Merge pull request #10 from edahelsinki/python311

Enable Python 3.11 and test it

Author: Aggrathon

.github/workflows/python-pytest.yml

@@ -16,7 +16,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
       - uses: actions/checkout@v3

README.md

@@ -52,10 +52,10 @@ Alternatively you can download the repo and run `python -m build` to build a whe
 
 ### Numba
 
-SLISE uses [Numba](https://numba.pydata.org/) to speed up the calculations. You might want to install the following optional libraries to get the most out of Numba:
+SLISE uses [Numba](https://numba.pydata.org/) to speed up the calculations. You might want to install the optional libraries to get the most out of Numba:
 
 ```sh
-pip install tbb icc_rt
+pip install "slise[tbb]"
 ```
 
 ## Other implementations

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "slise"
-version = "2.2.2"
+version = "2.2.3"
 authors = [{ name = "Anton Björklund", email = "[email protected]" }]
 description = "The SLISE algorithm for robust regression and explanations of black box models"
 readme = "README.md"
@@ -18,9 +18,8 @@ classifiers = [
     "Topic :: Scientific/Engineering :: Artificial Intelligence",
     "Topic :: Scientific/Engineering :: Visualization",
 ]
-requires-python = ">=3.6,<3.11" # Python>=3.11 is not supported due to PyLBFGS not being updated to cython>=3.0
+requires-python = ">=3.6"
 dependencies = [
-    "cython < 3.0",      # PyLBFGS is not updated to support cython>=3.0
     "numpy >= 1.20",
     "scipy >= 1.6",
     "numba >= 0.53",
@@ -29,7 +28,7 @@ dependencies = [
 ]
 
 [project.optional-dependencies]
-dev = ["pytest", "black[jupyter]", "pylint", "icc_rt", "IPython"]
+dev = ["pytest", "black[jupyter]", "pylint", "IPython"]
 tbb = ["tbb"]
 
 [project.urls]

tests/test_optim.py

@@ -250,8 +250,9 @@ def test_weights():
         regularised_regression(X, Y, 1e-4, 1e-4, weight=w2),
     )
     assert np.allclose(
-        regularised_regression(X2, Y2, 1e-4, 1e-4, weight=w1, max_iterations=300),
-        regularised_regression(X, Y, 1e-4, 1e-4, weight=w3, max_iterations=300),
+        regularised_regression(X2, Y2, 1e-4, 1e-4, weight=w1),
+        regularised_regression(X, Y, 1e-4, 1e-4, weight=w3),
+        atol=1e-5
     )