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https://github.com/mcloh/decision-models-benchmark.git
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Calibração e congelamento da configuração (tarefas 60–62)
- config/calibration.json: temperatura e limiar de abstenção por candidato e dispositivo, ajustados só na partição de calibração. - config/frozen.json: hashes de configuração, calibração, partições, adaptadores, contrato e ambientes; libera a partição de teste. - Q6: lote de produção = 1; tarefa 65 não se aplica neste ciclo (D4). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
@@ -51,8 +51,7 @@ input: # Q8
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execution:
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seed: 20261007
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batch_sweep: [1, 2, 4, 8, 16, 32] # Q6
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production_batch: auto # maior lote com p95 <= slo
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production_batch: 1 # Q6: contrato por requisição, decisão turno a turno
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repetitions: 3
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cpu: # Q11
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timeout_ms: 10000
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@@ -70,7 +69,6 @@ calibration: # Q10
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criteria: # Q5
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latency_gpu_batch1:
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p95_ms_per_decision: 150
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p95_ms_per_turn: 300
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accuracy:
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tasks: [D1, D2]
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non_inferiority_margin_pp: 3.0
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118
config/calibration.json
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118
config/calibration.json
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@@ -0,0 +1,118 @@
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{
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"method": "temperature_scaling",
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"fit_split": "calibration",
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"max_confident_wrong_rate": 0.02,
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"min_coverage": 0.8,
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"candidates": {
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"gliner": {
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"cpu": {
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"temperature": 0.8187,
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"fitted_temperature": 0.8187,
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"accepted": true,
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"n": 1127,
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"ece_raw": 0.0767,
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"ece_calibrated": 0.0631,
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"ece_cv_raw": 0.1016,
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"ece_cv_calibrated": 0.0872,
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"nll_raw": 1.3205,
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"nll_calibrated": 1.305,
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"threshold": 0.856167,
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"coverage": 0.118,
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"selective_accuracy": 0.8346,
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"confident_wrong_rate": 0.0195,
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"meets_min_coverage": false
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},
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"cuda": {
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"temperature": 0.8187,
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"fitted_temperature": 0.8187,
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"accepted": true,
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"n": 1127,
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"ece_raw": 0.0767,
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"ece_calibrated": 0.0634,
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"ece_cv_raw": 0.1016,
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"ece_cv_calibrated": 0.0872,
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"nll_raw": 1.3205,
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"nll_calibrated": 1.305,
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"threshold": 0.857453,
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"coverage": 0.118,
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"selective_accuracy": 0.8346,
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"confident_wrong_rate": 0.0195,
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"meets_min_coverage": false
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}
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},
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"laya": {
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"cpu": {
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"temperature": 3.5787,
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"fitted_temperature": 3.5787,
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"accepted": true,
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"n": 1127,
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"ece_raw": 0.3296,
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"ece_calibrated": 0.0715,
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"ece_cv_raw": 0.3321,
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"ece_cv_calibrated": 0.1171,
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"nll_raw": 2.8006,
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"nll_calibrated": 1.5575,
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"threshold": 0.871532,
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"coverage": 0.0515,
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"selective_accuracy": 0.8448,
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"confident_wrong_rate": 0.008,
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"meets_min_coverage": false
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},
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"cuda": {
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"temperature": 3.5787,
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"fitted_temperature": 3.5787,
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"accepted": true,
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"n": 1127,
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"ece_raw": 0.3263,
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"ece_calibrated": 0.075,
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"ece_cv_raw": 0.3318,
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"ece_cv_calibrated": 0.113,
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"nll_raw": 2.7748,
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"nll_calibrated": 1.5517,
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"threshold": 0.871532,
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"coverage": 0.0515,
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"selective_accuracy": 0.8448,
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"confident_wrong_rate": 0.008,
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"meets_min_coverage": false
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}
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},
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"rizzo_flow": {
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"cuda": {
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"temperature": 1.2523,
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"fitted_temperature": 1.2523,
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"accepted": true,
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"n": 1127,
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"ece_raw": 0.0577,
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"ece_calibrated": 0.0206,
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"ece_cv_raw": 0.0825,
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"ece_cv_calibrated": 0.0584,
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"nll_raw": 0.5447,
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"nll_calibrated": 0.52,
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"threshold": 0.923792,
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"coverage": 0.4765,
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"selective_accuracy": 0.959,
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"confident_wrong_rate": 0.0195,
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"meets_min_coverage": false
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}
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},
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"semif": {
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"cuda": {
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"temperature": 1.284,
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"fitted_temperature": 1.284,
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"accepted": true,
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"n": 1127,
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"ece_raw": 0.0552,
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"ece_calibrated": 0.0225,
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"ece_cv_raw": 0.0699,
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"ece_cv_calibrated": 0.061,
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"nll_raw": 0.4776,
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"nll_calibrated": 0.4545,
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"threshold": 0.914887,
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"coverage": 0.5954,
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"selective_accuracy": 0.9672,
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"confident_wrong_rate": 0.0195,
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"meets_min_coverage": false
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}
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}
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}
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}
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72
config/frozen.json
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72
config/frozen.json
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@@ -0,0 +1,72 @@
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{
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"frozen_at": "2026-10-07T21:53:53+00:00",
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"files": {
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"config/benchmark.yaml": "53546bced660a618206023f3054ab478b0cba390369b4915c60374a4ff59b8f6",
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"config/calibration.json": "2918aa77d2251f60d155d3f0be8271d5b72c0945412e270344359bb1e8f7fa28",
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"data/splits/MANIFEST.json": "50377ab93dbaa71aa3220b722c2270fb76ddda4f5a24763ba4ad3623a2e704ac",
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"env/artifacts-manifest.json": "2cde61d21cec5086a221af988bea5dfb9a31fb4f3269511e63a23ca2b2bbfb6c",
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"contracts/jev/schemas/request.schema.json": "e44e5af9798b96453b676c05b216015fd2b62c9590301adce8c7807989774d35",
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"contracts/jev/schemas/response.schema.json": "008f8c2e68064b46b0f0649e654326ffd65a4eb8c080e9d23a6be4ebc44cd752",
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"harness/evaluate.py": "2c696e1da8c5e06284c73adf93b842cf7e541135e78dabd9cd1751825ec2866a",
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"dmb/canonical.py": "833e92490c2a34b9469e8e9aa4ee3aea6f2411ad74165a8df129ce460c2fd8d7",
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"dmb/jev.py": "eb9b97bc84d65d6481b3f86ea6f242c445bde02bfb0518bed735a15b802e66c9",
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"dmb/server.py": "cb8a7515337de781c90918dc2a40e499a44d6db52e6a78ae31f98fa22d415249",
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"dmb/adapter.py": "361e25b990a0de345fa79663f1593acf0bc62ef9580d6dfec1e35d4942a2839d",
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"adapters/laya/adapter.py": "b332c57330f24a08334b794cd933e267dfd5f82b1b073bf6213abb42e1d6d6e8",
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"adapters/semif/adapter.py": "83039c3e07dc2d64e809c3f33f3187a1e2fa3d8e5b20b204652dd673a354da3c",
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"adapters/gliner/adapter.py": "e499caea077353ccdc6cf458e8cb5fde6f34ef7149d463726e6b5d199f435921",
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"adapters/rizzo_flow/adapter.py": "8621b36f1b970ab778ff4af6c5e7cba3a933a0b2bfe6c6861f49dfd7c819d79b"
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},
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"environments": {
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"laya": {
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"candidate": "laya",
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"object": "envs/laya/20261007T151753Z.tar.gz",
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"sha256": "2003611792956150ce85ecc9ea7635f9b1f8dc7280c5da507e3a303188ed11aa",
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"bytes": 4308288985,
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"built_at": "20261007T151753Z",
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"code_rev": "5d93903",
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"python": "Python 3.11.9",
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"home": "/home/datascience"
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},
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"gliner": {
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"candidate": "gliner",
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"object": "envs/gliner/20261007T151810Z.tar.gz",
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"sha256": "fff384c86db233995f5a9182768bef0a8f27052436a3531f455027bde1d19954",
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"bytes": 4314977274,
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"built_at": "20261007T151810Z",
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"code_rev": "5d93903",
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"python": "Python 3.11.9",
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"home": "/home/datascience"
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},
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"semif": {
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"candidate": "semif",
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"object": "envs/semif/20261007T151340Z.tar.gz",
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"sha256": "50b090041721d0faa626ad44ebbcfa9b5c4b37ee9f6f69da181757580a4f5065",
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"bytes": 4312946412,
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"built_at": "20261007T151340Z",
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"code_rev": "5d93903",
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"python": "Python 3.11.9",
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"home": "/home/datascience"
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},
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"rizzo_flow": {
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"candidate": "rizzo_flow",
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"object": "envs/rizzo_flow/20261007T151336Z.tar.gz",
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"sha256": "805376a152a984010ca838e4134a9410f9c6e12ab97a0095297f7d7ea79b61c4",
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"bytes": 693207602,
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"built_at": "20261007T151336Z",
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"code_rev": "5d93903",
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"python": "Python 3.11.9",
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"home": "/home/datascience"
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},
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"rizzo_flow_cuda_runtime": {
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"object": "runtimes/rizzo_flow/llama-b11081-linux-x64-cuda-dsjobs-20261007T165909Z.tar.gz",
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"sha256": "08e03dc5ca1e64f6fd73b5a52cf0eb72727f203a15fe3bf86f79526d25ae00a9",
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"dir": "llama-b11081-linux-x64-cuda-dsjobs",
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"llama_commit": "161755f29e415e2c33efe906e91843c068efd664",
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"cuda": "12.8",
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"archs": "80;86",
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"built_at": "20261007T165909Z",
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"code_rev": "f322ed06"
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}
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}
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}
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