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MAINT test building wheels (#156)
* manually trigger build * comment manual part * switching to Ubuntu 22.04 * fix bug random test * black * fix test ci * update cibuildwheels * try another image for manylinux i686 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Skip 3.11 for 32bit * fix syntax * remove failing binary * remove failing binary * remove all 32 bits * remove all 32 bits * remove musllinux * remove trigger of build wheels * syntax yml --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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.github/workflows/build-wheels.yml

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

pyproject.toml

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[tool.black]
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line-length = 79
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[tool.cibuildwheel]
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manylinux-i686-image = "manylinux2010"

sklearn_extra/robust/robust_weighted_estimator.py

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Maximum number of iterations.
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For more information, see the optimization scheme of base_estimator.
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c : float>0 or None, default=None
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c : float>0 or None, default=1
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Parameter used for Huber weighting procedure, used only if weightings
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is 'huber'. Measure the robustness of the weighting procedure. A small
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value of c means a more robust estimator.
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Can have a big effect on efficiency.
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If None, c is estimated at each step using half the Inter-quartile
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range, this tends to be conservative (robust).
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range, this tends to be unstable.
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k : int < sample_size/2, default=1
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Parameter used for mom weighting procedure, used only if weightings
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loss,
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weighting="huber",
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max_iter=100,
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c=None,
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c=1,
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k=0,
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tol=1e-5,
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n_iter_no_change=10,

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