evaluate.eval_crps()

Empirical CRPS averaged over all data elements.

Usage

Source

evaluate.eval_crps(
    pred,
    truth,
    *,
    batch_size=None,
)

A pure JAX scalar kernel (see Metric).

Parameters

pred: Float[ArrayLike, " sample *batch"]

Forecast samples with the sample axis first.

truth: Float[ArrayLike, " *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 inputs (e.g. draws sampled with device="host") this bounds accelerator memory by sample * batch_size values plus the CRPS sort workspace instead of the full panel, since both pred and truth are staged as NumPy views before chunking, whatever their own memory kind. 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 instead, which moves any host-committed operand back to device memory first (_device_view()) so either pred or truth (or both) may be host-committed regardless of batch_size. None (default) evaluates in one pass.

Returns

Array

The mean empirical CRPS as a scalar array.