FateWithShellmiaoComment/Fate/examples/dsl/v2/hetero_logistic_regression

Hetero Logistic Regression Configuration Usage Guide.

This section introduces the dsl and conf for usage of different type of task.

Example Task.

1. Train_task: dsl: hetero_lr_normal_dsl.json runtime_config : hetero_lr_normal_conf.json

2. Train, test and evaluation task: dsl: hetero_lr_validate_dsl.json runtime_config: hetero_lr_validate_conf.json

3. Cross Validation Task(with fold history data output of predict score): dsl: hetero_lr_cv_dsl.json runtime_config: hetero_lr_cv_conf.json

4. One vs Rest Task: dsl: hetero_lr_one_vs_all_dsl.json conf: hetero_lr_one_vs_all_conf.json

5. LR with feature engineering task dsl: hetero_lr_feature_engineering_dsl.json conf: hetero_lr_feature_engineering_conf.json

6. Multi-host training task: dsl: hetero_lr_multi_host_dsl.json conf: hetero_lr_multi_host_conf.json

7. lr_sparse training task: "conf": hetero_lr_sparse_conf.json, "dsl": hetero_lr_sparse_dsl.json

8. lr_sparse_sqn task: "conf": "hetero_lr_sparse_sqn_conf.json", "dsl": "hetero_lr_sparse_sqn_dsl.json"

9. lr_ovr_cv task: "conf": "hetero_lr_ovr_cv_conf.json", "dsl": "hetero_lr_ovr_cv_dsl.json"

10. lr_sparse_cv task: "conf": "hetero_lr_sparse_cv_conf.json", "dsl": "hetero_lr_sparse_cv_dsl.json"

11. lr_ovr_sqn task: "conf": "hetero_lr_ovr_sqn_conf.json", "dsl": "hetero_lr_ovr_sqn_dsl.json"

12. lr_sqn task: "conf": "hetero_lr_sqn_conf.json", "dsl": "hetero_lr_sqn_dsl.json"

13. early_stop_lr task: "conf": "hetero_lr_early_stop_conf.json", "dsl": "hetero_lr_early_stop_dsl.json"

14. Test Task: dsl: hetero-lr-normal-predict-dsl.json conf: hetero-lr-normal-predict-conf.json deps: Train_task

15. Warm start task: dsl: hetero_lr_warm_start_dsl.json conf: hetero_lr_warm_start_conf.json

Users can use following commands to running the task.

flow job submit -c ${runtime_config} -d ${dsl}

After having finished a successful training task, you can use it to predict, you can use the obtained model to perform prediction. You need to add the corresponding model id and model version to the configuration file