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- #
- # 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 Reader
- from pipeline.component import DataTransform
- from pipeline.component import Intersection
- from pipeline.component import HeteroSSHELR
- from pipeline.component import PositiveUnlabeled
- from pipeline.interface import Data
- 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}"}
- # 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
- 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)
- # define DataTransform components
- data_transform_0 = DataTransform(name="data_transform_0", output_format='dense')
- # configure DataTransform for guest
- data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True)
- # configure DataTransform for 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")
- # define SSHE-LR and PositiveUnlabeled components
- sshe_lr_0_param = {
- "name": "hetero_sshe_lr_0",
- "max_iter": 2
- }
- pu_0_param = {
- "name": "positive_unlabeled_0",
- "strategy": "probability",
- "threshold": 0.9
- }
- sshe_lr_1_param = {
- "name": "hetero_sshe_lr_1",
- "max_iter": 1
- }
- hetero_sshe_lr_0 = HeteroSSHELR(**sshe_lr_0_param)
- positive_unlabeled_0 = PositiveUnlabeled(**pu_0_param)
- hetero_sshe_lr_1 = HeteroSSHELR(**sshe_lr_1_param)
- # configure pipeline components
- 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_sshe_lr_0, data=Data(train_data=intersection_0.output.data))
- pipeline.add_component(positive_unlabeled_0,
- data=Data(data=[intersection_0.output.data, hetero_sshe_lr_0.output.data]))
- pipeline.add_component(hetero_sshe_lr_1, data=Data(train_data=positive_unlabeled_0.output.data))
- pipeline.compile()
- # fit model
- pipeline.fit()
- # query component summary
- prettify(pipeline.get_component("positive_unlabeled_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()
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