leaspy.samplers
.AbstractPopulationSampler
- class AbstractPopulationSampler(name: str, shape: Tuple[int, ...], *, acceptation_history_length: int = 25, mask: Tensor | None = None)
Bases:
AbstractSampler
Abstract class for samplers of population random variables.
- Parameters:
- name
str
The name of the random variable to sample.
- shape
tuple
ofint
The shape of the random variable to sample.
- acceptation_history_length
int
> 0 (default 25) Deepness (= number of iterations) of the history kept for computing the mean acceptation rate. (It is the same for population or individual variables.)
- mask
torch.Tensor
, optional If not None, mask should be 0/1 tensor indicating the sampling variable to adapt variance from 1 indices are kept for sampling while 0 are excluded.
- name
- Attributes:
- name
str
Name of variable
- shape
tuple
ofint
Shape of variable
- acceptation_history_length
int
Deepness (= number of iterations) of the history kept for computing the mean acceptation rate. (It is the same for population or individual variables.)
- acceptation_history
torch.Tensor
History of binary acceptations to compute mean acceptation rate for the sampler in MCMC-SAEM algorithm. It keeps the history of the last acceptation_history_length steps.
- mask
torch.Tensor
of obj:bool, optional If not None, mask should be 0/1 tensor indicating the sampling variable to adapt variance from 1 (True) indices are kept for sampling while 0 (False) are excluded.
- name
Methods
sample
(dataset, model, realizations, ...)Sample new realization (either population or individual) for a given
CollectionRealization
state,Dataset
,AbstractModel
, and temperature.- abstract sample(dataset: Dataset, model: AbstractModel, realizations: CollectionRealization, temperature_inv: float, **attachment_computation_kws) Tuple[Tensor, Tensor]
Sample new realization (either population or individual) for a given
CollectionRealization
state,Dataset
,AbstractModel
, and temperature.<!> Modifies in-place the realizations object, <!> as well as the model through its update_MCMC_toolbox for population variables.
- Parameters:
- dataset
Dataset
Dataset class object build with leaspy class object Data, model & algo
- model
AbstractModel
Model for loss computations and updates
- realizations
CollectionRealization
Contain the current state & information of all the variables of interest
- temperature_inv
float
> 0 Inverse of the temperature used in tempered MCMC-SAEM
- **attachment_computation_kws
Optional keyword arguments for attachment computations. As of now, we only use it for individual variables, and only attribute_type. It is used to know whether to compute attachments from the MCMC toolbox (esp. during fit) or to compute it from regular model parameters (esp. during personalization in mean/mode realization)
- dataset
- Returns:
- attachment, regularity_var
torch.Tensor
The attachment and regularity tensors (only for the current variable) at the end of this sampling step (globally or per individual, depending on variable type). The tensors are 0D for population variables, or 1D for individual variables (with length n_individuals).
- attachment, regularity_var