def test_spacy_ner_featurizer_config(spacy_nlp):
from rasa.nlu.featurizers.spacy_featurizer import SpacyFeaturizer
sentence = "hi there friend"
doc = spacy_nlp(sentence)
spacy_config = {"ner_feature_vectors": False}
ftr = SpacyFeaturizer.create(spacy_config, RasaNLUModelConfig())
greet = {"intent": "greet", "text_features": [0.5]}
message = Message(sentence, greet)
message.set("spacy_doc", doc)
ftr._set_spacy_features(message)
ftr._set_spacy_ner_features(message)
vecs = np.array(message.get("ner_features"))
assert vecs.shape[0] == len(doc)
assert vecs.shape[1] == 0
def test_spacy_intent_featurizer(spacy_nlp_component):
from rasa.nlu.featurizers.spacy_featurizer import SpacyFeaturizer
td = training_data.load_data("data/examples/rasa/demo-rasa.json")
spacy_nlp_component.train(td, config=None)
spacy_featurizer = SpacyFeaturizer()
spacy_featurizer.train(td, config=None)
intent_features_exist = np.array([
True if example.get("intent_features") is not None else False
for example in td.intent_examples
])
# no intent features should have been set
assert not any(intent_features_exist)
def test_spacy_ner_featurizer(sentence, expected, spacy_nlp):
from rasa.nlu.featurizers.spacy_featurizer import SpacyFeaturizer
doc = spacy_nlp(sentence)
token_vectors = [t.vector for t in doc]
spacy_config = {"ner_feature_vectors": True}
ftr = SpacyFeaturizer.create(spacy_config, RasaNLUModelConfig())
greet = {"intent": "greet", "text_features": [0.5]}
message = Message(sentence, greet)
message.set("spacy_doc", doc)
ftr._set_spacy_features(message)
ftr._set_spacy_ner_features(message)
vecs = message.get("ner_features")[0][:5]
assert np.allclose(token_vectors[0][:5], vecs, atol=1e-4)
assert np.allclose(vecs, expected, atol=1e-4)