Vertical Sampler

Introduction

Two sample methods are provided: random sample and stratified sample. Downsample and upsample are supported in both methods.

Parameters List

identity: str Federated identity of the party, should be one of label_trainer or trainer.

model_info:

• name: str Model name, should be vertical_sampler.

input:

• dataset:

  • type: str Input dataset type, support csv.

  • path: str If type is csv, folder path of train dataset.

  • name: bool If type is csv, file name of train dataset.

  • has_id: bool If type is csv, whether dataset has id column.

  • has_label: bool If type is csv, whether dataset has label column.

output:

• path: str Folder path of output.

• sample_id:

  • name: str File name of output id.

• dataset:

  • name: bool File name of output dataset.

train_info:

• train_params:

  • method: str Sample method, support random or stratify.

  • strategy: str Sample strategy, support downsample or upsample.

  • random_seed: int Random seed.

  • fraction: support three keys: percentage or number or labeled_percentage.

    • percentage: float Threshold of percentage filter.

    • number: int Threshold of number filter.

    • labeled_percentage: list Threshold of labeled_percentage filter.