Add goodness of fit metric

cebra/integrations/sklearn/metrics.py

@@ -108,6 +108,68 @@ def infonce_loss(
     return avg_loss
 
 
+def goodness_of_fit(model: cebra_sklearn_cebra.CEBRA) -> List[float]:
+    """Evaluate the goodness of fit (bits) for a given model.
+
+    This function calculates the goodness of fit for the provided model 
+    using the specified batch size. The goodness of fit is computed offline 
+    it is a way to normalize wrt batch size to compare models with 
+    different batch sizes or different implementations.
+
+    Args: 
+        model: The model to evaluate. This can be an instance of either 
+            `cebra_sklearn_cebra.CEBRA` or `cebra_solver.Solver`.
+        batch_size: Batch size used to train the model.
+
+    Returns: 
+        A list of float values representing the goodness of fit for the model.
+    """
+
+    if isinstance(model, cebra_sklearn_cebra.CEBRA):
+        if model.batch_size is None:
+            raise NotImplementedError(
+                "Batch size is None, please provide a model with a batch size to compute the goodness of fit."
+            )
+        if model.solver_name_ == 'single-session':
+            gof = _goodness_of_fit(loss=model.state_dict_["loss"],
+                                   batch_size=model.batch_size)
+        elif model.solver_name_ == 'multi-session':
+            # For the multisession implementation, the batch size is multiplied by the
+            # number of datasets to get the correct comparison.
+            gof = _goodness_of_fit(loss=model.state_dict_["loss"],
+                                   batch_size=model.batch_size *
+                                   model.num_sessions_)
+        else:
+            raise NotImplementedError(f"Invalid solver: {model.solver_name_}.")
+    elif isinstance(model, list):
+        raise ValueError(
+            f"Model should correspond to a single CEBRA model,"
+            f"got {type(model)}, containing {len(model)} elements.")
+    else:
+        raise ValueError(f"Provide CEBRA model, got {type(model)}.")
+    return gof
+
+
+def _goodness_of_fit(loss: List[float], batch_size: int) -> List[float]:
+    """
+    Compute offline the goodness of fit (bits) from a provided loss.
+    
+    This is a way to normalize wrt batch size to compare models with 
+    different batch sizes or different implementations.
+            
+    Args: 
+        loss: A list of size `max_iteration`, corresponding to the loss across training.
+        batch_size: Batch size used to train the model. For multisession implementation, 
+            you need to multiply the batch size by the number of datasets to get the correct 
+            comparison.
+    
+    Returns:
+        A list of float corresponding to the goodness of fit for the provided loss and batch size.
+    """
+    log_batch_size = np.log(batch_size)
+    return [(1 / np.log(2)) * (log_batch_size - lb) for lb in loss]
+
+
 def _consistency_scores(
     embeddings: List[Union[npt.NDArray, torch.Tensor]],
     datasets: List[Union[int, str]],

tests/test_sklearn_metrics.py

@@ -223,6 +223,57 @@ def test_sklearn_infonce_loss():
         )
 
 
+def test_sklearn_goodness_of_fit():
+    max_loss_iterations = 2
+    cebra_model = cebra_sklearn_cebra.CEBRA(
+        model_architecture="offset10-model",
+        max_iterations=5,
+        batch_size=128,
+    )
+
+    # Example data
+    X = torch.tensor(np.random.uniform(0, 1, (1000, 50)))
+    y_c1 = torch.tensor(np.random.uniform(0, 1, (1000, 5)))
+
+    X2 = torch.tensor(np.random.uniform(0, 1, (500, 20)))
+    y2_c1 = torch.tensor(np.random.uniform(0, 1, (500, 5)))
+
+    # Single session
+    cebra_model.fit(X, y_c1)
+
+    gof = cebra.sklearn.metrics.goodness_of_fit(cebra_model)
+    assert isinstance(gof, list)
+    _gof = cebra.sklearn.metrics._goodness_of_fit(
+        cebra_model.state_dict_["loss"], batch_size=128)
+    assert isinstance(_gof, list)
+    assert gof == _gof
+
+    # Multisession
+    cebra_model.fit([X, X2], [y_c1, y2_c1])
+
+    gof = cebra.sklearn.metrics.goodness_of_fit(cebra_model)
+    assert isinstance(gof, list)
+    _gof = cebra.sklearn.metrics._goodness_of_fit(
+        cebra_model.state_dict_["loss"], batch_size=128 * 2)
+    assert isinstance(_gof, list)
+    assert gof == _gof
+
+    # Multiple models passed
+    with pytest.raises(ValueError, match="single.*model"):
+        _ = cebra.sklearn.metrics.goodness_of_fit([cebra_model, cebra_model])
+
+    # No batch size
+    cebra_model_no_bs = cebra_sklearn_cebra.CEBRA(
+        model_architecture="offset10-model",
+        max_iterations=max_loss_iterations,
+        batch_size=None,
+    )
+
+    cebra_model_no_bs.fit(X)
+    with pytest.raises(NotImplementedError, match="Batch.*size"):
+        gof = cebra.sklearn.metrics.goodness_of_fit(cebra_model_no_bs)
+
+
 def test_sklearn_datasets_consistency():
     # Example data
     np.random.seed(42)