ML之MIC：利用某数据集计算机最大信息系数MIC并可视化MIC矩阵热图及其代码实现

ML之MIC：利用某数据集计算机最大信息系数MIC并可视化MIC矩阵热图及其代码实现

目录

利用某数据集计算机最大信息系数MIC并可视化MIC矩阵热图及其代码实现

实现结果

实现代码

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利用某数据集计算机最大信息系数MIC并可视化MIC矩阵热图及其代码实现

实现结果

正在执行B盘的数据
          0         1         2         3         4         5         6   \
0   0.993748  0.992363  0.865935  0.158754  0.199621  0.238159  0.859997   
1   0.992363  0.998222  0.584723  0.302727  0.307473  0.298183  0.695466   
2   0.865935  0.584723  0.999999  0.801459  0.805825  0.793084  0.935439   
3   0.158754  0.302727  0.801459  0.999574  0.999574  0.965256  0.963887   
4   0.199621  0.307473  0.805825  0.999574  0.999999  0.968664  0.966409   
5   0.238159  0.298183  0.793084  0.965256  0.968664  0.999999  0.935723   
6   0.859997  0.695466  0.935439  0.963887  0.966409  0.935723  0.999710   
7   0.632709  0.484949  0.818616  0.963887  0.966409  0.915654  0.995471   
8   0.241095  0.230026  0.545492  0.530788  0.669366  0.473332  0.486489   
9   0.368982  0.289529  0.250506  0.138713  0.215880  0.161387  0.137730   
10  0.423532  0.331815  0.331008  0.253744  0.262192  0.261714  0.295448   
11  0.841959  0.826301  0.772081  0.173843  0.239098  0.253886  0.781008   

          7         8         9         10        11  
0   0.632709  0.241095  0.368982  0.423532  0.841959  
1   0.484949  0.230026  0.289529  0.331815  0.826301  
2   0.818616  0.545492  0.250506  0.331008  0.772081  
3   0.963887  0.530788  0.138713  0.253744  0.173843  
4   0.966409  0.669366  0.215880  0.262192  0.239098  
5   0.915654  0.473332  0.161387  0.261714  0.253886  
6   0.995471  0.486489  0.137730  0.295448  0.781008  
7   0.999864  0.473332  0.108656  0.261138  0.573823  
8   0.473332  0.995335  0.275280  0.295224  0.190111  
9   0.108656  0.275280  0.999993  0.901033  0.408306  
10  0.261138  0.295224  0.901033  0.999993  0.374089  
11  0.573823  0.190111  0.408306  0.374089  0.999935  

实现代码

相关文章：ML之MIC：利用某数据集计算机最大信息系数MIC并可视化MIC矩阵热图及其代码实现

from minepy import MINE
import seaborn as sns


def MIC_matirx_ShowHeatMap(DataFrame):
    colormap = plt.cm.RdBu
    ylabels = DataFrame.columns.values.tolist()
    f, ax = plt.subplots(figsize=(14, 14))
    ax.set_title('MIC Matirx HeatMap')
    sns.heatmap(DataFrame.astype(float),
                cmap=colormap,ax=ax,annot=True,
                yticklabels=ylabels,xticklabels=ylabels)
    plt.show()
    
MIC_matirx_ShowHeatMap(data_MIC_matirx)