contrib.blackjax.MultiPathfinderFit

The result of fitting a forecasting model with multi-path BlackJAX Pathfinder.

Usage

Source

contrib.blackjax.MultiPathfinderFit(
    state, model, covariates, data, elbos, log_weights, pareto_k
)

A plain-data (picklable) container, mirroring PathfinderFit: it holds the raw blackjax MultipathfinderState (the pooled per-path approximations), the model and its data/covariates, the per-path ELBOs, and the Pareto-smoothed importance sampling (PSIS) weights/diagnostic over the pooled draws. Draws are produced lazily by multipathfinder_samples().

Attributes

state: Any

The blackjax MultipathfinderState (per-path PathfinderState objects, the pooled samples, and their log target/approximation densities).

model: ForecastModel

The forecasting model that was fit.

covariates: Array

In-sample covariates used at fit time (time at axis -2).

data: Array

In-sample data used at fit time (time at axis -2).

elbos: tuple[float, …]

The evidence lower bound of each path’s fitted approximation, in path order; converted eagerly with float() so pickling this fit never carries JAX tracers.

log_weights: Array

Normalized PSIS log importance weights over the flattened pool of num_paths * num_elbo_samples draws (path-major, matching state.samples); a fit-time diagnostic, valid only for that exact stored pool. multipathfinder_samples() draws fresh samples and recomputes its own weights, so it never consumes these.

pareto_k: float

The Pareto shape-parameter diagnostic for the fit-time PSIS weights: below 0.5 is reliable, 0.5 to 0.7 is borderline, and above 0.7 indicates the importance weights are unreliable. multipathfinder_samples() reads it to decide whether resample="auto" uses PSIS or ELBO-weighted path sampling.