Some love to model comparison

README.md

@@ -99,7 +99,8 @@ Check out some of our walk-through notebooks below. We are actively working on p
 4. [SBML model using an external simulator](examples/From_ABC_to_BayesFlow.ipynb)
 5. [Hyperparameter optimization](examples/Hyperparameter_Optimization.ipynb)
 6. [Bayesian experimental design](examples/Bayesian_Experimental_Design.ipynb)
-7. More coming soon...
+7. [Simple model comparison example (One-Sample T-Test)](examples/One_Sample_TTest.ipynb)
+8. More coming soon...
 
 ## Documentation \& Help
 

bayesflow/approximators/model_comparison_approximator.py

@@ -1,6 +1,7 @@
 from collections.abc import Mapping, Sequence
 
 import keras
+import numpy as np
 from keras.saving import (
     deserialize_keras_object as deserialize,
     register_keras_serializable as serializable,
@@ -198,3 +199,44 @@ def get_config(self):
         }
 
         return base_config | config
+
+    def predict(
+        self,
+        *,
+        conditions: dict[str, np.ndarray],
+        logits: bool = False,
+        **kwargs,
+    ) -> np.ndarray:
+        conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
+        conditions = keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
+
+        output = self._predict(**conditions, **kwargs)
+
+        if not logits:
+            output = keras.ops.softmax(output)
+
+        output = keras.ops.convert_to_numpy(output)
+
+        return output
+
+    def _predict(self, classifier_conditions: Tensor = None, summary_variables: Tensor = None, **kwargs) -> Tensor:
+        if self.summary_network is None:
+            if summary_variables is not None:
+                raise ValueError("Cannot use summary variables without a summary network.")
+        else:
+            if summary_variables is None:
+                raise ValueError("Summary variables are required when a summary network is present")
+
+            summary_outputs = self.summary_network(
+                summary_variables, **filter_kwargs(kwargs, self.summary_network.call)
+            )
+
+            if classifier_conditions is None:
+                classifier_conditions = summary_outputs
+            else:
+                classifier_conditions = keras.ops.concatenate([classifier_conditions, summary_outputs], axis=1)
+
+        output = self.classifier_network(classifier_conditions)
+        output = self.logits_projector(output)
+
+        return output

bayesflow/diagnostics/plots/mc_calibration.py

@@ -34,10 +34,10 @@ def mc_calibration(
 
     Parameters
     ----------
-    true_models       : np.ndarray of shape (num_data_sets, num_models)
-        The one-hot-encoded true model indices per data set.
     pred_models       : np.ndarray of shape (num_data_sets, num_models)
         The predicted posterior model probabilities (PMPs) per data set.
+    true_models       : np.ndarray of shape (num_data_sets, num_models)
+        The one-hot-encoded true model indices per data set.
     model_names       : list or None, optional, default: None
         The model names for nice plot titles. Inferred if None.
     num_bins          : int, optional, default: 10

bayesflow/diagnostics/plots/mc_confusion_matrix.py

@@ -13,8 +13,8 @@
 
 
 def mc_confusion_matrix(
-    true_models: dict[str, np.ndarray] | np.ndarray,
     pred_models: dict[str, np.ndarray] | np.ndarray,
+    true_models: dict[str, np.ndarray] | np.ndarray,
     model_names: Sequence[str] = None,
     fig_size: tuple = (5, 5),
     label_fontsize: int = 16,
@@ -23,18 +23,18 @@ def mc_confusion_matrix(
     tick_fontsize: int = 12,
     xtick_rotation: int = None,
     ytick_rotation: int = None,
-    normalize: bool = True,
+    normalize: str = None,
     cmap: matplotlib.colors.Colormap | str = None,
     title: bool = True,
 ) -> plt.Figure:
     """Plots a confusion matrix for validating a neural network trained for Bayesian model comparison.
 
     Parameters
     ----------
-    true_models    : np.ndarray of shape (num_data_sets, num_models)
-        The one-hot-encoded true model indices per data set.
     pred_models    : np.ndarray of shape (num_data_sets, num_models)
         The predicted posterior model probabilities (PMPs) per data set.
+    true_models    : np.ndarray of shape (num_data_sets, num_models)
+        The one-hot-encoded true model indices per data set.
     model_names    : list or None, optional, default: None
         The model names for nice plot titles. Inferred if None.
     fig_size       : tuple or None, optional, default: (5, 5)
@@ -51,9 +51,11 @@ def mc_confusion_matrix(
         Rotation of x-axis tick labels (helps with long model names).
     ytick_rotation: int, optional, default: None
         Rotation of y-axis tick labels (helps with long model names).
-    normalize      : bool, optional, default: True
-        A flag for normalization of the confusion matrix.
-        If True, each row of the confusion matrix is normalized to sum to 1.
+    normalize : {'true', 'pred', 'all'}, default=None
+        Passed to sklearn.metrics.confusion_matrix.
+        Normalizes confusion matrix over the true (rows), predicted (columns)
+        conditions or all the population. If None, confusion matrix will not be
+        normalized.
     cmap           : matplotlib.colors.Colormap or str, optional, default: None
         Colormap to be used for the cells. If a str, it should be the name of a registered colormap,
         e.g., 'viridis'. Default colormap matches the BayesFlow defaults by ranging from white to red.
@@ -77,29 +79,26 @@ def mc_confusion_matrix(
     pred_models = ops.argmax(pred_models, axis=1)
 
     # Compute confusion matrix
-    cm = confusion_matrix(true_models, pred_models)
-
-    # if normalize:
-    #     # Sum along rows and keep dimensions for broadcasting
-    #     cm_sum = ops.sum(cm, axis=1, keepdims=True)
-    #
-    #     # Broadcast division for normalization
-    #     cm_normalized = cm / cm_sum
+    cm = confusion_matrix(true_models, pred_models, normalize=normalize)
 
     # Initialize figure
     fig, ax = make_figure(1, 1, figsize=fig_size)
+    ax = ax[0]
     im = ax.imshow(cm, interpolation="nearest", cmap=cmap)
     cbar = ax.figure.colorbar(im, ax=ax, shrink=0.75)
 
     cbar.ax.tick_params(labelsize=value_fontsize)
 
-    ax.set(xticks=ops.arange(cm.shape[1]), yticks=ops.arange(cm.shape[0]))
+    ax.set_xticks(range(cm.shape[0]))
     ax.set_xticklabels(model_names, fontsize=tick_fontsize)
     if xtick_rotation:
         plt.xticks(rotation=xtick_rotation, ha="right")
+
+    ax.set_yticks(range(cm.shape[1]))
     ax.set_yticklabels(model_names, fontsize=tick_fontsize)
     if ytick_rotation:
         plt.yticks(rotation=ytick_rotation)
+
     ax.set_xlabel("Predicted model", fontsize=label_fontsize)
     ax.set_ylabel("True model", fontsize=label_fontsize)
 

bayesflow/simulators/model_comparison_simulator.py

@@ -2,12 +2,15 @@
 import numpy as np
 
 from bayesflow.types import Shape
-from bayesflow.utils import tree_stack
+from bayesflow.utils import tree_concatenate
 from bayesflow.utils.decorators import allow_batch_size
 
 from bayesflow.utils import numpy_utils as npu
 
+from types import FunctionType
+
 from .simulator import Simulator
+from .lambda_simulator import LambdaSimulator
 
 
 class ModelComparisonSimulator(Simulator):
@@ -18,10 +21,15 @@ def __init__(
         simulators: Sequence[Simulator],
         p: Sequence[float] = None,
         logits: Sequence[float] = None,
-        use_mixed_batches: bool = False,
+        use_mixed_batches: bool = True,
+        shared_simulator: Simulator | FunctionType = None,
     ):
         self.simulators = simulators
 
+        if isinstance(shared_simulator, FunctionType):
+            shared_simulator = LambdaSimulator(shared_simulator, is_batched=True)
+        self.shared_simulator = shared_simulator
+
         match logits, p:
             case (None, None):
                 logits = [0.0] * len(simulators)
@@ -43,30 +51,34 @@ def __init__(
 
     @allow_batch_size
     def sample(self, batch_shape: Shape, **kwargs) -> dict[str, np.ndarray]:
+        data = {}
+        if self.shared_simulator:
+            data |= self.shared_simulator.sample(batch_shape, **kwargs)
+
         if not self.use_mixed_batches:
             # draw one model index for the whole batch (faster)
             model_index = np.random.choice(len(self.simulators), p=npu.softmax(self.logits))
 
             simulator = self.simulators[model_index]
-            data = simulator.sample(batch_shape)
+            data = simulator.sample(batch_shape, **(kwargs | data))
 
             model_indices = np.full(batch_shape, model_index, dtype="int32")
+            model_indices = npu.one_hot(model_indices, len(self.simulators))
         else:
-            # draw a model index for each sample in the batch (slower)
-            model_indices = np.random.choice(len(self.simulators), p=npu.softmax(self.logits), size=batch_shape)
-
-            data = np.empty(batch_shape, dtype="object")
-
-            for index in np.ndindex(batch_shape):
-                simulator = self.simulators[int(model_indices[index])]
-                data[index] = simulator.sample(())
+            # generate data randomly from each model (slower)
+            model_counts = np.random.multinomial(n=batch_shape[0], pvals=npu.softmax(self.logits))
 
-            data = data.flatten().tolist()
-            data = tree_stack(data, axis=0, numpy=True)
+            sims = []
+            for n, simulator in zip(model_counts, self.simulators):
+                if n == 0:
+                    continue
+                sim = simulator.sample(n, **(kwargs | data))
+                sims.append(sim)
 
-            # restore batch shape
-            data = {key: np.reshape(value, batch_shape + np.shape(value)[1:]) for key, value in data.items()}
+            sims = tree_concatenate(sims, numpy=True)
+            data |= sims
 
-        model_indices = npu.one_hot(model_indices, len(self.simulators))
+            model_indices = np.eye(len(self.simulators), dtype="int32")
+            model_indices = np.repeat(model_indices, model_counts, axis=0)
 
         return data | {"model_indices": model_indices}