Fixes for numpy 2.0.

demo/guide-python/external_memory.py

@@ -84,7 +84,7 @@ def main(tmpdir: str) -> xgboost.Booster:
     it = Iterator(files)
     # For non-data arguments, specify it here once instead of passing them by the `next`
     # method.
-    missing = np.NaN
+    missing = np.nan
     Xy = xgboost.DMatrix(it, missing=missing, enable_categorical=False)
 
     # ``approx`` is also supported, but less efficient due to sketching. GPU behaves

python-package/xgboost/data.py

@@ -233,9 +233,9 @@ def _maybe_np_slice(data: DataType, dtype: Optional[NumpyDType]) -> np.ndarray:
         if not data.flags.c_contiguous:
             data = np.array(data, copy=True, dtype=dtype)
         else:
-            data = np.array(data, copy=False, dtype=dtype)
+            data = np.asarray(data, dtype=dtype)
     except AttributeError:
-        data = np.array(data, copy=False, dtype=dtype)
+        data = np.asarray(data, dtype=dtype)
     data, dtype = _ensure_np_dtype(data, dtype)
     return data
 
@@ -483,7 +483,7 @@ def cat_codes(ser: pd.Series) -> np.ndarray:
         if is_pd_cat_dtype(ser.dtype):
             return _ensure_np_dtype(
                 ser.cat.codes.astype(np.float32)
-                .replace(-1.0, np.NaN)
+                .replace(-1.0, np.nan)
                 .to_numpy(na_value=np.nan),
                 np.float32,
             )[0]
@@ -495,7 +495,7 @@ def cat_codes(ser: pd.Series) -> np.ndarray:
             .combine_chunks()
             .dictionary_encode()
             .indices.astype(np.float32)
-            .replace(-1.0, np.NaN)
+            .replace(-1.0, np.nan)
         )
 
     def nu_type(ser: pd.Series) -> np.ndarray:

python-package/xgboost/testing/__init__.py

@@ -437,7 +437,7 @@ def make_categorical(
             index = rng.randint(
                 low=0, high=n_samples - 1, size=int(n_samples * sparsity)
             )
-            df.iloc[index, i] = np.NaN
+            df.iloc[index, i] = np.nan
             if is_categorical_dtype(df.dtypes[i]):
                 assert n_categories == np.unique(df.dtypes[i].categories).size
 

python-package/xgboost/testing/dask.py

@@ -66,7 +66,7 @@ def check_uneven_nan(client: Client, tree_method: str, n_workers: int) -> None:
         X = pd.DataFrame({"a": range(10000), "b": range(10000, 0, -1)})
         y = pd.Series([*[0] * 5000, *[1] * 5000])
 
-        X["a"][:3000:1000] = np.NaN
+        X["a"][:3000:1000] = np.nan
 
         client.wait_for_workers(n_workers=n_workers)
 

tests/python-gpu/test_from_cudf.py

@@ -10,7 +10,7 @@
 cudf = pytest.importorskip("cudf")
 
 
-def dmatrix_from_cudf(input_type, DMatrixT, missing=np.NAN):
+def dmatrix_from_cudf(input_type, DMatrixT, missing=np.nan):
     """Test constructing DMatrix from cudf"""
     import pandas as pd
 
@@ -38,8 +38,8 @@ def dmatrix_from_cudf(input_type, DMatrixT, missing=np.NAN):
 
 def _test_from_cudf(DMatrixT):
     """Test constructing DMatrix from cudf"""
-    dmatrix_from_cudf(np.float32, DMatrixT, np.NAN)
-    dmatrix_from_cudf(np.float64, DMatrixT, np.NAN)
+    dmatrix_from_cudf(np.float32, DMatrixT, np.nan)
+    dmatrix_from_cudf(np.float64, DMatrixT, np.nan)
 
     dmatrix_from_cudf(np.int8, DMatrixT, 2)
     dmatrix_from_cudf(np.int32, DMatrixT, -2)
@@ -66,7 +66,7 @@ def _test_from_cudf(DMatrixT):
         )
 
     # Test when number of elements is less than 8
-    X = cudf.DataFrame({"x": cudf.Series([0, 1, 2, np.NAN, 4], dtype=np.int32)})
+    X = cudf.DataFrame({"x": cudf.Series([0, 1, 2, np.nan, 4], dtype=np.int32)})
     dtrain = DMatrixT(X)
     assert dtrain.num_col() == 1
     assert dtrain.num_row() == 5
@@ -225,7 +225,7 @@ def test_cudf_categorical(self) -> None:
         assert len(interfaces) == X.shape[1]
 
         # test missing value
-        X = cudf.DataFrame({"f0": ["a", "b", np.NaN]})
+        X = cudf.DataFrame({"f0": ["a", "b", np.nan]})
         X["f0"] = X["f0"].astype("category")
         df, cat_codes, _, _ = xgb.data._transform_cudf_df(
             X, None, None, enable_categorical=True

tests/python-gpu/test_from_cupy.py

@@ -18,7 +18,7 @@ def test_array_interface() -> None:
     np.testing.assert_equal(cp.asnumpy(arr), cp.asnumpy(ret))
 
 
-def dmatrix_from_cupy(input_type, DMatrixT, missing=np.NAN):
+def dmatrix_from_cupy(input_type, DMatrixT, missing=np.nan):
     """Test constructing DMatrix from cupy"""
     kRows = 80
     kCols = 3
@@ -46,9 +46,9 @@ def dmatrix_from_cupy(input_type, DMatrixT, missing=np.NAN):
 
 def _test_from_cupy(DMatrixT):
     """Test constructing DMatrix from cupy"""
-    dmatrix_from_cupy(np.float16, DMatrixT, np.NAN)
-    dmatrix_from_cupy(np.float32, DMatrixT, np.NAN)
-    dmatrix_from_cupy(np.float64, DMatrixT, np.NAN)
+    dmatrix_from_cupy(np.float16, DMatrixT, np.nan)
+    dmatrix_from_cupy(np.float32, DMatrixT, np.nan)
+    dmatrix_from_cupy(np.float64, DMatrixT, np.nan)
 
     dmatrix_from_cupy(np.uint8, DMatrixT, 2)
     dmatrix_from_cupy(np.uint32, DMatrixT, 3)

tests/python/test_dmatrix.py

@@ -147,7 +147,7 @@ def test_feature_names_slice(self):
         assert dm.slice([0, 1]).num_col() == dm.num_col()
         assert dm.slice([0, 1]).feature_names == dm.feature_names
 
-        with pytest.raises(ValueError, match=r"Duplicates found: \['bar'\]"):
+        with pytest.raises(ValueError, match=r"Duplicates found: \[.*'bar'.*\]"):
             dm.feature_names = ["bar"] * (data.shape[1] - 2) + ["a", "b"]
 
         dm.feature_types = list("qiqiq")
@@ -264,7 +264,7 @@ def test_sparse_dmatrix_csr(self):
         assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
         watchlist = [(dtrain, "train")]
         param = {"max_depth": 3, "objective": "binary:logistic"}
-        bst = xgb.train(param, dtrain, 5, watchlist)
+        bst = xgb.train(param, dtrain, 5, evals=watchlist)
         bst.predict(dtrain)
 
         i32 = csr_matrix((x.data.astype(np.int32), x.indices, x.indptr), shape=x.shape)
@@ -302,7 +302,7 @@ def test_sparse_dmatrix_csc(self):
         assert (dtrain.num_row(), dtrain.num_col()) == (nrow, ncol)
         watchlist = [(dtrain, "train")]
         param = {"max_depth": 3, "objective": "binary:logistic"}
-        bst = xgb.train(param, dtrain, 5, watchlist)
+        bst = xgb.train(param, dtrain, 5, evals=watchlist)
         bst.predict(dtrain)
 
     def test_unknown_data(self):
@@ -320,9 +320,10 @@ class Data:
         X = rng.rand(10, 10)
         y = rng.rand(10)
         X = sparse.dok_matrix(X)
-        Xy = xgb.DMatrix(X, y)
-        assert Xy.num_row() == 10
-        assert Xy.num_col() == 10
+        with pytest.warns(UserWarning, match="dok_matrix"):
+            Xy = xgb.DMatrix(X, y)
+            assert Xy.num_row() == 10
+            assert Xy.num_col() == 10
 
     @pytest.mark.skipif(**tm.no_pandas())
     def test_np_categorical(self):
@@ -343,8 +344,8 @@ def test_scipy_categorical(self):
         X = X.values.astype(np.float32)
         feature_types = ["c"] * n_features
 
-        X[1, 3] = np.NAN
-        X[2, 4] = np.NAN
+        X[1, 3] = np.nan
+        X[2, 4] = np.nan
         X = sparse.csr_matrix(X)
 
         Xy = xgb.DMatrix(X, y, feature_types=feature_types)

tests/python/test_predict.py

@@ -241,7 +241,7 @@ def test_dtypes(self) -> None:
 
         # unsupported types
         for dtype in [
-            np.string_,
+            np.bytes_,
             np.complex64,
             np.complex128,
         ]:

tests/python/test_quantile_dmatrix.py

@@ -333,7 +333,7 @@ def test_dtypes(self) -> None:
 
         # unsupported types
         for dtype in [
-            np.string_,
+            np.bytes_,
             np.complex64,
             np.complex128,
         ]:

tests/python/test_with_pandas.py

@@ -248,7 +248,7 @@ def test_pandas_categorical(self, data_split_mode=DataSplitMode.ROW):
         assert transformed.columns[0].min() == 0
 
         # test missing value
-        X = pd.DataFrame({"f0": ["a", "b", np.NaN]})
+        X = pd.DataFrame({"f0": ["a", "b", np.nan]})
         X["f0"] = X["f0"].astype("category")
         arr, _, _ = xgb.data._transform_pandas_df(X, enable_categorical=True)
         for c in arr.columns:

tests/python/test_with_sklearn.py

@@ -1098,7 +1098,7 @@ def test_pandas_input():
     np.testing.assert_equal(model.feature_names_in_, np.array(feature_names))
 
     columns = list(train.columns)
-    random.shuffle(columns, lambda: 0.1)
+    random.shuffle(columns)
     df_incorrect = df[columns]
     with pytest.raises(ValueError):
         model.predict(df_incorrect)

tests/test_distributed/test_with_spark/test_spark_local.py

@@ -1653,9 +1653,9 @@ def ltr_data(spark: SparkSession) -> Generator[LTRData, None, None]:
             [1.0, 2.0, 3.0],
             [4.0, 5.0, 6.0],
             [9.0, 4.0, 8.0],
-            [np.NaN, 1.0, 5.5],
-            [np.NaN, 6.0, 7.5],
-            [np.NaN, 8.0, 9.5],
+            [np.nan, 1.0, 5.5],
+            [np.nan, 6.0, 7.5],
+            [np.nan, 8.0, 9.5],
         ]
     )
     qid_train = np.array([0, 0, 0, 1, 1, 1])
@@ -1666,9 +1666,9 @@ def ltr_data(spark: SparkSession) -> Generator[LTRData, None, None]:
             [1.5, 2.0, 3.0],
             [4.5, 5.0, 6.0],
             [9.0, 4.5, 8.0],
-            [np.NaN, 1.0, 6.0],
-            [np.NaN, 6.0, 7.0],
-            [np.NaN, 8.0, 10.5],
+            [np.nan, 1.0, 6.0],
+            [np.nan, 6.0, 7.0],
+            [np.nan, 8.0, 10.5],
         ]
     )