94 lines
4.2 KiB
Python
94 lines
4.2 KiB
Python
import pandas as pd
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import numpy as np
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from classification_model.WaveSelect.Lar import Lar
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from classification_model.WaveSelect.Spa import SPA
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from classification_model.WaveSelect.Spa_acc import SPA_acc
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from classification_model.WaveSelect.Uve import UVE
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from classification_model.WaveSelect.Cars import CARS_Cloud
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from classification_model.WaveSelect.Pca import Pca
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from classification_model.WaveSelect.GA import GA
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from classification_model.WaveSelect.ReliefF import ReliefF
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from sklearn.model_selection import train_test_split
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# from WaveSelect.MRMR import MRMRFeatureSelection
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import os
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import matplotlib.pyplot as plt
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def SpctrumFeatureSelcet(method, X, y, name='', result_dir='', column_names=None):
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"""
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:param method: 波长筛选/降维的方法，包括：Cars, Lars, Uve, Spa, Pca。
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:param X: 光谱数据，可以是 pandas DataFrame 或 numpy array (n_samples, n_features)。
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:param y: 光谱数据对应的标签 (n_samples,)。
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:param name: 结果图像的文件名。
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:param result_dir: 保存结果的文件夹路径。
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:param column_names: 如果 X 是 numpy array，需要提供列名列表。
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:return:
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- X_Feature: 选择/降维后的数据 (n_samples, n_features)。
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- y: 对应的标签。
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- selected_columns: 选择的特征列名或索引。
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"""
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global X_Feature
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# 判断输入数据类型并转换为 DataFrame（如有必要）
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if isinstance(X, np.ndarray):
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if column_names is None:
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column_names = [f"{i}" for i in range(X.shape[1])] # 默认列名
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X_df = pd.DataFrame(X, columns=column_names)
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else:
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X_df = X
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# 根据所选方法执行特征选择
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if method == "None":
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X_Feature = X_df
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selected_columns = X_df.columns
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elif method == "Cars":
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save_path = os.path.join(result_dir, f"{name}_cars.png")
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# 调用 CARS_Cloud 并获取结果
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Featuresecletidx = CARS_Cloud(X_df.values, y, N=50, f=20, cv=10, save_fig=True,save_path=save_path)
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Featuresecletidx = Featuresecletidx.astype(int)
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = Featuresecletidx
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elif method == "Lars":
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Featuresecletidx = Lar(X_df.values, y)
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = X_df.columns[Featuresecletidx]
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elif method == "Uve":
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uve = UVE(X_df.values, y, 20)
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uve.calcCriteria()
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uve.evalCriteria(cv=5)
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Featuresecletidx = uve.cutFeature() # 返回所选特征的索引
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = X_df.columns[Featuresecletidx]
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elif method == "Spa":
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save_path = os.path.join(result_dir, f"{name}_spa.png")
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Xcal, Xval, ycal, yval = train_test_split(X_df, y, test_size=0.3)
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Featuresecletidx, var_sel_phase2 = SPA().spa(
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Xcal, ycal, m_min=2, m_max=50, Xval=Xval, yval=yval, autoscaling=1,save_path=save_path)
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = X_df.columns[Featuresecletidx]
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elif method == "Spa_acc":
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save_path = os.path.join(result_dir, f"{name}_spa_acc.png")
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Xcal, Xval, ycal, yval = train_test_split(X_df, y, test_size=0.3)
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Featuresecletidx, var_sel_phase2 = SPA_acc().spa(
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Xcal, ycal, m_min=2, m_max=50, Xval=Xval, yval=yval, autoscaling=1,save_path=save_path)
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = X_df.columns[Featuresecletidx]
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elif method == "GA":
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Featuresecletidx = GA(X_df.values, y, 10)
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = X_df.columns[Featuresecletidx]
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elif method == "Pca":
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X_Feature = Pca(X_df.values)
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selected_columns = [f"PC{i+1}" for i in range(X_Feature.shape[1])]
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elif method == "ReliefF":
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relieff = ReliefF(n_neighbors=20, n_features_to_keep=20)
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Featuresecletidx = relieff.fit(X_df.values, y)
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X_Feature = X_df.iloc[:, Featuresecletidx]
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selected_columns = X_df.columns[Featuresecletidx]
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else:
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print("没有这个波长筛选方法！")
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return None, None
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return X_Feature, y, selected_columns # 返回所选特征数据、标签和列名
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