def objective_with_logging(trial):
'num_leaves': trial.suggest_int('num_leaves', 2, 256),
'feature_fraction': trial.suggest_uniform('feature_fraction', 0.2, 1.0),
'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.2, 1.0),
'min_child_samples': trial.suggest_int('min_child_samples', 3, 100),
# create a trial-level Run
run_trial_level = neptune.init(api_token='ANONYMOUS',
project='common/optuna-integration')
# log sweep id to trial-level Run
run_trial_level['sys/tags'].add('trial-level')
run_trial_level['sweep-id'] = sweep_id
# log parameters of a trial-level Run
run_trial_level['parameters'] = param
# run training and calculate the score for this parameter configuration
# log score of a trial-level Run
run_trial_level['score'] = score