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python - Optuna catboost 修剪

问题描述

有没有办法用 CatBoost 和 Optuna 进行修剪（在 LightGBM 中很容易，但在 Catboost 中我找不到任何提示）。我的代码是这样的

def objective(trial):
    param = {
        'iterations':trial.suggest_int('iterations', 100,1500, step=100),
        'learning_rate':trial.suggest_uniform("learning_rate", 0.001, 0.3),
        'random_strength':trial.suggest_int("random_strength", 1,10),
        'max_bin':trial.suggest_categorical('max_bin', [2,3,4,5,6,8,10,20,30]),
        'grow_policy':trial.suggest_categorical('grow_policy', ['SymmetricTree', 'Depthwise', 'Lossguide']),        
        "colsample_bylevel": trial.suggest_uniform("colsample_bylevel", 0.1, 1),
        'od_type' : "Iter",
        'od_wait' : 30,
        "depth": trial.suggest_int("max_depth", 1,12),
        "l2_leaf_reg": trial.suggest_loguniform("l2_leaf_reg", 1e-8, 100),
        'custom_metric' : ['AUC'],
        "loss_function": "Logloss",
        }
    
    if param['grow_policy'] == "SymmetricTree": 
        param["boosting_type"]= trial.suggest_categorical("boosting_type", ["Ordered", "Plain"])
    else:
        param["boosting_type"] = "Plain"
        
    # Added subsample manually
    param["subsample"] = trial.suggest_float("subsample", 0.1, 1)

### CV ###

    # How to add a callback for pruning?
    scores = cv(train_dataset,
            param,
            fold_count=5, 
            early_stopping_rounds=30,         
            plot=False, verbose=False)
    
    return scores['test-AUC-mean'].mean()

标签： pythoncross-validationcatboostpruningoptuna

解决方案

不，因为 catboost 不像其他增强库那样提供任何回调。但是，catboost 计划在不久的将来引入回调函数。功能发布后，optuna 可能会实现 catboost 的集成，例如LightGBM. 另请参阅 github https://github.com/optuna/optuna/issues/2464上的功能请求。

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