def main(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]
backend = config.backend
work_mode = config.work_mode
# specify input data name & namespace in database
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).algorithm_param(table=guest_train_data)
# configure Reader for host
reader_0.get_party_instance(
role='host', party_id=host).algorithm_param(table=host_train_data)
# define DataIO components
dataio_0 = DataIO(name="dataio_0")
# get DataIO party instance of guest
dataio_0_guest_party_instance = dataio_0.get_party_instance(role='guest',
party_id=guest)
# configure DataIO for guest
dataio_0_guest_party_instance.algorithm_param(with_label=True,
output_format="dense")
# get and configure DataIO party instance of host
dataio_0.get_party_instance(
role='host', party_id=host).algorithm_param(with_label=False)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
intersection_0.get_party_instance(
role="guest", party_id=guest).algorithm_param(intersect_method="rsa",
sync_intersect_ids=True,
only_output_key=True)
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
# set data input sources of intersection components
pipeline.add_component(intersection_0,
data=Data(data=dataio_0.output.data))
# compile pipeline once finished adding modules, this step will form conf and dsl files for running job
pipeline.compile()
# fit model
pipeline.fit(backend=backend, work_mode=work_mode)
# query component summary
print(pipeline.get_component("hetero_lr_0").get_summary())
def main(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]
backend = config.backend
work_mode = config.work_mode
# specify input data name & namespace in database
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 Intersection components
intersections = []
for i in range(200):
intersection_tmp = Intersection(name="intersection_" + str(i))
intersection_tmp.get_party_instance(role="guest",
party_id=guest).component_param(
intersect_method="raw",
sync_intersect_ids=True,
only_output_key=True)
intersections.append(intersection_tmp)
union_0 = Union(name="union_0")
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
# set data input sources of intersection components
for i in range(len(intersections)):
pipeline.add_component(intersections[i],
data=Data(data=reader_0.output.data))
# set data output of intersection components
intersection_outputs = [
intersection_tmp.output.data for intersection_tmp in intersections
]
pipeline.add_component(union_0, data=Data(data=intersection_outputs))
# compile pipeline once finished adding modules, this step will form conf and dsl files for running job
pipeline.compile()
# fit model
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
