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)
