Fix failing tests due to scikit-learn versions

src/spikeinterface/curation/tests/common.py

@@ -4,6 +4,8 @@
 
 from spikeinterface.core import generate_ground_truth_recording, create_sorting_analyzer
 from spikeinterface.core.generate import inject_some_split_units
+from spikeinterface.curation import train_model
+from pathlib import Path
 
 job_kwargs = dict(n_jobs=-1)
 
@@ -82,6 +84,31 @@ def sorting_analyzer_with_splits():
     return make_sorting_analyzer_with_splits(sorting_analyzer)
 
 
+def make_trained_pipeline():
+    """
+    Makes a model saved at "./trained_pipeline" which will be used by other tests in the module.
+    If the model already exists, this function does nothing.
+    """
+    trained_model_folder = Path(__file__).parent / Path("trained_pipeline")
+    if not trained_model_folder.is_dir():
+        analyzer = make_sorting_analyzer(sparse=True)
+        analyzer.compute(
+            {
+                "quality_metrics": {"metric_names": ["snr", "num_spikes"]},
+                "template_metrics": {"metric_names": ["half_width"]},
+            }
+        )
+        train_model(
+            analyzers=[analyzer] * 5,
+            labels=[[1, 0, 1, 0, 1]] * 5,
+            folder=trained_model_folder,
+            classifiers=["RandomForestClassifier"],
+            imputation_strategies=["median"],
+            scaling_techniques=["standard_scaler"],
+        )
+    return
+
+
 if __name__ == "__main__":
     sorting_analyzer = make_sorting_analyzer(sparse=False)
     print(sorting_analyzer)

src/spikeinterface/curation/tests/test_model_based_curation.py

@@ -1,6 +1,6 @@
 import pytest
 from pathlib import Path
-from spikeinterface.curation.tests.common import make_sorting_analyzer, sorting_analyzer_for_curation
+from spikeinterface.curation.tests.common import sorting_analyzer_for_curation, make_trained_pipeline
 from spikeinterface.curation.model_based_curation import ModelBasedClassification
 from spikeinterface.curation import auto_label_units, load_model
 from spikeinterface.curation.train_manual_curation import _get_computed_metrics
@@ -19,6 +19,7 @@ def model():
     It has been trained locally and, when applied to `sorting_analyzer_for_curation` will label its 5 units with
     the following labels: [1,0,1,0,1]."""
 
+    make_trained_pipeline()
     model = load_model(Path(__file__).parent / "trained_pipeline/", trusted=["numpy.dtype"])
     return model
 
@@ -45,6 +46,7 @@ def test_metric_ordering_independence(sorting_analyzer_for_curation, model):
     sorting_analyzer_for_curation.compute("template_metrics", metric_names=["half_width"])
     sorting_analyzer_for_curation.compute("quality_metrics", metric_names=["num_spikes", "snr"])
 
+    make_trained_pipeline()
     model_folder = Path(__file__).parent / Path("trained_pipeline")
 
     prediction_prob_dataframe_1 = auto_label_units(
@@ -148,6 +150,7 @@ def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_cura
     )
     sorting_analyzer_for_curation.compute("template_metrics", metric_names=["half_width"])
 
+    make_trained_pipeline()
     model_folder = Path(__file__).parent / Path("trained_pipeline")
 
     model, model_info = load_model(model_folder=model_folder, trusted=["numpy.dtype"])