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))
    binning_param = {
        "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": -1,
            "transform_names": None,
            "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=intersection_0.output.data))
    statistic_param = {
        "name": "statistic_0",
        "statistics": ["95%", "coefficient_of_variance", "stddev"],
        "column_indexes": -1,
        "column_names": []
    }
    statistic_0 = DataStatistics(**statistic_param)
    pipeline.add_component(statistic_0, data=Data(data=intersection_0.output.data))
    selection_param = {
        "select_names": [],
        "filter_methods": [
            "manually",
            "unique_value",
            "iv_value_thres",
            "coefficient_of_variation_value_thres",
            "iv_percentile",
            "outlier_cols"
        ],
        "manually_param": {
            "filter_out_indexes": [],
            "filter_out_names": []
        },
        "unique_param": {
            "eps": 1e-06
        },
        "iv_value_param": {
            "value_threshold": 0.1
        },
        "iv_percentile_param": {
            "percentile_threshold": 0.9
        },
        "variance_coe_param": {
            "value_threshold": 0.3
        },
        "outlier_param": {
            "percentile": 0.95,
            "upper_threshold": 2.0
        }}
    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(select_col_indexes=[0, 1, 3])
    hetero_feature_selection_0.get_party_instance(role="host",
                                                  party_id=host).component_param(select_col_indexes=[0, 2, 3])
    pipeline.add_component(hetero_feature_selection_0, data=Data(data=intersection_0.output.data),
                           model=Model(isometric_model=[hetero_feature_binning_0.output.model,
                                                        statistic_0.output.model]))
    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()
    pipeline.fit()