## summaries.register_augment()


Register an [augment](augment.md#greenwood.augment) adapter for a model class.


Usage

``` python
summaries.register_augment(
    class_path,
    fn,
)
```


This is the extension point for adding [augment()](augment.md#greenwood.augment) support to new estimator classes. Each adapter is a callable that accepts a fitted model and (optionally) the original data, and returns an observation-level DataFrame with per-row predictions or residuals appended.


## Parameters


`class_path: str`  
Fully qualified class name used as the registry key, e.g., `"greenwood._cox.CoxPH"`.

`fn: Tidier`  
Callable with signature `fn(model, data=None, *, format=None, **kwargs) -> DataFrame`.


## Examples

Register a custom augment adapter:


``` python
from greenwood.summaries import register_augment

# Define an augment adapter that appends per-row predictions
def _augment_my_model(model, data=None, *, format=None, **kwargs):
    from greenwood._backends import to_dataframe

    preds = model.predict(data)
    return to_dataframe({"prediction": preds}, format=format)

# Register the adapter for the custom model class
register_augment("mypackage.MyModel", _augment_my_model)
```