def classifier_for_twitter_corpus():
svc = LogisticRegression(class_weight='auto', penalty='l2')
f_measure = []
entities = []
predicted = []
right = []
train_data = Bunch()
test_data = Bunch()
try:
for i in range(1, 6):
f_train = open("input/twitter_corpus/" + str(i) + "/train.txt")
f_test = open("input/twitter_corpus/" + str(i) + "/test.txt")
train_data.reviews = load_data(f_train)
test_data.reviews = load_data(f_test)
train_data.labels = extract_labels(train_data.reviews)
test_data.labels = extract_labels(test_data.reviews)
train_data.entities = extract_entities(train_data.reviews)
features_train = extract_features_for_twitter_corpus(
train_data.reviews, True)
svc.fit(numpy.array(features_train),
numpy.array(train_data.labels))
features_test = extract_features_for_twitter_corpus(
test_data.reviews, False)
predicted_block = svc.predict(numpy.array(features_test))
predicted.extend(predicted_block)
right.extend(test_data.labels)
print metrics.f1_score(test_data.labels,
predicted_block,
average='macro')
f_measure.append(
metrics.f1_score(test_data.labels,
predicted_block,
average='macro'))
entities.extend(test_data.reviews)
print str(f_measure)
print classification_report(right, predicted, digits=3)
print metrics.precision_score(right, predicted, average='macro')
print metrics.recall_score(right, predicted, average='macro')
print metrics.f1_score(right, predicted, average='macro')
except FileNotFoundError:
print "Please download Twitter corpus and put it into input/twitter_corpus folder"
def classifier_for_cadec_corpus():
svc = LinearSVC(class_weight='auto', penalty='l2')
f_measure = []
entities = []
predicted = []
right = []
train_data = Bunch()
test_data = Bunch()
for i in range(1, 6):
print i
f_train = open("input/cadec_corpus/" + str(i) + "/train.txt")
f_test = open("input/cadec_corpus/" + str(i) + "/test.txt")
train_data.reviews = load_data(f_train)
test_data.reviews = load_data(f_test)
train_data.labels = extract_labels(train_data.reviews)
test_data.labels = extract_labels(test_data.reviews)
train_data.entities = extract_entities(train_data.reviews)
features_train = extract_features_for_cadec_corpus(
train_data.reviews, True)
svc.fit(numpy.array(features_train), numpy.array(train_data.labels))
features_test = extract_features_for_cadec_corpus(
test_data.reviews, False)
predicted_block = svc.predict(numpy.array(features_test))
predicted.extend(predicted_block)
right.extend(test_data.labels)
print metrics.f1_score(test_data.labels,
predicted_block,
average='macro')
f_measure.append(
metrics.f1_score(test_data.labels,
predicted_block,
average='macro'))
entities.extend(test_data.reviews)
print str(f_measure)
print classification_report(right, predicted, digits=3)
print metrics.precision_score(right, predicted, average='macro')
print metrics.recall_score(right, predicted, average='macro')
print metrics.f1_score(right, predicted, average='macro')
