forked from Open-CT/openbrain
123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
# This is a sample Python script.
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# Press ⌃R to execute it or replace it with your code.
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# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
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import os
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import pandas as pd
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filePath= "file1" #输入文件夹
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bigname=111
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# -*- coding: utf-8 -*-
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import numpy as np
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from scipy.cluster.hierarchy import dendrogram, linkage, fcluster, set_link_color_palette
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from matplotlib import pyplot as plt
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def os_file(path): #遍历输入文件夹有多少个文件
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filenames = os.listdir(path)
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for filename in filenames:
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print(filename)
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filename1=filename[0:-5] #删掉了文件后缀
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print(filename1)
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if filename!=".DS_Store":
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file_reader(filePath + "/" + filename,filename1)
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def file_reader(path,name):
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df1 = pd.read_excel(path, sheet_name="Sheet1") #输入的文件必须放在Sheet1
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df1 = np.array(df1)
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importance=df1[-1,:] #获取最后一行的所有列信息（权重）
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importance = np.delete(importance, 0, axis=0)
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df1=df1[0:-1,:] #获取除了权重行的所有数据
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#print(importance)
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# 把word和数组分开
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word = df1[:, 0] #获取第一列所有行（词）
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data = np.delete(df1, 0, axis=1) #获取除第一列的其他列信息（打分）
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# print("data1")
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#print(data)
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data=data*importance #权重与数值相乘
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#print(word)
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#print("data2")
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#print(data)
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nums,indics=hierarchy_cluster(data,word,name)
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print(indics)
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for i in range(len(indics)):
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group = "为一组"
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for j in range(len(indics[i])):
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group=word[indics[i][j]]+" "+group
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print(group)
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def hierarchy_cluster(data,word,name, method='complete', threshold=600.0): #complete-linkage
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'''层次聚类
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Arguments:
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data [[0, float, ...], [float, 0, ...]] -- 文档 i 和文档 j 的距离
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Keyword Arguments:
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method {str} -- [linkage的方式： single、complete、average、centroid、median、ward] (default: {'average'})
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threshold {float} -- 聚类簇之间的距离
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Return:
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cluster_number int -- 聚类个数
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cluster [[idx1, idx2,..], [idx3]] -- 每一类下的索引
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'''
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data = np.array(data)
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plt.figure(figsize=(10, 15), dpi=300) #代表宽和高的尺寸，dpi代表分辨率
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Z = linkage(data, method=method,metric='euclidean') #欧式距离公式
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cluster_assignments = fcluster(Z, threshold, criterion='distance')
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num_clusters = cluster_assignments.max()
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indices = get_cluster_indices(cluster_assignments)
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z = linkage(data, method='ward')
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print(z.shape)
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dendrogram(z, labels=word, color_threshold=80,orientation='right', leaf_font_size=8,above_threshold_color='black')
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set_link_color_palette(['#0000FF', '#4A766E', '#2F4F4F','871F78','FF7F00','E47833','FF6666','FFCCFF']) #color_threshold是画线位置；orientation调成left图换方向
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#n_clusters=10 #leaf_font_size词间距
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#color_threshold=25
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plt.grid(True, which='minor', ls='--') #minor代表不显示网格线，major代表显示
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#name = "college" #设置图片标题
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plt.title(name, fontdict={'fontproperties':'Times New Roman','size': 10}) #标题的字体字号
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plt.yticks(fontproperties='Times New Roman', size=8) #设置y轴字体和字号，大小及加粗
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plt.xticks(fontproperties='Times New Roman', size=8)
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plt.plot(linewidth = '0.5') #设置线粗细
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f = plt.gcf()
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f.savefig(name + ".png")
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plt.show()
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f.clear()
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return num_clusters,indices
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def get_cluster_indices(cluster_assignments): #层次聚类的实现函数
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'''映射每一类至原数据索引
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Arguments:
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cluster_assignments 层次聚类后的结果
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Returns:
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[[idx1, idx2,..], [idx3]] -- 每一类下的索引
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'''
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n = cluster_assignments.max()
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indices = []
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for cluster_number in range(1, n + 1):
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indices.append(np.where(cluster_assignments == cluster_number)[0])
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return indices
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if __name__ == "__main__":
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os_file(filePath)
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