TST make sklearn integration test compatible with 0.24

python-package/lightgbm/sklearn.py

@@ -315,8 +315,16 @@ def __init__(self, boosting_type='gbdt', num_leaves=31, max_depth=-1,
         self.set_params(**kwargs)
 
     def _more_tags(self):
-        return {'allow_nan': True,
-                'X_types': ['2darray', 'sparse', '1dlabels']}
+        return {
+            'allow_nan': True,
+            'X_types': ['2darray', 'sparse', '1dlabels'],
+            '_xfail_checks': {
+                'check_no_attributes_set_in_init':
+                'scikit-learn incorrectly asserts that  private attributes '
+                'cannot be set in __init__: '
+                '(see https://github.com/microsoft/LightGBM/issues/2628)'
+            }
+        }
 
     def get_params(self, deep=True):
         """Get parameters for this estimator.

tests/python_package_test/test_sklearn.py

@@ -4,20 +4,21 @@
 import math
 import os
 import unittest
-import warnings
 
 import lightgbm as lgb
 import numpy as np
+import pytest
 from sklearn import __version__ as sk_version
 from sklearn.base import clone
 from sklearn.datasets import load_svmlight_file, make_multilabel_classification
-from sklearn.exceptions import SkipTestWarning
 from sklearn.metrics import log_loss, mean_squared_error
 from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, train_test_split
 from sklearn.multioutput import (MultiOutputClassifier, ClassifierChain, MultiOutputRegressor,
                                  RegressorChain)
-from sklearn.utils.estimator_checks import (_yield_all_checks, SkipTest,
-                                            check_parameters_default_constructible)
+from sklearn.utils.estimator_checks import (
+    check_parameters_default_constructible,
+    parametrize_with_checks,
+)
 from sklearn.utils.validation import check_is_fitted
 
 from .utils import load_boston, load_breast_cancer, load_digits, load_iris, load_linnerud
@@ -452,29 +453,6 @@ def test_feature_importances_type(self):
         importance_gain_top1 = sorted(importances_gain, reverse=True)[0]
         self.assertNotEqual(importance_split_top1, importance_gain_top1)
 
-    # sklearn <0.19 cannot accept instance, but many tests could be passed only with min_data=1 and min_data_in_bin=1
-    @unittest.skipIf(sk_version < '0.19.0', 'scikit-learn version is less than 0.19')
-    def test_sklearn_integration(self):
-        # we cannot use `check_estimator` directly since there is no skip test mechanism
-        for name, estimator in ((lgb.sklearn.LGBMClassifier.__name__, lgb.sklearn.LGBMClassifier),
-                                (lgb.sklearn.LGBMRegressor.__name__, lgb.sklearn.LGBMRegressor)):
-            check_parameters_default_constructible(name, estimator)
-            # we cannot leave default params (see https://github.com/microsoft/LightGBM/issues/833)
-            estimator = estimator(min_child_samples=1, min_data_in_bin=1)
-            for check in _yield_all_checks(name, estimator):
-                check_name = check.func.__name__ if hasattr(check, 'func') else check.__name__
-                if check_name == 'check_estimators_nan_inf':
-                    continue  # skip test because LightGBM deals with nan
-                elif check_name == "check_no_attributes_set_in_init":
-                    # skip test because scikit-learn incorrectly asserts that
-                    # private attributes cannot be set in __init__
-                    # (see https://github.com/microsoft/LightGBM/issues/2628)
-                    continue
-                try:
-                    check(name, estimator)
-                except SkipTest as message:
-                    warnings.warn(message, SkipTestWarning)
-
     @unittest.skipIf(not lgb.compat.PANDAS_INSTALLED, 'pandas is not installed')
     def test_pandas_categorical(self):
         import pandas as pd
@@ -1149,3 +1127,24 @@ def test_check_is_fitted(self):
         rnk.fit(X, y, group=np.ones(X.shape[0]))
         for model in models:
             check_is_fitted(model)
+
+
+def _tested_estimators():
+    for Estimator in [lgb.sklearn.LGBMClassifier, lgb.sklearn.LGBMRegressor]:
+        yield Estimator()
+
+
+@pytest.mark.skipif(
+    sk_version < "0.23.0", reason="scikit-learn version is less than 0.23"
+)
+@parametrize_with_checks(list(_tested_estimators()))
+def test_sklearn_integration(estimator, check, request):
+    estimator.set_params(min_child_samples=1, min_data_in_bin=1)
+    check(estimator)
+
+
+@pytest.mark.parametrize("estimator", list(_tested_estimators()))
+def test_parameters_default_constructible(estimator):
+    name, Estimator = estimator.__class__.__name__, estimator.__class__
+    # Test that estimators are default-constructible
+    check_parameters_default_constructible(name, Estimator)