> For the complete documentation index, see [llms.txt](https://legacy-docs.aquariumlearning.com/aquarium/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://legacy-docs.aquariumlearning.com/aquarium/concepts/custom-metrics/stratified-metrics.md).

# Stratified Metrics

Often you want to get a more nuanced understanding of how your model performs on  specific slices of the labels. For example, in a self driving use case, you might want to understand how you perform on near vs far objects. Or you might have a fish-eye camera and what to understand how your model performs as objects approach the edge of the image. 

With stratified metrics, you can filter the metrics view based on per-label attributes, looking at a specific 'stratum' of your label set. For example, if you attach an attribute called “range,” you can evaluate performance on just large or small labels independently.

### Object Level Metadata Metrics Filtering

To enable label attributes metrics filtering, you can specify object metadata schema for the dataset or inference set using [`update_dataset_object_metadata_schema`](https://aquarium-not-pypi.web.app/aquariumlearning/docs/#aquariumlearning.Client.update_dataset_object_metadata_schema). For example,

```python
import aquariumlearning as al

al_client = al.Client()
al_client.set_credentials(api_key=API_KEY)

schema_fields = [
    {"name": "width_bucket", "type": "STRING"},
    {"name": "width", "type": "FLOAT"}
]
al_client.update_dataset_object_metadata_schema(<PROJECT>, <DATASET>, schema_fields)
```

Then, use the "Object Level Filters" option to select the attribute and enter a value (ie `medium`) or numeric range (ie `<10`, `<=10`, `=10`,`>=10`, `>10`).&#x20;

![](/files/DwTPnXZVaSjyK4JLtkw7)

### Example Usage (Deprecated)

```python
stratified_metrics_definitions = [
    al.StratifiedMetricsDefinition(
        name='range_bucket',
        ordered_values=['0-10', '10-20', '20-30', '30-40', '40-50', '50+']
    )
]

...

range_bucket = get_cuboid_range_bucket((0,0,0), label['position'])
frame.add_label_3d_cuboid(user_attrs={'range_bucket': range_bucket}, **label)

...

range_bucket = get_cuboid_range_bucket((0,0,0), inference['position'])
inf_frame.add_inference_3d_cuboid(user_attrs={'range_bucket': range_bucket}, **inference)

...

al_client.create_project(
    aquarium_project, 
    label_class_map, 
    stratified_metrics=stratified_metrics_definitions
)
```