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]
guest_train_data = {"name": "tag_value_1000_140", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "tag_value_1000_140", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host)
reader_0 = Reader(name="reader_0")
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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
data_transform_0 = DataTransform(name="data_transform_0", input_format="tag",
tag_with_value=True, with_match_id=True)
data_transform_1 = DataTransform(name="data_transform_1")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data),
model=Model(model=data_transform_0.output.model))
pipeline.compile()
pipeline.fit()
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]
guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host)
reader_0 = Reader(name="reader_0")
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)
data_transform_0 = DataTransform(name="data_transform_0")
data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True)
data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
hetero_nn_0 = HeteroNN(name="hetero_nn_0", epochs=100,
interactive_layer_lr=0.15, batch_size=-1, early_stop="diff",
selector_param={"method": "relative"})
guest_nn_0 = hetero_nn_0.get_party_instance(role='guest', party_id=guest)
guest_nn_0.add_bottom_model(Dense(units=3, input_shape=(10,), activation="relu",
kernel_initializer=initializers.Constant(value=1)))
guest_nn_0.set_interactve_layer(Dense(units=2, input_shape=(2,),
kernel_initializer=initializers.Constant(value=1)))
guest_nn_0.add_top_model(Dense(units=1, input_shape=(2,), activation="sigmoid",
kernel_initializer=initializers.Constant(value=1)))
host_nn_0 = hetero_nn_0.get_party_instance(role='host', party_id=host)
host_nn_0.add_bottom_model(Dense(units=3, input_shape=(20,), activation="relu",
kernel_initializer=initializers.Constant(value=1)))
host_nn_0.set_interactve_layer(Dense(units=2, input_shape=(2,),
kernel_initializer=initializers.Constant(value=1)))
hetero_nn_0.compile(optimizer=optimizers.SGD(lr=0.15), loss="binary_crossentropy")
hetero_nn_1 = HeteroNN(name="hetero_nn_1")
evaluation_0 = Evaluation(name="evaluation_0")
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
pipeline.add_component(hetero_nn_0, data=Data(train_data=intersection_0.output.data))
pipeline.add_component(hetero_nn_1, data=Data(test_data=intersection_0.output.data),
model=Model(model=hetero_nn_0.output.model))
pipeline.add_component(evaluation_0, data=Data(data=hetero_nn_0.output.data))
pipeline.compile()
pipeline.fit()
print(hetero_nn_0.get_config(roles={"guest": [guest],
"host": [host]}))
print(pipeline.get_component("hetero_nn_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]
guest_train_data = {
"name": "breast_homo_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_homo_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 DataTransform components
data_transform_0 = DataTransform(
name="data_transform_0", with_label=True,
output_format="dense") # start component numbering at 0
homo_binning_0 = HomoFeatureBinning(name='homo_binning_0',
sample_bins=1000)
homo_binning_1 = HomoFeatureBinning(name='homo_binning_1',
sample_bins=1000)
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
# set data input sources of intersection components
pipeline.add_component(homo_binning_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(homo_binning_1,
data=Data(data=data_transform_0.output.data),
model=Model(model=homo_binning_0.output.model))
# compile pipeline once finished adding modules, this step will form conf and dsl files for running job
pipeline.compile()
# fit model
pipeline.fit()
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]
guest_train_data = {"name": "motor_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "motor_hetero_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)
reader_0 = Reader(name="reader_0")
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)
data_transform_0 = DataTransform(name="data_transform_0")
data_transform_0.get_party_instance(
role='guest',
party_id=guest).component_param(
with_label=True,
label_name="motor_speed",
label_type="float",
output_format="dense")
data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0", only_output_key=False)
hetero_linr_0 = HeteroLinR(name="hetero_linr_0", penalty="L2", optimizer="sgd", tol=0.001,
alpha=0.01, max_iter=5, early_stop="weight_diff", batch_size=-1,
learning_rate=0.15, decay=0.0, decay_sqrt=False,
callback_param={"callbacks": ["ModelCheckpoint"]},
init_param={"init_method": "zeros"},
floating_point_precision=23)
evaluation_0 = Evaluation(name="evaluation_0", eval_type="regression", pos_label=1)
hetero_linr_1 = HeteroLinR(name="hetero_linr_1", max_iter=15,
penalty="L2", optimizer="sgd", tol=0.001,
alpha=0.01, early_stop="weight_diff", batch_size=-1,
learning_rate=0.15, decay=0.0, decay_sqrt=False,
floating_point_precision=23
)
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
pipeline.add_component(hetero_linr_0, data=Data(train_data=intersection_0.output.data))
pipeline.add_component(hetero_linr_1, data=Data(train_data=intersection_0.output.data),
model=Model(hetero_linr_0.output.model))
pipeline.add_component(evaluation_0, data=Data(data=hetero_linr_1.output.data))
pipeline.compile()
pipeline.fit()
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
pipeline = PipeLine().set_initiator(role='guest',
party_id=guest).set_roles(guest=guest)
reader_0 = Reader(name="reader_0")
reader_0.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_train_data)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_train_data)
data_transform_0 = DataTransform(name="data_transform_0",
with_label=True,
output_format="dense",
label_name="y",
missing_fill=False,
outlier_replace=False)
data_transform_1 = DataTransform(name="data_transform_1",
with_label=True,
output_format="dense",
label_name="y",
missing_fill=False,
outlier_replace=False)
union_0 = Union(name="union_0", allow_missing=False, need_run=True)
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(
union_0,
data=Data(
data=[data_transform_0.output.data, data_transform_1.output.data]))
pipeline.compile()
pipeline.fit()
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": "motor_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "motor_hetero_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)
reader_0 = Reader(name="reader_0")
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 = DataIO(name="dataio_0")
dataio_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, label_name="motor_speed",
label_type="float", output_format="dense")
dataio_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
hetero_data_split_0 = HeteroDataSplit(name="hetero_data_split_0", stratified=True,
test_size=0.3, validate_size=0.2, split_points=[0.0, 0.2])
hetero_linr_0 = HeteroLinR(name="hetero_linr_0", penalty="L2", optimizer="sgd", tol=0.001,
alpha=0.01, max_iter=10, early_stop="weight_diff", batch_size=-1,
learning_rate=0.15, decay=0.0, decay_sqrt=False,
init_param={"init_method": "zeros"},
encrypted_mode_calculator_param={"mode": "fast"})
hetero_linr_1 = HeteroLinR()
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=dataio_0.output.data))
pipeline.add_component(hetero_data_split_0, data=Data(data=intersection_0.output.data))
pipeline.add_component(hetero_linr_0, data=Data(train_data=hetero_data_split_0.output.data.train_data,
validate_data=hetero_data_split_0.output.data.validate_data))
pipeline.add_component(hetero_linr_1, data=Data(test_data=hetero_data_split_0.output.data.test_data),
model=Model(model=hetero_linr_0.output.model))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
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
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
pipeline = PipeLine().set_initiator(role='guest',
party_id=guest).set_roles(guest=guest,
host=host)
reader_0 = Reader(name="reader_0")
reader_0.get_party_instance(
role='guest', party_id=guest).algorithm_param(table=guest_train_data)
reader_0.get_party_instance(
role='host', party_id=host).algorithm_param(table=host_train_data)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest', party_id=guest).algorithm_param(table=guest_train_data)
reader_1.get_party_instance(
role='host', party_id=host).algorithm_param(table=host_train_data)
dataio_0 = DataIO(name="dataio_0")
dataio_0.get_party_instance(
role='guest', party_id=guest).algorithm_param(with_label=True)
dataio_0.get_party_instance(
role='host', party_id=host).algorithm_param(with_label=False)
dataio_1 = DataIO(name="dataio_1")
dataio_1.get_party_instance(
role='guest', party_id=guest).algorithm_param(with_label=True)
dataio_1.get_party_instance(
role='host', party_id=host).algorithm_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
intersection_1 = Intersection(name="intersection_1")
hetero_nn_0 = HeteroNN(name="hetero_nn_0",
epochs=100,
validation_freqs=1,
interactive_layer_lr=0.15,
batch_size=-1,
early_stop="diff",
early_stopping_rounds=15,
use_first_metric_only=True)
hetero_nn_0.add_bottom_model(
Dense(units=3,
input_shape=(10, ),
activation="relu",
kernel_initializer=initializers.Constant(value=1)))
hetero_nn_0.set_interactve_layer(
Dense(units=2,
input_shape=(2, ),
kernel_initializer=initializers.Constant(value=1)))
hetero_nn_0.add_top_model(
Dense(units=1,
input_shape=(2, ),
activation="sigmoid",
kernel_initializer=initializers.Constant(value=1)))
hetero_nn_0.compile(optimizer=optimizers.SGD(lr=0.15),
metrics=["AUC"],
loss="binary_crossentropy")
hetero_nn_1 = HeteroNN(name="hetero_nn_1")
evaluation_0 = Evaluation(name="evaluation_0")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(dataio_1, data=Data(data=reader_1.output.data))
pipeline.add_component(intersection_0,
data=Data(data=dataio_0.output.data))
pipeline.add_component(intersection_1,
data=Data(data=dataio_1.output.data))
pipeline.add_component(hetero_nn_0,
data=Data(train_data=intersection_0.output.data,
validate_data=intersection_1.output.data))
pipeline.add_component(hetero_nn_1,
data=Data(test_data=intersection_1.output.data),
model=Model(model=hetero_nn_0.output.model))
pipeline.add_component(
evaluation_0,
data=Data(data=[hetero_nn_0.output.data, hetero_nn_1.output.data]))
pipeline.compile()
pipeline.fit(backend=backend, work_mode=work_mode)
print(pipeline.get_component("hetero_nn_0").get_summary())
print(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml",
param="./sshe_linr_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]
if isinstance(param, str):
param = JobConfig.load_from_file(param)
guest_train_data = {
"name": "motor_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "motor_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 DataTransform components
data_transform_0 = DataTransform(
name="data_transform_0") # start component numbering at 0
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(
role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(
with_label=True,
output_format="dense",
label_name=param["label_name"],
label_type="float")
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(
role='host', party_id=host).component_param(with_label=False)
# define Intersection component
intersection_0 = Intersection(name="intersection_0")
param = {
"penalty": param["penalty"],
"max_iter": param["max_iter"],
"optimizer": param["optimizer"],
"learning_rate": param["learning_rate"],
"init_param": param["init_param"],
"batch_size": param["batch_size"],
"alpha": param["alpha"],
"early_stop": param["early_stop"],
"reveal_strategy": param["reveal_strategy"],
"tol": 1e-6,
"reveal_every_iter": True
}
hetero_sshe_linr_0 = HeteroSSHELinR(name='hetero_sshe_linr_0', **param)
hetero_sshe_linr_1 = HeteroSSHELinR(name='hetero_sshe_linr_1')
evaluation_0 = Evaluation(name='evaluation_0',
eval_type="regression",
metrics=[
"r2_score", "mean_squared_error",
"root_mean_squared_error",
"explained_variance"
])
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(hetero_sshe_linr_0,
data=Data(train_data=intersection_0.output.data))
pipeline.add_component(hetero_sshe_linr_1,
data=Data(test_data=intersection_0.output.data),
model=Model(hetero_sshe_linr_0.output.model))
pipeline.add_component(evaluation_0,
data=Data(data=hetero_sshe_linr_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()
metric_summary = parse_summary_result(
pipeline.get_component("evaluation_0").get_summary())
data_linr_0 = extract_data(
pipeline.get_component("hetero_sshe_linr_0").get_output_data().get(
"data"), "predict_result")
data_linr_1 = extract_data(
pipeline.get_component("hetero_sshe_linr_1").get_output_data().get(
"data"), "predict_result")
desc_linr_0 = regression_metric.Describe().compute(data_linr_0)
desc_linr_1 = regression_metric.Describe().compute(data_linr_1)
metric_summary["script_metrics"] = {
"linr_train": desc_linr_0,
"linr_validate": desc_linr_1
}
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
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
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
pipeline = PipeLine().set_initiator(role='guest',
party_id=guest).set_roles(guest=guest,
host=host)
reader_0 = Reader(name="reader_0")
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 = DataIO(name="dataio_0")
dataio_0.get_party_instance(
role='guest', party_id=guest).component_param(with_label=True)
dataio_0.get_party_instance(
role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
hetero_pearson_0 = HeteroPearson(name='hetero_pearson_0',
column_indexes=-1)
hetero_binning_0 = HeteroFeatureBinning(name='hetero_binning_0')
selection_param = {
"name": "hetero_feature_selection_0",
"select_col_indexes": -1,
"select_names": [],
"filter_methods": ["vif_filter", "correlation_filter"],
"vif_param": {
"threshold": 5
},
"correlation_param": {
"threshold": 0.5,
"select_federated": False
}
}
hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0,
data=Data(data=dataio_0.output.data))
pipeline.add_component(hetero_binning_0,
data=Data(data=intersection_0.output.data))
pipeline.add_component(hetero_pearson_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_pearson_0.output.model, hetero_binning_0.output.model
]))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
# predict
# deploy required components
pipeline.deploy_component([
dataio_0, intersection_0, hetero_binning_0, hetero_feature_selection_0
])
predict_pipeline = PipeLine()
# add data reader onto predict pipeline
predict_pipeline.add_component(reader_0)
# add selected components from train pipeline onto predict pipeline
# specify data source
predict_pipeline.add_component(
pipeline,
data=Data(
predict_input={pipeline.dataio_0.input.data: reader_0.output.data
}))
# run predict model
predict_pipeline.predict(job_parameters)
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[0]
arbiter = parties.arbiter[0]
guest_train_data = {
"name": "breast_homo_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_homo_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).component_param(table=guest_train_data)
# configure Reader for host
reader_0.get_party_instance(
role='host', party_id=hosts).component_param(table=host_train_data)
data_transform_0 = DataTransform(name="data_transform_0",
output_format='dense',
with_label=True)
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
lr_param = {
"penalty": "L2",
"optimizer": "sgd",
"tol": 1e-05,
"alpha": 0.01,
"early_stop": "diff",
"batch_size": -1,
"learning_rate": 0.15,
"decay": 1,
"decay_sqrt": True,
"init_param": {
"init_method": "zeros"
},
"encrypt_param": {
"method": None
},
"cv_param": {
"n_splits": 4,
"shuffle": True,
"random_seed": 33,
"need_cv": False
},
"callback_param": {
"callbacks": ["ModelCheckpoint", "EarlyStopping"]
}
}
homo_lr_0 = HomoLR(name="homo_lr_0", max_iter=3, **lr_param)
homo_lr_1 = HomoLR(name="homo_lr_1", max_iter=30, **lr_param)
homo_lr_2 = HomoLR(name="homo_lr_2", max_iter=30, **lr_param)
pipeline.add_component(homo_lr_0,
data=Data(train_data=data_transform_0.output.data))
pipeline.add_component(homo_lr_1,
data=Data(train_data=data_transform_0.output.data),
model=Model(model=homo_lr_0.output.model))
pipeline.add_component(homo_lr_2,
data=Data(train_data=data_transform_0.output.data))
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
pipeline.add_component(
evaluation_0,
data=Data(data=[homo_lr_1.output.data, homo_lr_2.output.data]))
pipeline.compile()
# fit model
pipeline.fit()
# query component summary
prettify(pipeline.get_component("evaluation_0").get_summary())
return pipeline
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]
guest_train_data = [{
"name": "motor_hetero_guest",
"namespace": f"experiment{namespace}"
}, {
"name": "motor_hetero_guest",
"namespace": f"experiment{namespace}"
}]
host_train_data = [{
"name": "motor_hetero_host",
"namespace": f"experiment{namespace}"
}, {
"name": "motor_hetero_host",
"namespace": f"experiment{namespace}"
}]
pipeline = PipeLine().set_initiator(role='guest',
party_id=guest).set_roles(guest=guest,
host=host)
reader_0 = Reader(name="reader_0")
reader_0.get_party_instance(
role='guest',
party_id=guest).component_param(table=guest_train_data[0])
reader_0.get_party_instance(
role='host', party_id=host).component_param(table=host_train_data[0])
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest',
party_id=guest).component_param(table=guest_train_data[1])
reader_1.get_party_instance(
role='host', party_id=host).component_param(table=host_train_data[1])
data_transform_0 = DataTransform(name="data_transform_0")
data_transform_1 = DataTransform(name="data_transform_1")
data_transform_0.get_party_instance(
role='guest', party_id=guest).component_param(with_label=True,
label_name="motor_speed",
label_type="float",
output_format="dense")
data_transform_0.get_party_instance(
role='host', party_id=host).component_param(with_label=False)
data_transform_1.get_party_instance(
role='guest', party_id=guest).component_param(with_label=True,
label_name="motor_speed",
label_type="float",
output_format="dense")
data_transform_1.get_party_instance(
role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
intersect_1 = Intersection(name="intersection_1")
hetero_linr_0 = HeteroSSHELinR(
name="hetero_linr_0",
penalty="L2",
optimizer="sgd",
tol=0.001,
alpha=0.01,
max_iter=20,
early_stop="weight_diff",
batch_size=-1,
learning_rate=0.15,
decay=0.0,
decay_sqrt=False,
init_param={"init_method": "zeros"},
callback_param={
"callbacks": ["EarlyStopping", "PerformanceEvaluate"],
"validation_freqs": 1,
"early_stopping_rounds": 5,
"metrics": ["mean_absolute_error", "root_mean_squared_error"],
"use_first_metric_only": False,
"save_freq": 1
},
reveal_every_iter=True,
reveal_strategy="respectively")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(intersection_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(intersect_1,
data=Data(data=data_transform_1.output.data))
pipeline.add_component(hetero_linr_0,
data=Data(train_data=intersection_0.output.data,
validate_data=intersect_1.output.data))
pipeline.compile()
pipeline.fit()
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]
guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)
reader_0 = Reader(name="reader_0")
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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
data_transform_0 = DataTransform(name="data_transform_0")
data_transform_1 = DataTransform(name="data_transform_1")
data_transform_0.get_party_instance(
role='guest', party_id=guest).component_param(
with_label=True, output_format="dense")
data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
data_transform_1.get_party_instance(
role='guest', party_id=guest).component_param(
with_label=True, output_format="dense")
data_transform_1.get_party_instance(role='host', party_id=host).component_param(with_label=False)
intersect_0 = Intersection(name="intersection_0")
intersect_1 = Intersection(name="intersection_1")
union_0 = Union(name="union_0")
hetero_lr_0 = HeteroLR(name="hetero_lr_0", max_iter=3, early_stop="weight_diff",
optimizer="nesterov_momentum_sgd", tol=1E-4, alpha=0.01,
learning_rate=0.15, init_param={"init_method": "random_uniform"})
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary", pos_label=1)
evaluation_0.get_party_instance(role='host', party_id=host).component_param(need_run=False)
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(
data_transform_1, data=Data(
data=reader_1.output.data), model=Model(
data_transform_0.output.model))
pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
pipeline.add_component(intersect_1, data=Data(data=data_transform_1.output.data))
pipeline.add_component(union_0, data=Data(data=[intersect_0.output.data, intersect_1.output.data]))
pipeline.add_component(hetero_lr_0, data=Data(train_data=union_0.output.data))
pipeline.add_component(evaluation_0, data=Data(data=hetero_lr_0.output.data))
pipeline.compile()
pipeline.fit()
def make_feature_engineering_dsl(config, namespace, lr_param, is_multi_host=False, has_validate=False,
is_cv=False, is_ovr=False):
parties = config.parties
guest = parties.guest[0]
if is_multi_host:
hosts = parties.host
else:
hosts = parties.host[0]
arbiter = parties.arbiter[0]
if is_ovr:
guest_train_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}
guest_eval_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
host_eval_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}
else:
guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_eval_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
train_line = []
# 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).component_param(table=guest_train_data)
# configure Reader for host
reader_0.get_party_instance(role='host', party_id=hosts).component_param(table=host_train_data)
# define DataTransform components
data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense")
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(role='host', party_id=hosts).component_param(with_label=False)
train_line.append(data_transform_0)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
train_line.append(intersection_0)
feature_scale_0 = FeatureScale(name='feature_scale_0', method="standard_scale",
need_run=True)
pipeline.add_component(feature_scale_0, data=Data(data=intersection_0.output.data))
train_line.append(feature_scale_0)
binning_param = {
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"adjustment_factor": 0.5,
"local_only": False,
"need_run": True,
"transform_param": {
"transform_cols": -1,
"transform_type": "bin_num"
}
}
hetero_feature_binning_0 = HeteroFeatureBinning(name='hetero_feature_binning_0',
**binning_param)
pipeline.add_component(hetero_feature_binning_0, data=Data(data=feature_scale_0.output.data))
train_line.append(hetero_feature_binning_0)
selection_param = {
"select_col_indexes": -1,
"filter_methods": [
"manually",
"iv_value_thres",
"iv_percentile"
],
"manually_param": {
"filter_out_indexes": None
},
"iv_value_param": {
"value_threshold": 1.0
},
"iv_percentile_param": {
"percentile_threshold": 0.9
},
"need_run": True
}
hetero_feature_selection_0 = HeteroFeatureSelection(name='hetero_feature_selection_0',
**selection_param)
pipeline.add_component(hetero_feature_selection_0, data=Data(data=hetero_feature_binning_0.output.data),
model=Model(isometric_model=[hetero_feature_binning_0.output.model]))
train_line.append(hetero_feature_selection_0)
onehot_param = {
"transform_col_indexes": -1,
"transform_col_names": None,
"need_run": True
}
one_hot_encoder_0 = OneHotEncoder(name='one_hot_encoder_0', **onehot_param)
pipeline.add_component(one_hot_encoder_0, data=Data(data=hetero_feature_selection_0.output.data))
train_line.append(one_hot_encoder_0)
last_cpn = None
if has_validate:
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_eval_data)
reader_1.get_party_instance(role='host', party_id=hosts).component_param(table=host_eval_data)
pipeline.add_component(reader_1)
last_cpn = reader_1
for cpn in train_line:
cpn_name = cpn.name
new_name = "_".join(cpn_name.split('_')[:-1] + ['1'])
validate_cpn = type(cpn)(name=new_name)
if hasattr(cpn.output, "model"):
pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data),
model=Model(cpn.output.model))
else:
pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data))
last_cpn = validate_cpn
hetero_lr_0 = HeteroLR(**lr_param)
if has_validate:
pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_encoder_0.output.data,
validate_data=last_cpn.output.data))
else:
pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_encoder_0.output.data))
if is_cv:
pipeline.compile()
return pipeline
evaluation_data = [hetero_lr_0.output.data]
if has_validate:
hetero_lr_1 = HeteroLR(name='hetero_lr_1')
pipeline.add_component(hetero_lr_1, data=Data(test_data=last_cpn.output.data),
model=Model(hetero_lr_0.output.model))
evaluation_data.append(hetero_lr_1.output.data)
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
pipeline.add_component(evaluation_0, data=Data(data=evaluation_data))
pipeline.compile()
return pipeline
def main(config="../../config.yaml",
param="./vechile_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
if isinstance(param, str):
param = JobConfig.load_from_file(param)
assert isinstance(param, dict)
"""
guest = 9999
host = 10000
arbiter = 9999
backend = 0
work_mode = 1
param = {"penalty": "L2", "max_iter": 5}
"""
data_set = param.get("data_guest").split('/')[-1]
if data_set == "vehicle_scale_hetero_guest.csv":
guest_data_table = 'vehicle_scale_hetero_guest'
host_data_table = 'vehicle_scale_hetero_host'
else:
raise ValueError(f"Cannot recognized data_set: {data_set}")
guest_train_data = {
"name": guest_data_table,
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": host_data_table,
"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 component
intersection_0 = Intersection(name="intersection_0")
lr_param = {
"validation_freqs": None,
"early_stopping_rounds": None,
}
config_param = {
"penalty": param["penalty"],
"max_iter": param["max_iter"],
"alpha": param["alpha"],
"learning_rate": param["learning_rate"],
"optimizer": param["optimizer"],
"batch_size": param["batch_size"],
"early_stop": "diff",
"init_param": {
"init_method": param.get("init_method", 'random_uniform'),
"random_seed": param.get("random_seed", 103)
}
}
lr_param.update(config_param)
print(f"lr_param: {lr_param}, data_set: {data_set}")
hetero_lr_0 = HeteroLR(name='hetero_lr_0', **lr_param)
hetero_lr_1 = HeteroLR(name='hetero_lr_1')
evaluation_0 = Evaluation(name='evaluation_0', eval_type="multi")
# 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))
pipeline.add_component(intersection_0,
data=Data(data=dataio_0.output.data))
pipeline.add_component(hetero_lr_0,
data=Data(train_data=intersection_0.output.data))
pipeline.add_component(hetero_lr_1,
data=Data(test_data=intersection_0.output.data),
model=Model(hetero_lr_0.output.model))
pipeline.add_component(evaluation_0,
data=Data(data=hetero_lr_0.output.data))
# 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
result_summary = parse_summary_result(
pipeline.get_component("evaluation_0").get_summary())
lr_0_data = pipeline.get_component("hetero_lr_0").get_output_data().get(
"data")
lr_1_data = pipeline.get_component("hetero_lr_1").get_output_data().get(
"data")
lr_0_score_label = extract_data(lr_0_data, "predict_result", keep_id=True)
lr_1_score_label = extract_data(lr_1_data, "predict_result", keep_id=True)
metric_lr = {
"score_diversity_ratio":
classification_metric.Distribution.compute(lr_0_score_label,
lr_1_score_label)
}
result_summary["distribution_metrics"] = {"hetero_lr": metric_lr}
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, result_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]
guest_train_data = {
"name": "breast_homo_guest",
"namespace": f"experiment_sid{namespace}"
}
host_train_data = {
"name": "breast_homo_host",
"namespace": f"experiment_sid{namespace}"
}
guest_eval_data = {
"name": "breast_homo_guest",
"namespace": f"experiment_sid{namespace}"
}
host_eval_data = {
"name": "breast_homo_host",
"namespace": f"experiment_sid{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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_eval_data)
reader_1.get_party_instance(
role='host', party_id=host).component_param(table=host_eval_data)
# define DataTransform components
data_transform_0 = DataTransform(name="data_transform_0",
with_match_id=True,
with_label=True,
output_format="dense")
data_transform_1 = DataTransform(
name="data_transform_1") # start component numbering at 0
scale_0 = FeatureScale(name='scale_0')
scale_1 = FeatureScale(name='scale_1')
param = {
"penalty": "L2",
"optimizer": "sgd",
"tol": 1e-05,
"alpha": 0.01,
"max_iter": 3,
"early_stop": "diff",
"batch_size": 320,
"learning_rate": 0.15,
"validation_freqs": 1,
"init_param": {
"init_method": "zeros"
},
"encrypt_param": {
"method": None
},
"cv_param": {
"n_splits": 4,
"shuffle": True,
"random_seed": 33,
"need_cv": False
}
}
homo_lr_0 = HomoLR(name='homo_lr_0', **param)
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
# set data input sources of intersection components
pipeline.add_component(scale_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(scale_1,
data=Data(data=data_transform_1.output.data),
model=Model(scale_0.output.model))
pipeline.add_component(homo_lr_0,
data=Data(train_data=scale_0.output.data,
validate_data=scale_1.output.data))
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
evaluation_0.get_party_instance(
role='host', party_id=host).component_param(need_run=False)
pipeline.add_component(evaluation_0, data=Data(data=homo_lr_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()
# query component summary
print(
json.dumps(pipeline.get_component("homo_lr_0").get_summary(),
indent=4,
ensure_ascii=False))
print(
json.dumps(pipeline.get_component("evaluation_0").get_summary(),
indent=4,
ensure_ascii=False))
def main():
# parties config
guest = 9999
host = 10000
arbiter = 10000
# 0 for eggroll, 1 for spark
backend = Backend.EGGROLL
# 0 for standalone, 1 for cluster
work_mode = WorkMode.STANDALONE
# use the work mode below for cluster deployment
# work_mode = WorkMode.CLUSTER
# specify input data name & namespace in database
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": "experiment"
}
host_train_data = {"name": "breast_hetero_host", "namespace": "experiment"}
guest_eval_data = {
"name": "breast_hetero_guest",
"namespace": "experiment"
}
host_eval_data = {"name": "breast_hetero_host", "namespace": "experiment"}
# 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).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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role="guest", party_id=guest).algorithm_param(table=guest_eval_data)
reader_1.get_party_instance(
role="host", party_id=host).algorithm_param(table=host_eval_data)
# define DataIO components
dataio_0 = DataIO(name="dataio_0")
dataio_1 = DataIO(name="dataio_1")
# 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_1 = Intersection(name="intersection_1")
# define HeteroLR component
hetero_lr_0 = HeteroLR(name="hetero_lr_0",
early_stop="weight_diff",
learning_rate=0.15,
optimizer="rmsprop",
max_iter=10,
early_stopping_rounds=2,
validation_freqs=1)
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
# set dataio_1 to replicate model from dataio_0
pipeline.add_component(dataio_1,
data=Data(data=reader_1.output.data),
model=Model(dataio_0.output.model))
# set data input sources of intersection components
pipeline.add_component(intersection_0,
data=Data(data=dataio_0.output.data))
pipeline.add_component(intersection_1,
data=Data(data=dataio_1.output.data))
# set train & validate data of hetero_lr_0 component
pipeline.add_component(hetero_lr_0,
data=Data(train_data=intersection_0.output.data,
validate_data=intersection_1.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
import json
print(
json.dumps(pipeline.get_component("hetero_lr_0").get_summary(),
indent=4))
# predict
# deploy required components
pipeline.deploy_component([dataio_0, intersection_0, hetero_lr_0])
# initiate predict pipeline
predict_pipeline = PipeLine()
reader_2 = Reader(name="reader_2")
reader_2.get_party_instance(
role="guest", party_id=guest).algorithm_param(table=guest_eval_data)
reader_2.get_party_instance(
role="host", party_id=host).algorithm_param(table=host_eval_data)
# add data reader onto predict pipeline
predict_pipeline.add_component(reader_2)
# add selected components from train pipeline onto predict pipeline
# specify data source
predict_pipeline.add_component(
pipeline,
data=Data(
predict_input={pipeline.dataio_0.input.data: reader_2.output.data
}))
# run predict model
predict_pipeline.predict(backend=backend, work_mode=work_mode)
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]
guest_train_data = {
"name": "expect",
"namespace": f"experiment{namespace}"
}
host_train_data = {"name": "actual", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest',
party_id=guest).set_roles(guest=guest,
host=host)
reader_0 = Reader(name="reader_0")
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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_train_data)
reader_1.get_party_instance(
role='host', party_id=host).component_param(table=host_train_data)
data_transform_0 = DataTransform(name="data_transform_0")
data_transform_1 = DataTransform(name="data_transform_1")
data_transform_0.get_party_instance(
role='guest', party_id=guest).component_param(with_label=False,
output_format="dense")
data_transform_1.get_party_instance(
role='guest', party_id=guest).component_param(with_label=False,
output_format="dense")
data_transform_0.get_party_instance(
role='host', party_id=host).component_param(with_label=False,
output_format="dense")
data_transform_1.get_party_instance(
role='host', party_id=host).component_param(with_label=False,
output_format="dense")
psi_0 = PSI(name='psi_0', max_bin_num=20)
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(psi_0,
data=Data(
train_data=data_transform_0.output.data,
validate_data=data_transform_1.output.data))
pipeline.compile()
pipeline.fit()
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]
guest_train_data = {
"name": "mock_string",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "mock_string",
"namespace": f"experiment{namespace}"
}
guest_eval_data = {
"name": "mock_string",
"namespace": f"experiment{namespace}"
}
host_eval_data = {
"name": "mock_string",
"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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_eval_data)
reader_1.get_party_instance(
role='host', party_id=host).component_param(table=host_eval_data)
# define DataTransform components
data_transform_0 = DataTransform(
name="data_transform_0",
with_label=True,
output_format="dense",
label_name='y',
data_type="str") # start component numbering at 0
data_transform_1 = DataTransform(name="data_transform_1")
homo_onehot_param = {
"transform_col_indexes": -1,
"transform_col_names": [],
"need_alignment": True
}
homo_onehot_0 = HomoOneHotEncoder(name='homo_onehot_0',
**homo_onehot_param)
homo_onehot_1 = HomoOneHotEncoder(name='homo_onehot_1')
scale_0 = FeatureScale(name='scale_0', method="standard_scale")
scale_1 = FeatureScale(name='scale_1')
homo_lr_param = {
"penalty": "L2",
"optimizer": "sgd",
"tol": 1e-05,
"alpha": 0.01,
"max_iter": 3,
"early_stop": "diff",
"batch_size": 500,
"learning_rate": 0.15,
"decay": 1,
"decay_sqrt": True,
"init_param": {
"init_method": "zeros"
},
"encrypt_param": {
"method": "Paillier"
},
"cv_param": {
"n_splits": 4,
"shuffle": True,
"random_seed": 33,
"need_cv": False
}
}
homo_lr_0 = HomoLR(name='homo_lr_0', **homo_lr_param)
homo_lr_1 = HomoLR(name='homo_lr_1')
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
# set data_transform_1 to replicate model from data_transform_0
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(homo_onehot_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(homo_onehot_1,
data=Data(data=data_transform_1.output.data),
model=Model(homo_onehot_0.output.model))
pipeline.add_component(scale_0, data=Data(data=homo_onehot_0.output.data))
pipeline.add_component(scale_1,
data=Data(data=homo_onehot_1.output.data),
model=Model(scale_0.output.model))
pipeline.add_component(homo_lr_0,
data=Data(train_data=scale_0.output.data))
pipeline.add_component(homo_lr_1,
data=Data(test_data=scale_1.output.data),
model=Model(homo_lr_0.output.model))
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
evaluation_0.get_party_instance(
role='host', party_id=host).component_param(need_run=False)
pipeline.add_component(
evaluation_0,
data=Data(data=[homo_lr_0.output.data, homo_lr_1.output.data]))
pipeline.compile()
# fit model
pipeline.fit()
# query component summary
print(
json.dumps(pipeline.get_component("homo_lr_0").get_summary(),
indent=4,
ensure_ascii=False))
print(
json.dumps(pipeline.get_component("evaluation_0").get_summary(),
indent=4,
ensure_ascii=False))
def make_normal_dsl(config,
namespace,
selection_param,
is_multi_host=False,
host_dense_output=True,
**kwargs):
parties = config.parties
guest = parties.guest[0]
if is_multi_host:
hosts = parties.host
else:
hosts = parties.host[0]
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
guest_eval_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_eval_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=hosts)
# 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=hosts).component_param(table=host_train_data)
# define DataTransform components
data_transform_0 = DataTransform(
name="data_transform_0") # start component numbering at 0
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(
role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(
with_label=True, output_format="dense")
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(
role='host', party_id=hosts).component_param(with_label=False)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0,
data=Data(data=data_transform_0.output.data))
last_cpn = intersection_0
selection_include_model = []
if 'binning_param' in kwargs:
hetero_feature_binning_0 = HeteroFeatureBinning(
**kwargs['binning_param'])
pipeline.add_component(hetero_feature_binning_0,
data=Data(data=last_cpn.output.data))
selection_include_model.append(hetero_feature_binning_0)
# last_cpn = hetero_feature_binning_0
if 'statistic_param' in kwargs:
# print(f"param: {kwargs['statistic_param']}, kwargs: {kwargs}")
statistic_0 = DataStatistics(**kwargs['statistic_param'])
pipeline.add_component(statistic_0,
data=Data(data=last_cpn.output.data))
# last_cpn = statistic_0
selection_include_model.append(statistic_0)
if 'psi_param' in kwargs:
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest',
party_id=guest).component_param(table=guest_eval_data)
reader_1.get_party_instance(
role='host', party_id=hosts).component_param(table=host_eval_data)
data_transform_1 = DataTransform(name="data_transform_1")
intersection_1 = Intersection(name="intersection_1")
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(intersection_1,
data=Data(data=data_transform_1.output.data))
psi_0 = PSI(**kwargs['psi_param'])
pipeline.add_component(psi_0,
data=Data(
train_data=intersection_0.output.data,
validate_data=intersection_1.output.data))
# last_cpn = statistic_0
selection_include_model.append(psi_0)
if 'sbt_param' in kwargs:
secureboost_0 = HeteroSecureBoost(**kwargs['sbt_param'])
pipeline.add_component(
secureboost_0, data=Data(train_data=intersection_0.output.data))
selection_include_model.append(secureboost_0)
if "fast_sbt_param" in kwargs:
fast_sbt_0 = HeteroFastSecureBoost(**kwargs['fast_sbt_param'])
pipeline.add_component(
fast_sbt_0, data=Data(train_data=intersection_0.output.data))
selection_include_model.append(fast_sbt_0)
hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param)
pipeline.add_component(
hetero_feature_selection_0,
data=Data(data=intersection_0.output.data),
model=Model(
isometric_model=[x.output.model for x in selection_include_model]))
# compile pipeline once finished adding modules, this step will form conf and dsl files for running job
pipeline.compile()
return pipeline
def make_single_predict_pipeline(config,
namespace,
selection_param,
is_multi_host=False,
**kwargs):
parties = config.parties
guest = parties.guest[0]
if is_multi_host:
hosts = parties.host
else:
hosts = parties.host[0]
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
guest_eval_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_eval_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=hosts)
# 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=hosts).component_param(table=host_train_data)
# define DataTransform components
data_transform_0 = DataTransform(
name="data_transform_0") # start component numbering at 0
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(
role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(
with_label=True, output_format="dense")
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(
role='host', party_id=hosts).component_param(with_label=False)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0,
data=Data(data=data_transform_0.output.data))
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_eval_data)
reader_1.get_party_instance(
role='host', party_id=hosts).component_param(table=host_eval_data)
data_transform_1 = DataTransform(name="data_transform_1")
intersection_1 = Intersection(name="intersection_1")
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(intersection_1,
data=Data(data=data_transform_1.output.data))
sample_0 = FederatedSample(name='sample_0', fractions=0.9)
pipeline.add_component(sample_0,
data=Data(data=intersection_0.output.data))
if "binning_param" not in kwargs:
raise ValueError("Binning_param is needed")
hetero_feature_binning_0 = HeteroFeatureBinning(**kwargs['binning_param'])
pipeline.add_component(hetero_feature_binning_0,
data=Data(data=sample_0.output.data))
hetero_feature_binning_1 = HeteroFeatureBinning(
name='hetero_feature_binning_1')
pipeline.add_component(hetero_feature_binning_1,
data=Data(data=intersection_1.output.data),
model=Model(hetero_feature_binning_0.output.model))
hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param)
pipeline.add_component(
hetero_feature_selection_0,
data=Data(data=hetero_feature_binning_0.output.data),
model=Model(isometric_model=[hetero_feature_binning_0.output.model]))
hetero_feature_selection_1 = HeteroFeatureSelection(
name='hetero_feature_selection_1')
pipeline.add_component(
hetero_feature_selection_1,
data=Data(data=hetero_feature_binning_1.output.data),
model=Model(hetero_feature_selection_0.output.model))
scale_0 = FeatureScale(name='scale_0')
scale_1 = FeatureScale(name='scale_1')
pipeline.add_component(
scale_0, data=Data(data=hetero_feature_selection_0.output.data))
pipeline.add_component(
scale_1,
data=Data(data=hetero_feature_selection_1.output.data),
model=Model(scale_0.output.model))
pipeline.compile()
return pipeline
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]
# data sets
guest_train_data = {
"name": "vehicle_scale_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "vehicle_scale_hetero_host",
"namespace": f"experiment{namespace}"
}
guest_validate_data = {
"name": "vehicle_scale_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_validate_data = {
"name": "vehicle_scale_hetero_host",
"namespace": f"experiment{namespace}"
}
# init pipeline
pipeline = PipeLine().set_initiator(role="guest",
party_id=guest).set_roles(
guest=guest,
host=host,
)
# set data reader and data-io
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)
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)
data_transform_0, data_transform_1 = DataTransform(
name="data_transform_0"), DataTransform(name="data_transform_1")
data_transform_0.get_party_instance(
role="guest", party_id=guest).component_param(with_label=True,
output_format="dense")
data_transform_0.get_party_instance(
role="host", party_id=host).component_param(with_label=False)
data_transform_1.get_party_instance(
role="guest", party_id=guest).component_param(with_label=True,
output_format="dense")
data_transform_1.get_party_instance(
role="host", party_id=host).component_param(with_label=False)
# data intersect component
intersect_0 = Intersection(name="intersection_0")
intersect_1 = Intersection(name="intersection_1")
# secure boost component
hetero_secure_boost_0 = HeteroSecureBoost(
name="hetero_secure_boost_0",
num_trees=3,
task_type="classification",
objective_param={"objective": "cross_entropy"},
encrypt_param={"method": "Paillier"},
tree_param={"max_depth": 3},
validation_freqs=1,
boosting_strategy='mix')
# evaluation component
evaluation_0 = Evaluation(name="evaluation_0", eval_type="multi")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(intersect_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(intersect_1,
data=Data(data=data_transform_1.output.data))
pipeline.add_component(hetero_secure_boost_0,
data=Data(train_data=intersect_0.output.data,
validate_data=intersect_1.output.data))
pipeline.add_component(evaluation_0,
data=Data(data=hetero_secure_boost_0.output.data))
pipeline.compile()
pipeline.fit()
print("fitting hetero secureboost done, result:")
print(pipeline.get_component("hetero_secure_boost_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]
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 DataTransform components
data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense")
# get and configure DataTransform party instance of host
data_transform_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(data_transform_0, data=Data(data=reader_0.output.data))
# set data input sources of intersection components
pipeline.add_component(intersection_0, data=Data(data=data_transform_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
pipeline.fit()
# 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": "motor_hetero_guest",
"namespace": f"experiment{namespace}"
}, {
"name": "motor_hetero_guest",
"namespace": f"experiment{namespace}"
}]
host_train_data = [{
"name": "motor_hetero_host",
"namespace": f"experiment{namespace}"
}, {
"name": "motor_hetero_host",
"namespace": f"experiment{namespace}"
}]
pipeline = PipeLine().set_initiator(
role='guest', party_id=guest).set_roles(guest=guest,
host=host,
arbiter=arbiter)
reader_0 = Reader(name="reader_0")
reader_0.get_party_instance(
role='guest',
party_id=guest).algorithm_param(table=guest_train_data[0])
reader_0.get_party_instance(
role='host', party_id=host).algorithm_param(table=host_train_data[0])
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest',
party_id=guest).algorithm_param(table=guest_train_data[1])
reader_1.get_party_instance(
role='host', party_id=host).algorithm_param(table=host_train_data[1])
dataio_0 = DataIO(name="dataio_0")
dataio_1 = DataIO(name="dataio_1")
dataio_0.get_party_instance(role='guest', party_id=guest).algorithm_param(
with_label=True,
label_name="motor_speed",
label_type="float",
output_format="dense")
dataio_0.get_party_instance(
role='host', party_id=host).algorithm_param(with_label=False)
dataio_1.get_party_instance(role='guest', party_id=guest).algorithm_param(
with_label=True,
label_name="motor_speed",
label_type="float",
output_format="dense")
dataio_1.get_party_instance(
role='host', party_id=host).algorithm_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
intersect_1 = Intersection(name="intersection_1")
hetero_linr_0 = HeteroLinR(
name="hetero_linr_0",
penalty="L2",
optimizer="sgd",
tol=0.001,
alpha=0.01,
max_iter=20,
early_stop="weight_diff",
batch_size=-1,
learning_rate=0.15,
decay=0.0,
decay_sqrt=False,
init_param={"init_method": "zeros"},
encrypted_mode_calculator_param={"mode": "fast"},
validation_freqs=1,
early_stopping_rounds=5,
metrics=["mean_absolute_error", "root_mean_squared_error"],
use_first_metric_only=False)
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(dataio_1,
data=Data(data=reader_1.output.data),
model=Model(dataio_0.output.model))
pipeline.add_component(intersection_0,
data=Data(data=dataio_0.output.data))
pipeline.add_component(intersect_1, data=Data(data=dataio_1.output.data))
pipeline.add_component(hetero_linr_0,
data=Data(train_data=intersection_0.output.data,
validate_data=intersect_1.output.data))
pipeline.compile()
pipeline.fit(backend=backend, work_mode=work_mode)
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]
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment_sid{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment_sid{namespace}"
}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).\
set_roles(guest=guest, host=host, arbiter=arbiter)
reader_0 = Reader(name="reader_0")
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)
data_transform_0 = DataTransform(name="data_transform_0",
with_match_id=True)
data_transform_0.get_party_instance(
role='guest', party_id=guest).component_param(with_label=True)
data_transform_0.get_party_instance(
role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
feature_scale_0 = FeatureScale(name='feature_scale_0',
method="standard_scale",
need_run=True)
binning_param = {
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"adjustment_factor": 0.5,
"local_only": False,
"need_run": True,
"transform_param": {
"transform_cols": -1,
"transform_type": "bin_num"
}
}
hetero_feature_binning_0 = HeteroFeatureBinning(
name='hetero_feature_binning_0', **binning_param)
statistic_0 = DataStatistics(name='statistic_0', statistics=["95%"])
pearson_0 = HeteroPearson(name='pearson_0', column_indexes=-1)
onehot_0 = OneHotEncoder(name='onehot_0')
selection_param = {
"name":
"hetero_feature_selection_0",
"select_col_indexes":
-1,
"select_names": [],
"filter_methods": [
"manually", "unique_value", "iv_filter",
"coefficient_of_variation_value_thres", "outlier_cols"
],
"manually_param": {
"filter_out_indexes": [0, 1, 2],
"filter_out_names": ["x3"]
},
"unique_param": {
"eps": 1e-06
},
"iv_param": {
"metrics": ["iv", "iv", "iv"],
"filter_type": ["threshold", "top_k", "top_percentile"],
"threshold": [0.001, 100, 0.99]
},
"variance_coe_param": {
"value_threshold": 0.3
},
"outlier_param": {
"percentile": 0.95,
"upper_threshold": 2.0
}
}
hetero_feature_selection_0 = HeteroFeatureSelection(**selection_param)
lr_param = {
"name": "hetero_lr_0",
"penalty": "L2",
"optimizer": "rmsprop",
"tol": 0.0001,
"alpha": 0.01,
"max_iter": 30,
"early_stop": "diff",
"batch_size": 320,
"learning_rate": 0.15,
"init_param": {
"init_method": "zeros"
},
"sqn_param": {
"update_interval_L": 3,
"memory_M": 5,
"sample_size": 5000,
"random_seed": None
},
"cv_param": {
"n_splits": 5,
"shuffle": False,
"random_seed": 103,
"need_cv": False
}
}
hetero_lr_0 = HeteroLR(**lr_param)
evaluation_0 = Evaluation(name='evaluation_0')
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(feature_scale_0,
data=Data(data=intersection_0.output.data))
pipeline.add_component(hetero_feature_binning_0,
data=Data(data=feature_scale_0.output.data))
pipeline.add_component(statistic_0,
data=Data(data=feature_scale_0.output.data))
pipeline.add_component(pearson_0,
data=Data(data=feature_scale_0.output.data))
pipeline.add_component(
hetero_feature_selection_0,
data=Data(data=hetero_feature_binning_0.output.data),
model=Model(isometric_model=[
hetero_feature_binning_0.output.model, statistic_0.output.model
]))
pipeline.add_component(
onehot_0, data=Data(data=hetero_feature_selection_0.output.data))
pipeline.add_component(hetero_lr_0,
data=Data(train_data=onehot_0.output.data))
pipeline.add_component(evaluation_0,
data=Data(data=hetero_lr_0.output.data))
pipeline.compile()
pipeline.fit()
def main(config="../../config.yaml", param="./lr_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]
if isinstance(param, str):
param = JobConfig.load_from_file(param)
assert isinstance(param, dict)
data_set = param.get("data_guest").split('/')[-1]
if data_set == "default_credit_hetero_guest.csv":
guest_data_table = 'default_credit_hetero_guest'
host_data_table = 'default_credit_hetero_host'
elif data_set == 'breast_hetero_guest.csv':
guest_data_table = 'breast_hetero_guest'
host_data_table = 'breast_hetero_host'
elif data_set == 'give_credit_hetero_guest.csv':
guest_data_table = 'give_credit_hetero_guest'
host_data_table = 'give_credit_hetero_host'
elif data_set == 'epsilon_5k_hetero_guest.csv':
guest_data_table = 'epsilon_5k_hetero_guest'
host_data_table = 'epsilon_5k_hetero_host'
else:
raise ValueError(f"Cannot recognized data_set: {data_set}")
guest_train_data = {"name": guest_data_table, "namespace": f"experiment{namespace}"}
host_train_data = {"name": host_data_table, "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 DataTransform components
data_transform_0 = DataTransform(name="data_transform_0") # start component numbering at 0
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(with_label=True, output_format="dense")
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
# define Intersection component
intersection_0 = Intersection(name="intersection_0")
lr_param = {
}
config_param = {
"penalty": param["penalty"],
"max_iter": param["max_iter"],
"alpha": param["alpha"],
"learning_rate": param["learning_rate"],
"optimizer": param["optimizer"],
"batch_size": param["batch_size"],
"shuffle": False,
"masked_rate": 0,
"early_stop": "diff",
"tol": 1e-5,
"floating_point_precision": param.get("floating_point_precision"),
"init_param": {
"init_method": param.get("init_method", 'random_uniform'),
"random_seed": param.get("random_seed", 103)
}
}
lr_param.update(config_param)
print(f"lr_param: {lr_param}, data_set: {data_set}")
hetero_lr_0 = HeteroLR(name='hetero_lr_0', **lr_param)
hetero_lr_1 = HeteroLR(name='hetero_lr_1')
evaluation_0 = Evaluation(name='evaluation_0', eval_type="binary")
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
pipeline.add_component(hetero_lr_0, data=Data(train_data=intersection_0.output.data))
pipeline.add_component(hetero_lr_1, data=Data(test_data=intersection_0.output.data),
model=Model(hetero_lr_0.output.model))
pipeline.add_component(evaluation_0, data=Data(data=hetero_lr_0.output.data))
# compile pipeline once finished adding modules, this step will form conf and dsl files for running job
pipeline.compile()
# fit model
job_parameters = JobParameters()
pipeline.fit(job_parameters)
lr_0_data = pipeline.get_component("hetero_lr_0").get_output_data().get("data")
lr_1_data = pipeline.get_component("hetero_lr_1").get_output_data().get("data")
lr_0_score = extract_data(lr_0_data, "predict_result")
lr_0_label = extract_data(lr_0_data, "label")
lr_1_score = extract_data(lr_1_data, "predict_result")
lr_1_label = extract_data(lr_1_data, "label")
lr_0_score_label = extract_data(lr_0_data, "predict_result", keep_id=True)
lr_1_score_label = extract_data(lr_1_data, "predict_result", keep_id=True)
result_summary = parse_summary_result(pipeline.get_component("evaluation_0").get_summary())
metric_lr = {
"score_diversity_ratio": classification_metric.Distribution.compute(lr_0_score_label, lr_1_score_label),
"ks_2samp": classification_metric.KSTest.compute(lr_0_score, lr_1_score),
"mAP_D_value": classification_metric.AveragePrecisionScore().compute(lr_0_score, lr_1_score, lr_0_label,
lr_1_label)}
result_summary["distribution_metrics"] = {"hetero_lr": metric_lr}
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, result_summary
def make_normal_dsl(config, namespace, lr_param, is_multi_host=False, has_validate=False,
is_cv=False, is_ovr=False, is_dense=True, need_evaluation=True):
parties = config.parties
guest = parties.guest[0]
if is_multi_host:
hosts = parties.host
else:
hosts = parties.host[0]
arbiter = parties.arbiter[0]
if is_ovr:
guest_train_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}
guest_eval_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
host_eval_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}
else:
guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
guest_eval_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_eval_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
train_line = []
# 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).component_param(table=guest_train_data)
# configure Reader for host
reader_0.get_party_instance(role='host', party_id=hosts).component_param(table=host_train_data)
# define DataTransform components
if is_dense:
data_transform_0 = DataTransform(name="data_transform_0", output_format='dense')
else:
data_transform_0 = DataTransform(name="data_transform_0", output_format='sparse')
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(with_label=True)
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(role='host', party_id=hosts).component_param(with_label=False)
train_line.append(data_transform_0)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
train_line.append(intersection_0)
last_cpn = None
if has_validate:
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_eval_data)
reader_1.get_party_instance(role='host', party_id=hosts).component_param(table=host_eval_data)
pipeline.add_component(reader_1)
last_cpn = reader_1
for cpn in train_line:
cpn_name = cpn.name
new_name = "_".join(cpn_name.split('_')[:-1] + ['1'])
validate_cpn = type(cpn)(name=new_name)
if hasattr(cpn.output, "model"):
pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data),
model=Model(cpn.output.model))
else:
pipeline.add_component(validate_cpn, data=Data(data=last_cpn.output.data))
last_cpn = validate_cpn
hetero_lr_0 = HeteroLR(**lr_param)
if has_validate:
pipeline.add_component(hetero_lr_0, data=Data(train_data=intersection_0.output.data,
validate_data=last_cpn.output.data))
else:
pipeline.add_component(hetero_lr_0, data=Data(train_data=intersection_0.output.data))
if is_cv:
pipeline.compile()
return pipeline
evaluation_data = [hetero_lr_0.output.data]
if has_validate:
hetero_lr_1 = HeteroLR(name='hetero_lr_1')
pipeline.add_component(hetero_lr_1, data=Data(test_data=last_cpn.output.data),
model=Model(hetero_lr_0.output.model))
evaluation_data.append(hetero_lr_1.output.data)
if need_evaluation:
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
pipeline.add_component(evaluation_0, data=Data(data=evaluation_data))
pipeline.compile()
return pipeline
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[0]
arbiter = parties.arbiter[0]
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
# guest_train_data = {"name": "default_credit_hetero_guest", "namespace": f"experiment{namespace}"}
# host_train_data = {"name": "default_credit_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).component_param(table=guest_train_data)
# configure Reader for host
reader_0.get_party_instance(
role='host', party_id=hosts).component_param(table=host_train_data)
data_transform_0 = DataTransform(name="data_transform_0",
output_format='dense')
# get DataTransform party instance of guest
data_transform_0_guest_party_instance = data_transform_0.get_party_instance(
role='guest', party_id=guest)
# configure DataTransform for guest
data_transform_0_guest_party_instance.component_param(with_label=True)
# get and configure DataTransform party instance of host
data_transform_0.get_party_instance(
role='host', party_id=hosts).component_param(with_label=False)
# define Intersection components
intersection_0 = Intersection(name="intersection_0")
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0,
data=Data(data=data_transform_0.output.data))
param = {
"model_id": "arbiter-9999#guest-10000#host-9999#model",
"model_version": "202108311438379703480",
"component_name": "hetero_lr_0",
"step_index": 2
}
model_loader_0 = ModelLoader(name="model_loader_0", **param)
lr_param = {
"penalty": "L2",
"optimizer": "rmsprop",
"tol": 0.0001,
"alpha": 0.01,
"early_stop": "diff",
"batch_size": -1,
"learning_rate": 0.15,
"init_param": {
"init_method": "zeros",
"fit_intercept": True
},
"encrypt_param": {
"key_length": 1024
},
"callback_param": {
"callbacks": ["ModelCheckpoint"],
"validation_freqs": 1,
"early_stopping_rounds": 1,
"metrics": None,
"use_first_metric_only": False,
"save_freq": 1
}
}
hetero_lr_0 = HeteroLR(name="hetero_lr_0", max_iter=30, **lr_param)
pipeline.add_component(model_loader_0)
pipeline.add_component(hetero_lr_0,
data=Data(train_data=intersection_0.output.data),
model=Model(model=model_loader_0.output.model))
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
pipeline.add_component(evaluation_0,
data=Data(data=hetero_lr_0.output.data))
pipeline.compile()
# fit model
pipeline.fit()
# query component summary
prettify(pipeline.get_component("hetero_lr_0").get_summary())
prettify(pipeline.get_component("evaluation_0").get_summary())
return pipeline
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
guest_train_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
guest_validate_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}
host_validate_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host)
data_transform_0, data_transform_1 = DataTransform(name="data_transform_0"), DataTransform(name='data_transform_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)
data_transform_0.get_party_instance(
role='guest', party_id=guest).component_param(
with_label=True, output_format="dense")
data_transform_0.get_party_instance(
role='host', party_id=host).component_param(
with_label=False, 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)
data_transform_1.get_party_instance(
role='guest', party_id=guest).component_param(
with_label=True, output_format="dense")
data_transform_1.get_party_instance(
role='host', party_id=host).component_param(
with_label=True, output_format="dense")
intersection_0 = Intersection(name="intersection_0")
intersection_1 = Intersection(name="intersection_1")
param = {
"method": "quantile",
"optimal_binning_param": {
"metric_method": "gini",
"min_bin_pct": 0.05,
"max_bin_pct": 0.8,
"init_bucket_method": "quantile",
"init_bin_nums": 100,
"mixture": True
},
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": [0, 1, 2],
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
hetero_feature_binning_0 = HeteroFeatureBinning(name="hetero_feature_binning_0", **param)
hetero_feature_binning_1 = HeteroFeatureBinning(name='hetero_feature_binning_1')
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(reader_1)
pipeline.add_component(
data_transform_1, data=Data(
data=reader_1.output.data), model=Model(
data_transform_0.output.model))
pipeline.add_component(intersection_0, data=Data(data=data_transform_0.output.data))
pipeline.add_component(intersection_1, data=Data(data=data_transform_1.output.data))
pipeline.add_component(hetero_feature_binning_0, data=Data(data=intersection_0.output.data))
pipeline.add_component(hetero_feature_binning_1, data=Data(data=intersection_1.output.data),
model=Model(hetero_feature_binning_0.output.model))
pipeline.compile()
pipeline.fit()
# predict
# deploy required components
pipeline.deploy_component([data_transform_0, intersection_0, hetero_feature_binning_0])
predict_pipeline = PipeLine()
# add data reader onto predict pipeline
predict_pipeline.add_component(reader_1)
# add selected components from train pipeline onto predict pipeline
# specify data source
predict_pipeline.add_component(
pipeline, data=Data(
predict_input={
pipeline.data_transform_0.input.data: reader_1.output.data}))
# run predict model
predict_pipeline.predict()
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]
guest_train_data = {"name": "breast_homo_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_homo_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)
reader_0 = Reader(name="reader_0")
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)
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_train_data)
reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_train_data)
data_transform_0 = DataTransform(name="data_transform_0", with_label=True)
data_transform_1 = DataTransform(name="data_transform_1")
federated_sample_0 = FederatedSample(name="federated_sample_0", mode="stratified", method="downsample",
fractions=[[0, 1.0], [1, 1.0]], task_type="h**o")
homo_binning_0 = HomoFeatureBinning(name='homo_binning_0', sample_bins=10, method="recursive_query")
homo_binning_1 = HomoFeatureBinning(name='homo_binning_1')
homo_onehot_0 = HomoOneHotEncoder(name='homo_onehot_0', need_alignment=True)
homo_onehot_1 = HomoOneHotEncoder(name='homo_onehot_1')
homo_lr_0 = HomoLR(name="homo_lr_0", penalty="L2", tol=0.0001, alpha=1.0,
optimizer="rmsprop", max_iter=5)
homo_lr_1 = HomoLR(name="homo_lr_1")
local_baseline_0 = LocalBaseline(name="local_baseline_0", model_name="LogisticRegression",
model_opts={"penalty": "l2", "tol": 0.0001, "C": 1.0, "fit_intercept": True,
"solver": "lbfgs", "max_iter": 5, "multi_class": "ovr"})
local_baseline_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True)
local_baseline_0.get_party_instance(role='host', party_id=host).component_param(need_run=True)
local_baseline_1 = LocalBaseline(name="local_baseline_1")
homo_secureboost_0 = HomoSecureBoost(name="homo_secureboost_0", num_trees=3)
homo_secureboost_1 = HomoSecureBoost(name="homo_secureboost_1", num_trees=3)
evaluation_0 = Evaluation(name="evaluation_0")
evaluation_1 = Evaluation(name="evaluation_1")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1, data=Data(data=reader_1.output.data),
model=Model(model=data_transform_0.output.model))
pipeline.add_component(federated_sample_0, data=Data(data=data_transform_0.output.data))
pipeline.add_component(homo_binning_0, data=Data(data=federated_sample_0.output.data))
pipeline.add_component(homo_binning_1, data=Data(data=data_transform_1.output.data),
model=Model(model=homo_binning_0.output.model))
pipeline.add_component(homo_onehot_0, data=Data(data=homo_binning_0.output.data))
pipeline.add_component(homo_onehot_1, data=Data(data=homo_binning_1.output.data),
model=Model(model=homo_onehot_0.output.model))
pipeline.add_component(homo_lr_0, data=Data(data=homo_onehot_0.output.data))
pipeline.add_component(homo_lr_1, data=Data(data=homo_onehot_1.output.data),
model=Model(model=homo_lr_0.output.model))
pipeline.add_component(local_baseline_0, data=Data(data=homo_onehot_0.output.data))
pipeline.add_component(local_baseline_1, data=Data(data=homo_onehot_1.output.data),
model=Model(model=local_baseline_0.output.model))
pipeline.add_component(homo_secureboost_0, data=Data(data=homo_onehot_0.output.data))
pipeline.add_component(homo_secureboost_1, data=Data(data=homo_onehot_1.output.data),
model=Model(model=homo_secureboost_0.output.model))
pipeline.add_component(evaluation_0,
data=Data(
data=[homo_lr_0.output.data, homo_lr_1.output.data,
local_baseline_0.output.data, local_baseline_1.output.data]))
pipeline.add_component(evaluation_1,
data=Data(
data=[homo_secureboost_0.output.data, homo_secureboost_1.output.data]))
pipeline.compile()
pipeline.fit()
print(pipeline.get_component("evaluation_0").get_summary())
print(pipeline.get_component("evaluation_1").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]
# data sets
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
guest_validate_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_validate_data = {
"name": "breast_hetero_host",
"namespace": f"experiment{namespace}"
}
# init pipeline
pipeline = PipeLine().set_initiator(role="guest",
party_id=guest).set_roles(
guest=guest,
host=host,
)
# set data reader and data-io
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)
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)
data_transform_0, data_transform_1 = DataTransform(
name="data_transform_0"), DataTransform(name="data_transform_1")
data_transform_0.get_party_instance(
role="guest", party_id=guest).component_param(with_label=True,
output_format="dense")
data_transform_0.get_party_instance(
role="host", party_id=host).component_param(with_label=False)
data_transform_1.get_party_instance(
role="guest", party_id=guest).component_param(with_label=True,
output_format="dense")
data_transform_1.get_party_instance(
role="host", party_id=host).component_param(with_label=False)
# data intersect component
intersect_0 = Intersection(name="intersection_0")
intersect_1 = Intersection(name="intersection_1")
# secure boost component
hetero_secure_boost_0 = HeteroSecureBoost(
name="hetero_secure_boost_0",
num_trees=3,
task_type="classification",
objective_param={"objective": "cross_entropy"},
encrypt_param={"method": "paillier"},
tree_param={"max_depth": 3},
validation_freqs=1)
# evaluation component
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary")
evaluation_1 = Evaluation(name="evaluation_1", eval_type="binary")
# transformer
transformer_0 = SBTTransformer(name='sbt_transformer_0', dense_format=True)
# local baseline
def get_local_baseline(idx):
return LocalBaseline(name="local_baseline_{}".format(idx),
model_name="LogisticRegression",
model_opts={
"penalty": "l2",
"tol": 0.0001,
"C": 1.0,
"fit_intercept": True,
"solver": "lbfgs",
"max_iter": 50
})
local_baseline_0 = get_local_baseline(0)
local_baseline_0.get_party_instance(
role='guest', party_id=guest).component_param(need_run=True)
local_baseline_0.get_party_instance(
role='host', party_id=host).component_param(need_run=False)
local_baseline_1 = get_local_baseline(1)
local_baseline_1.get_party_instance(
role='guest', party_id=guest).component_param(need_run=True)
local_baseline_1.get_party_instance(
role='host', party_id=host).component_param(need_run=False)
evaluation_1.get_party_instance(
role='host', party_id=host).component_param(need_run=False)
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(data_transform_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(data_transform_1,
data=Data(data=reader_1.output.data),
model=Model(data_transform_0.output.model))
pipeline.add_component(intersect_0,
data=Data(data=data_transform_0.output.data))
pipeline.add_component(intersect_1,
data=Data(data=data_transform_1.output.data))
pipeline.add_component(hetero_secure_boost_0,
data=Data(train_data=intersect_0.output.data,
validate_data=intersect_1.output.data))
pipeline.add_component(
transformer_0,
data=Data(data=intersect_0.output.data),
model=Model(isometric_model=hetero_secure_boost_0.output.model))
pipeline.add_component(local_baseline_0,
data=Data(data=transformer_0.output.data))
pipeline.add_component(local_baseline_1,
data=Data(data=intersect_0.output.data))
pipeline.add_component(evaluation_0,
data=Data(data=local_baseline_0.output.data))
pipeline.add_component(evaluation_1,
data=Data(data=local_baseline_1.output.data))
pipeline.compile()
pipeline.fit()
