# Classification template

# 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_1.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
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

#Initialising the ANN
classifier = Sequential()

#Adding the input layer and the first hidden layer with dropout
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu',input_dim = 11))
classifier.add(Dropout(p = 0.1))

#Add the second hidden layer.
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(p = 0.1))


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

#Compile 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)


# Predicting the Test set results
y_pred = classifier.predict(X_test)
 #It will convert the probabilities to the TRue/False statements.
y_pred = (y_pred > 0.5)


# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print("Confusion Matrix: \n",cm)

#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(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='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)
accuracies_mean = accuracies.mean()
accuracies_variance = accuracies.std()

#Improving ANN
#Dropout Regularization fitted.

#Tuning the ANN
#Applying GridSearch

from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV

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























