leaspy.models.mixture¶

Classes¶

`TimeReparametrizedMixtureModel`	A time-reparametrized model tailored to handle mixture models with multiple clusters.
`RiemanianManifoldMixtureModel`	A riemannian manifold model tailored to handle mixture models with multiple clusters.
`LogisticMixtureInitializationMixin`
`LogisticMultivariateMixtureModel`	Mixture Manifold model for multiple variables of interest (logistic formulation).

Module Contents¶

class TimeReparametrizedMixtureModel(name, **kwargs)[source]¶

Bases: leaspy.models.mcmc_saem_compatible.McmcSaemCompatibleModel

A time-reparametrized model tailored to handle mixture models with multiple clusters.

This class extends TimeReparametrizedModel to incorporate mixture-specific behaviors, including support for multiple clusters (n_clusters) and corresponding vectorized parameters.

Parameters:

namestr

Name of the model.

source_dimensionOptional[int]

Number of sources. Dimension of spatial components (default is None).

**kwargs: :obj:`dict`

Additional hyperparameters for the model. Must include:

‘n_clusters’: int
Number of mixture components (must be ≥ 2).
‘dimension’ or ‘features’: int or list
Dimensionality of the input data.
‘obs_models’: str, list, or dict (optional)
Specification of the observation model(s). Defaults to “gaussian-diagonal”.

Raises:

LeaspyModelInputError: If inconsistent hyperparameters.

Parameters:

name (str)

property xi_mean: Tensor¶

Return the mean of xi as a tensor.

Return type:: Tensor

property xi_std: Tensor¶

Return the standard deviation of xi as a tensor.

Return type:: Tensor

property tau_std: Tensor¶

Return the standard deviation of tau as a tensor.

Return type:: Tensor

property noise_std: Tensor¶

Return the standard deviation of the model as a tensor.

Return type:: Tensor

property sources_mean: Tensor¶

Return the mean of the sources as a tensor.

Return type:: Tensor

property sources_std: Tensor¶

Return the standard deviation of the sources as a tensor.

Return type:: Tensor

source_dimension: int | None = None¶

get_variables_specs()[source]¶

Return the specifications of the variables (latent variables, derived variables, model ‘parameters’) that are part of the model.

Returns:

NamedVariables: A dictionary-like object containing specifications for the variables

Return type:

NamedVariables

property has_sources: bool¶

Indicates whether the model includes sources.

Returns:

bool: True if source_dimension is a positive integer. False otherwise.

Return type:

bool

static time_reparametrization(*, t, alpha, tau)[source]¶

Tensorized time reparametrization formula.

Warning

Shapes of tensors must be compatible between them.

Parameters:

ttorch.Tensor: Timepoints to reparametrize
alphatorch.Tensor: Acceleration factors of individual(s)
tautorch.Tensor: Time-shift(s) of individual(s)

Returns:

torch.Tensor: Reparametrized time of same shape as timepoints

Parameters:

t (TensorOrWeightedTensor[float])
alpha (Tensor)
tau (Tensor)

Return type:

TensorOrWeightedTensor[float]

put_individual_parameters(state, dataset)[source]¶

Initialize individual latent parameters in the given state if not already set.

Parameters:

stateState: The current state object that holds all the variables
datasetDataset: Dataset used to initialize latent variables accordingly.

Parameters:

state (State)
dataset (Dataset)

to_dict(*, with_mixing_matrix=True)[source]¶

Export model object as dictionary ready for JSON saving.

Parameters:

with_mixing_matrixbool (default True): Save the mixing matrix in the exported file in its ‘parameters’ section.

Warning

It is not a real parameter and its value will be overwritten at model loading (orthonormal basis is recomputed from other “true” parameters and mixing matrix is then deduced from this orthonormal basis and the betas)! It was integrated historically because it is used for convenience in browser webtool and only there…

Returns:

KwargsType: The object as a dictionary.

Parameters:

with_mixing_matrix (bool)

Return type:

KwargsType

class RiemanianManifoldMixtureModel(name, variables_to_track=None, **kwargs)[source]¶

Bases: TimeReparametrizedMixtureModel

A riemannian manifold model tailored to handle mixture models with multiple clusters.

This class extends RiemanianManifoldModel to incorporate mixture-specific behaviors, mainly the handling of sources for multiple clusters.

Parameters:

namestr: The name of the model.
**kwargs: Hyperparameters of the model (including noise_model)

Raises:

LeaspyModelInputError

If hyperparameters are inconsistent

Parameters:

name (str)
variables_to_track (Optional[Iterable[str]])

tracked_variables¶

tracked_variables_ordered = ['g', 'v0', 'noise_std', 'tau_mean', 'tau_std', 'xi_mean', 'xi_std', 'nll_attach',...¶

classmethod compute_sufficient_statistics(state)[source]¶

Compute the model’s sufficient statistics.

Parameters:

stateState: The state to pick values from.

Returns:

SuffStatsRW: The computed sufficient statistics.

Parameters:

state (State)

Return type:

SuffStatsRW

get_variables_specs()[source]¶

Return the specifications of the variables (latent variables, derived variables, model ‘parameters’) that are part of the model.

Returns:

NamedVariables: A dictionary-like object mapping variable names to their specifications. These include ModelParameter, Hyperparameter, PopulationLatentVariable, and LinkedVariable instances.

Return type:

NamedVariables

static metric(*, g)[source]¶

Abstractmethod:
Parameters:: g (Tensor)
Return type:: Tensor

classmethod model_no_sources(*, rt, metric, v0, g)[source]¶

Return the model output when sources(spatial components) are not present.

Parameters:

rttorch.Tensor: The reparametrized time.
metricAny: The metric tensor used for computing the spatial/temporal influence.
v0Any: The values of the population parameter v0 for each feature.
gAny: The values of the population parameter g for each feature.

Returns:

torch.Tensor: The model output without contribution from source shifts.

Parameters:

rt (Tensor)

Return type:

Tensor

Notes

This implementation delegates to model_with_sources with space_shifts set to a zero tensor of shape (1, 1), effectively removing source effects.

classmethod model_with_sources(*, rt, space_shifts, metric, v0, g)[source]¶

Abstractmethod:

Parameters:

rt (Tensor)
space_shifts (Tensor)

Return type:

Tensor

class LogisticMixtureInitializationMixin[source]¶

class LogisticMultivariateMixtureModel(name, **kwargs)[source]¶

Bases: LogisticMixtureInitializationMixin, RiemanianManifoldMixtureModel

Mixture Manifold model for multiple variables of interest (logistic formulation).

Parameters:: name (str)

get_variables_specs()[source]¶

Return the specifications of the variables (latent variables, derived variables, model ‘parameters’) that are part of the model.

Returns:

NamedVariables: A dictionary-like object mapping variable names to their specifications. These include ModelParameter, Hyperparameter, PopulationLatentVariable, and LinkedVariable instances.

Return type:

NamedVariables

static metric(*, g)[source]¶

Compute the metric tensor from input tensor g. This function calculates the metric as ((g + 1)^2 / g) element-wise.

Parameters:

gt torch.Tensor: Input tensor with values of the population parameter g for each feature.

Returns:

torch.Tensor: The computed metric tensor, same shape as g(number of features)

Parameters:

g (Tensor)

Return type:

Tensor

classmethod model_with_sources(*, rt, space_shifts, metric, v0, g)[source]¶

Return the model output when sources(spatial components) are present.

Parameters:

rtTensorOrWeightedTensor`[:obj:`float]: Tensor containing the reparametrized time.
space_shifts~leaspy.uitls.weighted_tensor._weighted_tensor.TensorOrWeightedTensor`[:obj:`float]: Tensor containing the values of the space-shifts
metric :`~leaspy.uitls.weighted_tensor._weighted_tensor.TensorOrWeightedTensor`[:obj:`float`]: Tensor containing the metric tensor used for computing the spatial/temporal influence.
v0~leaspy.uitls.weighted_tensor._weighted_tensor.TensorOrWeightedTensor`[:obj:`float]: Tensor containing the values of the population parameter v0 for each feature.
g~leaspy.uitls.weighted_tensor._weighted_tensor.TensorOrWeightedTensor`[:obj:`float]: Tensor containing the values of the population parameter g for each feature.

Returns:

torch.Tensor: Weighted value tensor after applying sigmoid transformation, representing the model output with sources.

Parameters:

rt (TensorOrWeightedTensor[float])
space_shifts (TensorOrWeightedTensor[float])
metric (TensorOrWeightedTensor[float])
v0 (TensorOrWeightedTensor[float])
g (TensorOrWeightedTensor[float])

Return type:

Tensor