From a5a83709de69976b0277dbd9f9fa4ce2e178417d Mon Sep 17 00:00:00 2001 From: "Thomas J. Fan" Date: Wed, 20 Jan 2021 14:31:15 -0500 Subject: [PATCH] TST Migrates test_consistency.py to pytest --- tests/python_package_test/test_consistency.py | 151 +++++++++--------- 1 file changed, 77 insertions(+), 74 deletions(-) diff --git a/tests/python_package_test/test_consistency.py b/tests/python_package_test/test_consistency.py index ce6223d13935..7c585c335b04 100644 --- a/tests/python_package_test/test_consistency.py +++ b/tests/python_package_test/test_consistency.py @@ -64,77 +64,80 @@ def path(self, suffix): return os.path.join(self.directory, self.prefix + suffix) -class TestEngine(unittest.TestCase): - - def test_binary(self): - fd = FileLoader('../../examples/binary_classification', 'binary') - X_train, y_train, _ = fd.load_dataset('.train') - X_test, _, X_test_fn = fd.load_dataset('.test') - weight_train = fd.load_field('.train.weight') - lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train) - gbm = lgb.LGBMClassifier(**fd.params) - gbm.fit(X_train, y_train, sample_weight=weight_train) - sk_pred = gbm.predict_proba(X_test)[:, 1] - fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) - fd.file_load_check(lgb_train, '.train') - - def test_binary_linear(self): - fd = FileLoader('../../examples/binary_classification', 'binary', 'train_linear.conf') - X_train, y_train, _ = fd.load_dataset('.train') - X_test, _, X_test_fn = fd.load_dataset('.test') - weight_train = fd.load_field('.train.weight') - lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train) - gbm = lgb.LGBMClassifier(**fd.params) - gbm.fit(X_train, y_train, sample_weight=weight_train) - sk_pred = gbm.predict_proba(X_test)[:, 1] - fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) - fd.file_load_check(lgb_train, '.train') - - def test_multiclass(self): - fd = FileLoader('../../examples/multiclass_classification', 'multiclass') - X_train, y_train, _ = fd.load_dataset('.train') - X_test, _, X_test_fn = fd.load_dataset('.test') - lgb_train = lgb.Dataset(X_train, y_train) - gbm = lgb.LGBMClassifier(**fd.params) - gbm.fit(X_train, y_train) - sk_pred = gbm.predict_proba(X_test) - fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) - fd.file_load_check(lgb_train, '.train') - - def test_regression(self): - fd = FileLoader('../../examples/regression', 'regression') - X_train, y_train, _ = fd.load_dataset('.train') - X_test, _, X_test_fn = fd.load_dataset('.test') - init_score_train = fd.load_field('.train.init') - lgb_train = lgb.Dataset(X_train, y_train, init_score=init_score_train) - gbm = lgb.LGBMRegressor(**fd.params) - gbm.fit(X_train, y_train, init_score=init_score_train) - sk_pred = gbm.predict(X_test) - fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) - fd.file_load_check(lgb_train, '.train') - - def test_lambdarank(self): - fd = FileLoader('../../examples/lambdarank', 'rank') - X_train, y_train, _ = fd.load_dataset('.train', is_sparse=True) - X_test, _, X_test_fn = fd.load_dataset('.test', is_sparse=True) - group_train = fd.load_field('.train.query') - lgb_train = lgb.Dataset(X_train, y_train, group=group_train) - params = dict(fd.params) - params['force_col_wise'] = True - gbm = lgb.LGBMRanker(**params) - gbm.fit(X_train, y_train, group=group_train) - sk_pred = gbm.predict(X_test) - fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) - fd.file_load_check(lgb_train, '.train') - - def test_xendcg(self): - fd = FileLoader('../../examples/xendcg', 'rank') - X_train, y_train, _ = fd.load_dataset('.train', is_sparse=True) - X_test, _, X_test_fn = fd.load_dataset('.test', is_sparse=True) - group_train = fd.load_field('.train.query') - lgb_train = lgb.Dataset(X_train, y_train, group=group_train) - gbm = lgb.LGBMRanker(**fd.params) - gbm.fit(X_train, y_train, group=group_train) - sk_pred = gbm.predict(X_test) - fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) - fd.file_load_check(lgb_train, '.train') +def test_binary(): + fd = FileLoader('../../examples/binary_classification', 'binary') + X_train, y_train, _ = fd.load_dataset('.train') + X_test, _, X_test_fn = fd.load_dataset('.test') + weight_train = fd.load_field('.train.weight') + lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train) + gbm = lgb.LGBMClassifier(**fd.params) + gbm.fit(X_train, y_train, sample_weight=weight_train) + sk_pred = gbm.predict_proba(X_test)[:, 1] + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, '.train') + + +def test_binary_linear(): + fd = FileLoader('../../examples/binary_classification', 'binary', 'train_linear.conf') + X_train, y_train, _ = fd.load_dataset('.train') + X_test, _, X_test_fn = fd.load_dataset('.test') + weight_train = fd.load_field('.train.weight') + lgb_train = lgb.Dataset(X_train, y_train, params=fd.params, weight=weight_train) + gbm = lgb.LGBMClassifier(**fd.params) + gbm.fit(X_train, y_train, sample_weight=weight_train) + sk_pred = gbm.predict_proba(X_test)[:, 1] + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, '.train') + + +def test_multiclass(): + fd = FileLoader('../../examples/multiclass_classification', 'multiclass') + X_train, y_train, _ = fd.load_dataset('.train') + X_test, _, X_test_fn = fd.load_dataset('.test') + lgb_train = lgb.Dataset(X_train, y_train) + gbm = lgb.LGBMClassifier(**fd.params) + gbm.fit(X_train, y_train) + sk_pred = gbm.predict_proba(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, '.train') + + +def test_regression(): + fd = FileLoader('../../examples/regression', 'regression') + X_train, y_train, _ = fd.load_dataset('.train') + X_test, _, X_test_fn = fd.load_dataset('.test') + init_score_train = fd.load_field('.train.init') + lgb_train = lgb.Dataset(X_train, y_train, init_score=init_score_train) + gbm = lgb.LGBMRegressor(**fd.params) + gbm.fit(X_train, y_train, init_score=init_score_train) + sk_pred = gbm.predict(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, '.train') + + +def test_lambdarank(): + fd = FileLoader('../../examples/lambdarank', 'rank') + X_train, y_train, _ = fd.load_dataset('.train', is_sparse=True) + X_test, _, X_test_fn = fd.load_dataset('.test', is_sparse=True) + group_train = fd.load_field('.train.query') + lgb_train = lgb.Dataset(X_train, y_train, group=group_train) + params = dict(fd.params) + params['force_col_wise'] = True + gbm = lgb.LGBMRanker(**params) + gbm.fit(X_train, y_train, group=group_train) + sk_pred = gbm.predict(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, '.train') + + +def test_xendcg(): + fd = FileLoader('../../examples/xendcg', 'rank') + X_train, y_train, _ = fd.load_dataset('.train', is_sparse=True) + X_test, _, X_test_fn = fd.load_dataset('.test', is_sparse=True) + group_train = fd.load_field('.train.query') + lgb_train = lgb.Dataset(X_train, y_train, group=group_train) + gbm = lgb.LGBMRanker(**fd.params) + gbm.fit(X_train, y_train, group=group_train) + sk_pred = gbm.predict(X_test) + fd.train_predict_check(lgb_train, X_test, X_test_fn, sk_pred) + fd.file_load_check(lgb_train, '.train')