code/downsample_summary.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Read downsample experiment results and generate:
  results/downsample/summary.csv  — all metrics by dataset/frac/model
  results/downsample/curve_<model>.png  — AUROC vs training samples, per model facet
Plus print a readable table.
"""
import os, json, csv
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np

PROJ = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image"
RES = f"{PROJ}/results/downsample"
OUT = RES
FRACTIONS = [100, 50, 25, 10, 5]
MODELS = ["retfound", "resnet", "vit"]
MLABEL = {"retfound": "RetFound (ViT-L, CFP)", "resnet": "ResNet-50", "vit": "ViT-B/16"}
MCOLOR = {"retfound": "#4C72B0", "resnet": "#55A868", "vit": "#C44E52"}
DSETS = ["adam", "airogs", "papila"]
DNAME = {"adam": "ADAM (AMD)", "airogs": "AIROGS (Glaucoma)", "papila": "PAPILA (Glaucoma)"}

TRAIN_COUNTS = {  # stratified train set size per fraction
    "adam":   {100: 280, 50: 140, 25: 70, 10: 28, 5: 14},
    "airogs": {100: 5000, 50: 2500, 25: 1250, 10: 500, 5: 250},
    "papila": {100: 294, 50: 146, 25: 73, 10: 29, 5: 15},
}


def load(dsk, frac, model):
    p = os.path.join(RES, dsk, f"{frac:03d}", model, "metrics.json")
    try:
        return json.load(open(p))
    except Exception:
        return None


def main():
    rows = []
    for dsk in DSETS:
        for frac in FRACTIONS:
            for model in MODELS:
                m = load(dsk, frac, model)
                n = TRAIN_COUNTS[dsk][frac]
                if m:
                    au = m.get("auroc") if m.get("task") == "binary" else m.get("auroc_macro_ovr")
                    rows.append({"dataset": dsk, "frac": frac, "n_train": n, "model": model,
                                 "acc": m.get("accuracy"), "auroc": au, "f1": m.get("f1_macro"),
                                 "kappa": m.get("cohen_kappa"), "mcc": m.get("mcc"),
                                 "n_test": m.get("n_test")})
    # write CSV
    os.makedirs(OUT, exist_ok=True)
    csvp = os.path.join(OUT, "summary.csv")
    with open(csvp, "w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=["dataset", "frac", "n_train", "model",
                                          "acc", "auroc", "f1", "kappa", "mcc", "n_test"])
        w.writeheader(); w.writerows(rows)
    print(f"wrote {csvp} ({len(rows)} rows)\n")

    # print table
    for dsk in DSETS:
        print(f"\n### {DNAME[dsk]}")
        print(f"{'Frac':>5}  {'n':>5}  ", end="")
        for model in MODELS:
            print(f"  {model[:8]:>8}", end="")
        print()
        for frac in FRACTIONS:
            print(f"{frac:>4}%  {TRAIN_COUNTS[dsk][frac]:>5}  ", end="")
            for model in MODELS:
                m = load(dsk, frac, model)
                au = m.get("auroc") if m and m.get("task") == "binary" else (m.get("auroc_macro_ovr") if m else None)
                print(f"  {au:>8.4f}" if au else "      NaN  ", end="")
            print()

    # learn curve plots: one facet per model, 3x1 layout
    for model in MODELS:
        fig, axes = plt.subplots(1, 3, figsize=(15, 4.5), sharey=True)
        for i, dsk in enumerate(DSETS):
            ax = axes[i]
            xs, ys = [], []
            for frac in sorted(FRACTIONS):
                m = load(dsk, frac, model)
                n = TRAIN_COUNTS[dsk][frac]
                au = m.get("auroc") if m and m.get("task") == "binary" else (m.get("auroc_macro_ovr") if m else None)
                if au is not None:
                    xs.append(n); ys.append(au)
            if xs:
                ax.plot(xs, ys, "o-", color=MCOLOR[model], lw=2, markersize=7)
                # annotate
                for x, y in zip(xs, ys):
                    ax.text(x, y, f" {y:.3f}", fontsize=8, va="bottom")
            ax.set_xscale("log")
            ax.set_xlabel("Training samples (log scale)")
            ax.set_ylabel("AUROC" if i == 0 else "")
            ax.set_title(f"{DNAME[dsk]}", fontsize=11, fontweight="bold")
            ax.grid(True, ls=":", alpha=0.4)
            ax.set_xticks(sorted([TRAIN_COUNTS[dsk][f] for f in FRACTIONS]))
            ax.set_xticklabels([str(TRAIN_COUNTS[dsk][f]) for f in FRACTIONS], fontsize=8)
            ax.set_ylim(0.35, 1.02)
        fig.suptitle(f"{MLABEL[model]} · Data Scarcity Curve", fontsize=13, fontweight="bold", y=1.02)
        fig.tight_layout()
        figp = os.path.join(OUT, f"curve_{model}.png")
        fig.savefig(figp, dpi=150, bbox_inches="tight")
        plt.close(fig)
        print(f"  wrote {figp}")

    # combined: all models on ADAM only (paper-style)
    fig, ax = plt.subplots(figsize=(6, 4.5))
    for model in MODELS:
        xs, ys = [], []
        for frac in sorted(FRACTIONS):
            m = load("adam", frac, model)
            au = m.get("auroc") if m and m.get("task") == "binary" else (m.get("auroc_macro_ovr") if m else None)
            _n = TRAIN_COUNTS["adam"][frac]
            if au is not None:
                xs.append(_n); ys.append(au)
        ax.plot(xs, ys, "o-", label=MLABEL[model], color=MCOLOR[model], lw=2, markersize=7)
        for x, y in zip(xs, ys):
            ax.text(x, y, f" {y:.3f}", fontsize=7, va="bottom", color=MCOLOR[model])
    ax.set_xscale("log"); ax.set_xlabel("Training samples (log scale)"); ax.set_ylabel("AUROC")
    ax.set_title("ADAM (AMD) · 3 models", fontweight="bold")
    ax.set_xticks(sorted([TRAIN_COUNTS["adam"][f] for f in FRACTIONS]))
    ax.set_xticklabels([str(TRAIN_COUNTS["adam"][f]) for f in FRACTIONS])
    ax.set_ylim(0.4, 1.02); ax.legend(fontsize=8); ax.grid(True, ls=":", alpha=0.4)
    fig.tight_layout()
    fig.savefig(os.path.join(OUT, "curve_adam_combined.png"), dpi=150, bbox_inches="tight")
    plt.close(fig)
    print("  wrote adam combined curve")

    print(f"\n=== summary csv: {csvp} ===")


if __name__ == "__main__":
    main()