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Heterogeneous Pearson Correlation Coefficient

Introduction

Pearson Correlation Coefficient is a measure of the linear correlation between two variables, X and Y, defined as,

\rho_{X,Y} = \frac{cov(X, Y)}{\sigma_X\sigma_Y} = \frac{E[(X-\mu_X)(Y-\mu_Y)]}{\sigma_X\sigma_Y} = E\left[\left(\frac{X-\mu_X}{\sigma_X}\cdot\frac{Y-\mu_Y}{\sigma_Y}\right)\right]

Let

\tilde{X} = \frac{X-\mu_X}{\sigma_X}, \tilde{Y}=\frac{Y-\mu_Y}{\sigma_Y}

then,

\rho_{X, Y} = E[\tilde{X}\tilde{Y}]

Implementation Detail

We use an MPC protocol called SPDZ for Heterogeneous Pearson Correlation Coefficient calculation. For more details, one can refer [here]

Param

pearson_param

Classes

PearsonParam(column_names=None, column_indexes=None, cross_parties=True, need_run=True, use_mix_rand=False, calc_local_vif=True)

Bases: BaseParam

param for pearson correlation

Parameters:

Name Type Description Default
column_names list of string

list of column names

 None
column_index list of int

list of column index

 required
cross_parties bool, default

if True, calculate correlation of columns from both party

 True
need_run bool

set False to skip this party

 True
use_mix_rand bool, defalut

mix system random and pseudo random for quicker calculation

 False
calc_loca_vif bool, default True

calculate VIF for columns in local

 required
Source code in python/federatedml/param/pearson_param.py 
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def __init__(
    self,
    column_names=None,
    column_indexes=None,
    cross_parties=True,
    need_run=True,
    use_mix_rand=False,
    calc_local_vif=True,
):
    super().__init__()
    self.column_names = column_names
    self.column_indexes = column_indexes
    self.cross_parties = cross_parties
    self.need_run = need_run
    self.use_mix_rand = use_mix_rand
    self.calc_local_vif = calc_local_vif
Attributes
column_names = column_names instance-attribute
column_indexes = column_indexes instance-attribute
cross_parties = cross_parties instance-attribute
need_run = need_run instance-attribute
use_mix_rand = use_mix_rand instance-attribute
calc_local_vif = calc_local_vif instance-attribute
Functions
check()
Source code in python/federatedml/param/pearson_param.py 
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def check(self):
    if not isinstance(self.use_mix_rand, bool):
        raise ValueError(
            f"use_mix_rand accept bool type only, {type(self.use_mix_rand)} got"
        )
    if self.cross_parties and (not self.need_run):
        raise ValueError(
            f"need_run should be True(which is default) when cross_parties is True."
        )

    self.column_indexes = [] if self.column_indexes is None else self.column_indexes
    self.column_names = [] if self.column_names is None else self.column_names
    if not isinstance(self.column_names, list):
        raise ValueError(
            f"type mismatch, column_names with type {type(self.column_names)}"
        )
    for name in self.column_names:
        if not isinstance(name, str):
            raise ValueError(
                f"type mismatch, column_names with element {name}(type is {type(name)})"
            )

    if isinstance(self.column_indexes, list):
        for idx in self.column_indexes:
            if not isinstance(idx, int):
                raise ValueError(
                    f"type mismatch, column_indexes with element {idx}(type is {type(idx)})"
                )

    if isinstance(self.column_indexes, int) and self.column_indexes != -1:
        raise ValueError(
            f"column_indexes with type int and value {self.column_indexes}(only -1 allowed)"
        )

    if self.need_run:
        if isinstance(self.column_indexes, list) and isinstance(
            self.column_names, list
        ):
            if len(self.column_indexes) == 0 and len(self.column_names) == 0:
                raise ValueError(f"provide at least one column")

How to Use

  • params

  • column_indexes
    -1 or list of int. If -1 provided, all columns are used for calculation. If a list of int provided, columns with given indexes are used for calculation.

  • column_names
    names of columns use for calculation.

Note

if both params are provided, the union of columns indicated are used for calculation.

Examples

Example 
## Hetero Pearson Pipeline Example Usage Guide.

#### Example Tasks

This section introduces the Pipeline scripts for different types of tasks.

1. Cross parties Task:

    script: pipeline_hetero_pearson.py

2. Host only Task:

    script: pipeline_hetero_pearson_host_only.py

3. Sole Task:

    script: pipeline_hetero_pearson_sole.py

4. Use Mix Rand schema Task:

    script: pipeline_hetero_pearson_mix_rand.py


Users can run a pipeline job directly:

    python ${pipeline_script}
pipeline_hetero_pearson_mix_rand.py 
import argparse

from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform, HeteroPearson, Intersection, Reader
from pipeline.interface import Data
from pipeline.utils.tools import load_job_config


def main(config="../../config.yaml", namespace=""):
    common_param = dict(column_indexes=-1, use_mix_rand=True)
    pipeline = run_pearson_pipeline(
        config=config,
        namespace=namespace,
        data=dataset.breast,
        common_param=common_param,
    )
    print(pipeline.get_component("hetero_pearson_0").get_model_param())
    print(pipeline.get_component("hetero_pearson_0").get_summary())


def run_pearson_pipeline(
    config,
    namespace,
    data,
    common_param=None,
    guest_only_param=None,
    host_only_param=None,
):
    if isinstance(config, str):
        config = load_job_config(config)
    guest_data = data["guest"]
    host_data = data["host"][0]

    guest_data["namespace"] = f"{guest_data['namespace']}{namespace}"
    host_data["namespace"] = f"{host_data['namespace']}{namespace}"

    pipeline = (
        PipeLine()
        .set_initiator(role="guest", party_id=config.parties.guest[0])
        .set_roles(guest=config.parties.guest[0], host=config.parties.host[0])
    )

    reader_0 = Reader(name="reader_0")
    reader_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(table=guest_data)
    reader_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(table=host_data)

    data_transform_0 = DataTransform(name="data_transform_0")
    data_transform_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(with_label=True, output_format="dense")
    data_transform_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(with_label=False)

    intersect_0 = Intersection(name="intersection_0")

    if common_param is None:
        common_param = {}
    hetero_pearson_component = HeteroPearson(name="hetero_pearson_0", **common_param)

    if guest_only_param:
        hetero_pearson_component.get_party_instance(
            "guest", config.parties.guest[0]
        ).component_param(**guest_only_param)

    if host_only_param:
        hetero_pearson_component.get_party_instance(
            "host", config.parties.host[0]
        ).component_param(**host_only_param)

    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(
        hetero_pearson_component, data=Data(train_data=intersect_0.output.data)
    )

    pipeline.compile()
    pipeline.fit()
    return pipeline


class dataset_meta(type):
    @property
    def breast(cls):
        return {
            "guest": {"name": "breast_hetero_guest", "namespace": "experiment"},
            "host": [{"name": "breast_hetero_host", "namespace": "experiment"}],
        }


class dataset(metaclass=dataset_meta):
    ...


if __name__ == "__main__":
    parser = argparse.ArgumentParser("PIPELINE DEMO")
    parser.add_argument("-config", type=str, help="config file")
    args = parser.parse_args()
    if args.config is not None:
        main(args.config)
    else:
        main()
hetero_pearson_testsuite.json 
{
    "data": [
        {
            "file": "examples/data/breast_hetero_guest.csv",
            "head": 1,
            "partition": 16,
            "table_name": "breast_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/breast_hetero_host.csv",
            "head": 1,
            "partition": 16,
            "table_name": "breast_hetero_host",
            "namespace": "experiment",
            "role": "host_0"
        }
    ],
    "pipeline_tasks": {
        "default": {
            "script": "./pipeline_hetero_pearson.py"
        },
        "host_only": {
            "script": "./pipeline_hetero_pearson_host_only.py"
        },
        "sole": {
            "script": "./pipeline_hetero_pearson_sole.py"
        },
        "mix_rand": {
            "script": "./pipeline_hetero_pearson_mix_rand.py"
        }
    }
}
init.py 
import os
import sys

additional_path = os.path.realpath("../")
if additional_path not in sys.path:
    sys.path.append(additional_path)
pipeline_hetero_pearson_sole.py 
import argparse

from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform, HeteroPearson, Intersection, Reader
from pipeline.interface import Data
from pipeline.utils.tools import load_job_config


def main(config="../../config.yaml", namespace=""):
    common_param = dict(column_indexes=-1, cross_parties=False)
    pipeline = run_pearson_pipeline(
        config=config,
        namespace=namespace,
        data=dataset.breast,
        common_param=common_param,
    )
    print(pipeline.get_component("hetero_pearson_0").get_model_param())
    print(pipeline.get_component("hetero_pearson_0").get_summary())


def run_pearson_pipeline(
    config,
    namespace,
    data,
    common_param=None,
    guest_only_param=None,
    host_only_param=None,
):
    if isinstance(config, str):
        config = load_job_config(config)
    guest_data = data["guest"]
    host_data = data["host"][0]

    guest_data["namespace"] = f"{guest_data['namespace']}{namespace}"
    host_data["namespace"] = f"{host_data['namespace']}{namespace}"

    pipeline = (
        PipeLine()
        .set_initiator(role="guest", party_id=config.parties.guest[0])
        .set_roles(guest=config.parties.guest[0], host=config.parties.host[0])
    )

    reader_0 = Reader(name="reader_0")
    reader_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(table=guest_data)
    reader_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(table=host_data)

    data_transform_0 = DataTransform(name="data_transform_0")
    data_transform_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(with_label=True, output_format="dense")
    data_transform_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(with_label=False)

    intersect_0 = Intersection(name="intersection_0")

    if common_param is None:
        common_param = {}
    hetero_pearson_component = HeteroPearson(name="hetero_pearson_0", **common_param)

    if guest_only_param:
        hetero_pearson_component.get_party_instance(
            "guest", config.parties.guest[0]
        ).component_param(**guest_only_param)

    if host_only_param:
        hetero_pearson_component.get_party_instance(
            "host", config.parties.host[0]
        ).component_param(**host_only_param)

    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(
        hetero_pearson_component, data=Data(train_data=intersect_0.output.data)
    )

    pipeline.compile()
    pipeline.fit()
    return pipeline


class dataset_meta(type):
    @property
    def breast(cls):
        return {
            "guest": {"name": "breast_hetero_guest", "namespace": "experiment"},
            "host": [{"name": "breast_hetero_host", "namespace": "experiment"}],
        }


class dataset(metaclass=dataset_meta):
    ...


if __name__ == "__main__":
    parser = argparse.ArgumentParser("PIPELINE DEMO")
    parser.add_argument("-config", type=str, help="config file")
    args = parser.parse_args()
    if args.config is not None:
        main(args.config)
    else:
        main()
pipeline_hetero_pearson.py 
import argparse

from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform, HeteroPearson, Intersection, Reader
from pipeline.interface import Data
from pipeline.utils.tools import load_job_config


def main(config="../../config.yaml", namespace=""):
    common_param = dict(column_indexes=-1)
    pipeline = run_pearson_pipeline(
        config=config,
        namespace=namespace,
        data=dataset.breast,
        common_param=common_param,
    )
    print(pipeline.get_component("hetero_pearson_0").get_model_param())
    print(pipeline.get_component("hetero_pearson_0").get_summary())


def run_pearson_pipeline(
    config,
    namespace,
    data,
    common_param=None,
    guest_only_param=None,
    host_only_param=None,
):
    if isinstance(config, str):
        config = load_job_config(config)
    guest_data = data["guest"]
    host_data = data["host"][0]

    guest_data["namespace"] = f"{guest_data['namespace']}{namespace}"
    host_data["namespace"] = f"{host_data['namespace']}{namespace}"

    pipeline = (
        PipeLine()
        .set_initiator(role="guest", party_id=config.parties.guest[0])
        .set_roles(guest=config.parties.guest[0], host=config.parties.host[0])
    )

    reader_0 = Reader(name="reader_0")
    reader_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(table=guest_data)
    reader_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(table=host_data)

    data_transform_0 = DataTransform(name="data_transform_0")
    data_transform_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(with_label=True, output_format="dense")
    data_transform_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(with_label=False)

    intersect_0 = Intersection(name="intersection_0")

    if common_param is None:
        common_param = {}
    hetero_pearson_component = HeteroPearson(name="hetero_pearson_0", **common_param)

    if guest_only_param:
        hetero_pearson_component.get_party_instance(
            "guest", config.parties.guest[0]
        ).component_param(**guest_only_param)

    if host_only_param:
        hetero_pearson_component.get_party_instance(
            "host", config.parties.host[0]
        ).component_param(**host_only_param)

    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(
        hetero_pearson_component, data=Data(train_data=intersect_0.output.data)
    )

    pipeline.compile()
    pipeline.fit()
    return pipeline


class dataset_meta(type):
    @property
    def breast(cls):
        return {
            "guest": {"name": "breast_hetero_guest", "namespace": "experiment"},
            "host": [{"name": "breast_hetero_host", "namespace": "experiment"}],
        }


class dataset(metaclass=dataset_meta):
    ...


if __name__ == "__main__":
    parser = argparse.ArgumentParser("PIPELINE DEMO")
    parser.add_argument("-config", type=str, help="config file")
    args = parser.parse_args()
    if args.config is not None:
        main(args.config)
    else:
        main()
pipeline_hetero_pearson_host_only.py 
import argparse

from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform, HeteroPearson, Intersection, Reader
from pipeline.interface import Data
from pipeline.utils.tools import load_job_config


def main(config="../../config.yaml", namespace=""):
    common_param = dict(column_indexes=-1, cross_parties=False)
    guest_only_param = dict(need_run=False)
    pipeline = run_pearson_pipeline(
        config=config,
        namespace=namespace,
        data=dataset.breast,
        common_param=common_param,
        guest_only_param=guest_only_param,
    )


def run_pearson_pipeline(
    config,
    namespace,
    data,
    common_param=None,
    guest_only_param=None,
    host_only_param=None,
):
    if isinstance(config, str):
        config = load_job_config(config)
    guest_data = data["guest"]
    host_data = data["host"][0]

    guest_data["namespace"] = f"{guest_data['namespace']}{namespace}"
    host_data["namespace"] = f"{host_data['namespace']}{namespace}"

    pipeline = (
        PipeLine()
        .set_initiator(role="guest", party_id=config.parties.guest[0])
        .set_roles(guest=config.parties.guest[0], host=config.parties.host[0])
    )

    reader_0 = Reader(name="reader_0")
    reader_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(table=guest_data)
    reader_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(table=host_data)

    data_transform_0 = DataTransform(name="data_transform_0")
    data_transform_0.get_party_instance(
        role="guest", party_id=config.parties.guest[0]
    ).component_param(with_label=True, output_format="dense")
    data_transform_0.get_party_instance(
        role="host", party_id=config.parties.host[0]
    ).component_param(with_label=False)

    intersect_0 = Intersection(name="intersection_0")

    if common_param is None:
        common_param = {}
    hetero_pearson_component = HeteroPearson(name="hetero_pearson_0", **common_param)

    if guest_only_param:
        hetero_pearson_component.get_party_instance(
            "guest", config.parties.guest[0]
        ).component_param(**guest_only_param)

    if host_only_param:
        hetero_pearson_component.get_party_instance(
            "host", config.parties.host[0]
        ).component_param(**host_only_param)

    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(
        hetero_pearson_component, data=Data(train_data=intersect_0.output.data)
    )

    pipeline.compile()
    pipeline.fit()
    return pipeline


class dataset_meta(type):
    @property
    def breast(cls):
        return {
            "guest": {"name": "breast_hetero_guest", "namespace": "experiment"},
            "host": [{"name": "breast_hetero_host", "namespace": "experiment"}],
        }


class dataset(metaclass=dataset_meta):
    ...


if __name__ == "__main__":
    parser = argparse.ArgumentParser("PIPELINE DEMO")
    parser.add_argument("-config", type=str, help="config file")
    args = parser.parse_args()
    if args.config is not None:
        main(args.config)
    else:
        main()

## Hetero Pearson Configuration Usage Guide.

This section introduces the dsl and conf for usage of different type of task.

#### Training Task.


1. Base Cross Parties Task:

    dsl: test_hetero_pearson_default_dsl.json

    runtime_config : test_hetero_pearson_default_conf.json

2. Host Only Task:

    dsl: test_hetero_pearson_host_only_dsl.json

    runtime_config : test_hetero_pearson_host_only_conf.json

3. Sole Task:

    dsl: test_hetero_pearson_sole_dsl.json

    runtime_config : test_hetero_pearson_sole_conf.json

4. Use Mix Rand Task:

    dsl: test_hetero_pearson_mix_rand_dsl.json

    runtime_config : test_hetero_pearson_mix_rand_conf.json



Users can use following commands to run a task.

    flow job submit -c ${runtime_config} -d ${dsl}
test_hetero_pearson_host_only_dsl.json 
{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_pearson_0": {
            "module": "HeteroPearson",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        }
    }
}
test_hetero_pearson_sole_dsl.json 
{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_pearson_0": {
            "module": "HeteroPearson",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        }
    }
}
test_hetero_pearson_mix_rand_dsl.json 
{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_pearson_0": {
            "module": "HeteroPearson",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        }
    }
}
test_hetero_pearson_mix_rand_conf.json 
{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_pearson_0": {
                "column_indexes": -1,
                "use_mix_rand": true
            }
        },
        "role": {
            "host": {
                "0": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "guest": {
                "0": {
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}
hetero_pearson_testsuite.json 
{
    "data": [
        {
            "file": "examples/data/breast_hetero_guest.csv",
            "head": 1,
            "partition": 16,
            "table_name": "breast_hetero_guest",
            "namespace": "experiment",
            "role": "guest_0"
        },
        {
            "file": "examples/data/breast_hetero_host.csv",
            "head": 1,
            "partition": 16,
            "table_name": "breast_hetero_host",
            "namespace": "experiment",
            "role": "host_0"
        }
    ],
    "tasks": {
        "default": {
            "conf": "./test_hetero_pearson_default_conf.json",
            "dsl": "./test_hetero_pearson_default_dsl.json"
        },
        "host_only": {
            "conf": "./test_hetero_pearson_host_only_conf.json",
            "dsl": "./test_hetero_pearson_host_only_dsl.json"
        },
        "sole": {
            "conf": "./test_hetero_pearson_sole_conf.json",
            "dsl": "./test_hetero_pearson_sole_dsl.json"
        },
        "mix_rand": {
            "conf": "./test_hetero_pearson_mix_rand_conf.json",
            "dsl": "./test_hetero_pearson_mix_rand_dsl.json"
        }
    }
}
test_hetero_pearson_default_dsl.json 
{
    "components": {
        "reader_0": {
            "module": "Reader",
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "data_transform_0": {
            "module": "DataTransform",
            "input": {
                "data": {
                    "data": [
                        "reader_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        },
        "intersection_0": {
            "module": "Intersection",
            "input": {
                "data": {
                    "data": [
                        "data_transform_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ]
            }
        },
        "hetero_pearson_0": {
            "module": "HeteroPearson",
            "input": {
                "data": {
                    "train_data": [
                        "intersection_0.data"
                    ]
                }
            },
            "output": {
                "data": [
                    "data"
                ],
                "model": [
                    "model"
                ]
            }
        }
    }
}
test_hetero_pearson_host_only_conf.json 
{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_pearson_0": {
                "column_indexes": -1,
                "cross_parties": false
            }
        },
        "role": {
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    }
                }
            },
            "guest": {
                "0": {
                    "hetero_pearson_0": {
                        "need_run": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            }
        }
    }
}
test_hetero_pearson_sole_conf.json 
{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_pearson_0": {
                "column_indexes": -1,
                "cross_parties": false
            }
        },
        "role": {
            "host": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": false
                    }
                }
            },
            "guest": {
                "0": {
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    },
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    }
                }
            }
        }
    }
}
test_hetero_pearson_default_conf.json 
{
    "dsl_version": 2,
    "initiator": {
        "role": "guest",
        "party_id": 9999
    },
    "role": {
        "host": [
            10000
        ],
        "guest": [
            9999
        ]
    },
    "component_parameters": {
        "common": {
            "hetero_pearson_0": {
                "column_indexes": -1
            }
        },
        "role": {
            "guest": {
                "0": {
                    "data_transform_0": {
                        "with_label": true,
                        "output_format": "dense"
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_guest",
                            "namespace": "experiment"
                        }
                    }
                }
            },
            "host": {
                "0": {
                    "data_transform_0": {
                        "with_label": false
                    },
                    "reader_0": {
                        "table": {
                            "name": "breast_hetero_host",
                            "namespace": "experiment"
                        }
                    }
                }
            }
        }
    }
}
Last update: 2021-11-15