最大信息系数MIC的python代码

素颜马尾好姑娘i 2023-09-29 12:44 19阅读 0赞

**完整代码**

import numpy as np
    import logging
    import sys
    import pandas as pd
    
    class CyrusMIC(object):
        logger = logging.getLogger()
        logger.setLevel(logging.INFO)
        screen_handler = logging.StreamHandler(sys.stdout)
        screen_handler.setLevel(logging.INFO)
        formatter = logging.Formatter('%(asctime)s - %(module)s.%(funcName)s:%(lineno)d - %(levelname)s - %(message)s')
        screen_handler.setFormatter(formatter)
        logger.addHandler(screen_handler)
        def __init__(self,x_num=[None,None],y_num=[None,None]):
            self.x_max_num = x_num[1]
            self.x_min_num = x_num[0]
            self.y_min_num = y_num[0]
            self.y_max_num = y_num[1]
            self.x = None
            self.y = None
        def cal_mut_info(self,p_matrix):
            """
            计算互信息值
            :param p_matrix: 变量X和Y的构成的概率矩阵
            :return: 互信息值
            """
            mut_info = 0
            p_matrix = np.array(p_matrix)
            for i in range(p_matrix.shape[0]):
                for j in range(p_matrix.shape[1]):
                    if p_matrix[i,j] != 0:
                        mut_info += p_matrix[i,j]*np.log2(p_matrix[i,j]/(p_matrix[i,:].sum()*p_matrix[:,j].sum()))
            self.logger.info("信息系数为：{}".format(mut_info/np.log2(min(p_matrix.shape[0],p_matrix.shape[1]))))
            return mut_info/np.log2(min(p_matrix.shape[0],p_matrix.shape[1]))
    
        def divide_bin(self,x_num,y_num):
            """
            指定在两个变量方向上需划分的网格数，返回概率矩阵
            :param x_num:
            :param y_num:
            :return: p_matrix
            """
            p_matrix = np.zeros([x_num,y_num])
            x_bin = np.linspace(self.x.min(),self.x.max()+1,x_num+1)
            y_bin = np.linspace(self.y.min(),self.y.max()+1,y_num+1)
            for i in range(x_num):
                for j in range(y_num):
                    p_matrix[i,j] = sum([1 if (self.x[value] < x_bin[i + 1] and self.x[value] >= x_bin[i] and self.y[value] < y_bin[j + 1] and
                          self.y[value] >= y_bin[j]) else 0 for value in range(self.x.shape[0])])/self.x.shape[0]
            return p_matrix
    
        def cal_MIC(self,x,y):
            self.x = np.array(x).reshape((-1,))
            self.y = np.array(y).reshape((-1,))
            if not self.x_max_num:
                self.x_max_num = int(round(self.x.shape[0]**0.3,0))
                self.y_max_num = self.x_max_num
                self.x_min_num = 2
                self.y_min_num = 2
            mics = []
            for i in range(self.x_min_num,self.x_max_num+1):
                for j in range(self.y_min_num,self.x_max_num+1):
                    self.logger.info("划分区间数量为：[{},{}]".format(i,j))
                    mics.append(self.cal_mut_info(self.divide_bin(i,j)))
            self.logger.info("最大信息系数为:{}".format(max(mics)))
            return max(mics)
    
    if __name__ == '__main__':
        import matplotlib.pyplot as plt
        from matplotlib.font_manager import FontProperties
        font_set = FontProperties(fname=r"c:\windows\fonts\simsun.ttc", size=15)
        df = pd.read_excel("xxx.xls")
        x = df['a']
        y = df['b']
        fig = plt.figure(figsize=(5, 5))
        ax = fig.add_subplot(1, 1, 1)
        ax.set_title('xxx', fontsize=11, fontstyle='oblique', fontproperties=font_set)
        ax.set_xlabel('xxx', fontsize=11, fontfamily='sans-serif', fontstyle='italic', fontproperties=font_set)
        ax.set_ylabel('xxx', fontsize=11, fontstyle='oblique', fontproperties=font_set)
        plt.scatter(x, y, c='g')
        mic_tool = CyrusMIC()
        mic_tool.cal_MIC(x, y)
        plt.show()

**效果展示**  
![在这里插入图片描述][47b35fdb07bc4e6785a3f0954e1f4b15.png]

[47b35fdb07bc4e6785a3f0954e1f4b15.png]: https://img-blog.csdnimg.cn/47b35fdb07bc4e6785a3f0954e1f4b15.png