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- import argparse
- from pipeline.utils.tools import load_job_config
- from pipeline.backend.pipeline import PipeLine
- from pipeline.component import DataTransform
- from pipeline.component import FeatureImputation
- from pipeline.component import Intersection
- from pipeline.component import Reader
- from pipeline.interface import Data
- def main(config="../../config.yaml", namespace=""):
-
- if isinstance(config, str):
- config = load_job_config(config)
- parties = config.parties
- guest = parties.guest[0]
- host = parties.host[0]
- guest_train_data = {"name": "dvisits_hetero_guest", "namespace": f"experiment{namespace}"}
- host_train_data = {"name": "dvisits_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", with_label=False)
- intersection_0 = Intersection(name="intersection_0")
- feature_imputation_0 = FeatureImputation(name="feature_imputation_0",
- default_value=42,
- missing_impute=[0])
- feature_imputation_0.get_party_instance(role='guest', party_id=guest).component_param(
- col_missing_fill_method={"doctorco": "min",
- "hscore": "designated"})
- 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(feature_imputation_0, data=Data(data=intersection_0.output.data))
- pipeline.compile()
- pipeline.fit()
-
-
- pipeline.deploy_component([data_transform_0, intersection_0,
- feature_imputation_0])
- predict_pipeline = PipeLine()
-
- predict_pipeline.add_component(reader_0)
-
-
- predict_pipeline.add_component(
- pipeline, data=Data(
- predict_input={
- pipeline.data_transform_0.input.data: reader_0.output.data}))
-
- predict_pipeline.predict()
- 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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