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
# 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)

# Making the ANN
# Importing keras and packages
# import keras
# What we use to initialize the NN
from keras.models import Sequential
# What we use to create the layer in the NN
from keras.layers import Dense

# Initializing ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
# units is the number of nodes in the layer
# kernel_initializer is defining the initial random weights of the layers
# activation is the 'decision function': relu being rectifier
classifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))

# Adding 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
# optimizer: algorithm you want to use to find the optimal set of weights, adam is stochastic gradient descent
# loss: loss function - binary_crossentropy is the logarithmic loss function
classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Fitting classifier to the Training set
classifier.fit(X_train, y_train, batch_size=10, nb_epoch=100)

# 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)