# # 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 FeatureScale from pipeline.component import FederatedSample from pipeline.component import HeteroFeatureBinning from pipeline.component import HeteroFeatureSelection from pipeline.component import HeteroLR from pipeline.component import HeteroSecureBoost from pipeline.component import Intersection from pipeline.component import OneHotEncoder from pipeline.component import Union from pipeline.component import LocalBaseline from pipeline.component import HeteroLinR from pipeline.component import HeteroPoisson from pipeline.component import HeteroSSHELR from pipeline.component import HeteroSSHELinR from pipeline.component import Reader from pipeline.interface import Data from pipeline.interface import Model """Note: This script is used for components regression only""" 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_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "breast_hetero_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) reader_2 = Reader(name="reader_2") reader_2.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data) reader_2.get_party_instance(role='host', party_id=host).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param( with_label=True, missing_fill=True, outlier_replace=True) data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False, missing_fill=True, outlier_replace=True) data_transform_1 = DataTransform(name="data_transform_1") data_transform_2 = DataTransform(name="data_transform_2") intersection_0 = Intersection(name="intersection_0") intersection_1 = Intersection(name="intersection_1") intersection_2 = Intersection(name="intersection_2") union_0 = Union(name="union_0") federated_sample_0 = FederatedSample(name="federated_sample_0", mode="stratified", method="downsample", fractions=[[0, 1.0], [1, 1.0]]) feature_scale_0 = FeatureScale(name="feature_scale_0") feature_scale_1 = FeatureScale(name="feature_scale_1") hetero_feature_binning_0 = HeteroFeatureBinning(name="hetero_feature_binning_0") hetero_feature_binning_1 = HeteroFeatureBinning(name="hetero_feature_binning_1") hetero_feature_selection_0 = HeteroFeatureSelection(name="hetero_feature_selection_0") hetero_feature_selection_1 = HeteroFeatureSelection(name="hetero_feature_selection_1") one_hot_0 = OneHotEncoder(name="one_hot_0") one_hot_1 = OneHotEncoder(name="one_hot_1") hetero_lr_0 = HeteroLR(name="hetero_lr_0", penalty="L2", optimizer="rmsprop", tol=1e-5, init_param={"init_method": "random_uniform"}, alpha=0.01, max_iter=3, early_stop="diff", batch_size=320, learning_rate=0.15) hetero_lr_1 = HeteroLR(name="hetero_lr_1") hetero_lr_2 = HeteroLR(name="hetero_lr_2", penalty="L2", optimizer="rmsprop", tol=1e-5, init_param={"init_method": "random_uniform"}, alpha=0.01, max_iter=3, early_stop="diff", batch_size=320, learning_rate=0.15, cv_param={"n_splits": 5, "shuffle": True, "random_seed": 103, "need_cv": True}) hetero_sshe_lr_0 = HeteroSSHELR(name="hetero_sshe_lr_0", reveal_every_iter=True, reveal_strategy="respectively", penalty="L2", optimizer="rmsprop", tol=1e-5, batch_size=320, learning_rate=0.15, init_param={"init_method": "random_uniform"}, alpha=0.01, max_iter=3) hetero_sshe_lr_1 = HeteroSSHELR(name="hetero_sshe_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=False) local_baseline_1 = LocalBaseline(name="local_baseline_1") hetero_secureboost_0 = HeteroSecureBoost(name="hetero_secureboost_0", num_trees=3) hetero_secureboost_1 = HeteroSecureBoost(name="hetero_secureboost_1") hetero_secureboost_2 = HeteroSecureBoost(name="hetero_secureboost_2", num_trees=3, cv_param={"shuffle": False, "need_cv": True}) hetero_linr_0 = HeteroLinR(name="hetero_linr_0", penalty="L2", optimizer="sgd", tol=0.001, alpha=0.01, max_iter=3, early_stop="weight_diff", batch_size=-1, learning_rate=0.15, decay=0.0, decay_sqrt=False, init_param={"init_method": "zeros"}, floating_point_precision=23) hetero_linr_1 = HeteroLinR(name="hetero_linr_1") hetero_sshe_linr_0 = HeteroSSHELinR(name="hetero_sshe_linr_0", max_iter=5, early_stop="weight_diff", batch_size=-1) hetero_sshe_linr_1 = HeteroSSHELinR(name="hetero_sshe_linr_1") hetero_poisson_0 = HeteroPoisson(name="hetero_poisson_0", early_stop="weight_diff", max_iter=10, alpha=100.0, batch_size=-1, learning_rate=0.01, optimizer="rmsprop", exposure_colname="exposure", decay_sqrt=False, tol=0.001, init_param={"init_method": "zeros"}, penalty="L2") hetero_poisson_1 = HeteroPoisson(name="hetero_poisson_1") evaluation_0 = Evaluation(name="evaluation_0") evaluation_1 = Evaluation(name="evaluation_1") evaluation_2 = Evaluation(name="evaluation_2") pipeline.add_component(reader_0) pipeline.add_component(reader_1) pipeline.add_component(reader_2) 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(data_transform_2, data=Data(data=reader_2.output.data), model=Model(model=data_transform_0.output.model)) 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)) pipeline.add_component(intersection_2, data=Data(data=data_transform_2.output.data)) pipeline.add_component(union_0, data=Data(data=[intersection_0.output.data, intersection_2.output.data])) pipeline.add_component(federated_sample_0, data=Data(data=intersection_1.output.data)) pipeline.add_component(feature_scale_0, data=Data(data=union_0.output.data)) pipeline.add_component(feature_scale_1, data=Data(data=federated_sample_0.output.data), model=Model(model=feature_scale_0.output.model)) pipeline.add_component(hetero_feature_binning_0, data=Data(data=feature_scale_0.output.data)) pipeline.add_component(hetero_feature_binning_1, data=Data(data=feature_scale_1.output.data), model=Model(model=hetero_feature_binning_0.output.model)) pipeline.add_component(hetero_feature_selection_0, data=Data(data=hetero_feature_binning_0.output.data)) pipeline.add_component(hetero_feature_selection_1, data=Data(data=hetero_feature_binning_1.output.data), model=Model(model=hetero_feature_selection_0.output.model)) pipeline.add_component(one_hot_0, data=Data(data=hetero_feature_selection_0.output.data)) pipeline.add_component(one_hot_1, data=Data(data=hetero_feature_selection_1.output.data), model=Model(model=one_hot_0.output.model)) pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_lr_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=hetero_lr_0.output.model)) pipeline.add_component(hetero_lr_2, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(local_baseline_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(local_baseline_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=local_baseline_0.output.model)) pipeline.add_component(hetero_sshe_lr_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_sshe_lr_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=hetero_sshe_lr_0.output.model)) pipeline.add_component(hetero_secureboost_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_secureboost_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=hetero_secureboost_0.output.model)) pipeline.add_component(hetero_secureboost_2, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_linr_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_linr_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=hetero_linr_0.output.model)) pipeline.add_component(hetero_sshe_linr_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_sshe_linr_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=hetero_sshe_linr_0.output.model)) pipeline.add_component(hetero_poisson_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(hetero_poisson_1, data=Data(test_data=one_hot_1.output.data), model=Model(model=hetero_poisson_0.output.model)) pipeline.add_component(evaluation_0, data=Data(data=[hetero_lr_0.output.data, hetero_lr_1.output.data, hetero_sshe_lr_0.output.data, hetero_sshe_lr_1.output.data, local_baseline_0.output.data, local_baseline_1.output.data])) pipeline.add_component(evaluation_1, data=Data( data=[hetero_linr_0.output.data, hetero_linr_1.output.data, hetero_sshe_linr_0.output.data, hetero_linr_1.output.data])) pipeline.add_component(evaluation_2, data=Data( data=[hetero_poisson_0.output.data, hetero_poisson_1.output.data])) pipeline.compile() pipeline.fit() print(pipeline.get_component("evaluation_0").get_summary()) print(pipeline.get_component("evaluation_1").get_summary()) print(pipeline.get_component("evaluation_2").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()