def persist(self, file_name: Text,
model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this model into the passed directory.
Returns the metadata necessary to load the model again.
"""
file_name = file_name + ".pkl"
if self.vectorizers:
# vectorizer instance was not None, some models could have been trained
attribute_vocabularies = self._collect_vectorizer_vocabularies()
if self._is_any_model_trained(attribute_vocabularies):
# Definitely need to persist some vocabularies
featurizer_file = os.path.join(model_dir, file_name)
if self.use_shared_vocab:
# Only persist vocabulary from one attribute. Can be loaded and
# distributed to all attributes.
vocab = attribute_vocabularies[TEXT_ATTRIBUTE]
else:
vocab = attribute_vocabularies
utils.json_pickle(featurizer_file, vocab)
return {"file": file_name}
def persist(self, file_name: Text,
model_dir: Text) -> Optional[Dict[Text, Any]]:
file_name = file_name + ".pkl"
featurizer_file = os.path.join(model_dir, file_name)
utils.json_pickle(featurizer_file, self)
return {"file": file_name}
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this model into the passed directory."""
classifier_file_name = file_name+"_classifier.pkl"
if self.clf:
utils.json_pickle(os.path.join(
model_dir, classifier_file_name), self.clf)
return {"classifier": classifier_file_name}
def persist(self, file_name: Text,
model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this model into the passed directory."""
mapping_file_name = file_name + "_mapping.pkl"
if self.grams_mapping:
utils.json_pickle(os.path.join(model_dir, mapping_file_name),
self.grams_mapping)
return {"grams_mapping": mapping_file_name}
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this model into the passed directory.
Returns the metadata necessary to load the model again.
"""
file_name = file_name + ".pkl"
if self.vectorizer:
featurizer_file = os.path.join(model_dir, file_name)
utils.json_pickle(featurizer_file, self.vectorizer.vocabulary_)
return {"file": file_name}
def persist(self, file_name: Text,
model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this component to disk for future loading."""
model_file_name = file_name + '_model.h5'
encoder_file_name = file_name + '_encoder.pkl'
if self.model and self.le:
with self.graph.as_default(), self.session.as_default():
self.model.save(os.path.join(model_dir, model_file_name))
utils.json_pickle(os.path.join(model_dir, encoder_file_name),
self.le.classes_)
return {'model': model_file_name, 'encoder': encoder_file_name}
def persist(self, file_name: Text, model_dir: Text) -> Optional[Dict[Text, Any]]:
"""Persist this model into the passed directory."""
classifier_file_name = file_name + "_classifier.pkl"
encoder_file_name = file_name + "_encoder.pkl"
if self.clf and self.le:
utils.json_pickle(
os.path.join(model_dir, encoder_file_name), self.le.classes_
)
utils.json_pickle(
os.path.join(model_dir, classifier_file_name), self.clf.best_estimator_
)
return {"classifier": classifier_file_name, "encoder": encoder_file_name}
def persist(self, file_name, model_dir):
"""Persist this model into the passed directory."""
classifier_file = os.path.join(model_dir, FASTAI_MODEL_FILE_NAME)
utils.json_pickle(classifier_file, self)
return {"classifier_file": FASTAI_MODEL_FILE_NAME}
def persist(self, file_name: Text,
model_dir: Text) -> Optional[Dict[Text, Any]]:
vectorizer_file = os.path.join(model_dir, self.VECTOR_PATH)
utils.json_pickle(vectorizer_file, self)
return {"vectorizer_file": self.VECTOR_PATH}
