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68bc1c7 | 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 | from __future__ import annotations
import pickle
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
import numpy as np
from app.ml.archive_model import clamp
FEATURE_ORDER: tuple[str, ...] = (
"base_anemia_risk",
"uncertainty",
"predicted_hemoglobin",
"predicted_hemoglobin_missing",
"brightness_score",
"contrast_score",
"blur_score",
"framing_score",
"lighting_score",
"glare_risk",
"shadow_risk",
"lighting_balanced",
"lighting_overexposed",
"lighting_glare_heavy",
"lighting_shadow_heavy",
"lighting_flat_contrast",
"lighting_dim",
"base_likely",
)
@dataclass
class RuntimeScreeningRefiner:
version: str = "runtime-screening-refiner-v1"
method: str = "logistic-regression"
threshold: float = 0.53
feature_order: tuple[str, ...] = FEATURE_ORDER
model: Any = None
report: dict[str, object] = field(default_factory=dict)
def _feature_vector(
self,
*,
base_anemia_risk: float,
uncertainty: float,
predicted_hemoglobin: float | None,
quality,
base_likely: bool,
) -> list[float]:
hb_missing = predicted_hemoglobin is None
hb_value = 13.5 if predicted_hemoglobin is None else float(predicted_hemoglobin)
lighting = str(getattr(quality, "lighting_condition", "balanced"))
return [
float(base_anemia_risk),
float(uncertainty),
hb_value,
float(hb_missing),
float(getattr(quality, "brightness_score", 0.0)),
float(getattr(quality, "contrast_score", 0.0)),
float(getattr(quality, "blur_score", 0.0)),
float(getattr(quality, "framing_score", 0.0)),
float(getattr(quality, "lighting_score", 0.0)),
float(getattr(quality, "glare_risk", 0.0)),
float(getattr(quality, "shadow_risk", 0.0)),
float(lighting == "balanced"),
float(lighting == "overexposed"),
float(lighting == "glare_heavy"),
float(lighting == "shadow_heavy"),
float(lighting == "flat_contrast"),
float(lighting == "dim"),
float(base_likely),
]
def refine(
self,
*,
base_anemia_risk: float,
uncertainty: float,
predicted_hemoglobin: float | None,
quality,
base_likely: bool,
) -> float:
if self.model is None:
return clamp(float(base_anemia_risk), 0.0, 1.0)
vector = np.asarray(
[
self._feature_vector(
base_anemia_risk=base_anemia_risk,
uncertainty=uncertainty,
predicted_hemoglobin=predicted_hemoglobin,
quality=quality,
base_likely=base_likely,
)
],
dtype=np.float32,
)
probability = float(self.model.predict_proba(vector)[0, 1])
return clamp(probability, 0.0, 1.0)
def save(self, path: str | Path) -> None:
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("wb") as handle:
pickle.dump(self, handle)
@classmethod
def load(cls, path: str | Path) -> "RuntimeScreeningRefiner":
with Path(path).open("rb") as handle:
return pickle.load(handle)
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