File size: 6,267 Bytes
306ab7a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 | """Recompute every statistic referenced in the paper from the released CSV
snapshot. Single source of truth for paper updates.
Outputs:
- Spearman rho_s + two-sided p for Stars / VS Code installs / SO questions
- Pearson r on log-targets (Appendix robustness check 2)
- Leave-one-out range on the GitHub-stars correlation (robustness check 1)
- Single- vs combined-factor sub-composite (robustness check 3)
- Dirichlet bootstrap (1000 draws from Dir(3.5, 2.5, 2.0, 2.0))
- Inter-factor Spearman matrix on n=50
"""
import csv, json, math, random, sys
from pathlib import Path
from reproduce_table3 import (
spearman, two_sided_p,
load_latest_scores, load_latest_signals, load_registry,
benchmark_sentiment_subcomposite,
)
def pearson(xs, ys):
n = len(xs)
mx, my = sum(xs)/n, sum(ys)/n
num = sum((xs[i]-mx)*(ys[i]-my) for i in range(n))
dx = math.sqrt(sum((xs[i]-mx)**2 for i in range(n)))
dy = math.sqrt(sum((ys[i]-my)**2 for i in range(n)))
return num/(dx*dy) if dx and dy else float("nan")
def main():
csv_dir = Path(__file__).parent.parent / "data" / "csv"
scores = load_latest_scores(csv_dir)
signals = load_latest_signals(csv_dir)
registry = load_registry(csv_dir)
eligible = [n for n, c in registry.items() if c.get("github_repo") and n in scores and n in signals]
print(f"\n=== Sample size ===")
print(f"50 agents in registry, n = {len(eligible)} have github_repo + scores + signals")
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)
# --- Table 3 (Spearman) ---
print(f"\n=== Table 3: Spearman correlations (n={len(bs)}) ===")
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, len(bs))
sig = "p<0.001" if p<0.001 else f"p={p:.3f}"
print(f" {label:<32} rho_s={rho:+.3f} {sig}")
# --- Pearson on log targets (Appendix Robustness check 2) ---
so_log = [math.log10(q + 1) for q in so_q]
print(f"\n=== Robustness check 2: Pearson on log targets ===")
for label, ys in [("GitHub stars (log)", stars_log),
("VS Code installs (log)", installs_log),
("Stack Overflow questions (log)", so_log)]:
r = pearson(bs, ys)
print(f" {label:<32} r={r:+.3f}")
# --- Robustness check 3: alternative sub-composites ---
print(f"\n=== Robustness check 3: alternative sub-composites vs stars ===")
bench_only, sent_only = [], []
for name in eligible:
sub = json.loads(scores[name].get("sub_scores") or "[0.5, 0, 0]")
bench_only.append(sub[0] if sub else 0.5)
sent_raw = float(scores[name].get("sentiment") or 0.0)
sent_only.append(max(0.0, min(1.0, (sent_raw + 1.0) / 2.0)))
print(f" Benchmark only: rho_s={spearman(bench_only, stars_log):+.3f}")
print(f" Sentiment only: rho_s={spearman(sent_only, stars_log):+.3f}")
print(f" B+S combined: rho_s={spearman(bs, stars_log):+.3f}")
# --- Robustness check 1: leave-one-out ---
print(f"\n=== Robustness check 1: leave-one-out (GitHub stars) ===")
rhos = []
for i in range(len(bs)):
bs_loo = bs[:i] + bs[i+1:]
sl_loo = stars_log[:i] + stars_log[i+1:]
rhos.append(spearman(bs_loo, sl_loo))
print(f" n iterations: {len(rhos)}")
print(f" range: [{min(rhos):.3f}, {max(rhos):.3f}]")
print(f" median: {sorted(rhos)[len(rhos)//2]:.3f}")
full = spearman(bs, stars_log)
print(f" full sample: {full:.3f}")
print(f" max |delta from full|: {max(abs(r-full) for r in rhos):.3f}")
# --- Dirichlet bootstrap ---
print(f"\n=== Dirichlet bootstrap (1000 draws from Dir(3.5,2.5,2.0,2.0)) ===")
# We perturb the four-factor weights, then re-derive a B+S sub-composite using
# the perturbed B and S weights renormalized to sum to 1 over {B, S}.
random.seed(12345)
def dirichlet(alphas):
ys = [random.gammavariate(a, 1.0) for a in alphas]
s = sum(ys)
return [y/s for y in ys]
rho_samples = []
for _ in range(1000):
w_B, w_A, w_S, w_E = dirichlet([3.5, 2.5, 2.0, 2.0])
# Resampled B+S sub-composite, renormalized over {B, S}
denom = w_B + w_S
if denom == 0:
continue
wb, ws = w_B / denom, w_S / denom
bs_pert = [wb * bench_only[i] + ws * sent_only[i] for i in range(len(eligible))]
rho_samples.append(spearman(bs_pert, stars_log))
rho_samples.sort()
lo, hi = rho_samples[25], rho_samples[975] # 95% interval
median = rho_samples[len(rho_samples)//2]
print(f" 95% interval: [{lo:.3f}, {hi:.3f}]")
print(f" median: {median:.3f}")
print(f" min, max: [{min(rho_samples):.3f}, {max(rho_samples):.3f}]")
print(f" fraction >0: {sum(1 for r in rho_samples if r>0)/len(rho_samples):.3f}")
# --- Inter-factor Spearman on n=50 (Table 2) ---
print(f"\n=== Table 2: inter-factor Spearman correlations (n=50) ===")
# Build per-agent factor vectors
all_agents = [n for n in registry if n in scores]
Bs, Ss, As, Es = [], [], [], []
for name in all_agents:
sub = json.loads(scores[name].get("sub_scores") or "[0.5, 0, 0]")
Bs.append(sub[0] if len(sub) > 0 else 0.5)
sent_raw = float(scores[name].get("sentiment") or 0.0)
Ss.append(max(0.0, min(1.0, (sent_raw + 1.0) / 2.0)))
As.append(float(scores[name].get("adoption") or 0))
Es.append(float(scores[name].get("reliability") or 0))
pairs = [("B-A", Bs, As), ("B-S", Bs, Ss), ("B-E", Bs, Es),
("A-S", As, Ss), ("A-E", As, Es), ("S-E", Ss, Es)]
for label, x, y in pairs:
print(f" {label}: rho={spearman(x, y):+.3f}")
print(f" n = {len(all_agents)}")
if __name__ == "__main__":
main()
|