"""Guarded self-correction loop — fits the market-anchor correction table.

What it does (the "tablo üreteci" of the feedback loop):

  1. MEASURE: on settled real-odds matches, per implied-probability band, the
     gap between the RAW de-vigged probability and the actual rate — for BOTH
     the home side (ms_home) and the away side (ms_away).
  2. BRAKE: a band only earns a correction if it passes the safety gates —
        * min sample  (>= MIN_N matches in the band, fitted on TRAIN window)
        * shrinkage   (delta = SHRINK x measured gap — never the full gap)
        * clipping    (|delta| <= CLIP)
        * materiality (|delta| >= MIN_DELTA, else 0 — don't chase noise)
  3. PROVE: the candidate table must beat the CURRENTLY ACTIVE corrections
     out-of-sample (most recent TEST_DAYS, never seen during fitting) on
     combined home+away ECE. If it doesn't, nothing is written.
  4. WRITE: versioned artifact `config/market_anchor_corrections.json`
     (+ timestamped copy under `config/history/`). The engine reads the table
     at runtime (models/market_anchor.py) — the loop never modifies code.

Run weekly (cron) or manually after big data ingests:
    python scripts/fit_anchor_corrections.py [--days 540] [--test-days 90]
    python scripts/fit_anchor_corrections.py --dry-run   # measure only
"""

from __future__ import annotations

import argparse
import json
import os
import shutil
import sys
import time
from collections import defaultdict
from typing import Any, Callable, Dict, List, Optional, Tuple

sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))

import psycopg2  # noqa: E402
from psycopg2.extras import RealDictCursor  # noqa: E402

from data.db import get_clean_dsn  # noqa: E402
from models.market_anchor import (  # noqa: E402
    away_favorite_delta,
    home_favorite_delta,
)

# ── safety gates ─────────────────────────────────────────────────────
MIN_N = 1500        # band needs this many TRAIN matches to earn a correction
SHRINK = 0.5        # apply only half of the measured gap
CLIP = 0.05         # never correct more than 5 points
MIN_DELTA = 0.004   # below this the correction is noise — emit 0
ACCEPT_MARGIN = 0.0002  # candidate must beat active combined ECE by this

BANDS: Tuple[Tuple[float, float], ...] = (
    (0.05, 0.15), (0.15, 0.25), (0.25, 0.35), (0.35, 0.45),
    (0.45, 0.55), (0.55, 0.65), (0.65, 0.75), (0.75, 0.85), (0.85, 1.01),
)

REAL_ODDS_MIN_OVERROUND = 0.05


def fetch(days: int) -> List[Dict[str, Any]]:
    since_ms = int((time.time() - days * 86400) * 1000)
    sql = """
        SELECT f.implied_home AS p1, f.implied_draw AS px, f.implied_away AS p2,
               m.mst_utc,
               (m.winner = 'home')::int AS home_won,
               (m.winner = 'away')::int AS away_won
        FROM football_ai_features f
        JOIN matches m ON m.id = f.match_id
        WHERE m.sport = 'football'
          AND m.winner IN ('home', 'away', 'draw')
          AND f.odds_overround > %s
          AND m.mst_utc >= %s
    """
    out: List[Dict[str, Any]] = []
    with psycopg2.connect(get_clean_dsn()) as conn:
        with conn.cursor() as cur:
            cur.execute("SET statement_timeout = '120s'")
        with conn.cursor("fit_stream", cursor_factory=RealDictCursor) as cur:
            cur.itersize = 5000
            cur.execute(sql, (REAL_ODDS_MIN_OVERROUND, since_ms))
            for r in cur:
                p1, px, p2 = r["p1"], r["px"], r["p2"]
                if p1 is None or px is None or p2 is None:
                    continue
                if abs(float(p1) + float(px) + float(p2) - 1.0) > 0.02:
                    continue
                out.append({
                    "p1": float(p1), "p2": float(p2),
                    "y1": int(r["home_won"]), "y2": int(r["away_won"]),
                    "mst_utc": int(r["mst_utc"]),
                })
    return out


def band_of(p: float) -> Optional[int]:
    for i, (lo, hi) in enumerate(BANDS):
        if lo <= p < hi:
            return i
    return None


def fit_candidate(
    train: List[Dict[str, Any]], pkey: str, ykey: str
) -> List[Dict[str, Any]]:
    n = defaultdict(int); sp = defaultdict(float); sy = defaultdict(int)
    for r in train:
        b = band_of(r[pkey])
        if b is None:
            continue
        n[b] += 1; sp[b] += r[pkey]; sy[b] += r[ykey]
    bands: List[Dict[str, Any]] = []
    for i, (lo, hi) in enumerate(BANDS):
        if n[i] < MIN_N:
            continue  # gate: not enough evidence — no correction for this band
        raw_gap = (sy[i] / n[i]) - (sp[i] / n[i])
        delta = max(-CLIP, min(CLIP, SHRINK * raw_gap))
        if abs(delta) < MIN_DELTA:
            delta = 0.0
        bands.append({"lo": lo, "hi": hi, "delta": round(delta, 4),
                      "n": n[i], "raw_gap": round(raw_gap, 4)})
    return bands


def table_delta_fn(table: List[Dict[str, Any]]) -> Callable[[float], float]:
    def fn(p: float) -> float:
        for b in table:
            if b["lo"] <= p < b["hi"]:
                return b["delta"]
        return 0.0
    return fn


def ece(rows: List[Dict[str, Any]], pkey: str, ykey: str,
        delta_fn: Callable[[float], float]) -> float:
    n = defaultdict(int); sp = defaultdict(float); sy = defaultdict(int)
    for r in rows:
        pc = min(0.98, r[pkey] + delta_fn(r[pkey]))
        b = min(19, int(pc * 20))
        n[b] += 1; sp[b] += pc; sy[b] += r[ykey]
    total = sum(n.values())
    if not total:
        return 0.0
    return sum(n[b] * abs(sp[b] / n[b] - sy[b] / n[b]) for b in n) / total


def print_bands(title: str, bands: List[Dict[str, Any]]) -> None:
    print(f"\ncandidate bands — {title} (after gates):")
    print(f"{'band':>12} {'n':>8} {'raw_gap_pt':>11} {'delta_pt':>9}")
    for b in bands:
        print(f"{b['lo']:>5.2f}-{b['hi']:<5.2f} {b['n']:>8} "
              f"{100 * b['raw_gap']:>11.2f} {100 * b['delta']:>9.2f}")


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--days", type=int, default=540, help="total lookback")
    ap.add_argument("--test-days", type=int, default=90,
                    help="most recent window held out for acceptance")
    ap.add_argument("--dry-run", action="store_true",
                    help="measure and report only — never write")
    args = ap.parse_args()

    rows = fetch(args.days)
    cutoff_ms = int((time.time() - args.test_days * 86400) * 1000)
    train = [r for r in rows if r["mst_utc"] < cutoff_ms]
    test = [r for r in rows if r["mst_utc"] >= cutoff_ms]
    print(f"matches: total={len(rows)}  train={len(train)}  test(OOS)={len(test)}")
    if len(train) < 10 * MIN_N or len(test) < 2000:
        print("ABORT: not enough data for a safe fit — keeping active table.")
        return

    cand_home = fit_candidate(train, "p1", "y1")
    cand_away = fit_candidate(train, "p2", "y2")
    print_bands("ms_home", cand_home)
    print_bands("ms_away", cand_away)

    # active = whatever the engine currently loads (artifact or fallback)
    ece_h_act = ece(test, "p1", "y1", home_favorite_delta)
    ece_a_act = ece(test, "p2", "y2", away_favorite_delta)
    ece_h_cand = ece(test, "p1", "y1", table_delta_fn(cand_home))
    ece_a_cand = ece(test, "p2", "y2", table_delta_fn(cand_away))
    ece_h_raw = ece(test, "p1", "y1", lambda _p: 0.0)
    ece_a_raw = ece(test, "p2", "y2", lambda _p: 0.0)

    print(f"\nOOS ECE (home/away/combined):")
    print(f"  raw (devig only) : {100 * ece_h_raw:.3f}% / {100 * ece_a_raw:.3f}% "
          f"/ {100 * (ece_h_raw + ece_a_raw):.3f}%")
    print(f"  ACTIVE table     : {100 * ece_h_act:.3f}% / {100 * ece_a_act:.3f}% "
          f"/ {100 * (ece_h_act + ece_a_act):.3f}%")
    print(f"  CANDIDATE table  : {100 * ece_h_cand:.3f}% / {100 * ece_a_cand:.3f}% "
          f"/ {100 * (ece_h_cand + ece_a_cand):.3f}%")

    if args.dry_run:
        print("\n(dry-run: nothing written)")
        return

    combined_act = ece_h_act + ece_a_act
    combined_cand = ece_h_cand + ece_a_cand
    if combined_cand > combined_act - ACCEPT_MARGIN:
        print("\nREJECTED: candidate does not beat the active table "
              "out-of-sample. Active corrections stay. (Bu fren tasarim geregi:"
              " kanitlayamayan tablo yazilmaz.)")
        return

    cfg_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "config"))
    path = os.path.join(cfg_dir, "market_anchor_corrections.json")
    artifact = {
        "version": time.strftime("%Y-%m-%dT%H:%M:%S"),
        "fitted_on": {"days": args.days, "test_days": args.test_days,
                      "n_train": len(train), "n_test": len(test)},
        "validated": {
            "ece_home": {"raw": round(ece_h_raw, 5), "active_before": round(ece_h_act, 5),
                         "candidate_oos": round(ece_h_cand, 5)},
            "ece_away": {"raw": round(ece_a_raw, 5), "active_before": round(ece_a_act, 5),
                         "candidate_oos": round(ece_a_cand, 5)},
        },
        "gates": {"min_n": MIN_N, "shrink": SHRINK, "clip": CLIP,
                  "min_delta": MIN_DELTA},
        "corrections": {"ms_home": cand_home, "ms_away": cand_away},
    }
    hist_dir = os.path.join(cfg_dir, "history")
    os.makedirs(hist_dir, exist_ok=True)
    if os.path.exists(path):
        shutil.copy2(path, os.path.join(
            hist_dir, f"market_anchor_corrections-{int(time.time())}.json"))
    with open(path, "w", encoding="utf-8") as fh:
        json.dump(artifact, fh, ensure_ascii=False, indent=2)
    print(f"\nACCEPTED: wrote {path}")

    # The deployed ai-engine container has NO volume mounts, so the file above
    # is invisible to it — app_settings is the shared medium. Running engines
    # re-read it within ~10 minutes (TTL in models/market_anchor.py).
    try:
        with psycopg2.connect(get_clean_dsn()) as conn:
            with conn.cursor() as cur:
                cur.execute(
                    """
                    INSERT INTO app_settings (key, value, updated_at)
                    VALUES ('market_anchor_corrections', %s, now())
                    ON CONFLICT (key)
                    DO UPDATE SET value = EXCLUDED.value, updated_at = now()
                    """,
                    (json.dumps(artifact, ensure_ascii=False),),
                )
            conn.commit()
        print("ACCEPTED: upserted app_settings['market_anchor_corrections'] "
              "(live engines refresh within ~10 min)")
    except Exception as exc:  # file artifact still written — warn only
        print(f"WARN: app_settings upsert failed: {exc}")


if __name__ == "__main__":
    main()