pandas XGBoost: AttributeError: 'DataFrame' 对象没有属性 'feature_names'
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XGBoost: AttributeError: 'DataFrame' object has no attribute 'feature_names'
提问by Abdullah Al Imran
I've trained an XGBoost Classifier for binary classification. While training the model on train data using CV and predicting on the test data, I face the error AttributeError: 'DataFrame' object has no attribute 'feature_names'.
我已经训练了一个用于二元分类的 XGBoost 分类器。在使用 CV 训练训练数据模型并预测测试数据时，我面临错误AttributeError: 'DataFrame' object has no attribute 'feature_names'。
My code is as follows:
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
How to deal with it?
如何处理？
Here is the complete error trace:
这是完整的错误跟踪：
Fold 1
[0] train-auc:0.919667 valid-auc:0.822968
Multiple 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:0.977704
Stopping. Best iteration:
[376] train-auc:1 valid-auc:0.977862
Exception 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by Abdullah Al Imran
The problem has been solved. The problem is, I didn't converted the X_train.iloc[val_idx]to xgb.DMatrix. After converting X_train.iloc[val_idx]and X_testto xgb.DMatrixthe plroblem was gone!
问题已经解决。问题是，我没有X_train.iloc[val_idx]将xgb.DMatrix. 转换后X_train.iloc[val_idx]，并X_test以xgb.DMatrix该plroblem不见了！
Updated the following two lines:
更新了以下两行：
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
