def evaluate_classifier(classifier, validationFeatures):
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(validationFeatures):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
accuracy = nltk.classify.util.accuracy(classifier, validationFeatures)
pos_precision = precision(refsets['POSITIVE_TIME'], testsets['POSITIVE_TIME'])
pos_recall = recall(refsets['POSITIVE_TIME'], testsets['POSITIVE_TIME'])
pos_f_measure = f_measure(refsets['POSITIVE_TIME'], testsets['POSITIVE_TIME'])
neg_precision = precision(refsets['NEGATIVE_TIME'], testsets['NEGATIVE_TIME'])
neg_recall = recall(refsets['NEGATIVE_TIME'], testsets['NEGATIVE_TIME'])
neg_f_measure = f_measure(refsets['NEGATIVE_TIME'], testsets['NEGATIVE_TIME'])
print 'accuracy:', accuracy
print 'pos precision:', pos_precision
print 'pos recall:', pos_recall
print 'pos f-measure', pos_f_measure
print 'neg precision:', neg_precision
print 'neg recall:', neg_recall
print 'neg f-measure', neg_f_measure
return {'accuracy': accuracy, 'pos precision': pos_precision, 'pos recall': pos_recall, 'pos f-measure': pos_f_measure, 'neg precision': neg_precision, 'neg recall': neg_recall, 'neg f-measure': neg_f_measure}
def get_accuracy_measures(classifier, testing_data, p_label):
actuallabels =collections.defaultdict(set)
predictedlabels = collections.defaultdict(set)
for i, (tokens, label) in enumerate(testing_data):
actuallabels[label].add(i)
predicted = classifier.classify(tokens)
predictedlabels[predicted].add(i)
result=[]
result.append(precision(actuallabels[p_label], predictedlabels[p_label]))
result.append(recall(actuallabels[p_label], predictedlabels[p_label]))
result.append(f_measure(actuallabels[p_label], predictedlabels[p_label]))
return result
def calcPrecRecallFMeasure(reference, prediction):
precision = nltk.f_measure(reference, prediction, alpha=1.0)
recall = nltk.f_measure(reference, prediction, alpha=0)
f_measure = nltk.f_measure(reference, prediction, alpha=0.5)
return [precision, recall, f_measure]
print "Training..."
classifier = svm.SVC(kernel="linear", decision_function_shape="ovr", probability=False)
classifier.fit(train_set, train_label)
print "Testing..."
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
refsets[1] = set()
refsets[-1] = set()
refsets[0] = set()
testsets[1] = set()
testsets[-1] = set()
testsets[0] = set()
for i in range(len(test_set)):
refsets[test_label[i]].add(i)
observed = classifier.predict([test_set[i]])
testsets[observed[0]].add(i)
print "Saving results..."
results.write('pos precision:' + str(precision(refsets[1], testsets[1])) + "\n")
results.write('pos recall:' + str(recall(refsets[1], testsets[1])) + "\n")
results.write('pos F-measure:' + str(f_measure(refsets[1], testsets[1])) + "\n")
results.write('neg precision:' + str(precision(refsets[-1], testsets[-1])) + "\n")
results.write('neg recall:' + str(recall(refsets[-1], testsets[-1])) + "\n")
results.write('neg F-measure:' + str(f_measure(refsets[-1], testsets[-1])) + "\n")
results.close()
end = time.time()
print "Duration: ", end - start, " seconds"
print "2-train.py done!"
def evaluate_classifier(featx):
#negfeats = [(featx(mark_negation(f)), 'neg') for f in word_split(negdata)]
#posfeats = [(featx(mark_negation(f)), 'pos') for f in word_split(posdata)]
negfeats = [(featx(f), 'neg') for f in word_split(negdata)]
#print negfeats[1:25]
#raw_input('>')
posfeats = [(featx(f), 'pos') for f in word_split(posdata)]
negcutoff = len(negfeats) * 3 / 4
poscutoff = len(posfeats) * 3 / 4
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
print "No of training reviews:", len(trainfeats)
#print trainfeats
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
print "No of testing reviews:", len(testfeats)
# using 3 classifiers
classifier_list = ['nb', 'svm', 'maxent'] #
NB_pred = []
new_label = []
for cl in classifier_list:
if cl == 'maxent':
classifierName = 'Maximum Entropy'
classifier = MaxentClassifier.train(trainfeats,
'GIS',
trace=0,
encoding=None,
labels=None,
gaussian_prior_sigma=0,
max_iter=1)
elif cl == 'svm':
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False)
classifier.train(trainfeats)
else:
classifierName = 'Naive Bayes'
classifier = NaiveBayesClassifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
original_label = []
for i, (feats, label) in enumerate(testfeats):
refsets[label].add(i)
original_label.append(label)
#print feats
#raw_input('> ')
observed = classifier.classify(feats)
NB_pred.append(observed)
testsets[observed].add(i)
#print refsets['pos']
#print testsets['pos']
#print original_label
#print NB_Pred
#cm = confusion_matrix(original_label,NB_pred)
#print cm
#print "The accuracy score is {:.2%}".format(accuracy_score(original_label,NB_pred))
new_label = original_label
accuracy = nltk.classify.util.accuracy(classifier, testfeats)
pos_precision = nltk.precision(refsets['pos'], testsets['pos'])
pos_recall = nltk.recall(refsets['pos'], testsets['pos'])
pos_fmeasure = nltk.f_measure(refsets['pos'], testsets['pos'])
neg_precision = nltk.precision(refsets['neg'], testsets['neg'])
neg_recall = nltk.recall(refsets['neg'], testsets['neg'])
neg_fmeasure = nltk.f_measure(refsets['neg'], testsets['neg'])
print('')
print('---------------------------------------')
print('SINGLE FOLD RESULT ' + '(' + classifierName + ')')
print('---------------------------------------')
print('accuracy:', accuracy)
print('precision', (pos_precision + neg_precision) / 2)
print('recall', (pos_recall + neg_recall) / 2)
print('f-measure', (pos_fmeasure + neg_fmeasure) / 2)
#classifier.show_most_informative_features(50)
print('')
#print len(NB_pred)
ME_pred = NB_pred[982:]
SVM_pred = NB_pred[491:982]
NB_pred = NB_pred[0:491]
#print NB_pred
#print "-----------------------"
#print ME_pred
#print "-----------------------"
#print SVM_pred
#print "-----------------------"
#cm = confusion_matrix(SVM_pred,NB_pred)
#print cm
#print "The accuracy score is {:.2%}".format(accuracy_score(SVM_pred,NB_pred))
#cm = confusion_matrix(ME_pred,NB_pred)
#print cm
#print "The accuracy score is {:.2%}".format(accuracy_score(ME_pred,NB_pred))
#cm = confusion_matrix(SVM_pred,ME_pred)
#print cm
#print "The accuracy score is {:.2%}".format(accuracy_score(SVM_pred,ME_pred))
final_pred = []
for i in range(0, 491):
c1 = 0
if NB_pred[i] == 'pos':
c1 = c1 + 1
if ME_pred[i] == 'pos':
c1 = c1 + 1
if SVM_pred[i] == 'pos':
c1 = c1 + 1
#print i
if c1 == 3 or c1 == 2:
final_pred.append('pos')
else:
final_pred.append('neg')
print "-----------------------"
#print final_pred
print "-----------------------"
#print new_label
print "Results of ensemble: NB + SVM + ME::"
print "----------Confusion Matrix--------------"
cm = confusion_matrix(final_pred, new_label)
print cm
print ""
print "The accuracy score of ensemble is {:.2%}".format(
accuracy_score(final_pred, new_label))
print "##############################################"
## CROSS VALIDATION
trainfeats = negfeats + posfeats
# SHUFFLE TRAIN SET
# As in cross validation, the test chunk might have only negative or only positive data
random.shuffle(trainfeats)
n = 5 # 5-fold cross-validation
for cl in classifier_list:
subset_size = len(trainfeats) / n
accuracy = []
pos_precision = []
pos_recall = []
neg_precision = []
neg_recall = []
pos_fmeasure = []
neg_fmeasure = []
cv_count = 1
for i in range(n):
testing_this_round = trainfeats[i * subset_size:][:subset_size]
training_this_round = trainfeats[:i * subset_size] + trainfeats[
(i + 1) * subset_size:]
if cl == 'maxent':
classifierName = 'Maximum Entropy'
classifier = MaxentClassifier.train(training_this_round,
'GIS',
trace=0,
encoding=None,
labels=None,
gaussian_prior_sigma=0,
max_iter=1)
elif cl == 'svm':
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False)
classifier.train(training_this_round)
else:
classifierName = 'Naive Bayes'
classifier = NaiveBayesClassifier.train(training_this_round)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_this_round):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
cv_accuracy = nltk.classify.util.accuracy(classifier,
testing_this_round)
cv_pos_precision = nltk.precision(refsets['pos'], testsets['pos'])
cv_pos_recall = nltk.recall(refsets['pos'], testsets['pos'])
cv_pos_fmeasure = nltk.f_measure(refsets['pos'], testsets['pos'])
cv_neg_precision = nltk.precision(refsets['neg'], testsets['neg'])
cv_neg_recall = nltk.recall(refsets['neg'], testsets['neg'])
cv_neg_fmeasure = nltk.f_measure(refsets['neg'], testsets['neg'])
accuracy.append(cv_accuracy)
pos_precision.append(cv_pos_precision)
pos_recall.append(cv_pos_recall)
neg_precision.append(cv_neg_precision)
neg_recall.append(cv_neg_recall)
pos_fmeasure.append(cv_pos_fmeasure)
neg_fmeasure.append(cv_neg_fmeasure)
cv_count += 1
print('---------------------------------------')
print('N-FOLD CROSS VALIDATION RESULT ' + '(' + classifierName + ')')
print('---------------------------------------')
print('accuracy:', sum(accuracy) / n)
print('precision',
(sum(pos_precision) / n + sum(neg_precision) / n) / 2)
print('recall', (sum(pos_recall) / n + sum(neg_recall) / n) / 2)
print('f-measure', (sum(pos_fmeasure) / n + sum(neg_fmeasure) / n) / 2)
if cl == 'maxent':
maxent_accuracy_next = (sum(accuracy) / n)
maxent_accuracy.append(maxent_accuracy_next)
elif cl == 'svm':
svm_accuracy_next = (sum(accuracy) / n)
svm_accuracy.append(svm_accuracy_next)
else:
nb_accuracy_next = (sum(accuracy) / n)
nb_accuracy.append(nb_accuracy_next)
observed = classifier.classify(feats)
testsets[observed].add(i)
for i, (feats, label) in enumerate(testdata_features):
refdatasets[label].add(i)
observed = classifier.classify(feats)
testdatasets[observed].add(i)
accuracy_scores.append(util.accuracy(classifier, test_features))
accuracy_data_scores.append(util.accuracy(classifier, testdata_features))
print('train: {} test: {}'.format(len(train_set), len(test_set)))
print('=================== Results ===================')
print('Accuracy {:f}'.format(accuracy_scores[-1]))
print(' Positive Negative')
print('F1 [{:f} {:f}]'.format(
f_measure(refsets['pos'], testsets['pos']),
f_measure(refsets['neg'], testsets['neg'])))
print('Precision [{:f} {:f}]'.format(
precision(refsets['pos'], testsets['pos']),
precision(refsets['neg'], testsets['neg'])))
print('Recall [{:f} {:f}]'.format(
recall(refsets['pos'], testsets['pos']),
recall(refsets['neg'], testsets['neg'])))
print('===============================================\n')
print('testData: {}'.format(len(testSentences)))
print('=================== Results ===================')
print('Accuracy TestData {:f}'.format(accuracy_data_scores[-1]))
print('F1 [{:f} {:f}]'.format(
f_measure(refdatasets['pos'], testdatasets['pos']),
f_measure(refdatasets['neg'], testdatasets['neg'])))
print('Precision [{:f} {:f}]'.format(
test_tweets = BuildFeatureVector(all_tweet_array[training_size:]) print len(test_tweets) training_set = nltk.classify.apply_features(extract_features, train_tweets) test_set = nltk.classify.apply_features(extract_features, test_tweets) NBClassifier = nltk.NaiveBayesClassifier.train(training_set) NBClassifier.show_most_informative_features(20) TestSet(all_tweet_array[training_size:]) print '' print 'TRAINING accuracy:', nltk.classify.accuracy(NBClassifier, training_set) print 'TEST accuracy:', nltk.classify.accuracy(NBClassifier, test_set) print '' print 'NEU precision:', precision(refSet['NEU'], testSet['NEU']) print 'NEU recall:', recall(refSet['NEU'], testSet['NEU']) print 'NEU F-measure:', f_measure(refSet['NEU'], testSet['NEU']) print '' print 'POS precision:', precision(refSet['POZ'], testSet['POZ']) print 'POS recall:', recall(refSet['POZ'], testSet['POZ']) print 'POS F-measure:', f_measure(refSet['POZ'], testSet['POZ']) print '' print 'NEG precision:', precision(refSet['NEG'], testSet['NEG']) print 'NEG recall:', recall(refSet['NEG'], testSet['NEG']) print 'NEG F-measure:', f_measure(refSet['NEG'], testSet['NEG']) print '' print ConfusionMatrix(refSetF, testSetF)
def do_test():
# global classifier
print('Start training')
k_splits = 10
response = {
'type': 1,
'status': 'start',
'step': 0,
'max_step': k_splits,
'trainset': 0,
'testset': 0
}
socketio.emit('test_result', response, namespace='/test')
socketio.sleep(.1)
data = sentiment.prepare_data()
from sklearn.model_selection import KFold
import numpy as np
import collections
k_fold = KFold(n_splits=k_splits, random_state=1992, shuffle=True)
featuresets = np.array(data)
accuracy_scores = []
index = 0
for train_set, test_set in k_fold.split(featuresets):
index += 1
sentiment.word_features = sentiment.get_word_features(
sentiment.get_words_in_reviews(featuresets[train_set].tolist()))
train_features = nltk.classify.apply_features(
sentiment.extract_features, featuresets[train_set].tolist())
test_features = nltk.classify.apply_features(
sentiment.extract_features, featuresets[test_set].tolist())
classifier = nltk.NaiveBayesClassifier.train(train_features)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_features):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
accuracy_scores.append(
nltk.classify.util.accuracy(classifier, test_features))
f1_pos = nltk.f_measure(refsets['pos'], testsets['pos'])
f1_neg = nltk.f_measure(refsets['neg'], testsets['neg'])
pre_pos = nltk.precision(refsets['pos'], testsets['pos'])
pre_neg = nltk.precision(refsets['neg'], testsets['neg'])
re_pos = nltk.recall(refsets['pos'], testsets['pos'])
re_neg = nltk.recall(refsets['neg'], testsets['neg'])
response = {
'type': 1,
'status': 'progress',
'step': index,
'max_step': k_splits,
'trainset': len(train_set),
'testset': len(test_set),
'accuracy': accuracy_scores[-1],
'f1_pos': f1_pos,
'f1_neg': f1_neg,
'pre_pos': pre_pos,
'pre_neg': pre_neg,
're_pos': re_pos,
're_neg': re_neg
}
socketio.emit('test_result', response, namespace='/test')
socketio.sleep(.1)
print("Success training")
response = {
'type': 1,
'status': 'success',
'step': index,
'max_step': k_splits,
'trainset': len(train_set),
'testset': len(test_set)
}
socketio.emit('test_result', response, namespace='/test')
socketio.sleep(.1)
for w in token_features:
features[w] = (w in words)
return features
featuresets = [(extract_features(tweet), category)
for (rev, category) in document]
training_set = featuresets[:1500]
testing_set = featuresets[500:]
classifier = nltk.NaiveBayesClassifier.train(training_set)
print("Classifier accuracy percent:",
(nltk.classify.accuracy(classifier, testing_set)) * 100)
# print(classifier.show_most_informative_features())
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print("Pos Precision:", (nltk.precision(refsets['pos'], testsets['pos'])))
print("Pos Recall:", (nltk.recall(refsets['pos'], testsets['pos'])))
print("Pos F-Score:", (nltk.f_measure(refsets['pos'], testsets['pos'])))
print("Neg Precision:", (nltk.precision(refsets['neg'], testsets['neg'])))
print("Neg Recall:", (nltk.recall(refsets['neg'], testsets['neg'])))
print("Neg F-Score:", (nltk.f_measure(refsets['neg'], testsets['neg'])))
def evaluate_classifier(featx):
negfeats = [(featx(f), 'neg') for f in word_split(negdata)]
posfeats = [(featx(f), 'pos') for f in word_split(posdata)]
negcutoff = int(len(negfeats) * 3 / 4)
poscutoff = int(len(posfeats) * 3 / 4)
#print(negcutoff)
trainfeats = negfeats[:negcutoff] + posfeats[:poscutoff]
#print(trainfeats)
testfeats = negfeats[negcutoff:] + posfeats[poscutoff:]
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False).train(trainfeats)
#classifier.train(trainfeats)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
#print(testfeats)
for i, (feats, label) in enumerate(testfeats):
#print(feats,'---',label)
refsets[label].add(i)
observed = classifier.classify(feats)
#print(observed)
testsets[observed].add(i)
#print(testsets)
accuracy = nltk.classify.util.accuracy(classifier, testfeats)
pos_precision = nltk.precision(refsets['pos'], testsets['pos'])
pos_recall = nltk.recall(refsets['pos'], testsets['pos'])
pos_fmeasure = nltk.f_measure(refsets['pos'], testsets['pos'])
neg_precision = nltk.precision(refsets['neg'], testsets['neg'])
neg_recall = nltk.recall(refsets['neg'], testsets['neg'])
neg_fmeasure = nltk.f_measure(refsets['neg'], testsets['neg'])
print('')
print('---------------------------------------')
print('SINGLE FOLD RESULT ' + '(' + classifierName + ')')
print('---------------------------------------')
print('accuracy:', accuracy)
print('precision', (pos_precision + neg_precision) / 2)
print('recall', (pos_recall + neg_recall) / 2)
print('f-measure', (pos_fmeasure + neg_fmeasure) / 2)
#classifier.show_most_informative_features()
print('')
## CROSS VALIDATION
trainfeats = negfeats + posfeats
# SHUFFLE TRAIN SET
# As in cross validation, the test chunk might have only negative or only positive data
random.shuffle(trainfeats)
n = 5 # 5-fold cross-validation
subset_size = int(len(trainfeats) / n)
accuracy = []
pos_precision = []
pos_recall = []
neg_precision = []
neg_recall = []
pos_fmeasure = []
neg_fmeasure = []
cv_count = 1
for i in range(n):
testing_this_round = trainfeats[i * subset_size:][:subset_size]
training_this_round = trainfeats[:i * subset_size] + trainfeats[
(i + 1) * subset_size:]
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False)
classifier.train(training_this_round)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_this_round):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
cv_accuracy = nltk.classify.util.accuracy(classifier,
testing_this_round)
cv_pos_precision = nltk.precision(refsets['pos'], testsets['pos'])
cv_pos_recall = nltk.recall(refsets['pos'], testsets['pos'])
cv_pos_fmeasure = nltk.f_measure(refsets['pos'], testsets['pos'])
cv_neg_precision = nltk.precision(refsets['neg'], testsets['neg'])
cv_neg_recall = nltk.recall(refsets['neg'], testsets['neg'])
cv_neg_fmeasure = nltk.f_measure(refsets['neg'], testsets['neg'])
accuracy.append(cv_accuracy)
pos_precision.append(cv_pos_precision)
pos_recall.append(cv_pos_recall)
neg_precision.append(cv_neg_precision)
neg_recall.append(cv_neg_recall)
pos_fmeasure.append(cv_pos_fmeasure)
neg_fmeasure.append(cv_neg_fmeasure)
cv_count += 1
print('---------------------------------------')
print('N-FOLD CROSS VALIDATION RESULT ' + '(' + classifierName + ')')
print('---------------------------------------')
print('accuracy:', sum(accuracy) / n)
print('precision', (sum(pos_precision) / n + sum(neg_precision) / n) / 2)
print('recall', (sum(pos_recall) / n + sum(neg_recall) / n) / 2)
print('f-measure', (sum(pos_fmeasure) / n + sum(neg_fmeasure) / n) / 2)
print('')
refsets['neg'] = set()
refsets['neu'] = set()
testsets['pos'] = set()
testsets['neg'] = set()
testsets['neu'] = set()
print "Testing..."
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = NBClassifier.classify(feats)
testsets[observed].add(i)
print "Saving results..."
results.write('pos precision:' + str(precision(refsets["pos"], testsets["pos"])) + "\n")
results.write('pos recall:' + str(recall(refsets["pos"], testsets["pos"])) + "\n")
results.write('pos F-measure:' + str(f_measure(refsets["pos"], testsets["pos"])) + "\n")
results.write('neg precision:' + str(precision(refsets["neg"], testsets["neg"])) + "\n")
results.write('neg recall:' + str(recall(refsets["neg"], testsets["neg"])) + "\n")
results.write('neg F-measure:' + str(f_measure(refsets["neg"], testsets["neg"])) + "\n")
results.write('neu precision:' + str(precision(refsets["neutral"], testsets["neutral"])) + "\n")
results.write('neu recall:' + str(recall(refsets["neutral"], testsets["neutral"])) + "\n")
results.write('neu F-measure:' + str(f_measure(refsets["neutral"], testsets["neutral"])) + "\n")
results.write("\nMost informative features:\n")
mif = NBClassifier.most_informative_features()
for f in mif:
results.write(str(f) + "\n")
results.close()
end = time.time()
print "Duration: ", end - start, " seconds"
def author_beng_nbc():
#1st Set
bankc = open("/python27/Bankim500_1.txt", "r").read()
bankw = bankc.split()
bankz = reduce(concat, [['bankim', x] for x in bankw[1:]], bankw[0:1])
#print a3
it = iter(bankz)
bankt = zip(it, it)
#print a4
#2nd Set
bibhuc = open("/python27/Bibhuti500_1.txt", "r").read()
bibhuw = bibhuc.split()
bibhuz = reduce(concat, [['bibhuti', x] for x in bibhuw[1:]], bibhuw[0:1])
#print b3
it1 = iter(bibhuz)
bibhut = zip(it1, it1)
#print b4
#3rd Set
rabindrac = open("/python27/Rabindra500_1.txt", "r").read()
rabindraw = rabindrac.split()
rabindraz = reduce(concat, [['rabindra', x] for x in rabindraw[1:]],
rabindraw[0:1])
#print a3
it2 = iter(rabindraz)
rabindrat = zip(it2, it2)
#4th Set
saratc = open("/python27/Sarat500_1.txt", "r").read()
saratw = saratc.split()
saratz = reduce(concat, [['sarat', x] for x in saratw[1:]], saratw[0:1])
#print a3
it3 = iter(saratz)
saratt = zip(it3, it3)
add1 = bankt + bibhut + rabindrat + saratt
#print c1
training_data = add1
vocabulary = set(
chain(*[word_tokenize(i[0].lower()) for i in training_data]))
feature_set = [
({i: (i in word_tokenize(sentence.lower()))
for i in vocabulary}, tag) for sentence, tag in training_data
]
#print "###",feature_set
from nltk.classify import NaiveBayesClassifier as nbc
train_set, test_set = feature_set[:300], feature_set[300:]
print len(train_set)
print len(test_set)
classifier = nbc.train(train_set)
test_sentence = "আলীপুরের উকিল বিশেষ কিছু হয় বলিয়া মনে হয় না বালিগঞ্জের ওদিকে কোথায় একটা টিউশনি আছে"
featurized_test_sentence = {
i: (i in word_tokenize(test_sentence.lower()))
for i in vocabulary
}
print "test_sent:", test_sentence
print "tag:", classifier.classify(featurized_test_sentence)
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(test_set):
refsets[label].add(i)
observed = classifier.classify(feats)
testsets[observed].add(i)
print 'bankim precision:', nltk.precision(refsets['bankim'],
testsets['bankim'])
print 'bankim recall:', nltk.recall(refsets['bankim'], testsets['bankim'])
print 'bankim F-measure:', nltk.f_measure(refsets['bankim'],
testsets['bankim'])
print 'bibhuti precision:', nltk.precision(refsets['bibhuti'],
testsets['bibhuti'])
print 'bibhuti recall:', nltk.recall(refsets['bibhuti'],
testsets['bibhuti'])
print 'bibhuti F-measure:', nltk.f_measure(refsets['bibhuti'],
testsets['bibhuti'])
print 'bankim precision:', nltk.precision(refsets['rabindra'],
testsets['rabindra'])
print 'bankim recall:', nltk.recall(refsets['rabindra'],
testsets['rabindra'])
print 'bankim F-measure:', nltk.f_measure(refsets['rabindra'],
testsets['rabindra'])
print 'bibhuti precision:', nltk.precision(refsets['sarat'],
testsets['sarat'])
print 'bibhuti recall:', nltk.recall(refsets['sarat'], testsets['sarat'])
print 'bibhuti F-measure:', nltk.f_measure(refsets['sarat'],
testsets['sarat'])
tweets = neg_tweets[:cutoff] + pos_tweets[:cutoff]
test_tweets = neg_tweets[cutoff:] + pos_tweets[cutoff:]
all_words = []
words_frequency = []
print(tweets)
#Get all the words
for (words, sentiment) in tweets:
all_words.extend(words)
#extract the features
wordlist = nltk.FreqDist(all_words)
word_features = wordlist.keys()
training_set = nltk.classify.apply_features(extract_features, tweets)
classifier = NaiveBayesClassifier.train(training_set)
refsets = {'pos': set([]), 'neg': set([])}
testsets = {'pos': set([]), 'neg': set([])}
classifier.show_most_informative_features()
for i, (feats, label) in enumerate(test_tweets):
refsets.get(label).add(i)
testsets[classifier.classify(extract_features(feats))].add(i)
print('pos precision:', nltk.precision(refsets['pos'], testsets['pos']))
print('pos recall:', nltk.recall(refsets['pos'], testsets['pos']))
print('pos F-measure:', nltk.f_measure(refsets['pos'], testsets['pos']))
print('neg precision:', nltk.precision(refsets['neg'], testsets['neg']))
print('neg recall:', nltk.recall(refsets['neg'], testsets['neg']))
print('neg F-measure:', nltk.f_measure(refsets['neg'], testsets['neg']))
def evaluate_mult_classifiers(feature_x, n_folds=5):
# 5-fold default for cross-validation
# train_feats = 75% of pos_data + 75% of neg_data
# test_feats = 25% of pos_data + 25% of neg_data
neg_feats = [(feature_x(i), 'neg') for i in word_split(neg_data)]
pos_feats = [(feature_x(i), 'pos') for i in word_split(pos_data)]
neg_cutoff = int(len(neg_feats) * 0.75)
pos_cutoff = int(len(pos_feats) * 0.75)
train_feats = neg_feats[:neg_cutoff] + pos_feats[:pos_cutoff]
test_feats = neg_feats[neg_cutoff:] + pos_feats[pos_cutoff:]
classifier_list = ['NB', 'SVM']
## CROSS VALIDATION
train_feats = neg_feats + pos_feats
# Shuffle training set
random.shuffle(train_feats)
for cl in classifier_list:
subset_size = int(len(train_feats) / n_folds)
accuracy = []
pos_precision = []
pos_recall = []
neg_precision = []
neg_recall = []
pos_fmeasure = []
neg_fmeasure = []
cv_count = 1
print('--------------------------')
print('Beginning Cross-validation')
print('--------------------------')
for i in range(n_folds):
testing_this_round = train_feats[i * subset_size:][:subset_size]
training_this_round = train_feats[:i * subset_size] + train_feats[
(i + 1) * subset_size:]
if cl == 'NB':
classifierName = 'Naive Bayes'
# Using NLTK NaiveBayesClassifier
classifier = NaiveBayesClassifier.train(training_this_round)
else:
classifierName = 'SVM'
classifier = SklearnClassifier(LinearSVC(), sparse=False)
classifier.train(training_this_round)
ref_sets = collections.defaultdict(set)
test_sets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testing_this_round):
ref_sets[label].add(i)
observed = classifier.classify(feats)
test_sets[observed].add(i)
cv_accuracy = nltk.classify.util.accuracy(classifier,
testing_this_round)
cv_pos_precision = nltk.precision(ref_sets['pos'],
test_sets['pos'])
cv_pos_recall = nltk.recall(ref_sets['pos'], test_sets['pos'])
cv_pos_fmeasure = nltk.f_measure(ref_sets['pos'], test_sets['pos'])
cv_neg_precision = nltk.precision(ref_sets['neg'],
test_sets['neg'])
cv_neg_recall = nltk.recall(ref_sets['neg'], test_sets['neg'])
cv_neg_fmeasure = nltk.f_measure(ref_sets['neg'], test_sets['neg'])
print('Fold: {} Acc : {:.4F}'.format(cv_count, cv_accuracy))
print('Fold: {} pos_prec : {:.4F} neg_prec : {:.4F}'.format(
cv_count, cv_pos_precision, cv_neg_precision))
print('Fold: {} pos_recall: {:.4F} neg_recall: {:.4F}'.format(
cv_count, cv_pos_recall, cv_neg_recall))
print('Fold: {} pos_fmeas : {:.4F} neg_fmeas : {:.4F}'.format(
cv_count, cv_pos_fmeasure, cv_neg_fmeasure))
print('--')
accuracy.append(cv_accuracy)
pos_precision.append(cv_pos_precision)
pos_recall.append(cv_pos_recall)
neg_precision.append(cv_neg_precision)
neg_recall.append(cv_neg_recall)
pos_fmeasure.append(cv_pos_fmeasure)
neg_fmeasure.append(cv_neg_fmeasure)
cv_count += 1
print('----------------------------------------------------------')
print('{}-Fold Cross Validation results for {} Classifier'.format(
n_folds, classifierName))
print('----------------------------------------------------------')
print('accuracy : {:.4F}'.format(sum(accuracy) / n_folds))
print('precision: {:.4F}'.format(
(sum(pos_precision) / n_folds + sum(neg_precision) / n_folds) / 2))
print('recall : {:.4F}'.format(
(sum(pos_recall) / n_folds + sum(neg_recall) / n_folds) / 2))
print('f-measure: {:.4F}'.format(
(sum(pos_fmeasure) / n_folds + sum(neg_fmeasure) / n_folds) / 2))
print('\n')
NBayesClassifier=nltk.NaiveBayesClassifier.train(trainFeat)
#NBResultLabels=[NBayesClassifier.classify(extract_features(tweet[0])) for tweet in testData]
print("Accuracy : " + str(nltk.classify.util.accuracy(NBayesClassifier, testFeat)*100) + " %")
refsets = collections.defaultdict(set)
testsets = collections.defaultdict(set)
for i, (feats, label) in enumerate(testFeat):
refsets[label].add(i)
observed = NBayesClassifier.classify(feats)
testsets[observed].add(i)
print 'sarcasm precision : ' + str((precision(refsets['sarcasm'], testsets['sarcasm'])*100)) + " %"
print 'sarcasm recall : ' + str((recall(refsets['sarcasm'], testsets['sarcasm'])*100)) + " %"
print 'sarcasm F-measure : ' + str((f_measure(refsets['sarcasm'], testsets['sarcasm'])*100)) + " %"
print 'non-sarcasm precision : ' + str((precision(refsets['non-sarcasm'], testsets['non-sarcasm'])*100)) + " %"
print 'non-sarcasm recall : ' + str((recall(refsets['non-sarcasm'], testsets['non-sarcasm'])*100)) + " %"
print 'non-sarcasm F-measure : ' + str((f_measure(refsets['non-sarcasm'], testsets['non-sarcasm'])*100)) + " %"
#NBayesClassifier.show_most_informative_features(100)
# print(NBResultLabels)
# if NBResultLabels.count('positive')>NBResultLabels.count('negative'):
# print "NB Result Sarcastic Sentiment\t\t:" + str(100*NBResultLabels.count('sarcasm')/len(NBResultLabels))+"%"
# else:
# print "NB Result Non-Sarcastic Sentiment\t:" + str(100*NBResultLabels.count('non-sarcasm')/len(NBResultLabels))+"%"