ModelComparisonSimulator#
- class bayesflow.simulators.ModelComparisonSimulator(simulators: Sequence[Simulator], p: Sequence[float] = None, logits: Sequence[float] = None, use_mixed_batches: bool = True, shared_simulator: Simulator | Callable[[Sequence[int]], dict[str, any]] = None)[source]#
Bases:
SimulatorWraps a sequence of simulators for use with a model comparison approximator.
Initialize a multimodel simulator that can generate data for mixture / model comparison problems.
- Parameters:
- simulatorsSequence[Simulator]
A sequence of simulator instances, each representing a different model.
- pSequence[float], optional
A sequence of probabilities associated with each simulator. Must sum to 1. Mutually exclusive with logits.
- logitsSequence[float], optional
A sequence of logits corresponding to model probabilities. Mutually exclusive with p. If neither p nor logits is provided, defaults to uniform logits.
- use_mixed_batchesbool, optional
If True, samples in a batch are drawn from different models. If False, the entire batch is drawn from a single model chosen according to the model probabilities. Default is True.
- shared_simulatorSimulator or Callable, optional
A shared simulator whose outputs are passed to all model simulators. If a function is provided, it is wrapped in a LambdaSimulator with batching enabled.
- sample(batch_shape: tuple[int, ...], **kwargs) dict[str, ndarray][source]#
Sample from the model comparison simulator.
- Parameters:
- batch_shapeShape
The shape of the batch to sample. Typically, a tuple indicating the number of samples, but the user can also supply an int.
- **kwargs
Additional keyword arguments passed to each simulator. These may include outputs from the shared simulator.
- Returns:
- datadict of str to np.ndarray
- A dictionary containing the sampled outputs. Includes:
outputs from the selected simulator(s)
optionally, outputs from the shared simulator
“model_indices”: a one-hot encoded array indicating the model origin of each sample