def test_crf_json_from_non_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = False
sentence = u"I need a home cleaning close-by"
doc = {"spacy_doc": spacy_nlp(sentence)}
rs = ext._from_crf_to_json(Message(sentence, doc),
[{'O': 1.0},
{'O': 1.0},
{'O': 1.0},
{'what': 1.0},
{'what': 1.0},
{'where': 1.0},
{'where': 1.0},
{'where': 1.0}])
# non BILOU will split multi-word entities - hence 5
assert len(rs) == 5, "There should be five entities"
for r in rs:
assert r['confidence'] # confidence should exist
del r['confidence']
assert rs[0] == {'start': 9, 'end': 13,
'value': 'home', 'entity': 'what'}
assert rs[1] == {'start': 14, 'end': 22,
'value': 'cleaning', 'entity': 'what'}
assert rs[2] == {'start': 23, 'end': 28,
'value': 'close', 'entity': 'where'}
assert rs[3] == {'start': 28, 'end': 29,
'value': '-', 'entity': 'where'}
assert rs[4] == {'start': 29, 'end': 31,
'value': 'by', 'entity': 'where'}
def test_crf_json_from_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = True
sentence = u"I need a home cleaning close-by"
doc = {"spacy_doc": spacy_nlp(sentence)}
r = ext._from_crf_to_json(Message(sentence, doc),
[{'O': 1.0},
{'O': 1.0},
{'O': 1.0},
{'B-what': 1.0},
{'L-what': 1.0},
{'B-where': 1.0},
{'I-where': 1.0},
{'L-where': 1.0}])
assert len(r) == 2, "There should be two entities"
assert r[0]["confidence"] # confidence should exist
del r[0]["confidence"]
assert r[0] == {'start': 9, 'end': 22,
'value': 'home cleaning', 'entity': 'what'}
assert r[1]["confidence"] # confidence should exist
del r[1]["confidence"]
assert r[1] == {'start': 23, 'end': 31,
'value': 'close-by', 'entity': 'where'}
def test_crf_json_from_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = True
sentence = u"I need a home cleaning close-by"
doc = {"spacy_doc": spacy_nlp(sentence)}
r = ext._from_crf_to_json(Message(sentence, doc),
[{'O': 1.0},
{'O': 1.0},
{'O': 1.0},
{'B-what': 1.0},
{'L-what': 1.0},
{'B-where': 1.0},
{'I-where': 1.0},
{'L-where': 1.0}])
assert len(r) == 2, "There should be two entities"
assert r[0]["confidence"] # confidence should exist
del r[0]["confidence"]
assert r[0] == {'start': 9, 'end': 22,
'value': 'home cleaning', 'entity': 'what'}
assert r[1]["confidence"] # confidence should exist
del r[1]["confidence"]
assert r[1] == {'start': 23, 'end': 31,
'value': 'close-by', 'entity': 'where'}
def test_crf_json_from_non_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor(component_config={"BILOU_flag": False})
sentence = u"I need a home cleaning close-by"
doc = {"spacy_doc": spacy_nlp(sentence)}
rs = ext._from_crf_to_json(Message(sentence, doc),
[{'O': 1.0},
{'O': 1.0},
{'O': 1.0},
{'what': 1.0},
{'what': 1.0},
{'where': 1.0},
{'where': 1.0},
{'where': 1.0}])
# non BILOU will split multi-word entities - hence 5
assert len(rs) == 5, "There should be five entities"
for r in rs:
assert r['confidence'] # confidence should exist
del r['confidence']
assert rs[0] == {'start': 9, 'end': 13,
'value': 'home', 'entity': 'what'}
assert rs[1] == {'start': 14, 'end': 22,
'value': 'cleaning', 'entity': 'what'}
assert rs[2] == {'start': 23, 'end': 28,
'value': 'close', 'entity': 'where'}
assert rs[3] == {'start': 28, 'end': 29,
'value': '-', 'entity': 'where'}
assert rs[4] == {'start': 29, 'end': 31,
'value': 'by', 'entity': 'where'}
def test_crf_json_from_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = True
sentence = u"I need a home cleaning close-by"
r = ext._from_crf_to_json(spacy_nlp(sentence), ['O', 'O', 'O', 'B-what', 'L-what', 'B-where', 'I-where', 'L-where'])
assert len(r) == 2, "There should be two entities"
assert r[0] == {u'start': 9, u'end': 22, u'value': u'home cleaning', u'entity': u'what'}
assert r[1] == {u'start': 23, u'end': 31, u'value': u'close-by', u'entity': u'where'}
def test_crf_json_from_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = True
sentence = u"I need a home cleaning close-by"
r = ext._from_crf_to_json(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}),
['O', 'O', 'O', 'B-what', 'L-what', 'B-where', 'I-where', 'L-where'])
assert len(r) == 2, "There should be two entities"
assert r[0] == {u'start': 9, u'end': 22, u'value': u'home cleaning', u'entity': u'what'}
assert r[1] == {u'start': 23, u'end': 31, u'value': u'close-by', u'entity': u'where'}
def test_crf_json_from_non_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = False
sentence = u"I need a home cleaning close-by"
r = ext._from_crf_to_json(spacy_nlp(sentence), ['O', 'O', 'O', 'what', 'what', 'where', 'where', 'where'])
assert len(r) == 5, "There should be five entities" # non BILOU will split multi-word entities - hence 5
assert r[0] == {u'start': 9, u'end': 13, u'value': u'home', u'entity': u'what'}
assert r[1] == {u'start': 14, u'end': 22, u'value': u'cleaning', u'entity': u'what'}
assert r[2] == {u'start': 23, u'end': 28, u'value': u'close', u'entity': u'where'}
assert r[3] == {u'start': 28, u'end': 29, u'value': u'-', u'entity': u'where'}
assert r[4] == {u'start': 29, u'end': 31, u'value': u'by', u'entity': u'where'}
def test_crf_json_from_non_BILOU(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
ext.BILOU_flag = False
sentence = u"I need a home cleaning close-by"
r = ext._from_crf_to_json(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}),
['O', 'O', 'O', 'what', 'what', 'where', 'where', 'where'])
assert len(r) == 5, "There should be five entities" # non BILOU will split multi-word entities - hence 5
assert r[0] == {u'start': 9, u'end': 13, u'value': u'home', u'entity': u'what'}
assert r[1] == {u'start': 14, u'end': 22, u'value': u'cleaning', u'entity': u'what'}
assert r[2] == {u'start': 23, u'end': 28, u'value': u'close', u'entity': u'where'}
assert r[3] == {u'start': 28, u'end': 29, u'value': u'-', u'entity': u'where'}
assert r[4] == {u'start': 29, u'end': 31, u'value': u'by', u'entity': u'where'}
def test_crf_extractor(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
examples = [
Message("anywhere in the west", {
"intent": "restaurant_search",
"entities": [{"start": 16, "end": 20, "value": "west", "entity": "location"}],
"spacy_doc": spacy_nlp("anywhere in the west")
}),
Message("central indian restaurant", {
"intent": "restaurant_search",
"entities": [{"start": 0, "end": 7, "value": "central", "entity": "location"}],
"spacy_doc": spacy_nlp("central indian restaurant")
})]
config = {"ner_crf": {"BILOU_flag": True, "features": ext.crf_features}}
ext.train(TrainingData(training_examples=examples), config)
sentence = 'anywhere in the west'
crf_format = ext._from_text_to_crf(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
assert [word[0] for word in crf_format] == ['anywhere', 'in', 'the', 'west']
feats = ext._sentence_to_features(crf_format)
assert 'BOS' in feats[0]
assert 'EOS' in feats[-1]
assert feats[1]['0:low'] == "in"
sentence = 'anywhere in the west'
ext.extract_entities(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
def test_crf_extractor(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
examples = [
Message("anywhere in the west", {
"intent": "restaurant_search",
"entities": [{"start": 16, "end": 20, "value": "west", "entity": "location"}],
"spacy_doc": spacy_nlp("anywhere in the west")
}),
Message("central indian restaurant", {
"intent": "restaurant_search",
"entities": [{"start": 0, "end": 7, "value": "central", "entity": "location"}],
"spacy_doc": spacy_nlp("central indian restaurant")
})]
config = {"ner_crf": {"BILOU_flag": True, "features": ext.crf_features}}
ext.train(TrainingData(training_examples=examples), config)
sentence = 'anywhere in the west'
crf_format = ext._from_text_to_crf(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
assert [word[0] for word in crf_format] == ['anywhere', 'in', 'the', 'west']
feats = ext._sentence_to_features(crf_format)
assert 'BOS' in feats[0]
assert 'EOS' in feats[-1]
assert feats[1]['0:low'] == "in"
sentence = 'anywhere in the west'
ext.extract_entities(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
def test_crf_extractor(spacy_nlp, ner_crf_pos_feature_config):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor(component_config=ner_crf_pos_feature_config)
examples = [
Message("anywhere in the west", {
"intent": "restaurant_search",
"entities": [{"start": 16, "end": 20,
"value": "west", "entity": "location"}],
"spacy_doc": spacy_nlp("anywhere in the west")
}),
Message("central indian restaurant", {
"intent": "restaurant_search",
"entities": [
{"start": 0, "end": 7, "value": "central",
"entity": "location", "extractor": "random_extractor"},
{"start": 8, "end": 14, "value": "indian",
"entity": "cuisine", "extractor": "ner_crf"}
],
"spacy_doc": spacy_nlp("central indian restaurant")
})]
# uses BILOU and the default features
ext.train(TrainingData(training_examples=examples), RasaNLUModelConfig())
sentence = 'anywhere in the west'
doc = {"spacy_doc": spacy_nlp(sentence)}
crf_format = ext._from_text_to_crf(Message(sentence, doc))
assert [word[0] for word in crf_format] == ['anywhere', 'in', 'the', 'west']
feats = ext._sentence_to_features(crf_format)
assert 'BOS' in feats[0]
assert 'EOS' in feats[-1]
assert feats[1]['0:low'] == "in"
sentence = 'anywhere in the west'
ext.extract_entities(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
filtered = ext.filter_trainable_entities(examples)
assert filtered[0].get('entities') == [
{"start": 16, "end": 20, "value": "west", "entity": "location"}
], 'Entity without extractor remains'
assert filtered[1].get('entities') == [
{"start": 8, "end": 14,
"value": "indian", "entity": "cuisine", "extractor": "ner_crf"}
], 'Only ner_crf entity annotation remains'
assert examples[1].get('entities')[0] == {
"start": 0, "end": 7,
"value": "central", "entity": "location",
"extractor": "random_extractor"
}, 'Original examples are not mutated'
def test_crf_extractor(spacy_nlp, ner_crf_pos_feature_config):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor(component_config=ner_crf_pos_feature_config)
examples = [
Message("anywhere in the west", {
"intent": "restaurant_search",
"entities": [{"start": 16, "end": 20,
"value": "west", "entity": "location"}],
"spacy_doc": spacy_nlp("anywhere in the west")
}),
Message("central indian restaurant", {
"intent": "restaurant_search",
"entities": [
{"start": 0, "end": 7, "value": "central",
"entity": "location", "extractor": "random_extractor"},
{"start": 8, "end": 14, "value": "indian",
"entity": "cuisine", "extractor": "CRFEntityExtractor"}
],
"spacy_doc": spacy_nlp("central indian restaurant")
})]
# uses BILOU and the default features
ext.train(TrainingData(training_examples=examples), RasaNLUModelConfig())
sentence = 'anywhere in the west'
doc = {"spacy_doc": spacy_nlp(sentence)}
crf_format = ext._from_text_to_crf(Message(sentence, doc))
assert [word[0] for word in crf_format] == ['anywhere', 'in',
'the', 'west']
feats = ext._sentence_to_features(crf_format)
assert 'BOS' in feats[0]
assert 'EOS' in feats[-1]
assert feats[1]['0:low'] == "in"
sentence = 'anywhere in the west'
ext.extract_entities(Message(sentence, {"spacy_doc": spacy_nlp(sentence)}))
filtered = ext.filter_trainable_entities(examples)
assert filtered[0].get('entities') == [
{"start": 16, "end": 20, "value": "west", "entity": "location"}
], 'Entity without extractor remains'
assert filtered[1].get('entities') == [
{"start": 8, "end": 14,
"value": "indian", "entity": "cuisine",
"extractor": "CRFEntityExtractor"}
], 'Only CRFEntityExtractor entity annotation remains'
assert examples[1].get('entities')[0] == {
"start": 0, "end": 7,
"value": "central", "entity": "location",
"extractor": "random_extractor"
}, 'Original examples are not mutated'
def test_crf_extractor(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
examples = [{
"text":
"anywhere in the west",
"intent":
"restaurant_search",
"entities": [{
"start": 16,
"end": 20,
"value": "west",
"entity": "location"
}]
}, {
"text":
"central indian restaurant",
"intent":
"restaurant_search",
"entities": [{
"start": 0,
"end": 7,
"value": "central",
"entity": "location"
}]
}]
ext.train(TrainingData(entity_examples_only=examples), spacy_nlp, True,
ext.crf_features)
crf_format = ext._from_text_to_crf('anywhere in the west', spacy_nlp)
assert ([word[0]
for word in crf_format] == ['anywhere', 'in', 'the', 'west'])
feats = ext._sentence_to_features(crf_format)
assert ('BOS' in feats[0])
assert ('EOS' in feats[-1])
assert ('0:low:in' in feats[1])
ext.extract_entities('anywhere in the west', spacy_nlp)
def test_crf_extractor(spacy_nlp):
from rasa_nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor()
examples = [
{
"text": "anywhere in the west",
"intent": "restaurant_search",
"entities": [{"start": 16, "end": 20, "value": "west", "entity": "location"}]
},
{
"text": "central indian restaurant",
"intent": "restaurant_search",
"entities": [{"start": 0, "end": 7, "value": "central", "entity": "location"}]
}]
ext.train(TrainingData(training_examples=examples), spacy_nlp, True, ext.crf_features)
crf_format = ext._from_text_to_crf('anywhere in the west', spacy_nlp)
assert ([word[0] for word in crf_format] == ['anywhere', 'in', 'the', 'west'])
feats = ext._sentence_to_features(crf_format)
assert ('BOS' in feats[0])
assert ('EOS' in feats[-1])
assert ('0:low:in' in feats[1])
ext.extract_entities('anywhere in the west', spacy_nlp)
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)
