add test documentation

src/spikeinterface/curation/tests/test_train_manual_curation.py

@@ -173,19 +173,17 @@ def test_saved_files(trainer):
     assert set(model_info.keys()) == set(["metric_params", "requirements", "label_conversion"])
 
 
-def test_train_model_using_two_csvs():
-
-    metrics_path_1 = make_temp_training_csv()
-    metrics_path_2 = make_temp_training_csv()
+def test_train_model():
+    """A simple function test to check that `train_model` doesn't fail with one csv inputs"""
 
+    metrics_path = make_temp_training_csv()
     folder = tempfile.mkdtemp()
     metric_names = ["metric1", "metric2", "metric3"]
-
     trainer = train_model(
         mode="csv",
-        metrics_paths=[metrics_path_1, metrics_path_2],
+        metrics_paths=[metrics_path],
         folder=folder,
-        labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]],
+        labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1]],
         metric_names=metric_names,
         imputation_strategies=["median"],
         scaling_techniques=["standard_scaler"],
@@ -195,16 +193,21 @@ def test_train_model_using_two_csvs():
     assert isinstance(trainer, CurationModelTrainer)
 
 
-def test_train_model():
+def test_train_model_using_two_csvs():
+    """Models can be trained using more than one set of training data. This test checks
+    that `train_model` works with two inputs, from csv files."""
+
+    metrics_path_1 = make_temp_training_csv()
+    metrics_path_2 = make_temp_training_csv()
 
-    metrics_path = make_temp_training_csv()
     folder = tempfile.mkdtemp()
     metric_names = ["metric1", "metric2", "metric3"]
+
     trainer = train_model(
         mode="csv",
-        metrics_paths=[metrics_path],
+        metrics_paths=[metrics_path_1, metrics_path_2],
         folder=folder,
-        labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1]],
+        labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [0, 1, 0, 1, 0, 1, 0, 1, 0, 1]],
         metric_names=metric_names,
         imputation_strategies=["median"],
         scaling_techniques=["standard_scaler"],
@@ -215,6 +218,9 @@ def test_train_model():
 
 
 def test_train_using_two_sorting_analyzers():
+    """Models can be trained using more than one set of training data. This test checks
+    that `train_model` works with two inputs, from sorting analzyers. It also checks that
+    an error is raised if the sorting_analyzers have different sets of metrics computed."""
 
     sorting_analyzer_1 = make_sorting_analyzer()
     sorting_analyzer_1.compute({"quality_metrics": {"metric_names": ["num_spikes", "snr"]}})
@@ -238,8 +244,7 @@ def test_train_using_two_sorting_analyzers():
 
     assert isinstance(trainer, CurationModelTrainer)
 
-    # Xheck that there is an error raised if the metric names are different
-
+    # Check that there is an error raised if the metric names are different
     sorting_analyzer_2 = make_sorting_analyzer()
     sorting_analyzer_2.compute({"quality_metrics": {"metric_names": ["num_spikes"], "delete_existing_metrics": True}})