def METHOD_NAME(config="../../config.yaml", param="./lr_config.yaml", namespace=""):
    # obtain config
    if isinstance(config, str):
        config = load_job_config(config)
    parties = config.parties
    guest = parties.guest[0]
    host = parties.host[0]
    arbiter = parties.arbiter[0]
    if isinstance(param, str):
        param = JobConfig.load_from_file(param)
    assert isinstance(param, dict)
    data_set = param.get("data_guest").split('/')[-1]
    if data_set == "default_credit_hetero_guest.csv":
        guest_data_table = 'default_credit_hetero_guest'
        host_data_table = 'default_credit_hetero_host'
    elif data_set == 'breast_hetero_guest.csv':
        guest_data_table = 'breast_hetero_guest'
        host_data_table = 'breast_hetero_host'
    elif data_set == 'give_credit_hetero_guest.csv':
        guest_data_table = 'give_credit_hetero_guest'
        host_data_table = 'give_credit_hetero_host'
    elif data_set == 'epsilon_5k_hetero_guest.csv':
        guest_data_table = 'epsilon_5k_hetero_guest'
        host_data_table = 'epsilon_5k_hetero_host'
    else:
        raise ValueError(f"Cannot recognized data_set: {data_set}")
    guest_train_data = {"name": guest_data_table, "namespace": f"experiment{namespace}"}
    host_train_data = {"name": host_data_table, "namespace": f"experiment{namespace}"}
    # initialize pipeline
    pipeline = PipeLine()
    # set job initiator
    pipeline.set_initiator(role='guest', party_id=guest)
    # set participants information
    pipeline.set_roles(guest=guest, host=host, arbiter=arbiter)
    # define Reader components to read in data
    reader_0 = Reader(name="reader_0")
    # configure Reader for guest
    reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
    # configure Reader for host
    reader_0.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
    # define DataTransform components
    data_transform_0 = DataTransform(name="data_transform_0")  # start component numbering at 0
    # get DataTransform party instance of guest
    data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
    # configure DataTransform for guest
    data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense")
    # get and configure DataTransform party instance of host
    data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
    # define Intersection component
    intersection_0 = Intersection(name="intersection_0")
    lr_param = {
    }
    config_param = {
        "penalty": param["penalty"],
        "max_iter": param["max_iter"],
        "alpha": param["alpha"],
        "learning_rate": param["learning_rate"],
        "optimizer": param["optimizer"],  # use sgd
        "batch_size": param["batch_size"],
        "early_stop": "diff",
        "tol": 1e-4,
        "init_param": {
            "init_method": param.get("init_method", 'random_uniform'),
            "random_seed": param.get("random_seed", 103),
            "fit_intercept": True
        },
        "reveal_strategy": param.get("reveal_strategy", "respectively"),
        "reveal_every_iter": True
    }
    lr_param.update(config_param)
    print(f"lr_param: {lr_param}, data_set: {data_set}")
    hetero_sshe_lr_0 = HeteroSSHELR(name='hetero_sshe_lr_0', **lr_param)
    hetero_sshe_lr_1 = HeteroSSHELR(name='hetero_sshe_lr_1')
    evaluation_0 = Evaluation(name='evaluation_0', eval_type="binary")
    # add components to pipeline, in order of task execution
    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(hetero_sshe_lr_0, data=Data(train_data=intersection_0.output.data))
    pipeline.add_component(hetero_sshe_lr_1, data=Data(test_data=intersection_0.output.data),
                           model=Model(hetero_sshe_lr_0.output.model))
    pipeline.add_component(evaluation_0, data=Data(data=hetero_sshe_lr_0.output.data))
    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()
    # fit model
    pipeline.fit()
    lr_0_data = pipeline.get_component("hetero_sshe_lr_0").get_output_data()
    lr_1_data = pipeline.get_component("hetero_sshe_lr_1").get_output_data()
    lr_0_score = extract_data(lr_0_data, "predict_result")
    lr_0_label = extract_data(lr_0_data, "label")
    lr_1_score = extract_data(lr_1_data, "predict_result")
    lr_1_label = extract_data(lr_1_data, "label")
    lr_0_score_label = extract_data(lr_0_data, "predict_result", keep_id=True)
    lr_1_score_label = extract_data(lr_1_data, "predict_result", keep_id=True)
    result_summary = parse_summary_result(pipeline.get_component("evaluation_0").get_summary())
    metric_lr = {
        "score_diversity_ratio": classification_metric.Distribution.compute(lr_0_score_label, lr_1_score_label),
        "ks_2samp": classification_metric.KSTest.compute(lr_0_score, lr_1_score),
        "mAP_D_value": classification_metric.AveragePrecisionScore().compute(lr_0_score, lr_1_score, lr_0_label,
                                                                             lr_1_label)}
    result_summary["distribution_metrics"] = {"hetero_lr": metric_lr}
    data_summary = {"train": {"guest": guest_train_data["name"], "host": host_train_data["name"]},
                    "test": {"guest": guest_train_data["name"], "host": host_train_data["name"]}
                    }
    print(f"result_summary: {result_summary}; data_summary: {data_summary}")
    return data_summary, result_summary