已知交叉验证得到的特征、标签的训练集和测试集，请用python代码对训练集实现五种进行特征选择的方法

好的，以下是五种进行特征选择的方法的Python代码实现：

1. 方差选择法：

from sklearn.feature_selection import VarianceThreshold

sel = VarianceThreshold(threshold=(.8 * (1 - .8))) # 根据数据特征选择阈值
X_train_sel = sel.fit_transform(X_train) # 训练集特征选择
X_test_sel = sel.transform(X_test) # 测试集特征选择

2. 相关系数法：

from sklearn.feature_selection import SelectKBest
from scipy.stats import pearsonr

sel = SelectKBest(lambda X, Y: array(list(map(lambda x: pearsonr(x, Y), X.T))).T, k=2)
X_train_sel = sel.fit_transform(X_train, y_train) # 训练集特征选择
X_test_sel = sel.transform(X_test) # 测试集特征选择

3. 卡方检验：

from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

sel = SelectKBest(chi2, k=2)
X_train_sel = sel.fit_transform(X_train, y_train) # 训练集特征选择
X_test_sel = sel.transform(X_test) # 测试集特征选择

4. 互信息法：

from sklearn.feature_selection import SelectKBest
from minepy import MINE

mic = MINE()
sel = SelectKBest(lambda X, Y: array(list(map(lambda x: mic.compute_score(x, Y), X.T))).T, k=2)
X_train_sel = sel.fit_transform(X_train, y_train) # 训练集特征选择
X_test_sel = sel.transform(X_test) # 测试集特征选择

5. Wrapper方法：

from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression

model = LogisticRegression()
sel = RFE(estimator=model, n_features_to_select=2)
X_train_sel = sel.fit_transform(X_train, y_train) # 训练集特征选择
X_test_sel = sel.transform(X_test) # 测试集特征选择

以上就是五种特征选择方法的Python代码实现。