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, 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'
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)
