hyperopt-xgb.py 文件源码

def optimize(
    # trials,
        random_state=SEED):
    """
    This is the optimization function that given a space (space here) of
    hyperparameters and a scoring function (score here),
    finds the best hyperparameters.
    """

    # space = {
    #     'n_estimators': hp.choice('n_estimators', [1000, 1100]),
    #     'eta': hp.quniform('eta', 0.01, 0.1, 0.025),
    #     'max_depth': hp.choice('max_depth', [4, 5, 7, 9, 17]),
    #     'min_child_weight': hp.choice('min_child_weight', [3, 5, 7]),
    #     'subsample': hp.choice('subsample', [0.4, 0.6, 0.8]),
    #     'gamma': hp.choice('gamma', [0.3, 0.4]),
    #     'colsample_bytree': hp.quniform('colsample_bytree', 0.4, 0.7, 0.1),
    #     'lambda': hp.choice('lambda', [0.01, 0.1, 0.9, 1.0]),
    #     'alpha': hp.choice('alpha', [0, 0.1, 0.5, 1.0]),
    #     'eval_metric': 'auc',
    #     'objective': 'binary:logistic',
    #     # Increase this number if you have more cores.
    #     # Otherwise, remove it and it will default
    #     # to the maxium number.
    #     'nthread': 4,
    #     'booster': 'gbtree',
    #     'tree_method': 'exact',
    #     'silent': 1,
    #     'seed': random_state
    # }
    space = {
        'n_estimators': hp.choice('n_estimators', [1000]),
        'eta': hp.choice('eta', [0.01]),
        'max_depth': hp.choice('max_depth', [4]),
        'min_child_weight': hp.choice('min_child_weight', [5]),
        'subsample': hp.choice('subsample', [0.4]),
        'gamma': hp.choice('gamma', [0.4, 0.8]),
        'colsample_bytree': hp.choice('colsample_bytree', [0.4]),
        'lambda': hp.choice('lambda', [0.9, 0.93]),
        'alpha': hp.choice('alpha', [0.5]),
        'eval_metric': 'auc',
        'objective': 'binary:logistic',
        # Increase this number if you have more cores.
        # Otherwise, remove it and it will default
        # to the maxium number.
        'nthread': 4,
        'booster': 'gbtree',
        'tree_method': 'exact',
        'silent': 1,
        'seed': random_state
    }
    # Use the fmin function from Hyperopt to find the best hyperparameters
    best = fmin(score, space, algo=tpe.suggest,
                # trials=trials,
                max_evals=4)
    return best