pydpf.filtering.SIS

class pydpf.filtering.SIS(*, initial_proposal: Module | None = None, proposal: Module | None = None)

Bases: Module

Module that represents a sequential importance sampling (SIS) algorithm. A SIS algorthm is fully specified by its importance sampling

procedures, the user should supply a proposal kernel that may depend on the time-step; and a special case for time 0.

Parameters:

initial_proposal: Module | None

A callable object that takes the number of particles and the data/observations at time-step zero and returns an importance sample of the posterior, i.e. particle position and log weights. Also returns the observation likelihood (if applicable).

proposal: Module | None

A callable object that implements the proposal kernel. Takes the state and log weights at the previous time step, the discreet time index i.e. how many iterations the filter has run for; and the data/observations at the current time-step. And returns an importance sample of the posterior at the current time step, i.e. particle position and log weights. Also returns the observation factor likelihood.

Notes

SMC filters can, in general, be described as special cases of sequential importance sampling (SIS). We provide this generic SIS class that can be extended for a given use case, or used by directly supplying the relevant functions. SIS iteratively importance samples a Markov-Chain. An SIS algorithm is defined by supplying an initial distribution and a Markov kernel.

This implementation is more general than the standard SIS algorithm. There is no independence requirements for the samples within a batch. This means that the particles can be drawn from an arbitrary joint distribution on depended on the data and the particles at the previous time-step. Both the usual particle filter [1] and interacting multiple model particle filter [2] are special cases of this algorithm.

References

[1]

Chopin N, Papaspiliopoulos O (2020). An Introduction to Sequential Monte Carlo, chapter Particle Filtering, pp. 129–165. Springer.

[2]

Boers Y, Driessen J (2003). “Interacting multiple model particle filter.” IEE Proc. Radar, Sonar Nav., 150, 344–349. ISSN 1350-2395.

__init__(*, initial_proposal: Module | None = None, proposal: Module | None = None)

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

Methods

__init__(*[, initial_proposal, proposal])	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

forward(n_particles: int, time_extent: int, aggregation_function: dict[str, Module] | Module, observation: Tensor, *, gradient_regulariser: Function | None = None, ground_truth: Tensor | None = None, control: Tensor | None = None, time: Tensor | None = None, series_metadata: Tensor | None = None) → dict[str, Tensor] | Tensor

Run a forward pass of the SIS filter.

Parameters:

n_particles: int

The number of particles to draw per filter.

time_extent: int

The maximum time-step to run to, including time 0, the filter will draw {time_extent + 1} importance sample populations.

aggregation_function: dict[str, Module] or Module

A module that’s forward function processes the filtering outputs (the particle locations, the normalised log weights, the log sum of the unnormalised weights, the data, the time-step) into an output per time-step. Or a string indexed dictionary of such items.

observation: Tensor

The observations of the hidden variable system.

gradient_regulariser: torch.autograd.Function or None. Default None.

A autograd function to apply to the particles at the start of every time-step. It should leave the forward pass unchanged but may modify the gradients during the backward pass the intended usage is to regularise the gradient in some way. Optional.

ground_truth: Tensor or None. Default None.

The ground truth latent state. Should only be pass to the aggregation function and not used in the proposal functions. Optional.

control: Tensor or None. Default None.

The control actions. Optional.

time: Tensor or None. Default None.

The continuous time each time-step occurs at. Optional.

series_metadata: Tensor or None. Default None.

The series_metadata. Optional.

Returns:

output: Tensor or dict[str, Tensor]

The output of the filter, formed from stacking the output of aggregation_function for every time-step. Or if aggregation_function is a dictionary, a dictionary of these output Tensors one for each aggregation function.

Notes

To save memory during inference runs we allow the user to pass a function that takes a population of particles and processes this into an output for each time-step. For example, if the goal was the filtering mean then it would be wasteful to store the full population of the particles for every time-step. The memory savings are most impactful during inference as pytorch retains many intermediate tensors for gradient computation otherwise.