def setUp(self): self.training = [['spam', './tests/fixtures/plain.eml'], ['ham', './tests/fixtures/small.eml'], ['scram', './tests/fixtures/plain.eml']] self.trainer = SpamTrainer(self.training) with io.open('./tests/fixtures/plain.eml', 'rb') as eml_file: self.email = EmailObject(eml_file)
def setUp(self): self.training = [['spam', './tests/fixtures/plain.eml'], ['ham', './tests/fixtures/small.eml'], ['scram', './tests/fixtures/plain.eml']] self.trainer = SpamTrainer(self.training) file = io.open('./tests/fixtures/plain.eml', 'r') self.email = EmailObject(file)
def label_to_training_data(fold_file): training_data = [] for line in io.open(fold_file, 'rb'): label_file = line.rstrip().split(b' ') training_data.append(label_file) return SpamTrainer(training_data)
def test_give_preference_to_whatever_hast_the_most(self): trainer = self.trainer score = trainer.score(self.email) preference = trainer.preference()[-1] preference_score = score[preference] expected = SpamTrainer.Classification(preference, preference_score) self.assertEqual(trainer.classify(self.email), expected)
class TestSpamTrainer(unittest.TestCase): def setUp(self): self.training = [['spam', './tests/fixtures/plain.eml'], ['ham', './tests/fixtures/small.eml'], ['scram', './tests/fixtures/plain.eml']] self.trainer = SpamTrainer(self.training) with io.open('./tests/fixtures/plain.eml', 'rb') as eml_file: self.email = EmailObject(eml_file) def test_multiple_categories(self): categories = self.trainer.categories expected = set([k for k, v in self.training]) self.assertEqual(categories, expected) def test_counts_all_at_zero(self): for cat in ['_all', 'spam', 'ham', 'scram']: self.assertEqual(self.trainer.total_for(cat), 0) def test_preference_category(self): trainer = self.trainer expected = sorted(trainer.categories, key=lambda cat: trainer.total_for(cat)) self.assertEqual(trainer.preference(), expected) def test_probability_being_1_over_n(self): trainer = self.trainer scores = list(trainer.score(self.email).values()) self.assertAlmostEqual(scores[0], scores[-1]) for i in range(len(scores) - 1): self.assertAlmostEqual(scores[i], scores[i + 1]) def test_adds_up_to_one(self): trainer = self.trainer scores = list(trainer.normalized_score(self.email).values()) self.assertAlmostEqual(sum(scores), 1) self.assertAlmostEqual(scores[0], 1 / 2.0) def test_give_preference_to_whatever_has_the_most(self): trainer = self.trainer score = trainer.score(self.email) preference = trainer.preference()[-1] preference_score = score[preference] expected = SpamTrainer.Classification(preference, preference_score) self.assertEqual(trainer.classify(self.email), expected)
class TestSpamTrainer(unittest.TestCase): def setUp(self): self.training = [['spam', './tests/fixtures/plain.eml'], ['ham', './tests/fixtures/small.eml'], ['scram', './tests/fixtures/plain.eml']] self.trainer = SpamTrainer(self.training) file = io.open('./tests/fixtures/plain.eml', 'r') self.email = EmailObject(file) def test_multiple_categories(self): categories = self.trainer.categories expected = sets.Set([k for k,v in self.training]) self.assertEqual(categories, expected) def test_counts_all_at_zero(self): for cat in ['_all', 'spam', 'ham', 'scram']: self.assertEqual(self.trainer.total_for(cat), 0) def test_preference_category(self): trainer = self.trainer expected = sorted(trainer.categories, key=lambda cat: trainer.total_for(cat)) self.assertEqual(trainer.preference(), expected) def test_probability_being_1_over_n(self): trainer = self.trainer scores = trainer.score(self.email).values() self.assertAlmostEqual(scores[0], scores[-1]) for i in range(len(scores)-1): self.assertAlmostEqual(scores[i], scores[i+1]) def test_adds_up_to_one(self): trainer = self.trainer scores = trainer.normalized_score(self.email).values() self.assertAlmostEqual(sum(scores), 1) self.assertAlmostEqual(scores[0], 1/2.0) def test_give_preference_to_whatever_has_the_most(self): trainer = self.trainer score = trainer.score(self.email) preference = trainer.preference()[-1] preference_score = score[preference] expected = SpamTrainer.Classification(preference, preference_score) self.assertEqual(trainer.classify(self.email), expected)