magli.eilers.LinGAM¶

class magli.eilers.LinGAM(n_splines: int = 10, lam: float = 1.0, spline_order: int = 3, constraint: str | None = None)[source]¶

Penalised B-spline GAM for a single smooth term y = f(x) + eps.

property n_splines: int¶

property lam: float¶

property constraint: str | None¶

property n_coefs: int¶: Effective number of spline coefficients (reduced for periodic).

property edge_knots_: ndarray | None¶

property design_matrix_: ndarray | None¶

property knots_: ndarray | None¶

aic() → float¶: Akaike Information Criterion (AIC).

bic() → float¶: Bayesian Information Criterion (BIC).

coef_: ndarray | None = None¶

confidence_intervals(x: ArrayLike, width: float = 0.95) → ndarray¶: Compute confidence intervals for the linear predictor.

deviance_explained() → float¶: Fraction of null deviance explained (pseudo R-squared).

fit(x: ArrayLike, y: ArrayLike, weights: ArrayLike | None = None) → GAMCore¶: Fit the model.

fit_intercept: bool = True¶

formula: str = ''¶

gridsearch(x: ArrayLike, y: ArrayLike, lam: ArrayLike | None = None, n_splines: ArrayLike | None = None, gamma: float = 1.4, n_jobs: int = 1, weights: ArrayLike | None = None) → LinGAM[source]¶: Select optimal (lam, n_splines) by minimising GCV.

property is_fitted: bool¶

loglikelihood() → float¶: Gaussian log-likelihood of the fitted model.

property n_terms: int¶

partial_dependence(term_idx: int, x: ArrayLike) → ndarray¶: Evaluate the partial contribution of term term_idx.

plot_diagnostics(x: ArrayLike, y: ArrayLike, figsize: Tuple[int, int] | None = None) → Tuple[Any, ndarray]¶: Diagnostic plots for model validation.

predict(x: ArrayLike) → ndarray¶: Predict target values for feature data x.

prediction_intervals(x: ArrayLike, width: float = 0.95) → ndarray¶: Compute prediction intervals for new observations.

statistics_: Dict[str, Any] | None = None¶

plot_decomposition(x: ArrayLike, y: ArrayLike, n_grid: int = 300, figsize: Tuple[int, int] = (16, 5)) → Tuple[source]¶