Let XGBoostError inherit ValueError. (#5696)

dmlc · May 26, 2020 · f145241 · f145241
1 parent 8438c7d
commit f145241
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Showing 2 changed files with 15 additions and 21 deletions.
diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py
@@ -26,7 +26,7 @@
 c_bst_ulong = ctypes.c_uint64
 
 
-class XGBoostError(Exception):
+class XGBoostError(ValueError):
     """Error thrown by xgboost trainer."""
 
 

diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
@@ -537,16 +537,13 @@ def fit(self, X, y, sample_weight=None, base_margin=None,
             else:
                 params.update({'eval_metric': eval_metric})
 
-        try:
-            self._Booster = train(params, train_dmatrix,
-                                  self.get_num_boosting_rounds(), evals=evals,
-                                  early_stopping_rounds=early_stopping_rounds,
-                                  evals_result=evals_result,
-                                  obj=obj, feval=feval,
-                                  verbose_eval=verbose, xgb_model=xgb_model,
-                                  callbacks=callbacks)
-        except XGBoostError as e:
-            raise ValueError(e)
+        self._Booster = train(params, train_dmatrix,
+                              self.get_num_boosting_rounds(), evals=evals,
+                              early_stopping_rounds=early_stopping_rounds,
+                              evals_result=evals_result,
+                              obj=obj, feval=feval,
+                              verbose_eval=verbose, xgb_model=xgb_model,
+                              callbacks=callbacks)
 
         if evals_result:
             for val in evals_result.items():
@@ -1230,16 +1227,13 @@ def _dmat_init(group, **params):
                     'Custom evaluation metric is not yet supported for XGBRanker.')
             params.update({'eval_metric': eval_metric})
 
-        try:
-            self._Booster = train(params, train_dmatrix,
-                                  self.n_estimators,
-                                  early_stopping_rounds=early_stopping_rounds,
-                                  evals=evals,
-                                  evals_result=evals_result, feval=feval,
-                                  verbose_eval=verbose, xgb_model=xgb_model,
-                                  callbacks=callbacks)
-        except XGBoostError as e:
-            raise ValueError(e)
+        self._Booster = train(params, train_dmatrix,
+                              self.n_estimators,
+                              early_stopping_rounds=early_stopping_rounds,
+                              evals=evals,
+                              evals_result=evals_result, feval=feval,
+                              verbose_eval=verbose, xgb_model=xgb_model,
+                              callbacks=callbacks)
 
         self.objective = params["objective"]