tensorflow / tensorflow/tensorboard
[Feature Request] Generalize pr_curves to visualize any 2D scalar data
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- Dominant language
- TypeScript
- Stars
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- Forks
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- Avg merge
- 4d 22h
- Merged PRs (30d)
- 1
Description
The pr_curves plugin is great to visualize any 2D scalar data, but currently it's geared only towards precisio-recall curves. For instance, the summaries have PR-specific fields (e.g. Threshold, Precision, Recall, TP, FP, TN, FN) and the plots have hard-coded default ranges of [-0.05, 1.05]. This plugin can be generalized for any 2D data, by taking X and Y values, rather than Recall and Precision values. I have achieved this by filling in dummy values for some fields (Threshold, TP, FP, TN, FN) and commenting out _defaultXRange and _defaulYRange in tf-pr-curve-card.html. This is useful for plotting metrics of time-series data, i.e. per-time-step metrics vs prediction time steps.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the pr_curves plugin and inspect tf-pr-curve-card.html, especially the default-range handling noted in the issue. Trace how summaries expose PR-specific fields, then define what generic X and Y inputs must provide. Done means arbitrary 2D scalar data can render without dummy PR fields or hard-coded PR ranges.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- typescript
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100