class SpacySklearnInterpreter(Interpreter):
def __init__(self,
entity_extractor=None,
intent_classifier=None,
language_name='en',
**kwargs):
self.nlp = spacy.load(language_name,
parser=False,
entity=False,
matcher=False)
self.featurizer = SpacyFeaturizer(self.nlp)
with open(intent_classifier, 'rb') as f:
self.classifier = cloudpickle.load(f)
self.extractor = SpacyEntityExtractor(self.nlp, entity_extractor)
def get_intent(self, text):
X = self.featurizer.create_bow_vecs([text])
return self.classifier.predict(X)[0]
def parse(self, text):
intent = self.get_intent(text)
entities = self.extractor.extract_entities(self.nlp, text)
return {'text': text, 'intent': intent, 'entities': entities}
class SpacySklearnInterpreter(Interpreter):
def __init__(self, entity_extractor=None, intent_classifier=None, language_name='en', **kwargs):
self.nlp = spacy.load(language_name, parser=False, entity=False, matcher=False)
self.featurizer = SpacyFeaturizer(self.nlp)
with open(intent_classifier, 'rb') as f:
self.classifier = cloudpickle.load(f)
self.extractor = SpacyEntityExtractor(self.nlp, entity_extractor)
def get_intent(self, text):
"""Returns the most likely intent and its probability for the input text.
:param text: text to classify
:return: tuple of most likely intent name and its probability"""
X = self.featurizer.create_bow_vecs([text])
intent_ids, probabilities = self.classifier.predict(X)
intents = self.classifier.transform_labels_num2str(intent_ids)
return intents[0], probabilities[0]
def parse(self, text):
"""Parse the input text, classify it and return an object containing its intent and entities."""
intent, probability = self.get_intent(text)
entities = self.extractor.extract_entities(self.nlp, text)
return {'text': text, 'intent': intent, 'entities': entities, 'confidence': probability}
class SpacySklearnTrainer(Trainer):
SUPPORTED_LANGUAGES = {"en", "de"}
def __init__(self, config, language_name):
self.ensure_language_support(language_name)
self.name = "spacy_sklearn"
self.language_name = language_name
self.training_data = None
self.nlp = spacy.load(self.language_name, parser=False, entity=False)
self.featurizer = SpacyFeaturizer(self.nlp)
self.intent_classifier = SklearnIntentClassifier()
self.entity_extractor = SpacyEntityExtractor()
def train(self, data, test_split_size=0.1):
self.training_data = data
self.train_entity_extractor(data.entity_examples)
self.train_intent_classifier(data.intent_examples, test_split_size)
def train_entity_extractor(self, entity_examples):
self.entity_extractor.train(self.nlp, entity_examples)
def train_intent_classifier(self, intent_examples, test_split_size=0.1):
labels = [e["intent"] for e in intent_examples]
sentences = [e["text"] for e in intent_examples]
y = self.intent_classifier.transform_labels_str2num(labels)
X = self.featurizer.create_bow_vecs(sentences)
self.intent_classifier.train(X, y, test_split_size)
def persist(self, path, persistor=None, create_unique_subfolder=True):
timestamp = datetime.datetime.now().strftime('%Y%m%d-%H%M%S')
if create_unique_subfolder:
dir_name = os.path.join(path, "model_" + timestamp)
os.mkdir(dir_name)
else:
dir_name = path
data_file = os.path.join(dir_name, "training_data.json")
classifier_file = os.path.join(dir_name, "intent_classifier.pkl")
ner_dir = os.path.join(dir_name, 'ner')
if not os.path.exists(ner_dir):
os.mkdir(ner_dir)
entity_extractor_config_file = os.path.join(ner_dir, "config.json")
entity_extractor_file = os.path.join(ner_dir, "model")
write_training_metadata(dir_name, timestamp, data_file, self.name,
self.language_name, classifier_file, ner_dir)
with open(data_file, 'w') as f:
f.write(self.training_data.as_json(indent=2))
with open(classifier_file, 'wb') as f:
cloudpickle.dump(self.intent_classifier, f)
with open(entity_extractor_config_file, 'w') as f:
json.dump(self.entity_extractor.ner.cfg, f)
self.entity_extractor.ner.model.dump(entity_extractor_file)
if persistor is not None:
persistor.send_tar_to_s3(dir_name)
def test_sentence(sentence, language, _ref):
import spacy
from rasa_nlu.featurizers.spacy_featurizer import SpacyFeaturizer
nlp = spacy.load(language, tagger=False, parser=False)
doc = nlp(sentence)
ftr = SpacyFeaturizer(nlp)
vecs = ftr.create_bow_vecs([sentence])
assert np.allclose(doc.vector[:5], _ref, atol=1e-5)
assert np.allclose(vecs[0], doc.vector, atol=1e-5)