Expose feature_types to sklearn interface. (#7821)

tests/python/test_with_dask.py

@@ -306,6 +306,13 @@ def test_categorical(client: "Client") -> None:
     run_categorical(client, "approx", X, X_onehot, y)
     run_categorical(client, "hist", X, X_onehot, y)
 
+    ft = ["c"] * X.shape[1]
+    reg = xgb.dask.DaskXGBRegressor(
+        tree_method="hist", feature_types=ft, enable_categorical=True
+    )
+    reg.fit(X, y)
+    assert reg.get_booster().feature_types == ft
+
 
 def test_dask_predict_shape_infer(client: "Client") -> None:
     X, y = make_classification(n_samples=1000, n_informative=5, n_classes=3)

tests/python/test_with_sklearn.py

@@ -1273,6 +1273,38 @@ def test_estimator_reg(estimator, check):
     check(estimator)
 
 
+def test_categorical():
+    X, y = tm.make_categorical(n_samples=32, n_features=2, n_categories=3, onehot=False)
+    ft = ["c"] * X.shape[1]
+    reg = xgb.XGBRegressor(
+        tree_method="hist",
+        feature_types=ft,
+        max_cat_to_onehot=1,
+        enable_categorical=True,
+    )
+    reg.fit(X.values, y, eval_set=[(X.values, y)])
+    from_cat = reg.evals_result()["validation_0"]["rmse"]
+    predt_cat = reg.predict(X.values)
+    assert reg.get_booster().feature_types == ft
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = os.path.join(tmpdir, "model.json")
+        reg.save_model(path)
+        reg = xgb.XGBRegressor()
+        reg.load_model(path)
+        assert reg.feature_types == ft
+
+    onehot, y = tm.make_categorical(
+        n_samples=32, n_features=2, n_categories=3, onehot=True
+    )
+    reg = xgb.XGBRegressor(tree_method="hist")
+    reg.fit(onehot, y, eval_set=[(onehot, y)])
+    from_enc = reg.evals_result()["validation_0"]["rmse"]
+    predt_enc = reg.predict(onehot)
+
+    np.testing.assert_allclose(from_cat, from_enc)
+    np.testing.assert_allclose(predt_cat, predt_enc)
+
+
 def test_prediction_config():
     reg = xgb.XGBRegressor()
     assert reg._can_use_inplace_predict() is True