# # 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 pipeline.backend.pipeline import PipeLine from pipeline.component import DataTransform from pipeline.component import HeteroFeatureBinning from pipeline.component import Intersection from pipeline.component import Reader from pipeline.interface import Data from pipeline.utils.tools import load_job_config def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] host = parties.host[0] guest_train_data = {"name": "default_credit_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "default_credit_hetero_host", "namespace": f"experiment{namespace}"} pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host) reader_0 = Reader(name="reader_0") reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data) reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True) data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False) intersection_0 = Intersection(name="intersection_0") param = { "method": "optimal", "optimal_binning_param": { "metric_method": "chi_square", "min_bin_pct": 0.05, "max_bin_pct": 0.8, "init_bucket_method": "quantile", "init_bin_nums": 100, "mixture": True }, "compress_thres": 10000, "head_size": 10000, "error": 0.001, "bin_num": 10, "bin_indexes": -1, "bin_names": None, "category_indexes": None, "category_names": None, "adjustment_factor": 0.5, "local_only": False, "transform_param": { "transform_cols": -1, "transform_names": None, "transform_type": "bin_num" } } hetero_feature_binning_0 = HeteroFeatureBinning(name="hetero_feature_binning_0", **param) pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_0.output.data)) pipeline.compile() pipeline.fit() if __name__ == "__main__": parser = argparse.ArgumentParser("PIPELINE DEMO") parser.add_argument("-config", type=str, help="config file") args = parser.parse_args() if args.config is not None: main(args.config) else: main()