FateWithShellmiaoComment/Fate/doc/federatedml_component/data_split.md

Data Split

Data Split module splits data into desired train, test, and/or validate sets. The module is based on sklearn train_test_split method while its output can include an extra validate data set.

Use

Data Split supports homogeneous (both Guest & Host have y) and heterogeneous (only Guest has y) mode.

Here lists supported split modes and scenario.

Split Mode	Federated Heterogeneous	Federated Homogeneous(Local)
Random	✓	✓
Stratified	✓(continuous label split into intervals)	✓

The module takes one table input as specified in job config file. Table must be uploaded beforehand as with other FederatedML models. Module parameters should be specified in job config file. Any parameter unspecified will take the default value detailed in parameter definition below.

Data Split module always outputs three tables (train, test, and validate sets). Each table may be used as input of another module. Below are the rules regarding set sizes:

1. if all three set sizes are None, the original data input will be split in the following ratio: 80% to train set, 20% to validate set, and an empty test set;

2. if only test size or validate size is given, train size is set to be of complement given size;

3. only one of the three sizes is needed to split input data, but all three may be specified. The module takes either int (instance count) or float (fraction) value for set sizes, but mixed-type inputs cannot be used.