diff --git a/examples/python-guide/notebooks/interactive_plot_example.ipynb b/examples/python-guide/notebooks/interactive_plot_example.ipynb index d4671c05d..ac7d85550 100644 --- a/examples/python-guide/notebooks/interactive_plot_example.ipynb +++ b/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)])" ] }, { diff --git a/examples/python-guide/plot_example.py b/examples/python-guide/plot_example.py index 7cf51b036..de70565e1 100644 --- a/examples/python-guide/plot_example.py +++ b/examples/python-guide/plot_example.py @@ -43,7 +43,7 @@ gbm = lgb.train(params, 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') diff --git a/python-package/lightgbm/engine.py b/python-package/lightgbm/engine.py index 7c2568961..6ad651172 100644 --- a/python-package/lightgbm/engine.py +++ b/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 diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py index c316f8220..2d0ed6d86 100644 --- a/tests/python_package_test/test_engine.py +++ b/tests/python_package_test/test_engine.py @@ -65,7 +65,6 @@ def test_binary(): gbm = lgb.train(params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = log_loss(y_test, gbm.predict(X_test)) assert ret < 0.14 @@ -92,7 +91,6 @@ def test_rf(): gbm = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = log_loss(y_test, gbm.predict(X_test)) assert ret < 0.19 @@ -112,7 +110,6 @@ def test_regression(): gbm = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = mean_squared_error(y_test, gbm.predict(X_test)) assert ret < 7 @@ -138,7 +135,6 @@ def test_missing_value_handle(): gbm = lgb.train(params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = mean_squared_error(y_train, gbm.predict(X_train)) assert ret < 0.005 @@ -164,7 +160,6 @@ def test_missing_value_handle_more_na(): gbm = lgb.train(params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = mean_squared_error(y_train, gbm.predict(X_train)) assert ret < 0.005 @@ -195,7 +190,6 @@ def test_missing_value_handle_na(): gbm = lgb.train(params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) pred = gbm.predict(X_train) np.testing.assert_allclose(pred, y) @@ -228,7 +222,6 @@ def test_missing_value_handle_zero(): gbm = lgb.train(params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) pred = gbm.predict(X_train) np.testing.assert_allclose(pred, y) @@ -261,7 +254,6 @@ def test_missing_value_handle_none(): gbm = lgb.train(params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) pred = gbm.predict(X_train) assert pred[0] == pytest.approx(pred[1]) @@ -300,7 +292,6 @@ def test_categorical_handle(): gbm = lgb.train(params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) pred = gbm.predict(X_train) np.testing.assert_allclose(pred, y) @@ -338,7 +329,6 @@ def test_categorical_handle_na(): gbm = lgb.train(params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) pred = gbm.predict(X_train) np.testing.assert_allclose(pred, y) @@ -376,7 +366,6 @@ def test_categorical_non_zero_inputs(): gbm = lgb.train(params, lgb_train, num_boost_round=1, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) pred = gbm.predict(X_train) np.testing.assert_allclose(pred, y) @@ -400,7 +389,6 @@ def test_multiclass(): gbm = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = multi_logloss(y_test, gbm.predict(X_test)) assert ret < 0.16 @@ -429,7 +417,6 @@ def test_multiclass_rf(): gbm = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = multi_logloss(y_test, gbm.predict(X_test)) assert ret < 0.23 @@ -470,7 +457,7 @@ def test_multi_class_error(): predict_default = est.predict(X) results = {} est = lgb.train(dict(params, multi_error_top_k=1), lgb_data, num_boost_round=10, - valid_sets=[lgb_data], evals_result=results, verbose_eval=False) + valid_sets=[lgb_data], evals_result=results) predict_1 = est.predict(X) # check that default gives same result as k = 1 np.testing.assert_allclose(predict_1, predict_default) @@ -480,14 +467,14 @@ def test_multi_class_error(): # check against independent calculation for k = 2 results = {} est = lgb.train(dict(params, multi_error_top_k=2), lgb_data, num_boost_round=10, - valid_sets=[lgb_data], evals_result=results, verbose_eval=False) + valid_sets=[lgb_data], evals_result=results) predict_2 = est.predict(X) err = top_k_error(y, predict_2, 2) assert results['training']['multi_error@2'][-1] == pytest.approx(err) # check against independent calculation for k = 10 results = {} est = lgb.train(dict(params, multi_error_top_k=10), lgb_data, num_boost_round=10, - valid_sets=[lgb_data], evals_result=results, verbose_eval=False) + valid_sets=[lgb_data], evals_result=results) predict_3 = est.predict(X) err = top_k_error(y, predict_3, 10) assert results['training']['multi_error@10'][-1] == pytest.approx(err) @@ -498,11 +485,11 @@ def test_multi_class_error(): params['num_classes'] = 2 results = {} lgb.train(params, lgb_data, num_boost_round=10, - valid_sets=[lgb_data], evals_result=results, verbose_eval=False) + valid_sets=[lgb_data], evals_result=results) assert results['training']['multi_error'][-1] == pytest.approx(1) results = {} lgb.train(dict(params, multi_error_top_k=2), lgb_data, num_boost_round=10, - valid_sets=[lgb_data], evals_result=results, verbose_eval=False) + valid_sets=[lgb_data], evals_result=results) assert results['training']['multi_error@2'][-1] == pytest.approx(0) @@ -626,7 +613,6 @@ def test_early_stopping(): num_boost_round=10, valid_sets=lgb_eval, valid_names=valid_set_name, - verbose_eval=False, early_stopping_rounds=5) assert gbm.best_iteration == 10 assert valid_set_name in gbm.best_score @@ -636,7 +622,6 @@ def test_early_stopping(): num_boost_round=40, valid_sets=lgb_eval, valid_names=valid_set_name, - verbose_eval=False, early_stopping_rounds=5) assert gbm.best_iteration <= 39 assert valid_set_name in gbm.best_score @@ -735,7 +720,6 @@ def test_continue_train(): gbm = lgb.train(params, lgb_train, num_boost_round=30, valid_sets=lgb_eval, - verbose_eval=False, # test custom eval metrics feval=(lambda p, d: ('custom_mae', mean_absolute_error(p, d.get_label()), False)), evals_result=evals_result, @@ -776,7 +760,6 @@ def test_continue_train_dart(): gbm = lgb.train(params, lgb_train, num_boost_round=50, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result, init_model=init_gbm) ret = mean_absolute_error(y_test, gbm.predict(X_test)) @@ -800,7 +783,6 @@ def test_continue_train_multiclass(): gbm = lgb.train(params, lgb_train, num_boost_round=30, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result, init_model=init_gbm) ret = multi_logloss(y_test, gbm.predict(X_test)) @@ -815,21 +797,20 @@ def test_cv(): # shuffle = False, override metric in params params_with_metric = {'metric': 'l2', 'verbose': -1} cv_res = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, - nfold=3, stratified=False, shuffle=False, - metrics='l1', verbose_eval=False) + nfold=3, stratified=False, shuffle=False, metrics='l1') assert 'l1-mean' in cv_res assert 'l2-mean' not in cv_res assert len(cv_res['l1-mean']) == 10 # shuffle = True, callbacks - cv_res = lgb.cv(params, lgb_train, num_boost_round=10, nfold=3, stratified=False, shuffle=True, - metrics='l1', verbose_eval=False, + cv_res = lgb.cv(params, lgb_train, num_boost_round=10, nfold=3, + stratified=False, shuffle=True, metrics='l1', callbacks=[lgb.reset_parameter(learning_rate=lambda i: 0.1 - 0.001 * i)]) assert 'l1-mean' in cv_res assert len(cv_res['l1-mean']) == 10 # enable display training loss cv_res = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, nfold=3, stratified=False, shuffle=False, - metrics='l1', verbose_eval=False, eval_train_metric=True) + metrics='l1', eval_train_metric=True) assert 'train l1-mean' in cv_res assert 'valid l1-mean' in cv_res assert 'train l2-mean' not in cv_res @@ -839,10 +820,8 @@ def test_cv(): # self defined folds tss = TimeSeriesSplit(3) folds = tss.split(X_train) - cv_res_gen = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=folds, - verbose_eval=False) - cv_res_obj = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=tss, - verbose_eval=False) + cv_res_gen = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=folds) + cv_res_obj = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=tss) np.testing.assert_allclose(cv_res_gen['l2-mean'], cv_res_obj['l2-mean']) # LambdaRank rank_example_dir = Path(__file__).absolute().parents[2] / 'examples' / 'lambdarank' @@ -851,19 +830,16 @@ def test_cv(): params_lambdarank = {'objective': 'lambdarank', 'verbose': -1, 'eval_at': 3} lgb_train = lgb.Dataset(X_train, y_train, group=q_train) # ... with l2 metric - cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, - metrics='l2', verbose_eval=False) + cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, metrics='l2') assert len(cv_res_lambda) == 2 assert not np.isnan(cv_res_lambda['l2-mean']).any() # ... with NDCG (default) metric - cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, - verbose_eval=False) + cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3) assert len(cv_res_lambda) == 2 assert not np.isnan(cv_res_lambda['ndcg@3-mean']).any() # self defined folds with lambdarank cv_res_lambda_obj = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, - folds=GroupKFold(n_splits=3), - verbose_eval=False) + folds=GroupKFold(n_splits=3)) np.testing.assert_allclose(cv_res_lambda['ndcg@3-mean'], cv_res_lambda_obj['ndcg@3-mean']) @@ -880,7 +856,6 @@ def test_cvbooster(): cv_res = lgb.cv(params, lgb_train, num_boost_round=25, early_stopping_rounds=5, - verbose_eval=False, nfold=3, return_cvbooster=True) assert 'cvbooster' in cv_res @@ -901,7 +876,6 @@ def test_cvbooster(): # without early stopping cv_res = lgb.cv(params, lgb_train, num_boost_round=20, - verbose_eval=False, nfold=3, return_cvbooster=True) cvb = cv_res['cvbooster'] @@ -1099,7 +1073,7 @@ def test_reference_chain(): evals_result = {} lgb.train(params, tmp_dat_train, num_boost_round=20, valid_sets=[tmp_dat_train, tmp_dat_val], - verbose_eval=False, evals_result=evals_result) + evals_result=evals_result) assert len(evals_result['training']['rmse']) == 20 assert len(evals_result['valid_1']['rmse']) == 20 @@ -1706,14 +1680,14 @@ def test_metrics(): params_metric_none_verbose = {'metric': 'None', 'verbose': -1} def get_cv_result(params=params_obj_verbose, **kwargs): - return lgb.cv(params, lgb_train, num_boost_round=2, verbose_eval=False, **kwargs) + return lgb.cv(params, lgb_train, num_boost_round=2, **kwargs) def train_booster(params=params_obj_verbose, **kwargs): lgb.train(params, lgb_train, num_boost_round=2, valid_sets=[lgb_valid], evals_result=evals_result, - verbose_eval=False, **kwargs) + **kwargs) # no fobj, no feval # default metric @@ -2248,7 +2222,7 @@ def test_early_stopping_for_only_first_metric(): } gbm = lgb.train(dict(params, first_metric_only=first_metric_only), lgb_train, num_boost_round=25, valid_sets=valid_sets, feval=feval, - early_stopping_rounds=5, verbose_eval=False) + early_stopping_rounds=5) assert assumed_iteration == gbm.best_iteration def metrics_combination_cv_regression(metric_list, assumed_iteration, @@ -2265,7 +2239,7 @@ def test_early_stopping_for_only_first_metric(): ret = lgb.cv(dict(params, first_metric_only=first_metric_only), train_set=lgb_train, num_boost_round=25, stratified=False, feval=feval, - early_stopping_rounds=5, verbose_eval=False, + early_stopping_rounds=5, eval_train_metric=eval_train_metric) assert assumed_iteration == len(ret[list(ret.keys())[0]]) @@ -2363,7 +2337,6 @@ def test_node_level_subcol(): gbm = lgb.train(params, lgb_train, num_boost_round=25, valid_sets=lgb_eval, - verbose_eval=False, evals_result=evals_result) ret = log_loss(y_test, gbm.predict(X_test)) assert ret < 0.14 diff --git a/tests/python_package_test/test_plotting.py b/tests/python_package_test/test_plotting.py index 05bcb6da7..e5e8b6eed 100644 --- a/tests/python_package_test/test_plotting.py +++ b/tests/python_package_test/test_plotting.py @@ -198,8 +198,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data): valid_sets=[train_data, test_data], valid_names=['v1', 'v2'], num_boost_round=10, - evals_result=evals_result0, - verbose_eval=False) + evals_result=evals_result0) with pytest.warns(UserWarning, match="More than one metric available, picking one to plot."): ax0 = lgb.plot_metric(evals_result0) assert isinstance(ax0, matplotlib.axes.Axes) @@ -259,8 +258,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data): evals_result1 = {} lgb.train(params, train_data, num_boost_round=10, - evals_result=evals_result1, - verbose_eval=False) + evals_result=evals_result1) with pytest.raises(ValueError, match="eval results cannot be empty."): lgb.plot_metric(evals_result1)