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]
    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)
    # 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")
    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))
    fast_sbt_param = {
        "task_type": "classification",
        "learning_rate": 0.1,
        "num_trees": 4,
        "subsample_feature_rate": 1,
        "n_iter_no_change": False,
        "work_mode": "layered",
        "guest_depth": 2,
        "host_depth": 3,
        "tol": 0.0001,
        "bin_num": 50,
        "metrics": ["Recall", "ks", "auc", "roc"],
        "objective_param": {
            "objective": "cross_entropy"
        },
        "encrypt_param": {
            "method": "paillier"
        },
        "predict_param": {
            "threshold": 0.5
        },
        "validation_freqs": 1
    }
    fast_sbt_0 = HeteroFastSecureBoost(name="fast_secureboost_0", **fast_sbt_param)
    pipeline.add_component(fast_sbt_0, data=Data(train_data=intersection_0.output.data))
    selection_param = {
        "select_col_indexes": -1,
        "select_names": [],
        "filter_methods": [
            "hetero_fast_sbt_filter"
        ],
        "sbt_param": {
            "metrics": "feature_importance",
            "filter_type": "threshold",
            "take_high": True,
            "threshold": 0.03
        }}
    hetero_feature_selection_0 = HeteroFeatureSelection(name="hetero_feature_selection_0", **selection_param)
    pipeline.add_component(hetero_feature_selection_0, data=Data(data=intersection_0.output.data),
                           model=Model(isometric_model=fast_sbt_0.output.model))
    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()
    pipeline.fit()