Ejercicio_Random_Forest.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejercicio Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Artículo completo en www.aprendemachinelearning.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:03:27.419272Z",
     "start_time": "2019-06-10T22:03:11.727446Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jbagnato/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n",
      "/Users/jbagnato/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from pylab import rcParams\n",
    "\n",
    "from imblearn.under_sampling import NearMiss\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.combine import SMOTETomek\n",
    "from imblearn.ensemble import BalancedBaggingClassifier\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "#set up graphic style in this case I am using the color scheme from xkcd.com\n",
    "rcParams['figure.figsize'] = 14, 8.7 # Golden Mean\n",
    "LABELS = [\"Normal\",\"Fraud\"]\n",
    "#col_list = [\"cerulean\",\"scarlet\"]# https://xkcd.com/color/rgb/\n",
    "#sns.set(style='white', font_scale=1.75, palette=sns.xkcd_palette(col_list))\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargamos Datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:03:31.407634Z",
     "start_time": "2019-06-10T22:03:27.423198Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
       "        text-align: right;\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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...         V21       V22       V23       V24  \\\n",
       "0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   \n",
       "1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   \n",
       "2  0.247676 -1.514654  ...    0.247998  0.771679  0.909412 -0.689281   \n",
       "3  0.377436 -1.387024  ...   -0.108300  0.005274 -0.190321 -1.175575   \n",
       "4 -0.270533  0.817739  ...   -0.009431  0.798278 -0.137458  0.141267   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descargar desde https://www.kaggle.com/mlg-ulb/creditcardfraud/data\n",
    "\n",
    "df = pd.read_csv(\"creditcard.csv\")\n",
    "df.head(n=5) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:03:41.130420Z",
     "start_time": "2019-06-10T22:03:41.124669Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(284807, 31)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vemos Desbalanceo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:04:01.076121Z",
     "start_time": "2019-06-10T22:04:01.062081Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    284315\n",
       "1       492\n",
       "Name: Class, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(df['Class'], sort = True) #class comparison 0=Normal 1=Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:04:35.859822Z",
     "start_time": "2019-06-10T22:04:35.771943Z"
    }
   },
   "outputs": [],
   "source": [
    "normal_df = df[df.Class == 0] #registros normales\n",
    "fraud_df = df[df.Class == 1] #casos de fraude"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:05:06.841086Z",
     "start_time": "2019-06-10T22:05:06.636496Z"
    }
   },
   "outputs": [],
   "source": [
    "y = df['Class']\n",
    "X = df.drop('Class', axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:05:29.371422Z",
     "start_time": "2019-06-10T22:05:29.366934Z"
    }
   },
   "outputs": [],
   "source": [
    "def mostrar_resultados(y_test, pred_y):\n",
    "    conf_matrix = confusion_matrix(y_test, pred_y)\n",
    "    plt.figure(figsize=(8, 8))\n",
    "    sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "    plt.title(\"Confusion matrix\")\n",
    "    plt.ylabel('True class')\n",
    "    plt.xlabel('Predicted class')\n",
    "    plt.show()\n",
    "    print (classification_report(y_test, pred_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ejecutamos Modelo con LogisticRegresion para poder Comparar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:06:08.148478Z",
     "start_time": "2019-06-10T22:05:48.871946Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jbagnato/anaconda3/envs/python36/lib/python3.6/site-packages/sklearn/utils/optimize.py:203: ConvergenceWarning: newton-cg failed to converge. Increase the number of iterations.\n",
      "  \"number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "def run_model_balanced(X_train, X_test, y_train, y_test):\n",
    "    clf = LogisticRegression(C=1.0,penalty='l2',random_state=1,solver=\"newton-cg\",class_weight=\"balanced\")\n",
    "    clf.fit(X_train, y_train)\n",
    "    return clf\n",
    "\n",
    "model = run_model_balanced(X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Veamos como responde en el test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:06:08.578474Z",
     "start_time": "2019-06-10T22:06:08.151477Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.98      0.99     85302\n",
      "           1       0.07      0.91      0.13       141\n",
      "\n",
      "    accuracy                           0.98     85443\n",
      "   macro avg       0.53      0.95      0.56     85443\n",
      "weighted avg       1.00      0.98      0.99     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_y = model.predict(X_test)\n",
    "mostrar_resultados(y_test, pred_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Probamos con Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ATENCION: Este modelo toma algo más de tiempo en entrenar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:38:18.090801Z",
     "start_time": "2019-06-10T22:37:01.497858Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100building tree 2 of 100building tree 3 of 100\n",
      "\n",
      "building tree 4 of 100\n",
      "\n",
      "building tree 5 of 100\n",
      "building tree 6 of 100\n",
      "building tree 7 of 100\n",
      "building tree 8 of 100\n",
      "building tree 9 of 100\n",
      "building tree 10 of 100\n",
      "building tree 11 of 100\n",
      "building tree 12 of 100\n",
      "building tree 13 of 100\n",
      "building tree 14 of 100\n",
      "building tree 15 of 100\n",
      "building tree 16 of 100\n",
      "building tree 17 of 100\n",
      "building tree 18 of 100\n",
      "building tree 19 of 100\n",
      "building tree 20 of 100\n",
      "building tree 21 of 100\n",
      "building tree 22 of 100\n",
      "building tree 23 of 100\n",
      "building tree 24 of 100\n",
      "building tree 25 of 100\n",
      "building tree 26 of 100\n",
      "building tree 27 of 100\n",
      "building tree 28 of 100\n",
      "building tree 29 of 100\n",
      "building tree 30 of 100\n",
      "building tree 31 of 100\n",
      "building tree 32 of 100\n",
      "building tree 33 of 100\n",
      "building tree 34 of 100\n",
      "building tree 35 of 100\n",
      "building tree 36 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:   25.4s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 100\n",
      "building tree 38 of 100\n",
      "building tree 39 of 100\n",
      "building tree 40 of 100\n",
      "building tree 41 of 100\n",
      "building tree 42 of 100\n",
      "building tree 43 of 100\n",
      "building tree 44 of 100\n",
      "building tree 45 of 100\n",
      "building tree 46 of 100\n",
      "building tree 47 of 100\n",
      "building tree 48 of 100\n",
      "building tree 49 of 100\n",
      "building tree 50 of 100\n",
      "building tree 51 of 100\n",
      "building tree 52 of 100\n",
      "building tree 53 of 100\n",
      "building tree 54 of 100\n",
      "building tree 55 of 100\n",
      "building tree 56 of 100\n",
      "building tree 57 of 100\n",
      "building tree 58 of 100\n",
      "building tree 59 of 100\n",
      "building tree 60 of 100\n",
      "building tree 61 of 100building tree 62 of 100\n",
      "\n",
      "building tree 63 of 100\n",
      "building tree 64 of 100\n",
      "building tree 65 of 100\n",
      "building tree 66 of 100\n",
      "building tree 67 of 100\n",
      "building tree 68 of 100\n",
      "building tree 69 of 100\n",
      "building tree 70 of 100\n",
      "building tree 71 of 100\n",
      "building tree 72 of 100\n",
      "building tree 73 of 100\n",
      "building tree 74 of 100\n",
      "building tree 75 of 100\n",
      "building tree 76 of 100\n",
      "building tree 77 of 100\n",
      "building tree 78 of 100\n",
      "building tree 79 of 100\n",
      "building tree 80 of 100\n",
      "building tree 81 of 100\n",
      "building tree 82 of 100\n",
      "building tree 83 of 100\n",
      "building tree 84 of 100\n",
      "building tree 85 of 100\n",
      "building tree 86 of 100\n",
      "building tree 87 of 100\n",
      "building tree 88 of 100\n",
      "building tree 89 of 100\n",
      "building tree 90 of 100\n",
      "building tree 91 of 100\n",
      "building tree 92 of 100\n",
      "building tree 93 of 100\n",
      "building tree 94 of 100\n",
      "building tree 95 of 100\n",
      "building tree 96 of 100\n",
      "building tree 97 of 100\n",
      "building tree 98 of 100\n",
      "building tree 99 of 100\n",
      "building tree 100 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:  1.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features='sqrt', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=4,\n",
       "                       oob_score=False, random_state=None, verbose=2,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Crear el modelo con 100 arboles\n",
    "model = RandomForestClassifier(n_estimators=100, \n",
    "                               bootstrap = True,verbose=2,\n",
    "                               max_features = 'sqrt')\n",
    "# entrenar!\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Revisemos los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:22:41.766131Z",
     "start_time": "2019-06-10T22:22:40.529577Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85302\n",
      "           1       0.91      0.80      0.85       141\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.96      0.90      0.93     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_y = model.predict(X_test)\n",
    "mostrar_resultados(y_test, pred_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otro Bosque: Random Forest -más veloz-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:33:52.023322Z",
     "start_time": "2019-06-10T22:33:21.079817Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 1 of 100building tree 2 of 100building tree 3 of 100\n",
      "building tree 4 of 100\n",
      "\n",
      "\n",
      "building tree 5 of 100\n",
      "building tree 6 of 100\n",
      "building tree 7 of 100\n",
      "building tree 8 of 100\n",
      "building tree 9 of 100\n",
      "building tree 10 of 100\n",
      "building tree 11 of 100\n",
      "building tree 12 of 100\n",
      "building tree 13 of 100\n",
      "building tree 14 of 100\n",
      "building tree 15 of 100\n",
      "building tree 16 of 100\n",
      "building tree 17 of 100\n",
      "building tree 18 of 100\n",
      "building tree 19 of 100\n",
      "building tree 20 of 100\n",
      "building tree 21 of 100\n",
      "building tree 22 of 100\n",
      "building tree 23 of 100\n",
      "building tree 24 of 100\n",
      "building tree 25 of 100\n",
      "building tree 26 of 100\n",
      "building tree 27 of 100\n",
      "building tree 28 of 100\n",
      "building tree 29 of 100\n",
      "building tree 30 of 100\n",
      "building tree 31 of 100\n",
      "building tree 32 of 100\n",
      "building tree 33 of 100\n",
      "building tree 34 of 100\n",
      "building tree 35 of 100\n",
      "building tree 36 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    9.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "building tree 37 of 100\n",
      "building tree 38 of 100\n",
      "building tree 39 of 100\n",
      "building tree 40 of 100\n",
      "building tree 41 of 100\n",
      "building tree 42 of 100\n",
      "building tree 43 of 100\n",
      "building tree 44 of 100\n",
      "building tree 45 of 100\n",
      "building tree 46 of 100\n",
      "building tree 47 of 100\n",
      "building tree 48 of 100\n",
      "building tree 49 of 100\n",
      "building tree 50 of 100\n",
      "building tree 51 of 100\n",
      "building tree 52 of 100\n",
      "building tree 53 of 100\n",
      "building tree 54 of 100\n",
      "building tree 55 of 100\n",
      "building tree 56 of 100\n",
      "building tree 57 of 100\n",
      "building tree 58 of 100\n",
      "building tree 59 of 100building tree 60 of 100\n",
      "\n",
      "building tree 61 of 100\n",
      "building tree 62 of 100\n",
      "building tree 63 of 100\n",
      "building tree 64 of 100\n",
      "building tree 65 of 100\n",
      "building tree 66 of 100\n",
      "building tree 67 of 100\n",
      "building tree 68 of 100\n",
      "building tree 69 of 100\n",
      "building tree 70 of 100\n",
      "building tree 71 of 100\n",
      "building tree 72 of 100\n",
      "building tree 73 of 100\n",
      "building tree 74 of 100\n",
      "building tree 75 of 100\n",
      "building tree 76 of 100\n",
      "building tree 77 of 100\n",
      "building tree 78 of 100\n",
      "building tree 79 of 100\n",
      "building tree 80 of 100\n",
      "building tree 81 of 100\n",
      "building tree 82 of 100\n",
      "building tree 83 of 100\n",
      "building tree 84 of 100\n",
      "building tree 85 of 100\n",
      "building tree 86 of 100\n",
      "building tree 87 of 100\n",
      "building tree 88 of 100\n",
      "building tree 89 of 100\n",
      "building tree 90 of 100\n",
      "building tree 91 of 100\n",
      "building tree 92 of 100\n",
      "building tree 93 of 100\n",
      "building tree 94 of 100\n",
      "building tree 95 of 100\n",
      "building tree 96 of 100\n",
      "building tree 97 of 100\n",
      "building tree 98 of 100\n",
      "building tree 99 of 100\n",
      "building tree 100 of 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:   28.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "                       criterion='gini', max_depth=6, max_features='sqrt',\n",
       "                       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                       min_impurity_split=None, min_samples_leaf=1,\n",
       "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                       n_estimators=100, n_jobs=4, oob_score=True,\n",
       "                       random_state=50, verbose=2, warm_start=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# otro modelo, variando hiperparámetros\n",
    "model = RandomForestClassifier(n_estimators=100, class_weight=\"balanced\",\n",
    "                               max_features = 'sqrt', verbose=2, max_depth=6,\n",
    "                               oob_score=True, random_state=50)\n",
    "# a entrenar\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Veamos la Confusion Matrix con el conjunto de Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:34:14.432791Z",
     "start_time": "2019-06-10T22:34:13.720456Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend ThreadingBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  33 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=4)]: Done 100 out of 100 | elapsed:    0.3s finished\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00     85302\n",
      "           1       0.68      0.84      0.75       141\n",
      "\n",
      "    accuracy                           1.00     85443\n",
      "   macro avg       0.84      0.92      0.88     85443\n",
      "weighted avg       1.00      1.00      1.00     85443\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pred_y = model.predict(X_test)\n",
    "mostrar_resultados(y_test, pred_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comprobamos Resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:39:44.407125Z",
     "start_time": "2019-06-10T22:39:44.384718Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Calculate roc auc\n",
    "roc_value = roc_auc_score(y_test, pred_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-10T22:40:00.264424Z",
     "start_time": "2019-06-10T22:40:00.257696Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9216517085479026\n"
     ]
    }
   ],
   "source": [
    "print(roc_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El valor de roc cuanto más cerca de 1, mejor. si fuera 0.5 daría igual que fuesen valores aleatorios y sería un mal modelo"
   ]
  }
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