Appscanner实验还原code2

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))