class TestDecisionTreeClassifier(unittest.TestCase):
def setUp(self):
self.classifier = DecisionTreeClassifier(train_set)
def test_classify(self):
res = self.classifier.classify("I feel happy this morning")
assert_equal(res, 'positive')
assert_equal(len(self.classifier.train_set), len(train_set))
def test_accuracy(self):
acc = self.classifier.accuracy(test_set)
assert_true(isinstance(acc, float))
def test_update(self):
original_length = len(self.classifier.train_set)
self.classifier.update([("lorem ipsum", "positive")])
new_length = len(self.classifier.train_set)
assert_equal(original_length + 1, new_length)
def test_custom_feature_extractor(self):
cl = DecisionTreeClassifier(train_set, custom_extractor)
cl.classify("Yay! I'm so happy it works.")
assert_equal(cl.train_features[0][1], 'positive')
def test_pseudocode(self):
code = self.classifier.pseudocode()
assert_true("if" in code)
def test_pp(self):
pp = self.classifier.pprint(width=60)
assert_true(isinstance(pp, unicode))
class TestDecisionTreeClassifier(unittest.TestCase):
def setUp(self):
self.classifier = DecisionTreeClassifier(train_set)
def test_classify(self):
res = self.classifier.classify("I feel happy this morning")
assert_equal(res, 'positive')
assert_equal(len(self.classifier.train_set), len(train_set))
def test_accuracy(self):
acc = self.classifier.accuracy(test_set)
assert_true(isinstance(acc, float))
def test_update(self):
original_length = len(self.classifier.train_set)
self.classifier.update([("lorem ipsum", "positive")])
new_length = len(self.classifier.train_set)
assert_equal(original_length + 1, new_length)
def test_custom_feature_extractor(self):
cl = DecisionTreeClassifier(train_set, custom_extractor)
cl.classify("Yay! I'm so happy it works.")
assert_equal(cl.train_features[0][1], 'positive')
def test_pseudocode(self):
code = self.classifier.pseudocode()
assert_true("if" in code)
def test_pp(self):
pp = self.classifier.pprint(width=60)
assert_true(isinstance(pp, unicode))
def test_custom_feature_extractor(self):
cl = DecisionTreeClassifier(train_set, custom_extractor)
cl.classify("Yay! I'm so happy it works.")
assert_equal(cl.train_features[0][1], 'positive')
def test_custom_feature_extractor(self):
cl = DecisionTreeClassifier(train_set, custom_extractor)
cl.classify("Yay! I'm so happy it works.")
assert_equal(cl.train_features[0][1], 'positive')