rpotter6298
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
"""Result dataclasses and serialisation helpers for V2 fold outputs."""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Optional
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import numpy as np
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# ---------------------------------------------------------------------------
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# Primitive helpers
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# ---------------------------------------------------------------------------
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def _nan() -> float:
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return float("nan")
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def _f(v) -> Optional[float]:
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"""Round a scalar to 6 dp, return None for nan/None."""
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if v is None or (isinstance(v, float) and np.isnan(v)):
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return None
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return round(float(v), 6)
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def _sv(vec) -> Optional[str]:
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"""Serialise a vector to a pipe-separated string, or None."""
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if vec is None:
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return None
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return "|".join(f"{float(v):.4f}" for v in vec)
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# ---------------------------------------------------------------------------
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# Dataclasses
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# ---------------------------------------------------------------------------
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@dataclass
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class FoldResult:
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mode: str
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fold: int
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# Epoch where each model hit its peak val AUC
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best_epoch_single: int # SingleEyeHT — selected by ensemble val AUC
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best_epoch_bilat: int # BilateralHT — selected by bilateral val AUC
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# Classic (eye-level eval of SingleEyeHT; n = 2 * ensemble_val_n)
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classic_val_auc: float
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classic_val_acc: float
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classic_val_kappa: float
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classic_val_mcc: float
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classic_val_f1: float
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classic_val_recall: Optional[str]
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classic_val_ece: float
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classic_val_threshold: float
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classic_val_bias: Optional[str]
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classic_val_n: int
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# Ensemble (patient-level eval of same SingleEyeHT)
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ensemble_val_auc: float
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ensemble_val_acc: float
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ensemble_val_kappa: float
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ensemble_val_mcc: float
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ensemble_val_f1: float
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ensemble_val_recall: Optional[str]
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ensemble_val_ece: float
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ensemble_val_threshold: float
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ensemble_val_bias: Optional[str]
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ensemble_val_n: int
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# Bilateral (BilateralHT patient-level)
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bilat_val_auc: float
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bilat_val_acc: float
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bilat_val_kappa: float
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bilat_val_mcc: float
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bilat_val_f1: float
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bilat_val_recall: Optional[str]
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bilat_val_ece: float
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bilat_val_threshold: float
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bilat_val_bias: Optional[str]
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bilat_val_n: int
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# Holdout metrics (evaluated at best val epoch; nan if no holdout)
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classic_holdout_auc: float
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classic_holdout_acc: float
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ensemble_holdout_auc: float
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ensemble_holdout_acc: float
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bilat_holdout_auc: float
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bilat_holdout_acc: float
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holdout_n: int # number of holdout bilateral samples
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# Training sample counts
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single_train_n: int
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bilat_train_n: int
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# Fused head (ensemble + --fused-head; nan / None if --fused-head not used)
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fused_val_auc: float = float("nan")
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fused_val_acc: float = float("nan")
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fused_val_kappa: float = float("nan")
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fused_val_mcc: float = float("nan")
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fused_val_f1: float = float("nan")
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fused_val_recall: Optional[str] = None
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fused_val_ece: float = float("nan")
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fused_val_threshold: float = float("nan")
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fused_val_bias: Optional[str] = None
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fused_val_n: int = 0
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fused_holdout_auc: float = float("nan")
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fused_holdout_acc: float = float("nan")
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@dataclass
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class FoldArtifacts:
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y_true_classic: Optional[np.ndarray]
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probs_classic: Optional[np.ndarray]
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y_true_ensemble: Optional[np.ndarray]
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probs_ensemble: Optional[np.ndarray]
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y_true_bilat: Optional[np.ndarray]
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probs_bilat: Optional[np.ndarray]
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y_true_fused: Optional[np.ndarray] = None
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probs_fused: Optional[np.ndarray] = None
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