Add cluster-bootstrap 95% CIs for the paper's AUC tables

benchmarks/conference_study/analyses/ci_auc.py

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+"""Cluster-bootstrap-over-papers 95% CIs for the pairwise-accuracy (Mann-Whitney
+AUC) cells in the conference study.
+
+Each quality proxy splits the papers into two sides: a high-quality group and a
+low-quality group. The AUC is the pairwise accuracy of that split, the
+probability that the system puts more comments on a randomly drawn low-quality
+paper than on a randomly drawn high-quality one (0.5 means no separation).
+
+Resampling unit = paper, stratified by (proxy, side): within each proxy we
+resample the high-side papers and the low-side papers with replacement and take
+2.5/97.5 percentiles. No significance tests.
+
+Paper sets, result dirs, and the tables to print are defined in a JSON config
+(--config, defaults to ci_auc_tables.json). The config and the result JSONs and
+manifests it points at are kept locally (gitignored, large); the math is covered
+by tests/test_ci_auc.py on in-memory data. Three table kinds, one per paper
+format: "comment_volume" (Table 1, mean comments + delta + overall AUC),
+"by_proxy" (Table 2, accuracy per quality proxy), and "by_severity" (Table 9,
+accuracy per severity tier).
+"""
+import sys
+from pathlib import Path
+from collections import defaultdict
+import numpy as np
+
+HERE = Path(__file__).resolve().parent
+from compute_auc import load_manifest, load_counts, cell_summary, auc_from  # noqa: E402
+
+B = 5000
+RNG = np.random.default_rng(42)
+
+
+def by_proxy_totals(counts_for_cell, slug_to_mem):
+    """proxy_id -> {'high': [counts], 'low': [counts]} (total comments)."""
+    by_proxy = defaultdict(lambda: {"high": [], "low": []})
+    for slug, rec in counts_for_cell.items():
+        for m in slug_to_mem.get(slug, []):
+            # "pair" is the manifest's field name for a quality proxy.
+            by_proxy[m["pair"]][m["side"]].append(rec["total"])
+    return by_proxy
+
+
+def cell_aucs(by_proxy):
+    """Return (overall_auc, {proxy: auc})."""
+    per = {}
+    H = T = 0.0
+    for proxy_id, d in by_proxy.items():
+        a, hits, tot = auc_from(d["high"], d["low"])
+        per[proxy_id] = a
+        H += hits; T += tot
+    return (H / T if T else np.nan), per
+
+
+def bootstrap(by_proxy):
+    """Cluster-bootstrap CIs for one cell's overall and per-proxy AUC.
+
+    Each of B draws resamples papers with replacement, stratified by (proxy,
+    side): within every proxy the high-side and low-side papers are resampled
+    on their own. Per draw, each proxy's resampled AUC is recorded, and hits and
+    pairs are pooled across proxies for that draw's overall AUC. Returns
+    ((overall_lo, overall_hi), {proxy_id: (lo, hi)}) from the 2.5/97.5
+    percentiles of those draws.
+    """
+    overalls = []
+    per_lists = defaultdict(list)
+    proxy_ids = sorted(by_proxy)
+    arrs = {proxy_id: (np.asarray(by_proxy[proxy_id]["high"]), np.asarray(by_proxy[proxy_id]["low"])) for proxy_id in proxy_ids}
+    for _ in range(B):
+        H = T = 0.0
+        for proxy_id in proxy_ids:
+            high, low = arrs[proxy_id]
+            if high.size == 0 or low.size == 0:
+                continue
+            high_resampled = high[RNG.integers(0, high.size, high.size)]
+            low_resampled = low[RNG.integers(0, low.size, low.size)]
+            a, hits, tot = auc_from(high_resampled, low_resampled)
+            per_lists[proxy_id].append(a)
+            H += hits; T += tot
+        overalls.append(H / T if T else np.nan)
+    # A proxy with papers on only one side never gets a draw appended, so guard
+    # the empty case (mirrors the nan the point-estimate paths return there).
+    ci = lambda xs: tuple(np.percentile(xs, [2.5, 97.5])) if xs else (float("nan"), float("nan"))
+    return ci(overalls), {proxy_id: ci(per_lists[proxy_id]) for proxy_id in proxy_ids}
+
+
+PROXY = {1: "Community", 2: "Conference", 3: "Reviewer", 4: "Composite"}
+
+
+# ---- per-severity-tier AUCs (appendix tier tables) ----
+
+def by_proxy_recs(counts_for_cell, slug_to_mem):
+    """proxy_id -> {'high': [rec], 'low': [rec]}.
+
+    A rec is one paper's comment counts from load_counts:
+    {'total', 'major', 'moderate', 'minor'}. Keeps the whole rec (not just the
+    total) so per-severity-tier AUCs can be computed."""
+    by_proxy = defaultdict(lambda: {"high": [], "low": []})
+    for slug, rec in counts_for_cell.items():
+        for m in slug_to_mem.get(slug, []):
+            by_proxy[m["pair"]][m["side"]].append(rec)
+    return by_proxy
+
+
+def bootstrap_tiers(by_proxy, tiers):
+    """Cluster-bootstrap CI per tier. Returns {tier: (lo, hi)}.
+
+    'tiers' may include 'total' (the overall comment count) alongside the
+    severity tiers. One paper resample per (proxy, side) is shared across all
+    tiers within a draw, so the tiers stay correlated. Supplies the CIs for the
+    volume table (overall) and the tier table (overall + per-severity).
+    """
+    proxy_ids = sorted(by_proxy)
+    draws = {t: [] for t in tiers}
+    for _ in range(B):
+        Ht = {t: 0.0 for t in tiers}; Tt = {t: 0.0 for t in tiers}
+        for proxy_id in proxy_ids:
+            high = by_proxy[proxy_id]["high"]; low = by_proxy[proxy_id]["low"]
+            if not high or not low:
+                continue
+            high_idx = RNG.integers(0, len(high), len(high))
+            low_idx = RNG.integers(0, len(low), len(low))
+            for t in tiers:
+                high_resampled = [high[j][t] for j in high_idx]
+                low_resampled = [low[j][t] for j in low_idx]
+                _, hits, tot = auc_from(high_resampled, low_resampled)
+                Ht[t] += hits; Tt[t] += tot
+        for t in tiers:
+            draws[t].append(Ht[t] / Tt[t] if Tt[t] else np.nan)
+    return {t: tuple(np.percentile(draws[t], [2.5, 97.5])) for t in tiers}
+
+
+def auc_cell(point, lo_hi):
+    """Format one AUC point estimate with its CI as 'point [lo, hi]'."""
+    lo, hi = lo_hi
+    return f"{point:.2f} [{lo:.2f}, {hi:.2f}]"
+
+
+def _labels_and_width(cells):
+    """Row labels ('method__model') and the first-column width to align them."""
+    labels = [f"{method}__{model}" for method, model in cells]
+    return labels, max([len("method__model")] + [len(x) for x in labels]) + 2
+
+
+def run_comment_volume(manifest, dirs, cells):
+    """Table 1 (model-aggregate): mean comments on the high/low-quality groups,
+    their difference (delta, % increase), and overall AUC with CI."""
+    slug_to_mem = load_manifest(manifest)
+    counts = load_counts(dirs, set(slug_to_mem))
+    labels, w = _labels_and_width(cells)
+    print(f"{'method__model':<{w}}{'c_high':>8}{'c_low':>8}{'delta':>8}{'%inc':>8}   {'Overall':<18}".rstrip())
+    for (method, model), label in zip(cells, labels):
+        cc = counts.get((method, model))
+        if not cc:
+            print(f"{label:<{w}}NO DATA"); continue
+        s = cell_summary(cc, slug_to_mem)
+        overall_ci = bootstrap_tiers(by_proxy_recs(cc, slug_to_mem), ["total"])["total"]
+        row = (f"{label:<{w}}{s['c_high']:>8.2f}{s['c_low']:>8.2f}{s['delta']:>+8.2f}"
+               f"{s['pct_increase']:>7.1f}%   {auc_cell(s['auc_overall'], overall_ci)}")
+        print(row.rstrip())
+
+
+def run_by_proxy(manifest, dirs, cells):
+    """Table 2 (system-deltas): mean comments and per-quality-proxy AUC plus
+    overall, with CIs, one (method, model) per row."""
+    slug_to_mem = load_manifest(manifest)
+    counts = load_counts(dirs, set(slug_to_mem))
+    labels, w = _labels_and_width(cells)
+    proxy_ids = sorted(PROXY)  # Community / Conference / Reviewer / Composite
+    head = (f"{'method__model':<{w}}{'c_bar':>8}   "
+            + "".join(f"{PROXY[p]:<18}" for p in proxy_ids) + f"{'Overall':<18}")
+    print(head.rstrip())
+    for (method, model), label in zip(cells, labels):
+        cc = counts.get((method, model))
+        if not cc:
+            print(f"{label:<{w}}NO DATA"); continue
+        s = cell_summary(cc, slug_to_mem)
+        by_proxy = by_proxy_totals(cc, slug_to_mem)
+        overall, per = cell_aucs(by_proxy)
+        (olo, ohi), perci = bootstrap(by_proxy)
+        c_bar = (s["c_high"] + s["c_low"]) / 2
+        row = f"{label:<{w}}{c_bar:>8.2f}   "
+        row += "".join(f"{(auc_cell(per[p], perci[p]) if p in per else '-'):<18}" for p in proxy_ids)
+        row += f"{auc_cell(overall, (olo, ohi)):<18}"
+        print(row.rstrip())
+
+
+def run_by_severity(manifest, dirs, cells):
+    """Table 9 (severity-aggregate): mean comments on the low-quality group and
+    per-severity-tier AUC (Major / Moderate / Minor) plus overall, with CIs."""
+    slug_to_mem = load_manifest(manifest)
+    counts = load_counts(dirs, set(slug_to_mem))
+    labels, w = _labels_and_width(cells)
+    # (column header, cell_summary point key, bootstrap tier key)
+    TIER_COLS = [("Major", "auc_major", "major"), ("Moderate", "auc_moderate", "moderate"),
+                 ("Minor", "auc_minor", "minor"), ("Overall", "auc_overall", "total")]
+    print((f"{'method__model':<{w}}{'c_low':>8}   "
+           + "".join(f"{name:<18}" for name, _, _ in TIER_COLS)).rstrip())
+    for (method, model), label in zip(cells, labels):
+        cc = counts.get((method, model))
+        if not cc:
+            print(f"{label:<{w}}NO DATA"); continue
+        s = cell_summary(cc, slug_to_mem)
+        ci = bootstrap_tiers(by_proxy_recs(cc, slug_to_mem), [t for _, _, t in TIER_COLS])
+        row = f"{label:<{w}}{s['c_low']:>8.2f}   "
+        row += "".join(f"{auc_cell(s[pkey], ci[tkey]):<18}" for _, pkey, tkey in TIER_COLS)
+        print(row.rstrip())
+
+
+# Table kind -> the report function that prints it. cells come from the config.
+# Each kind maps to one paper-table format: comment_volume = Table 1
+# (model-aggregate), by_proxy = Table 2 (system-deltas), by_severity = Table 9
+# (severity-aggregate).
+DISPATCH = {"comment_volume": run_comment_volume, "by_proxy": run_by_proxy, "by_severity": run_by_severity}
+
+
+def run_cohort(config, name, base):
+    """Run every table defined for one cohort. manifest and dirs in the config
+    are resolved relative to `base` (the config file's directory)."""
+    cohort = config["cohorts"][name]
+    manifest = base / cohort["manifest"]
+    dirs = [base / d for d in cohort["dirs"]]
+    for table in cohort["tables"]:
+        print(f"\n########## {table['title']} ##########")
+        cells = [tuple(c) for c in table["cells"]]
+        DISPATCH[table["kind"]](manifest, dirs, cells)
+
+
+if __name__ == "__main__":
+    import argparse
+    import json
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--config", type=Path, default=HERE / "ci_auc_tables.json",
+                    help="JSON defining cohorts, result dirs, and tables. "
+                         "Paths inside it are relative to the config's directory. "
+                         "Defaults to ci_auc_tables.json.")
+    ap.add_argument("--cohort", help="cohort name from the config "
+                                     "(default: the first one defined)")
+    args = ap.parse_args()
+    config = json.loads(args.config.read_text())
+    cohort = args.cohort or next(iter(config["cohorts"]))
+    if cohort not in config["cohorts"]:
+        ap.error(f"unknown cohort {cohort!r}; choices: {list(config['cohorts'])}")
+    run_cohort(config, cohort, args.config.resolve().parent)