# # 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 import torch as t from torch import nn from pipeline import fate_torch_hook from pipeline.backend.pipeline import PipeLine from pipeline.component.nn import DatasetParam from pipeline.component import DataTransform from pipeline.component import Evaluation from pipeline.component import HeteroNN 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 fate_torch_hook(t) def build(param, shape1, shape2): guest_bottom = t.nn.Sequential( nn.Linear(shape1, param["bottom_layer_units"]), nn.ReLU() ) host_bottom = t.nn.Sequential( nn.Linear(shape2, param["bottom_layer_units"]), nn.ReLU() ) interactive_layer = t.nn.InteractiveLayer( guest_dim=param["bottom_layer_units"], host_dim=param["bottom_layer_units"], host_num=1, out_dim=param["interactive_layer_units"]) act = nn.Sigmoid() if param["top_layer_units"] == 1 else nn.Softmax(dim=1) top_layer = t.nn.Sequential( t.nn.Linear( param["interactive_layer_units"], param["top_layer_units"]), act) return guest_bottom, host_bottom, interactive_layer, top_layer def main( config="../../config.yaml", param="./hetero_nn_breast_config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) if isinstance(param, str): param = JobConfig.load_from_file(param) parties = config.parties guest = parties.guest[0] host = parties.host[0] guest_train_data = { "name": param["guest_table_name"], "namespace": f"experiment{namespace}"} host_train_data = { "name": param["host_table_name"], "namespace": f"experiment{namespace}"} pipeline = PipeLine().set_initiator( role='guest', party_id=guest).set_roles( guest=guest, host=host) 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) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param( with_label=True) data_transform_0.get_party_instance( role='host', party_id=host).component_param( with_label=False) intersection_0 = Intersection(name="intersection_0") guest_bottom, host_bottom, interactive_layer, top_layer = build( param, param['shape1'], param['shape2']) if param["loss"] == "categorical_crossentropy": loss = t.nn.CrossEntropyLoss() ds_param = DatasetParam( dataset_name='table', flatten_label=True, label_dtype='long') else: loss = t.nn.BCELoss() ds_param = DatasetParam(dataset_name='table') hetero_nn_0 = HeteroNN( name="hetero_nn_0", epochs=param["epochs"], interactive_layer_lr=param["learning_rate"], batch_size=param["batch_size"], seed=100, dataset=ds_param) guest_nn_0 = hetero_nn_0.get_party_instance(role='guest', party_id=guest) host_nn_0 = hetero_nn_0.get_party_instance(role='host', party_id=host) guest_nn_0.add_bottom_model(guest_bottom) guest_nn_0.add_top_model(top_layer) host_nn_0.add_bottom_model(host_bottom) hetero_nn_0.set_interactive_layer(interactive_layer) hetero_nn_0.compile( optimizer=t.optim.Adam( lr=param['learning_rate']), loss=loss) hetero_nn_1 = HeteroNN(name="hetero_nn_1") if param["loss"] == "categorical_crossentropy": eval_type = "multi" else: eval_type = "binary" evaluation_0 = Evaluation(name="evaluation_0", eval_type=eval_type) 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_nn_0, data=Data( train_data=intersection_0.output.data)) pipeline.add_component( hetero_nn_1, data=Data( test_data=intersection_0.output.data), model=Model( hetero_nn_0.output.model)) pipeline.add_component( evaluation_0, data=Data( data=hetero_nn_0.output.data)) pipeline.compile() pipeline.fit() nn_0_data = pipeline.get_component("hetero_nn_0").get_output_data() nn_1_data = pipeline.get_component("hetero_nn_1").get_output_data() nn_0_score = extract_data(nn_0_data, "predict_result") nn_0_label = extract_data(nn_0_data, "label") nn_1_score = extract_data(nn_1_data, "predict_result") nn_1_label = extract_data(nn_1_data, "label") nn_0_score_label = extract_data(nn_0_data, "predict_result", keep_id=True) nn_1_score_label = extract_data(nn_1_data, "predict_result", keep_id=True) metric_summary = parse_summary_result( pipeline.get_component("evaluation_0").get_summary()) if eval_type == "binary": metric_nn = { "score_diversity_ratio": classification_metric.Distribution.compute( nn_0_score_label, nn_1_score_label), "ks_2samp": classification_metric.KSTest.compute( nn_0_score, nn_1_score), "mAP_D_value": classification_metric.AveragePrecisionScore().compute( nn_0_score, nn_1_score, nn_0_label, nn_1_label)} metric_summary["distribution_metrics"] = {"hetero_nn": metric_nn} elif eval_type == "multi": metric_nn = { "score_diversity_ratio": classification_metric.Distribution.compute( nn_0_score_label, nn_1_score_label)} metric_summary["distribution_metrics"] = {"hetero_nn": metric_nn} data_summary = { "train": { "guest": guest_train_data["name"], "host": host_train_data["name"]}, "test": { "guest": guest_train_data["name"], "host": host_train_data["name"]}} return data_summary, metric_summary if __name__ == "__main__": parser = argparse.ArgumentParser("BENCHMARK-QUALITY PIPELINE JOB") parser.add_argument("-config", type=str, help="config file") parser.add_argument("-param", type=str, help="config file for params") args = parser.parse_args() if args.config is not None: main(args.config, args.param) else: main()