def _init_model(self, params: LogisticRegressionParam):
super()._init_model(params)
self.encrypted_mode_calculator_param = params.encrypted_mode_calculator_param
if self.role == consts.HOST:
self.init_param_obj.fit_intercept = False
self.cipher = PaillierEncrypt()
self.cipher.generate_key(self.model_param.encrypt_param.key_length)
self.transfer_variable = SSHEModelTransferVariable()
self.one_vs_rest_obj = one_vs_rest_factory(self,
role=self.role,
mode=self.mode,
has_arbiter=False)
self.converge_func_name = params.early_stop
self.reveal_every_iter = params.reveal_every_iter
self.q_field = self._transfer_q_field()
LOGGER.debug(f"q_field: {self.q_field}")
if not self.reveal_every_iter:
self.self_optimizer = copy.deepcopy(self.optimizer)
self.remote_optimizer = copy.deepcopy(self.optimizer)
self.batch_generator = batch_generator.Guest(
) if self.role == consts.GUEST else batch_generator.Host()
self.batch_generator.register_batch_generator(
BatchGeneratorTransferVariable(), has_arbiter=False)
self.fixedpoint_encoder = FixedPointEndec(n=self.q_field)
self.converge_transfer_variable = ConvergeCheckerTransferVariable()
self.secure_matrix_obj = SecureMatrix(party=self.local_party,
q_field=self.q_field,
other_party=self.other_party)
def __init__(self):
super().__init__()
self.data_batch_count = []
self.role = consts.GUEST
self.cipher = paillier_cipher.Guest()
self.batch_generator = batch_generator.Guest()
self.gradient_loss_operator = hetero_poisson_gradient_and_loss.Guest()
self.converge_procedure = convergence.Guest()
def prepare_fit(self, data_instances, validate_data):
# self.transfer_variable = SSHEModelTransferVariable()
self.batch_generator = batch_generator.Guest()
self.batch_generator.register_batch_generator(BatchGeneratorTransferVariable(), has_arbiter=False)
self.header = copy.deepcopy(data_instances.schema.get("header", []))
self._abnormal_detection(data_instances)
self.check_abnormal_values(data_instances)
self.check_abnormal_values(validate_data)
def __init__(self):
super().__init__()
self.data_batch_count = []
# self.guest_forward = None
self.role = consts.GUEST
self.cipher = paillier_cipher.Guest()
self.batch_generator = batch_generator.Guest()
self.gradient_loss_operator = hetero_linr_gradient_and_loss.Guest()
self.converge_procedure = convergence.Guest()
self.encrypted_calculator = None
def __init__(self):
super(HeteroNNGuest, self).__init__()
self.task_type = None
self.converge_func = None
self.batch_generator = batch_generator.Guest()
self.data_keys = []
self.model_builder = None
self.label_dict = {}
self.model = None
self.history_loss = []
self.iter_epoch = 0
self.num_label = 2
self.input_shape = None
def __init__(self):
super(HeteroNNGuest, self).__init__()
self.task_type = None
self.converge_func = None
self.batch_generator = batch_generator.Guest()
self.data_keys = []
self.model_builder = None
self.label_dict = {}
self.model = None
self.role = consts.GUEST
self.history_loss = []
self.num_label = 2
self.input_shape = None
self._summary_buf = {"history_loss": [],
"is_converged": False,
"best_iteration": -1}
