scikit-learn-contrib · TimotheeMathieu · Mar 27, 2023 · Feb 20, 2023 · Feb 20, 2023 · Feb 20, 2023
diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml
@@ -1,14 +1,14 @@
 name: build_wheels
 
-on: # [push, pull_request]
-  release:
-    types:
-      - created
-  workflow_dispatch:
-    inputs:
-      version:
-        description: 'Manually trigger wheel build in Github UI'
-        required: true
+on: #[push, pull_request]
+   release:
+     types:
+       - created
+   workflow_dispatch:
+     inputs:
+       version:
+         description: 'Manually trigger wheel build in Github UI'
+         required: true
 
 
 jobs:
@@ -17,7 +17,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ubuntu-18.04, windows-latest, macos-latest]
+        os: [ubuntu-22.04, windows-latest, macos-latest]
 
     steps:
       - uses: actions/checkout@v2
@@ -28,12 +28,13 @@ jobs:
           python-version: '3.8'
       - name: Install cibuildwheel
         run: |
-          python -m pip install cibuildwheel==1.10.0
+          python -m pip install cibuildwheel==2.12.1
       - name: Build wheels
         env:
           # We only build for Python 3.6+. On Linux manylinux2010 is used.
           # Skipping pypy wheels for now since scipy & scikit-learn haven't build them yet.
-          CIBW_SKIP: "pp* *p27* *p35*"
+          # Skip python3.11 for 32bit.
+          CIBW_SKIP: "pp* *-win32 *-manylinux_i686 *musllinux*"
           CIBW_TEST_REQUIRES: "pytest pandas scikit-learn"
           CIBW_TEST_COMMAND: "pytest --pyargs sklearn_extra"
         run: |

diff --git a/pyproject.toml b/pyproject.toml
@@ -14,3 +14,6 @@ requires = [
 
 [tool.black]
 line-length = 79
+
+[tool.cibuildwheel]
+manylinux-i686-image = "manylinux2010"
diff --git a/sklearn_extra/robust/robust_weighted_estimator.py b/sklearn_extra/robust/robust_weighted_estimator.py
@@ -129,13 +129,13 @@ class _RobustWeightedEstimator(BaseEstimator):
         Maximum number of iterations.
         For more information, see the optimization scheme of base_estimator.
 
-    c : float>0 or None, default=None
+    c : float>0 or None, default=1
         Parameter used for Huber weighting procedure, used only if weightings
         is 'huber'. Measure the robustness of the weighting procedure. A small
         value of c means a more robust estimator.
         Can have a big effect on efficiency.
         If None, c is estimated at each step using half the Inter-quartile
-        range, this tends to be conservative (robust).
+        range, this tends to be unstable.
 
     k : int < sample_size/2, default=1
         Parameter used for mom weighting procedure, used only if weightings
@@ -211,7 +211,7 @@ def __init__(
         loss,
         weighting="huber",
         max_iter=100,
-        c=None,
+        c=1,
         k=0,
         tol=1e-5,
         n_iter_no_change=10,