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Update for scikit-learn 1.8.0 and pandas 3.0.0 compatibility #106

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Merged
larsoner merged 4 commits into from
Feb 11, 2026
Merged

Update for scikit-learn 1.8.0 and pandas 3.0.0 compatibility #106

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6397978
fix: use sklearn validate_data in FeatureFunctionTransformer fit (for…
rcmdnk Feb 1, 2026
86b2092
test: use pandas Index.to_list in feature name tests (fix for pandas …
rcmdnk Feb 1, 2026
cd4afd4
style: wrap long assert in test_univariate for readability
rcmdnk Feb 1, 2026
a1d432a
fix: require scikit-learn>=1.6 in setup files
rcmdnk Feb 11, 2026
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9 changes: 8 additions & 1 deletion mne_features/feature_extraction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
import joblib
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import FunctionTransformer
from sklearn.utils.validation import validate_data
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@larsoner larsoner Feb 10, 2026

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This is going to lead to an import issue on any version of sklearn that doesn't have this, right? I think over in MNE-Python I figured out this meant 1.6+

https://github.com/mne-tools/mne-python/blob/87d1051d6932383e825ba377b21a221b2670db11/mne/decoding/_fixes.py#L6-L7

Can you add a corresponding pin to >=1.6 to

mne-features/setup.py

Line 63 in 331ca47

install_requires=['numpy', 'scipy', 'numba', 'scikit-learn', 'mne',


from .bivariate import get_bivariate_funcs, get_bivariate_func_names
from .univariate import get_univariate_funcs, get_univariate_func_names
Expand Down Expand Up @@ -103,7 +104,13 @@ def fit(self, X, y=None):
-------
self
"""
self._check_input(X, reset=True)
validate_data(
self,
X,
reset=True,
accept_sparse=self.accept_sparse,
skip_check_array=not self.validate,
)
_feature_func = _get_python_func(self.func)
if hasattr(_feature_func, 'get_feature_names'):
_params = self.get_params()
Expand Down
22 changes: 12 additions & 10 deletions mne_features/tests/test_univariate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -306,7 +306,7 @@ def test_feature_names_quantile():
df = extract_features(
_data, sfreq, selected_funcs, funcs_params={'quantile__q': q},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, col_names)
assert_equal(df.columns.get_level_values(1).to_list(), col_names)


def test_feature_names_spect_edge_freq():
Expand All @@ -325,7 +325,7 @@ def test_feature_names_spect_edge_freq():
_data, sfreq, selected_funcs,
funcs_params={'spect_edge_freq__edge': edge},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, col_names)
assert_equal(df.columns.get_level_values(1).to_list(), col_names)


def test_feature_names_spect_slope():
Expand All @@ -338,7 +338,7 @@ def test_feature_names_spect_slope():
col_names = ['ch%s_%s' % (ch, stat) for ch in range(n_chans) for
stat in stats]
df = extract_features(_data, sfreq, selected_funcs, return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, col_names)
assert_equal(df.columns.get_level_values(1).to_list(), col_names)


def test_feature_names_wavelet_coef_energy(wavelet_name='db4'):
Expand All @@ -357,7 +357,7 @@ def test_feature_names_wavelet_coef_energy(wavelet_name='db4'):
_data, sfreq, selected_funcs,
funcs_params={'wavelet_coef_energy__wavelet_name': wavelet_name},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, col_names)
assert_equal(df.columns.get_level_values(1).to_list(), col_names)


def test_feature_names_teager_kaiser_energy(wavelet_name='db4'):
Expand All @@ -376,7 +376,7 @@ def test_feature_names_teager_kaiser_energy(wavelet_name='db4'):
_data, sfreq, selected_funcs,
funcs_params={'teager_kaiser_energy__wavelet_name': wavelet_name},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, col_names)
assert_equal(df.columns.get_level_values(1).to_list(), col_names)


def test_feature_names_pow_freq_bands():
Expand All @@ -402,15 +402,17 @@ def test_feature_names_pow_freq_bands():
funcs_params={'pow_freq_bands__ratios': 'only',
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df_only.columns.get_level_values(1).values, ratios_names)
assert_equal(
df_only.columns.get_level_values(1).to_list(), ratios_names
)

# With `ratios = 'all'`:
df_all = extract_features(
_data, sfreq, selected_funcs,
funcs_params={'pow_freq_bands__ratios': 'all',
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df_all.columns.get_level_values(1).values,
assert_equal(df_all.columns.get_level_values(1).to_list(),
pow_names + ratios_names)

# With `ratios = None`:
Expand All @@ -419,7 +421,7 @@ def test_feature_names_pow_freq_bands():
funcs_params={'pow_freq_bands__ratios': None,
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, pow_names)
assert_equal(df.columns.get_level_values(1).to_list(), pow_names)

# With `ratios = 'only'` and `ratios_triu = True`:
df_only = extract_features(
Expand All @@ -428,7 +430,7 @@ def test_feature_names_pow_freq_bands():
'pow_freq_bands__ratios_triu': True,
'pow_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df_only.columns.get_level_values(1).values,
assert_equal(df_only.columns.get_level_values(1).to_list(),
ratios_names[::2])


Expand Down Expand Up @@ -531,7 +533,7 @@ def test_feature_names_energy_freq_bands():
_data, sfreq, selected_funcs,
funcs_params={'energy_freq_bands__freq_bands': fb},
return_as_df=True)
assert_equal(df.columns.get_level_values(1).values, feat_names)
assert_equal(df.columns.get_level_values(1).to_list(), feat_names)


def test_spect_slope():
Expand Down
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