Fix :class:pyriemann_qiskit.utils.filtering.ChannelSelection (#354)

* Fix :class:`pyriemann_qiskit.utils.filtering.ChannelSelection` that caused incorrect channel selection Details: NeuroTechX/moabb#685 * [pre-commit.ci] auto fixes from pre-commit.com hooks * Update filtering.py Remove static method get_indices (not used anymore) --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: gcattan <[email protected]>
pyRiemann · Feb 12, 2025 · a5b7f6d · a5b7f6d
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diff --git a/doc/whatsnew.rst b/doc/whatsnew.rst
@@ -5,6 +5,16 @@
 What's new in the package
 =========================
 
+
+
+Develop branch
+----------------
+
+- Fix :class:`pyriemann_qiskit.utils.filtering.ChannelSelection` that caused incorrect channel selection
+
+
+
+
 v0.4.0
 ------
 

diff --git a/pyriemann_qiskit/utils/filtering.py b/pyriemann_qiskit/utils/filtering.py
@@ -139,13 +139,6 @@ def __init__(self, n_channels, cov_est="lwf"):
         self.n_channels = n_channels
         self.cov_est = cov_est
 
-    @staticmethod
-    def _get_indices(maxes, mean_cov):
-        indices = []
-        for v in maxes:
-            indices.extend(np.argwhere(mean_cov == v).flatten())
-        return np.unique(indices)
-
     def fit(self, X, y=None, **kwargs):
         """Select channel based on covariances
 
@@ -166,19 +159,10 @@ def fit(self, X, y=None, **kwargs):
         covs = Covariances(estimator=self.cov_est).fit_transform(X)
         # Get the average covariance between the channels.
         mean_cov = np.mean(covs, axis=0)
-        n_feats, _ = mean_cov.shape
         # Select the `n_channels` channels having the maximum covariances.
-        all_max = []
-        for i in range(n_feats):
-            for j in range(i, n_feats):
-                self._chs_idx = ChannelSelection._get_indices(all_max, mean_cov)
-
-                if len(self._chs_idx) < self.n_channels:
-                    all_max.append(mean_cov[i, j])
-                else:
-                    if mean_cov[i, j] > max(all_max):
-                        all_max[np.argmin(all_max)] = mean_cov[i, j]
-
+        self._chs_idx = np.argpartition(
+            np.max(mean_cov, axis=0), -self.n_channels, axis=None
+        )[-self.n_channels :]
         return self
 
     def transform(self, X, **kwargs):