def run_experiment(train_data, test_data, batch_size, learning_rate, epoch):
Xa_train, Xb_train, Xc_train, y_train = train_data
Xa_test, Xb_test, Xc_test, y_test = test_data
print("################################ Wire Federated Models ############################")
# create local models for both party A, party B and party C
party_a_local_model = LocalModel(input_dim=Xa_train.shape[1], output_dim=60, learning_rate=learning_rate)
party_b_local_model = LocalModel(input_dim=Xb_train.shape[1], output_dim=60, learning_rate=learning_rate)
party_c_local_model = LocalModel(input_dim=Xc_train.shape[1], output_dim=60, learning_rate=learning_rate)
# create lr model for both party A, party B and party C. Each party has a part of the whole lr model and
# only party A has the bias since only party A has the labels.
party_a_dense_model = DenseModel(party_a_local_model.get_output_dim(), 1, learning_rate=learning_rate, bias=True)
party_b_dense_model = DenseModel(party_b_local_model.get_output_dim(), 1, learning_rate=learning_rate, bias=False)
party_c_dense_model = DenseModel(party_c_local_model.get_output_dim(), 1, learning_rate=learning_rate, bias=False)
partyA = VFLGuestModel(local_model=party_a_local_model)
partyA.set_dense_model(party_a_dense_model)
partyB = VFLHostModel(local_model=party_b_local_model)
partyB.set_dense_model(party_b_dense_model)
partyC = VFLHostModel(local_model=party_c_local_model)
partyC.set_dense_model(party_c_dense_model)
party_B_id = "B"
party_C_id = "C"
federatedLearning = VerticalMultiplePartyLogisticRegressionFederatedLearning(partyA)
federatedLearning.add_party(id=party_B_id, party_model=partyB)
federatedLearning.add_party(id=party_C_id, party_model=partyC)
print("################################ Train Federated Models ############################")
fl_fixture = FederatedLearningFixture(federatedLearning)
train_data = {federatedLearning.get_main_party_id(): {"X": Xa_train, "Y": y_train},
"party_list": {party_B_id: Xb_train, party_C_id: Xc_train}}
test_data = {federatedLearning.get_main_party_id(): {"X": Xa_test, "Y": y_test},
"party_list": {party_B_id: Xb_test, party_C_id: Xc_test}}
fl_fixture.fit(train_data=train_data, test_data=test_data, epochs=epoch, batch_size=batch_size)
def run_experiment(train_data, test_data, batch_size, learning_rate, epoch):
print("hyper-parameters:")
print("batch size: {0}".format(batch_size))
print("learning rate: {0}".format(learning_rate))
Xa_train, Xb_train, y_train = train_data
Xa_test, Xb_test, y_test = test_data
print("################################ Wire Federated Models ############################")
# create local models for both party A and party B
party_a_local_model = LocalModel(input_dim=Xa_train.shape[1], output_dim=10, learning_rate=learning_rate)
party_b_local_model = LocalModel(input_dim=Xb_train.shape[1], output_dim=20, learning_rate=learning_rate)
# create lr model for both party A and party B. Each party has a part of the whole lr model and only party A has
# the bias since only party A has the labels.
party_a_dense_model = DenseModel(party_a_local_model.get_output_dim(), 1, learning_rate=learning_rate, bias=True)
party_b_dense_model = DenseModel(party_b_local_model.get_output_dim(), 1, learning_rate=learning_rate, bias=False)
partyA = VFLGuestModel(local_model=party_a_local_model)
partyA.set_dense_model(party_a_dense_model)
partyB = VFLHostModel(local_model=party_b_local_model)
partyB.set_dense_model(party_b_dense_model)
party_B_id = "B"
federatedLearning = VerticalMultiplePartyLogisticRegressionFederatedLearning(partyA)
federatedLearning.add_party(id=party_B_id, party_model=partyB)
federatedLearning.set_debug(is_debug=False)
print("################################ Train Federated Models ############################")
fl_fixture = FederatedLearningFixture(federatedLearning)
# only party A has labels (i.e., Y), other parties only have features (e.g., X).
# 'party_list' stores X for all other parties.
# Since this is two-party VFL, 'party_list' only stores the X of party B.
train_data = {federatedLearning.get_main_party_id(): {"X": Xa_train, "Y": y_train},
"party_list": {party_B_id: Xb_train}}
test_data = {federatedLearning.get_main_party_id(): {"X": Xa_test, "Y": y_test},
"party_list": {party_B_id: Xb_test}}
fl_fixture.fit(train_data=train_data, test_data=test_data, epochs=epoch, batch_size=batch_size)