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))
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_spacy(spacy_nlp):
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
tk = SpacyTokenizer()
assert [t.text for t in tk.tokenize(spacy_nlp("Forecast for lunch"))] == ['Forecast', 'for', 'lunch']
assert [t.offset for t in tk.tokenize(spacy_nlp("Forecast for lunch"))] == [0, 9, 13]
assert [t.text for t in tk.tokenize(spacy_nlp("hey ńöñàśçií how're you?"))] == \
['hey', 'ńöñàśçií', 'how', '\'re', 'you', '?']
assert [t.offset for t in tk.tokenize(spacy_nlp("hey ńöñàśçií how're you?"))] == [0, 4, 13, 16, 20, 23]
def __init__(self, resource_name, backend, language_name):
self.intent_examples = []
self.entity_examples = []
self.resource_name = resource_name
self.files = util.recursively_find_files(resource_name)
self.fformat = self.guess_format(self.files)
self.tokenizer = None
self.language_name = language_name
if backend in ['mitie', 'mitie_sklearn']:
from rasa_nlu.tokenizers.mitie_tokenizer import MITIETokenizer
self.tokenizer = MITIETokenizer()
elif backend in ['spacy_sklearn']:
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
self.tokenizer = SpacyTokenizer(language_name)
else:
from rasa_nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
self.tokenizer = WhitespaceTokenizer()
warnings.warn(
"backend not recognised by TrainingData : defaulting to tokenizing by splitting on whitespace"
)
if self.fformat == 'luis':
self.load_luis_data(self.files[0])
elif self.fformat == 'wit':
self.load_wit_data(self.files[0])
elif self.fformat == 'api':
self.load_api_data(self.files)
elif self.fformat == 'rasa_nlu':
self.load_data(self.files[0])
else:
raise ValueError("unknown training file format : {0}".format(
self.fformat))
def init_tokenizer(self, backend, nlp):
if backend in [mitie.MITIE_BACKEND_NAME, mitie.MITIE_SKLEARN_BACKEND_NAME]:
from rasa_nlu.tokenizers.mitie_tokenizer import MITIETokenizer
self.tokenizer = MITIETokenizer()
elif backend in [spacy.SPACY_BACKEND_NAME]:
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
self.tokenizer = SpacyTokenizer(nlp)
else:
from rasa_nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
self.tokenizer = WhitespaceTokenizer()
warnings.warn(
"backend not recognised by TrainingData : defaulting to tokenizing by splitting on whitespace")
def tokenizer_from_name(name, language):
from rasa_nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
from rasa_nlu.tokenizers.mitie_tokenizer import MitieTokenizer
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
if name == MitieTokenizer.name:
return MitieTokenizer()
elif name == SpacyTokenizer.name:
import spacy
nlp = spacy.load(language, parser=False, entity=False)
return SpacyTokenizer(nlp)
elif name == WhitespaceTokenizer.name:
return WhitespaceTokenizer()
def test_spacy(spacy_nlp):
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
tk = SpacyTokenizer()
assert [t.text for t in tk.tokenize(spacy_nlp("Forecast for lunch"))
] == ['Forecast', 'for', 'lunch']
assert [t.offset for t in tk.tokenize(spacy_nlp("Forecast for lunch"))
] == [0, 9, 13]
assert [t.text for t in tk.tokenize(spacy_nlp("hey ńöñàśçií how're you?"))] == \
['hey', 'ńöñàśçií', 'how', '\'re', 'you', '?']
assert [
t.offset for t in tk.tokenize(spacy_nlp("hey ńöñàśçií how're you?"))
] == [0, 4, 13, 16, 20, 23]
def tokenize_sentence(sentence, expected_result, language):
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
tk = SpacyTokenizer(language)
assert tk.tokenize(sentence) == expected_result
def tokenize_sentence(sentence, expected_result):
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
tk = SpacyTokenizer(spacy_nlp_en)
assert tk.tokenize(sentence) == expected_result
from rasa_nlu.train import load_data
from rasa_nlu.config import RasaNLUModelConfig
from rasa_nlu.utils.spacy_utils import SpacyNLP
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
import spacy
config = RasaNLUModelConfig()
training_data = load_data("data/examples/rasa/demo-rasa.json")
SpacyNLP(nlp=spacy.load("en")).train(training_data, config)
SpacyTokenizer().train(training_data, config)
print(training_data.training_examples[25].as_dict())
crf = CRFEntityExtractor()
filtered_data = crf.filter_trainable_entities(training_data.training_examples)
# Create Dataset
# dataset = crf._create_dataset(filtered_data)
## Convert Examples
dataset = []
## Convert JSON TO CRF
for training_example in filtered_data:
entity_offsets = crf._convert_example(training_example)
print("Entity Offset", entity_offsets)
# b = crf._from_json_to_crf(training_example, entity_offsets)
# print("JSON to CRF", b)
def tokenize_sentence(sentence, expected_result):
from rasa_nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
tk = SpacyTokenizer()
assert tk.tokenize(sentence, spacy_nlp) == expected_result
