coco_pipe.report.foundation#
Reports for foundation-model extraction and decoding workflows.
Functions#
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Build a dataset-level extraction coverage and provenance report. |
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Build the foundation decoding sweep report. |
Module Contents#
- coco_pipe.report.foundation.make_foundation_embedding_report(records, title='Foundation Embedding Extraction', config=None, output_path=None, asset_urls='inline')#
Build a dataset-level extraction coverage and provenance report.
- Parameters:
- Return type:
- coco_pipe.report.foundation.make_foundation_decoding_report(records, *, capability_records=(), title='Foundation Model Decoding', dataset_name='dataset', strategy_note=None, group_by=('condition', 'target'), per_result_sections='compact', include_hp_tuning=True, frame=None, config=None, output_path=None, asset_urls='inline')#
Build the foundation decoding sweep report.
Renders the shared sweep skeleton for a foundation sweep: scientific overview, a preflight capability matrix, per-train-mode leaderboards, the model/train-mode comparison figures + per-result diagnostics, and a failures section.
recordsare the per-model foundation decoding rows;capability_recordsare the preflight model/train-mode decisions. Pass a pre-built frame (fromprepare_sweep_frame()) to avoid re-preparing it when the caller already has one.- Parameters:
records (collections.abc.Iterable[collections.abc.Mapping[str, Any]])
capability_records (collections.abc.Iterable[collections.abc.Mapping[str, Any]])
title (str)
dataset_name (str)
strategy_note (str | None)
per_result_sections (str)
include_hp_tuning (bool)
frame (pandas.DataFrame | None)
config (collections.abc.Mapping[str, Any] | None)
output_path (str | pathlib.Path | None)
- Return type: