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f559cc0 | 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 | from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import torch
from sklearn.metrics import accuracy_score, f1_score, mean_absolute_error, precision_score, recall_score, roc_auc_score
from app.config import (
DEFAULT_ARCHIVE_MODEL_PATH,
DEFAULT_EFFICIENTNET_MODEL_PATH,
DEFAULT_RUNTIME_STACK_REPORT_PATH,
)
from app.ml.archive_model import load_archive_model, predict_with_archive_model
from app.ml.efficientnet_model import load_efficientnet_checkpoint
from app.ml.features import extract_eye_features
from app.ml.runtime_stack import (
DEFAULT_SOURCE_THRESHOLDS,
RUNTIME_STACK_VERSION,
build_runtime_stack_prediction,
decision_threshold_for_source,
)
from app.services.conjunctiva_roi import ConjunctivaRoiExtractor
from train_efficientnet import ARCHIVE_ROOT, _balanced_group_split, _build_records, _load_image_with_fallback
def main() -> None:
records = _build_records(ARCHIVE_ROOT)
if not records:
raise RuntimeError(f"No evaluation records found in {ARCHIVE_ROOT}.")
_, val_records = _balanced_group_split(records, test_size=0.2, n_splits=32)
archive_model = load_archive_model(DEFAULT_ARCHIVE_MODEL_PATH)
efficientnet_bundle = (
load_efficientnet_checkpoint(DEFAULT_EFFICIENTNET_MODEL_PATH)
if Path(DEFAULT_EFFICIENTNET_MODEL_PATH).exists()
else None
)
roi_extractor = ConjunctivaRoiExtractor()
runtime_rows: list[dict[str, float | int | str]] = []
full_rows: list[dict[str, float | int | str]] = []
prepared_images: list[object] = []
prepared_sources: list[str] = []
prepared_archive_predictions: list[dict[str, float]] = []
prepared_records = []
for record in val_records:
image = _load_image_with_fallback(record.image_path)
source_hint = record.source
if record.source == "roi_original":
image = roi_extractor.extract(image).image
image = image.convert("RGB")
archive_prediction = predict_with_archive_model(
archive_model,
extract_eye_features(image),
source_hint=source_hint,
)
prepared_records.append(record)
prepared_images.append(image)
prepared_sources.append(source_hint)
prepared_archive_predictions.append(archive_prediction)
efficientnet_predictions = _predict_efficientnet_batch(efficientnet_bundle, prepared_images)
for record, source_hint, archive_prediction, efficientnet_prediction in zip(
prepared_records,
prepared_sources,
prepared_archive_predictions,
efficientnet_predictions,
strict=True,
):
runtime_prediction = build_runtime_stack_prediction(
archive_prediction,
efficientnet_prediction=efficientnet_prediction,
source_hint=source_hint, # type: ignore[arg-type]
)
row = {
"label": int(record.label),
"source": str(record.source),
"risk": float(runtime_prediction["anemia_risk"]),
"predicted_hb": float(runtime_prediction["predicted_hemoglobin"]),
"target_hb": float(record.hb),
}
full_rows.append(row)
if record.source == "roi_original":
runtime_rows.append(row)
runtime_metrics = _evaluate_rows(runtime_rows, source_aware=False)
full_metrics = _evaluate_rows(full_rows, source_aware=True)
report = {
"primary_model": RUNTIME_STACK_VERSION,
"record_count": len(records),
"subject_count": len({record.subject_id for record in records}),
"selected_mode": "archive_evidence_fusion_runtime",
"source_thresholds": DEFAULT_SOURCE_THRESHOLDS,
"metrics": runtime_metrics,
"full_validation": full_metrics,
}
DEFAULT_RUNTIME_STACK_REPORT_PATH.write_text(json.dumps(report, indent=2), encoding="utf-8")
print("\nRuntime stack metrics (ROI-gated uploads)")
for key in ("accuracy", "precision", "recall", "f1", "auc", "hb_mae"):
print(f"{key}: {runtime_metrics[key]:.4f}")
print("\nFull validation metrics (all sources)")
for key in ("accuracy", "precision", "recall", "f1", "auc", "hb_mae"):
print(f"{key}: {full_metrics[key]:.4f}")
def _evaluate_rows(
rows: list[dict[str, float | int | str]],
*,
source_aware: bool,
) -> dict[str, float | int | str]:
labels = np.asarray([int(row["label"]) for row in rows], dtype=np.int32)
probabilities = np.asarray([float(row["risk"]) for row in rows], dtype=np.float32)
predicted_hb = np.asarray([float(row["predicted_hb"]) for row in rows], dtype=np.float32)
target_hb = np.asarray([float(row["target_hb"]) for row in rows], dtype=np.float32)
if source_aware:
predictions = np.asarray(
[
1
if float(row["risk"]) >= decision_threshold_for_source(str(row["source"])) # type: ignore[arg-type]
else 0
for row in rows
],
dtype=np.int32,
)
split_strategy = "group-shuffle-balance-select: source-aware"
else:
threshold = decision_threshold_for_source("roi_original")
predictions = (probabilities >= threshold).astype(np.int32)
split_strategy = "group-shuffle-balance-select: roi_original"
return {
"accuracy": round(float(accuracy_score(labels, predictions)), 4),
"precision": round(float(precision_score(labels, predictions, zero_division=0)), 4),
"recall": round(float(recall_score(labels, predictions, zero_division=0)), 4),
"f1": round(float(f1_score(labels, predictions, zero_division=0)), 4),
"auc": round(float(roc_auc_score(labels, probabilities)), 4),
"hb_mae": round(float(mean_absolute_error(target_hb, predicted_hb)), 4),
"validation_size": int(len(rows)),
"split_strategy": split_strategy,
}
def _predict_efficientnet_batch(
bundle: dict[str, object] | None,
images: list[object],
) -> list[dict[str, float] | None]:
if bundle is None:
return [None] * len(images)
transform = bundle["transform"]
model = bundle["model"]
hb_mean = float(bundle.get("hb_mean", 0.0))
hb_std = max(float(bundle.get("hb_std", 1.0)), 1e-6)
tensors = torch.stack([transform(image) for image in images], dim=0)
with torch.no_grad():
output = model(tensors)
probabilities = torch.sigmoid(output[:, 0]).cpu().numpy()
hemoglobin = ((output[:, 1].cpu().numpy()) * hb_std) + hb_mean
results: list[dict[str, float]] = []
for probability, hb_value in zip(probabilities, hemoglobin, strict=True):
margin_uncertainty = 1.0 - min(1.0, abs(float(probability) - 0.5) * 2.0)
results.append(
{
"anemia_risk": float(probability),
"predicted_hemoglobin": float(hb_value),
"uncertainty": float(np.clip((margin_uncertainty * 0.2) + 0.05, 0.05, 0.95)),
}
)
return results
if __name__ == "__main__":
main()
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