def test_np_chunk_baseline(self):
"""predicting sequences using baseline feature"""
train, test = self.split_np_chunk_corpus(Document)
classifier = HMM()
classifier.train(train)
test_result = compute_cm(classifier, test)
_, _, f1, accuracy = test_result.print_out()
self.assertGreater(accuracy, 0.55)
def test_np_chunk_baseline(self):
"""Test NP chunking with word and postag feature"""
train, test = self.split_np_chunk_corpus(Document)
classifier = HMM()
classifier.train(train)
test_result = compute_cm(classifier, test)
_, _, f1, accuracy = test_result.print_out()
self.assertGreater(accuracy, 0.55)
self.assertTrue(all(i>=.90 for i in f1), 'not all greater than 90.0%')
def test_sets(self):
"""Test NP chunking with word and postag feature"""
train, test = self.split_np_chunk_corpus(Document)
train1, train2, test1, test2 = copy.deepcopy(train), copy.deepcopy(
train), copy.deepcopy(test), copy.deepcopy(test)
for i in range(len(train)):
for j in range(len(train[i].data)):
train1[i].data[j] = train[i].data[j][0]
train2[i].data[j] = train[i].data[j][1]
for l in range(len(test)):
for k in range(len(test[l].data)):
test1[l].data[k] = test[l].data[k][0]
test2[l].data[k] = test[l].data[k][1]
classifier = HMM()
print "\n---------------FEATURES = WORDS---------------"
classifier.train(train1)
test_result = compute_cm(classifier, test1)
_, _, f1, accuracy = test_result.print_out()
self.assertGreater(accuracy, 0.55)
self.assertTrue(all(i >= .90 for i in f1),
'not all greater than 90.0%')
print "\n---------------FEATURES = POS---------------"
classifier.train(train2)
test_result = compute_cm(classifier, test2)
_, _, f1, accuracy = test_result.print_out()
self.assertGreater(accuracy, 0.55)
self.assertTrue(all(i >= .90 for i in f1),
'not all greater than 90.0%')
print "\n---------------FEATURES = WORDS+POS---------------"
classifier.train(train)
test_result = compute_cm(classifier, test)
_, _, f1, accuracy = test_result.print_out()
self.assertGreater(accuracy, 0.55)
self.assertTrue(all(i >= .90 for i in f1),
'not all greater than 90.0%')