Revert everything to last successful build

.github/CODEOWNERS

@@ -1 +1 @@
-* @MarcAntoineSchmidtQC @jtilly @lbittarello @stanmart
+* @MarcAntoineSchmidtQC @xhochy @jtilly @lbittarello

.readthedocs.yml

@@ -4,7 +4,7 @@ build:
   tools:
     python: mambaforge-4.10
   commands:
-    - mamba install -c conda-forge -c nodefaults pixi==0.39.4
+    - mamba install -c conda-forge -c nodefaults pixi==0.27.0
     - pixi run -e docs postinstall
     - pixi run readthedocs
 sphinx:

CHANGELOG.rst

@@ -7,18 +7,8 @@
 Changelog
 =========
 
-4.1.1 - 2025-01-29
-------------------
-
-**Bug fix:**
-
-- A more robust :meth:`DenseMatrix._get_col_stds` results in more accurate :meth:`StandardizedMatrix.sandwich` results.
-
-**Other changes:**
-
-- Build wheel for pypi on python 3.13.
-- Build and test with python 3.13 in CI.
-
+Unreleased
+----------
 
 4.1.0 - 2024-11-07
 ------------------

build_tools/prepare_macos_wheel.sh

@@ -0,0 +1,11 @@
+#!/bin/bash
+
+set -exuo pipefail
+
+if [[ "${ARCHFLAGS:-}" == *arm64 ]]; then
+    export CONDA_SUBDIR="osx-arm64"
+else
+    export CONDA_SUBDIR="osx-64"
+fi
+
+/Users/runner/micromamba-bin/micromamba create -y -p $CONDA/envs/build -c conda-forge jemalloc-local "xsimd<11|>12.1" llvm-openmp

docs/api.rst

@@ -1,8 +1,6 @@
 tabmat package
 ========================
 
-.. autofunction:: tabmat.from_df
-
 .. autofunction:: tabmat.from_pandas
 
 .. autofunction:: tabmat.from_csc

pixi.toml

@@ -20,8 +20,8 @@ test = "pytest tests --doctest-modules src"
 
 [feature.docs.tasks]
 make-docs = "cd docs && make html"
-serve-docs = { cmd = "python -m http.server --directory docs/_build/html", depends-on = ["make-docs"] }
-readthedocs = { cmd = "rm -rf $READTHEDOCS_OUTPUT/html && cp -r docs/_build/html $READTHEDOCS_OUTPUT/html", depends-on = ["make-docs"] }
+serve-docs = { cmd = "python -m http.server --directory docs/_build/html", depends_on = ["make-docs"] }
+readthedocs = { cmd = "rm -rf $READTHEDOCS_OUTPUT/html && cp -r docs/_build/html $READTHEDOCS_OUTPUT/html", depends_on = ["make-docs"] }
 
 [feature.benchmark.tasks.benchmark-generate-matrices]
 cmd = "python src/tabmat/benchmark/generate_matrices.py"
@@ -38,7 +38,7 @@ outputs = [
 ]
 [feature.benchmark.tasks.benchmark-run]
 cmd = "python src/tabmat/benchmark/main.py --include_baseline --bench_memory --n_iterations 100"
-depends-on = ["benchmark-generate-matrices"]
+depends_on = ["benchmark-generate-matrices"]
 inputs = [
     "src/tabmat/benchmark/main.py",
     "benchmark/data/dense_cat_data.pkl",
@@ -62,7 +62,7 @@ outputs = [
 ]
 [feature.benchmark.tasks.benchmark-visualize]
 cmd = "python src/tabmat/benchmark/visualize_benchmarks.py"
-depends-on = ["benchmark-run"]
+depends_on = ["benchmark-run"]
 inputs = [
     "src/tabmat/benchmark/visualize_benchmarks.py",
     "benchmark/data/dense_cat_bench.csv",
@@ -94,8 +94,6 @@ jemalloc-local = "*"
 [host-dependencies]
 python = ">=3.9"
 pip = "*"
-setuptools = "*"
-wheel = "*"
 
 [dependencies]
 formulaic = ">=0.6.4"
@@ -148,17 +146,15 @@ python = "3.10.*"
 python = "3.11.*"
 [feature.py312.dependencies]
 python = "3.12.*"
-[feature.py313.dependencies]
-python = "3.13.*"
 
 [feature.oldies.dependencies]
 setuptools = "62.*"
-python = "=3.9.19"
-numpy = "=1.24.0"
-pandas = "=1.4.4"
-scipy = "=1.7.3"
-formulaic = "=0.6.4"
-narwhals = "=1.4.1"
+python = "3.9.19"
+numpy = "1.24.0"
+pandas = "1.4.4"
+scipy = "1.7.3"
+formulaic = "0.6.4"
+narwhals = "1.4.1"
 
 [environments]
 default = ["dev", "test"]
@@ -168,7 +164,6 @@ py39 = ["py39", "test"]
 py310 = ["py310", "test"]
 py311 = ["py311", "test"]
 py312 = ["py312", "test"]
-py313 = ["py313", "test"]
 oldies = ["oldies", "test"]
 lint = { features = ["lint"], no-default-feature = true}
-nightly = ["py313", "test"]
+nightly = ["py312", "test"]

pyproject.toml

@@ -49,20 +49,21 @@ skip = [
   "cp36*",
   "cp37*",
   "cp38*",
+  "cp313-*",
 ]
 test-requires = ["pytest", "pytest-xdist"]
 test-command = "pytest {package}/tests/test_matrices.py"
 # note: ARCHS_LINUX defined in build_wheels.yml file.
 
 [tool.cibuildwheel.macos]
 before-build = [
-  "/Users/runner/micromamba-bin/micromamba create -y -p $MAMBA_ROOT_PREFIX/envs/build -c conda-forge jemalloc-local \"xsimd<11|>12.1\" llvm-openmp",
+  "bash build_tools/prepare_macos_wheel.sh",
 ]
 
 [tool.cibuildwheel.macos.environment]
-LDFLAGS="-Wl,-rpath,$MAMBA_ROOT_PREFIX/envs/build/lib -L$MAMBA_ROOT_PREFIX/envs/build/lib -headerpad_max_install_names"
-CFLAGS="-I$MAMBA_ROOT_PREFIX/envs/build/include"
-CXXFLAGS="-I$MAMBA_ROOT_PREFIX/envs/build/include"
+LDFLAGS="-Wl,-rpath,$CONDA/envs/build/lib -L$CONDA/envs/build/lib -headerpad_max_install_names"
+CFLAGS="-I$CONDA/envs/build/include"
+CXXFLAGS="-I$CONDA/envs/build/include"
 CXX="/usr/bin/clang++"
 CC="/usr/bin/clang"
 JE_INSTALL_SUFFIX="local"

setup.py

@@ -154,7 +154,6 @@
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
         "Programming Language :: Python :: 3.12",
-        "Programming Language :: Python :: 3.13",
     ],
     package_dir={"": "src"},
     packages=find_packages(where="src"),

src/tabmat/categorical_matrix.py

@@ -169,7 +169,6 @@ def matvec(mat, vec):
 
 import narwhals.stable.v1 as nw
 import numpy as np
-import numpy.typing
 from scipy import sparse as sps
 
 from .dense_matrix import DenseMatrix
@@ -340,7 +339,7 @@ def __init__(
         cat_vec,
         categories: Optional[np.ndarray] = None,
         drop_first: bool = False,
-        dtype: numpy.typing.DTypeLike = np.float64,
+        dtype: np.dtype = np.float64,
         column_name: Optional[str] = None,
         term_name: Optional[str] = None,
         column_name_format: str = "{name}[{category}]",

src/tabmat/constructor.py

@@ -5,7 +5,6 @@
 
 import narwhals.stable.v1 as nw
 import numpy as np
-import numpy.typing
 from formulaic import Formula, ModelSpec
 from formulaic.materializers.types import NAAction
 from formulaic.parser import DefaultFormulaParser
@@ -29,7 +28,7 @@
 @nw.narwhalify(eager_only=True)
 def from_df(
     df,
-    dtype: numpy.typing.DTypeLike = np.float64,
+    dtype: np.dtype = np.float64,
     sparse_threshold: float = 0.1,
     cat_threshold: int = 4,
     object_as_cat: bool = False,
@@ -208,7 +207,7 @@ def from_df(
 
 def from_pandas(
     df,
-    dtype: numpy.typing.DTypeLike = np.float64,
+    dtype: np.dtype = np.float64,
     sparse_threshold: float = 0.1,
     cat_threshold: int = 4,
     object_as_cat: bool = False,
@@ -301,7 +300,7 @@ def from_formula(
     data,
     ensure_full_rank: bool = False,
     na_action: Union[str, NAAction] = NAAction.IGNORE,
-    dtype: numpy.typing.DTypeLike = np.float64,
+    dtype: np.dtype = np.float64,
     sparse_threshold: float = 0.1,
     cat_threshold: int = 4,
     interaction_separator: str = ":",

src/tabmat/dense_matrix.py

@@ -164,8 +164,8 @@ def _cross_sandwich(
         raise TypeError
 
     def _get_col_stds(self, weights: np.ndarray, col_means: np.ndarray) -> np.ndarray:
-        """Get standard deviations of columns using weights `weights`."""
-        sqrt_arg = transpose_square_dot_weights(self._array, weights, col_means)
+        """Get standard deviations of columns."""
+        sqrt_arg = transpose_square_dot_weights(self._array, weights) - col_means**2
         # Minor floating point errors above can result in a very slightly
         # negative sqrt_arg (e.g. -5e-16). We just set those values equal to
         # zero.

src/tabmat/ext/dense.pyx

@@ -100,7 +100,7 @@ def dense_matvec(np.ndarray X, floating[:] v, int[:] rows, int[:] cols):
         raise Exception("The matrix X is not contiguous.")
     return out
 
-def transpose_square_dot_weights(np.ndarray X, floating[:] weights, floating[:] shift):
+def transpose_square_dot_weights(np.ndarray X, floating[:] weights):
     cdef floating* Xp = <floating*>X.data
     cdef int nrows = weights.shape[0]
     cdef int ncols = X.shape[1]
@@ -112,11 +112,11 @@ def transpose_square_dot_weights(np.ndarray X, floating[:] weights, floating[:]
     if X.flags["C_CONTIGUOUS"]:
         for j in prange(ncols, nogil=True):
             for i in range(nrows):
-                outp[j] = outp[j] + weights[i] * ((Xp[i * ncols + j] - shift[j]) ** 2)
+                outp[j] = outp[j] + weights[i] * (Xp[i * ncols + j] ** 2)
     elif X.flags["F_CONTIGUOUS"]:
         for j in prange(ncols, nogil=True):
             for i in range(nrows):
-                outp[j] = outp[j] + weights[i] * ((Xp[j * nrows + i] - shift[j]) ** 2)
+                outp[j] = outp[j] + weights[i] * (Xp[j * nrows + i] ** 2)
     else:
         raise Exception("The matrix X is not contiguous.")
     return out