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))