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- import json
- from pipeline.backend.pipeline import PipeLine
- from pipeline.component import Reader, DataTransform, Intersection, HeteroSecureBoost, Evaluation
- from pipeline.interface import Data
- guest_train_data = {"name": "breast_hetero_guest", "namespace": "experiment"}
- host_train_data = {"name": "breast_hetero_host", "namespace": "experiment"}
- pipeline = PipeLine().set_initiator(role="guest", party_id=9999).set_roles(guest=9999, host=10000)
- reader_0 = Reader(name="reader_0")
- reader_0.get_party_instance(role="guest", party_id=9999).component_param(table=guest_train_data)
- reader_0.get_party_instance(role="host", party_id=10000).component_param(table=host_train_data)
- data_transform_0 = DataTransform(name="data_transform_0", with_label=True)
- data_transform_0.get_party_instance(role="host", party_id=10000).component_param(with_label=False)
- intersect_0 = Intersection(name="intersection_0")
- hetero_secureboost_0 = HeteroSecureBoost(name="hetero_secureboost_0",
- num_trees=5,
- bin_num=16,
- task_type="classification",
- objective_param={"objective": "cross_entropy"},
- encrypt_param={"method": "paillier"},
- tree_param={"max_depth": 3})
- evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
- pipeline.add_component(reader_0)\
- .add_component(data_transform_0, data=Data(data=reader_0.output.data))\
- .add_component(intersect_0, data=Data(data=data_transform_0.output.data))\
- .add_component(hetero_secureboost_0, data=Data(train_data=intersect_0.output.data))\
- .add_component(evaluation_0, data=Data(data=hetero_secureboost_0.output.data))
- pipeline.compile().fit()
- print(f"Evaluation summary:\n{json.dumps(pipeline.get_component('evaluation_0').get_summary(), indent=4)}")
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