Merge branch 'main' of https://github.com/scikit-learn-contrib/skglm into documentation-update

Author: floriankozikowski

.circleci/config.yml

@@ -2,7 +2,7 @@ version: 2
 jobs:
     build_docs:
       docker:
-        - image: circleci/python:3.8.1-buster
+        - image: cimg/python:3.10
       steps:
         - checkout
         - run:
@@ -32,6 +32,13 @@ jobs:
             keys:
               - pip-cache
 
+        # Install Xvfb and related dependencies
+        - run:
+            name: Install Xvfb and dependencies
+            command: |
+              sudo apt-get update
+              sudo apt-get install -y xvfb
+
         - run:
             name: Spin up Xvfb
             command: |

.github/workflows/flake8.yml

@@ -30,4 +30,4 @@ jobs:
       - name: Check doc style with pydocstyle
         run: |
           pip install pydocstyle
-          pydocstyle skglm --ignore='D100',D102,'D104','D107','D203','D213','D413'
+          pydocstyle skglm --ignore='D100',D102,'D104','D105','D107','D203','D213','D413',

.github/workflows/main.yml

@@ -25,9 +25,8 @@ jobs:
     - name: Install other dependencies
       run: |
         conda install rpy2 -c conda-forge
-        pip install celer
         pip install statsmodels cvxopt
-        pip install git+https://github.com/jolars/sortedl1.git
+        pip install sortedl1
         # for testing Cox estimator
         pip install lifelines
         pip install pandas

doc/changes/0.4.rst

@@ -1,6 +1,6 @@
 .. _changes_0_4:
 
-Version 0.4 (in progress)
+Version 0.4 (2023/04/08)
 -------------------------
 - Add :ref:`GroupLasso Estimator <skglm.GroupLasso>` (PR: :gh:`228`)
 - Add support and tutorial for positive coefficients to :ref:`Group Lasso Penalty <skglm.penalties.WeightedGroupL2>` (PR: :gh:`221`)

doc/changes/0.5.rst

@@ -0,0 +1,4 @@
+.. _changes_0_5:
+
+Version 0.5 (in progress)
+-------------------------

doc/changes/whats_new.rst

@@ -5,6 +5,8 @@ What's new
 
 .. currentmodule:: skglm
 
+.. include:: 0.5.rst
+
 .. include:: 0.4.rst
 
 .. include:: 0.3.rst

examples/plot_sparse_recovery.py

@@ -18,7 +18,6 @@
 from skglm.utils.data import make_correlated_data
 from skglm.solvers import AndersonCD
 from skglm.datafits import Quadratic
-from skglm.utils.jit_compilation import compiled_clone
 from skglm.penalties import L1, MCPenalty, L0_5, L2_3, SCAD
 
 cmap = plt.get_cmap('tab10')
@@ -74,7 +73,7 @@
 for idx, estimator in enumerate(penalties.keys()):
     print(f'Running {estimator}...')
     estimator_path = solver.path(
-        X, y, compiled_clone(datafit), compiled_clone(penalties[estimator]),
+        X, y, datafit, penalties[estimator],
         alphas=alphas)
 
     f1_temp = np.zeros(n_alphas)

examples/plot_survival_analysis.py

@@ -15,6 +15,7 @@
 # Let's first generate synthetic data on which to run the Cox estimator,
 # using ``skglm`` data utils.
 #
+
 from skglm.utils.data import make_dummy_survival_data
 
 n_samples, n_features = 500, 100
@@ -59,18 +60,16 @@
 # Todo so, we need to combine a Cox datafit and a :math:`\ell_1` penalty
 # and solve the resulting problem using skglm Proximal Newton solver ``ProxNewton``.
 # We set the intensity of the :math:`\ell_1` regularization to ``alpha=1e-2``.
-from skglm.datafits import Cox
 from skglm.penalties import L1
+from skglm.datafits import Cox
 from skglm.solvers import ProxNewton
 
-from skglm.utils.jit_compilation import compiled_clone
-
 # regularization intensity
 alpha = 1e-2
 
 # skglm internals: init datafit and penalty
-datafit = compiled_clone(Cox())
-penalty = compiled_clone(L1(alpha))
+datafit = Cox()
+penalty = L1(alpha)
 
 datafit.initialize(X, y)
 
@@ -230,7 +229,7 @@
 # We only need to pass in ``use_efron=True`` to the ``Cox`` datafit.
 
 # ensure using Efron estimate
-datafit = compiled_clone(Cox(use_efron=True))
+datafit = Cox(use_efron=True)
 datafit.initialize(X, y)
 
 # solve the problem

pyproject.toml

@@ -22,6 +22,16 @@ dependencies = [
 ]
 dynamic = ["version"]
 
+requires-python = ">=3.9"
+
+classifiers = [
+  "Programming Language :: Python :: 3 :: Only",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: 3.13",
+]
 
 [tool.setuptools.dynamic]
 version = {attr = "skglm.__version__"}
@@ -38,6 +48,7 @@ test = [
     "flake8",
     "coverage",
     "numpydoc",
+    "celer",
 ]
 
 doc = [
@@ -56,3 +67,7 @@ doc = [
     "lifelines",
     "pydata_sphinx_theme",
 ]
+
+
+[tool.setuptools]
+license-files = []

skglm/__init__.py

@@ -1,4 +1,4 @@
-__version__ = '0.4dev'
+__version__ = '0.5dev'
 
 from skglm.estimators import (  # noqa F401
     Lasso, WeightedLasso, ElasticNet, MCPRegression, MultiTaskLasso, LinearSVC,