"""Run optimized pipeline with SMOTE + F2 tuning + CatBoost 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, Tuple

import numpy as np
import pandas as pd
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from sklearn.metrics import (
    accuracy_score,
    average_precision_score,
    confusion_matrix,
    f1_score,
    precision_score,
    recall_score,
    roc_auc_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
    print("⚠️  SMOTE not installed. Install: pip install imbalanced-learn")

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 (skip missing peak_table.csv)."""
    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/optimized_pipeline_m2"),
    )
    parser.add_argument(
        "--threshold-steps",
        type=int,
        default=199,
    )
    parser.add_argument(
        "--use-smote",
        type=bool,
        default=True,
        help="Use SMOTE for class imbalance",
    )
    parser.add_argument(
        "--use-catboost",
        type=bool,
        default=True,
        help="Add CatBoost to ensemble",
    )
    parser.add_argument(
        "--use-f2-metric",
        type=bool,
        default=False,
        help="Use F2 instead of F1 for recall optimization (default: F1)",
    )
    parser.add_argument(
        "--use-gpu",
        type=bool,
        default=True,
        help="Use GPU acceleration on Mac (MPS)",
    )
    return parser.parse_args()


def _tune_threshold_f1(y_true: np.ndarray, prob: np.ndarray, steps: int) -> Tuple[float, float]:
    """Tune threshold for F1 score."""
    thresholds = np.linspace(0.01, 0.99, steps)
    best_f1 = -1.0
    best_th = 0.5
    for th in thresholds:
        pred = (prob >= th).astype(int)
        score = f1_score(y_true, pred, zero_division=0)
        if score > best_f1:
            best_f1 = float(score)
            best_th = float(th)
    return best_th, best_f1


def _tune_threshold_f2(y_true: np.ndarray, prob: np.ndarray, steps: int) -> Tuple[float, float]:
    """Tune threshold for F2 score (emphasize recall)."""
    thresholds = np.linspace(0.01, 0.99, steps)
    best_f2 = -1.0
    best_th = 0.5
    for th in thresholds:
        pred = (prob >= th).astype(int)
        # F2 = 5 * (precision * recall) / (4 * precision + recall)
        precision = precision_score(y_true, pred, zero_division=0)
        recall = recall_score(y_true, pred, zero_division=0)
        f2 = (5 * precision * recall) / (4 * precision + recall + EPS)
        if f2 > best_f2:
            best_f2 = f2
            best_th = float(th)
    return best_th, best_f2


def _metrics(y_true: np.ndarray, prob: np.ndarray, threshold: float) -> Dict[str, float]:
    pred = (prob >= threshold).astype(int)
    tn, fp, fn, tp = confusion_matrix(y_true, pred, labels=[0, 1]).ravel()
    specificity = tn / (tn + fp + EPS)
    precision = precision_score(y_true, pred, zero_division=0)
    recall = recall_score(y_true, pred, zero_division=0)
    f2 = (5 * precision * recall) / (4 * precision + recall + EPS)
    return {
        "roc_auc": float(roc_auc_score(y_true, prob)),
        "pr_auc": float(average_precision_score(y_true, prob)),
        "f1": float(f1_score(y_true, pred, zero_division=0)),
        "f2": float(f2),
        "precision": float(precision),
        "recall": float(recall),
        "specificity": float(specificity),
        "accuracy": float(accuracy_score(y_true, pred)),
        "tp": int(tp),
        "fp": int(fp),
        "tn": int(tn),
        "fn": int(fn),
    }


def run(args: argparse.Namespace) -> Tuple[pd.DataFrame, pd.DataFrame]:
    args.out_dir.mkdir(parents=True, exist_ok=True)

    print("=" * 80)
    print("🚀 OPTIMIZED PIPELINE (SMOTE + F2 + CatBoost)")
    print("=" * 80)
    print(f"SMOTE: {args.use_smote}")
    print(f"CatBoost: {args.use_catboost}")
    print(f"F2 Tuning: {args.use_f2_metric}")
    print(f"Feature Scaling: True")
    print("=" * 80)

    per_split_rows: List[Dict[str, object]] = []
    trial_rows: List[Dict[str, object]] = []

    total_start = time.time()

    for benchmark_name in args.benchmarks:
        print(f"\n📊 Processing benchmark: {benchmark_name}")
        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_idx, split_id in enumerate(sorted(labels["split_id"].unique())):
            print(f"  Split {split_idx + 1}/{len(labels['split_id'].unique())}: {split_id}", end=" ")
            split_start = time.time()

            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"
            test_idx = partition == "test"

            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]
            x_test = x_all[test_idx].values
            y_test = y_all[test_idx]

            feature_names = x_all.columns.tolist()

            # ============ STEP 1: SMOTE ============
            if args.use_smote and SMOTE_AVAILABLE:
                smote = SMOTE(random_state=42, k_neighbors=5)
                x_train, y_train = smote.fit_resample(x_train, y_train)
                print(f"SMOTE: {len(y_train)} samples (balanced)", end=" | ")
            else:
                print(f"No SMOTE: {len(y_train)} samples", end=" | ")

            # ============ STEP 2: Feature Scaling ============
            scaler = StandardScaler()
            x_train_scaled = scaler.fit_transform(x_train)
            x_dev_scaled = scaler.transform(x_dev)
            x_test_scaled = scaler.transform(x_test)

            # ============ STEP 3: Model Training ============
            pos = float((y_train == 1).sum())
            neg = float((y_train == 0).sum())
            spw = max(1.0, neg / max(pos, 1.0))

            # LGBM with better regularization
            lgbm = LGBMClassifier(
                n_estimators=1200,
                learning_rate=0.015,
                num_leaves=100,
                subsample=0.85,
                colsample_bytree=0.85,
                min_child_samples=20,
                lambda_l1=1.0,
                lambda_l2=1.0,
                class_weight="balanced",
                random_state=42,
                verbose=-1,
                n_jobs=-1,
            )

            # XGBoost with optimized params
            xgb = XGBClassifier(
                n_estimators=1200,
                learning_rate=0.015,
                max_depth=7,
                min_child_weight=5,
                subsample=0.85,
                colsample_bytree=0.85,
                reg_alpha=1.0,
                reg_lambda=1.0,
                scale_pos_weight=spw,
                eval_metric="logloss",
                random_state=42,
                n_jobs=-1,
                tree_method="hist",
            )

            lgbm.fit(x_train_scaled, y_train)
            xgb.fit(x_train_scaled, y_train)

            p_dev_l = lgbm.predict_proba(x_dev_scaled)[:, 1]
            p_dev_x = xgb.predict_proba(x_dev_scaled)[:, 1]
            p_test_l = lgbm.predict_proba(x_test_scaled)[:, 1]
            p_test_x = xgb.predict_proba(x_test_scaled)[:, 1]

            # ============ STEP 4: CatBoost (Optional) ============
            p_dev_c = None
            p_test_c = None
            if args.use_catboost:
                cb = CatBoostClassifier(
                    iterations=1200,
                    learning_rate=0.015,
                    depth=7,
                    l2_leaf_reg=1.0,
                    scale_pos_weight=spw,
                    random_state=42,
                    verbose=0,
                    task_type="CPU",
                )
                cb.fit(x_train_scaled, y_train)
                p_dev_c = cb.predict_proba(x_dev_scaled)[:, 1]
                p_test_c = cb.predict_proba(x_test_scaled)[:, 1]

            # ============ STEP 5: F2 Threshold Tuning ============
            if args.use_f2_metric:
                th_l, dev_f_l = _tune_threshold_f2(y_dev, p_dev_l, args.threshold_steps)
                th_x, dev_f_x = _tune_threshold_f2(y_dev, p_dev_x, args.threshold_steps)
            else:
                th_l, dev_f_l = _tune_threshold_f1(y_dev, p_dev_l, args.threshold_steps)
                th_x, dev_f_x = _tune_threshold_f1(y_dev, p_dev_x, args.threshold_steps)

            # ============ STEP 6: Ensemble Fusion ============
            best_weight_lx = 0.5
            best_th_lx = 0.5
            best_dev_f_lx = -1.0
            for w in np.linspace(0.0, 1.0, 21):
                p_dev_lx = (w * p_dev_l) + ((1.0 - w) * p_dev_x)
                if args.use_f2_metric:
                    th_lx, dev_f_lx = _tune_threshold_f2(y_dev, p_dev_lx, args.threshold_steps)
                else:
                    th_lx, dev_f_lx = _tune_threshold_f1(y_dev, p_dev_lx, args.threshold_steps)
                if dev_f_lx > best_dev_f_lx:
                    best_dev_f_lx = dev_f_lx
                    best_th_lx = th_lx
                    best_weight_lx = float(w)

            p_test_lx = (best_weight_lx * p_test_l) + ((1.0 - best_weight_lx) * p_test_x)

            # ============ STEP 7: 3-Model Ensemble (with CatBoost) ============
            if args.use_catboost:
                best_weight_3 = [1/3, 1/3, 1/3]
                best_th_3 = 0.5
                best_dev_f_3 = -1.0
                for w_l in np.linspace(0.0, 1.0, 11):
                    for w_x in np.linspace(0.0, 1.0 - w_l, 6):
                        w_c = 1.0 - w_l - w_x
                        p_dev_3 = (w_l * p_dev_l) + (w_x * p_dev_x) + (w_c * p_dev_c)
                        if args.use_f2_metric:
                            th_3, dev_f_3 = _tune_threshold_f2(y_dev, p_dev_3, args.threshold_steps)
                        else:
                            th_3, dev_f_3 = _tune_threshold_f1(y_dev, p_dev_3, args.threshold_steps)
                        if dev_f_3 > best_dev_f_3:
                            best_dev_f_3 = dev_f_3
                            best_th_3 = th_3
                            best_weight_3 = [w_l, w_x, w_c]

                p_test_3 = (best_weight_3[0] * p_test_l) + (best_weight_3[1] * p_test_x) + (best_weight_3[2] * p_test_c)
            else:
                p_test_3 = p_test_lx
                best_th_3 = best_th_lx
                best_dev_f_3 = best_dev_f_lx
                best_weight_3 = [best_weight_lx, 1.0 - best_weight_lx, 0.0]

            model_payload = {
                "lightgbm_best": {
                    "threshold": th_l,
                    "dev_f": dev_f_l,
                    "dev_pr_auc": float(average_precision_score(y_dev, p_dev_l)),
                    "test_prob": p_test_l,
                },
                "xgboost_best": {
                    "threshold": th_x,
                    "dev_f": dev_f_x,
                    "dev_pr_auc": float(average_precision_score(y_dev, p_dev_x)),
                    "test_prob": p_test_x,
                },
                "fusion_lx_best": {
                    "threshold": best_th_lx,
                    "dev_f": best_dev_f_lx,
                    "dev_pr_auc": float(
                        average_precision_score(y_dev, best_weight_lx * p_dev_l + (1.0 - best_weight_lx) * p_dev_x)
                    ),
                    "test_prob": p_test_lx,
                    "weight_lgbm": best_weight_lx,
                },
            }

            if args.use_catboost:
                model_payload["catboost_best"] = {
                    "threshold": np.percentile(p_dev_c, 50),
                    "dev_f": float(f1_score(y_dev, (p_dev_c >= np.percentile(p_dev_c, 50)).astype(int), zero_division=0)),
                    "dev_pr_auc": float(average_precision_score(y_dev, p_dev_c)),
                    "test_prob": p_test_c,
                }
                model_payload["fusion_3_best"] = {
                    "threshold": best_th_3,
                    "dev_f": best_dev_f_3,
                    "dev_pr_auc": float(
                        average_precision_score(
                            y_dev,
                            best_weight_3[0] * p_dev_l + best_weight_3[1] * p_dev_x + best_weight_3[2] * p_dev_c,
                        )
                    ),
                    "test_prob": p_test_3,
                    "weight_lgbm": best_weight_3[0],
                    "weight_xgb": best_weight_3[1],
                    "weight_catboost": best_weight_3[2],
                }

            for model_name, payload in model_payload.items():
                trial_rows.append(
                    {
                        "benchmark": benchmark_name,
                        "split_id": split_id,
                        "model": model_name,
                        "threshold": float(payload["threshold"]),
                        "dev_score": float(payload["dev_f"]),
                        "dev_pr_auc": float(payload["dev_pr_auc"]),
                    }
                )

                m = _metrics(y_test, payload["test_prob"], float(payload["threshold"]))
                per_split_rows.append(
                    {
                        "benchmark": benchmark_name,
                        "split_id": split_id,
                        "model": model_name,
                        "threshold": float(payload["threshold"]),
                        "dev_score": float(payload["dev_f"]),
                        "dev_pr_auc": float(payload["dev_pr_auc"]),
                        "n_train": int((y_train == 1).sum() + (y_train == 0).sum()),
                        "n_dev": int(dev_idx.sum()),
                        "n_test": int(test_idx.sum()),
                        "test_positive_rate": float(y_test.mean()),
                        **m,
                    }
                )

            split_elapsed = time.time() - split_start
            print(f"✅ {split_elapsed:.1f}s")

    per_split = pd.DataFrame(per_split_rows).sort_values(["benchmark", "model", "split_id"])
    trials = pd.DataFrame(trial_rows).sort_values(["benchmark", "model", "split_id"])

    summary = (
        per_split.groupby(["benchmark", "model"], as_index=False)
        .agg(
            roc_auc_mean=("roc_auc", "mean"),
            roc_auc_std=("roc_auc", "std"),
            pr_auc_mean=("pr_auc", "mean"),
            pr_auc_std=("pr_auc", "std"),
            f1_mean=("f1", "mean"),
            f1_std=("f1", "std"),
            f2_mean=("f2", "mean"),
            f2_std=("f2", "std"),
            precision_mean=("precision", "mean"),
            recall_mean=("recall", "mean"),
            specificity_mean=("specificity", "mean"),
            accuracy_mean=("accuracy", "mean"),
        )
        .sort_values(["benchmark", "model"])
    )

    per_split.to_csv(args.out_dir / "optimized_pipeline_per_split.csv", index=False)
    summary.to_csv(args.out_dir / "optimized_pipeline_summary.csv", index=False)
    trials.to_csv(args.out_dir / "optimized_pipeline_trials_dev.csv", index=False)
    (args.out_dir / "run_args.json").write_text(
        json.dumps(
            {
                "use_smote": args.use_smote,
                "use_catboost": args.use_catboost,
                "use_f2_metric": args.use_f2_metric,
                "use_gpu": args.use_gpu,
                "pipeline": "LGBM+XGB+CatBoost with SMOTE, F2 tuning, feature scaling, and 3-model ensemble",
            },
            indent=2,
        )
    )

    total_elapsed = time.time() - total_start
    print(f"\n{'=' * 80}")
    print(f"✅ Total time: {total_elapsed / 60:.1f} minutes")
    print(f"{'=' * 80}\n")

    return per_split, summary


def main() -> None:
    args = _parse_args()
    _, summary = run(args)
    print("\n📊 SUMMARY RESULTS:")
    print("=" * 80)
    print(summary.to_string(index=False))
    print("=" * 80)

    # Highlight F1 scores > 0.5
    print("\n✅ F1 SCORES > 0.5 CHECK:")
    f1_cols = [c for c in summary.columns if 'f1_mean' in c]
    for _, row in summary.iterrows():
        f1_score = row['f1_mean']
        status = "✅ PASS" if f1_score > 0.5 else "⚠️  BELOW TARGET"
        print(f"  {row['benchmark']:40s} | {row['model']:20s} | F1={f1_score:.4f} | {status}")


if __name__ == "__main__":
    main()