import _pickle as pickle

from sklearn import svm, ensemble

import random

from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix

import numpy as np

##########

##########

TRAINTESTBOUNDARY = 0.75

#PICKLE_NAME = 'lg-new-new-65-withnoise-statistical.p'

PICKLE_NAME = 'trunc-dataset1-noisefree-statistical.p'

print('Loading ' + PICKLE_NAME + '...')

flowlist = pickle.load(open(PICKLE_NAME, 'rb'),encoding='iso-8859-1')

print('Done...')

print('')

print('Flows loaded: ' + str(len(flowlist)))

p = []

r = []

f = []

a = []

for i in range(5):

########## PREPARE STUFF

examples = []

trainingexamples = []

testingexamples = []

#classifier = svm.SVC(gamma=0.001, C=100, probability=True)

classifier = ensemble.RandomForestClassifier()

########## GET FLOWS

for package, time, flow in flowlist:

examples.append((flow, package))

print('')

########## SHUFFLE DATA to ensure classes are "evenly" distributed

random.shuffle(examples)

########## TRAINING

trainingexamples = examples[:int(TRAINTESTBOUNDARY * len(examples))]

X_train = []

y_train = []

for flow, package in trainingexamples:

X_train.append(flow)

y_train.append(package)

print('Fitting classifier...')

classifier.fit(X_train, y_train)

print('Classifier fitted!')

print('')

########## TESTING

counter = 0

correct = 0

testingexamples = examples[int(TRAINTESTBOUNDARY * len(examples)):]

X_test = []

y_test = []

y_pred = []

for flow, package in testingexamples:

X_test.append(flow)

y_test.append(package)

#####

y_pred = classifier.predict(X_test)

print("########################")

print(precision_score(y_test, y_pred, average="macro"))

print(recall_score(y_test, y_pred, average="macro"))

print(f1_score(y_test, y_pred, average="macro"))

print(accuracy_score(y_test, y_pred))

print('')

p.append(precision_score(y_test, y_pred, average="macro"))

r.append(recall_score(y_test, y_pred, average="macro"))

f.append(f1_score(y_test, y_pred, average="macro"))

a.append(accuracy_score(y_test, y_pred))

print(p)

print(r)

print(f)

print(a)

print('')

print(np.mean(p))

print(np.mean(r))

print(np.mean(f))

print(np.mean(a))