MAINT: Improve sklearn compliance

mne/decoding/base.py

@@ -10,7 +10,7 @@
 import datetime as dt
 import numbers
 from ..parallel import parallel_func
-from ..fixes import BaseEstimator, is_classifier, _get_check_scoring
+from ..fixes import BaseEstimator, _get_check_scoring
 from ..utils import warn, verbose
 
 
@@ -224,6 +224,8 @@ def classes_(self):
 def _set_cv(cv, estimator=None, X=None, y=None):
     """Set the default CV depending on whether clf is classifier/regressor."""
     # Detect whether classification or regression
+    from sklearn.base import is_classifier
+
     if estimator in ["classifier", "regressor"]:
         est_is_classifier = estimator == "classifier"
     else:
@@ -440,8 +442,7 @@ def cross_val_multiscore(
         Array of scores of the estimator for each run of the cross validation.
     """
     # This code is copied from sklearn
-
-    from sklearn.base import clone
+    from sklearn.base import clone, is_classifier
     from sklearn.utils import indexable
     from sklearn.model_selection._split import check_cv
 

mne/decoding/receptive_field.py

@@ -9,7 +9,6 @@
 
 from .base import get_coef, BaseEstimator, _check_estimator
 from .time_delaying_ridge import TimeDelayingRidge
-from ..fixes import is_regressor
 from ..utils import _validate_type, verbose, fill_doc
 
 
@@ -183,7 +182,7 @@ def fit(self, X, y):
                 "scoring must be one of %s, got"
                 "%s " % (sorted(_SCORERS.keys()), self.scoring)
             )
-        from sklearn.base import clone
+        from sklearn.base import clone, is_regressor
 
         X, y, _, self._y_dim = self._check_dimensions(X, y)
 

mne/decoding/tests/test_base.py

@@ -14,7 +14,6 @@
 import pytest
 
 from mne import create_info, EpochsArray
-from mne.fixes import is_regressor, is_classifier
 from mne.utils import requires_sklearn
 from mne.decoding.base import (
     _get_inverse_funcs,
@@ -69,7 +68,12 @@ def _make_data(n_samples=1000, n_features=5, n_targets=3):
 @requires_sklearn
 def test_get_coef():
     """Test getting linear coefficients (filters/patterns) from estimators."""
-    from sklearn.base import TransformerMixin, BaseEstimator
+    from sklearn.base import (
+        TransformerMixin,
+        BaseEstimator,
+        is_classifier,
+        is_regressor,
+    )
     from sklearn.pipeline import make_pipeline
     from sklearn.preprocessing import StandardScaler
     from sklearn import svm
@@ -336,6 +340,7 @@ def test_get_coef_multiclass_full(n_classes, n_channels, n_times):
 def test_linearmodel():
     """Test LinearModel class for computing filters and patterns."""
     # check categorical target fit in standard linear model
+    from sklearn.utils.estimator_checks import check_estimator
     from sklearn.linear_model import LinearRegression
 
     rng = np.random.RandomState(0)
@@ -388,6 +393,7 @@ def test_linearmodel():
     with pytest.raises(ValueError):
         wrong_y = rng.rand(n, n_features, 99)
         clf.fit(X, wrong_y)
+    check_estimator(clf)
 
 
 @requires_sklearn

mne/decoding/tests/test_receptive_field.py

@@ -596,3 +596,35 @@ def test_linalg_warning():
             (RuntimeWarning, UserWarning), match="[Singular|scipy.linalg.solve]"
         ):
             rf.fit(y, X)
+
+
+@requires_sklearn
+def test_tdr_sklearn_compliance():
+    """Test sklearn estimator compliance."""
+    from sklearn.utils.estimator_checks import check_estimator
+
+    tdr = TimeDelayingRidge(0, 10, 1.0, 0.1, "laplacian", n_jobs=1)
+    # We don't actually comply with a bunch of the regressor specs :(
+    ignores = (
+        "check_supervised_y_no_nan",
+        "check_regressor",
+        "check_parameters_default_constructible",
+        "check_estimators_unfitted",
+        "_invariance",
+        "check_fit2d_1sample",
+    )
+    for est, check in check_estimator(tdr, generate_only=True):
+        if any(ignore in str(check) for ignore in ignores):
+            continue
+        check(est)
+
+
+@requires_sklearn
+def test_rf_sklearn_compliance():
+    """Test sklearn RF compliance."""
+    from sklearn.linear_model import Ridge
+    from sklearn.utils.estimator_checks import check_estimator
+
+    rf = ReceptiveField(-0.1, 0.2, 1000.0, estimator=Ridge(), patterns=True)
+    for est, check in check_estimator(rf, generate_only=True):
+        check(est)

mne/decoding/time_delaying_ridge.py

@@ -10,7 +10,7 @@
 from ..cuda import _setup_cuda_fft_multiply_repeated
 from ..filter import next_fast_len
 from ..fixes import jit
-from ..utils import warn, ProgressBar, logger
+from ..utils import warn, ProgressBar, logger, _validate_type, _check_option
 
 
 def _compute_corrs(
@@ -301,6 +301,9 @@ def __init__(
         self.edge_correction = edge_correction
         self.n_jobs = n_jobs
 
+    def _more_tags(self):
+        return {"no_validation": True}
+
     @property
     def _smin(self):
         return int(round(self.tmin * self.sfreq))
@@ -324,12 +327,21 @@ def fit(self, X, y):
         self : instance of TimeDelayingRidge
             Returns the modified instance.
         """
+        _validate_type(X, "array-like", "X")
+        _validate_type(y, "array-like", "y")
+        X = np.asarray(X, dtype=float)
+        y = np.asarray(y, dtype=float)
         if X.ndim == 3:
             assert y.ndim == 3
             assert X.shape[:2] == y.shape[:2]
         else:
-            assert X.ndim == 2 and y.ndim == 2
-            assert X.shape[0] == y.shape[0]
+            if X.ndim == 1:
+                X = X[:, np.newaxis]
+            if y.ndim == 1:
+                y = y[:, np.newaxis]
+            assert X.ndim == 2
+            assert y.ndim == 2
+        _check_option("y.shape[0]", y.shape[0], (X.shape[0],))
         # These are split into two functions because it's possible that we
         # might want to allow people to do them separately (e.g., to test
         # different regularization parameters).

mne/fixes.py

@@ -176,38 +176,6 @@ def _read_volume_info(fobj):
 # adapted from scikit-learn
 
 
-def is_classifier(estimator):
-    """Returns True if the given estimator is (probably) a classifier.
-
-    Parameters
-    ----------
-    estimator : object
-        Estimator object to test.
-
-    Returns
-    -------
-    out : bool
-        True if estimator is a classifier and False otherwise.
-    """
-    return getattr(estimator, "_estimator_type", None) == "classifier"
-
-
-def is_regressor(estimator):
-    """Returns True if the given estimator is (probably) a regressor.
-
-    Parameters
-    ----------
-    estimator : object
-        Estimator object to test.
-
-    Returns
-    -------
-    out : bool
-        True if estimator is a regressor and False otherwise.
-    """
-    return getattr(estimator, "_estimator_type", None) == "regressor"
-
-
 _DEFAULT_TAGS = {
     "non_deterministic": False,
     "requires_positive_X": False,