#!/usr/bin/env python3
"""PROTAC-Bench standardized evaluation script.

Evaluates predictions under the Leave-One-Target-Out (LOTO) protocol
with 65 cold-target folds and paired Wilcoxon significance testing.

Usage:
    python evaluate.py --predictions my_preds.csv --output results.json
    python evaluate.py --predictions my_preds.csv --baseline baseline_predictions.csv --output results.json
"""
import argparse
import json
import sys
from pathlib import Path

import numpy as np
import pandas as pd
from scipy import stats as sp_stats
from sklearn.metrics import roc_auc_score


def _find_data_dir():
    """Locate data directory: supports repo layout (../data/) or flat copy (.)."""
    candidates = [
        Path(__file__).resolve().parent.parent / "data",
        Path(__file__).resolve().parent / "data",
        Path(__file__).resolve().parent,  # flat: CSV/JSON beside script
    ]
    for d in candidates:
        if (d / "protac_bench.csv").exists() and (d / "loto_folds.json").exists():
            return d
    return candidates[0]  # fallback to original

DATA_DIR = _find_data_dir()


def auroc_safe(y_true, y_prob):
    """AUROC that returns 0.5 for degenerate (single-class) folds."""
    if len(np.unique(y_true)) < 2:
        return 0.5
    return roc_auc_score(y_true, y_prob)


def load_folds():
    with open(DATA_DIR / "loto_folds.json") as f:
        return json.load(f)


def load_labels():
    df = pd.read_csv(DATA_DIR / "protac_bench.csv")
    return df["label"].values


def evaluate_predictions(pred_probs, labels, folds):
    """Compute per-fold and overall LOTO AUROC."""
    per_fold = []
    for uid, fold in sorted(folds.items()):
        idx = fold["test_indices"]
        y_true = labels[idx]
        y_pred = pred_probs[idx]
        auc = auroc_safe(y_true, y_pred)
        per_fold.append({
            "target_uniprot": uid,
            "n_entries": fold["n_entries"],
            "activity_rate": fold["activity_rate"],
            "auroc": round(auc, 6),
        })
    aurocs = [f["auroc"] for f in per_fold]
    # Tertile analysis by target size
    sizes = [f["n_entries"] for f in per_fold]
    sorted_by_size = sorted(zip(sizes, aurocs))
    n = len(sorted_by_size)
    t1 = [a for _, a in sorted_by_size[: n // 3]]
    t2 = [a for _, a in sorted_by_size[n // 3: 2 * n // 3]]
    t3 = [a for _, a in sorted_by_size[2 * n // 3:]]
    summary = {
        "mean_auroc": round(float(np.mean(aurocs)), 6),
        "std_auroc": round(float(np.std(aurocs)), 6),
        "median_auroc": round(float(np.median(aurocs)), 6),
        "n_folds": len(per_fold),
        "tertile_small": round(float(np.mean(t1)), 4),
        "tertile_medium": round(float(np.mean(t2)), 4),
        "tertile_large": round(float(np.mean(t3)), 4),
    }
    return summary, per_fold


def paired_wilcoxon(aurocs_a, aurocs_b):
    """Two-sided paired Wilcoxon signed-rank test."""
    diff = np.array(aurocs_a) - np.array(aurocs_b)
    if np.all(diff == 0):
        return {"delta": 0.0, "p_value": 1.0}
    try:
        stat, pval = sp_stats.wilcoxon(diff, alternative="two-sided")
    except Exception:
        pval = 1.0
    return {
        "delta": round(float(np.mean(diff)), 6),
        "p_value": round(float(pval), 6),
    }


def load_predictions(path):
    """Load predictions CSV (columns: index, predicted_probability)."""
    df = pd.read_csv(path)
    if "predicted_probability" not in df.columns:
        sys.exit(f"Error: predictions file must have 'predicted_probability' column. "
                 f"Found: {list(df.columns)}")
    if "index" in df.columns:
        df = df.sort_values("index").reset_index(drop=True)
    return df["predicted_probability"].values


def main():
    parser = argparse.ArgumentParser(description="PROTAC-Bench LOTO Evaluation")
    parser.add_argument("--predictions", required=True, help="Path to predictions CSV")
    parser.add_argument("--baseline", default=None,
                        help="Path to baseline predictions CSV (for Wilcoxon test)")
    parser.add_argument("--output", default="results.json", help="Output JSON path")
    args = parser.parse_args()

    labels = load_labels()
    folds = load_folds()

    # Evaluate submitted predictions
    pred_probs = load_predictions(args.predictions)
    if len(pred_probs) != len(labels):
        sys.exit(f"Error: predictions ({len(pred_probs)}) != dataset ({len(labels)})")
    summary, per_fold = evaluate_predictions(pred_probs, labels, folds)

    result = {"summary": summary, "per_fold": per_fold}

    # Wilcoxon test vs baseline if provided
    if args.baseline:
        base_probs = load_predictions(args.baseline)
        if len(base_probs) != len(labels):
            sys.exit(f"Error: baseline predictions ({len(base_probs)}) != dataset ({len(labels)})")
        base_summary, base_per_fold = evaluate_predictions(base_probs, labels, folds)
        aurocs_pred = [f["auroc"] for f in per_fold]
        aurocs_base = [f["auroc"] for f in base_per_fold]
        wilcoxon = paired_wilcoxon(aurocs_pred, aurocs_base)
        result["vs_baseline"] = {
            "baseline_mean_auroc": base_summary["mean_auroc"],
            "submitted_mean_auroc": summary["mean_auroc"],
            **wilcoxon,
        }
        print(f"Submitted: {summary['mean_auroc']:.4f}  "
              f"Baseline: {base_summary['mean_auroc']:.4f}  "
              f"Delta: {wilcoxon['delta']:+.4f}  "
              f"p={wilcoxon['p_value']:.4f}")
    else:
        print(f"LOTO AUROC: {summary['mean_auroc']:.4f} ± {summary['std_auroc']:.4f} "
              f"(median={summary['median_auroc']:.4f}, n={summary['n_folds']} folds)")

    with open(args.output, "w") as f:
        json.dump(result, f, indent=2)
    print(f"Results saved to {args.output}")


if __name__ == "__main__":
    main()