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Running on Zero
| """Phase 7 — Safety Risk Fusion configuration. | |
| Every risk-fusion parameter lives here; no threshold or weight is hardcoded | |
| in the risk engine. These are heuristic engineering choices, NOT scientifically | |
| validated values, and none of them was tuned on any dataset split (let alone | |
| the test set). | |
| The fusion inputs are four, and they are deliberately kept semantically | |
| separate: | |
| - ``near_miss_probability`` — calibrated P(class_1) from the Phase 4 | |
| near-miss/conflict model. NOTE: the class-1 label semantics are unconfirmed | |
| (see reports/data_dictionary.md); this pipeline only echoes the Phase 4 | |
| artifact's own output. | |
| - ``accident_probability`` — calibrated accident probability from the | |
| Phase 6 detector. Raw detector confidence is never used as a probability. | |
| - ``severity`` — CALLER-SUPPLIED relative severity in [0, 1]. | |
| Neither source dataset carries severity labels; the meaning of this value | |
| is owned entirely by the caller. This pipeline does not measure severity. | |
| - ``recency`` — derived from the CALLER-SUPPLIED event age in | |
| hours via an exponential decay (see RECENCY_HALF_LIFE_HOURS). | |
| """ | |
| from __future__ import annotations | |
| from pathlib import Path | |
| # --------------------------------------------------------------------------- | |
| # Artifact paths (Phase 4 near-miss + Phase 6 accident, loaded read-only) | |
| # --------------------------------------------------------------------------- | |
| PROJECT_ROOT = Path(__file__).resolve().parents[1] | |
| NEAR_MISS_MODEL_PATH = PROJECT_ROOT / "models" / "near_miss" / "random_forest_D_calibrated.joblib" | |
| ACCIDENT_DETECTOR_PATH = PROJECT_ROOT / "models" / "accident" / "calibrated_accident_detector.pth" | |
| # --------------------------------------------------------------------------- | |
| # Fusion weights | |
| # --------------------------------------------------------------------------- | |
| # Weight of each input in the linear fusion. Inputs present in an assessment | |
| # contribute weight * value; the weights of the PRESENT inputs are then | |
| # renormalized (see src/risk_engine.py), so the raw values below only define | |
| # relative importance. All weights must be > 0. | |
| RISK_WEIGHTS: dict[str, float] = { | |
| "near_miss": 0.35, | |
| "accident": 0.40, | |
| "severity": 0.15, | |
| "recency": 0.10, | |
| } | |
| # --------------------------------------------------------------------------- | |
| # Risk levels | |
| # --------------------------------------------------------------------------- | |
| # risk_score is on a 0-100 scale (RISK_SCORE_SCALE). Boundary semantics: | |
| # score < medium_threshold -> LOW | |
| # medium_threshold <= score < high -> MEDIUM | |
| # high_threshold <= score < critical -> HIGH | |
| # score >= critical_threshold -> CRITICAL | |
| # So each value here is the LOWER bound of its level (inclusive), and LOW | |
| # implicitly starts at 0. | |
| RISK_SCORE_SCALE: float = 100.0 | |
| RISK_LEVEL_THRESHOLDS: dict[str, float] = { | |
| "medium": 25.0, | |
| "high": 50.0, | |
| "critical": 75.0, | |
| } | |
| # --------------------------------------------------------------------------- | |
| # Recency decay | |
| # --------------------------------------------------------------------------- | |
| # recency_factor = 0.5 ** (event_age_hours / RECENCY_HALF_LIFE_HOURS) | |
| # An event exactly this many hours old contributes 0.5; twice as old 0.25; | |
| # age 0 contributes 1.0. Must be > 0. | |
| RECENCY_HALF_LIFE_HOURS: float = 24.0 | |