leaspy.samplers.base

Classes

AbstractSampler	Abstract sampler class.
AbstractIndividualSampler	Abstract class for samplers of individual random variables.
AbstractPopulationSampler	Abstract class for samplers of population random variables.

Module Contents

class AbstractSampler(name, shape, *, acceptation_history_length=25)

Bases: abc.ABC

Abstract sampler class.

Parameters:

namestr

The name of the random variable to sample.

shapetuple of int

The shape of the random variable to sample.

acceptation_history_lengthint > 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.)

Attributes:

namestr

Name of variable

shapetuple of int

Shape of variable

acceptation_history_lengthint

Deepness (= number of iterations) of the history kept for computing the mean acceptation rate. (Same for population or individual variables by default.)

acceptation_historytorch.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.

Raises:

LeaspyModelInputError

Parameters:

• name (str)

• shape (Tuple[int, Ellipsis])

• acceptation_history_length (int)

name

shape

acceptation_history_length = 25

acceptation_history

property ndim: int

Number of dimensions.

Return type:

int

property shape_acceptation: Tuple[int, Ellipsis]

Abstractmethod:

Return type:

Tuple[int, Ellipsis]

Return the shape of acceptation tensor for a single iteration.

Returns:

tuple of int

The shape of the acceptation history.

Return type:

Tuple[int, Ellipsis]

abstractmethod sample(state, *, temperature_inv)

Apply a sampling step

<!> It will modify in-place the internal state, caching all intermediate values needed to efficient.

Parameters:

stateState

Object containing values for all model variables, including latent variables

temperature_invfloat > 0

Inverse of the temperature used in tempered MCMC-SAEM

Parameters:

• state (State)

• temperature_inv (float)

Return type:

None

class AbstractIndividualSampler(name, shape, *, n_patients, acceptation_history_length=25)

Bases: AbstractSampler

Abstract class for samplers of individual random variables.

Parameters:

namestr

The name of the random variable to sample.

shapetuple of int

The shape of the random variable to sample.

n_patientsint

Number of patients.

acceptation_history_lengthint > 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.)

Attributes:

namestr

Name of variable

shapetuple of int

Shape of variable

n_patientsint

Number of patients.

acceptation_history_lengthint

Deepness (= number of iterations) of the history kept for computing the mean acceptation rate. (It is the same for population or individual variables.)

acceptation_historytorch.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.

Parameters:

• name (str)

• shape (Tuple[int, Ellipsis])

• n_patients (int)

• acceptation_history_length (int)

n_patients

class AbstractPopulationSampler(name, shape, *, acceptation_history_length=25, mask=None)

Bases: AbstractSampler

Abstract class for samplers of population random variables.

Parameters:

namestr

The name of the random variable to sample.

shapetuple of int

The shape of the random variable to sample.

acceptation_history_lengthint > 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.)

masktorch.Tensor, optional

A binary (0/1) tensor indicating which elements to sample. Elements with value 1 (True) are included in the sampling; elements with 0 (False) are excluded.

Attributes:

namestr

Name of variable

shapetuple of int

Shape of variable

acceptation_history_lengthint

Deepness (= number of iterations) of the history kept for computing the mean acceptation rate. (It is the same for population or individual variables.)

acceptation_historytorch.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.

masktorch.Tensor of bool, optional

A binary (0/1) tensor indicating which elements to sample. Elements with value 1 (True) are included in the sampling; elements with 0 (False) are excluded.

Parameters:

• name (str)

• shape (Tuple[int, Ellipsis])

• acceptation_history_length (int)

• mask (Optional[Tensor])

mask = None