Fix tests

src/spikeinterface/curation/tests/test_train_manual_curation.py

@@ -17,13 +17,15 @@ def trainer():
     scaling_techniques = ["standard_scaler"]
     classifiers = ["LogisticRegression"]
     metric_names = ["metric1", "metric2", "metric3"]
+    search_kwargs = {"cv": 3}
     return CurationModelTrainer(
         labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1]],
         folder=folder,
         metric_names=metric_names,
         imputation_strategies=imputation_strategies,
         scaling_techniques=scaling_techniques,
         classifiers=classifiers,
+        search_kwargs=search_kwargs,
     )
 
 
@@ -187,6 +189,7 @@ def test_train_model():
         scaling_techniques=["standard_scaler"],
         classifiers=["LogisticRegression"],
         overwrite=True,
+        search_kwargs={"cv": 3, "scoring": "balanced_accuracy", "n_iter": 1},
     )
     assert isinstance(trainer, CurationModelTrainer)
 

src/spikeinterface/curation/train_manual_curation.py

@@ -544,7 +544,12 @@ def _evaluate(
         )
 
         test_accuracies, models = zip(*results)
-        scoring_method = self.search_kwargs.get("scoring")
+
+        if self.search_kwargs is None or self.search_kwargs.get("scoring"):
+            scoring_method = "balanced_accuracy"
+        else:
+            scoring_method = self.search_kwargs.get("scoring")
+
         self.test_accuracies_df = pd.DataFrame(test_accuracies).sort_values(scoring_method, ascending=False)
 
         best_model_id = int(self.test_accuracies_df.iloc[0]["model_id"])
@@ -597,7 +602,7 @@ def _train_and_evaluate(
         if self.verbose is True:
             print(f"Running {classifier.__class__.__name__} with imputation {imputation_strategy} and scaling {scaler}")
         model, param_space = self.get_classifier_search_space(classifier.__class__.__name__)
-        print("search kwargs:", search_kwargs, flush=True)
+
         try:
             from skopt import BayesSearchCV
 
@@ -610,7 +615,7 @@ def _train_and_evaluate(
             )
         except:
             if self.verbose is True:
-                print("BayesSearchCV from scikit-optimize not available, using GridSearchCV")
+                print("BayesSearchCV from scikit-optimize not available, using RandomizedSearchCV")
             from sklearn.model_selection import RandomizedSearchCV
 
             model = RandomizedSearchCV(model, param_space, n_jobs=self.n_jobs, **search_kwargs)