MRMR算法 python

MRMR (最大相关最小冗余) 算法是一种特征选择算法，它可以在高维数据中选择最具代表性的特征。下面是一个使用Python实现MRMR算法的示例代码：

import numpy as np

def mrmr(X, y, n_selected_features):
    """
    MRMR Algorithm for feature selection
    X: array-like, shape (n_samples, n_features)
        input data
    y: array-like, shape (n_samples,)
        target variable
    n_selected_features: int
        number of selected features
    """
    # Initialize the list of selected features and the list of remaining features
    selected_features = []
    remaining_features = list(range(X.shape[1]))
    
    # Calculate the mutual information between each feature and the target variable
    mi = mutual_info_classif(X, y)
    
    # Select the feature with the highest mutual information with the target variable
    best_feature_idx = np.argmax(mi)
    selected_features.append(best_feature_idx)
    remaining_features.remove(best_feature_idx)
    
    # Select the remaining features using the MRMR criterion
    while len(selected_features) < n_selected_features:
        mrmr_score = []
        for feature_idx in remaining_features:
            # Calculate the relevance between the feature and the target variable
            relevance = mi[feature_idx]
            # Calculate the redundancy between the feature and the selected features
            redundancy = np.mean([mutual_info_score(X[:, feature_idx], X[:, selected_feature]) for selected_feature in selected_features])
            # Calculate the MRMR criterion
            mrmr_score.append(relevance - redundancy)
        # Select the feature with the highest MRMR score
        best_feature_idx = remaining_features[np.argmax(mrmr_score)]
        selected_features.append(best_feature_idx)
        remaining_features.remove(best_feature_idx)
    
    return selected_features

在上面的代码中，`X`是输入数据，`y`是目标变量，`n_selected_features`是要选择的特征数量。该代码使用`mutual_info_classif`和`mutual_info_score`函数计算特征与目标变量之间的互信息和特征之间的互信息，并使用MRMR准则选择最具代表性的特征。