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] guest_train_data = { "name": "nus_wide_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "nus_wide_host", "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, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=False) hetero_ftl_0 = HeteroFTL(name='hetero_ftl_0', epochs=10, alpha=1, batch_size=-1, mode='plain') hetero_ftl_0.add_nn_layer( Dense(units=32, activation='sigmoid', kernel_initializer=initializers.RandomNormal(stddev=1.0), bias_initializer=initializers.Zeros())) hetero_ftl_0.compile(optimizer=optimizers.Adam(lr=0.01)) evaluation_0 = Evaluation(name='evaluation_0', eval_type="binary") pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(hetero_ftl_0, data=Data(train_data=data_transform_0.output.data)) pipeline.add_component(evaluation_0, data=Data(data=hetero_ftl_0.output.data)) pipeline.compile() pipeline.fit() # predict # deploy required components pipeline.deploy_component([data_transform_0, hetero_ftl_0]) predict_pipeline = PipeLine() # add data reader onto predict pipeline predict_pipeline.add_component(reader_0) # 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_0.output.data })) # run predict model predict_pipeline.predict()
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}"} # 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", with_label=True, output_format="dense") # start component numbering at 0 scale_0 = FeatureScale(name='scale_0') param = { "penalty": "L2", "optimizer": "sgd", "tol": 1e-05, "alpha": 0.01, "max_iter": 30, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "decay": 1, "decay_sqrt": True, "init_param": { "init_method": "zeros" }, "encrypt_param": { "method": None }, "cv_param": { "n_splits": 4, "shuffle": True, "random_seed": 33, "need_cv": False } } homo_lr_0 = HomoLR(name='homo_lr_0', **param) # 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)) # set data input sources of intersection components pipeline.add_component(scale_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(homo_lr_0, data=Data(train_data=scale_0.output.data)) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") evaluation_0.get_party_instance(role='host', party_id=host).component_param(need_run=False) pipeline.add_component(evaluation_0, data=Data(data=homo_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() deploy_components = [data_transform_0, scale_0, homo_lr_0] pipeline.deploy_component(components=deploy_components) # predict_pipeline = PipeLine() # # add data reader onto predict pipeline predict_pipeline.add_component(reader_0) # # 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_0.output.data})) predict_pipeline.compile() predict_pipeline.predict() dsl_json = predict_pipeline.get_predict_dsl() conf_json = predict_pipeline.get_predict_conf() # import json json.dump(dsl_json, open('./h**o-lr-normal-predict-dsl.json', 'w'), indent=4) json.dump(conf_json, open('./h**o-lr-normal-predict-conf.json', 'w'), indent=4) # query component summary print(json.dumps(pipeline.get_component("homo_lr_0").get_summary(), indent=4, ensure_ascii=False)) print(json.dumps(pipeline.get_component("evaluation_0").get_summary(), indent=4, ensure_ascii=False))
def main(config="../../config.yaml", namespace=""): if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host[0] guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } # guest_train_data = {"name": "default_credit_hetero_guest", "namespace": f"experiment{namespace}"} # host_train_data = {"name": "default_credit_hetero_host", "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=hosts) # 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=hosts).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0", output_format='dense') # 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) # get and configure DataTransform party instance of host data_transform_0.get_party_instance( role='host', party_id=hosts).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") 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)) lr_param = { "name": "hetero_sshe_lr_0", "penalty": "L1", "optimizer": "rmsprop", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "init_param": { "init_method": "zeros", "fit_intercept": True }, "encrypt_param": { "key_length": 1024 }, "reveal_every_iter": True, "reveal_strategy": "respectively" } hetero_sshe_lr_0 = HeteroSSHELR(**lr_param) pipeline.add_component(hetero_sshe_lr_0, data=Data(train_data=intersection_0.output.data)) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") pipeline.add_component(evaluation_0, data=Data(data=hetero_sshe_lr_0.output.data)) pipeline.compile() # fit model pipeline.fit() # query component summary prettify(pipeline.get_component("hetero_sshe_lr_0").get_summary()) prettify(pipeline.get_component("evaluation_0").get_summary()) pipeline.deploy_component( [data_transform_0, intersection_0, hetero_sshe_lr_0]) predict_pipeline = PipeLine() # add data reader onto predict pipeline predict_pipeline.add_component(reader_0) # 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_0.output.data })) # run predict model predict_pipeline.predict() return pipeline
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = [{ "name": "breast_hetero_host", "namespace": f"experiment{namespace}" }, { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" }] pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=hosts) 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=hosts[0]).component_param(table=host_train_data[0]) reader_0.get_party_instance( role='host', party_id=hosts[1]).component_param(table=host_train_data[1]) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param(with_label=False, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=hosts[0]).component_param(with_label=False, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=hosts[1]).component_param(with_label=False, output_format="dense") param = { "intersect_method": "raw", "sync_intersect_ids": True, "only_output_key": True, "raw_params": { "use_hash": True, "hash_method": "sha256", "salt": "12345", "join_role": "guest" } } intersect_0 = Intersection(name="intersect_0", **param) pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data)) pipeline.compile() pipeline.fit()
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host guest_train_data = { "name": "breast_homo_test", "namespace": f"experiment_sid{namespace}" } host_train_data = { "name": "breast_homo_test", "namespace": f"experiment_sid{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=hosts) # 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=hosts).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0", with_match_id=True) # get and configure DataTransform party instance of guest data_transform_0.get_party_instance( role="guest", party_id=guest).component_param(with_label=False, output_format="dense") # get and configure DataTransform party instance of host data_transform_0.get_party_instance( role="host", party_id=hosts).component_param(with_label=False) # define FeldmanVerifiableSum components feldmanverifiablesum_0 = FeldmanVerifiableSum( name="feldmanverifiablesum_0") feldmanverifiablesum_0.get_party_instance( role="guest", party_id=guest).component_param(sum_cols=[1, 2, 3], q_n=6) feldmanverifiablesum_0.get_party_instance( role="host", party_id=hosts).component_param(sum_cols=[1, 2, 3], q_n=6) # 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(feldmanverifiablesum_0, data=Data(data=data_transform_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()
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] # data sets guest_train_data = { "name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}" } # init pipeline pipeline = PipeLine().set_initiator(role="guest", party_id=guest).set_roles( guest=guest, host=host, ) # set data reader and data-io 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, output_format="dense") data_transform_0.get_party_instance( role="host", party_id=host).component_param(with_label=False) # data intersect component intersect_0 = Intersection(name="intersection_0") # secure boost component hetero_secure_boost_0 = HeteroSecureBoost( name="hetero_secure_boost_0", num_trees=3, task_type="classification", objective_param={"objective": "cross_entropy"}, encrypt_param={"method": "Paillier"}, tree_param={"max_depth": 3}, validation_freqs=1, cv_param={ "need_cv": True, "n_splits": 5, "shuffle": False, "random_seed": 103 }) pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(hetero_secure_boost_0, data=Data(train_data=intersect_0.output.data)) pipeline.compile() pipeline.fit() print("fitting hetero secureboost done, result:") print(pipeline.get_component("hetero_secure_boost_0").get_summary())
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] guest_train_data = { "name": "expect", "namespace": f"experiment{namespace}" } host_train_data = {"name": "actual", "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) 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") data_transform_1 = DataTransform(name="data_transform_1") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param(with_label=False, output_format="dense") data_transform_1.get_party_instance( role='guest', party_id=guest).component_param(with_label=False, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=False, output_format="dense") data_transform_1.get_party_instance( role='host', party_id=host).component_param(with_label=False, output_format="dense") psi_0 = PSI(name='psi_0', max_bin_num=20) 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(data_transform_0.output.model)) pipeline.add_component(psi_0, data=Data( train_data=data_transform_0.output.data, validate_data=data_transform_1.output.data)) pipeline.compile() pipeline.fit()
def main(config="../../config.yaml", namespace=""): if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host[0] guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } guest_eval_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_eval_data = { "name": "breast_hetero_host", "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=hosts) # 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=hosts).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=hosts).component_param(table=host_eval_data) data_transform_0 = DataTransform(name="data_transform_0", output_format='dense') data_transform_1 = DataTransform(name="data_transform_1", output_format='dense') # 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) # get and configure DataTransform party instance of host data_transform_0.get_party_instance( role='host', party_id=hosts).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") intersection_1 = Intersection(name="intersection_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(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)) lr_param = { "name": "hetero_sshe_lr_0", "penalty": "L2", "optimizer": "rmsprop", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "early_stop": "diff", "batch_size": -1, "callback_param": { "callbacks": ["EarlyStopping", "PerformanceEvaluate"], "validation_freqs": 1, "early_stopping_rounds": 3 }, "learning_rate": 0.15, "init_param": { "init_method": "zeros" }, "reveal_strategy": "respectively", "reveal_every_iter": True } hetero_sshe_lr_0 = HeteroSSHELR(**lr_param) pipeline.add_component(hetero_sshe_lr_0, data=Data(train_data=intersection_0.output.data, validate_data=intersection_1.output.data)) evaluation_data = [hetero_sshe_lr_0.output.data] hetero_sshe_lr_1 = HeteroSSHELR(name='hetero_sshe_lr_1') pipeline.add_component(hetero_sshe_lr_1, data=Data(test_data=intersection_1.output.data), model=Model(hetero_sshe_lr_0.output.model)) evaluation_data.append(hetero_sshe_lr_1.output.data) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") pipeline.add_component(evaluation_0, data=Data(data=evaluation_data)) pipeline.compile() # fit model pipeline.fit() # query component summary prettify(pipeline.get_component("hetero_sshe_lr_0").get_summary()) prettify(pipeline.get_component("evaluation_0").get_summary()) return pipeline
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 guest_train_data = { "name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}" } guest_validate_data = { "name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}" } host_validate_data = { "name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}" } pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host) data_transform_0, data_transform_1 = DataTransform( name="data_transform_0"), DataTransform(name='data_transform_1') reader_0, reader_1 = Reader(name="reader_0"), Reader(name='reader_1') 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.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=False, output_format="dense") reader_1.get_party_instance( role='guest', party_id=guest).component_param(table=guest_validate_data) reader_1.get_party_instance( role='host', party_id=host).component_param(table=host_validate_data) data_transform_1.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, output_format="dense") data_transform_1.get_party_instance( role='host', party_id=host).component_param(with_label=True, output_format="dense") intersection_0 = Intersection(name="intersection_0") intersection_1 = Intersection(name="intersection_1") param = { "method": "quantile", "optimal_binning_param": { "metric_method": "gini", "min_bin_pct": 0.05, "max_bin_pct": 0.8, "init_bucket_method": "quantile", "init_bin_nums": 100, "mixture": True }, "compress_thres": 10000, "head_size": 10000, "error": 0.001, "bin_num": 10, "bin_indexes": -1, "bin_names": None, "category_indexes": [0, 1, 2], "category_names": None, "adjustment_factor": 0.5, "local_only": False, "transform_param": { "transform_cols": -1, "transform_names": None, "transform_type": "bin_num" } } hetero_feature_binning_0 = HeteroFeatureBinning( name="hetero_feature_binning_0", **param) hetero_feature_binning_1 = HeteroFeatureBinning( name='hetero_feature_binning_1') pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(reader_1) pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data), 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(hetero_feature_binning_0, data=Data(data=intersection_0.output.data)) pipeline.add_component(hetero_feature_binning_1, data=Data(data=intersection_1.output.data), model=Model(hetero_feature_binning_0.output.model)) pipeline.compile() pipeline.fit() # predict # deploy required components pipeline.deploy_component( [data_transform_0, intersection_0, hetero_feature_binning_0]) predict_pipeline = PipeLine() # add data reader onto predict pipeline predict_pipeline.add_component(reader_1) # 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_1.output.data })) # run predict model predict_pipeline.predict()
def make_asymmetric_dsl(config, namespace, guest_param, host_param, dataset='breast', is_multi_host=False, host_dense_output=True): parties = config.parties guest = parties.guest[0] hosts = parties.host if dataset == 'breast': guest_table_name = 'breast_hetero_guest' host_table_name = 'breast_hetero_host' elif dataset == 'default_credit': guest_table_name = 'default_credit_hetero_guest' host_table_name = 'default_credit_hetero_host' else: raise ValueError(f"dataset: {dataset} cannot be recognized") guest_train_data = { "name": guest_table_name, "namespace": f"experiment{namespace}" } host_train_data = { "name": host_table_name, "namespace": f"experiment{namespace}" } # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information if is_multi_host: pipeline.set_roles(guest=guest, host=hosts) else: pipeline.set_roles(guest=guest, host=hosts[0]) # 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=hosts[0]).component_param(table=host_train_data) if is_multi_host: reader_0.get_party_instance( role='host', party_id=hosts[1]).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 if host_dense_output: output_format = 'dense' else: output_format = 'sparse' if is_multi_host: data_transform_0.get_party_instance(role='host', party_id=hosts). \ component_param(with_label=False, output_format=output_format) else: data_transform_0.get_party_instance(role='host', party_id=hosts[0]). \ component_param(with_label=False, output_format=output_format) # define Intersection components intersection_0 = Intersection(name="intersection_0") hetero_feature_binning_0 = HeteroFeatureBinning( name="hetero_feature_binning_0") hetero_feature_binning_0.get_party_instance( role='guest', party_id=guest).component_param(**guest_param) if is_multi_host: hetero_feature_binning_0.get_party_instance( role='host', party_id=hosts).component_param(**host_param) else: hetero_feature_binning_0.get_party_instance( role='host', party_id=hosts[0]).component_param(**host_param) # 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)) # set data input sources of intersection components pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data)) # set train & validate data of hetero_lr_0 component pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_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(work_mode=work_mode) return pipeline
def make_add_one_hot_dsl(config, namespace, bin_param, is_multi_host=False): parties = config.parties guest = parties.guest[0] hosts = parties.host guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } guest_eval_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_eval_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information if is_multi_host: pipeline.set_roles(guest=guest, host=hosts) else: pipeline.set_roles(guest=guest, host=hosts[0]) # 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=hosts[0]).component_param(table=host_train_data) if is_multi_host: reader_0.get_party_instance( role='host', party_id=hosts[1]).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=hosts[0]).component_param(table=host_eval_data) if is_multi_host: reader_1.get_party_instance( role='host', party_id=hosts[1]).component_param(table=host_eval_data) # define DataTransform components data_transform_0 = DataTransform( name="data_transform_0") # start component numbering at 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=hosts[0]).component_param(with_label=False) if is_multi_host: data_transform_0.get_party_instance( role='host', party_id=hosts[1]).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") intersection_1 = Intersection(name="intersection_1") hetero_feature_binning_0 = HeteroFeatureBinning(**bin_param) hetero_feature_binning_1 = HeteroFeatureBinning( name='hetero_feature_binning_1') one_hot_encoder_0 = OneHotEncoder(name='one_hot_encoder_0', transform_col_indexes=-1, transform_col_names=None, need_run=True) # 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_feature_binning_0, data=Data(data=intersection_0.output.data)) pipeline.add_component(hetero_feature_binning_1, data=Data(data=intersection_1.output.data), model=Model(hetero_feature_binning_0.output.model)) pipeline.add_component( one_hot_encoder_0, data=Data(data=hetero_feature_binning_0.output.data)) # compile pipeline once finished adding modules, this step will form conf and dsl files for running job pipeline.compile() # pipeline.fit(work_mode=work_mode) return pipeline
def main(config="../../config.yaml", namespace=""): if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host[0] arbiter = parties.arbiter[0] guest_train_data = { "name": "vehicle_scale_homo_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "vehicle_scale_homo_host", "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=hosts, 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=hosts).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0", output_format='dense', with_label=True) pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) lr_param = { "penalty": "L2", "optimizer": "sgd", "tol": 1e-05, "alpha": 0.01, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "decay": 1, "decay_sqrt": True, "init_param": { "init_method": "zeros" }, "encrypt_param": { "method": "Paillier" }, "cv_param": { "n_splits": 4, "shuffle": True, "random_seed": 33, "need_cv": False }, "callback_param": { "callbacks": ["ModelCheckpoint", "EarlyStopping"] } } homo_lr_0 = HomoLR(name="homo_lr_0", max_iter=1, **lr_param) homo_lr_1 = HomoLR(name="homo_lr_1") pipeline.add_component(homo_lr_0, data=Data(train_data=data_transform_0.output.data)) pipeline.add_component(homo_lr_1, data=Data(test_data=data_transform_0.output.data), model=Model(model=homo_lr_0.output.model)) evaluation_0 = Evaluation(name="evaluation_0", eval_type="multi") pipeline.add_component( evaluation_0, data=Data(data=[homo_lr_0.output.data, homo_lr_1.output.data])) pipeline.compile() # fit model pipeline.fit() # query component summary prettify(pipeline.get_component("evaluation_0").get_summary()) return pipeline
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) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, label_name="y", label_type="int", output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=False) intersection_0 = Intersection(name="intersection_0") sample_weight_0 = SampleWeight(name="sample_weight_0") sample_weight_0.get_party_instance( role='guest', party_id=guest).component_param(need_run=True, class_weight="balanced") sample_weight_0.get_party_instance( role='host', party_id=host).component_param(need_run=False) sample_weight_1 = SampleWeight(name="sample_weight_1") hetero_lr_0 = HeteroLR(name="hetero_lr_0", optimizer="nesterov_momentum_sgd", tol=0.001, alpha=0.01, max_iter=20, early_stop="weight_diff", batch_size=-1, learning_rate=0.15, init_param={"init_method": "zeros"}) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary", pos_label=1) # evaluation_0.get_party_instance(role='host', party_id=host).component_param(need_run=False) 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(sample_weight_0, data=Data(data=intersection_0.output.data)) pipeline.add_component(sample_weight_1, data=Data(data=intersection_0.output.data), model=Model(model=sample_weight_0.output.model)) pipeline.add_component(hetero_lr_0, data=Data(train_data=sample_weight_1.output.data)) pipeline.add_component(evaluation_0, data=Data(data=hetero_lr_0.output.data)) pipeline.compile() pipeline.fit() # predict # deploy required components pipeline.deploy_component( [data_transform_0, intersection_0, sample_weight_0, hetero_lr_0]) predict_pipeline = PipeLine() # add data reader onto predict pipeline predict_pipeline.add_component(reader_0) # 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_0.output.data })) # run predict model predict_pipeline.predict()
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}" } guest_eval_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_eval_data = { "name": "breast_hetero_host", "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) 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") # start component numbering at 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") param = {"k": 3, "max_iter": 10} hetero_kmeans_0 = HeteroKmeans(name='hetero_kmeans_0', **param) hetero_kmeans_1 = HeteroKmeans(name='hetero_kmeans_1') evaluation_0 = Evaluation(name='evaluation_0', eval_type='clustering') evaluation_1 = Evaluation(name='evaluation_1', eval_type='clustering') # 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)) 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_kmeans_0, data=Data(train_data=intersection_0.output.data)) pipeline.add_component(hetero_kmeans_1, data=Data(train_data=intersection_1.output.data)) # print(f"data: {hetero_kmeans_0.output.data.data[0]}") pipeline.add_component(evaluation_0, data=Data(data=hetero_kmeans_0.output.data.data[0])) pipeline.add_component(evaluation_1, data=Data(data=hetero_kmeans_1.output.data.data[0])) # 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 print(pipeline.get_component("hetero_kmeans_0").get_summary())
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_sid{namespace}" } host_train_data = { "name": "breast_homo_host", "namespace": f"experiment_sid{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", with_match_id=True, with_label=True, output_format="dense") # start component numbering at 0 scale_0 = FeatureScale(name='scale_0') sample_weight_0 = SampleWeight(name="sample_weight_0", class_weight={ "0": 1, "1": 2 }) param = { "penalty": "L2", "optimizer": "sgd", "tol": 1e-05, "alpha": 0.01, "max_iter": 3, "early_stop": "diff", "batch_size": 320, "learning_rate": 0.15, "decay": 1.0, "decay_sqrt": True, "init_param": { "init_method": "zeros" }, "encrypt_param": { "method": "Paillier" }, "cv_param": { "n_splits": 5, "shuffle": True, "random_seed": 33, "need_cv": False } } homo_lr_0 = HomoLR(name='homo_lr_0', **param) evaluation_0 = Evaluation(name='evaluation_0') # 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)) # set data input sources of intersection components pipeline.add_component(scale_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(sample_weight_0, data=Data(data=scale_0.output.data)) pipeline.add_component(homo_lr_0, data=Data(train_data=sample_weight_0.output.data)) pipeline.add_component(evaluation_0, data=Data(data=homo_lr_0.output.data)) evaluation_0.get_party_instance( role='host', party_id=host).component_param(need_run=False) # 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 print( json.dumps(pipeline.get_component("evaluation_0").get_summary(), indent=4, ensure_ascii=False))
def main(config="../../config.yaml", namespace=""): if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] host = parties.host[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) 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") param = { "name": "hetero_feature_binning_0", "method": "quantile", "compress_thres": 10000, "head_size": 10000, "error": 0.001, "bin_num": 10, "bin_indexes": -1, "bin_names": None, "category_indexes": None, "category_names": None, "adjustment_factor": 0.5, "local_only": False, "transform_param": { "transform_cols": [0, 1, 2], "transform_names": None, "transform_type": "woe" } } hetero_feature_binning_0 = HeteroFeatureBinning(**param) hetero_feature_binning_0.get_party_instance( role="host", party_id=host).component_param( transform_param={"transform_type": None}) 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_feature_binning_0, data=Data(data=intersection_0.output.data)) pipeline.compile() pipeline.fit() pipeline.deploy_component( [data_transform_0, intersection_0, hetero_feature_binning_0]) predict_pipeline = PipeLine() # add data reader onto predict pipeline predict_pipeline.add_component(reader_0) # 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_0.output.data })) # run predict model predict_pipeline.predict()
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] 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) 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=False, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=False, output_format="dense") param = { "intersect_method": "rsa", "sync_intersect_ids": False, "only_output_key": True, "rsa_params": { "hash_method": "sha256", "final_hash_method": "sha256", "key_length": 2048 } } intersect_0 = Intersection(name="intersect_0", **param) cache_loader_0 = CacheLoader(name="cache_loader_0", job_id="", component_name="intersect_0", cache_name="cache") pipeline.add_component(reader_0) pipeline.add_component(cache_loader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data), cache=Cache(cache_loader_0.output.cache)) pipeline.compile() pipeline.fit()
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}"} # 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 components intersection_0 = Intersection(name="intersection_0") param = { "name": 'hetero_feature_binning_0', "method": 'optimal', "optimal_binning_param": { "metric_method": "iv" }, "bin_indexes": -1 } hetero_feature_binning_0 = HeteroFeatureBinning(**param) param = { "name": 'hetero_feature_selection_0', "filter_methods": ["manually", "iv_filter"], "manually_param": { "filter_out_indexes": [1] }, "iv_param": { "metrics": ["iv", "iv"], "filter_type": ["top_k", "threshold"], "take_high": [True, True], "threshold": [10, 0.001] }, "select_col_indexes": -1 } hetero_feature_selection_0 = HeteroFeatureSelection(**param) param = { "k": 3, "max_iter": 10 } hetero_kmeans_0 = HeteroKmeans(name='hetero_kmeans_0', **param) evaluation_0 = Evaluation(name='evaluation_0', eval_type='clustering') # 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)) # set data input sources of intersection components pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data)) # set train & validate data of hetero_lr_0 component pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_0.output.data)) pipeline.add_component(hetero_feature_selection_0, data=Data(data=intersection_0.output.data), model=Model(isometric_model=hetero_feature_binning_0.output.model)) pipeline.add_component(hetero_kmeans_0, data=Data(train_data=hetero_feature_selection_0.output.data)) print(f"data: {hetero_kmeans_0.output.data.data[0]}") pipeline.add_component(evaluation_0, data=Data(data=hetero_kmeans_0.output.data.data[0])) # 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 print(pipeline.get_component("hetero_kmeans_0").get_summary())
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_mini_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_mini_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) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=False) intersection_0 = Intersection(name="intersection_0") hetero_lr_0 = HeteroLR(name="hetero_lr_0", early_stop="diff", max_iter=5, penalty="None", optimizer="sgd", tol=0.001, batch_size=-1, learning_rate=0.15, decay=0.0, decay_sqrt=False, init_param={"init_method": "zeros"}, encrypted_mode_calculator_param={"mode": "fast"}, stepwise_param={ "score_name": "AIC", "direction": "backward", "need_stepwise": True, "max_step": 2, "nvmin": 2 }) 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_lr_0, data=Data(train_data=intersection_0.output.data)) pipeline.compile() pipeline.fit()
def make_feature_engineering_dsl(config, namespace, lr_param, is_multi_host=False, has_validate=False, is_cv=False, is_ovr=False): parties = config.parties guest = parties.guest[0] if is_multi_host: hosts = parties.host else: hosts = parties.host[0] arbiter = parties.arbiter[0] if is_ovr: guest_train_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"} guest_eval_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"} host_eval_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"} else: guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"} guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"} host_eval_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"} train_line = [] # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information pipeline.set_roles(guest=guest, host=hosts, 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=hosts).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=hosts).component_param(with_label=False) train_line.append(data_transform_0) # define Intersection components intersection_0 = Intersection(name="intersection_0") 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)) train_line.append(intersection_0) feature_scale_0 = FeatureScale(name='feature_scale_0', method="standard_scale", need_run=True) pipeline.add_component(feature_scale_0, data=Data(data=intersection_0.output.data)) train_line.append(feature_scale_0) binning_param = { "method": "quantile", "compress_thres": 10000, "head_size": 10000, "error": 0.001, "bin_num": 10, "bin_indexes": -1, "adjustment_factor": 0.5, "local_only": False, "need_run": True, "transform_param": { "transform_cols": -1, "transform_type": "bin_num" } } hetero_feature_binning_0 = HeteroFeatureBinning(name='hetero_feature_binning_0', **binning_param) pipeline.add_component(hetero_feature_binning_0, data=Data(data=feature_scale_0.output.data)) train_line.append(hetero_feature_binning_0) selection_param = { "select_col_indexes": -1, "filter_methods": [ "manually", "iv_value_thres", "iv_percentile" ], "manually_param": { "filter_out_indexes": None }, "iv_value_param": { "value_threshold": 1.0 }, "iv_percentile_param": { "percentile_threshold": 0.9 }, "need_run": True } hetero_feature_selection_0 = HeteroFeatureSelection(name='hetero_feature_selection_0', **selection_param) pipeline.add_component(hetero_feature_selection_0, data=Data(data=hetero_feature_binning_0.output.data), model=Model(isometric_model=[hetero_feature_binning_0.output.model])) train_line.append(hetero_feature_selection_0) onehot_param = { "transform_col_indexes": -1, "transform_col_names": None, "need_run": True } one_hot_encoder_0 = OneHotEncoder(name='one_hot_encoder_0', **onehot_param) pipeline.add_component(one_hot_encoder_0, data=Data(data=hetero_feature_selection_0.output.data)) train_line.append(one_hot_encoder_0) last_cpn = None if has_validate: 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=hosts).component_param(table=host_eval_data) pipeline.add_component(reader_1) last_cpn = reader_1 for cpn in train_line: cpn_name = cpn.name new_name = "_".join(cpn_name.split('_')[:-1] + ['1']) validate_cpn = type(cpn)(name=new_name) if hasattr(cpn.output, "model"): pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data), model=Model(cpn.output.model)) else: pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data)) last_cpn = validate_cpn hetero_lr_0 = HeteroLR(**lr_param) if has_validate: pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_encoder_0.output.data, validate_data=last_cpn.output.data)) else: pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_encoder_0.output.data)) if is_cv: pipeline.compile() return pipeline evaluation_data = [hetero_lr_0.output.data] if has_validate: hetero_lr_1 = HeteroLR(name='hetero_lr_1') pipeline.add_component(hetero_lr_1, data=Data(test_data=last_cpn.output.data), model=Model(hetero_lr_0.output.model)) evaluation_data.append(hetero_lr_1.output.data) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") pipeline.add_component(evaluation_0, data=Data(data=evaluation_data)) pipeline.compile() return pipeline
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": "mock_string", "namespace": f"experiment{namespace}" } host_train_data = { "name": "mock_string", "namespace": f"experiment{namespace}" } guest_eval_data = { "name": "mock_string", "namespace": f"experiment{namespace}" } host_eval_data = { "name": "mock_string", "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) 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", with_label=True, output_format="dense", label_name='y', data_type="str") # start component numbering at 0 data_transform_1 = DataTransform(name="data_transform_1") homo_onehot_param = { "transform_col_indexes": -1, "transform_col_names": [], "need_alignment": True } homo_onehot_0 = HomoOneHotEncoder(name='homo_onehot_0', **homo_onehot_param) homo_onehot_1 = HomoOneHotEncoder(name='homo_onehot_1') scale_0 = FeatureScale(name='scale_0', method="standard_scale") scale_1 = FeatureScale(name='scale_1') homo_lr_param = { "penalty": "L2", "optimizer": "sgd", "tol": 1e-05, "alpha": 0.01, "max_iter": 3, "early_stop": "diff", "batch_size": 500, "learning_rate": 0.15, "decay": 1, "decay_sqrt": True, "init_param": { "init_method": "zeros" }, "encrypt_param": { "method": "Paillier" }, "cv_param": { "n_splits": 4, "shuffle": True, "random_seed": 33, "need_cv": False } } homo_lr_0 = HomoLR(name='homo_lr_0', **homo_lr_param) homo_lr_1 = HomoLR(name='homo_lr_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)) pipeline.add_component(homo_onehot_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(homo_onehot_1, data=Data(data=data_transform_1.output.data), model=Model(homo_onehot_0.output.model)) pipeline.add_component(scale_0, data=Data(data=homo_onehot_0.output.data)) pipeline.add_component(scale_1, data=Data(data=homo_onehot_1.output.data), model=Model(scale_0.output.model)) pipeline.add_component(homo_lr_0, data=Data(train_data=scale_0.output.data)) pipeline.add_component(homo_lr_1, data=Data(test_data=scale_1.output.data), model=Model(homo_lr_0.output.model)) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") evaluation_0.get_party_instance( role='host', party_id=host).component_param(need_run=False) pipeline.add_component( evaluation_0, data=Data(data=[homo_lr_0.output.data, homo_lr_1.output.data])) pipeline.compile() # fit model pipeline.fit() # query component summary print( json.dumps(pipeline.get_component("homo_lr_0").get_summary(), indent=4, ensure_ascii=False)) print( json.dumps(pipeline.get_component("evaluation_0").get_summary(), indent=4, ensure_ascii=False))
def make_normal_dsl(config, namespace, lr_param, is_multi_host=False, has_validate=False, is_cv=False, is_ovr=False, is_dense=True, need_evaluation=True): parties = config.parties guest = parties.guest[0] if is_multi_host: hosts = parties.host else: hosts = parties.host[0] arbiter = parties.arbiter[0] if is_ovr: guest_train_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"} guest_eval_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"} host_eval_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"} else: guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"} host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"} guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"} host_eval_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"} train_line = [] # initialize pipeline pipeline = PipeLine() # set job initiator pipeline.set_initiator(role='guest', party_id=guest) # set participants information pipeline.set_roles(guest=guest, host=hosts, 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=hosts).component_param(table=host_train_data) # define DataTransform components if is_dense: data_transform_0 = DataTransform(name="data_transform_0", output_format='dense') else: data_transform_0 = DataTransform(name="data_transform_0", output_format='sparse') # 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) # get and configure DataTransform party instance of host data_transform_0.get_party_instance(role='host', party_id=hosts).component_param(with_label=False) train_line.append(data_transform_0) # define Intersection components intersection_0 = Intersection(name="intersection_0") 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)) train_line.append(intersection_0) last_cpn = None if has_validate: 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=hosts).component_param(table=host_eval_data) pipeline.add_component(reader_1) last_cpn = reader_1 for cpn in train_line: cpn_name = cpn.name new_name = "_".join(cpn_name.split('_')[:-1] + ['1']) validate_cpn = type(cpn)(name=new_name) if hasattr(cpn.output, "model"): pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data), model=Model(cpn.output.model)) else: pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data)) last_cpn = validate_cpn hetero_lr_0 = HeteroLR(**lr_param) if has_validate: pipeline.add_component(hetero_lr_0, data=Data(train_data=intersection_0.output.data, validate_data=last_cpn.output.data)) else: pipeline.add_component(hetero_lr_0, data=Data(train_data=intersection_0.output.data)) if is_cv: pipeline.compile() return pipeline evaluation_data = [hetero_lr_0.output.data] if has_validate: hetero_lr_1 = HeteroLR(name='hetero_lr_1') pipeline.add_component(hetero_lr_1, data=Data(test_data=last_cpn.output.data), model=Model(hetero_lr_0.output.model)) evaluation_data.append(hetero_lr_1.output.data) if need_evaluation: evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") pipeline.add_component(evaluation_0, data=Data(data=evaluation_data)) pipeline.compile() return pipeline
def main(config="../../config.yaml", namespace=""): # obtain config if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host arbiter = parties.arbiter[0] guest_train_data = { "name": "motor_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = [{ "name": "motor_hetero_host", "namespace": f"experiment{namespace}" }, { "name": "motor_hetero_host", "namespace": f"experiment{namespace}" }] pipeline = PipeLine().set_initiator( role='guest', party_id=guest).set_roles(guest=guest, host=hosts, 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=hosts[0]).component_param(table=host_train_data[0]) reader_0.get_party_instance( role='host', party_id=hosts[1]).component_param(table=host_train_data[1]) data_transform_0 = DataTransform(name="data_transform_0") data_transform_0.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, label_name="motor_speed", label_type="float", output_format="dense") data_transform_0.get_party_instance( role='host', party_id=hosts).component_param(with_label=False) intersection_0 = Intersection(name="intersection_0") hetero_linr_0 = HeteroLinR( name="hetero_linr_0", penalty="None", optimizer="sgd", tol=0.001, alpha=0.01, max_iter=20, early_stop="weight_diff", batch_size=-1, learning_rate=0.15, decay=0.0, decay_sqrt=False, init_param={"init_method": "zeros"}, encrypted_mode_calculator_param={"mode": "fast"}, cv_param={ "n_splits": 5, "shuffle": False, "random_seed": 42, "need_cv": True }) 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_linr_0, data=Data(train_data=intersection_0.output.data)) pipeline.compile() pipeline.fit()
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}" } guest_validate_data = { "name": "breast_homo_test", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_homo_host", "namespace": f"experiment{namespace}" } host_validate_data = { "name": "breast_homo_test", "namespace": f"experiment{namespace}" } pipeline = PipeLine().set_initiator( role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter) data_transform_0, data_transform_1 = DataTransform( name="data_transform_0"), DataTransform(name='data_transform_1') reader_0, reader_1 = Reader(name="reader_0"), Reader(name='reader_1') 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.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, output_format="dense") data_transform_0.get_party_instance( role='host', party_id=host).component_param(with_label=True, output_format="dense") reader_1.get_party_instance( role='guest', party_id=guest).component_param(table=guest_validate_data) reader_1.get_party_instance( role='host', party_id=host).component_param(table=host_validate_data) data_transform_1.get_party_instance( role='guest', party_id=guest).component_param(with_label=True, output_format="dense") data_transform_1.get_party_instance( role='host', party_id=host).component_param(with_label=True, output_format="dense") homo_secureboost_0 = HomoSecureBoost( name="homo_secureboost_0", num_trees=3, task_type='classification', objective_param={"objective": "cross_entropy"}, tree_param={"max_depth": 3}, validation_freqs=1, backend="memory") evaluation_0 = Evaluation(name='evaluation_0', eval_type='binary') pipeline.add_component(reader_0) pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data)) pipeline.add_component(reader_1) pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data), model=Model(data_transform_0.output.model)) pipeline.add_component(homo_secureboost_0, data=Data( train_data=data_transform_0.output.data, validate_data=data_transform_1.output.data)) pipeline.add_component(evaluation_0, data=Data(homo_secureboost_0.output.data)) pipeline.compile() pipeline.fit()
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] # data sets guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } guest_validate_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_validate_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } # init pipeline pipeline = PipeLine().set_initiator(role="guest", party_id=guest).set_roles( guest=guest, host=host, ) # set data reader and data-io reader_0, reader_1 = Reader(name="reader_0"), Reader(name="reader_1") 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.get_party_instance( role="guest", party_id=guest).component_param(table=guest_validate_data) reader_1.get_party_instance( role="host", party_id=host).component_param(table=host_validate_data) data_transform_0, data_transform_1 = DataTransform( name="data_transform_0"), DataTransform(name="data_transform_1") data_transform_0.get_party_instance( role="guest", party_id=guest).component_param(with_label=True, output_format="dense") data_transform_0.get_party_instance( role="host", party_id=host).component_param(with_label=False) data_transform_1.get_party_instance( role="guest", party_id=guest).component_param(with_label=True, output_format="dense") data_transform_1.get_party_instance( role="host", party_id=host).component_param(with_label=False) # data intersect component intersect_0 = Intersection(name="intersection_0") intersect_1 = Intersection(name="intersection_1") # secure boost component hetero_secure_boost_0 = HeteroSecureBoost( name="hetero_secure_boost_0", num_trees=3, task_type="classification", objective_param={"objective": "cross_entropy"}, encrypt_param={"method": "Paillier"}, tree_param={"max_depth": 3}, cipher_compress_error=8, validation_freqs=1) # evaluation component evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") 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(data_transform_0.output.model)) pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data)) pipeline.add_component(intersect_1, data=Data(data=data_transform_1.output.data)) pipeline.add_component(hetero_secure_boost_0, data=Data(train_data=intersect_0.output.data, validate_data=intersect_1.output.data)) pipeline.add_component(evaluation_0, data=Data(data=hetero_secure_boost_0.output.data)) pipeline.compile() pipeline.fit() print("fitting hetero secureboost done, result:") print(pipeline.get_component("hetero_secure_boost_0").get_summary())
def make_single_predict_pipeline(config, namespace, selection_param, is_multi_host=False, **kwargs): parties = config.parties guest = parties.guest[0] if is_multi_host: hosts = parties.host else: hosts = parties.host[0] guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } guest_eval_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_eval_data = { "name": "breast_hetero_host", "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=hosts) # 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=hosts).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=hosts).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") 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)) 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=hosts).component_param(table=host_eval_data) data_transform_1 = DataTransform(name="data_transform_1") intersection_1 = Intersection(name="intersection_1") pipeline.add_component(reader_1) pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data), model=Model(data_transform_0.output.model)) pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data)) sample_0 = FederatedSample(name='sample_0', fractions=0.9) pipeline.add_component(sample_0, data=Data(data=intersection_0.output.data)) if "binning_param" not in kwargs: raise ValueError("Binning_param is needed") hetero_feature_binning_0 = HeteroFeatureBinning(**kwargs['binning_param']) pipeline.add_component(hetero_feature_binning_0, data=Data(data=sample_0.output.data)) hetero_feature_binning_1 = HeteroFeatureBinning( name='hetero_feature_binning_1') pipeline.add_component(hetero_feature_binning_1, data=Data(data=intersection_1.output.data), model=Model(hetero_feature_binning_0.output.model)) hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param) pipeline.add_component( hetero_feature_selection_0, data=Data(data=hetero_feature_binning_0.output.data), model=Model(isometric_model=[hetero_feature_binning_0.output.model])) hetero_feature_selection_1 = HeteroFeatureSelection( name='hetero_feature_selection_1') pipeline.add_component( hetero_feature_selection_1, data=Data(data=hetero_feature_binning_1.output.data), model=Model(hetero_feature_selection_0.output.model)) scale_0 = FeatureScale(name='scale_0') scale_1 = FeatureScale(name='scale_1') pipeline.add_component( scale_0, data=Data(data=hetero_feature_selection_0.output.data)) pipeline.add_component( scale_1, data=Data(data=hetero_feature_selection_1.output.data), model=Model(scale_0.output.model)) pipeline.compile() return pipeline
def main(config="../../config.yaml", namespace=""): if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] hosts = parties.host[0] guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "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=hosts) # 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=hosts).component_param(table=host_train_data) data_transform_0 = DataTransform(name="data_transform_0", output_format='dense') # 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) # get and configure DataTransform party instance of host data_transform_0.get_party_instance( role='host', party_id=hosts).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") selection_param = { "select_col_indexes": -1, "filter_methods": ["manually"] } hetero_feature_selection_0 = HeteroFeatureSelection( name="hetero_feature_selection_0", **selection_param) hetero_feature_selection_0.get_party_instance( role='guest', party_id=guest).component_param( manually_param={"left_col_indexes": [0]}) 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_feature_selection_0, data=Data(data=intersection_0.output.data)) lr_param = { "name": "hetero_sshe_lr_0", "penalty": None, "optimizer": "sgd", "tol": 0.0001, "alpha": 0.01, "max_iter": 30, "early_stop": "diff", "batch_size": -1, "learning_rate": 0.15, "init_param": { "init_method": "random_uniform" }, "reveal_strategy": "encrypted_reveal_in_host", "reveal_every_iter": False } hetero_sshe_lr_0 = HeteroSSHELR(**lr_param) pipeline.add_component( hetero_sshe_lr_0, data=Data(train_data=hetero_feature_selection_0.output.data)) evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary") pipeline.add_component(evaluation_0, data=Data(data=hetero_sshe_lr_0.output.data)) pipeline.compile() # fit model pipeline.fit() # query component summary prettify(pipeline.get_component("hetero_sshe_lr_0").get_summary()) prettify(pipeline.get_component("evaluation_0").get_summary()) return pipeline
def make_normal_dsl(config, namespace, selection_param, is_multi_host=False, host_dense_output=True, **kwargs): parties = config.parties guest = parties.guest[0] if is_multi_host: hosts = parties.host else: hosts = parties.host[0] guest_train_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_train_data = { "name": "breast_hetero_host", "namespace": f"experiment{namespace}" } guest_eval_data = { "name": "breast_hetero_guest", "namespace": f"experiment{namespace}" } host_eval_data = { "name": "breast_hetero_host", "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=hosts) # 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=hosts).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=hosts).component_param(with_label=False) # define Intersection components intersection_0 = Intersection(name="intersection_0") 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)) last_cpn = intersection_0 selection_include_model = [] if 'binning_param' in kwargs: hetero_feature_binning_0 = HeteroFeatureBinning( **kwargs['binning_param']) pipeline.add_component(hetero_feature_binning_0, data=Data(data=last_cpn.output.data)) selection_include_model.append(hetero_feature_binning_0) # last_cpn = hetero_feature_binning_0 if 'statistic_param' in kwargs: # print(f"param: {kwargs['statistic_param']}, kwargs: {kwargs}") statistic_0 = DataStatistics(**kwargs['statistic_param']) pipeline.add_component(statistic_0, data=Data(data=last_cpn.output.data)) # last_cpn = statistic_0 selection_include_model.append(statistic_0) if 'psi_param' in kwargs: 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=hosts).component_param(table=host_eval_data) data_transform_1 = DataTransform(name="data_transform_1") intersection_1 = Intersection(name="intersection_1") pipeline.add_component(reader_1) pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data), model=Model(data_transform_0.output.model)) pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data)) psi_0 = PSI(**kwargs['psi_param']) pipeline.add_component(psi_0, data=Data( train_data=intersection_0.output.data, validate_data=intersection_1.output.data)) # last_cpn = statistic_0 selection_include_model.append(psi_0) if 'sbt_param' in kwargs: secureboost_0 = HeteroSecureBoost(**kwargs['sbt_param']) pipeline.add_component( secureboost_0, data=Data(train_data=intersection_0.output.data)) selection_include_model.append(secureboost_0) if "fast_sbt_param" in kwargs: fast_sbt_0 = HeteroFastSecureBoost(**kwargs['fast_sbt_param']) pipeline.add_component( fast_sbt_0, data=Data(train_data=intersection_0.output.data)) selection_include_model.append(fast_sbt_0) hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param) pipeline.add_component( hetero_feature_selection_0, data=Data(data=intersection_0.output.data), model=Model( isometric_model=[x.output.model for x in selection_include_model])) # compile pipeline once finished adding modules, this step will form conf and dsl files for running job pipeline.compile() return pipeline
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) 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) intersection_0 = Intersection(name="intersection_0") federated_sample_0 = FederatedSample(name="federated_sample_0", mode="stratified", method="upsample", fractions=[[0, 1.5], [1, 2.0]]) feature_scale_0 = FeatureScale(name="feature_scale_0", method="min_max_scale", mode="cap", feat_upper=1, feat_lower=0) hetero_feature_binning_0 = HeteroFeatureBinning( name="hetero_feature_binning_0") hetero_feature_selection_0 = HeteroFeatureSelection( name="hetero_feature_selection_0") one_hot_0 = OneHotEncoder(name="one_hot_0") 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=10, early_stop="diff", batch_size=320, learning_rate=0.15) evaluation_0 = Evaluation(name="evaluation_0") 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(federated_sample_0, data=Data(data=intersection_0.output.data)) pipeline.add_component(feature_scale_0, data=Data(data=federated_sample_0.output.data)) pipeline.add_component(hetero_feature_binning_0, data=Data(data=feature_scale_0.output.data)) pipeline.add_component( hetero_feature_selection_0, data=Data(data=hetero_feature_binning_0.output.data)) pipeline.add_component( one_hot_0, data=Data(data=hetero_feature_selection_0.output.data)) pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_0.output.data)) pipeline.add_component(evaluation_0, data=Data(data=hetero_lr_0.output.data)) pipeline.compile() pipeline.fit() print(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""): if isinstance(config, str): config = load_job_config(config) parties = config.parties guest = parties.guest[0] host = parties.host[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) 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") param = { "model_id": "guest-10000#host-9999#model", "model_version": "202108301602196678300", "component_name": "hetero_feature_binning_0", "step_index": None } model_loader_0 = ModelLoader(name="model_loader_0", **param) selection_param = { "name": "hetero_feature_selection_0", "select_col_indexes": -1, "select_names": [], "filter_methods": ["iv_filter"], "iv_param": { "metrics": ["iv", "iv", "iv"], "filter_type": ["threshold", "top_k", "top_percentile"], "take_high": True, "threshold": [0.03, 15, 0.7], "host_thresholds": [[0.15], None, None], "select_federated": True } } hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param) 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(model_loader_0) pipeline.add_component( hetero_feature_selection_0, data=Data(data=intersection_0.output.data), model=Model(isometric_model=model_loader_0.output.model)) pipeline.compile() pipeline.fit()