Skip to content

laplace.laplace #

Laplace #

Laplace(model: Module, likelihood: Likelihood | str, subset_of_weights: SubsetOfWeights | str = SubsetOfWeights.LAST_LAYER, hessian_structure: HessianStructure | str = HessianStructure.KRON, *args, **kwargs) -> BaseLaplace

Simplified Laplace access using strings instead of different classes.

Parameters:

  • model (Module) –
  • likelihood (Likelihood or str in {'classification', 'regression'}) –
  • subset_of_weights (SubsetofWeights or {'last_layer', 'subnetwork', 'all'}, default: SubsetOfWeights.LAST_LAYER ) –

    subset of weights to consider for inference

  • hessian_structure (HessianStructure or str in {'diag', 'kron', 'full', 'lowrank', 'gp'}, default: HessianStructure.KRON ) –

    structure of the Hessian approximation (note that in case of 'gp', we are not actually doing any Hessian approximation, the inference is instead done in the functional space)

Returns:

  • laplace ( BaseLaplace ) –

    chosen subclass of BaseLaplace instantiated with additional arguments

Source code in laplace/laplace.py 
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
def Laplace(
    model: torch.nn.Module,
    likelihood: Likelihood | str,
    subset_of_weights: SubsetOfWeights | str = SubsetOfWeights.LAST_LAYER,
    hessian_structure: HessianStructure | str = HessianStructure.KRON,
    *args,
    **kwargs,
) -> BaseLaplace:
    """Simplified Laplace access using strings instead of different classes.

    Parameters
    ----------
    model : torch.nn.Module
    likelihood : Likelihood or str in {'classification', 'regression'}
    subset_of_weights : SubsetofWeights or {'last_layer', 'subnetwork', 'all'}, default=SubsetOfWeights.LAST_LAYER
        subset of weights to consider for inference
    hessian_structure : HessianStructure or str in {'diag', 'kron', 'full', 'lowrank', 'gp'}, default=HessianStructure.KRON
        structure of the Hessian approximation (note that in case of 'gp',
        we are not actually doing any Hessian approximation, the inference is instead done in the functional space)
    Returns
    -------
    laplace : BaseLaplace
        chosen subclass of BaseLaplace instantiated with additional arguments
    """
    if subset_of_weights == "subnetwork" and hessian_structure not in ["full", "diag"]:
        raise ValueError(
            "Subnetwork Laplace requires a full or diagonal Hessian approximation!"
        )
    laplace_map = {
        subclass._key: subclass
        for subclass in _all_subclasses(BaseLaplace)
        if hasattr(subclass, "_key")
    }
    laplace_class = laplace_map[(subset_of_weights, hessian_structure)]
    return laplace_class(model, likelihood, *args, **kwargs)