[tests][dask] add scikit-learn compatibility tests (fixes #3894)

tests/python_package_test/test_dask.py

@@ -23,6 +23,7 @@
 import joblib
 import numpy as np
 import pandas as pd
+import sklearn.utils.estimator_checks as sklearn_checks
 from dask.array.utils import assert_eq
 from dask.distributed import Client, LocalCluster, default_client, wait
 from distributed.utils_test import client, cluster_fixture, gen_cluster, loop
@@ -1079,3 +1080,36 @@ def test_dask_methods_and_sklearn_equivalents_have_similar_signatures(methods):
     for param in dask_spec.args:
         error_msg = f"param '{param}' has different default values in the methods"
         assert dask_params[param].default == sklearn_params[param].default, error_msg
+
+
+def sklearn_checks_to_run():
+    check_names = [
+        "check_estimator_get_tags_default_keys",
+        "check_get_params_invariance",
+        "check_set_params"
+    ]
+    for check_name in check_names:
+        check_func = getattr(sklearn_checks, check_name, None)
+        if check_func:
+            yield check_func
+
+
+def _tested_estimators():
+    for Estimator in [lgb.DaskLGBMClassifier, lgb.DaskLGBMRegressor]:
+        yield Estimator()
+
+
+@pytest.mark.parametrize("estimator", _tested_estimators())
+@pytest.mark.parametrize("check", sklearn_checks_to_run())
+def test_sklearn_integration(estimator, check, client):
+    estimator.set_params(local_listen_port=18000, time_out=5)
+    name = type(estimator).__name__
+    check(name, estimator)
+    client.close(timeout=CLIENT_CLOSE_TIMEOUT)
+
+
+# this test is separate because it takes a not-yet-constructed estimator
+@pytest.mark.parametrize("estimator", list(_tested_estimators()))
+def test_parameters_default_constructible(estimator):
+    name, Estimator = estimator.__class__.__name__, estimator.__class__
+    sklearn_checks.check_parameters_default_constructible(name, Estimator)