Simplify scikit-learn dependencies to fix uv resolution conflicts

- Use single constraint: scikit-learn>=1.3.0,<1.7.0 (resolves to 1.6.1)
- Works with Python 3.9-3.12 and NumPy 2.0
- Eliminates dependency resolution errors
- All tests pass locally (107 main + 4 compatibility)

Author: xRiskLab

.github/workflows/compatibility.yml

@@ -20,7 +20,7 @@ jobs:
             sklearn-version: '1.3.2'
           - python-version: '3.12'
             python-version-short: 'py312'
-            sklearn-version: '1.7.2'
+            sklearn-version: '1.6.1'
 
     steps:
     - uses: actions/checkout@v4

pyproject.toml

@@ -48,9 +48,7 @@ dependencies = [
     "numpy>=1.21.0",
     "pandas>=1.3.0",
     "scipy>=1.7.0",
-    "scikit-learn>=1.3.0,<1.4.0; python_version < '3.10'",
-    "scikit-learn>=1.3.0,<1.7.0; python_version >= '3.10' and python_version < '3.12'",
-    "scikit-learn>=1.7.0,<1.8.0; python_version >= '3.12'",
+    "scikit-learn>=1.3.0,<1.7.0",
     "rich>=13.7.0",
     "numba>=0.60.0",
     "packaging>=21.0",

tests/test_compatibility.py

@@ -13,7 +13,7 @@
 # Focus on edge cases and critical combinations
 COMPATIBILITY_MATRIX = [
     ("3.9", "1.3.2", "Min supported: Python 3.9 + sklearn 1.3.2"),
-    ("3.12", "1.7.2", "Python 3.12 + sklearn 1.7.2 + NumPy 2.0"),
+    ("3.12", "1.6.1", "Python 3.12 + sklearn 1.6.1 + NumPy 2.0"),
 ]
 
 
@@ -32,8 +32,8 @@ def get_numpy_constraint(python_ver, sklearn_ver):
     """Get appropriate numpy version constraint."""
     if sklearn_ver in ["1.3.0", "1.3.2"] or python_ver == "3.9":
         return "numpy>=1.21,<2.0"
-    elif sklearn_ver in ["1.7.2"]:
-        # scikit-learn 1.7.2+ should support NumPy 2.0
+    elif sklearn_ver in ["1.6.1"]:
+        # scikit-learn 1.6.1+ should support NumPy 2.0
         return "numpy>=1.21,<2.1"
     else:
         return "numpy>=1.21,<2.1"