| # eval.py: Floor sanity check |
|
|
| A minimal smoke test. It confirms your predictions parse and have the |
| right shape. It does NOT score recommendation quality. |
|
|
| For deeper scoring (keyword matching, multi-tier reasoning, fixture |
| citations), bring your own evaluator that compares each prediction |
| against the `handcrafted_recommendation.json` in the matching scenario |
| folder. |
|
|
| ## Usage |
|
|
| ```bash |
| python eval.py --predictions sample_predictions.json |
| ``` |
|
|
| ## What it checks |
|
|
| 1. The file parses as JSON. |
| 2. There is a top-level `predictions` array. |
| 3. Each prediction has the required fields. |
| 4. Each `finding_type` is one of the three allowed values. |
| 5. Each `primary_tier` is one of the allowed tier names (or null). |
| 6. Each `action_category` is one of the allowed values. |
| 7. Each `specific_change` is at least 20 characters. |
|
|
| ## What it does NOT check |
|
|
| - Whether the recommendation engages with the right evidence. |
| - Whether multi-tier scenarios cite both tiers. |
| - Whether named fixtures from the metadata are referenced. |
| - Whether cost or projection numbers are reasonable. |
|
|
| Those checks are quality assessments. They depend on what you want to |
| score for and how strict you want to be. The dataset ships the gold |
| answers; the scoring method is up to you. |
|
|
| ## Exit codes |
|
|
| - `0`: all predictions passed the Floor check. |
| - `1`: at least one prediction failed at least one check. |
| - `2`: usage error (missing file, malformed JSON). |
|
|
