Ejercicio_PCA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio Aplicando PCA: Principal Component Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lee el articulo completo en www.aprendemachinelearning.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importamos librerias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:36.607020Z",
     "start_time": "2018-10-08T14:32:28.345589Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (16, 9)\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cargamos datos de entrada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:36.786815Z",
     "start_time": "2018-10-08T14:32:36.610868Z"
    }
   },
   "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>ingresos</th>\n",
       "      <th>gastos_comunes</th>\n",
       "      <th>pago_coche</th>\n",
       "      <th>gastos_otros</th>\n",
       "      <th>ahorros</th>\n",
       "      <th>vivienda</th>\n",
       "      <th>estado_civil</th>\n",
       "      <th>hijos</th>\n",
       "      <th>trabajo</th>\n",
       "      <th>comprar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>4653</td>\n",
       "      <td>1024</td>\n",
       "      <td>447</td>\n",
       "      <td>703</td>\n",
       "      <td>16952</td>\n",
       "      <td>392957</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>3390</td>\n",
       "      <td>881</td>\n",
       "      <td>194</td>\n",
       "      <td>584</td>\n",
       "      <td>34601</td>\n",
       "      <td>304112</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2969</td>\n",
       "      <td>623</td>\n",
       "      <td>72</td>\n",
       "      <td>620</td>\n",
       "      <td>29663</td>\n",
       "      <td>289205</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>4169</td>\n",
       "      <td>1292</td>\n",
       "      <td>48</td>\n",
       "      <td>333</td>\n",
       "      <td>28503</td>\n",
       "      <td>239872</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>4054</td>\n",
       "      <td>1095</td>\n",
       "      <td>371</td>\n",
       "      <td>440</td>\n",
       "      <td>22183</td>\n",
       "      <td>223971</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>3831</td>\n",
       "      <td>690</td>\n",
       "      <td>352</td>\n",
       "      <td>488</td>\n",
       "      <td>10723</td>\n",
       "      <td>363120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>3961</td>\n",
       "      <td>1030</td>\n",
       "      <td>270</td>\n",
       "      <td>475</td>\n",
       "      <td>21880</td>\n",
       "      <td>280421</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>3184</td>\n",
       "      <td>955</td>\n",
       "      <td>276</td>\n",
       "      <td>684</td>\n",
       "      <td>35565</td>\n",
       "      <td>388025</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>3334</td>\n",
       "      <td>867</td>\n",
       "      <td>369</td>\n",
       "      <td>652</td>\n",
       "      <td>19985</td>\n",
       "      <td>376892</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>3988</td>\n",
       "      <td>1157</td>\n",
       "      <td>105</td>\n",
       "      <td>382</td>\n",
       "      <td>11980</td>\n",
       "      <td>257580</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ingresos  gastos_comunes  pago_coche  gastos_otros  ahorros  vivienda  \\\n",
       "192      4653            1024         447           703    16952    392957   \n",
       "193      3390             881         194           584    34601    304112   \n",
       "194      2969             623          72           620    29663    289205   \n",
       "195      4169            1292          48           333    28503    239872   \n",
       "196      4054            1095         371           440    22183    223971   \n",
       "197      3831             690         352           488    10723    363120   \n",
       "198      3961            1030         270           475    21880    280421   \n",
       "199      3184             955         276           684    35565    388025   \n",
       "200      3334             867         369           652    19985    376892   \n",
       "201      3988            1157         105           382    11980    257580   \n",
       "\n",
       "     estado_civil  hijos  trabajo  comprar  \n",
       "192             0      0        3        0  \n",
       "193             2      2        5        0  \n",
       "194             2      2        6        0  \n",
       "195             2      1        7        1  \n",
       "196             1      0        3        0  \n",
       "197             0      0        2        0  \n",
       "198             2      3        8        0  \n",
       "199             1      3        8        0  \n",
       "200             1      2        5        0  \n",
       "201             0      0        4        0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe = pd.read_csv(r\"comprar_alquilar.csv\")\n",
    "dataframe.tail(10)\n",
    "# REFERENCIAS:\n",
    "# ingresos y gastos son mensuales de 1 personas o 2 si están casados.\n",
    "# trabajo: 0-sin trabajo 1-autonomo 2-asalariado 3-empresario 4-Autonomos 5-Asalariados 6-Autonomo y Asalariado 7-Empresario y Autonomo 8 Empresarios o empresario y autónomo\n",
    "# estado_civil: 0-soltero 1-casado 2-divorciado\n",
    "# hijos: Cantidad de hijos menores (no trabajan)\n",
    "# comprar: 0-mejor alquilar 1-Comprar casa\n",
    "# hipoteca fija a 30 años con interes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualicemos las dimensiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:41.251348Z",
     "start_time": "2018-10-08T14:32:36.791824Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes =plt.subplots(5,2, figsize=(12, 9)) # 2 columnas cada una con 5 figuras, total 10 features\n",
    "alquilar=dataframe[dataframe['comprar']==0] # conviene alquilar casa\n",
    "comprar=dataframe[dataframe['comprar']==1] # conviene comprar casa\n",
    "ax=axes.ravel()\n",
    "for i in range(len(dataframe.columns)):\n",
    "  _,bins=np.histogram(dataframe[dataframe.columns[i]],bins=40)\n",
    "  ax[i].hist(alquilar.values[:,i],bins=bins,color='r',alpha=.5)# rojo alquilar\n",
    "  ax[i].hist(comprar.values[:,i],bins=bins,color='g',alpha=0.3)# verde para comprar\n",
    "  ax[i].set_title(dataframe.columns[i],fontsize=9)\n",
    "  ax[i].axes.get_xaxis().set_visible(False)\n",
    "  ax[i].set_yticks(())\n",
    "ax[0].legend(['alquilar','comprar'],loc='best',fontsize=8)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ejemplo de como se correlacionan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:41.891242Z",
     "start_time": "2018-10-08T14:32:41.257866Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)#fisrt plot\n",
    "plt.scatter(dataframe['ingresos'], dataframe['ahorros'], s=dataframe['vivienda']*0.0005, color='magenta', label='check', alpha=0.3)\n",
    "plt.xlabel('Ingresos',fontsize=12)\n",
    "plt.ylabel('Ahorros',fontsize=12)\n",
    "plt.subplot(1,2,2)# 2nd plot\n",
    "plt.scatter(dataframe['gastos_comunes'], dataframe['gastos_otros'], s=dataframe['vivienda']*0.0005, color='purple', label='check', alpha=0.3)\n",
    "plt.xlabel('Gastos Comunes',fontsize=12)\n",
    "plt.ylabel('Vivienda',fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vamos a Normalizar ó Estandalizar los datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:41.912248Z",
     "start_time": "2018-10-08T14:32:41.895141Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after scaling minimum [-1.75791345 -2.16475652 -1.69895439 -1.87508126 -1.64128552 -1.44667719\n",
      " -1.22708822 -0.90342598 -1.77508704]\n"
     ]
    }
   ],
   "source": [
    "scaler=StandardScaler()#instantiate\n",
    "df = dataframe.drop(['comprar'], axis=1)\n",
    "scaler.fit(df) # calcula la media y estandar para cada dimension\n",
    "X_scaled=scaler.transform(df)# transforma los datos a su nueva escala"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aplicamos PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:41.946179Z",
     "start_time": "2018-10-08T14:32:41.915977Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of X_pca (202, 9)\n"
     ]
    }
   ],
   "source": [
    "pca=PCA(n_components=9)\n",
    "#pca=PCA(.85)\n",
    "pca.fit(X_scaled) # buscar los componentes principales\n",
    "X_pca=pca.transform(X_scaled) \n",
    "#revisemos la forma del array\n",
    "print(\"shape of X_pca\", X_pca.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Veamos cuán buenos predictores son las nuevas dimensiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:41.967301Z",
     "start_time": "2018-10-08T14:32:41.949323Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.29913557 0.23294923 0.11711241 0.10685622 0.09635279 0.05629866\n",
      " 0.04145579 0.03017819 0.01966115]\n",
      "suma: 0.852406211771431\n"
     ]
    }
   ],
   "source": [
    "expl = pca.explained_variance_ratio_\n",
    "print(expl)\n",
    "print('suma:',sum(expl[0:5]))\n",
    "#con 5 componentes tenemos algo mas del 85% de varianza explicada"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### graficamos la variacion explicada acumulada"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.230724Z",
     "start_time": "2018-10-08T14:32:41.978652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.cumsum(pca.explained_variance_ratio_))\n",
    "plt.xlabel('number of components')\n",
    "plt.ylabel('cumulative explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:46:25.061457Z",
     "start_time": "2018-10-08T14:46:25.047874Z"
    }
   },
   "source": [
    "## Graficamos los 2 componentes principales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.526893Z",
     "start_time": "2018-10-08T14:32:42.234234Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xax=X_pca[:,0]\n",
    "Yax=X_pca[:,1]\n",
    "labels=dataframe['comprar'].values\n",
    "#labels=[0,1] # Malignant, Beningn\n",
    "cdict={0:'red',1:'green'}\n",
    "labl={0:'Alquilar',1:'Comprar'}\n",
    "marker={0:'*',1:'o'}\n",
    "alpha={0:.3, 1:.5}\n",
    "fig,ax=plt.subplots(figsize=(7,5))\n",
    "fig.patch.set_facecolor('white')\n",
    "for l in np.unique(labels):\n",
    "    ix=np.where(labels==l)\n",
    "    ax.scatter(Xax[ix],Yax[ix],c=cdict[l],label=labl[l],s=40,marker=marker[l],alpha=alpha[l])\n",
    "\n",
    "plt.xlabel(\"First Principal Component\",fontsize=14)\n",
    "plt.ylabel(\"Second Principal Component\",fontsize=14)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# please check the scatter plot of the remaining component and you will see and understand the difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Veamos la correlacion entre las nuevas componentes y las dimensiones originales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.848773Z",
     "start_time": "2018-10-08T14:32:42.531140Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/fastai-cpu/lib/python3.6/site-packages/matplotlib/figure.py:2299: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  warnings.warn(\"This figure includes Axes that are not compatible \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 518.4x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(pca.components_[0:5],cmap='viridis')\n",
    "plt.yticks([0,1,2,3,4],['1st Comp','2nd Comp','3rd Comp','4th Comp','5th Comp'],fontsize=10)\n",
    "plt.colorbar()\n",
    "plt.xticks(range(len(df.columns)),df.columns,rotation=65,ha='left')\n",
    "plt.tight_layout()\n",
    "plt.show()# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.891453Z",
     "start_time": "2018-10-08T14:32:42.859081Z"
    }
   },
   "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>ingresos</th>\n",
       "      <th>gastos_comunes</th>\n",
       "      <th>pago_coche</th>\n",
       "      <th>gastos_otros</th>\n",
       "      <th>ahorros</th>\n",
       "      <th>vivienda</th>\n",
       "      <th>estado_civil</th>\n",
       "      <th>hijos</th>\n",
       "      <th>trabajo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.493720</td>\n",
       "      <td>-0.318229</td>\n",
       "      <td>0.123771</td>\n",
       "      <td>0.111876</td>\n",
       "      <td>-0.444650</td>\n",
       "      <td>-0.462609</td>\n",
       "      <td>0.252279</td>\n",
       "      <td>0.259859</td>\n",
       "      <td>0.290248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.306959</td>\n",
       "      <td>0.147373</td>\n",
       "      <td>-0.091641</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.288435</td>\n",
       "      <td>0.148985</td>\n",
       "      <td>0.469044</td>\n",
       "      <td>0.521894</td>\n",
       "      <td>0.527156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.091419</td>\n",
       "      <td>0.363453</td>\n",
       "      <td>0.516144</td>\n",
       "      <td>-0.701986</td>\n",
       "      <td>-0.191767</td>\n",
       "      <td>-0.140513</td>\n",
       "      <td>0.172885</td>\n",
       "      <td>-0.112184</td>\n",
       "      <td>0.036910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.111222</td>\n",
       "      <td>0.081630</td>\n",
       "      <td>0.780268</td>\n",
       "      <td>0.590704</td>\n",
       "      <td>0.022839</td>\n",
       "      <td>0.150085</td>\n",
       "      <td>-0.007663</td>\n",
       "      <td>0.007404</td>\n",
       "      <td>-0.007493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.088897</td>\n",
       "      <td>0.763006</td>\n",
       "      <td>-0.265554</td>\n",
       "      <td>0.330744</td>\n",
       "      <td>-0.289002</td>\n",
       "      <td>-0.365556</td>\n",
       "      <td>-0.104639</td>\n",
       "      <td>0.043668</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.075477</td>\n",
       "      <td>0.067903</td>\n",
       "      <td>-0.153697</td>\n",
       "      <td>0.154422</td>\n",
       "      <td>-0.230181</td>\n",
       "      <td>0.290799</td>\n",
       "      <td>0.767070</td>\n",
       "      <td>-0.387964</td>\n",
       "      <td>-0.256446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.154319</td>\n",
       "      <td>-0.105598</td>\n",
       "      <td>0.027333</td>\n",
       "      <td>0.101876</td>\n",
       "      <td>0.524584</td>\n",
       "      <td>-0.641034</td>\n",
       "      <td>0.212365</td>\n",
       "      <td>-0.460067</td>\n",
       "      <td>0.105053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.012732</td>\n",
       "      <td>-0.010274</td>\n",
       "      <td>0.061759</td>\n",
       "      <td>-0.037977</td>\n",
       "      <td>0.212759</td>\n",
       "      <td>-0.260847</td>\n",
       "      <td>0.204816</td>\n",
       "      <td>0.532216</td>\n",
       "      <td>-0.745630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.776990</td>\n",
       "      <td>-0.374440</td>\n",
       "      <td>-0.048557</td>\n",
       "      <td>0.026631</td>\n",
       "      <td>-0.474168</td>\n",
       "      <td>-0.156101</td>\n",
       "      <td>-0.003289</td>\n",
       "      <td>0.011051</td>\n",
       "      <td>-0.060657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ingresos  gastos_comunes  pago_coche  gastos_otros   ahorros  vivienda  \\\n",
       "0 -0.493720       -0.318229    0.123771      0.111876 -0.444650 -0.462609   \n",
       "1  0.306959        0.147373   -0.091641      0.000112  0.288435  0.148985   \n",
       "2  0.091419        0.363453    0.516144     -0.701986 -0.191767 -0.140513   \n",
       "3  0.111222        0.081630    0.780268      0.590704  0.022839  0.150085   \n",
       "4  0.088897        0.763006   -0.265554      0.330744 -0.289002 -0.365556   \n",
       "5 -0.075477        0.067903   -0.153697      0.154422 -0.230181  0.290799   \n",
       "6  0.154319       -0.105598    0.027333      0.101876  0.524584 -0.641034   \n",
       "7  0.012732       -0.010274    0.061759     -0.037977  0.212759 -0.260847   \n",
       "8  0.776990       -0.374440   -0.048557      0.026631 -0.474168 -0.156101   \n",
       "\n",
       "   estado_civil     hijos   trabajo  \n",
       "0      0.252279  0.259859  0.290248  \n",
       "1      0.469044  0.521894  0.527156  \n",
       "2      0.172885 -0.112184  0.036910  \n",
       "3     -0.007663  0.007404 -0.007493  \n",
       "4     -0.104639  0.043668  0.000009  \n",
       "5      0.767070 -0.387964 -0.256446  \n",
       "6      0.212365 -0.460067  0.105053  \n",
       "7      0.204816  0.532216 -0.745630  \n",
       "8     -0.003289  0.011051 -0.060657  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "components = pd.DataFrame(pca.components_, columns = df.columns)\n",
    "components"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparo nuevas entradas para validar el algoritmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.939121Z",
     "start_time": "2018-10-08T14:32:42.896568Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ingresos  gastos_comunes  pago_coche  gastos_otros   ahorros  vivienda  \\\n",
      "0 -1.321915       -0.222950   -1.599863     -0.885014  0.646617 -0.700208   \n",
      "1 -1.147878        1.716967   -0.395722     -0.447960 -0.687805 -0.231969   \n",
      "2 -0.090369        1.836066   -1.359382      0.337505  0.742387  0.593030   \n",
      "3 -3.455898       -0.078279    0.202382     -1.573584 -0.068551 -0.051945   \n",
      "4 -0.916933       -1.184676   -0.190036      0.117804 -0.147753 -0.619750   \n",
      "\n",
      "   estado_civil     hijos   trabajo  comprar  \n",
      "0     -0.318031  0.751809  0.269984        1  \n",
      "1     -1.542487 -0.221799  0.438425        0  \n",
      "2      1.540495 -0.668120  0.153463        1  \n",
      "3     -0.909409 -0.684486 -0.129045        0  \n",
      "4      0.450419 -0.194341  0.687452        1  \n",
      "(202, 10)\n"
     ]
    }
   ],
   "source": [
    "# Split dataset in training and test datasets\n",
    "dfScaledPca = pd.DataFrame(X_pca, columns = df.columns)\n",
    "dfScaledPca[\"comprar\"]=dataframe[\"comprar\"]\n",
    "print (dfScaledPca.head())\n",
    "print(dfScaledPca.shape)\n",
    "X_train, X_test = train_test_split(dfScaledPca, test_size=0.2, random_state=4) #6 \n",
    "y_train =X_train[\"comprar\"]\n",
    "y_test = X_test[\"comprar\"]\n",
    "X_train = X_train.drop(['comprar'], axis=1)\n",
    "X_test = X_test.drop(['comprar'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediccion con Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Con datos de entrada con reducción de dimensiones por PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.964231Z",
     "start_time": "2018-10-08T14:32:42.945162Z"
    }
   },
   "outputs": [],
   "source": [
    "# Instantiate the classifier\n",
    "gnb = GaussianNB()\n",
    "# Train classifier\n",
    "gnb.fit(\n",
    "    X_train.values,\n",
    "    y_train\n",
    ")\n",
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resultados Obtenidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:42.984461Z",
     "start_time": "2018-10-08T14:32:42.968030Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión en el set de Entrenamiento: 0.93\n",
      "Precisión en el set de Test: 0.90\n"
     ]
    }
   ],
   "source": [
    "print('Precisión en el set de Entrenamiento: {:.2f}'\n",
    "     .format(gnb.score(X_train, y_train))) #batir  87\n",
    "print('Precisión en el set de Test: {:.2f}'\n",
    "     .format(gnb.score(X_test, y_test)))  #batir  90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:43.007910Z",
     "start_time": "2018-10-08T14:32:42.996517Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total de Muestras en Test: 41\n",
      "Fallos: 4\n"
     ]
    }
   ],
   "source": [
    "# Print Test results\n",
    "print(\"Total de Muestras en Test: {}\\nFallos: {}\"\n",
    "      .format(\n",
    "          X_test.shape[0],\n",
    "          (y_test != y_pred).sum()\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:32:43.034633Z",
     "start_time": "2018-10-08T14:32:43.016664Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[28  2]\n",
      " [ 2  9]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.93      0.93        30\n",
      "          1       0.82      0.82      0.82        11\n",
      "\n",
      "avg / total       0.90      0.90      0.90        41\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comprobamos con predicciones individuales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-08T14:52:03.448702Z",
     "start_time": "2018-10-08T14:52:03.440685Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "X_scaledTest=scaler.transform([[6830,1298,345,309,46761,429812,1,1,5], # 1\n",
    "                               [4054,1095,371,440,22183,223971,1,0,3], # 0\n",
    "                               [6455,1033,98,795,57463,321779,2,1,8], # 1\n",
    "                               [6470,1035,39,782,57439,606291,0,0,1] #0\n",
    "                    ])\n",
    "X_pcaTest=pca.transform(X_scaledTest) \n",
    "#print(X_pcaTest)\n",
    "print(gnb.predict(X_pcaTest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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