def accuracy(featureset):
accuracy = []
for i in range(10):
print(i)
random.shuffle(featureset)
train = featureset[:trainsize]
test = featureset[trainsize:]
nltkNB = nltk.NaiveBayesClassifier.train(train)
nltkDT = nltk.DecisionTreeClassifier.train(train)
BNB.train(train)
MNB.train(train)
L1.train(train)
L2.train(train)
SVC.train(train)
LSVC.train(train)
NuSVC.train(train)
#we report accuracy using list of tuple
#each tuple represents each iteration
#[(acc1,acc2...acc9),(acc1,acc2...acc9),...]
acc1 = nltk.classify.accuracy(nltkNB, test)
acc2 = nltk.classify.accuracy(nltkDT, test)
acc3 = nltk.classify.accuracy(BNB, test)
acc4 = nltk.classify.accuracy(MNB, test)
acc5 = nltk.classify.accuracy(L1, test)
acc6 = nltk.classify.accuracy(L2, test)
acc7 = nltk.classify.accuracy(SVC, test)
acc8 = nltk.classify.accuracy(LSVC, test)
acc9 = nltk.classify.accuracy(NuSVC, test)
accuracy.append((acc1, acc2, acc3, acc4, acc5, acc6, acc7, acc8, acc9))
print([statistics.mean(x) for x in accuracy])
return accuracy
SGDClassifier.train(train_set)
print("SGDClassifier Accuracy Percentage: ",
(nltk.classify.accuracy(SGDClassifier, test_set)) * 100)
SVC = SklearnClassifier(SVC())
SVC.train(train_set)
print("SVC Accuracy Percentage: ",
(nltk.classify.accuracy(SVC, test_set)) * 100)
LinearSVC = SklearnClassifier(LinearSVC())
LinearSVC.train(train_set)
print("LinearSVC Accuracy Percentage: ",
(nltk.classify.accuracy(LinearSVC, test_set)) * 100)
NuSVC = SklearnClassifier(NuSVC())
NuSVC.train(train_set)
print("NuSVC Accuracy Percentage: ",
(nltk.classify.accuracy(NuSVC, test_set)) * 100)
voted_classifier = VoteClassifier(classifier, MNB_classifier, BernoulliNB,
LogisticRegression, SGDClassifier, LinearSVC,
NuSVC)
print("voted_classifier accuracy percentage:",
(nltk.classify.accuracy(voted_classifier, test_set)) * 100)
#print("Classification:", voted_classifier.classify(test_set[0][0]), "Confidence:", voted_classifier.confidence(test_set[0][0])*100)
#print("Classification:", voted_classifier.classify(test_set[1][0]), "Confidence:", voted_classifier.confidence(test_set[1][0])*100)
#print("Classification:", voted_classifier.classify(test_set[2][0]), "Confidence:", voted_classifier.confidence(test_set[2][0])*100)
#print("Classification:", voted_classifier.classify(test_set[3][0]), "Confidence:", voted_classifier.confidence(test_set[3][0])*100)
#print("Classification:", voted_classifier.classify(test_set[4][0]), "Confidence:", voted_classifier.confidence(test_set[4][0])*100)
#print("Classification:", voted_classifier.classify(test_set[5][0]), "Confidence:", voted_classifier.confidence(test_set[5][0])*100)
#GaussianNB.train(training_set)
#print("GaussianNB accuracy percent:", (nltk.classify.accuracy(GaussianNB, testing_set))*100)
#BernoulliNB = SklearnClassifier(BernoulliNB())
#BernoulliNB.train(training_set)
#print("BernoulliNB accuracy percent:", (nltk.classify.accuracy(BernoulliNB, testing_set))*100)
# LogisticRegression, SGDClassifier
# SVC, LinearSVC, NuSVC
LogisticRegression = SklearnClassifier(LogisticRegression())
LogisticRegression.train(training_set)
print("LogisticRegression accuracy percent:", (nltk.classify.accuracy(LogisticRegression, testing_set))*100)
SGDClassifier = SklearnClassifier(SGDClassifier())
SGDClassifier.train(training_set)
print("SGDClassifier accuracy percent:", (nltk.classify.accuracy(SGDClassifier, testing_set))*100)
SVC = SklearnClassifier(SVC())
SVC.train(training_set)
print("SVC accuracy percent:", (nltk.classify.accuracy(SVC, testing_set))*100)
LinearSVC = SklearnClassifier(LinearSVC())
LinearSVC.train(training_set)
print("LinearSVC accuracy percent:", (nltk.classify.accuracy(LinearSVC, testing_set))*100)
NuSVC = SklearnClassifier(NuSVC())
NuSVC.train(training_set)
print("NuSVC accuracy percent:", (nltk.classify.accuracy(NuSVC, testing_set))*100)
