From b9a69c15f84e53f7a52ef1e18278271b3a512746 Mon Sep 17 00:00:00 2001
From: ariasm <mildarias181@gmail.com>
Date: Fri, 2 Jul 2021 00:22:19 -0500
Subject: [PATCH] Tarea de Aprendizaje Supervisado y no Supervisado
---
README.md | 4 +-
...lizado_y_NoNormalizado_Mildred_Arias.ipynb | 1758 ++
reporte/Ejercicio1_1_Normalizado.ipynb | 1286 ++
reporte/Ejercicio2_Mildred_Arias.ipynb | 2149 +++
reporte/Ejercicio3_MildredArias.ipynb | 484 +
reporte/asignaciones.md | 13 +
reporte/common.py | 11 +
reporte/datasets/HR_comma_sep.csv | 15000 ++++++++++++++++
reporte/datasets/housing-data.csv | 48 +
reporte/datasets/titanic-train.csv | 892 +
reporte/datasets/user_visit_duration.csv | 101 +
reporte/datasets/weight-height.csv | 10001 +++++++++++
reporte/environment.yml | 28 +
reporte/img/2021_LA-CoNGA_UCV.jpg | Bin 0 -> 102665 bytes
reporte/img/autoencoder.PNG | Bin 0 -> 146998 bytes
reporte/img/con_mat.PNG | Bin 0 -> 58789 bytes
reporte/ml-in-science-tarea-01 | 1 +
reporte/presentacion.pdf | 0
18 files changed, 31775 insertions(+), 1 deletion(-)
create mode 100644 reporte/Ejercicio11_Normalizado_y_NoNormalizado_Mildred_Arias.ipynb
create mode 100644 reporte/Ejercicio1_1_Normalizado.ipynb
create mode 100644 reporte/Ejercicio2_Mildred_Arias.ipynb
create mode 100644 reporte/Ejercicio3_MildredArias.ipynb
create mode 100644 reporte/asignaciones.md
create mode 100644 reporte/common.py
create mode 100644 reporte/datasets/HR_comma_sep.csv
create mode 100644 reporte/datasets/housing-data.csv
create mode 100644 reporte/datasets/titanic-train.csv
create mode 100644 reporte/datasets/user_visit_duration.csv
create mode 100644 reporte/datasets/weight-height.csv
create mode 100644 reporte/environment.yml
create mode 100644 reporte/img/2021_LA-CoNGA_UCV.jpg
create mode 100644 reporte/img/autoencoder.PNG
create mode 100644 reporte/img/con_mat.PNG
create mode 160000 reporte/ml-in-science-tarea-01
create mode 100644 reporte/presentacion.pdf
diff --git a/README.md b/README.md
index 70374c1..440991b 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,5 @@
# ml-in-science-tarea-01
-ML in science --> tarea-01
\ No newline at end of file
+ML in science --> tarea-01
+Tarea de Aprendizaje supervisado y no supervisado
+Los notebooks se encuentran en la carpeta Reporte
\ No newline at end of file
diff --git a/reporte/Ejercicio11_Normalizado_y_NoNormalizado_Mildred_Arias.ipynb b/reporte/Ejercicio11_Normalizado_y_NoNormalizado_Mildred_Arias.ipynb
new file mode 100644
index 0000000..2ce8fe8
--- /dev/null
+++ b/reporte/Ejercicio11_Normalizado_y_NoNormalizado_Mildred_Arias.ipynb
@@ -0,0 +1,1758 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ejercicio1_1 Caso no normalizado y normalizado\n",
+ "Mildred Arias, mildarias181@gmail.com"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "from tensorflow.keras.optimizers import Adam, SGD,RMSprop\n",
+ "from sklearn.metrics import r2_score\n",
+ "from tensorflow.keras.layers.experimental import preprocessing\n",
+ "from sklearn.metrics import mean_squared_error as mse"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loading the data housing-data.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('common.py') as fin:\n",
+ " msg = fin.read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import seaborn as sb\n",
+ "import warnings\n",
+ "import logging\n",
+ "\n",
+ "logging.getLogger(\"tensorflow\").disabled = True\n",
+ "warnings.simplefilter(\"ignore\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(msg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exec(msg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = './datasets/housing-data.csv'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Trabajando sobre datos no normalizados"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2104</td>\n",
+ " <td>3</td>\n",
+ " <td>70</td>\n",
+ " <td>399900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1600</td>\n",
+ " <td>3</td>\n",
+ " <td>28</td>\n",
+ " <td>329900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2400</td>\n",
+ " <td>3</td>\n",
+ " <td>44</td>\n",
+ " <td>369000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1416</td>\n",
+ " <td>2</td>\n",
+ " <td>49</td>\n",
+ " <td>232000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>3000</td>\n",
+ " <td>4</td>\n",
+ " <td>75</td>\n",
+ " <td>539900</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "0 2104 3 70 399900\n",
+ "1 1600 3 28 329900\n",
+ "2 2400 3 44 369000\n",
+ "3 1416 2 49 232000\n",
+ "4 3000 4 75 539900"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(data_path)\n",
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 47 entries, 0 to 46\n",
+ "Data columns (total 4 columns):\n",
+ " # Column Non-Null Count Dtype\n",
+ "--- ------ -------------- -----\n",
+ " 0 sqft 47 non-null int64\n",
+ " 1 bdrms 47 non-null int64\n",
+ " 2 age 47 non-null int64\n",
+ " 3 price 47 non-null int64\n",
+ "dtypes: int64(4)\n",
+ "memory usage: 1.6 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info() # tambien es posible obtener informacion usando df.describe()\n",
+ "# df.describe()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Grafica de los histogramas para cada feature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x288 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15,4))\n",
+ "\n",
+ "for (i, feature) in enumerate(['sqft', \n",
+ " 'bdrms', \n",
+ " 'age', \n",
+ " 'price']):\n",
+ " plt.subplot(1, 4, i+1)\n",
+ " df[feature].plot(kind='hist', title=feature)\n",
+ " plt.xlabel(feature)\n",
+ " \n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. sqft'}, xlabel='sqft', ylabel='price'>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='sqft', #aqui vemos la dispersion de nuestros datos de sqft vs price\n",
+ " y='price', title='price Vs. sqft',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. bdrms'}, xlabel='bdrms', ylabel='price'>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='bdrms', #aqui vemos la dispersion de nuestros datos de bdrms vs price\n",
+ " y='price', title='price Vs. bdrms',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. age'}, xlabel='age', ylabel='price'>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='age', #aqui vemos la dispersion de nuestros datos de age vs price\n",
+ " y='price', title='price Vs. age',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Creamos dos variables X, y: X debe es una matriz con tres columnas (sqft, bdrms, age); y es un vector con una columna (price)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X: (47, 4)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2104</td>\n",
+ " <td>3</td>\n",
+ " <td>70</td>\n",
+ " <td>399900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1600</td>\n",
+ " <td>3</td>\n",
+ " <td>28</td>\n",
+ " <td>329900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2400</td>\n",
+ " <td>3</td>\n",
+ " <td>44</td>\n",
+ " <td>369000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1416</td>\n",
+ " <td>2</td>\n",
+ " <td>49</td>\n",
+ " <td>232000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>3000</td>\n",
+ " <td>4</td>\n",
+ " <td>75</td>\n",
+ " <td>539900</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "0 2104 3 70 399900\n",
+ "1 1600 3 28 329900\n",
+ "2 2400 3 44 369000\n",
+ "3 1416 2 49 232000\n",
+ "4 3000 4 75 539900"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X = df.copy()\n",
+ "print('X: ',X.shape) # devuelve una tupla con el tamaño del array\n",
+ "y = X.pop('price') ##pop () es una función incorporada en Python que elimina en X el ultimo valor de la lista que es \"price\" y devuelve ese valor en la lista del Ãndice dado.\n",
+ "df.head() #aqui se muestra lo que tengo en X y en y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Definimos nuestro Modelo\n",
+ "Añadimos una neurona con tres neuronas de entrada y una de salida. Dense: Es que cada una de las neuronas de entrada se va a conectar con cada una de las neuronas de salida"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = Sequential() #modelo secuencial construir capa a capa modelo secuencial\n",
+ "model.add(Dense(1, input_shape=(3,))) #añadimos una neurona con tres neuronas de entrada y una de salida Dense= es que se va a conectar con todas las neuronas. La red crea una funcion que toma tus tres variables para predecir la cuarta\n",
+ "model.compile(Adam(lr=0.8),loss='mse') #adam el tipo de algoritmo estocastico bais(optimaizer) bad propagation, funcion de costo mse=error cuadratico medio\n",
+ "#model.compile(optimizer='adam', loss='mean_squared_error')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Dividimos la data en train y test"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Proceso de validacion del modelo. Queremos tener una medida de que tambien puede generalizar el modelo que queremos implementar, por eso dividimos la data en un conjunto de entrenamiento y en un conjunto de validación."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(37, 3)\n",
+ "(47, 3)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.8)\n",
+ "print(X_train.shape) #X_train nuestra data de entrenamiento 37 datos se debe al train_size\n",
+ "print(X.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Proceso de entrenamiento\n",
+ "La red que acabamos de construir le vamos a pasar los datos y va a empezar a ajustar y va a calcular cual es el error, para eso le vamos a pasar como parametro la data de entrada que es X_train, le damos los valores reales y_train para que pueda calcularnos el error y ademas hay que pasarle el numero de veces que quiero que repita el proceso de iteracion sobre la data que es epochs. En este caso epochs es 30.\n",
+ "\n",
+ "\n",
+ "validation_data: Podemos pasarle como parametros la data para validar el metodo. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/30\n",
+ "1/1 [==============================] - 0s 355ms/step - loss: 119236493312.0000 - val_loss: 168646115328.0000\n",
+ "Epoch 2/30\n",
+ "1/1 [==============================] - 0s 18ms/step - loss: 118106734592.0000 - val_loss: 167005962240.0000\n",
+ "Epoch 3/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 116982743040.0000 - val_loss: 165374312448.0000\n",
+ "Epoch 4/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 115864616960.0000 - val_loss: 163751329792.0000\n",
+ "Epoch 5/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 114752462848.0000 - val_loss: 162137145344.0000\n",
+ "Epoch 6/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 113646362624.0000 - val_loss: 160531890176.0000\n",
+ "Epoch 7/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 112546398208.0000 - val_loss: 158935711744.0000\n",
+ "Epoch 8/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 111452667904.0000 - val_loss: 157348708352.0000\n",
+ "Epoch 9/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 110365270016.0000 - val_loss: 155771011072.0000\n",
+ "Epoch 10/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 109284286464.0000 - val_loss: 154202750976.0000\n",
+ "Epoch 11/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 108209766400.0000 - val_loss: 152644026368.0000\n",
+ "Epoch 12/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 107141824512.0000 - val_loss: 151094935552.0000\n",
+ "Epoch 13/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 106080509952.0000 - val_loss: 149555593216.0000\n",
+ "Epoch 14/30\n",
+ "1/1 [==============================] - 0s 18ms/step - loss: 105025912832.0000 - val_loss: 148026114048.0000\n",
+ "Epoch 15/30\n",
+ "1/1 [==============================] - 0s 18ms/step - loss: 103978074112.0000 - val_loss: 146506547200.0000\n",
+ "Epoch 16/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 102937075712.0000 - val_loss: 144997023744.0000\n",
+ "Epoch 17/30\n",
+ "1/1 [==============================] - 0s 18ms/step - loss: 101902966784.0000 - val_loss: 143497592832.0000\n",
+ "Epoch 18/30\n",
+ "1/1 [==============================] - 0s 15ms/step - loss: 100875812864.0000 - val_loss: 142008352768.0000\n",
+ "Epoch 19/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 99855654912.0000 - val_loss: 140529369088.0000\n",
+ "Epoch 20/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 98842566656.0000 - val_loss: 139060707328.0000\n",
+ "Epoch 21/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 97836556288.0000 - val_loss: 137602400256.0000\n",
+ "Epoch 22/30\n",
+ "1/1 [==============================] - 0s 20ms/step - loss: 96837697536.0000 - val_loss: 136154554368.0000\n",
+ "Epoch 23/30\n",
+ "1/1 [==============================] - 0s 25ms/step - loss: 95845998592.0000 - val_loss: 134717177856.0000\n",
+ "Epoch 24/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 94861500416.0000 - val_loss: 133290328064.0000\n",
+ "Epoch 25/30\n",
+ "1/1 [==============================] - 0s 16ms/step - loss: 93884260352.0000 - val_loss: 131874054144.0000\n",
+ "Epoch 26/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 92914278400.0000 - val_loss: 130468372480.0000\n",
+ "Epoch 27/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 91951587328.0000 - val_loss: 129073324032.0000\n",
+ "Epoch 28/30\n",
+ "1/1 [==============================] - 0s 19ms/step - loss: 90996195328.0000 - val_loss: 127688925184.0000\n",
+ "Epoch 29/30\n",
+ "1/1 [==============================] - 0s 18ms/step - loss: 90048143360.0000 - val_loss: 126315200512.0000\n",
+ "Epoch 30/30\n",
+ "1/1 [==============================] - 0s 17ms/step - loss: 89107406848.0000 - val_loss: 124952182784.0000\n"
+ ]
+ }
+ ],
+ "source": [
+ "history = model.fit(X_train, y_train, epochs=30, verbose=1, validation_data=(X_test, y_test), batch_size=128)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Para evaluar modelos de regresion se utiliza la metrica ð‘…2: R2, el coeficiente de determinación, determina la capacidad de un modelo para predecir futuros resultados. El mejor resultado posible es 1.0, y ocurre cuando la predicción coincide con los valores de la variable objetivo. R2 puede tomar valores negativos pues la predicción puede ser arbitrariamente mala."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 45874.812, 28354.068, 24553.754, 36886.637, 29694.287,\n",
+ " 39521.41 , 36256.977, 53078.016, 58048.523, 22521.916,\n",
+ " 32403.309, 104487.13 , 30835.26 , 44237.8 , 53870.082,\n",
+ " 64081.695, 36343.457, 75071.805, 32101.148, 35774.203,\n",
+ " 54414.906, 59674.945, 52779.793, 33743.87 , 36333.47 ,\n",
+ " 37790.39 , 31175.38 , 49231.746, 47261.344, 110517.71 ,\n",
+ " 63862.555, 39782.54 , 48002.344, 41607.04 , 51660.008,\n",
+ " 49513.535, 65000.406], dtype=float32)"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train_pred = model.predict(X_train).ravel() # podemos hacer predicciones .predict obtenemos un vector de los valores predichos por la red\n",
+ "y_train_pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([55556.824, 74536.57 , 46812.17 , 34082.57 , 44855.83 , 47595.293,\n",
+ " 49983.945, 95820.695, 78198.336, 62006.48 ], dtype=float32)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test_pred = model.predict(X_test).ravel()\n",
+ "y_test_pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "R2 score (Train Set):\t -5.732\n",
+ "R2 score (Test Set):\t -5.105\n"
+ ]
+ }
+ ],
+ "source": [
+ "r2 = r2_score(y_train, y_train_pred)## el r cuadratico la metrica\n",
+ "print('R2 score (Train Set):\\t', '{:0.3f}'.format(r2))\n",
+ "\n",
+ "r2 = r2_score(y_test, y_test_pred)\n",
+ "print('R2 score (Test Set):\\t', '{:0.3f}'.format(r2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error (Train Set):\t 88174037247.1\n",
+ "Mean Squared Error (Test Set):\t 124952174760.8\n"
+ ]
+ }
+ ],
+ "source": [
+ "err = mse(y_train, y_train_pred) ##errores cuadraticos medio sobre el conjunto de entrenamiento y validacion\n",
+ "print('Mean Squared Error (Train Set):\\t', '{:0.1f}'.format(err))\n",
+ "\n",
+ "err = mse(y_test, y_test_pred)\n",
+ "print('Mean Squared Error (Test Set):\\t', '{:0.1f}'.format(err))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Pred')"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(y_train,y_train_pred, alpha= 0.4) #Un segundo método más poderoso para crear diagramas de dispersión es la función plt.scatter, que se puede usar de manera muy similar a la función plt.plot:\n",
+ "plt.plot(y_train,y_train) #x and y must have same first dimension, but have shapes (47,) and (37,)\n",
+ "\n",
+ "plt.xlabel('Data')\n",
+ "plt.ylabel('Pred')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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kAVMIEcheBtouhQajKZDkEjO8+9H65Je89d0sek4O02BnIiLyREQrhBhjahpjdhtjwo0xfe6xPqcxZpkxZosxJtAYky3KukhjTJjzNTs2i5dY5OYGxd7AdA2FSu9Ry209wUl7kWPbd9QctJjpe29w+boGOxMRkdjz0BBijHEHRgK1gEJAE2NMobuaDQImWmuLAp8DX0dZd9VaW9z5qhtLdcuT4u0L1T7GdFmPV4Hq9HCfSoB3L24fWEGVAQFMWn+YSA12JiIisSA6Z0LKAuHW2v3W2hvAJKDeXW0KAcud7wPusV7im9S5oPEv0GouadKmZ6TXCCa6fcqvf86i9ogVBO854+oKRUQknotOCMkKHIny+ahzWVSbgYbO9w2A5MaYtM7PPsaYEGPMWmNM/ccpVlwgdyXoEMzu/J0o6HmKOd4f0/XSMN4dt5hW49az59QlV1coIiLxlHnYo5jGmEZATWttO+fnFkA5a22XKG2yAN8BuYFg4FWgsLX2vDEmq7X2mDEmD46zJdWstfvu+o32QHuAjBkzlpo0aVKs7WBUERER+Pr6PpFtJ3QRERGk9DHkPDSFbEfnchMPRt6qz+ibNSmfLSkN8nmR0tu4usw4ScddzKjfYk59F3Pqu5h7UN/5+/uHWmtL3708OiGkPNDPWlvD+fkDAGvt1/dp7wvsstZmu8e6CcBca+20+/1e6dKlbUhIyANriqnAwED8/PyeyLYTujv67uw+WNwXds/nb68sfHS5MUHu5ejol5e2FfOQxMvdpbXGNTruYkb9FnPqu5hT38Xcg/rOGHPPEBKdyzEbgHzGmNzGGC/gDeCOp1yMMemMMf9s6wNgnHN5amOM9z9tgArAjujtjsRZaZ+BJn9Ai5mkTpmS7z2HMiPp18xfspiqgwOZsfGoZuoVEZGHemgIsdbeAroAi4CdwBRr7XZjzOfGmH+edvEDdhtj9gAZgS+dywsCIcaYzThuWP3GWqsQklA84w9vr4TagylgjjDP+yM+ZTRfTgmm3shVrN1/1tUViohIHOYRnUbW2vnA/LuWfRLl/TTgP5dYrLWrgSKPWaPEZe4eUKYdFH4VEzSQGutHU813FaPON6TFmBfxK5SVD2o9S570usYqIiJ30oipEjuSpIaaX2E6rcUzdwW6Rk5kQ+qP8A5fSPWhQfSbvZ1zmqlXRESiUAiR2JUuHzSbAs2nkypZUr5zG8iCNENYu3YFVQYGMDpoH9duRrq6ShERiQMUQuTJyPsidFwFtQaS71Y4C7w/YETyiYxesJ5qg4OYFXZMM/WKiCRyCiHy5Lh7Qrn20G0Tpmx7/CMWsD75e7Q2c3hvUgj1v1/N+gPnXF2liIi4iEKIPHlJ00Ct/tBpDR65XuCtq+PYmLYv+f8O5vXRq+nwSwj7z0S4ukoREXnKFELk6UlfAJpNhWbTSZ4kCQNvfcOKTMM4vTeU6kODdfOqiEgioxAiT1++F6Hjanh5ENmvhzPDvQ+/Z/qDeWs26+ZVEZFERCFEXMPdA8q+5bhfpNzblD0/n7W+veibagmDF2zlxSFBzN58XDeviogkYAoh4lpJUkPNr6HTWtxzV6Tx+bFsTvcxNdw20O2Pjbp5VUQkAVMIkbghXT5oOhla/EmSJMn4+PJXbMg6jJR/7+D10Wt086qISAKkECJxyzNVnfPRDCH91f38fKsX83JOZtfecKoPDebTWds4G3Hd1VWKiEgsUAiRuMfdA8q0ha4bMeU789yZ+QR49+T7HAFMXReO38BARgaE6+ZVEZF4TiFE4q4kqaDGl9B5HW55/Kh+cgyb035ElwybGbhoF1UHBTI99Ci3b+vmVRGR+EghROK+tM9Ak9+h1Rw8k6amw5kv2Zp9MC/4HODdqZt55buVrAr/y9VViojII1IIkfgjd2XoEAR1vyX5laMMuvAuK/L+htflEzT7cR2tx69n98lLrq5SRESiSSFE4hc3dyjZErpthErvkv3EUmZEdmV6gQB2HDpBreHB9Jm+hdMXr7m6UhEReQiFEImfvJNDtU+gawjm2dqUOjSWtcl6MTT/DmZsPEyVgYEMXbKHy9dvubpSERG5D4UQid9S5YBG46DtEtxSZafeoS/YmuUb2uc8wfBle/EbFMjv6w5zK/K2qysVEZG7KIRIwpC9LLRdAg1/xPv637xztAebCkykTIrzfPjnVmoNX8Gynac0DLyISByiECIJh5sbFH0NumwA/76kPr6Ckec7ElB0Kd6REbT9OYQmY9ey5eh5V1cqIiIohEhC5JUUqvSCrqGYIq+Te8945tiuTC6xjf0nL1D3u1V0+2MTR85dcXWlIiKJmkKIJFwpMkP9kdAhCJOhEOV2fsWa1B8zuPhpFu84QbXBQXwxdwfnr9xwdaUiIomSQogkfJmLQas58MbvuNtIXt3Vg815RvN2wav8tOoAlQcEMCZ4n4aBFxF5yhRCJHEwBp6tDZ3WQs1v8D4VRs99bdlYfB5+2eCr+buoNjiImZuOaRh4EZGnRCFEEhcPL3i+I3TbBGU7kHr3ZEacbkvg85tIn8TSY3IYdUeuZLWGgRcReeIUQiRxSpoGan0DndZB7srkChvIn7e7M6XCMf6OuEHTH9fRZvx69pzSMPAiIk+KQogkbunyOibHazkbkyQVZUN7sSLdVwx74Tohh/6m5rBgek/bwskLGgZeRCS2KYSIAOSpAu2DoN5I3C4cpf7GNoQ++zs9SvswY9NR/AYFMGjRbi5du+nqSkVEEgyFEJF/uLlDiebQNRSq9MYrfBHddjQh9PlV1C3gy3cB4VQZGMiEVQe4cUvDwIuIPC6FEJG7efuC/4eOMPJcA1KEfseA461ZVfUABTMkpd+cHbw0NIh5W05oGHgRkcegECJyPymzQsPR8FYApC9A1tUf8evNd5hV4zI+7m50/n0j9b9fzbr9Z11dqYhIvKQQIvIwWUtC63nQ+DfM7VsUC3qLhWmHMLq6N6cuXKPxmLW0+3kDe/UkjYjII1EIEYkOY6BgnX8HOzMnwqgR3IiVBWfQzz8N6/afo8awYPpM38Kpi3qSRkQkOhRCRB7FP4OddQ+D8p3x2DqZ1iGvsq5iKO3KZWT6xqNUGRjA4MV6kkZE5GEUQkRiIklqqPEldFkPeauRdFV/PtzXgjW1TvHSs+n5dnk4fgMD+Xn1QW5pGHgRkXtSCBF5HGnyQONfoM1CSJ6JdEt78O2ld1jawJA3gy+fzt7ORyuvMnfLcT1JIyJyF4UQkdiQszy0WwYNf4Srf5N3QRMmpRjOpIZp8HCDLr9vov7IVazZpydpRET+oRAiElvc3KDoa9BlA1T7FHNgJc8vrMOkDL8wrG52Tl+6TpOxa3lzwgZ2nbzo6mpFRFxOIUQktnkmgUo9HTP1lmxJtuMLqR9Um+AKW/jwpdxsOHiOWsNX8N7UzRw/f9XV1YqIuEy0QogxpqYxZrcxJtwY0+ce63MaY5YZY7YYYwKNMdmirGtljNnrfLWKzeJF4jTf9FBnKBvKDIecL+C5vB/ttzRmTZ2/aftCLmaHHcd/UCBfL9jJhat6kkZEEp+HhhBjjDswEqgFFAKaGGMK3dVsEDDRWlsU+Bz42vndNMCnQDmgLPCpMSZ17JUvEvddSZYDmk6GlrMgSUp853ag78murGjiw8tFMjMmeD+VBwQwNng/125GurpcEZGnJjpnQsoC4dba/dbaG8AkoN5dbQoBy53vA6KsrwEssdaes9b+DSwBaj5+2SLxUB4/aB8M9X+AiyfIOK0+QxnE4pZZKZY9FV/O30m1wUHM2HiU23qsV0QSgeiEkKzAkSifjzqXRbUZaOh83wBIboxJG83viiQebm5QvKljcjz/vhC+nHxTqzEx8wwmt8hP6mSe9JyymdrfriRozxk91isiCZp52B9yxphGQE1rbTvn5xZAOWttlyhtsgDfAbmBYOBVoDDQDvCx1n7hbPcxcNVaO+iu32gPtAfImDFjqUmTJsXO3t0lIiICX1/fJ7LthE59F3MP6juv63+T6+AfZD6xhFseSTiY4zVmutVkyj7LmauWQmndeD2/F7lSuj/lql1Px1zMqe9iTn0Xcw/qO39//1Brbem7l0cnhJQH+llrazg/fwBgrf36Pu19gV3W2mzGmCaAn7W2g3PdaCDQWvvH/X6vdOnSNiQk5IE1xVRgYCB+fn5PZNsJnfou5qLVd6d3wuKPIXwJpMrJzaqf8OvFkoxYHs7fV27ySrEsvFc9PznTJnsqNccFOuZiTn0Xc+q7mHtQ3xlj7hlConM5ZgOQzxiT2xjjBbwBzL5r4+mMMf9s6wNgnPP9IqC6MSa184bU6s5lIhJVhoLQfBq0+BO8k+M5oy1tdrVnZdNkdK2al6U7TlFtcBCfztrGXxHXXV2tiEiseGgIsdbeArrgCA87gSnW2u3GmM+NMXWdzfyA3caYPUBG4Evnd88B/8MRZDYAnzuXici9PFMVOgRDvZFw/gjJfn2Zd89/ycp2OWlcJju/rjtMlQEBDFu6h4jrt1xdrYjIY/GITiNr7Xxg/l3LPonyfhow7T7fHcf/nxkRkYdxc4cSzeG5BrD6O1g1nLS75vNlmXa81bETA4LPMGzpXn5de4hu1fLxRpkceHlo3EERiX/0J5dIXOWVDPx6O0ZeLdEM1o8m168V+T7XSma2L8kz6X35ZNZ2XhoaxJzNx/VYr4jEOwohInFd8ozwynDouBpyPA9LPqH4rOpMKn+E8a1LkcTTna5/bKLeyFWsCv/L1dWKiESbQohIfJGhIDSbAi1nQ5JUmBlv4R/8BvPqGoa8Xoxzl2/Q7Md1tPhpHduOXXB1tSIiD6UQIhLf5KkC7YOgwWiIOIP7xDo03N2L5a0y07d2QbYeu0Cdb1fSfdImDp+94upqRUTuSyFEJD5yc4Nib0DXEKj2KRxcifeYCrS78C0rOj9HZ/9nWLT9JNWGBNJv9nY91isicZJCiEh85pkEKvV03Lxapi1snEjy0WXplWQOQT3K0ahUdn5Ze4gqAwIYukSP9YpI3KIQIpIQJEsHLw+ETuscE+Ut/4KMEyrwde7NLO5egcr50zN82V6qDAhgwqoD3Lh129UVi4gohIgkKOnywhu/QZuFkCIzzOrMMzNe5ofn/2Zm5wrkz5icfnN2UG1IIDM3HdNjvSLiUgohIglRzvLQbhk0Gg83IuDXVyke0JrfX0nCz2+WJbm3Jz0mh1H725UE7D6t2XpFxCUUQkQSKmOgcEPovB5qfgMnwjCjq1Bl+8fMbZmT4W8U5/L1W7QZv4E3xqxl0+G/XV2xiCQyCiEiCZ2HNzzfEbqFQYVusG0Gbt+Vpt6ZMSztVJzP6j7HvjMRNPh+NW//Ekr46QhXVywiiYRCiEhikSQVvPS547He5xrAqmF4jSxJK7cFBPaswDsv5mfF3jNUHxpEn+lbOHnhmqsrFpEETiFEJLFJlQMajnbM1pupCCzsg+/YF+ieeRtBvfxoWT4X0zcepcrAAL5esJPzV264umIRSaAUQkQSq8zFoOUsaDbdMd7I1Nakm1SbfsUusvxdP14ukpkxwfupPCCA7wPDuXoj0tUVi0gCoxAikpgZA/lehLdXQt3v4OIxGF+T7IvaMbRaMuZ3q0SZXGkYsHA3VQYG8Nu6Q9yM1BgjIhI7FEJEBNzcoWQL6BoKVfvCgSAYWY6Cof34qVFOpr5dnhxpkvLRn9t4aUgQczYf1xgjIvLYFEJE5P95JYPKvRxP0pRuAxt/huHFKXNwDFPfLMJPrUrj7eFO1z82UXfkSoL3nNEYIyISYwohIvJfvumh9mDHMPB5q0Hg15gRJal2eR7zu5ZnaONinL9yk5bj1tN07DqNMSIiMaIQIiL3ly4vNP4F2i6BNHlg7ju4/1CeBj5hLOtZmX6vFGLPqUs0+H41HX4JIfz0JVdXLCLxiEKIiDxc9rLw5kJ443fH58nN8J5Ym9Y5zhD0vj89X8rPqvCzVB8azPvTNnP8/FXX1isi8YJCiIhEjzHwbG3otBbqDIVz++Gnl/Cd2ZpuxQzB7/vTpkJuZm46jt+gQL6Yu4NzlzXGiIjcn0KIiDwadw8o/SZ02wR+H0L4chhZljSBH/CxXzoCevlRr1gWxq06QOUBAQxfupeI67dcXbWIxEEKISISM96+4Ncbuoc5nqQJGQ8jSpA1bAQD6z7Doh6VqZA3LUOX7qHKgADGrTzA9Vsa8ExE/p9CiIg8Ht8MjidpOq+DZ/wh8Cv4tiT5jkxjdNPi/NnpBQpkSs7nc3dQdVAQU0OOEKkxRkQEhRARiS3p8kHjX+HNxZA6N8ztAT+Up8TlVfzerhy/ti1HWl8vek3bQo1hwSzcdlJjjIgkcgohIhK7cpRzPEnT+DewFiY3g3E1qOgdzqzOFfihWUmstbz9ayj1v1/N6vC/XF2xiLiIQoiIxD5joGAd55M0w+DvQzCuBmZSM2plusiiHpUZ8GpRzly8RtMf19H8x3VsPnLe1VWLyFOmECIiT467h+Om1W4bnXPSBMP3z+MxrwevF/Bg+Xt+fFynEDtOXKTeyFV0/DWU8NMRrq5aRJ4SD1cXICKJwD9z0pRqA8EDYcNPsGUKPuU70bZCd14v7cdPKw8wNng/i7afpFGpbJRLptl6RRI6nQkRkacnWTqo1R+6bHAMfLZiMAwvTvKwH+nhl5Pg9/1p/YJjwLPewVf5fM4O/oq47uqqReQJUQgRkacvTW5o9BO0D4RMRWBhH/iuDGkPzOGT2s8S0MuP8lk8mLD6AFUGBDBk8W4uXrvp6qpFJJYphIiI62QpAS1nQfMZ4J0CpreFsX5kPbuWtkW8WfxOFfwKZGDE8nAqDwhgdNA+rt3UgGciCYVCiIi4ljGQtxp0CIYGY+DK3/BLfYpu/pS8kfsY2awkc7tWpFi2VHy9YBdVBgbw69pD3IzUPSMi8Z1CiIjEDW5uUKyx436RGl+R/NI+GF0ZprejcJJz/PxmWSa3f57sqZPSd+Y2qg0OYuamY9zW6Ksi8ZZCiIjELZ4+UL4z68qNgoo9Yedc+K4MLOhNuYyWqW+XZ3zrMiTz9qDH5DBeHrGCJTtOafRVkXhIIURE4qRbnr7w4qeO2XpLNIP1Y2F4cUzwQPxzJ2Ve14qMaFKCazcjeWtiCA1/WM3qfRp9VSQ+UQgRkbgtRWZ4Zbhj9NVn/CDgSxhRArfQn6hbOD1Lelbhm4ZFOHnhGk3HrqPFTxp9VSS+UAgRkfghfX7HBHltl0LavDDvXRhZFs+dM3mjTHYC3vOjb+2CbD/uGH317V9C2XvqkqurFpEHUAgRkfglexloMx+aTgEPH5jWBsb643NkJe0q5SGolx89XszHyvC/qD4smJ6Twzh89oqrqxaRe4hWCDHG1DTG7DbGhBtj+txjfQ5jTIAxZpMxZosx5mXn8lzGmKvGmDDna1Rs74CIJELGQP4a8PZKqP8DRJyBiXXhl4Yk/3snPV7Mz4r3/WlfKQ/ztp6g6uBAPvpzKycvXHN15SISxUNDiDHGHRgJ1AIKAU2MMYXuatYXmGKtLQG8AXwfZd0+a21x5+vtWKpbRATc3KF4U+gaCtW/hOMbYXQlmP4WqW8c54OXCxL8vj9NyuZgSsgRqgwM4Kv5Ozl3+YarKxcRoncmpCwQbq3db629AUwC6t3VxgIpnO9TAsdjr0QRkYfw9IEXukC3MOdjvXPg29KwoDcZ3S7xv/qFWf6uH3WKZuHHFfupPCCAoUv2cElDwYu4lHnYs/XGmEZATWttO+fnFkA5a22XKG0yA4uB1EAy4EVrbagxJhewHdgDXAT6WmtX3OM32gPtATJmzFhq0qRJsbBr/xUREYGvr+8T2XZCp76LOfVdzDxOv3ldP0uug5PIfGIpke5eHM1WnyPZ6xHpkZTjEbeZsfcGIaciSeYJtfN4Ui2HJ97uJpb3wHV0zMWc+i7mHtR3/v7+odba0ncvj60Q0tO5rcHGmPLAT0BhwBPwtdaeNcaUAmYCz1lrL97v90qXLm1DQkIevKcxFBgYiJ+f3xPZdkKnvos59V3MxEq//bUXln0OO2dD0nRQuReUbgMe3mw7doFBi3cTuPsMGZJ707VqXhqXyYGXR/y/X1/HXMyp72LuQX1njLlnCInOf23HgOxRPmdzLouqLTAFwFq7BvAB0llrr1trzzqXhwL7gPzR+E0RkceXLh80/gXaLYcMBWFhb/iuNGyeTOHMyZnQpixT3y5PrrTJ+HjWdqoODmRa6FEiNRS8yFMRnRCyAchnjMltjPHCcePp7LvaHAaqARhjCuIIIWeMMemdN7ZijMkD5AP2x1bxIiLRkq0UtJrjmK3XJxX82d5xA+uexZTJmZrJHZ7n5zfLkjqpF+9N3UyNYcHM33pC89KIPGEPDSHW2ltAF2ARsBPHUzDbjTGfG2PqOpu9C7xljNkM/AG0to7rPJWBLcaYMGAa8La19twT2A8RkQf7Z7be9kHw6k9w4zL8/hpMqI05GkKV/OmZ3aUCo5qXxACdftvIK9+tJGDXac1LI/KEeESnkbV2PjD/rmWfRHm/A6hwj+9NB6Y/Zo0iIrHHzQ2KNIKCdWHjzxA0AH56EZ6tg6n2CTULF+ClQpmYFXaMYUv30mbCBkrlTM271fPzwjPpXF29SIIS/+/AEhGJCQ8vKPuWY4I8/76wPwi+fx5mdcb90jEalszGsner8FWDIhw/f5WmY9fRdOxaNh7+29WViyQYCiEikrh5+0KVXtB9M5TrCFumwIiSsLgvntfP07RcDgLe8+OTOoXYc+oSDb9fzZsTNrD9+AVXVy4S7ymEiIgAJEsLNb9yjL5a+FVYMxKGF4PggfjYa7xZMTdBvfzpVaMAIQfPUXvESjr/tpHw05okTySmFEJERKJKlQMa/AAdV0PuyrD8CxheHNaPJZn7bTr752VF76p0q5qXwN2nqT40mJ5TNEmeSEwohIiI3EuGgvDGb9B2KaTLD/Pfc4wxsmUKKb3d6Vm9AMHv+9O2Ym7mbXFMkvfhn1s5ceGqqysXiTcUQkREHiR7GWg9F5pPB58UMOMt5xgji0ibzIuPahf6d5K8qSFHqDIwkP/N3cFfEdddXblInKcQIiLyMMZA3hehfXCUMUZeh/G14NAaMqbw+XeSvHrFsjB+1QEqDwhgwMJdnL+iGXtF7kchREQkuv4ZY6TLBqg9BM7th/E14ffGcHIb2dMkZeBrxVjSswovFszID0H7qNQ/gGFLNWOvyL0ohIiIPCp3TyjTFrqFQbVP4fAaGFURZrSHcwd4Jr0vI5qUYEH3SryQNy3Dlu6l0oAAfgjcx5Ubt1xdvUicoRAiIhJTXkmhUk/HGCMVusOOWfBdGZjfCyJO82ymFIxuUZo5XSpSInsq+i/cReUBAYxbeYBrNyNdXb2IyymEiIg8riSp4aXPHKOvlmgOG35yjDGy7HO4ep4i2VIyvk1ZpncsT/6Myfl87g78Bgby69pD3Lh129XVi7iMQoiISGxJkQVeGea4Z6RALVgx2BFGVg6FG1colTMNv7/1PL+3K0fW1EnoO3MbVQcHMjXkCLciFUYk8VEIERGJbWmfgUbjoMMKyF4WlvaDESVgw48QeZMX8qZj2tvlmdCmDKmTetFr2haqDw1mVtgxbt/WjL2SeCiEiIg8KZmLQrOp0GYBpMkN8979d8AzYy1+BTIwu0sFRrcohae7G90nhVFr+AoWbjuJtQojkvAphIiIPGk5X3AEkaZTwSu5Y8CzURVh9wIMUOO5TCzoXokRTUpw8/Zt3v41lDrfrmTZzlMKI5KgKYSIiDwNxkD+6tDBOeDZzSvwxxswrgYcXImbm6FusSws7lGZQa8V49K1W7T9OYQG368meM8ZhRFJkBRCRESepqgDntUZCucPw4Ta8EtDOB6Gh7sbjUplY9m7Vfi6YRFOX7xGy3HreX30GtbsO+vq6kVilUKIiIgruHtC6Tcdj/W+9Dkc3whjqsDU1vDXXjzd3WhSNgcBvfz4X73nOHzuCk3GrqXp2LWEHDzn6upFYoVCiIiIK3kmcQx01n0zVO4FexbDyHIwuytcOIq3hzstyuciqJc/H9cpxJ5Tl2g0ag0tx61n85Hzrq5e5LEohIiIxAU+KaFqX+geBmXfgs2THI/1LvwAIs7g4+lO24q5CX7fnw9qPcvWo+epN3IV7X7ewPbjF1xdvUiMKISIiMQlvhmgVn/ouhGKvg7rRjkGPFv+BVw9T1IvDzpUeYYVvavyXvX8rD9wjtojVtLx11D2nLrk6upFHolCiIhIXJQqO9QbCZ3XO56qCR54x+irvt4edKmajxW9q9KtWj5W7P2LGsOC6fbHJk5EaPRViR8UQkRE4rJ0+eC1CY5He/8dfbU4rB8Lt26QMoknPV/Kz8re/nSs8gxLd57iw5VX6TkljIN/XXZx8SIPphAiIhIfZC7mHH11IaR5Bua/B9+VgrDf4XYkqZJ68X7NZwl+358auTyZv/UE1YYE8f60zRw5d8XV1Yvck0KIiEh8krM8tJkPzac7Zu+d2RG+Lw87ZoO1pPP15o1nvQh+359W5XMxM+w4/oMC+WDGFo7+rTAicYtCiIhIfGMM5H0R2gfBaz8DFqa0gDF+EL4MrCVDch8+eaUQwb38aVYuB9NDj+E/KJC+M7dy4sJVV++BCKAQIiISfxkDz9WHjmug3vdw5Rz82pDiYR/BoTUAZErpw2f1ChPYy4/XS2dn8oYjVBkQSL/Z2zl18Zpr65dETyFERCS+c/eAEs2gawjUGkDSK8dgfE34tREcDwMgS6okfNmgCMvf9aNhyaz8svYQlQcE8L+5Ozhz6bpr65dESyFERCSh8PCGch1Y+/xoeLEfHN3gGAp+cgs4vQuA7GmS8s2rRVn+bhVeKZaF8asOUGnAcr6ev5OzEQoj8nQphIiIJDC33X2g4jvQYwtU6Q37lsMP5WFGBzh3AICcaZMx6LViLO1ZhVqFMzN2xX4qDQig/8Jd/H35hov3QBILhRARkYTKJyX4fwjdt0D5zrBjJnxXGua+AxePA5AnvS9DGxdn8TuVqVYwI6OC9lGx/3IGLtrF+SsKI/JkKYSIiCR0ydJC9S+gWxiUbAUbJzrmpVn0EVz+C4C8GZLzbZMSLOpRGb8CGRgZsI+K/QMYvHg3F67cdG39kmAphIiIJBYpMkOdIdA1FJ5rCGu/d85L8yVcc0yClz9jckY2K8nCHpWolC8d3y4Pp2L/5QxdsocLVxVGJHYphIiIJDapc0GDH6DTWsd4I8EDYFhRWDEEbjiGen82Uwp+aF6K+d0q8ULetAxftpeK/ZczfOleLl5TGJHYoRAiIpJYpS8Ar//snJemHCz7DIYXh7Wj4JbjSZlCWVIwukVp5nWrSPk8aRm6dA8Vv1nOt8v2cklhRB6TQoiISGKXuRg0mwJvLnYEk4W9YURJCJ0AkY6g8VyWlIxpWZq5XStSNndaBi/ZQ6UBAYwMCCfi+i3X1i/xlkKIiIg45CgHredCy9mO+0fmdHc8TbN5EtyOBKBw1pT82Ko0c7pUpFSO1AxctJuK/ZcrjEiMKISIiMid8lSBtkug6RTwTg5/dnBMkrf9T7h9G4Ai2VLyU+syzOpcgRLZUzFw0W4q9V/OD4H7uKwwItEUrRBijKlpjNltjAk3xvS5x/ocxpgAY8wmY8wWY8zLUdZ94PzebmNMjdgsXkREnhBjIH8NaB8Mr090fJ7aGsZUht0LwVoAimVPxfg2Zfmz0wsUzZaK/gt3UWlAAKOD9nHlhsKIPNhDQ4gxxh0YCdQCCgFNjDGF7mrWF5hirS0BvAF87/xuIefn54CawPfO7YmISHzg5gaF6kHH1dBwLFyPgD8aw48vwr6Af8NIiRyp+fnNsszo9AJFsqbk6wW7qNhfYUQeLDpnQsoC4dba/dbaG8AkoN5dbSyQwvk+JXDc+b4eMMlae91aewAId25PRETiEzd3KPo6dNkAr4yASyfhl/owoc6/M/YClHSGkekdX6Cwwog8RHRCSFbgSJTPR53LouoHNDfGHAXmA10f4bsiIhJfuHtCqVbQbSPUGghn9zpn7H0Vjm38t1mpnKmZqDAiD2Gs81TafRsY0wioaa1t5/zcAihnre0SpU1P57YGG2PKAz8BhYERwFpr7a/Odj8BC6y10+76jfZAe4CMGTOWmjRpUmzt3x0iIiLw9fV9IttO6NR3Mae+ixn1W8w9zb5zi7xO1mPzyHF4Bp63LnEmXTkO5mrKZd9cd7QL/zuSmftusu2vSJJ7Qa3cnlTL7om3h3kqdUaXjruYe1Df+fv7h1prS9+93CMa2z0GZI/yOZtzWVRtcdzzgbV2jTHGB0gXze9irR0DjAEoXbq09fPzi0ZZjy4wMJAnte2ETn0Xc+q7mFG/xdzT77sacO0LWDeK9Ku/JX1ID3iuAfh9AOnzA+AHtANCD/3N8GV7mbL7DMuOGtpXzkOL8jlJ6hWdv46ePB13MReTvovO5ZgNQD5jTG5jjBeOG01n39XmMFANwBhTEPABzjjbvWGM8TbG5AbyAesfqUIREYn7fFJAlfeh+2ao1BP2LILvy8GMDnBu/7/Nol6mec55maaSLtMkWg8NIdbaW0AXYBGwE8dTMNuNMZ8bY+o6m70LvGWM2Qz8AbS2DtuBKcAOYCHQ2Vob+SR2RERE4oCkaaDaJ9BjC5TvDDtmwbelYVYXOH/432b/H0bKUyhLCoWRRCpa57+stfNx3HAaddknUd7vACrc57tfAl8+Ro0iIhLfJEsH1b+A8l1g5VAIGecYebVUK6j0LqTIAkCpnGn4pW05Qg+dY9jSvXy9YBejg/fzVqU8tCyfk2TeceMyjTwZGjFVRESenOSZoFZ/6LYJSjR3zEczvDgs/AAiTv/b7J8w8s/TNP0X7tJw8ImAQoiIiDx5KbPBK8OgaygUeQ3WjYbhxWDJJ3Dl3L/N/rlMM6PTCxRzDgdfsf9yvluuWXsTIoUQERF5elLngvojofN6eLY2rBoBw4rC8i/h6vl/m5XMkZoJbcoys3MFSuZIzaDFe6jYP4Bvl+3losJIgqEQIiIiT1+6vPDqj9BpDeStCsEDYHhRCBoI1y/926x49lSMa12G2V0qUCZXagYv2UPFb5YzfOleLlxVGInvFEJERMR1MhR0TJDXYQXkrAABXzjOjKwcCjcu/9usaLZU/NiqDHO6VKRs7rQMXbqHiv2XM3TJHoWReEwhREREXC9zUWjyB7y1HLKWgqX9HGFk9Xdw48q/zYpkS8mPrUozt2tFyudJy/Ble6nYfzlDluzhwhWFkfhGIUREROKOrKWg+TR4czFkKgyLP4IRxWHtKLh57d9mhbOmZEzL0szvVokKz6RjxLK9VOi/nEGLdvP35Ruuq18eiUKIiIjEPTnKQctZ0Ho+pM0HC3vDiBKw4Ue4df3fZoWypGBUi1Is6F6JyvnT8V1AOBX7L6f/wl2cjbj+gB+QuEAhRERE4q5cFaDNPGg1B1LlgHnvwrelHOONRP7/5ZeCmVPwfbNSLOpRmaoFMzIqaB+VBgTw9fydnLmkMBJXKYSIiEjcl7syvLkQms8A34wwp7sjjGz6DSL/fzCzApmS822TEix5pzI1nsvE2BX7qTRgOf+bu4PTF6894AfEFRRCREQkfjAG8laDdkuh6VRIkhpmdYKRZWDzZLj9/1OT5c2QnKGNi7O0ZxVqF8nChNUHqTgggH6zt3PiwlUX7oREpRAiIiLxizGQvzq0D4Q3fgfPpPBne/j+edg6DW7f/rdpnvS+DH69GMvfrUKD4ln5de0hqgwIpO/MrRw7rzDiagohIiISPxnjGHW1wwrHWCPGHaa3hR9egO1/3hFGcqZNRv9GRQl4z49XS2Vj8oYj+A0M4IMZWzhy7soDfkSeJIUQERGJ39zcoFA96LgKXv0JbCRMbQ2jKsKOWXeEkexpkvJ1wyIE9vLnjTI5mB56DP9Bgbw/bTOHzl6+/2/IE6EQIiIiCYObOxRpBJ3WQsMfIfIGTGkJoyvDzrlg7b9Ns6ZKwv/qFyb4fX+aP5+TWWHHqTo4iDFbrrPvTIQLdyJxUQgREZGExc0dir4GnddBgzFw8zJMbuYII7vm3xFGMqX0oV/d51jxvj+tX8hFyMlbvDgkiK5/bGL3yUsP+BGJDR6uLkBEROSJcHOHYo2h8KuwdQoEDYBJTSBLCfD7APJVd9xXAmRI4cPHdQpRzPMUO25n5pc1B5mz+Tg1n8tE12p5eS5LShfvTMKkMyEiIpKwuXtA8abQZQPUGwlXzsHvr8OP1WDv0jvOjKTwNvSp9Swre1ela9W8rAr/i9ojVtLu5w1sPnLedfuQQCmEiIhI4uDuCSWaQ9dQeGUERJyB316Fn16C8GV3hJHUybx4t3oBVvapSs+X8rPh4N/UG7mKluPWE3LwnAt3ImFRCBERkcTF3RNKtXKEkTrD4OIJ+LUhjKtJ6nNhd4SRlEk86VYtHyt7+/N+zQJsO3aBRqPW0GTMWtbsO4uN0lYenUKIiIgkTh5eULoNdNsItQfDhSMU2/IpjK8F+wPvCCPJfTzp5JeXlb396Vu7IOFnImgydi2vj15D8J4zCiMxpBAiIiKJm4c3lGkH3TaxJ18H+PsQTKwH41+G/UF3hJGkXh60q5SHFe/70++VQhw5d5WW49bT4PvVLNt5SmHkESmEiIiIAHh4czzry9BtE7w8CP4+CBPr3jOM+Hi607pCboLe9+PLBoU5c+k6bX8Ooc63K1m47QS3byuMRIdCiIiISFSePlD2rShh5IAjjEyoDQeC7wgj3h7uNCuXk8BefgxoVJTL12/x9q8bqTEsmFlhx7gVefsBPyQKISIiIvfybxgJg1oD4dx++PmV/w8jUZu6u/F66ews7VmF4W8UxxjoPimMF4cEMSXkCDcVRu5JIURERORBPH2gXHtnGBkAZ/c5wsj42nBgxR1NPdzdqFc8Kwu7V2ZU85Ik8/bg/Wlb8BsYyC9rD3HtZqRr9iGOUggRERGJDk8fKNcBum92hpFw+LnOPcOIm5uhZuHMzO1akfGty5AhhTcfz9xGlYEB/LTyAFdvKIyAQoiIiMij+TeMhEHN/g8MI8YY/J/NwIyOL/Bbu3LkTpeM/83dQcX+y/k+MJxL1266Zh/iCIUQERGRmPBMAs+/HSWM7HWGkZf/cwOrMYYKedMxqX15pr5dnsJZUzJg4W4q9g9g2NI9XLiSOMOIQoiIiMjj+DeMbHaGkX/uGfnvo70AZXKl4ec3yzKrcwXK5k7DsKV7qdB/Of0X7uKviOsu2gnXUAgRERGJDVHDSK0B//9o7/hasC/gP2GkWPZUjG1ZmgXdK+FXID2jgvZRsf9yPpuznRMXrrpoJ54uhRAREZHY9M89I93CnOOMHIJf6sO4GrBv+X/CSMHMKfiuaUmWvFOF2kWyMHHNISoPCOCDGVs4fPaKS3bhaVEIEREReRL+GWeke5gjjFw4Cr80gJ+q/2fWXoC8GXwZ/HoxAt/zo3GZ7EwPPYb/4EDemRzG3lOXXLMPT5hCiIiIyJPk4f3/I7DWHgIXjztm7f3pJdi79D9hJHuapHxRvwgrevvT5oVcLNx2kurDgun4ayjbjl1w0U48GQohIiIiT4OHN5Rp65i1t85QuHQSfnsVfnwR9i75TxjJmMKHvnUKsapPVTr75WXl3r+o8+1KWo9fT+ihcy7aidilECIiIvI0eXhD6Teh60Z4ZThEnIbfGsHYqrBn0X/CSJpkXrxXowAr+1Tlver52XzkPK/+sIY3xqxhVfhf8XrmXoUQERERV/DwglKtoWsovDICrvwFv78OY6rArnn/CSMpk3jSpWo+VvWpSt/aBdl/5jLNflxHg+9Xs3THqXgZRhRCREREXMnDC0q1cpwZqTcSrl2ESU1hVCXYMRtu3zn5XVIvD9pVykPw+/58Ub8wf0Vcp93EEGoNX8GczceJvB1/wohCiIiISFzg7gklmkOXEGgwGm5dhSktYFRF2P7nf8KIj6c7zZ/PScB7fgx6rRg3Im/T9Y9NvDgkiMkbDnPjVtyfuTdaIcQYU9MYs9sYE26M6XOP9UONMWHO1x5jzPko6yKjrJsdi7WLiIgkPO4eUOwN6LweGv4It2/C1NbwQ3nYOg1u3zn5nae7G41KZWPJO1X4oVlJknq503v6VqoMDGD8qrg9Wd5DQ4gxxh0YCdQCCgFNjDGForax1r5jrS1urS0OfAvMiLL66j/rrLV1Y690ERGRBMzNHYq+Bp3WQqNxgIHpbWFkOdg8GSJv3dHc3c1Qq4hj5t4JbcqQLXUSPpvjmCxvZEA4F+PgZHnRORNSFgi31u631t4AJgH1HtC+CfBHbBQnIiKS6Lm5Q+FXoeNqeH2i4+maP9vDyDKw6TeIvDNcGGPwK5CBqW+/wJQOjsnyBi7aTYVvljNo0W7OxqH5aaITQrICR6J8Pupc9h/GmJxAbmB5lMU+xpgQY8xaY0z9mBYqIiKSqLm5QaF60GEFNP4NvHxhVif4rjRsnAi3bvznK2VzOybLm9OlIhXzpmNkYDgV4tD8NOZhj/QYYxoBNa217ZyfWwDlrLVd7tG2N5DNWts1yrKs1tpjxpg8OMJJNWvtvru+1x5oD5AxY8ZSkyZNeszdureIiAh8fX2fyLYTOvVdzKnvYkb9FnPqu5iLV31nLWnPbiDnocmkuBTONe/0HM7xKicyv4h187znV45H3Gbe/pusOXELA1TI6kHt3J5kTPb4z6k8qO/8/f1DrbWl714enRBSHuhnra3h/PwBgLX263u03QR0ttauvs+2JgBzrbXT7vd7pUuXtiEhIQ+sKaYCAwPx8/N7IttO6NR3Mae+ixn1W8yp72IuXvadtY4RV4MHwNENkDwLVOwBJVs6Zva9hyPnrjAmeD+TQ45wK/I2dYpmoZP/MzybKUWMy3hQ3xlj7hlCohN9NgD5jDG5jTFewBvAf55yMcY8C6QG1kRZltoY4+18nw6oAOyIxm+KiIhIdBgD+atD2yXQYiakzgUL3ofhxWD1d3Dj8n++kj1NUv5XvzAr3/fnrUp5WLbzFL+vO/zUS/d4WANr7S1jTBdgEeAOjLPWbjfGfA6EWGv/CSRvAJPsnadWCgKjjTG3cQSeb6y1CiEiIiKxzRh4xt/xOrgSggbA4o9g5RAo38UxiZ538ju+kiGFDx+8XJCOfs+4ZJCzh4YQAGvtfGD+Xcs+uetzv3t8bzVQ5DHqExERkUeVq6LjdXid4zLNss9g9Qh4vhOUbQ9JUt3RPFVSL5eUqRFTRUREEqoc5aD5dGi3HLI/DwFfwrCisPxLuOL6mXgVQkRERBK6bKWg6SToEAx5KjvOjgwrAkv7weW/XFaWQoiIiEhikbkYNP4VOq6B/DVg5TBHGFn0EVw69dTLUQgRERFJbDIWcgwF33k9FKwLa3+AZZ8/9TKidWOqiIiIJEDp80PD0eDXG9yefiRQCBEREUns0uRxyc/qcoyIiIi4hEKIiIiIuIRCiIiIiLiEQoiIiIi4hEKIiIiIuIRCiIiIiLiEQoiIiIi4hEKIiIiIuIRCiIiIiLiEQoiIiIi4hLHWurqGOxhjzgCHntDm0wGum7M4flPfxZz6LmbUbzGnvos59V3MPajvclpr09+9MM6FkCfJGBNirS3t6jriI/VdzKnvYkb9FnPqu5hT38VcTPpOl2NERETEJRRCRERExCUSWwgZ4+oC4jH1Xcyp72JG/RZz6ruYU9/F3CP3XaK6J0RERETijsR2JkRERETiiEQTQowxNY0xu40x4caYPq6uJz4xxhw0xmw1xoQZY0JcXU9cZowZZ4w5bYzZFmVZGmPMEmPMXuc/U7uyxrjoPv3WzxhzzHnchRljXnZljXGVMSa7MSbAGLPDGLPdGNPduVzH3QM8oN903D2EMcbHGLPeGLPZ2XefOZfnNsasc/49O9kY4/XQbSWGyzHGGHdgD/AScBTYADSx1u5waWHxhDHmIFDaWqtn5x/CGFMZiAAmWmsLO5cNAM5Za79xBuDU1trerqwzrrlPv/UDIqy1g1xZW1xnjMkMZLbWbjTGJAdCgfpAa3Tc3dcD+u11dNw9kDHGAMmstRHGGE9gJdAd6AnMsNZOMsaMAjZba3940LYSy5mQskC4tXa/tfYGMAmo5+KaJAGy1gYD5+5aXA/42fn+Zxx/0EkU9+k3iQZr7Qlr7Ubn+0vATiArOu4e6AH9Jg9hHSKcHz2dLwtUBaY5l0frmEssISQrcCTK56PoYHsUFlhsjAk1xrR3dTHxUEZr7Qnn+5NARlcWE890McZscV6u0eWEhzDG5AJKAOvQcRdtd/Ub6Lh7KGOMuzEmDDgNLAH2AeettbecTaL192xiCSHyeCpaa0sCtYDOzlPnEgPWcf0z4V8DjR0/AM8AxYETwGCXVhPHGWN8gelAD2vtxajrdNzd3z36TcddNFhrI621xYFsOK42PBuT7SSWEHIMyB7lczbnMokGa+0x5z9PA3/iOOAk+k45rz//cx36tIvriRestaecf9DdBsai4+6+nNflpwO/WWtnOBfruHuIe/WbjrtHY609DwQA5YFUxhgP56po/T2bWELIBiCf885dL+ANYLaLa4oXjDHJnDdtYYxJBlQHtj34W3KX2UAr5/tWwCwX1hJv/PMXqFMDdNzdk/MmwZ+AndbaIVFW6bh7gPv1m467hzPGpDfGpHK+T4LjoY+dOMJII2ezaB1zieLpGADnY1bDAHdgnLX2S9dWFD8YY/LgOPsB4AH8rr67P2PMH4AfjtkkTwGfAjOBKUAOHDNEv26t1U2YUdyn3/xwnBK3wEGgQ5R7HMTJGFMRWAFsBW47F3+I4/4GHXf38YB+a4KOuwcyxhTFceOpO46TGVOstZ87/76YBKQBNgHNrbXXH7itxBJCREREJG5JLJdjREREJI5RCBERERGXUAgRERERl1AIEREREZdQCBERERGXUAgRERERl1AIEREREZdQCBERERGX+D+X1avRmPf4wAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "max_val = pd.DataFrame(history.history).max()\n",
+ "(pd.DataFrame(history.history)/max_val).plot(figsize=(9,5))\n",
+ "#plt.gca().set_ylim(0.99, 1)\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Mejorando el Modelo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Como podemos notar nuestro modelo no es el mas apropiado, esto se puede ver en el plot de loss y val-loss ya que es raro que sobre los datos que el modelo no ha visto para entrenar, el modelo se porte mejor. Algo es evidente de la grafica es que no ha terminado de entrenar. Esto se nota porque aun la pendiente del loss no es cero. Entonces vamos a jugar un poco con la data de entrada, el \"learning rate\" , \"epochs\", batch_size y con el optimizer. Primero vamos a normalizar nuestra data de entrada\n",
+ "\n",
+ "\n",
+ "Loss: El error cuadratico medio para el conjunto de entrenamiento\n",
+ "Val_loss: El error cuadratico medio para el conjunto de validacion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>47.00</td>\n",
+ " <td>47.00</td>\n",
+ " <td>47.00</td>\n",
+ " <td>47.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.32</td>\n",
+ " <td>0.54</td>\n",
+ " <td>0.51</td>\n",
+ " <td>0.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.22</td>\n",
+ " <td>0.19</td>\n",
+ " <td>0.31</td>\n",
+ " <td>0.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.00</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.16</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.26</td>\n",
+ " <td>0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.29</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.53</td>\n",
+ " <td>0.25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.39</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.76</td>\n",
+ " <td>0.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.00</td>\n",
+ " <td>1.00</td>\n",
+ " <td>1.00</td>\n",
+ " <td>1.00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "count 47.00 47.00 47.00 47.00\n",
+ "mean 0.32 0.54 0.51 0.32\n",
+ "std 0.22 0.19 0.31 0.24\n",
+ "min 0.00 0.00 0.00 0.00\n",
+ "25% 0.16 0.50 0.26 0.15\n",
+ "50% 0.29 0.50 0.53 0.25\n",
+ "75% 0.39 0.75 0.76 0.40\n",
+ "max 1.00 1.00 1.00 1.00"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mms = MinMaxScaler()\n",
+ "norma=1\n",
+ "if norma==1:\n",
+ " df['sqft'] = mms.fit_transform(df[['sqft']])##se esta sobreescribiendo \n",
+ " df['bdrms'] = mms.fit_transform(df[['bdrms']])\n",
+ " df['age'] = mms.fit_transform(df[['age']])\n",
+ " df['price'] = mms.fit_transform(df[['price']])\n",
+ "df\n",
+ "df.head()\n",
+ "df.describe().round(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. sqft'}, xlabel='sqft', ylabel='price'>"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='sqft',#aqui vemos la dispersion de nuestros datos de sqft vs price\n",
+ " y='price', title='price Vs. sqft',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. bdrms'}, xlabel='bdrms', ylabel='price'>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='bdrms', #aqui vemos la dispersion de nuestros datos de bdrms vs price\n",
+ " y='price', title='price Vs. bdrms',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. age'}, xlabel='age', ylabel='price'>"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='age', #aqui vemos la dispersion de nuestros datos de age vs price\n",
+ " y='price', title='price Vs. age',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Creamos dos variables X, y: Donde X es una matriz con tres columnas (sqft, bdrms, age); y es un vector con una columna (price)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X: (47, 4)\n",
+ "X: (47, 3) y: (47,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.345284</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.878378</td>\n",
+ " <td>0.433962</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.206288</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.310811</td>\n",
+ " <td>0.301887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.426917</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.527027</td>\n",
+ " <td>0.375660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.155543</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.594595</td>\n",
+ " <td>0.117170</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.592388</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.945946</td>\n",
+ " <td>0.698113</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>0.312466</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.756757</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>0.188086</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.094595</td>\n",
+ " <td>0.273585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>0.158577</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.702703</td>\n",
+ " <td>0.054904</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>0.145615</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.121622</td>\n",
+ " <td>0.079434</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>0.177055</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.135135</td>\n",
+ " <td>0.136981</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>0.300055</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.027027</td>\n",
+ " <td>0.132262</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>0.316602</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.297297</td>\n",
+ " <td>0.334151</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>0.286266</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.540541</td>\n",
+ " <td>0.302074</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.00</td>\n",
+ " <td>0.594595</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>0.114727</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.716216</td>\n",
+ " <td>0.169811</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>0.399338</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.972973</td>\n",
+ " <td>0.528302</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>0.129068</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.770270</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>0.105902</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.986486</td>\n",
+ " <td>0.056604</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>0.484556</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.622826</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>0.600938</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.216216</td>\n",
+ " <td>0.809623</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>0.252344</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.527027</td>\n",
+ " <td>0.156604</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>0.285714</td>\n",
+ " <td>0.25</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.160566</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>0.207391</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.108108</td>\n",
+ " <td>0.137736</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>0.306122</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.648649</td>\n",
+ " <td>0.169811</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>0.837838</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.418919</td>\n",
+ " <td>0.762264</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>0.068395</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.743243</td>\n",
+ " <td>0.150943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>0.167126</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.324324</td>\n",
+ " <td>0.555849</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>0.461666</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.108108</td>\n",
+ " <td>0.564340</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>0.371760</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.310811</td>\n",
+ " <td>0.575660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>0.492278</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.270270</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>0.272201</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.472973</td>\n",
+ " <td>0.339623</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>0.040816</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>0.327634</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.945946</td>\n",
+ " <td>0.273585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>0.630171</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.837838</td>\n",
+ " <td>0.773585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>0.264479</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.256757</td>\n",
+ " <td>0.218868</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>0.161335</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.608108</td>\n",
+ " <td>0.150943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td>0.106729</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.229730</td>\n",
+ " <td>0.113208</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td>0.353006</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.310811</td>\n",
+ " <td>0.330377</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td>0.927468</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.824324</td>\n",
+ " <td>0.715283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39</th>\n",
+ " <td>0.361280</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.513514</td>\n",
+ " <td>0.220943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>0.223938</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.472973</td>\n",
+ " <td>0.374717</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td>0.382239</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.432432</td>\n",
+ " <td>0.301887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42</th>\n",
+ " <td>0.472973</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.702703</td>\n",
+ " <td>0.271887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43</th>\n",
+ " <td>0.095974</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.959459</td>\n",
+ " <td>0.243585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.878378</td>\n",
+ " <td>0.018868</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>0.275786</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.797297</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>0.096801</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.081081</td>\n",
+ " <td>0.131321</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "0 0.345284 0.50 0.878378 0.433962\n",
+ "1 0.206288 0.50 0.310811 0.301887\n",
+ "2 0.426917 0.50 0.527027 0.375660\n",
+ "3 0.155543 0.25 0.594595 0.117170\n",
+ "4 0.592388 0.75 0.945946 0.698113\n",
+ "5 0.312466 0.75 0.756757 0.245283\n",
+ "6 0.188086 0.50 0.094595 0.273585\n",
+ "7 0.158577 0.50 0.702703 0.054904\n",
+ "8 0.145615 0.50 0.121622 0.079434\n",
+ "9 0.177055 0.50 0.135135 0.136981\n",
+ "10 0.300055 0.75 0.027027 0.132262\n",
+ "11 0.316602 0.50 0.297297 0.334151\n",
+ "12 0.286266 0.50 0.540541 0.302074\n",
+ "13 1.000000 1.00 0.594595 1.000000\n",
+ "14 0.114727 0.50 0.716216 0.169811\n",
+ "15 0.399338 0.75 0.972973 0.528302\n",
+ "16 0.129068 0.25 0.770270 0.245283\n",
+ "17 0.105902 0.50 0.986486 0.056604\n",
+ "18 0.484556 0.75 0.000000 0.622826\n",
+ "19 0.600938 0.75 0.216216 0.809623\n",
+ "20 0.252344 0.50 0.527027 0.156604\n",
+ "21 0.285714 0.25 1.000000 0.160566\n",
+ "22 0.207391 0.50 0.108108 0.137736\n",
+ "23 0.306122 0.75 0.648649 0.169811\n",
+ "24 0.837838 0.50 0.418919 0.762264\n",
+ "25 0.068395 0.50 0.743243 0.150943\n",
+ "26 0.167126 0.50 0.324324 0.555849\n",
+ "27 0.461666 0.50 0.108108 0.564340\n",
+ "28 0.371760 0.50 0.310811 0.575660\n",
+ "29 0.492278 0.50 0.270270 0.245283\n",
+ "30 0.272201 0.25 0.472973 0.339623\n",
+ "31 0.040816 0.00 0.000000 0.000000\n",
+ "32 0.327634 0.75 0.945946 0.273585\n",
+ "33 0.630171 0.50 0.837838 0.773585\n",
+ "34 0.264479 0.75 0.256757 0.218868\n",
+ "35 0.161335 0.50 0.608108 0.150943\n",
+ "36 0.106729 0.50 0.229730 0.113208\n",
+ "37 0.353006 0.75 0.310811 0.330377\n",
+ "38 0.927468 0.75 0.824324 0.715283\n",
+ "39 0.361280 0.75 0.513514 0.220943\n",
+ "40 0.223938 0.25 0.472973 0.374717\n",
+ "41 0.382239 0.50 0.432432 0.301887\n",
+ "42 0.472973 0.75 0.702703 0.271887\n",
+ "43 0.095974 0.50 0.959459 0.243585\n",
+ "44 0.000000 0.25 0.878378 0.018868\n",
+ "45 0.275786 0.75 0.797297 0.245283\n",
+ "46 0.096801 0.50 0.081081 0.131321"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X = df.copy()\n",
+ "print('X: ',X.shape)\n",
+ "y = X.pop('price')##pop () es una función incorporada en Python que elimina en X el ultimo valor de la lista que es \"price\" y devuelve ese valor en la lista del Ãndice dado.\n",
+ "print('X: ',X.shape,'y: ',y.shape)# devuelve una tupla con el tamaño del array\n",
+ "df#aqui se muestra lo que tengo en X y en y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###### La red crea una funcion que toma mis tres variables para predecir la cuarta\n",
+ "Añadimos una neurona con tres neuronas de entrada y una de salida. Dense: Es que cada una de las neuronas de entrada se va a conectar con cada una de las neuronas de salida"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Vamos a jugar un poco variando el learning rate, optimizer y los epochs, para minimizar el error cuadratico medio."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = Sequential() #modelo secuencial construir capa a capa modelo secuencial\n",
+ "model.add(Dense(1, input_shape=(3,))) #añadimos una neurona con tres neuronas de entrada y una de salida Dense= es que se va a conectar con todas las neuronas. La red crea una funcion que toma tus tres variables para predecir la cuarta\n",
+ "model.compile(SGD(lr=0.5),loss='mse') #SGD es simplemente una técnica de optimización y no corresponde a una familia especÃfica de modelos de aprendizaje automático. Es solo una forma de entrenar a un modelo, funcion de costo mse=error cuadratico medio"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dividimos la data en un conjunto de entrenamiento y en un conjunto de validacion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((47, 3), (37, 3), (10, 3))"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train_good, X_test_good, y_train_good, y_test_good = train_test_split(X, y, train_size=.8) \n",
+ "X.shape,X_train.shape,X_test.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "##Reseteamos los parametros para volver a entrenar\n",
+ "resetWeights = [np.array([[1.], [1.], [1.]]), np.array([0.])]\n",
+ "model.set_weights(resetWeights)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Proceso de entrenamiento\n",
+ "La red que acabamos de construir le vamos a pasar los datos y va a empezar a ajustar y va a calcular cual es el error, para eso le vamos a pasar como parametro la data de entrada que es X_train, le damos los valores reales y_train para que pueda calcularnos el error y ademas hay que pasarle el numero de veces que quiero que repita el proceso de iteracion sobre la data que es epochs. En este caso epochs es 25.\n",
+ "\n",
+ "validation_data: Podemos pasarle como parametros la data para validar el metodo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "history= model.fit(X_train_good, y_train_good, epochs=25, verbose=0, validation_data=(X_test_good, y_test_good), batch_size=128)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "El fit nos da la funcion de costo sobre el entrenamiento (loss) y la funcion de costo sobre el conjunto de validacion (val-loss). Ambos son errores cuadraticos medios"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.40062317, 0.1656473 , 0.09780446, 0.3996801 , 0.3744507 ,\n",
+ " 0.24628997, 0.5183667 , 0.27982935, 0.87289023, -0.1345935 ,\n",
+ " 0.1682773 , 0.15616915, 0.58320975, 0.08491531, 0.55509377,\n",
+ " 0.42054245, 0.38758525, 0.3389252 , -0.01455881, 0.11535427,\n",
+ " 0.16773415, 0.42394587, 0.16555041, 0.37932554, 1.0048788 ,\n",
+ " 0.1966702 , 0.37767038, 0.1838966 , 0.12886813, 0.18238062,\n",
+ " 0.6852132 , 0.34121117, 0.12063059, 0.4799975 , 0.32952914,\n",
+ " 0.6257061 , 0.13772684], dtype=float32)"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train_pred_good = model.predict(X_train_good).ravel()#obtenemos un vector de los valores predichos por la red \n",
+ "y_train_pred_good"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.34071425, 0.15412337, 0.16259345, 0.41189036, 0.2113426 ,\n",
+ " 0.07653832, 0.3227703 , 0.48040447, 0.1866323 , 0.2730481 ],\n",
+ " dtype=float32)"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test_pred_good = model.predict(X_test_good).ravel()\n",
+ "y_test_pred_good"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Metrica R2\n",
+ "Para evaluar modelos de regresion se utiliza la metrica ð‘…2. R2 es el coeficiente de determinación, determina la capacidad de un modelo para predecir futuros resultados. El mejor resultado posible es 1, y ocurre cuando la predicción coincide con los valores de la variable objetivo."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "R2 score (Train Set):\t 0.654\n",
+ "R2 score (Test Set):\t 0.440\n"
+ ]
+ }
+ ],
+ "source": [
+ "r2 = r2_score(y_train_good, y_train_pred_good)\n",
+ "print('R2 score (Train Set):\\t', '{:0.3f}'.format(r2))\n",
+ "\n",
+ "r2 = r2_score(y_test_good, y_test_pred_good)\n",
+ "print('R2 score (Test Set):\\t', '{:0.3f}'.format(r2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error (Train Set):\t 0.0\n",
+ "Mean Squared Error (Test Set):\t 0.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "err = mse(y_train_good, y_train_pred_good) ##errores cuadraticos medio sobre el conjunto de entrenamiento y validacion\n",
+ "print('Mean Squared Error (Train Set):\\t', '{:0.1f}'.format(err))\n",
+ "\n",
+ "err = mse(y_test_good, y_test_pred_good)\n",
+ "print('Mean Squared Error (Test Set):\\t', '{:0.1f}'.format(err))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Pred')"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(y_train_good,y_train_pred_good, alpha= 0.4) #Un segundo método más poderoso para crear diagramas de dispersión es la función plt.scatter, que se puede usar de manera muy similar a la función plt.plot:\n",
+ "plt.plot(y_train_good,y_train_good) #x and y must have same first dimension, but have shapes (47,) and (37,)\n",
+ "\n",
+ "plt.xlabel('Data')\n",
+ "plt.ylabel('Pred')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "max_val = pd.DataFrame(history.history).max()\n",
+ "(pd.DataFrame(history.history)/max_val).plot(figsize=(9,5))\n",
+ "#plt.gca().set_ylim(0.99, 1)\n",
+ "plt.grid(True)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loss: El error cuadratico medio para el conjunto de entrenamiento\n",
+ "Val_loss: El error cuadratico medio para el conjunto de validacion\n",
+ "\n",
+ "Es razonable que el algoritmo se comporte mejor sobre los datos con los cuales se entreno y sea un poco peor con datos que no vio durante el entrenamiento. si val_loss hubiese estado por debajo= Es raro que sobre datos que el modelo no ha visto para entrenar, el modelo se pote mejor.\n",
+ " \t\n",
+ "Algo que es evidente de la grafica es que termino de entrenar. Esto se nota porque la pendiente del loss es cero."
+ ]
+ }
+ ],
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/reporte/Ejercicio1_1_Normalizado.ipynb b/reporte/Ejercicio1_1_Normalizado.ipynb
new file mode 100644
index 0000000..8c46ed8
--- /dev/null
+++ b/reporte/Ejercicio1_1_Normalizado.ipynb
@@ -0,0 +1,1286 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Tarea 1\n",
+ "#### Mildred Arias, mildarias181@gmail.com"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "from tensorflow.keras.optimizers import Adam, SGD,RMSprop\n",
+ "from tensorflow.keras.layers.experimental import preprocessing"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Usamos el archivo common.py para importar las librerias mas usadas en esta parte"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('common.py') as fin: #cargando nuestras librerias\n",
+ " msg = fin.read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import seaborn as sb\n",
+ "import warnings\n",
+ "import logging\n",
+ "\n",
+ "logging.getLogger(\"tensorflow\").disabled = True\n",
+ "warnings.simplefilter(\"ignore\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(msg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exec(msg)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loading the data housing-data.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = './datasets/housing-data.csv' #cargando nuestra data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Apredizaje Supervisado\n",
+ "### Modelo Lineal - Regresion\n",
+ "\n",
+ "Tenemos una data sobre ciertas categorias como: sqft,bdrms,age y price. Deseamos hacer un modelo simple que permita predecir el price en funcion de las otras categorias."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2104</td>\n",
+ " <td>3</td>\n",
+ " <td>70</td>\n",
+ " <td>399900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>1600</td>\n",
+ " <td>3</td>\n",
+ " <td>28</td>\n",
+ " <td>329900</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>2400</td>\n",
+ " <td>3</td>\n",
+ " <td>44</td>\n",
+ " <td>369000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>1416</td>\n",
+ " <td>2</td>\n",
+ " <td>49</td>\n",
+ " <td>232000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>3000</td>\n",
+ " <td>4</td>\n",
+ " <td>75</td>\n",
+ " <td>539900</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "0 2104 3 70 399900\n",
+ "1 1600 3 28 329900\n",
+ "2 2400 3 44 369000\n",
+ "3 1416 2 49 232000\n",
+ "4 3000 4 75 539900"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(data_path)#usamos las herramientas de las librerias que cargamos por ejemplo panda para leer el dataset y hacer una exploracion de la data\n",
+ "df.head() # tambien es posible obtener informacion usando df.describe()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'pandas.core.frame.DataFrame'>\n",
+ "RangeIndex: 47 entries, 0 to 46\n",
+ "Data columns (total 4 columns):\n",
+ " # Column Non-Null Count Dtype\n",
+ "--- ------ -------------- -----\n",
+ " 0 sqft 47 non-null int64\n",
+ " 1 bdrms 47 non-null int64\n",
+ " 2 age 47 non-null int64\n",
+ " 3 price 47 non-null int64\n",
+ "dtypes: int64(4)\n",
+ "memory usage: 1.6 KB\n"
+ ]
+ }
+ ],
+ "source": [
+ "df.info() #para ver que tipo de data tenemos"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Vamos a normalizar nuestra data para mejorar el modelo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>count</th>\n",
+ " <td>47.00</td>\n",
+ " <td>47.00</td>\n",
+ " <td>47.00</td>\n",
+ " <td>47.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>mean</th>\n",
+ " <td>0.32</td>\n",
+ " <td>0.54</td>\n",
+ " <td>0.51</td>\n",
+ " <td>0.32</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>std</th>\n",
+ " <td>0.22</td>\n",
+ " <td>0.19</td>\n",
+ " <td>0.31</td>\n",
+ " <td>0.24</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>min</th>\n",
+ " <td>0.00</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.00</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25%</th>\n",
+ " <td>0.16</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.26</td>\n",
+ " <td>0.15</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>50%</th>\n",
+ " <td>0.29</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.53</td>\n",
+ " <td>0.25</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>75%</th>\n",
+ " <td>0.39</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.76</td>\n",
+ " <td>0.40</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>max</th>\n",
+ " <td>1.00</td>\n",
+ " <td>1.00</td>\n",
+ " <td>1.00</td>\n",
+ " <td>1.00</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "count 47.00 47.00 47.00 47.00\n",
+ "mean 0.32 0.54 0.51 0.32\n",
+ "std 0.22 0.19 0.31 0.24\n",
+ "min 0.00 0.00 0.00 0.00\n",
+ "25% 0.16 0.50 0.26 0.15\n",
+ "50% 0.29 0.50 0.53 0.25\n",
+ "75% 0.39 0.75 0.76 0.40\n",
+ "max 1.00 1.00 1.00 1.00"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mms = MinMaxScaler()\n",
+ "norma=1\n",
+ "if norma==1:\n",
+ " df['sqft'] = mms.fit_transform(df[['sqft']])##se esta sobreescribiendo \n",
+ " df['bdrms'] = mms.fit_transform(df[['bdrms']])\n",
+ " df['age'] = mms.fit_transform(df[['age']])\n",
+ " df['price'] = mms.fit_transform(df[['price']])\n",
+ "df\n",
+ "df.head()\n",
+ "df.describe().round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Grafica de los histogramas para cada feature"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x288 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15,4))\n",
+ "\n",
+ "for (i, feature) in enumerate(['sqft', \n",
+ " 'bdrms', \n",
+ " 'age', \n",
+ " 'price']):\n",
+ " plt.subplot(1, 4, i+1)\n",
+ " df[feature].plot(kind='hist', title=feature)\n",
+ " plt.xlabel(feature)\n",
+ " \n",
+ "plt.tight_layout()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. sqft'}, xlabel='sqft', ylabel='price'>"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='sqft', #aqui vemos la dispersion de nuestros datos de sqft vs price\n",
+ " y='price', title='price Vs. sqft',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. bdrms'}, xlabel='bdrms', ylabel='price'>"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='bdrms', #aqui vemos la dispersion de nuestros datos de bdrms vs price\n",
+ " y='price', title='price Vs. bdrms',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<AxesSubplot:title={'center':'price Vs. age'}, xlabel='age', ylabel='price'>"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df.plot(kind='scatter', x='age', #aqui vemos la dispersion de nuestros datos de age vs price\n",
+ " y='price', title='price Vs. age',\n",
+ " alpha=.4)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Creamos dos variables X, y: Donde X es una matriz con tres columnas (sqft, bdrms, age); y es un vector con una columna (price)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "X: (47, 4)\n",
+ "X: (47, 3) y: (47,)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>sqft</th>\n",
+ " <th>bdrms</th>\n",
+ " <th>age</th>\n",
+ " <th>price</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.345284</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.878378</td>\n",
+ " <td>0.433962</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.206288</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.310811</td>\n",
+ " <td>0.301887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.426917</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.527027</td>\n",
+ " <td>0.375660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.155543</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.594595</td>\n",
+ " <td>0.117170</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.592388</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.945946</td>\n",
+ " <td>0.698113</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>0.312466</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.756757</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>0.188086</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.094595</td>\n",
+ " <td>0.273585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>0.158577</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.702703</td>\n",
+ " <td>0.054904</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>0.145615</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.121622</td>\n",
+ " <td>0.079434</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>0.177055</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.135135</td>\n",
+ " <td>0.136981</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>10</th>\n",
+ " <td>0.300055</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.027027</td>\n",
+ " <td>0.132262</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>11</th>\n",
+ " <td>0.316602</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.297297</td>\n",
+ " <td>0.334151</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>12</th>\n",
+ " <td>0.286266</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.540541</td>\n",
+ " <td>0.302074</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>13</th>\n",
+ " <td>1.000000</td>\n",
+ " <td>1.00</td>\n",
+ " <td>0.594595</td>\n",
+ " <td>1.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14</th>\n",
+ " <td>0.114727</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.716216</td>\n",
+ " <td>0.169811</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>15</th>\n",
+ " <td>0.399338</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.972973</td>\n",
+ " <td>0.528302</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>16</th>\n",
+ " <td>0.129068</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.770270</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>17</th>\n",
+ " <td>0.105902</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.986486</td>\n",
+ " <td>0.056604</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>18</th>\n",
+ " <td>0.484556</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.622826</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>19</th>\n",
+ " <td>0.600938</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.216216</td>\n",
+ " <td>0.809623</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>20</th>\n",
+ " <td>0.252344</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.527027</td>\n",
+ " <td>0.156604</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>21</th>\n",
+ " <td>0.285714</td>\n",
+ " <td>0.25</td>\n",
+ " <td>1.000000</td>\n",
+ " <td>0.160566</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>22</th>\n",
+ " <td>0.207391</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.108108</td>\n",
+ " <td>0.137736</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>23</th>\n",
+ " <td>0.306122</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.648649</td>\n",
+ " <td>0.169811</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>24</th>\n",
+ " <td>0.837838</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.418919</td>\n",
+ " <td>0.762264</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>25</th>\n",
+ " <td>0.068395</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.743243</td>\n",
+ " <td>0.150943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>26</th>\n",
+ " <td>0.167126</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.324324</td>\n",
+ " <td>0.555849</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>27</th>\n",
+ " <td>0.461666</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.108108</td>\n",
+ " <td>0.564340</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>28</th>\n",
+ " <td>0.371760</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.310811</td>\n",
+ " <td>0.575660</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>29</th>\n",
+ " <td>0.492278</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.270270</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>30</th>\n",
+ " <td>0.272201</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.472973</td>\n",
+ " <td>0.339623</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>31</th>\n",
+ " <td>0.040816</td>\n",
+ " <td>0.00</td>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.000000</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>32</th>\n",
+ " <td>0.327634</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.945946</td>\n",
+ " <td>0.273585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>33</th>\n",
+ " <td>0.630171</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.837838</td>\n",
+ " <td>0.773585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>34</th>\n",
+ " <td>0.264479</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.256757</td>\n",
+ " <td>0.218868</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>35</th>\n",
+ " <td>0.161335</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.608108</td>\n",
+ " <td>0.150943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>36</th>\n",
+ " <td>0.106729</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.229730</td>\n",
+ " <td>0.113208</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>37</th>\n",
+ " <td>0.353006</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.310811</td>\n",
+ " <td>0.330377</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>38</th>\n",
+ " <td>0.927468</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.824324</td>\n",
+ " <td>0.715283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>39</th>\n",
+ " <td>0.361280</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.513514</td>\n",
+ " <td>0.220943</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>40</th>\n",
+ " <td>0.223938</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.472973</td>\n",
+ " <td>0.374717</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>41</th>\n",
+ " <td>0.382239</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.432432</td>\n",
+ " <td>0.301887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>42</th>\n",
+ " <td>0.472973</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.702703</td>\n",
+ " <td>0.271887</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>43</th>\n",
+ " <td>0.095974</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.959459</td>\n",
+ " <td>0.243585</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>44</th>\n",
+ " <td>0.000000</td>\n",
+ " <td>0.25</td>\n",
+ " <td>0.878378</td>\n",
+ " <td>0.018868</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>45</th>\n",
+ " <td>0.275786</td>\n",
+ " <td>0.75</td>\n",
+ " <td>0.797297</td>\n",
+ " <td>0.245283</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>46</th>\n",
+ " <td>0.096801</td>\n",
+ " <td>0.50</td>\n",
+ " <td>0.081081</td>\n",
+ " <td>0.131321</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " sqft bdrms age price\n",
+ "0 0.345284 0.50 0.878378 0.433962\n",
+ "1 0.206288 0.50 0.310811 0.301887\n",
+ "2 0.426917 0.50 0.527027 0.375660\n",
+ "3 0.155543 0.25 0.594595 0.117170\n",
+ "4 0.592388 0.75 0.945946 0.698113\n",
+ "5 0.312466 0.75 0.756757 0.245283\n",
+ "6 0.188086 0.50 0.094595 0.273585\n",
+ "7 0.158577 0.50 0.702703 0.054904\n",
+ "8 0.145615 0.50 0.121622 0.079434\n",
+ "9 0.177055 0.50 0.135135 0.136981\n",
+ "10 0.300055 0.75 0.027027 0.132262\n",
+ "11 0.316602 0.50 0.297297 0.334151\n",
+ "12 0.286266 0.50 0.540541 0.302074\n",
+ "13 1.000000 1.00 0.594595 1.000000\n",
+ "14 0.114727 0.50 0.716216 0.169811\n",
+ "15 0.399338 0.75 0.972973 0.528302\n",
+ "16 0.129068 0.25 0.770270 0.245283\n",
+ "17 0.105902 0.50 0.986486 0.056604\n",
+ "18 0.484556 0.75 0.000000 0.622826\n",
+ "19 0.600938 0.75 0.216216 0.809623\n",
+ "20 0.252344 0.50 0.527027 0.156604\n",
+ "21 0.285714 0.25 1.000000 0.160566\n",
+ "22 0.207391 0.50 0.108108 0.137736\n",
+ "23 0.306122 0.75 0.648649 0.169811\n",
+ "24 0.837838 0.50 0.418919 0.762264\n",
+ "25 0.068395 0.50 0.743243 0.150943\n",
+ "26 0.167126 0.50 0.324324 0.555849\n",
+ "27 0.461666 0.50 0.108108 0.564340\n",
+ "28 0.371760 0.50 0.310811 0.575660\n",
+ "29 0.492278 0.50 0.270270 0.245283\n",
+ "30 0.272201 0.25 0.472973 0.339623\n",
+ "31 0.040816 0.00 0.000000 0.000000\n",
+ "32 0.327634 0.75 0.945946 0.273585\n",
+ "33 0.630171 0.50 0.837838 0.773585\n",
+ "34 0.264479 0.75 0.256757 0.218868\n",
+ "35 0.161335 0.50 0.608108 0.150943\n",
+ "36 0.106729 0.50 0.229730 0.113208\n",
+ "37 0.353006 0.75 0.310811 0.330377\n",
+ "38 0.927468 0.75 0.824324 0.715283\n",
+ "39 0.361280 0.75 0.513514 0.220943\n",
+ "40 0.223938 0.25 0.472973 0.374717\n",
+ "41 0.382239 0.50 0.432432 0.301887\n",
+ "42 0.472973 0.75 0.702703 0.271887\n",
+ "43 0.095974 0.50 0.959459 0.243585\n",
+ "44 0.000000 0.25 0.878378 0.018868\n",
+ "45 0.275786 0.75 0.797297 0.245283\n",
+ "46 0.096801 0.50 0.081081 0.131321"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X = df.copy()\n",
+ "print('X: ',X.shape)\n",
+ "y = X.pop('price') ##pop () es una función incorporada en Python que elimina en X el ultimo valor de la lista que es \"price\" y devuelve ese valor en la lista del Ãndice dado.\n",
+ "print('X: ',X.shape,'y: ',y.shape) # devuelve una tupla con el tamaño del array\n",
+ "df #aqui se muestra lo que tengo en X y en y"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Configuramos un modelo en Keras, hacemos una instancia de la clase Sequential el cual es el proceso de construir capa a capa una red neural"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = Sequential() #nuestro modelo"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "###### La red crea una funcion que toma mis tres variables para predecir la cuarta"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Añadimos una neurona con tres neuronas de entrada y una de salida. Dense: Es que cada una de las neuronas de entrada se va a conectar con cada una de las neuronas de salida"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.add(Dense(1, input_shape=(3,))) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 1) 4 \n",
+ "=================================================================\n",
+ "Total params: 4\n",
+ "Trainable params: 4\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "model.summary() #resumen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Vamos a jugar un poco variando el learning rate, optimizer y los epochs, para minimizar el error cuadratico medio."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.compile(SGD(lr=0.5),loss='mse') #SGD es simplemente una técnica de optimización y no corresponde a una familia especÃfica de modelos de aprendizaje automático. Es solo una forma de entrenar a un modelo, funcion de costo mse=error cuadratico medio\n",
+ "#model.compile(optimizer='adam', loss='mean_squared_error'). SGD"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Dividimos la data en un conjunto de entrenamiento y en un conjunto de validacion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((47, 3), (37, 3), (10, 3))"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.8) \n",
+ "X.shape,X_train.shape,X_test.shape #el conjunto de entrenamiento (37,3) y el conjunto de validacion (10,3)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Proceso de entrenamiento\n",
+ "La red que acabamos de construir le vamos a pasar los datos y va a empezar a ajustar y va a calcular cual es el error, para eso le vamos a pasar como parametro la data de entrada que es X_train, le damos los valores reales y_train para que pueda calcularnos el error y ademas hay que pasarle el numero de veces que quiero que repita el proceso de iteracion sobre la data que es epochs. En este caso epochs es 26\n",
+ "\n",
+ "\n",
+ "validation_data: Podemos pasarle como parametros la data para validar el metodo. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "history=model.fit(X_train, y_train, epochs=26, verbose=0, validation_data=(X_test, y_test), batch_size=128) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "El fit nos da la funcion de costo sobre el entrenamiento (loss) y la funcion de costo sobre el conjunto de validacion (val-loss). Ambos son errores cuadraticos medios"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import r2_score"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0.28972116, 0.34357217, 0.11388129, 0.15114206, 0.1630593 ,\n",
+ " 0.4051389 , 0.36461574, 0.4030922 , 0.6329508 , 0.13596803,\n",
+ " 0.9667417 , 0.1738388 , 0.24288484, 0.11301818, 0.2199685 ,\n",
+ " 0.03674576, 0.3836158 , 0.46214712, 0.4013667 , 0.33957607,\n",
+ " 0.18666184, 0.17927027, -0.12030627, 0.563338 , 0.5269118 ,\n",
+ " 0.50495195, 0.45357943, 0.36387026, 0.26534283, 0.32390457,\n",
+ " 0.11280227, 0.20408604, 0.14657238, 0.55343765, 0.40828496,\n",
+ " 0.8474002 , 0.4316901 ], dtype=float32)"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_train_pred = model.predict(X_train).ravel() #obtenemos un vector de los valores predichos por la red \n",
+ "y_train_pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.21360523, 0.4237535 , 0.65084404, 0.33994046, 0.19165653,\n",
+ " 0.17712346, 0.21206221, 0.19159901, 0.21179748, 0.28513914],\n",
+ " dtype=float32)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y_test_pred = model.predict(X_test).ravel()\n",
+ "y_test_pred"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Metrica R2\n",
+ "Para evaluar modelos de regresion se utiliza la metrica ð‘…2. R2 es el coeficiente de determinación, determina la capacidad de un modelo para predecir futuros resultados. El mejor resultado posible es 1, y ocurre cuando la predicción coincide con los valores de la variable objetivo."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "R2 score (Train Set):\t 0.611\n",
+ "R2 score (Test Set):\t 0.606\n"
+ ]
+ }
+ ],
+ "source": [
+ "r2 = r2_score(y_train, y_train_pred)## el r cuadratico la metrica\n",
+ "print('R2 score (Train Set):\\t', '{:0.3f}'.format(r2))\n",
+ "\n",
+ "r2 = r2_score(y_test, y_test_pred)\n",
+ "print('R2 score (Test Set):\\t', '{:0.3f}'.format(r2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'Pred')"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(y_train,y_train_pred, alpha= 0.4) #Un segundo método más poderoso para crear diagramas de dispersión es la función plt.scatter, que se puede usar de manera muy similar a la función plt.plot:\n",
+ "plt.plot(y_train,y_train) #x and y must have same first dimension, but have shapes (47,) and (37,)\n",
+ "\n",
+ "plt.xlabel('Data')\n",
+ "plt.ylabel('Pred')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 648x360 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "max_val = pd.DataFrame(history.history).max()\n",
+ "(pd.DataFrame(history.history)/max_val).plot(figsize=(9,5))\n",
+ "#plt.gca().set_ylim(0.99, 1)\n",
+ "plt.grid(True)\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loss: El error cuadratico medio para el conjunto de entrenamiento\n",
+ "Val_loss: El error cuadratico medio para el conjunto de validacion\n",
+ "\n",
+ "Es razonable que el algoritmo se comporte mejor sobre los datos con los cuales se entreno y sea un poco peor con datos que no vio durante el entrenamiento.\n",
+ " \t\n",
+ "Algo que es evidente de la grafica es que termino de entrenar. Esto se nota porque la pendiente del loss es cero."
+ ]
+ }
+ ],
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/reporte/Ejercicio2_Mildred_Arias.ipynb b/reporte/Ejercicio2_Mildred_Arias.ipynb
new file mode 100644
index 0000000..537029b
--- /dev/null
+++ b/reporte/Ejercicio2_Mildred_Arias.ipynb
@@ -0,0 +1,2149 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ejercicio 2. Mildred Arias\n",
+ "#### mildarias181@gmail.com"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.metrics import confusion_matrix\n",
+ "from sklearn.metrics import precision_score, recall_score, f1_score\n",
+ "from sklearn.model_selection import cross_val_score\n",
+ "from sklearn.model_selection import KFold\n",
+ "#from tensorflow.keras.wrappers.scikit_learn import KerasClassifier\n",
+ "from sklearn.preprocessing import MinMaxScaler\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from tensorflow.keras.models import Sequential\n",
+ "import tensorflow as tf\n",
+ "from tensorflow.keras import layers\n",
+ "from tensorflow.keras.layers import Dense\n",
+ "from tensorflow.keras.optimizers import Adam, SGD\n",
+ "from tensorflow.keras.layers.experimental import preprocessing\n",
+ "from tensorflow.keras.layers import Activation\n",
+ "from sklearn.metrics import accuracy_score"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loading the data HR_comma_sep.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('common.py') as fin:\n",
+ " msg = fin.read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import seaborn as sb\n",
+ "import warnings\n",
+ "import logging\n",
+ "\n",
+ "logging.getLogger(\"tensorflow\").disabled = True\n",
+ "warnings.simplefilter(\"ignore\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "print(msg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exec(msg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "data_path = './datasets/HR_comma_sep.csv'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>satisfaction_level</th>\n",
+ " <th>last_evaluation</th>\n",
+ " <th>number_project</th>\n",
+ " <th>average_montly_hours</th>\n",
+ " <th>time_spend_company</th>\n",
+ " <th>Work_accident</th>\n",
+ " <th>left</th>\n",
+ " <th>promotion_last_5years</th>\n",
+ " <th>sales</th>\n",
+ " <th>salary</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.38</td>\n",
+ " <td>0.53</td>\n",
+ " <td>2</td>\n",
+ " <td>157</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>low</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.80</td>\n",
+ " <td>0.86</td>\n",
+ " <td>5</td>\n",
+ " <td>262</td>\n",
+ " <td>6</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.11</td>\n",
+ " <td>0.88</td>\n",
+ " <td>7</td>\n",
+ " <td>272</td>\n",
+ " <td>4</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.72</td>\n",
+ " <td>0.87</td>\n",
+ " <td>5</td>\n",
+ " <td>223</td>\n",
+ " <td>5</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>low</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.37</td>\n",
+ " <td>0.52</td>\n",
+ " <td>2</td>\n",
+ " <td>159</td>\n",
+ " <td>3</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>low</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " satisfaction_level last_evaluation number_project average_montly_hours \\\n",
+ "0 0.38 0.53 2 157 \n",
+ "1 0.80 0.86 5 262 \n",
+ "2 0.11 0.88 7 272 \n",
+ "3 0.72 0.87 5 223 \n",
+ "4 0.37 0.52 2 159 \n",
+ "\n",
+ " time_spend_company Work_accident left promotion_last_5years sales \\\n",
+ "0 3 0 1 0 sales \n",
+ "1 6 0 1 0 sales \n",
+ "2 4 0 1 0 sales \n",
+ "3 5 0 1 0 sales \n",
+ "4 3 0 1 0 sales \n",
+ "\n",
+ " salary \n",
+ "0 low \n",
+ "1 medium \n",
+ "2 medium \n",
+ "3 low \n",
+ "4 low "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df = pd.read_csv(data_path)\n",
+ "df.head() #exploro mi data y me doy cuenta que debo normalizar las variables number_project, average_montly_hours y time_spend_company"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Nomalizando la data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>satisfaction_level</th>\n",
+ " <th>last_evaluation</th>\n",
+ " <th>number_project</th>\n",
+ " <th>average_montly_hours</th>\n",
+ " <th>time_spend_company</th>\n",
+ " <th>Work_accident</th>\n",
+ " <th>left</th>\n",
+ " <th>promotion_last_5years</th>\n",
+ " <th>sales</th>\n",
+ " <th>salary</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.38</td>\n",
+ " <td>0.53</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.285047</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>low</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.80</td>\n",
+ " <td>0.86</td>\n",
+ " <td>0.6</td>\n",
+ " <td>0.775701</td>\n",
+ " <td>0.500</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.11</td>\n",
+ " <td>0.88</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.822430</td>\n",
+ " <td>0.250</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>medium</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.72</td>\n",
+ " <td>0.87</td>\n",
+ " <td>0.6</td>\n",
+ " <td>0.593458</td>\n",
+ " <td>0.375</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>low</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.37</td>\n",
+ " <td>0.52</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.294393</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>sales</td>\n",
+ " <td>low</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " satisfaction_level last_evaluation number_project average_montly_hours \\\n",
+ "0 0.38 0.53 0.0 0.285047 \n",
+ "1 0.80 0.86 0.6 0.775701 \n",
+ "2 0.11 0.88 1.0 0.822430 \n",
+ "3 0.72 0.87 0.6 0.593458 \n",
+ "4 0.37 0.52 0.0 0.294393 \n",
+ "\n",
+ " time_spend_company Work_accident left promotion_last_5years sales \\\n",
+ "0 0.125 0 1 0 sales \n",
+ "1 0.500 0 1 0 sales \n",
+ "2 0.250 0 1 0 sales \n",
+ "3 0.375 0 1 0 sales \n",
+ "4 0.125 0 1 0 sales \n",
+ "\n",
+ " salary \n",
+ "0 low \n",
+ "1 medium \n",
+ "2 medium \n",
+ "3 low \n",
+ "4 low "
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mms = MinMaxScaler()\n",
+ "norma=1\n",
+ "if norma==1:\n",
+ "# df['satisfaction_level'] = mms.fit_transform(df[['satisfaction_level']])##se esta sobreescribiendo \n",
+ "# df['last_evaluation'] = mms.fit_transform(df[['last_evaluation']])\n",
+ " df['number_project'] = mms.fit_transform(df[['number_project']])\n",
+ " df['average_montly_hours'] = mms.fit_transform(df[['average_montly_hours']])\n",
+ " df['time_spend_company'] = mms.fit_transform(df[['time_spend_company']])\n",
+ "# df['Work_accident'] = mms.fit_transform(df[['Work_accident']])\n",
+ "# df['left'] = mms.fit_transform(df[['left']])\n",
+ "# df['promotion_last_5years'] = mms.fit_transform(df[['promotion_last_5years']])\n",
+ "df\n",
+ "df.head()\n",
+ "#df.describe().round(2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Ademas tengo variables categoricas las cuales debo transformar usando dummies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sales_dummy= pd.get_dummies(df['sales'], prefix='sales')\n",
+ "#df= pd.get_dummies(df, columns=['sales'], prefix='sales_', prefix_sep='')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "salary_dummy= pd.get_dummies(df['salary'], prefix='salary')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df= pd.concat([df,sales_dummy], axis=1) #hago una concatenacion a mi matriz original \n",
+ "df= pd.concat([df,salary_dummy], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
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+ " .dataframe thead th {\n",
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+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>satisfaction_level</th>\n",
+ " <th>last_evaluation</th>\n",
+ " <th>number_project</th>\n",
+ " <th>average_montly_hours</th>\n",
+ " <th>time_spend_company</th>\n",
+ " <th>Work_accident</th>\n",
+ " <th>left</th>\n",
+ " <th>promotion_last_5years</th>\n",
+ " <th>sales</th>\n",
+ " <th>salary</th>\n",
+ " <th>...</th>\n",
+ " <th>sales_hr</th>\n",
+ " <th>sales_management</th>\n",
+ " <th>sales_marketing</th>\n",
+ " <th>sales_product_mng</th>\n",
+ " <th>sales_sales</th>\n",
+ " <th>sales_support</th>\n",
+ " <th>sales_technical</th>\n",
+ " <th>salary_high</th>\n",
+ " <th>salary_low</th>\n",
+ " <th>salary_medium</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.38</td>\n",
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+ " <th>1</th>\n",
+ " <td>0.80</td>\n",
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+ " <td>0.6</td>\n",
+ " <td>0.775701</td>\n",
+ " <td>0.500</td>\n",
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+ " <td>sales</td>\n",
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+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.11</td>\n",
+ " <td>0.88</td>\n",
+ " <td>1.0</td>\n",
+ " <td>0.822430</td>\n",
+ " <td>0.250</td>\n",
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+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.72</td>\n",
+ " <td>0.87</td>\n",
+ " <td>0.6</td>\n",
+ " <td>0.593458</td>\n",
+ " <td>0.375</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
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+ " <td>support</td>\n",
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+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>14999 rows × 23 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " satisfaction_level last_evaluation number_project \\\n",
+ "0 0.38 0.53 0.0 \n",
+ "1 0.80 0.86 0.6 \n",
+ "2 0.11 0.88 1.0 \n",
+ "3 0.72 0.87 0.6 \n",
+ "4 0.37 0.52 0.0 \n",
+ "... ... ... ... \n",
+ "14994 0.40 0.57 0.0 \n",
+ "14995 0.37 0.48 0.0 \n",
+ "14996 0.37 0.53 0.0 \n",
+ "14997 0.11 0.96 0.8 \n",
+ "14998 0.37 0.52 0.0 \n",
+ "\n",
+ " average_montly_hours time_spend_company Work_accident left \\\n",
+ "0 0.285047 0.125 0 1 \n",
+ "1 0.775701 0.500 0 1 \n",
+ "2 0.822430 0.250 0 1 \n",
+ "3 0.593458 0.375 0 1 \n",
+ "4 0.294393 0.125 0 1 \n",
+ "... ... ... ... ... \n",
+ "14994 0.257009 0.125 0 1 \n",
+ "14995 0.299065 0.125 0 1 \n",
+ "14996 0.219626 0.125 0 1 \n",
+ "14997 0.859813 0.250 0 1 \n",
+ "14998 0.289720 0.125 0 1 \n",
+ "\n",
+ " promotion_last_5years sales salary ... sales_hr \\\n",
+ "0 0 sales low ... 0 \n",
+ "1 0 sales medium ... 0 \n",
+ "2 0 sales medium ... 0 \n",
+ "3 0 sales low ... 0 \n",
+ "4 0 sales low ... 0 \n",
+ "... ... ... ... ... ... \n",
+ "14994 0 support low ... 0 \n",
+ "14995 0 support low ... 0 \n",
+ "14996 0 support low ... 0 \n",
+ "14997 0 support low ... 0 \n",
+ "14998 0 support low ... 0 \n",
+ "\n",
+ " sales_management sales_marketing sales_product_mng sales_sales \\\n",
+ "0 0 0 0 1 \n",
+ "1 0 0 0 1 \n",
+ "2 0 0 0 1 \n",
+ "3 0 0 0 1 \n",
+ "4 0 0 0 1 \n",
+ "... ... ... ... ... \n",
+ "14994 0 0 0 0 \n",
+ "14995 0 0 0 0 \n",
+ "14996 0 0 0 0 \n",
+ "14997 0 0 0 0 \n",
+ "14998 0 0 0 0 \n",
+ "\n",
+ " sales_support sales_technical salary_high salary_low salary_medium \n",
+ "0 0 0 0 1 0 \n",
+ "1 0 0 0 0 1 \n",
+ "2 0 0 0 0 1 \n",
+ "3 0 0 0 1 0 \n",
+ "4 0 0 0 1 0 \n",
+ "... ... ... ... ... ... \n",
+ "14994 1 0 0 1 0 \n",
+ "14995 1 0 0 1 0 \n",
+ "14996 1 0 0 1 0 \n",
+ "14997 1 0 0 1 0 \n",
+ "14998 1 0 0 1 0 \n",
+ "\n",
+ "[14999 rows x 23 columns]"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "me doy cuenta que se unen las columnas de \"sales\" y \"salary\", y lo que quiero son las transformadas de ellas a numeros. Aplico .drop para quitarlas de la matriz y asi soluciono el problema"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_dummies= df.drop(['sales','salary'], axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
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+ " <td>0.37</td>\n",
+ " <td>0.48</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.299065</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14996</th>\n",
+ " <td>0.37</td>\n",
+ " <td>0.53</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.219626</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14997</th>\n",
+ " <td>0.11</td>\n",
+ " <td>0.96</td>\n",
+ " <td>0.8</td>\n",
+ " <td>0.859813</td>\n",
+ " <td>0.250</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>14998</th>\n",
+ " <td>0.37</td>\n",
+ " <td>0.52</td>\n",
+ " <td>0.0</td>\n",
+ " <td>0.289720</td>\n",
+ " <td>0.125</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>1</td>\n",
+ " <td>0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>14999 rows × 20 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " satisfaction_level last_evaluation number_project \\\n",
+ "0 0.38 0.53 0.0 \n",
+ "1 0.80 0.86 0.6 \n",
+ "2 0.11 0.88 1.0 \n",
+ "3 0.72 0.87 0.6 \n",
+ "4 0.37 0.52 0.0 \n",
+ "... ... ... ... \n",
+ "14994 0.40 0.57 0.0 \n",
+ "14995 0.37 0.48 0.0 \n",
+ "14996 0.37 0.53 0.0 \n",
+ "14997 0.11 0.96 0.8 \n",
+ "14998 0.37 0.52 0.0 \n",
+ "\n",
+ " average_montly_hours time_spend_company Work_accident \\\n",
+ "0 0.285047 0.125 0 \n",
+ "1 0.775701 0.500 0 \n",
+ "2 0.822430 0.250 0 \n",
+ "3 0.593458 0.375 0 \n",
+ "4 0.294393 0.125 0 \n",
+ "... ... ... ... \n",
+ "14994 0.257009 0.125 0 \n",
+ "14995 0.299065 0.125 0 \n",
+ "14996 0.219626 0.125 0 \n",
+ "14997 0.859813 0.250 0 \n",
+ "14998 0.289720 0.125 0 \n",
+ "\n",
+ " promotion_last_5years sales_IT sales_RandD sales_accounting \\\n",
+ "0 0 0 0 0 \n",
+ "1 0 0 0 0 \n",
+ "2 0 0 0 0 \n",
+ "3 0 0 0 0 \n",
+ "4 0 0 0 0 \n",
+ "... ... ... ... ... \n",
+ "14994 0 0 0 0 \n",
+ "14995 0 0 0 0 \n",
+ "14996 0 0 0 0 \n",
+ "14997 0 0 0 0 \n",
+ "14998 0 0 0 0 \n",
+ "\n",
+ " sales_hr sales_management sales_marketing sales_product_mng \\\n",
+ "0 0 0 0 0 \n",
+ "1 0 0 0 0 \n",
+ "2 0 0 0 0 \n",
+ "3 0 0 0 0 \n",
+ "4 0 0 0 0 \n",
+ "... ... ... ... ... \n",
+ "14994 0 0 0 0 \n",
+ "14995 0 0 0 0 \n",
+ "14996 0 0 0 0 \n",
+ "14997 0 0 0 0 \n",
+ "14998 0 0 0 0 \n",
+ "\n",
+ " sales_sales sales_support sales_technical salary_high salary_low \\\n",
+ "0 1 0 0 0 1 \n",
+ "1 1 0 0 0 0 \n",
+ "2 1 0 0 0 0 \n",
+ "3 1 0 0 0 1 \n",
+ "4 1 0 0 0 1 \n",
+ "... ... ... ... ... ... \n",
+ "14994 0 1 0 0 1 \n",
+ "14995 0 1 0 0 1 \n",
+ "14996 0 1 0 0 1 \n",
+ "14997 0 1 0 0 1 \n",
+ "14998 0 1 0 0 1 \n",
+ "\n",
+ " salary_medium \n",
+ "0 0 \n",
+ "1 1 \n",
+ "2 1 \n",
+ "3 0 \n",
+ "4 0 \n",
+ "... ... \n",
+ "14994 0 \n",
+ "14995 0 \n",
+ "14996 0 \n",
+ "14997 0 \n",
+ "14998 0 \n",
+ "\n",
+ "[14999 rows x 20 columns]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X = df_dummies.copy()\n",
+ "print('X: ',X.shape)\n",
+ "y = X.pop('left')\n",
+ "print('X: ',X.shape,'y: ',y.shape)\n",
+ "X"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "dividimos la data en conjunto de entrenamiento y de validacion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((14999, 20), (11999, 20), (3000, 20))"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.model_selection import train_test_split\n",
+ "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.8) \n",
+ "X.shape,X_train.shape,X_test.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "A continuacion definimos nuestro modelo usando Keras."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ "Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ "dense (Dense) (None, 1) 21 \n",
+ "_________________________________________________________________\n",
+ "activation (Activation) (None, 1) 0 \n",
+ "=================================================================\n",
+ "Total params: 21\n",
+ "Trainable params: 21\n",
+ "Non-trainable params: 0\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "logistic = Sequential()\n",
+ "logistic.add(Dense(1, input_dim=20)) #tenemos 20 neuronas de entrada y una de salida\n",
+ "logistic.add(Activation('sigmoid'))\n",
+ "logistic.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logistic.compile(optimizer=SGD(lr=0.4),\n",
+ " loss='binary_crossentropy',\n",
+ " metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[array([[-0.45290142],\n",
+ " [-0.02070558],\n",
+ " [-0.11722156],\n",
+ " [-0.5313923 ],\n",
+ " [ 0.35993773],\n",
+ " [ 0.03088701],\n",
+ " [-0.10258073],\n",
+ " [-0.4115773 ],\n",
+ " [-0.42994145],\n",
+ " [-0.24632755],\n",
+ " [-0.30499306],\n",
+ " [-0.1581924 ],\n",
+ " [-0.17323288],\n",
+ " [-0.37293303],\n",
+ " [ 0.09837979],\n",
+ " [ 0.2775755 ],\n",
+ " [-0.10734165],\n",
+ " [-0.12169164],\n",
+ " [ 0.15144175],\n",
+ " [ 0.21483314]], dtype=float32),\n",
+ " array([0.], dtype=float32)]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "params = logistic.get_weights()\n",
+ "params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[array([[0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.],\n",
+ " [0.]]),\n",
+ " array([0.])]"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "params = [np.zeros(w.shape) for w in params]\n",
+ "params"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logistic.set_weights(params)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Proceso de entrenamiento\n",
+ "La red que acabamos de construir le vamos a pasar los datos y va a empezar a ajustar y va a calcular cual es el error, para eso le vamos a pasar como parametro la data de entrada que es X_train, le damos los valores reales y_train para que pueda calcularnos el error y ademas hay que pasarle el numero de veces que quiero que repita el proceso de iteracion sobre la data que es epochs. En este caso epochs es 22.\n",
+ "\n",
+ "validation_data: Podemos pasarle como parametros la data para validar el metodo"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "history = logistic.fit(X_train, y_train, epochs=22, verbose=0, validation_data=(X_test, y_test), batch_size=64)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train Accuracy Score: 0.795\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_train_pred_class = logistic.predict(X_train) > .5\n",
+ "acc = accuracy_score(y_train, y_train_pred_class)\n",
+ "print('Train Accuracy Score: {:0.3f}'.format(acc))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Test Accuracy Score: 0.799\n"
+ ]
+ }
+ ],
+ "source": [
+ "y_test_pred_class = logistic.predict(X_test) > .5 ##y_pred \n",
+ "acc = accuracy_score(y_test, y_test_pred_class)\n",
+ "print('Test Accuracy Score: {:0.3f}'.format(acc))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 720x432 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pd.DataFrame(history.history).plot(figsize=(10,6))\n",
+ "plt.grid(True)\n",
+ "#plt.gca().set_ylim(0,1)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Loss: El error cuadratico medio para el conjunto de entrenamiento\n",
+ "Val_loss: El error cuadratico medio para el conjunto de validacion\n",
+ "\n",
+ "Es razonable que el algoritmo se comporte mejor sobre los datos con los cuales se entreno y sea un poco peor con datos que no vio durante el entrenamiento.\n",
+ " \t\n",
+ "Algo que es evidente de la grafica es que termino de entrenar. Esto se nota porque la pendiente del loss es cero."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Confusion Matrix"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "¿Es `accuracy` la mejor manera de comprobar el rendimiento de nuestro modelo? Nos dice lo bien que lo estamos haciendo en general, pero no nos da ninguna idea del tipo de errores que está cometiendo el modelo. Veamos como podemos hacerlo mejor. Esta tabla se llama matriz de confusion y da una mejor vision de las predicciones correctas e incorrectas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def nice_cm(y_test, y_test_pred_class, labels=['False', 'True']):\n",
+ " cm = confusion_matrix(y_test, y_test_pred_class)\n",
+ " pred_labels = ['Predicted ' + l for l in labels]\n",
+ " \n",
+ " df = pd.DataFrame(cm, index=labels,\n",
+ " columns=pred_labels)\n",
+ " return df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Predicted 0</th>\n",
+ " <th>Predicted 1</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>2105</td>\n",
+ " <td>166</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>436</td>\n",
+ " <td>293</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " Predicted 0 Predicted 1\n",
+ "0 2105 166\n",
+ "1 436 293"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nice_cm(y_test, y_test_pred_class, ['0', '1'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Precision:\t0.638\n",
+ "Recall: \t0.402\n",
+ "F1 Score: \t0.493\n"
+ ]
+ }
+ ],
+ "source": [
+ "precision = precision_score(y_test, y_test_pred_class) #La precisión es la relación tp / (tp + fp) donde tp es el número de verdaderos positivos y fp el número de falsos positivos. La precisión es intuitivamente la capacidad del clasificador de no etiquetar como positiva una muestra que es negativa.El mejor valor es 1 y el peor valor es 0.\n",
+ "print('Precision:\\t{:.3f}'.format(precision))\n",
+ "\n",
+ "recall = recall_score(y_test, y_test_pred_class) #La recuperación es la relación tp / (tp + fn) donde tp es el número de verdaderos positivos y fn el número de falsos negativos. El retiro es intuitivamente la capacidad del clasificador de encontrar todas las muestras positivas. El mejor valor es 1 y el peor valor es 0.\n",
+ "print('Recall: \\t{:.3f}'.format(recall))\n",
+ "\n",
+ "f1 = f1_score(y_test, y_test_pred_class) #La puntuación F1 se puede interpretar como un promedio ponderado de la precisión y la recuperación, donde una puntuación F1 alcanza su mejor valor en 1 y la peor puntuación en 0.\n",
+ "print('F1 Score: \\t{:.3f}'.format(f1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Aunque tuviéramos mucho cuidado al dividir nuestros datos de forma aleatoria, esa es sólo una de las muchas formas posibles de realizar una división. No sólo tendrÃamos una estimación más precisa del `accuracy` real, sino que también podrÃamos calcular la desviación estándar de las puntuaciones y, por lo tanto, conocer el error en el `accuracy`. Este proceso se conoce como `cross-validation` y la forma usual de implementarlo es a traves de `K-fold cross-validation`.\n",
+ "\n",
+ "En `K-fold cross-validation`, el conjunto de datos se divide en `K` subconjuntos aleatorios de igual tamaño. Entonces, cada uno de los `K` subconjuntos desempeña el papel de conjunto de prueba, mientras que los demás se agregan para formar un conjunto de entrenamiento. De este modo, obtenemos `K` estimaciones de la puntuación del modelo, cada una calculada a partir de un conjunto de pruebas que no se solapa con ninguno de los otros conjuntos de pruebas.\n",
+ "\n",
+ "Usando 5-Fold cross-validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tensorflow.keras.wrappers.scikit_learn import KerasClassifier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def build_logistic():\n",
+ " logistic = Sequential()\n",
+ " logistic.add(Dense(1, input_dim=20,\n",
+ " activation='sigmoid'))\n",
+ " logistic.compile(optimizer=SGD(lr=.5),\n",
+ " loss='binary_crossentropy',\n",
+ " metrics=['accuracy'])\n",
+ " return logistic"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "logistic = KerasClassifier(build_fn=build_logistic,\n",
+ " epochs=25, verbose=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cv = KFold(5, shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.79866666 0.79066664 0.79866666 0.79299998 0.79859954]\n"
+ ]
+ }
+ ],
+ "source": [
+ "scores = cross_val_score(logistic, X, y, cv=cv)\n",
+ "print(scores) #una forma de mejorar esos valores es picando la data en mas segmentos o a lo mejor tratar de utilizar otro metodo de validacion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cross Validation Accuracy: 0.7959 ± 0.0038\n"
+ ]
+ }
+ ],
+ "source": [
+ "m = scores.mean()\n",
+ "s = scores.std(ddof=1)\n",
+ "\n",
+ "print('Cross Validation Accuracy:',\n",
+ " '{:.4f} ± {:.4f}'.format(m, s))"
+ ]
+ }
+ ],
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/reporte/Ejercicio3_MildredArias.ipynb b/reporte/Ejercicio3_MildredArias.ipynb
new file mode 100644
index 0000000..f0e9a88
--- /dev/null
+++ b/reporte/Ejercicio3_MildredArias.ipynb
@@ -0,0 +1,484 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Ejercicio3_MildredArias\n",
+ "## Apredizaje No-Supervisado\n",
+ "mildarias181@gmail.com"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow.keras as tf\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "from sklearn.manifold import TSNE"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with open('common.py') as fin:\n",
+ " msg = fin.read()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib as mpl\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import seaborn as sb\n",
+ "import warnings\n",
+ "import logging\n",
+ "\n",
+ "logging.getLogger(\"tensorflow\").disabled = True\n",
+ "warnings.simplefilter(\"ignore\")\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "print(msg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "exec(msg)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Cargando la data. Trabajaremos con la archiconocida data MNIST"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(X_train, y_train), (X_test,y_test) = tf.datasets.fashion_mnist.load_data()\n",
+ "#print(X_train.shape,X_test.shape,y_train.shape, y_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train shape: (60000, 28, 28)\n",
+ "El valor maximo de un pixel es: 255\n",
+ "Label shape: (60000,)\n",
+ "Los labels son: [0 1 2 3 4 5 6 7 8 9]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Train shape: ', X_train.shape)\n",
+ "print('El valor maximo de un pixel es:', X_train.max())\n",
+ "print('Label shape: ', y_train.shape)\n",
+ "print('Los labels son:', np.unique(y_train))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Vemos que tenemos 60000 imagenes de 28x28 pixels. Examinemos varias de ellas"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 0 Axes>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 576x576 with 10 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 432x288 with 0 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "figure,eje= plt.subplots(5,2, sharex=True, figsize=(8,8))\n",
+ "eje[0][0].set_title(y_train[0])\n",
+ "eje[0][0].imshow(X_train[0,:,:])\n",
+ "eje[0][1].set_title(y_train[1])\n",
+ "eje[0][1].imshow(X_train[1,:,:])\n",
+ "eje[1][0].set_title(y_train[2])\n",
+ "eje[1][0].imshow(X_train[2,:,:])\n",
+ "eje[1][1].set_title(y_train[3])\n",
+ "eje[1][1].imshow(X_train[3,:,:])\n",
+ "eje[2][0].set_title(y_train[4])\n",
+ "eje[2][0].imshow(X_train[4,:,:])\n",
+ "eje[2][1].set_title(y_train[5])\n",
+ "eje[2][1].imshow(X_train[5,:,:])\n",
+ "eje[3][0].set_title(y_train[6])\n",
+ "eje[3][0].imshow(X_train[6,:,:])\n",
+ "eje[3][1].set_title(y_train[7])\n",
+ "eje[3][1].imshow(X_train[7,:,:])\n",
+ "eje[4][0].set_title(y_train[8])\n",
+ "eje[4][0].imshow(X_train[8,:,:])\n",
+ "eje[4][1].set_title(y_train[9])\n",
+ "eje[4][1].imshow(X_train[9,:,:])\n",
+ "plt.figure()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "255\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(X_train.max())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Normalizamos la data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_train = X_train.astype(np.float32) / 255\n",
+ "X_test = X_test.astype(np.float32) / 255"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(X_train.max())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Autoencoders\n",
+ "\n",
+ "Un `autoencoder` toma las entradas, las convierte en una representacion latente eficiente (de menor dimensionalidad) y luego produce algo que (con un poco de suerte) se parece mucho a las entradas. Un `autoencoder` se compone siempre de dos partes: un `encoder` (o red de reconocimiento) que convierte las entradas en una representacion latente, seguida de un `decoder` (o red generativa) que convierte la representacion interna a las salidas.\n",
+ "Los `autoencoder` poseen la misma arquitectura densa que hemos usado en los ejemplos anteriores con la particularidad de que el numero de neuronas de salidas es igual al numero de neuronas de entrada."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Entrenando la red\n",
+ "\n",
+ "Construimos un autoencoder simple, con una unica capa oculta (que sera nuestro bottleneck)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.random.seed(42)\n",
+ "\n",
+ "encoder = tf.models.Sequential([\n",
+ " tf.layers.Flatten(input_shape=[28, 28]),\n",
+ " #tf.layers.Dense(250, activation=\"sigmoid\"),\n",
+ " tf.layers.Dense(100)\n",
+ "])\n",
+ "decoder = tf.models.Sequential([\n",
+ " tf.layers.Dense(28*28, activation=\"sigmoid\", input_shape=[100]),\n",
+ " #tf.layers.Dense(28*28, activation=\"sigmoid\"),\n",
+ " tf.layers.Reshape([28, 28])\n",
+ "])\n",
+ "\n",
+ "autoencoder = tf.models.Sequential([encoder, decoder])\n",
+ "\n",
+ "autoencoder.compile(loss=\"mse\", optimizer=tf.optimizers.SGD(lr=1.9))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0482\n",
+ "Epoch 2/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0251\n",
+ "Epoch 3/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0199\n",
+ "Epoch 4/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0175\n",
+ "Epoch 5/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0159\n",
+ "Epoch 6/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0147\n",
+ "Epoch 7/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0138\n",
+ "Epoch 8/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0130\n",
+ "Epoch 9/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0124\n",
+ "Epoch 10/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0119\n",
+ "Epoch 11/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0115\n",
+ "Epoch 12/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0111\n",
+ "Epoch 13/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0108\n",
+ "Epoch 14/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0105\n",
+ "Epoch 15/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0103\n",
+ "Epoch 16/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0100\n",
+ "Epoch 17/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0098\n",
+ "Epoch 18/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0096\n",
+ "Epoch 19/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0094\n",
+ "Epoch 20/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0092\n"
+ ]
+ }
+ ],
+ "source": [
+ "f_history = autoencoder.fit(X_train, X_train, epochs=20)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Podemos visualizar algunas de las predicciones para las imagenes (en el espacio original), haciendo `predict` del autoencoder completo."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "f_codings = autoencoder.predict(X_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def plot_image(image):\n",
+ " plt.imshow(image, cmap=\"binary\")\n",
+ " plt.axis(\"off\")\n",
+ "\n",
+ "def show_reconstructions(model, images, n_images=5):\n",
+ " reconstructions = model.predict(images[:n_images])\n",
+ " fig = plt.figure(figsize=(n_images * 1.5, 3))\n",
+ " for image_index in range(n_images):\n",
+ " plt.subplot(2, n_images, 1 + image_index)\n",
+ " plot_image(images[image_index])\n",
+ " plt.subplot(2, n_images, 1 + n_images + image_index)\n",
+ " plot_image(reconstructions[image_index])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 540x216 with 10 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_reconstructions(autoencoder, X_train)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.random.seed(42)\n",
+ "\n",
+ "X_compressed = encoder.predict(X_train)\n",
+ "tsne = TSNE()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "para visualizar el espacio reducido t-SNE. Es un algoritmo de reduccion dimensional "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_2D = tsne.fit_transform(X_compressed)\n",
+ "X_2D = (X_2D - X_2D.min()) / (X_2D.max() - X_2D.min())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1080x720 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "N = 10000 # numero de puntos a graficar\n",
+ "\n",
+ "plt.figure(figsize=(15, 10))\n",
+ "cmap = plt.cm.tab10\n",
+ "plt.scatter(X_2D[:N, 0], X_2D[:N, 1], c=y_train[:N], s=10, cmap=cmap, alpha=.4)\n",
+ "image_positions = np.array([[1., 1.]])\n",
+ "for index, position in enumerate(X_2D[:N]):\n",
+ " dist = np.sum((position - image_positions) ** 2, axis=1)\n",
+ " if np.min(dist) > 0.02:\n",
+ " image_positions = np.r_[image_positions, [position]]\n",
+ " imagebox = mpl.offsetbox.AnnotationBbox(\n",
+ " mpl.offsetbox.OffsetImage(X_train[index], cmap=\"binary\"),\n",
+ " position, bboxprops={\"edgecolor\": cmap(y_train[index]), \"lw\": 2})\n",
+ " plt.gca().add_artist(imagebox)\n",
+ "plt.axis(\"off\")\n",
+ "#save_fig()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "El modelo mejora un poco si se aumenta el numero de neuronas, pero si aumentamos el numero de capas ocultas no funciona bien"
+ ]
+ }
+ ],
+ "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": "3.7.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/reporte/asignaciones.md b/reporte/asignaciones.md
new file mode 100644
index 0000000..d61724d
--- /dev/null
+++ b/reporte/asignaciones.md
@@ -0,0 +1,13 @@
+# Asignaciones
+
+Para la tercera sesión, es necesario realizar algunas actividades prácticas. Encontrarás estas asignaciones al final de cada jupyter notebook.
+
+## Asignación 1
+
+Consiste en dos ejercicios donde se reproduce lo trabajado en la sesión 2 relativa al aprendizaje supervisado con algunos elementos adicionales. Hay, además, algunas actividades marcadas como opcionales que serÃa interesante fueran abordadas. En general no son complicadas ni requiren un trabajo exhaustivo de código.
+
+## Asignación 2
+
+Consiste en un ejercicio donde se reproduce lo trabajado en la sesión 2 relativa al aprendizaje no-supervisado con algunos elementos adicionales.
+
+(*Nota: el trabajo mÃnimo requerido es enfrentarse a los ejercicios planteados en ambos cuadernos, pudiendo dejarse las secciones opcionales para más adelante*)
diff --git a/reporte/common.py b/reporte/common.py
new file mode 100644
index 0000000..04222f1
--- /dev/null
+++ b/reporte/common.py
@@ -0,0 +1,11 @@
+import pandas as pd
+import numpy as np
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+import seaborn as sb
+import warnings
+import logging
+
+logging.getLogger("tensorflow").disabled = True
+warnings.simplefilter("ignore")
\ No newline at end of file
diff --git a/reporte/datasets/HR_comma_sep.csv b/reporte/datasets/HR_comma_sep.csv
new file mode 100644
index 0000000..be6211c
--- /dev/null
+++ b/reporte/datasets/HR_comma_sep.csv
@@ -0,0 +1,15000 @@
+satisfaction_level,last_evaluation,number_project,average_montly_hours,time_spend_company,Work_accident,left,promotion_last_5years,sales,salary
+0.38,0.53,2,157,3,0,1,0,sales,low
+0.8,0.86,5,262,6,0,1,0,sales,medium
+0.11,0.88,7,272,4,0,1,0,sales,medium
+0.72,0.87,5,223,5,0,1,0,sales,low
+0.37,0.52,2,159,3,0,1,0,sales,low
+0.41,0.5,2,153,3,0,1,0,sales,low
+0.1,0.77,6,247,4,0,1,0,sales,low
+0.92,0.85,5,259,5,0,1,0,sales,low
+0.89,1,5,224,5,0,1,0,sales,low
+0.42,0.53,2,142,3,0,1,0,sales,low
+0.45,0.54,2,135,3,0,1,0,sales,low
+0.11,0.81,6,305,4,0,1,0,sales,low
+0.84,0.92,4,234,5,0,1,0,sales,low
+0.41,0.55,2,148,3,0,1,0,sales,low
+0.36,0.56,2,137,3,0,1,0,sales,low
+0.38,0.54,2,143,3,0,1,0,sales,low
+0.45,0.47,2,160,3,0,1,0,sales,low
+0.78,0.99,4,255,6,0,1,0,sales,low
+0.45,0.51,2,160,3,1,1,1,sales,low
+0.76,0.89,5,262,5,0,1,0,sales,low
+0.11,0.83,6,282,4,0,1,0,sales,low
+0.38,0.55,2,147,3,0,1,0,sales,low
+0.09,0.95,6,304,4,0,1,0,sales,low
+0.46,0.57,2,139,3,0,1,0,sales,low
+0.4,0.53,2,158,3,0,1,0,sales,low
+0.89,0.92,5,242,5,0,1,0,sales,low
+0.82,0.87,4,239,5,0,1,0,sales,low
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+0.46,0.5,2,156,3,0,1,0,technical,medium
+0.39,0.45,2,134,3,0,1,0,technical,medium
+0.09,0.88,6,269,4,0,1,0,technical,medium
+0.09,0.77,6,290,4,0,1,0,technical,medium
+0.37,0.51,2,132,3,0,1,0,technical,medium
+0.1,0.89,7,308,4,0,1,0,technical,medium
+0.77,1,4,232,5,0,1,0,technical,medium
+0.79,0.86,5,235,5,0,1,0,support,medium
+0.43,0.55,2,130,3,0,1,0,support,high
+0.38,0.53,2,146,3,0,1,0,support,low
+0.77,0.91,5,221,6,0,1,0,support,medium
+0.44,0.5,2,130,3,0,1,0,support,medium
+0.39,0.46,2,136,3,0,1,0,support,medium
+0.1,0.79,6,275,4,0,1,0,management,low
+0.1,0.9,6,299,4,0,1,0,marketing,low
+0.36,0.49,2,147,3,0,1,0,marketing,low
+0.1,0.97,7,306,4,0,1,0,marketing,low
+0.84,1,5,242,5,0,1,0,sales,low
+0.38,0.51,2,159,3,0,1,0,sales,low
+0.41,0.49,2,147,3,0,1,0,sales,low
+0.37,0.51,2,154,3,1,1,0,sales,low
+0.43,0.56,2,129,3,0,1,0,sales,low
+0.46,0.53,2,161,3,0,1,0,sales,low
+0.09,0.84,6,269,4,0,1,0,sales,low
+0.78,0.86,5,274,5,0,1,0,sales,low
+0.45,0.53,2,159,3,0,1,0,sales,low
+0.42,0.47,2,135,3,0,1,0,sales,low
+0.46,0.53,2,147,3,0,1,0,sales,low
+0.39,0.49,2,142,3,0,1,0,sales,low
+0.36,0.51,2,130,3,0,1,0,sales,low
+0.43,0.53,2,147,3,0,1,0,sales,medium
+0.85,0.87,5,246,5,1,1,0,sales,medium
+0.11,0.92,6,281,4,0,1,0,sales,medium
+0.11,0.9,6,253,4,0,1,0,sales,medium
+0.38,0.47,2,128,3,0,1,0,sales,medium
+0.43,0.57,2,129,3,0,1,0,sales,medium
+0.75,1,5,223,6,0,1,0,accounting,medium
+0.11,0.92,6,269,4,0,1,0,accounting,medium
+0.1,0.9,7,269,4,0,1,0,accounting,medium
+0.1,0.81,7,244,5,0,1,0,hr,medium
+0.37,0.5,2,154,3,0,1,0,hr,medium
+0.11,0.93,5,140,5,0,1,0,hr,medium
+0.45,0.46,2,159,3,0,1,0,hr,high
+0.44,0.48,2,158,3,0,1,0,technical,low
+0.44,0.56,2,133,3,0,1,0,technical,medium
+0.11,0.77,6,247,4,0,1,0,technical,medium
+0.79,0.93,5,268,5,0,1,0,technical,medium
+0.8,0.9,5,267,5,0,1,0,technical,medium
+0.1,0.87,7,251,5,0,1,0,technical,low
+0.09,0.93,6,279,4,0,1,0,technical,low
+0.7,0.84,6,161,4,0,1,0,technical,low
+0.72,0.84,4,256,5,0,1,0,technical,low
+0.11,0.8,6,304,4,0,1,0,technical,low
+0.39,0.51,2,137,3,0,1,0,technical,low
+0.4,0.49,2,144,3,0,1,0,support,low
+0.43,0.54,2,142,3,0,1,0,support,low
+0.76,0.87,5,262,5,0,1,0,support,low
+0.4,0.48,2,142,3,0,1,0,support,low
+0.09,0.89,6,282,4,0,1,0,support,low
+0.37,0.54,2,157,3,0,1,0,support,low
+0.87,0.91,5,228,5,0,1,0,support,low
+0.1,0.86,6,283,4,0,1,0,support,low
+0.11,0.86,6,286,4,0,1,0,support,low
+0.43,0.5,2,148,3,0,1,0,support,low
+0.1,0.81,6,245,4,0,1,0,support,low
+0.11,0.95,6,279,4,0,1,0,technical,low
+0.85,0.87,5,245,5,0,1,0,technical,low
+0.37,0.49,2,138,3,0,1,0,technical,low
+0.44,0.52,2,141,3,0,1,0,management,low
+0.1,0.83,7,302,5,0,1,0,IT,medium
+0.11,0.89,6,268,4,0,1,0,IT,medium
+0.87,0.88,5,240,5,0,1,0,IT,medium
+0.39,0.49,2,127,3,0,1,0,IT,medium
+0.1,0.94,7,264,4,0,1,0,IT,medium
+0.44,0.53,2,155,3,0,1,0,product_mng,medium
+0.4,0.49,2,143,3,0,1,0,product_mng,medium
+0.76,0.98,5,217,6,0,1,0,product_mng,medium
+0.46,0.55,2,147,3,0,1,0,product_mng,medium
+0.9,0.92,4,271,5,0,1,0,IT,medium
+0.85,0.87,4,273,5,0,1,0,RandD,medium
+0.1,0.78,5,285,4,1,1,0,RandD,medium
+0.43,0.49,2,131,3,0,1,0,RandD,high
+0.2,0.5,5,135,6,0,1,0,RandD,low
+0.81,0.92,5,239,5,0,1,0,RandD,medium
+0.83,0.85,5,237,5,0,1,0,marketing,medium
+0.14,0.75,4,277,5,1,1,0,sales,medium
+0.1,0.84,5,303,5,0,1,0,accounting,medium
+0.91,0.98,4,242,6,0,1,0,support,low
+0.37,0.57,2,158,3,0,1,0,technical,low
+0.42,0.57,2,147,3,1,1,0,management,low
+0.39,0.68,2,282,5,0,1,0,marketing,low
+0.39,0.54,2,154,3,0,1,0,marketing,low
+0.44,0.52,2,149,3,0,1,0,marketing,low
+0.37,0.45,2,149,3,0,1,0,sales,low
+0.39,0.53,2,146,3,0,1,0,sales,low
+0.72,0.94,4,258,5,0,1,0,sales,low
+0.37,0.49,2,148,3,0,1,0,sales,low
+0.82,0.94,5,236,5,0,1,0,sales,low
+0.42,0.52,2,134,3,0,1,0,sales,low
+0.59,1,2,155,5,0,1,0,sales,low
+0.82,0.86,5,257,5,0,1,0,sales,low
+0.73,0.97,6,189,2,0,1,0,sales,low
+0.78,0.66,3,164,3,0,1,0,sales,low
+0.09,0.95,6,271,4,0,1,0,sales,low
+0.1,0.97,6,280,4,0,1,0,sales,low
+0.45,0.46,2,149,3,0,1,0,sales,low
+0.83,0.81,5,219,5,0,1,0,sales,low
+0.43,0.51,2,128,3,0,1,0,sales,low
+0.4,0.47,2,128,3,0,1,0,sales,medium
+0.43,0.46,2,157,3,0,1,0,sales,medium
+0.78,0.93,4,225,5,0,1,0,sales,medium
+0.39,0.45,2,140,3,0,1,0,sales,medium
+0.11,0.97,6,310,4,0,1,0,accounting,medium
+0.36,0.52,2,143,3,0,1,0,accounting,medium
+0.36,0.54,2,153,3,0,1,0,accounting,medium
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+0.4,0.47,2,136,3,0,1,0,hr,medium
+0.81,0.85,4,251,6,0,1,0,hr,medium
+0.4,0.47,2,144,3,0,1,0,hr,medium
+0.09,0.93,6,296,4,0,1,0,technical,medium
+0.76,0.89,5,238,5,0,1,0,technical,high
+0.73,0.93,5,162,4,0,1,0,technical,low
+0.38,0.49,2,137,3,0,1,0,technical,medium
+0.72,0.84,5,257,5,0,1,0,technical,medium
+0.4,0.56,2,148,3,0,1,0,technical,medium
+0.91,0.99,5,254,5,0,1,0,technical,medium
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+0.43,0.57,2,159,3,1,1,0,technical,low
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+0.37,0.52,2,158,3,0,1,0,support,low
\ No newline at end of file
diff --git a/reporte/datasets/housing-data.csv b/reporte/datasets/housing-data.csv
new file mode 100644
index 0000000..d53b2ef
--- /dev/null
+++ b/reporte/datasets/housing-data.csv
@@ -0,0 +1,48 @@
+sqft,bdrms,age,price
+2104,3,70,399900
+1600,3,28,329900
+2400,3,44,369000
+1416,2,49,232000
+3000,4,75,539900
+1985,4,61,299900
+1534,3,12,314900
+1427,3,57,198999
+1380,3,14,212000
+1494,3,15,242500
+1940,4,7,239999
+2000,3,27,347000
+1890,3,45,329999
+4478,5,49,699900
+1268,3,58,259900
+2300,4,77,449900
+1320,2,62,299900
+1236,3,78,199900
+2609,4,5,499998
+3031,4,21,599000
+1767,3,44,252900
+1888,2,79,255000
+1604,3,13,242900
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+3890,3,36,573900
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+1458,3,29,464500
+2526,3,13,469000
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+2637,3,25,299900
+1839,2,40,349900
+1000,1,5,169900
+2040,4,75,314900
+3137,3,67,579900
+1811,4,24,285900
+1437,3,50,249900
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+2132,4,28,345000
+4215,4,66,549000
+2162,4,43,287000
+1664,2,40,368500
+2238,3,37,329900
+2567,4,57,314000
+1200,3,76,299000
+852,2,70,179900
+1852,4,64,299900
+1203,3,11,239500
diff --git a/reporte/datasets/titanic-train.csv b/reporte/datasets/titanic-train.csv
new file mode 100644
index 0000000..5cc466e
--- /dev/null
+++ b/reporte/datasets/titanic-train.csv
@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
+37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
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diff --git a/reporte/datasets/user_visit_duration.csv b/reporte/datasets/user_visit_duration.csv
new file mode 100644
index 0000000..02ac335
--- /dev/null
+++ b/reporte/datasets/user_visit_duration.csv
@@ -0,0 +1,101 @@
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diff --git a/reporte/datasets/weight-height.csv b/reporte/datasets/weight-height.csv
new file mode 100644
index 0000000..361c9b5
--- /dev/null
+++ b/reporte/datasets/weight-height.csv
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diff --git a/reporte/environment.yml b/reporte/environment.yml
new file mode 100644
index 0000000..c22a701
--- /dev/null
+++ b/reporte/environment.yml
@@ -0,0 +1,28 @@
+name: lacongaml
+channels:
+- defaults
+dependencies:
+- python=3.7.*
+- bz2file==0.98
+- cython==0.29.*
+- numpy==1.16.*
+- flask==1.0.*
+- gensim==3.4.*
+- h5py==2.9.*
+- jupyter==1.0.*
+- matplotlib==3.0.*
+- nomkl==3.0.*
+- pandas==0.24.*
+- pillow==5.4.*
+- pip==19.0.*
+- pytest==4.1.*
+- pyhamcrest==1.9.*
+- scikit-learn==0.20.*
+- scipy==1.2.*
+- seaborn==0.9.*
+- setuptools==40.8.*
+- twisted==18.9.*
+- pip:
+ - jupyter_contrib_nbextensions==0.5.*
+ - tensorflow==2.0.0-alpha0
+ - tensorflow-serving-api==1.13.*
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diff --git a/reporte/ml-in-science-tarea-01 b/reporte/ml-in-science-tarea-01
new file mode 160000
index 0000000..6ba57cf
--- /dev/null
+++ b/reporte/ml-in-science-tarea-01
@@ -0,0 +1 @@
+Subproject commit 6ba57cf5baec81c8e1976d3c5e4c88e511bd49f5
diff --git a/reporte/presentacion.pdf b/reporte/presentacion.pdf
new file mode 100644
index 0000000..e69de29
--
GitLab