From 6cfca9f94066e8d253450f1ae938692ab5607424 Mon Sep 17 00:00:00 2001
From: artieyue <3312002405@qq.com>
Date: Fri, 7 Oct 2022 18:33:27 +0800
Subject: [PATCH] 10.7 upload

---
 pydata-huang/层次聚类和可视化.py | 122 +++++++++++++++++++++++
 1 file changed, 122 insertions(+)
 create mode 100644 pydata-huang/层次聚类和可视化.py

diff --git a/pydata-huang/层次聚类和可视化.py b/pydata-huang/层次聚类和可视化.py
new file mode 100644
index 0000000..0c29c02
--- /dev/null
+++ b/pydata-huang/层次聚类和可视化.py
@@ -0,0 +1,122 @@
+# This is a sample Python script.
+
+# Press ⌃R to execute it or replace it with your code.
+# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.
+import os
+import pandas as pd
+filePath= "file1"   #输入文件夹
+bigname=111
+# -*- coding: utf-8 -*-
+import numpy as np
+from scipy.cluster.hierarchy import dendrogram, linkage, fcluster, set_link_color_palette
+from matplotlib import pyplot as plt
+
+def os_file(path):   #遍历输入文件夹有多少个文件
+    filenames = os.listdir(path)
+
+    for filename in filenames:
+        print(filename)
+        filename1=filename[0:-5]    #删掉了文件后缀
+        print(filename1)
+        if filename!=".DS_Store":
+            file_reader(filePath + "/" + filename,filename1)
+
+
+def file_reader(path,name):
+    df1 = pd.read_excel(path, sheet_name="Sheet1")  #输入的文件必须放在Sheet1
+    df1 = np.array(df1)
+    importance=df1[-1,:]        #获取最后一行的所有列信息（权重）
+    importance = np.delete(importance, 0, axis=0)
+    df1=df1[0:-1,:]      #获取除了权重行的所有数据
+    #print(importance)
+
+    # 把word和数组分开
+    word = df1[:, 0]    #获取第一列所有行（词）
+    data = np.delete(df1, 0, axis=1)   #获取除第一列的其他列信息（打分）
+   # print("data1")
+    #print(data)
+    data=data*importance  #权重与数值相乘
+    #print(word)
+    #print("data2")
+    #print(data)
+    nums,indics=hierarchy_cluster(data,word,name)
+    print(indics)
+    for i in range(len(indics)):
+        group = "为一组"
+        for j in range(len(indics[i])):
+            group=word[indics[i][j]]+" "+group
+        print(group)
+
+
+
+
+
+
+
+
+
+def hierarchy_cluster(data,word,name, method='complete', threshold=600.0):  #complete-linkage
+    '''层次聚类
+
+    Arguments:
+        data [[0, float, ...], [float, 0, ...]] -- 文档 i 和文档 j 的距离
+
+    Keyword Arguments:
+        method {str} -- [linkage的方式： single、complete、average、centroid、median、ward] (default: {'average'})
+        threshold {float} -- 聚类簇之间的距离
+    Return:
+        cluster_number int -- 聚类个数
+        cluster [[idx1, idx2,..], [idx3]] -- 每一类下的索引
+    '''
+    data = np.array(data)
+    plt.figure(figsize=(10, 15), dpi=300)      #代表宽和高的尺寸，dpi代表分辨率
+    Z = linkage(data, method=method,metric='euclidean')      #欧式距离公式
+
+    cluster_assignments = fcluster(Z, threshold, criterion='distance')
+
+
+    num_clusters = cluster_assignments.max()
+
+    indices = get_cluster_indices(cluster_assignments)
+    z = linkage(data, method='ward')
+    print(z.shape)
+    dendrogram(z, labels=word, color_threshold=80,orientation='right', leaf_font_size=8,above_threshold_color='black')
+    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
+    #n_clusters=10                                                     #leaf_font_size词间距
+    #color_threshold=25
+
+    plt.grid(True, which='minor', ls='--')        #minor代表不显示网格线，major代表显示
+    #name = "college"    #设置图片标题
+    plt.title(name, fontdict={'fontproperties':'Times New Roman','size': 10}) #标题的字体字号
+    plt.yticks(fontproperties='Times New Roman', size=8)  #设置y轴字体和字号，大小及加粗
+    plt.xticks(fontproperties='Times New Roman', size=8)
+    plt.plot(linewidth = '0.5')    #设置线粗细
+
+    f = plt.gcf()
+
+    f.savefig(name + ".png")
+    plt.show()
+    f.clear()
+
+    return num_clusters,indices
+
+
+def get_cluster_indices(cluster_assignments):   #层次聚类的实现函数
+    '''映射每一类至原数据索引
+
+    Arguments:
+        cluster_assignments 层次聚类后的结果
+
+    Returns:
+        [[idx1, idx2,..], [idx3]] -- 每一类下的索引
+    '''
+    n = cluster_assignments.max()
+    indices = []
+    for cluster_number in range(1, n + 1):
+        indices.append(np.where(cluster_assignments == cluster_number)[0])
+
+    return indices
+
+
+if __name__ == "__main__":
+    os_file(filePath)