# -*- coding: utf-8 -*-
"""
Created on Tue Sep 24 04:45:42 2019

@author: Mahamed
"""

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
df = pd.DataFrame(X)
y = dataset.iloc[:, 13].values


# Encoding categorical data
# Encoding the Independent Variable
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])

labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])

onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X=X[:,1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Fitting classifier to the Training set
# Create your classifier here keras ANN
# import keras library
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
#initializing the ANN
classifier = Sequential()
#Adding the input layer and the first hidden layer with dropout
classifier.add(Dense(output_dim = 6, init ='uniform', activation = 'relu', input_dim = 11) )
classifier.add(Dropout(p = 0.1))
#Adding the first hidden layer
classifier.add(Dense(output_dim = 6, init ='uniform', activation = 'relu' ))
classifier.add(Dropout(p = 0.1))

#Adding the output layer
classifier.add(Dense(output_dim = 1, init ='uniform', activation = 'sigmoid' ))
#Compiling ANN
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics = ['accuracy'])
#Fitting ANN
classifier.fit(X_train,y_train, batch_size= 10, epochs =100)



# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred= (y_pred > 0.5)

# Predicting a single new observation
"""Predict if the customer with the following informations will leave the bank:
Geography: France
Credit Score: 600
Gender: Male
Age: 40
Tenure: 3
Balance: 60000
Number of Products: 2
Has Credit Card: Yes
Is Active Member: Yes
Estimated Salary: 50000"""
new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
new_prediction = (new_prediction > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
#evaluating the ANN
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
def build_classifier():
    classifier = Sequential()
    classifier.add(Dense(output_dim = 6, init ='uniform', activation = 'relu', input_dim = 11) )
    classifier.add(Dense(output_dim = 6, init ='uniform', activation = 'relu' ))
    classifier.add(Dense(output_dim = 1, init ='uniform', activation = 'sigmoid' ))
    classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics = ['accuracy'])
    return classifier
classifier = KerasClassifier(build_fn=build_classifier, batch_size=10, epochs=100)
accuracies = cross_val_score(estimator = classifier,X=X_train, y=y_train,cv=10, n_jobs=1)

mean = accuracies.mean()
variance = accuracies.std()
#improving the ANN

#Dropout regularization to reduce overfitting if needed

#tuning the Ann

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense
def build_classifier(optimizer):
    classifier = Sequential()
    classifier.add(Dense(output_dim = 6, init ='uniform', activation = 'relu', input_dim = 11) )
    classifier.add(Dense(output_dim = 6, init ='uniform', activation = 'relu' ))
    classifier.add(Dense(output_dim = 1, init ='uniform', activation = 'sigmoid' ))
    classifier.compile(optimizer=optimizer, loss='binary_crossentropy', metrics = ['accuracy'])
    return classifier
classifier = KerasClassifier(build_fn=build_classifier)

parameters = {'batch_size': [25,32],
              
              'epochs' : [100, 500],
              'optimizer' :['adam','rmsprop']}
grid_search = GridSearchCV(estimator = classifier,
                           param_grid = parameters,
                           scoring = 'accuracy',
                           cv = 10)
grid_search=grid_search.fit(X_train,y_train)
best_parameters = grid_search.best_params_
best_accuracy =grid_search.best_score_
 
   

acc=(1550+178)/2000
