evaluate.VectorizedBacktestResult
Result of a backtest_vectorized() run (all windows at once).
Usage
evaluate.VectorizedBacktestResult(
t0, t1, t2, num_samples, losses, metrics, predictions=None
)Unlike BacktestResult this holds every window’s values stacked along a leading window axis, because the windows are fitted, drawn, and scored in single vmapped passes rather than one call each. There are no per-window walltimes (a single fused computation covers all windows).
Attributes
t0, t1, t2-
Train-begin, train/test split, and test-end time indices, each an integer array of shape
(num_windows,). num_samples: int-
Number of forecast samples drawn per window.
losses: Array-
SVI loss history with shape
(num_windows, num_steps). metrics: dict[str, Array]-
Mapping of metric name to a
(num_windows,)array of per-window values. predictions: Array | None-
Stacked out-of-sample forecast samples with shape
(num_windows, num_samples, *batch, test_window, obs), orNoneunlesskeep_predictions=True.
Methods
| Name | Description |
|---|---|
| to_dict() | Return a flat dictionary view. |
to_dict()
Return a flat dictionary view.
Usage
to_dict()Returns
dict[str, Any]- All fields as a plain dictionary.