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]
    hosts = parties.host
    guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
    guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
    host_eval_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=hosts)
    # 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=hosts).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=hosts).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 = {
        "statistics": ["95%", "coefficient_of_variance", "stddev"],
        "column_indexes": -1,
        "column_names": []
    }
    statistic_0 = DataStatistics(name="statistic_0", **statistic_param)
    pipeline.add_component(statistic_0, data=Data(data=intersection_0.output.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=hosts).component_param(table=host_eval_data)
    data_transform_1 = DataTransform(name="data_transform_1")
    intersection_1 = Intersection(name="intersection_1")
    pipeline.add_component(reader_1)
    pipeline.add_component(
        data_transform_1, data=Data(
            data=reader_1.output.data), model=Model(
            data_transform_0.output.model))
    pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data))
    psi_param = {
        "name": "psi_0",
        "max_bin_num": 20
    }
    psi_0 = PSI(**psi_param)
    pipeline.add_component(psi_0, data=Data(train_data=intersection_0.output.data,
                                            validate_data=intersection_1.output.data))
    secureboost_param = {
        "task_type": "classification",
        "learning_rate": 0.1,
        "num_trees": 5,
        "subsample_feature_rate": 1,
        "n_iter_no_change": False,
        "tol": 0.0001,
        "bin_num": 50,
        "objective_param": {
            "objective": "cross_entropy"
        },
        "encrypt_param": {
            "method": "paillier"
        },
        "predict_param": {
            "threshold": 0.5
        }
    }
    secureboost_0 = HeteroSecureBoost(name="secureboost_0", **secureboost_param)
    pipeline.add_component(secureboost_0, data=Data(train_data=intersection_0.output.data))
    selection_param = {
        "select_col_indexes": -1,
        "select_names": [],
        "filter_methods": [
            "iv_filter",
            "statistic_filter",
            "psi_filter",
            "hetero_sbt_filter"
        ],
        "iv_param": {
            "metrics": ["iv", "iv", "iv"],
            "filter_type": ["threshold", "top_k", "top_percentile"],
            "take_high": True,
            "threshold": [0.03, 15, 0.7],
            "host_thresholds": [[0.15], None, None],
            "select_federated": True
        },
        "statistic_param": {
            "metrics": ["skewness", "skewness", "kurtosis", "median"],
            "filter_type": "threshold",
            "take_high": [True, False, False, True],
            "threshold": [-10, 10, 2, -1.5]
        },
        "psi_param": {
            "metrics": "psi",
            "filter_type": "threshold",
            "take_high": False,
            "threshold": -0.1
        },
        "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=[hetero_feature_binning_0.output.model,
                                                        statistic_0.output.model,
                                                        psi_0.output.model,
                                                        secureboost_0.output.model]))
    # compile pipeline once finished adding modules, this step will form conf and dsl files for running job
    pipeline.compile()
    pipeline.fit()