plot_dialect_vs_none.py

"""exp0 headline: posts/comments that use ANY rationalist dialect vs NONE.

The honest, defensible claim from exp0 is a step, not a gradient: AF documents
that use any rationalist/EA dialect score higher than documents that use none.
This script tests that for posts and comments separately.

Karma is heavy-tailed, so we use Mann-Whitney U (rank-based) for significance
and report the rank-biserial / common-language effect size alongside means.

Outputs:
  figures/dialect_vs_none_karma.png
  results/dialect_vs_none.txt
"""
import json
import os
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats

PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, PROJECT_DIR)
from config import count_all_terms, word_count

DATA = Path(PROJECT_DIR) / "data"
FIG = Path(PROJECT_DIR) / "figures"
RES = Path(PROJECT_DIR) / "results"
FIG.mkdir(parents=True, exist_ok=True)
RES.mkdir(parents=True, exist_ok=True)
def load(path, year_range=None):
    """year_range=(lo, hi) to restrict; None to keep all years."""
    rows = []
    with open(path) as f:
        for line in f:
            p = json.loads(line)
            text = p.get("body") or ""
            wc = max(1, word_count(text))
            counts = count_all_terms(text)
            rows.append({
                "posted_at": p.get("posted_at"),
                "base_score": p.get("base_score"),
                "n_matches": sum(counts.values()),
                "dialect_rate": sum((c / wc) * 10000 for c in counts.values()),
            })
    df = pd.DataFrame(rows)
    df["posted_at"] = pd.to_datetime(df["posted_at"], errors="coerce", utc=True)
    df["year"] = df["posted_at"].dt.year
    df["base_score"] = pd.to_numeric(df["base_score"], errors="coerce")
    if year_range is not None:
        lo, hi = year_range
        df = df[(df["year"] >= lo) & (df["year"] <= hi)]
    df = df.dropna(subset=["base_score"]).reset_index(drop=True)
    # group: did the doc use ANY dialect term at all?
    df["uses_dialect"] = df["n_matches"] > 0
    return df


def boot_median_ci(a, n_boot=2000, seed=0):
    """95% bootstrap CI for the median (half-widths around the point median)."""
    rng = np.random.default_rng(seed)
    a = np.asarray(a)
    meds = np.median(rng.choice(a, size=(n_boot, len(a)), replace=True), axis=1)
    lo, hi = np.percentile(meds, [2.5, 97.5])
    m = float(np.median(a))
    return m, m - lo, hi - m


def analyze(name, df, lines):
    none = df.loc[~df["uses_dialect"], "base_score"].values
    some = df.loc[df["uses_dialect"], "base_score"].values
    n0, n1 = len(none), len(some)

    # Mann-Whitney U (two-sided), rank-based — robust to karma's heavy tail
    U, p = stats.mannwhitneyu(some, none, alternative="two-sided")
    # common-language effect size: P(random dialect doc outscores no-dialect doc)
    cles = U / (n0 * n1)
    rank_biserial = 2 * cles - 1

    med0, lo0, hi0 = boot_median_ci(none)
    med1, lo1, hi1 = boot_median_ci(some)

    stat = {
        "name": name, "n0": n0, "n1": n1,
        "mean0": float(np.mean(none)), "mean1": float(np.mean(some)),
        "med0": med0, "med1": med1,
        "ci0": (lo0, hi0), "ci1": (lo1, hi1),
        "p": p, "cles": cles, "rb": rank_biserial,
    }

    lines.append(f"{name}")
    lines.append("-" * 60)
    lines.append(f"  no dialect : n={n0:>6}  median={med0:6.1f}  mean={stat['mean0']:7.2f}")
    lines.append(f"  any dialect: n={n1:>6}  median={med1:6.1f}  mean={stat['mean1']:7.2f}")
    lines.append(f"  median uplift: {med1 - med0:+.1f} karma")
    lines.append(f"  mean uplift  : {stat['mean1'] - stat['mean0']:+.2f} karma "
                 "(mean is outlier-sensitive on heavy-tailed karma — see CLES)")
    lines.append(f"  Mann-Whitney U: p = {p:.2g}")
    lines.append(f"  effect size  : common-language (CLES) = {cles:.3f} "
                 f"(P a dialect doc outranks a no-dialect doc); "
                 f"rank-biserial = {rank_biserial:+.3f}")
    lines.append("")
    return stat


def panel(ax, stat, color):
    x = [0, 1]
    meds = [stat["med0"], stat["med1"]]
    yerr = np.array([[stat["ci0"][0], stat["ci1"][0]],
                     [stat["ci0"][1], stat["ci1"][1]]])
    ns = [stat["n0"], stat["n1"]]
    means = [stat["mean0"], stat["mean1"]]
    ax.bar(x, meds, 0.6, yerr=yerr, capsize=6, color=["tab:gray", color],
           alpha=0.88, error_kw={"elinewidth": 1.5, "ecolor": "black"})
    for i in x:
        top = meds[i] + yerr[1][i]
        ax.text(i, top, f"  n={ns[i]:,}\n  median {meds[i]:.0f}\n  (mean {means[i]:.1f})",
                ha="center", va="bottom", fontsize=8.5, color="dimgray")
    ax.set_xticks(x)
    ax.set_xticklabels(["uses NO\ndialect", "uses SOME\ndialect"], fontsize=10)
    ax.set_ylabel("median karma (base_score)", fontsize=11)
    ax.margins(y=0.34)
    ax.grid(alpha=0.3, axis="y")
    star = "p < 0.001" if stat["p"] < 1e-3 else f"p = {stat['p']:.3f}"
    ax.set_title(f"{stat['name']}\n"
                 f"Mann-Whitney {star}   ·   CLES = {stat['cles']:.2f}",
                 fontsize=11, fontweight="bold")


def main():
    lines = ["exp0 — does using rationalist dialect predict higher karma?",
             "Group split: 'any dialect' = >=1 dialect-term match in the doc.",
             "Posts:    2022-2025 window (comparable recent era).",
             "Comments: full crawl, which is a recent ~May-2026 snapshot only.",
             "=" * 60, ""]

    posts = load(DATA / "alignmentforum_posts.jsonl", year_range=(2022, 2025))
    comments = load(DATA / "alignmentforum_comments.jsonl", year_range=None)

    s_posts = analyze("Alignment Forum POSTS  (2022-2025)", posts, lines)
    s_comments = analyze("Alignment Forum COMMENTS  (2026 snapshot)", comments, lines)

    txt = "\n".join(lines)
    print(txt)
    (RES / "dialect_vs_none.txt").write_text(txt + "\n")
    print(f"\nwrote {RES / 'dialect_vs_none.txt'}")

    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5.8))
    panel(ax1, s_posts, "tab:blue")
    panel(ax2, s_comments, "tab:orange")
    fig.suptitle("Alignment Forum: documents using rationalist dialect outrank "
                 "documents using none\n"
                 "median karma, bootstrap 95% CI  ·  rank-based test (karma is heavy-tailed)",
                 fontsize=12.5, fontweight="bold")
    fig.tight_layout(rect=(0, 0, 1, 0.91))
    out = FIG / "dialect_vs_none_karma.png"
    fig.savefig(out, dpi=140)
    plt.close(fig)
    print(f"wrote {out}")


if __name__ == "__main__":
    main()