## kernels.harness.RetroTask


One unit of work: fit one model to one company x line cohort as of a


Usage

``` python
kernels.harness.RetroTask(
    model,
    warehouse,
    line,
    company_code,
    as_of,
    loss_field=None,
    clamp_paid=False,
    seed=None,
    fit_kwargs=dict()
)
```


training cutoff, score it against the realized outcome.

`warehouse` must already be concrete (a local path or a pinned `github://owner/repo@publish_id` with the mart cached) - see `data.schedule_p.pinned_source`. [fit_kwargs](kernels.harness.SamplerSettings.md#ibnr.kernels.harness.SamplerSettings.fit_kwargs) carries entry-specific arguments (`variant`, `growth_curve`) verbatim: unlike sampler settings they are NOT signature-filtered, so a typo fails loudly as an error row instead of silently fitting the default.


## Parameter Attributes


`model: str`  

`warehouse: str`  

`line: str`  

`company_code: str`  

`as_of: str`  

`loss_field: str | None = None`  

`clamp_paid: bool = ``False`  

`seed: int | None = None`  

`fit_kwargs: dict = dict()`\