evaluate.eval_crps()

Empirical CRPS averaged over all data elements.

Usage

Source

evaluate.eval_crps(pred: Float[Array, " sample *batch"] | Float[np.ndarray, " sample *batch"], truth: Float[Array, " *batch"] | Float[np.ndarray, " *batch"], batch_size: None = None) -> Array
 
evaluate.eval_crps(pred: Float[Array, " sample *batch"] | Float[np.ndarray, " sample *batch"], truth: Float[Array, " *batch"] | Float[np.ndarray, " *batch"], batch_size: int) -> Array | np.floating

A pure JAX scalar kernel (see ~numpyro_forecast.typing.Metric).

Parameters

pred: Float[Array, " sample *batch"] | Float[np.ndarray, " sample *batch"]

Forecast samples with the sample axis first.

truth: Float[Array, " *batch"] | Float[np.ndarray, " *batch"]

Ground-truth values (matching pred without the sample axis).

batch_size: int | None = None

Optional number of flattened data cells (the product of the batch shape, e.g. time times series) evaluated on the accelerator per pass; the sample axis is never chunked. With host-resident (NumPy) inputs this bounds accelerator memory by sample * batch_size values plus the CRPS sort workspace instead of the full panel. Chunking only changes the summation order of the final mean (results are equal to float tolerance, not bitwise); at or above the cell count the single-pass path runs. None (default) evaluates in one pass.

Returns

Array | np.floating

The mean empirical CRPS as a scalar (a NumPy scalar when chunked).