MAINT: Fixes for latest sklearn (#12951)

mne/decoding/base.py

@@ -11,6 +11,7 @@
 from sklearn import model_selection as models
 from sklearn.base import (  # noqa: F401
     BaseEstimator,
+    MetaEstimatorMixin,
     TransformerMixin,
     clone,
     is_classifier,
@@ -24,7 +25,7 @@
 from ..utils import _pl, logger, verbose, warn
 
 
-class LinearModel(BaseEstimator):
+class LinearModel(MetaEstimatorMixin, BaseEstimator):
     """Compute and store patterns from linear models.
 
     The linear model coefficients (filters) are used to extract discriminant
@@ -61,11 +62,14 @@ class LinearModel(BaseEstimator):
     .. footbibliography::
     """
 
+    # TODO: Properly refactor this using
+    # https://github.com/scikit-learn/scikit-learn/issues/30237#issuecomment-2465572885
     _model_attr_wrap = (
         "transform",
         "predict",
         "predict_proba",
         "_estimator_type",
+        "__tags__",
         "decision_function",
         "score",
         "classes_",
@@ -77,6 +81,12 @@ def __init__(self, model=None):
 
         self.model = model
 
+    def __sklearn_tags__(self):
+        """Get sklearn tags."""
+        from sklearn.utils import get_tags  # added in 1.6
+
+        return get_tags(self.model)
+
     def __getattr__(self, attr):
         """Wrap to model for some attributes."""
         if attr in LinearModel._model_attr_wrap:

mne/decoding/ems.py

@@ -13,7 +13,7 @@
 from .base import _set_cv
 
 
-class EMS(BaseEstimator, TransformerMixin):
+class EMS(TransformerMixin, BaseEstimator):
     """Transformer to compute event-matched spatial filters.
 
     This version of EMS :footcite:`SchurgerEtAl2013` operates on the entire

mne/decoding/receptive_field.py

@@ -6,17 +6,22 @@
 
 import numpy as np
 from scipy.stats import pearsonr
-from sklearn.base import clone, is_regressor
+from sklearn.base import (
+    BaseEstimator,
+    MetaEstimatorMixin,
+    clone,
+    is_regressor,
+)
 from sklearn.exceptions import NotFittedError
 from sklearn.metrics import r2_score
 
 from ..utils import _validate_type, fill_doc, pinv
-from .base import BaseEstimator, _check_estimator, get_coef
+from .base import _check_estimator, get_coef
 from .time_delaying_ridge import TimeDelayingRidge
 
 
 @fill_doc
-class ReceptiveField(BaseEstimator):
+class ReceptiveField(MetaEstimatorMixin, BaseEstimator):
     """Fit a receptive field model.
 
     This allows you to fit an encoding model (stimulus to brain) or a decoding

mne/decoding/search_light.py

@@ -5,7 +5,7 @@
 import logging
 
 import numpy as np
-from sklearn.base import BaseEstimator, TransformerMixin, clone
+from sklearn.base import BaseEstimator, MetaEstimatorMixin, TransformerMixin, clone
 from sklearn.metrics import check_scoring
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import check_array
@@ -16,7 +16,7 @@
 
 
 @fill_doc
-class SlidingEstimator(BaseEstimator, TransformerMixin):
+class SlidingEstimator(MetaEstimatorMixin, TransformerMixin, BaseEstimator):
     """Search Light.
 
     Fit, predict and score a series of models to each subset of the dataset
@@ -61,6 +61,20 @@ def __init__(
     def _estimator_type(self):
         return getattr(self.base_estimator, "_estimator_type", None)
 
+    def __sklearn_tags__(self):
+        """Get sklearn tags."""
+        from sklearn.utils import get_tags
+
+        tags = super().__sklearn_tags__()
+        sub_tags = get_tags(self.base_estimator)
+        tags.estimator_type = sub_tags.estimator_type
+        for kind in ("classifier", "regressor", "transformer"):
+            if tags.estimator_type == kind:
+                attr = f"{kind}_tags"
+                setattr(tags, attr, getattr(sub_tags, attr))
+                break
+        return tags
+
     def __repr__(self):  # noqa: D105
         repr_str = "<" + super().__repr__()
         if hasattr(self, "estimators_"):

mne/decoding/ssd.py

@@ -23,7 +23,7 @@
 
 
 @fill_doc
-class SSD(BaseEstimator, TransformerMixin):
+class SSD(TransformerMixin, BaseEstimator):
     """
     Signal decomposition using the Spatio-Spectral Decomposition (SSD).
 

mne/decoding/tests/test_base.py

@@ -94,10 +94,18 @@ def _make_data(n_samples=1000, n_features=5, n_targets=3):
 def test_get_coef():
     """Test getting linear coefficients (filters/patterns) from estimators."""
     lm_classification = LinearModel()
+    assert hasattr(lm_classification, "__sklearn_tags__")
+    print(lm_classification.__sklearn_tags__)
+    assert is_classifier(lm_classification.model)
     assert is_classifier(lm_classification)
+    assert not is_regressor(lm_classification.model)
+    assert not is_regressor(lm_classification)
 
     lm_regression = LinearModel(Ridge())
+    assert is_regressor(lm_regression.model)
     assert is_regressor(lm_regression)
+    assert not is_classifier(lm_regression.model)
+    assert not is_classifier(lm_regression)
 
     parameters = {"kernel": ["linear"], "C": [1, 10]}
     lm_gs_classification = LinearModel(
@@ -433,7 +441,8 @@ def test_cross_val_multiscore():
     # raise an error if scoring is defined at cross-val-score level and
     # search light, because search light does not return a 1-dimensional
     # prediction.
-    pytest.raises(ValueError, cross_val_multiscore, clf, X, y, cv=cv, scoring="roc_auc")
+    with pytest.raises(ValueError, match="multi_class must be"):
+        cross_val_multiscore(clf, X, y, cv=cv, scoring="roc_auc", n_jobs=1)
     clf = SlidingEstimator(logreg, scoring="roc_auc")
     scores_auc = cross_val_multiscore(clf, X, y, cv=cv, n_jobs=None)
     scores_auc_manual = list()

mne/decoding/tests/test_receptive_field.py

@@ -174,6 +174,7 @@ def test_time_delay():
 
 @pytest.mark.slowtest  # slow on Azure
 @pytest.mark.parametrize("n_jobs", n_jobs_test)
+@pytest.mark.filterwarnings("ignore:Estimator .* has no __sklearn_tags__.*")
 def test_receptive_field_basic(n_jobs):
     """Test model prep and fitting."""
     # Make sure estimator pulling works

mne/decoding/tests/test_search_light.py

@@ -56,6 +56,7 @@ def test_search_light():
     sl = SlidingEstimator(Ridge())
     assert not is_classifier(sl)
     sl = SlidingEstimator(LogisticRegression(solver="liblinear"))
+    assert is_classifier(sl.base_estimator)
     assert is_classifier(sl)
     # fit
     assert_equal(sl.__repr__()[:18], "<SlidingEstimator(")

mne/decoding/time_delaying_ridge.py

@@ -8,12 +8,12 @@
 from scipy import linalg
 from scipy.signal import fftconvolve
 from scipy.sparse.csgraph import laplacian
+from sklearn.base import BaseEstimator, RegressorMixin
 
 from ..cuda import _setup_cuda_fft_multiply_repeated
 from ..filter import next_fast_len
 from ..fixes import jit
 from ..utils import ProgressBar, _check_option, _validate_type, logger, warn
-from .base import BaseEstimator
 
 
 def _compute_corrs(
@@ -226,7 +226,7 @@ def _fit_corrs(x_xt, x_y, n_ch_x, reg_type, alpha, n_ch_in):
     return w
 
 
-class TimeDelayingRidge(BaseEstimator):
+class TimeDelayingRidge(RegressorMixin, BaseEstimator):
     """Ridge regression of data with time delays.
 
     Parameters

mne/fixes.py

@@ -175,6 +175,18 @@ def empirical_covariance(X, assume_centered=False):
 
 
 class _EstimatorMixin:
+    def __sklearn_tags__(self):
+        # If we get here, we should have sklearn installed
+        from sklearn.utils import Tags, TargetTags
+
+        return Tags(
+            estimator_type=None,
+            target_tags=TargetTags(required=False),
+            transformer_tags=None,
+            regressor_tags=None,
+            classifier_tags=None,
+        )
+
     def _param_names(self):
         return inspect.getfullargspec(self.__init__).args[1:]
 

mne/preprocessing/xdawn.py

@@ -79,7 +79,7 @@ def _least_square_evoked(epochs_data, events, tmin, sfreq):
         sel = events[:, 2] == this_class
 
         # build toeplitz matrix
-        trig = np.zeros((n_samples, 1))
+        trig = np.zeros((n_samples,))
         ix_trig = (events[sel, 0]) + n_min
         trig[ix_trig] = 1
         toeplitz.append(linalg.toeplitz(trig[0:window], trig))

tools/install_pre_requirements.sh

@@ -44,8 +44,11 @@ python -m pip install $STD_ARGS git+https://github.com/nilearn/nilearn
 
 echo "VTK"
 # No pre until PyVista fixes a bug
-# python -m pip install $STD_ARGS --only-binary ":all:" --extra-index-url "https://wheels.vtk.org" vtk
-python -m pip install $STD_ARGS vtk
+if [[ "${PLATFORM}" == "Windows" ]]; then
+	python -m pip install $STD_ARGS "vtk<9.4"  # 9.4 requires GLSL 1.5 and Azure win only has 1.3
+else
+	python -m pip install $STD_ARGS --only-binary ":all:" --extra-index-url "https://wheels.vtk.org" vtk
+fi
 python -c "import vtk"
 
 echo "PyVista"