fix: resolve test-suite warnings

pertpy/tools/_augur.py

@@ -230,7 +230,12 @@ def create_estimator(
                 random_state=random_state,
             )
         elif classifier == "logistic_regression_classifier":
-            return LogisticRegression(penalty=penalty, random_state=random_state)
+            # scikit-learn 1.8 deprecated `penalty` in favor of `l1_ratio` (the L1/L2 mix) and `C` (no penalty via C=inf).
+            if penalty in (None, "none"):
+                return LogisticRegression(C=np.inf, random_state=random_state)
+            l1_ratio = {"l2": 0.0, "l1": 1.0, "elasticnet": 0.5}[penalty]
+            solver = "saga" if l1_ratio > 0.0 else "lbfgs"
+            return LogisticRegression(l1_ratio=l1_ratio, solver=solver, random_state=random_state)
         else:
             raise ValueError("Invalid classifier")
 

pyproject.toml

@@ -175,7 +175,14 @@ filterwarnings = [
     "ignore:'cgi' is deprecated and slated for removal in Python 3.13:DeprecationWarning",
     "ignore:In the future, the default backend for leiden will be igraph instead of leidenalg:FutureWarning",
     "ignore:Transforming to str index:anndata.ImplicitModificationWarning",
-    "ignore:Failed to correctly find n_neighbors for some samples:UserWarning"
+    "ignore:Failed to correctly find n_neighbors for some samples:UserWarning",
+    # pydeseq2 emits these on the small synthetic data used by the Milo and compare_groups tests; they are not actionable in pertpy.
+    "ignore:The dispersion trend curve fitting did not converge:UserWarning",
+    "ignore:As the residual degrees of freedom is less than 3:UserWarning",
+    # Third-party deprecation and runtime notices that pertpy cannot resolve at the source.
+    "ignore:JAXopt is no longer maintained:DeprecationWarning",
+    "ignore:A worker stopped while some jobs were given to the executor:UserWarning",
+    "ignore:Data has categories outside of the nominated levels"
 ]
 
 [tool.hatch.envs.default]

tests/tools/_differential_gene_expression/test_statsmodels.py

@@ -8,7 +8,9 @@
     pytestmark = pytest.mark.skip(reason="formulaic_contrasts and formulaic not available")
 
 
-@pytest.mark.parametrize("kwargs", [{}, {"regression_model": sm.GLM, "family": sm.families.NegativeBinomial()}])
+@pytest.mark.parametrize(
+    "kwargs", [{}, {"regression_model": sm.GLM, "family": sm.families.NegativeBinomial(alpha=1.0)}]
+)
 def test_statsmodels(test_adata, kwargs):
     """Check that the method can be initialized and fitted, and perform basic checks on the result of test_contrasts."""
     from pertpy.tools._differential_gene_expression import Statsmodels

tests/tools/_perturbation_space/test_simple_perturbation_space.py

@@ -199,12 +199,14 @@ def test_linear_operations():
     # Input that has already been differenced — opt out of the auto-diff with ensure_consistency=False.
     ps_adata = ps.compute_control_diff(psadata)
 
-    ps_adata2 = ps.add(ps_adata, perturbations=["target1", "target2"], ensure_consistency=False)
+    with pytest.warns(UserWarning, match="Combining perturbations without"):
+        ps_adata2 = ps.add(ps_adata, perturbations=["target1", "target2"], ensure_consistency=False)
 
     test = ps_adata["control"].X + ps_adata["target1"].X + ps_adata["target2"].X
     np.testing.assert_allclose(test, ps_adata2["target1+target2"].X, rtol=1e-4)
 
-    ps_adata2 = ps.subtract(ps_adata, reference_key="target1", perturbations=["target1"], ensure_consistency=False)
+    with pytest.warns(UserWarning, match="Combining perturbations without"):
+        ps_adata2 = ps.subtract(ps_adata, reference_key="target1", perturbations=["target1"], ensure_consistency=False)
     ps_vector = ps_adata2["target1-target1"].X
     np.testing.assert_allclose(ps_adata2["control"].X, ps_adata2["target1-target1"].X, rtol=1e-4)