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Unverified Commit e7c6e67a authored by Nikita Titov's avatar Nikita Titov Committed by GitHub
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[tests] simplified test and added dumped data to gitignore (#2372)

parent 0f9720bf
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.gitignore
View file @ e7c6e67a
...@@ -373,9 +373,10 @@ ENV/ ...@@ -373,9 +373,10 @@ ENV/
# R testing artefact # R testing artefact
lightgbm.model lightgbm.model
# saved or dumped model # saved or dumped model/data
*.model *.model
*.pkl *.pkl
*.bin
# macOS # macOS
**/.DS_Store **/.DS_Store
......
tests/python_package_test/test_engine.py
View file @ e7c6e67a
...@@ -371,54 +371,48 @@ class TestEngine(unittest.TestCase): ...@@ -371,54 +371,48 @@ class TestEngine(unittest.TestCase):
self.assertLess(ret, 0.2) self.assertLess(ret, 0.2)
def test_multi_class_error(self): def test_multi_class_error(self):
X, y = load_digits(return_X_y=True) X, y = load_digits(10, True)
params = {'objective': 'multiclass', 'num_classes': 10, 'metric': 'multi_error', 'num_leaves': 4, 'seed': 0, params = {'objective': 'multiclass', 'num_classes': 10, 'metric': 'multi_error',
'num_rounds': 30, 'verbose': -1} 'num_leaves': 4, 'verbose': -1}
lgb_data = lgb.Dataset(X, label=y) lgb_data = lgb.Dataset(X, label=y)
results = {} est = lgb.train(params, lgb_data, num_boost_round=10)
est = lgb.train(params, lgb_data, valid_sets=[lgb_data], valid_names=['train'], evals_result=results)
predict_default = est.predict(X) predict_default = est.predict(X)
params = {'objective': 'multiclass', 'num_classes': 10, 'metric': 'multi_error', 'multi_error_top_k': 1,
'num_leaves': 4, 'seed': 0, 'num_rounds': 30, 'verbose': -1, 'metric_freq': 10}
results = {} results = {}
est = lgb.train(params, lgb_data, valid_sets=[lgb_data], valid_names=['train'], evals_result=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)
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)
# check against independent calculation for k = 1 # check against independent calculation for k = 1
err = top_k_error(y, predict_1, 1) err = top_k_error(y, predict_1, 1)
np.testing.assert_allclose(results['train']['multi_error'][-1], err) self.assertAlmostEqual(results['training']['multi_error'][-1], err)
# check against independent calculation for k = 2 # check against independent calculation for k = 2
params = {'objective': 'multiclass', 'num_classes': 10, 'metric': 'multi_error', 'multi_error_top_k': 2,
'num_leaves': 4, 'seed': 0, 'num_rounds': 30, 'verbose': -1, 'metric_freq': 10}
results = {} results = {}
est = lgb.train(params, lgb_data, valid_sets=[lgb_data], valid_names=['train'], evals_result=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)
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)
np.testing.assert_allclose(results['train']['multi_error@2'][-1], err) self.assertAlmostEqual(results['training']['multi_error@2'][-1], err)
# check against independent calculation for k = 10 # check against independent calculation for k = 10
params = {'objective': 'multiclass', 'num_classes': 10, 'metric': 'multi_error', 'multi_error_top_k': 10,
'num_leaves': 4, 'seed': 0, 'num_rounds': 30, 'verbose': -1, 'metric_freq': 10}
results = {} results = {}
est = lgb.train(params, lgb_data, valid_sets=[lgb_data], valid_names=['train'], evals_result=results) est = lgb.train(dict(params, multi_error_top_k=10), lgb_data, num_boost_round=10,
predict_2 = est.predict(X) valid_sets=[lgb_data], evals_result=results, verbose_eval=False)
err = top_k_error(y, predict_2, 10) predict_3 = est.predict(X)
np.testing.assert_allclose(results['train']['multi_error@10'][-1], err) err = top_k_error(y, predict_3, 10)
# check case where predictions are equal self.assertAlmostEqual(results['training']['multi_error@10'][-1], err)
# check cases where predictions are equal
X = np.array([[0, 0], [0, 0]]) X = np.array([[0, 0], [0, 0]])
y = np.array([0, 1]) y = np.array([0, 1])
lgb_data = lgb.Dataset(X, label=y) lgb_data = lgb.Dataset(X, label=y)
params = {'objective': 'multiclass', 'num_classes': 2, 'metric': 'multi_error', 'multi_error_top_k': 1, params['num_classes'] = 2
'num_leaves': 4, 'seed': 0, 'num_rounds': 1, 'verbose': -1, 'metric_freq': 10}
results = {} results = {}
lgb.train(params, lgb_data, valid_sets=[lgb_data], valid_names=['train'], evals_result=results) lgb.train(params, lgb_data, num_boost_round=10,
np.testing.assert_allclose(results['train']['multi_error'][-1], 1) valid_sets=[lgb_data], evals_result=results, verbose_eval=False)
lgb_data = lgb.Dataset(X, label=y) self.assertAlmostEqual(results['training']['multi_error'][-1], 1)
params = {'objective': 'multiclass', 'num_classes': 2, 'metric': 'multi_error', 'multi_error_top_k': 2,
'num_leaves': 4, 'seed': 0, 'num_rounds': 1, 'verbose': -1, 'metric_freq': 10}
results = {} results = {}
lgb.train(params, lgb_data, valid_sets=[lgb_data], valid_names=['train'], evals_result=results) lgb.train(dict(params, multi_error_top_k=2), lgb_data, num_boost_round=10,
np.testing.assert_allclose(results['train']['multi_error@2'][-1], 0) valid_sets=[lgb_data], evals_result=results, verbose_eval=False)
self.assertAlmostEqual(results['training']['multi_error@2'][-1], 0)
def test_early_stopping(self): def test_early_stopping(self):
X, y = load_breast_cancer(True) X, y = load_breast_cancer(True)
......
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