def test_crf_json_from_BILOU(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)
sentence = "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, ner_crf_pos_feature_config):
from rasa.nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ner_crf_pos_feature_config.update({"BILOU_flag": False})
ext = CRFEntityExtractor(component_config=ner_crf_pos_feature_config)
sentence = "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):
ext = CRFEntityExtractor(
component_config={
"features": [
["low", "title", "upper", "pos", "pos2"],
[
"low",
"bias",
"suffix3",
"suffix2",
"upper",
"title",
"digit",
"pos",
"pos2",
],
["low", "title", "upper", "pos", "pos2"],
]
}
)
sentence = "I need a home cleaning close-by"
message = Message(sentence, {SPACY_DOCS[TEXT]: spacy_nlp(sentence)})
tokenizer = SpacyTokenizer()
tokenizer.process(message)
r = ext._from_crf_to_json(
message,
[
{"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,
"features": [
["low", "title", "upper", "pos", "pos2"],
["low", "suffix3", "suffix2", "upper", "title", "digit", "pos", "pos2"],
["low", "title", "upper", "pos", "pos2"],
],
}
)
sentence = "I need a home cleaning close-by"
message = Message(sentence, {SPACY_DOCS[TEXT]: spacy_nlp(sentence)})
tokenizer = SpacyTokenizer()
tokenizer.process(message)
rs = ext._from_crf_to_json(
message,
[
{"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_non_BILOU(spacy_nlp, ner_crf_pos_feature_config):
from rasa.nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ner_crf_pos_feature_config.update({"BILOU_flag": False})
ext = CRFEntityExtractor(component_config=ner_crf_pos_feature_config)
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, ner_crf_pos_feature_config):
from rasa.nlu.extractors.crf_entity_extractor import CRFEntityExtractor
ext = CRFEntityExtractor(component_config=ner_crf_pos_feature_config)
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'
}
