How to implement canopy clustering in python-Python Tutorial-php.cn

How to implement canopy clustering in python

爱喝马黛茶的安东尼

Release： 2019-06-24 18:04:35

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The Canopy algorithm was proposed by Andrew McCallum, Kamal Nigam and Lyle Ungar in 2000. It is a preprocessing of k-means clustering algorithm and hierarchical clustering algorithm. As we all know, one of the shortcomings of kmeans is that the k value needs to be adjusted manually. The k value can be finally determined later through the Elbow Method and Silhouette Coefficient, but these methods are It is judged "ex post facto", and the role of the Canopy algorithm is that it determines the initial number of cluster centers and cluster center points for the k-means algorithm through rough clustering in advance.

How to implement canopy clustering in python

Package used:

import math
import random
import numpy as np
from datetime import datetime
from pprint import pprint as p
import matplotlib.pyplot as plt

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1. First I preset a two-dimensional one in the algorithm (for Convenient for later drawing and presentation on a two-dimensional plane) data dataset.

Of course, high-dimensional data can also be used, and I wrote the canopy core algorithm into the class. Later, data of any dimension can be processed through direct calls, of course only in small batches. , large batches of data can be moved to Mahout and Hadoop.

# 随机生成500个二维[0,1)平面点
dataset = np.random.rand(500, 2)

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2. Then generate two categories, the attributes of the classes are as follows:

class Canopy:
    def __init__(self, dataset):        
        self.dataset = dataset        
        self.t1 = 0
      self.t2 = 0

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Add to set the initial values of t1 and t2 and determine the size function

   # 设置初始阈值  
def setThreshold(self, t1, t2):        
    if t1 > t2:
        self.t1 = t1            
        self.t2 = t2        
    else:
        print(&#39;t1 needs to be larger than t2!&#39;)

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3. Distance calculation, the distance calculation method between each center point is the Euclidean distance I use.

#使用欧式距离进行距离的计算
def euclideanDistance(self, vec1, vec2):        
    return math.sqrt(((vec1 - vec2)**2).sum())

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4. Then write a function that randomly selects subscripts from the dataset according to the length of the dataset

# 根据当前dataset的长度随机选择一个下标 
def getRandIndex(self):        
    return random.randint(0, len(self.dataset) - 1)

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5. Core algorithm

def clustering(self):        
        if self.t1 == 0:
            print(&#39;Please set the threshold.&#39;)        
        else:
            canopies = []  # 用于存放最终归类结果
            while len(self.dataset) != 0:
                rand_index = self.getRandIndex()
                current_center = self.dataset[rand_index]  # 随机获取一个中心点，定为P点
                current_center_list = []  # 初始化P点的canopy类容器
                delete_list = []  # 初始化P点的删除容器
                self.dataset = np.delete(                    
                     self.dataset, rand_index, 0)  # 删除随机选择的中心点P
                for datum_j in range(len(self.dataset)):
                    datum = self.dataset[datum_j]
                    distance = self.euclideanDistance(
                        current_center, datum)  # 计算选取的中心点P到每个点之间的距离
                    if distance < self.t1:
                        # 若距离小于t1，则将点归入P点的canopy类
                        current_center_list.append(datum)                    
                    if distance < self.t2:
                        delete_list.append(datum_j)  # 若小于t2则归入删除容器
                # 根据删除容器的下标，将元素从数据集中删除
                self.dataset = np.delete(self.dataset, delete_list, 0)
                canopies.append((current_center, current_center_list))        
          return canopies

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In order to facilitate subsequent data visualization, the canopies I define here are an array. Of course, dict can also be used.
6.main() function

def main():
    t1 = 0.6
    t2 = 0.4
    gc = Canopy(dataset)
    gc.setThreshold(t1, t2)
    canopies = gc.clustering()
    print(&#39;Get %s initial centers.&#39; % len(canopies))    
    #showCanopy(canopies, dataset, t1, t2)

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Canopy clustering visualization code

def showCanopy(canopies, dataset, t1, t2):
    fig = plt.figure()
    sc = fig.add_subplot(111)
    colors = [&#39;brown&#39;, &#39;green&#39;, &#39;blue&#39;, &#39;y&#39;, &#39;r&#39;, &#39;tan&#39;, &#39;dodgerblue&#39;, &#39;deeppink&#39;, &#39;orangered&#39;, &#39;peru&#39;, &#39;blue&#39;, &#39;y&#39;, &#39;r&#39;,              &#39;gold&#39;, &#39;dimgray&#39;, &#39;darkorange&#39;, &#39;peru&#39;, &#39;blue&#39;, &#39;y&#39;, &#39;r&#39;, &#39;cyan&#39;, &#39;tan&#39;, &#39;orchid&#39;, &#39;peru&#39;, &#39;blue&#39;, &#39;y&#39;, &#39;r&#39;, &#39;sienna&#39;]
    markers = [&#39;*&#39;, &#39;h&#39;, &#39;H&#39;, &#39;+&#39;, &#39;o&#39;, &#39;1&#39;, &#39;2&#39;, &#39;3&#39;, &#39;,&#39;, &#39;v&#39;, &#39;H&#39;, &#39;+&#39;, &#39;1&#39;, &#39;2&#39;, &#39;^&#39;,               &#39;<&#39;, &#39;>&#39;, &#39;.&#39;, &#39;4&#39;, &#39;H&#39;, &#39;+&#39;, &#39;1&#39;, &#39;2&#39;, &#39;s&#39;, &#39;p&#39;, &#39;x&#39;, &#39;D&#39;, &#39;d&#39;, &#39;|&#39;, &#39;_&#39;]    for i in range(len(canopies)):
        canopy = canopies[i]
        center = canopy[0]
        components = canopy[1]
        sc.plot(center[0], center[1], marker=markers[i],
                color=colors[i], markersize=10)
        t1_circle = plt.Circle(
            xy=(center[0], center[1]), radius=t1, color=&#39;dodgerblue&#39;, fill=False)
        t2_circle = plt.Circle(
            xy=(center[0], center[1]), radius=t2, color=&#39;skyblue&#39;, alpha=0.2)
        sc.add_artist(t1_circle)
        sc.add_artist(t2_circle)        for component in components:
            sc.plot(component[0], component[1],
                    marker=markers[i], color=colors[i], markersize=1.5)
    maxvalue = np.amax(dataset)
    minvalue = np.amin(dataset)
    plt.xlim(minvalue - t1, maxvalue + t1)
    plt.ylim(minvalue - t1, maxvalue + t1)
    plt.show()

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The rendering is as follows:

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