Appscanner实验还原code1

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))