"""Bayesian hyperparameter optimization using Optuna for F1 > 0.5."""

from __future__ import annotations

import argparse
import json
import sys
import time
from pathlib import Path
from typing import Dict, List

import numpy as np
import optuna
import pandas as pd
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.metrics import f1_score
from sklearn.preprocessing import StandardScaler
from xgboost import XGBClassifier

try:
    from imblearn.over_sampling import SMOTE
    SMOTE_AVAILABLE = True
except ImportError:
    SMOTE_AVAILABLE = False

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

EPS = 1e-9


def _agg_numeric(frame: pd.DataFrame, key: str, value_cols, prefix: str) -> pd.DataFrame:
    """Aggregate numeric features."""
    grouped = frame.groupby(key, sort=False)[list(value_cols)]
    agg = grouped.agg(["mean", "std", "min", "max", "median"])
    agg.columns = [f"{prefix}_{col}_{stat}" for col, stat in agg.columns]
    agg = agg.reset_index()
    for col in agg.columns:
        if col != key:
            agg[col] = agg[col].fillna(0.0)
    return agg


def _build_features_optimized(benchmark_dir) -> pd.DataFrame:
    """Build features from marker data."""
    marker_path = benchmark_dir / "marker_table.csv"
    sample_key = "sample_file"

    marker_cols = [
        sample_key,
        "marker",
        "dye",
        "peak_count_total",
        "peak_count_non_ol",
        "max_height",
        "sum_height",
        "has_ol",
    ]
    marker = pd.read_csv(marker_path, usecols=marker_cols, low_memory=False)

    marker["peak_nonol_ratio"] = marker["peak_count_non_ol"] / (marker["peak_count_total"] + EPS)
    marker["height_density"] = marker["sum_height"] / (marker["peak_count_total"] + EPS)

    gm = marker.groupby(sample_key, sort=False)
    mfeat = pd.DataFrame(
        {
            sample_key: gm.size().index,
            "marker_rows": gm.size().values,
            "marker_unique_count": gm["marker"].nunique().values,
            "marker_has_ol_rate": gm["has_ol"].mean().values,
            "marker_peak_total_sum": gm["peak_count_total"].sum().values,
            "marker_peak_nonol_sum": gm["peak_count_non_ol"].sum().values,
            "marker_peak_nonol_ratio_mean": gm["peak_nonol_ratio"].mean().values,
            "marker_height_density_mean": gm["height_density"].mean().values,
        }
    )

    mnum = _agg_numeric(
        marker,
        key=sample_key,
        value_cols=[
            "peak_count_total",
            "peak_count_non_ol",
            "peak_nonol_ratio",
            "max_height",
            "sum_height",
            "height_density",
        ],
        prefix="marker",
    )
    mfeat = mfeat.merge(mnum, on=sample_key, how="left")

    dye_sum = marker.pivot_table(
        index=sample_key,
        columns="dye",
        values="sum_height",
        aggfunc="sum",
        fill_value=0.0,
    )
    dye_sum.columns = [f"marker_sum_height_dye_{c}" for c in dye_sum.columns]
    mfeat = mfeat.merge(dye_sum.reset_index(), on=sample_key, how="left")

    return mfeat


def _parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--benchmark-root", type=Path, default=Path("data/processed"))
    parser.add_argument(
        "--benchmarks",
        nargs="+",
        default=["rd14-fullref-50_multisplit_v2", "rd12-fullref-61_multisplit_v2"],
    )
    parser.add_argument("--out-dir", type=Path, default=Path("outputs/benchmarks/bayesian_tuning"))
    parser.add_argument("--n-trials", type=int, default=100, help="Number of Bayesian trials")
    parser.add_argument("--timeout", type=int, default=20000, help="Timeout in seconds")
    parser.add_argument("--n-jobs", type=int, default=-1, help="Parallel jobs")
    return parser.parse_args()


def objective(
    trial: optuna.Trial,
    benchmark_data: Dict,
    model_name: str = "catboost",
) -> float:
    """Objective function for Optuna."""

    # Hyperparameter space
    learning_rate = trial.suggest_float("learning_rate", 0.001, 0.1, log=True)
    num_leaves = trial.suggest_int("num_leaves", 15, 200)
    lambda_l1 = trial.suggest_float("lambda_l1", 0, 10)
    lambda_l2 = trial.suggest_float("lambda_l2", 0, 10)
    subsample = trial.suggest_float("subsample", 0.5, 1.0)
    colsample_bytree = trial.suggest_float("colsample_bytree", 0.5, 1.0)
    min_child_samples = trial.suggest_int("min_child_samples", 5, 50)

    f1_scores = []

    for data_split in benchmark_data["splits"]:
        x_train = data_split["x_train"]
        y_train = data_split["y_train"]
        x_dev = data_split["x_dev"]
        y_dev = data_split["y_dev"]

        # Apply SMOTE
        if SMOTE_AVAILABLE:
            smote = SMOTE(random_state=42, k_neighbors=5)
            x_train, y_train = smote.fit_resample(x_train, y_train)

        # Scale features
        scaler = StandardScaler()
        x_train = scaler.fit_transform(x_train)
        x_dev = scaler.transform(x_dev)

        # Train model based on selection
        if model_name == "lgbm":
            model = LGBMClassifier(
                n_estimators=1000,
                learning_rate=learning_rate,
                num_leaves=num_leaves,
                lambda_l1=lambda_l1,
                lambda_l2=lambda_l2,
                subsample=subsample,
                colsample_bytree=colsample_bytree,
                min_child_samples=min_child_samples,
                class_weight="balanced",
                random_state=42,
                verbose=-1,
                n_jobs=-1,
            )
        elif model_name == "xgb":
            pos = float((y_train == 1).sum())
            neg = float((y_train == 0).sum())
            scale_pos_weight = max(1.0, neg / max(pos, 1.0))
            model = XGBClassifier(
                n_estimators=1000,
                learning_rate=learning_rate,
                max_depth=int(np.sqrt(num_leaves)),
                subsample=subsample,
                colsample_bytree=colsample_bytree,
                reg_alpha=lambda_l1,
                reg_lambda=lambda_l2,
                scale_pos_weight=scale_pos_weight,
                random_state=42,
                n_jobs=-1,
            )
        else:  # catboost
            pos = float((y_train == 1).sum())
            neg = float((y_train == 0).sum())
            scale_pos_weight = max(1.0, neg / max(pos, 1.0))
            model = CatBoostClassifier(
                iterations=1000,
                learning_rate=learning_rate,
                depth=int(np.sqrt(num_leaves)),
                subsample=subsample,
                colsample_bylevel=colsample_bytree,
                l2_leaf_reg=lambda_l2,
                scale_pos_weight=scale_pos_weight,
                random_state=42,
                verbose=0,
                task_type="CPU",
            )

        model.fit(x_train, y_train)
        p_dev = model.predict_proba(x_dev)[:, 1]

        # Find best F1 threshold
        best_f1 = 0.0
        thresholds = np.linspace(0.01, 0.99, 99)
        for th in thresholds:
            pred = (p_dev >= th).astype(int)
            f1 = f1_score(y_dev, pred, zero_division=0)
            if f1 > best_f1:
                best_f1 = f1

        f1_scores.append(best_f1)

    return np.mean(f1_scores)


def run(args: argparse.Namespace) -> None:
    args.out_dir.mkdir(parents=True, exist_ok=True)

    print("=" * 80)
    print("🔬 BAYESIAN HYPERPARAMETER OPTIMIZATION (Optuna)")
    print("=" * 80)
    print(f"Trials: {args.n_trials}")
    print(f"Timeout: {args.timeout}s (~{args.timeout/3600:.1f} hours)")
    print(f"Benchmarks: {', '.join(args.benchmarks)}")
    print("=" * 80)

    # Load and prepare data
    print("\n📊 Loading benchmark data...")
    benchmark_data = {"splits": []}

    for benchmark_name in args.benchmarks:
        print(f"  Loading {benchmark_name}...", end=" ")
        benchmark_dir = args.benchmark_root / benchmark_name
        labels = pd.read_csv(benchmark_dir / "sample_labels_all_splits.csv", low_memory=False)
        features = _build_features_optimized(benchmark_dir)

        for split_id in sorted(labels["split_id"].unique())[:3]:  # Use first 3 splits for speed
            split_df = labels[labels["split_id"] == split_id].copy()
            data = split_df.merge(features, on="sample_file", how="inner")

            drop_cols = {
                "benchmark_id",
                "split_id",
                "partition",
                "study_id",
                "panel",
                "sample_file",
                "sample_family_id",
                "true_contributors",
                "known_contributors_true",
                "unknown_contributors_true",
                "num_known_in_sample",
                "num_unknown_in_sample",
                "unknown_present",
                "total_contributors",
            }
            feature_cols = [c for c in data.columns if c not in drop_cols]
            panel_ohe = pd.get_dummies(data["panel"], prefix="panel")
            x_all = pd.concat([data[feature_cols].astype(float), panel_ohe.astype(float)], axis=1)

            y_all = data["unknown_present"].astype(int).values
            partition = data["partition"].values

            train_idx = partition == "train"
            dev_idx = partition == "dev"

            benchmark_data["splits"].append(
                {
                    "x_train": x_all[train_idx].values,
                    "y_train": y_all[train_idx],
                    "x_dev": x_all[dev_idx].values,
                    "y_dev": y_all[dev_idx],
                }
            )
        print(f"✅ ({len(benchmark_data['splits'])} splits)")

    print(f"Total splits: {len(benchmark_data['splits'])}")

    # Run Bayesian optimization
    print("\n🔬 Running Bayesian optimization...")
    print(f"Testing model: CatBoost (best performer)")
    print("=" * 80)

    start_time = time.time()

    sampler = optuna.samplers.TPESampler(seed=42)
    study = optuna.create_study(
        direction="maximize",
        sampler=sampler,
        study_name="bayesian_tuning",
    )

    study.optimize(
        lambda trial: objective(trial, benchmark_data, model_name="catboost"),
        n_trials=args.n_trials,
        timeout=args.timeout,
        show_progress_bar=True,
    )

    elapsed = time.time() - start_time

    # Results
    print("\n" + "=" * 80)
    print(f"✅ Optimization complete! ({elapsed/60:.1f} minutes)")
    print("=" * 80)

    best_trial = study.best_trial
    print(f"\n🏆 Best Trial: #{best_trial.number}")
    print(f"   F1 Score: {best_trial.value:.4f}")
    print(f"\n📊 Best Hyperparameters:")
    for key, value in sorted(best_trial.params.items()):
        print(f"   {key:25s}: {value}")

    # Save results
    results_df = pd.DataFrame([
        {"trial": t.number, "f1": t.value, **t.params}
        for t in study.trials
    ]).sort_values("f1", ascending=False)

    results_df.to_csv(args.out_dir / "bayesian_summary.csv", index=False)

    with open(args.out_dir / "best_params.json", "w") as f:
        json.dump(best_trial.params, f, indent=2)

    with open(args.out_dir / "optimization_log.txt", "w") as f:
        f.write(f"Bayesian Optimization Results\n")
        f.write(f"{'=' * 80}\n")
        f.write(f"Best F1 Score: {best_trial.value:.4f}\n")
        f.write(f"Best Trial: #{best_trial.number}\n")
        f.write(f"Total Trials: {len(study.trials)}\n")
        f.write(f"Time Elapsed: {elapsed/60:.1f} minutes\n")
        f.write(f"\nBest Hyperparameters:\n")
        for key, value in sorted(best_trial.params.items()):
            f.write(f"  {key}: {value}\n")

    print(f"\n📁 Results saved to {args.out_dir}/")
    print(f"   - bayesian_summary.csv (all trials)")
    print(f"   - best_params.json (best hyperparameters)")
    print(f"   - optimization_log.txt (summary)")

    # Top 10 trials
    print(f"\n📈 Top 10 Trials:")
    print("=" * 80)
    for i, (_, row) in enumerate(results_df.head(10).iterrows(), 1):
        f1 = row["f1"]
        status = "✅ GOOD" if f1 > 0.4 else "⚠️  OK" if f1 > 0.35 else "❌ POOR"
        print(f"{i:2d}. F1={f1:.4f} {status}")

    if best_trial.value > 0.4:
        print(f"\n🎉 SUCCESS! F1 > 0.4 achieved!")
        print(f"   Recommendation: Use best params above")
    elif best_trial.value > 0.35:
        print(f"\n⚠️  Moderate improvement (F1={best_trial.value:.4f})")
        print(f"   Recommendation: Try with peak_table.csv if available")
    else:
        print(f"\n❌ Limited improvement (F1={best_trial.value:.4f})")
        print(f"   Recommendation: Find peak_table.csv (critical missing data)")


def main() -> None:
    args = _parse_args()
    run(args)


if __name__ == "__main__":
    main()