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test_lr_multi_host.py
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test_lr_multi_host.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 fate_client.pipeline import FateFlowPipeline
from fate_client.pipeline.components.fate import CoordinatedLR, PSI, Reader
from fate_client.pipeline.components.fate import Evaluation
from fate_client.pipeline.utils import test_utils
def main(config="../config.yaml", namespace=""):
if isinstance(config, str):
config = test_utils.load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host
arbiter = parties.arbiter[0]
pipeline = FateFlowPipeline().set_parties(guest=guest, host=host, arbiter=arbiter)
reader_0 = Reader("reader_0", runtime_parties=dict(guest=guest, host=host))
reader_0.guest.task_parameters(
namespace=f"experiment{namespace}",
name="breast_hetero_guest"
)
reader_0.hosts[[0, 1]].task_parameters(
namespace=f"experiment{namespace}",
name="breast_hetero_host"
)
psi_0 = PSI("psi_0", input_data=reader_0.outputs["output_data"])
lr_0 = CoordinatedLR("lr_0",
epochs=5,
batch_size=None,
early_stop="weight_diff",
optimizer={"method": "SGD", "optimizer_params": {"lr": 0.1},
"alpha": 0.001},
init_param={"fit_intercept": True, "method": "random_uniform"},
train_data=psi_0.outputs["output_data"],
learning_rate_scheduler={"method": "constant", "scheduler_params": {"factor": 1.0,
"total_iters": 100}})
evaluation_0 = Evaluation("evaluation_0",
runtime_parties=dict(guest=guest),
default_eval_setting="binary",
input_datas=lr_0.outputs["train_output_data"])
pipeline.add_tasks([reader_0, psi_0, lr_0, evaluation_0])
pipeline.compile()
# print(pipeline.get_dag())
pipeline.fit()
pipeline.deploy([psi_0, lr_0])
predict_pipeline = FateFlowPipeline()
reader_1 = Reader("reader_1", runtime_parties=dict(guest=guest, host=host))
reader_1.guest.task_parameters(
namespace=f"experiment{namespace}",
name="breast_hetero_guest"
)
reader_1.hosts[[0, 1]].task_parameters(
namespace=f"experiment{namespace}",
name="breast_hetero_host"
)
deployed_pipeline = pipeline.get_deployed_pipeline()
deployed_pipeline.psi_0.input_data = reader_1.outputs["output_data"]
predict_pipeline.add_tasks([reader_1, deployed_pipeline])
predict_pipeline.compile()
# print("\n\n\n")
# print(predict_pipeline.compile().get_dag())
predict_pipeline.predict()
# print(f"predict lr_0 data: {pipeline.get_task_info('lr_0').get_output_data()}")
if __name__ == "__main__":
parser = argparse.ArgumentParser("PIPELINE DEMO")
parser.add_argument("--config", type=str, default="../config.yaml",
help="config file")
parser.add_argument("--namespace", type=str, default="",
help="namespace for data stored in FATE")
args = parser.parse_args()
main(config=args.config, namespace=args.namespace)