Máquina Vector Soporte .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score,roc_auc_score\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "#from analytics_tools.analytics_tools3 import freq_discrete\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('Data/churn.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V_ACCT_LGHT</th>\n",
       "      <th>C_IP</th>\n",
       "      <th>C_VMP</th>\n",
       "      <th>V_N_VM_MESS</th>\n",
       "      <th>V_T_DAY_CHG</th>\n",
       "      <th>V_T_EVE_CALLS</th>\n",
       "      <th>V_T_EVE_CHG</th>\n",
       "      <th>V_T_NIG_CALLS</th>\n",
       "      <th>V_T_NIG_CHG</th>\n",
       "      <th>V_T_INT_CALLS</th>\n",
       "      <th>V_T_INT_CHG</th>\n",
       "      <th>V_CS_CALLS</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25</td>\n",
       "      <td>45.07</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>27.47</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>41.38</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>21.95</td>\n",
       "      <td>83</td>\n",
       "      <td>19.42</td>\n",
       "      <td>111</td>\n",
       "      <td>9.40</td>\n",
       "      <td>6</td>\n",
       "      <td>3.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>21.90</td>\n",
       "      <td>71</td>\n",
       "      <td>8.92</td>\n",
       "      <td>128</td>\n",
       "      <td>6.35</td>\n",
       "      <td>2</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   V_ACCT_LGHT C_IP C_VMP  V_N_VM_MESS  V_T_DAY_CHG  V_T_EVE_CALLS  \\\n",
       "0          128   No   Yes           25        45.07             99   \n",
       "1          107   No   Yes           26        27.47            103   \n",
       "2          137   No    No            0        41.38            110   \n",
       "3           65   No    No            0        21.95             83   \n",
       "4          168   No    No            0        21.90             71   \n",
       "\n",
       "   V_T_EVE_CHG  V_T_NIG_CALLS  V_T_NIG_CHG  V_T_INT_CALLS  V_T_INT_CHG  \\\n",
       "0        16.78             91        11.01              3         2.70   \n",
       "1        16.62            103        11.45              3         3.70   \n",
       "2        10.30            104         7.32              5         3.29   \n",
       "3        19.42            111         9.40              6         3.43   \n",
       "4         8.92            128         6.35              2         3.02   \n",
       "\n",
       "   V_CS_CALLS  TARGET  ID  \n",
       "0           1       0   1  \n",
       "1           1       0   2  \n",
       "2           0       0   3  \n",
       "3           4       1  11  \n",
       "4           1       0  13  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_disc = [v for v in df.columns if v[:2]=='C_']\n",
    "var_cont = [v for v in df.columns if v[:2]=='V_']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for v in var_disc:\n",
    "    df[v] = (df[v]=='Yes').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V_ACCT_LGHT</th>\n",
       "      <th>C_IP</th>\n",
       "      <th>C_VMP</th>\n",
       "      <th>V_N_VM_MESS</th>\n",
       "      <th>V_T_DAY_CHG</th>\n",
       "      <th>V_T_EVE_CALLS</th>\n",
       "      <th>V_T_EVE_CHG</th>\n",
       "      <th>V_T_NIG_CALLS</th>\n",
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       "      <th>V_T_INT_CHG</th>\n",
       "      <th>V_CS_CALLS</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>45.07</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
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       "      <td>2.70</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>107</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>27.47</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.38</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.95</td>\n",
       "      <td>83</td>\n",
       "      <td>19.42</td>\n",
       "      <td>111</td>\n",
       "      <td>9.40</td>\n",
       "      <td>6</td>\n",
       "      <td>3.43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.90</td>\n",
       "      <td>71</td>\n",
       "      <td>8.92</td>\n",
       "      <td>128</td>\n",
       "      <td>6.35</td>\n",
       "      <td>2</td>\n",
       "      <td>3.02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   V_ACCT_LGHT  C_IP  C_VMP  V_N_VM_MESS  V_T_DAY_CHG  V_T_EVE_CALLS  \\\n",
       "0          128     0      1           25        45.07             99   \n",
       "1          107     0      1           26        27.47            103   \n",
       "2          137     0      0            0        41.38            110   \n",
       "3           65     0      0            0        21.95             83   \n",
       "4          168     0      0            0        21.90             71   \n",
       "\n",
       "   V_T_EVE_CHG  V_T_NIG_CALLS  V_T_NIG_CHG  V_T_INT_CALLS  V_T_INT_CHG  \\\n",
       "0        16.78             91        11.01              3         2.70   \n",
       "1        16.62            103        11.45              3         3.70   \n",
       "2        10.30            104         7.32              5         3.29   \n",
       "3        19.42            111         9.40              6         3.43   \n",
       "4         8.92            128         6.35              2         3.02   \n",
       "\n",
       "   V_CS_CALLS  TARGET  ID  \n",
       "0           1       0   1  \n",
       "1           1       0   2  \n",
       "2           0       0   3  \n",
       "3           4       1  11  \n",
       "4           1       0  13  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[var_cont+var_disc].copy()\n",
    "y = df['TARGET'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "Xs = pd.DataFrame(scaler.transform(X),columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V_ACCT_LGHT</th>\n",
       "      <th>V_N_VM_MESS</th>\n",
       "      <th>V_T_DAY_CHG</th>\n",
       "      <th>V_T_EVE_CALLS</th>\n",
       "      <th>V_T_EVE_CHG</th>\n",
       "      <th>V_T_NIG_CALLS</th>\n",
       "      <th>V_T_NIG_CHG</th>\n",
       "      <th>V_T_INT_CALLS</th>\n",
       "      <th>V_T_INT_CHG</th>\n",
       "      <th>V_CS_CALLS</th>\n",
       "      <th>C_IP</th>\n",
       "      <th>C_VMP</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.633880</td>\n",
       "      <td>0.581395</td>\n",
       "      <td>0.841885</td>\n",
       "      <td>0.489362</td>\n",
       "      <td>0.492640</td>\n",
       "      <td>0.377778</td>\n",
       "      <td>0.693383</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.519126</td>\n",
       "      <td>0.604651</td>\n",
       "      <td>0.433248</td>\n",
       "      <td>0.531915</td>\n",
       "      <td>0.484789</td>\n",
       "      <td>0.511111</td>\n",
       "      <td>0.734390</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.776243</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.683060</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.756211</td>\n",
       "      <td>0.606383</td>\n",
       "      <td>0.174681</td>\n",
       "      <td>0.522222</td>\n",
       "      <td>0.349487</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.662983</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.289617</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.305085</td>\n",
       "      <td>0.319149</td>\n",
       "      <td>0.622179</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.543336</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.701657</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.852459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.303924</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.106968</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.259087</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>0.588398</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   V_ACCT_LGHT  V_N_VM_MESS  V_T_DAY_CHG  V_T_EVE_CALLS  V_T_EVE_CHG  \\\n",
       "0     0.633880     0.581395     0.841885       0.489362     0.492640   \n",
       "1     0.519126     0.604651     0.433248       0.531915     0.484789   \n",
       "2     0.683060     0.000000     0.756211       0.606383     0.174681   \n",
       "3     0.289617     0.000000     0.305085       0.319149     0.622179   \n",
       "4     0.852459     0.000000     0.303924       0.191489     0.106968   \n",
       "\n",
       "   V_T_NIG_CALLS  V_T_NIG_CHG  V_T_INT_CALLS  V_T_INT_CHG  V_CS_CALLS  C_IP  \\\n",
       "0       0.377778     0.693383       0.166667     0.500000    0.166667   0.0   \n",
       "1       0.511111     0.734390       0.166667     0.776243    0.166667   0.0   \n",
       "2       0.522222     0.349487       0.333333     0.662983    0.000000   0.0   \n",
       "3       0.600000     0.543336       0.416667     0.701657    0.666667   0.0   \n",
       "4       0.788889     0.259087       0.083333     0.588398    0.166667   0.0   \n",
       "\n",
       "   C_VMP  \n",
       "0    1.0  \n",
       "1    1.0  \n",
       "2    0.0  \n",
       "3    0.0  \n",
       "4    0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xt,Xv,yt,yv = train_test_split(Xs,y,train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC(probability=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 506 ms, sys: 16.1 ms, total: 522 ms\n",
      "Wall time: 520 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(Xt,yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metricas(model,Xt,Xv,yt,yv):\n",
    "    print \"Accuracy_Train %.2f, Accuracy_Validate %.2f \"%(accuracy_score(y_pred=model.predict(Xt),y_true=yt),accuracy_score(y_pred=model.predict(Xv),y_true=yv))\n",
    "    print \"ROC_Train %.2f, ROC_Validate %.2f \"%(roc_auc_score(y_score=model.predict_proba(Xt)[:,1],y_true=yt),roc_auc_score(y_score=model.predict_proba(Xv)[:,1],y_true=yv))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_Train 0.86, Accuracy_Validate 0.87 \n",
      "ROC_Train 0.90, ROC_Validate 0.88 \n"
     ]
    }
   ],
   "source": [
    "metricas(model,Xt,Xv,yt,yv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = dict(kernel = ['linear', 'poly', 'rbf', 'sigmoid'],\n",
    "                  degree = range(1,6),\n",
    "                  gamma = np.arange(0.5,1,0.05),\n",
    "                  coef0 = range(1,13),\n",
    "                  decision_function_shape = ['ovo', 'ovr']\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = RandomizedSearchCV(cv=3,\n",
    "                          verbose=True,\n",
    "                          param_distributions=param_grid,\n",
    "                          estimator=model,\n",
    "                          n_jobs=-1,\n",
    "                          n_iter=100,\n",
    "                          scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   10.1s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:  8.3min\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed: 12.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.82 s, sys: 257 ms, total: 3.07 s\n",
      "Wall time: 12min 57s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          fit_params={}, iid=True, n_iter=100, n_jobs=-1,\n",
       "          param_distributions={'decision_function_shape': ['ovo', 'ovr'], 'coef0': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], 'gamma': array([0.5 , 0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95]), 'degree': [1, 2, 3, 4, 5], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid']},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='roc_auc', verbose=True)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "grid.fit(Xs,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=2,\n",
       "  decision_function_shape='ovo', degree=4, gamma=0.8000000000000003,\n",
       "  kernel='rbf', max_iter=-1, probability=True, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_Train 0.92, Accuracy_Validate 0.92 \n",
      "ROC_Train 0.92, ROC_Validate 0.90 \n"
     ]
    }
   ],
   "source": [
    "metricas(grid.best_estimator_,Xt,Xv,yt,yv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1405.,  238.,   48.,   32.,   35.,   31.,   27.,   27.,   45.,\n",
       "          72.]),\n",
       " array([7.92598483e-04, 1.00712944e-01, 2.00633290e-01, 3.00553635e-01,\n",
       "        4.00473981e-01, 5.00394327e-01, 6.00314672e-01, 7.00235018e-01,\n",
       "        8.00155363e-01, 9.00075709e-01, 9.99996055e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc22552090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(grid.best_estimator_.predict_proba(Xt)[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}