Question

对于下面的函数，我没有得到估算器的数量作为输出，但相反我得到以下类型错误。

cv() got an unexpected keyword argument 'show_progress'

即使文档包含标志，我也会收到类型错误。我正在关注这个博客进行参数调整。谁能指出我哪里出错了？ the blog 有没有其他方法可以将估算器的数量作为输出？

def modelfit(alg, dtrain, predictors, useTrainCV=True, cv_folds=5, early_stopping_rounds=50):
if useTrainCV:
    xgb_param = alg.get_xgb_params()
    xgtrain = xgb.DMatrix(dtrain[predictors].values, label=dtrain[target].values, silent=False)
    cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=alg.get_params()['n_estimators'], nfold=cv_folds,
        metrics='auc', early_stopping_rounds=early_stopping_rounds, show_progress = True)
    alg.set_params(n_estimators=cvresult.shape[0])

#Fit the algorithm on the data
alg.fit(dtrain[predictors], dtrain[target],eval_metric='auc')

#Predict training set:
dtrain_predictions = alg.predict(dtrain[predictors])
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]

#Print model report:
print "\nModel Report"
print "Accuracy : %.4g" % metrics.accuracy_score(dtrain[target].values, dtrain_predictions)
print "AUC Score (Train): %f" % metrics.roc_auc_score(dtrain[target], dtrain_predprob)


feat_imp = pd.Series(alg.booster().get_fscore()).sort_values(ascending=False)
feat_imp.plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')

Answer 1

xgboost的最新版本（0.6）为xgb.cv提供了以下选项：

xgboost.cv(params, dtrain, num_boost_round=10, nfold=3, 
stratified=False, folds=None, metrics=(), obj=None, feval=None, 
maximize=False, early_stopping_rounds=None, fpreproc=None, 
as_pandas=True, verbose_eval=None, show_stdv=True, seed=0, 
callbacks=None, shuffle=True)

show_progress已弃用，取而代之的是verbose_eval。见here

Answer 2

删除show_progress = True，它应该运行

没有得到XGBoost的参数数量

2 个答案: