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":
classifier_file = os.path.join(model_dir, meta.get("classifier"))
encoder_file = os.path.join(model_dir, meta.get("encoder"))
if os.path.exists(classifier_file):
classifier = utils.pycloud_unpickle(classifier_file)
encoder = utils.pycloud_unpickle(encoder_file)
return cls(meta, classifier, encoder)
else:
return cls(meta)
def load(cls,
meta: Dict[Text, Any],
model_dir: Optional[Text] = None,
model_metadata: Optional[Metadata] = None,
cached_component: Optional['rasa_sium'] = None,
**kwargs: Any) -> 'rasa_sium':
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["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,
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: Text = None,
model_metadata: Metadata = None,
cached_component: Optional["CountVectorsFeaturizer"] = None,
**kwargs: Any) -> "CountVectorsFeaturizer":
file_name = meta.get("file")
featurizer_file = os.path.join(model_dir, file_name)
if os.path.exists(featurizer_file):
vectorizer = utils.pycloud_unpickle(featurizer_file)
return cls(meta, vectorizer)
else:
return cls(meta)
def load(cls,
meta: Dict[Text, Any],
model_dir: Optional[Text] = None,
model_metadata: Optional[Metadata] = None,
cached_component: Optional['N2GHelper'] = None,
**kwargs) -> 'N2GHelper':
"""Load this component from file."""
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)
