These prerequisites should be in place:
- Your model must have historic and related data export actions defined.
- You must run a data collection successfully.
- Data for this module depends on a forecast model trained with Amazon Ensemble.
To create a model-level explainability module and import action:
- Create a list called "Forecast Models."
- In Pivot view:
- Place Forecast Feature Names as Rows dimensions.
- Place Line Items as the Column dimension and remove Time.
- Place the Forecast Models in the Pages dimension (see screenshot 1 below).
- Select OK.
- Add a line item called Feature Contribution.
- In Blueprint view, select None in the Summary column for the Feature Contribution line item.
- From the regular view:
- Select Import from the Data dropdown.
- From the Select Source dialog:
- Select import_action_setup_model_explainability.csv.
- Click the Select button.
- From the File Options dialog:
- Use the Set Default File dropdown to select Admins Only.
- Select Next.
- From the Import dialog, Mapping tab:
- Map Source to Target. The table below provides an example. Your sources and targets will vary.
- Select Only update imported cells on the right (see screenshot 2 below).
- Note: You don't need to map within the Forecast Feature name tabs. The Forecast Feature name tab will have no Target items until you run a Forecast action.
- From the Forecast Explainability Line Items tab:
|Source items||Mapped To|
- Select Run Import.
This is how you define the import to the explainability module.
- Optional: from Import actions, rename import_action_setup_explainability to IMP Module Forecast Explainability.
Blueprint setup example:
9. Select OK.
Your explainability module is now ready. The next step is to import your model-level explainability.