began work on v3

2026-03-19 16:58:29 +01:00
parent 786457b30d
commit eb9eafe715
42 changed files with 11214 additions and 1 deletions
@@ -0,0 +1,138 @@
+"""Result dataclasses and serialisation helpers for V3 fold outputs."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Optional
+
+import numpy as np
+
+
+def _nan() -> float:
+    return float("nan")
+
+
+def _f(v) -> Optional[float]:
+    """Round a scalar to 6 dp, return None for nan/None."""
+    if v is None or (isinstance(v, float) and np.isnan(v)):
+        return None
+    return round(float(v), 6)
+
+
+def _sv(vec) -> Optional[str]:
+    """Serialise a vector to a pipe-separated string, or None."""
+    if vec is None:
+        return None
+    return "|".join(f"{float(v):.4f}" for v in vec)
+
+
+@dataclass
+class FoldResult:
+    mode: str
+    fold: int
+    # Epoch where each model hit its peak val AUC
+    best_epoch_single: int
+    best_epoch_bilat:  int
+    # Classic (eye-level eval of SingleEyeHT)
+    classic_val_auc:       float
+    classic_val_acc:       float
+    classic_val_kappa:     float
+    classic_val_mcc:       float
+    classic_val_f1:        float
+    classic_val_recall:    Optional[str]
+    classic_val_ece:       float
+    classic_val_threshold: float
+    classic_val_bias:      Optional[str]
+    classic_val_n:         int
+    # Ensemble (patient-level eval of SingleEyeHT)
+    ensemble_val_auc:       float
+    ensemble_val_acc:       float
+    ensemble_val_kappa:     float
+    ensemble_val_mcc:       float
+    ensemble_val_f1:        float
+    ensemble_val_recall:    Optional[str]
+    ensemble_val_ece:       float
+    ensemble_val_threshold: float
+    ensemble_val_bias:      Optional[str]
+    ensemble_val_n:         int
+    # Bilateral (BilateralHT patient-level)
+    bilat_val_auc:       float
+    bilat_val_acc:       float
+    bilat_val_kappa:     float
+    bilat_val_mcc:       float
+    bilat_val_f1:        float
+    bilat_val_recall:    Optional[str]
+    bilat_val_ece:       float
+    bilat_val_threshold: float
+    bilat_val_bias:      Optional[str]
+    bilat_val_n:         int
+    # Test metrics (evaluated once after training on final-epoch model)
+    ensemble_test_auc:   float = float("nan")
+    ensemble_test_acc:   float = float("nan")
+    ensemble_test_kappa: float = float("nan")
+    ensemble_test_f1:    float = float("nan")
+    ensemble_test_ece:   float = float("nan")
+    classic_test_auc:    float = float("nan")
+    classic_test_acc:    float = float("nan")
+    classic_test_kappa:  float = float("nan")
+    classic_test_f1:     float = float("nan")
+    classic_test_ece:    float = float("nan")
+    bilat_test_auc:      float = float("nan")
+    bilat_test_acc:      float = float("nan")
+    bilat_test_kappa:    float = float("nan")
+    bilat_test_f1:       float = float("nan")
+    bilat_test_ece:      float = float("nan")
+    test_n:              int   = 0
+    # Training sample counts
+    single_train_n: int = 0
+    bilat_train_n:  int = 0
+    # Fused head (optional)
+    fused_val_auc:       float        = float("nan")
+    fused_val_acc:       float        = float("nan")
+    fused_val_kappa:     float        = float("nan")
+    fused_val_mcc:       float        = float("nan")
+    fused_val_f1:        float        = float("nan")
+    fused_val_recall:    Optional[str] = None
+    fused_val_ece:       float        = float("nan")
+    fused_val_threshold: float        = float("nan")
+    fused_val_bias:      Optional[str] = None
+    fused_val_n:         int          = 0
+    fused_test_auc:      float        = float("nan")
+    fused_test_acc:      float        = float("nan")
+
+
+@dataclass
+class FoldArtifacts:
+    # Val split
+    y_true_classic:  Optional[np.ndarray]
+    probs_classic:   Optional[np.ndarray]
+    y_true_ensemble: Optional[np.ndarray]
+    probs_ensemble:  Optional[np.ndarray]
+    y_true_bilat:    Optional[np.ndarray]
+    probs_bilat:     Optional[np.ndarray]
+    y_true_fused:    Optional[np.ndarray] = None
+    probs_fused:     Optional[np.ndarray] = None
+    probs_ensemble_img: Optional[np.ndarray] = None
+    probs_ensemble_md:  Optional[np.ndarray] = None
+    probs_classic_img:  Optional[np.ndarray] = None
+    probs_classic_md:   Optional[np.ndarray] = None
+    # per-eye (pre-averaged) for ensemble mode
+    y_true_ensemble_pereye:    Optional[np.ndarray] = None
+    probs_ensemble_pereye:     Optional[np.ndarray] = None
+    probs_ensemble_img_pereye: Optional[np.ndarray] = None
+    probs_ensemble_md_pereye:  Optional[np.ndarray] = None
+    # raw logits — patient-level
+    logits_ensemble:     Optional[np.ndarray] = None
+    logits_ensemble_img: Optional[np.ndarray] = None
+    logits_ensemble_md:  Optional[np.ndarray] = None
+    logits_classic:      Optional[np.ndarray] = None
+    logits_classic_img:  Optional[np.ndarray] = None
+    logits_classic_md:   Optional[np.ndarray] = None
+    # raw logits — per-eye
+    logits_ensemble_pereye:     Optional[np.ndarray] = None
+    logits_ensemble_img_pereye: Optional[np.ndarray] = None
+    logits_ensemble_md_pereye:  Optional[np.ndarray] = None
+    # Test split equivalents
+    y_true_test:         Optional[np.ndarray] = None
+    probs_test:          Optional[np.ndarray] = None
+    probs_test_img:      Optional[np.ndarray] = None
+    probs_test_md:       Optional[np.ndarray] = None