pydpf.filtering.OptimalTransportDPF

class pydpf.filtering.OptimalTransportDPF(SSM: FilteringModel = None, regularisation: float = 0.1, decay_rate: float = 0.9, min_update_size: float = 0.01, max_iterations: int = 100, transport_gradient_clip: float = 1.0, *, use_REINFORCE_for_proposal: bool = False, use_REINFORCE_for_initial_proposal: bool = False)

Bases: ParticleFilter

Differentiable particle filter with optimal transport resampling.

Parameters:

SSM: FilteringModel

A FilteringModel that represents the SSM (and optionally a proposal model). See the documentation of FilteringModel for more complete information.

regularisation: float

The maximum strength of the entropy regularisation, in our implementation the initial regularisation automatically chosen per sample and exponentially decreased to the given regularisation.

decay_rate: float

The factor by which to decrease the entropy regularisation per Sinkhorn loop.

min_update_size: float

The size of update to the transport potentials below which the algorithm is considered converged and iteration should stop.

max_iterations: int

The maximum number iterations of the Sinkhorn loop, before stopping. Regardless of convergence.

transport_gradient_clip: float

The maximum per-element gradient of loss due to the transport matrix that should be passed. Higher valued gradients will be clipped to this value.

use_REINFORCE_for_proposal: bool. Default False.

Whether to use the REINFORCE estimator for the gradient due to the particle proposal process.

use_REINFORCE_for_initial_proposal: bool. Default False.

Whether to use the REINFORCE estimator for the gradient due to the initial particle proposal process.

Notes

See [1]. It is not recommended to set use_REINFORCE_for_proposal to True as it will lose the convergence properties of the reparameterised algorithm but retain the instability and costly runtime of OT filtering. Consider other algorithms if the proposal cannot be reparameterised.

References

[1]

Corenflos A, Thornton J, Deligiannidis G, Doucet A (2021). “Differentiable Particle Filtering via Entropy-Regularized Optimal Transport.” In Proc. Int. Conf. on Machine Learn. (ICML), pp. 2100–2111. Online.

__init__(SSM: FilteringModel = None, regularisation: float = 0.1, decay_rate: float = 0.9, min_update_size: float = 0.01, max_iterations: int = 100, transport_gradient_clip: float = 1.0, *, use_REINFORCE_for_proposal: bool = False, use_REINFORCE_for_initial_proposal: bool = False) → None

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Methods

__init__([SSM, regularisation, decay_rate, ...])	Initialize internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Return the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(n_particles, time_extent, ...[, ...])	Run a forward pass of the SIS filter.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules([remove_duplicate])	Return an iterator over all modules in the network.
mtia([device])	Move all model parameters and buffers to the MTIA.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post-hook to be run after module's load_state_dict() is called.
register_load_state_dict_pre_hook(hook)	Register a pre-hook to be run before module's load_state_dict() is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_post_hook(hook)	Register a post-hook for the state_dict() method.
register_state_dict_pre_hook(hook)	Register a pre-hook for the state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
set_submodule(target, module[, strict])	Set the submodule given by target if it exists, otherwise throw an error.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
update()	Update all constrained_parameters and cached_properties belonging to this Module.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

Attributes

T_destination
call_super_init
dump_patches
training