Skip to content

GitLab

"python-package/vscode:/vscode.git/clone" did not exist on "79d27770e480d2037e417e2727041d1ecb26d0ac"
Unverified Commit 9f13a9c8 authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
Browse files

[python] remove `verbose_eval` argument of `train()` and `cv()` functions (#4878) 

* remove `verbose_eval` argument

* update example Notebook
parent 80662618
Hide whitespace changes
Inline Side-by-side
Showing with 25 additions and 94 deletions
examples/python-guide/notebooks/interactive_plot_example.ipynb
View file @ 9f13a9c8
...@@ -149,7 +149,7 @@ ...@@ -149,7 +149,7 @@
" feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],\n", " feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],\n",
" categorical_feature=[21],\n", " categorical_feature=[21],\n",
" evals_result=evals_result,\n", " evals_result=evals_result,\n",
" verbose_eval=10)" " callbacks=[lgb.log_evaluation(10)])"
] ]
}, },
{ {
......
examples/python-guide/plot_example.py
View file @ 9f13a9c8
...@@ -43,7 +43,7 @@ gbm = lgb.train(params, ...@@ -43,7 +43,7 @@ gbm = lgb.train(params,
feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])], feature_name=[f'f{i + 1}' for i in range(X_train.shape[-1])],
categorical_feature=[21], categorical_feature=[21],
evals_result=evals_result, evals_result=evals_result,
verbose_eval=10) callbacks=[lgb.log_evaluation(10)])
print('Plotting metrics recorded during training...') print('Plotting metrics recorded during training...')
ax = lgb.plot_metric(evals_result, metric='l1') ax = lgb.plot_metric(evals_result, metric='l1')
......
python-package/lightgbm/engine.py
View file @ 9f13a9c8
...@@ -35,7 +35,6 @@ def train( ...@@ -35,7 +35,6 @@ def train(
categorical_feature: Union[List[str], List[int], str] = 'auto', categorical_feature: Union[List[str], List[int], str] = 'auto',
early_stopping_rounds: Optional[int] = None, early_stopping_rounds: Optional[int] = None,
evals_result: Optional[Dict[str, Any]] = None, evals_result: Optional[Dict[str, Any]] = None,
verbose_eval: Union[bool, int, str] = 'warn',
keep_training_booster: bool = False, keep_training_booster: bool = False,
callbacks: Optional[List[Callable]] = None callbacks: Optional[List[Callable]] = None
) -> Booster: ) -> Booster:
...@@ -133,17 +132,6 @@ def train( ...@@ -133,17 +132,6 @@ def train(
returns {'train': {'logloss': ['0.48253', '0.35953', ...]}, returns {'train': {'logloss': ['0.48253', '0.35953', ...]},
'eval': {'logloss': ['0.480385', '0.357756', ...]}}. '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) keep_training_booster : bool, optional (default=False)
Whether the returned Booster will be used to keep training. Whether the returned Booster will be used to keep training.
If False, the returned value will be converted into _InnerPredictor before returning. If False, the returned value will be converted into _InnerPredictor before returning.
...@@ -230,21 +218,8 @@ def train( ...@@ -230,21 +218,8 @@ def train(
callbacks_set = set(callbacks) callbacks_set = set(callbacks)
# Most of legacy advanced options becomes 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: 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: if evals_result is not None:
_log_warning("'evals_result' argument is deprecated and will be removed in a future release of LightGBM. " _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, ...@@ -426,8 +401,7 @@ def cv(params, train_set, num_boost_round=100,
metrics=None, fobj=None, feval=None, init_model=None, metrics=None, fobj=None, feval=None, init_model=None,
feature_name='auto', categorical_feature='auto', feature_name='auto', categorical_feature='auto',
early_stopping_rounds=None, fpreproc=None, early_stopping_rounds=None, fpreproc=None,
verbose_eval=None, show_stdv=True, seed=0, seed=0, callbacks=None, eval_train_metric=False,
callbacks=None, eval_train_metric=False,
return_cvbooster=False): return_cvbooster=False):
"""Perform the cross-validation with given parameters. """Perform the cross-validation with given parameters.
...@@ -522,13 +496,6 @@ def cv(params, train_set, num_boost_round=100, ...@@ -522,13 +496,6 @@ def cv(params, train_set, num_boost_round=100,
fpreproc : callable or None, optional (default=None) fpreproc : callable or None, optional (default=None)
Preprocessing function that takes (dtrain, dtest, params) Preprocessing function that takes (dtrain, dtest, params)
and returns transformed versions of those. 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 : int, optional (default=0)
Seed used to generate the folds (passed to numpy.random.seed). Seed used to generate the folds (passed to numpy.random.seed).
callbacks : list of callable, or None, optional (default=None) callbacks : list of callable, or None, optional (default=None)
...@@ -606,13 +573,6 @@ def cv(params, train_set, num_boost_round=100, ...@@ -606,13 +573,6 @@ def cv(params, train_set, num_boost_round=100,
callbacks = set(callbacks) callbacks = set(callbacks)
if early_stopping_rounds is not None and early_stopping_rounds > 0: 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)) 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_before_iter = {cb for cb in callbacks if getattr(cb, 'before_iteration', False)}
callbacks_after_iter = callbacks - callbacks_before_iter callbacks_after_iter = callbacks - callbacks_before_iter
......
tests/python_package_test/test_engine.py
View file @ 9f13a9c8
...@@ -65,7 +65,6 @@ def test_binary(): ...@@ -65,7 +65,6 @@ def test_binary():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=20, num_boost_round=20,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = log_loss(y_test, gbm.predict(X_test)) ret = log_loss(y_test, gbm.predict(X_test))
assert ret < 0.14 assert ret < 0.14
...@@ -92,7 +91,6 @@ def test_rf(): ...@@ -92,7 +91,6 @@ def test_rf():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=50, num_boost_round=50,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = log_loss(y_test, gbm.predict(X_test)) ret = log_loss(y_test, gbm.predict(X_test))
assert ret < 0.19 assert ret < 0.19
...@@ -112,7 +110,6 @@ def test_regression(): ...@@ -112,7 +110,6 @@ def test_regression():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=50, num_boost_round=50,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = mean_squared_error(y_test, gbm.predict(X_test)) ret = mean_squared_error(y_test, gbm.predict(X_test))
assert ret < 7 assert ret < 7
...@@ -138,7 +135,6 @@ def test_missing_value_handle(): ...@@ -138,7 +135,6 @@ def test_missing_value_handle():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=20, num_boost_round=20,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = mean_squared_error(y_train, gbm.predict(X_train)) ret = mean_squared_error(y_train, gbm.predict(X_train))
assert ret < 0.005 assert ret < 0.005
...@@ -164,7 +160,6 @@ def test_missing_value_handle_more_na(): ...@@ -164,7 +160,6 @@ def test_missing_value_handle_more_na():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=20, num_boost_round=20,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = mean_squared_error(y_train, gbm.predict(X_train)) ret = mean_squared_error(y_train, gbm.predict(X_train))
assert ret < 0.005 assert ret < 0.005
...@@ -195,7 +190,6 @@ def test_missing_value_handle_na(): ...@@ -195,7 +190,6 @@ def test_missing_value_handle_na():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=1, num_boost_round=1,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
pred = gbm.predict(X_train) pred = gbm.predict(X_train)
np.testing.assert_allclose(pred, y) np.testing.assert_allclose(pred, y)
...@@ -228,7 +222,6 @@ def test_missing_value_handle_zero(): ...@@ -228,7 +222,6 @@ def test_missing_value_handle_zero():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=1, num_boost_round=1,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
pred = gbm.predict(X_train) pred = gbm.predict(X_train)
np.testing.assert_allclose(pred, y) np.testing.assert_allclose(pred, y)
...@@ -261,7 +254,6 @@ def test_missing_value_handle_none(): ...@@ -261,7 +254,6 @@ def test_missing_value_handle_none():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=1, num_boost_round=1,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
pred = gbm.predict(X_train) pred = gbm.predict(X_train)
assert pred[0] == pytest.approx(pred[1]) assert pred[0] == pytest.approx(pred[1])
...@@ -300,7 +292,6 @@ def test_categorical_handle(): ...@@ -300,7 +292,6 @@ def test_categorical_handle():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=1, num_boost_round=1,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
pred = gbm.predict(X_train) pred = gbm.predict(X_train)
np.testing.assert_allclose(pred, y) np.testing.assert_allclose(pred, y)
...@@ -338,7 +329,6 @@ def test_categorical_handle_na(): ...@@ -338,7 +329,6 @@ def test_categorical_handle_na():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=1, num_boost_round=1,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
pred = gbm.predict(X_train) pred = gbm.predict(X_train)
np.testing.assert_allclose(pred, y) np.testing.assert_allclose(pred, y)
...@@ -376,7 +366,6 @@ def test_categorical_non_zero_inputs(): ...@@ -376,7 +366,6 @@ def test_categorical_non_zero_inputs():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=1, num_boost_round=1,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
pred = gbm.predict(X_train) pred = gbm.predict(X_train)
np.testing.assert_allclose(pred, y) np.testing.assert_allclose(pred, y)
...@@ -400,7 +389,6 @@ def test_multiclass(): ...@@ -400,7 +389,6 @@ def test_multiclass():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=50, num_boost_round=50,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = multi_logloss(y_test, gbm.predict(X_test)) ret = multi_logloss(y_test, gbm.predict(X_test))
assert ret < 0.16 assert ret < 0.16
...@@ -429,7 +417,6 @@ def test_multiclass_rf(): ...@@ -429,7 +417,6 @@ def test_multiclass_rf():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=50, num_boost_round=50,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = multi_logloss(y_test, gbm.predict(X_test)) ret = multi_logloss(y_test, gbm.predict(X_test))
assert ret < 0.23 assert ret < 0.23
...@@ -470,7 +457,7 @@ def test_multi_class_error(): ...@@ -470,7 +457,7 @@ def test_multi_class_error():
predict_default = est.predict(X) predict_default = est.predict(X)
results = {} results = {}
est = lgb.train(dict(params, multi_error_top_k=1), lgb_data, num_boost_round=10, 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) predict_1 = est.predict(X)
# check that default gives same result as k = 1 # check that default gives same result as k = 1
np.testing.assert_allclose(predict_1, predict_default) np.testing.assert_allclose(predict_1, predict_default)
...@@ -480,14 +467,14 @@ def test_multi_class_error(): ...@@ -480,14 +467,14 @@ def test_multi_class_error():
# check against independent calculation for k = 2 # check against independent calculation for k = 2
results = {} results = {}
est = lgb.train(dict(params, multi_error_top_k=2), lgb_data, num_boost_round=10, 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) predict_2 = est.predict(X)
err = top_k_error(y, predict_2, 2) err = top_k_error(y, predict_2, 2)
assert results['training']['multi_error@2'][-1] == pytest.approx(err) assert results['training']['multi_error@2'][-1] == pytest.approx(err)
# check against independent calculation for k = 10 # check against independent calculation for k = 10
results = {} results = {}
est = lgb.train(dict(params, multi_error_top_k=10), lgb_data, num_boost_round=10, 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) predict_3 = est.predict(X)
err = top_k_error(y, predict_3, 10) err = top_k_error(y, predict_3, 10)
assert results['training']['multi_error@10'][-1] == pytest.approx(err) assert results['training']['multi_error@10'][-1] == pytest.approx(err)
...@@ -498,11 +485,11 @@ def test_multi_class_error(): ...@@ -498,11 +485,11 @@ def test_multi_class_error():
params['num_classes'] = 2 params['num_classes'] = 2
results = {} results = {}
lgb.train(params, lgb_data, num_boost_round=10, 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) assert results['training']['multi_error'][-1] == pytest.approx(1)
results = {} results = {}
lgb.train(dict(params, multi_error_top_k=2), lgb_data, num_boost_round=10, 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) assert results['training']['multi_error@2'][-1] == pytest.approx(0)
...@@ -626,7 +613,6 @@ def test_early_stopping(): ...@@ -626,7 +613,6 @@ def test_early_stopping():
num_boost_round=10, num_boost_round=10,
valid_sets=lgb_eval, valid_sets=lgb_eval,
valid_names=valid_set_name, valid_names=valid_set_name,
verbose_eval=False,
early_stopping_rounds=5) early_stopping_rounds=5)
assert gbm.best_iteration == 10 assert gbm.best_iteration == 10
assert valid_set_name in gbm.best_score assert valid_set_name in gbm.best_score
...@@ -636,7 +622,6 @@ def test_early_stopping(): ...@@ -636,7 +622,6 @@ def test_early_stopping():
num_boost_round=40, num_boost_round=40,
valid_sets=lgb_eval, valid_sets=lgb_eval,
valid_names=valid_set_name, valid_names=valid_set_name,
verbose_eval=False,
early_stopping_rounds=5) early_stopping_rounds=5)
assert gbm.best_iteration <= 39 assert gbm.best_iteration <= 39
assert valid_set_name in gbm.best_score assert valid_set_name in gbm.best_score
...@@ -735,7 +720,6 @@ def test_continue_train(): ...@@ -735,7 +720,6 @@ def test_continue_train():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=30, num_boost_round=30,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
# test custom eval metrics # test custom eval metrics
feval=(lambda p, d: ('custom_mae', mean_absolute_error(p, d.get_label()), False)), feval=(lambda p, d: ('custom_mae', mean_absolute_error(p, d.get_label()), False)),
evals_result=evals_result, evals_result=evals_result,
...@@ -776,7 +760,6 @@ def test_continue_train_dart(): ...@@ -776,7 +760,6 @@ def test_continue_train_dart():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=50, num_boost_round=50,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result, evals_result=evals_result,
init_model=init_gbm) init_model=init_gbm)
ret = mean_absolute_error(y_test, gbm.predict(X_test)) ret = mean_absolute_error(y_test, gbm.predict(X_test))
...@@ -800,7 +783,6 @@ def test_continue_train_multiclass(): ...@@ -800,7 +783,6 @@ def test_continue_train_multiclass():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=30, num_boost_round=30,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result, evals_result=evals_result,
init_model=init_gbm) init_model=init_gbm)
ret = multi_logloss(y_test, gbm.predict(X_test)) ret = multi_logloss(y_test, gbm.predict(X_test))
...@@ -815,21 +797,20 @@ def test_cv(): ...@@ -815,21 +797,20 @@ def test_cv():
# shuffle = False, override metric in params # shuffle = False, override metric in params
params_with_metric = {'metric': 'l2', 'verbose': -1} params_with_metric = {'metric': 'l2', 'verbose': -1}
cv_res = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, cv_res = lgb.cv(params_with_metric, lgb_train, num_boost_round=10,
nfold=3, stratified=False, shuffle=False, nfold=3, stratified=False, shuffle=False, metrics='l1')
metrics='l1', verbose_eval=False)
assert 'l1-mean' in cv_res assert 'l1-mean' in cv_res
assert 'l2-mean' not in cv_res assert 'l2-mean' not in cv_res
assert len(cv_res['l1-mean']) == 10 assert len(cv_res['l1-mean']) == 10
# shuffle = True, callbacks # shuffle = True, callbacks
cv_res = lgb.cv(params, lgb_train, num_boost_round=10, nfold=3, stratified=False, shuffle=True, cv_res = lgb.cv(params, lgb_train, num_boost_round=10, nfold=3,
metrics='l1', verbose_eval=False, stratified=False, shuffle=True, metrics='l1',
callbacks=[lgb.reset_parameter(learning_rate=lambda i: 0.1 - 0.001 * i)]) callbacks=[lgb.reset_parameter(learning_rate=lambda i: 0.1 - 0.001 * i)])
assert 'l1-mean' in cv_res assert 'l1-mean' in cv_res
assert len(cv_res['l1-mean']) == 10 assert len(cv_res['l1-mean']) == 10
# enable display training loss # enable display training loss
cv_res = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, cv_res = lgb.cv(params_with_metric, lgb_train, num_boost_round=10,
nfold=3, stratified=False, shuffle=False, 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 'train l1-mean' in cv_res
assert 'valid l1-mean' in cv_res assert 'valid l1-mean' in cv_res
assert 'train l2-mean' not in cv_res assert 'train l2-mean' not in cv_res
...@@ -839,10 +820,8 @@ def test_cv(): ...@@ -839,10 +820,8 @@ def test_cv():
# self defined folds # self defined folds
tss = TimeSeriesSplit(3) tss = TimeSeriesSplit(3)
folds = tss.split(X_train) folds = tss.split(X_train)
cv_res_gen = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=folds, 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)
cv_res_obj = lgb.cv(params_with_metric, lgb_train, num_boost_round=10, folds=tss,
verbose_eval=False)
np.testing.assert_allclose(cv_res_gen['l2-mean'], cv_res_obj['l2-mean']) np.testing.assert_allclose(cv_res_gen['l2-mean'], cv_res_obj['l2-mean'])
# LambdaRank # LambdaRank
rank_example_dir = Path(__file__).absolute().parents[2] / 'examples' / 'lambdarank' rank_example_dir = Path(__file__).absolute().parents[2] / 'examples' / 'lambdarank'
...@@ -851,19 +830,16 @@ def test_cv(): ...@@ -851,19 +830,16 @@ def test_cv():
params_lambdarank = {'objective': 'lambdarank', 'verbose': -1, 'eval_at': 3} params_lambdarank = {'objective': 'lambdarank', 'verbose': -1, 'eval_at': 3}
lgb_train = lgb.Dataset(X_train, y_train, group=q_train) lgb_train = lgb.Dataset(X_train, y_train, group=q_train)
# ... with l2 metric # ... with l2 metric
cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, metrics='l2')
metrics='l2', verbose_eval=False)
assert len(cv_res_lambda) == 2 assert len(cv_res_lambda) == 2
assert not np.isnan(cv_res_lambda['l2-mean']).any() assert not np.isnan(cv_res_lambda['l2-mean']).any()
# ... with NDCG (default) metric # ... with NDCG (default) metric
cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3, cv_res_lambda = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, nfold=3)
verbose_eval=False)
assert len(cv_res_lambda) == 2 assert len(cv_res_lambda) == 2
assert not np.isnan(cv_res_lambda['ndcg@3-mean']).any() assert not np.isnan(cv_res_lambda['ndcg@3-mean']).any()
# self defined folds with lambdarank # self defined folds with lambdarank
cv_res_lambda_obj = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10, cv_res_lambda_obj = lgb.cv(params_lambdarank, lgb_train, num_boost_round=10,
folds=GroupKFold(n_splits=3), folds=GroupKFold(n_splits=3))
verbose_eval=False)
np.testing.assert_allclose(cv_res_lambda['ndcg@3-mean'], cv_res_lambda_obj['ndcg@3-mean']) np.testing.assert_allclose(cv_res_lambda['ndcg@3-mean'], cv_res_lambda_obj['ndcg@3-mean'])
...@@ -880,7 +856,6 @@ def test_cvbooster(): ...@@ -880,7 +856,6 @@ def test_cvbooster():
cv_res = lgb.cv(params, lgb_train, cv_res = lgb.cv(params, lgb_train,
num_boost_round=25, num_boost_round=25,
early_stopping_rounds=5, early_stopping_rounds=5,
verbose_eval=False,
nfold=3, nfold=3,
return_cvbooster=True) return_cvbooster=True)
assert 'cvbooster' in cv_res assert 'cvbooster' in cv_res
...@@ -901,7 +876,6 @@ def test_cvbooster(): ...@@ -901,7 +876,6 @@ def test_cvbooster():
# without early stopping # without early stopping
cv_res = lgb.cv(params, lgb_train, cv_res = lgb.cv(params, lgb_train,
num_boost_round=20, num_boost_round=20,
verbose_eval=False,
nfold=3, nfold=3,
return_cvbooster=True) return_cvbooster=True)
cvb = cv_res['cvbooster'] cvb = cv_res['cvbooster']
...@@ -1099,7 +1073,7 @@ def test_reference_chain(): ...@@ -1099,7 +1073,7 @@ def test_reference_chain():
evals_result = {} evals_result = {}
lgb.train(params, tmp_dat_train, num_boost_round=20, lgb.train(params, tmp_dat_train, num_boost_round=20,
valid_sets=[tmp_dat_train, tmp_dat_val], 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['training']['rmse']) == 20
assert len(evals_result['valid_1']['rmse']) == 20 assert len(evals_result['valid_1']['rmse']) == 20
...@@ -1706,14 +1680,14 @@ def test_metrics(): ...@@ -1706,14 +1680,14 @@ def test_metrics():
params_metric_none_verbose = {'metric': 'None', 'verbose': -1} params_metric_none_verbose = {'metric': 'None', 'verbose': -1}
def get_cv_result(params=params_obj_verbose, **kwargs): 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): def train_booster(params=params_obj_verbose, **kwargs):
lgb.train(params, lgb_train, lgb.train(params, lgb_train,
num_boost_round=2, num_boost_round=2,
valid_sets=[lgb_valid], valid_sets=[lgb_valid],
evals_result=evals_result, evals_result=evals_result,
verbose_eval=False, **kwargs) **kwargs)
# no fobj, no feval # no fobj, no feval
# default metric # default metric
...@@ -2248,7 +2222,7 @@ def test_early_stopping_for_only_first_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, gbm = lgb.train(dict(params, first_metric_only=first_metric_only), lgb_train,
num_boost_round=25, valid_sets=valid_sets, feval=feval, 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 assert assumed_iteration == gbm.best_iteration
def metrics_combination_cv_regression(metric_list, assumed_iteration, def metrics_combination_cv_regression(metric_list, assumed_iteration,
...@@ -2265,7 +2239,7 @@ def test_early_stopping_for_only_first_metric(): ...@@ -2265,7 +2239,7 @@ def test_early_stopping_for_only_first_metric():
ret = lgb.cv(dict(params, first_metric_only=first_metric_only), ret = lgb.cv(dict(params, first_metric_only=first_metric_only),
train_set=lgb_train, num_boost_round=25, train_set=lgb_train, num_boost_round=25,
stratified=False, feval=feval, stratified=False, feval=feval,
early_stopping_rounds=5, verbose_eval=False, early_stopping_rounds=5,
eval_train_metric=eval_train_metric) eval_train_metric=eval_train_metric)
assert assumed_iteration == len(ret[list(ret.keys())[0]]) assert assumed_iteration == len(ret[list(ret.keys())[0]])
...@@ -2363,7 +2337,6 @@ def test_node_level_subcol(): ...@@ -2363,7 +2337,6 @@ def test_node_level_subcol():
gbm = lgb.train(params, lgb_train, gbm = lgb.train(params, lgb_train,
num_boost_round=25, num_boost_round=25,
valid_sets=lgb_eval, valid_sets=lgb_eval,
verbose_eval=False,
evals_result=evals_result) evals_result=evals_result)
ret = log_loss(y_test, gbm.predict(X_test)) ret = log_loss(y_test, gbm.predict(X_test))
assert ret < 0.14 assert ret < 0.14
......
tests/python_package_test/test_plotting.py
View file @ 9f13a9c8
...@@ -198,8 +198,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data): ...@@ -198,8 +198,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data):
valid_sets=[train_data, test_data], valid_sets=[train_data, test_data],
valid_names=['v1', 'v2'], valid_names=['v1', 'v2'],
num_boost_round=10, num_boost_round=10,
evals_result=evals_result0, evals_result=evals_result0)
verbose_eval=False)
with pytest.warns(UserWarning, match="More than one metric available, picking one to plot."): with pytest.warns(UserWarning, match="More than one metric available, picking one to plot."):
ax0 = lgb.plot_metric(evals_result0) ax0 = lgb.plot_metric(evals_result0)
assert isinstance(ax0, matplotlib.axes.Axes) assert isinstance(ax0, matplotlib.axes.Axes)
...@@ -259,8 +258,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data): ...@@ -259,8 +258,7 @@ def test_plot_metrics(params, breast_cancer_split, train_data):
evals_result1 = {} evals_result1 = {}
lgb.train(params, train_data, lgb.train(params, train_data,
num_boost_round=10, num_boost_round=10,
evals_result=evals_result1, evals_result=evals_result1)
verbose_eval=False)
with pytest.raises(ValueError, match="eval results cannot be empty."): with pytest.raises(ValueError, match="eval results cannot be empty."):
lgb.plot_metric(evals_result1) lgb.plot_metric(evals_result1)
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment