XGBRegressorやGradientBoostingRegressorでMSEが計れません。

Question

題にもあるように、XGBRegressorやGradientBoostingRegressorを使って、MSEが図れないという問題に直面しています。

rmseを返すメソッドは以下のようになっています。

kfolds = KFold(n_splits = 10, shuffle = True, random_state = 42)

def cv_rmse(model, X = X):
    rmse = np.sqrt(-cross_val_score(model, X, y, scoring = "neg_mean_squared_error", cv = kfolds))
    return rmse

また、それぞれのRegressorの定義は、以下のようになります。

alphas_alt = [14.5, 14.6, 14.7, 14.8, 14.9, 15, 15.1, 15.2, 15.3, 15.4, 15.5]
alphas2 = [5e-05, 0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007, 0.0008]
e_alphas = [0.0001, 0.0002, 0.0003, 0.0004, 0.0005, 0.0006, 0.0007]
e_l1ratio = [0.8, 0.85, 0.9, 0.95, 0.99, 1]

ridge = make_pipeline(RobustScaler(), RidgeCV(alphas = (0.1, 10, 15), cv = kfolds))
lasso = make_pipeline(RobustScaler(), LassoCV(max_iter = 1e7, alphas = alphas2, random_state = 42, cv = kfolds))
elasticnet = make_pipeline(RobustScaler(), ElasticNetCV(max_iter = 1e7, alphas = e_alphas, cv = kfolds, l1_ratio = e_l1ratio))
svr = make_pipeline(RobustScaler(), SVR(C = 20, epsilon = 0.008, gamma = 0.0003))
lightgbm = LGBMRegressor(objective = 'regression', 
                         num_leaves = 4,
                         learning_rate = 0.01,
                         n_estimators = 5000,
                         max_bin = 200,
                         bagging_fraction = 0.75, 
                         bagging_freq = 5,
                         bagging_seed = 7, 
                         feature_fraction = 0.2,
                         feature_fraction_seed = 7, 
                         verbose = -1)
xgboost = xg.XGBRegressor(learning_rate=0.01,n_estimators=3460,
                                     max_depth=3, min_child_weight=0,
                                     gamma=0, subsample=0.7,
                                     colsample_bytree=0.7,
                                     objective='reg:linear', nthread=-1,
                                     scale_pos_weight=1, seed=27,
                                     reg_alpha=0.00006)
gbr = GradientBoostingRegressor(n_estimators = 3000,
                                learning_rate = 0.05, 
                                max_depth = 4, 
                                max_features = 'sqrt', 
                                min_samples_leaf = 15, 
                                min_samples_split = 10, 
                                loss = 'huber',
                                random_state = 42)

これらを、以下のようにしてMSEを図ろうとしています。

score = cv_rmse(ridge)
print("Ridge: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

score = cv_rmse(lasso)
print("Lasso: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

score = cv_rmse(elasticnet)
print("elastic net: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

score = cv_rmse(svr)
print("SVR: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

score = cv_rmse(lightgbm)
print("lightgbm: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

score = cv_rmse(gbr)
print("gradient_boosting_regressor: {:.4f} ({:.4f})".format(score.mean(), score.std()))

score = cv_rmse(xgboost)
print("xgboost: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))

ですが、gbrまでの5つは動くのですが、下二つのgradientboostingregressorとxgboostは、出力が

[nan nan nan nan nan nan nan nan nan nan]

となってしまいます。何が原因でしょうか。どなたかわかる方、ご教示いただけますと幸いです。