# # 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.backend.pipeline import PipeLine from pipeline.component import DataTransform from pipeline.component import Evaluation from pipeline.component import HeteroSSHELR from pipeline.component import Intersection from pipeline.component import Reader from pipeline.interface import Data, Model from pipeline.utils.tools import load_job_config, JobConfig from federatedml.evaluation.metrics import classification_metric from fate_test.utils import extract_data, parse_summary_result def main(config="../../config.yaml", param="./vehicle_sshe_lr_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] if isinstance(param, str): param = JobConfig.load_from_file(param) assert isinstance(param, dict) data_set = param.get("data_guest").split('/')[-1] if data_set == "vehicle_scale_hetero_guest.csv": guest_data_table = 'vehicle_scale_hetero_guest' host_data_table = 'vehicle_scale_hetero_host' else: raise ValueError(f"Cannot recognized data_set: {data_set}") guest_train_data = {"name": guest_data_table, "namespace": f"experiment{namespace}"} host_train_data = {"name": host_data_table, "namespace": f"experiment{namespace}"} # 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) # define DataTransform components data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0 # 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 component intersection_0 = Intersection(name="intersection_0") lr_param = { } config_param = { "penalty": param["penalty"], "max_iter": param["max_iter"], "alpha": param["alpha"], "learning_rate": param["learning_rate"], "optimizer": param["optimizer"], # use sgd "batch_size": param["batch_size"], "early_stop": "diff", "init_param": { "init_method": param.get("init_method", 'random_uniform'), "random_seed": param.get("random_seed", 103), "fit_intercept": True }, "reveal_strategy": param.get("reveal_strategy", "respectively"), "reveal_every_iter": True } lr_param.update(config_param) print(f"lr_param: {lr_param}, data_set: {data_set}") hetero_sshe_lr_0 = HeteroSSHELR(name='hetero_sshe_lr_0', **lr_param) hetero_sshe_lr_1 = HeteroSSHELR(name='hetero_sshe_lr_1') evaluation_0 = Evaluation(name='evaluation_0', eval_type="multi") # add components to pipeline, in order of task execution 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(hetero_sshe_lr_0, data=Data(train_data=intersection_0.output.data)) pipeline.add_component(hetero_sshe_lr_1, data=Data(test_data=intersection_0.output.data), model=Model(hetero_sshe_lr_0.output.model)) pipeline.add_component(evaluation_0, data=Data(data=hetero_sshe_lr_0.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 result_summary = parse_summary_result(pipeline.get_component("evaluation_0").get_summary()) lr_0_data = pipeline.get_component("hetero_sshe_lr_0").get_output_data() lr_1_data = pipeline.get_component("hetero_sshe_lr_1").get_output_data() lr_0_score_label = extract_data(lr_0_data, "predict_result", keep_id=True) lr_1_score_label = extract_data(lr_1_data, "predict_result", keep_id=True) metric_lr = { "score_diversity_ratio": classification_metric.Distribution.compute(lr_0_score_label, lr_1_score_label)} result_summary["distribution_metrics"] = {"hetero_lr": metric_lr} data_summary = {"train": {"guest": guest_train_data["name"], "host": host_train_data["name"]}, "test": {"guest": guest_train_data["name"], "host": host_train_data["name"]} } print(f"result_summary: {result_summary}; data_summary: {data_summary}") return data_summary, result_summary if __name__ == "__main__": parser = argparse.ArgumentParser("BENCHMARK-QUALITY PIPELINE JOB") parser.add_argument("-c", "--config", type=str, help="config file", default="../../config.yaml") parser.add_argument("-p", "--param", type=str, help="config file for params", default="./vehicle_sshe_lr_config.yaml") args = parser.parse_args() if args.config is not None: main(args.config, args.param) else: main()