legex/analysis.py

"""Cross-jurisdiction analysis of Goldenset vs prediction CSVs.

Builds on `legex.evaluation.score_country`: reuses the (tp, mismatch, missed,
hallucinated, tn) per-cell buckets and exposes paper-headline aggregates —
hallucination rate, recall-when-filled, miss rate — across countries, fields,
models, legal traditions, and language families.

Outputs CSV + LaTeX tables under ``--out`` (default ``data/analysis``).
"""

import argparse
import csv
import logging
import re
import sys
from collections import defaultdict
from pathlib import Path

from legex.config import settings
from legex.evaluation import SYSTEMS, _BUCKETS, _derived, score_country
from legex.utils import evaluable_countries

log = logging.getLogger(__name__)


# Paper §A. Static maps — adding a jurisdiction = adding two entries.
LEGAL_TRADITION: dict[str, str] = {
    "au": "common", "hk": "common", "in": "common", "nz": "common",
    "sg": "common", "uk": "common", "us": "common", "gh": "common",
    "ph": "common",
    "am": "civil", "at": "civil", "be": "civil", "br": "civil",
    "ch": "civil", "de": "civil", "es": "civil", "fr": "civil",
    "ge": "civil", "it": "civil", "li": "civil", "lu": "civil",
    "np": "civil", "rs": "civil", "tw": "civil", "xk": "civil",
    "al": "civil",
}

LANGUAGE_FAMILY: dict[str, str] = {
    "au": "en-latin", "hk": "en-latin", "in": "en-latin", "nz": "en-latin",
    "sg": "en-latin", "uk": "en-latin", "us": "en-latin", "gh": "en-latin",
    "ph": "en-latin",
    "at": "eu-latin", "be": "eu-latin", "br": "eu-latin", "ch": "eu-latin",
    "de": "eu-latin", "es": "eu-latin", "fr": "eu-latin", "it": "eu-latin",
    "li": "eu-latin", "lu": "eu-latin", "rs": "eu-latin", "al": "eu-latin",
    "xk": "eu-latin",
    "am": "non-latin", "ge": "non-latin", "np": "non-latin", "tw": "non-latin",
}


COST_BLOCK: tuple[str, ...] = (
    "dispute_value_nominal",
    "plaintiff_loosing_share",
    "court_cost_awarded_nominal",
    "party_compensation_awarded_nominal",
)


DERIVED_KEYS = (
    "accuracy",
    "recall_when_filled",
    "precision_when_emitted",
    "hallucination_rate",
    "miss_rate",
    "wrong_when_both_filled",
    "f1",
)


def derived_metrics(c: dict[str, int]) -> dict[str, float]:
    """Seven paper-headline metrics from a single bucket counter."""
    tp, mism, miss, hallu, tn = c["tp"], c["mismatch"], c["missed"], c["hallucinated"], c["tn"]
    total = tp + mism + miss + hallu + tn
    filled_gold = tp + mism + miss
    emitted = tp + mism + hallu
    empty_gold = hallu + tn
    both_filled = tp + mism
    p, r, f1 = _derived(c)
    return {
        "accuracy": (tp + tn) / total if total else 0.0,
        "recall_when_filled": r,
        "precision_when_emitted": p,
        "hallucination_rate": hallu / empty_gold if empty_gold else 0.0,
        "miss_rate": miss / filled_gold if filled_gold else 0.0,
        "wrong_when_both_filled": mism / both_filled if both_filled else 0.0,
        "f1": f1,
    }


def add_buckets(a: dict[str, int], b: dict[str, int]) -> dict[str, int]:
    return {k: a.get(k, 0) + b.get(k, 0) for k in _BUCKETS}


def sum_buckets(counters: dict[str, dict[str, int]], cols: tuple[str, ...] | None = None) -> dict[str, int]:
    """Sum bucket counts across `cols` (or all columns when None)."""
    out = {k: 0 for k in _BUCKETS}
    for col, c in counters.items():
        if cols is not None and col not in cols:
            continue
        for k in _BUCKETS:
            out[k] += c[k]
    return out


_INFERENCE_CSV_RE = re.compile(r"^inference_(.+)\.csv$")


def models_present(cc: str) -> list[str]:
    """Which systems have an inference_<system>.csv on disk for `cc`."""
    d = settings.data_dir / cc
    if not d.is_dir():
        return []
    found: set[str] = set()
    for p in d.glob("inference_*.csv"):
        m = _INFERENCE_CSV_RE.match(p.name)
        if m:
            found.add(m.group(1))
    return sorted(found)


def collect(
    countries: list[str], models: list[str]
) -> list[tuple[str, str, dict[str, dict[str, int]]]]:
    """Score every (country, system) pair. Returns rows of (cc, system, counters)."""
    rows: list[tuple[str, str, dict[str, dict[str, int]]]] = []
    for cc in countries:
        ms = models or models_present(cc)
        for model in ms:
            result = score_country(cc, model, verbose=False)
            if result is None:
                log.info(f"[{cc}/{model}] no scoreable data, skipping")
                continue
            counters, _coverage = result
            rows.append((cc, model, counters))
            log.info(f"[{cc}/{model}] scored {len(counters)} columns")
    return rows


def _fmt(v: float) -> str:
    return f"{v:.4f}"


def _write_csv(path: Path, header: list[str], rows: list[dict[str, object]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with open(path, "w", encoding="utf-8", newline="") as f:
        w = csv.DictWriter(f, fieldnames=header, extrasaction="ignore")
        w.writeheader()
        w.writerows(rows)


def write_per_country_per_column(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None:
    header = ["country", "model", "column", *_BUCKETS, *DERIVED_KEYS]
    out_rows: list[dict[str, object]] = []
    for cc, model, counters in rows:
        for col, c in counters.items():
            d = derived_metrics(c)
            out_rows.append({
                "country": cc, "model": model, "column": col,
                **c, **{k: _fmt(d[k]) for k in DERIVED_KEYS},
            })
    _write_csv(out / "per_country_per_column.csv", header, out_rows)


def write_per_country(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None:
    """One row per (cc, model): summed buckets across all label columns, plus
    cost-block-only summed buckets. Also adds tradition / language tags."""
    header = [
        "country", "model", "legal_tradition", "language_family",
        *_BUCKETS, *DERIVED_KEYS,
        *(f"cost_{k}" for k in _BUCKETS),
        *(f"cost_{k}" for k in DERIVED_KEYS),
    ]
    out_rows: list[dict[str, object]] = []
    for cc, model, counters in rows:
        all_b = sum_buckets(counters)
        cost_b = sum_buckets(counters, COST_BLOCK)
        d_all = derived_metrics(all_b)
        d_cost = derived_metrics(cost_b)
        out_rows.append({
            "country": cc, "model": model,
            "legal_tradition": LEGAL_TRADITION.get(cc, ""),
            "language_family": LANGUAGE_FAMILY.get(cc, ""),
            **all_b,
            **{k: _fmt(d_all[k]) for k in DERIVED_KEYS},
            **{f"cost_{k}": cost_b[k] for k in _BUCKETS},
            **{f"cost_{k}": _fmt(d_cost[k]) for k in DERIVED_KEYS},
        })
    _write_csv(out / "per_country.csv", header, out_rows)


def write_per_column(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None:
    """One row per (model, column): summed across countries."""
    agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in _BUCKETS})
    for _cc, model, counters in rows:
        for col, c in counters.items():
            for k in _BUCKETS:
                agg[(model, col)][k] += c[k]
    header = ["model", "column", *_BUCKETS, *DERIVED_KEYS]
    out_rows: list[dict[str, object]] = []
    for (model, col), c in sorted(agg.items()):
        d = derived_metrics(c)
        out_rows.append({
            "model": model, "column": col, **c,
            **{k: _fmt(d[k]) for k in DERIVED_KEYS},
        })
    _write_csv(out / "per_column.csv", header, out_rows)


def _write_grouped(
    out: Path, name: str, group_map: dict[str, str],
    rows: list[tuple[str, str, dict[str, dict[str, int]]]],
) -> None:
    agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in _BUCKETS})
    cost_agg: dict[tuple[str, str], dict[str, int]] = defaultdict(lambda: {k: 0 for k in _BUCKETS})
    counts: dict[tuple[str, str], int] = defaultdict(int)
    for cc, model, counters in rows:
        group = group_map.get(cc)
        if group is None:
            continue
        key = (model, group)
        all_b = sum_buckets(counters)
        cost_b = sum_buckets(counters, COST_BLOCK)
        for k in _BUCKETS:
            agg[key][k] += all_b[k]
            cost_agg[key][k] += cost_b[k]
        counts[key] += 1
    header = [
        "model", "group", "n_countries",
        *_BUCKETS, *DERIVED_KEYS,
        *(f"cost_{k}" for k in _BUCKETS),
        *(f"cost_{k}" for k in DERIVED_KEYS),
    ]
    out_rows: list[dict[str, object]] = []
    for (model, group), c in sorted(agg.items()):
        cost_c = cost_agg[(model, group)]
        d_all = derived_metrics(c)
        d_cost = derived_metrics(cost_c)
        out_rows.append({
            "model": model, "group": group,
            "n_countries": counts[(model, group)],
            **c,
            **{k: _fmt(d_all[k]) for k in DERIVED_KEYS},
            **{f"cost_{k}": cost_c[k] for k in _BUCKETS},
            **{f"cost_{k}": _fmt(d_cost[k]) for k in DERIVED_KEYS},
        })
    _write_csv(out / f"{name}.csv", header, out_rows)


def _latex_escape(s: str) -> str:
    return s.replace("\\", "\\textbackslash{}").replace("&", "\\&").replace("_", "\\_").replace("%", "\\%")


def _pct(v: float) -> str:
    return f"{v * 100:5.1f}\\%"


def write_headline_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None:
    """One LaTeX `tabular` per model: rows = jurisdiction, cols = headline metrics."""
    by_model: dict[str, list[tuple[str, dict[str, dict[str, int]]]]] = defaultdict(list)
    for cc, model, counters in rows:
        by_model[model].append((cc, counters))

    out.mkdir(parents=True, exist_ok=True)
    lines: list[str] = []
    for model in sorted(by_model):
        lines.append("% Auto-generated by legex-analysis.")
        lines.append("\\begin{table}[h]")
        lines.append(
            "\\caption{Headline extraction metrics by jurisdiction for model \\texttt{"
            f"{_latex_escape(model)}"
            "}. Recall when filled is over the cells where the expert recorded a value. "
            "False-fill rate is the share of legitimately-empty cells where the model "
            "invented a value.}"
        )
        lines.append("\\label{tab:headline-" + re.sub(r"[^a-zA-Z0-9]+", "-", model).strip("-") + "}")
        lines.append("\\centering\\small")
        lines.append("\\begin{tabular}{@{}lrr@{}}")
        lines.append("\\toprule")
        lines.append("Jurisdiction & Recall when filled & False-fill rate \\\\")
        lines.append("\\midrule")
        for cc, counters in sorted(by_model[model]):
            d_all = derived_metrics(sum_buckets(counters))
            lines.append(
                f"{cc.upper()} & {_pct(d_all['recall_when_filled'])} "
                f"& {_pct(d_all['hallucination_rate'])} \\\\"
            )
        lines.append("\\bottomrule")
        lines.append("\\end{tabular}")
        lines.append("\\end{table}")
        lines.append("")
    (out / "headline.tex").write_text("\n".join(lines), encoding="utf-8")


def write_per_field_latex(out: Path, rows: list[tuple[str, str, dict[str, dict[str, int]]]]) -> None:
    """One LaTeX table per model: rows = variable, cols = headline metrics (summed across jurisdictions)."""
    by_model_col: dict[str, dict[str, dict[str, int]]] = defaultdict(lambda: defaultdict(lambda: {k: 0 for k in _BUCKETS}))
    for _cc, model, counters in rows:
        for col, c in counters.items():
            for k in _BUCKETS:
                by_model_col[model][col][k] += c[k]

    out.mkdir(parents=True, exist_ok=True)
    lines: list[str] = []
    for model in sorted(by_model_col):
        lines.append("% Auto-generated by legex-analysis.")
        lines.append("\\begin{table}[h]")
        lines.append(
            "\\caption{Per-field extraction metrics, summed across jurisdictions, for model \\texttt{"
            f"{_latex_escape(model)}"
            "}. Recall when filled is over the cells where the expert recorded a value. False-fill "
            "rate is the share of legitimately-empty cells where the model invented a value.}"
        )
        lines.append("\\label{tab:per-field-" + re.sub(r"[^a-zA-Z0-9]+", "-", model).strip("-") + "}")
        lines.append("\\centering\\small")
        lines.append("\\begin{tabular}{@{}lrr@{}}")
        lines.append("\\toprule")
        lines.append("Variable & Recall when filled & False-fill rate \\\\")
        lines.append("\\midrule")
        for col, c in sorted(by_model_col[model].items()):
            d = derived_metrics(c)
            lines.append(
                f"\\texttt{{{_latex_escape(col)}}} "
                f"& {_pct(d['recall_when_filled'])} & {_pct(d['hallucination_rate'])} \\\\"
            )
        lines.append("\\bottomrule")
        lines.append("\\end{tabular}")
        lines.append("\\end{table}")
        lines.append("")
    (out / "per_field.tex").write_text("\n".join(lines), encoding="utf-8")


def analyse(
    countries: list[str] | None,
    models: list[str] | None,
    out_dir: Path,
) -> None:
    targets = countries or evaluable_countries()
    rows = collect(targets, models or [])
    if not rows:
        log.warning("no (country, system) pairs produced results; nothing to write")
        return
    out_dir.mkdir(parents=True, exist_ok=True)
    write_per_country_per_column(out_dir, rows)
    write_per_country(out_dir, rows)
    write_per_column(out_dir, rows)
    _write_grouped(out_dir, "per_tradition", LEGAL_TRADITION, rows)
    _write_grouped(out_dir, "per_language", LANGUAGE_FAMILY, rows)
    write_headline_latex(out_dir / "tables", rows)
    write_per_field_latex(out_dir / "tables", rows)
    log.info(f"wrote analysis for {len(rows)} (country, system) pairs to {out_dir}")


def main() -> None:
    logging.basicConfig(
        level=logging.INFO,
        format="%(asctime)s [%(levelname)s] %(message)s",
        handlers=[logging.StreamHandler(sys.stderr)],
    )
    parser = argparse.ArgumentParser(
        prog="legex-analysis",
        description="Cross-jurisdiction analysis of JSONL goldensets vs system inference CSVs.",
    )
    parser.add_argument(
        "--country", action="extend", nargs="+", dest="countries",
        help=(
            "Country code(s). Repeatable. "
            "Default: every jurisdiction with a goldenset_<cc>.jsonl minus the "
            "round-2 exclusion set (BE/NP/RS plus TW/BR/HK/IN)."
        ),
    )
    parser.add_argument(
        "--system", "--model", action="extend", nargs="+", dest="models",
        choices=list(SYSTEMS),
        help=f"Inference system(s). Repeatable. Default: every system with a CSV on disk per country ({list(SYSTEMS)}).",
    )
    parser.add_argument(
        "--out", type=Path, default=Path("data/analysis"),
        help="Output directory (default: data/analysis).",
    )
    args = parser.parse_args()
    analyse(
        countries=args.countries,
        models=args.models,
        out_dir=args.out,
    )


if __name__ == "__main__":
    main()