legex/analysis/goldenset_quality.py

"""Goldenset data-quality audit: empty vs. valid vs. malformed per variable.

This does *not* judge whether a label is the right answer (there is no
reference) — it checks whether each filled cell conforms to the variable's
expected type/format, so we can report coding hygiene. Three buckets per cell:

- ``empty``   — None / blank (the value is simply absent),
- ``valid``   — filled and conforms to the variable's type/format/range/vocab,
- ``invalid`` — filled but malformed, e.g. free text where a number is
  expected, a ratio outside ``[0, 1]``, a non-ISO date, or an ISIC code that is
  not in the controlled vocabulary.

Outputs two tables (CSV + Markdown), matching the request:

1. **By country** — one row per (country, variable) with absolute counts.
2. **Aggregated by variable** — one row per variable, summed over all
   countries, with counts and percentages.

Usage:
    uv run legex-goldenset-quality
    uv run legex-goldenset-quality --countries ch,de,br --out data/analysis/quality
"""

import argparse
import csv
import logging
import re
import sys
from collections import defaultdict
from datetime import date, datetime
from pathlib import Path

from openpyxl import load_workbook

from legex.config import settings
from legex.evaluation import is_label_column, normalise
from legex.utils import countries_with_goldenset, goldenset_path, goldenset_sheet

log = logging.getLogger("legex.goldenset_quality")

# --- Field type groups (the eleven substantive variables) -------------------
DATE_FIELDS = frozenset({"trial_start_date", "trial_end_date"})
RATIO_FIELDS = frozenset({"plaintiff_loosing_share"})
MONEY_FIELDS = frozenset(
    {"court_cost_awarded_nominal", "party_compensation_awarded_nominal"}
)
# dispute_value_nominal is money but also allows the literal "nonpecuniary".
DISPUTE_FIELD = "dispute_value_nominal"
COUNT_FIELDS = frozenset({"plaintiffs_all_count", "defendants_all_count"})
ISIC_FIELDS = frozenset(
    {"plaintiff_no1_ISIC1_industry_category", "defendant_no1_ISIC1_industry_category"}
)
STRING_FIELDS = frozenset({"legal_subject_judgement"})

# The eleven substantive schema variables. Stray columns (e.g. a workbook that
# duplicates the identifier as "Case Id") are ignored so the report stays on
# the schema.
SCHEMA_FIELDS = (
    DATE_FIELDS | RATIO_FIELDS | MONEY_FIELDS | {DISPUTE_FIELD}
    | COUNT_FIELDS | ISIC_FIELDS | STRING_FIELDS
)

# Controlled ISIC vocabulary (v3 prompt) plus the two documented fallbacks.
ISIC_VOCAB = frozenset(
    {
        "a_agriculture_forestry_fishing", "b_mining_quarrying", "c_manufacturing",
        "d_electricity_gas_steam_ac", "e_water_sewerage_waste_remediation",
        "f_construction", "g_wholesale_retail_trade", "h_transportation_storage",
        "i_accommodation_food_service", "j_publishing_broadcasting_content",
        "k_telecom_it_info_services", "l_financial_insurance", "m_real_estate",
        "n_professional_scientific_technical", "o_administrative_support",
        "p_public_admin_defence", "q_education", "r_human_health_social_work",
        "s_arts_entertainment_recreation", "t_other_service_activities",
        "u_households_as_employers", "v_extraterritorial_organisations",
        "no_allocation_possible",
    }
)

BUCKETS = ("empty", "valid", "invalid")

_NUM_RE = re.compile(r"^-?\d+(?:\.\d+)?$")


def _is_number(s: str) -> bool:
    """A clean plain number per the codebook (period decimal, no separators)."""
    return bool(_NUM_RE.match(s.strip()))


def _is_int(s: str) -> bool:
    s = s.strip()
    if _NUM_RE.match(s):
        f = float(s)
        return f.is_integer() and f >= 0
    return False


def _is_iso_date(value: object, normalised: str) -> bool:
    if isinstance(value, (date, datetime)):
        return True
    try:
        date.fromisoformat(normalised.strip())
        return True
    except ValueError:
        return False


def classify_cell(field: str, raw: object) -> str:
    """Return one of BUCKETS for a single (field, raw cell value)."""
    s = normalise(raw)
    if not s:
        return "empty"

    if field in DATE_FIELDS:
        return "valid" if _is_iso_date(raw, s) else "invalid"

    if field == DISPUTE_FIELD:
        if s.lower() == "nonpecuniary":
            return "valid"
        return "valid" if _is_number(s) else "invalid"

    if field in MONEY_FIELDS:
        return "valid" if _is_number(s) else "invalid"

    if field in RATIO_FIELDS:
        if not _is_number(s):
            return "invalid"
        return "valid" if 0.0 <= float(s) <= 1.0 else "invalid"

    if field in COUNT_FIELDS:
        return "valid" if _is_int(s) else "invalid"

    if field in ISIC_FIELDS:
        return "valid" if s.lower() in ISIC_VOCAB else "invalid"

    if field in STRING_FIELDS:
        # Free text: a bare number where a translated legal subject is expected
        # is the malformed case; otherwise any non-empty string is acceptable.
        return "invalid" if _is_number(s) else "valid"

    # Unknown / currency-like columns are excluded upstream; default to valid.
    return "valid"


# (country, field) -> {bucket: count}
Counts = dict[tuple[str, str], dict[str, int]]


def audit_country(cc: str) -> tuple[dict[str, dict[str, int]], int]:
    """Return ({field: {bucket: count}}, n_rows) for one country."""
    path = goldenset_path(cc)
    wb = load_workbook(path, read_only=True, data_only=True)
    try:
        ws = goldenset_sheet(wb)
        rows = ws.iter_rows(values_only=True)
        header = [str(c) if c is not None else "" for c in next(rows)]
        label_cols = [h for h in header if is_label_column(h) and h in SCHEMA_FIELDS]
        per_field: dict[str, dict[str, int]] = {
            f: {b: 0 for b in BUCKETS} for f in label_cols
        }
        n_rows = 0
        for row in rows:
            cells = dict(zip(header, row))
            # Count only substantively-annotated rows
            if not any(cells.get(f) not in (None, "") for f in label_cols):
                continue
            n_rows += 1
            for f in label_cols:
                per_field[f][classify_cell(f, cells.get(f))] += 1
        return per_field, n_rows
    finally:
        wb.close()


def _expected_hint(field: str) -> str:
    """Human-readable description of the valid form, for the hand-cleaning worklist."""
    if field in DATE_FIELDS:
        return "ISO date YYYY-MM-DD"
    if field == DISPUTE_FIELD:
        return "number or 'nonpecuniary'"
    if field in MONEY_FIELDS:
        return "number (period decimal, no thousands separators / currency symbols)"
    if field in RATIO_FIELDS:
        return "number in [0, 1]"
    if field in COUNT_FIELDS:
        return "integer >= 0"
    if field in ISIC_FIELDS:
        return "ISIC category from the controlled vocab, or no_allocation_possible"
    if field in STRING_FIELDS:
        return "text (not a bare number)"
    return ""


def collect_invalid(cc: str) -> list[tuple[str, str, str, str, str]]:
    """Return one (country, case_id, field, raw_value, expected) row per invalid cell."""
    path = goldenset_path(cc)
    wb = load_workbook(path, read_only=True, data_only=True)
    out: list[tuple[str, str, str, str, str]] = []
    try:
        ws = goldenset_sheet(wb)
        rows = ws.iter_rows(values_only=True)
        header = [str(c) if c is not None else "" for c in next(rows)]
        label_cols = [h for h in header if is_label_column(h) and h in SCHEMA_FIELDS]
        for row in rows:
            cells = dict(zip(header, row))
            if not any(cells.get(f) not in (None, "") for f in label_cols):
                continue
            case_id = cells.get("case_id")
            case_id = str(case_id) if case_id not in (None, "") else ""
            for f in label_cols:
                raw = cells.get(f)
                if classify_cell(f, raw) == "invalid":
                    out.append((cc, case_id, f, "" if raw is None else str(raw), _expected_hint(f)))
        return out
    finally:
        wb.close()


def _pct(n: int, total: int) -> float:
    return n / total if total else 0.0


def write_by_country_csv(counts: Counts, path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("w", encoding="utf-8", newline="") as f:
        w = csv.writer(f)
        w.writerow(["country", "variable", "n", "empty", "valid", "invalid",
                    "pct_empty", "pct_valid", "pct_invalid"])
        for (cc, field), c in sorted(counts.items()):
            total = c["empty"] + c["valid"] + c["invalid"]
            w.writerow([cc, field, total, c["empty"], c["valid"], c["invalid"],
                        f"{_pct(c['empty'], total):.4f}",
                        f"{_pct(c['valid'], total):.4f}",
                        f"{_pct(c['invalid'], total):.4f}"])


def write_by_variable_csv(agg: dict[str, dict[str, int]], path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("w", encoding="utf-8", newline="") as f:
        w = csv.writer(f)
        w.writerow(["variable", "n", "empty", "valid", "invalid",
                    "pct_empty", "pct_valid", "pct_invalid"])
        for field, c in sorted(agg.items()):
            total = c["empty"] + c["valid"] + c["invalid"]
            w.writerow([field, total, c["empty"], c["valid"], c["invalid"],
                        f"{_pct(c['empty'], total):.4f}",
                        f"{_pct(c['valid'], total):.4f}",
                        f"{_pct(c['invalid'], total):.4f}"])


def write_invalid_csv(rows: list[tuple[str, str, str, str, str]], path: Path) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    with path.open("w", encoding="utf-8", newline="") as f:
        w = csv.writer(f)
        w.writerow(["country", "case_id", "field", "raw_value", "expected"])
        w.writerows(rows)


def render_markdown(counts: Counts, agg: dict[str, dict[str, int]]) -> str:
    lines = ["# Goldenset data-quality audit", ""]
    lines.append("Buckets: **empty** (absent), **valid** (well-typed), "
                 "**invalid** (filled but malformed, e.g. text where a number "
                 "is expected, ratio outside [0,1], non-ISO date, unknown ISIC).")
    lines.append("")

    lines.append("## Aggregated by variable (all countries)")
    lines.append("")
    lines.append("| Variable | n | empty | valid | invalid | %empty | %valid | %invalid |")
    lines.append("|---|---:|---:|---:|---:|---:|---:|---:|")
    for field, c in sorted(agg.items()):
        total = c["empty"] + c["valid"] + c["invalid"]
        lines.append(
            f"| `{field}` | {total} | {c['empty']} | {c['valid']} | {c['invalid']} | "
            f"{_pct(c['empty'], total):.1%} | {_pct(c['valid'], total):.1%} | "
            f"{_pct(c['invalid'], total):.1%} |"
        )
    lines.append("")

    lines.append("## By country (absolute counts)")
    lines.append("")
    lines.append("| Country | Variable | n | empty | valid | invalid |")
    lines.append("|---|---|---:|---:|---:|---:|")
    for (cc, field), c in sorted(counts.items()):
        total = c["empty"] + c["valid"] + c["invalid"]
        lines.append(
            f"| `{cc}` | `{field}` | {total} | {c['empty']} | {c['valid']} | {c['invalid']} |"
        )
    return "\n".join(lines)


def main(argv: list[str] | None = None) -> int:
    logging.basicConfig(level=logging.INFO, format="%(message)s")
    parser = argparse.ArgumentParser(description="Goldenset data-quality audit.")
    parser.add_argument("--countries", default=None, help="Comma-separated codes (default: all).")
    parser.add_argument("--out", type=Path, default=None,
                        help="Output dir (default data/analysis/quality).")
    parser.add_argument("--list-invalid", action="store_true",
                        help="Also write invalid_cells.csv: one row per invalid cell "
                             "(country, case_id, field, raw_value, expected) for hand-cleaning.")
    args = parser.parse_args(argv)

    countries = (
        [c.strip() for c in args.countries.split(",") if c.strip()]
        if args.countries else countries_with_goldenset()
    )
    out_dir = args.out or (settings.data_dir / "analysis" / "quality")

    counts: Counts = {}
    agg: dict[str, dict[str, int]] = defaultdict(lambda: {b: 0 for b in BUCKETS})
    for cc in countries:
        gs = goldenset_path(cc)
        if not gs.exists():
            log.warning("[%s] no goldenset, skipping", cc)
            continue
        per_field, n_rows = audit_country(cc)
        log.info("[%s] %d rows, %d variables", cc, n_rows, len(per_field))
        for field, c in per_field.items():
            counts[(cc, field)] = c
            for b in BUCKETS:
                agg[field][b] += c[b]

    if not counts:
        log.error("no goldensets scored")
        return 1

    write_by_country_csv(counts, out_dir / "by_country.csv")
    write_by_variable_csv(agg, out_dir / "by_variable.csv")
    report = render_markdown(counts, dict(agg))
    (out_dir / "report.md").write_text(report, encoding="utf-8")

    if args.list_invalid:
        invalid_rows: list[tuple[str, str, str, str, str]] = []
        for cc in countries:
            if goldenset_path(cc).exists():
                invalid_rows.extend(collect_invalid(cc))
        write_invalid_csv(invalid_rows, out_dir / "invalid_cells.csv")
        log.info("wrote %s (%d invalid cells)",
                 out_dir / "invalid_cells.csv", len(invalid_rows))

    # Console summary: the aggregated-by-variable table.
    width = max((len(f) for f in agg), default=len("variable"))
    print(f"\n{'variable'.ljust(width)}  {'n':>5}  {'empty':>6}  {'valid':>6}  {'invalid':>7}")
    for field, c in sorted(agg.items()):
        total = c["empty"] + c["valid"] + c["invalid"]
        print(f"{field.ljust(width)}  {total:>5}  "
              f"{_pct(c['empty'], total):>6.1%}  {_pct(c['valid'], total):>6.1%}  "
              f"{_pct(c['invalid'], total):>7.1%}")
    log.info("\nwrote %s, %s, %s",
             out_dir / "by_country.csv", out_dir / "by_variable.csv", out_dir / "report.md")
    return 0


if __name__ == "__main__":
    sys.exit(main())