hyperopt_objective.py

def objective(params):
    """
    objective function for lightgbm.
    """
    # hyperopt casts as float
    params['num_boost_round'] = int(params['num_boost_round'])
    params['num_leaves'] = int(params['num_leaves'])

    # need to be passed as parameter
    params['is_unbalance'] = True
    params['verbose'] = -1
    params['seed'] = 1

    cv_result = lgb.cv(
        params,
        train,
        num_boost_round=params['num_boost_round'],
        metrics='binary_logloss',
        nfold=3,
        stratified=True,
        early_stopping_rounds=20)
    early_stop_dict[objective.i] = len(cv_result['binary_logloss-mean'])
    error = cv_result['binary_logloss-mean'][-1]
    objective.i+=1
    return error