def load(self, source: Union[str, Any], templatefile: str,
key: str) -> None: # type: ignore
"""
Load a policy and associated templates under the given key
If `source` is a string, it is taken as a path to a filename and the
policy is loaded as an `LocalKerasModel` object.
If `source` is not a string, it is taken as a custom object that
implements the `__len__` and `predict` methods.
:param source: the source of the policy model
:param templatefile: the path to a HDF5 file with the templates
:param key: the key or label
:raises PolicyException: if the length of the model output vector is not same as the number of templates
"""
self._logger.info(
f"Loading expansion policy model from {source} to {key}")
model = load_model(source, key, self._config.use_remote_models)
self._logger.info(f"Loading templates from {templatefile} to {key}")
templates: pd.DataFrame = pd.read_hdf(templatefile, "table")
if hasattr(model, "output_size"
) and len(templates) != model.output_size: # type: ignore
raise PolicyException(
f"The number of templates ({len(templates)}) does not agree with the " # type: ignore
f"output dimensions of the model ({model.output_size})")
self._items[key] = {"model": model, "templates": templates}
def __init__(self, key: str, config: Configuration, **kwargs: Any) -> None:
super().__init__(key, config, **kwargs)
source = kwargs["source"]
self._logger.info(
f"Loading filter policy model from {source} to {key}")
self.model = load_model(source, key, self._config.use_remote_models)
self._prod_fp_name = kwargs.get("prod_fp_name", "input_1")
self._rxn_fp_name = kwargs.get("rxn_fp_name", "input_2")
def load(self, source: Union[str, Any], key: str) -> None: # type: ignore
"""
Load a policy under the given key
If `source` is a string, it is taken as a path to a filename and the
policy is loaded as an `LocalKerasModel` object.
If `source` is not a string, it is taken as a custom object that
implements the `__len__` and `predict` methods.
:param source: the source of the policy model
:param key: the key or label
"""
self._logger.info(f"Loading filter policy model from {source} to {key}")
self._items[key] = {
"model": load_model(source, key, self._config.use_remote_models)
}
def __init__(self, key: str, config: Configuration, **kwargs: str) -> None:
super().__init__(key, config, **kwargs)
source = kwargs["source"]
templatefile = kwargs["templatefile"]
self._logger.info(
f"Loading template-based expansion policy model from {source} to {self.key}"
)
self.model = load_model(source, self.key,
self._config.use_remote_models)
self._logger.info(
f"Loading templates from {templatefile} to {self.key}")
self.templates: pd.DataFrame = pd.read_hdf(templatefile, "table")
if hasattr(self.model, "output_size") and len(
self.templates) != self.model.output_size: # type: ignore
raise PolicyException(
f"The number of templates ({len(self.templates)}) does not agree with the " # type: ignore
f"output dimensions of the model ({self.model.output_size})")