def is_converge(self, weight):
if self.pre_weight is None:
self.pre_weight = weight
return False
weight_diff = fate_operator.norm(self.pre_weight - weight)
self.pre_weight = weight
if weight_diff < self.eps * np.max([fate_operator.norm(weight), 1]):
return True
return False
def fit(self, data_instances=None, validate_data=None):
"""
Train linear model of role arbiter
Parameters
----------
data_instances: Table of Instance, input data
"""
LOGGER.info("Enter hetero linear model arbiter fit")
self.cipher_operator = self.cipher.paillier_keygen(
self.model_param.encrypt_param.key_length)
self.batch_generator.initialize_batch_generator()
self.gradient_loss_operator.set_total_batch_nums(
self.batch_generator.batch_num)
# self.validation_strategy = self.init_validation_strategy(data_instances, validate_data)
self.callback_list.on_train_begin(data_instances, validate_data)
if self.component_properties.is_warm_start:
self.callback_warm_start_init_iter(self.n_iter_)
while self.n_iter_ < self.max_iter:
self.callback_list.on_epoch_begin(self.n_iter_)
iter_loss = None
batch_data_generator = self.batch_generator.generate_batch_data()
total_gradient = None
self.optimizer.set_iters(self.n_iter_)
for batch_index in batch_data_generator:
# Compute and Transfer gradient info
gradient = self.gradient_loss_operator.compute_gradient_procedure(
self.cipher_operator, self.optimizer, self.n_iter_,
batch_index)
if total_gradient is None:
total_gradient = gradient
else:
total_gradient = total_gradient + gradient
training_info = {
"iteration": self.n_iter_,
"batch_index": batch_index
}
self.perform_subtasks(**training_info)
loss_list = self.gradient_loss_operator.compute_loss(
self.cipher_operator, self.n_iter_, batch_index)
if len(loss_list) == 1:
if iter_loss is None:
iter_loss = loss_list[0]
else:
iter_loss += loss_list[0]
# LOGGER.info("Get loss from guest:{}".format(de_loss))
# if converge
if iter_loss is not None:
iter_loss /= self.batch_generator.batch_num
if self.need_call_back_loss:
self.callback_loss(self.n_iter_, iter_loss)
self.loss_history.append(iter_loss)
if self.model_param.early_stop == 'weight_diff':
# LOGGER.debug("total_gradient: {}".format(total_gradient))
weight_diff = fate_operator.norm(total_gradient)
# LOGGER.info("iter: {}, weight_diff:{}, is_converged: {}".format(self.n_iter_,
# weight_diff, self.is_converged))
if weight_diff < self.model_param.tol:
self.is_converged = True
else:
if iter_loss is None:
raise ValueError(
"Multiple host situation, loss early stop function is not available."
"You should use 'weight_diff' instead")
self.is_converged = self.converge_func.is_converge(iter_loss)
LOGGER.info("iter: {}, loss:{}, is_converged: {}".format(
self.n_iter_, iter_loss, self.is_converged))
self.converge_procedure.sync_converge_info(self.is_converged,
suffix=(self.n_iter_, ))
self.callback_list.on_epoch_end(self.n_iter_)
self.n_iter_ += 1
if self.stop_training:
break
if self.is_converged:
break
LOGGER.debug(f"Finish_train, n_iter: {self.n_iter_}")
self.callback_list.on_train_end()
summary = {
"loss_history": self.loss_history,
"is_converged": self.is_converged,
"best_iteration": self.best_iteration
}
# if self.validation_strategy and self.validation_strategy.has_saved_best_model():
# self.load_model(self.validation_strategy.cur_best_model)
if self.loss_history is not None and len(self.loss_history) > 0:
summary["best_iter_loss"] = self.loss_history[self.best_iteration]
self.set_summary(summary)
LOGGER.debug("finish running linear model arbiter")
def fit_binary(self, data_instances=None, validate_data=None):
"""
Train FM model of role arbiter
Parameters
----------
data_instances: DTable of Instance, input data
"""
LOGGER.info("Enter hetero fm model arbiter fit")
self.cipher_operator = self.cipher.paillier_keygen(
self.model_param.encrypt_param.key_length)
self.batch_generator.initialize_batch_generator()
validation_strategy = self.init_validation_strategy()
while self.n_iter_ < self.max_iter:
iter_loss = None
batch_data_generator = self.batch_generator.generate_batch_data()
total_gradient = None
self.optimizer.set_iters(self.n_iter_)
for batch_index in batch_data_generator:
# Compute and Transfer gradient info
gradient = self.gradient_loss_operator.compute_gradient_procedure(
self.cipher_operator, self.optimizer, self.n_iter_,
batch_index)
if total_gradient is None:
total_gradient = gradient
else:
total_gradient = total_gradient + gradient
# training_info = {"iteration": self.n_iter_, "batch_index": batch_index}
# self.perform_subtasks(**training_info)
loss_list = self.gradient_loss_operator.compute_loss(
self.cipher_operator, self.n_iter_, batch_index)
if len(loss_list) == 1:
if iter_loss is None:
iter_loss = loss_list[0]
else:
iter_loss += loss_list[0]
# if converge
if iter_loss is not None:
iter_loss /= self.batch_generator.batch_num
if not self.in_one_vs_rest:
self.callback_loss(self.n_iter_, iter_loss)
if self.model_param.early_stop == 'weight_diff':
weight_diff = fate_operator.norm(total_gradient)
LOGGER.info(
"iter: {}, weight_diff:{}, is_converged: {}".format(
self.n_iter_, weight_diff, self.is_converged))
if weight_diff < self.model_param.tol:
self.is_converged = True
else:
self.is_converged = self.converge_func.is_converge(iter_loss)
LOGGER.info("iter: {}, loss:{}, is_converged: {}".format(
self.n_iter_, iter_loss, self.is_converged))
self.converge_procedure.sync_converge_info(self.is_converged,
suffix=(self.n_iter_, ))
validation_strategy.validate(self, self.n_iter_)
self.n_iter_ += 1
if self.is_converged:
break