lightgbm_objective.py

import lightgbm as lgb
from hyperopt import STATUS_OK

N_FOLDS = 10

# Create the dataset
train_set = lgb.Dataset(train_features, train_labels)

def objective(params, n_folds = N_FOLDS):
    """Objective function for Gradient Boosting Machine Hyperparameter Tuning"""
    
    # Perform n_fold cross validation with hyperparameters
    # Use early stopping and evalute based on ROC AUC
    cv_results = lgb.cv(params, train_set, nfold = n_folds, num_boost_round = 10000, 
                        early_stopping_rounds = 100, metrics = 'auc', seed = 50)
  
    # Extract the best score
    best_score = max(cv_results['auc-mean'])
    
    # Loss must be minimized
    loss = 1 - best_score
    
    # Dictionary with information for evaluation
    return {'loss': loss, 'params': params, 'status': STATUS_OK}