FateWithShellmiaoComment/Fate/python/fate_client/pipeline/demo/pipeline-mini-demo.py

#
#  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.
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from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform
from pipeline.component import HeteroLR
from pipeline.component import Intersection
from pipeline.component import Reader
from pipeline.interface import Data
from pipeline.interface import Model


def main():
    # parties config
    guest = 9999
    host = 10000
    arbiter = 10000

    # specify input data name & namespace in database
    guest_train_data = {"name": "breast_hetero_guest", "namespace": "experiment"}
    host_train_data = {"name": "breast_hetero_host", "namespace": "experiment"}

    guest_eval_data = {"name": "breast_hetero_guest", "namespace": "experiment"}
    host_eval_data = {"name": "breast_hetero_host", "namespace": "experiment"}

    # initialize pipeline
    pipeline = PipeLine()
    # set job initiator
    pipeline.set_initiator(role="guest", party_id=guest)
    # set participants information
    pipeline.set_roles(guest=guest, host=host, arbiter=arbiter)

    # 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=host).component_param(table=host_train_data)

    reader_1 = Reader(name="reader_1")
    reader_1.get_party_instance(role="guest", party_id=guest).component_param(table=guest_eval_data)
    reader_1.get_party_instance(role="host", party_id=host).component_param(table=host_eval_data)

    # define DataTransform components
    data_transform_0 = DataTransform(name="data_transform_0")
    data_transform_1 = DataTransform(name="data_transform_1")

    # 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, output_format="dense")
    # get and configure DataTransform party instance of host
    data_transform_0.get_party_instance(role="host", party_id=host).component_param(with_label=False)

    # define Intersection components
    intersection_0 = Intersection(name="intersection_0")
    intersection_1 = Intersection(name="intersection_1")

    # define HeteroLR component
    hetero_lr_0 = HeteroLR(name="hetero_lr_0", early_stop="weight_diff", learning_rate=0.15, optimizer="rmsprop",
                           max_iter=10, early_stopping_rounds=2, validation_freqs=1)

    # add components to pipeline, in order of task execution
    pipeline.add_component(reader_0)
    pipeline.add_component(reader_1)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    # set data_transform_1 to replicate model from data_transform_0
    pipeline.add_component(data_transform_1,
                           data=Data(data=reader_1.output.data), model=Model(data_transform_0.output.model))
    # set data input sources of intersection components
    pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data))
    # set train & validate data of hetero_lr_0 component
    pipeline.add_component(
        hetero_lr_0,
        data=Data(
            train_data=intersection_0.output.data,
            validate_data=intersection_1.output.data))

    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()

    # fit model
    pipeline.fit()
    # query component summary
    import json
    print(json.dumps(pipeline.get_component("hetero_lr_0").get_summary(), indent=4))

    # predict
    # deploy required components
    pipeline.deploy_component([data_transform_0, intersection_0, hetero_lr_0])

    # initiate predict pipeline
    predict_pipeline = PipeLine()

    reader_2 = Reader(name="reader_2")
    reader_2.get_party_instance(role="guest", party_id=guest).component_param(table=guest_eval_data)
    reader_2.get_party_instance(role="host", party_id=host).component_param(table=host_eval_data)
    # add data reader onto predict pipeline
    predict_pipeline.add_component(reader_2)
    # add selected components from train pipeline onto predict pipeline
    # specify data source
    predict_pipeline.add_component(pipeline, data=Data(
        predict_input={pipeline.data_transform_0.input.data: reader_2.output.data}))
    # run predict model
    predict_pipeline.predict()


if __name__ == "__main__":
    main()