[python] Remove `silent` argument (#4800)

* Update test_plotting.py

* Update dask.py

* Update sklearn.py

* Update test_sklearn.py

* Update basic.py

* Update engine.py

* Update test_engine.py

* Update basic.py

* Update basic.py

* Update engine.py

python-package/lightgbm/basic.py

@@ -412,8 +412,6 @@ class _ConfigAliases:
                "two_round": {"two_round",
                              "two_round_loading",
                              "use_two_round_loading"},
-               "verbosity": {"verbosity",
-                             "verbose"},
                "weight_column": {"weight_column",
                                  "weight"}}
 
@@ -1173,7 +1171,7 @@ class Dataset:
     """Dataset in LightGBM."""
 
     def __init__(self, data, label=None, reference=None,
-                 weight=None, group=None, init_score=None, silent='warn',
+                 weight=None, group=None, init_score=None,
                  feature_name='auto', categorical_feature='auto', params=None,
                  free_raw_data=True):
         """Initialize Dataset.
@@ -1197,8 +1195,6 @@ class Dataset:
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for Dataset.
-        silent : bool, optional (default=False)
-            Whether to print messages during construction.
         feature_name : list of str, or 'auto', optional (default="auto")
             Feature names.
             If 'auto' and data is pandas DataFrame, data columns names are used.
@@ -1223,7 +1219,6 @@ class Dataset:
         self.weight = weight
         self.group = group
         self.init_score = init_score
-        self.silent = silent
         self.feature_name = feature_name
         self.categorical_feature = categorical_feature
         self.params = deepcopy(params)
@@ -1465,8 +1460,7 @@ class Dataset:
 
     def _lazy_init(self, data, label=None, reference=None,
                    weight=None, group=None, init_score=None, predictor=None,
-                   silent=False, feature_name='auto',
-                   categorical_feature='auto', params=None):
+                   feature_name='auto', categorical_feature='auto', params=None):
         if data is None:
             self.handle = None
             return self
@@ -1488,14 +1482,6 @@ class Dataset:
             if key in args_names:
                 _log_warning(f'{key} keyword has been found in `params` and will be ignored.\n'
                              f'Please use {key} argument of the Dataset constructor to pass this parameter.')
-        # user can set verbose with params, it has higher priority
-        if silent != "warn":
-            _log_warning("'silent' argument is deprecated and will be removed in a future release of LightGBM. "
-                         "Pass 'verbose' parameter via 'params' instead.")
-        else:
-            silent = False
-        if not any(verbose_alias in params for verbose_alias in _ConfigAliases.get("verbosity")) and silent:
-            params["verbose"] = -1
         # get categorical features
         if categorical_feature is not None:
             categorical_indices = set()
@@ -1816,7 +1802,7 @@ class Dataset:
                     self._lazy_init(self.data, label=self.label, reference=self.reference,
                                     weight=self.weight, group=self.group,
                                     init_score=self.init_score, predictor=self._predictor,
-                                    silent=self.silent, feature_name=self.feature_name, params=self.params)
+                                    feature_name=self.feature_name, params=self.params)
                 else:
                     # construct subset
                     used_indices = list_to_1d_numpy(self.used_indices, np.int32, name='used_indices')
@@ -1847,14 +1833,12 @@ class Dataset:
                 self._lazy_init(self.data, label=self.label,
                                 weight=self.weight, group=self.group,
                                 init_score=self.init_score, predictor=self._predictor,
-                                silent=self.silent, feature_name=self.feature_name,
-                                categorical_feature=self.categorical_feature, params=self.params)
+                                feature_name=self.feature_name, categorical_feature=self.categorical_feature, params=self.params)
             if self.free_raw_data:
                 self.data = None
         return self
 
-    def create_valid(self, data, label=None, weight=None, group=None,
-                     init_score=None, silent='warn', params=None):
+    def create_valid(self, data, label=None, weight=None, group=None, init_score=None, params=None):
         """Create validation data align with current Dataset.
 
         Parameters
@@ -1874,8 +1858,6 @@ class Dataset:
             where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.
         init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), or None, optional (default=None)
             Init score for Dataset.
-        silent : bool, optional (default=False)
-            Whether to print messages during construction.
         params : dict or None, optional (default=None)
             Other parameters for validation Dataset.
 
@@ -1886,7 +1868,7 @@ class Dataset:
         """
         ret = Dataset(data, label=label, reference=self,
                       weight=weight, group=group, init_score=init_score,
-                      silent=silent, params=params, free_raw_data=self.free_raw_data)
+                      params=params, free_raw_data=self.free_raw_data)
         ret._predictor = self._predictor
         ret.pandas_categorical = self.pandas_categorical
         return ret
@@ -2562,7 +2544,7 @@ class Dataset:
 class Booster:
     """Booster in LightGBM."""
 
-    def __init__(self, params=None, train_set=None, model_file=None, model_str=None, silent='warn'):
+    def __init__(self, params=None, train_set=None, model_file=None, model_str=None):
         """Initialize the Booster.
 
         Parameters
@@ -2575,8 +2557,6 @@ class Booster:
             Path to the model file.
         model_str : str or None, optional (default=None)
             Model will be loaded from this string.
-        silent : bool, optional (default=False)
-            Whether to print messages during construction.
         """
         self.handle = None
         self.network = False
@@ -2587,14 +2567,6 @@ class Booster:
         self.best_iteration = -1
         self.best_score = {}
         params = {} if params is None else deepcopy(params)
-        # user can set verbose with params, it has higher priority
-        if silent != 'warn':
-            _log_warning("'silent' argument is deprecated and will be removed in a future release of LightGBM. "
-                         "Pass 'verbose' parameter via 'params' instead.")
-        else:
-            silent = False
-        if not any(verbose_alias in params for verbose_alias in _ConfigAliases.get("verbosity")) and silent:
-            params["verbose"] = -1
         if train_set is not None:
             # Training task
             if not isinstance(train_set, Dataset):
@@ -3388,12 +3360,6 @@ class Booster:
         _safe_call(_LIB.LGBM_BoosterGetNumClasses(
             self.handle,
             ctypes.byref(out_num_class)))
-        if verbose in {'warn', '_silent_false'}:
-            verbose = verbose == 'warn'
-        else:
-            _log_warning("'verbose' argument is deprecated and will be removed in a future release of LightGBM.")
-        if verbose:
-            _log_info(f'Finished loading model, total used {int(out_num_iterations.value)} iterations')
         self.__num_class = out_num_class.value
         self.pandas_categorical = _load_pandas_categorical(model_str=model_str)
         return self
@@ -3608,7 +3574,7 @@ class Booster:
             default_value=None
         )
         new_params["linear_tree"] = bool(out_is_linear.value)
-        train_set = Dataset(data, label, silent=True, params=new_params)
+        train_set = Dataset(data, label, params=new_params)
         new_params['refit_decay_rate'] = decay_rate
         new_booster = Booster(new_params, train_set)
         # Copy models

python-package/lightgbm/dask.py

@@ -1108,7 +1108,6 @@ class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):
         reg_lambda: float = 0.,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         n_jobs: int = -1,
-        silent: bool = "warn",
         importance_type: str = 'split',
         client: Optional[Client] = None,
         **kwargs: Any
@@ -1134,7 +1133,6 @@ class DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):
             reg_lambda=reg_lambda,
             random_state=random_state,
             n_jobs=n_jobs,
-            silent=silent,
             importance_type=importance_type,
             **kwargs
         )
@@ -1293,7 +1291,6 @@ class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):
         reg_lambda: float = 0.,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         n_jobs: int = -1,
-        silent: bool = "warn",
         importance_type: str = 'split',
         client: Optional[Client] = None,
         **kwargs: Any
@@ -1319,7 +1316,6 @@ class DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):
             reg_lambda=reg_lambda,
             random_state=random_state,
             n_jobs=n_jobs,
-            silent=silent,
             importance_type=importance_type,
             **kwargs
         )
@@ -1458,7 +1454,6 @@ class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):
         reg_lambda: float = 0.,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         n_jobs: int = -1,
-        silent: bool = "warn",
         importance_type: str = 'split',
         client: Optional[Client] = None,
         **kwargs: Any
@@ -1484,7 +1479,6 @@ class DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):
             reg_lambda=reg_lambda,
             random_state=random_state,
             n_jobs=n_jobs,
-            silent=silent,
             importance_type=importance_type,
             **kwargs
         )

python-package/lightgbm/sklearn.py

@@ -398,7 +398,6 @@ class LGBMModel(_LGBMModelBase):
         reg_lambda: float = 0.,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         n_jobs: int = -1,
-        silent: Union[bool, str] = 'warn',
         importance_type: str = 'split',
         **kwargs
     ):
@@ -463,8 +462,6 @@ class LGBMModel(_LGBMModelBase):
             If None, default seeds in C++ code are used.
         n_jobs : int, optional (default=-1)
             Number of parallel threads to use for training (can be changed at prediction time).
-        silent : bool, optional (default=True)
-            Whether to print messages while running boosting.
         importance_type : str, optional (default='split')
             The type of feature importance to be filled into ``feature_importances_``.
             If 'split', result contains numbers of times the feature is used in a model.
@@ -528,7 +525,6 @@ class LGBMModel(_LGBMModelBase):
         self.reg_lambda = reg_lambda
         self.random_state = random_state
         self.n_jobs = n_jobs
-        self.silent = silent
         self.importance_type = importance_type
         self._Booster = None
         self._evals_result = None
@@ -632,17 +628,6 @@ class LGBMModel(_LGBMModelBase):
             self._fobj = None
             params['objective'] = self._objective
 
-        # user can set verbose with kwargs, it has higher priority
-        if self.silent != "warn":
-            _log_warning("'silent' argument is deprecated and will be removed in a future release of LightGBM. "
-                         "Pass 'verbose' parameter via keyword arguments instead.")
-            silent = self.silent
-        else:
-            silent = True
-        if not any(verbose_alias in params for verbose_alias in _ConfigAliases.get("verbosity")) and silent:
-            params['verbose'] = -1
-        params.pop('silent', None)
-
         params.pop('importance_type', None)
         params.pop('n_estimators', None)
         params.pop('class_weight', None)

tests/python_package_test/test_engine.py

@@ -1686,8 +1686,8 @@ def test_fpreproc():
 def test_metrics():
     X, y = load_digits(n_class=2, return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    lgb_train = lgb.Dataset(X_train, y_train, silent=True)
-    lgb_valid = lgb.Dataset(X_test, y_test, reference=lgb_train, silent=True)
+    lgb_train = lgb.Dataset(X_train, y_train)
+    lgb_valid = lgb.Dataset(X_test, y_test, reference=lgb_train)
 
     evals_result = {}
     params_verbose = {'verbose': -1}
@@ -1991,7 +1991,7 @@ def test_metrics():
     assert 'error' in evals_result['valid_0']
 
     X, y = load_digits(n_class=3, return_X_y=True)
-    lgb_train = lgb.Dataset(X, y, silent=True)
+    lgb_train = lgb.Dataset(X, y)
 
     obj_multi_aliases = ['multiclass', 'softmax', 'multiclassova', 'multiclass_ova', 'ova', 'ovr']
     for obj_multi_alias in obj_multi_aliases:
@@ -2065,8 +2065,8 @@ def test_multiple_feval_train():
 
     X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.2)
 
-    train_dataset = lgb.Dataset(data=X_train, label=y_train, silent=True)
-    validation_dataset = lgb.Dataset(data=X_validation, label=y_validation, reference=train_dataset, silent=True)
+    train_dataset = lgb.Dataset(data=X_train, label=y_train)
+    validation_dataset = lgb.Dataset(data=X_validation, label=y_validation, reference=train_dataset)
     evals_result = {}
     lgb.train(
         params=params,
@@ -2087,7 +2087,7 @@ def test_multiple_feval_cv():
 
     params = {'verbose': -1, 'objective': 'binary', 'metric': 'binary_logloss'}
 
-    train_dataset = lgb.Dataset(data=X, label=y, silent=True)
+    train_dataset = lgb.Dataset(data=X, label=y)
 
     cv_results = lgb.cv(
         params=params,

tests/python_package_test/test_plotting.py

@@ -45,7 +45,7 @@ def test_plot_importance(params, breast_cancer_split, train_data):
     assert ax0.get_ylabel() == 'Features'
     assert len(ax0.patches) <= 30
 
-    gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1)
     gbm1.fit(X_train, y_train)
 
     ax1 = lgb.plot_importance(gbm1, color='r', title='t', xlabel='x', ylabel='y')
@@ -75,7 +75,7 @@ def test_plot_importance(params, breast_cancer_split, train_data):
     assert ax3.get_ylabel() == 'y @importance_type@'
     assert len(ax3.patches) <= 30
 
-    gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, importance_type="gain")
+    gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1, importance_type="gain")
     gbm2.fit(X_train, y_train)
 
     def get_bounds_of_first_patch(axes):
@@ -107,7 +107,7 @@ def test_plot_split_value_histogram(params, breast_cancer_split, train_data):
     assert ax0.get_ylabel() == 'Count'
     assert len(ax0.patches) <= 2
 
-    gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm1 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1)
     gbm1.fit(X_train, y_train)
 
     ax1 = lgb.plot_split_value_histogram(gbm1, gbm1.booster_.feature_name()[27], figsize=(10, 5),
@@ -142,7 +142,7 @@ def test_plot_split_value_histogram(params, breast_cancer_split, train_data):
                     reason='matplotlib or graphviz is not installed')
 def test_plot_tree(breast_cancer_split):
     X_train, _, y_train, _ = breast_cancer_split
-    gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1)
     gbm.fit(X_train, y_train, verbose=False)
 
     with pytest.raises(IndexError):
@@ -160,7 +160,7 @@ def test_create_tree_digraph(breast_cancer_split):
     X_train, _, y_train, _ = breast_cancer_split
 
     constraints = [-1, 1] * int(X_train.shape[1] / 2)
-    gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True, monotone_constraints=constraints)
+    gbm = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, verbose=-1, monotone_constraints=constraints)
     gbm.fit(X_train, y_train, verbose=False)
 
     with pytest.raises(IndexError):
@@ -264,7 +264,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data):
     with pytest.raises(ValueError, match="eval results cannot be empty."):
         lgb.plot_metric(evals_result1)
 
-    gbm2 = lgb.LGBMClassifier(n_estimators=10, num_leaves=3, silent=True)
+    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)
     ax4 = lgb.plot_metric(gbm2, title=None, xlabel=None, ylabel=None)
     assert isinstance(ax4, matplotlib.axes.Axes)

tests/python_package_test/test_sklearn.py

@@ -91,7 +91,7 @@ def multi_logloss(y_true, y_pred):
 def test_binary():
     X, y = load_breast_cancer(return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    gbm = lgb.LGBMClassifier(n_estimators=50, silent=True)
+    gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1)
     gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
     ret = log_loss(y_test, gbm.predict_proba(X_test))
     assert ret < 0.12
@@ -101,7 +101,7 @@ def test_binary():
 def test_regression():
     X, y = load_boston(return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    gbm = lgb.LGBMRegressor(n_estimators=50, silent=True)
+    gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1)
     gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
     ret = mean_squared_error(y_test, gbm.predict(X_test))
     assert ret < 7
@@ -111,7 +111,7 @@ def test_regression():
 def test_multiclass():
     X, y = load_digits(n_class=10, return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    gbm = lgb.LGBMClassifier(n_estimators=50, silent=True)
+    gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1)
     gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
     ret = multi_error(y_test, gbm.predict(X_test))
     assert ret < 0.05
@@ -195,7 +195,7 @@ def test_objective_aliases(custom_objective):
 def test_regression_with_custom_objective():
     X, y = load_boston(return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    gbm = lgb.LGBMRegressor(n_estimators=50, silent=True, objective=objective_ls)
+    gbm = lgb.LGBMRegressor(n_estimators=50, verbose=-1, objective=objective_ls)
     gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
     ret = mean_squared_error(y_test, gbm.predict(X_test))
     assert ret < 7.0
@@ -205,7 +205,7 @@ def test_regression_with_custom_objective():
 def test_binary_classification_with_custom_objective():
     X, y = load_digits(n_class=2, return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    gbm = lgb.LGBMClassifier(n_estimators=50, silent=True, objective=logregobj)
+    gbm = lgb.LGBMClassifier(n_estimators=50, verbose=-1, objective=logregobj)
     gbm.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=5, verbose=False)
     # prediction result is actually not transformed (is raw) due to custom objective
     y_pred_raw = gbm.predict_proba(X_test)
@@ -421,7 +421,7 @@ def test_regressor_chain():
 
 def test_clone_and_property():
     X, y = load_boston(return_X_y=True)
-    gbm = lgb.LGBMRegressor(n_estimators=10, silent=True)
+    gbm = lgb.LGBMRegressor(n_estimators=10, verbose=-1)
     gbm.fit(X, y, verbose=False)
 
     gbm_clone = clone(gbm)
@@ -429,7 +429,7 @@ def test_clone_and_property():
     assert isinstance(gbm.feature_importances_, np.ndarray)
 
     X, y = load_digits(n_class=2, return_X_y=True)
-    clf = lgb.LGBMClassifier(n_estimators=10, silent=True)
+    clf = lgb.LGBMClassifier(n_estimators=10, verbose=-1)
     clf.fit(X, y, verbose=False)
     assert sorted(clf.classes_) == [0, 1]
     assert clf.n_classes_ == 2
@@ -441,7 +441,7 @@ def test_joblib():
     X, y = load_boston(return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
     gbm = lgb.LGBMRegressor(n_estimators=10, objective=custom_asymmetric_obj,
-                            silent=True, importance_type='split')
+                            verbose=-1, importance_type='split')
     gbm.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)],
             eval_metric=mse, early_stopping_rounds=5, verbose=False,
             callbacks=[lgb.reset_parameter(learning_rate=list(np.arange(1, 0, -0.1)))])
@@ -694,7 +694,7 @@ def test_predict():
 def test_evaluate_train_set():
     X, y = load_boston(return_X_y=True)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
-    gbm = lgb.LGBMRegressor(n_estimators=10, silent=True)
+    gbm = lgb.LGBMRegressor(n_estimators=10, verbose=-1)
     gbm.fit(X_train, y_train, eval_set=[(X_train, y_train), (X_test, y_test)], verbose=False)
     assert len(gbm.evals_result_) == 2
     assert 'training' in gbm.evals_result_
@@ -1142,7 +1142,7 @@ def test_class_weight():
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
     y_train_str = y_train.astype('str')
     y_test_str = y_test.astype('str')
-    gbm = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', silent=True)
+    gbm = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', verbose=-1)
     gbm.fit(X_train, y_train,
             eval_set=[(X_train, y_train), (X_test, y_test), (X_test, y_test),
                       (X_test, y_test), (X_test, y_test)],
@@ -1154,7 +1154,7 @@ def test_class_weight():
                                      np.testing.assert_allclose,
                                      gbm.evals_result_[eval_set1][metric],
                                      gbm.evals_result_[eval_set2][metric])
-    gbm_str = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', silent=True)
+    gbm_str = lgb.LGBMClassifier(n_estimators=10, class_weight='balanced', verbose=-1)
     gbm_str.fit(X_train, y_train_str,
                 eval_set=[(X_train, y_train_str), (X_test, y_test_str),
                           (X_test, y_test_str), (X_test, y_test_str), (X_test, y_test_str)],