def test_regex_featurizer(sentence, expected, labeled_tokens, spacy_nlp):
    from rasa_nlu.featurizers.regex_featurizer import RegexFeaturizer
    patterns = [{
        "pattern": '[0-9]+',
        "name": "number",
        "usage": "intent"
    }, {
        "pattern": '\\bhey*',
        "name": "hello",
        "usage": "intent"
    }, {
        "pattern": '[0-1]+',
        "name": "binary",
        "usage": "intent"
    }]
    ftr = RegexFeaturizer(known_patterns=patterns)

    # adds tokens to the message
    tokenizer = SpacyTokenizer()
    message = Message(sentence)
    message.set("spacy_doc", spacy_nlp(sentence))
    tokenizer.process(message)

    result = ftr.features_for_patterns(message)
    assert np.allclose(result, expected, atol=1e-10)

    # the tokenizer should have added tokens
    assert len(message.get("tokens", [])) > 0
    # the number of regex matches on each token should match
    for i, token in enumerate(message.get("tokens")):
        token_matches = token.get("pattern").values()
        num_matches = sum(token_matches)
        assert (num_matches == labeled_tokens.count(i))
def test_lookup_tables(sentence, expected, labeled_tokens, spacy_nlp):
    from rasa_nlu.featurizers.regex_featurizer import RegexFeaturizer

    lookups = [{
        "name":
        'drinks',
        "elements":
        ["mojito", "lemonade", "sweet berry wine", "tea", "club?mate"]
    }, {
        "name": 'plates',
        "elements": "data/test/lookup_tables/plates.txt"
    }]
    ftr = RegexFeaturizer(lookup_tables=lookups)

    # adds tokens to the message
    tokenizer = SpacyTokenizer()
    message = Message(sentence)
    message.set("spacy_doc", spacy_nlp(sentence))
    tokenizer.process(message)

    result = ftr.features_for_patterns(message)
    assert np.allclose(result, expected, atol=1e-10)

    # the tokenizer should have added tokens
    assert len(message.get("tokens", [])) > 0
    # the number of regex matches on each token should match
    for i, token in enumerate(message.get("tokens")):
        token_matches = token.get("pattern").values()
        num_matches = sum(token_matches)
        assert (num_matches == labeled_tokens.count(i))
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def test_regex_featurizer(sentence, expected, labeled_tokens, spacy_nlp):
    from rasa_nlu.featurizers.regex_featurizer import RegexFeaturizer
    patterns = [
        {"pattern": '[0-9]+', "name": "number", "usage": "intent"},
        {"pattern": '\\bhey*', "name": "hello", "usage": "intent"},
        {"pattern": '[0-1]+', "name": "binary", "usage": "intent"}
    ]
    ftr = RegexFeaturizer(known_patterns=patterns)

    # adds tokens to the message
    tokenizer = SpacyTokenizer()
    message = Message(sentence)
    message.set("spacy_doc", spacy_nlp(sentence))
    tokenizer.process(message)

    result = ftr.features_for_patterns(message)
    assert np.allclose(result, expected, atol=1e-10)

    # the tokenizer should have added tokens
    assert len(message.get("tokens", [])) > 0
    # the number of regex matches on each token should match
    for i, token in enumerate(message.get("tokens")):
        token_matches = token.get("pattern").values()
        num_matches = sum(token_matches)
        assert(num_matches == labeled_tokens.count(i))
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def test_regex_featurizer(sentence, expected, labeled_tokens, spacy_nlp):
    from rasa_nlu.featurizers.regex_featurizer import RegexFeaturizer
    patterns = [
        {"pattern": '[0-9]+', "name": "number", "usage": "intent"},
        {"pattern": '\\bhey*', "name": "hello", "usage": "intent"}
    ]
    ftr = RegexFeaturizer(known_patterns=patterns)

    # adds tokens to the message
    tokenizer = SpacyTokenizer()
    message = Message(sentence)
    message.set("spacy_doc", spacy_nlp(sentence))
    tokenizer.process(message)

    result = ftr.features_for_patterns(message)
    assert np.allclose(result, expected, atol=1e-10)

    # the tokenizer should have added tokens
    assert len(message.get("tokens", [])) > 0
    for i, token in enumerate(message.get("tokens")):
        if i in labeled_tokens:
            assert token.get("pattern") in [0, 1]
        else:
            # if the token is not part of a regex the pattern should not be set
            assert token.get("pattern") is None
示例#5
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def test_lookup_tables(sentence, expected, labeled_tokens, spacy_nlp):
    from rasa_nlu.featurizers.regex_featurizer import RegexFeaturizer

    lookups = [
        {"name": 'drinks', "elements": ["mojito", "lemonade",
                                        "sweet berry wine",
                                        "tea", "club?mate"]},
        {"name": 'plates', "elements": "data/test/lookup_tables/plates.txt"}
    ]
    ftr = RegexFeaturizer(lookup_tables=lookups)

    # adds tokens to the message
    tokenizer = SpacyTokenizer()
    message = Message(sentence)
    message.set("spacy_doc", spacy_nlp(sentence))
    tokenizer.process(message)

    result = ftr.features_for_patterns(message)
    assert np.allclose(result, expected, atol=1e-10)

    # the tokenizer should have added tokens
    assert len(message.get("tokens", [])) > 0
    # the number of regex matches on each token should match
    for i, token in enumerate(message.get("tokens")):
        token_matches = token.get("pattern").values()
        num_matches = sum(token_matches)
        assert(num_matches == labeled_tokens.count(i))