Apply recommended pytest config

See scientific python development guide

.pre-commit-config.yaml

@@ -1,6 +1,3 @@
-default_language_version:
-  python: python3.11
-
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0

pyproject.toml

@@ -100,7 +100,7 @@ ignore = ["E501"] # line too long
 [tool.ruff.lint.pydocstyle]
 convention = "numpy"
 
-[tool.ruff.per-file-ignores]
+[tool.ruff.lint.per-file-ignores]
 "__init__.py" = ["F403"]
 
 [tool.setuptools.packages.find]
@@ -118,7 +118,16 @@ disable_error_code = ["import-untyped"]
 plugins = "numpy.typing.mypy_plugin"
 
 [tool.pytest.ini_options]
-addopts = "--capture=no"
+minversion = "7.0"
+addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"]
+xfail_strict = true
+filterwarnings = [
+    "error",
+    "ignore::scipy.sparse._base.SparseEfficiencyWarning",
+    "ignore:Sparse CSR tensor support is in beta state:UserWarning",
+]
+log_cli_level = "info"
+testpaths = ["test"]
 
 [tool.numpydoc_validation]
 checks = [

test/nn/cell/test_ccxn.py

@@ -19,18 +19,11 @@ def test_forward(self):
             att=False,
         ).to(device)
 
-        x_0 = torch.rand(2, 2)
-        x_1 = torch.rand(2, 2)
-
-        adjacency_1 = torch.rand(2, 2)
-        incidence_2 = torch.rand(2, 2)
-
-        x_0, x_1 = (
-            torch.tensor(x_0).float().to(device),
-            torch.tensor(x_1).float().to(device),
-        )
-        adjacency_1 = adjacency_1.float().to(device)
-        incidence_2 = incidence_2.float().to(device)
+        x_0 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        x_1 = torch.rand(2, 2, dtype=torch.float32, device=device)
+
+        adjacency_1 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        incidence_2 = torch.rand(2, 2, dtype=torch.float32, device=device)
 
         x_0, x_1, x_2 = model(x_0, x_1, adjacency_1, incidence_2)
         assert x_0.shape == torch.Size([2, 2])

test/nn/cell/test_cwn.py

@@ -19,21 +19,12 @@ def test_forward(self):
             n_layers=2,
         ).to(device)
 
-        x_0 = torch.rand(2, 2)
-        x_1 = torch.rand(2, 2)
-        x_2 = torch.rand(2, 2)
-        adjacency_1 = torch.rand(2, 2)
-        incidence_2 = torch.rand(2, 2)
-        incidence_1_t = torch.rand(2, 2)
-
-        x_0, x_1, x_2 = (
-            torch.tensor(x_0).float().to(device),
-            torch.tensor(x_1).float().to(device),
-            torch.tensor(x_2).float().to(device),
-        )
-        adjacency_1 = adjacency_1.float().to(device)
-        incidence_2 = incidence_2.float().to(device)
-        incidence_1_t = incidence_1_t.float().to(device)
+        x_0 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        x_1 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        x_2 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        adjacency_1 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        incidence_2 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        incidence_1_t = torch.rand(2, 2, dtype=torch.float32, device=device)
 
         x_0, x_1, x_2 = model(x_0, x_1, x_2, adjacency_1, incidence_2, incidence_1_t)
         assert x_0.shape == torch.Size([2, 16])

test/nn/combinatorial/test_hmc.py

@@ -1,4 +1,5 @@
 """Tests for the HMC class."""
+
 import numpy as np
 import torch
 
@@ -17,18 +18,13 @@ def test_forward(self):
         channels_per_layer = [[in_channels, intermediate_channels, final_channels]]
         model = HMC(channels_per_layer, negative_slope=0.2).to(device)
 
-        x_0 = torch.rand(2, 2)
-        x_1 = torch.rand(2, 2)
-        x_2 = torch.rand(2, 2)
+        x_0 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        x_1 = torch.rand(2, 2, dtype=torch.float32, device=device)
+        x_2 = torch.rand(2, 2, dtype=torch.float32, device=device)
         adjacency_0 = torch.from_numpy(
             np.random.default_rng().random((2, 2))
         ).to_sparse()
 
-        x_0, x_1, x_2 = (
-            torch.tensor(x_0).float().to(device),
-            torch.tensor(x_1).float().to(device),
-            torch.tensor(x_2).float().to(device),
-        )
         adjacency_0 = adjacency_0.float().to(device)
 
         x_0, x_1, x_2 = model(