def _grow_ensemble_not_ready_fn(): # Don't grow the ensemble, just update the stamp. return training_ops.grow_tree_ensemble( tree_ensemble_handle=self._ensemble_handle, stamp_token=ensemble_stamp, next_stamp_token=next_ensemble_stamp, learning_rate=0, partition_ids=[], gains=[], splits=[], learner_config=self._learner_config_serialized, dropout_seed=dropout_seed, center_bias=self._center_bias)
def _grow_ensemble_not_ready_fn(): # Don't grow the ensemble, just update the stamp. return training_ops.grow_tree_ensemble( tree_ensemble_handle=self._ensemble_handle, stamp_token=ensemble_stamp, next_stamp_token=next_ensemble_stamp, learning_rate=0, partition_ids=[], gains=[], splits=[], learner_config=self._learner_config_serialized, dropout_seed=dropout_seed, center_bias=self._center_bias)
def _grow_ensemble_ready_fn(): # Grow the ensemble given the current candidates. sizes = array_ops.unstack(split_sizes) partition_ids_list = list(array_ops.split(partition_ids, sizes, axis=0)) gains_list = list(array_ops.split(gains, sizes, axis=0)) split_info_list = list(array_ops.split(split_infos, sizes, axis=0)) return training_ops.grow_tree_ensemble( tree_ensemble_handle=self._ensemble_handle, stamp_token=ensemble_stamp, next_stamp_token=next_ensemble_stamp, learning_rate=learning_rate, partition_ids=partition_ids_list, gains=gains_list, splits=split_info_list, learner_config=self._learner_config_serialized, dropout_seed=dropout_seed, center_bias=self._center_bias)