model_stats_s2_vs_s3.py

"""Paired S3-vs-S2 statistics for each model under runs/, at TWO levels:

    * per_image   (Method 1)  -- pool every test image across all phases, pair
                                 S3 vs S2 by (phase, sample_id).
    * phase_level (Method 2)  -- one paired value per phase = the phase-mean of
                                 the metric; pair S3-mean vs S2-mean across phases.

Same paired machinery as ablation_stats.py: paired t-test, Wilcoxon signed-rank
(zero_method="wilcox"), sign-flip permutation, bootstrap CI for the mean delta,
Hodges-Lehmann estimate + CI, Cohen's dz, rank-biserial r, Shapiro-Wilk on the
diffs, Holm correction. HD95 is the only lower-is-better metric.

Auto-discovers every model folder directly under runs/ (each distinct MODEL_NAME).
One .xlsx per model + a combined cross-model summary.

Usage:
    python model_stats_s2_vs_s3.py                 # scans ./runs
    python model_stats_s2_vs_s3.py --runs-root X
"""
from __future__ import annotations

import argparse
import json
import pathlib
import re
from collections import defaultdict

import numpy as np
import pandas as pd
from scipy import stats

# metric -> higher_is_better
METRICS = {
    "biou_contour": True,   # manuscript's reported "BIoU" (1-px contour)
    "biou": True,           # Cheng et al. CVPR 2021 d-pixel band Boundary IoU
    "dice": True,
    "iou": True,
    "ppv": True,
    "sen": True,
    "hd95": False,          # lower is better
}
PRIMARY = "biou_contour"
ALPHA = 0.05
N_BOOT = 20000
N_BOOT_HL = 4000
N_PERM = 20000
SEED = 20260709

# S3 is the first arm, S2 the second: Mean_delta = S3 - S2, positive favours S3
LABEL = {2: "S2 (baseline)", 3: "S3 (refinement)"}


# ---------------------------------------------------------------- stats helpers
def holm(pvals):
    pvals = np.asarray(pvals, dtype=float)
    order = np.argsort(pvals)
    m = len(pvals)
    adj = np.empty(m)
    running = 0.0
    for rank, idx in enumerate(order):
        running = max(running, (m - rank) * pvals[idx])
        adj[idx] = min(running, 1.0)
    return adj


def boot_ci_mean(d, rng):
    idx = rng.integers(0, len(d), size=(N_BOOT, len(d)))
    return np.percentile(d[idx].mean(axis=1), [2.5, 97.5])


def hodges_lehmann(d):
    i, j = np.triu_indices(len(d), k=0)
    return float(np.median((d[i] + d[j]) / 2.0))


def boot_ci_hl(d, rng):
    idx = rng.integers(0, len(d), size=(N_BOOT_HL, len(d)))
    return np.percentile([hodges_lehmann(d[r]) for r in idx], [2.5, 97.5])


def sign_flip_perm_p(d, rng):
    obs = abs(d.mean())
    signs = rng.choice([-1.0, 1.0], size=(N_PERM, len(d)))
    null = np.abs((signs * d).mean(axis=1))
    return (np.sum(null >= obs - 1e-15) + 1) / (N_PERM + 1)


def rank_biserial(d):
    nz = d[d != 0]
    if len(nz) == 0:
        return 0.0
    r = stats.rankdata(np.abs(nz))
    rp, rm = r[nz > 0].sum(), r[nz < 0].sum()
    return float((rp - rm) / (rp + rm))


def analyse(x, y, metric, higher_better, level, rng):
    """x = S3 vector, y = S2 vector (paired). Returns (rows, delta)."""
    d = np.asarray(x, float) - np.asarray(y, float)
    n = len(d)
    mean_d, sd_d = float(d.mean()), float(d.std(ddof=1)) if n > 1 else float("nan")
    se = sd_d / np.sqrt(n) if n > 1 else float("nan")

    if n >= 2:
        t_stat, p_t = stats.ttest_rel(x, y)
        crit = stats.t.ppf(1 - ALPHA / 2, df=n - 1)
        t_ci = (mean_d - crit * se, mean_d + crit * se)
    else:
        t_stat, p_t, t_ci = np.nan, 1.0, (np.nan, np.nan)

    if n >= 1 and not np.allclose(d, 0):
        try:
            w_stat, p_w = stats.wilcoxon(x, y, zero_method="wilcox", alternative="two-sided")
        except ValueError:
            w_stat, p_w = np.nan, 1.0
    else:
        w_stat, p_w = np.nan, 1.0

    p_perm = sign_flip_perm_p(d, rng) if n >= 1 else 1.0
    b_lo, b_hi = boot_ci_mean(d, rng) if n >= 2 else (np.nan, np.nan)
    hl = hodges_lehmann(d) if n >= 1 else np.nan
    hl_lo, hl_hi = boot_ci_hl(d, rng) if n >= 2 else (np.nan, np.nan)
    shapiro_p = float(stats.shapiro(d).pvalue) if (n >= 3 and not np.allclose(d, 0)) else np.nan
    p_holm_c = holm([p_t, p_w, p_perm])

    better = (mean_d > 0) == higher_better
    winner = LABEL[3] if better else LABEL[2]

    rows = []
    for name, stat, p_raw, p_adj in [
        ("Paired t-test", float(t_stat), float(p_t), p_holm_c[0]),
        ("Wilcoxon signed-rank", float(w_stat), float(p_w), p_holm_c[1]),
        ("Sign-flip permutation", mean_d, float(p_perm), p_holm_c[2]),
    ]:
        rows.append({
            "Level": level, "Metric": metric, "Higher_is_better": higher_better,
            "Contrast": "S3 - S2", "Test": name, "n_pairs": n,
            "n_zero_diff": int(np.sum(d == 0)), "Statistic": stat,
            "Mean_delta": mean_d, "SD_delta": sd_d, "p_raw": p_raw,
            "p_Holm_within_metric_level": p_adj,
            "Sig_within_metric_level": "Yes" if p_adj < ALPHA else "No",
            "Better_arm": winner if p_adj < ALPHA else "n.s.",
            "t_CI_low": t_ci[0], "t_CI_high": t_ci[1],
            "boot_CI_low": b_lo, "boot_CI_high": b_hi,
            "CI_excludes_zero": "Yes" if (b_lo > 0) or (b_hi < 0) else "No",
            "HodgesLehmann_delta": hl, "HL_CI_low": hl_lo, "HL_CI_high": hl_hi,
            "Cohens_dz": mean_d / sd_d if (sd_d and sd_d > 0) else np.nan,
            "Rank_biserial_r": rank_biserial(d),
            "Shapiro_p_on_diffs": shapiro_p,
            "Diffs_normal_at_0.05": "n/a" if np.isnan(shapiro_p) else ("No" if shapiro_p < ALPHA else "Yes"),
        })
    return rows, d


# ---------------------------------------------------------------- discovery
def read_threshold(final_dir: pathlib.Path):
    rc = final_dir / "run_config.json"
    if rc.exists():
        try:
            return json.loads(rc.read_text()).get("threshold")
        except Exception:
            return None
    return None


def discover(runs_root: pathlib.Path):
    """model -> phase -> strategy -> {'per_sample': {sid: row}, 'threshold': float}"""
    data: dict[str, dict[int, dict[int, dict]]] = defaultdict(lambda: defaultdict(dict))
    for ev in runs_root.glob("*/**/strategy_*/final/evaluation.json"):
        final_dir = ev.parent
        ms = re.search(r"strategy_(\d+)", final_dir.parent.name)
        if not ms:
            continue
        s = int(ms.group(1))
        if s not in (2, 3):
            continue
        try:
            model = ev.relative_to(runs_root).parts[0]
        except ValueError:
            continue
        pm = re.search(r"phase_(\d+)", str(ev))
        phase = int(pm.group(1)) if pm else 1
        try:
            payload = json.loads(ev.read_text())
        except Exception:
            continue
        data[model][phase][s] = {
            "per_sample": {row["sample_id"]: row for row in payload.get("per_sample", [])},
            "threshold": read_threshold(final_dir),
        }
    return data


# ---------------------------------------------------------------- per model
def analyse_model(model, phase_map, rng):
    phases = sorted(p for p in phase_map if 2 in phase_map[p] and 3 in phase_map[p])
    if not phases:
        return None

    # per-image pooled arrays + per-phase means
    img = {m: {2: [], 3: []} for m in METRICS}
    phase_mean = {m: {2: [], 3: []} for m in METRICS}
    phase_used, n_img_per_phase, split_warnings = [], [], []
    thr = {2: None, 3: None}

    for p in phases:
        s2, s3 = phase_map[p][2], phase_map[p][3]
        thr[2] = thr[2] or s2.get("threshold")
        thr[3] = thr[3] or s3.get("threshold")
        set2, set3 = set(s2["per_sample"]), set(s3["per_sample"])
        if set2 != set3:
            split_warnings.append(f"phase {p}: S2/S3 sample_id sets differ "
                                  f"(|S2|={len(set2)}, |S3|={len(set3)}, common={len(set2 & set3)})")
        ids = sorted(set2 & set3)
        if not ids:
            continue
        phase_used.append(p)
        n_img_per_phase.append(len(ids))
        for m in METRICS:
            v2 = np.array([s2["per_sample"][i][m] for i in ids], float)
            v3 = np.array([s3["per_sample"][i][m] for i in ids], float)
            img[m][2].append(v2)
            img[m][3].append(v3)
            phase_mean[m][2].append(float(v2.mean()))
            phase_mean[m][3].append(float(v3.mean()))

    test_rows, desc_rows = [], []
    per_image = {"phase": np.concatenate([[p] * n for p, n in zip(phase_used, n_img_per_phase)]).astype(int)}
    per_phase = {"phase": np.array(phase_used, int)}

    for m, hib in METRICS.items():
        x_img = np.concatenate(img[m][3]) if img[m][3] else np.array([])
        y_img = np.concatenate(img[m][2]) if img[m][2] else np.array([])
        pm2 = np.array(phase_mean[m][2], float)
        pm3 = np.array(phase_mean[m][3], float)

        # descriptives, both levels
        for s, arr_img, arr_ph in [(2, y_img, pm2), (3, x_img, pm3)]:
            desc_rows.append({
                "Metric": m, "Higher_is_better": hib, "Arm": LABEL[s],
                "n_images": len(arr_img), "img_Mean": arr_img.mean() if len(arr_img) else np.nan,
                "img_SD": arr_img.std(ddof=1) if len(arr_img) > 1 else np.nan,
                "img_Median": np.median(arr_img) if len(arr_img) else np.nan,
                "n_phases": len(arr_ph), "phase_Mean": arr_ph.mean() if len(arr_ph) else np.nan,
                "phase_SD": arr_ph.std(ddof=1) if len(arr_ph) > 1 else np.nan,
            })

        # per_image tests
        rows, d_img = analyse(x_img, y_img, m, hib, "per_image", rng)
        test_rows.extend(rows)
        per_image[f"{m}__S2"] = y_img
        per_image[f"{m}__S3"] = x_img
        per_image[f"{m}__delta_S3_minus_S2"] = d_img

        # phase_level tests
        rows_ph, d_ph = analyse(pm3, pm2, m, hib, "phase_level", rng)
        test_rows.extend(rows_ph)
        per_phase[f"{m}__S2_phase_mean"] = pm2
        per_phase[f"{m}__S3_phase_mean"] = pm3
        per_phase[f"{m}__delta_S3_minus_S2"] = d_ph

    tests_df = pd.DataFrame(test_rows)
    # global Holm across this model's whole workbook (metrics x 2 levels x 3 tests)
    tests_df["p_Holm_global"] = holm(tests_df["p_raw"].values)
    tests_df["Sig_global"] = np.where(tests_df["p_Holm_global"] < ALPHA, "Yes", "No")

    return {
        "phases": phase_used,
        "thr": thr,
        "split_warnings": split_warnings,
        "tests": tests_df,
        "descriptives": pd.DataFrame(desc_rows),
        "per_image": pd.DataFrame(per_image),
        "per_phase": pd.DataFrame(per_phase),
    }


def readme_frame(model, res):
    return pd.DataFrame({"Field": [
        "Model", "Contrast", "Phases used", "n phases", "S2 threshold", "S3 threshold",
        "per_image level", "phase_level level", "Metrics", "PRIMARY", "hd95 direction",
        "Wilcoxon zero handling", "Bootstrap", "Permutation", "Holm scope",
        "Split check", "Split warnings", "Seed",
    ], "Value": [
        model, "S3 (refinement) - S2 (baseline); positive delta favours S3",
        ", ".join(map(str, res["phases"])), len(res["phases"]),
        str(res["thr"][2]), str(res["thr"][3]),
        "pool all test images across phases, paired by (phase, sample_id) [manuscript Method 1]",
        "one paired value per phase = phase-mean of the metric, paired across phases [manuscript Method 2]",
        ", ".join(METRICS), PRIMARY, "LOWER is better; direction handled in Better_arm",
        "zero_method='wilcox' (zero diffs dropped)",
        f"{N_BOOT} resamples for mean-delta CI; {N_BOOT_HL} for Hodges-Lehmann",
        f"{N_PERM} sign flips; p floor ~= 1/{N_PERM + 1}",
        "Holm across every test in this workbook (metrics x 2 levels x 3 tests) = p_Holm_global",
        "asserted S2 and S3 test sample_id sets identical within each phase",
        "; ".join(res["split_warnings"]) if res["split_warnings"] else "none",
        f"numpy default_rng({SEED})",
    ]})


def write_workbook(model, res, out_dir):
    out = out_dir / f"{model}__s2_vs_s3_stats.xlsx"
    try:
        with pd.ExcelWriter(out, engine="openpyxl") as xl:
            readme_frame(model, res).to_excel(xl, sheet_name="README", index=False)
            res["descriptives"].to_excel(xl, sheet_name="Descriptives", index=False)
            res["tests"].to_excel(xl, sheet_name="Tests", index=False)
            res["per_phase"].to_excel(xl, sheet_name="PerPhase", index=False)
            res["per_image"].to_excel(xl, sheet_name="PerImage", index=False)
            for sh in xl.book.worksheets:
                for col in sh.columns:
                    w = max((len(str(c.value)) if c.value is not None else 0) for c in col)
                    sh.column_dimensions[col[0].column_letter].width = min(max(w + 2, 12), 62)
                sh.freeze_panes = "A2"
        return out
    except Exception as exc:  # openpyxl missing -> CSV fallback
        print(f"[stats] xlsx failed for {model} ({exc}); writing CSVs instead")
        res["tests"].to_csv(out_dir / f"{model}__tests.csv", index=False)
        res["descriptives"].to_csv(out_dir / f"{model}__descriptives.csv", index=False)
        res["per_phase"].to_csv(out_dir / f"{model}__per_phase.csv", index=False)
        return None


def main() -> int:
    ap = argparse.ArgumentParser()
    ap.add_argument("--runs-root", default="runs")
    args = ap.parse_args()

    runs_root = pathlib.Path(args.runs_root).resolve()
    if not runs_root.is_dir():
        print(f"[stats] runs root not found: {runs_root}")
        return 1

    data = discover(runs_root)
    if not data:
        print(f"[stats] no evaluation.json found under {runs_root}")
        return 1

    out_dir = runs_root / "_stats"
    out_dir.mkdir(parents=True, exist_ok=True)
    rng = np.random.default_rng(SEED)

    summary_rows = []
    for model in sorted(data):
        res = analyse_model(model, data[model], rng)
        if res is None:
            print(f"[stats] {model}: no phase has both S2 and S3 -- skipped")
            continue
        wb = write_workbook(model, res, out_dir)
        print(f"[stats] {model:28s} phases={len(res['phases'])}  -> {wb.name if wb else '(csv)'}"
              + ("  [SPLIT WARNINGS]" if res["split_warnings"] else ""))

        # headline: Wilcoxon row per metric per level for the cross-model summary
        t = res["tests"]
        for m in METRICS:
            for level in ("per_image", "phase_level"):
                r = t[(t.Metric == m) & (t.Level == level) & (t.Test == "Wilcoxon signed-rank")]
                if r.empty:
                    continue
                r = r.iloc[0]
                summary_rows.append({
                    "Model": model, "Metric": m, "Level": level, "n_pairs": int(r.n_pairs),
                    "S3_minus_S2": r.Mean_delta, "Wilcoxon_p_raw": r.p_raw,
                    "p_Holm_global": r.p_Holm_global, "Sig_global": r.Sig_global,
                    "Better_arm": r.Better_arm, "Cohens_dz": r.Cohens_dz,
                    "boot_CI_low": r.boot_CI_low, "boot_CI_high": r.boot_CI_high,
                })

    summary_df = pd.DataFrame(summary_rows)
    summary_df.to_csv(out_dir / "ALL_MODELS_summary.csv", index=False)
    try:
        with pd.ExcelWriter(out_dir / "ALL_MODELS_summary.xlsx", engine="openpyxl") as xl:
            summary_df.to_excel(xl, sheet_name="Summary", index=False)
            for sh in xl.book.worksheets:
                for col in sh.columns:
                    w = max((len(str(c.value)) if c.value is not None else 0) for c in col)
                    sh.column_dimensions[col[0].column_letter].width = min(max(w + 2, 12), 40)
                sh.freeze_panes = "A2"
    except Exception:
        pass

    print(f"\n[stats] wrote per-model workbooks + ALL_MODELS_summary to {out_dir}")
    if not summary_df.empty:
        pd.set_option("display.width", 240, "display.max_columns", 40)
        prim = summary_df[summary_df.Metric == PRIMARY]
        print(f"\n=== PRIMARY ({PRIMARY}) S3-vs-S2, both levels ===")
        print(prim.to_string(index=False, float_format=lambda v: f"{v:.4g}"))
    return 0


if __name__ == "__main__":
    raise SystemExit(main())