Local Standard Scaler

Introduction

Local standard scaler is a module that standardize each column of features by subtracting the mean then and then scaling to unit variance.

Parameter List

identity: str The role of each participant in federated learning, should be label_trainer or trainer.

model_info:

• name: str Model name, should be local_standard_scaler.

input:

• trainset:

  • type: str Train dataset type, currently supported is csv.

  • path: str The folder path of train dataset.

  • name: str The file name of train dataset.

  • has_id: bool Whether the dataset has id column.

  • has_label: bool Whether the dataset has label column.

• valset:

  • type: str Validation dataset type, currently supported is csv.

  • path: str The folder path of validation dataset.

  • name: str The file name of validation dataset.

  • has_id: bool Whether the dataset has id column.

  • has_label: bool Whether the dataset has label column.

output:

• path: str Output folder path.

• model:

  • name: str File name of output model.

• trainset:

  • name: str File name of output train dataset.

• valset:

  • name: str File name of output validation dataset.

train_info:

• train_params:

  • with_mean: bool If True, center the data before scaling.

  • with_std: bool If True, scale the data to unit standard deviation.

  • feature_standard: map Fine-grained configuration for standardization. The format is defined as: {column_1: {"with_mean": True/False, "with_std": True/False}, ..., column_N: {"with_mean": True/False, "with_std": True/False}}.