leaspy.models.GenericModel

class GenericModel(name: str, **kwargs)

Bases: BaseModel

Generic model (temporary until AbstractModel is really abstract).

TODO: change naming after AbstractModel was renamed?

Parameters:

namestr

The name of the model.

**kwargs

Hyperparameters of the model.

Attributes:

namestr

The name of the model.

is_initializedbool

True if the model is initialized, False otherwise.

featureslist of str

List of model features (None if not initialization).

dimensionint (read-only)

The dimension of the model.

parametersdict

Contains internal parameters of the model.

Methods

compute_individual_trajectory(timepoints, ...)	Compute scores values at the given time-point(s) given a subject's individual parameters.
get_hyperparameters(*[, with_features, ...])	Get all model hyperparameters.
hyperparameters_ok()	Check all model hyperparameters are ok.
initialize(dataset[, method])	Initialize the model given a Dataset and an initialization method.
load_hyperparameters(hyperparameters, *[, ...])	Load model hyperparameters from a dict.
load_parameters(parameters, *[, list_converter])	Instantiate or update the model's parameters.
save(path, **kwargs)	Save Leaspy object as JSON model parameter file.
validate_compatibility_of_dataset(dataset)	Raise if the given Dataset is not compatible with the current model.

abstract compute_individual_trajectory(timepoints, individual_parameters: dict) → Tensor

Compute scores values at the given time-point(s) given a subject’s individual parameters.

Parameters:

timepointsscalar or array_like[scalar] (list, tuple, numpy.ndarray)

Contains the age(s) of the subject.

individual_parametersdict [ str, Any]

Contains the individual parameters. Each individual parameter should be a scalar or array_like.

Returns:

torch.Tensor

Contains the subject’s scores computed at the given age(s). The shape of the tensor is (1, n_tpts, n_features).

property dimension: int | None

The dimension of the model. If the private attribute is defined, then it takes precedence over the feature length. The associated setters are responsible for their coherence.

get_hyperparameters(*, with_features=True, with_properties=True, default=None) → Dict[str, Any]

Get all model hyperparameters.

Parameters:

with_features, with_propertiesbool (default True)

Whether to include features and respectively all _properties (i.e. _dynamic_ hyperparameters) in the returned dictionary.

defaultAny

Default value is something is an hyperparameter is missing (should not!).

Returns:

:obj:`dict` { hyperparam_namestr -> hyperparam_valueAny }

hyperparameters_ok() → bool

Check all model hyperparameters are ok.

Returns:

bool

initialize(dataset: Dataset, method: str = 'default') → None

Initialize the model given a Dataset and an initialization method.

After calling this method is_initialized should be True and model should be ready for use.

Parameters:

datasetDataset

The dataset we want to initialize from.

methodstr

A custom method to initialize the model

load_hyperparameters(hyperparameters: Dict[str, Any], *, with_defaults: bool = False) → None

Load model hyperparameters from a dict.

Parameters:

hyperparametersdict [ str, Any]

Contains the model’s hyperparameters.

with_defaultsbool (default False)

If True, it also resets hyperparameters that are part of the model but not included in hyperparameters to their default value.

Raises:

LeaspyModelInputError

If inconsistent hyperparameters.

load_parameters(parameters, *, list_converter=<built-in function array>) → None

Instantiate or update the model’s parameters.

Parameters:

parametersdict

Contains the model’s parameters.

list_convertercallable

The function to convert list objects.

save(path: str, **kwargs) → None

Save Leaspy object as JSON model parameter file.

Default save method: it can be overwritten in child class but should be generic…

Parameters:

pathstr

Path to store the model’s parameters.

**kwargs

Keyword arguments for json.dump method.

validate_compatibility_of_dataset(dataset: Dataset) → None

Raise if the given Dataset is not compatible with the current model.

Parameters:

datasetDataset

The Dataset we want to model.

Raises:

LeaspyModelInputError

• If the Dataset has a number of dimensions smaller than 2.

• If the Dataset does not have the same dimensionality as the model.

• If the Dataset’s headers do not match the model’s.