# Artificial Neural Network

# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git

# Installing Tensorflow
# pip install tensorflow

# Installing Keras
# pip install --upgrade keras

# Part 1 - Data Preprocessing

# 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
y = dataset.iloc[:, 13].values

# Encoding categorical data
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:]
X

# 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)
print('starr')

# Part 2 - Now let's make the ANN!

# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense

# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))

# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the Training set

classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
# Part 3 - Making predictions and evaluating the model

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

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

#Checking the model accuracy by k-fold cross validation
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score

def build_classifier():
    # Initialising the ANN
    classifier = Sequential()
    
    # Adding the input layer and the first hidden layer
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
    
    # Adding the second hidden layer
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
    
    # Adding the output layer
    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    
    # Compiling the ANN
    classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier
#appying cross-fold validation in ANN
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)




#parameter tunig 
#Checking the model accuracy by k-fold cross validation
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV

def build_classifier(optimizer):
    # Initialising the ANN
    classifier = Sequential()
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
    classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
    classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
    classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
    return classifier
classifier = KerasClassifier(build_fn=build_classifier)


parameters={'batch_size':[25,34],
            'epochs':[200,500],
            'optimizer':['adam','rmsprop']}


gridSearch=GridSearchCV(estimator=classifier,param_grid=parameters,scoring='accuracy',
                        cv=10)
gridSearch=gridSearch.fit(X_train,y_train)
best_parameters=gridSearch.best_params_
best_accuracy=gridSearch.best_score_







