Handle batch size = None for goodness of fit computation

cebra/integrations/sklearn/metrics.py

@@ -138,7 +138,7 @@ def goodness_of_fit_score(cebra_model: cebra_sklearn_cebra.CEBRA,
         >>> import cebra
         >>> import numpy as np
         >>> neural_data = np.random.uniform(0, 1, (1000, 20))
-        >>> cebra_model = cebra.CEBRA(max_iterations=10)
+        >>> cebra_model = cebra.CEBRA(max_iterations=10, batch_size = 512)
         >>> cebra_model.fit(neural_data)
         CEBRA(max_iterations=10)
         >>> gof = cebra.sklearn.metrics.goodness_of_fit_score(cebra_model, neural_data)
@@ -169,7 +169,7 @@ def goodness_of_fit_history(model):
         >>> import cebra
         >>> import numpy as np
         >>> neural_data = np.random.uniform(0, 1, (1000, 20))
-        >>> cebra_model = cebra.CEBRA(max_iterations=10)
+        >>> cebra_model = cebra.CEBRA(max_iterations=10, batch_size = 512)
         >>> cebra_model.fit(neural_data)
         CEBRA(max_iterations=10)
         >>> gof_history = cebra.sklearn.metrics.goodness_of_fit_history(cebra_model)
@@ -210,6 +210,11 @@ def infonce_to_goodness_of_fit(infonce: Union[float, Iterable[float]],
     """
     if not hasattr(model, "state_dict_"):
         raise RuntimeError("Fit the CEBRA model first.")
+    if model.batch_size is None:
+        raise ValueError(
+            "Computing the goodness of fit is not yet supported for "
+            "models trained on the full dataset (batchsize = None). " 
+        )
 
     nats_to_bits = np.log2(np.e)
     num_sessions = model.num_sessions_