Spaces:
Running on Zero
Running on Zero
File size: 18,397 Bytes
f234112 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 | """Phase 7 — Safety Risk Fusion orchestrator.
Connects the completed probability models without retraining them:
- Phase 4 near-miss/conflict model
(models/near_miss/random_forest_D_calibrated.joblib)
via the existing near_miss.probability_pipeline.ProbabilityPipeline
-> calibrated class-1 probability.
- Phase 6 calibrated accident detector
(models/accident/calibrated_accident_detector.pth)
via accident.calibrated_detector.CalibratedAccidentDetector
-> calibrated accident probability.
Model D is the deployment-realistic near-miss model. It does NOT require:
- PET
- FE_inv_PET
- FE_safety_index
The near-miss probability and accident probability remain separate
and are passed to risk_engine.assess_risk together with caller-supplied
severity and event age.
This module:
- does NOT retrain models
- does NOT control traffic signals
- does NOT treat the risk score as a probability
- does NOT substitute raw accident detector confidence for probability
"""
from __future__ import annotations
import json
import os
import sys
from typing import Any
import pandas as pd
_SRC_DIR = os.path.dirname(__file__)
if _SRC_DIR not in sys.path:
sys.path.insert(0, _SRC_DIR)
try:
# When src/ is directly on PYTHONPATH
import config as _config
from risk_engine import assess_risk
except ImportError:
# Package-style import
from . import config as _config
from .risk_engine import assess_risk
# ---------------------------------------------------------------------
# Version
# ---------------------------------------------------------------------
ENGINE_VERSION = "safety_risk_fusion_v1"
# ---------------------------------------------------------------------
# Phase 6 detector output keys
# ---------------------------------------------------------------------
_DETECTOR_PROBABILITY_KEY = "calibrated_accident_probability"
_DETECTOR_RAW_SIGNAL_KEY = "accident_detection_confidence"
# ---------------------------------------------------------------------
# Expected Model D features
# ---------------------------------------------------------------------
MODEL_D_FEATURES = [
"speed_object_1_kph",
"speed_object_2_kph",
"angle_degrees",
"acceleration_obj1_mps2",
"acceleration_obj2_mps2",
"min_dist_dual_check_m",
"object_count_at_frame1",
"target_dist_px",
"FE_log_mesafe",
"FE_dist_squared",
"class_object_1",
"class_object_2",
]
class SafetyIntelligence:
"""Lazy-loading fusion of near-miss Model D and accident models."""
def __init__(
self,
near_miss_artifact: str | None = None,
accident_checkpoint: str | None = None,
):
"""
Parameters
----------
near_miss_artifact:
Optional explicit path to the calibrated near-miss model.
accident_checkpoint:
Optional explicit path to the calibrated accident detector.
If paths are not supplied, values are taken from config.py.
"""
self.near_miss_artifact = near_miss_artifact
self.accident_checkpoint = accident_checkpoint
# Models are loaded lazily.
self._near_miss = None
self._accident = None
# =================================================================
# MODEL LOADING
# =================================================================
def _ensure_near_miss(self):
"""Load the calibrated Model D artifact once."""
if self._near_miss is None:
try:
from near_miss.probability_pipeline import ProbabilityPipeline
except ModuleNotFoundError:
# Repository-root pytest imports this module as ``src``.
from src.near_miss.probability_pipeline import ProbabilityPipeline
path = (
self.near_miss_artifact
or _config.NEAR_MISS_MODEL_PATH
)
self._near_miss = ProbabilityPipeline(path)
# Safety check:
# Make sure the configured artifact is actually Model D.
actual_features = list(self._near_miss.features)
if actual_features != MODEL_D_FEATURES:
raise ValueError(
"The configured near-miss artifact does not match "
"the deployment-realistic Model D feature schema.\n\n"
f"Expected {len(MODEL_D_FEATURES)} features:\n"
f"{MODEL_D_FEATURES}\n\n"
f"Loaded {len(actual_features)} features:\n"
f"{actual_features}\n\n"
"Make sure config.py points to:\n"
"models/near_miss/random_forest_D_calibrated.joblib"
)
return self._near_miss
def _ensure_accident(self):
"""Load the calibrated accident detector once."""
if self._accident is None:
from accident.calibrated_detector import (
CalibratedAccidentDetector
)
path = (
self.accident_checkpoint
or _config.ACCIDENT_DETECTOR_PATH
)
self._accident = CalibratedAccidentDetector(path)
return self._accident
# =================================================================
# MODEL STATUS
# =================================================================
@property
def near_miss_model_loaded(self) -> bool:
"""Return True if the near-miss model has been loaded."""
return self._near_miss is not None
@property
def accident_model_loaded(self) -> bool:
"""Return True if the accident model has been loaded."""
return self._accident is not None
# =================================================================
# NEAR-MISS PROBABILITY
# =================================================================
def _near_miss_probability(
self,
features: pd.DataFrame | dict[str, Any] | None,
) -> float | None:
"""
Calculate calibrated Model D class-1 probability.
Parameters
----------
features:
Either:
- pandas DataFrame containing Model D features
- dictionary containing one Model D feature vector
- None
Returns
-------
float | None
Calibrated class-1 probability.
"""
if features is None:
return None
pipe = self._ensure_near_miss()
# Convert dictionary into one-row DataFrame.
if isinstance(features, pd.DataFrame):
frame = features.copy()
elif isinstance(features, dict):
frame = pd.DataFrame([features])
else:
raise TypeError(
"near_miss_features must be a pandas DataFrame, "
"dictionary, or None."
)
# -------------------------------------------------------------
# Validate required Model D features
# -------------------------------------------------------------
missing = [
feature
for feature in pipe.features
if feature not in frame.columns
]
if missing:
raise ValueError(
"Input missing required Model D columns: "
f"{missing}"
)
# -------------------------------------------------------------
# Select ONLY model features
# -------------------------------------------------------------
model_frame = frame[pipe.features]
# -------------------------------------------------------------
# Run calibrated probability model
# -------------------------------------------------------------
probabilities = pipe.predict_proba(model_frame)
probability = float(probabilities[0, 1])
# Probability should always be in [0, 1].
if not 0.0 <= probability <= 1.0:
raise ValueError(
f"Near-miss model returned invalid probability: "
f"{probability}"
)
return round(probability, 6)
# =================================================================
# ACCIDENT PROBABILITY
# =================================================================
def _accident_probability(
self,
accident_detector_output: dict[str, Any] | None,
accident_probability: float | None,
) -> tuple[float | None, Any]:
"""
Resolve calibrated accident probability.
Explicit accident_probability has priority.
If detector output is supplied, only:
calibrated_accident_probability
is treated as a probability.
The raw:
accident_detection_confidence
value is preserved separately and NEVER treated as a probability.
"""
raw_signal = None
# -------------------------------------------------------------
# Explicit probability supplied by caller
# -------------------------------------------------------------
if accident_probability is not None:
resolved = float(accident_probability)
# -------------------------------------------------------------
# Probability supplied through detector output
# -------------------------------------------------------------
elif accident_detector_output is not None:
if _DETECTOR_PROBABILITY_KEY not in accident_detector_output:
raise ValueError(
"accident_detector_output is missing "
f"{_DETECTOR_PROBABILITY_KEY!r}.\n"
"The raw detector confidence is NOT a probability "
"and cannot be substituted."
)
resolved = float(
accident_detector_output[
_DETECTOR_PROBABILITY_KEY
]
)
raw_signal = accident_detector_output.get(
_DETECTOR_RAW_SIGNAL_KEY
)
# -------------------------------------------------------------
# No accident evidence supplied
# -------------------------------------------------------------
else:
resolved = None
# -------------------------------------------------------------
# Validate probability
# -------------------------------------------------------------
if resolved is not None:
if not 0.0 <= resolved <= 1.0:
raise ValueError(
"Accident probability must be between 0 and 1. "
f"Received: {resolved}"
)
return resolved, raw_signal
# =================================================================
# PUBLIC EVENT API
# =================================================================
def assess_event(
self,
near_miss_features: (
pd.DataFrame
| dict[str, Any]
| None
) = None,
accident_detector_output: dict[str, Any] | None = None,
accident_probability: float | None = None,
severity: float | None = None,
event_age_hours: float | None = None,
risk_config: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""
Fuse near-miss and accident evidence into a safety assessment.
Parameters
----------
near_miss_features:
Model D feature vector.
accident_detector_output:
Output dictionary from Phase 6 accident detector.
accident_probability:
Explicit calibrated accident probability.
severity:
Caller-supplied event severity in [0, 1].
event_age_hours:
Age of the event in hours.
risk_config:
Optional risk-engine configuration.
Returns
-------
dict
JSON-serializable safety assessment.
"""
# -------------------------------------------------------------
# Near-miss model
# -------------------------------------------------------------
near_miss_probability = self._near_miss_probability(
near_miss_features
)
# -------------------------------------------------------------
# Accident model
# -------------------------------------------------------------
resolved_accident, raw_signal = self._accident_probability(
accident_detector_output,
accident_probability,
)
# -------------------------------------------------------------
# Risk fusion
# -------------------------------------------------------------
assessment = assess_risk(
near_miss_probability=near_miss_probability,
accident_probability=resolved_accident,
severity=severity,
event_age_hours=event_age_hours,
config=risk_config,
)
# -------------------------------------------------------------
# Build final output
# -------------------------------------------------------------
output: dict[str, Any] = {
"engine_version": ENGINE_VERSION,
"near_miss_probability": (
assessment["near_miss_probability"]
),
"accident_probability": (
assessment["accident_probability"]
),
"risk_score": assessment["risk_score"],
"risk_level": assessment["risk_level"],
"severity": assessment["severity"],
"event_age_hours": assessment["event_age_hours"],
"explanation": list(
assessment["explanation"]
),
"present_inputs": assessment["present_inputs"],
"missing_inputs": assessment["missing_inputs"],
"model_provenance": {
"near_miss_artifact": str(
self.near_miss_artifact
or _config.NEAR_MISS_MODEL_PATH
),
"near_miss_model_loaded": (
self.near_miss_model_loaded
),
"near_miss_feature_configuration": (
"D_deployment_realistic"
),
"near_miss_features": MODEL_D_FEATURES,
"accident_checkpoint": str(
self.accident_checkpoint
or _config.ACCIDENT_DETECTOR_PATH
),
"accident_model_loaded": (
self.accident_model_loaded
),
"retrained": False,
},
}
# -------------------------------------------------------------
# Preserve raw accident detector confidence separately
# -------------------------------------------------------------
if raw_signal is not None:
output["detector_raw_signal"] = {
"accident_detection_confidence": raw_signal,
"note": (
"Raw detector confidence is a ranking signal "
"aggregated from accident-class box scores. "
"It is NOT a probability. The accident probability "
"is the calibrated 'accident_probability' value."
),
}
return output
# =================================================================
# IMAGE-BASED ACCIDENT + SAFETY ASSESSMENT
# =================================================================
def assess_image(
self,
image: Any,
near_miss_features: (
pd.DataFrame
| dict[str, Any]
| None
) = None,
severity: float | None = None,
event_age_hours: float | None = None,
score_threshold: float | None = None,
risk_config: dict[str, Any] | None = None,
) -> dict[str, Any]:
"""
Run the Phase 6 accident detector on an image and fuse
the result with near-miss evidence.
"""
# -------------------------------------------------------------
# Load accident detector
# -------------------------------------------------------------
detector = self._ensure_accident()
# -------------------------------------------------------------
# Run detector
# -------------------------------------------------------------
detector_output = detector.predict(
image,
score_threshold=score_threshold,
)
# -------------------------------------------------------------
# Fuse safety information
# -------------------------------------------------------------
output = self.assess_event(
near_miss_features=near_miss_features,
accident_detector_output=detector_output,
severity=severity,
event_age_hours=event_age_hours,
risk_config=risk_config,
)
# -------------------------------------------------------------
# Add detector information
# -------------------------------------------------------------
output["detector_output"] = {
"detector_version": detector_output.get(
"detector_version"
),
"image_size": detector_output.get(
"image_size"
),
"n_detections": len(
detector_output.get(
"detections",
[]
)
),
"detections": detector_output.get(
"detections",
[]
),
"calibration": detector_output.get(
"calibration"
),
}
return output
# =====================================================================
# JSON SERIALIZATION
# =====================================================================
def to_json(
output: dict[str, Any],
indent: int | None = 2,
) -> str:
"""
Convert safety assessment into JSON.
"""
return json.dumps(
output,
indent=indent,
) |