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
  import json
  
  from pipeline.backend.pipeline import PipeLine
  from pipeline.component import DataTransform
  from pipeline.component import Evaluation
  from pipeline.component import HeteroSSHELR
  from pipeline.component import Intersection
  from pipeline.component import Reader
  from pipeline.interface import Data
  from pipeline.interface import Model
  from pipeline.utils.tools import load_job_config
  
  
  def prettify(response, verbose=True):
      if verbose:
          print(json.dumps(response, indent=4, ensure_ascii=False))
          print()
      return response
  
  
  def main(config="../../config.yaml", namespace=""):
      if isinstance(config, str):
          config = load_job_config(config)
      parties = config.parties
      guest = parties.guest[0]
      hosts = parties.host[0]
      guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
      host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
      # guest_train_data = {"name": "default_credit_hetero_guest", "namespace": f"experiment{namespace}"}
      # host_train_data = {"name": "default_credit_hetero_host", "namespace": f"experiment{namespace}"}
  
      # initialize pipeline
      pipeline = PipeLine()
      # set job initiator
      pipeline.set_initiator(role='guest', party_id=guest)
      # set participants information
      pipeline.set_roles(guest=guest, host=hosts)
  
      # define Reader components to read in data
      reader_0 = Reader(name="reader_0")
      # configure Reader for guest
      reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
      # configure Reader for host
      reader_0.get_party_instance(role='host', party_id=hosts).component_param(table=host_train_data)
  
      data_transform_0 = DataTransform(name="data_transform_0", output_format='dense')
  
      # get DataTransform party instance of guest
      data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
      # configure DataTransform for guest
      data_transform_0_guest_party_instance.component_param(with_label=True)
      # get and configure DataTransform party instance of host
      data_transform_0.get_party_instance(role='host', party_id=hosts).component_param(with_label=False)
  
      # define Intersection components
      intersection_0 = Intersection(name="intersection_0")
  
      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))
  
      lr_param = {
          "penalty": "L2",
          "optimizer": "rmsprop",
          "tol": 0.0001,
          "alpha": 0.01,
          "early_stop": "diff",
          "batch_size": -1,
          "learning_rate": 0.15,
          "init_param": {
              "init_method": "zeros",
              "fit_intercept": True
          },
          "encrypt_param": {
              "key_length": 1024
          },
          "reveal_strategy": "respectively",
          "reveal_every_iter": True,
          "callback_param": {
              "callbacks": ["ModelCheckpoint"],
              "metrics": None,
              "use_first_metric_only": False,
              "save_freq": 1
          }
      }
  
      hetero_sshe_lr_0 = HeteroSSHELR(name="hetero_sshe_lr_0", max_iter=3, **lr_param)
      hetero_sshe_lr_1 = HeteroSSHELR(name="hetero_sshe_lr_1", max_iter=30, **lr_param)
  
      pipeline.add_component(hetero_sshe_lr_0, data=Data(train_data=intersection_0.output.data))
      pipeline.add_component(hetero_sshe_lr_1, data=Data(train_data=intersection_0.output.data),
                             model=Model(model=hetero_sshe_lr_0.output.model))
  
      evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
      pipeline.add_component(evaluation_0, data=Data(data=hetero_sshe_lr_1.output.data))
  
      pipeline.compile()
  
      # fit model
      pipeline.fit()
      # query component summary
      prettify(pipeline.get_component("hetero_sshe_lr_0").get_summary())
      prettify(pipeline.get_component("hetero_sshe_lr_1").get_summary())
      prettify(pipeline.get_component("evaluation_0").get_summary())
      return pipeline
  
  
  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()
  