ann.py

import numpy as np
# import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from keras.models import Sequential
from keras.layers import Dense
from sklearn.metrics import confusion_matrix, accuracy_score
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score


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


# Encoding categorical data
def preprocess_data(X):
    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:]
    return X


# Splitting the dataset into the Training set and Test set
def split_data(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
    return X_train, X_test, y_train, y_test


# Feature Scaling
def scale_features(X_train, X_test):
    sc = StandardScaler()
    X_train = sc.fit_transform(X_train)
    X_test = sc.transform(X_test)
    return X_train, X_test


def build_classifier():
    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


def simple_deep_learning_model():
    X, y = read_data()
    X = preprocess_data(X)
    X_train, X_test, y_train, y_test = split_data(X, y)
    X_train, X_test = scale_features(X_train, X_test)
    classifier = build_classifier()
    # 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)
    y_pred = (y_pred > 0.5)
    # Making the Confusion Matrix
    cm = confusion_matrix(y_test, y_pred)
    print (accuracy_score(y_pred, y_test))


def deep_learning_model_cross_validation():
    X, y = read_data()
    X = preprocess_data(X)
    X_train, X_test, y_train, y_test = split_data(X, y)
    X_train, X_test = scale_features(X_train, X_test)
    classifier = KerasClassifier(build_fn=build_classifier, batch_size=16, epochs=100)
    accuracies = cross_val_score(estimator=classifier, X=X_train, y=y_train, cv=10)
    print(np.mean(accuracies))


if __name__ == '__main__':
    # simple_deep_learning_model()
    deep_learning_model_cross_validation()