ValueError: feature_names mismatch: in xgboost in

问题:

I have trained an XGBoostRegressor model. When I have to use this trained model for predicting for a new input, the predict() function throws a feature_names mismatch error, although the input feature vector has the same structure as the training data.

Also, in order to build the feature vector in the same structure as the training data, I am doing a lot inefficient processing such as adding new empty columns (if data does not exist) and then rearranging the data columns so that it matches with the training structure. Is there a better and cleaner way of formatting the input so that it matches the training structure?

回答1:

From what I could find, the predict function does not take the DataFrame (or a sparse matrix) as input. It is one of the bugs which can be found here https://github.com/dmlc/xgboost/issues/1238

In order to get around this issue, use as_matrix() function in case of a DataFrame or toarray() in case of a sparse matrix.

This is the only workaround till the bug is fixed or the feature is implemented in a different manner.

回答2:

I also had this problem when i used pandas DataFrame (non-sparse representation).

I converted training and testing data into numpy ndarray.

          `X_train = X_train.as_matrix()
           X_test = X_test.as_matrix()` 

This how i got rid of that Error!

回答3:

I came across the same problem and it's been solved by adding passing the train dataframe column name to the test dataframe via adding the following code:

test_df = test_df[train_df.columns]

回答4:

Check the exception. What you should see are two arrays. One is the column names of the dataframe you’re passing in and the other is the XGBoost feature names. They should be the same length. If you put them side by side in an Excel spreadsheet you will see that they are not in the same order. My guess is that the XGBoost names were written to a dictionary so it would be a coincidence if the names in then two arrays were in the same order.

The fix is easy. Just reorder your dataframe columns to match the XGBoost names:

f_names = model.feature_names
df = df[f_names]

回答5:

Do this while creating the DMatrix for XGB:

dtrain = xgb.DMatrix(np.asmatrix(X_train), label=y_train)
dtest = xgb.DMatrix(np.asmatrix(X_test), label=y_test)

Do not pass X_train and X_test directly.

回答6:

Try converting data into ndarray before passing it to fit/predict. For eg: if your train data is train_df and test data is test_df. Use below code:

train_x = train_df.values
test_x = test_df.values

Now fit the model:

xgb.fit(train_x,train_y)

Finally, predict:

pred = xgb.predict(test_x)

Hope this helps!