label_anger_id.py

#!/usr/bin/env python
"""Split EmoTweetID's `anger` pool into `anger` vs `contempt` with laya - **Indonesian only**.

Difference from `label_anger.py`: the English translation (`tweet_en`) is never shown to the model.
The published run read every tweet twice, once in Indonesian and once in EmoTweetID's machine
translation, and pooled the two; the translation mistranslates the slang and code-mixing these
tweets are made of (`GUA MURKA` -> "THE CAVE IS ANGER"), so the English reading is what defined
122 of the 475 shipped labels.  Here the decision is made from `text` alone.

Stages (all on Indonesian text; the question wording stays laya's own English prompt, i.e. the
criteria are Ekman's, quoted in the language they were written in):

  id_core       `ekman` choice on all 475 rows.  Same stage name, questions and texts as the
                published `out/cache_id_core.json`, so this stage must reproduce that cache
                *exactly* - the run asserts it and the card quotes the agreement.  This is the
                primary reading.
  id_core_swap  the same choice with the two criteria in the opposite order.  Measured on the
                probe set, the option listed second is favoured (mean P(anger) on contempt probes
                0.31 with anger first vs 0.48 with contempt first), so the swapped pass records how
                much of the contempt shift is prompt-order artifact.  `p_anger_swapped` ships on
                every row; the average of the two orders is the debiased reading.
  id_diag       Ekman's core-feature probes (superiority, blocked_or_unfair) - only on the rows
                where the primary choice reading is not decisive (|margin| < 0.10).
  id_veto       the `not_anger_or_contempt` diagnostic on all rows (card: do not filter on it).

Output: out/anger_ekman_rows.csv, out/timings.json, out/cache_<stage>.json.
"""
import argparse
import hashlib
import json
import os
import re
import sys
import time

import numpy as np
import pandas as pd


def norm(s):
    return re.sub(r"\s+", " ", str(s)).strip()


def cache_path(out_dir, stage):
    return os.path.join(out_dir, "cache_%s.json" % stage)


def load_cache(path):
    if os.path.exists(path):
        with open(path) as f:
            return {k: v for k, v in json.load(f).items()}
    return {}


def save_cache(path, cache):
    tmp = path + ".tmp"
    with open(tmp, "w") as f:
        json.dump(cache, f)
    os.replace(tmp, path)


def run_stage(agent, laya_opt, questions, texts, out_dir, stage, max_len, head_max_len, budget):
    """Score `texts` with `questions`, one cache row per (text, stage) so a re-run resumes."""
    cache = load_cache(cache_path(out_dir, stage))
    todo_idx, todo_txt = [], []
    for t in texts:
        key = hashlib.sha1(("%s|%s" % (stage, t)).encode()).hexdigest()[:16]
        if key not in cache:
            todo_idx.append(key)
            todo_txt.append(t)
    print("[stage %s] %d rows to score, %d already cached" % (
        stage, len(todo_txt), len(texts) - len(todo_txt)), flush=True)
    t0 = time.time()
    if todo_txt:
        res, stats = laya_opt.score_texts(agent, questions, todo_txt, max_len=max_len,
                                          head_max_len=head_max_len, token_budget=budget,
                                          log=lambda *a: None)
        for k, r in zip(todo_idx, res):
            cache[k] = r
        save_cache(cache_path(out_dir, stage), cache)
        print("[stage %s] %s" % (stage, json.dumps(stats)), flush=True)
    else:
        stats = {"seconds": 0.0}
    out = []
    for t in texts:
        key = hashlib.sha1(("%s|%s" % (stage, t)).encode()).hexdigest()[:16]
        if key not in cache:
            raise RuntimeError("cache miss for %s after stage %s" % (key, stage))
        out.append(cache[key])
    return out, stats


def combine_id(probs, swap=None, sup=None, blk=None, choice_margin=0.10, noul_margin=0.05):
    """(label, label_source, margin, p_anger, p_contempt).

    Rules, in order - the single-reading version of `ekman_questions.combine`:

      1. the Indonesian choice reading is decisive (|p(anger) - p(contempt)| >= 0.10) -> its argmax
         (`ekman_choice_id`).
      2. not decisive -> Ekman's core-feature probes break the tie: superiority => contempt,
         blocked-or-unfair => anger (`ekman_noul_tiebreak`).
      3. not decisive and probes agree too -> the *order-averaged* reading decides if it has a
         side to pick (`ekman_choice_order_avg`).
      4. still nothing -> keep the upstream EmoTweetID label `anger`: this dataset only ever splits
         an existing anger pool, it does not re-litigate it (`kept_original_label`).
    """
    p_a, p_c = probs["anger"], probs["contempt"]
    m = p_a - p_c
    src = "ekman_choice_id"
    if abs(m) < choice_margin:
        m = m
        if sup is not None and blk is not None and abs(sup - blk) >= noul_margin:
            lab = "contempt" if sup > blk else "anger"
            return lab, "ekman_noul_tiebreak", m, p_a, p_c
        if swap is not None:
            ma = ((p_a + swap["anger"]) - (p_c + swap["contempt"])) / 2.0
            if abs(ma) >= choice_margin:
                return ("anger" if ma >= 0 else "contempt"), "ekman_choice_order_avg", m, p_a, p_c
        return "anger", "kept_original_label", m, p_a, p_c
    return ("anger" if p_a >= p_c else "contempt"), src, m, p_a, p_c


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--max-len", type=int, default=256)
    ap.add_argument("--head-max-len", type=int, default=144)
    ap.add_argument("--token-budget", type=int, default=6144)
    ap.add_argument("--margin", type=float, default=0.10)
    ap.add_argument("--out", default="build/out")
    ap.add_argument("--limit", type=int, default=0)
    ap.add_argument("--no-swap", action="store_true", help="skip the option-order control pass")
    ap.add_argument("--no-veto", action="store_true", help="skip the not_anger_or_contempt diagnostic")
    args = ap.parse_args()

    os.makedirs(args.out, exist_ok=True)
    sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
    import laya_opt
    from ekman_questions import CHOICE_MARGIN, EKMAN_QUESTIONS, assert_option_budget

    t_load = time.time()
    agent = laya_opt.load_agent()
    print("[laya] agent ready in %.1fs (%d params)" % (
        time.time() - t_load, sum(p.numel() for p in agent.model.parameters())), flush=True)
    assert_option_budget(agent.tok)
    print("[laya] option budget ok: every criterion fits laya's 48-token cap", flush=True)

    # ---- data: identical input to the published run, so the Indonesian stage is comparable -------
    f1 = pd.read_csv("data/file1.csv", index_col=0)
    f2 = pd.read_csv("data/file2.csv", index_col=0)
    assert len(f1) == len(f2) and (f1.index == f2.index).all(), "the two EmoTweetID files must align"
    df = f1.join(f2)
    df = df[df["label"] == "anger"].copy()
    df["row_src"] = df.index
    df["text_en"] = df["tweet_en"].map(norm)
    df["text_id"] = df["tweet"].map(norm)
    df = df[df["text_en"].str.len() > 0]
    if args.limit:
        df = df.iloc[:args.limit]
    n = len(df)
    print("[data] %d rows labelled anger (of %d annotated tweets)" % (n, len(f1)), flush=True)

    id_texts = df["text_id"].tolist()
    timings, stage_rows = {}, {}

    # ---- stage 1: the split itself, Indonesian, laya's own criteria order ------------------------
    qC = {"ekman": EKMAN_QUESTIONS["ekman"]}
    ansC, s = run_stage(agent, laya_opt, qC, id_texts, args.out, "id_core", args.max_len,
                        args.head_max_len, args.token_budget)
    timings["id_core"] = s["seconds"]; stage_rows["id_core"] = s.get("rows", 0)

    # ---- stage 1b: option-order control (contempt listed first) ---------------------------------
    ansS = [None] * n
    if not args.no_swap:
        qS = {"ekman": {"type": "choice",
                        "instructions": EKMAN_QUESTIONS["ekman"]["instructions"],
                        "criteria": {"contempt": EKMAN_QUESTIONS["ekman"]["criteria"]["contempt"],
                                     "anger": EKMAN_QUESTIONS["ekman"]["criteria"]["anger"]}}}
        ansS, s = run_stage(agent, laya_opt, qS, id_texts, args.out, "id_core_swap", args.max_len,
                            args.head_max_len, args.token_budget)
        timings["id_core_swap"] = s["seconds"]; stage_rows["id_core_swap"] = s.get("rows", 0)

    # ---- stage 2: core-feature probes, only where the choice reading could not settle it ---------
    ambiguous = [i for i in range(n)
                 if abs(ansC[i]["ekman"]["probabilities"]["anger"]
                        - ansC[i]["ekman"]["probabilities"]["contempt"]) < args.margin]
    print("[stage id_diag] %d/%d rows not decided by the Indonesian choice reading" % (
        len(ambiguous), n), flush=True)
    ansD = [dict() for _ in range(n)]
    if ambiguous:
        qD = {k: EKMAN_QUESTIONS[k] for k in ("superiority", "blocked_or_unfair")}
        sub = [id_texts[i] for i in ambiguous]
        res, s = run_stage(agent, laya_opt, qD, sub, args.out, "id_diag", args.max_len,
                           args.head_max_len, args.token_budget)
        for i, r in zip(ambiguous, res):
            ansD[i] = r
        timings["id_diag"] = s["seconds"]; stage_rows["id_diag"] = s.get("rows", 0)

    # ---- stage 3: the off-topic veto, as a diagnostic only ---------------------------------------
    ansV = [None] * n
    if not args.no_veto:
        qV = {"not_anger_or_contempt": EKMAN_QUESTIONS["not_anger_or_contempt"]}
        ansV, s = run_stage(agent, laya_opt, qV, id_texts, args.out, "id_veto", args.max_len,
                            args.head_max_len, args.token_budget)
        timings["id_veto"] = s["seconds"]; stage_rows["id_veto"] = s.get("rows", 0)

    # ---- combine ---------------------------------------------------------------------------------
    rows = []
    for i in range(n):
        probs = ansC[i]["ekman"]["probabilities"]
        swap = ansS[i]["ekman"]["probabilities"] if ansS[i] else None
        diag = ansD[i]
        sup = diag.get("superiority", {}).get("noul")
        blk = diag.get("blocked_or_unfair", {}).get("noul")
        label, src, m, p_a, p_c = combine_id(probs, swap, sup, blk, args.margin)
        rows.append({
            "row_src": int(df["row_src"].iloc[i]),
            "text": id_texts[i],
            "text_en": df["text_en"].iloc[i],
            "source_label": "anger",
            "label": label,
            "label_source": src,
            "ekman_p_anger": round(p_a, 4),
            "ekman_p_contempt": round(p_c, 4),
            "ekman_confidence": round(ansC[i]["ekman"]["confidence"], 4),
            "ekman_margin_id": round(m, 4),
            "p_anger_id": round(probs["anger"], 4),
            "p_anger_swapped": None if swap is None else round(swap["anger"], 4),
            "en_id_agree": None,
            "p_superiority": None if sup is None else round(sup, 4),
            "p_blocked_or_unfair": None if blk is None else round(blk, 4),
            "p_not_anger_or_contempt": None if ansV[i] is None else
                                      round(ansV[i]["not_anger_or_contempt"]["noul"], 4),
            "ambiguous": bool(abs(m) < CHOICE_MARGIN),
        })
    out = pd.DataFrame(rows)
    out.to_csv(os.path.join(args.out, "anger_ekman_rows.csv"), index=False)

    prev_path = os.path.join(args.out, "timings.json")
    if os.path.exists(prev_path):
        prev = json.load(open(prev_path))
        for k, v in prev.get("stages", {}).items():
            if timings.get(k, 0) in (0, 0.0) and v:
                timings[k] = v
        for k, v in prev.get("stage_rows", {}).items():
            stage_rows.setdefault(k, v)
    with open(os.path.join(args.out, "timings.json"), "w") as f:
        json.dump({"rows": n, "ambiguous_rows": len(ambiguous),
                   "total_laya_seconds": round(sum(timings.values()), 1), "stages": timings,
                   "stage_rows": {k: v for k, v in stage_rows.items() if v},
                   "max_len": args.max_len, "head_max_len": args.head_max_len,
                   "token_budget": args.token_budget, "margin": args.margin,
                   "language": "id-only"}, f, indent=2)

    print("\n=== laya anger/contempt split (Indonesian only) ===", flush=True)
    print(out["label"].value_counts().to_string())
    print("\nlabel provenance:\n" + out["label_source"].value_counts().to_string())
    print("\ncontempt share of the pool: %.1f%%" % (100.0 * (out["label"] == "contempt").mean()))
    print("ambiguous (|margin|<%.2f): %d" % (args.margin, out["ambiguous"].sum()))
    if out["p_anger_swapped"].notna().any():
        print("mean P(anger): as prompted %.3f | swapped %.3f" % (
            out["p_anger_id"].mean(), out["p_anger_swapped"].mean()))
        flips = int(((out["p_anger_id"] >= 0.5) != (out["p_anger_swapped"] >= 0.5)).sum())
        print("rows whose argmax flips with the option order: %d (%.1f%%)" % (flips, 100.0 * flips / n))
    if out["p_not_anger_or_contempt"].notna().any():
        print("p(not anger or contempt) > 0.5: %d" % (out["p_not_anger_or_contempt"] > 0.5).sum())
    print("laya seconds: %s" % json.dumps(timings))


if __name__ == "__main__":
    main()