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 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 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 _make_linsvmDIFF(self):
linsvm = LinearSVC()
return EntailmentClassifier(linsvm, self.vectors)
def _make_most_frequent(self):
dummy = DummyClassifier('most_frequent')
return EntailmentClassifier(dummy, self.vectors)
def _make_knn10(self):
neigh = KNeighborsClassifier(n_neighbors=10)
return EntailmentClassifier(neigh, self.vectors)
def _make_knnP(self):
neigh = GridSearchCV(KNeighborsClassifier(),
{'n_neighbors': self.params['k']},
score_func=f1_score)
return EntailmentClassifier(neigh, self.vectors)
