objective_function_search.py

import lightgbm as lgb

def objective(hyperparameters, iteration):
    """Objective function for grid and random search. Returns
       the cross validation score from a set of hyperparameters."""
    
    # Number of estimators will be found using early stopping
    if 'n_estimators' in hyperparameters.keys():
        del hyperparameters['n_estimators']
    
     # Perform n_folds cross validation
    cv_results = lgb.cv(hyperparameters, train_set, num_boost_round = 10000, nfold = N_FOLDS, 
                        early_stopping_rounds = 100, metrics = 'auc', seed = 42)
    
    # results to retun
    score = cv_results['auc-mean'][-1]
    estimators = len(cv_results['auc-mean'])
    hyperparameters['n_estimators'] = estimators 
    
    return [score, hyperparameters, iteration]