def load(cls, model_dir: Optional[Text] = None, model_metadata: Optional[Metadata] = None, cached_component: Optional['SklearnIntentClassifier'] = None, **kwargs: Any) -> 'SklearnIntentClassifier': meta = model_metadata.for_component(cls.name) file_name = meta.get("classifier_file", SKLEARN_MODEL_FILE_NAME) classifier_file = os.path.join(model_dir, file_name) if os.path.exists(classifier_file): return utils.pycloud_unpickle(classifier_file) else: return cls(meta)
def load(cls, model_dir: Optional[Text] = None, model_metadata: Optional['Metadata'] = None, cached_component: Optional['NGramFeaturizer'] = None, **kwargs: Any) -> 'NGramFeaturizer': meta = model_metadata.for_component(cls.name) file_name = meta.get("featurizer_file", NGRAM_MODEL_FILE_NAME) featurizer_file = os.path.join(model_dir, file_name) if os.path.exists(featurizer_file): return utils.pycloud_unpickle(featurizer_file) else: return NGramFeaturizer(meta)
def load(cls, meta: Dict[Text, Any], model_dir: Optional[Text] = None, model_metadata: Optional[Metadata] = None, cached_component: Optional['SklearnIntentClassifier'] = None, **kwargs: Any) -> 'SklearnIntentClassifier': file_name = meta.get("file") classifier_file = os.path.join(model_dir, file_name) if os.path.exists(classifier_file): return utils.pycloud_unpickle(classifier_file) else: return cls(meta)
def load(cls, meta: Dict[Text, Any], model_dir: Optional[Text] = None, model_metadata: Optional[Metadata] = None, cached_component: Optional['SklearnIntentClassifier'] = None, **kwargs: Any ) -> 'SklearnIntentClassifier': file_name = meta.get("file") classifier_file = os.path.join(model_dir, file_name) if os.path.exists(classifier_file): return utils.pycloud_unpickle(classifier_file) else: return cls(meta)
def load(cls, meta: Dict[Text, Any], model_dir: Text = None, model_metadata: Metadata = None, cached_component: Optional['CountVectorsFeaturizer'] = None, **kwargs: Any) -> 'CountVectorsFeaturizer': if model_dir and meta.get("file"): file_name = meta.get("file") featurizer_file = os.path.join(model_dir, file_name) return utils.pycloud_unpickle(featurizer_file) else: logger.warning("Failed to load featurizer. Maybe path {} " "doesn't exist".format(os.path.abspath(model_dir))) return CountVectorsFeaturizer(meta)
def load(cls, meta: Dict[Text, Any], model_dir: Optional[Text] = None, model_metadata: Optional['Metadata'] = None, cached_component: Optional['NGramFeaturizer'] = None, **kwargs: Any ) -> 'NGramFeaturizer': file_name = meta.get("file") featurizer_file = os.path.join(model_dir, file_name) if os.path.exists(featurizer_file): return utils.pycloud_unpickle(featurizer_file) else: return NGramFeaturizer(meta)
def load(cls, model_dir=None, # type: Optional[Text] model_metadata=None, # type: Optional[Metadata] cached_component=None, # type: Optional[Component] **kwargs # type: **Any ): # type: (...) -> SklearnIntentClassifier meta = model_metadata.for_component(cls.name) file_name = meta.get("classifier_file", SKLEARN_MODEL_FILE_NAME) classifier_file = os.path.join(model_dir, file_name) if os.path.exists(classifier_file): return utils.pycloud_unpickle(classifier_file) else: return cls(meta)
def load(cls, model_dir=None, # type: Optional[Text] model_metadata=None, # type: Optional[Metadata] cached_component=None, # type: Optional[NGramFeaturizer] **kwargs # type: **Any ): # type: (...) -> NGramFeaturizer meta = model_metadata.for_component(cls.name) file_name = meta.get("featurizer_file", NGRAM_MODEL_FILE_NAME) featurizer_file = os.path.join(model_dir, file_name) if os.path.exists(featurizer_file): return utils.pycloud_unpickle(featurizer_file) else: return NGramFeaturizer(meta)
def load(cls, model_dir=None, # type: Optional[Text] model_metadata=None, # type: Optional[Metadata] cached_component=None, # type: Optional[Component] **kwargs # type: **Any ): # type: (...) -> SklearnIntentClassifier meta = model_metadata.for_component(cls.name) file_name = meta.get("classifier_file", SKLEARN_MODEL_FILE_NAME) classifier_file = os.path.join(model_dir, file_name) if os.path.exists(classifier_file): return utils.pycloud_unpickle(classifier_file) else: return cls(meta)
def load(cls, model_dir=None, # type: Optional[Text] model_metadata=None, # type: Optional[Metadata] cached_component=None, # type: Optional[NGramFeaturizer] **kwargs # type: **Any ): # type: (...) -> NGramFeaturizer meta = model_metadata.for_component(cls.name) file_name = meta.get("classifier_file", NGRAM_MODEL_FILE_NAME) classifier_file = os.path.join(model_dir, file_name) if os.path.exists(classifier_file): return utils.pycloud_unpickle(classifier_file) else: return NGramFeaturizer(meta)
def load(cls, model_dir=None, # type: Text model_metadata=None, # type: Metadata cached_component=None, # type: Optional[Component] **kwargs # type: **Any ): # type: (...) -> CountVectorsFeaturizer meta = model_metadata.for_component(cls.name) if model_dir and meta.get("featurizer_file"): file_name = meta.get("featurizer_file") featurizer_file = os.path.join(model_dir, file_name) return utils.pycloud_unpickle(featurizer_file) else: logger.warning("Failed to load featurizer. Maybe path {} " "doesn't exist".format(os.path.abspath(model_dir)))
def load(cls, model_dir=None, # type: Text model_metadata=None, # type: Metadata cached_component=None, # type: Optional[Component] **kwargs # type: **Any ): # type: (...) -> CountVectorsFeaturizer meta = model_metadata.for_component(cls.name) if model_dir and meta.get("featurizer_file"): file_name = meta.get("featurizer_file") featurizer_file = os.path.join(model_dir, file_name) return utils.pycloud_unpickle(featurizer_file) else: logger.warning("Failed to load featurizer. Maybe path {} " "doesn't exist".format(os.path.abspath(model_dir))) return CountVectorsFeaturizer(meta)
def load( cls, model_dir=None, # type: Text model_metadata=None, # type: Metadata cached_component=None, # type: Optional[Component] **kwargs # type: **Any ): # type: (...) -> TfIdfCharWordFeaturizer meta = model_metadata.for_component(cls.name) pklfile = TfIdfCharWordFeaturizer.name + ".pkl" if model_dir: featurizer_file = os.path.join(model_dir, pklfile) return utils.pycloud_unpickle(featurizer_file) else: logger.warning("Failed to load featurizer. Maybe path {} " "doesn't exist".format(os.path.abspath(model_dir))) return TfIdfCharWordFeaturizer(meta)