[breaking] Remove label encoder deprecated in 1.3. (#7357)

tests/python-gpu/test_from_cudf.py

@@ -239,7 +239,7 @@ def test_cudf_training_with_sklearn():
     y_cudf_series = ss(data=y.iloc[:, 0])
 
     for y_obj in [y_cudf, y_cudf_series]:
-        clf = xgb.XGBClassifier(gpu_id=0, tree_method='gpu_hist', use_label_encoder=False)
+        clf = xgb.XGBClassifier(gpu_id=0, tree_method='gpu_hist')
         clf.fit(X_cudf, y_obj, sample_weight=cudf_weights, base_margin=cudf_base_margin,
                 eval_set=[(X_cudf, y_obj)])
         pred = clf.predict(X_cudf)

tests/python-gpu/test_from_cupy.py

@@ -122,7 +122,7 @@ def test_cupy_training_with_sklearn():
     base_margin = np.random.random(50)
     cupy_base_margin = cp.array(base_margin)
 
-    clf = xgb.XGBClassifier(gpu_id=0, tree_method="gpu_hist", use_label_encoder=False)
+    clf = xgb.XGBClassifier(gpu_id=0, tree_method="gpu_hist")
     clf.fit(
         X,
         y,

tests/python-gpu/test_gpu_basic_models.py

@@ -7,6 +7,7 @@ sys.path.append("tests/python")
 # Don't import the test class, otherwise they will run twice.
 import test_callback as test_cb  # noqa
 import test_basic_models as test_bm
+import testing as tm
 rng = np.random.RandomState(1994)
 
 
@@ -14,16 +15,12 @@ class TestGPUBasicModels:
     cpu_test_cb = test_cb.TestCallbacks()
     cpu_test_bm = test_bm.TestModels()
 
-    def run_cls(self, X, y, deterministic):
-        cls = xgb.XGBClassifier(tree_method='gpu_hist',
-                                deterministic_histogram=deterministic,
-                                single_precision_histogram=True)
+    def run_cls(self, X, y):
+        cls = xgb.XGBClassifier(tree_method='gpu_hist', single_precision_histogram=True)
         cls.fit(X, y)
         cls.get_booster().save_model('test_deterministic_gpu_hist-0.json')
 
-        cls = xgb.XGBClassifier(tree_method='gpu_hist',
-                                deterministic_histogram=deterministic,
-                                single_precision_histogram=True)
+        cls = xgb.XGBClassifier(tree_method='gpu_hist', single_precision_histogram=True)
         cls.fit(X, y)
         cls.get_booster().save_model('test_deterministic_gpu_hist-1.json')
 
@@ -49,19 +46,22 @@ class TestGPUBasicModels:
         kClasses = 4
         # Create large values to force rounding.
         X = np.random.randn(kRows, kCols) * 1e4
-        y = np.random.randint(0, kClasses, size=kRows) * 1e4
+        y = np.random.randint(0, kClasses, size=kRows)
 
-        model_0, model_1 = self.run_cls(X, y, True)
+        model_0, model_1 = self.run_cls(X, y)
         assert model_0 == model_1
 
+    @pytest.mark.skipif(**tm.no_sklearn())
     def test_invalid_gpu_id(self):
-        X = np.random.randn(10, 5) * 1e4
-        y = np.random.randint(0, 2, size=10) * 1e4
+        from sklearn.datasets import load_digits
+        X, y = load_digits(return_X_y=True)
         # should pass with invalid gpu id
         cls1 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999)
         cls1.fit(X, y)
         # should throw error with fail_on_invalid_gpu_id enabled
-        cls2 = xgb.XGBClassifier(tree_method='gpu_hist', gpu_id=9999, fail_on_invalid_gpu_id=True)
+        cls2 = xgb.XGBClassifier(
+            tree_method='gpu_hist', gpu_id=9999, fail_on_invalid_gpu_id=True
+        )
         try:
             cls2.fit(X, y)
             assert False, "Should have failed with with fail_on_invalid_gpu_id enabled"

tests/python-gpu/test_gpu_pickling.py

@@ -146,8 +146,10 @@ class TestPickling:
 
         os.remove(model_path)
 
+    @pytest.mark.skipif(**tm.no_sklearn())
     def test_predict_sklearn_pickle(self):
-        x, y = build_dataset()
+        from sklearn.datasets import load_digits
+        x, y = load_digits(return_X_y=True)
 
         kwargs = {'tree_method': 'gpu_hist',
                   'predictor': 'gpu_predictor',

tests/python-gpu/test_gpu_with_sklearn.py

@@ -56,7 +56,6 @@ def test_categorical():
     X, y = load_svmlight_file(os.path.join(data_dir, "agaricus.txt.train"))
     clf = xgb.XGBClassifier(
         tree_method="gpu_hist",
-        use_label_encoder=False,
         enable_categorical=True,
         n_estimators=10,
     )
@@ -98,3 +97,36 @@ def test_categorical():
 
     X = cudf.DataFrame(X)
     check_predt(X, y)
+
+
+@pytest.mark.skipif(**tm.no_cupy())
+@pytest.mark.skipif(**tm.no_cudf())
+def test_classififer():
+    from sklearn.datasets import load_digits
+    import cupy as cp
+    import cudf
+
+    X, y = load_digits(return_X_y=True)
+    y *= 10
+
+    clf = xgb.XGBClassifier(tree_method="gpu_hist", n_estimators=1)
+
+    # numpy
+    with pytest.raises(ValueError, match=r"Invalid classes.*"):
+        clf.fit(X, y)
+
+    # cupy
+    X, y = cp.array(X), cp.array(y)
+    with pytest.raises(ValueError, match=r"Invalid classes.*"):
+        clf.fit(X, y)
+
+    # cudf
+    X, y = cudf.DataFrame(X), cudf.DataFrame(y)
+    with pytest.raises(ValueError, match=r"Invalid classes.*"):
+        clf.fit(X, y)
+
+    # pandas
+    X, y = load_digits(return_X_y=True, as_frame=True)
+    y *= 10
+    with pytest.raises(ValueError, match=r"Invalid classes.*"):
+        clf.fit(X, y)