text: #
  #  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 HeteroSecureBoost
  from pipeline.component import Intersection
  from pipeline.component import Reader
  from pipeline.interface import Data
  from pipeline.component import Evaluation
  from pipeline.interface import Model
  
  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]
  
  
      # data sets
      guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
      host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
  
      guest_validate_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
      host_validate_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
  
      # init pipeline
      pipeline = PipeLine().set_initiator(role="guest", party_id=guest).set_roles(guest=guest, host=host,)
  
      # set data reader and data-io
  
      reader_0, reader_1 = Reader(name="reader_0"), Reader(name="reader_1")
      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)
      reader_1.get_party_instance(role="guest", party_id=guest).component_param(table=guest_validate_data)
      reader_1.get_party_instance(role="host", party_id=host).component_param(table=host_validate_data)
  
      data_transform_0, data_transform_1 = DataTransform(name="data_transform_0"), DataTransform(name="data_transform_1")
  
      data_transform_0.get_party_instance(role="guest", party_id=guest).component_param(with_label=True, output_format="dense")
      data_transform_0.get_party_instance(role="host", party_id=host).component_param(with_label=False)
      data_transform_1.get_party_instance(role="guest", party_id=guest).component_param(with_label=True, output_format="dense")
      data_transform_1.get_party_instance(role="host", party_id=host).component_param(with_label=False)
  
      # data intersect component
      intersect_0 = Intersection(name="intersection_0")
      intersect_1 = Intersection(name="intersection_1")
  
      # secure boost component
      hetero_secure_boost_0 = HeteroSecureBoost(name="hetero_secure_boost_0",
                                                num_trees=3,
                                                task_type="classification",
                                                objective_param={"objective": "cross_entropy"},
                                                encrypt_param={"method": "iterativeAffine"},
                                                tree_param={"max_depth": 3},
                                                validation_freqs=1)
  
      # evaluation component
      evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
  
      pipeline.add_component(reader_0)
      pipeline.add_component(reader_1)
      pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
      pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data), model=Model(data_transform_0.output.model))
      pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
      pipeline.add_component(intersect_1, data=Data(data=data_transform_1.output.data))
      pipeline.add_component(hetero_secure_boost_0, data=Data(train_data=intersect_0.output.data,
                                                              validate_data=intersect_1.output.data))
      pipeline.add_component(evaluation_0, data=Data(data=hetero_secure_boost_0.output.data))
  
      pipeline.compile()
      pipeline.fit()
  
      print("fitting hetero secureboost done, result:")
      print(pipeline.get_component("hetero_secure_boost_0").get_summary())
  
  
  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()
  