Appscanner实验还原code3

# Author: Baozi
#-*- codeing:utf-8 -*-
import _pickle as pickle
from sklearn import 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-65-withnoise-statistical.p' # 1a
TRAINING_PICKLE = 'trunc-dataset1a-noisefree-statistical.p' # 1a
# TESTING_PICKLE = 'motog-new-65-withnoise-statistical.p' # 2
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('') for THR in range(10): p = []
r = []
f = []
a = []
c = [] for i in range(5):
print(i)
########## PREPARE STUFF
trainingexamples = []
classifier = ensemble.RandomForestClassifier()
classifier2 = ensemble.RandomForestClassifier() ########## GET FLOWS
for package, time, flow in trainingflowlist:
trainingexamples.append((flow, package))
# print('') ########## SHUFFLE DATA to ensure classes are "evenly" distributed
random.shuffle(trainingexamples) ########## TRAINING PART 1
X1_train = []
y1_train = []
#####################################################
for flow, package in trainingexamples[:int(float(len(trainingexamples)) / 2)]:
X1_train.append(flow)
y1_train.append(package) # print('Fitting classifier...')
classifier.fit(X1_train, y1_train)
# print('Classifier fitted!')
# print('' ########## TRAINING PART 2 (REINFORCEMENT)
X2_train = []
y2_train = []
tmpx_train = []
tmpy_train = [] count = 0
count1 = 0
count2 = 0 ###############################################
for flow, package in trainingexamples[int(float(len(trainingexamples)) / 2):]:
# flow = np.array(flow).reshape(1,-1)
# tmp.append(flow)
tmpx_train.append(flow)
tmpy_train.append(package) predictions = classifier.predict(tmpx_train)
#print(type(predictions))#<class 'numpy.ndarray'>
#print(predictions[0])#com.myfitnesspal.android-auto.csv
for flow, package in trainingexamples[int(float(len(trainingexamples)) / 2):]:
X2_train.append(flow)
prediction = predictions[count] if (prediction == package):
y2_train.append(package)
count1 += 1
else:
y2_train.append('ambiguous')
count2 += 1
count += 1
print("Step Finished!!!!!!!!!!!")
# print(count1)
# print(count2) # print('Fitting 2nd classifier...')
classifier2.fit(X2_train, y2_train)
# print('2nd classifier fitted!'
# print('' ########## TESTING threshold = float(THR) / 10 X_test = []
y_test = []
tmpx_test = []
tmpy_test = []
count = 0
totalflows = 0
consideredflows = 0 for package, time, flow in testingflowlist:
tmpx_test.append(flow)
tmpy_test.append(package) predictionss = classifier2.predict(tmpx_test)#此时的分类器可以预测带有ambiguous标签的样本
prediction_proba = classifier2.predict_proba(tmpx_test)#此时的分类器可以预测带有ambiguous标签的样本
#print(type(prediction_proba))#<class 'numpy.ndarray'>
print(prediction_proba[0]) for package, time, flow in testingflowlist:
prediction = predictionss[count]
if (prediction != 'ambiguous'):
prediction_probability = max(prediction_proba[0])
totalflows += 1 if (prediction_probability >= threshold):
consideredflows += 1 X_test.append(flow)
y_test.append(package)
count += 1 y_pred = classifier2.predict(X_test) p.append(precision_score(y_test, y_pred, average="macro") * 100)
r.append(recall_score(y_test, y_pred, average="macro") * 100)
f.append(f1_score(y_test, y_pred, average="macro") * 100)
a.append(accuracy_score(y_test, y_pred) * 100)
c.append(float(consideredflows) * 100 / totalflows) print('Threshold: ' + str(threshold))
print(np.mean(p))
print(np.mean(r))
print(np.mean(f))
print(np.mean(a))
print(np.mean(c))
print('')
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