MAINT: Update dependency specifiers (#13916)

mne-bot · autofix-ci[bot] · larsoner · web-flow · commit 3b3f3afb0e46 · 2026-05-25T22:18:23.000Z
Co-authored-by: autofix-ci[bot] &lt;114827586+autofix-ci[bot]@users.noreply.github.com&gt;
Co-authored-by: Eric Larson &lt;larson.eric.d@gmail.com&gt;
diff --git a/README.rst b/README.rst
@@ -75,7 +75,7 @@ The minimum required dependencies to run MNE-Python are:
 - `Python <https://www.python.org>`__ ≥ 3.10
 - `NumPy <https://numpy.org>`__ ≥ 1.26
 - `SciPy <https://scipy.org>`__ ≥ 1.13
-- `Matplotlib <https://matplotlib.org>`__ ≥ 3.8
+- `Matplotlib <https://matplotlib.org>`__ ≥ 3.9
 - `Pooch <https://www.fatiando.org/pooch/latest/>`__ ≥ 1.5
 - `tqdm <https://tqdm.github.io>`__ ≥ 4.66
 - `Jinja2 <https://palletsprojects.com/p/jinja/>`__ ≥ 3.1
diff --git a/doc/changes/dev/13916.dependency.rst b/doc/changes/dev/13916.dependency.rst
@@ -0,0 +1,5 @@
+Updated minimum for:
+
+- Optional dependency ``scikit-learn >= 1.5``
+
+Changes implemented via CI action created by `Thomas Binns`_.
diff --git a/environment.yml b/environment.yml
@@ -53,7 +53,7 @@ dependencies:
   - pyvistaqt >=0.11
   - qdarkstyle !=3.2.2
   - qtpy
-  - scikit-learn >=1.4
+  - scikit-learn >=1.5
   - scipy >=1.13
   - sip
   - snirf
diff --git a/mne/_fiff/proc_history.py b/mne/_fiff/proc_history.py
@@ -3,8 +3,8 @@
 # Copyright the MNE-Python contributors.
 
 import numpy as np
+from scipy.sparse import csc_array
 
-from ..fixes import _csc_array_cast
 from ..utils import _check_fname, warn
 from .constants import FIFF
 from .open import fiff_open, read_tag
@@ -211,7 +211,7 @@ def _write_proc_history(fid, info):
     FIFF.FIFF_DECOUPLER_MATRIX,
 )
 _sss_ctc_writers = (write_id, write_int, write_string, write_float_sparse)
-_sss_ctc_casters = (dict, np.array, str, _csc_array_cast)
+_sss_ctc_casters = (dict, np.array, str, csc_array)
 
 _sss_cal_keys = ("cal_chans", "cal_corrs")
 _sss_cal_ids = (FIFF.FIFF_SSS_CAL_CHANS, FIFF.FIFF_SSS_CAL_CORRS)
diff --git a/mne/decoding/tests/test_csp.py b/mne/decoding/tests/test_csp.py
@@ -499,7 +499,7 @@ def test_csp_component_ordering():
 @parametrize_with_checks([CSP(), SPoC()])
 def test_sklearn_compliance(estimator, check):
     """Test compliance with sklearn."""
-    pytest.importorskip("sklearn", minversion="1.5")  # TODO VERSION remove on 1.5+
+    pytest.importorskip("sklearn", minversion="1.6")  # TODO VERSION remove on 1.6+
     check(estimator)
 
 
diff --git a/mne/decoding/transformer.py b/mne/decoding/transformer.py
@@ -44,7 +44,7 @@ def _check_data(
         if isinstance(epochs_data, BaseEpochs):
             epochs_data = epochs_data.get_data(copy=False)
         kwargs = dict(dtype=np.float64, allow_nd=True, order="C")
-        if check_version("sklearn", "1.5"):  # TODO VERSION sklearn 1.5+
+        if check_version("sklearn", "1.6"):  # TODO VERSION sklearn 1.6+
             kwargs["force_writeable"] = True
         if hasattr(self, "n_features_in_") and check_n_features:
             if y is None:
diff --git a/mne/fixes.py b/mne/fixes.py
@@ -136,19 +136,6 @@ def _safe_svd(A, **kwargs):
         return linalg.svd(A, lapack_driver="gesvd", **kwargs)
 
 
-def _csc_array_cast(x):
-    from scipy.sparse import csc_array
-
-    return csc_array(x)
-
-
-# Can be replaced with sparse.eye_array once we depend on SciPy >= 1.12
-def _eye_array(n, *, format="csr"):  # noqa: A002
-    from scipy import sparse
-
-    return sparse.dia_array((np.ones(n), 0), shape=(n, n)).asformat(format)
-
-
 ###############################################################################
 # NumPy Generator (NumPy 1.17)
 
@@ -707,7 +694,7 @@ def _close_event(fig):
 
 
 ###############################################################################
-# SciPy 1.14+ minimum_phase half=True option
+# TODO VERSION SciPy 1.14+ minimum_phase half=True option
 
 
 def minimum_phase(h, method="homomorphic", n_fft=None, *, half=True):
@@ -778,8 +765,7 @@ def sph_harm_y(n, m, theta, phi, *, diff_n=0):
 
 
 ###############################################################################
-# TODO VERSION: Can be removed once pymatreader >= 1.2.2 is the minimum
-# supported version.
+# TODO VERSION: Can be removed once pymatreader >= 1.2.2 is the min version
 
 
 def _whosmat(fname):
@@ -898,6 +884,8 @@ def _whosmat_hdf5(fname):
 # workaround: Numpy won't allow to read from file-like objects with numpy.fromfile,
 # we try to use numpy.fromfile, if a blob is used we use numpy.frombuffer to read
 # from the file-like object.
+
+
 def read_from_file_or_buffer(
     file: str | bytes | os.PathLike | io.IOBase,
     dtype: numpy.typing.DTypeLike = float,
diff --git a/mne/morph.py b/mne/morph.py
@@ -9,7 +9,7 @@
 import numpy as np
 from scipy import sparse
 
-from .fixes import _eye_array, _get_img_fdata, _reshape_view
+from .fixes import _get_img_fdata, _reshape_view
 from .morph_map import read_morph_map
 from .parallel import parallel_func
 from .source_estimate import (
@@ -1231,7 +1231,7 @@ def _hemi_morph(tris, vertices_to, vertices_from, smooth, maps, warn):
     e = mesh_edges(tris)
     e.data[e.data == 2] = 1
     n_vertices = e.shape[0]
-    e += _eye_array(n_vertices, format="csr")
+    e += sparse.eye_array(n_vertices, format="csr")
     if isinstance(smooth, str):
         _check_option("smooth", smooth, ("nearest",), extra=" when used as a string.")
         mm = _surf_nearest(vertices_from, e).tocsr()
@@ -1380,7 +1380,7 @@ def _surf_upsampling_mat(idx_from, e, smooth):
     assert e.shape == (n_tot, n_tot)
     # our output matrix starts out as a smaller matrix, and will gradually
     # increase in size
-    data = _eye_array(len(idx_from), format="csr")
+    data = sparse.eye_array(len(idx_from), format="csr")
     _validate_type(smooth, ("int-like", str, None), "smoothing steps")
     if smooth is not None:  # number of steps
         smooth = _ensure_int(smooth, "smoothing steps")
diff --git a/mne/morph_map.py b/mne/morph_map.py
@@ -8,7 +8,7 @@
 import os
 
 import numpy as np
-from scipy.sparse import csr_array
+from scipy.sparse import csr_array, eye_array
 
 from ._fiff.constants import FIFF
 from ._fiff.open import fiff_open
@@ -22,7 +22,6 @@
     write_int,
     write_string,
 )
-from .fixes import _eye_array
 from .surface import (
     _compute_nearest,
     _find_nearest_tri_pts,
@@ -210,7 +209,7 @@ def _make_morph_map_hemi(subject_from, subject_to, subjects_dir, reg_from, reg_t
     if subject_from == subject_to and reg_from == reg_to:
         fname = subjects_dir / subject_from / "surf" / reg_from
         n_pts = len(read_surface(fname, verbose=False)[0])
-        return _eye_array(n_pts, format="csr")
+        return eye_array(n_pts, format="csr")
 
     # load surfaces and normalize points to be on unit sphere
     fname = subjects_dir / subject_from / "surf" / reg_from
diff --git a/mne/source_estimate.py b/mne/source_estimate.py
@@ -19,7 +19,7 @@
 from .cov import Covariance
 from .evoked import _get_peak
 from .filter import FilterMixin, _check_fun, resample
-from .fixes import _eye_array, _reshape_view, _safe_svd
+from .fixes import _reshape_view, _safe_svd
 from .parallel import parallel_func
 from .source_space._source_space import (
     SourceSpaces,
@@ -3188,7 +3188,7 @@ def spatio_temporal_tris_adjacency(tris, n_times, remap_vertices=False, verbose=
         tris = np.searchsorted(np.unique(tris), tris)
 
     edges = mesh_edges(tris)
-    edges = (edges + _eye_array(edges.shape[0])).tocoo()
+    edges = (edges + sparse.eye_array(edges.shape[0], format="csr")).tocoo()
     return _get_adjacency_from_edges(edges, n_times)
 
 
diff --git a/mne/stats/tests/test_cluster_level.py b/mne/stats/tests/test_cluster_level.py
@@ -16,7 +16,6 @@
 from scipy import linalg, sparse, stats
 
 from mne import MixedSourceEstimate, SourceEstimate, SourceSpaces, VolSourceEstimate
-from mne.fixes import _eye_array
 from mne.stats import combine_adjacency, ttest_ind_no_p
 from mne.stats.cluster_level import (
     f_oneway,
@@ -381,7 +380,7 @@ def test_cluster_permutation_with_adjacency(numba_conditional, monkeypatch):
             )
         )
         # nesting here is time then space:
-        adjacency_2a = combine_adjacency(_eye_array(2), adjacency)
+        adjacency_2a = combine_adjacency(sparse.eye_array(2), adjacency)
         assert_array_equal(
             adjacency_2.toarray().astype(bool), adjacency_2a.toarray().astype(bool)
         )
diff --git a/mne/stats/tests/test_permutations.py b/mne/stats/tests/test_permutations.py
@@ -5,9 +5,8 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose, assert_array_equal
-from scipy import stats
+from scipy import sparse, stats
 
-from mne.fixes import _eye_array
 from mne.stats import permutation_cluster_1samp_test
 from mne.stats.permutations import (
     _ci,
@@ -48,7 +47,7 @@ def test_permutation_t_test():
     assert_array_equal(is_significant, [True, True, False, False, False])
 
     # check equivalence with spatio_temporal_cluster_test
-    for adjacency in (_eye_array(n_tests), False):
+    for adjacency in (sparse.eye_array(n_tests), False):
         t_obs_clust, _, p_values_clust, _ = permutation_cluster_1samp_test(
             X, n_permutations=999, seed=0, adjacency=adjacency, out_type="mask"
         )
diff --git a/mne/tests/test_label.py b/mne/tests/test_label.py
@@ -18,6 +18,7 @@
     assert_array_less,
     assert_equal,
 )
+from scipy import sparse
 
 from mne import (
     grow_labels,
@@ -35,7 +36,6 @@
     write_labels_to_annot,
 )
 from mne.datasets import testing
-from mne.fixes import _eye_array
 from mne.label import (
     Label,
     _blend_colors,
@@ -130,7 +130,7 @@ def _stc_to_label(stc, src, smooth, subjects_dir=None):
         e = mesh_edges(this_tris)
         e.data[e.data == 2] = 1
         n_vertices = e.shape[0]
-        e = e + _eye_array(n_vertices)
+        e += sparse.eye_array(n_vertices)
 
         clusters = [this_vertno[np.any(this_data, axis=1)]]
 
diff --git a/mne/tests/test_morph_map.py b/mne/tests/test_morph_map.py
@@ -8,10 +8,10 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose
+from scipy import sparse
 
 from mne import read_morph_map
 from mne.datasets import testing
-from mne.fixes import _eye_array
 from mne.utils import _record_warnings, catch_logging
 
 data_path = testing.data_path(download=False)
@@ -58,4 +58,4 @@ def test_make_morph_maps(tmp_path):
     with _record_warnings():
         mmap = read_morph_map("sample", "sample", subjects_dir=tmp_path)
     for mm in mmap:
-        assert (mm - _eye_array(mm.shape[0])).sum() == 0
+        assert (mm - sparse.eye_array(mm.shape[0])).sum() == 0
diff --git a/mne/utils/tests/test_numerics.py b/mne/utils/tests/test_numerics.py
@@ -10,11 +10,11 @@
 import numpy as np
 import pytest
 from numpy.testing import assert_allclose, assert_array_equal
+from scipy import sparse
 
 from mne import pick_types, read_cov, read_evokeds
 from mne._fiff.pick import _picks_by_type
 from mne.epochs import make_fixed_length_epochs
-from mne.fixes import _eye_array
 from mne.io import read_raw_fif
 from mne.time_frequency import tfr_morlet
 from mne.utils import (
@@ -315,8 +315,8 @@ def test_object_size():
         (150, 500, np.ones(20)),
         (30, 400, dict()),
         (400, 1000, dict(a=np.ones(50))),
-        (200, 900, _eye_array(20, format="csc")),
-        (200, 900, _eye_array(20, format="csr")),
+        (200, 900, sparse.eye_array(20, format="csc")),
+        (200, 900, sparse.eye_array(20, format="csr")),
     ):
         size = object_size(obj)
         assert lower < size < upper, f"{lower} < {size} < {upper}:\n{obj}"
@@ -417,14 +417,14 @@ def test_hash():
     pytest.raises(RuntimeError, object_diff, d1, d2)
     pytest.raises(RuntimeError, object_hash, d1)
 
-    x = _eye_array(2, format="csc")
-    y = _eye_array(2, format="csr")
+    x = sparse.eye_array(2, format="csc")
+    y = sparse.eye_array(2, format="csr")
     assert "type mismatch" in object_diff(x, y)
-    y = _eye_array(2, format="csc")
+    y = sparse.eye_array(2, format="csc")
     assert len(object_diff(x, y)) == 0
     y[1, 1] = 2
     assert "elements" in object_diff(x, y)
-    y = _eye_array(3, format="csc")
+    y = sparse.eye_array(3, format="csc")
     assert "shape" in object_diff(x, y)
     y = 0
     assert "type mismatch" in object_diff(x, y)
@@ -598,12 +598,12 @@ def my_fun_2(*args):
     assert my_fun(1, np.array([2]), 3) == "int, ndarray, int"
     assert n_calls == [2, 1]
     assert len(_LRU_CACHES[fun_hash]) == 2
-    assert my_fun_2(1, _eye_array(1, format="csc")) == "int, csc_array"
+    assert my_fun_2(1, sparse.eye_array(1, format="csc")) == "int, csc_array"
     assert n_calls == [2, 2]
     assert len(_LRU_CACHES[fun_2_hash]) == 1  # other got popped
     # we could add support for this eventually, but don't bother for now
     with pytest.raises(RuntimeError, match="Unsupported sparse type"):
-        my_fun_2(1, _eye_array(1, format="coo"))
+        my_fun_2(1, sparse.eye_array(1, format="coo"))
     assert n_calls == [2, 2]  # never did any computation
 
 
diff --git a/pyproject.toml b/pyproject.toml
@@ -24,7 +24,7 @@ doc = [
   "pyvistaqt >= 0.11",  # released 2023-06-30, no newer version available
   "pyxdf",
   "pyzmq != 24.0.0",
-  "scikit-learn >= 1.4",  # released 2024-01-18, will become 1.5 on 2026-05-21
+  "scikit-learn >= 1.5",  # released 2024-05-21, will become 1.6 on 2026-12-09
   "seaborn >= 0.5, != 0.11.2",
   "selenium >= 4.27.1",
   "sphinx >= 6",
@@ -40,7 +40,7 @@ doc-full = ["rpy2", {include-group = "doc"}]
 # the 'old' environment CI test run
 lockfile_extras = [
   "pandas >= 2.2",  # released 2024-01-20, will become 2.3 on 2027-06-05
-  "scikit-learn >= 1.4",  # released 2024-01-18, will become 1.5 on 2026-05-21
+  "scikit-learn >= 1.5",  # released 2024-05-21, will become 1.6 on 2026-12-09
 ]
 test = [
   "codespell",
@@ -178,7 +178,7 @@ full-no-qt = [
   "pyvistaqt >= 0.11",  # released 2023-06-30, no newer version available
   "qdarkstyle != 3.2.2",
   "qtpy",
-  "scikit-learn >= 1.4",  # released 2024-01-18, will become 1.5 on 2026-05-21
+  "scikit-learn >= 1.5",  # released 2024-05-21, will become 1.6 on 2026-12-09
   "sip",
   "snirf",
   "statsmodels >= 0.6",
diff --git a/tools/pylock.ci-old.toml b/tools/pylock.ci-old.toml
@@ -448,12 +448,9 @@ wheels = [{ url = "https://files.pythonhosted.org/packages/ac/22/0fc6119373ee933
 
 [[packages]]
 name = "scikit-learn"
-version = "1.4.0"
-sdist = { url = "https://files.pythonhosted.org/packages/a0/25/f95b39549240d07c7fefa2ab0f81ac418f0ea192c53334f655bbed5015f9/scikit-learn-1.4.0.tar.gz", upload-time = 2024-01-18T09:22:23Z, size = 7706781, hashes = { sha256 = "d4373c984eba20e393216edd51a3e3eede56cbe93d4247516d205643c3b93121" } }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/3f/61/047b353f0ad550226ef962da182b4a09b689eb6df6bd84a03e44f9ee95bb/scikit_learn-1.4.0-1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", upload-time = 2024-01-18T20:53:12Z, size = 12113336, hashes = { sha256 = "e98632da8f6410e6fb6bf66937712c949b4010600ccd3f22a5388a83e610cc3c" } },
-    { url = "https://files.pythonhosted.org/packages/73/b9/3cb1d6aa07d10c66d8ab6d990163e973b3d29a945418058104affad3d4b6/scikit_learn-1.4.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", upload-time = 2024-01-18T09:21:34Z, size = 12113327, hashes = { sha256 = "0aba2a20d89936d6e72d95d05e3bf1db55bca5c5920926ad7b92c34f5e7d3bbe" } },
-]
+version = "1.5.0"
+sdist = { url = "https://files.pythonhosted.org/packages/bf/8a/06e499bca463905000f50e461c9445e949aafdd33ea3b62024aa2238b83d/scikit_learn-1.5.0.tar.gz", upload-time = 2024-05-21T16:34:07Z, size = 7820839, hashes = { sha256 = "789e3db01c750ed6d496fa2db7d50637857b451e57bcae863bff707c1247bef7" } }
+wheels = [{ url = "https://files.pythonhosted.org/packages/1e/7d/1a2ea8eb5b4df373c30c7418cf26305a4a05e2a0e56c80a8043b791595f3/scikit_learn-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", upload-time = 2024-05-21T16:33:13Z, size = 13343697, hashes = { sha256 = "2c75ea812cd83b1385bbfa94ae971f0d80adb338a9523f6bbcb5e0b0381151d4" } }]
 
 [[packages]]
 name = "scipy"
