def _get_meta(self):
# col_list = [str(x) for x in self.cols]
transform_param = feature_binning_meta_pb2.TransformMeta(
transform_cols=self.bin_inner_param.transform_bin_indexes,
transform_type=self.model_param.transform_param.transform_type)
meta_protobuf_obj = feature_binning_meta_pb2.FeatureBinningMeta(
method=self.model_param.method,
compress_thres=self.model_param.compress_thres,
head_size=self.model_param.head_size,
error=self.model_param.error,
bin_num=self.model_param.bin_num,
cols=self.bin_inner_param.bin_names,
adjustment_factor=self.model_param.adjustment_factor,
local_only=self.model_param.local_only,
need_run=self.need_run,
transform_param=transform_param)
return meta_protobuf_obj
def _get_meta(self):
col_list = [str(x) for x in self.cols]
LOGGER.debug("In get_meta, transform_cols_idx: {}".format(
self.transform_cols_idx))
if not isinstance(self.transform_cols_idx, (list, tuple)):
self.transform_cols_idx = []
transform_param = feature_binning_meta_pb2.TransformMeta(
transform_cols=self.transform_cols_idx,
transform_type=self.model_param.transform_param.transform_type)
meta_protobuf_obj = feature_binning_meta_pb2.FeatureBinningMeta(
method=self.model_param.method,
compress_thres=self.model_param.compress_thres,
head_size=self.model_param.head_size,
error=self.model_param.error,
bin_num=self.model_param.bin_num,
cols=col_list,
adjustment_factor=self.model_param.adjustment_factor,
local_only=self.model_param.local_only,
need_run=self.need_run,
transform_param=transform_param)
return meta_protobuf_obj
