def test_simple_error_matrix(self):
train, test = corpus.make_train_test_split("reflektor", proportion=0.4)
predictor = PhraseSentimentPredictor()
predictor.fit(train)
error = predictor.error_matrix(test)
for real, predicted in error.keys():
self.assertNotEqual(real, predicted)
score = predictor.score(test)
assert score > 0, "Test is valid only if score is more than 0"
N = float(len(test))
wrong = sum(len(xs) for xs in error.values())
self.assertEqual((N - wrong) / N, score)
def test_simple_error_matrix(self):
train, test = corpus.make_train_test_split("reflektor", proportion=0.4)
predictor = PhraseSentimentPredictor()
predictor.fit(train)
error = predictor.error_matrix(test)
for real, predicted in error.keys():
self.assertNotEqual(real, predicted)
score = predictor.score(test)
assert score > 0, "Test is valid only if score is more than 0"
N = float(len(test))
wrong = sum(len(xs) for xs in error.values())
self.assertEqual((N - wrong) / N, score)
def runThroughSetup(self, **kwargs):
predictor = PhraseSentimentPredictor(**kwargs)
predictor.fit(self.train[:self.samples])
return str(predictor.score(self.test))
