def _save_min_max_meta(self, name, namespace):
if self.scale_param.area == consts.ALL:
LOGGER.debug("save_min_max_meta with mode is all")
feat_upper = "None" if self.scale_param.feat_upper is None else str(self.scale_param.feat_upper)
feat_lower = "None" if self.scale_param.feat_lower is None else str(self.scale_param.feat_lower)
out_upper = "None" if self.scale_param.out_upper is None else str(self.scale_param.out_upper)
out_lower = "None" if self.scale_param.out_lower is None else str(self.scale_param.out_lower)
min_max_scale_meta = feature_scale_meta_pb2.MinMaxScaleMeta(feat_upper=feat_upper,
feat_lower=feat_lower,
out_upper=out_upper,
out_lower=out_lower)
minmax_scale_meta = {"0": min_max_scale_meta}
meta_protobuf_obj = feature_scale_meta_pb2.ScaleMeta(is_scale=True,
strategy=self.scale_param.method,
minmax_scale_meta=minmax_scale_meta)
else:
LOGGER.debug("save_min_max_meta with mode is {}".format(self.scale_param.mode))
meta_protobuf_obj = feature_scale_meta_pb2.ScaleMeta(is_scale=True)
buffer_type = "{}.meta".format(self.class_name)
model_manager.save_model(buffer_type=buffer_type,
proto_buffer=meta_protobuf_obj,
name=name,
namespace=namespace)
return buffer_type
def _save_standard_scale_meta(self, name, namespace):
with_mean = self.scale_param.with_mean
with_std = self.scale_param.with_std
standard_scale_meta = feature_scale_meta_pb2.StandardScaleMeta(with_mean=with_mean, with_std=with_std)
meta_protobuf_obj = feature_scale_meta_pb2.ScaleMeta(is_scale=True,
strategy=self.scale_param.method,
standard_scale_meta=standard_scale_meta)
buffer_type = "{}.meta".format(self.class_name)
model_manager.save_model(buffer_type=buffer_type,
proto_buffer=meta_protobuf_obj,
name=name,
namespace=namespace)
return buffer_type