fix return type

python-package/lightgbm/compat.py

@@ -45,22 +45,22 @@ def validate_data(
             # trap other keyword arguments that only work on scikit-learn >=1.6, like 'reset'
             **ignored_kwargs,
         ):
+            no_val_y = isinstance(y, str) and y == "no_validation"
+
             # NOTE: check_X_y() calls check_array() internally, so only need to call one or the other of them here
-            if isinstance(y, str) and y == "no_validation":
+            if no_val_y:
                 X = check_array(X, accept_sparse=accept_sparse, force_all_finite=ensure_all_finite)
-                return X  # noqa: RET504
-
-            # if we reach here, we're validating features and labels
-            X, y = check_X_y(
-                X,
-                y,
-                accept_sparse=accept_sparse,
-                force_all_finite=ensure_all_finite,
-                ensure_min_samples=ensure_min_samples,
-            )
+            else:
+                X, y = check_X_y(
+                    X,
+                    y,
+                    accept_sparse=accept_sparse,
+                    force_all_finite=ensure_all_finite,
+                    ensure_min_samples=ensure_min_samples,
+                )
 
-            # this only needs to be updated at fit() time
-            _estimator._n_features_in = X.shape[1]
+                # this only needs to be updated at fit() time
+                _estimator._n_features_in = X.shape[1]
 
             # raise the same error that scikit-learn's `validate_data()` does on scikit-learn>=1.6
             n_features = X.shape[1]
@@ -70,7 +70,10 @@ def validate_data(
                     f"is expecting {_estimator._n_features} features as input."
                 )
 
-            return X, y
+            if no_val_y:
+                return X
+            else:
+                return X, y
 
     SKLEARN_INSTALLED = True
     _LGBMBaseCrossValidator = BaseCrossValidator