gammy.models.numpy.GAM

GAM

class GAM(formula, tau, theta=None)[source]

Bases: object

Generalized additive model with NumPy backend

Parameters
formulagammy.formulae.Formula

Formula object containing the terms and prior

thetaGaussian

Model parameters vector

tauDelta

Observation noise precision (inverse variance)

__len__() → int[source]

Number of model parameters

property covariance_theta

Covariance estimate of model parameters

fit(input_data, y) → gammy.models.numpy.GAM[source]

Estimate model parameters

Parameters
input_datanp.ndarray

Input data

ynp.ndarray

Observations

formula

Model formula

property inv_mean_tau

Additive observation noise variance estimate

load(filepath: str) → gammy.models.numpy.GAM[source]

Load model from a file on disk

marginal_residual(input_data, y, i: int) → numpy.ndarray[source]

Calculate marginal residual for a given term

Parameters
input_datanp.ndarray

Input data

ynp.ndarray

Observations

marginal_residuals(input_data, y) → List[numpy.ndarray][source]

Marginal (partial) residuals

Parameters
input_datanp.ndarray

Input data

ynp.ndarray

Observations

property mean_theta

Mean estimate of model parameters

Posterior if model is fitted, otherwise prior.

predict(input_data) → numpy.ndarray[source]

Calculate mean of posterior predictive at inputs

Parameters
input_datanp.ndarray
predict_marginal(input_data, i: int) → numpy.ndarray[source]

Predict a term separately

Parameters
input_datanp.ndarray
predict_marginals(input_data) → List[numpy.ndarray][source]

Predict all terms separately

Parameters
input_datanp.ndarray
predict_variance(input_data) → Tuple[numpy.ndarray][source]

Predict mean and variance

Parameters
input_datanp.ndarray
predict_variance_marginal(input_data, i: int) → Tuple[numpy.ndarray][source]

Evaluate mean and variance for a given term

Parameters
input_datanp.ndarray
predict_variance_marginals(input_data) → List[Tuple[numpy.ndarray]][source]

Predict variance (theta) for marginal parameter distributions

Parameters
input_datanp.ndarray
predict_variance_theta(input_data) → Tuple[numpy.ndarray][source]

Predict observations with variance from model parameters

Parameters
input_datanp.ndarray
save(filepath: str) → None[source]

Save the model to disk

Supported file formats: JSON and HDF5

tau

Additive noise precision parameter

theta

Model parameters

theta_marginal(i: int) → gammy.models.numpy.Gaussian[source]

Extract marginal distribution for a specific term

property theta_marginals

Marginal distributions of model parameters