xgb_autotune.py 文件源码

def xgboostcv(max_depth,
              learning_rate,
              n_estimators,
              subsample,
              colsample_bytree,
              gamma,
              min_child_weight,
              silent=True,
              nthread=-1,
              seed=1234):

    clf = XGBClassifier(max_depth=int(max_depth),
                        learning_rate=learning_rate,
                        n_estimators=int(n_estimators),
                        silent=silent,
                        nthread=nthread,
                        subsample=subsample,
                        colsample_bytree=colsample_bytree,
                        gamma=gamma,
                        min_child_weight = min_child_weight,
                        seed=seed,
                        objective="binary:logistic")

    clf.fit(x0, y0, eval_metric="logloss", eval_set=[(x1, y1)],early_stopping_rounds=25)
    ll = -log_loss(y1, clf.predict_proba(x1))
    return ll