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}