[python] remove verbose_eval argument of train() and cv() functions

examples/python-guide/notebooks/interactive_plot_example.ipynb

@@ -149,7 +149,7 @@
     "                feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],\n",
     "                categorical_feature=[21],\n",
     "                evals_result=evals_result,\n",
-    "                verbose_eval=10)"
+    "                callbacks=[lgb.log_evaluation(10)])"
    ]
   },
   {

examples/python-guide/plot_example.py

@@ -43,7 +43,7 @@
                 feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],
                 categorical_feature=[21],
                 evals_result=evals_result,
-                verbose_eval=10)
+                callbacks=[lgb.log_evaluation(10)])
 
 print('Plotting metrics recorded during training...')
 ax = lgb.plot_metric(evals_result, metric='l1')

python-package/lightgbm/engine.py

@@ -35,7 +35,6 @@ def train(
     categorical_feature: Union[List[str], List[int], str] = 'auto',
     early_stopping_rounds: Optional[int] = None,
     evals_result: Optional[Dict[str, Any]] = None,
-    verbose_eval: Union[bool, int, str] = 'warn',
     keep_training_booster: bool = False,
     callbacks: Optional[List[Callable]] = None
 ) -> Booster:
@@ -133,17 +132,6 @@ def train(
         returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
         'eval': {'logloss': ['0.480385', '0.357756', ...]}}.
 
-    verbose_eval : bool or int, optional (default=True)
-        Requires at least one validation data.
-        If True, the eval metric on the valid set is printed at each boosting stage.
-        If int, the eval metric on the valid set is printed at every ``verbose_eval`` boosting stage.
-        The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
-
-        .. rubric:: Example
-
-        With ``verbose_eval`` = 4 and at least one item in ``valid_sets``,
-        an evaluation metric is printed every 4 (instead of 1) boosting stages.
-
     keep_training_booster : bool, optional (default=False)
         Whether the returned Booster will be used to keep training.
         If False, the returned value will be converted into _InnerPredictor before returning.
@@ -230,21 +218,8 @@ def train(
         callbacks_set = set(callbacks)
 
     # Most of legacy advanced options becomes callbacks
-    if verbose_eval != "warn":
-        _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
-                     "Pass 'log_evaluation()' callback via 'callbacks' argument instead.")
-    else:
-        if callbacks_set:  # assume user has already specified log_evaluation callback
-            verbose_eval = False
-        else:
-            verbose_eval = True
-    if verbose_eval is True:
-        callbacks_set.add(callback.log_evaluation())
-    elif isinstance(verbose_eval, int):
-        callbacks_set.add(callback.log_evaluation(verbose_eval))
-
     if early_stopping_rounds is not None and early_stopping_rounds > 0:
-        callbacks_set.add(callback.early_stopping(early_stopping_rounds, first_metric_only, verbose=bool(verbose_eval)))
+        callbacks_set.add(callback.early_stopping(early_stopping_rounds, first_metric_only))
 
     if evals_result is not None:
         _log_warning("'evals_result' argument is deprecated and will be removed in a future release of LightGBM. "
@@ -426,8 +401,7 @@ def cv(params, train_set, num_boost_round=100,
        metrics=None, fobj=None, feval=None, init_model=None,
        feature_name='auto', categorical_feature='auto',
        early_stopping_rounds=None, fpreproc=None,
-       verbose_eval=None, show_stdv=True, seed=0,
-       callbacks=None, eval_train_metric=False,
+       seed=0, callbacks=None, eval_train_metric=False,
        return_cvbooster=False):
     """Perform the cross-validation with given parameters.
 
@@ -522,13 +496,6 @@ def cv(params, train_set, num_boost_round=100,
     fpreproc : callable or None, optional (default=None)
         Preprocessing function that takes (dtrain, dtest, params)
         and returns transformed versions of those.
-    verbose_eval : bool, int, or None, optional (default=None)
-        Whether to display the progress.
-        If True, progress will be displayed at every boosting stage.
-        If int, progress will be displayed at every given ``verbose_eval`` boosting stage.
-    show_stdv : bool, optional (default=True)
-        Whether to display the standard deviation in progress.
-        Results are not affected by this parameter, and always contain std.
     seed : int, optional (default=0)
         Seed used to generate the folds (passed to numpy.random.seed).
     callbacks : list of callable, or None, optional (default=None)
@@ -606,13 +573,6 @@ def cv(params, train_set, num_boost_round=100,
         callbacks = set(callbacks)
     if early_stopping_rounds is not None and early_stopping_rounds > 0:
         callbacks.add(callback.early_stopping(early_stopping_rounds, first_metric_only, verbose=False))
-    if verbose_eval is not None:
-        _log_warning("'verbose_eval' argument is deprecated and will be removed in a future release of LightGBM. "
-                     "Pass 'log_evaluation()' callback via 'callbacks' argument instead.")
-    if verbose_eval is True:
-        callbacks.add(callback.log_evaluation(show_stdv=show_stdv))
-    elif isinstance(verbose_eval, int):
-        callbacks.add(callback.log_evaluation(verbose_eval, show_stdv=show_stdv))
 
     callbacks_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
     callbacks_after_iter = callbacks - callbacks_before_iter