我已经训练了一个用于二分类任务的XGBoost分类器。在使用交叉验证对训练数据进行训练并对测试数据进行预测时，我遇到了错误 AttributeError: 'DataFrame' object has no attribute 'feature_names'。
我的代码如下：
folds = StratifiedKFold(n_splits=5, shuffle=False, random_state=44000)oof = np.zeros(len(X_train))predictions = np.zeros(len(X_test))for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train, y_train)): print("Fold {}".format(fold_+1)) trn_data = xgb.DMatrix(X_train.iloc[trn_idx], y_train.iloc[trn_idx]) val_data = xgb.DMatrix(X_train.iloc[val_idx], y_train.iloc[val_idx]) clf = xgb.train(params = best_params, dtrain = trn_data, num_boost_round = 2000, evals = [(trn_data, 'train'), (val_data, 'valid')], maximize = False, early_stopping_rounds = 100, verbose_eval=100) oof[val_idx] = clf.predict(X_train.iloc[val_idx], ntree_limit=clf.best_ntree_limit) predictions += clf.predict(X_test, ntree_limit=clf.best_ntree_limit)/folds.n_splits如何处理这个问题？
以下是完整的错误跟踪信息：
Fold 1[0] train-auc:0.919667 valid-auc:0.822968Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.Will train until valid-auc hasn't improved in 100 rounds.[100] train-auc:1 valid-auc:0.974659[200] train-auc:1 valid-auc:0.97668[300] train-auc:1 valid-auc:0.977696[400] train-auc:1 valid-auc:0.977704Stopping. Best iteration:[376] train-auc:1 valid-auc:0.977862Exception ignored in: <bound method DMatrix.__del__ of <xgboost.core.DMatrix object at 0x7f3d9c285550>>Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/xgboost/core.py", line 368, in __del__ if self.handle is not None:AttributeError: 'DMatrix' object has no attribute 'handle'---------------------------------------------------------------------------AttributeError Traceback (most recent call last)<ipython-input-55-d52b20cc0183> in <module>() 19 verbose_eval=100) 20 ---> 21 oof[val_idx] = clf.predict(X_train.iloc[val_idx], ntree_limit=clf.best_ntree_limit) 22 23 predictions += clf.predict(X_test, ntree_limit=clf.best_ntree_limit)/folds.n_splits/usr/local/lib/python3.6/dist-packages/xgboost/core.py in predict(self, data, output_margin, ntree_limit, pred_leaf, pred_contribs, approx_contribs) 1042 option_mask |= 0x08 1043 -> 1044 self._validate_features(data) 1045 1046 length = c_bst_ulong()/usr/local/lib/python3.6/dist-packages/xgboost/core.py in _validate_features(self, data) 1271 else: 1272 # Booster can't accept data with different feature names-> 1273 if self.feature_names != data.feature_names: 1274 dat_missing = set(self.feature_names) - set(data.feature_names) 1275 my_missing = set(data.feature_names) - set(self.feature_names)/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py in __getattr__(self, name) 3612 if name in self._info_axis: 3613 return self[name]-> 3614 return object.__getattribute__(self, name) 3615 3616 def __setattr__(self, name, value):AttributeError: 'DataFrame' object has no attribute 'feature_names'回答：
问题已经解决了。问题在于，我没有将 X_train.iloc[val_idx] 转换为 xgb.DMatrix。在将 X_train.iloc[val_idx] 和 X_test 转换为 xgb.DMatrix 后，问题就解决了！
更新了以下两行代码：
oof[val_idx] = clf.predict(xgb.DMatrix(X_train.iloc[val_idx]), ntree_limit=clf.best_ntree_limit)predictions += clf.predict(xgb.DMatrix(X_test), ntree_limit=clf.best_ntree_limit)/folds.n_splits