import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sn
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split


def ip_to_bin(x):
    parts = x.split('.')
    return (int(parts[0]) << 24) + (int(parts[1]) << 16) + (int(parts[2]) << 8) + int(parts[3])


df = pd.read_csv('training4tuplabeled.csv',
                 converters={
                     'sourceIPAddress': lambda x: ip_to_bin(x),
                     'destinationIPAddress': lambda x: ip_to_bin(x)
                 })
df.drop(['flowStartMilliseconds'], 1, inplace=True)
X = np.array(df.drop(columns=['sublabel']))
y = np.array(df['sublabel'])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)

clf = RandomForestClassifier(n_estimators=50, n_jobs=-1, criterion='gini', random_state=0, class_weight="balanced")
clf.fit(X_train, y_train)

accuracy = clf.score(X_test, y_test)
print('Accuracy: ', accuracy)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
print("\n *************** TRAINING ****************")
cm_train = confusion_matrix(y_train, y_pred_train)
plt.figure(figsize=(10, 7))
sn.heatmap(cm_train, annot=True)
plt.xlabel('Truth')
plt.ylabel('Predicted')
plt.show()
print(classification_report(y_train, y_pred_train))
print("\n ************** VALIDATION ***************")
cm_test = confusion_matrix(y_test, y_pred_test)
plt.figure(figsize=(10, 7))
sn.heatmap(cm_test, annot=True)
plt.xlabel('Truth')
plt.ylabel('Predicted')
plt.show()
print(classification_report(y_test, y_pred_test))

example_measure = np.array([ip_to_bin('2.1.1.1'), ip_to_bin('2.1.1.2'), 0, 0, 1])