def __init__(self):
super(HomoFMArbiter, self).__init__()
# self.re_encrypt_times = [] # Record the times needed for each host
self.loss_history = []
self.is_converged = False
self.role = consts.ARBITER
self.aggregator = aggregator.Arbiter()
self.model_weights = None
self.host_predict_results = []
def fit(self, data_instances=None, validate_data=None):
self.aggregator = aggregator.Arbiter()
self.aggregator.register_aggregator(self.transfer_variable)
self._server_check_data()
host_ciphers = self.cipher.paillier_keygen(
key_length=self.model_param.encrypt_param.key_length,
suffix=('fit', ))
host_has_no_cipher_ids = [
idx for idx, cipher in host_ciphers.items() if cipher is None
]
self.re_encrypt_times = self.cipher.set_re_cipher_time(host_ciphers)
max_iter = self.max_iter
# validation_strategy = self.init_validation_strategy()
while self.n_iter_ < max_iter + 1:
suffix = (self.n_iter_, )
if ((self.n_iter_ + 1) % self.aggregate_iters
== 0) or self.n_iter_ == max_iter:
merged_model = self.aggregator.aggregate_and_broadcast(
ciphers_dict=host_ciphers, suffix=suffix)
total_loss = self.aggregator.aggregate_loss(
host_has_no_cipher_ids, suffix)
self.callback_loss(self.n_iter_, total_loss)
self.loss_history.append(total_loss)
if self.use_loss:
converge_var = total_loss
else:
converge_var = np.array(merged_model.unboxed)
self.is_converged = self.aggregator.send_converge_status(
self.converge_func.is_converge, (converge_var, ),
suffix=(self.n_iter_, ))
LOGGER.info(
"n_iters: {}, total_loss: {}, converge flag is :{}".format(
self.n_iter_, total_loss, self.is_converged))
self.model_weights = LogisticRegressionWeights(
merged_model.unboxed,
self.model_param.init_param.fit_intercept)
if self.header is None:
self.header = [
'x' + str(i)
for i in range(len(self.model_weights.coef_))
]
if self.is_converged or self.n_iter_ == max_iter:
break
self.cipher.re_cipher(iter_num=self.n_iter_,
re_encrypt_times=self.re_encrypt_times,
host_ciphers_dict=host_ciphers,
re_encrypt_batches=self.re_encrypt_batches)
# validation_strategy.validate(self, self.n_iter_)
self.n_iter_ += 1
LOGGER.info("Finish Training task, total iters: {}".format(
self.n_iter_))
