Add new regression and ensemble experiment configurations for V2-M and OrthoBridge
- Introduced multiple regression experiment configurations targeting vf_md, including: - cd_solo_reg_set.json: CD tower only regression setup. - img_solo_reg_set.json: Image tower only regression setup. - reg_head_epoch_sweep.json: Baseline regression sweeps at different epochs (50, 75, 100). - reg_head_set.json: Various regression setups including baseline and OrthoBridge configurations. - single_eye_reg.json: Single-eye regression setup for worst-eye aggregation analysis. - Added ensemble configurations for OrthoBridge with different inner bridges: - ortho_alts_ensemble.json: Ensemble tests with ConcatBridge, PairwiseAdditiveBridge, and GatedAdditiveBridge. - ortho_alts_tritower.json: Tritower tests with the same inner bridges. - Created V2-M specific configurations: - baseline_reg_nt50.json: Regression baseline with V2-M backbone. - geom_vec_gt.json and geom_vec_unet.json: Geometry vector injection experiments with V2-M. - single_l1_bridges.json: Single-eye ensemble experiments with various bridge types. - tritower_geom_gt.json: Tritower setup with GT contour-rasterized masks. - Promoted existing experiments to higher repetitions for robustness.
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rpotter6298
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"""V2M_F6 — Regression VF_MD with severity grouping (refuge V2-M backbone).
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V2-M counterpart to F6. Reads predictions from the refuge V2-M variant of
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baseline_reg_nt50; otherwise identical layout to F6 so panels can be
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compared side-by-side.
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Re-run anytime predictions.h5 changes:
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python -m v4.figures.V2M_F6_regression
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"""
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from __future__ import annotations
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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 numpy as np
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from sklearn.metrics import roc_curve, roc_auc_score
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from v4.figures.util.loaders import RESULTS_ROOT
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OUT = Path(__file__).parent / "output" / "V2M_F6_regression.png"
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RUN_DIR = RESULTS_ROOT / "v2m_variants" / "baseline_reg_nt50_v2m"
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# Prediction-side bin boundaries
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NP_THRESH = -1.097 # mean of measured-healthy MD
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SEV_PRED = -9.14 # midpoint of HAP -12 and mean predicted MD for severe truth
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# Actual (HAP / Mills) clinical boundaries
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HAP_SEV = -12.0
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HAP_MOD = -6.0
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LABELS = ["severe", "moderate", "low"]
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# Severity colors (consistent across figures)
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C_SEVERE = "#E53935"
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C_MODERATE = "#FFB300"
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C_LOW = "#3B6FB5"
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def _decode(arr):
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return np.array(
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[s.decode("utf-8") if isinstance(s, bytes) else str(s) for s in arr]
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)
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def _collect():
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actuals, preds = [], []
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for fp in sorted(RUN_DIR.rglob("predictions.h5")):
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with h5py.File(fp, "r") as f:
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if "hb" not in f:
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continue
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grp = f["hb"]
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logits = grp["logits"][:]
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y_true = grp["y_true"][:].astype(float)
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split = grp["split"][:]
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n_folds, n_epochs, _, n_heads, _ = logits.shape
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ep, head, out = n_epochs - 1, n_heads - 1, 0
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for fold in range(n_folds):
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labels = _decode(split[fold])
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m = (
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(labels == "test")
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& np.isfinite(y_true)
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& np.isfinite(logits[fold, ep, :, head, out])
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)
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actuals.append(y_true[m])
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preds.append(logits[fold, ep, m, head, out].astype(float))
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if not actuals:
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return None, None
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return np.concatenate(actuals), np.concatenate(preds)
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def _bin(values, sev, np_th):
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bins = np.full(values.shape, 2, dtype=int)
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bins[values <= np_th] = 1
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bins[values <= sev] = 0
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return bins
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def render() -> None:
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if not RUN_DIR.exists() or not any(RUN_DIR.rglob("predictions.h5")):
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print(f"[F6] no predictions.h5 yet under {RUN_DIR}. Run after data lands.")
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return
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a, p = _collect()
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if a is None:
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print("[F6] no usable predictions")
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return
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print(f"[F6] pooled n={a.size}")
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fig = plt.figure(figsize=(14, 10.5))
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gs = fig.add_gridspec(
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2, 2, hspace=0.40, wspace=0.30, left=0.07, right=0.96, top=0.92, bottom=0.07
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)
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ax_sc = fig.add_subplot(gs[0, 0])
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ax_rc = fig.add_subplot(gs[0, 1])
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ax_cm = fig.add_subplot(gs[1, 0])
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ax_tb = fig.add_subplot(gs[1, 1])
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ax_tb.axis("off")
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# ── (a) scatter ──────────────────────────────────────────────────────────
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ax_sc.scatter(a, p, s=10, alpha=0.4, color="#2563eb", edgecolor="none")
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lo, hi = -30, 6
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ax_sc.plot([lo, hi], [lo, hi], ls="--", color="#9ca3af", lw=1, label="ideal y=x")
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ax_sc.axvline(HAP_SEV, ls=":", color="#dc2626", lw=0.8, alpha=0.5)
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ax_sc.axvline(HAP_MOD, ls=":", color="#dc2626", lw=0.8, alpha=0.5)
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ax_sc.set_xlim(lo, hi)
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ax_sc.set_ylim(lo, hi)
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ax_sc.set_xlabel("Actual VF_MD (dB)")
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ax_sc.set_ylabel("Predicted VF_MD (dB)")
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ax_sc.set_title(f"(a) Predicted vs Actual MD (n={a.size})", fontsize=11)
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r = np.corrcoef(a, p)[0, 1]
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mae = float(np.mean(np.abs(p - a)))
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ax_sc.text(
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0.04,
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0.95,
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f"r = {r:.3f}\nMAE = {mae:.2f} dB",
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transform=ax_sc.transAxes,
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ha="left",
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va="top",
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fontsize=10,
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bbox=dict(facecolor="white", alpha=0.85, edgecolor="#d1d5db"),
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)
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# ── (b) three one-vs-rest ROCs ───────────────────────────────────────────
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# Severe vs rest: score = -p (more negative pred → more severe)
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# Low vs rest: score = +p (more positive pred → more "low" / no-problem)
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# Moderate vs rest: score = -|p - midpoint of moderate range|
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# (closer to midpoint → more moderate-like)
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mod_midpoint = 0.5 * (HAP_SEV + HAP_MOD) # -9 dB
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truth_severe = (a <= HAP_SEV).astype(int)
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truth_low = (a > HAP_MOD).astype(int)
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truth_moderate = ((a > HAP_SEV) & (a <= HAP_MOD)).astype(int)
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series = [
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("Severe (≤ −12 dB) vs rest", truth_severe, -p, C_SEVERE),
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(
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"Moderate (−12..−6) vs rest",
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truth_moderate,
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-np.abs(p - mod_midpoint),
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C_MODERATE,
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),
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("Low (> −6 dB) vs rest", truth_low, p, C_LOW),
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]
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for label, ybin, score, color in series:
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if len(np.unique(ybin)) < 2:
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continue
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fpr, tpr, _ = roc_curve(ybin, score)
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auc = roc_auc_score(ybin, score)
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ax_rc.plot(fpr, tpr, color=color, lw=1.8, label=f"{label} (AUC = {auc:.3f})")
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ax_rc.plot([0, 1], [0, 1], ls="--", color="#9ca3af", lw=0.8)
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ax_rc.set_xlim(0, 1)
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ax_rc.set_ylim(0, 1.02)
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ax_rc.set_xlabel("False positive rate")
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ax_rc.set_ylabel("True positive rate")
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ax_rc.set_title("(b) One-vs-rest ROC per severity tier", fontsize=11)
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ax_rc.legend(loc="lower right", fontsize=9, framealpha=0.95)
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ax_rc.grid(alpha=0.25, linestyle="--")
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# ── (c) confusion matrix ─────────────────────────────────────────────────
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t_act = _bin(a, HAP_SEV, HAP_MOD)
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t_pred = _bin(p, SEV_PRED, NP_THRESH)
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cm = np.zeros((3, 3), dtype=int)
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for x, y in zip(t_act, t_pred):
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cm[x, y] += 1
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cm_pct = cm / np.maximum(cm.sum(axis=1, keepdims=True), 1)
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ax_cm.imshow(cm_pct, cmap="Blues", vmin=0, vmax=1, aspect="equal")
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for i in range(3):
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for j in range(3):
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text_color = "white" if cm_pct[i, j] > 0.55 else "black"
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ax_cm.text(
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j,
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i,
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f"{cm[i,j]}\n({cm_pct[i,j]*100:.0f}%)",
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ha="center",
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va="center",
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fontsize=10,
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color=text_color,
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)
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ax_cm.set_xticks(range(3))
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ax_cm.set_xticklabels(LABELS, fontsize=10)
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ax_cm.set_yticks(range(3))
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ax_cm.set_yticklabels(LABELS, fontsize=10)
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ax_cm.set_xlabel("Predicted", fontsize=10)
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ax_cm.set_ylabel("Actual", fontsize=10)
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ax_cm.set_title("(c) 3-tier confusion", fontsize=11)
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# ── (d) per-class stats — sens / spec / PPV / NPV only ─────────────────
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# We deliberately drop TP/FN/FP/TN here because in a 3-tier setting a
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# "false negative" for severe could land in moderate (clinically
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# different from landing in low). The confusion matrix in panel (c)
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# already shows that distinction; sens/spec/PPV/NPV summarise the
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# one-vs-rest performance without the blanket-count obfuscation.
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ax_tb.set_title("(d) Per-class statistics", fontsize=11)
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ax_tb.set_xlim(0, 10)
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ax_tb.set_ylim(0, 5)
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headers = ["class", "n", "sens", "spec", "PPV", "NPV"]
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# Make the class column wider than the numeric columns to avoid clipping.
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col_widths = np.array([2.4, 1.1, 1.4, 1.4, 1.4, 1.4])
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col_widths *= 10.0 / col_widths.sum() # normalise to total width 10
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col_edges = np.concatenate([[0], np.cumsum(col_widths)])
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col_x = (col_edges[:-1] + col_edges[1:]) / 2 # column centers
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row_y = [3.5, 2.5, 1.5, 0.5] # 1 header + 3 data rows
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rows = []
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for c in range(3):
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ac = t_act == c
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pc = t_pred == c
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tp = int(np.sum(ac & pc))
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fn = int(np.sum(ac & ~pc))
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fp = int(np.sum(~ac & pc))
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tn = int(np.sum(~ac & ~pc))
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sens = tp / max(tp + fn, 1)
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spec = tn / max(tn + fp, 1)
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ppv = tp / max(tp + fp, 1)
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npv = tn / max(tn + fn, 1)
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rows.append(
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[
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LABELS[c],
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int(ac.sum()),
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f"{sens:.3f}",
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f"{spec:.3f}",
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f"{ppv:.3f}",
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f"{npv:.3f}",
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]
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)
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# Header band
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ax_tb.add_patch(
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plt.Rectangle(
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(0, 3.05), 10, 0.9, facecolor="#dbeafe", edgecolor="none", zorder=1
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)
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)
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for x, h in zip(col_x, headers):
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ax_tb.text(
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x,
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row_y[0],
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h,
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ha="center",
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va="center",
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fontsize=11,
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fontweight="bold",
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color="#1e3a8a",
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zorder=2,
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)
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# Data rows with zebra shading
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row_colors = ["#f8fafc", "#eef2f6", "#f8fafc"]
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severity_color = {"severe": C_SEVERE, "moderate": C_MODERATE, "low": C_LOW}
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for ri, row in enumerate(rows):
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ax_tb.add_patch(
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plt.Rectangle(
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(0, row_y[ri + 1] - 0.45),
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10,
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0.9,
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facecolor=row_colors[ri],
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edgecolor="none",
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zorder=1,
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)
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)
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for ci, val in enumerate(row):
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txt_color = "#222"
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weight = "normal"
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if ci == 0:
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txt_color = severity_color.get(val, "#222")
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weight = "bold"
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ax_tb.text(
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col_x[ci],
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row_y[ri + 1],
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str(val),
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ha="center",
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va="center",
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fontsize=11,
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fontweight=weight,
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color=txt_color,
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zorder=2,
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)
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# Subtle horizontal grid lines
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for y in [
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row_y[0] - 0.45,
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row_y[0] + 0.45,
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row_y[1] - 0.45,
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row_y[2] - 0.45,
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row_y[3] - 0.45,
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]:
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ax_tb.plot([0, 10], [y, y], color="#cbd5e1", lw=0.6, zorder=1.5)
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fig.suptitle(
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"Regression predicting VF_MD with severity grouping — refuge V2-M backbone",
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fontsize=13,
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fontweight="bold",
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y=0.97,
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)
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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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