Change metric_path to metric_pathS

doc/how_to/auto_curation_training.rst

@@ -45,14 +45,14 @@ We can also access the model, which is an sklearn ``Pipeline``, from the trainer
 
 
 The training function can also be run in “csv” mode, if you prefer to
-store metrics in a single .csv file. If the target labels are stored as a column in
+store metrics in as .csv files. If the target labels are stored as a column in
 the file, you can point to these with the ``target_label`` parameter
 
 .. code::
 
     trainer = train_model(
         mode="csv",
-        metrics_path = "/path/to/csv",
+        metrics_paths = ["/path/to/csv_file_1", "/path/to/csv_file_2"],
         target_label = "my_label",
         output_folder=output_folder,
     )

src/spikeinterface/curation/tests/test_train_manual_curation.py

@@ -39,7 +39,7 @@ def test_load_and_preprocess_full(trainer):
     temp_file_path = make_temp_training_csv()
 
     # Load and preprocess the data from the temporary CSV file
-    trainer.load_and_preprocess_csv(temp_file_path)
+    trainer.load_and_preprocess_csv([temp_file_path])
 
     # Assert that the data is loaded and preprocessed correctly
     assert trainer.X is not None
@@ -102,14 +102,36 @@ def test_evaluate_model_config(trainer):
     assert (trainer_folder / "model_info.json").is_file()
 
 
+def test_train_model_using_two_csvs():
+
+    metrics_path_1 = make_temp_training_csv()
+    metrics_path_2 = 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],
+        folder=folder,
+        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"],
+        classifiers=["LogisticRegression"],
+        overwrite=True,
+    )
+    assert isinstance(trainer, CurationModelTrainer)
+
+
 def test_train_model():
 
     metrics_path = make_temp_training_csv()
     folder = tempfile.mkdtemp()
     metric_names = ["metric1", "metric2", "metric3"]
     trainer = train_model(
         mode="csv",
-        metrics_path=metrics_path,
+        metrics_paths=[metrics_path],
         folder=folder,
         labels=[[0, 1, 0, 1, 0, 1, 0, 1, 0, 1]],
         metric_names=metric_names,

src/spikeinterface/curation/train_manual_curation.py

@@ -232,8 +232,8 @@ def _check_metrics_parameters(self):
                     "same parameters for each sorting_analyzer."
                 )
 
-    def load_and_preprocess_csv(self, path):
-        self._load_data_file(path)
+    def load_and_preprocess_csv(self, paths):
+        self._load_data_files(paths)
         self.process_test_data_for_classification()
 
     def process_test_data_for_classification(self):
@@ -443,10 +443,10 @@ def _get_metrics_for_classification(self, analyzer, analyzer_index):
 
         return calculated_metrics
 
-    def _load_data_file(self, path):
+    def _load_data_files(self, paths):
         import pandas as pd
 
-        self.testing_metrics = pd.read_csv(path, index_col=0)
+        self.testing_metrics = pd.concat([pd.read_csv(path, index_col=0) for path in paths], axis=0)
 
     def _evaluate(self, imputation_strategies, scaling_techniques, classifiers, X_train, X_test, y_train, y_test):
         from joblib import Parallel, delayed
@@ -552,7 +552,7 @@ def train_model(
     mode="analyzers",
     labels=None,
     analyzers=None,
-    metrics_path=None,
+    metrics_paths=None,
     folder=None,
     metric_names=None,
     imputation_strategies=None,
@@ -577,8 +577,8 @@ def train_model(
          List of SortingAnalyzer objects containing the quality metrics and labels to use for training, if using 'analyzers' mode.
     labels : list of list | None, default: None
         List of curated labels for each unit; must be in the same order as the metrics data.
-    metrics_path : str or None, default: None
-        The path to the CSV file containing the metrics data if using 'csv' mode.
+    metrics_paths : list of str or None, default: None
+        List of paths to the CSV files containing the metrics data if using 'csv' mode.
     folder : str | None, default: None
         The folder where outputs such as models and evaluation metrics will be saved.
     metric_names : list of str | None, default: None
@@ -633,8 +633,9 @@ def train_model(
         trainer.load_and_preprocess_analyzers(analyzers)
 
     elif mode == "csv":
-        assert Path(metrics_path).is_file(), "Valid metrics path must be provided for mode 'csv'"
-        trainer.load_and_preprocess_csv(metrics_path)
+        for metrics_path in metrics_paths:
+            assert Path(metrics_path).is_file(), f"{metrics_path} is not a file."
+        trainer.load_and_preprocess_csv(metrics_paths)
 
     trainer.evaluate_model_config()
     return trainer