Add autosave of training_data and labels, and add overwrite

examples/tutorials/curation/plot_2_train_a_model.py

@@ -77,6 +77,7 @@
     imputation_strategies = ["median"], # Defaults to all
     scaling_techniques = ["standard_scaler"], # Defaults to all
     classifiers = None, # Default to Random Forest only. Other classifiers you can try [ "AdaBoostClassifier","GradientBoostingClassifier","LogisticRegression","MLPClassifier"]
+    overwrite = True, # Whether or not to overwrite `output_folder` if it already exists. Default is False.
 )
 
 best_model = trainer.best_pipeline

examples/tutorials/curation/plot_3_upload_a_model.py

@@ -22,60 +22,16 @@
 #         metadata.json
 #
 # SpikeInterface doesn't require you to keep this folder structure, we just advise it as
-# best practice. So: let's make these files!
+# best practice.
 #
 # If you've used SpikeInterface to train your model, you have already created such a folder,
-# containing ``my_model_name.skops`` and ``model_info.json``.
-#
-# We'll now export the training data. If we trained the model using the function...
-#
-# .. code-block::
-#
-#     my_model = train_model(
-#         labels=list_of_labels,
-#         analyzers=[sorting_analyzer_1, sorting_analyzer_2],
-#         output_folder = "my_model_folder"
-#     )
-#
-# ...then the training data is contained in your metric extensions. If you've calculated both
-# ``quality_metrics`` and ``template_metrics`` for two `sorting_analyzer`s, you can extract
-# and export the training data as follows
-#
-# .. code-block::
-#
-#     import pandas as pd
-#
-#     training_data_1 = pd.concat([
-#         sorting_analyzer_1.get_extension("quality_metrics").get_data(),
-#         sorting_analyzer_1.get_extension("template_metrics").get_data()
-#     ],axis=1)
-#
-#     training_data_2 = pd.concat([
-#         sorting_analyzer_2.get_extension("quality_metrics").get_data(),
-#         sorting_analyzer_2.get_extension("template_metrics").get_data()
-#     ],axis=1)
-#
-#     training_data = pd.concat([
-#         training_data_1,
-#         training_data_2
-#     ])
-#
-#     training_data.to_csv("my_model_folder/training_data.csv")
-#
-# If you have used different metrics, or use a `.csv` files you will need to modify this code.
-#
-# Similarly, we can save the curated labels we used:
-#
-# .. code-block::
-#
-#     list_of_labels.to_csv("my_model_folder/labels.csv")
-#
-# Finally, we suggest adding any information which shows when a model is applicable
-# (and when it is *not*). Taking a model trained on mouse data and applying it to a primate is
-# likely a bad idea. And a model trained in tetrode data will have limited application on a silcone
-# high density probe. Hence we suggest the following dictionary as a minimal amount of information
-# needed. Note that we format the metadata so that the information in common with the NWB data
-# format is consistent with it,
+# containing everything except the ``metadata.json`` file. In this file, we suggest saving
+# any information which shows when a model is applicable (and when it is *not*). Taking
+# a model trained on mouse data and applying it to a primate is likely a bad idea (or a
+# great research paper!). And a model trained in tetrode data will have limited application
+# on a silconehigh density probe. Hence we suggest the following dictionary as a minimal
+# amount of information needed. Note that we format the metadata so that the information
+# in common with the NWB data format is consistent with it,
 #
 # .. code-block::
 #

src/spikeinterface/curation/tests/test_train_manual_curation.py

@@ -114,5 +114,6 @@ def test_train_model():
         imputation_strategies=["median"],
         scaling_techniques=["standard_scaler"],
         classifiers=["LogisticRegression"],
+        overwrite=True,
     )
     assert isinstance(trainer, CurationModelTrainer)

src/spikeinterface/curation/train_manual_curation.py

@@ -480,6 +480,11 @@ def _evaluate(self, imputation_strategies, scaling_techniques, classifiers, X_tr
     def _save(self):
         from skops.io import dump
         import sklearn
+        import pandas as pd
+
+        # export training data and labels
+        pd.DataFrame(self.X).to_csv(os.path.join(self.output_folder, f"training_data.csv"), index_label="unit_id")
+        pd.DataFrame(self.y).to_csv(os.path.join(self.output_folder, f"labels.csv"), index_label="unit_index")
 
         self.requirements["scikit-learn"] = sklearn.__version__
 
@@ -550,6 +555,7 @@ def train_model(
     imputation_strategies=None,
     scaling_techniques=None,
     classifiers=None,
+    overwrite=False,
     seed=None,
     **job_kwargs,
 ):
@@ -580,6 +586,8 @@ def train_model(
         A list of scaling techniques to apply. If None, default techniques will be used.
     classifiers : list of str | dict | None, default: None
         A list of classifiers to evaluate. Optionally, a dictionary of classifiers and their hyperparameter search spaces can be provided. If None, default classifiers will be used. Check the `get_default_classifier_search_spaces` method for the default search spaces & format for custom spaces.
+    overwrite : bool, default: False
+        Overwrites the `output_folder` if it already exists
     seed : int | None, default: None
         Random seed for reproducibility. If None, a random seed will be generated.
 
@@ -594,6 +602,11 @@ def train_model(
     and evaluating the models. The evaluation results are saved to the specified output folder.
     """
 
+    if overwrite is False:
+        assert not Path(
+            output_folder
+        ).exists(), f"folder {output_folder} already exists, choose another name or use overwrite=True"
+
     if labels is None:
         raise Exception("You must supply a list of curated labels using `labels = ...`")