"""LightGBM tuning fairness probe (Phase 2.5).

Reviewer R1 (W1.5 / Q1.3) and R2 (W2.3) ask whether the headline finding
"classical models lead long-horizon T1 forecasting" survives if LightGBM
is tuned rather than run at library defaults. The canonical Table 6
LightGBM row remains at library defaults per the project's no-tuning
rule (every method in the benchmark panel uses library defaults; see
project memory `feedback_use_library_defaults.md`). This probe is
**outside the panel** -- it is a one-time secondary analysis whose only
purpose is to answer the reviewers' fairness question: does a modest
hyperparameter sweep change the leaderboard?

Design: a small 3 x 3 x 2 = 18-cell grid

    n_estimators ∈ {100, 500, 1000}
    max_depth    ∈ {6, 10, 20}
    learning_rate ∈ {0.01, 0.1}

All other LightGBM settings are kept at library defaults. The grid is
run on T1 at the panel's headline T1 horizon (read from
``experiments.panel``). For every cell we save predictions under a
distinct tag (so the probe never overwrites the canonical run) and
record the primary T1 metric with cluster-bootstrap CIs. The summary
report names the best cell, the default-config cell, and the relative
delta -- this is what the camera-ready text quotes back when explaining
the LightGBM-vs-LLM contrast.

Per-launch authorisation: this is CPU-only and 18 fits on T1's full
panel (~5M rows). Wall-clock estimate is several hours on the shared
host; the user must authorise the launch.
"""

from __future__ import annotations

import argparse
import itertools
import json
import logging
import pickle
import time
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any

import numpy as np

logger = logging.getLogger(__name__)


# Grid as specified by the plan; deliberately modest so the wall-clock
# stays under one human-day on the shared CPU host.
_GRID_N_ESTIMATORS: tuple[int, ...] = (100, 500, 1000)
_GRID_MAX_DEPTH: tuple[int, ...] = (6, 10, 20)
_GRID_LEARNING_RATE: tuple[float, ...] = (0.01, 0.1)


@dataclass
class _GridCell:
    n_estimators: int
    max_depth: int
    learning_rate: float
    seed: int
    horizon: int
    n_train: int
    n_test: int
    primary_metric: str
    value: float
    ci_lo: float
    ci_hi: float
    fit_sec: float
    predict_sec: float
    is_default: bool


def _build_config(
    *, n_estimators: int, max_depth: int, learning_rate: float,
) -> Any:
    """Construct a ``LightGBMConfig`` with all other fields at defaults."""
    from projects.agent_builder.scripts.whatif_bench.methods._config import (
        LightGBMConfig,
    )
    cfg = LightGBMConfig()
    cfg.n_estimators = n_estimators
    cfg.max_depth = max_depth
    cfg.learning_rate = learning_rate
    return cfg


def _is_default_cell(n_estimators: int, max_depth: int, learning_rate: float) -> bool:
    from projects.agent_builder.scripts.whatif_bench.methods._config import (
        LightGBMConfig,
    )
    d = LightGBMConfig()
    # max_depth default is -1 (unlimited); the grid uses positive depths
    # only, so the default cell is never exactly reproduced by the grid.
    # Flag the conventional "closest to default" cell instead, which is
    # n=100, lr=0.1, max_depth=the largest grid value (closest proxy to
    # the unlimited default).
    return (
        n_estimators == d.n_estimators
        and learning_rate == d.learning_rate
        and max_depth == max(_GRID_MAX_DEPTH)
    )


def _save_predictions(
    *,
    pred_dir: Path,
    method_id: str,
    task: str,
    seed: int,
    cell_tag: str,
    granularity: str,
    y_pred: Any,
    y_test: Any,
    meta_test: Any,
) -> Path:
    pred_dir.mkdir(parents=True, exist_ok=True)
    tag = f"{method_id}_{task}_seed{seed}_{cell_tag}"
    out_path = pred_dir / f"{tag}.pkl"
    tmp = out_path.with_suffix(".pkl.tmp")
    with open(tmp, "wb") as f:
        pickle.dump({
            "method_id": method_id,
            "task": task,
            "seed": seed,
            "granularity": granularity,
            "cell_tag": cell_tag,
            "y_pred": y_pred,
            "y_test": y_test,
            "meta_test": meta_test,
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%S"),
        }, f)
    tmp.replace(out_path)
    return out_path


def run_grid(
    *,
    horizon: int | None = None,
    seed: int | None = None,
    granularity: str = "daily",
    pred_dir: Path | None = None,
) -> dict[str, Any]:
    """Sweep the 18-cell grid on T1 at the headline horizon.

    Returns a dict with one record per cell plus a flagged best cell.
    """
    import macrolens as ml
    from projects.agent_builder.scripts.whatif_bench.experiments import panel

    # Match DRAFT.md (Fig. 3 caption): T1 ablation horizon is 252, not the
    # panel.ABLATION_T1_HORIZON=21 used for the analyst-rebalancing view.
    horizon = horizon if horizon is not None else 252
    seed = seed if seed is not None else panel.PRIMARY_SEED
    pred_dir = pred_dir or Path(__file__).resolve().parents[1] / "predictions"

    train = ml.load("T1", "train", granularity=granularity, horizon=horizon)
    test = ml.load("T1", "test", granularity=granularity, horizon=horizon)

    cells: list[_GridCell] = []
    for n_est, max_d, lr in itertools.product(
        _GRID_N_ESTIMATORS, _GRID_MAX_DEPTH, _GRID_LEARNING_RATE,
    ):
        cell_tag = f"grid_n{n_est}_d{max_d}_lr{lr:.3g}".replace(".", "p")
        logger.info("grid cell: n=%d depth=%d lr=%.3g  (tag=%s)",
                    n_est, max_d, lr, cell_tag)
        cfg = _build_config(
            n_estimators=n_est, max_depth=max_d, learning_rate=lr,
        )
        model = ml.methods.LightGBMRegressor(task="T1", config=cfg)
        t0 = time.perf_counter()
        model.fit(train.X, train.y, seed=seed)
        fit_sec = time.perf_counter() - t0
        t1 = time.perf_counter()
        y_pred = model.predict(test.X)
        predict_sec = time.perf_counter() - t1

        _save_predictions(
            pred_dir=pred_dir, method_id="lightgbm_tuned", task="T1",
            seed=seed, cell_tag=cell_tag, granularity=granularity,
            y_pred=y_pred, y_test=test.y, meta_test=test.meta,
        )

        cluster_keys = None
        if hasattr(test.meta, "columns") and "ticker" in test.meta.columns:
            cluster_keys = test.meta["ticker"].values
        metrics = ml.score(
            "T1", test.y, y_pred,
            cluster_keys=cluster_keys, resample="cluster",
            n_boot="adaptive", seed=seed,
        )
        mv = metrics["mse"]
        value = float("nan") if mv.value is None else float(mv.value)
        ci_lo = float("nan") if mv.ci_lo is None else float(mv.ci_lo)
        ci_hi = float("nan") if mv.ci_hi is None else float(mv.ci_hi)

        cells.append(_GridCell(
            n_estimators=n_est, max_depth=max_d, learning_rate=lr,
            seed=seed, horizon=horizon,
            n_train=int(len(train.y)) if hasattr(train.y, "__len__") else -1,
            n_test=int(len(test.y)) if hasattr(test.y, "__len__") else -1,
            primary_metric="mse", value=value, ci_lo=ci_lo, ci_hi=ci_hi,
            fit_sec=fit_sec, predict_sec=predict_sec,
            is_default=_is_default_cell(n_est, max_d, lr),
        ))
        logger.info("  -> mse=%.4g [%.4g, %.4g]", value, ci_lo, ci_hi)

    # Identify the best (minimum) cell by primary metric.
    finite = [c for c in cells if np.isfinite(c.value)]
    best = min(finite, key=lambda c: c.value) if finite else None
    default = next((c for c in cells if c.is_default), None)
    delta = (
        (default.value - best.value) / abs(default.value)
        if (best is not None and default is not None and default.value != 0)
        else None
    )

    return {
        "probe": "lightgbm_tuned",
        "method_id": "lightgbm_tuned",
        "task": "T1",
        "granularity": granularity,
        "horizon": horizon,
        "seed": seed,
        "grid": {
            "n_estimators": list(_GRID_N_ESTIMATORS),
            "max_depth": list(_GRID_MAX_DEPTH),
            "learning_rate": list(_GRID_LEARNING_RATE),
        },
        "best_cell": asdict(best) if best is not None else None,
        "default_proxy_cell": asdict(default) if default is not None else None,
        "relative_improvement_over_default": delta,
        "cells": [asdict(c) for c in cells],
    }


def _default_probe_dir() -> Path:
    # Probe outputs live under experiments/ (experiment artifacts),
    # never under data_small_caps/ (raw + derived benchmark data).
    return Path(__file__).resolve().parents[1] / "probes_output"


def main() -> int:
    parser = argparse.ArgumentParser(
        description="LightGBM tuning fairness probe (fairness check; NOT in panel).",
    )
    parser.add_argument("--granularity", default="daily")
    parser.add_argument("--horizon", type=int, default=None,
                        help="T1 horizon (default: 252, matching DRAFT.md Fig. 3 caption).")
    parser.add_argument("--seed", type=int, default=None,
                        help="Seed (default: panel.PRIMARY_SEED).")
    parser.add_argument("--pred-dir", type=Path, default=None,
                        help="Override the per-cell predictions directory.")
    parser.add_argument("--output", type=Path, default=None,
                        help="Path to the summary JSON report.")
    args = parser.parse_args()

    logging.basicConfig(
        level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s",
    )

    report = run_grid(
        horizon=args.horizon, seed=args.seed,
        granularity=args.granularity, pred_dir=args.pred_dir,
    )

    out_path = args.output or _default_probe_dir() / "lightgbm_tuned.json"
    out_path.parent.mkdir(parents=True, exist_ok=True)
    out_path.write_text(json.dumps(report, indent=2, default=str))
    logger.info("tuned-grid report written to %s", out_path)
    return 0


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