rpotter6298
512ebd13b2
- Introduced `protocol.py` for shared data models used in server/client communication, including request and response schemas for registration, job submission, and status updates. - Implemented `server.py` to manage a SQLite job queue and client registry, handling job polling, status updates, and job completion. - Created a cheat sheet for server usage, detailing commands for starting the server, submitting jobs, and monitoring clients. - Added several experiment configuration files for various training setups, including geometry vector injections and baseline ensembles.
145 lines
5.3 KiB
Python
145 lines
5.3 KiB
Python
"""Result dataclasses and serialisation helpers for V3 fold outputs."""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Optional
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import numpy as np
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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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@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
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best_epoch_bilat: int
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# Classic (eye-level eval of SingleEyeHT)
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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 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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# Test metrics (evaluated once after training on final-epoch model)
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ensemble_test_auc: float = float("nan")
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ensemble_test_acc: float = float("nan")
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ensemble_test_kappa: float = float("nan")
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ensemble_test_f1: float = float("nan")
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ensemble_test_ece: float = float("nan")
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classic_test_auc: float = float("nan")
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classic_test_acc: float = float("nan")
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classic_test_kappa: float = float("nan")
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classic_test_f1: float = float("nan")
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classic_test_ece: float = float("nan")
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bilat_test_auc: float = float("nan")
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bilat_test_acc: float = float("nan")
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bilat_test_kappa: float = float("nan")
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bilat_test_f1: float = float("nan")
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bilat_test_ece: float = float("nan")
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test_n: int = 0
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# Training sample counts
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single_train_n: int = 0
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bilat_train_n: int = 0
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# Fused head (optional)
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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_test_auc: float = float("nan")
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fused_test_acc: float = float("nan")
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fused_test_kappa: float = float("nan")
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fused_test_f1: float = float("nan")
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fused_test_ece: float = float("nan")
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fused_test_n: int = 0
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@dataclass
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class FoldArtifacts:
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# Val split
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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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probs_ensemble_img: Optional[np.ndarray] = None
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probs_ensemble_md: Optional[np.ndarray] = None
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probs_classic_img: Optional[np.ndarray] = None
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probs_classic_md: Optional[np.ndarray] = None
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# per-eye (pre-averaged) for ensemble mode
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y_true_ensemble_pereye: Optional[np.ndarray] = None
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probs_ensemble_pereye: Optional[np.ndarray] = None
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probs_ensemble_img_pereye: Optional[np.ndarray] = None
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probs_ensemble_md_pereye: Optional[np.ndarray] = None
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# raw logits — patient-level
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logits_ensemble: Optional[np.ndarray] = None
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logits_ensemble_img: Optional[np.ndarray] = None
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logits_ensemble_md: Optional[np.ndarray] = None
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logits_classic: Optional[np.ndarray] = None
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logits_classic_img: Optional[np.ndarray] = None
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logits_classic_md: Optional[np.ndarray] = None
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# raw logits — per-eye
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logits_ensemble_pereye: Optional[np.ndarray] = None
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logits_ensemble_img_pereye: Optional[np.ndarray] = None
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logits_ensemble_md_pereye: Optional[np.ndarray] = None
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# Test split equivalents
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y_true_test: Optional[np.ndarray] = None
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probs_test: Optional[np.ndarray] = None
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probs_test_img: Optional[np.ndarray] = None
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probs_test_md: Optional[np.ndarray] = None
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y_true_fused_test: Optional[np.ndarray] = None
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probs_fused_test: Optional[np.ndarray] = None
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