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f271df0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 | """Multi-gate readiness skeleton — config-driven thresholds.
Extends existing QualityGate + LaunchGateEvaluator toward:
Scenario | Coverage | Regression | Safety | Release
Phases 4–6 fill Safety/ODD/Release deeply; Phase 1 returns honest ready=False
for unwired gates and real results for quality/launch when sequence artifacts exist.
"""
from __future__ import annotations
import json
from pathlib import Path
from typing import Any, Dict, List, Optional
from sensorflow.platform.entities import GateResult, QualityGateDef, Provenance, _id
DEFAULT_GATE_CONFIG_PATH = Path("runs/platform/gate_config.json")
# Config-driven defaults — never hard-code safety cutoffs in callers.
DEFAULT_GATE_CONFIG: Dict[str, Any] = {
"schema_version": "platform-gates-1.0",
"gates": {
"scenario": {
"enabled": True,
"ready": False, # Phase 5/6
"thresholds": {"min_scenario_types_covered": 5},
"message": "TODO(Phase 5): scenario coverage vs ODDDefinition",
},
"coverage": {
"enabled": True,
"ready": False,
"thresholds": {"min_cohort_support": 200, "min_dim_coverage": 0.8},
"message": "TODO(Phase 5): ODD combinatorial coverage via MegaEval cube",
},
"regression": {
"enabled": True,
"ready": True, # can use MegaEval compare / LabelEval regression when run_ids provided
"thresholds": {
"max_recall_drop": 0.010,
"max_precision_drop": 0.015,
"max_safety_recall_drop": 0.005,
},
"message": "Uses MegaEval promotion policy when candidate/baseline runs supplied",
},
"safety": {
"enabled": True,
"ready": False,
"thresholds": {}, # filled in Phase 4 from config only
"message": "TODO(Phase 4): SSAM DRAC/DeltaS conflict engine",
},
"release": {
"enabled": True,
"ready": False,
"thresholds": {"require_all_prior_passed": 1.0},
"message": "TODO(Phase 6): release orchestration after Safety+Regression",
},
"quality": {
"enabled": True,
"ready": True,
"thresholds": {
"map_3d": 0.65,
"orientation_error_deg": 5.0,
"id_swap_rate": 0.02,
"track_fragmentation_rate": 0.05,
"position_error_m": 2.0,
},
"message": "Delegates to sensorflow.quality_gate.QualityGate when sequence_id set",
},
"launch": {
"enabled": True,
"ready": True,
"thresholds": {
"map_3d": 0.65,
"orientation_error_deg": 5.0,
"id_swap_rate": 0.02,
"track_fragmentation_rate": 0.05,
},
"message": "Delegates to LaunchGateEvaluator when sequence_id set",
},
},
}
def load_gate_config(path: Optional[Path] = None) -> Dict[str, Any]:
path = path or DEFAULT_GATE_CONFIG_PATH
if path.exists():
with open(path) as f:
cfg = json.load(f)
# merge missing keys from defaults without wiping user thresholds
merged = json.loads(json.dumps(DEFAULT_GATE_CONFIG))
for gname, gcfg in (cfg.get("gates") or {}).items():
if gname in merged["gates"]:
merged["gates"][gname].update(gcfg)
else:
merged["gates"][gname] = gcfg
return merged
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
json.dump(DEFAULT_GATE_CONFIG, f, indent=2)
return json.loads(json.dumps(DEFAULT_GATE_CONFIG))
def save_gate_config(cfg: Dict[str, Any], path: Optional[Path] = None) -> Path:
path = path or DEFAULT_GATE_CONFIG_PATH
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "w") as f:
json.dump(cfg, f, indent=2)
return path
GATE_ORDER = ["scenario", "coverage", "regression", "safety", "quality", "launch", "release"]
def list_gate_defs(cfg: Optional[Dict[str, Any]] = None) -> List[QualityGateDef]:
cfg = cfg or load_gate_config()
out = []
for name in GATE_ORDER:
g = cfg["gates"].get(name, {})
out.append(QualityGateDef(
gate_id=f"gate-{name}",
name=name.title(),
gate_type=name, # type: ignore[arg-type]
thresholds=dict(g.get("thresholds") or {}),
config_path=str(DEFAULT_GATE_CONFIG_PATH),
enabled=bool(g.get("enabled", True)),
version="v1",
provenance=Provenance(source_system="platform", notes=g.get("message", "")),
))
return out
def evaluate_multi_gates(
*,
sequence_id: Optional[str] = None,
candidate_run_id: Optional[str] = None,
baseline_run_id: Optional[str] = None,
config_path: Optional[Path] = None,
) -> Dict[str, Any]:
cfg = load_gate_config(config_path)
results: List[GateResult] = []
for name in GATE_ORDER:
g = cfg["gates"][name]
if not g.get("enabled", True):
continue
ready = bool(g.get("ready", False))
thresholds = dict(g.get("thresholds") or {})
result = GateResult(
result_id=_id("gateres"),
gate_id=f"gate-{name}",
gate_type=name,
thresholds=thresholds,
ready=ready,
message=g.get("message", ""),
scope_ref=sequence_id or candidate_run_id,
passed=None,
)
if name == "quality" and sequence_id and ready:
result = _eval_quality(sequence_id, thresholds, result)
elif name == "launch" and sequence_id and ready:
result = _eval_launch(sequence_id, result)
elif name == "regression" and candidate_run_id and baseline_run_id and ready:
result = _eval_regression(candidate_run_id, baseline_run_id, thresholds, result)
elif not ready:
result.passed = None
result.message = g.get("message") or f"{name} gate skeleton — not yet wired"
else:
result.passed = None
result.message = f"{name} gate ready but missing required inputs"
results.append(result)
# Overall readiness: all ready gates that produced a boolean must pass;
# unwired gates block "release ready" but do not fail the suite silently.
wired = [r for r in results if r.ready and r.passed is not None]
unwired = [r for r in results if not r.ready]
all_wired_passed = all(r.passed for r in wired) if wired else False
return {
"config_path": str(config_path or DEFAULT_GATE_CONFIG_PATH),
"sequence_id": sequence_id,
"candidate_run_id": candidate_run_id,
"baseline_run_id": baseline_run_id,
"gates": [r.model_dump() for r in results],
"summary": {
"wired_count": len(wired),
"unwired_count": len(unwired),
"wired_all_passed": all_wired_passed,
"release_ready": False, # Phase 6
"unwired_gate_types": [r.gate_type for r in unwired],
},
}
def _eval_quality(sequence_id: str, thresholds: Dict[str, float], result: GateResult) -> GateResult:
try:
from sensorflow.quality_gate import QualityGate
from sensorflow.schemas.unified_frame import UnifiedSequence
manifest = Path("runs/pipeline") / sequence_id / "manifest.json"
tracks_path = Path("runs/pipeline") / sequence_id / "tracks.json"
if not manifest.exists() or not tracks_path.exists():
result.passed = False
result.failures = [{"metric": "artifacts", "message": "manifest/tracks missing"}]
result.message = "Quality gate: run ingest/auto-label/track first"
return result
sequence = UnifiedSequence.load(manifest)
pred_tracks = json.loads(tracks_path.read_text())
gate = QualityGate(thresholds=thresholds)
out = gate.evaluate(sequence, pred_tracks)
result.passed = bool(out.get("passed"))
result.metrics = out.get("metric_card") or {}
result.failures = out.get("quality_report", {}).get("failures") or out.get("failures") or []
result.message = "QualityGate evaluated"
except Exception as exc:
result.passed = False
result.failures = [{"error": str(exc)}]
result.message = f"Quality gate error: {exc}"
return result
def _eval_launch(sequence_id: str, result: GateResult) -> GateResult:
try:
from sensorflow.launch_gate_evaluator import LaunchGateEvaluator
evaluator = LaunchGateEvaluator()
out = evaluator.evaluate(sequence_id)
result.passed = bool(out.get("passed"))
result.failures = out.get("failures") or []
result.metrics = out.get("metrics") or {}
result.thresholds = evaluator.thresholds
result.message = "LaunchGateEvaluator evaluated"
except Exception as exc:
result.passed = False
result.failures = [{"error": str(exc)}]
result.message = f"Launch gate error: {exc}"
return result
def _eval_regression(
candidate_run_id: str,
baseline_run_id: str,
thresholds: Dict[str, float],
result: GateResult,
) -> GateResult:
try:
from sensorflow.platform.compare import compare_models
cmp = compare_models(
[baseline_run_id, candidate_run_id],
baseline_run_id=baseline_run_id,
policy=thresholds,
)
pair = (cmp.get("pairwise") or [{}])[0]
rec = pair.get("recommendation")
result.passed = rec == "PROMOTE"
result.failures = [{"blocker": b} for b in (pair.get("blockers") or [])]
result.metrics = {"recommendation": rec, "headline": pair.get("headline")}
result.message = f"Regression gate: {rec}"
except Exception as exc:
result.passed = False
result.failures = [{"error": str(exc)}]
result.message = f"Regression gate error: {exc}"
return result
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