Python wordFeatures 예제들

예제 #1

파일: test_str_classification.py 프로젝트: Mercurial1101/gt-nlp-class

def test_viterbi_trans():
    test_weights = defaultdict(float)
    test_tags = ['N','V','V','N']
    for i in range(len(sent)):
        for feat in features.wordFeatures(sent,test_tags[i],'X',i):
            test_weights[feat] = 1
        for feat in features.wordFeatures(sent,'X','X',i):
            test_weights[feat] = 1
    
    expected_output = test_tags
    expected_score = 8.0
    actual_output, actual_score=viterbi.viterbiTagger(sent,features.wordTransFeatures,test_weights,alltags)
    eq_(expected_output, actual_output, msg="UNEQUAL viterbi trans output Expected:%s, Actual:%s" %(expected_output, actual_output) )
    eq_(expected_score, actual_score, msg="UNEQUAL viterbi trans score Expected:%s, Actual:%s" %(expected_score, actual_score) )

예제 #2

파일: test_classification.py 프로젝트: Mercurial1101/gt-nlp-class

def test_basic_classifer():
    test_weights = defaultdict(float)
    test_tags = ['N','V','V','N']
    for i in range(len(sent)):
        for feat in features.wordFeatures(sent,test_tags[i],'X',i):
            test_weights[feat] = 1
        for feat in features.wordFeatures(sent,'X','X',i):
            test_weights[feat] = 1
    expected = test_tags
    actual = tagger_base.classifierTagger(sent,features.wordFeatures,test_weights,alltags)
    eq_ (expected, actual, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual) )

    expected_acc = 0.139539705577
    confusion = tagger_base.evalTagger(lambda words,alltags : tagger_base.classifierTagger(words,features.wordFeatures,test_weights,alltags),'test')
    actual_acc =scorer.accuracy(confusion)
    assert_almost_equals(expected_acc ,actual_acc,places = 3)

예제 #3

파일: test_str_classification.py 프로젝트: xiaochaowei/gt-nlp-class

def test_viterbi_trans():
    test_weights = defaultdict(float)
    test_tags = ['N', 'V', 'V', 'N']
    for i in range(len(sent)):
        for feat in features.wordFeatures(sent, test_tags[i], 'X', i):
            test_weights[feat] = 1
        for feat in features.wordFeatures(sent, 'X', 'X', i):
            test_weights[feat] = 1

    expected_output = test_tags
    expected_score = 8.0
    actual_output, actual_score = viterbi.viterbiTagger(
        sent, features.wordTransFeatures, test_weights, alltags)
    eq_(expected_output,
        actual_output,
        msg="UNEQUAL viterbi trans output Expected:%s, Actual:%s" %
        (expected_output, actual_output))
    eq_(expected_score,
        actual_score,
        msg="UNEQUAL viterbi trans score Expected:%s, Actual:%s" %
        (expected_score, actual_score))

예제 #4

파일: test_classification.py 프로젝트: xiaochaowei/gt-nlp-class

def test_basic_classifer():
    test_weights = defaultdict(float)
    test_tags = ['N', 'V', 'V', 'N']
    for i in range(len(sent)):
        for feat in features.wordFeatures(sent, test_tags[i], 'X', i):
            test_weights[feat] = 1
        for feat in features.wordFeatures(sent, 'X', 'X', i):
            test_weights[feat] = 1
    expected = test_tags
    actual = tagger_base.classifierTagger(sent, features.wordFeatures,
                                          test_weights, alltags)
    eq_(expected,
        actual,
        msg="UNEQUAL Expected:%s, Actual:%s" % (expected, actual))

    expected_acc = 0.139539705577
    confusion = tagger_base.evalTagger(
        lambda words, alltags: tagger_base.classifierTagger(
            words, features.wordFeatures, test_weights, alltags), 'test')
    actual_acc = scorer.accuracy(confusion)
    assert_almost_equals(expected_acc, actual_acc, places=3)