0.15.8

- Improved documentation for light.regression (__init__.py handling)
- Takes care of the FutureWarning: `torch.testing.assert_allclose()` is deprecated since 1.12 and will be removed in a future release. Please use `torch.testing.assert_close()` instead. You can find detailed upgrade instructions in pytorch/pytorch#61844.

Author: bartzbeielstein

docs/gen_ref_pages.py

@@ -1,12 +1,15 @@
 """Generate the code reference pages and navigation."""
 
 from pathlib import Path
-
 import mkdocs_gen_files
 
 nav = mkdocs_gen_files.Nav()
 
 for path in sorted(Path("src").rglob("*.py")):
+    # Ignore __init__.py files
+    if path.name == "__init__.py":
+        continue
+    
     module_path = path.relative_to("src").with_suffix("")
     doc_path = path.relative_to("src").with_suffix(".md")
     full_doc_path = Path("reference", doc_path)

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.15.7"
+version = "0.15.8"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/light/regression/netlightregression.py

@@ -68,8 +68,7 @@ class NetLightRegression(L.LightningModule):
             trainer.fit(net_light_base, train_loader)
             trainer.validate(net_light_base, val_loader)
             trainer.test(net_light_base, test_loader)
-
-              | Name   | Type       | Params | In sizes | Out sizes
+            | Name   | Type       | Params | In sizes | Out sizes
             -------------------------------------------------------------
             0 | layers | Sequential | 15.9 K | [8, 10]  | [8, 1]
             -------------------------------------------------------------

test/test_torch_minmax_scaler.py

@@ -12,8 +12,8 @@ def test_min_max_scaler_fit():
     scaler = TorchMinMaxScaler()
     scaler.fit(tensor)
 
-    torch.testing.assert_allclose(scaler.min, expected_min)
-    torch.testing.assert_allclose(scaler.max, expected_max)
+    torch.testing.assert_close(scaler.min, expected_min)
+    torch.testing.assert_close(scaler.max, expected_max)
 
 
 def test_min_max_scaler_transform():
@@ -25,7 +25,7 @@ def test_min_max_scaler_transform():
 
     expected_transformed = torch.tensor([[0.5, 0.0], [0.0, 0.5], [1.0, 1.0]])
 
-    torch.testing.assert_allclose(transformed, expected_transformed)
+    torch.testing.assert_close(transformed, expected_transformed)
 
 
 def test_min_max_scaler_fit_transform():
@@ -36,7 +36,7 @@ def test_min_max_scaler_fit_transform():
 
     expected_transformed = torch.tensor([[0.5, 0.0], [0.0, 0.5], [1.0, 1.0]])
 
-    torch.testing.assert_allclose(transformed, expected_transformed)
+    torch.testing.assert_close(transformed, expected_transformed)
 
 
 def test_input_validation():

test/test_torch_standard_scaler.py

@@ -12,8 +12,8 @@ def test_fit():
     scaler = TorchStandardScaler()
     scaler.fit(tensor)
 
-    torch.testing.assert_allclose(scaler.mean, expected_mean)
-    torch.testing.assert_allclose(scaler.std, expected_std, atol=1e-7, rtol=1e-7)
+    torch.testing.assert_close(scaler.mean, expected_mean)
+    torch.testing.assert_close(scaler.std, expected_std, atol=1e-7, rtol=1e-7)
 
 
 def test_transform():
@@ -25,7 +25,7 @@ def test_transform():
 
     expected_transformed = torch.tensor([[-1.0, -1.0], [1.0, 1.0]])
 
-    torch.testing.assert_allclose(transformed, expected_transformed)
+    torch.testing.assert_close(transformed, expected_transformed)
 
 
 def test_fit_transform():
@@ -36,7 +36,7 @@ def test_fit_transform():
 
     expected_transformed = torch.tensor([[-1.0, -1.0], [1.0, 1.0]])
 
-    torch.testing.assert_allclose(transformed, expected_transformed)
+    torch.testing.assert_close(transformed, expected_transformed)
 
 
 def test_input_not_tensor():