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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"""F7 — Backbone upgrade: refugelike → refuge V2-M.
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Four architecture configurations ordered from least to most complex, each
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shown as a paired box plot (refugelike vs refuge_efficientnet_v2_m). Same
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F2-style: black-bordered boxes, red median lines, baseline median dashed
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reference. Within each group a Wilcoxon p-value compares V2-M to refugelike.
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Configs (left → right, increasing architectural complexity):
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1. Single-eye img+cd ensemble (no bilateral aggregation)
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2. Bilateral img only (bilateral hb, single tower)
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3. Bilateral img+cd ensemble (production architecture)
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4. Bilateral tritower (img+cd+geom)
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Re-run anytime:
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python -m v4.figures.X1_v2m_punch
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"""
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from __future__ import annotations
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import json
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from pathlib import Path
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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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from scipy.stats import wilcoxon
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from v4.figures.util.loaders import RESULTS_ROOT
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OUT = Path(__file__).parent / "output" / "X1_v2m_backbone.png"
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# ── Style ───────────────────────────────────────────────────────────────────
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C_REFUGELIKE = "#dd8452" # orange — the older fundus-pretrained baseline
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C_REFUGE_V2M = "#4c72b0" # blue — the upgraded fundus-pretrained backbone
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C_MEDIAN = "#c44e52" # red — median line
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ALPHA = 0.82
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# (config_label, refugelike_run_path, refuge_v2m_run_path)
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CONFIGS = [
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(
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"Single\nimg only",
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RESULTS_ROOT / "refuge_v2m_baseline" / "img_solo_single_refugelike",
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RESULTS_ROOT / "refuge_v2m_baseline" / "img_solo_single_refuge_v2m",
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),
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(
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"Single ensemble\n(img+cd)",
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RESULTS_ROOT / "refuge_v2m_baseline" / "ensemble_single_refugelike",
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RESULTS_ROOT / "refuge_v2m_baseline" / "ensemble_single_refuge_v2m",
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),
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(
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"Bilateral ensemble\n(img+cd)",
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RESULTS_ROOT / "tri_v1" / "baseline_ensemble",
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RESULTS_ROOT / "efficientnet" / "refuge_efficientnetv2_m",
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),
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(
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"3-way fusion\n(img+cd+geom)",
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RESULTS_ROOT / "tri_v1" / "baseline_tri",
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RESULTS_ROOT / "refuge_v2m_baseline" / "tritower",
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),
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]
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def _wilcoxon_p(a: np.ndarray, b: np.ndarray) -> float:
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diffs = a - b
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if len(diffs) < 5 or np.all(diffs == 0):
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return float("nan")
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try:
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return float(wilcoxon(diffs, alternative="two-sided").pvalue)
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except Exception:
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return float("nan")
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def load_fold_aucs(run_dir: Path) -> np.ndarray:
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if not run_dir.exists():
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return np.array([])
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out: list[float] = []
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for rep in sorted(run_dir.glob("rep*")):
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s = next(iter(rep.rglob("summary.json")), None)
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if s is None: continue
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d = json.loads(s.read_text())
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eval_stage = d.get("eval_stage", "hb")
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key = f"{eval_stage}_test_auc"
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for fr in d.get("fold_results", []):
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v = fr.get(key)
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if v is not None and np.isfinite(v):
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out.append(float(v))
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return np.array(out)
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def render() -> None:
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data = []
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for label, refg_path, v2m_path in CONFIGS:
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refg = load_fold_aucs(refg_path)
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v2m = load_fold_aucs(v2m_path)
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data.append((label, refg, v2m))
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print(f" {label.replace(chr(10), ' '):<32s} refg n={len(refg):>3d} {refg.mean():.3f}±{refg.std():.3f} "
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f"v2m n={len(v2m):>3d} {v2m.mean():.3f}±{v2m.std():.3f}"
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if (len(refg) and len(v2m)) else f" {label} pending")
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# Layout: 4 groups of 2 boxes
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box_w = 0.46
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pair_gap = 0.10
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group_gap = 0.85
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group_width = 2 * box_w + pair_gap
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positions: list[tuple[float, float]] = []
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p = 0.0
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for _ in CONFIGS:
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positions.append((p, p + box_w + pair_gap))
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p += group_width + group_gap
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fig, ax = plt.subplots(figsize=(12.5, 5.8))
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fig.suptitle("Backbone Upgrade — refugelike → refuge V2-M",
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fontsize=13, fontweight="bold")
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boxprops_kw = dict(linewidth=1.2, edgecolor="black")
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medianprops = dict(color=C_MEDIAN, linewidth=2)
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whiskerprops = dict(color="black", linewidth=1.0)
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capprops = dict(color="black", linewidth=1.0)
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flierprops = dict(marker="o", markersize=3, alpha=0.55,
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markerfacecolor="#888", markeredgecolor="#444")
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for (label, refg, v2m), (xr, xv) in zip(data, positions):
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if len(refg):
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ax.boxplot(refg, positions=[xr], widths=box_w, patch_artist=True,
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manage_ticks=False,
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boxprops=dict(facecolor=C_REFUGELIKE, alpha=ALPHA, **boxprops_kw),
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medianprops=medianprops, whiskerprops=whiskerprops,
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capprops=capprops, flierprops=flierprops)
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if len(v2m):
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ax.boxplot(v2m, positions=[xv], widths=box_w, patch_artist=True,
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manage_ticks=False,
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boxprops=dict(facecolor=C_REFUGE_V2M, alpha=ALPHA, **boxprops_kw),
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medianprops=medianprops, whiskerprops=whiskerprops,
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capprops=capprops, flierprops=flierprops)
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# Group tick labels (config name + Wilcoxon p between paired boxes)
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tick_x = [(xr + xv) / 2 for xr, xv in positions]
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tick_lb = []
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for (label, refg, v2m), _ in zip(data, positions):
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if len(refg) and len(v2m):
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n = min(len(refg), len(v2m))
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p_val = _wilcoxon_p(v2m[:n], refg[:n])
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ps = f"p={p_val:.3f}" if not np.isnan(p_val) else "p=n/a"
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tick_lb.append(f"{label}\n{ps}")
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else:
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tick_lb.append(label)
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ax.set_xticks(tick_x)
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ax.set_xticklabels(tick_lb, fontsize=9.5)
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# Legend
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legend_handles = [
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mpatches.Patch(facecolor=C_REFUGELIKE, edgecolor="black",
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alpha=ALPHA, label="refugelike (ResNet50 + REFUGE)"),
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mpatches.Patch(facecolor=C_REFUGE_V2M, edgecolor="black",
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alpha=ALPHA, label="refuge V2-M (EfficientNetV2-M + REFUGE)"),
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]
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ax.legend(handles=legend_handles, loc="lower right", fontsize=9, framealpha=0.92)
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# Limits and grid
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xmin = positions[0][0] - box_w
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xmax = positions[-1][1] + box_w
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ax.set_xlim(xmin - 0.3, xmax + 0.3)
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ax.set_ylim(0.55, 1.0)
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ax.set_ylabel("Test AUC (10 reps × 5 folds)", fontsize=11)
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ax.grid(axis="y", alpha=0.3, linestyle="--")
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fig.tight_layout()
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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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