save patterns and filters - add linear model

SophieHerbst · May 15, 2024 · 8732634 · 8732634
1 parent 4362979
commit 8732634
Showing 1 changed file with 10 additions and 13 deletions.
diff --git a/mne_bids_pipeline/steps/sensor/_05_decoding_csp.py b/mne_bids_pipeline/steps/sensor/_05_decoding_csp.py
@@ -171,11 +171,11 @@ def one_subject_decoding(
     clf = make_pipeline(
         *preproc_steps,
         csp,
-        LogReg(
+        LinearModel(LogReg(
             solver="liblinear",  # much faster than the default
             random_state=cfg.random_state,
             n_jobs=1,
-        ),
+        )),
     )
     cv = StratifiedKFold(
         n_splits=cfg.decoding_n_splits,
@@ -244,9 +244,6 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
         # Get the data for all time points
         X = epochs_filt.get_data()
 
-
-
-
         cv_scores = cross_val_score(
             estimator=clf,
             X=X,
@@ -258,14 +255,14 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
         freq_decoding_table.loc[idx, "mean_crossval_score"] = cv_scores.mean()
         freq_decoding_table.at[idx, "scores"] = cv_scores
 
-        # PATTERNS
-        csp.fit_transform(X, y)
-        sensor_pattern_csp = csp.patterns_
-
         # COEFS
         clf.fit(X, y)
         weights_csp = mne.decoding.get_coef(clf, 'patterns_', inverse_transform=True)
 
+        # PATTERNS
+        csp.fit_transform(X, y)
+        sensor_pattern_csp = csp.patterns_
+
         # save scores
         # XXX right now this saves in working directory
         csp_fname = cond1 + cond2 + str(fmin) + str(fmax)
@@ -345,14 +342,14 @@ def _fmt_contrast(cond1, cond2, fmin, fmax, freq_range_name, tmin=None, tmax=Non
         msg += f": {cfg.decoding_metric}={score:0.3f}"
         logger.info(**gen_log_kwargs(msg))
 
-        # PATTERNS
-        csp.fit_transform(X, y)
-        sensor_pattern_csp = csp.patterns_
-
         # COEFS
         clf.fit(X, y)
         weights_csp = mne.decoding.get_coef(clf, 'patterns_', inverse_transform=True)
 
+        # PATTERNS
+        csp.fit_transform(X, y)
+        sensor_pattern_csp = csp.patterns_
+
         # save scores
         # XXX right now this saves in working directory
         csp_fname = cond1 + cond2 + str(fmin) + str(fmax) + str(tmin) + str(tmax)