Redes Neuronales Pro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "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.neural_network import MLPClassifier\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2801 entries, 0 to 2800\n",
      "Data columns (total 14 columns):\n",
      "V_ACCT_LGHT      2801 non-null int64\n",
      "C_IP             2801 non-null object\n",
      "C_VMP            2801 non-null object\n",
      "V_N_VM_MESS      2801 non-null int64\n",
      "V_T_DAY_CHG      2801 non-null float64\n",
      "V_T_EVE_CALLS    2801 non-null int64\n",
      "V_T_EVE_CHG      2801 non-null float64\n",
      "V_T_NIG_CALLS    2801 non-null int64\n",
      "V_T_NIG_CHG      2801 non-null float64\n",
      "V_T_INT_CALLS    2801 non-null int64\n",
      "V_T_INT_CHG      2801 non-null float64\n",
      "V_CS_CALLS       2801 non-null int64\n",
      "TARGET           2801 non-null int64\n",
      "ID               2801 non-null int64\n",
      "dtypes: float64(4), int64(8), object(2)\n",
      "memory usage: 306.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>V_ACCT_LGHT</th>\n",
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       "      <th>TARGET</th>\n",
       "      <th>ID</th>\n",
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       "      <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",
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       "      <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",
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       "      <td>2</td>\n",
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       "    <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",
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       "      <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",
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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   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": 7,
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "for v in var_disc:\n",
    "    df[v] = (df[v]=='Yes').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <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",
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       "      <td>19.42</td>\n",
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       "      <td>9.40</td>\n",
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       "      <td>0</td>\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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[var_cont+var_disc].copy()\n",
    "y = df['TARGET'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "Xs = pd.DataFrame(scaler.transform(X),columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
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       "      <th>V_ACCT_LGHT</th>\n",
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       "      <td>0.662983</td>\n",
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       "      <td>0.191489</td>\n",
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       "      <td>0.0</td>\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",
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       "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xt,Xv,yt,yv = train_test_split(Xs,y,train_size=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MLPClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.14 s, sys: 4.07 s, total: 7.21 s\n",
      "Wall time: 1.86 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "model.fit(Xt,yt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_Train 0.92, Accuracy_Validate 0.91 \n",
      "ROC_Train 0.91, ROC_Validate 0.90 \n"
     ]
    }
   ],
   "source": [
    "metricas(model,Xt,Xv,yt,yv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = dict( hidden_layer_sizes=[(a,b,c,) for a in range(24,31) for b in range(24,31) for c in range(24,31)],\n",
    "                  activation = ['identity', 'logistic', 'tanh', 'relu'],\n",
    "                  alpha = np.arange(0.0001,0.0006,0.0001),\n",
    "                  learning_rate = ['constant', 'invscaling', 'adaptive'],\n",
    "                  max_iter = range(2000,2100,100)\n",
    "                  \n",
    "                  \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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=10,\n",
    "                          scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed:    7.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.2 s, sys: 43.3 ms, total: 1.25 s\n",
      "Wall time: 8.96 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=3, error_score='raise',\n",
       "          estimator=MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100,), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False),\n",
       "          fit_params={}, iid=True, n_iter=10, n_jobs=-1,\n",
       "          param_distributions={'alpha': array([0.0001, 0.0002, 0.0003, 0.0004, 0.0005]), 'learning_rate': ['constant', 'invscaling', 'adaptive'], 'max_iter': [2000], 'activation': ['identity', 'logistic', 'tanh', 'relu'], 'hidden_layer_sizes': [(24, 24, 24), (24, 24, 25), (24, 24, 26), (24, 24, 27), (24, 24, ..., (30, 30, 24), (30, 30, 25), (30, 30, 26), (30, 30, 27), (30, 30, 28), (30, 30, 29), (30, 30, 30)]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score=True, scoring='roc_auc', verbose=True)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "grid.fit(Xs,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='relu', alpha=0.00030000000000000003,\n",
       "       batch_size='auto', beta_1=0.9, beta_2=0.999, early_stopping=False,\n",
       "       epsilon=1e-08, hidden_layer_sizes=(26, 30, 27),\n",
       "       learning_rate='constant', learning_rate_init=0.001, max_iter=2000,\n",
       "       momentum=0.9, nesterovs_momentum=True, power_t=0.5,\n",
       "       random_state=None, shuffle=True, solver='adam', tol=0.0001,\n",
       "       validation_fraction=0.1, verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy_Train 0.93, Accuracy_Validate 0.94 \n",
      "ROC_Train 0.91, ROC_Validate 0.91 \n"
     ]
    }
   ],
   "source": [
    "metricas(grid.best_estimator_,Xt,Xv,yt,yv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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