def testEntailmentClassifierEmptyData(self):
# Arrange
data, test_data, vectors = testData()
vectors.nouns = defaultdict(lambda: {})
neigh = KNeighborsClassifier(n_neighbors=1)
classifier = EntailmentClassifier(neigh, vectors)
# Act
classifier.fit(data)
classifier.predict(data)
def testEntailmentClassifierEmptyData(self):
# Arrange
data, test_data, vectors = testData()
vectors.nouns = defaultdict(lambda:{})
neigh = KNeighborsClassifier(n_neighbors=1)
classifier = EntailmentClassifier(neigh, vectors)
# Act
classifier.fit(data)
classifier.predict(data)
def runExperiment(self, classifier_name):
data, test_data, vectors = testData()
all_data = (data + test_data)*3
#print all_data
maker = ClassifierMaker(vectors)
classifier = maker.make(classifier_name)
num_folds = 3
experiment = EntailmentExperiment(all_data, classifier, num_folds)
results = [experiment.runFold(fold)
for fold in range(num_folds)]
#print results
return results
def testEntailmentClassifier(self):
for i in range(3):
# Arrange
data, test_data, vectors = testData()
expected = tuple(x[2] for x in test_data)
test_data = [x[:2] for x in test_data]
neigh = KNeighborsClassifier(n_neighbors=1)
classifier = EntailmentClassifier(neigh, vectors)
# Act
classifier.fit(data)
results = classifier.predict(test_data)
# Assert
self.assertEqual(tuple(results), expected)
def testEntailmentClassifier(self):
for i in range(3):
# Arrange
data, test_data, vectors = testData()
expected = tuple(x[2] for x in test_data)
test_data = [x[:2] for x in test_data]
neigh = KNeighborsClassifier(n_neighbors=1)
classifier = EntailmentClassifier(neigh, vectors)
# Act
classifier.fit(data)
results = classifier.predict(test_data)
# Assert
self.assertEqual(tuple(results), expected)
def testClassifierMakerClassifiers(self):
"Check that all classifiers that can be made are valid."
data, test_data, vectors = testData()
class_values = set(x[2] for x in data)
params = {'beta': [1.0, 2.0], 'costs': [1.0], 'k': [1]}
maker = ClassifierMaker(vectors, params)
names = maker.get_names()
for name in names:
classifier = maker.make(name)
classifier.fit(data)
results = classifier.predict(test_data)
self.assertEqual(len(results), len(test_data))
self.assertTrue(set(results) <= class_values)
def testClassifierMakerClassifiers(self):
"Check that all classifiers that can be made are valid."
data, test_data, vectors = testData()
class_values = set(x[2] for x in data)
params = {'beta':[1.0, 2.0], 'costs':[1.0]}
maker = ClassifierMaker(vectors, params)
names = maker.get_names()
for name in names:
classifier = maker.make(name)
classifier.fit(data)
results = classifier.predict(test_data)
self.assertEqual(len(results), len(test_data))
self.assertTrue(set(results) <= class_values)
def runExperiment(self, classifier_name):
data, test_data, vectors = testData()
all_data = (data + test_data)*3
#print all_data
maker = ClassifierMaker(vectors, params = {'k':[1]} )
classifier = maker.make(classifier_name)
num_folds = 3
experiment = EntailmentExperiment(all_data, classifier, num_folds)
results = [experiment.runFold(fold)
for fold in range(num_folds)]
#print results
return results
def testClassifierMakerNames(self):
data, test_data, vectors = testData()
maker = ClassifierMaker(vectors)
names = maker.get_names()
self.assertGreater(len(names), 0,
"Maker should have more than one classifier")
def testClassifierMakerNames(self):
data, test_data, vectors = testData()
maker = ClassifierMaker(vectors)
names = maker.get_names()
self.assertGreater(len(names), 0,
"Maker should have more than one classifier")
