def METHOD_NAME(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}"}
    guest_eval_data = {"name": "breast_homo_guest", "namespace": f"experiment{namespace}"}
    host_eval_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)
    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")  # start component numbering at 0
    data_transform_1 = DataTransform(name="data_transform_1")  # start component numbering at 0
    scale_0 = FeatureScale(name='scale_0')
    scale_1 = FeatureScale(name='scale_1')
    param = {
        "penalty": "L2",
        "optimizer": "sgd",
        "tol": 1e-05,
        "alpha": 0.01,
        "max_iter": 3,
        "early_stop": "diff",
        "batch_size": 320,
        "learning_rate": 0.15,
        "validation_freqs": 1,
        "init_param": {
            "init_method": "zeros"
        },
        "cv_param": {
            "n_splits": 4,
            "shuffle": True,
            "random_seed": 33,
            "need_cv": False
        }
    }
    homo_lr_0 = HomoLR(name='homo_lr_0', **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))
    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(scale_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(scale_1, data=Data(data=data_transform_1.output.data),
                           model=Model(scale_0.output.model))
    pipeline.add_component(homo_lr_0, data=Data(train_data=scale_0.output.data,
                                                validate_data=scale_1.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]))
    # 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("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))