def test_hamClassificationWithoutCache(self):
"""
Like L{test_spamClassification}, but ensure no instance cache is used to
satisfied word info lookups.
"""
self.classifier.train(StringIO("very nice words"), False)
classifier = Hammie(spam._SQLite3Classifier(self.path), mode='r')
self.assertTrue(classifier.score(StringIO("words, very nice")) < 0.01)
def test_spamClassificationWithoutCache(self):
"""
Like L{test_spamClassification}, but ensure no instance cache is used to
satisfied word info lookups.
"""
self.classifier.train(StringIO("spam words of spamfulness"), True)
classifier = Hammie(spam._SQLite3Classifier(self.path), mode='r')
self.assertTrue(
classifier.score(StringIO("spamfulness words of spam")) > 0.99)
def test_largeDocumentClassification(self):
"""
A document with more than 999 tokens can be successfully classified.
"""
words = []
for i in range(1000):
word = "word%d" % (i, )
words.append(word)
document = " ".join(words)
self.classifier.train(StringIO(document), False)
classifier = Hammie(spam._SQLite3Classifier(self.path), mode='r')
self.assertTrue(classifier.score(StringIO(document)) < 0.01)
def setUp(self):
self.path = self.mktemp()
self.bayes = spam._SQLite3Classifier(self.path)
self.classifier = Hammie(self.bayes, mode='r')
