hyperopt-xgb.py 文件源码

def score(params):
    logging.info("Training with params: ")
    logging.info(params)
    # Delete 'n_estimators' because it's only a constructor param
    # when you're using  XGB's sklearn API.
    # Instead, we have to save 'n_estimators' (# of boosting rounds)
    # to xgb.cv().
    num_boost_round = int(params['n_estimators'])
    del params['n_estimators']
    dtrain = xgb.DMatrix(X_train, label=y_train)
    # As of version 0.6, XGBoost returns a dataframe of the following form:
    # boosting iter | mean_test_err | mean_test_std | mean_train_err | mean_train_std
    # boost iter 1 mean_test_iter1 | mean_test_std1 | ... | ...
    # boost iter 2 mean_test_iter2 | mean_test_std2 | ... | ...
    # ...
    # boost iter n_estimators

    score_history = xgb.cv(params, dtrain, num_boost_round,
                           nfold=5, stratified=True,
                           early_stopping_rounds=250,
                           verbose_eval=500)
    # Only use scores from the final boosting round since that's the one
    # that performed the best.
    mean_final_round = score_history.tail(1).iloc[0, 0]
    std_final_round = score_history.tail(1).iloc[0, 1]
    logging.info("\tMean Score: {0}\n".format(mean_final_round))
    logging.info("\tStd Dev: {0}\n\n".format(std_final_round))
    # score() needs to return the loss (1 - score)
    # since optimize() should be finding the minimum, and AUC
    # naturally finds the maximum.
    loss = 1 - mean_final_round
    return {'loss': loss, 'status': STATUS_OK}