add docs to ModelComparisonApproximator

bayesflow/approximators/model_comparison_approximator.py

@@ -22,6 +22,20 @@ class ModelComparisonApproximator(Approximator):
     """
     Defines an approximator for model (simulator) comparison, where the (discrete) posterior model probabilities are
     learned with a classifier.
+
+    Parameters
+    ----------
+    adapter: Adapter
+        Adapter for data processing.
+    num_models: int
+        Number of models (simulators) that the approximator will compare
+    classifier_network: keras.Model
+        The network (e.g, an MLP) that is used for model classification.
+        The input of the classifier network is created by concatenating `classifier_variables`
+        and (optional) output of the summary_network.
+    summary_network: SummaryNetwork, optional
+        The summary network used for data summarisation (default is None).
+        The input of the summary network is `summary_variables`.
     """
 
     def __init__(
@@ -161,6 +175,60 @@ def fit(
         simulators: Sequence[Simulator] = None,
         **kwargs,
     ):
+        """
+        Trains the approximator on the provided dataset or on-demand generated from the given (multi-model) simulator.
+        If `dataset` is not provided, a dataset is built from the `simulator`.
+        If `simulator` is not provided, it will be build from a list of `simulators`.
+        If the model has not been built, it will be built using a batch from the dataset.
+
+        Parameters
+        ----------
+        dataset : keras.utils.PyDataset, optional
+            A dataset containing simulations for training. If provided, `simulator` must be None.
+        simulator : ModelComparisonSimulator, optional
+            A simulator used to generate a dataset. If provided, `dataset` must be None.
+        simulators: Sequence[Simulator], optional
+            A list of simulators (one simulator per model). If provided, `dataset` must be None.
+        **kwargs : dict
+            Additional keyword arguments passed to `keras.Model.fit()`, including (see also `build_dataset`):
+
+            batch_size : int or None, default='auto'
+                Number of samples per gradient update. Do not specify if `dataset` is provided as a
+                `keras.utils.PyDataset`, `tf.data.Dataset`, `torch.utils.data.DataLoader`, or a generator function.
+            epochs : int, default=1
+                Number of epochs to train the model.
+            verbose : {"auto", 0, 1, 2}, default="auto"
+                Verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch.
+            callbacks : list of keras.callbacks.Callback, optional
+                List of callbacks to apply during training.
+            validation_split : float, optional
+                Fraction of training data to use for validation (only supported if `dataset` consists of NumPy arrays
+                or tensors).
+            validation_data : tuple or dataset, optional
+                Data for validation, overriding `validation_split`.
+            shuffle : bool, default=True
+                Whether to shuffle the training data before each epoch (ignored for dataset generators).
+            initial_epoch : int, default=0
+                Epoch at which to start training (useful for resuming training).
+            steps_per_epoch : int or None, optional
+                Number of steps (batches) before declaring an epoch finished.
+            validation_steps : int or None, optional
+                Number of validation steps per validation epoch.
+            validation_batch_size : int or None, optional
+                Number of samples per validation batch (defaults to `batch_size`).
+            validation_freq : int, default=1
+                Specifies how many training epochs to run before performing validation.
+
+        Returns
+        -------
+        keras.callbacks.History
+            A history object containing the training loss and metrics values.
+
+        Raises
+        ------
+        ValueError
+            If both `dataset` and `simulator` or `simulators` are provided or neither is provided.
+        """
         if dataset is not None:
             if simulator is not None or simulators is not None:
                 raise ValueError(
@@ -207,6 +275,25 @@ def predict(
         logits: bool = False,
         **kwargs,
     ) -> np.ndarray:
+        """
+        Predicts posterior model probabilities given input conditions. The `conditions` dictionary is preprocessed
+        using the `adapter`. The output is converted to NumPy array after inference.
+
+        Parameters
+        ----------
+        conditions : dict[str, np.ndarray]
+            Dictionary of conditioning variables as NumPy arrays.
+        logits: bool, default=False
+            Should the posterior model probabilities be on the (unconstrained) logit space?
+            If `False`, the output is a unit simplex instead.
+        **kwargs : dict
+            Additional keyword arguments for the adapter and classification process.
+
+        Returns
+        -------
+        np.ndarray
+            Predicted posterior model probabilities given `conditions`.
+        """
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
         # at inference time, model_indices are predicted by the networks and thus ignored in conditions
         conditions.pop("model_indices", None)