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
})
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": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_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, 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, output_format="dense",
label_type="int", label_name="y")
data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0", intersect_method="rsa", sync_intersect_ids=True,
only_output_key=False)
hetero_lr_0 = HeteroLR(name="hetero_lr_0", penalty="L2", optimizer="nesterov_momentum_sgd",
tol=0.0001, alpha=0.0001, max_iter=30, batch_size=-1,
early_stop="diff", learning_rate=0.15, init_param={"init_method": "zeros"})
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=False)
evaluation_0 = Evaluation(name="evaluation_0", eval_type="multi", pos_label=1)
evaluation_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True)
evaluation_0.get_party_instance(role='host', party_id=host).component_param(need_run=False)
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(local_baseline_0, data=Data(train_data=intersection_0.output.data))
pipeline.add_component(evaluation_0, data=Data(data=[hetero_lr_0.output.data, local_baseline_0.output.data]))
pipeline.compile()
pipeline.fit()
# predict
pipeline.deploy_component([data_transform_0, intersection_0, hetero_lr_0, local_baseline_0])
predict_pipeline = PipeLine()
predict_pipeline.add_component(reader_0)
predict_pipeline.add_component(pipeline,
data=Data(predict_input={pipeline.data_transform_0.input.data: reader_0.output.data}))
predict_pipeline.add_component(evaluation_0, data=Data(data=[hetero_lr_0.output.data, local_baseline_0.output.data]))
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]
backend = config.backend
work_mode = config.work_mode
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)
dataio_0 = DataIO(name="dataio_0", with_label=True, output_format="dense",
label_type="int", label_name="y")
homo_lr_0 = HomoLR(name="homo_lr_0", penalty="L2", optimizer="sgd",
tol=0.0001, alpha=0.01, max_iter=30, batch_size=-1,
early_stop="weight_diff", learning_rate=0.15, init_param={"init_method": "zeros"})
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": "saga", "max_iter": 2})
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)
evaluation_0 = Evaluation(name="evaluation_0", eval_type="binary", pos_label=1)
evaluation_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True)
evaluation_0.get_party_instance(role='host', party_id=host).component_param(need_run=False)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(homo_lr_0, data=Data(train_data=dataio_0.output.data))
pipeline.add_component(local_baseline_0, data=Data(train_data=dataio_0.output.data))
pipeline.add_component(evaluation_0, data=Data(data=[homo_lr_0.output.data, local_baseline_0.output.data]))
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]
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]
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)
reader_2 = Reader(name="reader_2")
reader_2.get_party_instance(
role='guest', party_id=guest).component_param(table=guest_train_data)
reader_2.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,
missing_fill=True,
outlier_replace=True)
data_transform_0.get_party_instance(
role='host', party_id=host).component_param(with_label=False,
missing_fill=True,
outlier_replace=True)
data_transform_1 = DataTransform(name="data_transform_1")
data_transform_2 = DataTransform(name="data_transform_2")
intersection_0 = Intersection(name="intersection_0")
intersection_1 = Intersection(name="intersection_1")
intersection_2 = Intersection(name="intersection_2")
union_0 = Union(name="union_0")
federated_sample_0 = FederatedSample(name="federated_sample_0",
mode="stratified",
method="downsample",
fractions=[[0, 1.0], [1, 1.0]])
feature_scale_0 = FeatureScale(name="feature_scale_0")
feature_scale_1 = FeatureScale(name="feature_scale_1")
hetero_feature_binning_0 = HeteroFeatureBinning(
name="hetero_feature_binning_0")
hetero_feature_binning_1 = HeteroFeatureBinning(
name="hetero_feature_binning_1")
hetero_feature_selection_0 = HeteroFeatureSelection(
name="hetero_feature_selection_0")
hetero_feature_selection_1 = HeteroFeatureSelection(
name="hetero_feature_selection_1")
one_hot_0 = OneHotEncoder(name="one_hot_0")
one_hot_1 = OneHotEncoder(name="one_hot_1")
hetero_lr_0 = HeteroLR(name="hetero_lr_0",
penalty="L2",
optimizer="rmsprop",
tol=1e-5,
init_param={"init_method": "random_uniform"},
alpha=0.01,
max_iter=3,
early_stop="diff",
batch_size=320,
learning_rate=0.15)
hetero_lr_1 = HeteroLR(name="hetero_lr_1")
hetero_lr_2 = HeteroLR(name="hetero_lr_2",
penalty="L2",
optimizer="rmsprop",
tol=1e-5,
init_param={"init_method": "random_uniform"},
alpha=0.01,
max_iter=3,
early_stop="diff",
batch_size=320,
learning_rate=0.15,
cv_param={
"n_splits": 5,
"shuffle": True,
"random_seed": 103,
"need_cv": True
})
hetero_sshe_lr_0 = HeteroSSHELR(
name="hetero_sshe_lr_0",
reveal_every_iter=True,
reveal_strategy="respectively",
penalty="L2",
optimizer="rmsprop",
tol=1e-5,
batch_size=320,
learning_rate=0.15,
init_param={"init_method": "random_uniform"},
alpha=0.01,
max_iter=3)
hetero_sshe_lr_1 = HeteroSSHELR(name="hetero_sshe_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=False)
local_baseline_1 = LocalBaseline(name="local_baseline_1")
hetero_secureboost_0 = HeteroSecureBoost(name="hetero_secureboost_0",
num_trees=3)
hetero_secureboost_1 = HeteroSecureBoost(name="hetero_secureboost_1")
hetero_secureboost_2 = HeteroSecureBoost(name="hetero_secureboost_2",
num_trees=3,
cv_param={
"shuffle": False,
"need_cv": True
})
hetero_linr_0 = HeteroLinR(name="hetero_linr_0",
penalty="L2",
optimizer="sgd",
tol=0.001,
alpha=0.01,
max_iter=3,
early_stop="weight_diff",
batch_size=-1,
learning_rate=0.15,
decay=0.0,
decay_sqrt=False,
init_param={"init_method": "zeros"},
floating_point_precision=23)
hetero_linr_1 = HeteroLinR(name="hetero_linr_1")
hetero_sshe_linr_0 = HeteroSSHELinR(name="hetero_sshe_linr_0",
max_iter=5,
early_stop="weight_diff",
batch_size=-1)
hetero_sshe_linr_1 = HeteroSSHELinR(name="hetero_sshe_linr_1")
hetero_poisson_0 = HeteroPoisson(name="hetero_poisson_0",
early_stop="weight_diff",
max_iter=10,
alpha=100.0,
batch_size=-1,
learning_rate=0.01,
optimizer="rmsprop",
exposure_colname="exposure",
decay_sqrt=False,
tol=0.001,
init_param={"init_method": "zeros"},
penalty="L2")
hetero_poisson_1 = HeteroPoisson(name="hetero_poisson_1")
hetero_sshe_poisson_0 = HeteroSSHEPoisson(name="hetero_sshe_poisson_0",
max_iter=5)
hetero_sshe_poisson_1 = HeteroSSHEPoisson(name="hetero_sshe_poisson_1")
evaluation_0 = Evaluation(name="evaluation_0")
evaluation_1 = Evaluation(name="evaluation_1")
evaluation_2 = Evaluation(name="evaluation_2")
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(reader_2)
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(data_transform_2,
data=Data(data=reader_2.output.data),
model=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(intersection_2,
data=Data(data=data_transform_2.output.data))
pipeline.add_component(
union_0,
data=Data(
data=[intersection_0.output.data, intersection_2.output.data]))
pipeline.add_component(federated_sample_0,
data=Data(data=intersection_1.output.data))
pipeline.add_component(feature_scale_0,
data=Data(data=union_0.output.data))
pipeline.add_component(feature_scale_1,
data=Data(data=federated_sample_0.output.data),
model=Model(model=feature_scale_0.output.model))
pipeline.add_component(hetero_feature_binning_0,
data=Data(data=feature_scale_0.output.data))
pipeline.add_component(
hetero_feature_binning_1,
data=Data(data=feature_scale_1.output.data),
model=Model(model=hetero_feature_binning_0.output.model))
pipeline.add_component(
hetero_feature_selection_0,
data=Data(data=hetero_feature_binning_0.output.data))
pipeline.add_component(
hetero_feature_selection_1,
data=Data(data=hetero_feature_binning_1.output.data),
model=Model(model=hetero_feature_selection_0.output.model))
pipeline.add_component(
one_hot_0, data=Data(data=hetero_feature_selection_0.output.data))
pipeline.add_component(
one_hot_1,
data=Data(data=hetero_feature_selection_1.output.data),
model=Model(model=one_hot_0.output.model))
pipeline.add_component(hetero_lr_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(hetero_lr_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_lr_0.output.model))
pipeline.add_component(hetero_lr_2,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(local_baseline_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(local_baseline_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=local_baseline_0.output.model))
pipeline.add_component(hetero_sshe_lr_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(hetero_sshe_lr_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_sshe_lr_0.output.model))
pipeline.add_component(hetero_secureboost_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(
hetero_secureboost_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_secureboost_0.output.model))
pipeline.add_component(hetero_secureboost_2,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(hetero_linr_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(hetero_linr_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_linr_0.output.model))
pipeline.add_component(hetero_sshe_linr_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(hetero_sshe_linr_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_sshe_linr_0.output.model))
pipeline.add_component(hetero_poisson_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(hetero_poisson_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_poisson_0.output.model))
pipeline.add_component(
evaluation_0,
data=Data(data=[
hetero_lr_0.output.data, hetero_lr_1.output.data,
hetero_sshe_lr_0.output.data, hetero_sshe_lr_1.output.data,
local_baseline_0.output.data, local_baseline_1.output.data
]))
pipeline.add_component(hetero_sshe_poisson_0,
data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(
hetero_sshe_poisson_1,
data=Data(test_data=one_hot_1.output.data),
model=Model(model=hetero_sshe_poisson_0.output.model))
pipeline.add_component(
evaluation_1,
data=Data(data=[
hetero_linr_0.output.data, hetero_linr_1.output.data,
hetero_sshe_linr_0.output.data, hetero_linr_1.output.data
]))
pipeline.add_component(
evaluation_2,
data=Data(data=[
hetero_poisson_0.output.data, hetero_poisson_1.output.data,
hetero_sshe_poisson_0.output.data,
hetero_sshe_poisson_1.output.data
]))
pipeline.compile()
pipeline.fit()
print(pipeline.get_component("evaluation_0").get_summary())
print(pipeline.get_component("evaluation_1").get_summary())
print(pipeline.get_component("evaluation_2").get_summary())