Widen scikit-learn constraint to <2 - #103
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The <=1.4.2 pin came from a blanket version-pinning commit; the only sklearn usage is Ridge from sklearn.linear_model, which is stable across 1.x releases. Match the style of the numpy/pandas constraints. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01FT7d8d2E3FansicmSzJ1SW
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Both CI jobs aborted during pytest collection with
pyace/activelearning.py:9: from maxvolpy.maxvol import maxvol
E ModuleNotFoundError: No module named 'maxvolpy'
Interrupted: 2 errors during collection
so no test ran at all. This is pre-existing and unrelated to any particular
change - run 31264271622 (17 Aug, PR #103) fails identically.
`maxvolpy` is installed by the custom InstallMaxVolPyLocalPackage(install)
command in setup.py, which shells out to `cd lib/maxvolpy; python setup.py
install`. The workflow installs with `pip install .`, which builds a wheel and
never invokes the legacy `install` command, so that hook never runs.
(`python setup.py install` is also gone in setuptools >= 80.)
Build it explicitly instead. `--no-build-isolation` is required because
lib/maxvolpy/setup.py imports Cython and numpy at module level while declaring
no pyproject.toml build-system, so an isolated build environment fails with
ModuleNotFoundError: No module named 'Cython'. Cython and scipy are installed
first: the extension cimports scipy/linalg/cython_blas.pxd. numpy is
deliberately not installed here, to avoid resolving past setup.py's
`numpy<=1.26.4` pin; it is already present from the python-ace install.
Verified locally in a clean venv: with build isolation the build fails on the
missing Cython, without scipy it fails cythonizing _maxvol.pyx, and with the
command used here it builds a wheel and `from maxvolpy.maxvol import maxvol`
succeeds.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017QbfG2UVKxkeuiz9GtDLo4
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Description of Changes
The <=1.4.2 pin came from a blanket version-pinning commit; the only sklearn usage is Ridge from sklearn.linear_model, which is stable across 1.x releases. Match the style of the numpy/pandas constraints.
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