# # 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 from pipeline.utils.tools import load_job_config from pipeline.backend.pipeline import PipeLine from pipeline.component import DataTransform from pipeline.component import Evaluation from pipeline.component import HomoOneHotEncoder from pipeline.component.homo_feature_binning import HomoFeatureBinning from pipeline.component import FederatedSample from pipeline.component import HomoLR from pipeline.component import HomoSecureBoost from pipeline.component import LocalBaseline from pipeline.component import Reader from pipeline.interface import Data from pipeline.interface import Model def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] host = parties.host[0] arbiter = parties.arbiter[0] guest_train_data = {"name": "breast_homo_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "breast_homo_host", "namespace": f"experiment{namespace}"} pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter) 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) reader_1 = Reader(name="reader_1") reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data) reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0", with_label=True) data_transform_1 = DataTransform(name="data_transform_1") federated_sample_0 = FederatedSample(name="federated_sample_0", mode="stratified", method="downsample", fractions=[[0, 1.0], [1, 1.0]], task_type="homo") homo_binning_0 = HomoFeatureBinning(name='homo_binning_0', sample_bins=10, method="recursive_query") homo_binning_1 = HomoFeatureBinning(name='homo_binning_1') homo_onehot_0 = HomoOneHotEncoder(name='homo_onehot_0', need_alignment=True) homo_onehot_1 = HomoOneHotEncoder(name='homo_onehot_1') homo_lr_0 = HomoLR(name="homo_lr_0", penalty="L2", tol=0.0001, alpha=1.0, optimizer="rmsprop", max_iter=5) homo_lr_1 = HomoLR(name="homo_lr_1") local_baseline_0 = LocalBaseline(name="local_baseline_0", model_name="LogisticRegression", model_opts={"penalty": "l2", "tol": 0.0001, "C": 1.0, "fit_intercept": True, "solver": "lbfgs", "max_iter": 5, "multi_class": "ovr"}) local_baseline_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True) local_baseline_0.get_party_instance(role='host', party_id=host).component_param(need_run=True) local_baseline_1 = LocalBaseline(name="local_baseline_1") homo_secureboost_0 = HomoSecureBoost(name="homo_secureboost_0", num_trees=3) homo_secureboost_1 = HomoSecureBoost(name="homo_secureboost_1", num_trees=3) evaluation_0 = Evaluation(name="evaluation_0") evaluation_1 = Evaluation(name="evaluation_1") pipeline.add_component(reader_0) pipeline.add_component(reader_1) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data), model=Model(model=data_transform_0.output.model)) pipeline.add_component(federated_sample_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(homo_binning_0, data=Data(data=federated_sample_0.output.data)) pipeline.add_component(homo_binning_1, data=Data(data=data_transform_1.output.data), model=Model(model=homo_binning_0.output.model)) pipeline.add_component(homo_onehot_0, data=Data(data=homo_binning_0.output.data)) pipeline.add_component(homo_onehot_1, data=Data(data=homo_binning_1.output.data), model=Model(model=homo_onehot_0.output.model)) pipeline.add_component(homo_lr_0, data=Data(data=homo_onehot_0.output.data)) pipeline.add_component(homo_lr_1, data=Data(data=homo_onehot_1.output.data), model=Model(model=homo_lr_0.output.model)) pipeline.add_component(local_baseline_0, data=Data(data=homo_onehot_0.output.data)) pipeline.add_component(local_baseline_1, data=Data(data=homo_onehot_1.output.data), model=Model(model=local_baseline_0.output.model)) pipeline.add_component(homo_secureboost_0, data=Data(data=homo_onehot_0.output.data)) pipeline.add_component(homo_secureboost_1, data=Data(data=homo_onehot_1.output.data), model=Model(model=homo_secureboost_0.output.model)) pipeline.add_component(evaluation_0, data=Data( data=[homo_lr_0.output.data, homo_lr_1.output.data, local_baseline_0.output.data, local_baseline_1.output.data])) pipeline.add_component(evaluation_1, data=Data( data=[homo_secureboost_0.output.data, homo_secureboost_1.output.data])) pipeline.compile() pipeline.fit() print(pipeline.get_component("evaluation_0").get_summary()) print(pipeline.get_component("evaluation_1").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()