rpotter6298
708fbc70ce
- Introduced `bridge_attention_ceiling_check.py` for variance decomposition analysis on bridge attention configurations. - Added `bridge_attention_readout.py` to perform per-tower gate and contribution readouts, including AUC sanity checks. - Created multiple JSON configuration files for backbone replication experiments, including anonymous CV variants and basic backbones. - Implemented sensitivity experiments to evaluate the impact of axial length inclusion and EfficientNetV2-M performance at higher resolutions. - Added a memory probe script to assess GPU memory usage during training with EfficientNetV2-M.
346 lines
11 KiB
Python
346 lines
11 KiB
Python
"""F4 — Bilateral lift via confidence strips.
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Six-panel grid in the same v3-style as F3. Rows are aggregation mode,
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columns are tower configuration:
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Clinical Image Fusion (img+cd)
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Single | cd_solo_single | img_solo_single | ensemble_single (Hadamard L1)
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Bilateral| cd_solo_bilat | img_solo_bilat | baseline_ensemble (L2 concat default)
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All refugelike. Single-eye panels eval at the appropriate eye-level fusion
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stage; bilateral panels eval at hb. Points are coloured by VF-MD severity.
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Each panel shows per-patient or per-eye P(Glaucoma) with TN / TP rates and
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AUC printed in.
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Re-run anytime:
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python -m v4.figures.F4_bilateral
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"""
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from __future__ import annotations
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import warnings
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warnings.filterwarnings("ignore")
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from pathlib import Path
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import h5py
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import matplotlib.patches as mpatches
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import numpy as np
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import pandas as pd
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from sklearn.metrics import roc_auc_score
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from v4.figures.util.loaders import REPO_ROOT, RESULTS_ROOT
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OUT = Path(__file__).parent / "output" / "F4_bilateral.png"
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# Palette (matches F3) ───────────────────────────────────────────────────────
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C_NORMAL = "#78909C"
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C_EARLY = "#29B6F6"
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C_MODERATE = "#FFB300"
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C_SEVERE = "#E53935"
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C_UNKNOWN = "#BDBDBD"
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SEV_COLORS = {
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"normal": C_NORMAL,
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"early": C_EARLY,
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"moderate": C_MODERATE,
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"severe": C_SEVERE,
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"unknown": C_UNKNOWN,
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}
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SEV_LABELS = {
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"normal": "Normal",
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"early": "Glaucoma — early (VF_MD > −6)",
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"moderate": "Glaucoma — moderate (−12 to −6)",
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"severe": "Glaucoma — severe (VF_MD < −12)",
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"unknown": "Glaucoma — VF_MD not recorded",
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}
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SEV_ORDER = ["severe", "moderate", "unknown", "early", "normal"]
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SEV_ALPHA = {
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"normal": 0.55,
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"unknown": 0.55,
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"early": 0.55,
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"moderate": 0.55,
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"severe": 0.55,
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}
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SEV_SIZE = {"normal": 8, "unknown": 8, "early": 8, "moderate": 8, "severe": 8}
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# Panel grid: [row][col] = (label, run_dir, eval_stage)
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GRID = [
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[
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("Single · Clinical", RESULTS_ROOT / "phase2_v4" / "cd_solo_single", "cd_fuse"),
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(
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"Single · Image",
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RESULTS_ROOT / "refuge_v2m_baseline" / "img_solo_single_refugelike",
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"img_fuse",
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),
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(
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"Single · Fusion",
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RESULTS_ROOT / "refuge_v2m_baseline" / "ensemble_single_refugelike",
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"nt",
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),
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],
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[
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(
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"Bilateral · Clinical",
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RESULTS_ROOT / "phase4_v4" / "cd_solo_bilateral",
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"hb",
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),
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(
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"Bilateral · Image",
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RESULTS_ROOT / "refuge_v2m_baseline" / "img_solo_bilateral_refugelike",
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"hb",
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),
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("Bilateral · Fusion", RESULTS_ROOT / "tri_v1" / "baseline_ensemble", "hb"),
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],
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]
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CLINICAL_DIR = REPO_ROOT / "Papila" / "ClinicalData"
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# ── Same helpers as F3 ───────────────────────────────────────────────────────
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def load_vfi() -> pd.DataFrame:
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od = pd.read_excel(CLINICAL_DIR / "patient_data_od.xlsx", header=1)
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os_ = pd.read_excel(CLINICAL_DIR / "patient_data_os.xlsx", header=1)
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def _clean(df):
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df = df.copy()
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if "Patient ID" not in df.columns and "ID" in df.columns:
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df.rename(columns={"ID": "Patient ID"}, inplace=True)
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df["Patient ID"] = (
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df["Patient ID"].astype(str).str.extract(r"(\d+)")[0].astype(int)
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)
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df["Diagnosis"] = pd.to_numeric(df["Diagnosis"], errors="coerce")
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df["VF_MD"] = pd.to_numeric(df["VF_MD"], errors="coerce")
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df = df[df["Diagnosis"].isin([0, 1])].copy()
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return df[["Patient ID", "Diagnosis", "VF_MD"]]
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both = pd.concat([_clean(od), _clean(os_)], ignore_index=True)
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diag = (
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both.groupby("Patient ID")["Diagnosis"]
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.agg(lambda x: x.mode().iloc[0])
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.reset_index()
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)
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vf = both.groupby("Patient ID")["VF_MD"].min().reset_index()
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out = diag.merge(vf, on="Patient ID").rename(
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columns={"Patient ID": "patient_id", "Diagnosis": "diagnosis", "VF_MD": "vf_md"}
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)
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def _sev(row):
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if int(row["diagnosis"]) == 0:
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return "normal"
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v = row["vf_md"]
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if pd.isna(v):
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return "unknown"
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if v > -6:
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return "early"
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if v > -12:
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return "moderate"
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return "severe"
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out["severity"] = out.apply(_sev, axis=1)
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return out
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def collect_predictions(run_dir: Path, eval_stage: str) -> pd.DataFrame:
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if not run_dir.exists():
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return pd.DataFrame()
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rows: list[dict] = []
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for rep in sorted(run_dir.glob("rep*")):
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fp = next(iter(rep.rglob("predictions.h5")), None)
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if fp is None:
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continue
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with h5py.File(fp, "r") as f:
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if eval_stage not in f:
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continue
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grp = f[eval_stage]
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logits = grp["logits"][:]
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y_true = grp["y_true"][:].astype(int)
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split = grp["split"][:]
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eid0 = grp["entity_id_0"][:]
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n_folds, n_epochs, n_samples, n_heads, n_outputs = logits.shape
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if n_outputs != 2:
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continue
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ep, head = n_epochs - 1, n_heads - 1
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for fold in range(n_folds):
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labels = np.array(
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[
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s.decode() if isinstance(s, bytes) else str(s)
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for s in split[fold]
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]
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)
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test_mask = labels == "test"
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if not test_mask.any():
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continue
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lg = logits[fold, ep, test_mask, head, :]
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e = np.exp(lg - lg.max(axis=1, keepdims=True))
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p = e / e.sum(axis=1, keepdims=True)
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for k, idx in enumerate(np.where(test_mask)[0]):
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rows.append(
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{
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"rep": rep.name,
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"fold": fold,
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"patient_id": int(eid0[idx]),
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"y_true": int(y_true[idx]),
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"prob_glaucoma": float(p[k, 1]),
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}
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)
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return pd.DataFrame(rows)
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C_NORMAL_VIOLIN = "#4c72b0"
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C_GLAUCOMA_VIOLIN = "#c44e52"
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def _draw_panel(ax, df: pd.DataFrame, vfi: pd.DataFrame, label: str):
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df = df.merge(vfi[["patient_id", "severity"]], on="patient_id", how="left")
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df["severity"] = df["severity"].fillna("unknown")
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rng = np.random.default_rng(42)
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x_pos = {0: 0.0, 1: 1.0}
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jitter_scale = 0.18
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# Violin density behind everything (per true class)
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data_by_class = [
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df.loc[df["y_true"] == cls, "prob_glaucoma"].values for cls in [0, 1]
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]
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if all(len(d) > 0 for d in data_by_class):
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vp = ax.violinplot(
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data_by_class,
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positions=[0, 1],
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widths=0.7,
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showmedians=False,
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showextrema=False,
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)
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for body, color in zip(vp["bodies"], [C_NORMAL_VIOLIN, C_GLAUCOMA_VIOLIN]):
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body.set_facecolor(color)
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body.set_alpha(0.30)
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body.set_edgecolor("none")
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body.set_zorder(2)
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for sev in SEV_ORDER:
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mask = df["severity"] == sev
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if not mask.any():
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continue
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sub = df[mask]
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jitter = rng.uniform(-jitter_scale, jitter_scale, len(sub))
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x = np.array([x_pos[int(v)] for v in sub["y_true"]]) + jitter
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ax.scatter(
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x,
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sub["prob_glaucoma"].values,
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c=SEV_COLORS[sev],
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s=SEV_SIZE[sev],
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alpha=SEV_ALPHA[sev],
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linewidths=0,
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zorder=3,
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)
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xtick_labels = []
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for cls, xc in x_pos.items():
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vals = df.loc[df["y_true"] == cls, "prob_glaucoma"]
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if not len(vals):
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xtick_labels.append("Normal" if cls == 0 else "Glaucoma")
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continue
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med = float(np.median(vals))
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ax.plot(
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[xc - jitter_scale - 0.04, xc + jitter_scale + 0.04],
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[med, med],
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color="#222",
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lw=2.0,
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zorder=5,
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)
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if cls == 0:
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rate = (vals <= 0.5).mean() * 100
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xtick_labels.append(f"Normal\nTN {rate:.0f}%")
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else:
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rate = (vals > 0.5).mean() * 100
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xtick_labels.append(f"Glaucoma\nTP {rate:.0f}%")
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ax.axhline(0.5, color="#888", lw=1.2, ls="--", alpha=0.7, zorder=2)
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ax.set_xticks([0, 1])
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ax.set_xticklabels(xtick_labels, fontsize=9)
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ax.set_ylim(-0.04, 1.04)
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ax.set_xlim(-0.55, 1.55)
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ax.set_title(label, fontsize=10.5, fontweight="bold")
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ax.grid(axis="y", alpha=0.3, zorder=1)
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fold_aucs = []
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for _, g in df.groupby(["rep", "fold"]):
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if g["y_true"].nunique() < 2:
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continue
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try:
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fold_aucs.append(roc_auc_score(g["y_true"], g["prob_glaucoma"]))
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except Exception:
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pass
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if fold_aucs:
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ax.text(
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0.66,
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0.0,
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f"AUC = {np.mean(fold_aucs):.3f} ± {np.std(fold_aucs):.3f}",
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transform=ax.transAxes,
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ha="right",
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va="bottom",
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fontsize=8.5,
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color="#333",
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bbox=dict(facecolor="white", alpha=0.7, edgecolor="none", pad=2),
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)
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def render() -> None:
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vfi = load_vfi()
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fig, axes = plt.subplots(2, 3, figsize=(13, 11), sharey=True)
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fig.patch.set_facecolor("#e8e8e8")
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fig.suptitle("Single → Bilateral Aggregation Lift", fontsize=13, fontweight="bold")
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for ri, row in enumerate(GRID):
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for ci, (lbl, path, stage) in enumerate(row):
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ax = axes[ri, ci]
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ax.set_facecolor("#e8e8e8")
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df = collect_predictions(path, stage)
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if len(df):
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_draw_panel(ax, df, vfi, lbl)
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print(f" [{ri},{ci}] {lbl:<22s} n={len(df):>5d}")
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else:
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ax.text(
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0.5,
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0.5,
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"(pending)",
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ha="center",
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va="center",
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fontsize=12,
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color="#888",
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transform=ax.transAxes,
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)
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ax.set_xticks([])
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ax.set_yticks([])
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ax.set_title(lbl, fontsize=10.5, fontweight="bold")
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print(f" [{ri},{ci}] {lbl:<22s} no data yet")
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for ri in range(2):
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axes[ri, 0].set_ylabel("Predicted P(Glaucoma)", fontsize=10.5)
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legend_patches = [
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mpatches.Patch(color=SEV_COLORS[s], label=SEV_LABELS[s])
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for s in ["normal", "early", "moderate", "severe", "unknown"]
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]
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fig.legend(
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handles=legend_patches,
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fontsize=9,
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loc="lower center",
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ncol=len(legend_patches),
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framealpha=0.75,
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bbox_to_anchor=(0.5, -0.005),
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)
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fig.tight_layout(rect=[0, 0.04, 1, 0.97])
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OUT.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(OUT, dpi=180, bbox_inches="tight")
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plt.close(fig)
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print(f"saved {OUT}")
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if __name__ == "__main__":
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render()
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