leaspy.models.constant module

class ConstantModel(name: str, **kwargs)

Bases: GenericModel

ConstantModel is a benchmark model that predicts constant values (no matter what the patient’s ages are).

These constant values depend on the algorithm setting and the patient’s values provided during calibration.

It could predict:

last: last value seen during calibration (even if NaN).
last_known: last non NaN value seen during calibration.
max: maximum (=worst) value seen during calibration.
mean: average of values seen during calibration.

Warning

Depending on features, the last_known / max value may correspond to different visits.

Warning

For a given feature, value will be NaN if and only if all values for this feature were NaN.

Parameters:

namestr: The model’s name.
**kwargs: Hyperparameters for the model. None supported for now.

See also

ConstantPredictionAlgorithm

Attributes:

namestr

The model’s name.

is_initializedbool

Always True (no true initialization needed for constant model).

featureslist of str

List of the model features. Unlike most models features will be determined at personalization only (because it does not needed any fit).

dimensionint

The dimension of the model.

parametersdict

The model has no parameters: empty dictionary. The prediction_type parameter should be defined during personalization. Example:

>>> AlgorithmSettings('constant_prediction', prediction_type='last_known')

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.

compute_individual_trajectory(timepoints: Tensor, 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.

property features: List[str] | None

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.