Add test for _one_side_trunc_norm_sampling

python_tests/preferential/samplers/test_gp.py

@@ -0,0 +1,29 @@
+import sys
+from unittest.mock import patch
+
+import numpy as np
+import pytest
+import torch
+
+
+if sys.version_info >= (3, 8):
+    from optuna_dashboard.preferential.samplers.gp import _one_side_trunc_norm_sampling
+else:
+    pytest.skip("BoTorch dropped Python3.7 support", allow_module_level=True)
+
+
+def test_one_side_trunc_norm_sampling() -> None:
+    for lower in np.linspace(-10, 10, 100):
+        assert _one_side_trunc_norm_sampling(torch.tensor([lower], dtype=torch.float64)) >= lower
+
+    with patch.object(torch, "rand", return_value=torch.tensor([0.4], dtype=torch.float64)):
+        sampled_value = _one_side_trunc_norm_sampling(torch.tensor([0.1], dtype=torch.float64))
+        assert np.allclose(sampled_value.numpy(), 0.899967154837563)
+
+    with patch.object(torch, "rand", return_value=torch.tensor([0.8], dtype=torch.float64)):
+        sampled_value = _one_side_trunc_norm_sampling(torch.tensor([-2.3], dtype=torch.float64))
+        assert np.allclose(sampled_value.numpy(), -0.8113606739551955)
+
+    with patch.object(torch, "rand", return_value=torch.tensor([0.1], dtype=torch.float64)):
+        sampled_value = _one_side_trunc_norm_sampling(torch.tensor([5], dtype=torch.float64))
+        assert np.allclose(sampled_value.numpy(), 5.426934003050024)