8000 FIX convergence criterion of MeanShift (#28951) · scikit-learn/scikit-learn@e796d0a · GitHub
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akikunoogriseljeremiedbb
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FIX convergence criterion of MeanShift (#28951)
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org> Co-authored-by: Jérémie du Boisberranger <jeremie@probabl.ai>
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‎doc/whats_new/v1.5.rst‎

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@@ -183,6 +183,9 @@ Changelog
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:mod:`sklearn.cluster`
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......................
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- |Fix| The :class:`cluster.MeanShift` class now properly converges for constant data.
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:pr:`28951` by :user:`Akihiro Kuno <akikuno>`.
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- |FIX| Create copy of precomputed sparse matrix within the `fit` method of
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:class:`~cluster.OPTICS` to avoid in-place modification of the sparse matrix.
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:pr:`28491` by :user:`Thanh Lam Dang <lamdang2k>`.

‎sklearn/cluster/_mean_shift.py‎

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@@ -122,7 +122,7 @@ def _mean_shift_single_seed(my_mean, X, nbrs, max_iter):
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my_mean = np.mean(points_within, axis=0)
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# If converged or at max_iter, adds the cluster
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if (
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np.linalg.norm(my_mean - my_old_mean) < stop_thresh
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np.linalg.norm(my_mean - my_old_mean) <= stop_thresh
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or completed_iterations == max_iter
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):
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break

‎sklearn/cluster/tests/test_mean_shift.py‎

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)
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def test_convergence_of_1d_constant_data():
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# Test convergence using 1D constant data
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# Non-regression test for:
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# https://github.com/scikit-learn/scikit-learn/issues/28926
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model = MeanShift()
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n_iter = model.fit(np.ones(10).reshape(-1, 1)).n_iter_
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assert n_iter < model.max_iter
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def test_estimate_bandwidth():
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# Test estimate_bandwidth
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bandwidth = estimate_bandwidth(X, n_samples=200)

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