初始提交

classification_model/WaveSelect/Cars.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import copy
+from sklearn.cross_decomposition import PLSRegression
+from sklearn.metrics import mean_squared_error
+from sklearn.model_selection import KFold
+
+
+def PC_Cross_Validation(X, y, pc, cv):
+    '''
+        X : 光谱矩阵 (DataFrame) nxm
+        y : 浓度阵 (Series) （化学值）
+        pc: 最大主成分数
+        cv: 交叉验证数量
+    return :
+        RMSECV: 各主成分数对应的RMSECV
+        rindex: 最佳主成分数
+    '''
+    kf = KFold(n_splits=cv)
+    RMSECV = []
+    for i in range(pc):
+        RMSE = []
+        for train_index, test_index in kf.split(X):
+            x_train, x_test = X.iloc[train_index], X.iloc[test_index]
+            y_train, y_test = y.iloc[train_index], y.iloc[test_index]
+            pls = PLSRegression(n_components=i + 1)
+            pls.fit(x_train, y_train)
+            y_predict = pls.predict(x_test)
+            RMSE.append(np.sqrt(mean_squared_error(y_test, y_predict)))
+        RMSE_mean = np.mean(RMSE)
+        RMSECV.append(RMSE_mean)
+    rindex = np.argmin(RMSECV)
+    return RMSECV, rindex
+
+
+def Cross_Validation(X, y, pc, cv):
+    '''
+     X : 光谱矩阵 (DataFrame) nxm
+     y : 浓度阵 (Series) （化学值）
+     pc: 最大主成分数
+     cv: 交叉验证数量
+     return :
+            RMSECV: 各主成分数对应的RMSECV
+    '''
+    kf = KFold(n_splits=cv)
+    RMSE = []
+    for train_index, test_index in kf.split(X):
+        x_train, x_test = X.iloc[train_index], X.iloc[test_index]
+        y_train, y_test = y.iloc[train_index], y.iloc[test_index]
+        pls = PLSRegression(n_components=pc)
+        pls.fit(x_train, y_train)
+        y_predict = pls.predict(x_test)
+        RMSE.append(np.sqrt(mean_squared_error(y_test, y_predict)))
+    RMSE_mean = np.mean(RMSE)
+    return RMSE_mean
+
+
+def CARS_Cloud(X, y, N=50, f=20, cv=10, save_fig=False, save_path=None):
+    '''
+    X : 光谱矩阵 (DataFrame 或 ndarray)
+    y : 浓度阵 (Series 或 ndarray)
+    N : 蒙特卡洛迭代次数
+    f : 最大特征数
+    cv : 交叉验证的次数
+    save_fig : 是否保存图像
+    save_path : 图像保存路径
+    return :
+        OptWave : 选择的波长
+    '''
+    p = 0.8
+    m, n = X.shape
+    u = np.power((n / 2), (1 / (N - 1)))
+    k = (1 / (N - 1)) * np.log(n / 2)
+    cal_num = np.round(m * p)
+    b2 = np.arange(n)
+    x = X  # 将 DataFrame 转换为 numpy 数组
+    y = y # 将 Series 转换为 numpy 数组
+    D = np.vstack((np.array(b2).reshape(1, -1), x))
+    WaveData = []
+    WaveNum = []
+    RMSECV = []
+    r = []
+
+    for i in range(1, N + 1):
+        r.append(u * np.exp(-1 * k * i))
+        wave_num = int(np.round(r[i - 1] * n))
+        WaveNum = np.hstack((WaveNum, wave_num))
+        cal_index = np.random.choice(np.arange(m), size=int(cal_num), replace=False)
+        wave_index = b2[:wave_num].reshape(1, -1)[0]
+
+        # 使用 np.ix_ 来进行行列索引
+        xcal = x[np.ix_(cal_index, wave_index)]  # 选择对应的行和列
+        ycal = y[cal_index]  # 选择对应的 y
+
+        # 将 ycal 转换为一维数组
+        ycal = ycal.ravel()  # 使其成为一维数组
+
+        x = x[:, wave_index]  # 更新 x
+        D = D[:, wave_index]  # 更新 D
+        d = D[0, :].reshape(1, -1)
+        wnum = n - wave_num
+        if wnum > 0:
+            d = np.hstack((d, np.full((1, wnum), -1)))
+        if len(WaveData) == 0:
+            WaveData = d
+        else:
+            WaveData = np.vstack((WaveData, d.reshape(1, -1)))
+
+        if wave_num < f:
+            f = wave_num
+
+        pls = PLSRegression(n_components=f)
+        pls.fit(xcal, ycal)
+        beta = pls.coef_
+
+        # 针对新版sklearn处理 coef_ 的方式
+        if beta.shape[0] == 1:  # 新版sklearn，(1, x)
+            b = np.abs(beta[0])  # 从第一行提取数据
+            coeff = beta[0, b2]  # 修改为beta[0, b2]，因为coef只有一行
+        else:  # 旧版sklearn，(x, 1)
+            b = np.abs(beta[:, 0])  # 从列中提取数据
+            coeff = beta[b2, 0]  # 修改为beta[b2, 0]，因为coef只有一列
+
+        b2 = np.argsort(-b, axis=0)
+        coef = copy.deepcopy(beta)
+        coeff = coef[b2, :].reshape(len(b2), -1)
+        rmsecv, rindex = PC_Cross_Validation(pd.DataFrame(xcal), pd.Series(ycal), f, cv)
+        RMSECV.append(Cross_Validation(pd.DataFrame(xcal), pd.Series(ycal), rindex + 1, cv))
+
+    WAVE = []
+    for i in range(WaveData.shape[0]):
+        wd = WaveData[i, :]
+        WD = np.ones((len(wd)))
+        for j in range(len(wd)):
+            ind = np.where(wd == j)
+            if len(ind[0]) == 0:
+                WD[j] = 0
+            else:
+                WD[j] = wd[ind[0]]
+        if len(WAVE) == 0:
+            WAVE = copy.deepcopy(WD)
+        else:
+            WAVE = np.vstack((WAVE, WD.reshape(1, -1)))
+
+    MinIndex = np.argmin(RMSECV)
+    Optimal = WAVE[MinIndex, :]
+    boindex = np.where(Optimal != 0)
+    OptWave = boindex[0]
+
+    plt.figure(figsize=(12, 10))
+    # 设置字体为新罗马
+    plt.rcParams['font.sans-serif'] = ['Times New Roman']  # 使用 Times New Roman 字体
+    plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题
+    fonts = 20
+
+    plt.subplot(211)
+    plt.xlabel('Monte Carlo Iterations', fontsize=fonts)
+    plt.ylabel('Number of Selected Wavelengths', fontsize=fonts)
+    plt.title('Optimal Iteration: ' + str(MinIndex), fontsize=fonts)
+    plt.plot(np.arange(N), WaveNum)
+
+    plt.subplot(212)
+    plt.xlabel('Monte Carlo Iterations', fontsize=fonts)
+    plt.ylabel('RMSECV', fontsize=fonts)
+    plt.plot(np.arange(N), RMSECV)
+
+    # 保存图像
+    if save_fig:
+        plt.savefig(save_path)  # 保存图像到文件
+        print(f"The figure has been saved as {save_path}")
+
+
+    # plt.show()
+
+    return OptWave