Use RandomState

tests/python-gpu/test_device_quantile_dmatrix.py

@@ -3,7 +3,10 @@
 import xgboost as xgb
 import unittest
 import pytest
-import cupy as cp
+import sys
+
+sys.path.append("tests/python")
+import testing as tm
 
 
 class TestDeviceQuantileDMatrix(unittest.TestCase):
@@ -12,6 +15,8 @@ def test_dmatrix_numpy_init(self):
         with pytest.raises(AssertionError, match='is not supported for DeviceQuantileDMatrix'):
             dm = xgb.DeviceQuantileDMatrix(data, np.ones(5, dtype=np.float64))
 
+    @pytest.mark.skipif(**tm.no_cupy())
     def test_dmatrix_cupy_init(self):
+        import cupy as cp
         data = cp.random.randn(5, 5)
         dm = xgb.DeviceQuantileDMatrix(data, cp.ones(5, dtype=np.float64))

tests/python-gpu/test_gpu_updaters.py

@@ -47,6 +47,7 @@ def test_gpu_hist_device_dmatrix(self):
         device_dmatrix_datasets = ["Boston", "Cancer", "Digits"]
         for param in test_param:
             param['tree_method'] = 'gpu_hist'
+
             gpu_results_device_dmatrix = run_suite(param, select_datasets=device_dmatrix_datasets,
                                                    DMatrixT=xgb.DeviceQuantileDMatrix,
                                                    dmatrix_params={'max_bin': param['max_bin']})

tests/python/regression_test_utilities.py

@@ -65,7 +65,7 @@ def get_sparse():
     n = 2000
     sparsity = 0.75
     X, y = datasets.make_regression(n, random_state=rng)
-    flag = np.random.binomial(1, sparsity, X.shape)
+    flag = rng.binomial(1, sparsity, X.shape)
     for i in range(X.shape[0]):
         for j in range(X.shape[1]):
             if flag[i, j]:
@@ -85,16 +85,16 @@ def get_small_weights():
 
 @memory.cache
 def get_weights_regression(min_weight, max_weight):
-    np.random.seed(199)
+    rng = np.random.RandomState(199)
     n = 2000
     sparsity = 0.25
-    X, y = datasets.make_regression(n, random_state=199)
-    flag = np.random.binomial(1, sparsity, X.shape)
+    X, y = datasets.make_regression(n, random_state=rng)
+    flag = rng.binomial(1, sparsity, X.shape)
     for i in range(X.shape[0]):
         for j in range(X.shape[1]):
             if flag[i, j]:
                 X[i, j] = np.nan
-    w = np.random.uniform(min_weight, max_weight, n)
+    w = rng.uniform(min_weight, max_weight, n)
     return X, y, w