import pandas as pd
import numpy as np
from classification_model.WaveSelect.Lar import Lar
from classification_model.WaveSelect.Spa import SPA
from classification_model.WaveSelect.Spa_acc import SPA_acc
from classification_model.WaveSelect.Uve import UVE
from classification_model.WaveSelect.Cars import CARS_Cloud
from classification_model.WaveSelect.Pca import Pca
from classification_model.WaveSelect.GA import GA
from classification_model.WaveSelect.ReliefF import ReliefF
from sklearn.model_selection import train_test_split
# from WaveSelect.MRMR import MRMRFeatureSelection
import os
import matplotlib.pyplot as plt
def SpctrumFeatureSelcet(method, X, y, name='', result_dir='', column_names=None):
    """
    :param method: 波长筛选/降维的方法，包括：Cars, Lars, Uve, Spa, Pca。
    :param X: 光谱数据，可以是 pandas DataFrame 或 numpy array (n_samples, n_features)。
    :param y: 光谱数据对应的标签 (n_samples,)。
    :param name: 结果图像的文件名。
    :param result_dir: 保存结果的文件夹路径。
    :param column_names: 如果 X 是 numpy array，需要提供列名列表。
    :return:
        - X_Feature: 选择/降维后的数据 (n_samples, n_features)。
        - y: 对应的标签。
        - selected_columns: 选择的特征列名或索引。
    """
    global X_Feature

    # 判断输入数据类型并转换为 DataFrame（如有必要）
    if isinstance(X, np.ndarray):
        if column_names is None:
            column_names = [f"{i}" for i in range(X.shape[1])]  # 默认列名
        X_df = pd.DataFrame(X, columns=column_names)
    else:
        X_df = X

    # 根据所选方法执行特征选择
    if method == "None":
        X_Feature = X_df
        selected_columns = X_df.columns
    elif method == "Cars":
        save_path = os.path.join(result_dir, f"{name}_cars.png")
        # 调用 CARS_Cloud 并获取结果
        Featuresecletidx = CARS_Cloud(X_df.values, y, N=50, f=20, cv=10,  save_fig=True,save_path=save_path)
        Featuresecletidx = Featuresecletidx.astype(int)
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = Featuresecletidx

    elif method == "Lars":
        Featuresecletidx = Lar(X_df.values, y)
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = X_df.columns[Featuresecletidx]
    elif method == "Uve":
        uve = UVE(X_df.values, y, 20)
        uve.calcCriteria()
        uve.evalCriteria(cv=5)
        Featuresecletidx = uve.cutFeature()  # 返回所选特征的索引
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = X_df.columns[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=0.3)
        Featuresecletidx, var_sel_phase2 = SPA().spa(
            Xcal, ycal, m_min=2, m_max=50, Xval=Xval, yval=yval, autoscaling=1,save_path=save_path)
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = X_df.columns[Featuresecletidx]
    elif method == "Spa_acc":
        save_path = os.path.join(result_dir, f"{name}_spa_acc.png")

        Xcal, Xval, ycal, yval = train_test_split(X_df, y, test_size=0.3)
        Featuresecletidx, var_sel_phase2 = SPA_acc().spa(
            Xcal, ycal, m_min=2, m_max=50, Xval=Xval, yval=yval, autoscaling=1,save_path=save_path)
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = X_df.columns[Featuresecletidx]
    elif method == "GA":
        Featuresecletidx = GA(X_df.values, y, 10)
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = X_df.columns[Featuresecletidx]
    elif method == "Pca":
        X_Feature = Pca(X_df.values)
        selected_columns = [f"PC{i+1}" for i in range(X_Feature.shape[1])]
    elif method == "ReliefF":
        relieff = ReliefF(n_neighbors=20, n_features_to_keep=20)
        Featuresecletidx = relieff.fit(X_df.values, y)
        X_Feature = X_df.iloc[:, Featuresecletidx]
        selected_columns = X_df.columns[Featuresecletidx]
    else:
        print("没有这个波长筛选方法！")
        return None, None

    return X_Feature, y, selected_columns  # 返回所选特征数据、标签和列名