# Custom Metrics

You can write your own custom metrics to be calculated and stored for later use within the [**Metrics Dashboard**](https://docs.tensorleap.ai/user-interface/project/dashboards/dashlets/metrics-dashboard).

Tensorleap stores metrics and metadata for every sample. This allows the user to investigate certain populations within the dataset. This also means that the **custom metric** is calculated per prediction on a **single** sample.

This function can get multiple `np.ndarray` arrays as inputs which will be exposed in the **Metric** UI connections. These are of shape *`(batch, dim-1,...,dim-n)`*. \
The function returns a `np.ndarray` that contains a batched metric result. 

Example of usage:

```python
@tensorleap_custom_metric(name='metrics',
                          direction=MetricDirection.Downward,
                          compute_insights=None)
def custom_metric_distances(
    y_true: npt.NDArray[np.float32],
    y_pred: npt.NDArray[np.float32]
) -> dict[str, npt.NDArray[np.float32]]:
    diff = y_true - y_pred
    axis = tuple(range(1, diff.ndim))

    return {
        "mean_difference": np.mean(diff, axis=axis),
        "mean_absolute_difference": np.mean(np.abs(diff), axis=axis)
    }
```

The [tensorleap\_custom\_metric](https://docs.tensorleap.ai/tensorleap-integration/python-api/code_loader/decorators/tensorleap_custom_metric "mention") decorator registers each metric collection into the Tensorleap integration, and can be added to the model analysis using the [integration test](https://docs.tensorleap.ai/tensorleap-integration/integration-test).