review suggestions

Co-authored-by: Eric Larson <larson.eric.d@gmail.com>

Author: Genuster

mne/decoding/_covs_ged.py

@@ -30,9 +30,8 @@ def _concat_cov(x_class, *, cov_kind, log_rank, reg, cov_method_params, rank, in
         log_rank=log_rank,
         log_ch_type="data",
     )
-    weight = x_class.shape[0]
 
-    return cov, weight
+    return cov, n_channels  # the weight here is just the number of channels
 
 
 def _epoch_cov(x_class, *, cov_kind, log_rank, reg, cov_method_params, rank, info):

mne/decoding/base.py

@@ -46,7 +46,7 @@ class _GEDTransformer(MNETransformerMixin, BaseEstimator):
     mod_ged_callable : callable
         Function used to modify (e.g. sort or normalize) generalized
         eigenvalues and eigenvectors.
-    mod_params : dict
+    mod_params : dict | None
         Parameters passed to mod_ged_callable.
     dec_type : "single" | "multi"
         When "single" and cov_callable returns > 2 covariances,
@@ -93,7 +93,8 @@ def __init__(
         cov_callable,
         cov_params,
         mod_ged_callable,
-        mod_params,
+        *,
+        mod_params=None,
         dec_type="single",
         restr_type=None,
         R_func=None,
@@ -120,6 +121,7 @@ def fit(self, X, y=None):
         covs = np.stack(covs)
         self._validate_covariances(covs)
         self._validate_covariances([C_ref])
+        mod_params = self.mod_params if self.mod_params is not None else dict()
         if self.dec_type == "single":
             if len(covs) > 2:
                 sample_weights = kwargs["sample_weights"]
@@ -133,7 +135,7 @@ def fit(self, X, y=None):
                 evals, evecs = _smart_ged(S, R, restr_mat, R_func=self.R_func)
 
             evals, evecs = self.mod_ged_callable(
-                evals, evecs, covs, **self.mod_params, **kwargs
+                evals, evecs, covs, **mod_params, **kwargs
             )
             self.evals_ = evals
             self.filters_ = evecs.T
@@ -153,7 +155,7 @@ def fit(self, X, y=None):
                 evals, evecs = _smart_ged(S, R, restr_mat, R_func=self.R_func)
 
                 evals, evecs = self.mod_ged_callable(
-                    evals, evecs, covs, **self.mod_params, **kwargs
+                    evals, evecs, covs, **mod_params, **kwargs
                 )
                 all_evals.append(evals)
                 all_evecs.append(evecs.T)

mne/decoding/csp.py

@@ -134,13 +134,12 @@ def __init__(
             norm_trace=norm_trace,
         )
 
-        mod_params = dict(evecs_order=component_order)
         super().__init__(
             n_components=n_components,
             cov_callable=_csp_estimate,
             cov_params=cov_params,
             mod_ged_callable=_csp_mod,
-            mod_params=mod_params,
+            mod_params=dict(evecs_order=component_order),
             dec_type="single",
             restr_type="restricting",
             R_func=sum,
@@ -906,13 +905,11 @@ def __init__(
             rank=rank,
         )
 
-        mod_params = dict()
         super(CSP, self).__init__(
             n_components,
             _spoc_estimate,
             cov_params,
             _spoc_mod,
-            mod_params,
             dec_type="single",
             restr_type=None,
             R_func=None,

mne/decoding/tests/test_ged.py

@@ -187,7 +187,6 @@ def test_ged_binary_cov():
         cov_callable=_mock_cov_callable,
         cov_params=dict(),
         mod_ged_callable=_mock_mod_ged_callable,
-        mod_params=dict(),
         dec_type="single",
         restr_type="restricting",
         R_func=None,
@@ -215,7 +214,6 @@ def test_ged_binary_cov():
         cov_callable=_mock_cov_callable,
         cov_params=dict(),
         mod_ged_callable=_mock_mod_ged_callable,
-        mod_params=dict(),
         dec_type="multi",
         restr_type="restricting",
         R_func=None,
@@ -245,7 +243,6 @@ def test_ged_multicov():
         cov_callable=_mock_cov_callable,
         cov_params=dict(),
         mod_ged_callable=_mock_mod_ged_callable,
-        mod_params=dict(),
         dec_type="single",
         restr_type="restricting",
         R_func=None,
@@ -272,7 +269,6 @@ def test_ged_multicov():
         cov_callable=_mock_cov_callable,
         cov_params=dict(),
         mod_ged_callable=_mock_mod_ged_callable,
-        mod_params=dict(),
         dec_type="multi",
         restr_type="restricting",
         R_func=None,
@@ -299,7 +295,6 @@ def test_ged_multicov():
         cov_callable=_mock_cov_callable,
         cov_params=dict(cov_method_params=dict(reg="oas"), compute_C_ref=False),
         mod_ged_callable=_mock_mod_ged_callable,
-        mod_params=dict(),
         dec_type="single",
         restr_type="restricting",
         R_func=None,
@@ -317,7 +312,6 @@ def test_ged_invalid_cov():
         cov_callable=_mock_cov_callable,
         cov_params=dict(),
         mod_ged_callable=_mock_mod_ged_callable,
-        mod_params=dict(),
         dec_type="single",
         restr_type=None,
         R_func=None,

mne/preprocessing/xdawn.py

@@ -265,13 +265,11 @@ def __init__(self, n_components=2, reg=None, signal_cov=None, method_params=None
 
         cov_params = dict(reg=reg, cov_method_params=method_params, R=signal_cov)
 
-        mod_params = dict()
         super().__init__(
             n_components,
             _xdawn_estimate,
             cov_params,
             _xdawn_mod,
-            mod_params,
             dec_type="multi",
             restr_type=None,
             R_func=None,