最大信息系数MIC的python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_csv('G:\毕业论文\B数据集\水稻稻叶瘟\oston.csv', encoding='gbk') # x = df.a # y = df.b df = pd.read_excel("xxx.xls") x = df['x'] y = df['x'] 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()