OLS Bug + misc

.github/workflows/pytest.yml

@@ -20,7 +20,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ['ubuntu-latest']
-        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
+        python-version: ['3.11', '3.12', '3.13', '3.14']
     steps:
       - uses: actions/checkout@v6
       - name: Install uv

.github/workflows/pytest_upstream_nightly.yml

@@ -12,7 +12,7 @@ jobs:
       fail-fast: false
       matrix:
         os: ['ubuntu-latest']
-        python-version: ['3.10', '3.11', '3.12', '3.13', '3.14']
+        python-version: ['3.11', '3.12', '3.13', '3.14']
     steps:
       - uses: actions/checkout@v6
       - name: Install uv
@@ -42,4 +42,4 @@ jobs:
       - name: Install dependencies
         run: uv sync --extra test
       - name: Run tests
-        run: uv run --with "pandas>=3,<4" pytest chainladder -m "not r"
+        run: uv run --with "pandas>=3,<4" pytest chainladder -m "not r"

chainladder/utils/tests/test_tri_w.py

@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import chainladder as cl
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from chainladder import Triangle
+
+
+class TestFullTri:
+    """Test weight generation on full triangles"""
+
+    def test_triangleweight_full_triangle(self, raa: Triangle) -> None:
+        """
+        Testing new path that allows weights on full triangles
+        """
+        ult = cl.Chainladder().fit(raa)
+        tw = cl.TriangleWeight(n_periods=4).fit(raa)
+        tw_full = cl.TriangleWeight(n_periods=4).fit(ult.full_triangle_)
+        assert tw.w_.iloc[:, :, :, 0] == tw_full.w_.iloc[:, :, :, 0]
+
+    def test_triangleweight_full_irregular_triangle(self) -> None:
+        """
+        Testing unequal grains
+        """
+        prism = cl.load_sample("prism_oydq")["Paid"]
+        ult = cl.Chainladder().fit(prism)
+        tw = cl.TriangleWeight(n_periods=4).fit(prism)
+        tw_full = cl.TriangleWeight(n_periods=4).fit(ult.full_triangle_)
+        assert tw.w_.iloc[:, :, :, 0] == tw_full.w_.iloc[:, :, :, 0]

chainladder/utils/tests/test_utilities.py

@@ -1296,13 +1296,3 @@ def test_triangleweight_drop_valuation_all(raa: Triangle) -> None:
                 "1990",
             ]
         ).fit(raa)
-
-
-def test_triangleweight_full_triangle(raa: Triangle) -> None:
-    """
-    Testing new path that allows weights on full triangles
-    """
-    ult = cl.Chainladder().fit(raa)
-    tw = cl.TriangleWeight(n_periods=4).fit(raa)
-    tw_full = cl.TriangleWeight(n_periods=4).fit(ult.full_triangle_)
-    assert tw.w_.iloc[:, :, :, 0] == tw_full.w_.iloc[:, :, :, 0]

chainladder/utils/tests/test_wtd_reg.py

@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+import numpy as np
+from chainladder.utils.sparse import sp
+from chainladder.utils.weighted_regression import WeightedRegression
+
+
+class TestOLS:
+    """Test the OLS calculations"""
+
+    def test_missing_data(self) -> None:
+        """Check that having nan in X and/or y still results in the right OLS coefficients."""
+        data = [
+            {
+                "module": np,
+                "X": [
+                    np.array([[[[1.0], [2.0], [3.0], [4.0], [5.0]]]]),
+                    np.array([[[[1.0], [np.nan], [3.0], [4.0], [5.0]]]]),
+                ],
+                "y": [
+                    np.array([[[[1.0], [2.0], [3.0], [4.0], [5.0]]]]),
+                    np.array([[[[1.0], [2.0], [np.nan], [4.0], [5.0]]]]),
+                ],
+                "w": np.array([[[[1.0], [1.0], [1.0], [1.0], [1.0]]]]),
+                "slope": np.array([[[[1.0]]]]),
+            }
+        ]
+        data.append({
+            "module": sp,
+            "X": [sp.COO.from_numpy(i, fill_value=np.nan) for i in data[0]["X"]],
+            "y": [sp.COO.from_numpy(i, fill_value=np.nan) for i in data[0]["y"]],
+            "w": sp.COO.from_numpy(data[0]["w"]),
+            "slope": sp.COO.from_numpy(data[0]["slope"]),
+        })
+        for i in data:
+            for x in i["X"]:
+                for y in i["y"]:
+                    assert i["module"].all(
+                        WeightedRegression(xp=i["module"])
+                        .fit(x, y, i["w"], "regression")
+                        .slope_
+                        == i["slope"]
+                    )

chainladder/utils/weighted_regression.py

@@ -195,12 +195,17 @@ def _fit_ols(self):
         if xp != sp:
             x[w == 0] = xp.nan
             y[w == 0] = xp.nan
+            w[np.isnan(x)] = 0
+            w[np.isnan(y)] = 0
         else:
-            w2 = w.copy()
+            x2, y2, w2 = x.copy(), y.copy(), w.copy()
             w2 = sp.COO(
                 data=w2.data, coords=w2.coords, fill_value=sp.nan, shape=w2.shape
             )
             x, y = x * w2, y * w2
+            x2 = sp.COO(data=1.0, coords=x2.coords, fill_value=sp.nan, shape=x2.shape)
+            y2 = sp.COO(data=1.0, coords=y2.coords, fill_value=sp.nan, shape=y2.shape)
+            w = w * x2 * y2
 
         with warnings.catch_warnings():
             warnings.simplefilter("ignore", category=RuntimeWarning)

pyproject.toml

@@ -14,15 +14,14 @@ maintainers = [
 description = "Chainladder Package - P&C Loss Reserving package"
 readme = "README.rst"
 license = {text = "MPL-2.0"}
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 classifiers = [
     "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
     "Operating System :: OS Independent",
     "Programming Language :: Python :: 3",
-    "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
@@ -32,7 +31,7 @@ keywords = ["actuarial", "reserving", "insurance", "chainladder", "IBNR"]
 dependencies = [
     "pandas >=2.3.3, !=3.0.4",
     "scikit-learn>1.4.2",
-    "sparse>=0.9",
+    "sparse>=0.18",
     "numpy>=2.0",
     "matplotlib",  # Required for TriangleDisplay.heatmap()
     "dill",