| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778 |
- #
- # 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 json
- from pipeline.backend.pipeline import PipeLine
- from pipeline.component import Reader, DataTransform, Intersection, HeteroSecureBoost, Evaluation
- from pipeline.interface import Data
- # table name & namespace in data storage
- # data should be uploaded before running modeling task
- guest_train_data = {"name": "breast_hetero_guest", "namespace": "experiment"}
- host_train_data = {"name": "breast_hetero_host", "namespace": "experiment"}
- # initialize pipeline
- # Party ids are indicators of parties involved in federated learning. For standalone mode,
- # arbitrary integers can be used as party id.
- pipeline = PipeLine().set_initiator(role="guest", party_id=9999).set_roles(guest=9999, host=10000)
- # define components
- # Reader is a component to obtain the uploaded data. This component are very likely to be needed.
- reader_0 = Reader(name="reader_0")
- # By the following way, you can set different parameters for different party.
- 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 provided some preprocessing to the raw data, including extract label, convert data format,
- # filling missing value and so on. You may refer to the algorithm list doc for more details.
- 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)
- # Perform PSI for hetero-scenario.
- intersect_0 = Intersection(name="intersection_0")
- # Define a hetero-secureboost component. The following parameters will be set for all parties involved.
- 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})
- # To show the evaluation result, an "Evaluation" component is needed.
- evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
- # add components to pipeline, in order of task execution
- # The components are connected by indicating upstream data output as their input.
- # Typically, a feature engineering component will indicate input data as "data" while
- # the modeling component will use "train_data". Please check out carefully of the difference
- # between hetero_secureboost_0 input and other components below.
- # Here we are just showing a simple example, for more details of other components, please check
- # out the examples in "example/pipeline/{component you are interested in}
- 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))
- # compile & fit pipeline
- pipeline.compile().fit()
- # query component summary
- print(f"Evaluation summary:\n{json.dumps(pipeline.get_component('evaluation_0').get_summary(), indent=4)}")
|
