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5d10875 | 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 | """ScenarioMiningAgent — similar-failure retrieval + clustering.
Embedding + attribute retrieval of similar historical failures, following the
megaeval embedding recipe (structural features + one-hot attributes + seeded
random projection, cosine retrieval — see MegaStore._build_embeddings in
sensorflow/megaeval/runs.py, which operates on run artifacts and is therefore
mirrored here for failure records rather than re-invented).
Clustering method is SELECTED FROM THE DATA SHAPE and the choice is justified
in the output (it is not always DBSCAN):
* n < 15 -> single-linkage agglomerative on cosine distance
(density parameters are unidentifiable at tiny n)
* mostly categorical -> attribute-signature grouping (exact modes)
* otherwise -> density-based (DBSCAN-style) via pairwise radius
graph connected components
"""
from __future__ import annotations
from typing import Any, Dict, List, Tuple
import numpy as np
from sensorflow.agentic.agents.base import BaseAgent, compact, no_escalation
from sensorflow.agentic.models import AgentEscalation, FailureEvent
GT_CLASSES = ["pedestrian", "cyclist", "motorcycle", "vehicle", "truck"]
PRED_CLASSES = ["pedestrian", "cyclist", "motorcycle", "vehicle", "truck",
"construction_cone", "background"]
TIMES = ["day", "night"]
WEATHERS = ["clear", "rain"]
EMBED_DIM = 16
EMBED_SEED = 1234 # same seeded-projection convention as megaeval
def historical_corpus(seed: int = 77, n: int = 48) -> List[Dict]:
"""Deterministic synthetic archive of previously-triaged failures.
Deliberately contains NO pedestrian->construction_cone precedent, so the
motivating failure is genuinely novel against the archive.
"""
rng = np.random.default_rng(seed)
pairs = [("pedestrian", "cyclist"), ("cyclist", "motorcycle"),
("vehicle", "truck"), ("truck", "vehicle"),
("cyclist", "vehicle"), ("pedestrian", "background"),
("motorcycle", "cyclist"), ("vehicle", "background")]
out = []
for i in range(n):
gt, pred = pairs[int(rng.integers(0, len(pairs)))]
out.append({
"historical_id": f"hist-{i:04d}",
"gt_class": gt, "predicted_class": pred,
"construction_zone": bool(rng.random() < 0.15),
"time_of_day": TIMES[int(rng.random() < 0.4)],
"weather": WEATHERS[int(rng.random() < 0.3)],
"distance_m": round(float(rng.uniform(8, 70)), 1),
"resolved": bool(rng.random() < 0.8),
"resolution": "retrained" if rng.random() < 0.5 else "data_fix",
})
return out
def _featurize(rec: Dict) -> np.ndarray:
f: List[float] = []
f.extend(1.0 if rec["gt_class"] == c else 0.0 for c in GT_CLASSES)
f.extend(1.0 if rec["predicted_class"] == c else 0.0 for c in PRED_CLASSES)
f.append(1.0 if rec.get("construction_zone") else 0.0)
f.append(1.0 if rec.get("time_of_day") == "night" else 0.0)
f.append(1.0 if rec.get("weather") == "rain" else 0.0)
f.append(min(float(rec.get("distance_m", 30.0)) / 70.0, 1.0))
return np.asarray(f, dtype=np.float64)
def embed(records: List[Dict]) -> np.ndarray:
X = np.vstack([_featurize(r) for r in records])
rng = np.random.default_rng(EMBED_SEED)
proj = rng.normal(size=(X.shape[1], EMBED_DIM)) / np.sqrt(X.shape[1])
E = X @ proj
return E / np.clip(np.linalg.norm(E, axis=1, keepdims=True), 1e-9, None)
def _select_clustering(n: int, categorical_share: float) -> Tuple[str, str]:
if n < 15:
return ("single_linkage_agglomerative",
f"only n={n} member instances: density-based methods "
"(DBSCAN) need an eps/minPts neighborhood that is "
"unidentifiable at this sample size; single-linkage on "
"cosine distance degrades gracefully")
if categorical_share > 0.8:
return ("attribute_signature_grouping",
f"{categorical_share:.0%} of features are categorical "
"one-hots: exact signature grouping is better-defined than "
"euclidean density in a nearly-discrete space")
return ("density_radius_components",
f"n={n} with mixed features: density-based clustering "
"(DBSCAN-style radius graph components) is appropriate")
def _cluster(records: List[Dict], emb: np.ndarray, method: str) -> List[List[int]]:
n = len(records)
if method == "attribute_signature_grouping":
groups: Dict[Tuple, List[int]] = {}
for i, r in enumerate(records):
key = (r.get("construction_zone"), r.get("time_of_day"),
r.get("weather"))
groups.setdefault(key, []).append(i)
return list(groups.values())
# graph-based (single-linkage threshold == radius components on cosine)
sims = emb @ emb.T
thr = 0.92 if method == "single_linkage_agglomerative" else 0.95
parent = list(range(n))
def find(a):
while parent[a] != a:
parent[a] = parent[parent[a]]
a = parent[a]
return a
for i in range(n):
for j in range(i + 1, n):
if sims[i, j] >= thr:
parent[find(i)] = find(j)
groups2: Dict[int, List[int]] = {}
for i in range(n):
groups2.setdefault(find(i), []).append(i)
return list(groups2.values())
class ScenarioMiningAgent(BaseAgent):
name = "scenario_mining"
version = "1.0.0"
epistemic_status = "DERIVED"
def _analyze(self, failure_id: str, failure: FailureEvent = None,
**inputs) -> Tuple[Dict[str, Any], float, str, AgentEscalation]:
assert failure is not None
corpus = historical_corpus()
# ---- retrieval: embedding + attribute filters --------------------
query = {
"gt_class": failure.gt_class or "vehicle",
"predicted_class": failure.predicted_class or "background",
"construction_zone": any(i.construction_zone for i in failure.instances),
"time_of_day": (failure.instances[0].time_of_day
if failure.instances else "day"),
"weather": (failure.instances[0].weather
if failure.instances else "clear"),
"distance_m": (float(np.mean([i.distance_m for i in failure.instances]))
if failure.instances else 30.0),
}
all_emb = embed(corpus + [query])
emb, q = all_emb[:-1], all_emb[-1]
sims = emb @ q
order = np.argsort(-sims)[:8]
similar = [{**corpus[int(i)], "similarity": round(float(sims[int(i)]), 4)}
for i in order]
exact_precedent = [r for r in corpus
if r["gt_class"] == query["gt_class"]
and r["predicted_class"] == query["predicted_class"]]
novelty = "novel" if not exact_precedent else (
"known_regression" if any(not r["resolved"] for r in exact_precedent)
else "known_stable")
# ---- clustering of the failure's own instances -------------------
inst_records = [{
"gt_class": i.gt_class, "predicted_class": i.predicted_class,
"construction_zone": i.construction_zone,
"time_of_day": i.time_of_day, "weather": i.weather,
"distance_m": i.distance_m,
} for i in failure.instances]
cluster_reports: List[Dict] = []
method = justification = None
if inst_records:
feat_dim = _featurize(inst_records[0]).size
categorical_share = (feat_dim - 1) / feat_dim # all but distance
method, justification = _select_clustering(len(inst_records),
categorical_share)
inst_emb = embed(inst_records)
clusters = _cluster(inst_records, inst_emb, method)
total = len(inst_records)
for idx, members in enumerate(sorted(clusters, key=len, reverse=True)):
recs = [inst_records[m] for m in members]
dominant = {
"construction_zone": (sum(r["construction_zone"] for r in recs)
/ len(recs)),
"night_share": (sum(r["time_of_day"] == "night" for r in recs)
/ len(recs)),
"rain_share": (sum(r["weather"] == "rain" for r in recs)
/ len(recs)),
"mean_distance_m": round(float(np.mean(
[r["distance_m"] for r in recs])), 1),
}
cluster_reports.append({
"cluster_id": f"{failure.failure_id}-c{idx}",
"size": len(members),
"population_share": round(len(members) / total, 3),
"dominant_conditions": dominant,
"representative_examples": [
failure.instances[m].instance_id for m in members[:3]],
})
output = {
"similar_historical": similar,
"exact_precedent_count": len(exact_precedent),
"novelty": novelty,
"novelty_basis": ("no archived failure shares the "
f"{query['gt_class']}->{query['predicted_class']} "
"signature" if novelty == "novel" else
f"{len(exact_precedent)} archived precedents"),
"clustering_method": method,
"clustering_justification": justification,
"clusters": cluster_reports,
"retrieval": ("cosine over seeded random-projection embeddings of "
"structural+categorical features (megaeval recipe) "
"+ attribute retrieval"),
}
confidence = 0.75 if inst_records else 0.4
return (output, confidence,
"retrieval and clustering are deterministic; confidence "
"reflects instance coverage",
no_escalation())
def _llm_prompt(self, failure_id, output, **inputs):
return ("Summarize the cluster structure and novelty finding of this "
"failure mining result: " + compact(output))
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