[python][sklearn] remove verbose argument from fit() method (#4832)

python-package/lightgbm/dask.py

@@ -1205,7 +1205,7 @@ def fit(
                  + _base_doc[_base_doc.find('eval_metric :'):])
 
     _base_doc = (_base_doc[:_base_doc.find('early_stopping_rounds :')]
-                 + _base_doc[_base_doc.find('verbose :'):])
+                 + _base_doc[_base_doc.find('feature_name :'):])
 
     # DaskLGBMClassifier support for callbacks and init_model is not tested
     fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
@@ -1388,7 +1388,7 @@ def fit(
                  + _base_doc[_base_doc.find('eval_metric :'):])
 
     _base_doc = (_base_doc[:_base_doc.find('early_stopping_rounds :')]
-                 + _base_doc[_base_doc.find('verbose :'):])
+                 + _base_doc[_base_doc.find('feature_name :'):])
 
     # DaskLGBMRegressor support for callbacks and init_model is not tested
     fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs
@@ -1554,7 +1554,7 @@ def fit(
     _base_doc = (_base_doc[:_base_doc.find('early_stopping_rounds :')]
                  + "eval_at : iterable of int, optional (default=(1, 2, 3, 4, 5))\n"
                  + f"{' ':8}The evaluation positions of the specified metric.\n"
-                 + f"{' ':4}{_base_doc[_base_doc.find('verbose :'):]}")
+                 + f"{' ':4}{_base_doc[_base_doc.find('feature_name :'):]}")
 
     # DaskLGBMRanker support for callbacks and init_model is not tested
     fit.__doc__ = f"""{_base_doc[:_base_doc.find('callbacks :')]}**kwargs

python-package/lightgbm/sklearn.py

@@ -258,17 +258,6 @@ def __call__(self, preds, dataset):
         If there's more than one, will check all of them. But the training data is ignored anyway.
         To check only the first metric, set the ``first_metric_only`` parameter to ``True``
         in additional parameters ``**kwargs`` of the model constructor.
-    verbose : bool or int, optional (default=True)
-        Requires at least one evaluation data.
-        If True, the eval metric on the eval set is printed at each boosting stage.
-        If int, the eval metric on the eval set is printed at every ``verbose`` boosting stage.
-        The last boosting stage or the boosting stage found by using ``early_stopping_rounds`` is also printed.
-
-        .. rubric:: Example
-
-        With ``verbose`` = 4 and at least one item in ``eval_set``,
-        an evaluation metric is printed every 4 (instead of 1) boosting stages.
-
     feature_name : list of str, or 'auto', optional (default='auto')
         Feature names.
         If 'auto' and data is pandas DataFrame, data columns names are used.
@@ -597,7 +586,7 @@ def fit(self, X, y,
             sample_weight=None, init_score=None, group=None,
             eval_set=None, eval_names=None, eval_sample_weight=None,
             eval_class_weight=None, eval_init_score=None, eval_group=None,
-            eval_metric=None, early_stopping_rounds=None, verbose='warn',
+            eval_metric=None, early_stopping_rounds=None,
             feature_name='auto', categorical_feature='auto',
             callbacks=None, init_model=None):
         """Docstring is set after definition, using a template."""
@@ -752,16 +741,6 @@ def _get_meta_data(collection, name, i):
         else:
             callbacks = copy.copy(callbacks)  # don't use deepcopy here to allow non-serializable objects
 
-        if verbose != 'warn':
-            _log_warning("'verbose' argument is deprecated and will be removed in a future release of LightGBM. "
-                         "Pass 'log_evaluation()' callback via 'callbacks' argument instead.")
-        else:
-            if callbacks:  # assume user has already specified log_evaluation callback
-                verbose = False
-            else:
-                verbose = True
-        callbacks.append(log_evaluation(int(verbose)))
-
         evals_result = {}
         callbacks.append(record_evaluation(evals_result))
 
@@ -931,13 +910,13 @@ def fit(self, X, y,
             sample_weight=None, init_score=None,
             eval_set=None, eval_names=None, eval_sample_weight=None,
             eval_init_score=None, eval_metric=None, early_stopping_rounds=None,
-            verbose='warn', feature_name='auto', categorical_feature='auto',
+            feature_name='auto', categorical_feature='auto',
             callbacks=None, init_model=None):
         """Docstring is inherited from the LGBMModel."""
         super().fit(X, y, sample_weight=sample_weight, init_score=init_score,
                     eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight,
                     eval_init_score=eval_init_score, eval_metric=eval_metric,
-                    early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name,
+                    early_stopping_rounds=early_stopping_rounds, feature_name=feature_name,
                     categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model)
         return self
 
@@ -957,7 +936,7 @@ def fit(self, X, y,
             sample_weight=None, init_score=None,
             eval_set=None, eval_names=None, eval_sample_weight=None,
             eval_class_weight=None, eval_init_score=None, eval_metric=None,
-            early_stopping_rounds=None, verbose='warn',
+            early_stopping_rounds=None,
             feature_name='auto', categorical_feature='auto',
             callbacks=None, init_model=None):
         """Docstring is inherited from the LGBMModel."""
@@ -1004,7 +983,7 @@ def fit(self, X, y,
                     eval_names=eval_names, eval_sample_weight=eval_sample_weight,
                     eval_class_weight=eval_class_weight, eval_init_score=eval_init_score,
                     eval_metric=eval_metric, early_stopping_rounds=early_stopping_rounds,
-                    verbose=verbose, feature_name=feature_name, categorical_feature=categorical_feature,
+                    feature_name=feature_name, categorical_feature=categorical_feature,
                     callbacks=callbacks, init_model=init_model)
         return self
 
@@ -1079,7 +1058,7 @@ def fit(self, X, y,
             sample_weight=None, init_score=None, group=None,
             eval_set=None, eval_names=None, eval_sample_weight=None,
             eval_init_score=None, eval_group=None, eval_metric=None,
-            eval_at=(1, 2, 3, 4, 5), early_stopping_rounds=None, verbose='warn',
+            eval_at=(1, 2, 3, 4, 5), early_stopping_rounds=None,
             feature_name='auto', categorical_feature='auto',
             callbacks=None, init_model=None):
         """Docstring is inherited from the LGBMModel."""
@@ -1103,7 +1082,7 @@ def fit(self, X, y,
         super().fit(X, y, sample_weight=sample_weight, init_score=init_score, group=group,
                     eval_set=eval_set, eval_names=eval_names, eval_sample_weight=eval_sample_weight,
                     eval_init_score=eval_init_score, eval_group=eval_group, eval_metric=eval_metric,
-                    early_stopping_rounds=early_stopping_rounds, verbose=verbose, feature_name=feature_name,
+                    early_stopping_rounds=early_stopping_rounds, feature_name=feature_name,
                     categorical_feature=categorical_feature, callbacks=callbacks, init_model=init_model)
         return self
 

tests/python_package_test/test_dask.py

@@ -900,8 +900,7 @@ def test_eval_set_no_early_stopping(task, output, eval_sizes, eval_names_prefix,
             'eval_names': eval_names,
             'eval_sample_weight': eval_sample_weight,
             'eval_init_score': eval_init_score,
-            'eval_metric': eval_metrics,
-            'verbose': True
+            'eval_metric': eval_metrics
         }
         if task == 'ranking':
             fit_params.update(

tests/python_package_test/test_plotting.py

@@ -143,7 +143,7 @@ def test_plot_split_value_histogram(params, breast_cancer_split, train_data):
 def test_plot_tree(breast_cancer_split):
     X_train, _, y_train, _ = breast_cancer_split
     gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1)
-    gbm.fit(X_train, y_train, verbose=False)
+    gbm.fit(X_train, y_train)
 
     with pytest.raises(IndexError):
         lgb.plot_tree(gbm, tree_index=83)
@@ -161,7 +161,7 @@ def test_create_tree_digraph(breast_cancer_split):
 
     constraints = [-1, 1] * int(X_train.shape[1] / 2)
     gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1, monotone_constraints=constraints)
-    gbm.fit(X_train, y_train, verbose=False)
+    gbm.fit(X_train, y_train)
 
     with pytest.raises(IndexError):
         lgb.create_tree_digraph(gbm, tree_index=83)
@@ -265,7 +265,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data):
         lgb.plot_metric(evals_result1)
 
     gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1)
-    gbm2.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=False)
+    gbm2.fit(X_train, y_train, eval_set=[(X_test, y_test)])
     ax4 = lgb.plot_metric(gbm2, title=None, xlabel=None, ylabel=None)
     assert isinstance(ax4, matplotlib.axes.Axes)
     assert ax4.get_title() == ''