test_feature_selection_binning_lr.py

#
#  Copyright 2019 The FATE Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
import argparse

from fate_client.pipeline import FateFlowPipeline
from fate_client.pipeline.components.fate import (PSI, HeteroFeatureSelection, HeteroFeatureBinning,
                                                  Reader, SSHELR, FeatureScale)
from fate_client.pipeline.utils import test_utils


def main(config=".../config.yaml", namespace=""):
    if isinstance(config, str):
        config = test_utils.load_job_config(config)
    parties = config.parties
    guest = parties.guest[0]
    host = parties.host[0]

    pipeline = FateFlowPipeline().set_parties(guest=guest, host=host)
    if config.task_cores:
        pipeline.conf.set("task_cores", config.task_cores)
    if config.timeout:
        pipeline.conf.set("timeout", config.timeout)

    reader_0 = Reader("reader_0")
    reader_0.guest.task_parameters(
        namespace=f"experiment{namespace}",
        name="breast_hetero_guest"
    )
    reader_0.hosts[0].task_parameters(
        namespace=f"experiment{namespace}",
        name="breast_hetero_host"
    )
    psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])
    scale_0 = FeatureScale("scale_0", train_data=psi_0.outputs["output_data"], method="standard")

    binning_0 = HeteroFeatureBinning("binning_0",
                                     method="quantile",
                                     n_bins=10,
                                     transform_method="bin_idx",
                                     train_data=scale_0.outputs["train_output_data"]
                                     )
    selection_0 = HeteroFeatureSelection("selection_0",
                                         method=["iv"],
                                         train_data=psi_0.outputs["output_data"],
                                         input_models=[binning_0.outputs["output_model"]],
                                         iv_param={"metrics": "iv", "filter_type": "threshold", "threshold": 0.1,
                                                   "select_federated": True})

    lr_0 = SSHELR("lr_0",
                  learning_rate=0.05,
                  epochs=10,
                  batch_size=300,
                  init_param={"fit_intercept": True, "method": "zeros"},
                  train_data=selection_0.outputs["train_output_data"],
                  reveal_every_epoch=False,
                  early_stop="diff",
                  reveal_loss_freq=3)

    pipeline.add_tasks([reader_0, psi_0, scale_0, binning_0, selection_0, lr_0])

    # pipeline.add_task(hetero_feature_binning_0)
    pipeline.compile()
    # print(pipeline.get_dag())
    pipeline.fit()

    # print(pipeline.get_task_info("feature_scale_1").get_output_model())

    pipeline.deploy([psi_0, scale_0, selection_0, lr_0])

    predict_pipeline = FateFlowPipeline()

    reader_1 = Reader("reader_1")
    reader_1.guest.task_parameters(
        namespace=f"experiment{namespace}",
        name="breast_hetero_guest"
    )
    reader_1.hosts[0].task_parameters(
        namespace=f"experiment{namespace}",
        name="breast_hetero_host"
    )

    deployed_pipeline = pipeline.get_deployed_pipeline()
    deployed_pipeline.psi_0.input_data = reader_1.outputs["output_data"]

    predict_pipeline.add_tasks([reader_1, deployed_pipeline])

    predict_pipeline.compile()
    predict_pipeline.predict()


if __name__ == "__main__":
    parser = argparse.ArgumentParser("PIPELINE DEMO")
    parser.add_argument("--config", type=str, default="../config.yaml",
                        help="config file")
    parser.add_argument("--namespace", type=str, default="",
                        help="namespace for data stored in FATE")
    args = parser.parse_args()
    main(config=args.config, namespace=args.namespace)