add some minor tests and fixes

Author: Genuster

mne/decoding/base.py

@@ -215,7 +215,7 @@ def get_spatial_filter(self, info):
         check_is_fitted(self, ["filters_", "patterns_", "evals_"])
         sp_filter = SpatialFilter(
             info,
-            evecs=self.filters_,
+            filters=self.filters_,
             evals=self.evals_,
             patterns=self.patterns_,
             patterns_method="pinv",
@@ -463,7 +463,7 @@ def get_spatial_filter(self, info):
         check_is_fitted(self, ["filters_", "patterns_"])
         sp_filter = SpatialFilter(
             info,
-            evecs=self.filters_.T,
+            filters=self.filters_,
             patterns=self.patterns_,
             patterns_method="haufe",
         )

mne/decoding/tests/test_base.py

@@ -418,6 +418,14 @@ def test_linearmodel():
         wrong_y = rng.rand(n, n_features, 99)
         clf.fit(X, wrong_y)
 
+    # check get_spatial_filter
+    info = create_info(n_features, 1000.0, "eeg")
+    sp_filter = clf.get_spatial_filter(info)
+    assert sp_filter.patterns_method == "haufe"
+    np.testing.assert_array_equal(sp_filter.filters, clf.filters_)
+    np.testing.assert_array_equal(sp_filter.patterns, clf.patterns_)
+    assert sp_filter.evals is None
+
 
 def test_cross_val_multiscore():
     """Test cross_val_multiscore for computing scores on decoding over time."""

mne/decoding/tests/test_ged.py

@@ -145,7 +145,7 @@ def test_sklearn_compliance(estimator, check):
     check(estimator)
 
 
-def _get_X_y(event_id):
+def _get_X_y(event_id, return_info=False):
     raw = read_raw(raw_fname, preload=False)
     events = read_events(event_name)
     picks = pick_types(
@@ -166,6 +166,8 @@ def _get_X_y(event_id):
     )
     X = epochs.get_data(copy=False, units=dict(eeg="uV", grad="fT/cm", mag="fT"))
     y = epochs.events[:, -1]
+    if return_info:
+        return X, y, epochs.info
     return X, y
 
 
@@ -386,3 +388,22 @@ def test__no_op_mod():
     assert evals is evals_no_op
     assert evecs is evecs_no_op
     assert sorter_no_op is None
+
+
+def test_get_spatial_filter():
+    """Test instantiation of spatial filter."""
+    event_id = dict(aud_l=1, vis_l=3)
+    X, y, info = _get_X_y(event_id, return_info=True)
+
+    ged = _GEDTransformer(
+        n_components=4,
+        cov_callable=_mock_cov_callable,
+        mod_ged_callable=_mock_mod_ged_callable,
+        restr_type="restricting",
+    )
+    ged.fit(X, y)
+    sp_filter = ged.get_spatial_filter(info)
+    assert sp_filter.patterns_method == "pinv"
+    np.testing.assert_array_equal(sp_filter.filters, ged.filters_)
+    np.testing.assert_array_equal(sp_filter.patterns, ged.patterns_)
+    np.testing.assert_array_equal(sp_filter.evals, ged.evals_)

mne/viz/decoding/ged.py

@@ -294,10 +294,12 @@ def __init__(
             self.patterns = patterns
             self.patterns_method = patterns_method
 
-        if n_comps > n_chs:
+        # In case of multi-target classification in LinearModel
+        # number of targets can be greater than number of channels.
+        if patterns_method != "haufe" and n_comps > n_chs:
             raise ValueError(
                 "Number of components can't be greater "
-                "than number of channels in filters,"
+                "than number of channels in filters, "
                 "perhaps the provided matrix is transposed?"
             )
         if self.filters.shape != self.patterns.shape:

mne/viz/decoding/tests/test_ged.py

@@ -0,0 +1,18 @@
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+import numpy as np
+import pytest
+
+from mne import create_info
+from mne.viz import SpatialFilter
+
+
+def test_plot_scree_raises():
+    """Tests that plot_scree can't plot without evals."""
+    info = create_info(2, 1000.0, "eeg")
+    filters = np.array([[1, 2], [3, 4]])
+    sp_filter = SpatialFilter(info, filters, evals=None)
+    with pytest.raises(AttributeError):
+        sp_filter.plot_scree()