a-scoreable-file-in-the-first-hour
SolidUse at the first stage of a run whose deliverable is a predictions file, and again at every stage when one still does not exist. Covers why a trivial submission written early dominates a good one written late, what the first version should contain, and how to improve it in place without ever leaving it invalid.
Install
Quality Score: 82/100
Skill Content
Details
- Author
- tangxiangru
- Repository
- tangxiangru/AutoR
- Created
- 6 months ago
- Last Updated
- 2 weeks ago
- Language
- Python
- License
- NOASSERTION
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
add-the-baseline-back-on-the-split-you-cannot-score
Use at study design, implementation and every write thereafter, whenever the value you submit is assembled from parts - a fitted baseline plus a model's residual, a level plus a shape, a de-trended prediction that has to be re-trended, any inverse transform - and the validation arrays and the graded arrays are produced by separate calls. Covers assembling every split through one function, using the baseline you already fitted as a label-free reference vector on the graded split, why row count, header, dtype and finiteness cannot see this class of error, and putting the gate inside the writer rather than in a script somebody has to remember to run.
chemistry-reproduce-the-scoring-path-before-you-replace-it
Use at implementation, experimentation and analysis when you are reproducing a published benchmark number and the source's scoring path is one you can read — which rows are scored, in what order, how many the loader drops, which epoch is reported, how tasks are pooled, over how many seeds. Covers implementing that path exactly before improving it, the one-row-per-step ladder from the published rule down to your own honest estimate, and why one un-replicated step makes the reproduction gap you report uninterpretable.
a-combination-is-not-the-candidate-set
Use once more than one trained artifact exists on disk -- two checkpoints, two seeds, two architectures, a continuation run -- and something is deciding which of them, or which combination of them, writes the predictions file. Covers the ballot that lists every artifact as a submission on its own before any blend, re-running it whenever a training job finishes, the known-bad canary that tests the objective, and persisting a rejected candidate's predictions.