def main(config="../../config.yaml", namespace=""):
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
hosts = parties.host
arbiter = parties.arbiter[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = [{
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}, {
"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, arbiter=arbiter)
# 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=hosts[0]).algorithm_param(table=host_train_data[0])
reader_0.get_party_instance(
role='host',
party_id=hosts[1]).algorithm_param(table=host_train_data[1])
# define DataIO components
dataio_0 = DataIO(name="dataio_0") # start component numbering at 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=hosts[0]).algorithm_param(with_label=False)
dataio_0.get_party_instance(
role='host', party_id=hosts[1]).algorithm_param(with_label=False)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
param = {"k": 3, "max_iter": 10}
hetero_kmeans_0 = HeteroKmeans(name='hetero_kmeans_0', **param)
evaluation_0 = Evaluation(name='evaluation_0', eval_type='clustering')
# 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))
pipeline.add_component(hetero_kmeans_0,
data=Data(train_data=intersection_0.output.data))
# print(f"data: {hetero_kmeans_0.output.data.data[0]}")
pipeline.add_component(evaluation_0,
data=Data(data=hetero_kmeans_0.output.data.data[0]))
# 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_kmeans_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]
arbiter = parties.arbiter[0]
backend = config.backend
work_mode = config.work_mode
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, arbiter=arbiter)
# 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 DataIO components
dataio_0 = DataIO(name="dataio_0") # start component numbering at 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.component_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).component_param(with_label=False)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
param = {
"name": 'hetero_feature_binning_0',
"method": 'optimal',
"optimal_binning_param": {
"metric_method": "iv"
},
"bin_indexes": -1
}
hetero_feature_binning_0 = HeteroFeatureBinning(**param)
param = {
"name": 'hetero_feature_selection_0',
"filter_methods": ["manually", "iv_filter"],
"manually_param": {
"filter_out_indexes": [1]
},
"iv_param": {
"metrics": ["iv", "iv"],
"filter_type": ["top_k", "threshold"],
"take_high": [True, True],
"threshold": [10, 0.001]
},
"select_col_indexes": -1
}
hetero_feature_selection_0 = HeteroFeatureSelection(**param)
param = {"k": 3, "max_iter": 10}
hetero_kmeans_0 = HeteroKmeans(name='hetero_kmeans_0', **param)
evaluation_0 = Evaluation(name='evaluation_0', eval_type='clustering')
# 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))
# set train & validate data of hetero_lr_0 component
pipeline.add_component(hetero_feature_binning_0,
data=Data(data=intersection_0.output.data))
pipeline.add_component(
hetero_feature_selection_0,
data=Data(data=intersection_0.output.data),
model=Model(isometric_model=hetero_feature_binning_0.output.model))
pipeline.add_component(
hetero_kmeans_0,
data=Data(train_data=hetero_feature_selection_0.output.data))
print(f"data: {hetero_kmeans_0.output.data.data[0]}")
pipeline.add_component(evaluation_0,
data=Data(data=hetero_kmeans_0.output.data.data[0]))
# 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)
# query component summary
print(pipeline.get_component("hetero_kmeans_0").get_summary())