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ad91e86 | 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 | """Validation-only logistic calibration of online detector recall."""
from __future__ import annotations
import json
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
FEATURES = (
"coverage", "range_m", "angle_cos", "projected_pixels",
"depth_quality", "category_recall",
)
@dataclass(frozen=True)
class DetectionCalibrator:
intercept: float
coefficients: tuple[float, ...]
means: tuple[float, ...]
scales: tuple[float, ...]
@classmethod
def fit(cls, validation_rows: list[dict]) -> "DetectionCalibrator":
if not validation_rows or {int(row["detected"]) for row in validation_rows} != {0, 1}:
raise ValueError("calibration requires positive and negative validation observations")
features = np.asarray(
[[float(row[key]) for key in FEATURES] for row in validation_rows], dtype=float
)
if not np.isfinite(features).all():
raise ValueError("calibration features must be finite")
scaler = StandardScaler().fit(features)
classifier = LogisticRegression(max_iter=1000).fit(
scaler.transform(features), [int(row["detected"]) for row in validation_rows]
)
return cls(
float(classifier.intercept_[0]), tuple(classifier.coef_[0].tolist()),
tuple(scaler.mean_.tolist()), tuple(scaler.scale_.tolist()),
)
def predict(self, online_features: dict) -> float:
values = np.asarray([float(online_features[key]) for key in FEATURES])
standardized = (values - self.means) / self.scales
logit = self.intercept + float(np.dot(self.coefficients, standardized))
return float(1.0 / (1.0 + np.exp(-np.clip(logit, -30, 30))))
def save(self, path: Path) -> None:
path.write_text(json.dumps({
"intercept": self.intercept, "coefficients": self.coefficients,
"means": self.means, "scales": self.scales,
"features": FEATURES,
}, indent=2) + "\n", encoding="utf-8")
@classmethod
def load(cls, path: Path) -> "DetectionCalibrator":
data = json.loads(path.read_text(encoding="utf-8"))
if tuple(data["features"]) != FEATURES:
raise ValueError("calibrator feature schema mismatch")
return cls(
float(data["intercept"]), tuple(data["coefficients"]),
tuple(data["means"]), tuple(data["scales"]),
)
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