"""Reproduce Table 3 (cross-factor predictive validity) from the paper. Reads only the released CSVs — no DB, no network. Expected output (paper, n=35): GitHub stars (log) rho_s = 0.52 p < 0.01 VS Code installs (log) rho_s = 0.44 p < 0.05 Stack Overflow question volume rho_s = 0.49 p < 0.01 Usage: python reproduce_table3.py [--csv-dir ../data/csv] """ import argparse import csv import json import math import sys from pathlib import Path from typing import Dict, List, Tuple # ─────────────────────────────────────────────────────────────────────────────── # Tiny stats utilities (avoid scipy/pandas dependency for reviewer reproducibility) # ─────────────────────────────────────────────────────────────────────────────── def _rank(values: List[float]) -> List[float]: """Average-rank a sequence, handling ties.""" indexed = sorted(enumerate(values), key=lambda kv: kv[1]) ranks = [0.0] * len(values) i = 0 while i < len(indexed): j = i while j + 1 < len(indexed) and indexed[j + 1][1] == indexed[i][1]: j += 1 avg = (i + j) / 2.0 + 1.0 # 1-indexed average rank for k in range(i, j + 1): ranks[indexed[k][0]] = avg i = j + 1 return ranks def spearman(xs: List[float], ys: List[float]) -> float: rx, ry = _rank(xs), _rank(ys) n = len(xs) mx, my = sum(rx) / n, sum(ry) / n num = sum((rx[i] - mx) * (ry[i] - my) for i in range(n)) dx = math.sqrt(sum((rx[i] - mx) ** 2 for i in range(n))) dy = math.sqrt(sum((ry[i] - my) ** 2 for i in range(n))) return num / (dx * dy) if dx and dy else float("nan") def two_sided_p(rho: float, n: int) -> float: """Approximate two-sided p-value via the t-distribution approximation.""" if abs(rho) >= 1.0 or n < 4: return 0.0 if abs(rho) >= 1.0 else 1.0 t = rho * math.sqrt((n - 2) / (1 - rho * rho)) df = n - 2 # Survival of |t| via Student-t CDF approx (good enough at df ≥ 10) x = df / (df + t * t) # Regularized incomplete beta via continued fraction return _student_t_sf(t, df) * 2 def _student_t_sf(t: float, df: int) -> float: """One-sided survival function of Student-t (|t|).""" t = abs(t) a = df / 2.0 b = 0.5 x = df / (df + t * t) return 0.5 * _betai(a, b, x) def _betai(a: float, b: float, x: float) -> float: if x <= 0.0: return 0.0 if x >= 1.0: return 1.0 bt = math.exp(math.lgamma(a + b) - math.lgamma(a) - math.lgamma(b) + a * math.log(x) + b * math.log(1.0 - x)) if x < (a + 1.0) / (a + b + 2.0): return bt * _betacf(a, b, x) / a return 1.0 - bt * _betacf(b, a, 1.0 - x) / b def _betacf(a: float, b: float, x: float, max_iter: int = 200, eps: float = 1e-12) -> float: qab, qap, qam = a + b, a + 1.0, a - 1.0 c, d = 1.0, 1.0 - qab * x / qap if abs(d) < 1e-30: d = 1e-30 d, h = 1.0 / d, 1.0 / d for m in range(1, max_iter + 1): m2 = 2 * m aa = m * (b - m) * x / ((qam + m2) * (a + m2)) d = 1.0 + aa * d if abs(d) < 1e-30: d = 1e-30 c = 1.0 + aa / c if abs(c) < 1e-30: c = 1e-30 d = 1.0 / d h *= d * c aa = -(a + m) * (qab + m) * x / ((a + m2) * (qap + m2)) d = 1.0 + aa * d if abs(d) < 1e-30: d = 1e-30 c = 1.0 + aa / c if abs(c) < 1e-30: c = 1e-30 d = 1.0 / d delta = d * c h *= delta if abs(delta - 1.0) < eps: return h return h # ─────────────────────────────────────────────────────────────────────────────── # Load inputs # ─────────────────────────────────────────────────────────────────────────────── def load_latest_scores(csv_dir: Path) -> Dict[str, dict]: """Latest agent_scores row per agent_name.""" rows: Dict[str, dict] = {} with (csv_dir / "agent_scores.csv").open() as f: for r in csv.DictReader(f): ts = r["computed_at"] cur = rows.get(r["agent_name"]) if cur is None or ts > cur["computed_at"]: rows[r["agent_name"]] = r return rows def load_latest_signals(csv_dir: Path) -> Dict[str, dict]: """Latest signals_json row per agent_name (parsed).""" rows: Dict[str, dict] = {} with (csv_dir / "agent_signals_raw.csv").open() as f: for r in csv.DictReader(f): ts = r["collected_at"] cur = rows.get(r["agent_name"]) if cur is None or ts > cur["collected_at"]: rows[r["agent_name"]] = r out: Dict[str, dict] = {} for name, row in rows.items(): try: out[name] = json.loads(row["signals_json"]) except Exception: out[name] = {} return out def load_registry(csv_dir: Path) -> Dict[str, dict]: out: Dict[str, dict] = {} with (csv_dir / "agent_registry.csv").open() as f: for r in csv.DictReader(f): out[r["name"]] = r return out # ─────────────────────────────────────────────────────────────────────────────── # Sub-composite construction (paper Section 5.2) # ─────────────────────────────────────────────────────────────────────────────── def benchmark_sentiment_subcomposite(score_row: dict, signals: dict) -> float: """Benchmark + Sentiment only, normalized to weights summing to 1. Default paper weights: w_B=0.35, w_S=0.20. Renormalized: 0.6364 B + 0.3636 S. Excludes Adoption and Ecosystem to break GitHub-signal circularity. """ sub = json.loads(score_row.get("sub_scores") or "[0.5, 0, 0]") b = sub[0] if sub else 0.5 sent_raw = float(score_row.get("sentiment") or 0.0) # Paper S(a) is in [0,1]; raw sentiment is in [-1,1]. Linear remap: s_norm = max(0.0, min(1.0, (sent_raw + 1.0) / 2.0)) return 0.6364 * b + 0.3636 * s_norm # ─────────────────────────────────────────────────────────────────────────────── # Main # ─────────────────────────────────────────────────────────────────────────────── def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--csv-dir", default="../data/csv", help="Directory containing the released CSVs") args = parser.parse_args() csv_dir = Path(args.csv_dir).resolve() if not csv_dir.exists(): print(f"error: csv-dir {csv_dir} does not exist", file=sys.stderr) return 1 scores = load_latest_scores(csv_dir) signals = load_latest_signals(csv_dir) registry = load_registry(csv_dir) # The paper's n=35 subset: agents with a public GitHub repo. public_repo_agents = [ name for name, cfg in registry.items() if cfg.get("github_repo") ] print(f"Agents with public GitHub repos in registry: {len(public_repo_agents)}") # Restrict to those with both a score row and a signals row eligible = [a for a in public_repo_agents if a in scores and a in signals] print(f"Of those, with scoring and signal data: {len(eligible)}\n") bs = [] stars_log = [] installs_log = [] so_q = [] for name in eligible: s = signals[name] bs.append(benchmark_sentiment_subcomposite(scores[name], s)) stars_log.append(math.log10((s.get("github_stars") or 0) + 1)) installs_log.append(math.log10((s.get("vscode_installs") or 0) + 1)) so_q.append(s.get("so_questions") or 0) n = len(bs) print(f"{'External signal':<32} {'rho_s':>8} {'p (two-sided)':>15}") print("-" * 60) for label, ys in [("GitHub stars (log)", stars_log), ("VS Code installs (log)", installs_log), ("Stack Overflow question volume", so_q)]: rho = spearman(bs, ys) p = two_sided_p(rho, n) print(f"{label:<32} {rho:>8.3f} {p:>15.4g}") print(f"\nn = {n}") print("\nPaper-reported values (Table 3, n=35):") print(" GitHub stars (log) rho_s = 0.52 p < 0.01") print(" VS Code installs (log) rho_s = 0.44 p < 0.05 (illustrative; 11/35 non-zero)") print(" Stack Overflow question volume rho_s = 0.49 p < 0.01") return 0 if __name__ == "__main__": sys.exit(main())