def persist(self, file_name: Text, model_dir: Text) -> Dict[Text, Any]:
"""Persist this model into the passed directory.
Return the metadata necessary to load the model again.
"""
if self.model is None:
return {"file": None}
model_dir = Path(model_dir)
tf_model_file = model_dir / f"{file_name}.tf_model"
io_utils.create_directory_for_file(tf_model_file)
self.model.save(str(tf_model_file))
io_utils.pickle_dump(model_dir / f"{file_name}.data_example.pkl",
self.data_example)
io_utils.pickle_dump(model_dir / f"{file_name}.label_data.pkl",
self._label_data)
io_utils.json_pickle(
model_dir / f"{file_name}.index_label_id_mapping.pkl",
self.index_label_id_mapping,
)
io_utils.json_pickle(
model_dir / f"{file_name}.index_tag_id_mapping.pkl",
self.index_tag_id_mapping,
)
return {"file": file_name}
def persist(self, path: Text) -> None:
"""Persists the policy to a storage."""
if self.model is None:
logger.debug("Method `persist(...)` was called "
"without a trained model present. "
"Nothing to persist then!")
return
model_path = Path(path)
tf_model_file = model_path / f"{SAVE_MODEL_FILE_NAME}.tf_model"
io_utils.create_directory_for_file(tf_model_file)
self.featurizer.persist(path)
self.model.save(str(tf_model_file))
io_utils.json_pickle(
model_path / f"{SAVE_MODEL_FILE_NAME}.priority.pkl", self.priority)
io_utils.pickle_dump(model_path / f"{SAVE_MODEL_FILE_NAME}.meta.pkl",
self.config)
io_utils.json_pickle(
model_path / f"{SAVE_MODEL_FILE_NAME}.data_example.pkl",
self.data_example)
io_utils.json_pickle(
model_path / f"{SAVE_MODEL_FILE_NAME}.label_data.pkl",
self._label_data)
def persist(self, file_name: Text, model_dir: Text) -> Dict[Text, Any]:
gensim_model_path = os.path.join(model_dir, f'{file_name}.model')
hash_embedding_path = os.path.join(model_dir,
f'{file_name}.hash_embedding.pkl')
self.model.save(gensim_model_path)
io_utils.pickle_dump(hash_embedding_path, self.hash_embedding)
return {"file": file_name}
def persist(self, file_name: Text, model_dir: Text) -> Dict[Text, Any]:
tf_model_path = os.path.join(model_dir, f'{file_name}.tf_model')
vocab_path = os.path.join(model_dir, f'{file_name}.vocab.pkl')
os.makedirs(tf_model_path)
tf.keras.models.save_model(self.model, tf_model_path)
io_utils.pickle_dump(vocab_path, self.vocab)
return {"file": file_name}
def persist_model_utilities(self, model_path: Path) -> None:
"""Persists model's utility attributes like model weights, etc.
Args:
model_path: Path where model is to be persisted
"""
super().persist_model_utilities(model_path)
io_utils.pickle_dump(
model_path / f"{self._metadata_filename()}.label_quantiles.pkl",
self.label_quantiles,
)
def persist(self, file_name: Text,
model_dir: Text) -> Optional[Dict[Text, Any]]:
from rasa.utils.io import pickle_dump
file_name = file_name + ".pickle"
path = os.path.join(model_dir, file_name)
persisted = {"canonicals": self.canonicals}
pickle_dump(path, persisted)
return {"file": file_name}
def persist(self, path: Union[Text, Path]) -> None:
"""Persists the policy to a storage."""
if self.model is None:
logger.debug(
"Method `persist(...)` was called without a trained model present. "
"Nothing to persist then!")
return
model_path = Path(path)
tf_model_file = model_path / f"{SAVE_MODEL_FILE_NAME}.tf_model"
rasa.shared.utils.io.create_directory_for_file(tf_model_file)
self.featurizer.persist(path)
if self.model.checkpoint_model:
self.model.copy_best(str(tf_model_file))
else:
self.model.save(str(tf_model_file))
io_utils.json_pickle(
model_path / f"{SAVE_MODEL_FILE_NAME}.priority.pkl", self.priority)
io_utils.pickle_dump(model_path / f"{SAVE_MODEL_FILE_NAME}.meta.pkl",
self.config)
io_utils.pickle_dump(
model_path / f"{SAVE_MODEL_FILE_NAME}.data_example.pkl",
self.data_example)
io_utils.pickle_dump(
model_path / f"{SAVE_MODEL_FILE_NAME}.zero_state_features.pkl",
self.zero_state_features,
)
io_utils.pickle_dump(
model_path / f"{SAVE_MODEL_FILE_NAME}.label_data.pkl",
dict(self._label_data.data),
)
def persist_model_utilities(self, model_path: Path) -> None:
"""Persists model's utility attributes like model weights, etc.
Args:
model_path: Path where model is to be persisted
"""
model_filename = self._metadata_filename()
io_utils.json_pickle(model_path / f"{model_filename}.priority.pkl",
self.priority)
io_utils.pickle_dump(model_path / f"{model_filename}.meta.pkl",
self.config)
io_utils.pickle_dump(
model_path / f"{model_filename}.data_example.pkl",
self.data_example,
)
io_utils.pickle_dump(
model_path / f"{model_filename}.fake_features.pkl",
self.fake_features,
)
io_utils.pickle_dump(
model_path / f"{model_filename}.label_data.pkl",
dict(self._label_data.data),
)
entity_tag_specs = ([
tag_spec._asdict() for tag_spec in self._entity_tag_specs
] if self._entity_tag_specs else [])
rasa.shared.utils.io.dump_obj_as_json_to_file(
model_path / f"{model_filename}.entity_tag_specs.json",
entity_tag_specs,
)
def persist(self, path: Text) -> None:
if self.model:
self.featurizer.persist(path)
meta = {"priority": self.priority}
meta_file = os.path.join(path, "sklearn_policy.json")
io_utils.dump_obj_as_json_to_file(meta_file, meta)
filename = os.path.join(path, "sklearn_model.pkl")
io_utils.pickle_dump(filename, self._state)
zero_features_filename = os.path.join(path,
"zero_state_features.pkl")
io_utils.pickle_dump(zero_features_filename,
self.zero_state_features)
else:
rasa.shared.utils.io.raise_warning(
"Persist called without a trained model present. "
"Nothing to persist then!")
def persist(self, path: Union[Text, Path]) -> None:
"""Persists the policy properties (see parent class for more information)."""
if self.model:
self.featurizer.persist(path)
meta = {"priority": self.priority}
path = Path(path)
meta_file = path / "sklearn_policy.json"
rasa.shared.utils.io.dump_obj_as_json_to_file(meta_file, meta)
filename = path / "sklearn_model.pkl"
rasa.utils.io.pickle_dump(filename, self._state)
zero_features_filename = path / "zero_state_features.pkl"
io_utils.pickle_dump(zero_features_filename,
self.zero_state_features)
else:
rasa.shared.utils.io.raise_warning(
"Persist called without a trained model present. "
"Nothing to persist then!")
def persist(self, file_name: Text, model_dir: Text) -> Dict[Text, Any]:
tf_model_path = os.path.join(model_dir, f'{file_name}.tf_model')
os.makedirs(tf_model_path)
tf.keras.models.save_model(self.model, tf_model_path)
io_utils.pickle_dump(
os.path.join(model_dir, f'{file_name}.char_to_idx.pkl'),
self.char_to_idx)
io_utils.pickle_dump(
os.path.join(model_dir, f'{file_name}.index_label_id_mapping.pkl'),
self.index_label_id_mapping)
io_utils.pickle_dump(
os.path.join(model_dir, f'{file_name}.index_tag_id_mapping .pkl'),
self.index_tag_id_mapping)
return {"file": file_name}
def persist(self, file_name, model_dir):
"""Persist this model into the passed directory."""
classifier_file = os.path.join(model_dir, SENTIMENT_MODEL_FILE_NAME)
utils_io.pickle_dump(classifier_file, self)
return {"classifier_file": SENTIMENT_MODEL_FILE_NAME}
