src/cli/run_aggressive_tuning.py

"""Run aggressive hyperparameter tuned pipeline 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

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:
    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(benchmark_dir) -> pd.DataFrame:
    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 _tune_threshold(y_true: np.ndarray, prob: np.ndarray, steps: int) -> Tuple[float, float]:
    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 _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("🚀 AGGRESSIVE HYPERPARAMETER TUNING PIPELINE")
    print("=" * 80)

    per_split_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(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]
            x_all = data[feature_cols].astype(float).values

            y_all = data["unknown_present"].astype(int).values
            partition = data["partition"].values
            train_idx, dev_idx, test_idx = partition == "train", partition == "dev", partition == "test"

            x_train, y_train = x_all[train_idx], y_all[train_idx]
            x_dev, y_dev = x_all[dev_idx], y_all[dev_idx]
            x_test, y_test = x_all[test_idx], y_all[test_idx]

            if SMOTE_AVAILABLE:
                smote = SMOTE(random_state=42, k_neighbors=3)  # Reduced k_neighbors
                x_train, y_train = smote.fit_resample(x_train, y_train)

            scaler = StandardScaler()
            x_train = scaler.fit_transform(x_train)
            x_dev = scaler.transform(x_dev)
            x_test = scaler.transform(x_test)

            pos = float((y_train == 1).sum())
            neg = float((y_train == 0).sum())
            spw = max(1.0, neg / max(pos, 1.0))

            # AGGRESSIVE HYPERPARAMETERS
            lgbm = LGBMClassifier(
                n_estimators=2000,  # More trees
                learning_rate=0.005,  # Slower learning
                num_leaves=50,  # Fewer leaves (less overfitting)
                subsample=0.7,  # Lower subsample
                colsample_bytree=0.7,
                min_child_samples=40,  # Stricter constraints
                lambda_l1=5.0,  # More L1 regularization
                lambda_l2=5.0,
                class_weight="balanced",
                random_state=42,
                verbose=-1,
                n_jobs=-1,
            )

            xgb = XGBClassifier(
                n_estimators=2000,
                learning_rate=0.005,
                max_depth=5,  # Shallower trees
                min_child_weight=10,  # Stricter constraints
                subsample=0.7,
                colsample_bytree=0.7,
                reg_alpha=5.0,  # More regularization
                reg_lambda=5.0,
                scale_pos_weight=spw,
                eval_metric="logloss",
                random_state=42,
                n_jobs=-1,
            )

            cb = CatBoostClassifier(
                iterations=2000,
                learning_rate=0.005,
                depth=5,
                l2_leaf_reg=5.0,
                scale_pos_weight=spw,
                random_state=42,
                verbose=0,
                task_type="CPU",
            )

            lgbm.fit(x_train, y_train)
            xgb.fit(x_train, y_train)
            cb.fit(x_train, y_train)

            p_dev_l = lgbm.predict_proba(x_dev)[:, 1]
            p_dev_x = xgb.predict_proba(x_dev)[:, 1]
            p_dev_c = cb.predict_proba(x_dev)[:, 1]
            p_test_l = lgbm.predict_proba(x_test)[:, 1]
            p_test_x = xgb.predict_proba(x_test)[:, 1]
            p_test_c = cb.predict_proba(x_test)[:, 1]

            th_l, dev_f1_l = _tune_threshold(y_dev, p_dev_l, args.threshold_steps)
            th_x, dev_f1_x = _tune_threshold(y_dev, p_dev_x, args.threshold_steps)
            th_c, dev_f1_c = _tune_threshold(y_dev, p_dev_c, args.threshold_steps)

            # Best ensemble
            best_weight = [1/3, 1/3, 1/3]
            best_th = 0.5
            best_f1 = -1.0
            for w_l in np.linspace(0, 1, 6):
                for w_x in np.linspace(0, 1-w_l, 6):
                    w_c = 1.0 - w_l - w_x
                    p_dev = (w_l * p_dev_l) + (w_x * p_dev_x) + (w_c * p_dev_c)
                    th, dev_f1 = _tune_threshold(y_dev, p_dev, args.threshold_steps)
                    if dev_f1 > best_f1:
                        best_f1, best_th = dev_f1, th
                        best_weight = [w_l, w_x, w_c]

            p_test_ens = (best_weight[0] * p_test_l) + (best_weight[1] * p_test_x) + (best_weight[2] * p_test_c)

            for name, prob, th in [("lgbm", p_test_l, th_l), ("xgb", p_test_x, th_x), ("cb", p_test_c, th_c), ("ensemble", p_test_ens, best_th)]:
                m = _metrics(y_test, prob, th)
                per_split_rows.append({"benchmark": benchmark_name, "split_id": split_id, "model": name, **m})

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

    per_split = pd.DataFrame(per_split_rows).sort_values(["benchmark", "model", "split_id"])
    summary = per_split.groupby(["benchmark", "model"], as_index=False).agg(
        f1_mean=("f1", "mean"), f1_std=("f1", "std"),
        precision_mean=("precision", "mean"), recall_mean=("recall", "mean"),
    )

    per_split.to_csv(args.out_dir / "aggressive_tuning_per_split.csv", index=False)
    summary.to_csv(args.out_dir / "aggressive_tuning_summary.csv", index=False)

    print(f"\n{'='*80}\nTotal: {(time.time()-total_start)/60:.1f} min\n{'='*80}\n")
    return per_split, summary


def main() -> None:
    parser = argparse.ArgumentParser()
    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/aggressive_tuning"))
    parser.add_argument("--threshold-steps", type=int, default=199)
    args = parser.parse_args()

    _, summary = run(args)
    print("\n📊 RESULTS:")
    print(summary[["benchmark", "model", "f1_mean", "precision_mean", "recall_mean"]].to_string(index=False))

    best_f1 = summary["f1_mean"].max()
    if best_f1 > 0.5:
        print(f"\n✅ SUCCESS! Best F1 = {best_f1:.4f}")
    else:
        print(f"\n⚠️  Target not met. Best F1 = {best_f1:.4f}")


if __name__ == "__main__":
    main()