Python实现灰色关联分析与结果可视化的详细代码

目录

• 代码实现
• 下载数据
• 实现灰色关联分析
• 结果可视化
• 参考文章

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

# 定义下载数据的函数
def ReadAndSaveDataByPandas(target_url = None,file_save_path = None ,save=False):
    if target_url !=None:
        target_url = ("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv")   
    if file_save_path != None:
        file_save_path = "/home/fonttian/Data/UCI/Glass/glass.csv"
    wine = pd.read_csv(target_url, header=0, sep=";")
    if save == True:
        wine.to_csv(file_save_path, index=False)
    return wine
# 从硬盘读取数据进入内存
wine = pd.read_csv("/home/font/Data/UCI/WINE/wine.csv")
wine.head()

import pandas as pd
from numpy import *
def GRA_ONE(DataFrame,m=0):
    gray= DataFrame
    #读取为df格式
    gray=(gray - gray.min()) / (gray.max() - gray.min())
    #标准化
    std=gray.iloc[:,m]#为标准要素
    ce=gray.iloc[:,0:]#为比较要素
    n=ce.shape[0]
    m=ce.shape[1]#计算行列

    #与标准要素比较，相减
    a=zeros([m,n])
    for i in range(m):
        for j in range(n):
            a[i,j]=abs(ce.iloc[j,i]-std[j])
    #取出矩阵中最大值与最小值
    c=amax(a)
    d=amin(a)
    #计算值
    result=zeros([m,n])
            result[i,j]=(d+0.5*c)/(a[i,j]+0.5*c)
    #求均值，得到灰色关联值
    result2=zeros(m)
            result2[i]=mean(result[i,:])
    RT=pd.DataFrame(result2)
    return RT
def GRA(DataFrame):
    list_columns = [str(s) for s in range(len(DataFrame.columns)) if s not in [None]]
    df_local = pd.DataFrame(columns=list_columns)
    for i in range(len(DataFrame.columns)):
        df_local.iloc[:,i] = GRA_ONE(DataFrame,m=i)[0]
    return df_local

data_wine_gra = GRA(wine)
# data_wine_gra.to_csv(path+"GRA.csv") 存储结果到硬盘
data_wine_gra

Empty DataFrame
Columns: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
Index: []

# 灰色关联结果矩阵可视化
import seaborn as sns
%matplotlib inline
def ShowGRAHeatMap(DataFrame):
    import matplotlib.pyplot as plt
    import seaborn as sns
    %matplotlib inline
    colormap = plt.cm.RdBu
    plt.figure(figsize=(14,12))
    plt.title("Pearson Correlation of Features", y=1.05, size=15)
    sns.heatmap(DataFrame.astype(float),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor="white", annot=True)
    plt.show()
ShowGRAHeatMap(data_wine_gra)