import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from WaveSelect.Lar import Lar
from WaveSelect.Spa import SPA
from WaveSelect.Uve import UVE
from WaveSelect.Cars import CARS_Cloud
from WaveSelect.GA import GA
from WaveSelect.ReliefF import ReliefF
from sklearn.model_selection import train_test_split
from collections import Counter
from tqdm import tqdm
import os

def IntegratedWaveSelect(methods, X, y, strategy="voting", column_names=None,  name='', result_dir=''):
    # 检查并确保 X 是 DataFrame
    if isinstance(X, np.ndarray):
        if column_names is None:
            column_names = [f"{i}" for i in range(X.shape[1])]
        X_df = pd.DataFrame(X, columns=column_names)
    else:
        X_df = X

    feature_indices_list = []

    for method in tqdm(methods, desc="Processing Feature Selection Methods"):
        print(f"Applying method: {method}")  # 调试信息
        if method == "Cars":
            save_path = os.path.join(result_dir, f"{name}_cars.png")
            Featuresecletidx = CARS_Cloud(X, y, N=50, f=20, cv=10, save_path=save_path)
            Featuresecletidx = Featuresecletidx.astype(int)
            feature_indices_list.append(Featuresecletidx)
        elif method == "Lars":
            Featuresecletidx = Lar(X_df.values, y)
            feature_indices_list.append(Featuresecletidx)
        elif method == "Uve":
            uve = UVE(X_df.values, y, 20)
            uve.calcCriteria()
            uve.evalCriteria(cv=5)
            Featuresecletidx = uve.cutFeature()
            feature_indices_list.append(Featuresecletidx)
        elif method == "Spa":
            save_path = os.path.join(result_dir, f"{name}_spa.png")
            Xcal, Xval, ycal, yval = train_test_split(X_df, y, test_size=0.2)
            Featuresecletidx = SPA().spa(
                Xcal=Xcal.values, ycal=ycal, m_min=4, m_max=32, Xval=Xval.values, yval=yval, autoscaling=1,save_path=save_path)
            feature_indices_list.append(Featuresecletidx)
        elif method == "GA":
            Featuresecletidx = GA(X_df.values, y, 10)
            feature_indices_list.append(Featuresecletidx)
        elif method == "ReliefF":
            relieff = ReliefF(n_neighbors=20, n_features_to_keep=20)
            Featuresecletidx = relieff.fit(X_df.values, y)
            feature_indices_list.append(Featuresecletidx)
        else:
            print(f"No such method: {method}")
            continue

        print(f"Selected indices by {method}: {Featuresecletidx}")  # 调试信息

    print("Feature indices list after all methods:", feature_indices_list)  # 调试信息

    if strategy == "voting":
        if feature_indices_list:
            all_indices = np.concatenate(feature_indices_list)
            print("All indices concatenated:", all_indices)  # 调试信息
            counter = Counter(all_indices)
            print("Counter result:", counter)  # 调试信息
            selected_features = [
                idx for idx, count in tqdm(counter.items(), desc="Voting Selection")
                if count > len(methods) / 2
            ]
            print("Selected features after voting:", selected_features)  # 调试信息
        else:
            print("No features selected by any method.")  # 提示信息
            selected_features = []
    elif strategy == "weighted":
        weights = {method: 1 for method in methods}
        weighted_counts = Counter()
        for method, indices in zip(methods, feature_indices_list):
            for idx in indices:
                weighted_counts[idx] += weights[method]
        print("Weighted counts:", weighted_counts)  # 调试信息
        selected_features = [
            idx for idx, count in tqdm(weighted_counts.items(), desc="Weighted Selection")
            if count >= np.mean(list(weighted_counts.values()))
        ]
        print("Selected features after weighted strategy:", selected_features)  # 调试信息
    elif strategy == "bagging":
        num_iterations = 5
        selected_features = set()
        for _ in tqdm(range(num_iterations), desc="Bagging Iterations"):
            X_sample, _, y_sample, _ = train_test_split(X_df, y, test_size=0.5)
            sub_feature_indices_list = []
            for method in methods:
                if method == "Spa":
                    Xcal, Xval, ycal, yval = train_test_split(X_sample, y_sample, test_size=0.2)
                    sub_feature_indices = SPA().spa(Xcal=Xcal.values, ycal=ycal, m_min=4, m_max=32, Xval=Xval.values,
                                                    yval=yval, autoscaling=1)
                elif method == "Cars":
                    sub_feature_indices = CARS_Cloud(X_sample.values, y_sample)
                else:
                    continue
                sub_feature_indices_list.append(sub_feature_indices)
            for sub_indices in sub_feature_indices_list:
                selected_features.update(sub_indices)
        selected_features = list(selected_features)
        print("Selected features after bagging strategy:", selected_features)  # 调试信息
    else:
        raise ValueError("Invalid strategy. Choose from 'voting', 'weighted', or 'bagging'.")

    selected_features = list(map(int, selected_features))  # 确保索引是整数类型
    X_Feature = X_df.iloc[:, selected_features]
    selected_columns = X_df.columns[selected_features]





    # 关闭图像以释放资源
    plt.close()

    return X_Feature, y, selected_columns