"""
Distributional evaluation metrics for perturbation prediction.

Complements cell-eval (which focuses on conditional mean accuracy) with
metrics that measure distributional fidelity:

1. Per-perturbation MMD (Maximum Mean Discrepancy)
2. Per-perturbation Energy Distance
3. Variance Ratio (per-gene variance match)
4. Gene-Gene Correlation Preservation
5. Classifier Two-Sample Test (C2ST)
6. k-NN Precision / Recall
"""

import numpy as np
import anndata as ad
import pandas as pd
from scipy.spatial.distance import cdist
from scipy.stats import pearsonr
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler


# ── Helpers ──────────────────────────────────────────────────────────

def _get_pert_cells(adata, pert):
    mask = adata.obs["perturbation"] == pert
    X = adata[mask].X
    return np.asarray(X) if not isinstance(X, np.ndarray) else X


def _rbf_kernel(X, Y, sigma):
    D2 = cdist(X, Y, metric="sqeuclidean")
    return np.exp(-D2 / (2 * sigma ** 2))


# ── 1. MMD (multi-sigma RBF) ────────────────────────────────────────

def mmd_multi_sigma(X, Y, scales=(0.5, 1.0, 2.0, 4.0)):
    D2_all = cdist(X, X, "sqeuclidean")
    median_sq = np.median(D2_all[np.triu_indices(len(X), k=1)])
    median_sq = max(median_sq, 1e-12)

    vals = []
    for s in scales:
        sigma = np.sqrt(s * median_sq)
        Kxx = _rbf_kernel(X, X, sigma)
        Kyy = _rbf_kernel(Y, Y, sigma)
        Kxy = _rbf_kernel(X, Y, sigma)
        m, n = len(X), len(Y)
        t_xx = (Kxx.sum() - np.trace(Kxx)) / (m * (m - 1))
        t_yy = (Kyy.sum() - np.trace(Kyy)) / (n * (n - 1))
        t_xy = Kxy.mean()
        vals.append(t_xx + t_yy - 2 * t_xy)
    return float(np.mean(vals))


# ── 2. Energy Distance ──────────────────────────────────────────────

def energy_distance(X, Y):
    Dxy = cdist(X, Y, "euclidean").mean()
    Dxx = cdist(X, X, "euclidean").mean()
    Dyy = cdist(Y, Y, "euclidean").mean()
    return float(2 * Dxy - Dxx - Dyy)


# ── 3. Variance Ratio ───────────────────────────────────────────────

def variance_ratio(pred_cells, real_cells, var_threshold=0.001):
    """Per-gene variance ratio: pred_var / real_var. Perfect = 1.0.
    Only considers genes with real_var > var_threshold (skip sparse zeros)."""
    pv = pred_cells.var(axis=0)
    rv = real_cells.var(axis=0)
    mask = rv > var_threshold
    if mask.sum() < 10:
        return {"var_ratio_median": np.nan, "var_ratio_mean": np.nan,
                "var_ratio_q25": np.nan, "var_ratio_q75": np.nan,
                "n_active_genes": int(mask.sum())}
    ratio = pv[mask] / rv[mask]
    return {
        "var_ratio_median": float(np.median(ratio)),
        "var_ratio_mean": float(np.mean(ratio)),
        "var_ratio_q25": float(np.percentile(ratio, 25)),
        "var_ratio_q75": float(np.percentile(ratio, 75)),
        "n_active_genes": int(mask.sum()),
    }


# ── 4. Gene-Gene Correlation Preservation ───────────────────────────

def gene_corr_preservation(pred_cells, real_cells, top_k=200):
    """Pearson correlation between flattened gene-gene correlation matrices.
    Uses top_k highest-variance genes for tractability."""
    gene_var = real_cells.var(axis=0)
    top_idx = np.argsort(gene_var)[-top_k:]

    pred_sub = pred_cells[:, top_idx]
    real_sub = real_cells[:, top_idx]

    pred_corr = np.corrcoef(pred_sub.T)
    real_corr = np.corrcoef(real_sub.T)

    idx = np.triu_indices(top_k, k=1)
    r, _ = pearsonr(pred_corr[idx], real_corr[idx])
    return float(r)


# ── 5. C2ST (Classifier Two-Sample Test) ────────────────────────────

def c2st(pred_cells, real_cells, max_samples=500):
    """Logistic regression C2ST. Returns accuracy (0.5 = indistinguishable)."""
    n_pred = min(len(pred_cells), max_samples)
    n_real = min(len(real_cells), max_samples)
    idx_p = np.random.choice(len(pred_cells), n_pred, replace=False)
    idx_r = np.random.choice(len(real_cells), n_real, replace=False)

    X = np.vstack([pred_cells[idx_p], real_cells[idx_r]])
    y = np.concatenate([np.zeros(n_pred), np.ones(n_real)])

    scaler = StandardScaler()
    X_s = scaler.fit_transform(X)

    clf = LogisticRegression(max_iter=500, C=1.0, solver="lbfgs")
    n_cv = min(5, min(n_pred, n_real))
    if n_cv < 2:
        return float("nan")
    scores = cross_val_score(clf, X_s, y, cv=n_cv, scoring="accuracy")
    return float(scores.mean())


# ── 6. k-NN Precision / Recall ──────────────────────────────────────

def knn_precision_recall(pred_cells, real_cells, k=10):
    """
    Precision: fraction of pred cells whose k-NN ball overlaps real data.
    Recall: fraction of real cells whose k-NN ball overlaps pred data.
    """
    D_rr = cdist(real_cells, real_cells, "euclidean")
    np.fill_diagonal(D_rr, np.inf)
    real_knn_dist = np.sort(D_rr, axis=1)[:, k - 1]

    D_pp = cdist(pred_cells, pred_cells, "euclidean")
    np.fill_diagonal(D_pp, np.inf)
    pred_knn_dist = np.sort(D_pp, axis=1)[:, k - 1]

    D_pr = cdist(pred_cells, real_cells, "euclidean")
    precision = float((D_pr <= real_knn_dist[None, :]).any(axis=1).mean())

    D_rp = D_pr.T
    recall = float((D_rp <= pred_knn_dist[None, :]).any(axis=1).mean())

    return precision, recall


# ── Main ─────────────────────────────────────────────────────────────

def evaluate_model(pred_path, real_path, name):
    pred = ad.read_h5ad(pred_path)
    real = ad.read_h5ad(real_path)

    perts = [p for p in pred.obs["perturbation"].unique() if p != "control"]

    rows = []
    for pert in perts:
        pc = _get_pert_cells(pred, pert)
        rc = _get_pert_cells(real, pert)

        if len(pc) < 5 or len(rc) < 5:
            continue

        mmd = mmd_multi_sigma(pc, rc)
        edist = energy_distance(pc, rc)
        vr = variance_ratio(pc, rc)
        gc = gene_corr_preservation(pc, rc, top_k=min(200, pc.shape[1]))
        c2st_acc = c2st(pc, rc)
        prec, rec = knn_precision_recall(pc, rc, k=min(5, len(rc) - 1))

        rows.append({
            "perturbation": pert,
            "mmd": mmd,
            "energy_distance": edist,
            **vr,
            "gene_corr_preservation": gc,
            "c2st_accuracy": c2st_acc,
            "knn_precision": prec,
            "knn_recall": rec,
        })

    df = pd.DataFrame(rows)
    return df


def print_comparison(results: dict[str, pd.DataFrame]):
    metrics = [
        "mmd", "energy_distance",
        "var_ratio_median", "var_ratio_mean",
        "gene_corr_preservation", "c2st_accuracy",
        "knn_precision", "knn_recall",
    ]
    lower_better = {"mmd", "energy_distance"}
    target_metrics = {
        "var_ratio_median": 1.0,
        "var_ratio_mean": 1.0,
        "c2st_accuracy": 0.5,
    }

    names = list(results.keys())
    short = {n: n[:12] for n in names}

    header = f"{'Metric':<28}" + "".join(f"{short[n]:>14}" for n in names) + "   Best"
    print(header)
    print("-" * len(header))

    for m in metrics:
        vals = []
        for n in names:
            vals.append(float(results[n][m].mean()))

        if m in target_metrics:
            target = target_metrics[m]
            dists = [abs(v - target) for v in vals]
            best_idx = dists.index(min(dists))
        elif m in lower_better:
            best_idx = vals.index(min(vals))
        else:
            best_idx = vals.index(max(vals))

        short_names = [short[n] for n in names]
        line = f"{m:<28}" + "".join(f"{v:>14.4f}" for v in vals)
        line += f"   <- {short_names[best_idx]}"
        print(line)

    print("\n=== Variance Ratio (median [Q25, Q75], 1.0 = perfect) ===")
    for n in names:
        df = results[n]
        med = df["var_ratio_median"].mean()
        q25 = df["var_ratio_q25"].mean()
        q75 = df["var_ratio_q75"].mean()
        print(f"  {short[n]:<14} {med:.3f} [{q25:.3f}, {q75:.3f}]")


if __name__ == "__main__":
    np.random.seed(42)

    BASE = "/home/hp250092/ku50001222/qian/aivc/lfj"

    models = {
        "A6_SDE50": (
            f"{BASE}/GRN/result/SB/A6_dsm_aniso/iteration_195000/pred.h5ad",
            f"{BASE}/GRN/result/SB/A6_dsm_aniso/iteration_195000/real.h5ad",
        ),
        "A6_ODE_RK4": (
            f"{BASE}/GRN/result/SB/A6_dsm_aniso/eval_only/pred.h5ad",
            f"{BASE}/GRN/result/SB/A6_dsm_aniso/eval_only/real.h5ad",
        ),
        "A1_Euler": (
            f"{BASE}/GRN/result/SB/A1_baseline/iteration_195000/pred.h5ad",
            f"{BASE}/GRN/result/SB/A1_baseline/iteration_195000/real.h5ad",
        ),
        "A1_RK4": (
            f"{BASE}/GRN/result/SB/A1_baseline/eval_only/pred.h5ad",
            f"{BASE}/GRN/result/SB/A1_baseline/eval_only/real.h5ad",
        ),
        "scDFM": (
            f"{BASE}/GRN/baseline/flow-fusion_differential_perceiver-norman-origin-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-use_negative_edge_True-topk_30/iteration_200000/pred.h5ad",
            f"{BASE}/GRN/baseline/flow-fusion_differential_perceiver-norman-origin-predict_y-gamma_0.5-perturbation_function_crisper-lr_5e-05-dim_model_128-infer_top_gene_1000-split_method_additive-use_mmd_loss_True-fold_1-use_negative_edge_True-topk_30/iteration_200000/real.h5ad",
        ),
    }

    results = {}
    for name, (pred_path, real_path) in models.items():
        print(f"\n>>> Evaluating {name} ...")
        df = evaluate_model(pred_path, real_path, name)
        results[name] = df
        out_dir = str(pred_path).rsplit("/", 1)[0]
        df.to_csv(f"{out_dir}/distributional_results.csv", index=False)
        print(f"    saved to {out_dir}/distributional_results.csv")

    print("\n" + "=" * 100)
    print("DISTRIBUTIONAL METRICS COMPARISON (mean over 39 perturbations)")
    print("=" * 100 + "\n")
    print_comparison(results)