Initial support for multioutput regression. (#7514)

* Add num target model parameter, which is configured from input labels.
* Change elementwise metric and indexing for weights.
* Add demo.
* Add tests.

tests/python-gpu/test_from_cudf.py

@@ -60,8 +60,9 @@ def _test_from_cudf(DMatrixT):
     assert dtrain.feature_names == ['x']
     assert dtrain.feature_types == ['int']
 
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError, match=r".*multi.*"):
         dtrain = DMatrixT(cd, label=cd)
+        xgb.train({"tree_method": "gpu_hist", "objective": "multi:softprob"}, dtrain)
 
     # Test when number of elements is less than 8
     X = cudf.DataFrame({'x': cudf.Series([0, 1, 2, np.NAN, 4],

tests/python-gpu/test_from_cupy.py

@@ -50,9 +50,10 @@ def _test_from_cupy(DMatrixT):
     dmatrix_from_cupy(np.int32, DMatrixT, -2)
     dmatrix_from_cupy(np.int64, DMatrixT, -3)
 
-    with pytest.raises(Exception):
+    with pytest.raises(ValueError):
         X = cp.random.randn(2, 2, dtype="float32")
-        DMatrixT(X, label=X)
+        y = cp.random.randn(2, 2, 3, dtype="float32")
+        DMatrixT(X, label=y)
 
 
 def _test_cupy_training(DMatrixT):

tests/python-gpu/test_gpu_with_dask.py

@@ -277,7 +277,9 @@ def run_gpu_hist(
     X = to_cp(dataset.X, DMatrixT)
     X = da.from_array(X, chunks=(chunk, dataset.X.shape[1]))
     y = to_cp(dataset.y, DMatrixT)
-    y = da.from_array(y, chunks=(chunk,))
+    y_chunk = chunk if len(dataset.y.shape) == 1 else (chunk, dataset.y.shape[1])
+    y = da.from_array(y, chunks=y_chunk)
+
     if dataset.w is not None:
         w = to_cp(dataset.w, DMatrixT)
         w = da.from_array(w, chunks=(chunk,))

tests/python-gpu/test_gpu_with_sklearn.py

@@ -52,8 +52,12 @@ def test_boost_from_prediction_gpu_hist():
     X, y = load_digits(return_X_y=True)
     X, y = cp.array(X), cp.array(y)
 
-    twskl.run_boost_from_prediction_multi_clasas(tree_method, X, y, None)
-    twskl.run_boost_from_prediction_multi_clasas(tree_method, X, y, cudf.DataFrame)
+    twskl.run_boost_from_prediction_multi_clasas(
+        xgb.XGBClassifier, tree_method, X, y, None
+    )
+    twskl.run_boost_from_prediction_multi_clasas(
+        xgb.XGBClassifier, tree_method, X, y, cudf.DataFrame
+    )
 
 
 def test_num_parallel_tree():