n_samples.

tests/python-gpu/test_gpu_eval_metrics.py

@@ -1,5 +1,6 @@
 import sys
 import xgboost
+import pytest
 
 sys.path.append("tests/python")
 import test_eval_metrics as test_em  # noqa
@@ -8,17 +9,20 @@
 class TestGPUEvalMetrics:
     cpu_test = test_em.TestEvalMetrics()
 
-    def test_roc_auc_binary(self):
-        self.cpu_test.run_roc_auc_binary("gpu_hist")
+    @pytest.mark.parametrize("n_samples", [4, 100, 1000])
+    def test_roc_auc_binary(self, n_samples):
+        self.cpu_test.run_roc_auc_binary("gpu_hist", n_samples)
 
-    def test_roc_auc_multi(self):
-        self.cpu_test.run_roc_auc_multi("gpu_hist")
+    @pytest.mark.parametrize("n_samples", [4, 100, 1000])
+    def test_roc_auc_multi(self, n_samples):
+        self.cpu_test.run_roc_auc_multi("gpu_hist", n_samples)
 
-    def test_roc_auc_ltr(self):
+    @pytest.mark.parametrize("n_samples", [4, 100, 1000])
+    def test_roc_auc_ltr(self, n_samples):
         import numpy as np
 
         rng = np.random.RandomState(1994)
-        n_samples = 100
+        n_samples = n_samples
         n_features = 10
         X = rng.randn(n_samples, n_features)
         y = rng.randint(0, 16, size=n_samples)

tests/python/test_eval_metrics.py

@@ -104,13 +104,13 @@ def test_eval_metrics(self):
         assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
         assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
 
-    def run_roc_auc_binary(self, tree_method):
+    def run_roc_auc_binary(self, tree_method, n_samples):
         import numpy as np
         from sklearn.datasets import make_classification
         from sklearn.metrics import roc_auc_score
 
         rng = np.random.RandomState(1994)
-        n_samples = 1000
+        n_samples = n_samples
         n_features = 10
 
         X, y = make_classification(
@@ -142,16 +142,17 @@ def run_roc_auc_binary(self, tree_method):
         np.testing.assert_allclose(skl_auc, auc, rtol=1e-6)
 
     @pytest.mark.skipif(**tm.no_sklearn())
-    def test_roc_auc(self):
-        self.run_roc_auc_binary("hist")
+    @pytest.mark.parametrize("n_samples", [4, 100, 1000])
+    def test_roc_auc(self, n_samples):
+        self.run_roc_auc_binary("hist", n_samples)
 
-    def run_roc_auc_multi(self, tree_method):
+    def run_roc_auc_multi(self, tree_method, n_samples):
         import numpy as np
         from sklearn.datasets import make_classification
         from sklearn.metrics import roc_auc_score
 
         rng = np.random.RandomState(1994)
-        n_samples = 1000
+        n_samples = n_samples
         n_features = 10
         n_classes = 4
 
@@ -186,5 +187,6 @@ def run_roc_auc_multi(self, tree_method):
         auc = float(booster.eval(xgb.DMatrix(X, y)).split(":")[1])
         np.testing.assert_allclose(skl_auc, auc, rtol=1e-6)
 
-    def test_roc_auc_multi(self):
-        self.run_roc_auc_multi("hist")
+    @pytest.mark.parametrize("n_samples", [4, 100, 1000])
+    def test_roc_auc_multi(self, n_samples):
+        self.run_roc_auc_multi("hist", n_samples)