## evaluate.eval_coverage()


Empirical coverage of the central `alpha` prediction interval.


Usage

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


The central `alpha` interval is bounded by the `(1 - alpha) / 2` and `1 - (1 - alpha) / 2` quantiles of the forecast samples; the metric is the fraction of ground-truth values that fall inside it. A well-calibrated forecast has coverage close to `alpha`. A pure JAX scalar kernel (see `~numpyro_forecast.typing.Metric`); bind a non-default level with `functools.partial(eval_coverage, alpha=...)`.


## Parameters


`pred: Float[Array, ``" sample *batch"] | Float[np.ndarray, `<span class="st">`" sample *batch"``]`</span>  
Forecast samples with the sample axis first.

`truth: Float[Array, ``" *batch"] | Float[np.ndarray, `<span class="st">`" *batch"``]`</span>  
Ground-truth values (matching `pred` without the sample axis).

`alpha: float = _DEFAULT_COVERAGE_ALPHA`\  
Nominal interval level in `(0, 1)`; defaults to `0.9`.

`batch_size: int | None = None`  
Optional number of flattened data cells evaluated on the accelerator per pass (see [eval_crps()](evaluate.eval_crps.md#numpyro_forecast.evaluate.eval_crps); the sample axis is never chunked). Coverage is a count of exact 0/1 indicators, so chunking recovers the identical count; only the precision of the final division differs from the single pass. `None` (default) evaluates in one pass.


## Returns


`Array | np.floating`  
The fraction of ground truth inside the central `alpha` interval, as a scalar (a NumPy scalar when chunked).


## Raises


`ValueError`  
If `alpha` is not strictly inside `(0, 1)`.