#!/usr/bin/env python3
"""Offline M1 segment-quality and leave-one-out analysis for BUG-1105.

This deliberately reuses ``native_case`` and does not modify production
rectification code.  The output contains both full-sample fixed-threshold
figures and case-level leave-one-out validation; no threshold is selected from
the validation case itself.
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path
from typing import Any, Sequence

ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from scripts.research.varga_resolution_lib import (  # noqa: E402
    RADII,
    THRESHOLDS,
    VARGA_PREFIXES,
    native_case,
    segment_metrics,
    choose_loo_threshold,
    threshold_scan,
)

HOLDOUT = ROOT / "references" / "real_case_calibration" / "minute_rectification_holdout_v5.json"
SCHEMA = "bug-1105-varga-resolution-m1-v1"
MODES = ("raw", "percent", "uniform")


def load_cases() -> list[dict[str, Any]]:
    return list(json.loads(HOLDOUT.read_text(encoding="utf-8")).get("cases") or [])


def is_lmt(case: dict[str, Any]) -> bool:
    return int(str(case.get("birth", {}).get("date", "9999"))[:4]) < 1900


def metrics_for_case(case: dict[str, Any], radius: int) -> dict[str, Any]:
    result = native_case(case, radius, do_reconcile=False)
    state = result["state"]
    rows = result["chart_rows"]
    true_time = result["true_time"]
    return {
        "case_id": str(case["case_id"]),
        "radius": radius,
        "lmt_before_1900": is_lmt(case),
        "answered_count": int((state.get("result") or {}).get("questions") or 0),
        "probe_count": len(result.get("probes") or []),
        "by_varga": {
            prefix: {
                mode: segment_metrics(state, rows, prefix, true_time, mode)
                for mode in MODES
            }
            for prefix in VARGA_PREFIXES
        },
    }


def aggregate_rows(items: Sequence[dict[str, Any]], prefix: str, mode: str) -> dict[str, Any]:
    rows = [item["by_varga"][prefix][mode] for item in items]
    denominator = len(rows)
    return {
        "denominator": denominator,
        "top_share_mean": round(sum(float(r["top_share"] or 0) for r in rows) / denominator, 8) if denominator else None,
        "truth_retained": sum(bool(r["truth_retained"]) for r in rows),
        "truth_retained_rate": round(sum(bool(r["truth_retained"]) for r in rows) / denominator, 8) if denominator else None,
        "truth_excluded": sum(not bool(r["truth_retained"]) for r in rows),
        "top_segment_correct": sum(bool(r["top_segment_correct"]) for r in rows),
        "top_segment_correct_rate": round(sum(bool(r["top_segment_correct"]) for r in rows) / denominator, 8) if denominator else None,
        "top_segment_ties": sum(bool(r.get("top_segment_tie")) for r in rows),
        "top_segment_tie_rate": round(sum(bool(r.get("top_segment_tie")) for r in rows) / denominator, 8) if denominator else None,
        "valid_segment_count_le_2": sum(int(r["valid_segment_count"]) <= 2 for r in rows),
        "valid_segment_count_le_2_rate": round(sum(int(r["valid_segment_count"]) <= 2 for r in rows) / denominator, 8) if denominator else None,
        "thresholds_full_fit": threshold_scan(rows),
    }


def stratified_rows(items: Sequence[dict[str, Any]], prefix: str, mode: str) -> dict[str, Any]:
    """Keep the pre-1900 LMT stratum auditable without changing denominators."""
    return {
        "all": aggregate_rows(items, prefix, mode),
        "lmt_before_1900": aggregate_rows(
            [item for item in items if item["lmt_before_1900"]], prefix, mode
        ),
        "post_1900": aggregate_rows(
            [item for item in items if not item["lmt_before_1900"]], prefix, mode
        ),
    }


def summarize_loo_rows(rows: Sequence[dict[str, Any]]) -> dict[str, Any]:
    eligible = [row for row in rows if row["eligible"]]
    return {
        "eligible": len(eligible),
        "denominator": len(rows),
        "coverage": round(len(eligible) / len(rows), 8) if rows else None,
        "accuracy": round(sum(bool(row["top_segment_correct"]) for row in eligible) / len(eligible), 8) if eligible else None,
        "truth_retained": round(sum(bool(row["truth_retained"]) for row in eligible) / len(eligible), 8) if eligible else None,
    }


def loo_fixed(items: Sequence[dict[str, Any]], prefix: str, mode: str) -> dict[str, Any]:
    out: dict[str, Any] = {}
    for threshold in THRESHOLDS:
        selected = [item["by_varga"][prefix][mode] for item in items if float(item["by_varga"][prefix][mode]["top_share"] or 0) >= threshold]
        out[str(threshold)] = {
            "eligible": len(selected),
            "denominator": len(items),
            "coverage": round(len(selected) / len(items), 8) if items else None,
            "accuracy": round(sum(bool(row["top_segment_correct"]) for row in selected) / len(selected), 8) if selected else None,
            "truth_retained": round(sum(bool(row["truth_retained"]) for row in selected) / len(selected), 8) if selected else None,
        }
    return out


def loo_selected(items: Sequence[dict[str, Any]], prefix: str, mode: str) -> dict[str, Any]:
    validations: list[dict[str, Any]] = []
    for index, item in enumerate(items):
        training = [other["by_varga"][prefix][mode] for j, other in enumerate(items) if j != index]
        threshold = choose_loo_threshold(training)
        row = item["by_varga"][prefix][mode]
        eligible = threshold is not None and float(row["top_share"] or 0) >= threshold
        validations.append({
            "case_id": item["case_id"],
            "threshold": threshold,
            "eligible": bool(eligible),
            "top_segment_correct": bool(row["top_segment_correct"]) if eligible else None,
            "truth_retained": bool(row["truth_retained"]) if eligible else None,
            "lmt_before_1900": item["lmt_before_1900"],
        })
    eligible = [row for row in validations if row["eligible"]]
    return {
        "eligible": len(eligible),
        "denominator": len(validations),
        "coverage": round(len(eligible) / len(validations), 8) if validations else None,
        "accuracy": round(sum(bool(row["top_segment_correct"]) for row in eligible) / len(eligible), 8) if eligible else None,
        "truth_retained": round(sum(bool(row["truth_retained"]) for row in eligible) / len(eligible), 8) if eligible else None,
        "selected_threshold_counts": {str(t): sum(row["threshold"] == t for row in validations) for t in THRESHOLDS},
        "validation_rows": validations,
    }


def main() -> int:
    parser = argparse.ArgumentParser()
    parser.add_argument("--radii", default=",".join(str(value) for value in RADII))
    parser.add_argument("--limit", type=int, default=0)
    parser.add_argument("--json-out", required=True)
    args = parser.parse_args()
    radii = tuple(int(value) for value in str(args.radii).split(",") if value.strip())
    cases = load_cases()
    if args.limit:
        cases = cases[: args.limit]
    items: list[dict[str, Any]] = []
    errors: list[dict[str, Any]] = []
    for case in cases:
        for radius in radii:
            label = f"{case.get('case_id')} ±{radius}"
            try:
                items.append(metrics_for_case(case, radius))
                print(label, flush=True)
            except Exception as exc:  # noqa: BLE001
                errors.append({"case_id": str(case.get("case_id")), "radius": radius, "error": f"{type(exc).__name__}: {exc}"})
                print(label, "ERROR", type(exc).__name__, exc, flush=True)
    aggregates: list[dict[str, Any]] = []
    for radius in radii:
        subset = [item for item in items if item["radius"] == radius]
        by_varga: dict[str, Any] = {}
        for prefix in VARGA_PREFIXES:
            by_varga[prefix] = {}
            for mode in MODES:
                loo = loo_selected(subset, prefix, mode)
                by_varga[prefix][mode] = {
                    "full_fit": stratified_rows(subset, prefix, mode),
                    "loo_fixed_thresholds": loo_fixed(subset, prefix, mode),
                    "loo_selected_threshold": {
                        **summarize_loo_rows(loo["validation_rows"]),
                        "selected_threshold_counts": loo["selected_threshold_counts"],
                        "validation_rows": loo["validation_rows"],
                        "lmt_before_1900": summarize_loo_rows([row for row in loo["validation_rows"] if row["lmt_before_1900"]]),
                        "post_1900": summarize_loo_rows([row for row in loo["validation_rows"] if not row["lmt_before_1900"]]),
                    },
                }
        aggregates.append({
            "radius": radius,
            "case_count": len(subset),
            "lmt_case_count": sum(item["lmt_before_1900"] for item in subset),
            "by_varga": by_varga,
        })
    payload = {
        "schema": SCHEMA,
        "metadata": {
            "holdout": str(HOLDOUT.relative_to(ROOT)).replace("\\", "/"),
            "case_count_requested": len(cases),
            "case_count_completed": len({item["case_id"] for item in items}),
            "radii": list(radii),
            "vargas": list(VARGA_PREFIXES),
            "modes": list(MODES),
            "thresholds": list(THRESHOLDS),
            "loo": "one case held out; threshold selected only from the other cases; validation case never used for selection",
            "lmt_stratum": "birth year < 1900, reported separately",
            "production_code_modified": False,
            "deterministic_json": True,
        },
        "aggregates": aggregates,
        "items": items,
        "errors": errors,
    }
    out = Path(args.json_out)
    out.parent.mkdir(parents=True, exist_ok=True)
    out.write_text(json.dumps(payload, ensure_ascii=False, indent=2, sort_keys=True) + "\n", encoding="utf-8")
    return 0 if not errors and len(items) == len(cases) * len(radii) else 1


if __name__ == "__main__":
    raise SystemExit(main())