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fate-sbt.py
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fate-sbt.py
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#
# Copyright 2019 The FATE Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import argparse
from pipeline.backend.pipeline import PipeLine
from pipeline.component.dataio import DataIO
from pipeline.component.homo_secureboost import HomoSecureBoost
from pipeline.component.reader import Reader
from pipeline.interface.data import Data
from pipeline.component.evaluation import Evaluation
from pipeline.interface.model import Model
from pipeline.utils.tools import JobConfig
from pipeline.utils.tools import load_job_config
from pipeline.runtime.entity import JobParameters
from federatedml.evaluation.metrics import regression_metric, classification_metric
from fate_test.utils import extract_data, parse_summary_result
def main(config="../../config.yaml", param='./xgb_config_binary.yaml', namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
if isinstance(param, str):
param = JobConfig.load_from_file(param)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
arbiter = parties.arbiter[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {"name": param['data_guest_train'], "namespace": f"experiment{namespace}"}
guest_validate_data = {"name": param['data_guest_val'], "namespace": f"experiment{namespace}"}
host_train_data = {"name": param['data_host_train'], "namespace": f"experiment{namespace}"}
host_validate_data = {"name": param['data_host_val'], "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)
dataio_0, dataio_1 = DataIO(name="dataio_0"), DataIO(name='dataio_1')
reader_0, reader_1 = Reader(name="reader_0"), Reader(name='reader_1')
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)
dataio_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, output_format="dense")
dataio_0.get_party_instance(role='host', party_id=host).component_param(with_label=True, output_format="dense")
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_validate_data)
reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_validate_data)
dataio_1.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, output_format="dense")
dataio_1.get_party_instance(role='host', party_id=host).component_param(with_label=True, output_format="dense")
homo_secureboost_0 = HomoSecureBoost(name="homo_secureboost_0",
num_trees=param['tree_num'],
task_type=param['task_type'],
objective_param={"objective": param['loss_func']},
tree_param={"max_depth": param['tree_depth']},
validation_freqs=1,
subsample_feature_rate=1,
learning_rate=param['learning_rate'],
bin_num=50
)
homo_secureboost_1 = HomoSecureBoost(name="homo_secureboost_1")
evaluation_0 = Evaluation(name='evaluation_0', eval_type=param['eval_type'])
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(reader_1)
pipeline.add_component(dataio_1, data=Data(data=reader_1.output.data), model=Model(dataio_0.output.model))
pipeline.add_component(homo_secureboost_0, data=Data(train_data=dataio_0.output.data,
validate_data=dataio_1.output.data))
pipeline.add_component(homo_secureboost_1, data=Data(test_data=dataio_1.output.data),
model=Model(homo_secureboost_0.output.model))
pipeline.add_component(evaluation_0, data=Data(homo_secureboost_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
sbt_0_data = pipeline.get_component("homo_secureboost_0").get_output_data().get("data")
sbt_1_data = pipeline.get_component("homo_secureboost_1").get_output_data().get("data")
sbt_0_score = extract_data(sbt_0_data, "predict_result")
sbt_0_label = extract_data(sbt_0_data, "label")
sbt_1_score = extract_data(sbt_1_data, "predict_result")
sbt_1_label = extract_data(sbt_1_data, "label")
sbt_0_score_label = extract_data(sbt_0_data, "predict_result", keep_id=True)
sbt_1_score_label = extract_data(sbt_1_data, "predict_result", keep_id=True)
metric_summary = parse_summary_result(pipeline.get_component("evaluation_0").get_summary())
if param['eval_type'] == "regression":
desc_sbt_0 = regression_metric.Describe().compute(sbt_0_score)
desc_sbt_1 = regression_metric.Describe().compute(sbt_1_score)
metric_summary["script_metrics"] = {"sbt_train": desc_sbt_0,
"sbt_validate": desc_sbt_1}
elif param['eval_type'] == "binary":
metric_sbt = {
"score_diversity_ratio": classification_metric.Distribution.compute(sbt_0_score_label, sbt_1_score_label),
"ks_2samp": classification_metric.KSTest.compute(sbt_0_score, sbt_1_score),
"mAP_D_value": classification_metric.AveragePrecisionScore().compute(sbt_0_score, sbt_1_score, sbt_0_label,
sbt_1_label)}
metric_summary["distribution_metrics"] = {"homo_sbt": metric_sbt}
elif param['eval_type'] == "multi":
metric_sbt = {
"score_diversity_ratio": classification_metric.Distribution.compute(sbt_0_score_label, sbt_1_score_label)}
metric_summary["distribution_metrics"] = {"homo_sbt": metric_sbt}
data_summary = {"train": {"guest": guest_train_data["name"], "host": host_train_data["name"]},
"test": {"guest": guest_train_data["name"], "host": host_train_data["name"]}
}
return data_summary, metric_summary
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()