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

##########

##########

#TRAINING_PICKLE = 'motog-old-110-noisefree-statistical.p'     # 1

TRAINING_PICKLE = 'trunc-dataset1a-noisefree-statistical.p'     #

#TESTING_PICKLE  = 'lg-new-new-110-noisefree-statistical.p'    # 5

TESTING_PICKLE  = 'trunc-dataset2-noisefree-statistical.p'    #

print('Loading pickles...')

trainingflowlist = pickle.load(open(TRAINING_PICKLE, 'rb'),encoding='iso-8859-1')

testingflowlist = pickle.load(open(TESTING_PICKLE, 'rb'),encoding='iso-8859-1')

print('Done...')

print('')

print('Training with ' + TRAINING_PICKLE + ': ' + str(len(trainingflowlist)))

print('Testing with ' + TESTING_PICKLE + ': ' + str(len(testingflowlist)))

print('')

p = []

r = []

f = []

a = []

for i in range(10):

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

    trainingexamples = []

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

    classifier = ensemble.RandomForestClassifier()

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

    for package, time, flow in trainingflowlist:

        trainingexamples.append((flow, package))

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

    random.shuffle(trainingexamples)

    ########## TRAINING

    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

    X_test = []

    y_test = []

    for package, time, flow in testingflowlist:

        X_test.append(flow)

        y_test.append(package)

    y_pred = classifier.predict(X_test)

    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))