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}"}
    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", with_label=True, output_format="dense",
                                     label_type="int", label_name="y")
    homo_lr_0 = HomoLR(name="homo_lr_0", penalty="L2", optimizer="sgd",
                       tol=0.0001, alpha=0.01, max_iter=30, batch_size=-1,
                       early_stop="weight_diff", learning_rate=0.15, init_param={"init_method": "zeros"})
    local_baseline_0 = LocalBaseline(name="local_baseline_0", model_name="LogisticRegression",
                                     model_opts={"penalty": "l2", "tol": 0.0001, "C": 1.0, "fit_intercept": True,
                                                 "solver": "saga", "max_iter": 2})
    local_baseline_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True)
    local_baseline_0.get_party_instance(role='host', party_id=host).component_param(need_run=False)
    evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary", pos_label=1)
    evaluation_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True)
    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(homo_lr_0, data=Data(train_data=data_transform_0.output.data))
    pipeline.add_component(local_baseline_0, data=Data(train_data=data_transform_0.output.data))
    pipeline.add_component(evaluation_0, data=Data(data=[homo_lr_0.output.data, local_baseline_0.output.data]))
    pipeline.compile()
    pipeline.fit()
    # predict
    pipeline.deploy_component([data_transform_0, homo_lr_0, local_baseline_0])
    predict_pipeline = PipeLine()
    predict_pipeline.add_component(reader_0)
    predict_pipeline.add_component(
        pipeline, data=Data(
            predict_input={
                pipeline.data_transform_0.input.data: reader_0.output.data}))
    predict_pipeline.add_component(evaluation_0, data=Data(data=[homo_lr_0.output.data, local_baseline_0.output.data]))
    predict_pipeline.predict()