def test_get_most_common_tag():
expected = 0.63
weights = most_common.get_most_common_weights(TRAIN_FILE)
confusion = tagger_base.evalTagger(tagger_base.makeClassifierTagger(weights),'mcc')
actual = scorer.accuracy(confusion)
ok_(expected < actual, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected, actual))
def test_get_most_common_tag():
expected = 0.63
weights = most_common.get_most_common_weights(TRAIN_FILE)
confusion = tagger_base.evalTagger(
tagger_base.makeClassifierTagger(weights), 'mcc')
actual = scorer.accuracy(confusion)
ok_(expected < actual,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected, actual))
def test_classifier_tagger():
expected = 0.136844287788
noun_weights = most_common.get_noun_weights()
noun_tagger = tagger_base.makeClassifierTagger(noun_weights)
confusion = tagger_base.evalTagger(noun_tagger,'nouns')
actual = scorer.accuracy(confusion)
assert_almost_equals(expected, actual,places=3, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
def test_classifier_tagger():
expected = 0.136844287788
noun_weights = most_common.get_noun_weights()
noun_tagger = tagger_base.makeClassifierTagger(noun_weights)
confusion = tagger_base.evalTagger(noun_tagger, 'nouns')
actual = scorer.accuracy(confusion)
assert_almost_equals(expected,
actual,
places=3,
msg="UNEQUAL Expected:%s, Actual:%s" %
(expected, actual))
