leaspy.models.utils.ordinal module

class OrdinalModelMixin

Bases: object

Mix-in to add some useful properties & methods for models supporting the ordinal and ranking noise (univariate or multivariate).

Attributes:

is_ordinal: Property to check if the model is of ordinal sub-type.
is_ordinal_ranking: Property to check if the model is of ordinal-ranking sub-type (working with survival functions).
ordinal_infos: Property to return the ordinal info dictionary.

Methods

`check_noise_model_compatibility`(model)	Check compatibility between the model instance and provided noise model.
`compute_appropriate_ordinal_model`(...)	Post-process model values (or their gradient) if needed.
`compute_ordinal_model_sufficient_statistics`(...)	Compute the sufficient statistics given realizations.
`compute_ordinal_pdf_from_ordinal_sf`(ordinal_sf)	Computes the probability density (or its jacobian) of an ordinal model [P(X = l), l=0..L] from ordinal_sf which are the survival function probabilities [P(X > l), i.e. P(X >= l+1), l=0..L-1] (or its jacobian).
`get_additional_ordinal_population_random_variable_information`()	Return the information of additional population random variables for the ordinal model.
`get_ordinal_parameters_updates_from_sufficient_statistics`(...)	Return a dictionary computed from provided sufficient statistics for updating the parameters.
`postprocess_model_estimation`(estimation, *)	Extra layer of processing used to output nice estimated values in main API Leaspy.estimate.
`update_ordinal_population_random_variable_information`(...)	Update (in-place) the provided variable information dictionary.

check_noise_model_compatibility(model: BaseNoiseModel) → None: Check compatibility between the model instance and provided noise model.

compute_appropriate_ordinal_model(model_or_model_grad: Tensor) → Tensor: Post-process model values (or their gradient) if needed.

compute_ordinal_model_sufficient_statistics(realizations: CollectionRealization) → Dict[str, Tensor]: Compute the sufficient statistics given realizations.

static compute_ordinal_pdf_from_ordinal_sf(ordinal_sf: Tensor, dim_ordinal_levels: int = 3) → Tensor

Computes the probability density (or its jacobian) of an ordinal model [P(X = l), l=0..L] from ordinal_sf which are the survival function probabilities [P(X > l), i.e. P(X >= l+1), l=0..L-1] (or its jacobian).

Parameters:

ordinal_sftorch.FloatTensor: Survival function values : ordinal_sf[…, l] is the proba to be superior or equal to l+1 Dimensions are: * 0=individual * 1=visit * 2=feature * 3=ordinal_level [l=0..L-1] * [4=individual_parameter_dim_when_gradient]
dim_ordinal_levelsint, default = 3: The dimension of the tensor where the ordinal levels are.

Returns:

ordinal_pdftorch.FloatTensor (same shape as input, except for dimension 3 which has one more element): ordinal_pdf[…, l] is the proba to be equal to l (l=0..L)

get_additional_ordinal_population_random_variable_information() → Dict[str, Any]: Return the information of additional population random variables for the ordinal model.

get_ordinal_parameters_updates_from_sufficient_statistics(sufficient_statistics: Dict[str, Tensor]) → Dict[str, Tensor]: Return a dictionary computed from provided sufficient statistics for updating the parameters.

property is_ordinal: bool: Property to check if the model is of ordinal sub-type.

property is_ordinal_ranking: bool: Property to check if the model is of ordinal-ranking sub-type (working with survival functions).

property ordinal_infos: dict | None: Property to return the ordinal info dictionary.

postprocess_model_estimation(estimation: ndarray, *, ordinal_method: str = 'MLE', **kws) → ndarray | Dict[Hashable, ndarray]

Extra layer of processing used to output nice estimated values in main API Leaspy.estimate.

Parameters:

estimationnumpy.ndarray[float]: The raw estimated values by model (from compute_individual_trajectory)
ordinal_methodstr: <!> Only used for ordinal models. * ‘MLE’ or ‘maximum_likelihood’ returns maximum likelihood estimator for each point (int) * ‘E’ or ‘expectation’ returns expectation (float) * ‘P’ or ‘probabilities’ returns probabilities of all-possible levels for a given feature:

{feature_name: array[float]<0..max_level_ft>}
**kws: Some extra keywords arguments that may be handled in the future.

Returns:

numpy.ndarray[float] or dict[str, numpy.ndarray[float]]: Post-processed values. In case using ‘probabilities’ mode, the values are a dictionary with keys being: (feature_name: str, feature_level: int<0..max_level_for_feature>) Otherwise it is a standard numpy.ndarray corresponding to different model features (in order)

update_ordinal_population_random_variable_information(variables_info: Dict[str, Any]) → None

Update (in-place) the provided variable information dictionary.

Nota: this is needed due to different signification of v0 in ordinal model (common per-level velocity)

Parameters:

variables_infoDictParams: The variables information to be updated with ordinal logic.