leaspy.models.utils.attributes.logistic_ordinal_attributes module

class LogisticOrdinalAttributes(name, dimension, source_dimension, ordinal_infos)

Bases: LogisticAttributes

Attributes of leaspy logistic models with ordinal noise model.

Contains the common attributes & methods to update the logistic ordinal model’s attributes.

Parameters:

namestr

dimensionint

source_dimensionint

ordinal_infosdict[str, Any]

Dictionary containing the required informations for all the ordinal levels of each item

See also

UnivariateModel

MultivariateModel

Attributes:

namestr (default ‘logistic’)

Name of the associated leaspy model.

dimensionint

source_dimensionint

univariatebool

Whether model is univariate or not (i.e. dimension == 1)

has_sourcesbool

Whether model has sources or not (not univariate and source_dimension >= 1)

update_possibilitiesset[str] (default {‘all’, ‘g’, ‘v0’, ‘betas’} )

Contains the available parameters to update. Different models have different parameters.

max_level: int

Maximum level of ordinal features

batch_deltasbool

True if samplers for deltas are batched or False if deltas are sampled separately. If True attribute deltas is a Tensor. Otherwise deltas are a dictionary associating names of features to the Tensor of corresponding deltas.

positionstorch.Tensor [dimension] (default None)

positions = exp(realizations[‘g’]) such that “p0” = 1 / (1 + positions)

velocitiestorch.Tensor [dimension] (default None)

Always positive: exp(realizations[‘v0’])

deltasUnion[torch.Tensor [dimension, max_level], dict[str, torch.Tensor]] (default None)

Always positive: exp(realizations[‘deltas’])

orthonormal_basistorch.Tensor [dimension, dimension - 1] (default None)

betastorch.Tensor [dimension - 1, source_dimension] (default None)

mixing_matrixtorch.Tensor [dimension, source_dimension] (default None)

Matrix A such that w_i = A * s_i.

Methods

get_attributes()	Returns the attributes of the model, which is a tuple of three torch tensors.
get_deltas()	Returns the deltas attributes stacked in one tensor
move_to_device(device)	Move the tensor attributes of this class to the specified device.
update(names_of_changed_values, values)	Update model group average parameter(s).

get_attributes() → Tuple[FloatTensor, FloatTensor, FloatTensor]

Returns the attributes of the model, which is a tuple of three torch tensors.

For the precise definitions of those attributes please refer to the exact attributes class associated to your model.

Returns:

positions: torch.Tensor

velocities: torch.Tensor

mixing_matrix: torch.Tensor

get_deltas()

Returns the deltas attributes stacked in one tensor

There are max_level - 1 = nb_levels - 2 “true” deltas since:

• we only model nb_levels - 1 curves, not P(X >= 0) since it’s constant = 1

• the first curve P(X >= 1) is the anchor (“delta = 0”) for it

In the returned tensor, we prepend the delta=0 for all features (for the anchor curve). For the impossible levels, delta=+inf, so that survival function is always 0

Returns:

The deltas concatenated in a big tensor: shape (dimension, max_level)

move_to_device(device: device)

Move the tensor attributes of this class to the specified device.

Parameters:

devicetorch.device

update(names_of_changed_values: set, values: dict)

Update model group average parameter(s).

Parameters:

names_of_changed_valuesset[str]

Elements of set must be either:

• all (update everything)

• g correspond to the attribute positions.

• v0 (only for multivariate models) correspond to the attribute velocities. When we are sure that the v0 change is only a scalar multiplication (in particular, when we reparametrize log(v0) <- log(v0) + mean(xi)), we may update velocities using v0_collinear, otherwise we always assume v0 is NOT collinear to previous value (no need to perform the verification it is - would not be really efficient)

• betas correspond to the linear combination of columns from the orthonormal basis so to derive the mixing_matrix.

• deltas correspond to the attribute :attr: deltas.

valuesdict [str, torch.Tensor]

New values used to update the model’s group average parameters

Raises:

LeaspyModelInputError

If names_of_changed_values contains unknown parameters.