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))
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_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
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))
