innovate.diffuse.logistic module

class innovate.diffuse.logistic.LogisticModel(covariates: Sequence[str] = None, t_event: float = None)[source]

Bases: DiffusionModel

Implementation of the Logistic Diffusion Model. This is a wrapper around the SymmetricGrowth dynamics model.

bounds(t: Sequence[float], y: Sequence[float]) → Dict[str, tuple][source]

Return parameter bounds for the logistic model, including covariate effects.

Parameters:

  • t (Sequence[float]) – Time points of the observations.

  • y (Sequence[float]) – Observed values corresponding to each time point.

Returns:

Dictionary mapping parameter names to their (lower, upper) bounds.

Return type:

Dict[str, tuple]

cumulative_adoption(t: Sequence[float], *params, **param_kwargs) → Sequence[float][source]
differential_equation(t, y, params, covariates, t_eval)[source]

Differential equation for the logistic model.

initial_guesses(t: Sequence[float], y: Sequence[float]) → Dict[str, float][source]

Returns initial guesses for the model parameters.

property param_names: Sequence[str]

Return the list of parameter names for the logistic model, including base parameters and covariate-specific coefficients.

Returns:

List of parameter names, with covariate effects prefixed by ‘beta_L_’, ‘beta_k_’, and ‘beta_x0_’ for each covariate.

Return type:

names (Sequence[str])

property params_: Dict[str, float]

Returns a dictionary of fitted model parameters.

predict(t: Sequence[float], covariates: Dict[str, Sequence[float]] = None) → Sequence[float][source]

Predicts the cumulative values of the logistic diffusion process at specified time points.

Parameters:

  • t (Sequence[float]) – Time points at which to compute predictions.

  • covariates (Dict[str, Sequence[float]], optional) – Covariate values for each time point.

Returns:

Predicted cumulative values of the logistic model at each time point.

Return type:

Sequence[float]

Raises:

RuntimeError – If the model parameters have not been set (i.e., the model is not fitted).

predict_adoption_rate(t: Sequence[float], covariates: Dict[str, Sequence[float]] = None) → Sequence[float][source]

Predicts the rate of adoption (new adoptions per unit of time).

score(t: Sequence[float], y: Sequence[float], covariates: Dict[str, Sequence[float]] = None) → float[source]

Compute the coefficient of determination (R²) between observed values and model predictions.

Parameters:

  • t (Sequence[float]) – Time points at which observations were made.

  • y (Sequence[float]) – Observed values corresponding to time points.

  • covariates (Dict[str, Sequence[float]], optional) – Covariate values for each time point.

Returns:

The R² score indicating the proportion of variance explained by the model predictions.

Return type:

float

Raises:

RuntimeError – If the model has not been fitted.