"""Feature selection strategies for the error predictor.

Strategies:

1. ``rank_by_roc_auc`` — univariate ROC-AUC ranking against the
   "model is wrong" label.
2. ``greedy_forward_select`` — TOHA-style Algorithm 1: greedy forward CV-AUC.
3. ``top_k_per_group`` — paper-faithful "up to K of each type" using
   univariate ranking inside each group.
4. ``shapley_top_k_per_group`` — **paper-faithful**: fit a logistic regression
   on the wrongness label, compute exact Shapley values for it via
   ``shap.LinearExplainer``, then keep top-K per group by mean |Shapley|.
"""
from __future__ import annotations

from typing import List, Tuple

import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import roc_auc_score
from sklearn.preprocessing import StandardScaler
from tqdm import tqdm


def _safe_auc(y_true: np.ndarray, score: np.ndarray) -> float:
    if len(np.unique(y_true)) < 2:
        return 0.5
    # Higher score should mean "more likely to be wrong".
    return float(roc_auc_score(y_true, score))


def rank_by_roc_auc(X: np.ndarray, y_wrong: np.ndarray) -> np.ndarray:
    """Return per-feature ROC-AUC scores (vs the better of feature/feature-negated)."""
    n_features = X.shape[1]
    scores = np.zeros(n_features, dtype=np.float32)
    for j in range(n_features):
        v = X[:, j]
        auc_pos = _safe_auc(y_wrong, v)
        auc_neg = _safe_auc(y_wrong, -v)
        scores[j] = max(auc_pos, auc_neg)
    return scores


def top_k_by_univariate_auc(X: np.ndarray, y_wrong: np.ndarray, k: int) -> List[int]:
    scores = rank_by_roc_auc(X, y_wrong)
    order = np.argsort(scores)[::-1]
    return order[:k].tolist()


def shapley_top_k_per_group(
    X: np.ndarray,
    y_wrong: np.ndarray,
    group_of: List[str],
    k_per_group: dict,
    *,
    random_state: int = 42,
    nsamples_background: int = 100,
) -> List[int]:
    """Paper-faithful Shapley-based feature selection.

    Train a logistic regression on the binary wrongness target, then use
    ``shap.LinearExplainer`` (exact Shapley values for linear models, O(N·F))
    to obtain per-sample contributions. Score each feature by mean absolute
    Shapley value. Keep the top-K within each named group.
    """
    import shap
    from sklearn.linear_model import LogisticRegression
    from sklearn.preprocessing import StandardScaler

    assert len(group_of) == X.shape[1], (len(group_of), X.shape[1])
    scaler = StandardScaler()
    Xs = scaler.fit_transform(X).astype(np.float32)

    clf = LogisticRegression(max_iter=2000, n_jobs=1, solver="lbfgs", random_state=random_state)
    clf.fit(Xs, y_wrong)

    bg = shap.sample(Xs, min(nsamples_background, Xs.shape[0]), random_state=random_state)
    explainer = shap.LinearExplainer(clf, bg, feature_perturbation="interventional")
    shap_values = explainer.shap_values(Xs)        # (N, F)
    importance = np.abs(shap_values).mean(axis=0)  # (F,)

    selected: List[int] = []
    by_group: dict = {}
    for i, g in enumerate(group_of):
        by_group.setdefault(g, []).append(i)
    for g, idx_list in by_group.items():
        k = k_per_group.get(g, len(idx_list))
        if k >= len(idx_list):
            selected.extend(idx_list)
            continue
        scores = importance[idx_list]
        order = np.argsort(scores)[::-1][:k]
        selected.extend([idx_list[j] for j in order])
    return sorted(selected)


def top_k_per_group(
    X: np.ndarray,
    y_wrong: np.ndarray,
    group_of: List[str],
    k_per_group: dict,
) -> List[int]:
    """Per-group univariate top-K selection.

    Args:
        X: (N, F) feature matrix.
        y_wrong: (N,) binary target.
        group_of: list of length F naming each column's group ("ripser", "cb", …).
        k_per_group: dict {group_name: int}. Groups not in the dict keep all columns.
    Returns:
        Sorted list of selected column indices.
    """
    assert len(group_of) == X.shape[1], (len(group_of), X.shape[1])
    selected: List[int] = []
    by_group: dict = {}
    for i, g in enumerate(group_of):
        by_group.setdefault(g, []).append(i)
    for g, idx_list in by_group.items():
        k = k_per_group.get(g, len(idx_list))
        if k >= len(idx_list):
            selected.extend(idx_list)
            continue
        Xg = X[:, idx_list]
        scores = rank_by_roc_auc(Xg, y_wrong)
        order = np.argsort(scores)[::-1][:k]
        selected.extend([idx_list[j] for j in order])
    return sorted(selected)


def greedy_forward_select(
    X: np.ndarray,
    y_wrong: np.ndarray,
    *,
    max_features: int = 30,
    min_gain: float = 1e-3,
    cv_folds: int = 5,
    candidate_pool: List[int] | None = None,
    random_state: int = 0,
) -> Tuple[List[int], List[float]]:
    """TOHA-style Algorithm 1."""
    pool = list(range(X.shape[1])) if candidate_pool is None else list(candidate_pool)
    selected: List[int] = []
    history: List[float] = []
    best_auc = 0.5

    # Standardize once up front so the inner LogReg converges quickly.
    Xz = StandardScaler().fit_transform(X).astype(np.float32)

    skf = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=random_state)

    pbar = tqdm(total=max_features, desc="TOHA-greedy")
    while pool and len(selected) < max_features:
        best_j, best_step_auc = -1, best_auc
        for j in pool:
            cols = selected + [j]
            Xs = Xz[:, cols]
            fold_aucs = []
            for tr, va in skf.split(Xs, y_wrong):
                clf = LogisticRegression(max_iter=2000, n_jobs=1, solver="lbfgs")
                clf.fit(Xs[tr], y_wrong[tr])
                proba = clf.predict_proba(Xs[va])[:, 1]
                fold_aucs.append(_safe_auc(y_wrong[va], proba))
            mean_auc = float(np.mean(fold_aucs))
            if mean_auc > best_step_auc:
                best_step_auc = mean_auc
                best_j = j
        if best_j == -1 or (best_step_auc - best_auc) < min_gain:
            break
        selected.append(best_j)
        pool.remove(best_j)
        history.append(best_step_auc)
        pbar.update(1)
        pbar.set_postfix(auc=f"{best_step_auc:.4f}", picked=best_j)
        best_auc = best_step_auc
    pbar.close()
    return selected, history