timmy
179f8fe6e9
Quality gates / quality (pull_request) Successful in 13m25s
The ROADMAP gates (macro-F1 >= 0.80, precision@coverage >= 0.90, ECE <= 0.05) were prose with no tool behind them. scripts/calibration_report.py now computes them from prediction records and refuses input it cannot honestly score: - a split is re-derived from the pseudonymous subject key with the ingestion rule, so a corpus cannot re-label its own splits; a contributor spanning two splits is refused before scoring - the threshold is selected on validation only, and selecting on test or train is refused - coverage, precision-at-coverage, macro-F1, per-class precision/recall, confusion, and expected calibration error are computed on accepted suggestions, with classes that abstained away named in excludedClasses instead of being scored as a silent zero - exit codes carry the verdict: 0 GO, 3 BLOCKED, 2 refused The release demo records the real harness against committed synthetic fixtures: a blocked three-candidate comparison, a fixture engineered to clear every gate, and a leaked corpus refused. Every number on the card is read back from the report the run wrote, and the captions state that no model was measured, because a fixture is not evidence about accuracy. The harness suite is wired into CI, check:syntax, and the release builder's own gates.
3 lines
302 B
JSON
3 lines
302 B
JSON
{"model":"frozen-encoder","subjectKey":"cd1a2b3c4d5e6f01","split":"validation","truth":{"bristolType":4},"prediction":{"bristolType":4,"confidence":0.7}}
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{"model":"frozen-encoder","subjectKey":"cd1a2b3c4d5e6f01","split":"test","truth":{"bristolType":6},"prediction":{"bristolType":6,"confidence":0.7}}
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