| """Floor sanity check for the synthesized cloud-optimization recommendations dataset. |
| |
| This is a minimal smoke test. It reads your predictions file and confirms: |
| 1. Each prediction parses as JSON. |
| 2. Required fields are present. |
| 3. finding_type is one of the three allowed values. |
| 4. primary_tier is one of the allowed tier names (or null). |
| 5. action_category is one of the allowed values. |
| 6. specific_change is a non-empty string of reasonable length. |
| |
| This script does NOT score recommendation quality. It only confirms that |
| predictions are well-formed and on-topic. 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: |
| python eval.py --predictions sample_predictions.json |
| """ |
|
|
| from __future__ import annotations |
| import argparse |
| import json |
| import sys |
| from pathlib import Path |
|
|
|
|
| REQUIRED_FIELDS = ["scenario_id", "finding_type", "specific_change", |
| "primary_tier", "action_category"] |
|
|
| ALLOWED_FINDING_TYPES = { |
| "issue_found", "no_issue_found", "diagnostic_deferral", "insufficient_data", |
| } |
| ALLOWED_TIERS = {"compute", "database", "cache", "network", "deferred", None} |
| ALLOWED_ACTION_CATEGORIES = { |
| "rightsizing", "scaling_policy_change", "query_cache_optimization", |
| "cache_capacity_adjustment", "pool_sizing", "replica_adjustment", |
| "load_balancer_reconfiguration", "network_topology_change", "sla_review", |
| None, |
| } |
|
|
|
|
| def check_one(prediction: dict) -> list[str]: |
| """Return a list of error messages. Empty list means the prediction passes.""" |
| errors = [] |
| sid = prediction.get("scenario_id", "?") |
| for f in REQUIRED_FIELDS: |
| if f not in prediction: |
| errors.append(f"scenario {sid}: missing required field {f!r}") |
|
|
| ft = prediction.get("finding_type") |
| if ft not in ALLOWED_FINDING_TYPES: |
| errors.append(f"scenario {sid}: finding_type {ft!r} not in {sorted(ALLOWED_FINDING_TYPES)}") |
|
|
| pt = prediction.get("primary_tier") |
| if pt not in ALLOWED_TIERS: |
| errors.append(f"scenario {sid}: primary_tier {pt!r} not in {sorted(t for t in ALLOWED_TIERS if t)}") |
|
|
| ac = prediction.get("action_category") |
| if ac not in ALLOWED_ACTION_CATEGORIES: |
| errors.append(f"scenario {sid}: action_category {ac!r} not allowed") |
|
|
| sc = prediction.get("specific_change") or "" |
| if len(sc.strip()) < 20: |
| errors.append(f"scenario {sid}: specific_change too short ({len(sc.strip())} chars, need >=20)") |
|
|
| return errors |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser(description=__doc__, |
| formatter_class=argparse.RawDescriptionHelpFormatter) |
| ap.add_argument("--predictions", required=True, type=Path, |
| help="path to predictions JSON file") |
| args = ap.parse_args() |
|
|
| if not args.predictions.exists(): |
| print(f"ERROR: file not found: {args.predictions}", file=sys.stderr) |
| sys.exit(2) |
|
|
| doc = json.loads(args.predictions.read_text()) |
| preds = doc.get("predictions", []) |
|
|
| if not preds: |
| print("ERROR: predictions file has no 'predictions' array", file=sys.stderr) |
| sys.exit(2) |
|
|
| print() |
| print("=" * 70) |
| print(f" Floor sanity check: {len(preds)} prediction(s)") |
| print("=" * 70) |
|
|
| total_errors = 0 |
| for p in preds: |
| errs = check_one(p) |
| sid = p.get("scenario_id", "?") |
| if errs: |
| print(f" ✗ {sid}: {len(errs)} problem(s)") |
| for e in errs: |
| print(f" {e}") |
| total_errors += len(errs) |
| else: |
| print(f" ✓ {sid}: parseable and on-topic") |
|
|
| print() |
| if total_errors == 0: |
| print(f" All {len(preds)} prediction(s) passed the Floor sanity check.") |
| print(" Quality scoring beyond this is up to you.") |
| sys.exit(0) |
| else: |
| print(f" {total_errors} problem(s) across the predictions file.") |
| sys.exit(1) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
