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 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,
entity_synonyms=None,
intent_classifier=None,
language_name='en',
**kwargs):
self.extractor = None
self.classifier = None
self.ent_synonyms = None
self.nlp = spacy.load(language_name,
parser=False,
entity=False,
matcher=False)
self.featurizer = SpacyFeaturizer(self.nlp)
ensure_proper_language_model(self.nlp)
if intent_classifier:
with open(intent_classifier, 'rb') as f:
self.classifier = cloudpickle.load(f)
if entity_extractor:
self.extractor = SpacyEntityExtractor(self.nlp, entity_extractor)
self.ent_synonyms = Interpreter.load_synonyms(entity_synonyms)
def get_intent(self, doc):
"""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"""
if self.classifier:
X = self.featurizer.features_for_doc(doc).reshape(1, -1)
intent_ids, probabilities = self.classifier.predict(X)
intents = self.classifier.transform_labels_num2str(intent_ids)
intent, score = intents[0], probabilities[0]
else:
intent, score = "None", 0.0
return intent, score
def get_entities(self, doc):
if self.extractor:
return self.extractor.extract_entities(doc)
return []
def parse(self, text):
"""Parse the input text, classify it and return an object containing its intent and entities."""
doc = self.nlp(text)
intent, probability = self.get_intent(doc)
entities = self.get_entities(doc)
if self.ent_synonyms:
Interpreter.replace_synonyms(entities, self.ent_synonyms)
return {
'text': text,
'intent': intent,
'entities': entities,
'confidence': probability
}