RE-ORGANIZE FEATURE IMPORTANCE LIKE TABLE TO GROUP-PARTS TABLE

Reorganizes a feature-importance-style flat table into a compact Group-Parts Table by grouping rows on a chosen group column, sorting features within each group by descending value, and serializing them as colon-delimited part strings. Optionally normalizes each feature’s value as a ratio of the group’s total. Use this worker when you need a human-readable or downstream-consumable summary of which features contribute most within each group interval.

When to use

Tagged: feature-importance, group-parts, interval, pivot, ratio, reshape, rollup.

Inputs

Label	ID	Type	Default	Required	Description
Dataset	dataset	dataset	—	✓	Input dataset in tabular (array-of-rows) format containing at minimum the group, feature, and value columns; must be a non-empty dataset object.
Group Column	group_column	text	—		Name of the column whose distinct values define the groups (e.g. an interval or category ID); select a text or numeric column from the linked dataset.
Feature Column	feature_column	text	—		Name of the column that identifies each feature or part within a group (e.g. component name or variable label); select a text or numeric column from the linked dataset.
Value Column	value_column	text	—		Name of the column holding the numeric importance or contribution value for each feature row; select a text or numeric column from the linked dataset.
Return Ratio On Each Interval	return_ratio	text	—		Boolean flag (default true); when true each feature’s value is normalized as a fraction of the group’s total sum and the output encodes ‘feature:ratio=X’, otherwise raw values are used with ‘feature:value=X’.

Outputs

Label	ID	Type	Description
Output Group Parts Table	dataset	dataset	Output Group-Parts Table: one row per group containing the group-column value and a ‘Parts’ string of ‘::’-delimited ‘feature:ratio=X’ (or ‘feature:value=X’) entries sorted by descending importance.

Disciplines

• ai_ml.prognosis
• data.dataset.transform
• data.statistics

Runnable example

A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_get_group_parts_table

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