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)
 
 
+@pytest.fixture(scope="module")
+def trained_pipeline_path():
+    """
+    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")
+    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"],
+    )
+    yield trained_model_folder
+
+
 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, trained_pipeline_path
 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
@@ -14,12 +14,12 @@
 
 
 @pytest.fixture
-def model():
+def model(trained_pipeline_path):
     """A toy model, created using the `sorting_analyzer_for_curation` from `spikeinterface.curation.tests.common`.
     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]."""
 
-    model = load_model(Path(__file__).parent / "trained_pipeline/", trusted=["numpy.dtype"])
+    model = load_model(trained_pipeline_path, trusted=["numpy.dtype"])
     return model
 
 
@@ -38,26 +38,24 @@ def test_model_based_classification_init(sorting_analyzer_for_curation, model):
     assert np.all(model_based_classification.required_metrics == model_based_classification.pipeline.feature_names_in_)
 
 
-def test_metric_ordering_independence(sorting_analyzer_for_curation, model):
+def test_metric_ordering_independence(sorting_analyzer_for_curation, trained_pipeline_path):
     """The function `auto_label_units` needs the correct metrics to have been computed. However,
     it should be independent of the order of computation. We test this here."""
 
     sorting_analyzer_for_curation.compute("template_metrics", metric_names=["half_width"])
     sorting_analyzer_for_curation.compute("quality_metrics", metric_names=["num_spikes", "snr"])
 
-    model_folder = Path(__file__).parent / Path("trained_pipeline")
-
     prediction_prob_dataframe_1 = auto_label_units(
         sorting_analyzer=sorting_analyzer_for_curation,
-        model_folder=model_folder,
+        model_folder=trained_pipeline_path,
         trusted=["numpy.dtype"],
     )
 
     sorting_analyzer_for_curation.compute("quality_metrics", metric_names=["snr", "num_spikes"])
 
     prediction_prob_dataframe_2 = auto_label_units(
         sorting_analyzer=sorting_analyzer_for_curation,
-        model_folder=model_folder,
+        model_folder=trained_pipeline_path,
         trusted=["numpy.dtype"],
     )
 
@@ -137,7 +135,7 @@ def test_model_based_classification_predict_labels(sorting_analyzer_for_curation
 
 
 @pytest.mark.skip(reason="We need to retrain the model to reflect any changes in metric computation")
-def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_curation):
+def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_curation, trained_pipeline_path):
     """We track whether the metric parameters used to compute the metrics used to train
     a model are the same as the parameters used to compute the metrics in the sorting
     analyzer which is being curated. If they are different, an error or warning will
@@ -148,9 +146,7 @@ def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_cura
     )
     sorting_analyzer_for_curation.compute("template_metrics", metric_names=["half_width"])
 
-    model_folder = Path(__file__).parent / Path("trained_pipeline")
-
-    model, model_info = load_model(model_folder=model_folder, trusted=["numpy.dtype"])
+    model, model_info = load_model(model_folder=trained_pipeline_path, trusted=["numpy.dtype"])
     model_based_classification = ModelBasedClassification(sorting_analyzer_for_curation, model)
 
     # an error should be raised if `enforce_metric_params` is True
@@ -169,6 +165,6 @@ def test_exception_raised_when_metric_params_not_equal(sorting_analyzer_for_cura
     )
     sorting_analyzer_for_curation.compute("template_metrics", metric_names=["half_width"])
 
-    model, model_info = load_model(model_folder=model_folder, trusted=["numpy.dtype"])
+    model, model_info = load_model(model_folder=trained_pipeline_path, trusted=["numpy.dtype"])
     model_based_classification = ModelBasedClassification(sorting_analyzer_for_curation, model)
     model_based_classification._check_params_for_classification(enforce_metric_params=True, model_info=model_info)