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)
#! test for score(), since training data is uniform across the categories there is no reason for the score to differ across them.
def test_probability_being_1_over_n(self):
trainer = self.trainer
scores = list(trainer.score(self.email).values())
#* compare first and last item in scores, assertAlmostEqual() used to ignore rounding errors? followed by comparing each nth element of scores with the n+1th element.
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)
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)
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)