#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 12 15:43:06 2017

@author: amine
"""

#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('kddcup.data_10_percent_corrected')

#change Multi-class to binary-class
dataset['normal.'] = dataset['normal.'].replace(['back.', 'buffer_overflow.', 'ftp_write.', 'guess_passwd.', 'imap.', 'ipsweep.', 'land.', 'loadmodule.', 'multihop.', 'neptune.', 'nmap.', 'perl.', 'phf.', 'pod.', 'portsweep.', 'rootkit.', 'satan.', 'smurf.', 'spy.', 'teardrop.', 'warezclient.', 'warezmaster.'], 'attack')

x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 41].values

#encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_x_1 = LabelEncoder()
labelencoder_x_2 = LabelEncoder()
labelencoder_x_3 = LabelEncoder()
x[:, 1] = labelencoder_x_1.fit_transform(x[:, 1])
x[:, 2] = labelencoder_x_2.fit_transform(x[:, 2])
x[:, 3] = labelencoder_x_3.fit_transform(x[:, 3])
onehotencoder_1 = OneHotEncoder(categorical_features = [1])
x = onehotencoder_1.fit_transform(x).toarray()
onehotencoder_2 = OneHotEncoder(categorical_features = [4])
x = onehotencoder_2.fit_transform(x).toarray()
onehotencoder_3 = OneHotEncoder(categorical_features = [70])
x = onehotencoder_3.fit_transform(x).toarray()
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

#splitting the dataset into the training set and test set
from sklearn.cross_validation import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.3, random_state = 0)

#feature scaling
from sklearn.preprocessing import StandardScaler
sc_x = StandardScaler()
x_train = sc_x.fit_transform(x_train)
x_test = sc_x.transform(x_test)

# 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(output_dim = 60, init = 'uniform', activation = 'relu', input_dim = 118))

#Adding a second hidden layer
classifier.add(Dense(output_dim = 60, init = 'uniform', activation = 'relu'))

#Adding a third hidden layer
classifier.add(Dense(output_dim = 60, init = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(output_dim = 1, init = '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, nb_epoch = 20)

# 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)

#the performance of the classification model
print("the Accuracy is: "+ str((cm[0,0]+cm[1,1])/(cm[0,0]+cm[0,1]+cm[1,0]+cm[1,1])))
recall = cm[1,1]/(cm[0,1]+cm[1,1])
print("Recall is : "+ str(recall))
print("False Positive rate: "+ str(cm[1,0]/(cm[0,0]+cm[1,0])))
precision = cm[1,1]/(cm[1,0]+cm[1,1])
print("Precision is: "+ str(precision))
print("F-measure is: "+ str(2*((precision*recall)/(precision+recall))))
from math import log
print("Entropy is: "+ str(-precision*log(precision)))




