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}"
}
# 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", 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(dataio_0, data=Data(data=reader_0.output.data))
# set data input sources of intersection components
pipeline.add_component(homo_binning_0,
data=Data(data=dataio_0.output.data))
pipeline.add_component(homo_binning_1,
data=Data(data=dataio_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
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": "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)
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,
input_format="tag",
tag_with_value=True)
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_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]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {
"name": "vehicle_scale_homo_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "vehicle_scale_homo_host",
"namespace": f"experiment{namespace}"
}
pipeline = PipeLine().set_initiator(
role='guest', party_id=guest).set_roles(guest=guest,
host=host,
arbiter=arbiter)
dataio_0 = DataIO(name="dataio_0")
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.get_party_instance(role='guest', party_id=guest).component_param(
with_label=True, output_format="dense")
dataio_0.get_party_instance(role='host', party_id=host).component_param(
with_label=True, output_format="dense")
homo_secureboost_0 = HomoSecureBoost(
name="homo_secureboost_0",
num_trees=3,
task_type='classification',
objective_param={"objective": "cross_entropy"},
tree_param={"max_depth": 3},
cv_param={
"need_cv": True,
"shuffle": False,
"n_splits": 5
})
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(homo_secureboost_0,
data=Data(train_data=dataio_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)
backend = config.backend
work_mode = config.work_mode
selection_param = {
"name": "hetero_feature_selection_0",
"select_col_indexes": -1,
"select_names": [],
"filter_methods": ["manually"],
"manually_param": {
"filter_out_indexes": None,
"filter_out_names": None,
"left_col_indexes": [0, 1, 2],
"left_col_names": ["x3"]
}
}
pipeline = common_tools.make_normal_dsl(config, namespace, selection_param)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
common_tools.prettify(
pipeline.get_component("hetero_feature_selection_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
param = {
"name": "hetero_feature_binning_0",
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
pipeline = common_tools.make_add_one_hot_dsl(config, namespace, param)
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)
backend = config.backend
work_mode = config.work_mode
lr_param = {
"name": "hetero_lr_0",
"penalty": "L2",
"optimizer": "nesterov_momentum_sgd",
"tol": 1e-05,
"alpha": 0.0001,
"max_iter": 10,
"early_stop": "diff",
"multi_class": "ovr",
"batch_size": -1,
"learning_rate": 0.15,
"init_param": {
"init_method": "zeros"
}
}
pipeline = common_tools.make_normal_dsl(config, namespace, lr_param, is_ovr=True)
# fit model
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
# query component summary
common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
binning_param = {
"name": 'hetero_feature_binning_0',
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
selection_param = {
"name": "hetero_feature_selection_0",
"select_col_indexes": -1,
"select_names": [],
"filter_methods": ["manually", "iv_value_thres", "iv_percentile"],
"manually_param": {
"filter_out_indexes": [],
"filter_out_names": []
},
"unique_param": {
"eps": 1e-06
},
"iv_value_param": {
"value_threshold": 0.1
},
"iv_percentile_param": {
"percentile_threshold": 0.9
},
"variance_coe_param": {
"value_threshold": 0.3
},
"outlier_param": {
"percentile": 0.95,
"upper_threshold": 2.0
}
}
pipeline = common_tools.make_single_predict_pipeline(
config, namespace, selection_param, binning_param=binning_param)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
common_tools.prettify(
pipeline.get_component("hetero_feature_selection_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
backend = config.backend
work_mode = config.work_mode
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=False, output_format="dense")
dataio_0.get_party_instance(role='host', party_id=host).component_param(
with_label=False, output_format="dense")
param = {
"intersect_method": "raw",
"sync_intersect_ids": True,
"join_role": "host",
"with_encode": True,
"only_output_key": True,
"encode_params": {
"encode_method": "sm3",
"salt": "12345",
"base64": False
}
}
intersect_0 = Intersection(name="intersect_0", **param)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersect_0, data=Data(data=dataio_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]
backend = config.backend
work_mode = config.work_mode
# 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}"}
# 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(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, output_format="dense")
dataio_0.get_party_instance(role="host", party_id=host).component_param(with_label=False)
# data intersect component
intersect_0 = Intersection(name="intersection_0")
# 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": "iterativeAffine"},
tree_param={"max_depth": 3},
validation_freqs=1,
cv_param={
"need_cv": True,
"n_splits": 5,
"shuffle": False,
"random_seed": 103
}
)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersect_0, data=Data(data=dataio_0.output.data))
pipeline.add_component(hetero_secure_boost_0, data=Data(train_data=intersect_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
print("fitting hetero secureboost done, result:")
print(pipeline.get_component("hetero_secure_boost_0").get_summary())
def run_homo_nn_pipeline(config, namespace, data: dict, nn_component,
num_host):
if isinstance(config, str):
config = load_job_config(config)
guest_train_data = data["guest"]
host_train_data = data["host"][:num_host]
for d in [guest_train_data, *host_train_data]:
d["namespace"] = f"{d['namespace']}{namespace}"
hosts = config.parties.host[:num_host]
pipeline = (PipeLine().set_initiator(
role="guest", party_id=config.parties.guest[0]).set_roles(
guest=config.parties.guest[0],
host=hosts,
arbiter=config.parties.arbiter))
reader_0 = Reader(name="reader_0")
reader_0.get_party_instance(
role="guest", party_id=config.parties.guest[0]).component_param(
table=guest_train_data)
for i in range(num_host):
reader_0.get_party_instance(
role="host",
party_id=hosts[i]).component_param(table=host_train_data[i])
dataio_0 = DataIO(name="dataio_0", with_label=True)
dataio_0.get_party_instance(
role="guest", party_id=config.parties.guest[0]).component_param(
with_label=True, output_format="dense")
dataio_0.get_party_instance(
role="host", party_id=hosts).component_param(with_label=True)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(nn_component,
data=Data(train_data=dataio_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=config.backend,
work_mode=config.work_mode)
pipeline.fit(job_parameters)
print(pipeline.get_component("homo_nn_0").get_summary())
pipeline.deploy_component([dataio_0, nn_component])
# predict
predict_pipeline = PipeLine()
predict_pipeline.add_component(reader_0)
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=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
arbiter = parties.arbiter[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
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, missing_fill=True,
outlier_replace=True)
dataio_0.get_party_instance(role='host', party_id=host).component_param(with_label=False, missing_fill=True,
outlier_replace=True)
intersection_0 = Intersection(name="intersection_0")
federated_sample_0 = FederatedSample(name="federated_sample_0", mode="stratified", method="upsample",
fractions=[[0, 1.5], [1, 2.0]])
feature_scale_0 = FeatureScale(name="feature_scale_0", method="min_max_scale", mode="normal")
feature_scale_0.get_party_instance(role='guest', party_id=guest).component_param(feat_upper=[1, 2, 1, 1, 0.5, 1, 2, 2, 1, 1])
hetero_feature_binning_0 = HeteroFeatureBinning(name="hetero_feature_binning_0")
hetero_feature_selection_0 = HeteroFeatureSelection(name="hetero_feature_selection_0")
one_hot_0 = OneHotEncoder(name="one_hot_0")
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=10, early_stop="diff", batch_size=320, learning_rate=0.15)
evaluation_0 = Evaluation(name="evaluation_0")
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(federated_sample_0, data=Data(data=intersection_0.output.data))
pipeline.add_component(feature_scale_0, data=Data(data=federated_sample_0.output.data))
pipeline.add_component(hetero_feature_binning_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))
pipeline.add_component(one_hot_0, data=Data(data=hetero_feature_selection_0.output.data))
pipeline.add_component(hetero_lr_0, data=Data(train_data=one_hot_0.output.data))
pipeline.add_component(evaluation_0, data=Data(data=hetero_lr_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
print(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
backend = config.backend
work_mode = config.work_mode
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)
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,
output_format="dense",
label_name="y",
label_type="int")
dataio_0.get_party_instance(role='host',
party_id=host).component_param(with_label=True)
homo_data_split_0 = HomoDataSplit(name="homo_data_split_0",
stratified=True,
test_size=0.3,
validate_size=0.2)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(homo_data_split_0,
data=Data(data=dataio_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
print(pipeline.get_component("homo_data_split_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]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}
# initialize pipeline
pipeline = PipeLine()
# set job initiator
pipeline.set_initiator(role="guest", party_id=guest).set_roles(guest=guest)
# 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)
# define ColumnExpand components
column_expand_0 = ColumnExpand(name="column_expand_0")
column_expand_0.get_party_instance(
role="guest", party_id=guest).component_param(
need_run=True,
method="manual",
append_header=["x_0", "x_1", "x_2", "x_3"],
fill_value=[0, 0.2, 0.5, 1])
# define DataIO components
dataio_0 = DataIO(name="dataio_0") # start component numbering at 0
# get DataIO party instance of guest
dataio_0_guest_party_instance = dataio_0.get_party_instance(role="guest",
party_id=guest)
# configure DataIO for guest
dataio_0_guest_party_instance.component_param(with_label=True,
output_format="dense")
# add components to pipeline, in order of task execution
pipeline.add_component(reader_0)
pipeline.add_component(column_expand_0,
data=Data(data=reader_0.output.data))
pipeline.add_component(dataio_0,
data=Data(data=column_expand_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)
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
pipeline = make_normal_dsl(config, namespace)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
common_tools.prettify(pipeline.get_component("hetero_feature_selection_0").get_summary())
common_tools.prettify(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {
"name": "ionosphere_scale_hetero_guest",
"namespace": f"experiment{namespace}"
}
host_train_data = {
"name": "ionosphere_scale_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,
label_name="LABEL",
missing_fill=True,
missing_fill_method="mean",
outlier_replace=True)
data_transform_0.get_party_instance(role='host',
party_id=host).component_param(
with_label=False,
missing_fill=True,
missing_fill_method="designated",
default_value=0,
outlier_replace=False)
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0,
data=Data(data=reader_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]
hosts = parties.host
arbiter = parties.arbiter[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {"name": "breast_hetero_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "breast_hetero_host", "namespace": f"experiment{namespace}"}
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=hosts, 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=hosts).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="y",
label_type="int", output_format="dense")
dataio_0.get_party_instance(role='host', party_id=hosts).component_param(with_label=False)
intersection_0 = Intersection(name="intersection_0")
sample_weight_0 = SampleWeight(name="sample_weight_0")
sample_weight_0.get_party_instance(role='guest', party_id=guest).component_param(need_run=True,
class_weight="balanced")
sample_weight_0.get_party_instance(role='host', party_id=hosts).component_param(need_run=False)
hetero_lr_0 = HeteroLR(name="hetero_lr_0", optimizer="nesterov_momentum_sgd", tol=0.001,
alpha=0.01, max_iter=20, early_stop="weight_diff", batch_size=-1,
learning_rate=0.15,
init_param={"init_method": "zeros"})
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(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersection_0, data=Data(data=dataio_0.output.data))
pipeline.add_component(sample_weight_0, data=Data(data=intersection_0.output.data))
pipeline.add_component(hetero_lr_0, data=Data(train_data=sample_weight_0.output.data))
pipeline.add_component(evaluation_0, data=Data(data=hetero_lr_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)
backend = config.backend
work_mode = config.work_mode
fast_sbt_param = {
"name": "fast_secureboost_0",
"task_type": "classification",
"learning_rate": 0.1,
"num_trees": 4,
"subsample_feature_rate": 1,
"n_iter_no_change": False,
"work_mode": "layered",
"guest_depth": 2,
"host_depth": 3,
"tol": 0.0001,
"bin_num": 50,
"metrics": ["Recall", "ks", "auc", "roc"],
"objective_param": {
"objective": "cross_entropy"
},
"encrypt_param": {
"method": "iterativeAffine"
},
"predict_param": {
"threshold": 0.5
},
"validation_freqs": 1
}
selection_param = {
"name": "hetero_feature_selection_0",
"select_col_indexes": -1,
"select_names": [],
"filter_methods": [
"hetero_fast_sbt_filter"
],
"sbt_param": {
"metrics": "feature_importance",
"filter_type": "threshold",
"take_high": True,
"threshold": 0.03
}}
pipeline = common_tools.make_normal_dsl(config, namespace, selection_param,
fast_sbt_param=fast_sbt_param)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
common_tools.prettify(pipeline.get_component("hetero_feature_selection_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]
backend = config.backend
work_mode = config.work_mode
guest_train_data = [{
"name": "breast_hetero_guest",
"namespace": f"experiment{namespace}"
}, {
"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[0])
reader_1 = Reader(name="reader_1")
reader_1.get_party_instance(
role='guest',
party_id=guest).component_param(table=guest_train_data[1])
union_0 = Union(name="union_0", allow_missing=False, keep_duplicate=True)
dataio_0 = DataIO(name="dataio_0",
with_label=True,
output_format="dense",
label_name="y",
missing_fill=False,
outlier_replace=False)
pipeline.add_component(reader_0)
pipeline.add_component(reader_1)
pipeline.add_component(
union_0, data=Data(data=[reader_0.output.data, reader_1.output.data]))
pipeline.add_component(dataio_0, data=Data(data=union_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)
backend = config.backend
work_mode = config.work_mode
binning_param = {
"name": 'hetero_feature_binning_0',
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
selection_param = {
"name": "hetero_feature_selection_0",
"select_col_indexes": -1,
"select_names": [],
"filter_methods": ["iv_value_thres", "iv_filter"],
"iv_value_param": {
"value_threshold": 1,
"local_only": True
},
"iv_top_k_param": {
"k": 7,
"local_only": False
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
selection_param,
binning_param=binning_param)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
common_tools.prettify(
pipeline.get_component("hetero_feature_selection_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {"name": "nus_wide_guest", "namespace": f"experiment{namespace}"}
host_train_data = {"name": "nus_wide_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, output_format="dense")
dataio_0.get_party_instance(role='host', party_id=host).component_param(with_label=False)
hetero_ftl_0 = HeteroFTL(name='hetero_ftl_0',
epochs=10,
alpha=1,
batch_size=-1,
mode='plain',
communication_efficient=True,
local_round=5)
hetero_ftl_0.add_nn_layer(Dense(units=32, activation='sigmoid',
kernel_initializer=initializers.RandomNormal(stddev=1.0,
dtype="float32"),
bias_initializer=initializers.Zeros()))
hetero_ftl_0.compile(optimizer=optimizers.Adam(lr=0.01))
evaluation_0 = Evaluation(name='evaluation_0', eval_type="binary")
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(hetero_ftl_0, data=Data(train_data=dataio_0.output.data))
pipeline.add_component(evaluation_0, data=Data(data=hetero_ftl_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]
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)
backend = config.backend
work_mode = config.work_mode
lr_param = {
"name": "hetero_lr_0",
"penalty": "L2",
"optimizer": "rmsprop",
"tol": 0.0001,
"alpha": 0.01,
"max_iter": 30,
"early_stop": "diff",
"batch_size": -1,
"learning_rate": 0.15,
"validation_freqs": 1,
"early_stopping_rounds": 3,
"metrics": [],
"use_first_metric_only": False,
"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
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
lr_param,
has_validate=True)
# fit model
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
# query component summary
common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
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
}
}
pipeline = common_tools.make_normal_dsl(config, namespace, lr_param)
# dsl_json = predict_pipeline.get_predict_dsl()
# conf_json = predict_pipeline.get_predict_conf()
# import json
# json.dump(dsl_json, open('./hetero-lr-normal-predict-dsl.json', 'w'), indent=4)
# json.dump(conf_json, open('./hetero-lr-normal-predict-conf.json', 'w'), indent=4)
# fit model
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
# query component summary
common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
common_tools.prettify(pipeline.get_component("evaluation_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host[0]
arbiter = parties.arbiter[0]
backend = config.backend
work_mode = config.work_mode
guest_train_data = {"name": "breast_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]
backend = config.backend
work_mode = config.work_mode
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)
dataio_0 = DataIO(name="dataio_0")
dataio_1 = DataIO(name="dataio_1")
dataio_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=False, output_format="dense")
dataio_1.get_party_instance(role='guest', party_id=guest).component_param(with_label=False, output_format="dense")
dataio_0.get_party_instance(role='host', party_id=host).component_param(with_label=False, output_format="dense")
dataio_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(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(psi_0, data=Data(train_data=dataio_0.output.data, validate_data=dataio_1.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)
backend = config.backend
work_mode = config.work_mode
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_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
guest_param = copy.deepcopy(param)
guest_param["method"] = 'quantile'
host_param = copy.deepcopy(param)
host_param["method"] = 'optimal'
pipeline = common_tools.make_asymmetric_dsl(config,
namespace,
guest_param=guest_param,
host_param=host_param)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
def run_pearson_pipeline(config, namespace, data, common_param=None, guest_only_param=None, host_only_param=None):
if isinstance(config, str):
config = load_job_config(config)
guest_data = data["guest"]
host_data = data["host"][0]
guest_data["namespace"] = f"{guest_data['namespace']}{namespace}"
host_data["namespace"] = f"{host_data['namespace']}{namespace}"
pipeline = PipeLine() \
.set_initiator(role='guest', party_id=config.parties.guest[0]) \
.set_roles(guest=config.parties.guest[0], host=config.parties.host[0])
reader_0 = Reader(name="reader_0")
reader_0.get_party_instance(role='guest', party_id=config.parties.guest[0]).component_param(table=guest_data)
reader_0.get_party_instance(role='host', party_id=config.parties.host[0]).component_param(table=host_data)
dataio_0 = DataIO(name="dataio_0")
dataio_0.get_party_instance(role='guest', party_id=config.parties.guest[0]) \
.component_param(with_label=True, output_format="dense")
dataio_0.get_party_instance(role='host', party_id=config.parties.host[0]).component_param(with_label=False)
intersect_0 = Intersection(name="intersection_0")
if common_param is None:
common_param = {}
hetero_pearson_component = HeteroPearson(name="hetero_pearson_0", **common_param)
if guest_only_param:
hetero_pearson_component.get_party_instance("guest", config.parties.guest[0]).component_param(**guest_only_param)
if host_only_param:
hetero_pearson_component.get_party_instance("host", config.parties.host[0]).component_param(**host_only_param)
pipeline.add_component(reader_0)
pipeline.add_component(dataio_0, data=Data(data=reader_0.output.data))
pipeline.add_component(intersect_0, data=Data(data=dataio_0.output.data))
pipeline.add_component(hetero_pearson_component, data=Data(train_data=intersect_0.output.data))
pipeline.compile()
job_parameters = JobParameters(backend=config.backend, work_mode=config.work_mode)
pipeline.fit(job_parameters)
return pipeline
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
param = {
"name": "hetero_feature_binning_0",
"method": "optimal",
"optimal_binning_param": {
"metric_method": "iv",
"min_bin_pct": 0.05,
"max_bin_pct": 0.8,
"init_bucket_method": "quantile",
"init_bin_nums": 100,
"mixture": False
},
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
is_multi_host=True,
bin_param=param,
host_dense_output=False)
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)
backend = config.backend
work_mode = config.work_mode
lr_param = {
"name": "hetero_lr_0",
"penalty": "L2",
"optimizer": "nesterov_momentum_sgd",
"tol": 0.0001,
"alpha": 0.01,
"max_iter": 10,
"early_stop": "diff",
"batch_size": -1,
"learning_rate": 0.15,
"init_param": {
"init_method": "random_uniform"
},
"cv_param": {
"n_splits": 3,
"shuffle": False,
"random_seed": 103,
"need_cv": True
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
lr_param,
is_dense=False,
need_evaluation=False)
# fit model
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
# query component summary
common_tools.prettify(pipeline.get_component("hetero_lr_0").get_summary())
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
parties = config.parties
guest = parties.guest[0]
host = parties.host
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}"
}
# 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", with_label=True,
output_format="dense") # start component numbering at 0
scale_0 = FeatureScale(name='scale_0')
param = {
"penalty": "L2",
"optimizer": "sgd",
"tol": 1e-05,
"alpha": 0.01,
"max_iter": 30,
"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
}
}
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(dataio_0, data=Data(data=reader_0.output.data))
# set data input sources of intersection components
pipeline.add_component(scale_0, data=Data(data=dataio_0.output.data))
pipeline.add_component(homo_lr_0,
data=Data(train_data=scale_0.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
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
# 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))