> ## Documentation Index
> Fetch the complete documentation index at: https://aidflow.aigensys.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AgentSelectorWorker.transform

**def AgentSelectorWorker.transform**(self, data) [\[source\]](https://github.com/aigensys-labs/aidflow/blob/main/AID/\TRUENAS\aig_modeling_Products\AID\1_0_Lib\AID\selector\select\agent_selector.py#L599)

Generate a fresh selection audit report on any dataset.

Re-evaluates the champion agent's diagnostics (performance,
SHAP drift) on a new data sample without re-fitting.
Use this to:

* Monitor the champion on a subsequent OOT window.
* Detect distribution shift before it impacts production.
* Generate a fresh audit report for compliance.

#### Parameters

**`data`** : *`pDF`*
The dataset to evaluate (must contain target + features).\
Typically a new OOT sample.

#### Returns

*`AgentSelectorResult`*
Updated performance and SHAP diagnostics on the\
new data, with the same champion predictor.

> [!NOTE]
>
> **No Re-fitting**: This method does not re-train the
> champion. It only re-computes the diagnostics on the
> new data. The `agent_predictor` remains the same
> fitted instance from `fit`.
>
> **Use Case**: If your OOT window shifts (e.g., next
> month's data), call `transform` on the new sample to
> verify that the champion's feature behavior remains
> stable. If SHAP drift increases, consider re-selecting.


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