: 0.4145 - auc: 0.8274 - accuracy: 0.7807 - val_loss: 0.4081 - val_auc: 0.8428 - val_accuracy: 0.7809\n", "Epoch 72/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4130 - auc: 0.8277 - accuracy: 0.7807 - val_loss: 0.4071 - val_auc: 0.8423 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 854us/step - loss: 0.4130 - auc: 0.8277 - accuracy: 0.7807 - val_loss: 0.4071 - val_auc: 0.8423 - val_accuracy: 0.7809\n", "Epoch 73/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4167 - auc: 0.8254 - accuracy: 0.7807 - val_loss: 0.4189 - val_auc: 0.8252 - val_accuracy: 0.7809\n", "Epoch 74/100\n", @@ -393,13 +393,13 @@ "Epoch 75/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4214 - auc: 0.8210 - accuracy: 0.7788 - val_loss: 0.4046 - val_auc: 0.8412 - val_accuracy: 0.7809\n", "Epoch 76/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4152 - auc: 0.8244 - accuracy: 0.7829 - val_loss: 0.4126 - val_auc: 0.8334 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 861us/step - loss: 0.4152 - auc: 0.8244 - accuracy: 0.7829 - val_loss: 0.4126 - val_auc: 0.8334 - val_accuracy: 0.7809\n", "Epoch 77/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4164 - auc: 0.8233 - accuracy: 0.7824 - val_loss: 0.4039 - val_auc: 0.8473 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 861us/step - loss: 0.4164 - auc: 0.8233 - accuracy: 0.7824 - val_loss: 0.4039 - val_auc: 0.8473 - val_accuracy: 0.7809\n", "Epoch 78/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4182 - auc: 0.8252 - accuracy: 0.7782 - val_loss: 0.4039 - val_auc: 0.8453 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 850us/step - loss: 0.4182 - auc: 0.8252 - accuracy: 0.7782 - val_loss: 0.4039 - val_auc: 0.8453 - val_accuracy: 0.7809\n", "Epoch 79/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4208 - auc: 0.8203 - accuracy: 0.7800 - val_loss: 0.4195 - val_auc: 0.8250 - val_accuracy: 0.7809\n", + "814/814 [==============================] - 1s 849us/step - loss: 0.4208 - auc: 0.8203 - accuracy: 0.7800 - val_loss: 0.4195 - val_auc: 0.8250 - val_accuracy: 0.7809\n", "Epoch 80/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4163 - auc: 0.8194 - accuracy: 0.7849 - val_loss: 0.4041 - val_auc: 0.8456 - val_accuracy: 0.7809\n", "Epoch 81/100\n", @@ -407,41 +407,41 @@ "Epoch 82/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4201 - auc: 0.8251 - accuracy: 0.7801 - val_loss: 0.4147 - val_auc: 0.8437 - val_accuracy: 0.7809\n", "Epoch 83/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4425 - auc: 0.8009 - accuracy: 0.7678 - val_loss: 0.4289 - val_auc: 0.7964 - val_accuracy: 0.7622\n", + "814/814 [==============================] - 1s 962us/step - loss: 0.4425 - auc: 0.8009 - accuracy: 0.7678 - val_loss: 0.4289 - val_auc: 0.7964 - val_accuracy: 0.7622\n", "Epoch 84/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4290 - auc: 0.8145 - accuracy: 0.7657 - val_loss: 0.4060 - val_auc: 0.8465 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 826us/step - loss: 0.4290 - auc: 0.8145 - accuracy: 0.7657 - val_loss: 0.4060 - val_auc: 0.8465 - val_accuracy: 0.7778\n", "Epoch 85/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4165 - auc: 0.8256 - accuracy: 0.7814 - val_loss: 0.4072 - val_auc: 0.8431 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 861us/step - loss: 0.4165 - auc: 0.8256 - accuracy: 0.7814 - val_loss: 0.4072 - val_auc: 0.8431 - val_accuracy: 0.7778\n", "Epoch 86/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4186 - auc: 0.8243 - accuracy: 0.7804 - val_loss: 0.4067 - val_auc: 0.8494 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 786us/step - loss: 0.4186 - auc: 0.8243 - accuracy: 0.7804 - val_loss: 0.4067 - val_auc: 0.8494 - val_accuracy: 0.7778\n", "Epoch 87/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4183 - auc: 0.8266 - accuracy: 0.7777 - val_loss: 0.5333 - val_auc: 0.8080 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 829us/step - loss: 0.4183 - auc: 0.8266 - accuracy: 0.7777 - val_loss: 0.5333 - val_auc: 0.8080 - val_accuracy: 0.7778\n", "Epoch 88/100\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4175 - auc: 0.8226 - accuracy: 0.7800 - val_loss: 0.4095 - val_auc: 0.8325 - val_accuracy: 0.7778\n", "Epoch 89/100\n", - "814/814 [==============================] - 1s 919us/step - loss: 0.4150 - auc: 0.8246 - accuracy: 0.7782 - val_loss: 0.4113 - val_auc: 0.8418 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4150 - auc: 0.8246 - accuracy: 0.7782 - val_loss: 0.4113 - val_auc: 0.8418 - val_accuracy: 0.7778\n", "Epoch 90/100\n", - "814/814 [==============================] - 1s 911us/step - loss: 0.4196 - auc: 0.8217 - accuracy: 0.7817 - val_loss: 0.4053 - val_auc: 0.8459 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 943us/step - loss: 0.4196 - auc: 0.8217 - accuracy: 0.7817 - val_loss: 0.4053 - val_auc: 0.8459 - val_accuracy: 0.7807\n", "Epoch 91/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4105 - auc: 0.8299 - accuracy: 0.7837 - val_loss: 0.4293 - val_auc: 0.8365 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 806us/step - loss: 0.4105 - auc: 0.8299 - accuracy: 0.7837 - val_loss: 0.4293 - val_auc: 0.8365 - val_accuracy: 0.7807\n", "Epoch 92/100\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4143 - auc: 0.8283 - accuracy: 0.7808 - val_loss: 0.4032 - val_auc: 0.8467 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 796us/step - loss: 0.4143 - auc: 0.8283 - accuracy: 0.7808 - val_loss: 0.4032 - val_auc: 0.8467 - val_accuracy: 0.7807\n", "Epoch 93/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4191 - auc: 0.8282 - accuracy: 0.7778 - val_loss: 0.4044 - val_auc: 0.8389 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 814us/step - loss: 0.4191 - auc: 0.8282 - accuracy: 0.7778 - val_loss: 0.4044 - val_auc: 0.8389 - val_accuracy: 0.7778\n", "Epoch 94/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4139 - auc: 0.8265 - accuracy: 0.7827 - val_loss: 0.4036 - val_auc: 0.8446 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 842us/step - loss: 0.4139 - auc: 0.8265 - accuracy: 0.7827 - val_loss: 0.4036 - val_auc: 0.8446 - val_accuracy: 0.7807\n", "Epoch 95/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4196 - auc: 0.8228 - accuracy: 0.7780 - val_loss: 0.4046 - val_auc: 0.8440 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 854us/step - loss: 0.4196 - auc: 0.8228 - accuracy: 0.7780 - val_loss: 0.4046 - val_auc: 0.8440 - val_accuracy: 0.7807\n", "Epoch 96/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4110 - auc: 0.8321 - accuracy: 0.7811 - val_loss: 0.4076 - val_auc: 0.8443 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4110 - auc: 0.8321 - accuracy: 0.7811 - val_loss: 0.4076 - val_auc: 0.8443 - val_accuracy: 0.7807\n", "Epoch 97/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4211 - auc: 0.8257 - accuracy: 0.7777 - val_loss: 0.4051 - val_auc: 0.8416 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4211 - auc: 0.8257 - accuracy: 0.7777 - val_loss: 0.4051 - val_auc: 0.8416 - val_accuracy: 0.7778\n", "Epoch 98/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4191 - auc: 0.8211 - accuracy: 0.7766 - val_loss: 0.4035 - val_auc: 0.8457 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4191 - auc: 0.8211 - accuracy: 0.7766 - val_loss: 0.4035 - val_auc: 0.8457 - val_accuracy: 0.7807\n", "Epoch 99/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4165 - auc: 0.8289 - accuracy: 0.7818 - val_loss: 0.4028 - val_auc: 0.8498 - val_accuracy: 0.7807\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4165 - auc: 0.8289 - accuracy: 0.7818 - val_loss: 0.4028 - val_auc: 0.8498 - val_accuracy: 0.7807\n", "Epoch 100/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4178 - auc: 0.8275 - accuracy: 0.7811 - val_loss: 0.4019 - val_auc: 0.8465 - val_accuracy: 0.7807\n" + "814/814 [==============================] - 1s 988us/step - loss: 0.4178 - auc: 0.8275 - accuracy: 0.7811 - val_loss: 0.4019 - val_auc: 0.8465 - val_accuracy: 0.7807\n" ] } ], @@ -451,7 +451,7 @@ }, { "cell_type": "markdown", - "id": "ac9e323b", + "id": "c6f50f8c", "metadata": {}, "source": [ "#### Métricas" @@ -459,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "1d8cf3ea", + "id": "bc6bc5e7", "metadata": {}, "source": [ "Para evaluar los resultados obtenidos, observaremos la curva de aprendizaje tanto de la accuracy como del AUC" @@ -468,7 +468,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "f0ec8405", + "id": "77a0ed63", "metadata": {}, "outputs": [ { @@ -498,7 +498,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "525f16e1", + "id": "ad85fc78", "metadata": {}, "outputs": [ { @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "c4e5d56f", + "id": "f85c6f9e", "metadata": {}, "source": [ "Llegado este punto es importante destacar algunas cosas. En primer lugar, el calculo de AUC provisto por keras tomo una cierta cantidad de samples a la hora de calcular esta metrica (en este caso tomamos 200, que además es lo que toma la función por default) y no es del todo representativa e incluso su valor es distinto al real. Por esto, vamos a calcularlo con la función de sklearn.metrics y de paso aprovecharemos para obtener otras metricas que resultan interesantes para evaluar el modelo" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "676924fe", + "id": "0c3521c9", "metadata": {}, "outputs": [ { @@ -594,7 +594,7 @@ }, { "cell_type": "markdown", - "id": "0c0019a4", + "id": "0b23ee45", "metadata": {}, "source": [ "Obtuvimos un AUC-ROC de 0.85 tanto para el set de train como para el set de test. Aún así, en otros modelos obtuvimos mejores valores y además el recall con respecto a las instancias de alto valor adquisitivo es bastante bajo. Busquemos complejizar más la red" @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "30777e33", + "id": "efddc9f7", "metadata": {}, "source": [ "### Segundo diseño de la red" @@ -610,7 +610,7 @@ }, { "cell_type": "markdown", - "id": "903765ee", + "id": "e4016a07", "metadata": {}, "source": [ "#### Diseño y entrenamiento" @@ -618,7 +618,7 @@ }, { "cell_type": "markdown", - "id": "c05a2e43", + "id": "af2e14ea", "metadata": {}, "source": [ "Para complejizar la red agregaremos más capas. Como la red se volverá más compleja utilizaremos relu como función de activación" @@ -627,7 +627,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "83fd05dd", + "id": "00c3ee97", "metadata": {}, "outputs": [], "source": [ @@ -638,7 +638,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "975c5335", + "id": "5f5913c0", "metadata": {}, "outputs": [], "source": [ @@ -651,7 +651,7 @@ }, { "cell_type": "markdown", - "id": "eea5586d", + "id": "c29e1359", "metadata": {}, "source": [ "Compilamos y mostramos un resumen de la red" @@ -660,7 +660,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "390629ab", + "id": "91c607e5", "metadata": {}, "outputs": [ { @@ -694,7 +694,7 @@ }, { "cell_type": "markdown", - "id": "09d1d02c", + "id": "cf4f7e45", "metadata": {}, "source": [ "Vemos que pasamos de alrededor de 100 params a 800. Finalmente, entrenamos nuestra red" @@ -703,7 +703,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "0cb8703f", + "id": "6fb2bdbf", "metadata": {}, "outputs": [ { @@ -711,205 +711,205 @@ "output_type": "stream", "text": [ "Epoch 1/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 2.7978 - auc: 0.5132 - accuracy: 0.7431 - val_loss: 0.5308 - val_auc: 0.6194 - val_accuracy: 0.7593\n", + "814/814 [==============================] - 1s 1ms/step - loss: 2.7978 - auc: 0.5132 - accuracy: 0.7431 - val_loss: 0.5308 - val_auc: 0.6194 - val_accuracy: 0.7593\n", "Epoch 2/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.5255 - auc: 0.6169 - accuracy: 0.7607 - val_loss: 0.5099 - val_auc: 0.6823 - val_accuracy: 0.7591\n", + "814/814 [==============================] - 1s 866us/step - loss: 0.5255 - auc: 0.6169 - accuracy: 0.7607 - val_loss: 0.5099 - val_auc: 0.6823 - val_accuracy: 0.7591\n", "Epoch 3/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.5070 - auc: 0.6890 - accuracy: 0.7622 - val_loss: 0.4888 - val_auc: 0.7753 - val_accuracy: 0.7593\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.5070 - auc: 0.6890 - accuracy: 0.7622 - val_loss: 0.4888 - val_auc: 0.7753 - val_accuracy: 0.7593\n", "Epoch 4/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4976 - auc: 0.7145 - accuracy: 0.7614 - val_loss: 0.4662 - val_auc: 0.7853 - val_accuracy: 0.7593\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4976 - auc: 0.7145 - accuracy: 0.7614 - val_loss: 0.4662 - val_auc: 0.7853 - val_accuracy: 0.7593\n", "Epoch 5/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4878 - auc: 0.7420 - accuracy: 0.7559 - val_loss: 0.4836 - val_auc: 0.7997 - val_accuracy: 0.7593\n", + "814/814 [==============================] - 1s 922us/step - loss: 0.4878 - auc: 0.7420 - accuracy: 0.7559 - val_loss: 0.4836 - val_auc: 0.7997 - val_accuracy: 0.7593\n", "Epoch 6/100\n", - 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val_auc: 0.8729 - val_accuracy: 0.8116\n", "Epoch 94/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3736 - auc: 0.8625 - accuracy: 0.8106 - val_loss: 0.3652 - val_auc: 0.8706 - val_accuracy: 0.8168\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3736 - auc: 0.8625 - accuracy: 0.8106 - val_loss: 0.3652 - val_auc: 0.8706 - val_accuracy: 0.8168\n", "Epoch 95/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3834 - auc: 0.8558 - accuracy: 0.8036 - val_loss: 0.3615 - val_auc: 0.8731 - val_accuracy: 0.8191\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3834 - auc: 0.8558 - accuracy: 0.8036 - val_loss: 0.3615 - val_auc: 0.8731 - val_accuracy: 0.8191\n", "Epoch 96/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3707 - auc: 0.8661 - accuracy: 0.8129 - val_loss: 0.3686 - val_auc: 0.8715 - val_accuracy: 0.8162\n", + "814/814 [==============================] - 1s 884us/step - loss: 0.3707 - auc: 0.8661 - accuracy: 0.8129 - val_loss: 0.3686 - val_auc: 0.8715 - val_accuracy: 0.8162\n", "Epoch 97/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3772 - auc: 0.8633 - accuracy: 0.8107 - val_loss: 0.3640 - val_auc: 0.8727 - val_accuracy: 0.8142\n", + "814/814 [==============================] - 1s 886us/step - loss: 0.3772 - auc: 0.8633 - accuracy: 0.8107 - val_loss: 0.3640 - val_auc: 0.8727 - val_accuracy: 0.8142\n", "Epoch 98/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3796 - auc: 0.8580 - accuracy: 0.8077 - val_loss: 0.3702 - val_auc: 0.8697 - val_accuracy: 0.8096\n", + "814/814 [==============================] - 1s 873us/step - loss: 0.3796 - auc: 0.8580 - accuracy: 0.8077 - val_loss: 0.3702 - val_auc: 0.8697 - val_accuracy: 0.8096\n", "Epoch 99/100\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3736 - auc: 0.8641 - accuracy: 0.8146 - val_loss: 0.3636 - val_auc: 0.8722 - val_accuracy: 0.8153\n", + "814/814 [==============================] - 1s 980us/step - loss: 0.3736 - auc: 0.8641 - accuracy: 0.8146 - val_loss: 0.3636 - val_auc: 0.8722 - val_accuracy: 0.8153\n", "Epoch 100/100\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3778 - auc: 0.8612 - accuracy: 0.8104 - val_loss: 0.3715 - val_auc: 0.8721 - val_accuracy: 0.8090\n" + "814/814 [==============================] - 1s 1ms/step - loss: 0.3778 - auc: 0.8612 - accuracy: 0.8104 - val_loss: 0.3715 - val_auc: 0.8721 - val_accuracy: 0.8090\n" ] } ], @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "1bb39532", + "id": "306295ea", "metadata": {}, "source": [ "#### Métricas" @@ -927,7 +927,7 @@ }, { "cell_type": "markdown", - "id": "d0a0a2f6", + "id": "3f4fec03", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -936,7 +936,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "81f3ab3c", + "id": "f812c9e0", "metadata": {}, "outputs": [ { @@ -966,7 +966,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "5ba99dd7", + "id": "c82f0b13", "metadata": {}, "outputs": [ { @@ -996,7 +996,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "c4079bf1", + "id": "716e6f63", "metadata": {}, "outputs": [ { @@ -1054,7 +1054,7 @@ }, { "cell_type": "markdown", - "id": "9cc2f259", + "id": "cb5d19a1", "metadata": {}, "source": [ "Visualizando lo obtenido vemos una mejora interesante. No solo obtuvimos un mejor score de AUC-ROC sino que mejoro mucho el recall de la clase de altos ingresos. Aun así notamos algunos problemas en la curva de aprendizaje de la metrica accuracy. Por lo que probaremos bajar el learning rate " @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "dbed8473", + "id": "cbcf2e02", "metadata": {}, "source": [ "### Tercer diseño de la red" @@ -1070,7 +1070,7 @@ }, { "cell_type": "markdown", - "id": "9c7515bb", + "id": "6d566b34", "metadata": {}, "source": [ "#### Diseño y entrenamiento" @@ -1078,7 +1078,7 @@ }, { "cell_type": "markdown", - "id": "ff2ec180", + "id": "6d7c69af", "metadata": {}, "source": [ "Realizaremos el mismo entrenamiento que antes pero bajando el learning rate (hasta ahora utilizabamos el default de SGD que es 0.01)" @@ -1087,7 +1087,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "45c3135a", + "id": "96649fde", "metadata": {}, "outputs": [], "source": [ @@ -1098,7 +1098,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "96a40ca7", + "id": "4e1426fb", "metadata": {}, "outputs": [], "source": [ @@ -1111,7 +1111,7 @@ }, { "cell_type": "markdown", - "id": "090dcb4f", + "id": "97fce84d", "metadata": {}, "source": [ "Compilamos y mostramos un resumen de la red" @@ -1120,7 +1120,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "f11e76c5", + "id": "733a4672", "metadata": {}, "outputs": [ { @@ -1154,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "6871cdc1", + "id": "d11eab1b", "metadata": {}, "source": [ "Entrenamos aumentando también la cantidad de epochs. Sino aumentasemos los epochs parecia que la red podia seguir aprendiendo (esperable al haber disminuido el learning rate)" @@ -1163,7 +1163,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "047a2980", + "id": "7ec7de08", "metadata": {}, "outputs": [ { @@ -1171,705 +1171,705 @@ "output_type": "stream", "text": [ "Epoch 1/350\n", - "814/814 [==============================] - 2s 2ms/step - loss: 3.2669 - auc: 0.5280 - accuracy: 0.5703 - val_loss: 0.5560 - val_auc: 0.5480 - val_accuracy: 0.7591\n", + "814/814 [==============================] - 1s 1ms/step - loss: 3.2669 - auc: 0.5280 - accuracy: 0.5703 - val_loss: 0.5560 - val_auc: 0.5480 - val_accuracy: 0.7591\n", "Epoch 2/350\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.5410 - auc: 0.5771 - accuracy: 0.7603 - val_loss: 0.5392 - val_auc: 0.5671 - val_accuracy: 0.7591\n", + "814/814 [==============================] - 1s 857us/step - loss: 0.5410 - auc: 0.5771 - accuracy: 0.7603 - val_loss: 0.5392 - val_auc: 0.5671 - val_accuracy: 0.7591\n", "Epoch 3/350\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.5286 - auc: 0.5870 - accuracy: 0.7619 - val_loss: 0.5287 - val_auc: 0.5898 - val_accuracy: 0.7591\n", + "814/814 [==============================] - 1s 869us/step - loss: 0.5286 - auc: 0.5870 - accuracy: 0.7619 - val_loss: 0.5287 - val_auc: 0.5898 - val_accuracy: 0.7591\n", "Epoch 4/350\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.5241 - auc: 0.6012 - accuracy: 0.7612 - val_loss: 0.5240 - val_auc: 0.6077 - val_accuracy: 0.7591\n", + "814/814 [==============================] - 1s 880us/step - loss: 0.5241 - auc: 0.6012 - accuracy: 0.7612 - val_loss: 0.5240 - val_auc: 0.6077 - val_accuracy: 0.7591\n", "Epoch 5/350\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.5277 - auc: 0.6152 - accuracy: 0.7557 - val_loss: 0.5221 - val_auc: 0.6147 - val_accuracy: 0.7591\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.5277 - 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loss: 0.3922 - auc: 0.8468 - accuracy: 0.7980 - val_loss: 0.3892 - val_auc: 0.8540 - val_accuracy: 0.8022\n", "Epoch 350/350\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3924 - auc: 0.8495 - accuracy: 0.8023 - val_loss: 0.3906 - val_auc: 0.8530 - val_accuracy: 0.8029\n" + "814/814 [==============================] - 1s 925us/step - loss: 0.3924 - auc: 0.8495 - accuracy: 0.8023 - val_loss: 0.3906 - val_auc: 0.8530 - val_accuracy: 0.8029\n" ] } ], @@ -1879,7 +1879,7 @@ }, { "cell_type": "markdown", - "id": "de93c1f0", + "id": "612ef44f", "metadata": {}, "source": [ "#### Métricas" @@ -1887,7 +1887,7 @@ }, { "cell_type": "markdown", - "id": "c101cc78", + "id": "22665121", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -1896,7 +1896,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "a76086b1", + "id": "d6666a05", "metadata": {}, "outputs": [ { @@ -1926,7 +1926,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "43af1f69", + "id": "14f90c21", "metadata": {}, "outputs": [ { @@ -1956,7 +1956,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "78c4ca11", + "id": "c270b58f", "metadata": {}, "outputs": [ { @@ -2014,7 +2014,7 @@ }, { "cell_type": "markdown", - "id": "ed632c73", + "id": "42711a94", "metadata": {}, "source": [ "Si bien se estabilizo un poco mas el entrenamiento, perdimos score en metricas. Es importante destacar que la curva de aprendizaje viendo la métrica accuracy se estanca durante muchos epochs. Busquemos usar un mejor optimizador (en cuanto a complejidad del mismo)" @@ -2022,7 +2022,7 @@ }, { "cell_type": "markdown", - "id": "1f94e465", + "id": "3e37e2fe", "metadata": {}, "source": [ "### Cuarto entrenamiento" @@ -2030,7 +2030,7 @@ }, { "cell_type": "markdown", - "id": "3db7aa36", + "id": "ce10fc19", "metadata": {}, "source": [ "#### Diseño" @@ -2038,7 +2038,7 @@ }, { "cell_type": "markdown", - "id": "e5e07e58", + "id": "16d39413", "metadata": {}, "source": [ "Ahora cambiamos el optimizador por RMSprop y agregamos algo de regularización ya que sino corremos riesgo de overfittear" @@ -2047,7 +2047,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "3105b082", + "id": "734975ae", "metadata": {}, "outputs": [], "source": [ @@ -2058,7 +2058,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "3b85aff9", + "id": "786f165a", "metadata": {}, "outputs": [], "source": [ @@ -2071,7 +2071,7 @@ }, { "cell_type": "markdown", - "id": "996baa26", + "id": "9cd8ba7b", "metadata": {}, "source": [ "Compilamos y mostramos un resumen de la red" @@ -2080,7 +2080,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "0e5069a0", + "id": "da5c6197", "metadata": {}, "outputs": [ { @@ -2114,7 +2114,7 @@ }, { "cell_type": "markdown", - "id": "518bf2c3", + "id": "658bd8b8", "metadata": {}, "source": [ "Entrenamos 200 epochs" @@ -2123,7 +2123,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "6afa694c", + "id": "d8bcfeaf", "metadata": {}, "outputs": [ { @@ -2131,51 +2131,51 @@ "output_type": "stream", "text": [ "Epoch 1/200\n", - "814/814 [==============================] - 3s 2ms/step - loss: 4.2603 - auc: 0.5244 - accuracy: 0.3538 - val_loss: 0.5725 - val_auc: 0.6612 - val_accuracy: 0.7777\n", + "814/814 [==============================] - 2s 2ms/step - loss: 4.2603 - auc: 0.5244 - accuracy: 0.3538 - val_loss: 0.5725 - val_auc: 0.6612 - val_accuracy: 0.7777\n", "Epoch 2/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.5125 - auc: 0.7287 - accuracy: 0.7936 - val_loss: 0.4975 - val_auc: 0.8027 - val_accuracy: 0.7927\n", + "814/814 [==============================] - 1s 973us/step - loss: 0.5125 - auc: 0.7287 - accuracy: 0.7936 - val_loss: 0.4975 - val_auc: 0.8027 - val_accuracy: 0.7927\n", "Epoch 3/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4676 - auc: 0.8049 - accuracy: 0.7963 - val_loss: 0.4607 - val_auc: 0.8283 - val_accuracy: 0.7952\n", + "814/814 [==============================] - 1s 955us/step - loss: 0.4676 - auc: 0.8049 - accuracy: 0.7963 - val_loss: 0.4607 - val_auc: 0.8283 - val_accuracy: 0.7952\n", "Epoch 4/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.4373 - auc: 0.8290 - accuracy: 0.8023 - val_loss: 0.4404 - val_auc: 0.8477 - val_accuracy: 0.7990\n", + "814/814 [==============================] - 1s 978us/step - loss: 0.4373 - auc: 0.8290 - accuracy: 0.8023 - val_loss: 0.4404 - val_auc: 0.8477 - val_accuracy: 0.7990\n", "Epoch 5/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4392 - auc: 0.8427 - accuracy: 0.7999 - val_loss: 0.4247 - val_auc: 0.8581 - val_accuracy: 0.8013\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4392 - auc: 0.8427 - accuracy: 0.7999 - val_loss: 0.4247 - val_auc: 0.8581 - val_accuracy: 0.8013\n", "Epoch 6/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4191 - auc: 0.8537 - accuracy: 0.8140 - val_loss: 0.4180 - val_auc: 0.8686 - val_accuracy: 0.8099\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4191 - auc: 0.8537 - accuracy: 0.8140 - val_loss: 0.4180 - val_auc: 0.8686 - val_accuracy: 0.8099\n", "Epoch 7/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.4249 - auc: 0.8548 - accuracy: 0.8148 - val_loss: 0.4166 - val_auc: 0.8592 - val_accuracy: 0.8151\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.4249 - auc: 0.8548 - accuracy: 0.8148 - val_loss: 0.4166 - val_auc: 0.8592 - val_accuracy: 0.8151\n", "Epoch 8/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3931 - 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loss: 0.3841 - auc: 0.8693 - accuracy: 0.8255 - val_loss: 0.3786 - val_auc: 0.8858 - val_accuracy: 0.8270\n", "Epoch 17/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3703 - auc: 0.8783 - accuracy: 0.8301 - val_loss: 0.3884 - val_auc: 0.8682 - val_accuracy: 0.8197\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3703 - auc: 0.8783 - accuracy: 0.8301 - val_loss: 0.3884 - val_auc: 0.8682 - val_accuracy: 0.8197\n", "Epoch 18/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3756 - auc: 0.8783 - accuracy: 0.8328 - val_loss: 0.3694 - val_auc: 0.8854 - val_accuracy: 0.8276\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3756 - auc: 0.8783 - accuracy: 0.8328 - val_loss: 0.3694 - val_auc: 0.8854 - val_accuracy: 0.8276\n", "Epoch 19/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3842 - auc: 0.8740 - accuracy: 0.8235 - val_loss: 0.3959 - val_auc: 0.8846 - val_accuracy: 0.8251\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3842 - auc: 0.8740 - accuracy: 0.8235 - val_loss: 0.3959 - val_auc: 0.8846 - val_accuracy: 0.8251\n", "Epoch 20/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3616 - auc: 0.8839 - accuracy: 0.8339 - val_loss: 0.4069 - val_auc: 0.8834 - val_accuracy: 0.8237\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3616 - auc: 0.8839 - accuracy: 0.8339 - val_loss: 0.4069 - val_auc: 0.8834 - val_accuracy: 0.8237\n", "Epoch 21/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3736 - auc: 0.8796 - accuracy: 0.8301 - val_loss: 0.3669 - val_auc: 0.8872 - val_accuracy: 0.8299\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3736 - auc: 0.8796 - accuracy: 0.8301 - val_loss: 0.3669 - val_auc: 0.8872 - val_accuracy: 0.8299\n", "Epoch 22/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3660 - auc: 0.8823 - accuracy: 0.8291 - val_loss: 0.3668 - val_auc: 0.8882 - val_accuracy: 0.8303\n", + "814/814 [==============================] - 1s 975us/step - loss: 0.3660 - auc: 0.8823 - accuracy: 0.8291 - val_loss: 0.3668 - val_auc: 0.8882 - val_accuracy: 0.8303\n", "Epoch 23/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3623 - auc: 0.8835 - accuracy: 0.8329 - val_loss: 0.3843 - val_auc: 0.8860 - val_accuracy: 0.8271\n", + "814/814 [==============================] - 1s 934us/step - loss: 0.3623 - auc: 0.8835 - accuracy: 0.8329 - val_loss: 0.3843 - val_auc: 0.8860 - val_accuracy: 0.8271\n", "Epoch 24/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3616 - auc: 0.8839 - accuracy: 0.8333 - val_loss: 0.3649 - val_auc: 0.8870 - val_accuracy: 0.8287\n", "Epoch 25/200\n", @@ -2185,9 +2185,9 @@ "Epoch 27/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3704 - auc: 0.8826 - accuracy: 0.8304 - val_loss: 0.3617 - val_auc: 0.8883 - val_accuracy: 0.8299\n", "Epoch 28/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3697 - auc: 0.8819 - accuracy: 0.8291 - val_loss: 0.3865 - val_auc: 0.8881 - val_accuracy: 0.8259\n", + "814/814 [==============================] - 1s 972us/step - loss: 0.3697 - auc: 0.8819 - accuracy: 0.8291 - val_loss: 0.3865 - val_auc: 0.8881 - val_accuracy: 0.8259\n", "Epoch 29/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3648 - auc: 0.8817 - accuracy: 0.8341 - val_loss: 0.3721 - val_auc: 0.8900 - val_accuracy: 0.8287\n", + "814/814 [==============================] - 1s 980us/step - loss: 0.3648 - auc: 0.8817 - accuracy: 0.8341 - val_loss: 0.3721 - val_auc: 0.8900 - val_accuracy: 0.8287\n", "Epoch 30/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3753 - auc: 0.8839 - accuracy: 0.8333 - val_loss: 0.3950 - val_auc: 0.8871 - val_accuracy: 0.8268\n", "Epoch 31/200\n", @@ -2199,11 +2199,11 @@ "Epoch 34/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3690 - auc: 0.8900 - accuracy: 0.8341 - val_loss: 0.3799 - val_auc: 0.8773 - val_accuracy: 0.8323\n", "Epoch 35/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3581 - auc: 0.8872 - accuracy: 0.8313 - val_loss: 0.4458 - val_auc: 0.8290 - val_accuracy: 0.8027\n", + "814/814 [==============================] - 1s 989us/step - loss: 0.3581 - auc: 0.8872 - accuracy: 0.8313 - val_loss: 0.4458 - val_auc: 0.8290 - val_accuracy: 0.8027\n", "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3655 - auc: 0.8829 - accuracy: 0.8273 - val_loss: 0.3577 - val_auc: 0.8926 - val_accuracy: 0.8326\n", + "814/814 [==============================] - 1s 968us/step - loss: 0.3655 - auc: 0.8829 - accuracy: 0.8273 - val_loss: 0.3577 - val_auc: 0.8926 - val_accuracy: 0.8326\n", "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3618 - auc: 0.8847 - accuracy: 0.8322 - val_loss: 0.3574 - val_auc: 0.8915 - val_accuracy: 0.8325\n", + "814/814 [==============================] - 1s 979us/step - loss: 0.3618 - auc: 0.8847 - accuracy: 0.8322 - val_loss: 0.3574 - val_auc: 0.8915 - val_accuracy: 0.8325\n", "Epoch 38/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3575 - auc: 0.8892 - accuracy: 0.8317 - val_loss: 0.3590 - val_auc: 0.8909 - val_accuracy: 0.8326\n", "Epoch 39/200\n", @@ -2213,9 +2213,9 @@ "Epoch 41/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3521 - auc: 0.8909 - accuracy: 0.8303 - val_loss: 0.3561 - val_auc: 0.8929 - val_accuracy: 0.8320\n", "Epoch 42/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3655 - auc: 0.8834 - accuracy: 0.8299 - val_loss: 0.3574 - val_auc: 0.8915 - val_accuracy: 0.8325\n", + "814/814 [==============================] - 1s 977us/step - loss: 0.3655 - auc: 0.8834 - accuracy: 0.8299 - val_loss: 0.3574 - val_auc: 0.8915 - val_accuracy: 0.8325\n", "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3684 - auc: 0.8877 - accuracy: 0.8370 - val_loss: 0.3924 - val_auc: 0.8709 - val_accuracy: 0.8145\n", + "814/814 [==============================] - 1s 947us/step - loss: 0.3684 - auc: 0.8877 - accuracy: 0.8370 - val_loss: 0.3924 - val_auc: 0.8709 - val_accuracy: 0.8145\n", "Epoch 44/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3584 - auc: 0.8887 - accuracy: 0.8336 - val_loss: 0.3771 - val_auc: 0.8904 - val_accuracy: 0.8299\n", "Epoch 45/200\n", @@ -2225,11 +2225,11 @@ "Epoch 47/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.8949 - accuracy: 0.8371 - val_loss: 0.3745 - val_auc: 0.8782 - val_accuracy: 0.8222\n", "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3540 - auc: 0.8903 - accuracy: 0.8348 - val_loss: 0.3681 - val_auc: 0.8943 - val_accuracy: 0.8308\n", + "814/814 [==============================] - 1s 990us/step - loss: 0.3540 - auc: 0.8903 - accuracy: 0.8348 - val_loss: 0.3681 - val_auc: 0.8943 - val_accuracy: 0.8308\n", "Epoch 49/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3522 - auc: 0.8909 - accuracy: 0.8309 - val_loss: 0.3618 - val_auc: 0.8935 - val_accuracy: 0.8323\n", + "814/814 [==============================] - 1s 944us/step - loss: 0.3522 - auc: 0.8909 - accuracy: 0.8309 - val_loss: 0.3618 - val_auc: 0.8935 - val_accuracy: 0.8323\n", "Epoch 50/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3536 - auc: 0.8909 - accuracy: 0.8345 - val_loss: 0.3675 - val_auc: 0.8935 - val_accuracy: 0.8316\n", + "814/814 [==============================] - 1s 979us/step - loss: 0.3536 - auc: 0.8909 - accuracy: 0.8345 - val_loss: 0.3675 - val_auc: 0.8935 - val_accuracy: 0.8316\n", "Epoch 51/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3564 - auc: 0.8891 - accuracy: 0.8345 - val_loss: 0.4143 - val_auc: 0.8873 - val_accuracy: 0.8270\n", "Epoch 52/200\n", @@ -2239,9 +2239,9 @@ "Epoch 54/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3565 - auc: 0.8871 - accuracy: 0.8342 - val_loss: 0.3563 - val_auc: 0.8962 - val_accuracy: 0.8348\n", "Epoch 55/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3589 - auc: 0.8875 - accuracy: 0.8320 - val_loss: 0.3820 - val_auc: 0.8917 - val_accuracy: 0.8293\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3589 - auc: 0.8875 - accuracy: 0.8320 - val_loss: 0.3820 - val_auc: 0.8917 - val_accuracy: 0.8293\n", "Epoch 56/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3592 - auc: 0.8892 - accuracy: 0.8354 - val_loss: 0.3539 - val_auc: 0.8961 - val_accuracy: 0.8353\n", + "814/814 [==============================] - 1s 948us/step - loss: 0.3592 - auc: 0.8892 - accuracy: 0.8354 - val_loss: 0.3539 - val_auc: 0.8961 - val_accuracy: 0.8353\n", "Epoch 57/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3556 - auc: 0.8912 - accuracy: 0.8351 - val_loss: 0.3561 - val_auc: 0.8966 - val_accuracy: 0.8337\n", "Epoch 58/200\n", @@ -2251,9 +2251,9 @@ "Epoch 60/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3658 - auc: 0.8887 - accuracy: 0.8329 - val_loss: 0.3575 - val_auc: 0.8933 - val_accuracy: 0.8334\n", "Epoch 61/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3535 - auc: 0.8906 - accuracy: 0.8363 - val_loss: 0.3546 - val_auc: 0.8965 - val_accuracy: 0.8340\n", + "814/814 [==============================] - 1s 954us/step - loss: 0.3535 - auc: 0.8906 - accuracy: 0.8363 - val_loss: 0.3546 - val_auc: 0.8965 - val_accuracy: 0.8340\n", "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3554 - auc: 0.8896 - accuracy: 0.8325 - val_loss: 0.3601 - val_auc: 0.8949 - val_accuracy: 0.8319\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3554 - auc: 0.8896 - accuracy: 0.8325 - val_loss: 0.3601 - val_auc: 0.8949 - val_accuracy: 0.8319\n", "Epoch 63/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3539 - auc: 0.8909 - accuracy: 0.8365 - val_loss: 0.3569 - val_auc: 0.8963 - val_accuracy: 0.8322\n", "Epoch 64/200\n", @@ -2265,7 +2265,7 @@ "Epoch 67/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3467 - auc: 0.8926 - accuracy: 0.8350 - val_loss: 0.3627 - val_auc: 0.8954 - val_accuracy: 0.8317\n", "Epoch 68/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8949 - accuracy: 0.8349 - val_loss: 0.3798 - val_auc: 0.8747 - val_accuracy: 0.8187\n", + "814/814 [==============================] - 1s 971us/step - loss: 0.3478 - auc: 0.8949 - accuracy: 0.8349 - val_loss: 0.3798 - val_auc: 0.8747 - val_accuracy: 0.8187\n", "Epoch 69/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3526 - auc: 0.8913 - accuracy: 0.8329 - val_loss: 0.3506 - val_auc: 0.8971 - val_accuracy: 0.8345\n", "Epoch 70/200\n", @@ -2275,9 +2275,9 @@ "Epoch 72/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3538 - auc: 0.8937 - accuracy: 0.8364 - val_loss: 0.3710 - val_auc: 0.8944 - val_accuracy: 0.8300\n", "Epoch 73/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3558 - auc: 0.8929 - accuracy: 0.8351 - val_loss: 0.3554 - val_auc: 0.8933 - val_accuracy: 0.8339\n", + "814/814 [==============================] - 1s 975us/step - loss: 0.3558 - auc: 0.8929 - accuracy: 0.8351 - val_loss: 0.3554 - val_auc: 0.8933 - val_accuracy: 0.8339\n", "Epoch 74/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3565 - auc: 0.8894 - accuracy: 0.8313 - val_loss: 0.3646 - val_auc: 0.8942 - val_accuracy: 0.8322\n", + "814/814 [==============================] - 1s 947us/step - loss: 0.3565 - auc: 0.8894 - accuracy: 0.8313 - val_loss: 0.3646 - val_auc: 0.8942 - val_accuracy: 0.8322\n", "Epoch 75/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3579 - auc: 0.8921 - accuracy: 0.8308 - val_loss: 0.3542 - val_auc: 0.8943 - val_accuracy: 0.8343\n", "Epoch 76/200\n", @@ -2289,7 +2289,7 @@ "Epoch 79/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3589 - auc: 0.8890 - accuracy: 0.8318 - val_loss: 0.3837 - val_auc: 0.8698 - val_accuracy: 0.8145\n", "Epoch 80/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3585 - auc: 0.8906 - accuracy: 0.8384 - val_loss: 0.3847 - val_auc: 0.8940 - val_accuracy: 0.8300\n", + "814/814 [==============================] - 1s 990us/step - loss: 0.3585 - auc: 0.8906 - accuracy: 0.8384 - val_loss: 0.3847 - val_auc: 0.8940 - val_accuracy: 0.8300\n", "Epoch 81/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3470 - auc: 0.8943 - accuracy: 0.8349 - val_loss: 0.3696 - val_auc: 0.8966 - val_accuracy: 0.8279\n", "Epoch 82/200\n", @@ -2311,11 +2311,11 @@ "Epoch 90/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3544 - auc: 0.8918 - accuracy: 0.8471 - val_loss: 0.3530 - val_auc: 0.8950 - val_accuracy: 0.8440\n", "Epoch 91/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3432 - auc: 0.8961 - accuracy: 0.8458 - val_loss: 0.3635 - val_auc: 0.8960 - val_accuracy: 0.8392\n", + "814/814 [==============================] - 1s 997us/step - loss: 0.3432 - auc: 0.8961 - accuracy: 0.8458 - val_loss: 0.3635 - val_auc: 0.8960 - val_accuracy: 0.8392\n", "Epoch 92/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3464 - auc: 0.8953 - accuracy: 0.8473 - val_loss: 0.9888 - val_auc: 0.7175 - val_accuracy: 0.7955\n", + "814/814 [==============================] - 1s 981us/step - loss: 0.3464 - auc: 0.8953 - accuracy: 0.8473 - val_loss: 0.9888 - val_auc: 0.7175 - val_accuracy: 0.7955\n", "Epoch 93/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3652 - auc: 0.8890 - accuracy: 0.8383 - val_loss: 0.3726 - val_auc: 0.8824 - val_accuracy: 0.8299\n", + "814/814 [==============================] - 1s 988us/step - loss: 0.3652 - auc: 0.8890 - accuracy: 0.8383 - val_loss: 0.3726 - val_auc: 0.8824 - val_accuracy: 0.8299\n", "Epoch 94/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3490 - auc: 0.8933 - accuracy: 0.8446 - val_loss: 0.3616 - val_auc: 0.8963 - val_accuracy: 0.8423\n", "Epoch 95/200\n", @@ -2323,9 +2323,9 @@ "Epoch 96/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3476 - auc: 0.8965 - accuracy: 0.8447 - val_loss: 0.3715 - val_auc: 0.8952 - val_accuracy: 0.8357\n", "Epoch 97/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3494 - auc: 0.8968 - accuracy: 0.8458 - val_loss: 0.3538 - val_auc: 0.8989 - val_accuracy: 0.8443\n", + "814/814 [==============================] - 1s 988us/step - loss: 0.3494 - auc: 0.8968 - accuracy: 0.8458 - val_loss: 0.3538 - val_auc: 0.8989 - val_accuracy: 0.8443\n", "Epoch 98/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3573 - auc: 0.8906 - accuracy: 0.8435 - val_loss: 0.3706 - val_auc: 0.8962 - val_accuracy: 0.8449\n", + "814/814 [==============================] - 1s 976us/step - loss: 0.3573 - auc: 0.8906 - accuracy: 0.8435 - val_loss: 0.3706 - val_auc: 0.8962 - val_accuracy: 0.8449\n", "Epoch 99/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3502 - auc: 0.8939 - accuracy: 0.8467 - val_loss: 0.3651 - val_auc: 0.8968 - val_accuracy: 0.8420\n", "Epoch 100/200\n", @@ -2337,9 +2337,9 @@ "Epoch 103/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3502 - auc: 0.8925 - accuracy: 0.8417 - val_loss: 0.3626 - val_auc: 0.8941 - val_accuracy: 0.8369\n", "Epoch 104/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3553 - auc: 0.8927 - accuracy: 0.8430 - val_loss: 0.3617 - val_auc: 0.8976 - val_accuracy: 0.8382\n", + "814/814 [==============================] - 1s 984us/step - loss: 0.3553 - auc: 0.8927 - accuracy: 0.8430 - val_loss: 0.3617 - val_auc: 0.8976 - val_accuracy: 0.8382\n", "Epoch 105/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3464 - auc: 0.8949 - accuracy: 0.8473 - val_loss: 0.3520 - val_auc: 0.8979 - val_accuracy: 0.8466\n", + "814/814 [==============================] - 1s 1000us/step - loss: 0.3464 - auc: 0.8949 - accuracy: 0.8473 - val_loss: 0.3520 - val_auc: 0.8979 - val_accuracy: 0.8466\n", "Epoch 106/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3491 - auc: 0.8942 - accuracy: 0.8449 - val_loss: 0.3551 - val_auc: 0.8993 - val_accuracy: 0.8437\n", "Epoch 107/200\n", @@ -2349,9 +2349,9 @@ "Epoch 109/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3529 - auc: 0.8947 - accuracy: 0.8455 - val_loss: 0.3583 - val_auc: 0.8992 - val_accuracy: 0.8420\n", "Epoch 110/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3577 - auc: 0.8900 - accuracy: 0.8460 - val_loss: 0.3678 - val_auc: 0.8964 - val_accuracy: 0.8412\n", + "814/814 [==============================] - 1s 980us/step - loss: 0.3577 - auc: 0.8900 - accuracy: 0.8460 - val_loss: 0.3678 - val_auc: 0.8964 - val_accuracy: 0.8412\n", "Epoch 111/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3523 - auc: 0.8939 - accuracy: 0.8467 - val_loss: 0.3582 - val_auc: 0.8981 - val_accuracy: 0.8425\n", + "814/814 [==============================] - 1s 955us/step - loss: 0.3523 - auc: 0.8939 - accuracy: 0.8467 - val_loss: 0.3582 - val_auc: 0.8981 - val_accuracy: 0.8425\n", "Epoch 112/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3563 - auc: 0.8965 - accuracy: 0.8453 - val_loss: 0.3751 - val_auc: 0.8965 - val_accuracy: 0.8462\n", "Epoch 113/200\n", @@ -2361,9 +2361,9 @@ "Epoch 115/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3372 - auc: 0.8978 - accuracy: 0.8457 - val_loss: 0.3627 - val_auc: 0.8935 - val_accuracy: 0.8357\n", "Epoch 116/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3456 - auc: 0.8966 - accuracy: 0.8467 - val_loss: 0.4200 - val_auc: 0.8694 - val_accuracy: 0.8311\n", + "814/814 [==============================] - 1s 984us/step - loss: 0.3456 - auc: 0.8966 - accuracy: 0.8467 - val_loss: 0.4200 - val_auc: 0.8694 - val_accuracy: 0.8311\n", "Epoch 117/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3563 - auc: 0.8923 - accuracy: 0.8410 - val_loss: 0.3525 - val_auc: 0.8993 - val_accuracy: 0.8451\n", + "814/814 [==============================] - 1s 978us/step - loss: 0.3563 - auc: 0.8923 - accuracy: 0.8410 - val_loss: 0.3525 - val_auc: 0.8993 - val_accuracy: 0.8451\n", "Epoch 118/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3543 - auc: 0.8944 - accuracy: 0.8448 - val_loss: 0.3584 - val_auc: 0.8942 - val_accuracy: 0.8409\n", "Epoch 119/200\n", @@ -2395,7 +2395,7 @@ "Epoch 132/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8955 - accuracy: 0.8441 - val_loss: 0.3924 - val_auc: 0.8925 - val_accuracy: 0.8383\n", "Epoch 133/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3539 - auc: 0.8921 - accuracy: 0.8432 - val_loss: 0.3682 - val_auc: 0.8980 - val_accuracy: 0.8425\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3539 - auc: 0.8921 - accuracy: 0.8432 - val_loss: 0.3682 - val_auc: 0.8980 - val_accuracy: 0.8425\n", "Epoch 134/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.8965 - accuracy: 0.8466 - val_loss: 0.3506 - val_auc: 0.9003 - val_accuracy: 0.8454\n", "Epoch 135/200\n", @@ -2403,7 +2403,7 @@ "Epoch 136/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3474 - auc: 0.8954 - accuracy: 0.8435 - val_loss: 0.3560 - val_auc: 0.8915 - val_accuracy: 0.8411\n", "Epoch 137/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3604 - auc: 0.8942 - accuracy: 0.8447 - val_loss: 0.3507 - val_auc: 0.9006 - val_accuracy: 0.8480\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3604 - auc: 0.8942 - accuracy: 0.8447 - val_loss: 0.3507 - val_auc: 0.9006 - val_accuracy: 0.8480\n", "Epoch 138/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3417 - auc: 0.8971 - accuracy: 0.8472 - val_loss: 0.3536 - val_auc: 0.8998 - val_accuracy: 0.8449\n", "Epoch 139/200\n", @@ -2417,11 +2417,11 @@ "Epoch 143/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3401 - auc: 0.8982 - accuracy: 0.8499 - val_loss: 0.3633 - val_auc: 0.8988 - val_accuracy: 0.8462\n", "Epoch 144/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3403 - auc: 0.8979 - accuracy: 0.8481 - val_loss: 0.3525 - val_auc: 0.8970 - val_accuracy: 0.8419\n", + "814/814 [==============================] - 1s 959us/step - loss: 0.3403 - auc: 0.8979 - accuracy: 0.8481 - val_loss: 0.3525 - val_auc: 0.8970 - val_accuracy: 0.8419\n", "Epoch 145/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3580 - auc: 0.8941 - accuracy: 0.8440 - val_loss: 0.3700 - val_auc: 0.8838 - val_accuracy: 0.8328\n", + "814/814 [==============================] - 1s 956us/step - loss: 0.3580 - auc: 0.8941 - accuracy: 0.8440 - val_loss: 0.3700 - val_auc: 0.8838 - val_accuracy: 0.8328\n", "Epoch 146/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.8961 - accuracy: 0.8485 - val_loss: 0.3480 - val_auc: 0.8993 - val_accuracy: 0.8445\n", + "814/814 [==============================] - 1s 1000us/step - loss: 0.3457 - auc: 0.8961 - accuracy: 0.8485 - val_loss: 0.3480 - val_auc: 0.8993 - val_accuracy: 0.8445\n", "Epoch 147/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3570 - auc: 0.8944 - accuracy: 0.8463 - val_loss: 0.3617 - val_auc: 0.8973 - val_accuracy: 0.8426\n", "Epoch 148/200\n", @@ -2431,7 +2431,7 @@ "Epoch 150/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3435 - auc: 0.8974 - accuracy: 0.8469 - val_loss: 0.3505 - val_auc: 0.9006 - val_accuracy: 0.8472\n", "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3551 - auc: 0.8946 - accuracy: 0.8480 - val_loss: 0.3620 - val_auc: 0.8981 - val_accuracy: 0.8440\n", + "814/814 [==============================] - 1s 986us/step - loss: 0.3551 - auc: 0.8946 - accuracy: 0.8480 - val_loss: 0.3620 - val_auc: 0.8981 - val_accuracy: 0.8440\n", "Epoch 152/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3512 - auc: 0.8960 - accuracy: 0.8469 - val_loss: 0.3534 - val_auc: 0.8989 - val_accuracy: 0.8435\n", "Epoch 153/200\n", @@ -2443,9 +2443,9 @@ "Epoch 156/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3447 - auc: 0.8967 - accuracy: 0.8492 - val_loss: 0.3450 - val_auc: 0.9018 - val_accuracy: 0.8495\n", "Epoch 157/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8978 - accuracy: 0.8497 - val_loss: 0.3459 - val_auc: 0.9009 - val_accuracy: 0.8463\n", + "814/814 [==============================] - 1s 987us/step - loss: 0.3424 - auc: 0.8978 - accuracy: 0.8497 - val_loss: 0.3459 - val_auc: 0.9009 - val_accuracy: 0.8463\n", "Epoch 158/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3394 - auc: 0.8995 - accuracy: 0.8491 - val_loss: 0.5073 - val_auc: 0.7976 - val_accuracy: 0.8058\n", + "814/814 [==============================] - 1s 994us/step - loss: 0.3394 - auc: 0.8995 - accuracy: 0.8491 - val_loss: 0.5073 - val_auc: 0.7976 - val_accuracy: 0.8058\n", "Epoch 159/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3533 - auc: 0.8962 - accuracy: 0.8465 - val_loss: 0.3470 - val_auc: 0.9021 - val_accuracy: 0.8481\n", "Epoch 160/200\n", @@ -2455,9 +2455,9 @@ "Epoch 162/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3566 - auc: 0.8968 - accuracy: 0.8459 - val_loss: 0.3815 - val_auc: 0.8724 - val_accuracy: 0.8251\n", "Epoch 163/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3414 - auc: 0.8978 - accuracy: 0.8474 - val_loss: 0.3612 - val_auc: 0.8996 - val_accuracy: 0.8446\n", + "814/814 [==============================] - 1s 991us/step - loss: 0.3414 - auc: 0.8978 - accuracy: 0.8474 - val_loss: 0.3612 - val_auc: 0.8996 - val_accuracy: 0.8446\n", "Epoch 164/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3456 - auc: 0.9012 - accuracy: 0.8472 - val_loss: 0.3471 - val_auc: 0.9011 - val_accuracy: 0.8474\n", + "814/814 [==============================] - 1s 984us/step - loss: 0.3456 - auc: 0.9012 - accuracy: 0.8472 - val_loss: 0.3471 - val_auc: 0.9011 - val_accuracy: 0.8474\n", "Epoch 165/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.8969 - accuracy: 0.8474 - val_loss: 0.3468 - val_auc: 0.9012 - val_accuracy: 0.8472\n", "Epoch 166/200\n", @@ -2467,9 +2467,9 @@ "Epoch 168/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.8973 - accuracy: 0.8445 - val_loss: 0.3570 - val_auc: 0.8989 - val_accuracy: 0.8440\n", "Epoch 169/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3461 - auc: 0.8978 - accuracy: 0.8471 - val_loss: 0.3518 - val_auc: 0.9025 - val_accuracy: 0.8481\n", + "814/814 [==============================] - 1s 973us/step - loss: 0.3461 - auc: 0.8978 - accuracy: 0.8471 - val_loss: 0.3518 - val_auc: 0.9025 - val_accuracy: 0.8481\n", "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3496 - auc: 0.8946 - accuracy: 0.8469 - val_loss: 0.3587 - val_auc: 0.8991 - val_accuracy: 0.8417\n", + "814/814 [==============================] - 1s 978us/step - loss: 0.3496 - auc: 0.8946 - accuracy: 0.8469 - val_loss: 0.3587 - val_auc: 0.8991 - val_accuracy: 0.8417\n", "Epoch 171/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3443 - auc: 0.9001 - accuracy: 0.8520 - val_loss: 0.3443 - val_auc: 0.9006 - val_accuracy: 0.8503\n", "Epoch 172/200\n", @@ -2481,9 +2481,9 @@ "Epoch 175/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3411 - auc: 0.8982 - accuracy: 0.8480 - val_loss: 0.3419 - val_auc: 0.9023 - val_accuracy: 0.8478\n", "Epoch 176/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3469 - auc: 0.8985 - accuracy: 0.8468 - val_loss: 0.3506 - val_auc: 0.9006 - val_accuracy: 0.8442\n", + "814/814 [==============================] - 1s 986us/step - loss: 0.3469 - auc: 0.8985 - accuracy: 0.8468 - val_loss: 0.3506 - val_auc: 0.9006 - val_accuracy: 0.8442\n", "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3422 - auc: 0.8975 - accuracy: 0.8508 - val_loss: 0.3573 - val_auc: 0.8902 - val_accuracy: 0.8369\n", + "814/814 [==============================] - 1s 978us/step - loss: 0.3422 - auc: 0.8975 - accuracy: 0.8508 - val_loss: 0.3573 - val_auc: 0.8902 - val_accuracy: 0.8369\n", "Epoch 178/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3451 - auc: 0.8988 - accuracy: 0.8471 - val_loss: 0.3439 - val_auc: 0.9032 - val_accuracy: 0.8500\n", "Epoch 179/200\n", @@ -2493,9 +2493,9 @@ "Epoch 181/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3440 - auc: 0.8985 - accuracy: 0.8453 - val_loss: 0.3528 - val_auc: 0.8983 - val_accuracy: 0.8452\n", "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3389 - auc: 0.9004 - accuracy: 0.8489 - val_loss: 0.3559 - val_auc: 0.9004 - val_accuracy: 0.8429\n", + "814/814 [==============================] - 1s 996us/step - loss: 0.3389 - auc: 0.9004 - accuracy: 0.8489 - val_loss: 0.3559 - val_auc: 0.9004 - val_accuracy: 0.8429\n", "Epoch 183/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3433 - auc: 0.9008 - accuracy: 0.8491 - val_loss: 0.3677 - val_auc: 0.8982 - val_accuracy: 0.8408\n", + "814/814 [==============================] - 1s 984us/step - loss: 0.3433 - auc: 0.9008 - accuracy: 0.8491 - val_loss: 0.3677 - val_auc: 0.8982 - val_accuracy: 0.8408\n", "Epoch 184/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3427 - auc: 0.8999 - accuracy: 0.8491 - val_loss: 0.3573 - val_auc: 0.8924 - val_accuracy: 0.8354\n", "Epoch 185/200\n", @@ -2507,7 +2507,7 @@ "Epoch 188/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3408 - auc: 0.8978 - accuracy: 0.8494 - val_loss: 0.3396 - val_auc: 0.9040 - val_accuracy: 0.8492\n", "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3447 - auc: 0.8982 - accuracy: 0.8481 - val_loss: 0.3640 - val_auc: 0.9016 - val_accuracy: 0.8446\n", + "814/814 [==============================] - 1s 970us/step - loss: 0.3447 - auc: 0.8982 - accuracy: 0.8481 - val_loss: 0.3640 - val_auc: 0.9016 - val_accuracy: 0.8446\n", "Epoch 190/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3472 - auc: 0.8989 - accuracy: 0.8500 - val_loss: 0.3530 - val_auc: 0.9009 - val_accuracy: 0.8432\n", "Epoch 191/200\n", @@ -2523,7 +2523,7 @@ "Epoch 196/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3379 - auc: 0.8994 - accuracy: 0.8509 - val_loss: 0.3418 - val_auc: 0.9013 - val_accuracy: 0.8488\n", "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3394 - auc: 0.8981 - accuracy: 0.8490 - val_loss: 0.3455 - val_auc: 0.9013 - val_accuracy: 0.8458\n", + "814/814 [==============================] - 1s 998us/step - loss: 0.3394 - auc: 0.8981 - accuracy: 0.8490 - val_loss: 0.3455 - val_auc: 0.9013 - val_accuracy: 0.8458\n", "Epoch 198/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3450 - auc: 0.8966 - accuracy: 0.8479 - val_loss: 0.3628 - val_auc: 0.8992 - val_accuracy: 0.8446\n", "Epoch 199/200\n", @@ -2539,7 +2539,7 @@ }, { "cell_type": "markdown", - "id": "c51f0b60", + "id": "d9c4bf0a", "metadata": {}, "source": [ "#### Métricas" @@ -2547,7 +2547,7 @@ }, { "cell_type": "markdown", - "id": "d9152242", + "id": "6270bd11", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -2556,7 +2556,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "1317b544", + "id": "9d08735f", "metadata": {}, "outputs": [ { @@ -2586,7 +2586,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "53bdbc0a", + "id": "d79cab18", "metadata": {}, "outputs": [ { @@ -2616,7 +2616,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "8e9dd85b", + "id": "1a6aaaf1", "metadata": {}, "outputs": [ { @@ -2674,7 +2674,7 @@ }, { "cell_type": "markdown", - "id": "e4f8e909", + "id": "c2234f8a", "metadata": {}, "source": [ "Observamos que mejoro considerablemente el AUC-ROC. También se observan mejoras significativas en la medición de precision y recall de la clase con altos ingresos. Por último, destacar la estabilización de la curva de aprendizaje" @@ -2682,7 +2682,7 @@ }, { "cell_type": "markdown", - "id": "f6ac03a9", + "id": "959c641b", "metadata": {}, "source": [ "### Quinto entrenamiento" @@ -2690,7 +2690,7 @@ }, { "cell_type": "markdown", - "id": "b9d11f93", + "id": "4281f80a", "metadata": {}, "source": [ "#### Diseño" @@ -2698,7 +2698,7 @@ }, { "cell_type": "markdown", - "id": "60cd6e08", + "id": "392b4eb0", "metadata": {}, "source": [ "Realizamos un retoque final a la red, modificando la regularización a l1 y agregando una capa más de 16 neuronas. No agrandamos más al red en este caso porque no encontramos mejora alguna e incluso empeoraba en algunas configuraciones" @@ -2707,7 +2707,7 @@ { "cell_type": "code", "execution_count": 32, - "id": "bc27ac8b", + "id": "34f7a519", "metadata": {}, "outputs": [], "source": [ @@ -2718,7 +2718,7 @@ { "cell_type": "code", "execution_count": 33, - "id": "f6d22b43", + "id": "267d9c44", "metadata": {}, "outputs": [], "source": [ @@ -2732,7 +2732,7 @@ }, { "cell_type": "markdown", - "id": "66f32dae", + "id": "d4c4b22e", "metadata": {}, "source": [ "Compilamos y mostramos un resumen de la red" @@ -2741,7 +2741,7 @@ { "cell_type": "code", "execution_count": 34, - "id": "d879abb5", + "id": "2546b39e", "metadata": {}, "outputs": [ { @@ -2777,7 +2777,7 @@ }, { "cell_type": "markdown", - "id": "78a0a4c2", + "id": "fc819eb8", "metadata": {}, "source": [ "Tenemos 1200 params, mientrás que anteriormente teniamos aproximadamente 800" @@ -2786,7 +2786,7 @@ { "cell_type": "code", "execution_count": 35, - "id": "d482e1c6", + "id": "7f2e4e11", "metadata": {}, "outputs": [ { @@ -2794,11 +2794,11 @@ "output_type": "stream", "text": [ "Epoch 1/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 2.1281 - auc: 0.3829 - accuracy: 0.7333 - val_loss: 0.5417 - val_auc: 0.5710 - val_accuracy: 0.7792\n", + "814/814 [==============================] - 2s 1ms/step - loss: 2.1281 - auc: 0.3829 - accuracy: 0.7333 - val_loss: 0.5417 - val_auc: 0.5710 - val_accuracy: 0.7792\n", "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5233 - auc: 0.6245 - accuracy: 0.7791 - val_loss: 0.4984 - val_auc: 0.7327 - val_accuracy: 0.7778\n", + "814/814 [==============================] - 1s 997us/step - loss: 0.5233 - auc: 0.6245 - accuracy: 0.7791 - val_loss: 0.4984 - val_auc: 0.7327 - val_accuracy: 0.7778\n", "Epoch 3/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4870 - auc: 0.7412 - accuracy: 0.7815 - val_loss: 0.4661 - val_auc: 0.7939 - val_accuracy: 0.7781\n", + "814/814 [==============================] - 1s 985us/step - loss: 0.4870 - auc: 0.7412 - accuracy: 0.7815 - val_loss: 0.4661 - val_auc: 0.7939 - val_accuracy: 0.7781\n", "Epoch 4/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4573 - auc: 0.7933 - accuracy: 0.7834 - val_loss: 0.4377 - val_auc: 0.8270 - val_accuracy: 0.7795\n", "Epoch 5/200\n", @@ -2808,7 +2808,7 @@ "Epoch 7/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3990 - auc: 0.8537 - accuracy: 0.8075 - val_loss: 0.3898 - val_auc: 0.8648 - val_accuracy: 0.8085\n", "Epoch 8/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3866 - auc: 0.8646 - accuracy: 0.8199 - val_loss: 0.3785 - val_auc: 0.8769 - val_accuracy: 0.8228\n", + "814/814 [==============================] - 1s 1000us/step - loss: 0.3866 - auc: 0.8646 - accuracy: 0.8199 - val_loss: 0.3785 - val_auc: 0.8769 - val_accuracy: 0.8228\n", "Epoch 9/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3782 - auc: 0.8710 - accuracy: 0.8265 - val_loss: 0.3722 - val_auc: 0.8805 - val_accuracy: 0.8282\n", "Epoch 10/200\n", @@ -2820,9 +2820,9 @@ "Epoch 13/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3620 - auc: 0.8833 - accuracy: 0.8330 - val_loss: 0.3680 - val_auc: 0.8839 - val_accuracy: 0.8296\n", "Epoch 14/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3689 - auc: 0.8820 - accuracy: 0.8288 - val_loss: 0.3701 - val_auc: 0.8843 - val_accuracy: 0.8313\n", + "814/814 [==============================] - 1s 989us/step - loss: 0.3689 - auc: 0.8820 - accuracy: 0.8288 - val_loss: 0.3701 - val_auc: 0.8843 - val_accuracy: 0.8313\n", "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3680 - auc: 0.8821 - accuracy: 0.8321 - val_loss: 0.3677 - val_auc: 0.8842 - val_accuracy: 0.8259\n", + "814/814 [==============================] - 1s 990us/step - loss: 0.3680 - auc: 0.8821 - accuracy: 0.8321 - val_loss: 0.3677 - val_auc: 0.8842 - val_accuracy: 0.8259\n", "Epoch 16/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3709 - auc: 0.8783 - accuracy: 0.8276 - val_loss: 0.3672 - val_auc: 0.8854 - val_accuracy: 0.8296\n", "Epoch 17/200\n", @@ -2832,9 +2832,9 @@ "Epoch 19/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3738 - auc: 0.8785 - accuracy: 0.8248 - val_loss: 0.3697 - val_auc: 0.8853 - val_accuracy: 0.8270\n", "Epoch 20/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3594 - auc: 0.8871 - accuracy: 0.8337 - val_loss: 0.3788 - val_auc: 0.8830 - val_accuracy: 0.8227\n", + "814/814 [==============================] - 1s 990us/step - loss: 0.3594 - auc: 0.8871 - accuracy: 0.8337 - val_loss: 0.3788 - val_auc: 0.8830 - val_accuracy: 0.8227\n", "Epoch 21/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3627 - auc: 0.8853 - accuracy: 0.8312 - val_loss: 0.3659 - val_auc: 0.8871 - val_accuracy: 0.8276\n", + "814/814 [==============================] - 1s 993us/step - loss: 0.3627 - auc: 0.8853 - accuracy: 0.8312 - val_loss: 0.3659 - val_auc: 0.8871 - val_accuracy: 0.8276\n", "Epoch 22/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3618 - auc: 0.8851 - accuracy: 0.8311 - val_loss: 0.3647 - val_auc: 0.8883 - val_accuracy: 0.8296\n", "Epoch 23/200\n", @@ -2844,9 +2844,9 @@ "Epoch 25/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3606 - auc: 0.8859 - accuracy: 0.8358 - val_loss: 0.3659 - val_auc: 0.8879 - val_accuracy: 0.8294\n", "Epoch 26/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3614 - auc: 0.8873 - accuracy: 0.8323 - val_loss: 0.3657 - val_auc: 0.8881 - val_accuracy: 0.8296\n", + "814/814 [==============================] - 1s 995us/step - loss: 0.3614 - auc: 0.8873 - accuracy: 0.8323 - val_loss: 0.3657 - val_auc: 0.8881 - val_accuracy: 0.8296\n", "Epoch 27/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3651 - auc: 0.8855 - accuracy: 0.8304 - val_loss: 0.3648 - val_auc: 0.8883 - val_accuracy: 0.8322\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3651 - auc: 0.8855 - accuracy: 0.8304 - val_loss: 0.3648 - val_auc: 0.8883 - val_accuracy: 0.8322\n", "Epoch 28/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3644 - auc: 0.8855 - accuracy: 0.8350 - val_loss: 0.3832 - val_auc: 0.8787 - val_accuracy: 0.8334\n", "Epoch 29/200\n", @@ -2856,9 +2856,9 @@ "Epoch 31/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3631 - auc: 0.8860 - accuracy: 0.8346 - val_loss: 0.3676 - val_auc: 0.8892 - val_accuracy: 0.8316\n", "Epoch 32/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3615 - auc: 0.8858 - accuracy: 0.8355 - val_loss: 0.3666 - val_auc: 0.8886 - val_accuracy: 0.8337\n", + "814/814 [==============================] - 1s 997us/step - loss: 0.3615 - auc: 0.8858 - accuracy: 0.8355 - val_loss: 0.3666 - val_auc: 0.8886 - val_accuracy: 0.8337\n", "Epoch 33/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3626 - auc: 0.8885 - accuracy: 0.8333 - val_loss: 0.3636 - val_auc: 0.8902 - val_accuracy: 0.8310\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3626 - auc: 0.8885 - accuracy: 0.8333 - val_loss: 0.3636 - val_auc: 0.8902 - val_accuracy: 0.8310\n", "Epoch 34/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3594 - auc: 0.8921 - accuracy: 0.8362 - val_loss: 0.3648 - val_auc: 0.8904 - val_accuracy: 0.8351\n", "Epoch 35/200\n", @@ -2868,7 +2868,7 @@ "Epoch 37/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3620 - auc: 0.8871 - accuracy: 0.8338 - val_loss: 0.3635 - val_auc: 0.8905 - val_accuracy: 0.8326\n", "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3566 - auc: 0.8911 - accuracy: 0.8347 - val_loss: 0.3645 - val_auc: 0.8902 - val_accuracy: 0.8326\n", + "814/814 [==============================] - 1s 999us/step - loss: 0.3566 - auc: 0.8911 - accuracy: 0.8347 - val_loss: 0.3645 - val_auc: 0.8902 - val_accuracy: 0.8326\n", "Epoch 39/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3607 - auc: 0.8879 - accuracy: 0.8378 - val_loss: 0.3653 - val_auc: 0.8902 - val_accuracy: 0.8305\n", "Epoch 40/200\n", @@ -2880,9 +2880,9 @@ "Epoch 43/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3609 - auc: 0.8899 - accuracy: 0.8376 - val_loss: 0.3688 - val_auc: 0.8919 - val_accuracy: 0.8300\n", "Epoch 44/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3550 - auc: 0.8912 - accuracy: 0.8355 - val_loss: 0.3700 - val_auc: 0.8889 - val_accuracy: 0.8306\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3550 - auc: 0.8912 - accuracy: 0.8355 - val_loss: 0.3700 - val_auc: 0.8889 - val_accuracy: 0.8306\n", "Epoch 45/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3638 - auc: 0.8875 - accuracy: 0.8312 - val_loss: 0.3669 - val_auc: 0.8904 - val_accuracy: 0.8316\n", + "814/814 [==============================] - 1s 988us/step - loss: 0.3638 - auc: 0.8875 - accuracy: 0.8312 - val_loss: 0.3669 - val_auc: 0.8904 - val_accuracy: 0.8316\n", "Epoch 46/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3550 - auc: 0.8901 - accuracy: 0.8406 - val_loss: 0.3657 - val_auc: 0.8919 - val_accuracy: 0.8353\n", "Epoch 47/200\n", @@ -2918,7 +2918,7 @@ "Epoch 62/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3598 - auc: 0.8898 - accuracy: 0.8352 - val_loss: 0.3610 - val_auc: 0.8929 - val_accuracy: 0.8320\n", "Epoch 63/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8929 - accuracy: 0.8381 - val_loss: 0.3592 - val_auc: 0.8934 - val_accuracy: 0.8351\n", + "814/814 [==============================] - 1s 997us/step - loss: 0.3508 - auc: 0.8929 - accuracy: 0.8381 - val_loss: 0.3592 - val_auc: 0.8934 - val_accuracy: 0.8351\n", "Epoch 64/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3551 - auc: 0.8919 - accuracy: 0.8367 - val_loss: 0.4057 - val_auc: 0.8647 - val_accuracy: 0.8187\n", "Epoch 65/200\n", @@ -2936,15 +2936,15 @@ "Epoch 71/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3537 - auc: 0.8932 - accuracy: 0.8395 - val_loss: 0.3570 - val_auc: 0.8940 - val_accuracy: 0.8349\n", "Epoch 72/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8939 - accuracy: 0.8381 - val_loss: 0.3653 - val_auc: 0.8909 - val_accuracy: 0.8310\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3527 - auc: 0.8939 - accuracy: 0.8381 - val_loss: 0.3653 - val_auc: 0.8909 - val_accuracy: 0.8310\n", "Epoch 73/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3559 - auc: 0.8922 - accuracy: 0.8381 - val_loss: 0.3575 - val_auc: 0.8941 - val_accuracy: 0.8343\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3559 - auc: 0.8922 - accuracy: 0.8381 - val_loss: 0.3575 - val_auc: 0.8941 - val_accuracy: 0.8343\n", "Epoch 74/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3549 - auc: 0.8918 - accuracy: 0.8347 - val_loss: 0.3610 - val_auc: 0.8925 - val_accuracy: 0.8322\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3549 - auc: 0.8918 - accuracy: 0.8347 - val_loss: 0.3610 - val_auc: 0.8925 - val_accuracy: 0.8322\n", "Epoch 75/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3590 - auc: 0.8913 - accuracy: 0.8321 - val_loss: 0.3756 - val_auc: 0.8821 - val_accuracy: 0.8277\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3590 - auc: 0.8913 - accuracy: 0.8321 - val_loss: 0.3756 - val_auc: 0.8821 - val_accuracy: 0.8277\n", "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3536 - auc: 0.8906 - accuracy: 0.8364 - val_loss: 0.3589 - val_auc: 0.8935 - val_accuracy: 0.8337\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3536 - auc: 0.8906 - accuracy: 0.8364 - val_loss: 0.3589 - val_auc: 0.8935 - val_accuracy: 0.8337\n", "Epoch 77/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3547 - auc: 0.8913 - accuracy: 0.8361 - val_loss: 0.3624 - val_auc: 0.8925 - val_accuracy: 0.8322\n", "Epoch 78/200\n", @@ -2968,17 +2968,17 @@ "Epoch 87/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3518 - auc: 0.8968 - accuracy: 0.8371 - val_loss: 0.3557 - val_auc: 0.8976 - val_accuracy: 0.8345\n", "Epoch 88/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3523 - auc: 0.8977 - accuracy: 0.8386 - val_loss: 0.3565 - val_auc: 0.8963 - val_accuracy: 0.8372\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3523 - auc: 0.8977 - accuracy: 0.8386 - val_loss: 0.3565 - val_auc: 0.8963 - val_accuracy: 0.8372\n", "Epoch 89/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3607 - auc: 0.8921 - accuracy: 0.8381 - val_loss: 0.3532 - val_auc: 0.8977 - val_accuracy: 0.8362\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3607 - auc: 0.8921 - accuracy: 0.8381 - val_loss: 0.3532 - val_auc: 0.8977 - val_accuracy: 0.8362\n", "Epoch 90/200\n", "814/814 [==============================] - 1s 2ms/step - loss: 0.3546 - auc: 0.8919 - accuracy: 0.8379 - val_loss: 0.3550 - val_auc: 0.8964 - val_accuracy: 0.8340\n", "Epoch 91/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8953 - accuracy: 0.8402 - val_loss: 0.3596 - val_auc: 0.8943 - val_accuracy: 0.8340\n", "Epoch 92/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3506 - auc: 0.8944 - accuracy: 0.8399 - val_loss: 0.7212 - val_auc: 0.7225 - val_accuracy: 0.7966\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3506 - auc: 0.8944 - accuracy: 0.8399 - val_loss: 0.7212 - val_auc: 0.7225 - val_accuracy: 0.7966\n", "Epoch 93/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3601 - auc: 0.8911 - accuracy: 0.8286 - val_loss: 0.4017 - val_auc: 0.8668 - val_accuracy: 0.8190\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3601 - auc: 0.8911 - accuracy: 0.8286 - val_loss: 0.4017 - val_auc: 0.8668 - val_accuracy: 0.8190\n", "Epoch 94/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3507 - auc: 0.8942 - accuracy: 0.8383 - val_loss: 0.3569 - val_auc: 0.8959 - val_accuracy: 0.8366\n", "Epoch 95/200\n", @@ -3008,7 +3008,7 @@ "Epoch 107/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3578 - auc: 0.8920 - accuracy: 0.8339 - val_loss: 0.3517 - val_auc: 0.8993 - val_accuracy: 0.8366\n", "Epoch 108/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3528 - auc: 0.8937 - accuracy: 0.8374 - val_loss: 0.3565 - val_auc: 0.8987 - val_accuracy: 0.8340\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3528 - auc: 0.8937 - accuracy: 0.8374 - val_loss: 0.3565 - val_auc: 0.8987 - val_accuracy: 0.8340\n", "Epoch 109/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3547 - auc: 0.8944 - accuracy: 0.8371 - val_loss: 0.3600 - val_auc: 0.8973 - val_accuracy: 0.8320\n", "Epoch 110/200\n", @@ -3032,7 +3032,7 @@ "Epoch 119/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3482 - auc: 0.8967 - accuracy: 0.8401 - val_loss: 0.3517 - val_auc: 0.8992 - val_accuracy: 0.8340\n", "Epoch 120/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3500 - auc: 0.8950 - accuracy: 0.8405 - val_loss: 0.3504 - val_auc: 0.8995 - val_accuracy: 0.8357\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3500 - auc: 0.8950 - accuracy: 0.8405 - val_loss: 0.3504 - val_auc: 0.8995 - val_accuracy: 0.8357\n", "Epoch 121/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3569 - auc: 0.8914 - accuracy: 0.8342 - val_loss: 0.3622 - val_auc: 0.8957 - val_accuracy: 0.8337\n", "Epoch 122/200\n", @@ -3042,7 +3042,7 @@ "Epoch 124/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8980 - accuracy: 0.8380 - val_loss: 0.4591 - val_auc: 0.8297 - val_accuracy: 0.8033\n", "Epoch 125/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3520 - auc: 0.8984 - accuracy: 0.8393 - val_loss: 0.3535 - val_auc: 0.8995 - val_accuracy: 0.8342\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3520 - auc: 0.8984 - accuracy: 0.8393 - val_loss: 0.3535 - val_auc: 0.8995 - val_accuracy: 0.8342\n", "Epoch 126/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3426 - auc: 0.9026 - accuracy: 0.8397 - val_loss: 0.3506 - val_auc: 0.9007 - val_accuracy: 0.8365\n", "Epoch 127/200\n", @@ -3098,9 +3098,9 @@ "Epoch 152/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3545 - auc: 0.8988 - accuracy: 0.8376 - val_loss: 0.3499 - val_auc: 0.9024 - val_accuracy: 0.8392\n", "Epoch 153/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3487 - auc: 0.8991 - accuracy: 0.8378 - val_loss: 0.3523 - val_auc: 0.9009 - val_accuracy: 0.8365\n", + "814/814 [==============================] - 1s 996us/step - loss: 0.3487 - auc: 0.8991 - accuracy: 0.8378 - val_loss: 0.3523 - val_auc: 0.9009 - val_accuracy: 0.8365\n", "Epoch 154/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3504 - auc: 0.8974 - accuracy: 0.8392 - val_loss: 0.3504 - val_auc: 0.9008 - val_accuracy: 0.8391\n", + "814/814 [==============================] - 1s 999us/step - loss: 0.3504 - auc: 0.8974 - accuracy: 0.8392 - val_loss: 0.3504 - val_auc: 0.9008 - val_accuracy: 0.8391\n", "Epoch 155/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3461 - auc: 0.9010 - accuracy: 0.8423 - val_loss: 0.3524 - val_auc: 0.9006 - val_accuracy: 0.8425\n", "Epoch 156/200\n", @@ -3110,11 +3110,11 @@ "Epoch 158/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3457 - auc: 0.9011 - accuracy: 0.8481 - val_loss: 0.3501 - val_auc: 0.9007 - val_accuracy: 0.8486\n", "Epoch 159/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3508 - auc: 0.8996 - accuracy: 0.8452 - val_loss: 0.3535 - val_auc: 0.9003 - val_accuracy: 0.8486\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3508 - auc: 0.8996 - accuracy: 0.8452 - val_loss: 0.3535 - val_auc: 0.9003 - val_accuracy: 0.8486\n", "Epoch 160/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3485 - auc: 0.8996 - accuracy: 0.8452 - val_loss: 0.3683 - val_auc: 0.8900 - val_accuracy: 0.8379\n", "Epoch 161/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3480 - auc: 0.9020 - accuracy: 0.8495 - val_loss: 0.3519 - val_auc: 0.9018 - val_accuracy: 0.8455\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3480 - auc: 0.9020 - accuracy: 0.8495 - val_loss: 0.3519 - val_auc: 0.9018 - val_accuracy: 0.8455\n", "Epoch 162/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3506 - auc: 0.9001 - accuracy: 0.8446 - val_loss: 0.3490 - val_auc: 0.9009 - val_accuracy: 0.8505\n", "Epoch 163/200\n", @@ -3184,13 +3184,13 @@ "Epoch 195/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3558 - auc: 0.8958 - accuracy: 0.8434 - val_loss: 0.3509 - val_auc: 0.9034 - val_accuracy: 0.8488\n", "Epoch 196/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3477 - auc: 0.8992 - accuracy: 0.8471 - val_loss: 0.3617 - val_auc: 0.8964 - val_accuracy: 0.8497\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3477 - auc: 0.8992 - accuracy: 0.8471 - val_loss: 0.3617 - val_auc: 0.8964 - val_accuracy: 0.8497\n", "Epoch 197/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3478 - auc: 0.8983 - accuracy: 0.8512 - val_loss: 0.3587 - val_auc: 0.9002 - val_accuracy: 0.8465\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3478 - auc: 0.8983 - accuracy: 0.8512 - val_loss: 0.3587 - val_auc: 0.9002 - val_accuracy: 0.8465\n", "Epoch 198/200\n", "814/814 [==============================] - 1s 2ms/step - loss: 0.3490 - auc: 0.8990 - accuracy: 0.8465 - val_loss: 0.3607 - val_auc: 0.9016 - val_accuracy: 0.8483\n", "Epoch 199/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3487 - auc: 0.9014 - accuracy: 0.8491 - val_loss: 0.3519 - val_auc: 0.9022 - val_accuracy: 0.8508\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3487 - auc: 0.9014 - accuracy: 0.8491 - val_loss: 0.3519 - val_auc: 0.9022 - val_accuracy: 0.8508\n", "Epoch 200/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3511 - auc: 0.9000 - accuracy: 0.8438 - val_loss: 0.3594 - val_auc: 0.9009 - val_accuracy: 0.8437\n" ] @@ -3202,7 +3202,7 @@ }, { "cell_type": "markdown", - "id": "97cd8879", + "id": "1142cef0", "metadata": {}, "source": [ "#### Métricas" @@ -3210,7 +3210,7 @@ }, { "cell_type": "markdown", - "id": "a05c7996", + "id": "ff0f574f", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -3219,7 +3219,7 @@ { "cell_type": "code", "execution_count": 36, - "id": "afb2129c", + "id": "634188be", "metadata": {}, "outputs": [ { @@ -3249,7 +3249,7 @@ { "cell_type": "code", "execution_count": 37, - "id": "cdf5e54d", + "id": "93a36827", "metadata": {}, "outputs": [ { @@ -3279,7 +3279,7 @@ { "cell_type": "code", "execution_count": 38, - "id": "0353c4d4", + "id": "9d82df7a", "metadata": {}, "outputs": [ { @@ -3337,7 +3337,7 @@ }, { "cell_type": "markdown", - "id": "2754ff31", + "id": "3e5b14ab", "metadata": {}, "source": [ "Observamos que mejoro levemente el AUC-ROC y también mejoro el recall en la clase de altos ingresos. Pasamos al siguiente preprocesamiento" @@ -3345,7 +3345,7 @@ }, { "cell_type": "markdown", - "id": "b6b7ab77", + "id": "a17843e6", "metadata": {}, "source": [ "## Segundo preprocesamiento" @@ -3353,7 +3353,7 @@ }, { "cell_type": "markdown", - "id": "5d54c638", + "id": "f1b9e7de", "metadata": {}, "source": [ "Volveremos a entrenar una red, pero en este caso realizaremos otro preprocesamiento a nuestros datos. Volvemos a cargar el dataset" @@ -3361,8 +3361,8 @@ }, { "cell_type": "code", - "execution_count": 39, - "id": "2f221507", + "execution_count": 40, + "id": "09d26076", "metadata": {}, "outputs": [], "source": [ @@ -3371,7 +3371,7 @@ }, { "cell_type": "markdown", - "id": "791e365c", + "id": "1ed47afc", "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. Luego, escalamos nuestro datos con StandardScaler de sklearn" @@ -3379,8 +3379,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "f26ff186", + "execution_count": 41, + "id": "7fb82fa5", "metadata": {}, "outputs": [ { @@ -3398,7 +3398,7 @@ }, { "cell_type": "markdown", - "id": "ff8bb683", + "id": "c6e8bd3d", "metadata": {}, "source": [ "Observemos cuantas features tenemos" @@ -3406,8 +3406,8 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "6b52b097", + "execution_count": 42, + "id": "c682d3a1", "metadata": {}, "outputs": [ { @@ -3416,7 +3416,7 @@ "(32561, 43)" ] }, - "execution_count": 41, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -3427,7 +3427,7 @@ }, { "cell_type": "markdown", - "id": "0f0d5a5e", + "id": "9ef47f08", "metadata": {}, "source": [ "Luego vamos a realizar un split del dataset para dividir en train y test" @@ -3435,8 +3435,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "cecd5221", + "execution_count": 43, + "id": "8b31039b", "metadata": {}, "outputs": [], "source": [ @@ -3445,7 +3445,7 @@ }, { "cell_type": "markdown", - "id": "24cbb3d8", + "id": "5ee87f17", "metadata": {}, "source": [ "Finalemente, escalamos los datos" @@ -3453,8 +3453,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "e43793f8", + "execution_count": 44, + "id": "d7e438f1", "metadata": {}, "outputs": [], "source": [ @@ -3464,7 +3464,7 @@ }, { "cell_type": "markdown", - "id": "a2630754", + "id": "4e66e87b", "metadata": {}, "source": [ "### Primer diseño de la red" @@ -3472,7 +3472,7 @@ }, { "cell_type": "markdown", - "id": "5fb5f571", + "id": "356c2265", "metadata": {}, "source": [ "#### Diseño" @@ -3480,7 +3480,7 @@ }, { "cell_type": "markdown", - "id": "f6868907", + "id": "bfd5fb6c", "metadata": {}, "source": [ "Como ya fuimos de menos a más en el anterior preprocesado, comenzemos con una red algo más compleja. Usaremos función de activación relu en las capas, optimizador SGD (learning rate 0.01) y obviamente sigmoidea como función de activación en la última capa" @@ -3488,8 +3488,8 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "6e64457f", + "execution_count": 45, + "id": "f09343a0", "metadata": {}, "outputs": [], "source": [ @@ -3499,8 +3499,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "id": "5a0293b4", + "execution_count": 46, + "id": "e7862c31", "metadata": {}, "outputs": [], "source": [ @@ -3513,7 +3513,7 @@ }, { "cell_type": "markdown", - "id": "69247a32", + "id": "d32a3985", "metadata": {}, "source": [ "Vemos un resumen de nuestra red" @@ -3521,8 +3521,8 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "0ebc03df", + "execution_count": 47, + "id": "fc9934f2", "metadata": {}, "outputs": [ { @@ -3556,7 +3556,7 @@ }, { "cell_type": "markdown", - "id": "5c05307c", + "id": "97f8e1d3", "metadata": {}, "source": [ "Finalmente entrenamos" @@ -3564,8 +3564,8 @@ }, { "cell_type": "code", - "execution_count": 47, - "id": "0686cbd3", + "execution_count": 48, + "id": "495902e2", "metadata": {}, "outputs": [ { @@ -3575,7 +3575,7 @@ "Epoch 1/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.6538 - auc: 0.6197 - accuracy: 0.6581 - val_loss: 0.5826 - val_auc: 0.6542 - val_accuracy: 0.7382\n", "Epoch 2/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.5671 - auc: 0.6664 - accuracy: 0.7483 - val_loss: 0.5361 - val_auc: 0.6975 - val_accuracy: 0.7546\n", + "814/814 [==============================] - 1s 963us/step - loss: 0.5671 - auc: 0.6664 - accuracy: 0.7483 - val_loss: 0.5361 - val_auc: 0.6975 - val_accuracy: 0.7546\n", "Epoch 3/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.5241 - auc: 0.7092 - accuracy: 0.7617 - val_loss: 0.5047 - val_auc: 0.7412 - val_accuracy: 0.7623\n", "Epoch 4/200\n", @@ -3583,37 +3583,37 @@ "Epoch 5/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4743 - auc: 0.7839 - accuracy: 0.7702 - val_loss: 0.4565 - val_auc: 0.7986 - val_accuracy: 0.7855\n", "Epoch 6/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.4443 - auc: 0.8097 - accuracy: 0.7977 - val_loss: 0.4373 - val_auc: 0.8173 - val_accuracy: 0.8041\n", + "814/814 [==============================] - 1s 997us/step - loss: 0.4443 - auc: 0.8097 - accuracy: 0.7977 - val_loss: 0.4373 - val_auc: 0.8173 - val_accuracy: 0.8041\n", "Epoch 7/200\n", - "814/814 [==============================] - 1s 910us/step - loss: 0.4296 - auc: 0.8253 - accuracy: 0.8079 - val_loss: 0.4213 - val_auc: 0.8307 - val_accuracy: 0.8159\n", + "814/814 [==============================] - 1s 971us/step - loss: 0.4296 - auc: 0.8253 - accuracy: 0.8079 - val_loss: 0.4213 - val_auc: 0.8307 - val_accuracy: 0.8159\n", "Epoch 8/200\n", - "814/814 [==============================] - 1s 969us/step - loss: 0.4149 - auc: 0.8362 - accuracy: 0.8168 - val_loss: 0.4080 - val_auc: 0.8418 - val_accuracy: 0.8205\n", + "814/814 [==============================] - 1s 947us/step - loss: 0.4149 - auc: 0.8362 - accuracy: 0.8168 - val_loss: 0.4080 - val_auc: 0.8418 - val_accuracy: 0.8205\n", "Epoch 9/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3990 - auc: 0.8484 - accuracy: 0.8217 - val_loss: 0.3972 - val_auc: 0.8503 - val_accuracy: 0.8217\n", + "814/814 [==============================] - 1s 946us/step - loss: 0.3990 - auc: 0.8484 - accuracy: 0.8217 - val_loss: 0.3972 - val_auc: 0.8503 - val_accuracy: 0.8217\n", "Epoch 10/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3962 - auc: 0.8513 - accuracy: 0.8202 - val_loss: 0.3886 - val_auc: 0.8569 - val_accuracy: 0.8242\n", + "814/814 [==============================] - 1s 996us/step - loss: 0.3962 - auc: 0.8513 - accuracy: 0.8202 - val_loss: 0.3886 - val_auc: 0.8569 - val_accuracy: 0.8242\n", "Epoch 11/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3834 - auc: 0.8603 - accuracy: 0.8253 - val_loss: 0.3817 - val_auc: 0.8622 - val_accuracy: 0.8280\n", + "814/814 [==============================] - 1s 919us/step - loss: 0.3834 - auc: 0.8603 - accuracy: 0.8253 - val_loss: 0.3817 - val_auc: 0.8622 - val_accuracy: 0.8280\n", "Epoch 12/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3756 - auc: 0.8653 - accuracy: 0.8318 - val_loss: 0.3763 - val_auc: 0.8662 - val_accuracy: 0.8296\n", + "814/814 [==============================] - 1s 944us/step - loss: 0.3756 - auc: 0.8653 - accuracy: 0.8318 - val_loss: 0.3763 - val_auc: 0.8662 - val_accuracy: 0.8296\n", "Epoch 13/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3651 - auc: 0.8741 - accuracy: 0.8384 - val_loss: 0.3721 - val_auc: 0.8692 - val_accuracy: 0.8294\n", + "814/814 [==============================] - 1s 930us/step - loss: 0.3651 - auc: 0.8741 - accuracy: 0.8384 - val_loss: 0.3721 - val_auc: 0.8692 - val_accuracy: 0.8294\n", "Epoch 14/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3685 - auc: 0.8739 - accuracy: 0.8333 - val_loss: 0.3687 - val_auc: 0.8719 - val_accuracy: 0.8302\n", "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3673 - auc: 0.8740 - accuracy: 0.8307 - val_loss: 0.3659 - val_auc: 0.8742 - val_accuracy: 0.8320\n", + "814/814 [==============================] - 1s 934us/step - loss: 0.3673 - auc: 0.8740 - accuracy: 0.8307 - val_loss: 0.3659 - val_auc: 0.8742 - val_accuracy: 0.8320\n", "Epoch 16/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3679 - auc: 0.8726 - accuracy: 0.8303 - val_loss: 0.3635 - val_auc: 0.8760 - val_accuracy: 0.8333\n", "Epoch 17/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3607 - auc: 0.8793 - accuracy: 0.8340 - val_loss: 0.3614 - val_auc: 0.8776 - val_accuracy: 0.8333\n", + "814/814 [==============================] - 1s 958us/step - loss: 0.3607 - auc: 0.8793 - accuracy: 0.8340 - val_loss: 0.3614 - val_auc: 0.8776 - val_accuracy: 0.8333\n", "Epoch 18/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3620 - auc: 0.8743 - accuracy: 0.8340 - val_loss: 0.3596 - val_auc: 0.8791 - val_accuracy: 0.8334\n", + "814/814 [==============================] - 1s 968us/step - loss: 0.3620 - auc: 0.8743 - accuracy: 0.8340 - val_loss: 0.3596 - val_auc: 0.8791 - val_accuracy: 0.8334\n", "Epoch 19/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3655 - auc: 0.8762 - accuracy: 0.8315 - val_loss: 0.3581 - val_auc: 0.8802 - val_accuracy: 0.8342\n", "Epoch 20/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3562 - auc: 0.8822 - accuracy: 0.8361 - val_loss: 0.3567 - val_auc: 0.8812 - val_accuracy: 0.8343\n", + "814/814 [==============================] - 1s 933us/step - loss: 0.3562 - auc: 0.8822 - accuracy: 0.8361 - val_loss: 0.3567 - val_auc: 0.8812 - val_accuracy: 0.8343\n", "Epoch 21/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3560 - auc: 0.8812 - accuracy: 0.8344 - val_loss: 0.3555 - val_auc: 0.8822 - val_accuracy: 0.8342\n", + "814/814 [==============================] - 1s 951us/step - loss: 0.3560 - auc: 0.8812 - accuracy: 0.8344 - val_loss: 0.3555 - val_auc: 0.8822 - val_accuracy: 0.8342\n", "Epoch 22/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3532 - auc: 0.8835 - accuracy: 0.8375 - val_loss: 0.3544 - val_auc: 0.8831 - val_accuracy: 0.8343\n", "Epoch 23/200\n", @@ -3621,7 +3621,7 @@ "Epoch 24/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3472 - auc: 0.8863 - accuracy: 0.8386 - val_loss: 0.3524 - val_auc: 0.8847 - val_accuracy: 0.8359\n", "Epoch 25/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3527 - auc: 0.8832 - accuracy: 0.8382 - val_loss: 0.3515 - val_auc: 0.8852 - val_accuracy: 0.8369\n", + "814/814 [==============================] - 1s 987us/step - loss: 0.3527 - auc: 0.8832 - accuracy: 0.8382 - val_loss: 0.3515 - val_auc: 0.8852 - val_accuracy: 0.8369\n", "Epoch 26/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8886 - accuracy: 0.8372 - val_loss: 0.3507 - val_auc: 0.8858 - val_accuracy: 0.8369\n", "Epoch 27/200\n", @@ -3629,47 +3629,47 @@ "Epoch 28/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3506 - auc: 0.8862 - accuracy: 0.8386 - val_loss: 0.3492 - val_auc: 0.8869 - val_accuracy: 0.8371\n", "Epoch 29/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3426 - auc: 0.8897 - accuracy: 0.8430 - val_loss: 0.3485 - val_auc: 0.8873 - val_accuracy: 0.8377\n", + "814/814 [==============================] - 1s 975us/step - loss: 0.3426 - auc: 0.8897 - accuracy: 0.8430 - val_loss: 0.3485 - val_auc: 0.8873 - val_accuracy: 0.8377\n", "Epoch 30/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3484 - auc: 0.8885 - accuracy: 0.8379 - val_loss: 0.3479 - val_auc: 0.8878 - val_accuracy: 0.8374\n", + "814/814 [==============================] - 1s 936us/step - loss: 0.3484 - auc: 0.8885 - accuracy: 0.8379 - val_loss: 0.3479 - val_auc: 0.8878 - val_accuracy: 0.8374\n", "Epoch 31/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3480 - auc: 0.8881 - accuracy: 0.8374 - val_loss: 0.3472 - val_auc: 0.8883 - val_accuracy: 0.8383\n", + "814/814 [==============================] - 1s 986us/step - loss: 0.3480 - auc: 0.8881 - accuracy: 0.8374 - val_loss: 0.3472 - val_auc: 0.8883 - val_accuracy: 0.8383\n", "Epoch 32/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3484 - auc: 0.8874 - accuracy: 0.8407 - val_loss: 0.3467 - val_auc: 0.8887 - val_accuracy: 0.8382\n", "Epoch 33/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3481 - auc: 0.8899 - accuracy: 0.8396 - val_loss: 0.3461 - val_auc: 0.8891 - val_accuracy: 0.8392\n", "Epoch 34/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3416 - auc: 0.8936 - accuracy: 0.8392 - val_loss: 0.3455 - val_auc: 0.8896 - val_accuracy: 0.8391\n", + "814/814 [==============================] - 1s 939us/step - loss: 0.3416 - auc: 0.8936 - accuracy: 0.8392 - val_loss: 0.3455 - val_auc: 0.8896 - val_accuracy: 0.8391\n", "Epoch 35/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3438 - auc: 0.8910 - accuracy: 0.8401 - val_loss: 0.3450 - val_auc: 0.8899 - val_accuracy: 0.8392\n", + "814/814 [==============================] - 1s 910us/step - loss: 0.3438 - auc: 0.8910 - accuracy: 0.8401 - val_loss: 0.3450 - val_auc: 0.8899 - val_accuracy: 0.8392\n", "Epoch 36/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3424 - auc: 0.8903 - accuracy: 0.8380 - val_loss: 0.3445 - val_auc: 0.8903 - val_accuracy: 0.8392\n", + "814/814 [==============================] - 1s 917us/step - loss: 0.3424 - auc: 0.8903 - accuracy: 0.8380 - val_loss: 0.3445 - val_auc: 0.8903 - val_accuracy: 0.8392\n", "Epoch 37/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3454 - auc: 0.8898 - accuracy: 0.8403 - val_loss: 0.3440 - val_auc: 0.8905 - val_accuracy: 0.8388\n", + "814/814 [==============================] - 1s 919us/step - loss: 0.3454 - auc: 0.8898 - accuracy: 0.8403 - val_loss: 0.3440 - val_auc: 0.8905 - val_accuracy: 0.8388\n", "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3434 - auc: 0.8922 - accuracy: 0.8408 - val_loss: 0.3435 - val_auc: 0.8909 - val_accuracy: 0.8392\n", + "814/814 [==============================] - 1s 886us/step - loss: 0.3434 - auc: 0.8922 - accuracy: 0.8408 - val_loss: 0.3435 - val_auc: 0.8909 - val_accuracy: 0.8392\n", "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3399 - auc: 0.8921 - accuracy: 0.8427 - val_loss: 0.3431 - val_auc: 0.8912 - val_accuracy: 0.8396\n", + "814/814 [==============================] - 1s 907us/step - loss: 0.3399 - auc: 0.8921 - accuracy: 0.8427 - val_loss: 0.3431 - val_auc: 0.8912 - val_accuracy: 0.8396\n", "Epoch 40/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3400 - auc: 0.8913 - accuracy: 0.8426 - val_loss: 0.3426 - val_auc: 0.8916 - val_accuracy: 0.8403\n", + "814/814 [==============================] - 1s 877us/step - loss: 0.3400 - auc: 0.8913 - accuracy: 0.8426 - val_loss: 0.3426 - val_auc: 0.8916 - val_accuracy: 0.8403\n", "Epoch 41/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3408 - auc: 0.8923 - accuracy: 0.8378 - val_loss: 0.3422 - val_auc: 0.8918 - val_accuracy: 0.8409\n", + "814/814 [==============================] - 1s 875us/step - loss: 0.3408 - auc: 0.8923 - accuracy: 0.8378 - val_loss: 0.3422 - val_auc: 0.8918 - val_accuracy: 0.8409\n", "Epoch 42/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3452 - auc: 0.8908 - accuracy: 0.8368 - val_loss: 0.3418 - val_auc: 0.8921 - val_accuracy: 0.8409\n", + "814/814 [==============================] - 1s 890us/step - loss: 0.3452 - auc: 0.8908 - accuracy: 0.8368 - val_loss: 0.3418 - val_auc: 0.8921 - val_accuracy: 0.8409\n", "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3389 - auc: 0.8930 - accuracy: 0.8426 - val_loss: 0.3414 - val_auc: 0.8923 - val_accuracy: 0.8412\n", + "814/814 [==============================] - 1s 889us/step - loss: 0.3389 - auc: 0.8930 - accuracy: 0.8426 - val_loss: 0.3414 - val_auc: 0.8923 - val_accuracy: 0.8412\n", "Epoch 44/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3392 - auc: 0.8945 - accuracy: 0.8425 - val_loss: 0.3410 - val_auc: 0.8925 - val_accuracy: 0.8412\n", + "814/814 [==============================] - 1s 883us/step - loss: 0.3392 - auc: 0.8945 - accuracy: 0.8425 - val_loss: 0.3410 - val_auc: 0.8925 - val_accuracy: 0.8412\n", "Epoch 45/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3444 - auc: 0.8917 - accuracy: 0.8386 - val_loss: 0.3407 - val_auc: 0.8928 - val_accuracy: 0.8409\n", "Epoch 46/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3365 - auc: 0.8941 - accuracy: 0.8442 - val_loss: 0.3403 - val_auc: 0.8931 - val_accuracy: 0.8415\n", "Epoch 47/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3345 - auc: 0.8980 - accuracy: 0.8446 - val_loss: 0.3400 - val_auc: 0.8933 - val_accuracy: 0.8411\n", + "814/814 [==============================] - 1s 891us/step - loss: 0.3345 - auc: 0.8980 - accuracy: 0.8446 - val_loss: 0.3400 - val_auc: 0.8933 - val_accuracy: 0.8411\n", "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3365 - auc: 0.8953 - accuracy: 0.8424 - val_loss: 0.3396 - val_auc: 0.8936 - val_accuracy: 0.8417\n", + "814/814 [==============================] - 1s 888us/step - loss: 0.3365 - auc: 0.8953 - accuracy: 0.8424 - val_loss: 0.3396 - val_auc: 0.8936 - val_accuracy: 0.8417\n", "Epoch 49/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3395 - auc: 0.8951 - accuracy: 0.8427 - val_loss: 0.3393 - val_auc: 0.8938 - val_accuracy: 0.8417\n", + "814/814 [==============================] - 1s 892us/step - loss: 0.3395 - auc: 0.8951 - accuracy: 0.8427 - val_loss: 0.3393 - val_auc: 0.8938 - val_accuracy: 0.8417\n", "Epoch 50/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3356 - auc: 0.8965 - accuracy: 0.8407 - val_loss: 0.3390 - val_auc: 0.8939 - val_accuracy: 0.8422\n", "Epoch 51/200\n", @@ -3677,15 +3677,15 @@ "Epoch 52/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3396 - auc: 0.8902 - accuracy: 0.8393 - val_loss: 0.3385 - val_auc: 0.8944 - val_accuracy: 0.8431\n", "Epoch 53/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3373 - auc: 0.8956 - accuracy: 0.8371 - val_loss: 0.3382 - val_auc: 0.8945 - val_accuracy: 0.8434\n", + "814/814 [==============================] - 1s 988us/step - loss: 0.3373 - auc: 0.8956 - accuracy: 0.8371 - val_loss: 0.3382 - val_auc: 0.8945 - val_accuracy: 0.8434\n", "Epoch 54/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3423 - auc: 0.8911 - accuracy: 0.8391 - val_loss: 0.3379 - val_auc: 0.8948 - val_accuracy: 0.8439\n", + "814/814 [==============================] - 1s 925us/step - loss: 0.3423 - auc: 0.8911 - accuracy: 0.8391 - val_loss: 0.3379 - val_auc: 0.8948 - val_accuracy: 0.8439\n", "Epoch 55/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3389 - auc: 0.8942 - accuracy: 0.8399 - val_loss: 0.3376 - val_auc: 0.8949 - val_accuracy: 0.8442\n", + "814/814 [==============================] - 1s 864us/step - loss: 0.3389 - auc: 0.8942 - accuracy: 0.8399 - val_loss: 0.3376 - val_auc: 0.8949 - val_accuracy: 0.8442\n", "Epoch 56/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3307 - auc: 0.8980 - accuracy: 0.8454 - val_loss: 0.3374 - val_auc: 0.8951 - val_accuracy: 0.8443\n", + "814/814 [==============================] - 1s 931us/step - loss: 0.3307 - auc: 0.8980 - accuracy: 0.8454 - val_loss: 0.3374 - val_auc: 0.8951 - val_accuracy: 0.8443\n", "Epoch 57/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3355 - auc: 0.8966 - accuracy: 0.8453 - val_loss: 0.3371 - val_auc: 0.8953 - val_accuracy: 0.8446\n", + "814/814 [==============================] - 1s 961us/step - loss: 0.3355 - auc: 0.8966 - accuracy: 0.8453 - val_loss: 0.3371 - val_auc: 0.8953 - val_accuracy: 0.8446\n", "Epoch 58/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3354 - auc: 0.8967 - accuracy: 0.8446 - val_loss: 0.3369 - val_auc: 0.8955 - val_accuracy: 0.8457\n", "Epoch 59/200\n", @@ -3695,9 +3695,9 @@ "Epoch 61/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3344 - auc: 0.8963 - accuracy: 0.8443 - val_loss: 0.3362 - val_auc: 0.8960 - val_accuracy: 0.8460\n", "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3368 - auc: 0.8976 - accuracy: 0.8423 - val_loss: 0.3359 - val_auc: 0.8962 - val_accuracy: 0.8462\n", + "814/814 [==============================] - 1s 917us/step - loss: 0.3368 - auc: 0.8976 - accuracy: 0.8423 - val_loss: 0.3359 - val_auc: 0.8962 - val_accuracy: 0.8462\n", "Epoch 63/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3324 - auc: 0.8971 - accuracy: 0.8452 - val_loss: 0.3357 - val_auc: 0.8963 - val_accuracy: 0.8455\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3324 - auc: 0.8971 - accuracy: 0.8452 - val_loss: 0.3357 - val_auc: 0.8963 - val_accuracy: 0.8455\n", "Epoch 64/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3343 - auc: 0.8972 - accuracy: 0.8439 - val_loss: 0.3355 - val_auc: 0.8965 - val_accuracy: 0.8457\n", "Epoch 65/200\n", @@ -3709,13 +3709,13 @@ "Epoch 68/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3297 - auc: 0.9018 - accuracy: 0.8465 - val_loss: 0.3347 - val_auc: 0.8970 - val_accuracy: 0.8466\n", "Epoch 69/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3365 - auc: 0.8963 - accuracy: 0.8402 - val_loss: 0.3346 - val_auc: 0.8970 - val_accuracy: 0.8462\n", + "814/814 [==============================] - 1s 961us/step - loss: 0.3365 - auc: 0.8963 - accuracy: 0.8402 - val_loss: 0.3346 - val_auc: 0.8970 - val_accuracy: 0.8462\n", "Epoch 70/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3347 - auc: 0.8976 - accuracy: 0.8414 - val_loss: 0.3344 - val_auc: 0.8971 - val_accuracy: 0.8466\n", + "814/814 [==============================] - 1s 953us/step - loss: 0.3347 - auc: 0.8976 - accuracy: 0.8414 - val_loss: 0.3344 - val_auc: 0.8971 - val_accuracy: 0.8466\n", "Epoch 71/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3323 - auc: 0.8986 - accuracy: 0.8470 - val_loss: 0.3342 - val_auc: 0.8973 - val_accuracy: 0.8468\n", + "814/814 [==============================] - 1s 859us/step - loss: 0.3323 - auc: 0.8986 - accuracy: 0.8470 - val_loss: 0.3342 - val_auc: 0.8973 - val_accuracy: 0.8468\n", "Epoch 72/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3276 - auc: 0.9017 - accuracy: 0.8475 - val_loss: 0.3341 - val_auc: 0.8974 - val_accuracy: 0.8465\n", + "814/814 [==============================] - 1s 863us/step - loss: 0.3276 - auc: 0.9017 - accuracy: 0.8475 - val_loss: 0.3341 - val_auc: 0.8974 - val_accuracy: 0.8465\n", "Epoch 73/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3318 - auc: 0.8984 - accuracy: 0.8444 - val_loss: 0.3339 - val_auc: 0.8975 - val_accuracy: 0.8465\n", "Epoch 74/200\n", @@ -3723,17 +3723,17 @@ "Epoch 75/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3320 - auc: 0.8998 - accuracy: 0.8446 - val_loss: 0.3336 - val_auc: 0.8977 - val_accuracy: 0.8463\n", "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3305 - auc: 0.8979 - accuracy: 0.8429 - val_loss: 0.3335 - val_auc: 0.8978 - val_accuracy: 0.8465\n", + "814/814 [==============================] - 1s 944us/step - loss: 0.3305 - auc: 0.8979 - accuracy: 0.8429 - val_loss: 0.3335 - val_auc: 0.8978 - val_accuracy: 0.8465\n", "Epoch 77/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3295 - auc: 0.8994 - accuracy: 0.8464 - val_loss: 0.3333 - val_auc: 0.8978 - val_accuracy: 0.8462\n", + "814/814 [==============================] - 1s 840us/step - loss: 0.3295 - auc: 0.8994 - accuracy: 0.8464 - val_loss: 0.3333 - val_auc: 0.8978 - val_accuracy: 0.8462\n", "Epoch 78/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3303 - auc: 0.9010 - accuracy: 0.8448 - val_loss: 0.3331 - val_auc: 0.8979 - val_accuracy: 0.8462\n", + "814/814 [==============================] - 1s 853us/step - loss: 0.3303 - auc: 0.9010 - accuracy: 0.8448 - val_loss: 0.3331 - val_auc: 0.8979 - val_accuracy: 0.8462\n", "Epoch 79/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3322 - auc: 0.8981 - accuracy: 0.8432 - val_loss: 0.3330 - val_auc: 0.8980 - val_accuracy: 0.8462\n", + "814/814 [==============================] - 1s 923us/step - loss: 0.3322 - auc: 0.8981 - accuracy: 0.8432 - val_loss: 0.3330 - val_auc: 0.8980 - val_accuracy: 0.8462\n", "Epoch 80/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3246 - auc: 0.9004 - accuracy: 0.8491 - val_loss: 0.3329 - val_auc: 0.8980 - val_accuracy: 0.8463\n", + "814/814 [==============================] - 1s 865us/step - loss: 0.3246 - auc: 0.9004 - accuracy: 0.8491 - val_loss: 0.3329 - val_auc: 0.8980 - val_accuracy: 0.8463\n", "Epoch 81/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3270 - auc: 0.9007 - accuracy: 0.8463 - val_loss: 0.3328 - val_auc: 0.8982 - val_accuracy: 0.8468\n", + "814/814 [==============================] - 1s 956us/step - loss: 0.3270 - auc: 0.9007 - accuracy: 0.8463 - val_loss: 0.3328 - val_auc: 0.8982 - val_accuracy: 0.8468\n", "Epoch 82/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3261 - auc: 0.9034 - accuracy: 0.8462 - val_loss: 0.3326 - val_auc: 0.8983 - val_accuracy: 0.8466\n", "Epoch 83/200\n", @@ -3743,13 +3743,13 @@ "Epoch 85/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3196 - auc: 0.9055 - accuracy: 0.8507 - val_loss: 0.3323 - val_auc: 0.8986 - val_accuracy: 0.8469\n", "Epoch 86/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3291 - auc: 0.8992 - accuracy: 0.8451 - val_loss: 0.3322 - val_auc: 0.8985 - val_accuracy: 0.8463\n", + "814/814 [==============================] - 1s 917us/step - loss: 0.3291 - auc: 0.8992 - accuracy: 0.8451 - val_loss: 0.3322 - val_auc: 0.8985 - val_accuracy: 0.8463\n", "Epoch 87/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3273 - auc: 0.9021 - accuracy: 0.8436 - val_loss: 0.3321 - val_auc: 0.8986 - val_accuracy: 0.8465\n", + "814/814 [==============================] - 1s 952us/step - loss: 0.3273 - auc: 0.9021 - accuracy: 0.8436 - val_loss: 0.3321 - val_auc: 0.8986 - val_accuracy: 0.8465\n", "Epoch 88/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3249 - auc: 0.9034 - accuracy: 0.8472 - val_loss: 0.3320 - val_auc: 0.8987 - val_accuracy: 0.8468\n", + "814/814 [==============================] - 1s 953us/step - loss: 0.3249 - auc: 0.9034 - accuracy: 0.8472 - val_loss: 0.3320 - val_auc: 0.8987 - val_accuracy: 0.8468\n", "Epoch 89/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3233 - auc: 0.9038 - accuracy: 0.8482 - val_loss: 0.3318 - val_auc: 0.8988 - val_accuracy: 0.8469\n", + "814/814 [==============================] - 1s 913us/step - loss: 0.3233 - auc: 0.9038 - accuracy: 0.8482 - val_loss: 0.3318 - val_auc: 0.8988 - val_accuracy: 0.8469\n", "Epoch 90/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3291 - auc: 0.8991 - accuracy: 0.8472 - val_loss: 0.3317 - val_auc: 0.8989 - val_accuracy: 0.8474\n", "Epoch 91/200\n", @@ -3759,13 +3759,13 @@ "Epoch 93/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3361 - auc: 0.8969 - accuracy: 0.8402 - val_loss: 0.3314 - val_auc: 0.8991 - val_accuracy: 0.8474\n", "Epoch 94/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3269 - auc: 0.9011 - accuracy: 0.8455 - val_loss: 0.3312 - val_auc: 0.8992 - val_accuracy: 0.8472\n", + "814/814 [==============================] - 1s 912us/step - loss: 0.3269 - auc: 0.9011 - accuracy: 0.8455 - val_loss: 0.3312 - val_auc: 0.8992 - val_accuracy: 0.8472\n", "Epoch 95/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3325 - auc: 0.8987 - accuracy: 0.8427 - val_loss: 0.3311 - val_auc: 0.8993 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 924us/step - loss: 0.3325 - auc: 0.8987 - accuracy: 0.8427 - val_loss: 0.3311 - val_auc: 0.8993 - val_accuracy: 0.8475\n", "Epoch 96/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3250 - auc: 0.9032 - accuracy: 0.8474 - val_loss: 0.3310 - val_auc: 0.8994 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 875us/step - loss: 0.3250 - auc: 0.9032 - accuracy: 0.8474 - val_loss: 0.3310 - val_auc: 0.8994 - val_accuracy: 0.8475\n", "Epoch 97/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3278 - auc: 0.9032 - accuracy: 0.8463 - val_loss: 0.3309 - val_auc: 0.8994 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 884us/step - loss: 0.3278 - auc: 0.9032 - accuracy: 0.8463 - val_loss: 0.3309 - val_auc: 0.8994 - val_accuracy: 0.8475\n", "Epoch 98/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3305 - auc: 0.8995 - accuracy: 0.8445 - val_loss: 0.3309 - val_auc: 0.8995 - val_accuracy: 0.8477\n", "Epoch 99/200\n", @@ -3775,9 +3775,9 @@ "Epoch 101/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3326 - auc: 0.8994 - accuracy: 0.8420 - val_loss: 0.3306 - val_auc: 0.8997 - val_accuracy: 0.8474\n", "Epoch 102/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3238 - auc: 0.9046 - accuracy: 0.8470 - val_loss: 0.3305 - val_auc: 0.8997 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 929us/step - loss: 0.3238 - auc: 0.9046 - accuracy: 0.8470 - val_loss: 0.3305 - val_auc: 0.8997 - val_accuracy: 0.8475\n", "Epoch 103/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3262 - auc: 0.9027 - accuracy: 0.8436 - val_loss: 0.3304 - val_auc: 0.8997 - val_accuracy: 0.8474\n", + "814/814 [==============================] - 1s 991us/step - loss: 0.3262 - auc: 0.9027 - accuracy: 0.8436 - val_loss: 0.3304 - val_auc: 0.8997 - val_accuracy: 0.8474\n", "Epoch 104/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3257 - auc: 0.9020 - accuracy: 0.8474 - val_loss: 0.3303 - val_auc: 0.8999 - val_accuracy: 0.8480\n", "Epoch 105/200\n", @@ -3791,17 +3791,17 @@ "Epoch 109/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3256 - auc: 0.9021 - accuracy: 0.8455 - val_loss: 0.3299 - val_auc: 0.9001 - val_accuracy: 0.8478\n", "Epoch 110/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3277 - auc: 0.8999 - accuracy: 0.8456 - val_loss: 0.3298 - val_auc: 0.9002 - val_accuracy: 0.8480\n", + "814/814 [==============================] - 1s 947us/step - loss: 0.3277 - auc: 0.8999 - accuracy: 0.8456 - val_loss: 0.3298 - val_auc: 0.9002 - val_accuracy: 0.8480\n", "Epoch 111/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3241 - auc: 0.9022 - accuracy: 0.8451 - val_loss: 0.3298 - val_auc: 0.9003 - val_accuracy: 0.8480\n", + "814/814 [==============================] - 1s 742us/step - loss: 0.3241 - auc: 0.9022 - accuracy: 0.8451 - val_loss: 0.3298 - val_auc: 0.9003 - val_accuracy: 0.8480\n", "Epoch 112/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3208 - auc: 0.9069 - accuracy: 0.8500 - val_loss: 0.3297 - val_auc: 0.9003 - val_accuracy: 0.8478\n", + "814/814 [==============================] - 1s 762us/step - loss: 0.3208 - auc: 0.9069 - accuracy: 0.8500 - val_loss: 0.3297 - val_auc: 0.9003 - val_accuracy: 0.8478\n", "Epoch 113/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3281 - auc: 0.9015 - accuracy: 0.8476 - val_loss: 0.3296 - val_auc: 0.9004 - val_accuracy: 0.8480\n", + "814/814 [==============================] - 1s 850us/step - loss: 0.3281 - auc: 0.9015 - accuracy: 0.8476 - val_loss: 0.3296 - val_auc: 0.9004 - val_accuracy: 0.8480\n", "Epoch 114/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3280 - auc: 0.9001 - accuracy: 0.8450 - val_loss: 0.3296 - val_auc: 0.9004 - val_accuracy: 0.8485\n", + "814/814 [==============================] - 1s 859us/step - loss: 0.3280 - auc: 0.9001 - accuracy: 0.8450 - val_loss: 0.3296 - val_auc: 0.9004 - val_accuracy: 0.8485\n", "Epoch 115/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3198 - auc: 0.9035 - accuracy: 0.8493 - val_loss: 0.3295 - val_auc: 0.9005 - val_accuracy: 0.8477\n", + "814/814 [==============================] - 1s 898us/step - loss: 0.3198 - auc: 0.9035 - accuracy: 0.8493 - val_loss: 0.3295 - val_auc: 0.9005 - val_accuracy: 0.8477\n", "Epoch 116/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3202 - auc: 0.9057 - accuracy: 0.8503 - val_loss: 0.3294 - val_auc: 0.9005 - val_accuracy: 0.8478\n", "Epoch 117/200\n", @@ -3811,31 +3811,31 @@ "Epoch 119/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3219 - auc: 0.9039 - accuracy: 0.8487 - val_loss: 0.3292 - val_auc: 0.9007 - val_accuracy: 0.8481\n", "Epoch 120/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3252 - auc: 0.9013 - accuracy: 0.8466 - val_loss: 0.3291 - val_auc: 0.9007 - val_accuracy: 0.8483\n", + "814/814 [==============================] - 1s 808us/step - loss: 0.3252 - auc: 0.9013 - accuracy: 0.8466 - val_loss: 0.3291 - val_auc: 0.9007 - val_accuracy: 0.8483\n", "Epoch 121/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3265 - auc: 0.9011 - accuracy: 0.8469 - val_loss: 0.3291 - val_auc: 0.9008 - val_accuracy: 0.8483\n", + "814/814 [==============================] - 1s 767us/step - loss: 0.3265 - auc: 0.9011 - accuracy: 0.8469 - 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val_accuracy: 0.8483\n", + "814/814 [==============================] - 1s 777us/step - loss: 0.3210 - auc: 0.9064 - accuracy: 0.8496 - val_loss: 0.3271 - val_auc: 0.9021 - val_accuracy: 0.8483\n", "Epoch 156/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3232 - auc: 0.9038 - accuracy: 0.8500 - val_loss: 0.3270 - val_auc: 0.9022 - val_accuracy: 0.8494\n", + "814/814 [==============================] - 1s 752us/step - loss: 0.3232 - auc: 0.9038 - accuracy: 0.8500 - val_loss: 0.3270 - val_auc: 0.9022 - val_accuracy: 0.8494\n", "Epoch 157/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3180 - auc: 0.9062 - accuracy: 0.8519 - val_loss: 0.3270 - val_auc: 0.9022 - val_accuracy: 0.8485\n", + "814/814 [==============================] - 1s 751us/step - loss: 0.3180 - auc: 0.9062 - accuracy: 0.8519 - val_loss: 0.3270 - val_auc: 0.9022 - val_accuracy: 0.8485\n", "Epoch 158/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - 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1s 953us/step - loss: 0.3186 - auc: 0.9085 - accuracy: 0.8515 - val_loss: 0.3268 - val_auc: 0.9024 - val_accuracy: 0.8485\n", "Epoch 162/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3223 - auc: 0.9057 - accuracy: 0.8505 - val_loss: 0.3268 - val_auc: 0.9024 - val_accuracy: 0.8483\n", "Epoch 163/200\n", @@ -3901,11 +3901,11 @@ "Epoch 164/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3166 - auc: 0.9094 - accuracy: 0.8514 - val_loss: 0.3267 - val_auc: 0.9025 - val_accuracy: 0.8483\n", "Epoch 165/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3223 - auc: 0.9044 - accuracy: 0.8508 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3223 - auc: 0.9044 - accuracy: 0.8508 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8475\n", "Epoch 166/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3150 - auc: 0.9083 - accuracy: 0.8508 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8481\n", + "814/814 [==============================] - 1s 760us/step - loss: 0.3150 - auc: 0.9083 - accuracy: 0.8508 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8481\n", "Epoch 167/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3217 - auc: 0.9047 - accuracy: 0.8485 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8481\n", + "814/814 [==============================] - 1s 830us/step - loss: 0.3217 - auc: 0.9047 - accuracy: 0.8485 - val_loss: 0.3266 - val_auc: 0.9025 - val_accuracy: 0.8481\n", "Epoch 168/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3217 - auc: 0.9057 - accuracy: 0.8478 - val_loss: 0.3265 - val_auc: 0.9025 - val_accuracy: 0.8481\n", "Epoch 169/200\n", @@ -3915,25 +3915,25 @@ "Epoch 171/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3182 - auc: 0.9073 - accuracy: 0.8512 - val_loss: 0.3264 - val_auc: 0.9027 - val_accuracy: 0.8480\n", "Epoch 172/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3211 - auc: 0.9064 - accuracy: 0.8511 - val_loss: 0.3264 - val_auc: 0.9027 - val_accuracy: 0.8474\n", + "814/814 [==============================] - 1s 888us/step - loss: 0.3211 - auc: 0.9064 - accuracy: 0.8511 - val_loss: 0.3264 - val_auc: 0.9027 - val_accuracy: 0.8474\n", "Epoch 173/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3223 - auc: 0.9055 - accuracy: 0.8470 - val_loss: 0.3263 - val_auc: 0.9027 - val_accuracy: 0.8480\n", + "814/814 [==============================] - 1s 884us/step - loss: 0.3223 - auc: 0.9055 - accuracy: 0.8470 - val_loss: 0.3263 - val_auc: 0.9027 - val_accuracy: 0.8480\n", "Epoch 174/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3254 - auc: 0.9033 - accuracy: 0.8471 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 862us/step - loss: 0.3254 - auc: 0.9033 - accuracy: 0.8471 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", "Epoch 175/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3177 - auc: 0.9073 - accuracy: 0.8482 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", + "814/814 [==============================] - 1s 983us/step - loss: 0.3177 - auc: 0.9073 - accuracy: 0.8482 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", "Epoch 176/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3182 - auc: 0.9067 - accuracy: 0.8492 - val_loss: 0.3263 - val_auc: 0.9028 - val_accuracy: 0.8475\n", "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3205 - auc: 0.9046 - accuracy: 0.8495 - val_loss: 0.3262 - val_auc: 0.9028 - val_accuracy: 0.8478\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3205 - auc: 0.9046 - accuracy: 0.8495 - val_loss: 0.3262 - val_auc: 0.9028 - val_accuracy: 0.8478\n", "Epoch 178/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3191 - auc: 0.9064 - accuracy: 0.8501 - val_loss: 0.3262 - val_auc: 0.9028 - val_accuracy: 0.8480\n", "Epoch 179/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9048 - accuracy: 0.8493 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8475\n", "Epoch 180/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3180 - auc: 0.9073 - accuracy: 0.8513 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8477\n", + "814/814 [==============================] - 1s 978us/step - loss: 0.3180 - auc: 0.9073 - accuracy: 0.8513 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8477\n", "Epoch 181/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3191 - auc: 0.9072 - accuracy: 0.8490 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8466\n", + "814/814 [==============================] - 1s 974us/step - loss: 0.3191 - auc: 0.9072 - accuracy: 0.8490 - val_loss: 0.3261 - val_auc: 0.9028 - val_accuracy: 0.8466\n", "Epoch 182/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3188 - auc: 0.9061 - accuracy: 0.8494 - val_loss: 0.3260 - val_auc: 0.9030 - val_accuracy: 0.8481\n", "Epoch 183/200\n", @@ -3947,21 +3947,21 @@ "Epoch 187/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3094 - auc: 0.9126 - accuracy: 0.8550 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8468\n", "Epoch 188/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3193 - auc: 0.9056 - accuracy: 0.8518 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8477\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3193 - auc: 0.9056 - accuracy: 0.8518 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8477\n", "Epoch 189/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3144 - auc: 0.9089 - accuracy: 0.8499 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8477\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3144 - auc: 0.9089 - accuracy: 0.8499 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8477\n", "Epoch 190/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3192 - auc: 0.9063 - accuracy: 0.8511 - val_loss: 0.3258 - val_auc: 0.9030 - val_accuracy: 0.8472\n", "Epoch 191/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3212 - auc: 0.9078 - accuracy: 0.8474 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8465\n", "Epoch 192/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3245 - auc: 0.9053 - accuracy: 0.8467 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8466\n", + "814/814 [==============================] - 1s 906us/step - loss: 0.3245 - auc: 0.9053 - accuracy: 0.8467 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8466\n", "Epoch 193/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - auc: 0.9071 - accuracy: 0.8504 - val_loss: 0.3257 - val_auc: 0.9031 - val_accuracy: 0.8465\n", "Epoch 194/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9057 - accuracy: 0.8498 - val_loss: 0.3257 - val_auc: 0.9032 - val_accuracy: 0.8466\n", "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3264 - auc: 0.9019 - accuracy: 0.8464 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8468\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3264 - auc: 0.9019 - accuracy: 0.8464 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8468\n", "Epoch 196/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3180 - auc: 0.9062 - accuracy: 0.8493 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8471\n", "Epoch 197/200\n", @@ -3969,9 +3969,9 @@ "Epoch 198/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3164 - auc: 0.9078 - accuracy: 0.8510 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8481\n", "Epoch 199/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3169 - auc: 0.9082 - accuracy: 0.8531 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8474\n", + "814/814 [==============================] - 1s 936us/step - loss: 0.3169 - auc: 0.9082 - accuracy: 0.8531 - val_loss: 0.3256 - val_auc: 0.9032 - val_accuracy: 0.8474\n", "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3167 - auc: 0.9084 - accuracy: 0.8486 - val_loss: 0.3255 - val_auc: 0.9032 - val_accuracy: 0.8462\n" + "814/814 [==============================] - 1s 927us/step - loss: 0.3167 - auc: 0.9084 - accuracy: 0.8486 - val_loss: 0.3255 - val_auc: 0.9032 - val_accuracy: 0.8462\n" ] } ], @@ -3981,7 +3981,7 @@ }, { "cell_type": "markdown", - "id": "70f3f807", + "id": "067cd491", "metadata": {}, "source": [ "#### Métricas" @@ -3989,7 +3989,7 @@ }, { "cell_type": "markdown", - "id": "36b6952c", + "id": "d38b10a8", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -3997,8 +3997,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "id": "9d43e624", + "execution_count": 49, + "id": "7b8cdcd8", "metadata": {}, "outputs": [ { @@ -4027,8 +4027,8 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "600d21ea", + "execution_count": 50, + "id": "1f64c554", "metadata": {}, "outputs": [ { @@ -4057,8 +4057,8 @@ }, { "cell_type": "code", - "execution_count": 50, - "id": "da6c2b75", + "execution_count": 51, + "id": "2f2db9be", "metadata": {}, "outputs": [ { @@ -4116,7 +4116,7 @@ }, { "cell_type": "markdown", - "id": "8027d735", + "id": "0f1690b1", "metadata": {}, "source": [ "Lo obtenido es muy prometedor, practicamente empato los resultados del último modelo del primer preprocesado. Veamos que sucede si mejoramos el optimizador" @@ -4124,7 +4124,7 @@ }, { "cell_type": "markdown", - "id": "cc55ca28", + "id": "b5eb3c0e", "metadata": {}, "source": [ "### Segundo diseño de la red" @@ -4132,7 +4132,7 @@ }, { "cell_type": "markdown", - "id": "deaef32a", + "id": "48034b57", "metadata": {}, "source": [ "#### Diseño" @@ -4140,7 +4140,7 @@ }, { "cell_type": "markdown", - "id": "d8010901", + "id": "ad8de06d", "metadata": {}, "source": [ "Seguiremos con la misma estrucctura pero modificando el learning rate a 0.0001 y cambiando el optimziador" @@ -4148,8 +4148,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "d9669f8d", + "execution_count": 52, + "id": "0e01edea", "metadata": {}, "outputs": [], "source": [ @@ -4159,8 +4159,8 @@ }, { "cell_type": "code", - "execution_count": 52, - "id": "e1b46529", + "execution_count": 53, + "id": "18f572f6", "metadata": {}, "outputs": [], "source": [ @@ -4173,7 +4173,7 @@ }, { "cell_type": "markdown", - "id": "9f1e562d", + "id": "d7f8b482", "metadata": {}, "source": [ "Vemos un resumen de nuestra red" @@ -4181,8 +4181,8 @@ }, { "cell_type": "code", - "execution_count": 53, - "id": "771075d1", + "execution_count": 54, + "id": "d54b6a6e", "metadata": {}, "outputs": [ { @@ -4216,7 +4216,7 @@ }, { "cell_type": "markdown", - "id": "bd6a2b2f", + "id": "761a20c1", "metadata": {}, "source": [ "Finalmente entrenamos" @@ -4224,8 +4224,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "33aa2617", + "execution_count": 55, + "id": "63bcc1dd", "metadata": {}, "outputs": [ { @@ -4261,13 +4261,13 @@ "Epoch 14/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3374 - auc: 0.8956 - accuracy: 0.8443 - val_loss: 0.3385 - val_auc: 0.8940 - val_accuracy: 0.8415\n", "Epoch 15/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3387 - auc: 0.8947 - accuracy: 0.8413 - val_loss: 0.3377 - val_auc: 0.8946 - val_accuracy: 0.8425\n", + "814/814 [==============================] - 1s 914us/step - loss: 0.3387 - auc: 0.8947 - accuracy: 0.8413 - val_loss: 0.3377 - val_auc: 0.8946 - val_accuracy: 0.8425\n", "Epoch 16/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3387 - auc: 0.8937 - accuracy: 0.8412 - val_loss: 0.3369 - val_auc: 0.8951 - val_accuracy: 0.8435\n", + "814/814 [==============================] - 1s 951us/step - loss: 0.3387 - auc: 0.8937 - accuracy: 0.8412 - val_loss: 0.3369 - val_auc: 0.8951 - val_accuracy: 0.8435\n", "Epoch 17/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3327 - auc: 0.8984 - accuracy: 0.8463 - val_loss: 0.3361 - val_auc: 0.8956 - val_accuracy: 0.8435\n", + "814/814 [==============================] - 1s 936us/step - loss: 0.3327 - auc: 0.8984 - accuracy: 0.8463 - val_loss: 0.3361 - val_auc: 0.8956 - val_accuracy: 0.8435\n", "Epoch 18/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3350 - auc: 0.8940 - accuracy: 0.8456 - val_loss: 0.3354 - val_auc: 0.8961 - val_accuracy: 0.8449\n", + "814/814 [==============================] - 1s 966us/step - loss: 0.3350 - auc: 0.8940 - accuracy: 0.8456 - val_loss: 0.3354 - val_auc: 0.8961 - val_accuracy: 0.8449\n", "Epoch 19/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3403 - auc: 0.8941 - accuracy: 0.8419 - val_loss: 0.3349 - val_auc: 0.8964 - val_accuracy: 0.8462\n", "Epoch 20/200\n", @@ -4279,9 +4279,9 @@ "Epoch 23/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3258 - auc: 0.9007 - accuracy: 0.8507 - val_loss: 0.3329 - val_auc: 0.8977 - val_accuracy: 0.8481\n", "Epoch 24/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3240 - auc: 0.9017 - accuracy: 0.8497 - val_loss: 0.3325 - val_auc: 0.8980 - val_accuracy: 0.8480\n", + "814/814 [==============================] - 1s 972us/step - loss: 0.3240 - auc: 0.9017 - accuracy: 0.8497 - val_loss: 0.3325 - val_auc: 0.8980 - val_accuracy: 0.8480\n", "Epoch 25/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3288 - auc: 0.8994 - accuracy: 0.8483 - val_loss: 0.3323 - val_auc: 0.8983 - val_accuracy: 0.8474\n", + "814/814 [==============================] - 1s 982us/step - loss: 0.3288 - auc: 0.8994 - accuracy: 0.8483 - val_loss: 0.3323 - val_auc: 0.8983 - val_accuracy: 0.8474\n", "Epoch 26/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3254 - auc: 0.9030 - accuracy: 0.8492 - val_loss: 0.3317 - val_auc: 0.8984 - val_accuracy: 0.8483\n", "Epoch 27/200\n", @@ -4295,7 +4295,7 @@ "Epoch 31/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3252 - auc: 0.9027 - accuracy: 0.8500 - val_loss: 0.3300 - val_auc: 0.8996 - val_accuracy: 0.8498\n", "Epoch 32/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3261 - auc: 0.9015 - accuracy: 0.8536 - val_loss: 0.3299 - val_auc: 0.8997 - val_accuracy: 0.8503\n", + "814/814 [==============================] - 1s 958us/step - loss: 0.3261 - auc: 0.9015 - accuracy: 0.8536 - val_loss: 0.3299 - val_auc: 0.8997 - val_accuracy: 0.8503\n", "Epoch 33/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3257 - auc: 0.9038 - accuracy: 0.8519 - val_loss: 0.3296 - val_auc: 0.9000 - val_accuracy: 0.8501\n", "Epoch 34/200\n", @@ -4307,13 +4307,13 @@ "Epoch 37/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3246 - auc: 0.9032 - accuracy: 0.8516 - val_loss: 0.3291 - val_auc: 0.9004 - val_accuracy: 0.8506\n", "Epoch 38/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3208 - auc: 0.9063 - accuracy: 0.8517 - val_loss: 0.3291 - val_auc: 0.9005 - val_accuracy: 0.8506\n", + "814/814 [==============================] - 1s 985us/step - loss: 0.3208 - auc: 0.9063 - accuracy: 0.8517 - val_loss: 0.3291 - val_auc: 0.9005 - val_accuracy: 0.8506\n", "Epoch 39/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9048 - accuracy: 0.8543 - val_loss: 0.3291 - val_auc: 0.9006 - val_accuracy: 0.8509\n", + "814/814 [==============================] - 1s 969us/step - loss: 0.3200 - auc: 0.9048 - accuracy: 0.8543 - val_loss: 0.3291 - val_auc: 0.9006 - val_accuracy: 0.8509\n", "Epoch 40/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3216 - auc: 0.9029 - accuracy: 0.8514 - val_loss: 0.3289 - val_auc: 0.9008 - val_accuracy: 0.8505\n", + "814/814 [==============================] - 1s 915us/step - loss: 0.3216 - auc: 0.9029 - accuracy: 0.8514 - val_loss: 0.3289 - val_auc: 0.9008 - val_accuracy: 0.8505\n", "Epoch 41/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3202 - auc: 0.9052 - accuracy: 0.8506 - val_loss: 0.3288 - val_auc: 0.9008 - val_accuracy: 0.8506\n", + "814/814 [==============================] - 1s 979us/step - loss: 0.3202 - auc: 0.9052 - accuracy: 0.8506 - val_loss: 0.3288 - val_auc: 0.9008 - val_accuracy: 0.8506\n", "Epoch 42/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3259 - auc: 0.9027 - accuracy: 0.8485 - val_loss: 0.3288 - val_auc: 0.9009 - val_accuracy: 0.8512\n", "Epoch 43/200\n", @@ -4323,11 +4323,11 @@ "Epoch 45/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3260 - auc: 0.9030 - accuracy: 0.8475 - val_loss: 0.3289 - val_auc: 0.9010 - val_accuracy: 0.8500\n", "Epoch 46/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3179 - auc: 0.9057 - accuracy: 0.8564 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8500\n", + "814/814 [==============================] - 1s 977us/step - loss: 0.3179 - auc: 0.9057 - accuracy: 0.8564 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8500\n", "Epoch 47/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3165 - auc: 0.9086 - accuracy: 0.8530 - val_loss: 0.3287 - val_auc: 0.9010 - val_accuracy: 0.8498\n", + "814/814 [==============================] - 1s 937us/step - loss: 0.3165 - auc: 0.9086 - accuracy: 0.8530 - val_loss: 0.3287 - val_auc: 0.9010 - val_accuracy: 0.8498\n", "Epoch 48/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3186 - auc: 0.9066 - accuracy: 0.8522 - val_loss: 0.3287 - val_auc: 0.9011 - val_accuracy: 0.8500\n", + "814/814 [==============================] - 1s 982us/step - loss: 0.3186 - auc: 0.9066 - accuracy: 0.8522 - val_loss: 0.3287 - val_auc: 0.9011 - val_accuracy: 0.8500\n", "Epoch 49/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3220 - auc: 0.9053 - accuracy: 0.8516 - val_loss: 0.3287 - val_auc: 0.9011 - val_accuracy: 0.8500\n", "Epoch 50/200\n", @@ -4337,9 +4337,9 @@ "Epoch 52/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3225 - auc: 0.9011 - accuracy: 0.8507 - val_loss: 0.3291 - val_auc: 0.9012 - val_accuracy: 0.8506\n", "Epoch 53/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3228 - auc: 0.9048 - accuracy: 0.8476 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8501\n", + "814/814 [==============================] - 1s 957us/step - loss: 0.3228 - auc: 0.9048 - accuracy: 0.8476 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8501\n", "Epoch 54/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3231 - auc: 0.9031 - accuracy: 0.8499 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8503\n", + "814/814 [==============================] - 1s 940us/step - loss: 0.3231 - auc: 0.9031 - accuracy: 0.8499 - val_loss: 0.3288 - val_auc: 0.9011 - val_accuracy: 0.8503\n", "Epoch 55/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3222 - auc: 0.9046 - accuracy: 0.8496 - val_loss: 0.3287 - val_auc: 0.9012 - val_accuracy: 0.8497\n", "Epoch 56/200\n", @@ -4351,11 +4351,11 @@ "Epoch 59/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3167 - auc: 0.9085 - accuracy: 0.8560 - val_loss: 0.3285 - val_auc: 0.9015 - val_accuracy: 0.8498\n", "Epoch 60/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3231 - auc: 0.9052 - accuracy: 0.8523 - val_loss: 0.3285 - val_auc: 0.9015 - val_accuracy: 0.8498\n", + "814/814 [==============================] - 1s 939us/step - loss: 0.3231 - auc: 0.9052 - accuracy: 0.8523 - val_loss: 0.3285 - val_auc: 0.9015 - val_accuracy: 0.8498\n", "Epoch 61/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3172 - auc: 0.9070 - accuracy: 0.8535 - val_loss: 0.3284 - val_auc: 0.9014 - val_accuracy: 0.8497\n", + "814/814 [==============================] - 1s 937us/step - loss: 0.3172 - auc: 0.9070 - accuracy: 0.8535 - val_loss: 0.3284 - val_auc: 0.9014 - val_accuracy: 0.8497\n", "Epoch 62/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3207 - auc: 0.9071 - accuracy: 0.8529 - val_loss: 0.3283 - val_auc: 0.9016 - val_accuracy: 0.8503\n", + "814/814 [==============================] - 1s 945us/step - loss: 0.3207 - auc: 0.9071 - accuracy: 0.8529 - val_loss: 0.3283 - val_auc: 0.9016 - val_accuracy: 0.8503\n", "Epoch 63/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3157 - auc: 0.9069 - accuracy: 0.8571 - val_loss: 0.3284 - val_auc: 0.9016 - val_accuracy: 0.8497\n", "Epoch 64/200\n", @@ -4365,11 +4365,11 @@ "Epoch 66/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3166 - auc: 0.9074 - accuracy: 0.8558 - val_loss: 0.3283 - val_auc: 0.9018 - val_accuracy: 0.8494\n", "Epoch 67/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3126 - auc: 0.9088 - accuracy: 0.8554 - val_loss: 0.3285 - val_auc: 0.9020 - val_accuracy: 0.8483\n", + "814/814 [==============================] - 1s 944us/step - loss: 0.3126 - auc: 0.9088 - accuracy: 0.8554 - val_loss: 0.3285 - val_auc: 0.9020 - val_accuracy: 0.8483\n", "Epoch 68/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3127 - auc: 0.9114 - accuracy: 0.8563 - val_loss: 0.3279 - val_auc: 0.9020 - val_accuracy: 0.8498\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3127 - auc: 0.9114 - accuracy: 0.8563 - val_loss: 0.3279 - val_auc: 0.9020 - val_accuracy: 0.8498\n", "Epoch 69/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3193 - auc: 0.9069 - accuracy: 0.8518 - val_loss: 0.3280 - val_auc: 0.9020 - val_accuracy: 0.8498\n", + "814/814 [==============================] - 1s 986us/step - loss: 0.3193 - auc: 0.9069 - accuracy: 0.8518 - val_loss: 0.3280 - val_auc: 0.9020 - val_accuracy: 0.8498\n", "Epoch 70/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3200 - auc: 0.9068 - accuracy: 0.8538 - val_loss: 0.3279 - val_auc: 0.9021 - val_accuracy: 0.8498\n", "Epoch 71/200\n", @@ -4379,11 +4379,11 @@ "Epoch 73/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3168 - auc: 0.9078 - accuracy: 0.8551 - val_loss: 0.3280 - val_auc: 0.9021 - val_accuracy: 0.8503\n", "Epoch 74/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3154 - auc: 0.9093 - accuracy: 0.8546 - val_loss: 0.3281 - val_auc: 0.9022 - val_accuracy: 0.8506\n", + "814/814 [==============================] - 1s 991us/step - loss: 0.3154 - auc: 0.9093 - accuracy: 0.8546 - val_loss: 0.3281 - val_auc: 0.9022 - val_accuracy: 0.8506\n", "Epoch 75/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3174 - auc: 0.9087 - accuracy: 0.8542 - val_loss: 0.3279 - val_auc: 0.9024 - val_accuracy: 0.8501\n", + "814/814 [==============================] - 1s 942us/step - loss: 0.3174 - auc: 0.9087 - accuracy: 0.8542 - val_loss: 0.3279 - val_auc: 0.9024 - val_accuracy: 0.8501\n", "Epoch 76/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3152 - auc: 0.9073 - accuracy: 0.8531 - val_loss: 0.3282 - val_auc: 0.9023 - val_accuracy: 0.8503\n", + "814/814 [==============================] - 1s 955us/step - loss: 0.3152 - auc: 0.9073 - accuracy: 0.8531 - val_loss: 0.3282 - val_auc: 0.9023 - val_accuracy: 0.8503\n", "Epoch 77/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3137 - auc: 0.9093 - accuracy: 0.8560 - val_loss: 0.3280 - val_auc: 0.9023 - val_accuracy: 0.8498\n", "Epoch 78/200\n", @@ -4393,11 +4393,11 @@ "Epoch 80/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3135 - auc: 0.9079 - accuracy: 0.8582 - val_loss: 0.3280 - val_auc: 0.9025 - val_accuracy: 0.8500\n", "Epoch 81/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9084 - accuracy: 0.8553 - val_loss: 0.3277 - val_auc: 0.9028 - val_accuracy: 0.8500\n", + "814/814 [==============================] - 1s 940us/step - loss: 0.3142 - auc: 0.9084 - accuracy: 0.8553 - val_loss: 0.3277 - val_auc: 0.9028 - val_accuracy: 0.8500\n", "Epoch 82/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3126 - auc: 0.9114 - accuracy: 0.8557 - val_loss: 0.3277 - val_auc: 0.9027 - val_accuracy: 0.8508\n", + "814/814 [==============================] - 1s 954us/step - loss: 0.3126 - auc: 0.9114 - accuracy: 0.8557 - val_loss: 0.3277 - val_auc: 0.9027 - val_accuracy: 0.8508\n", "Epoch 83/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3204 - auc: 0.9074 - accuracy: 0.8532 - val_loss: 0.3278 - val_auc: 0.9029 - val_accuracy: 0.8501\n", + "814/814 [==============================] - 1s 985us/step - loss: 0.3204 - auc: 0.9074 - accuracy: 0.8532 - val_loss: 0.3278 - val_auc: 0.9029 - val_accuracy: 0.8501\n", "Epoch 84/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3140 - auc: 0.9097 - accuracy: 0.8550 - val_loss: 0.3276 - val_auc: 0.9030 - val_accuracy: 0.8509\n", "Epoch 85/200\n", @@ -4405,11 +4405,11 @@ "Epoch 86/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9084 - accuracy: 0.8579 - val_loss: 0.3275 - val_auc: 0.9031 - val_accuracy: 0.8511\n", "Epoch 87/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3129 - auc: 0.9112 - accuracy: 0.8573 - val_loss: 0.3274 - val_auc: 0.9032 - val_accuracy: 0.8512\n", + "814/814 [==============================] - 1s 931us/step - loss: 0.3129 - auc: 0.9112 - accuracy: 0.8573 - val_loss: 0.3274 - val_auc: 0.9032 - val_accuracy: 0.8512\n", "Epoch 88/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3116 - auc: 0.9117 - accuracy: 0.8608 - val_loss: 0.3273 - val_auc: 0.9033 - val_accuracy: 0.8505\n", + "814/814 [==============================] - 1s 866us/step - loss: 0.3116 - auc: 0.9117 - accuracy: 0.8608 - val_loss: 0.3273 - val_auc: 0.9033 - val_accuracy: 0.8505\n", "Epoch 89/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3123 - auc: 0.9106 - accuracy: 0.8589 - val_loss: 0.3272 - val_auc: 0.9034 - val_accuracy: 0.8517\n", + "814/814 [==============================] - 1s 931us/step - loss: 0.3123 - auc: 0.9106 - accuracy: 0.8589 - val_loss: 0.3272 - val_auc: 0.9034 - val_accuracy: 0.8517\n", "Epoch 90/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3173 - auc: 0.9070 - accuracy: 0.8587 - val_loss: 0.3273 - val_auc: 0.9035 - val_accuracy: 0.8511\n", "Epoch 91/200\n", @@ -4421,11 +4421,11 @@ "Epoch 94/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3129 - auc: 0.9100 - accuracy: 0.8556 - val_loss: 0.3271 - val_auc: 0.9038 - val_accuracy: 0.8512\n", "Epoch 95/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3172 - auc: 0.9082 - accuracy: 0.8547 - val_loss: 0.3271 - val_auc: 0.9038 - val_accuracy: 0.8501\n", + "814/814 [==============================] - 1s 940us/step - loss: 0.3172 - auc: 0.9082 - accuracy: 0.8547 - val_loss: 0.3271 - val_auc: 0.9038 - val_accuracy: 0.8501\n", "Epoch 96/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3104 - auc: 0.9123 - accuracy: 0.8604 - val_loss: 0.3268 - val_auc: 0.9041 - val_accuracy: 0.8506\n", + "814/814 [==============================] - 1s 944us/step - loss: 0.3104 - auc: 0.9123 - accuracy: 0.8604 - val_loss: 0.3268 - val_auc: 0.9041 - val_accuracy: 0.8506\n", "Epoch 97/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3139 - auc: 0.9112 - accuracy: 0.8553 - val_loss: 0.3267 - val_auc: 0.9040 - val_accuracy: 0.8505\n", + "814/814 [==============================] - 1s 982us/step - loss: 0.3139 - auc: 0.9112 - accuracy: 0.8553 - val_loss: 0.3267 - val_auc: 0.9040 - val_accuracy: 0.8505\n", "Epoch 98/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3178 - auc: 0.9085 - accuracy: 0.8542 - val_loss: 0.3268 - val_auc: 0.9040 - val_accuracy: 0.8508\n", "Epoch 99/200\n", @@ -4435,11 +4435,11 @@ "Epoch 101/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3203 - auc: 0.9073 - accuracy: 0.8546 - val_loss: 0.3270 - val_auc: 0.9041 - val_accuracy: 0.8509\n", "Epoch 102/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3090 - auc: 0.9139 - accuracy: 0.8574 - val_loss: 0.3268 - val_auc: 0.9042 - val_accuracy: 0.8509\n", + "814/814 [==============================] - 1s 951us/step - loss: 0.3090 - auc: 0.9139 - accuracy: 0.8574 - val_loss: 0.3268 - val_auc: 0.9042 - val_accuracy: 0.8509\n", "Epoch 103/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3128 - auc: 0.9107 - accuracy: 0.8551 - val_loss: 0.3267 - val_auc: 0.9044 - val_accuracy: 0.8511\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3128 - auc: 0.9107 - accuracy: 0.8551 - val_loss: 0.3267 - val_auc: 0.9044 - val_accuracy: 0.8511\n", "Epoch 104/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3134 - auc: 0.9099 - accuracy: 0.8583 - val_loss: 0.3268 - val_auc: 0.9044 - val_accuracy: 0.8514\n", + "814/814 [==============================] - 1s 939us/step - loss: 0.3134 - auc: 0.9099 - accuracy: 0.8583 - val_loss: 0.3268 - val_auc: 0.9044 - val_accuracy: 0.8514\n", "Epoch 105/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3091 - auc: 0.9121 - accuracy: 0.8577 - val_loss: 0.3265 - val_auc: 0.9046 - val_accuracy: 0.8515\n", "Epoch 106/200\n", @@ -4449,9 +4449,9 @@ "Epoch 108/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3148 - auc: 0.9093 - accuracy: 0.8589 - val_loss: 0.3266 - val_auc: 0.9046 - val_accuracy: 0.8512\n", "Epoch 109/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9099 - accuracy: 0.8552 - val_loss: 0.3265 - val_auc: 0.9046 - val_accuracy: 0.8506\n", + "814/814 [==============================] - 1s 982us/step - loss: 0.3142 - auc: 0.9099 - accuracy: 0.8552 - val_loss: 0.3265 - val_auc: 0.9046 - val_accuracy: 0.8506\n", "Epoch 110/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3114 - auc: 0.9103 - accuracy: 0.8600 - val_loss: 0.3267 - val_auc: 0.9047 - val_accuracy: 0.8518\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3114 - auc: 0.9103 - accuracy: 0.8600 - val_loss: 0.3267 - val_auc: 0.9047 - val_accuracy: 0.8518\n", "Epoch 111/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3105 - auc: 0.9108 - accuracy: 0.8572 - val_loss: 0.3264 - val_auc: 0.9050 - val_accuracy: 0.8524\n", "Epoch 112/200\n", @@ -4461,11 +4461,11 @@ "Epoch 114/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3157 - auc: 0.9081 - accuracy: 0.8569 - val_loss: 0.3264 - val_auc: 0.9051 - val_accuracy: 0.8508\n", "Epoch 115/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3061 - auc: 0.9116 - accuracy: 0.8606 - val_loss: 0.3265 - val_auc: 0.9051 - val_accuracy: 0.8515\n", + "814/814 [==============================] - 1s 965us/step - loss: 0.3061 - auc: 0.9116 - accuracy: 0.8606 - val_loss: 0.3265 - val_auc: 0.9051 - val_accuracy: 0.8515\n", "Epoch 116/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3059 - auc: 0.9145 - accuracy: 0.8629 - val_loss: 0.3264 - val_auc: 0.9051 - val_accuracy: 0.8515\n", + "814/814 [==============================] - 1s 949us/step - loss: 0.3059 - auc: 0.9145 - accuracy: 0.8629 - val_loss: 0.3264 - val_auc: 0.9051 - val_accuracy: 0.8515\n", "Epoch 117/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3147 - auc: 0.9102 - accuracy: 0.8559 - val_loss: 0.3268 - val_auc: 0.9051 - val_accuracy: 0.8521\n", + "814/814 [==============================] - 1s 985us/step - loss: 0.3147 - auc: 0.9102 - accuracy: 0.8559 - val_loss: 0.3268 - val_auc: 0.9051 - val_accuracy: 0.8521\n", "Epoch 118/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9114 - accuracy: 0.8591 - val_loss: 0.3265 - val_auc: 0.9050 - val_accuracy: 0.8512\n", "Epoch 119/200\n", @@ -4475,9 +4475,9 @@ "Epoch 121/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3160 - auc: 0.9088 - accuracy: 0.8571 - val_loss: 0.3266 - val_auc: 0.9052 - val_accuracy: 0.8523\n", "Epoch 122/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3171 - auc: 0.9114 - accuracy: 0.8598 - val_loss: 0.3266 - val_auc: 0.9052 - val_accuracy: 0.8523\n", + "814/814 [==============================] - 1s 946us/step - loss: 0.3171 - auc: 0.9114 - accuracy: 0.8598 - val_loss: 0.3266 - val_auc: 0.9052 - val_accuracy: 0.8523\n", "Epoch 123/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3085 - auc: 0.9136 - accuracy: 0.8602 - val_loss: 0.3263 - val_auc: 0.9052 - val_accuracy: 0.8511\n", + "814/814 [==============================] - 1s 976us/step - loss: 0.3085 - auc: 0.9136 - accuracy: 0.8602 - val_loss: 0.3263 - val_auc: 0.9052 - val_accuracy: 0.8511\n", "Epoch 124/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3057 - auc: 0.9145 - accuracy: 0.8617 - val_loss: 0.3266 - val_auc: 0.9054 - val_accuracy: 0.8524\n", "Epoch 125/200\n", @@ -4487,9 +4487,9 @@ "Epoch 127/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3045 - auc: 0.9158 - accuracy: 0.8627 - val_loss: 0.3262 - val_auc: 0.9053 - val_accuracy: 0.8512\n", "Epoch 128/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3099 - auc: 0.9114 - accuracy: 0.8596 - val_loss: 0.3267 - val_auc: 0.9054 - val_accuracy: 0.8526\n", + "814/814 [==============================] - 1s 948us/step - loss: 0.3099 - auc: 0.9114 - accuracy: 0.8596 - val_loss: 0.3267 - val_auc: 0.9054 - val_accuracy: 0.8526\n", "Epoch 129/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3075 - auc: 0.9133 - accuracy: 0.8606 - val_loss: 0.3267 - val_auc: 0.9055 - val_accuracy: 0.8524\n", + "814/814 [==============================] - 1s 952us/step - loss: 0.3075 - auc: 0.9133 - accuracy: 0.8606 - val_loss: 0.3267 - val_auc: 0.9055 - val_accuracy: 0.8524\n", "Epoch 130/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3142 - auc: 0.9100 - accuracy: 0.8609 - val_loss: 0.3266 - val_auc: 0.9055 - val_accuracy: 0.8524\n", "Epoch 131/200\n", @@ -4501,7 +4501,7 @@ "Epoch 134/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3114 - auc: 0.9115 - accuracy: 0.8605 - val_loss: 0.3268 - val_auc: 0.9054 - val_accuracy: 0.8523\n", "Epoch 135/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3111 - auc: 0.9118 - accuracy: 0.8605 - val_loss: 0.3266 - val_auc: 0.9053 - val_accuracy: 0.8521\n", + "814/814 [==============================] - 1s 986us/step - loss: 0.3111 - auc: 0.9118 - accuracy: 0.8605 - val_loss: 0.3266 - val_auc: 0.9053 - val_accuracy: 0.8521\n", "Epoch 136/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3112 - auc: 0.9128 - accuracy: 0.8598 - val_loss: 0.3266 - val_auc: 0.9054 - val_accuracy: 0.8524\n", "Epoch 137/200\n", @@ -4511,9 +4511,9 @@ "Epoch 139/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3125 - auc: 0.9109 - accuracy: 0.8583 - val_loss: 0.3267 - val_auc: 0.9054 - val_accuracy: 0.8523\n", "Epoch 140/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3144 - auc: 0.9105 - accuracy: 0.8587 - val_loss: 0.3270 - val_auc: 0.9054 - val_accuracy: 0.8523\n", + "814/814 [==============================] - 1s 987us/step - loss: 0.3144 - auc: 0.9105 - accuracy: 0.8587 - val_loss: 0.3270 - val_auc: 0.9054 - val_accuracy: 0.8523\n", "Epoch 141/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3055 - auc: 0.9136 - accuracy: 0.8618 - val_loss: 0.3270 - val_auc: 0.9055 - val_accuracy: 0.8529\n", + "814/814 [==============================] - 1s 999us/step - loss: 0.3055 - auc: 0.9136 - accuracy: 0.8618 - val_loss: 0.3270 - val_auc: 0.9055 - val_accuracy: 0.8529\n", "Epoch 142/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3056 - auc: 0.9157 - accuracy: 0.8607 - val_loss: 0.3270 - val_auc: 0.9056 - val_accuracy: 0.8529\n", "Epoch 143/200\n", @@ -4523,7 +4523,7 @@ "Epoch 145/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3163 - auc: 0.9109 - accuracy: 0.8589 - val_loss: 0.3268 - val_auc: 0.9057 - val_accuracy: 0.8529\n", "Epoch 146/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3073 - auc: 0.9143 - accuracy: 0.8620 - val_loss: 0.3273 - val_auc: 0.9056 - val_accuracy: 0.8521\n", + "814/814 [==============================] - 1s 972us/step - loss: 0.3073 - auc: 0.9143 - accuracy: 0.8620 - val_loss: 0.3273 - val_auc: 0.9056 - val_accuracy: 0.8521\n", "Epoch 147/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3106 - auc: 0.9142 - accuracy: 0.8608 - val_loss: 0.3273 - val_auc: 0.9055 - val_accuracy: 0.8515\n", "Epoch 148/200\n", @@ -4533,7 +4533,7 @@ "Epoch 150/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3046 - auc: 0.9142 - accuracy: 0.8625 - val_loss: 0.3276 - val_auc: 0.9056 - val_accuracy: 0.8526\n", "Epoch 151/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3126 - auc: 0.9115 - accuracy: 0.8629 - val_loss: 0.3276 - val_auc: 0.9058 - val_accuracy: 0.8532\n", + "814/814 [==============================] - 1s 990us/step - loss: 0.3126 - auc: 0.9115 - accuracy: 0.8629 - val_loss: 0.3276 - val_auc: 0.9058 - val_accuracy: 0.8532\n", "Epoch 152/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3108 - auc: 0.9129 - accuracy: 0.8620 - val_loss: 0.3272 - val_auc: 0.9057 - val_accuracy: 0.8531\n", "Epoch 153/200\n", @@ -4545,11 +4545,11 @@ "Epoch 156/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3104 - auc: 0.9117 - accuracy: 0.8622 - val_loss: 0.3270 - val_auc: 0.9059 - val_accuracy: 0.8523\n", "Epoch 157/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3048 - auc: 0.9139 - accuracy: 0.8636 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8535\n", + "814/814 [==============================] - 1s 985us/step - loss: 0.3048 - auc: 0.9139 - accuracy: 0.8636 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8535\n", "Epoch 158/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3033 - auc: 0.9157 - accuracy: 0.8621 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8528\n", + "814/814 [==============================] - 1s 984us/step - loss: 0.3033 - auc: 0.9157 - accuracy: 0.8621 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8528\n", "Epoch 159/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3101 - auc: 0.9131 - accuracy: 0.8612 - val_loss: 0.3269 - val_auc: 0.9058 - val_accuracy: 0.8531\n", + "814/814 [==============================] - 1s 992us/step - loss: 0.3101 - auc: 0.9131 - accuracy: 0.8612 - val_loss: 0.3269 - val_auc: 0.9058 - val_accuracy: 0.8531\n", "Epoch 160/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3078 - auc: 0.9141 - accuracy: 0.8610 - val_loss: 0.3275 - val_auc: 0.9059 - val_accuracy: 0.8528\n", "Epoch 161/200\n", @@ -4571,7 +4571,7 @@ "Epoch 169/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3071 - auc: 0.9151 - accuracy: 0.8618 - val_loss: 0.3271 - val_auc: 0.9059 - val_accuracy: 0.8528\n", "Epoch 170/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3081 - auc: 0.9133 - accuracy: 0.8590 - val_loss: 0.3274 - val_auc: 0.9060 - val_accuracy: 0.8524\n", + "814/814 [==============================] - 1s 970us/step - loss: 0.3081 - auc: 0.9133 - accuracy: 0.8590 - val_loss: 0.3274 - val_auc: 0.9060 - val_accuracy: 0.8524\n", "Epoch 171/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3041 - auc: 0.9163 - accuracy: 0.8632 - val_loss: 0.3270 - val_auc: 0.9060 - val_accuracy: 0.8540\n", "Epoch 172/200\n", @@ -4583,9 +4583,9 @@ "Epoch 175/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3057 - auc: 0.9145 - accuracy: 0.8610 - val_loss: 0.3272 - val_auc: 0.9060 - val_accuracy: 0.8532\n", "Epoch 176/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3091 - auc: 0.9124 - accuracy: 0.8601 - val_loss: 0.3277 - val_auc: 0.9061 - val_accuracy: 0.8529\n", + "814/814 [==============================] - 1s 951us/step - loss: 0.3091 - auc: 0.9124 - accuracy: 0.8601 - val_loss: 0.3277 - val_auc: 0.9061 - val_accuracy: 0.8529\n", "Epoch 177/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3082 - auc: 0.9118 - accuracy: 0.8631 - val_loss: 0.3273 - val_auc: 0.9060 - val_accuracy: 0.8538\n", + "814/814 [==============================] - 1s 995us/step - loss: 0.3082 - auc: 0.9118 - accuracy: 0.8631 - val_loss: 0.3273 - val_auc: 0.9060 - val_accuracy: 0.8538\n", "Epoch 178/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3078 - auc: 0.9136 - accuracy: 0.8617 - val_loss: 0.3279 - val_auc: 0.9062 - val_accuracy: 0.8531\n", "Epoch 179/200\n", @@ -4595,9 +4595,9 @@ "Epoch 181/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3073 - auc: 0.9144 - accuracy: 0.8613 - val_loss: 0.3275 - val_auc: 0.9062 - val_accuracy: 0.8531\n", "Epoch 182/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3038 - auc: 0.9152 - accuracy: 0.8601 - val_loss: 0.3279 - val_auc: 0.9062 - val_accuracy: 0.8528\n", + "814/814 [==============================] - 1s 950us/step - loss: 0.3038 - auc: 0.9152 - accuracy: 0.8601 - val_loss: 0.3279 - val_auc: 0.9062 - val_accuracy: 0.8528\n", "Epoch 183/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3052 - auc: 0.9161 - accuracy: 0.8615 - val_loss: 0.3272 - val_auc: 0.9062 - val_accuracy: 0.8528\n", + "814/814 [==============================] - 1s 990us/step - loss: 0.3052 - auc: 0.9161 - accuracy: 0.8615 - val_loss: 0.3272 - val_auc: 0.9062 - val_accuracy: 0.8528\n", "Epoch 184/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3040 - auc: 0.9156 - accuracy: 0.8609 - val_loss: 0.3275 - val_auc: 0.9062 - val_accuracy: 0.8528\n", "Epoch 185/200\n", @@ -4617,11 +4617,11 @@ "Epoch 192/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3118 - auc: 0.9135 - accuracy: 0.8612 - val_loss: 0.3270 - val_auc: 0.9063 - val_accuracy: 0.8535\n", "Epoch 193/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3064 - auc: 0.9138 - accuracy: 0.8604 - val_loss: 0.3271 - val_auc: 0.9064 - val_accuracy: 0.8534\n", + "814/814 [==============================] - 1s 906us/step - loss: 0.3064 - auc: 0.9138 - accuracy: 0.8604 - val_loss: 0.3271 - val_auc: 0.9064 - val_accuracy: 0.8534\n", "Epoch 194/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3090 - auc: 0.9138 - accuracy: 0.8623 - val_loss: 0.3270 - val_auc: 0.9065 - val_accuracy: 0.8524\n", + "814/814 [==============================] - 1s 857us/step - loss: 0.3090 - auc: 0.9138 - accuracy: 0.8623 - val_loss: 0.3270 - val_auc: 0.9065 - val_accuracy: 0.8524\n", "Epoch 195/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3140 - auc: 0.9098 - accuracy: 0.8569 - val_loss: 0.3271 - val_auc: 0.9063 - val_accuracy: 0.8535\n", + "814/814 [==============================] - 1s 867us/step - loss: 0.3140 - auc: 0.9098 - accuracy: 0.8569 - val_loss: 0.3271 - val_auc: 0.9063 - val_accuracy: 0.8535\n", "Epoch 196/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3058 - auc: 0.9134 - accuracy: 0.8624 - val_loss: 0.3269 - val_auc: 0.9064 - val_accuracy: 0.8535\n", "Epoch 197/200\n", @@ -4631,7 +4631,7 @@ "Epoch 199/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3068 - auc: 0.9148 - accuracy: 0.8637 - val_loss: 0.3271 - val_auc: 0.9065 - val_accuracy: 0.8537\n", "Epoch 200/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3053 - auc: 0.9160 - accuracy: 0.8609 - val_loss: 0.3270 - val_auc: 0.9065 - val_accuracy: 0.8537\n" + "814/814 [==============================] - 1s 872us/step - loss: 0.3053 - auc: 0.9160 - accuracy: 0.8609 - val_loss: 0.3270 - val_auc: 0.9065 - val_accuracy: 0.8537\n" ] } ], @@ -4641,7 +4641,7 @@ }, { "cell_type": "markdown", - "id": "91ec4f77", + "id": "bdd1ee3f", "metadata": {}, "source": [ "#### Métricas" @@ -4649,7 +4649,7 @@ }, { "cell_type": "markdown", - "id": "e6c279c2", + "id": "f65853c9", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -4657,8 +4657,8 @@ }, { "cell_type": "code", - "execution_count": 55, - "id": "e8d1472d", + "execution_count": 56, + "id": "d632408f", "metadata": {}, "outputs": [ { @@ -4687,8 +4687,8 @@ }, { "cell_type": "code", - "execution_count": 56, - "id": "2525f690", + "execution_count": 57, + "id": "a6199d3b", "metadata": {}, "outputs": [ { @@ -4717,8 +4717,8 @@ }, { "cell_type": "code", - "execution_count": 57, - "id": "4b0f2f6e", + "execution_count": 58, + "id": "c5eba18d", "metadata": {}, "outputs": [ { @@ -4776,7 +4776,7 @@ }, { "cell_type": "markdown", - "id": "d7bc30c5", + "id": "624b4da8", "metadata": {}, "source": [ "Observamos un incrememento en la métrica AUC-ROC. Busquemos seguir complejizando la red" @@ -4784,7 +4784,7 @@ }, { "cell_type": "markdown", - "id": "b8d77eaf", + "id": "2b8af1b6", "metadata": {}, "source": [ "### Tercer diseño de la red" @@ -4792,7 +4792,7 @@ }, { "cell_type": "markdown", - "id": "8518bb65", + "id": "74e6d729", "metadata": {}, "source": [ "#### Diseño" @@ -4800,7 +4800,7 @@ }, { "cell_type": "markdown", - "id": "0645164f", + "id": "abda52e1", "metadata": {}, "source": [ "Buscaremos agrandar la red para ver si obtenemos mejores resultados. Como corremos riesgo de overfittear, utilizaremos regularizaión l1" @@ -4808,8 +4808,8 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "9acc1255", + "execution_count": 59, + "id": "acc585ca", "metadata": {}, "outputs": [], "source": [ @@ -4819,8 +4819,8 @@ }, { "cell_type": "code", - "execution_count": 59, - "id": "040eb238", + "execution_count": 60, + "id": "4d510ab1", "metadata": {}, "outputs": [], "source": [ @@ -4835,7 +4835,7 @@ }, { "cell_type": "markdown", - "id": "2244d627", + "id": "4bd8b526", "metadata": {}, "source": [ "Vemos un resumen de nuestra red" @@ -4843,8 +4843,8 @@ }, { "cell_type": "code", - "execution_count": 60, - "id": "4eab6775", + "execution_count": 61, + "id": "98a1228e", "metadata": {}, "outputs": [ { @@ -4882,7 +4882,7 @@ }, { "cell_type": "markdown", - "id": "4b839d5a", + "id": "7148fa9d", "metadata": {}, "source": [ "Finalmente entrenamos" @@ -4890,8 +4890,8 @@ }, { "cell_type": "code", - "execution_count": 61, - "id": "2f441120", + "execution_count": 62, + "id": "8df46098", "metadata": {}, "outputs": [ { @@ -4899,7 +4899,7 @@ "output_type": "stream", "text": [ "Epoch 1/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.6365 - auc: 0.4833 - accuracy: 0.7509 - val_loss: 0.4872 - val_auc: 0.7732 - val_accuracy: 0.7593\n", + "814/814 [==============================] - 2s 1ms/step - loss: 0.6365 - auc: 0.4833 - accuracy: 0.7509 - val_loss: 0.4872 - val_auc: 0.7732 - val_accuracy: 0.7593\n", "Epoch 2/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.4643 - auc: 0.8002 - accuracy: 0.7607 - val_loss: 0.4135 - val_auc: 0.8524 - val_accuracy: 0.7593\n", "Epoch 3/200\n", @@ -4911,9 +4911,9 @@ "Epoch 6/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3611 - auc: 0.8849 - accuracy: 0.8381 - val_loss: 0.3588 - val_auc: 0.8868 - val_accuracy: 0.8377\n", "Epoch 7/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3583 - auc: 0.8874 - accuracy: 0.8364 - val_loss: 0.3543 - val_auc: 0.8897 - val_accuracy: 0.8392\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3583 - auc: 0.8874 - accuracy: 0.8364 - val_loss: 0.3543 - val_auc: 0.8897 - val_accuracy: 0.8392\n", "Epoch 8/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3519 - auc: 0.8905 - accuracy: 0.8404 - val_loss: 0.3514 - val_auc: 0.8914 - val_accuracy: 0.8415\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3519 - auc: 0.8905 - accuracy: 0.8404 - val_loss: 0.3514 - val_auc: 0.8914 - val_accuracy: 0.8415\n", "Epoch 9/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3495 - auc: 0.8915 - accuracy: 0.8408 - val_loss: 0.3490 - val_auc: 0.8928 - val_accuracy: 0.8434\n", "Epoch 10/200\n", @@ -4983,7 +4983,7 @@ "Epoch 42/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3295 - auc: 0.9055 - accuracy: 0.8496 - val_loss: 0.3337 - val_auc: 0.9020 - val_accuracy: 0.8471\n", "Epoch 43/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3264 - auc: 0.9059 - accuracy: 0.8542 - val_loss: 0.3335 - val_auc: 0.9019 - val_accuracy: 0.8478\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3264 - auc: 0.9059 - accuracy: 0.8542 - val_loss: 0.3335 - val_auc: 0.9019 - val_accuracy: 0.8478\n", "Epoch 44/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3238 - auc: 0.9079 - accuracy: 0.8542 - val_loss: 0.3342 - val_auc: 0.9018 - val_accuracy: 0.8468\n", "Epoch 45/200\n", @@ -5001,7 +5001,7 @@ "Epoch 51/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3267 - auc: 0.9064 - accuracy: 0.8534 - val_loss: 0.3330 - val_auc: 0.9026 - val_accuracy: 0.8472\n", "Epoch 52/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3269 - auc: 0.9032 - accuracy: 0.8526 - val_loss: 0.3334 - val_auc: 0.9027 - val_accuracy: 0.8472\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3269 - auc: 0.9032 - accuracy: 0.8526 - val_loss: 0.3334 - val_auc: 0.9027 - val_accuracy: 0.8472\n", "Epoch 53/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3270 - auc: 0.9071 - accuracy: 0.8496 - val_loss: 0.3330 - val_auc: 0.9027 - val_accuracy: 0.8471\n", "Epoch 54/200\n", @@ -5013,7 +5013,7 @@ "Epoch 57/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3244 - auc: 0.9080 - accuracy: 0.8525 - val_loss: 0.3328 - val_auc: 0.9030 - val_accuracy: 0.8488\n", "Epoch 58/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3227 - auc: 0.9092 - accuracy: 0.8602 - val_loss: 0.3324 - val_auc: 0.9033 - val_accuracy: 0.8474\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3227 - auc: 0.9092 - accuracy: 0.8602 - val_loss: 0.3324 - val_auc: 0.9033 - val_accuracy: 0.8474\n", "Epoch 59/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3219 - auc: 0.9103 - accuracy: 0.8561 - val_loss: 0.3323 - val_auc: 0.9033 - val_accuracy: 0.8488\n", "Epoch 60/200\n", @@ -5189,17 +5189,17 @@ "Epoch 145/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3218 - auc: 0.9127 - accuracy: 0.8584 - val_loss: 0.3360 - val_auc: 0.9044 - val_accuracy: 0.8491\n", "Epoch 146/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3153 - auc: 0.9159 - accuracy: 0.8615 - val_loss: 0.3366 - val_auc: 0.9048 - val_accuracy: 0.8524\n", + "814/814 [==============================] - 2s 2ms/step - loss: 0.3153 - auc: 0.9159 - accuracy: 0.8615 - val_loss: 0.3366 - val_auc: 0.9048 - val_accuracy: 0.8524\n", "Epoch 147/200\n", "814/814 [==============================] - 1s 2ms/step - loss: 0.3166 - auc: 0.9159 - accuracy: 0.8610 - val_loss: 0.3362 - val_auc: 0.9048 - val_accuracy: 0.8515\n", "Epoch 148/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3119 - auc: 0.9165 - accuracy: 0.8640 - val_loss: 0.3360 - val_auc: 0.9047 - val_accuracy: 0.8500\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3119 - auc: 0.9165 - accuracy: 0.8640 - val_loss: 0.3360 - val_auc: 0.9047 - val_accuracy: 0.8500\n", "Epoch 149/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9169 - accuracy: 0.8616 - val_loss: 0.3366 - val_auc: 0.9048 - val_accuracy: 0.8523\n", "Epoch 150/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3131 - auc: 0.9154 - accuracy: 0.8635 - val_loss: 0.3367 - val_auc: 0.9049 - val_accuracy: 0.8517\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3131 - auc: 0.9154 - accuracy: 0.8635 - val_loss: 0.3367 - val_auc: 0.9049 - val_accuracy: 0.8517\n", "Epoch 151/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3181 - auc: 0.9133 - accuracy: 0.8643 - val_loss: 0.3368 - val_auc: 0.9048 - val_accuracy: 0.8528\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3181 - auc: 0.9133 - accuracy: 0.8643 - val_loss: 0.3368 - val_auc: 0.9048 - val_accuracy: 0.8528\n", "Epoch 152/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3159 - auc: 0.9154 - accuracy: 0.8633 - val_loss: 0.3364 - val_auc: 0.9046 - val_accuracy: 0.8503\n", "Epoch 153/200\n", @@ -5209,9 +5209,9 @@ "Epoch 155/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9175 - accuracy: 0.8646 - val_loss: 0.3368 - val_auc: 0.9050 - val_accuracy: 0.8517\n", "Epoch 156/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3153 - auc: 0.9149 - accuracy: 0.8641 - val_loss: 0.3369 - val_auc: 0.9046 - val_accuracy: 0.8497\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3153 - auc: 0.9149 - accuracy: 0.8641 - val_loss: 0.3369 - val_auc: 0.9046 - val_accuracy: 0.8497\n", "Epoch 157/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3121 - auc: 0.9158 - accuracy: 0.8636 - val_loss: 0.3356 - val_auc: 0.9049 - val_accuracy: 0.8505\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3121 - auc: 0.9158 - accuracy: 0.8636 - val_loss: 0.3356 - val_auc: 0.9049 - val_accuracy: 0.8505\n", "Epoch 158/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3110 - auc: 0.9175 - accuracy: 0.8627 - val_loss: 0.3363 - val_auc: 0.9051 - val_accuracy: 0.8518\n", "Epoch 159/200\n", @@ -5257,13 +5257,13 @@ "Epoch 179/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3139 - auc: 0.9143 - accuracy: 0.8637 - val_loss: 0.3369 - val_auc: 0.9053 - val_accuracy: 0.8512\n", "Epoch 180/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3146 - auc: 0.9158 - accuracy: 0.8614 - val_loss: 0.3375 - val_auc: 0.9057 - val_accuracy: 0.8508\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9158 - accuracy: 0.8614 - val_loss: 0.3375 - val_auc: 0.9057 - val_accuracy: 0.8508\n", "Epoch 181/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3122 - auc: 0.9173 - accuracy: 0.8634 - val_loss: 0.3383 - val_auc: 0.9056 - val_accuracy: 0.8529\n", "Epoch 182/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3103 - auc: 0.9175 - accuracy: 0.8628 - val_loss: 0.3376 - val_auc: 0.9052 - val_accuracy: 0.8500\n", "Epoch 183/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3109 - auc: 0.9186 - accuracy: 0.8648 - val_loss: 0.3370 - val_auc: 0.9052 - val_accuracy: 0.8520\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3109 - auc: 0.9186 - accuracy: 0.8648 - val_loss: 0.3370 - val_auc: 0.9052 - val_accuracy: 0.8520\n", "Epoch 184/200\n", "814/814 [==============================] - 1s 2ms/step - loss: 0.3118 - auc: 0.9169 - accuracy: 0.8642 - val_loss: 0.3385 - val_auc: 0.9057 - val_accuracy: 0.8508\n", "Epoch 185/200\n", @@ -5271,7 +5271,7 @@ "Epoch 186/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3151 - auc: 0.9149 - accuracy: 0.8624 - val_loss: 0.3390 - val_auc: 0.9053 - val_accuracy: 0.8501\n", "Epoch 187/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3041 - auc: 0.9212 - accuracy: 0.8694 - val_loss: 0.3377 - val_auc: 0.9052 - val_accuracy: 0.8509\n", + "814/814 [==============================] - 1s 938us/step - loss: 0.3041 - auc: 0.9212 - accuracy: 0.8694 - val_loss: 0.3377 - val_auc: 0.9052 - val_accuracy: 0.8509\n", "Epoch 188/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3118 - auc: 0.9162 - accuracy: 0.8653 - val_loss: 0.3373 - val_auc: 0.9055 - val_accuracy: 0.8508\n", "Epoch 189/200\n", @@ -5283,19 +5283,19 @@ "Epoch 192/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3161 - auc: 0.9168 - accuracy: 0.8622 - val_loss: 0.3382 - val_auc: 0.9051 - val_accuracy: 0.8514\n", "Epoch 193/200\n", - "814/814 [==============================] - 2s 2ms/step - loss: 0.3121 - auc: 0.9166 - accuracy: 0.8636 - val_loss: 0.3384 - val_auc: 0.9055 - val_accuracy: 0.8520\n", + "814/814 [==============================] - 1s 981us/step - loss: 0.3121 - auc: 0.9166 - accuracy: 0.8636 - val_loss: 0.3384 - val_auc: 0.9055 - val_accuracy: 0.8520\n", "Epoch 194/200\n", - "814/814 [==============================] - 1s 2ms/step - loss: 0.3146 - auc: 0.9169 - accuracy: 0.8659 - val_loss: 0.3384 - val_auc: 0.9056 - val_accuracy: 0.8526\n", + "814/814 [==============================] - 1s 1ms/step - loss: 0.3146 - auc: 0.9169 - accuracy: 0.8659 - val_loss: 0.3384 - val_auc: 0.9056 - val_accuracy: 0.8526\n", "Epoch 195/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3198 - auc: 0.9129 - accuracy: 0.8597 - val_loss: 0.3401 - val_auc: 0.9054 - val_accuracy: 0.8529\n", "Epoch 196/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3118 - auc: 0.9167 - accuracy: 0.8638 - val_loss: 0.3387 - val_auc: 0.9055 - val_accuracy: 0.8532\n", "Epoch 197/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3110 - auc: 0.9159 - accuracy: 0.8640 - val_loss: 0.3395 - val_auc: 0.9055 - val_accuracy: 0.8532\n", + "814/814 [==============================] - 1s 2ms/step - loss: 0.3110 - auc: 0.9159 - accuracy: 0.8640 - val_loss: 0.3395 - val_auc: 0.9055 - val_accuracy: 0.8532\n", "Epoch 198/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3120 - auc: 0.9172 - accuracy: 0.8659 - val_loss: 0.3393 - val_auc: 0.9055 - val_accuracy: 0.8518\n", "Epoch 199/200\n", - "814/814 [==============================] - 1s 1ms/step - loss: 0.3102 - auc: 0.9185 - accuracy: 0.8683 - val_loss: 0.3398 - val_auc: 0.9048 - val_accuracy: 0.8535\n", + "814/814 [==============================] - 1s 939us/step - loss: 0.3102 - auc: 0.9185 - accuracy: 0.8683 - val_loss: 0.3398 - val_auc: 0.9048 - val_accuracy: 0.8535\n", "Epoch 200/200\n", "814/814 [==============================] - 1s 1ms/step - loss: 0.3095 - auc: 0.9198 - accuracy: 0.8658 - val_loss: 0.3393 - val_auc: 0.9050 - val_accuracy: 0.8508\n" ] @@ -5307,7 +5307,7 @@ }, { "cell_type": "markdown", - "id": "82daa228", + "id": "a9d88151", "metadata": {}, "source": [ "#### Métricas" @@ -5315,7 +5315,7 @@ }, { "cell_type": "markdown", - "id": "dc26c9a5", + "id": "6d875a22", "metadata": {}, "source": [ "Obtenemos las curvas de aprendizaje y demás metricas para establecer conclusiones" @@ -5323,8 +5323,8 @@ }, { "cell_type": "code", - "execution_count": 62, - "id": "e766b70f", + "execution_count": 63, + "id": "4dc657f6", "metadata": {}, "outputs": [ { @@ -5353,8 +5353,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "id": "69f5c1e8", + "execution_count": 64, + "id": "f35279ea", "metadata": {}, "outputs": [ { @@ -5383,8 +5383,8 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "6cd81c9f", + "execution_count": 65, + "id": "c4bc18ea", "metadata": {}, "outputs": [ { @@ -5442,7 +5442,7 @@ }, { "cell_type": "markdown", - "id": "89a5c96f", + "id": "f4daf8c5", "metadata": {}, "source": [ "Observamos que practicamente obtuvimos los mismo resultados que en el entrenamiento anterior por lo que pararemos aquí" @@ -5450,19 +5450,134 @@ }, { "cell_type": "markdown", - "id": "d7fd826e", + "id": "58d32289", "metadata": {}, "source": [ "## Holdout" ] }, + { + "cell_type": "markdown", + "id": "50d9a476", + "metadata": {}, + "source": [ + "Realizamos el testeo en el holdout" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "210eecf6", + "metadata": {}, + "outputs": [], + "source": [ + "df, df_for_prediction = obtener_datasets()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "ea6b5750", + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessing import aplicar_preparacion_holdout\n", + "\n", + "X_holdout = aplicar_preparacion_holdout(df_for_prediction, generalizada=True)" + ] + }, + { + "cell_type": "markdown", + "id": "fe97a5c7", + "metadata": {}, + "source": [ + "Luego aplicamos el preprocesado correspondiente" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "871a4b72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Aplicando 'conversion_numerica_generalizada' en las variables categóricas.\n" + ] + } + ], + "source": [ + "X_holdout_numerico = conversion_numerica_generalizada(X_holdout) " + ] + }, + { + "cell_type": "markdown", + "id": "f9f762b4", + "metadata": {}, + "source": [ + "Y escalamos" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "b9072ec5", + "metadata": {}, + "outputs": [], + "source": [ + "X_holdout_numerico = get_dataframe_scaled(X_holdout_numerico,StandardScaler())" + ] + }, + { + "cell_type": "markdown", + "id": "d420baff", + "metadata": {}, + "source": [ + "Predecimos" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "6b6d882a", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "'int' object is not callable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0my_pred_holdout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_holdout_numerico\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mX_holdout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tiene_alto_valor_adquisitivo'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_pred_holdout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX_holdout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tiene_alto_valor_adquisitivo'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_holdout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tiene_alto_valor_adquisitivo'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m 3980\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3981\u001b[0m \"\"\"\n\u001b[0;32m-> 3982\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_action\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_action\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3983\u001b[0m return self._constructor(new_values, index=self.index).__finalize__(\n\u001b[1;32m 3984\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"map\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.8/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m_map_values\u001b[0;34m(self, mapper, na_action)\u001b[0m\n\u001b[1;32m 1158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1159\u001b[0m \u001b[0;31m# mapper is a function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1160\u001b[0;31m \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap_f\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapper\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1162\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnew_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: 'int' object is not callable" + ] + } + ], + "source": [ + "y_pred_holdout = model_2.predict(X_holdout_numerico).round().astype(int)\n", + "X_holdout['tiene_alto_valor_adquisitivo'] = y_pred_holdout\n", + "X_holdout['tiene_alto_valor_adquisitivo'] = X_holdout['tiene_alto_valor_adquisitivo'].map(int())" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "9dde8a1f", + "id": "0ad11f94", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "name_model = '#5 - Red neuronal'\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": { diff --git a/parte_2/#6 - Boosting.ipynb b/parte_2/#6 - Boosting.ipynb index bf0917d..a69c4d3 100644 --- a/parte_2/#6 - Boosting.ipynb +++ b/parte_2/#6 - Boosting.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "ecdc2953", + "id": "9d29f1f3", "metadata": {}, "source": [ "# Modelo: Boosting" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "6f0b3c0f", + "id": "3d53b62b", "metadata": {}, "source": [ "El modelo a entrenar en el sigueinte notebook se ra un ensamble. En particular buscaremos hacer un boosting con árboles de decisión. Para ello utilizaremos GradientBoostingClassifier de sklearn" @@ -18,7 +18,7 @@ }, { "cell_type": "markdown", - "id": "a58c154a", + "id": "ad4f614c", "metadata": {}, "source": [ "## Librerias y funciones necesarias" @@ -26,7 +26,7 @@ }, { "cell_type": "markdown", - "id": "f9c8f559", + "id": "a3f17936", "metadata": {}, "source": [ "Comenzamos importando las librerias y funciones que serán necesarias para preprocesar nuestros datos, realizar nuestro entrenamiento y obtener metricas " @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "17b543ef", + "id": "610feb2a", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a6a745c3", + "id": "55c06020", "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "markdown", - "id": "f3c99e5e", + "id": "c4fe1117", "metadata": {}, "source": [ "## Primer preprocesamiento" @@ -84,7 +84,7 @@ }, { "cell_type": "markdown", - "id": "b0c91164", + "id": "43106519", "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" @@ -93,7 +93,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "2472688d", + "id": "ee745271", "metadata": {}, "outputs": [ { @@ -105,14 +105,14 @@ } ], "source": [ - "df, df_holdout = obtener_datasets()\n", + "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": "9600e2f1", + "id": "9101a6ea", "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" @@ -121,7 +121,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "ef8565d0", + "id": "809726c2", "metadata": {}, "outputs": [], "source": [ @@ -130,7 +130,7 @@ }, { "cell_type": "markdown", - "id": "414705b7", + "id": "e136d338", "metadata": {}, "source": [ "### Entrenamiento" @@ -138,7 +138,7 @@ }, { "cell_type": "markdown", - "id": "85bc0943", + "id": "eb54dcfa", "metadata": {}, "source": [ "Vamos a realizar un entrenamiento con 5 folds. Para ello utilizaremos StratifiedKFold para asegurarnos de obtener folds balanceados. Además, utilizaremos Gridsearch para la busqueda de hiperparámetros óptimos teniendo en cuenta la métrica AUC-ROC." @@ -147,7 +147,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "3ec28407", + "id": "6ec46d3a", "metadata": {}, "outputs": [], "source": [ @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "5cb9a10f", + "id": "9c3d3192", "metadata": {}, "source": [ "Ahora sí, entrenamos nuestro modelo" @@ -168,13 +168,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "abeccc62", + "id": "0c3816b6", "metadata": {}, "outputs": [ { "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", @@ -192,7 +192,7 @@ }, { "cell_type": "markdown", - "id": "08119667", + "id": "4a720b7d", "metadata": {}, "source": [ "Realizamos nuestras predicciones para una análisis más amplio" @@ -201,7 +201,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "e8d9f88e", + "id": "7e5f2962", "metadata": {}, "outputs": [], "source": [ @@ -210,7 +210,7 @@ }, { "cell_type": "markdown", - "id": "11f49428", + "id": "2e0d68a5", "metadata": {}, "source": [ "### Metricas" @@ -219,7 +219,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "b9bcd704", + "id": "f7cc5713", "metadata": {}, "outputs": [ { @@ -280,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "3044688a", + "id": "c935a483", "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" @@ -288,7 +288,7 @@ }, { "cell_type": "markdown", - "id": "2106746a", + "id": "35e9665f", "metadata": {}, "source": [ "## Segundo preprocesamiento" @@ -296,7 +296,7 @@ }, { "cell_type": "markdown", - "id": "1705fc2f", + "id": "c9cd7fc5", "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. También, utilizaremos rfecv para que seleccione solo las features que resultan mas importantes para un árbol de decisión a la hora de entrenar con nuestro dataset" @@ -305,7 +305,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "a72fda2b", + "id": "17d0184a", "metadata": {}, "outputs": [ { @@ -324,7 +324,7 @@ }, { "cell_type": "markdown", - "id": "e0d32aa7", + "id": "9fa6c6e7", "metadata": {}, "source": [ "Usamos rfecv" @@ -333,7 +333,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "17dfabe3", + "id": "92634b13", "metadata": {}, "outputs": [], "source": [ @@ -352,7 +352,7 @@ }, { "cell_type": "markdown", - "id": "f58e3504", + "id": "c4481695", "metadata": {}, "source": [ "Realizamos nuevamente el split" @@ -361,7 +361,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "babf9bab", + "id": "97de903a", "metadata": {}, "outputs": [], "source": [ @@ -370,7 +370,7 @@ }, { "cell_type": "markdown", - "id": "13219234", + "id": "019f4800", "metadata": {}, "source": [ "### Entrenamiento" @@ -378,7 +378,7 @@ }, { "cell_type": "markdown", - "id": "eab58b3d", + "id": "9855eb47", "metadata": {}, "source": [ "Volvemos a realizar un entrenamiento con 5 folds, utilizando las mismas librerias y funciones utilizadas anteriormente" @@ -387,7 +387,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "c562e142", + "id": "d9214b4e", "metadata": {}, "outputs": [], "source": [ @@ -399,7 +399,7 @@ }, { "cell_type": "markdown", - "id": "4149c4b0", + "id": "e7b25296", "metadata": {}, "source": [ "Entrenamos nuestro modelo" @@ -408,13 +408,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "67844d1b", + "id": "ebcb7de6", "metadata": {}, "outputs": [ { "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", @@ -432,7 +432,7 @@ }, { "cell_type": "markdown", - "id": "2f90ecd0", + "id": "58f7a06f", "metadata": {}, "source": [ "Realizamos nuestras predicciones para una análisis más amplio" @@ -441,7 +441,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "7bbb8776", + "id": "a49720b5", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "a4dbfce6", + "id": "a834a3fa", "metadata": {}, "source": [ "### Metricas" @@ -459,7 +459,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "bb0e1ad5", + "id": "6c0bb7a5", "metadata": {}, "outputs": [ { @@ -520,15 +520,15 @@ }, { "cell_type": "markdown", - "id": "a9866013", + "id": "af442b5f", "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 los holdouts con los dos modelos" + "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" ] }, { "cell_type": "markdown", - "id": "4808d70d", + "id": "5cdb8143", "metadata": {}, "source": [ "## Holdouts" @@ -536,19 +536,81 @@ }, { "cell_type": "markdown", - "id": "24c022f8", + "id": "316b859c", + "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": 16, + "id": "75380bf8", + "metadata": {}, + "outputs": [], + "source": [ + "from preprocessing import aplicar_preparacion_holdout\n", + "X_holdout = aplicar_preparacion_holdout(df_for_prediction, generalizada=False)" + ] + }, + { + "cell_type": "markdown", + "id": "1ff264dc", + "metadata": {}, + "source": [ + "Apliquemos el procesado con el que obtuvimos el mejor score AUC-ROC:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5106e8f4", + "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": "3df05c39", "metadata": {}, "source": [ - "Realizamos los testeos requeridos en el holdout" + "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": null, - "id": "4ecad4a9", + "execution_count": 18, + "id": "4b6fdf8d", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "y_pred_holdout = clf.predict(X_holdout_numerico)\n", + "X_holdout['tiene_alto_valor_adquisitivo'] = y_pred_holdout" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "d605c504", + "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": { diff --git a/parte_2/#6 - KNN.ipynb b/parte_2/#6 - KNN.ipynb deleted file mode 100644 index 7f17857..0000000 --- a/parte_2/#6 - KNN.ipynb +++ /dev/null @@ -1,802 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "de3696a8", - "metadata": {}, - "source": [ - "# Modelo: KNN" - ] - }, - { - "cell_type": "markdown", - "id": "9862aae2", - "metadata": {}, - "source": [ - "El siguiente modelo que vamos a entrenar se trata de KNN. Utilizaremos KNeighborsClassifier, distintos scalers y métricas de la libreria sklearn" - ] - }, - { - "cell_type": "markdown", - "id": "9d3fd605", - "metadata": {}, - "source": [ - "## Librerias y funciones necesarias" - ] - }, - { - "cell_type": "markdown", - "id": "ae78f0d5", - "metadata": {}, - "source": [ - "Comenzamos importando todas las librerias y funciones necesarias" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "95de3070", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import MinMaxScaler,Normalizer,StandardScaler\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" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "2482ef63", - "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": "fdfeb882", - "metadata": {}, - "source": [ - "## Primer preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "a801efc9", - "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. También, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo. Por último, vamos a escalar nuestro dataset con StandarScaler de sklearn" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "cca9336c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_holdout = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df = conversion_numerica(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "1190dbd6", - "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": 27, - "id": "b5fe8a04", - "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": "eb322dd1", - "metadata": {}, - "source": [ - "Finalemte escalamos los datos" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "be52357e", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = StandardScaler()\n", - "X_train = get_dataframe_scaled(X_train,scaler)\n", - "X_test = get_dataframe_scaled(X_test,scaler)" - ] - }, - { - "cell_type": "markdown", - "id": "da0028e8", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "77fb7bb3", - "metadata": {}, - "source": [ - "Vamos a realizar el entrenamiento de KNN, buscando hiperparámetros adecuados y utilizando K folds. Utilizamos Gridsearch y StratifiedKfold de sklearn. Variaremos la cantidad de vecinos asi como también si se ponderan las distancias o no de los mismos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "35e6aa26", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 16 candidates, totalling 80 fits\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n" - ] - } - ], - "source": [ - "parametros = {\"n_neighbors\":np.arange(20,60,5),\"weights\":[\"uniform\",\"distance\"]}\n", - "clf = KNeighborsClassifier()\n", - "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n", - "clf = GridSearchCV(clf, parametros, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "48ef821e", - "metadata": {}, - "source": [ - "Veamos que hiperprámetros resultaron óptimos" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d288dc1", - "metadata": {}, - "outputs": [], - "source": [ - "clf.best_params_" - ] - }, - { - "cell_type": "markdown", - "id": "fec9d977", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "markdown", - "id": "4fccedaf", - "metadata": {}, - "source": [ - "Evaluamos nuestro modelo en base a las métricas" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e602083", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22e366c3", - "metadata": {}, - "outputs": [], - "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(\"Los mejores hiperpametros elegidos: \", clf.best_params_)\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf, X_test, y_test, X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "7d3540c5", - "metadata": {}, - "source": [ - "Obtuvimos 0.88 de AUC-ROC sobre test. Veamos si on otro preprocesamiento podemos mejorar estos datos" - ] - }, - { - "cell_type": "markdown", - "id": "5a6ca60c", - "metadata": {}, - "source": [ - "## Segundo preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "42f6ecf7", - "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. También, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo. Por último, vamos a escalar nuestro dataset con Normalizer de sklearn" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "56d90104", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_holdout = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df = conversion_numerica(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "5dad34bc", - "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": 11, - "id": "95d346eb", - "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": "e9cc124f", - "metadata": {}, - "source": [ - "Luego escalamos" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6d50b41c", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = Normalizer()\n", - "X_train = get_dataframe_scaled(X_train,scaler)\n", - "X_test = get_dataframe_scaled(X_test,scaler)" - ] - }, - { - "cell_type": "markdown", - "id": "cb068b71", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "2b5055d2", - "metadata": {}, - "source": [ - "Vamos a realizar el entrenamiento de KNN, buscando hiperparámetros adecuados y utilizando K folds. Utilizamos Gridsearch y StratifiedKfold de sklearn. Variaremos la cantidad de vecinos asi como también si se ponderan las distancias o no de los mismos" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "bd3c7167", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 16 candidates, totalling 80 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 17 tasks | elapsed: 22.7s\n", - "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 1.5min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=KNeighborsClassifier(), n_jobs=-1,\n", - " param_grid={'n_neighbors': array([20, 25, 30, 35, 40, 45, 50, 55]),\n", - " 'weights': ['uniform', 'distance']},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parametros = {\"n_neighbors\":np.arange(20,60,5),\"weights\":[\"uniform\",\"distance\"]}\n", - "clf_2 = KNeighborsClassifier()\n", - "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n", - "clf_2 = GridSearchCV(clf_2, parametros, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "clf_2.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "b91d3814", - "metadata": {}, - "source": [ - "Veamos que hiperprámetros usar" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "2192eb08", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'n_neighbors': 25, 'weights': 'uniform'}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf_2.best_params_" - ] - }, - { - "cell_type": "markdown", - "id": "4d5311bb", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "6980702e", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf_2.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "e3e6675b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AUC-ROC score sobre test: 0.8734192185468728\n", - "AUC-ROC score sobre train: 0.8930391542184181\n", - "Accuracy sobre test: 0.8295716259788116\n", - "Accuracy sobre train: 0.8399877149877150\n", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.91 0.87 0.89 5155\n", - " Alto valor 0.58 0.67 0.62 1358\n", - "\n", - " accuracy 0.83 6513\n", - " macro avg 0.74 0.77 0.76 6513\n", - "weighted avg 0.84 0.83 0.83 6513\n", - "\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", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_2, X_test, y_test, X_train, y_train)\n" - ] - }, - { - "cell_type": "markdown", - "id": "57915444", - "metadata": {}, - "source": [ - "Vemos que nuesto score empeoro respecto del preprocesamiento anterior. Sigamos con otro preprocsado más" - ] - }, - { - "cell_type": "markdown", - "id": "cfd19838", - "metadata": {}, - "source": [ - "## Tercer preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "bd6ca85b", - "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. También, convertimos a númericas las variables categoricas para poder entrenar nuestro modelo. Por último, vamos a escalar nuestro dataset con MinMaxScaler de sklearn" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "f76564fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_holdout = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df = conversion_numerica(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "1c14d3e0", - "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": 18, - "id": "80c85fda", - "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": "fc9abd1d", - "metadata": {}, - "source": [ - "Luego escalamos" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "79aae0a1", - "metadata": {}, - "outputs": [], - "source": [ - "scaler = MinMaxScaler()\n", - "X_train = get_dataframe_scaled(X_train,scaler)\n", - "X_test = get_dataframe_scaled(X_test,scaler)" - ] - }, - { - "cell_type": "markdown", - "id": "baa741f5", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "02a4b0a1", - "metadata": {}, - "source": [ - "Vamos a realizar el entrenamiento de KNN, buscando hiperparámetros adecuados y utilizando K folds. Utilizamos Gridsearch y StratifiedKfold de sklearn. Variaremos la cantidad de vecinos asi como también si se ponderan las distancias o no de los mismos" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "eaef2dc4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fitting 5 folds for each of 16 candidates, totalling 80 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 17 tasks | elapsed: 48.7s\n", - "[Parallel(n_jobs=-1)]: Done 80 out of 80 | elapsed: 3.6min finished\n" - ] - }, - { - "data": { - "text/plain": [ - "GridSearchCV(cv=,\n", - " estimator=KNeighborsClassifier(), n_jobs=-1,\n", - " param_grid={'n_neighbors': array([20, 25, 30, 35, 40, 45, 50, 55]),\n", - " 'weights': ['uniform', 'distance']},\n", - " scoring='roc_auc', verbose=4)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "parametros = {\"n_neighbors\":np.arange(20,60,5),\"weights\":[\"uniform\",\"distance\"]}\n", - "clf_3 = KNeighborsClassifier()\n", - "cv = StratifiedKFold(n_splits=5,shuffle= True, random_state= 10).split(X_train, y_train)\n", - "clf_3 = GridSearchCV(clf_3, parametros, scoring='roc_auc', cv=cv, n_jobs = -1, verbose=4)\n", - "clf_3.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "0bf42148", - "metadata": {}, - "source": [ - "Veamos que hiperprámetros usar" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7f0b705d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'n_neighbors': 40, 'weights': 'uniform'}" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf_3.best_params_" - ] - }, - { - "cell_type": "markdown", - "id": "880766d6", - "metadata": {}, - "source": [ - "### Métricas" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "a8740371", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf_3.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "601945b4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.87 0.93 0.90 4945\n", - " Alto valor 0.70 0.56 0.62 1568\n", - "\n", - " accuracy 0.84 6513\n", - " macro avg 0.79 0.74 0.76 6513\n", - "weighted avg 0.83 0.84 0.83 6513\n", - "\n" - ] - }, - { - "data": { - "image/png": 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mXmgMaaloSLVxCtNFxHRJk/PS1YuDupmZla+GN6EfC5xesO+vwBm1nPNn4DvgvzUliIh58pT0MPA+aWDdDkVPWgQc1M3MrHw1fKDcRcB/CvbVVkv/EWlg3G5A16yW3ik73ElSp4iYWHheREyS9AiwZ97uMaSm9ULdgdHZz2Ozx64F5WgDdMhLVy8O6mZmVr4aWFPPmtlrDOJF9AHaAI8UOfY08DKwUT3zGgpsLUkF/ep9gXez8k2S9BXz952vAoj5+9pr5YFyZmZmc70FbFGw/TE7djhwZLGTsgFvOwGv5u0eQKqVb5WXbmVgHeDRgnS7Smqdt28fUi1+UCmFd03dzMzK1yK+Tz0ixgLPzFOEua0Fr0fEG5I2Jd1bfh9pspmlSU32SwF75eU1WNJA4DpJxzF38pl3gHvzLnE+cABwm6QrSKPuTwBOLbjNrU4O6mZmVr4aPlBuYRhOaqI/G1gMmESqUR8eEa8UpN2H1K9/NSnmPk6aJnZmLkFEfCxp2yzdo8BI0uC+C0stmIO6mZmVryacUS4nIp4h9W/nnn8MbF/Pc8cBh2VbbekGUf+++ho5qJuZWfkqz5p62Wr6r0BmZmbWKFxTNzOz8lUGze+VxEHdzMzKl5vfS+KgbmZm5cs19ZL43TIzM6sSrqmbmVn5ck29JA7qZmZWvlq4T70UDupmZla+XFMvid8tMzOzKuGaupmZlS/f0lYSB3UzMytfbn4viYO6mZmVL9fUS+KgbmZm5cs19ZL43TIzM6sSrqmbmVn5cvN7SRzUzcysfLn5vSQO6mZmVr5cUy+JvwKZmZlVCdfUzcysfLn5vSQO6mZmVr7c/F4SB3UzMytfrqmXxO+WmZlZlXBN3czMypdr6iVxUDczs/LlPvWSOKibmVn5ck29JA7qZmZWvlxTL4m/ApmZmdVAUidJX0sKSesVHDtM0oeSpkp6W9JORc7vKulaSaMlTZB0t6ReRdJtLGmwpCmSvpB0klT6NxoHdTMzK19q0bCt4f5CkVZtSfsC1wB3AP2BwcB9kjYqSHoHsC1wOHAAsAowQFKrvLxWBAYCw4GdgIuBM4HjSi2sm9/NzKx8NWHzu6S+wP+RguuVBYf/CtweEX/Jnj8taS3gNGCH7Px+wHbAdhHxeLZvGDAE2B24Mzv3BGAUsG9ETAeekrQ4cKqkyyJiWn3L7Jq6mZmVLUkN2hroMlIwH1ZQph8DKzM3KOfcDmwlqW32vD8wFngilyAihgFvkQX+vHT3ZwE9P69uQL9SCuygbmZmVkDSnsCapGbwQn2zx6EF+4cAbYA+eemGRUQUSdc3u05HYNkieQ0FIu9a9eLmdzMzK1sNrW1L6gJ0Kdg9PiLG13JOB+Ai4JSIGF+kDN2zx7EF+8dkjz3y0hWmyaXLpelWLK+ImC5pcl66enFN3czMypcauMGxwFcF27F1XPXPwHfAfxvzpSwKrqmbmVnZaoR+8YuA/xTsq62W/iPSwLjdgK7Z9TtlhztJ6sTcGnlXYETe6bka/OjscQypab1Q97w0Y/Pyyi9HG6BDXrp6cVA3M7OqlTWz1xjEi+hD6hd/pMixp4GXgf2z532ZdxBdX2A68Gn2fCiwtSQV9Kv3Bd7NyjdJ0lfM33e+CqmtobCvvVZufjczs7LVBKPf3wK2KNj+mB07HDgyIj4FPgT2Kjh3H+CpvFHsA0i18q3yXs/KwDrAo3nnDQB2ldS6IK+xwKBSCu+aupmZla1GaH4vSUSMBZ6poQyvR8Qb2c9nALdI+oRUg98H2BDYLC+vwZIGAtdJOg6YCpwFvAPcm3eJ80kT09wm6QrSqPsTgFMLbnOrk4O6mZmVrUUd1OsrIm7LRsn/KduGAbtFxOCCpPuQ+vWvJsXcx4GjI2JmXl4fS9o2S/coMBI4Hbiw1HI5qJuZWfkqg5geEc9QpCQRcS1wbR3njgMOy7ba0g0CCqeYLZn71M3MzKqEa+pmZla2yrX5vVw5qJuZWdlyUC+Ng7qZmZUtB/XSuE/dzMysSrimbmZmZcs19dI4qJuZWflyTC+Jg7qZmZUt19RL46BuZmZly0G9NB4oZ2ZmViVcUzczs7LlmnppHNTNzKx8OaaXxEHdzMzKlmvqpXGfupmZWZVwTd3MzMqWa+qlcVA3M7Oy5aBeGgd1MzMrWw7qpXGfupmZWZVwTd3MzMqXK+olcVBvxu699HD6b7oGADc9+BK/Pf3m+dIMvOYYNltvpVrzefS599jjmCvn2z/0kb/yo6UXq1dZbnzgJX53xtzrb7ruSjz+n2PqdS7ANr++mBde/7je6a2yjBk9irvvuIWXXniO4d9+w4wZM+ixWE9WXHkVNtlsC/rv/Iv5zvnhh5HcecsNvPTi84wY/i0tWrZgmWWWZbMtt2Hv/Q+iffsORa/15uuvcMzhv6qzTFffcDt9V1ujoS/N6uDm99I4qDdTe2+/7pyAXh8TJ09j4uRpRY+NHT+56P4fxkykbZvWNebZtk0rundJf1jf+ODLeY7NmDmTET+Mr7VM3Tq3p13b1kydNoP3P/q21rRWuV549mnO/uupTJyQPg9t2ralVatWDP/2a4Z/+zWffvzhfEH9rTde488nHsP4ceMA6NCxI7NnzeajD4fy0YdDeeyRB7jk39exxJJL1XrtHj1q/lLaqpX/fC4KDuql8aeyGerepQPnHb8HYydMZvjIcaz64151nnPxjU9x1lWPlnSdTQ48v9bjfzliR075bX+mTpvBHQNenefYS29/Rp9tTqn1/FxLwCPPvsuYGr5YWGV77eXBnPanPzJz5ky222Fn9j/4MPqssCIAEyaM5/133ub9d9+e55wfRn7PqSf8ngnjx7PKqqtx/Mmns8qqqxMRvP3m65z799P45qsvOfnYo7j6xjto2bJljde/f+CzC/X1Wd0c1EvjgXLN0LnH7c6Si3XhtMseZOToiU1SBkkcuPOGADz8zDuMnTClpPM332DlOU37Nz74UqOXz5re5MmTOedvf2HmzJnsd/CvOPWv/5gT0AE6d+7CRj/blMMOP2qe826/+QYmjB9P+w4dOOeiK1hl1dWB9Jlb+6frcfb5l9KyZUs++nAojz/60CJ9TWYLm4N6M7PFhqtw0C4b8co7n/Gfu19ssnJsvsHKLNerBwA3PFB6UD54l40A+Oa7MTw5eEijls3Kw4CH7+f770aw+BJL8uvDj673eS8Neg6AbbbfkcV69pzveJ8VVmTDjTedcw0rc2rg1sw4qDcj7dq25vJT92XGjFkcddbtRESTlSUXlL8eMYanXhpa0rmdO7Zj1y3XBuCWh19h9uymex228Dwx4GEAfr7lNrRuXfPYjELfDR8OwHI/+nGNaX7UJx175603mDZ1agNKaQubpAZtzU3F9KlLagfcCVwQEc81dXkq0WlH7MiPl12cf97wJO9++E1J5+67w3ocvOtGLNWzCxMnT2PYZyN4+Jl3uebuF5gwqbQ/il06tWOXLX4CwM0PvVzyl4u9t1+XDu3bAG56r1bTpk3jw6EfALBy31X58vPPuOHaK3n9lZeYMGE8PRbryTrrbsD+B/+K5X+8QtE8Zs+eVWP+s2fNytLM5osvPmPlVVYtmu6IXx3AZ59+zMyZM+mxWE/WXGttdt1jH9Za+6cNfIVWX80xMDdExdTUI2Iq8HOg5lEtVqO1+/bm6AO24Mvho/n7laUNeANYcbklWHKxzkyaMp1undvTb+0VOOsPv+C1u05hzZWXKSmvvbdfb05QvumhBW96f/GNj/nky5Eln2/l77vh3zJz5kwAvvryC3590N488dgjTJw0kTZt2vLdiOE89sgD/PqgvXj6yYHznLtkrzTw8/NPa77F8bNP5h4bNfL7GtO9/+7btGjREiIY8e03PPHYIxz1m4O59MJzmrSlyxYeSTtIelbSSEnTJH0q6SJJXfPSXC8pimzbF+TVRtL5kkZImiTpCUmrFLlm3+zYpCzteZLaLEj5K6amnnkc2BZ4uqkLUklatBD/+sv+tGrVkj+ecyeTp06v97nPvfYRNzwwmKcGD+G7UROANHp+7+3X469H7cxyvXrwwOVHst5eZzN63KR65ZkLys+//hGffvVDSa9llT5LssFafYAF64u3yjBhwtzbGW+5/j90696Dv55zIRv224QWLVrw8YdDOe/vpzN0yPucfcaprLTKqvRedjkANuy3CV9+/hlPPj6AQ359BL2WnvdL59AP3uPVlwfNeT5p0ryf206durDfQb9ki623o8+PV6Rtu3bMnj2boUPe579XX8HLg57n7ttvpnuPHhz0y98uxHfBoElq6j2Al4FLgVHAGsAZ2eO2eek+BQ4oOLdwgM+lwL7AscA3wKnAU5JWj4hxAJK6A/8DPgJ2B5YBLgI6AEdRooqpqWf+Cxwo6fLs29S6kn6avzV1AcvRMQduyU9XW44HnnqLR597r6Rzz7rqUW59+JU5AR1gzPjJXHXnc/T/3aVMnzGTXot35ZiDtqxXfn1/vBTrr7k8kCacKVXuC8GESVO594k3Sj7fKsPs2bPn+fmUM86i3882o0WL9CdrxZX7cvZFl9O+fXumTZvKnbfeOCf93vsfTMeOnZg+bRrHH/07XnrxeaZOncLkSZN45qnHOfm4o+e5ja0waKy0Sl+O+P1x9F1tDdq2awdAixYtWG31NTnv4iv4+ZbbAHDzf6+Z58uHLRyLuk89Im6OiBMj4p6IeCYiLgdOBraRtHRe0ikR8VLBNi6v3L2BXwMnRsR1ETEQ+AXQDfhdXj6HA12A3SJiYERcB5wIHF5wvXqptKD+MOlbzJHZz68Ar2bba9ljvUjqIql3/haz6l+DrRTLL7MYpx6+A+MnTuG48+5u1LzfHPIVdw18HYAdNluzXufMG5TfLOl6LVqI/XbcAIB7n3iTSVOq7//Lkg4dO875efk+K7DBRj+bL03Pnouz9XY7AvD6K3O/IC65VC/+ft7FdOrUma++/JwT/3AE2266PttvviGn/elYJk+ayJHHHD8nfecuXepdLkkcfvQfAZgyZQqvv/pyya/NSlQeo99HZY+lNIlvS4qxd+V2RMRoUovzDnnp+gNPZsdy7szOzW8ZqJdKa37fohHzOhY4PX/HzO/fonWvDRrxEk3vvON2p2P7tpx22YOMnTCZju3n/Uy2bJE+9a1atphzbPLUGfXuL3z13S84YKcN6dO77ulgW7ZsMSco3/PEGyV1AwBs97PV6bV46ta6yQPkqlrPnkvM+Xm55fvUmC537PvvRsyzf90NNuLGux7kvjtv5fVXX2bUDyPp0KEjP1lnXfY58BC+GzE3/bLLLV9S2ZbpvRxdu3Vn3NgxDP/mq5LOtcohqSXQGlgNOA14MCI+z0uyoqRxQHvgXeBvEXF/3vG+wPcRMaYg6yHAYQXprstPEBFjJQ3PjpWkooJ6RDTm9E4XAf/J39FqibWr7jd0uWyCljOP3oUzj96lxnT77bjBnIC74T7/4J0SR8fXx/abrM5SPVOt6Mb7Sw/Kh+yaavkfffE9L775SaOWzcpL127d6LFYT0aPqt+Yi2LNrD17Ls5vjjyG3xRJ/9zTTwGwWM/F5+tzt/LS0D51SV1Izdv5xkdEXX0nX5BahgEeA/bPO/YmqWX4fVJz+hHAfZL2iohck2h3YGyRfMeQ+u0pMV29VFrzOwCSVpf0O0knZ4+rl5pHRIyPiK/zN7VcoMGGzdr6a/4IgM+/GVVHyrlN7x9+/h2D3/60pOv07N6J/pvNXXzGqt/6G/YD4MvPP6sxTe7YUr3q3/UYETw5MN0Bsu0OO5dcrm+//opxY1Plq9fSvUs+30rTCH3qxwJfFWzH1uPSOwAbA78BVgUeymrvRMQlEfGvrM/9flIT+svAmY39+ktVUTV1SW2Bm4A9SL0l04C2QEi6GzgoItzRmmejfc+p9XhuFbaaVmmrzU9W6c1e260LUOcAvJ7dO7H9pum714IE5X37r0eb1q2YNWs2tzzkfszmYPsdd2Xgow/x+Wef8MrgF9mg37z96j/8MJInBz4CQL9NNqt3vvfccQsfDRtCx46d2GPv/ec7HhG11g6v+tfFALRr155119+w3te1BdMIg9/na5UF6hzhGBHvZD8OlvQq8BawGzDf4KSImC3pHuA8Se0jYgqppt21MC2pZp7ff17fdPVSaTX1s4EdSaMFu0VEe1LTx+HZ/rObrmjV5/hfbctVZxzI1v1WpUundnP2d+vcnl/vuQkDrv49bVq3YvjIcVx841O15rXfDuvTpnUrZs6cxS0Pv1JyWQ7Kmt6ffGkI344cV0dqqwbrbrDRnOlcz/7rqbz04vNzRsV//OFQTjnuaKZMmUKXrl3Za/+D5zn3qsv/yeAXn2PC+Lmfla+/+pJLLjibyy46F0kcc8LJRVdpO3ifXbnz1pv48vPP5lwvIhj6wXv86dij5twXf+Avf03nLsX+FltjamhNvVirbD2a3gu9A8wAVqwrYZ6hwJLZLWv5+mbH8tPN03ee3RPfqyBdvVRUTZ10v9/JEXFNbkf2n3ONpA6k2wCOr+lkK03b1q04eNeNODgLqOMmTGHW7Nl069x+zq1Fn341kn2Ou6bOe9QPyprenxg8hOElBuW1+/ZmrZVTM+eC9MVb5Trtb+fwhyN/zUfDhnDiH46gbdt2tGrVikmT0kJEnbt04e/nXULPnovPc95Tjw/glhuuBaB9hw7E7GDq1LRoUNu27TjmhJPZfsddi17zi88+5fJ/nsvl/zyX1q1b06FjJ6ZMmcz0aWnpYUnsvf/BHPyr3xU936rShqRBc0X7DSW1APYC3s9q6ZBGuc8mtSz/J0vXnTSi/W95pw8ATpHULSLGZvv2ys59vNSCVlpQ70HN31yGsgCDCqxm9z7xBi1bio3W+jE/XrYnPbp2pH27tnw/eiLvf/wtD/7vbW5+6OU6R7Gvs+qyc2adW5Cm94N3TX2ro8ZO4uFn3y39hVjF6tylK1f+91buvfNWnhr4KF9++TkzZs5g2eWWZ6Ofbcq+Bx7K4kssOd95h/z6cAY9/yyffDSM0aNGoRaiz49XZIN+P2P3vfevdXDcCaecwbtvv8mwoR8wZvQoJowfT5u2bVi+zwqsufZP2WW3Pees/GYL36Kee0bSvaRbpN8BpgA/AU7Int8v6UfADcBtwMekZvIjgPVIARyAiPha0n+A8yXNIk0+cwowDrgq75JXAkdneZ9NGpx3PnBlRHxbcvkraapDSW8C70XEQUWO3QSsERHrLGj+7dc5qnLeDGsWPn/2n01dBLOiluzSepGE21VOGtigv8vDzt2upHJK+hOwD7ACqYv6c+Be0roj4yX1IE2E9lNgCWA66UvAOdkEM/l5tQXOAg4COgMvAkdHxNCCdKsCl5EG5k0AbgROXZAxYpVWU/8bcJek5YF7gO9Ib+qeQD9Sk4WZmVWJRV1Tj4hzgBpHGGeTxBTvu5k/7TRSl3Ct3cIRMQTYuoRi1qiignpE3CtpN9KkMReSRsAH2ajEiHioCYtnZmbWpCoqqANExIPAg5I6kka+j42I+q0kYmZmFaVFCy+9WoqKC+o5WSB3MDczq2JeTr00ZR/UJV1aQvKIiGMWWmHMzGyRaoKlVyta2Qd1oJR5HANwUDczs2ap7IN6RNS8RJOZmVU1V9RLU/ZB3czMmi83v5emIoO6pBWBlYF2hcci4t5FXyIzM1sYHNRLU1FBPVsX9z5g89yu7DF/xqGWi7JMZma28Diml6bSVmk7F1gK2JQU0HcjBfhrgc+AjZqsZGZmZk2s0oL69qR5dHMLan8bEc9FxG+BB4DjmqxkZmbW6Bq69GpzU1HN76R53r+KiFmSJgGL5R17lDQfvJmZVYlmGJcbpNJq6l8BPbOfPwJ2yTvWD5i6yEtkZmYLjWvqpam0mvoTpJVs7gP+CdwgaUPS0ncbkBZ5MTMza5YqLaifBHQAiIibJE0kLbvaHjiKeReeNzOzCtcMK9sNUlFBPSImA5Pznt9HqrWbmVkVao5N6A1RUX3qkl6UdKSkxZu6LGZmtvBJDduam4oK6sBw4ALgG0kDJR0sqXNTF8rMzBYOD5QrTUUF9YjYk3Rb26+BmcB/gO8k3SNpD0ltm7SAZmZmTaiigjpAREyMiBsjYkegF/BHoAdwO/BdkxbOzMwalZvfS1NRA+UKRcQoSS8CPwJWAZZs4iKZmVkjao5N6A1RkUFd0grAvtm2GqmGfidwW1OWy8zMGpdjemkqKqhLOpYUyNcFxpGmhT0GeCYiZjdl2czMzJpaRQV14EzgQeBvwGMRMaOJy2NmZguRm99LU2lBfYlsAhozM2sGHNNLU1FB3QHdzKx5cU29NBV3S5uZmZkV56BuZmZla1HPKCdpB0nPShopaZqkTyVdJKlrQbqdJb0taaqkDyX9skhebSSdL2mEpEmSnpC0SpF0fbNjk7K050lqU3LhcVA3M7My1gSTz/QAXgYOB7YDLgIOBu6aWyZtQlpMbDDQH7gDuFbSngV5XQr8BjgF2B1oCzyV/wVBUnfgf0CbLM0pwG+z65asovrUzcyseVnUfeoRcXPBrmckTQOulrR0RHwL/AV4OSIOz9I8nc2fciZwN4Ck3qQpzY+MiOuyfa8CXwK/A87Lzj0c6ALsFhGjs3StgCsknZ1dr94qrqauZEdJ/5B0Vfa4gzyawsys6pTJNLGjssc22RojW5BXc8/cDqwqafns+bakGDsnXRa0Hwd2yDuvP/BkLqBn7szO3bbUglZUTT1rpngU2BAYS5pJbkngJOAlSTtExNgmK6CZmZUVSV1INeF84yNifB3ntQRak2YtPQ14MCI+l7Ratn9owSlDsse+wOfZ4/cRMaZIusPynvcFrstPEBFjJQ3PjpWk0mrqFwArANtFRI+IWDUiepD6PVbIjpuZWZVohIFyxwJfFWzH1uPSXwBTgNdJy37vn+3vnj2OLUifC9498tIVpsml65H3vL7p6qXSgvouwEkR8UT+zuz5ycCuTVIqMzNbKBqh+f0iYNmCrT6D0HYANiYNdFsVeCirvZe1imp+BzpS8/KqI7LjZmZWJVo0sGM8a2avtam9hvPeyX4cnA1wewvYDfgg29+14JRcDT7XNz6mSJpcuvz+8/qmq5dKq6m/CRxV+G1JUgvgaOCNJimVmZlVs3eAGcCKwCfZz4X93bnnQ/Mel8zGghWmy++PH1qYV3bLWy/m77evU6UF9ZOBLYGPJV0s6WRJ/yS9yZtnx83MrEqUyej3DUmD4z6NiGnA00DhPen7AEMi4vPs+ePAbGCPua9F3Ukj2h/NO28AsLWkbnn79srOfbzUglZU83tEPCfpZ8CppEELueaJF4CzIsI1dTOzKrKo71aWdC/wGql2PgX4CXBC9vz+LNnfSPevX0G6/WwLUkzaJ5dPRHwt6T/A+ZJmAd+QJpYZB1yVd8krSS3N90s6G1gGOB+4stR71KHCgjpARLxOmnXHzMyqXItFPwPJK6Tg/CdSa/bnwDXABRExHSAiXpC0O/B30u1pXwK/jojCe9ePASYC5wCdgReBrSNiXC5BRIyRtBVwGelLwwTgP6TKa8kqLqibmZktLBFxDikI15XuQeDBOtJMA47PttrSDQG2LqGYNSr7oC7pQeC4iPgo+7k2QWqOfwW4LntDzcysQnmy0NKUfVAnNVnkRrt3IQXu2iwDHEAa2HDowiuWmZktbI7ppSn7oB4RW+T9vHl9zpG0P6l/wszMKphwVC9F2Qf1BfQUabUcMzOrYE0wUK6iVVxQzyaa2RJYGWhXeDwiLoqI74BLFnXZzMzMmlJFBXVJSwHPkAJ6wJx2mfx+9gVaWN7MzMqPB8qVptJmlLuItK7tsqSAviGwPGnB+o9Iwd7MzKpEmcwoVzEqqqYObAb8nrQMHoAi4kvgbKWvc5eTFpw3M7Mq0NAFXZqbSqupdwVGRsRs0qo7S+QdGwxs0iSlMjMzKwOVFtQ/I61cA/A+cFDesd1YgGXqzMysfLn5vTSV1vz+CGmFmztJc+4+IOl70jJ4SwEnNWHZzMyskXmgXGkqKqhHxMl5Pw+QtDFpcZd2wBMRMaDJCmdmZo3OMb00FRXUC0XEa6Ql8szMzJq9ig7qklYDVgd+AJ6LiFlNXCQzM2tEHv1empKDuqTTGuviEVHnVK7ZrWonkJrZWwN3AeeS1ps9lHS/egDvS9oyIn5orPKZmVnTckgvzYLU1M+g7pXS6qs+87MfD/wDeIC0ePyfgbVI96MfDwwB1iQtKH8a6T52MzOrAh4oV5oFCerP0XhBvT5+CfwtIs4AkHQPcB9wTERcnqV5TNJM4P9wUDczqxpe0KU0JQf1+i5/2oj6AE/nPf8fqUXm9YJ0r5GmjzUzM2uWKmGgXFtgSt7z3M/TCtJNpzJej5mZ1ZOb30tTKUGwWHP/ouwCMDOzJuCYXppKCepPS5pdsO/5gn2VNuWtmZnVwTX10jRqUJfUDtiTtLDK0kBHar4jISJiq3pk+9dGKp6ZmVlVa7SgLunnwG3Aksy9dxzmBvX85nJRz+bziHBQNzNrpjz6vTSNEtQl9QEeAjoBHwBPAMcAE4GLSYF+S2AF0uxvVwEzG+PaZmZWvdz8XprGqqkfRwrojwG7RsQMSccAEyNizgx0kn4LXA6sExE7NdK1zcysSjmkl6axBpdtTWpO/3NEzKgpUURcTZr5rb+kIxrp2mZmZkbjBfXewCzgzbx9QbrHvNCV2bGDG+naZmZWpVpIDdpKJWkvSQ9I+lrSJElvSfqV8voBJD0jKYpsfQvy6irpWkmjJU2QdLekXkWuubGkwZKmSPpC0kn51ytFYzW/zwbGRkT+4LdJQBdJLfNXT4uICZLGA6s00rXNzKxKNUGX+rHA56Ru5ZHANsA1pBlL8wduv0hafyTf5wXP7yCtJHo4MBU4Cxggab2ImAkgaUVgIGksWm5tk3NIFeULSi18YwX1b4A+kpQX2L8C+mYFnFODl9QV6Mb8M8KZmZnNowkGyu1csNrn/yQtBhwr6W8RkZsfZWxEvFRTJpL6AdsB20XE49m+YaRFyHYH7sySngCMAvaNiOnAU5IWB06VdFlElBQrG6v5/UPSsqj5te8Xs8fCbzJ/yx4/aqRrm5lZlZIatpWqhuW73wS6kOZeqa/+wFhSDTyX9zDgLWCHgnT3ZwE953ZS5bdfCdcDGi+oP0UapNg/b9+/SX3n+0p6V9Itkt4mraQWwH8b6dpmZmYL0ybANxExIW/fz7M+96mSnpW0WcE5fYFhBd3SkGrqfQEkdSQ16w8tSDOUFCf7UqLGan6/E1gHaJfbERFvSjoWuJDUp7B6QfqLG+naZmZWpRZksFs+SV1Itex84yNifD3P3wTYl9THnvMscCOpxXlpUov0k5J+HhGDszTdSTX1QmOAHtnP3bLHedJFxHRJk/PS1VujBPWIGEFa97xw/6WSHidNHbssMA4YGBFPNcZ1zcysujVCl/qxwOkF+/4KnFH3tdWbNNjtaeDS3P6IOL0g3cPA+8BfmLdpfZFb6Au6RMRQ4O8L+zpmZlZ9GmGg3EXAfwr21VlLl9QNGEAaxLZH3gC5+UTEJEmPkCqwOWNIldlC3YHR2c9js8euBdduA3TIS1dvlbJK2yIx5tXLm7oIZvMYMW5qUxfBrAatm7oA9ZI1s9erqT1HUnvgYVKw7RcR4xbg0kOBrQvuCoPUT/5uVrZJknJ3iuVbhTROrbCvvU6NvlyppLUlnSjpcknXFhxrLWnpYjffm5mZFWrRwK1UklqRxn2tCmwfEd/U45yOwE7Aq3m7B5Bq5VvlpVuZNP7s0YJ0u0rK/5a0D6kWP6jU8jfmKm09gOuBHXO7SKP3DstL1hp4A+gpaZ2IeLexrm9mZtWnCe5Tv4IUoI8jTaC2Ud6xN4ENSPeW30eabGbpLO1SwF65hBExWNJA4DpJxzF38pl3gHvz8jwfOAC4TdIVwJpZ/qcW3OZWL421Sltb4HHSN5ApwEvAxhRMExsRkyVdQ5r/fS+yJggzM7NimmDp1W2zxwuLHOsDDAfaAGcDi5FmTx0EHB4RrxSk34fUp381Kd4+Dhydm00OICI+lrRtlu5R0ix2p9dw/To1Vk39cOCnpElo+kfEZ5KGA0sUSXsPKagX3tNnZmY2j0Ud1CNi+Xok276eeY0jtVYfVke6QcBGtaWpr8bqU9+X1NT++4j4rI6075LmtC35pnozMzOrWWPV1FclBer/1ZUwImZJGkcaQGBmZlajJuhTr2iNFdTbApPz+wnq0J40aMDMzKxGTdCnXtEaq/l9BNA5u1m/VpLWJAX1Lxvp2mZmVqUW9YIula6xgvpz2eMB9Uj7Z1L/u6eKNTMza0SNFdQvyR7PkLRBsQSSukj6N+lWtlmAp28zM7NatZAatDU3jbWgyxuSziTdW/e8pBfJVsWRdDWwHPAz0ly2ACdFxMeNcW0zM6tejT7taZVrtBnlIuKvkr4H/gFsnnfoMNLscgATgBMj4qrGuq6ZmVWvZljZbpBGXdAlIv4t6WbSSjUbA72AlsB3pBl37oqIMZCa4+u7nq2ZmZnVrdFXaYuICcB/s20+2YL1fwR+T5piz8zMrKjm2C/eEIts6dW8YH4MBWvHmpmZFeOYXpoGBXVJ2wCHAquTxjN8CtwQEfflpWlHCuYnkIK5gMnMv2i9mZnZPDz5TGkWOKhLOhs4Kfc0e1wd2FnSvyPiqGyimbuAlbI0Y0m3sl0SEaMWuNRmZtYsuPm9NAsU1CVtBvwpe/oD8AopaG9A6ic/QtLzwGVAT+B74ALgyoiY2NBCm5mZ2fwWtKb+2+zxOeAXETEWQFIP4H5gE+BGoDVwKXBKRExuUEnNzKzZcUW9NAsa1DciTfX6x1xAB4iI0ZL+CLya5X15RPyhoYU0M7PmyX3qpVnQoL4UMBN4q8ixN7NjLUnN72ZmZgtEOKqXYkFn4OsAjIqIKDwQEbOB3CC4Txe0YGZmZlaahXqfekTMWpj5m5lZdXPze2kW2eQzZmZmpXJQL01DgnoPSf+r6RhALccBIiK2asD1zcysysnD30vSkKDehnlXYyumtuPz9cebmZnZglvQoH5Do5bCzMysCDe/l2aBgnpE/LKxC2JmZlbIre+l8UA5MzMrW577vTQO6mZmVrbc/F6aBZ18xszMzMqMg7qZmZUtqWFb6dfTXpIekPS1pEmS3pL0KxXcWyfpMEkfSpoq6W1JOxXJq6ukayWNljRB0t2SehVJt7GkwZKmSPpC0kmF16svB3UzMytbLVCDtgVwLDAZOA7YGRgAXAOclksgad9s3x1Af2AwcJ+kjQryugPYFjgcOABYBRggqVVeXisCA4HhwE7AxcCZ2fVLpiLTtzdbU2f63nkrLyPGTW3qIpgVtfxi7RZJb/cVgz5v0N/lIzdevqRySuoZET8U7Lsa2AfoHhGzJQ0DXo+I/fPSDALGRsQO2fN+wCBgu4h4PNu3CjAE2Dci7sz2XQVsB6wcEdOzfWcDRwBLRcS0UsrvmrqZmVmmMKBn3gS6AB0l/RhYGbizIM3twFaS2mbP+wNjgSfy8h5GWt10h7zz+gP35wJ6Xl7dgH6llt+j383MrGw1dPS7pC6kgJxvfESMLyGbTYBvImKCpE2zfUML0gwhzbTaJzvWFxhWZDXTIdkxJHUEli2S11DSrKt9gWdKKKdr6mZmVr5aSA3aSH3kXxVsx9b3+pI2AfYFLsh2dc8exxYkHZM99shLV5gmly6XpluxvLJa++S8dPXmmrqZmZWtRph75iLgPwX76lVLl9SbNNjtaeDSBpdkEXBQNzOzqpU1s5fS1A6ApG6kke+jgD0iYnZ2KFcj7wqMyDslV4MfnZdu2SJZd89LMzYvr/xrtwE65KWrNze/m5lZ2WqE5veSSWoPPEwKtv0jYlze4Vz/d9+C0/oC04FP89KtUuR+8765PCJiEqk7oDCvVQAxf197nRzUzcysbDXB5DOtSCPbVwW2j4hv8o9HxKfAh8BeBafuAzyVN4p9AKlWvlVe3isD6wCP5p03ANhVUuuCvMaSbokriZvfzcysbDVBzfMK0iQwxwFdCiaUeTO7b/wM4BZJn5D62/cBNgQ2yyWMiMGSBgLXSToOmAqcBbwD3JuX5/mkiWluk3QFsCZwAnBqwW1u9eKgbmZmZWsBZ0ttiG2zxwuLHOsDfB4Rt0nqAPwp24YBu0XE4IL0+5AG6l1NirePA0dHxMxcgoj4WNK2WbpHgZHA6TVcv06eUS6PZ5SzcuMZ5axcLaoZ5W547asG/V0+ZL1lm9U6b66pm5lZ2WpWEbkROKibmVnZWtAR7M2Vg7qZmZUth/TS+JY2MzOzKuGaupmZlS23vpfGQd3MzMpWE9zSVtEc1M3MrGy5j7g0DupmZla2XFMvjb8EmZmZVQnX1M3MrGy5nl4aB3UzMytbbn4vjYO6mZmVLfcRl8bvl5mZWZVwTd3MzMqWm99L46BuZmZlyyG9NA7qZmZWtlxRL4371M3MzKqEa+pmZla2WrgBviQO6mZmVrbc/F4aB3UzMytbck29JA7qZmZWtlxTL40HypmZmVUJ19TNzKxseaBcaRzUzcysbLn5vTQO6mZmVrYc1EvjPnUzM7Mq4aBuZmZlSw38t0DXlFaUdKWktyTNlPRekTTPSIoiW9+CdF0lXStptKQJku6W1KtIfhtLGixpiqQvJJ2kBVjNxs3vxltvvsELzz3Le++9xzdff8Xo0aOYOnUa3bt3o++qq7HDjjvTf8ed6lwt6Zn/PcWDD9zPe++9w+hRo+jUuTNL91qaddffgH323Z/eyy473zkRwWMDHuWhB+5j6JAhjBs3jjZtWrP0Mr3ZaKN+7HfgQfTuPf951jxEBM8++RhPDHiITz4cyoTx42jdpg1LLrU066y/Eb/Yaz+WWrr3POfc9J9/c/N1V9b7GgMHvT3P888++YgXn32Kj4Z+wNdffs7YMWOYMnkynTp3ps8KK7Hpltuw3U670bp160Z5jVa7Fk3T/L46sCPwMqnyW1MF+EXg+IJ9nxc8vyPL73BgKnAWMEDSehExE9KXCGAg8ATwZ2At4BxgFnBBKQVXRJSSvqpNnUmzfDNOPP6PDBzw6JznHTp0YHYEU6dMmbNv/Q025JLLr6Bjx07znT9lyhROOv6PPPvM00BaKrFT585MnjSJWbNmAXDWOeex0867znPetGnTOO4PR/P8c8/O2dexY0emTZvGzJkzAWjXrh3nnn8Rm2+5VeO94AoyYtzUpi5Ck5k+bRp/O+U4Xhn8/Jx9HTqkz8esWenz0bZtO04+81z6bbr5nDR33XoD99x6Q615jx0zmohg5b6rc9l1t85z7NorLubOm/8753nbtu1o2bIlkydPmrOvzworcdY//81iPRdvyEusaMsv1m6RhNv/DR3VoL/LW/ZdrORySmoREbOzn68H1ouINQrSPANMjIidasmnHzAI2C4iHs/2rQIMAfaNiDuzfVcB2wErR8T0bN/ZwBHAUhExrd5ld1Cfq7kG9bvuvJ0WasGaa61F797L0qFjRwB+GDmS++69m3//6zJmzZrFHnvuzWl//ds8586ePZvDf/MrXn5pMEssuSTH/PE4ttxyazp07MjMmTP5+uuvePaZp1ljjTVZd7315zn3X5ddwtVXXgHAEf93NPvufwDdunVn1qxZvPnG65z99zP55OOP6NixI48MfJLu3XssmjekjDTnoH7DNf/i1v9eDcBBhx3BLnvuS5eu3Zg1axbvv/Mml19wNl989gkdOnTk+rsfoWu37vXK99uvv+JX++xMRHDUcaew8x77zHP82acGMnb0KFZd4ycss+xydOzUGYCJE8bz9OMDuOZfFzFt6lR+uv5G/OOSqxr3RVeQag7q+RoY1M8EjgZ6RF6wlfQG8E5EHJo9/xK4NyL+kJdmLeBtYIuIeKa+5XWfurHX3vuyx157s/IqfecEdICeiy/Ob353BL887DcAPPLwg8yYMWOec2+75SZefmkw3bt358abb2ennXedk0erVq1Yfvk+HHLor+YL6AAPP/QAALvsuhuHH3kU3bI/yi1btmS99TfgkstSwJ80aRKDXnyh8V+4lbWnHnsYgG122IUDDzucLl27AenzsdY663HGuZcAMHnyJF5/eVC9833i0QeICNq0acvm2/Sf7/jPt9qOXffan5VXXX1OQAfo1LkLO++xD4cfcyIAb7z6EiO//25BX57Vk9SwbSH7uaRJkqZKelbSZgXH+wLDYv7a85DsGJI6AssCQwvSDAUil66+HNStTmuuuRYAU6dOZfy4cXP2z5w5k/9eew2Qatq9ll66pHx/GDkSgNVWX6Po8WWXW46u2R/yyZMnl1psq3CjR/0AwEp9Vyt6fOney9K5S1cApkyp3+dj9uzZPDHgIQA23mwLOnfpUnK5+q6+5pyfR410UF/YGjpQTlIXSb0LttL/4+f3LHAMsD1wCNABeDJrcs/pDowtcu4YINf02C17nCdd1gw/OS9dvTioW53efON1IPW191hssTn7Xx48iJEjRyKJ/jvU2AJVo2V6pwFOH7w/38BSAL768kvGjRsLwGqrrV5y/lbZluq1DAAfDf2g6PFvv/6KCePTl8yVVike+Au9+drLjPxuBADb7rhrHamLe//tN+croy08LdSwDTgW+KpgO7ah5YqI0yPiuoh4PiLuADYHvgX+0tC8G6JigrqkVpJ+Kqn5jkxZhCZPnsynn3zCJf+8kBuuvw6A/Q88eJ4R8G+9lf64Lb3MMnTq3Jnbb72FffbcjQ3XW5tNNlqPgw/Ylztuu2W+JvucPfdKfZkPPnAfV15xOWPHjgFg1qxZvPbqKxxz9JEA7LTLrqy+xppF87DqtcMv9gTgiUcf5OZrr2R89gVv1qxZvPPma5xx0jEAbLX9Tqy8av2+9D3+8P0ALL7kUqyz/kb1Lsv06dP59uuvuOOm67j6sgsB2GLbHejWY7E6zrSGaoRb2i4iNW/nbxc1djkjYhLwCLBu3u4xQNciybsDo7Ofx2aP86ST1IZU+x9NCSrplrbZwEvADsCTTVyWqvTDyJFstfkm8+1v1ao1+x1wIEce9ft59n/5xRcAdOvWnWP/cDRPP/UkkujcuQuTJ0/i7bfe5O233uSxAY/yryuvoUOHDvOcv98BBzF8+HBuuekG/v2vy/j3vy6jU6dOTJ06lZkzZ9J72WU59oSTOOjgQxfaa7byteue+zFyxHDuu/MWbrr239x07b/p0LET06ZOZdasmfRauje/OepYdt/3oHrlN3HCeAY9l+7Q2Kb/zrRoUXedZqfN12fG9Onz7GvRogVbbbcjvz+pSStkVk8RMR4Y30SXHwpsLUkF/ep9gXchfRmQ9BXz952vAoj5+9prVTE19ez2gk9J33BsIWjRsiWLLdaTxRbrSZs2bYB0e9qBBx3ML3/1a1q2bDlP+vFZ0+eQD97n6aee5Be778FTz7zA84Nf4YWXXuPYE06iZcuWvPH6a5x/ztnzXa9ly5Ycd8JJnHraGbRt2xaAiRMnzrmdbeqUqUwYP55Z2XNrXlq2bMlvjj6Oo084lTZt0udj8qSJc25nmzZtKpMmTphz22Rdnn7iMaZPn4Yktt3xF/U6p0ePnnTvsRht27Wbs2+r7Xfi4N8cSbt27Ut7QbZAynygXF451RHYCXg1b/cAUszaKi/dysA6wKMF6XaVlD/5wT6kWnz9R4FSYbe0STqYdKP/9hHxbWPn31xvaStm9uzZfPvtN9xy0w3ccdutdOnShYsuuZyfrrvenDS/+82veGnQiwD8ZO11uPGW2+fL54LzzuGmG/5Lq1ateOzJp1l88SXmHBs1ahTHHnMUb735Btv335FDfvkrll++D+PHj+eVl1/i0osvZOTIkay/wYZcec11tGpVSQ1LjaM539I2dvQozjzlWN5/5y0233p79tz/EHovtzwTJozn7ddf4borL2X0DyP5yU/X5x8XX0nLOj4fRx+2Px8OeZ+11lmP8/91bcnlGTXyex594B7uuuV6EJx42llssvnWC/jqKt+iuqXtxY/GNOjv8s9W6r4g96l3ILUKA/wfsAJz++GfJdWqTwDuI002szRwHGmSmU0j4pW8vB4DVsuO5yafmU26TS5/8pm3gMeAK4A1gfOAUyOipMlnKqamntkLWBz4VNIrkh6S9GDe9kB9Myo2InL8+KZqoSk/LVq0oHfvZTnp5D/zx+NOZMyYMZx0wrFMyZuQpmOHube/HXjwIUXzOfjQXwJppPxrr7wyz7E/n3wib735Bjvv8gvOveAiVlt9DTp07MhSvXqxyy9246r/XE+bNm149ZWXue/euxfCq7Rydt7f/sz777zF1v135uQzz2WlvqvRvkMHllhyKbbZYRfOueQqWrdpw9tvvMpjD99Xa16ff/oxHw55H4Btd/rFApVnscWX4KBfH8FJZ5zNtKlTOf9vf2bUyO8XKC+rvxZSg7YFtARwV7ZtTuqHzz1fHRgOtAHOJs0Ed3m2b56AntmHNFPc1cCtwEfADrmADhARHwPbAr1JNfjjgdOBC0steKUF9U6k/oXBwKTseee8rZTbFOYbEXnpxY0+dqIq7L3vfrRp04bvv/uOF59/bs7+xZeYW+vu0+fHRc9dYokl6dQpzUI3YsTwOfs//eSTOfeeH/LLXxU9d4UVV2TTzTYH4H9PPtGg12CV5cvPP51z7/me+xf/wvijPiuwwcabAvDis/+rNb/HH0nf9zt06MimWzSsdv2zn2/FkkstzdQpU3j6iQENysvKU0R8HhGqYXsmIj6OiO0joldEtImI7hGxY5GATkSMi4jDsjSdI2KPYi3NETEoIjaKiHYRsWxEnFPk/vY6VVR7ZkRs0YjZXQT8J3/H7/9w7FeNmH/VaNu2LV27dWPk99/z1Vdfztm/4korlZRP/sj5Tz/5eM7PvZddrsZzlvvRjwD49ttvSrqWVbYvPvt0zs+9luldY7pleqfPznfDa+6NmzVzJv8b+AgAm221XaP0hS+2+BJ8N+Jbhn/zdYPzstp55dXSVFpNvdFExPiI+Dp/67IAE1E0B5MnTWLM6HS7Wf4I9n79fjbn58/y/gjn+/7775g4cSKQbn3LUd7I4+Hf1vwHedSoUQBF55y36tUibxWP7/NaeAqNGZ0+H/kzIRZ6edDzc9Jtu9OC3ZteaMTw9CWzfcEdHbYQqIFbM1NxQV3SOpLukjRc0rTs8U5J6zR12SrRzHqMLL/pxuuZOTPda/7TvOleey+7LOv8NN2SefONxRfQuOG/6R73tm3bssGGc+8L7rvqqnN+vvOO24qe+8PIkTz9VGp2X+sna9dZTqseK6w89+6eh++9s2ia0aN+mHOL2qqrr1VjXrmm997LLc/qa65d63UjgtmzZ9ea5skBDzH6hzQb4pprr1trWmu4plh6tZJVVFCXtCmpP3194DbgtOxxfWCQpPlvsrZaffzRRxy0/z48eP99fDdixJz9EcEnH3/M2X8/k3//6zIAttl2O1ZaaeV5zv/DscfTsmVL3n7rTc447VRG/ZCm9pwyZQo3Xv9fbrvlJgAOPPjQOXO7AyyzTG823eznANx+682cf+4/+D6bR3vatGm8+Pxz/PKQA5kwYQKtWrVm3/32X3hvgpWdpXotwwb9Un/5g/fczlWXnD9nUNr0adN49aUXOf7IXzJp4gRatWrFznvsWzSfsaNH8cqgtMpbfWaQmzRxAr87aA8euucOhn/zNfldmiO+/Zobrr6cf/7jDABWWW0N1u/nPzlWXirtlrYXgQnATvkjByW1JM3k0ykiFvi3rDne0jZ0yBD22fMXc563bduWDh06MHnyZKZNm7va3+ZbbMk5519E+/bz90fee/ddnPW3M5g5cyaS6NKlK5Mmzb3ffLv+O3D2OefPd0vaqFGjOPzXv+TDD4fN2dehQwemTp06p7bUpk0bzvz7P+i/Y+nT0FaD5n5L25/+cDifffzhnH3tO3RgWt7no3WbNhx3yplsse38C7MA3HPbjVx92YW0aNmSm+8bWOdSqRMnjGeP7Tad87x169Z06NiJqVOnMG3q3P+L1dZcm9PP+SfdmuHKgTmL6pa2Vz4d16C/yxv8uGuzqq5XWlCfDOwZEY8WObYDcHdELHAnV3MM6lOnTuXpp57klZdf4v333uWHH35g3LixtGnThiWXWoo11liLHXfehX4b/6zWfIYM+YAbr7+O1159hdGjRtOpU0dWXW0Ndt9zT7bdrvgfXIAZ06dz371388TjA/now2GpZt66NUv3WpoNNurHfgccyPLL92nsl10xmnNQB5gxYwaPPXQfLzz9BJ998hETJ0ygVetWLLnU0qy93gbsuud+9F5u+RrP/92Be/D5px+zwcab8rcLLq/zerNnz2bw80/z1uuvMOS9dxj1w0jGjx1Dy1at6NGjJyutuhqbbbkdm2y+1TwDP5ujRRXUX21gUF/fQb18SRoJnBAR1xc59kvgvIhY4Lnhm2NQt/LW3IO6la9FFtQ/a2BQ79O8gnpF9akDDwHnSprnRtPs+T+AB5ukVGZmtlB4oFxpKuo+deZOwzdQ0njge9LMP11I8+0e34RlMzMza1IVFdQjYky2AP1OwCbMXb7uBeCRbNEXMzOrEs186ELJKiqow5zV2h7ETe1mZlXPMb00ZR/UJZV0z0hElLSgvJmZlTFH9ZKUfVAHfoCSRqW3rDuJmZlZ9amEoP4rSgvqZmZWJZrjCPaGKPugXuyedDMzax48UK40ZR/Uzcys+XJML03FBXVJmwG/BVYG2hUej4ial2syMzOrYhU1o5yk7YD/AT2B9YCvSAPpVgE6Aq81XenMzKzReT31klRUUAf+ClwM7Jg9/0tEbEmqtc8gBXwzM6sSnia2NJUW1FcFBgCzSSPiOwJExBfAGcCfm6xkZmbW6KSGbc1NpQX1qUCLSEvLDQdWyDs2AVi2SUplZmYLhVvfS1NpA+XeJvWfPwE8BZwq6QdS0/vfgXebsGxmZmZNqtKC+sVAn+znU0hLsebmgP8a2K0JymRmZgtLc6xuN0BFBfWIeDTv528krQusCLQHhkbE9CYrnJmZNbrmONitISoqqEvaBngy61Mne/yoaUtlZmYLS3Mc7NYQlTZQbiDwraRLJG3U1IUxMzMrJ5UW1NcCriPdpz5I0qeSzpK0ZhOXy8zMFoKmGP0uaUVJV0p6S9JMSe/VkO4wSR9KmirpbUk7FUnTVdK1kkZLmiDpbkm9iqTbWNJgSVMkfSHpJKn0doqKCuoR8V5EnBoRKwIbAQ8AhwBvSXpX0slNW0IzM2tUTXNP2+qkyuPHwAdFiyXtC1wD3AH0BwYD9xVpRb4D2BY4HDiAdAfXAEmt8vJakdQSPRzYiTQo/EzguFILrqx7umJl32R2Av4N9IqIBV5PfepML/Fq5WXEuKlNXQSzopZfrN0i6e0eOnxyg/4u9+3VoeRySmoREbOzn68H1ouINQrSDANej4j98/YNAsZGxA7Z837AIGC7iHg827cKMATYNyLuzPZdBWwHrJwb8C3pbOAIYKmImFbfsldUTT2fpLaS9gTuIn0TWgJ4vGlLZWZmjakpZpTLBfSay6Qfk6Ynv7Pg0O3AVpLaZs/7A2NJc6vk8h4GvAXskHdef+D+gju4bge6Af1KKXtFBXVJLSXtIOkm4HvmBvPjgKUjon+TFtDMzJqDvtnj0IL9Q4A2zJ1PpS8wLOZvEh+Sy0NSR9JsqIV5DSVNh96XElTULW2kQN6N9C3nb8DtEfF1UxbIzMwWnoa28UvqAnQp2D0+IsY3INvu2ePYgv1jssceeekK0+TS5dJ0K5ZXREyXNDkvXb1UVE0duBRYNSLWjYgLHNDNzKpcwwfKHUtapjt/O3YRvoJFqqJq6hHx16Yug5mZLTqNMKPcRcB/CvY1pJYOc2vkXYEReftzNfjReemKLTTWPS/N2Ly85pDUBuiQl65eKiqom5mZlSJrZm9oEC+U6//uCwzL298XmA58mpdua0kq6FfvS7YAWURMkvQV8/edr0Jqayjsa69VpTW/m5lZM1KO66lHxKfAh8BeBYf2AZ7KG8U+gFQr32ru69HKwDrAo3nnDQB2ldS6IK+xpFvi6s01dTMzK1tNMfW7pA7MveXsR0CX7BZqgGcjYiRwBnCLpE+Ap0lBeENgs1w+ETFY0kDgOknHAVOBs4B3gHvzLnk+aWKa2yRdAawJnACcWupCZRU/+Uxj8uQzVm48+YyVq0U1+cwnI6c06O/yCou3X5DJZ5YHPqvh8BYR8UyW7jDgT8BypGb4UyLi4YK8upL69XcnVaQfB46OiG8L0m2cpVsbGAn8Czi3yO1wtZe9UoO6pPakWwHGRsSUxsjTQd3KjYO6latqDuqVrOL61CXtJOlVYALwNTBB0quSdqjjVDMzqzBq4L/mpqKCuqRfkBZxmU66z3B/0mxy04AHJe3adKUzM7PGVo4D5cpZRTW/S3oTeD8iDixy7GZg9YhYZ0Hzd/O7lRs3v1u5WlTN75//MLVBf5eX77loylkuKqqmTrqP78Yajt1EiXPkmplZmWuapVcrVqUF9dGkG/KLWYUSZ94xMzOrJpV2n/odwNmSpgB3R8TY7HaBvYC/kxasNzOzKtEcB7s1RKUF9ZNJEwFcDVwlaQbQmtTIci9wShOWzczMGllzHOzWEBUV1CNiGrCHpDWBTZk7Kf4LEfFukxbOzMwanWN6aSoqqEvaDHgjC+DvFhzrCKwbEc81SeHMzMyaWKUNlHsaWK2GY32z42ZmViV8n3ppKqqmTu0tMR2BRpku1szMykUzjMwNUPZBXdJGwMZ5u/aXtElBsnbArsCQRVYwMzNb6Jpjbbshyj6oA9sBp2c/B/D7ImlmkAL6kYuqUGZmZuWm0qaJnQ1sFBGvLIz8PU2slRtPE2vlalFNE/vt2OkN+ru8dLc2zaquXwk19TkiotIG9pmZWQO4+b00ZR/UJf20lPQR8cbCKouZmS1anlGuNGUf1IHXoF7N4srStVy4xTEzs0XGMb0klRDUt2jqApiZmVWCsg/qEfFsfdNK6rMwy2JmZouWK+qlKfugXhdJPYF9gP2BjXDzu5lZ1fBAudJUZFCX1AHYjRTItyat1PYm8MemLJeZmTUuD5QrTcUEdUktge1JgXwXoAMwgvQa9o2IO5uweGZmZk2u7IO6pJ+RAvleQE9gFHAzcCvwXvZ8RJMV0MzMFh5X1EtS9kEdeJ50q9rTwEXA4xExE0BS16YsmJmZLVyO6aWphKD+LrAm8HNgFtBT0n0RMaFpi2VmZgubB8qVpuynXY2InwBrAOcDKwHXAyMk3Ulamc3ztZuZmVFhC7rAPH3sewKLk4L6/cAlEfFcQ/L2gi5Wbrygi5WrRbWgy+hJsxr0d7lHx5bNqq5f9jX1QhHxYkT8H7A0sBNpwNw2wNOSPm3SwpmZWaOSGraVfj0dKimKbOcUpDtM0oeSpkp6W9JORfLqKulaSaMlTZB0t6ReC/5u1K0S+tSLiohZwKPAo5LaA78A9mvSQpmZWbXYHhiX9/yb3A+S9gWuAc4C/keaAO0+SZtGxEt559wBrA4cDkzN0g+QtF5uwHdjq7jm94XJze9Wbtz8buVqUTW/j53SsOb3bu1La36XdCjwX2DxiPihhjTDgNcjYv+8fYOAsRGxQ/a8HzAI2C4iHs/2rQIMYSHOrVJxze9mZmZNRdKPgZWBwqB8O7CVpLbZ8/7AWOCJXIKIGAa8BeywsMrnoG5mZmVLDfzXAO9LmiXpU0knZ7OaAvTNHocWpB8CtAH65KUbFvM3hw/Jy6PRVWyfupmZVb+G3qcuqQvQpWD3+IgYX8Mpw4HTgZdJd1ftAvwdWAY4CuiepRtbcN6Y7LFH9ti9SJpcuh5F9jcKB3UzMytbjdBxfywpSOf7K3BGscQRMRAYmLfrcUlTgD9KOqvhxVm43PxuZmbV7CJg2YLtohLzuJO0rPfazK2RF05TnqvBj84exxRJk0s3usj+RuGaupmZla8GVtWzZvaamtoXRK4vvS8wLG9/X2A68Gleuq0lqaBfvS9p+vOFwjV1MzMrW004UC7fvqS1R96MiE+BD0krh+bbB3gqIqZnzweQauVbzXkt0srAOqQ5VhYK19TNzKxsLeoFXSQNJE0ok6tN7wL8ljQVeW6Z7zOAWyR9QlpBdB9gQ2CzXD4RMTjL6zpJxzF38pl3gHsXVvkd1M3MzOYaChwG9Ca1Zn8I/AG4LJcgIm6T1AH4U7YNA3aLiMEFee1D6r+/mhRvHweOXlizyYFnlJuHZ5SzcuMZ5axcLaoZ5SZPb1iQ6tCmeS3e6pq6mZmVr2YVkhvOQd3MzMpWIw52axYc1M3MrGw1r8bzhnOfujW6bFrGY4GLapmK0WyR8ufSmgMHdWt0knoDXwHLRsTXTV0eM/Dn0poHTz5jZmZWJRzUzczMqoSDupmZWZVwULeFYTxpaUMPRrJy4s+lVT0PlDMzM6sSrqmbmZlVCQd1MzOzKuGgbmZmViUc1M3MzKqEg3qFk3SGpMjbpkoaIulESSX//0p6RtLDC6OsC0rS55Iub+pyWMNIejv7jG5a5Njm2bH18vadIWnjRVvKukk6NCtrz6Yui1khL+hSHaYAW2Y/twe2AM4hfWk7p8S8jgRmNV7RzEDS6sBa2dP9gefrcdrpwERg0MIql1m1cVCvDrMj4qW8509LWhPYnRKDekR80KglKyOS2kfElKYuRzN1ADAbeBbYS9LvI2JGE5epSUlqCbRo7u+DNS43v1evCUDr/B2SzpH0rqSJkr6RdJukXgVp5mt+l7SZpEGSpkj6QdJ1knrUdGFJHSVNknR8kWN3Sxqcl+5yScMkTc6a2a+U1LWuFydpd0lvZd0N30q6SFK7vOO55twds2uOB+6qK19rfJIE7Af8D7gIWAzYvo5zchNonJ/XtbR5dqxd9v/9bfb//5ak3erI7wxJoyUV/k6skeW9XfZ8R0lPSPpe0nhJL0uqtazZeT2y34sfst+TQZI2K0jzjKSHJR0iaRgwDfhJXXmblcJBvUpIapVtnSXtAuwB3F2QbAngbGBH4BhgeeBZSTW22EhaF3iC9CVhL+AkYGdgQFbTmE9ETAIeBPYtyKtzdu1bs10dgJbAqUB/4M/Az4H763itu2Sv7QPgF8B5wOHAzUWSXw18AuwGXFBbvrbQbEz6rN0KDARGkZrga9Mve7ws+7kf8Ea27xbgd6T/91+QPgf3ZJ+LmtwGdAe2K9i/H/A98GT2vA/wEHAQ6XfoReDR3BeKYrLfgwGk34uTSL8nE4Enst+ffOsBJwCnATuQVo0zazwR4a2CN+AMIIpstwMtazmvJbBMlnbbvP3PAA/nPb8X+AJonbdv2+y8nWvJf5cszUp5+w4GZgJL1nBOK+Bn2Xkr5+3/HLg87/kbwKCCc3+bnbdm9nzz7Pm/m/r/qLlvwL9I4z66Zs+vBCYBnfLS5P6/1svbF8DxBXmtle3/XcH+QcDrdZTjDeCWgn2f5H+2Co61yD6TA4Fb8/YfmpWhZ/Y891nfLi9N6+z35p68fc8A00lLvzb5/4u36txcU68OU4D1s20TUi18e+Ca/ESS+mfNguNIwTW3pvTKteS9KfBA5PX7RcTjwNjsWjV5LEuTX1vfF3g6Ir7LK9NBkt6UNBGYAbxQW5kkdQLWZv5WiDuyx8IyPVJLGW0hy1qB9gIejYhx2e5bSa00tTaZ1yA3cr6wK+UOYB1JHWs59zZgF0nts7JtAPw4258rb29JN0j6hvQ7MoP0Jbau35HxETEwtyP7fbmX+T+P70SEa+e20DioV4fZEfFatr0YEZcCZwK/lLQGgKT1SU3i35KaFvsBG2XntyuWaaY78F2R/d8BNfarR8R04B6yoC5pMWAb5ja9k/WD3gi8AuydlSf3h76mMnUDVFimLGBMK1KmYmW3RWdbYHHgIUndJHUD3gWGU3cTfDHdgRkRMbpg/3ekz0W3Ws69HehIaiaH1PT+BdnoeqVbQB8kBeLTSHeRrE9qWq/rd+T7IvuL/Y7482gLlUe/V68h2ePqwHukYDkO2DsiZgNI+lE98hlN6osvtGR2rDa3AYdJWov0JWIWqfaSsxfwVkT8LrdD0s/ryHMsqalznjJlg+vaFimTVyxqWrnA/d9sy7e4pCUiolhArMlooLWk7hExJm//kqT/67E1nRgRX0l6EdhX0t2kL5I3RUTuM7IisA7wi4h4IHdermZfR5nq+zviz6MtVK6pV681sscfssf2pKbE/D8qB9QjnxeAX+QPppO0DalG9EJNJ2WeAUaQakT7AQPymmBzZZpecE6tZYqIicBbwJ4Fh/bOK6+VAUkdgF1JAx+3KNj2I1Uq9qklixnMX0PO/f/uVbB/L+DNSIM0a3MbaYDaTsDS5DW9kz6PkPeZzL74/qyOPF8AukjaNu+8VqQv0v482iLlmnp1aCEp15TeBliXNJL8A+C5bP8TwB+AyyTdR6o5H1SPvM8iNU8+LOkyUu3jHFKT+aO1nRgRsyTdSRpYtAQFo+GzMv1L0l+AwaQ/tlvVo0xnAPdLupk04n0V0qj+eyLi3Xqcb4vGrkAn4NKIeKbwoKQTSTX5y2o4fwiwq6TnSQPrhkXEO5LuBS7KatDDgANJI+x3rUeZ7gIuAf4NfBARb+cdG0oaZ3JONqK9E2n99W/qyPMR0u/DzZL+RGpiPxroRfpcmi0yrqlXh/akoDgYeIr0B+VmYIvcALeIeJR0u82upH7DzUi1lWLm1OYj4nVSv2gXUh/5+aQ/Yv0joj4zz90GLAVMBgqnn70KuDAr773AstSjnzUiHiTVzNYEHgD+RLp17cB6lMcWnf2BL0ktNsXcAGwkaYUajv8f6W/UAOBV0pdVSP/P15D+3x8gfQ72jIiH6ipQRIwk/Y4U1tKJiGmkCZumkYL/maQvtc/Wkecs0hfSR0i/H/eQfl+2zX5/zBYZze1OMgNJrwPvRcQhTV0WMzMrjWvqBoCkpSTtT7oP+NWmLo+ZmZXOQd1y9iVNEnI7cG0Tl8XMzBaAm9/NzMyqhGvqZmZmVcJB3czMrEo4qJuZmVUJB3UzM7Mq4aBuZmZWJRzUzcqQpM8lhaRDC/Yvn+0PScsvzGuZWeVxULeqJen6vACYv02UNFTSNZJ+0tTlNDNrLA7q1hzMIC2ykdvakRaB+TXwmqTDm7BspZpBWsRkWPazmdkcDurWHAyKiKVyG9CBtEjNx6SVCv9VKTX2iPgmIvpmW12rh5lZM+Ogbs1OREyPiCdIK9bNIP0eVFJt3cysKAd1a7Yi4gPgtezpegCSDs363T/PnveXNEDS95JmS/pDfh6SVpL0b0kfSposaYKktySdLqlrTddW8jtJr0maJGmUpCclbV9bmeszUE5SO0lHSXpa0khJ0yR9lT0/RtJiteTfVtKfJQ2RNEXSD5Lul7R2HeXqIeksSe9kYxYmSXpf0nmSlqjtXDNrPK2augBmTezr7HG+ACzpOOAC0vry44DZBccPA/4NtM52TQbaAj/JtkMkbRMRnxSc15K0lvde2a5ZpDW8twS2lHTMgr4YSSuR1q1fOds1GxgLLA70BjbPXsv1RU7vDLxA+oIzLTt3MVKLxjaStoiIV4pcc23gMWDJbNeU7NzVsu1XknaMiJcX9HWZWf24pm7N3Y+yxzEF+5cEzgWuAHpFRHegE3A3gKQdgGuAmcDpwNIR0ZHUX/8zUgtAH+BeSYW/ZycwN6D/FegRET2AZUjB/kJSEC6JpG7AQFJA/w44COgSEYtl5VoTOKvIa835K9AT2B7omL3ezUhffDoAlxa5ZlfgQdL79RmwNdAxIjqR3ochpC8GD7rGbrYIRIQ3b1W5kWqjATxTw/H1SbXkAC7J9h2aPQ/g1hrOawl8kqXZq4Y0PYBvszS75+3vQKopB/DPIucJeCqvDIcWHF8+79jyBcf+ke2fAKxcwvv0eXbeZGDFIsf3yLvmcgXHTqnj3KVJLQUBnNvUnwlv3qp9c03dmh1JS0s6iFTDbAFMJ60lX+j8GrL4OfBj4IuIuKtYgogYDQzInm6bd2hboAupefrcIucFcHY9XkYxh2SPl0XEhwtw/t0R8XGR/Q+SgjLAGgXH9s4eby52bkR8C1yZPd1vAcpkZiVwn7o1Bz+XFDUcm0yqDRcGwSnA2zWcs3H22EvSiFqu2yl7XC5v37rZ49CIqOncF0jN+vX+/cwGzfXKnj5S3/MKvFpsZ0TMkPQ9qYm9e9412zA3yD9ZS75PAicBy0paPCJGLmD5zKwODurWHMwARmc/55qKvwaeB66OiC+LnDMqImYX2Q9zg2cb5g4Oq02HvJ9zfeU13mMeEdMk/QAsVY+8c/LL8UUJ5+WbUMuxqdlj67x9PUhdEVDL62HuYESAJQAHdbOFxEHdmoNBEbF5iefMquVYLpA9FRFbL1iRzMwan/vUzUr3Xfa4XK2pisvVUpeuKUHWrN2zxHzzm/J/VGOqxjWauV9+lqklXe+8n79feMUxMwd1s9INyh5XkrRyrSnn93r22FdSTU33m1BiK1pEfEEabQ+wU4llWiARMR14N3u6VS1Jc60ZX7o/3WzhclA3K93/mNtvfXE2mUxRklpL6pS363HSLW0tgROLpBdw8gKW6/rs8agF+LKxoO7MHg+U1KfwoKRewO+yp7ctojKZNVsO6mYliogZwJGk29L6A49L6pebZEZSC0mrSfoT8CGwdt65k4Fzsqd/lHSapM7ZeUsBN5BumZu8AEU7nzQBTCfgWUkHSOqQV6a1JF0sabcFyLsmVwBfkQYDPiFpi+yLCZL6ke6570Zqdr+wEa9rZkV4oJzZAoiIRyUdCFxLmt51EDBN0kTSfej5o8QLb6c7n3Rr256kWdxOkzSeFPwAjgGOo8S+8YgYm80d/wiwInAzMEvSWFKgb5slfauUfOu45jhJu5LuyV+B1IoxObuFsGOWbDSwq5vezRY+19TNFlBE3AasRKp5v0WaL70b6dawl4GLgE0i4sWC82aRJm05HHiDNPkNpIC4Y0Rc1oAyfQisBfwReBEYT5rT/XvgaeD3pMlkGk1EvAmsTpo05/1sdwtgKGnu/NUi4qXGvKaZFac0gZWZmZlVOtfUzczMqoSDupmZWZVwUDczM6sSDupmZmZVwkHdzMysSjiom5mZVQkHdTMzsyrhoG5mZlYlHNTNzMyqhIO6mZlZlXBQNzMzqxIO6mZmZlXCQd3MzKxKOKibmZlVCQd1MzOzKvH/JPtNob88DgwAAAAASUVORK5CYII=\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_3.predict_proba(X_test)[:, 1]))\n", - "#print(\"AUC-ROC score sobre train: \", \"%0.16f\" % roc_auc_score(y_train, clf_3.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_3.predict(X_train), y_train))\n", - "print(classification_report(y_test, y_pred, target_names=[\"Bajo valor\", \"Alto valor\"]))\n", - "#from sklearn.metrics import auc, confusion_matrix, roc_curve\n", - " \n", - "graficar_matriz_confusion(y_test, y_pred)\n", - "plot_roc_curves(clf_3, X_test, y_test, X_train, y_train)\n" - ] - }, - { - "cell_type": "markdown", - "id": "8136e1b1", - "metadata": {}, - "source": [ - "Mejoramos respecto del preprocesado anterior, practicamente obtuvimos algo similar al primer preprocesamiento. Para finalizar, testiemos en los holdouts" - ] - }, - { - "cell_type": "markdown", - "id": "e2de2081", - "metadata": {}, - "source": [ - "## Holdout" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9e6ed05b", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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" - }, - "toc-autonumbering": true - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/parte_2/boosting.ipynb b/parte_2/boosting.ipynb deleted file mode 100644 index a40d43f..0000000 --- a/parte_2/boosting.ipynb +++ /dev/null @@ -1,626 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "4f506374", - "metadata": {}, - "source": [ - "# Modelo: Boosting" - ] - }, - { - "cell_type": "markdown", - "id": "0dfc1a95", - "metadata": {}, - "source": [ - "El modelo a entrenar en el sigueinte notebook se ra un ensamble. En particular buscaremos hacer un boosting con árboles de decisión. Para ello utilizaremos GradientBoostingClassifier de sklearn" - ] - }, - { - "cell_type": "markdown", - "id": "391cf161", - "metadata": {}, - "source": [ - "## Librerias y funciones necesarias" - ] - }, - { - "cell_type": "markdown", - "id": "16cdfa7b", - "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": "713c6a1c", - "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": "38283f15", - "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": "718c496b", - "metadata": {}, - "source": [ - "## Primer preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "c9341762", - "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": "064cd0bd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Aplicando 'conversion_numerica' en las variables categóricas.\n" - ] - } - ], - "source": [ - "df, df_holdout = obtener_datasets()\n", - "X_df, y_df = aplicar_preparacion(df)\n", - "X_df = conversion_numerica(X_df) " - ] - }, - { - "cell_type": "markdown", - "id": "5d035169", - "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": "21d37537", - "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": "34e5c649", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "339a8420", - "metadata": {}, - "source": [ - "Vamos a realizar un entrenamiento con 5 folds. Para ello utilizaremos StratifieadKFold para asegurarnos de obtener folds balanceados. Además, utilizaremos Gridsearch para la busqueda de hiperparámetros óptimos teniendo en cuenta la métria AUC-ROC." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "237cd08d", - "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),\"min_samples_leaf\":np.arange(50,150,20)}\n", - "clf = GridSearchCV(clf, params, scoring='roc_auc', cv=cv, n_jobs = -1)\n" - ] - }, - { - "cell_type": "markdown", - "id": "e4b90a4a", - "metadata": {}, - "source": [ - "Ahora sí, entrenamos nuestro modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "1e975f3d", - "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", - " scoring='roc_auc')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "867b206e", - "metadata": {}, - "source": [ - "Realizamos nuestras predicciones para una análisis más amplio" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c2f742b2", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "de08702e", - "metadata": {}, - "source": [ - "### Metricas" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "630ffc27", - "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", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.94 0.89 0.92 5207\n", - " Alto valor 0.65 0.78 0.71 1306\n", - "\n", - " accuracy 0.87 6513\n", - " macro avg 0.80 0.84 0.81 6513\n", - "weighted avg 0.88 0.87 0.88 6513\n", - "\n", - "Los mejores hiperpametros elegidos: {'max_depth': 7, 'min_samples_leaf': 50}\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)" - ] - }, - { - "cell_type": "markdown", - "id": "702610da", - "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" - ] - }, - { - "cell_type": "markdown", - "id": "7ab6f6d1", - "metadata": {}, - "source": [ - "## Segundo preprocesamiento" - ] - }, - { - "cell_type": "markdown", - "id": "88b449ee", - "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. También, utilizaremos rfecv para que seleccione solo las features que resultan mas importantes para un árbol de decisión a la hora de entrenar con nuestro dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "2539de81", - "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": "f2ce1074", - "metadata": {}, - "source": [ - "Usamos rfecv" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d8b04ffd", - "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", - " estimator=clf_2,\n", - " X_df = X_df,\n", - " y_df = y_df,\n", - " min_features_to_select=20,\n", - " step=5,\n", - " n_jobs=-1,\n", - " scoring=\"roc_auc\",\n", - " cv=5\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "29fa7413", - "metadata": {}, - "source": [ - "Realizamos nuevamente el split" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "893505c9", - "metadata": {}, - "outputs": [], - "source": [ - "X_train, X_test, y_train, y_test = train_test_split(X_reducido, y_df, random_state=10, test_size=0.20, stratify=y_df)" - ] - }, - { - "cell_type": "markdown", - "id": "17051c0f", - "metadata": {}, - "source": [ - "### Entrenamiento" - ] - }, - { - "cell_type": "markdown", - "id": "5bbc5e65", - "metadata": {}, - "source": [ - "Volvemos a realizar un entrenamiento con 5 folds, utilizando las mismas librerias y funciones utilizadas anteriormente" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "bda7e752", - "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", - "clf_2 = GridSearchCV(clf_2, params, scoring='roc_auc', cv=cv, n_jobs = -1)\n" - ] - }, - { - "cell_type": "markdown", - "id": "a8c52e0f", - "metadata": {}, - "source": [ - "Entrenamos nuestro modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "c1eafc1c", - "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", - " scoring='roc_auc')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf_2.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "33330721", - "metadata": {}, - "source": [ - "Realizamos nuestras predicciones para una análisis más amplio" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "43f7e4b8", - "metadata": {}, - "outputs": [], - "source": [ - "y_pred = clf_2.predict(X_test)" - ] - }, - { - "cell_type": "markdown", - "id": "6345229d", - "metadata": {}, - "source": [ - "### Metricas" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "d58dbc47", - "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", - " precision recall f1-score support\n", - "\n", - " Bajo valor 0.94 0.90 0.92 5186\n", - " Alto valor 0.66 0.78 0.71 1327\n", - "\n", - " accuracy 0.87 6513\n", - " macro avg 0.80 0.84 0.81 6513\n", - "weighted avg 0.88 0.87 0.88 6513\n", - "\n", - "Los mejores hiperpametros elegidos: {'max_depth': 6, 'min_samples_leaf': 50}\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": "50a1496d", - "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 la precision para la clase de altos ingresos. Para finalizar pasamos a testear en los holdouts con los dos modelos" - ] - }, - { - "cell_type": "markdown", - "id": "c4b601af", - "metadata": {}, - "source": [ - "## Holdouts" - ] - }, - { - "cell_type": "markdown", - "id": "4f805300", - "metadata": {}, - "source": [ - "Realizamos los testeos requeridos en el holdout" - ] - }, - { - "cell_type": "markdown", - "id": "bf5ca1de", - "metadata": {}, - "source": [ - "### Primer modelo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ccab562b", - "metadata": {}, - "outputs": [], - "source": [ - "clf" - ] - }, - { - "cell_type": "markdown", - "id": "cd16ebfc", - "metadata": {}, - "source": [ - "### Segundo modelo" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "e80dad1b", - "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", - " scoring='roc_auc')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clf_2\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be05a8e2", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "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/predicciones/#2 - KNN.csv b/parte_2/predicciones/#2 - KNN.csv new file mode 100644 index 0000000..7d1c9ee --- /dev/null +++ b/parte_2/predicciones/#2 - KNN.csv @@ -0,0 +1,16282 @@ +id,tiene_alto_valor_adquisitivo +1,0 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