def fit(self, data_inst, validate_data=None):
self.federated_binning()
# initializing
self.feature_num = self.sync_feature_num()
if self.task_type == consts.CLASSIFICATION:
label_mapping = HomoLabelEncoderArbiter().label_alignment()
LOGGER.info('label mapping is {}'.format(label_mapping))
self.booster_dim = len(
label_mapping) if len(label_mapping) > 2 else 1
if self.n_iter_no_change:
self.check_convergence_func = converge_func_factory(
"diff", self.tol)
LOGGER.info('begin to fit a boosting tree')
for epoch_idx in range(self.boosting_round):
LOGGER.info('cur epoch idx is {}'.format(epoch_idx))
for class_idx in range(self.booster_dim):
model = self.fit_a_booster(epoch_idx, class_idx)
global_loss = self.aggregator.aggregate_loss(suffix=(epoch_idx, ))
self.history_loss.append(global_loss)
LOGGER.debug('cur epoch global loss is {}'.format(global_loss))
self.callback_metric("loss", "train",
[Metric(epoch_idx, global_loss)])
if self.n_iter_no_change:
should_stop = self.aggregator.broadcast_converge_status(
self.check_convergence, (global_loss, ),
suffix=(epoch_idx, ))
LOGGER.debug('stop flag sent')
if should_stop:
break
self.callback_meta(
"loss", "train",
MetricMeta(name="train",
metric_type="LOSS",
extra_metas={"Best": min(self.history_loss)}))
self.set_summary(self.generate_summary())
def server_fit(self, data_inst):
if not self.component_properties.is_warm_start:
label_mapping = HomoLabelEncoderArbiter().label_alignment()
LOGGER.info(f"label mapping: {label_mapping}")
else:
self.callback_warm_start_init_iter(self.aggregate_iteration_num + 1)
while self.aggregate_iteration_num + 1 < self.max_aggregate_iteration_num:
# update iteration num
self.aggregate_iteration_num += 1
self.callback_list.on_epoch_begin(self.aggregate_iteration_num)
self.model = self.aggregator.weighted_mean_model(suffix=_suffix(self))
self.aggregator.send_aggregated_model(model=self.model,
suffix=_suffix(self))
self.callback_list.on_epoch_end(self.aggregate_iteration_num)
if server_is_converged(self):
LOGGER.info(f"early stop at iter {self.aggregate_iteration_num}")
break
else:
LOGGER.warn(
f"reach max iter: {self.aggregate_iteration_num}, not converged")
self.set_summary(self._summary)
def dataset_align():
LOGGER.info("start label alignment")
label_mapping = HomoLabelEncoderArbiter().label_alignment()
LOGGER.info(f"label aligned, mapping: {label_mapping}")
def _server_check_data(self):
HomoLabelEncoderArbiter().label_alignment()