07 聚类算法 - 代码案例三 - K-Means算法和Mini Batch K-Means算法效果评估

03 聚类算法 - K-means聚类
04 聚类算法 - 代码案例一 - K-means聚类
05 聚类算法 - 二分K-Means、K-Means++、K-Means||、Canopy、Mini Batch K-Means算法
06 聚类算法 - 代码案例二 - K-Means算法和Mini Batch K-Means算法比较

需求： 基于scikit包中的创建模拟数据的API创建聚类数据，对K-Means算法和Mini Batch K-Means算法构建的模型进行评估。

常规操作：

import time
import numpy as np  
import matplotlib.pyplot as plt  
import matplotlib as mpl
from sklearn.cluster import MiniBatchKMeans, KMeans 
from sklearn import metrics
from sklearn.metrics.pairwise import pairwise_distances_argmin  
from sklearn.datasets.samples_generator import make_blobs  

## 设置属性防止中文乱码
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False

一、构建数据

centers = [[1, 1], [-1, -1], [1, -1]] 
clusters = len(centers)       

X, Y = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7, random_state=28) 
Y # 在实际工作中是人工给定的，专门用于判断聚类的效果的一个值

array([2, 0, 0, ..., 2, 2, 1])

二、构建k-means++模型

k_means = KMeans(init='k-means++', n_clusters=clusters, random_state=28)
t0 = time.time() 
k_means.fit(X)  
km_batch = time.time() - t0  
print ("K-Means算法模型训练消耗时间:%.4fs" % km_batch)

K-Means算法模型训练消耗时间:0.1211s

三、构建Mini Batch K-Means模型

batch_size = 100
mbk = MiniBatchKMeans(init='k-means++', n_clusters=clusters, 
    batch_size=batch_size, random_state=28)  
t0 = time.time()  
mbk.fit(X)  
mbk_batch = time.time() - t0  
print ("Mini Batch K-Means算法模型训练消耗时间:%.4fs" % mbk_batch)

Mini Batch K-Means算法模型训练消耗时间:0.0991s

km_y_hat = k_means.labels_
mbkm_y_hat = mbk.labels_
print(km_y_hat) # 样本所属的类别

[0 2 2 ... 1 1 0]

k_means_cluster_centers = k_means.cluster_centers_
mbk_means_cluster_centers = mbk.cluster_centers_
print ("K-Means算法聚类中心点:\ncenter=", k_means_cluster_centers)
print ("Mini Batch K-Means算法聚类中心点:\ncenter=", mbk_means_cluster_centers)
order = pairwise_distances_argmin(k_means_cluster_centers,  
                                  mbk_means_cluster_centers) 
order

K-Means算法聚类中心点:
center= [[-1.0600799 -1.05662982]
[ 1.02975208 -1.07435837]
[ 1.01491055 1.02216649]]
Mini Batch K-Means算法聚类中心点:
center= [[ 0.99602094 1.10688195]
[-1.00828286 -1.05983915]
[ 1.07892315 -0.94286826]]
array([1, 2, 0], dtype=int64)

效果评估:

score_funcs = [
    metrics.adjusted_rand_score,#ARI
    metrics.v_measure_score,#均一性和完整性的加权平均
    metrics.adjusted_mutual_info_score,#AMI
    metrics.mutual_info_score,#互信息
]

迭代对每个评估函数进行评估操作

for score_func in score_funcs:
    t0 = time.time()
    km_scores = score_func(Y,km_y_hat)
    print("K-Means算法:%s评估函数计算结果值:%.5f；计算消耗时间:%0.3fs" % 
      (score_func.__name__,km_scores, time.time() - t0))
    
    t0 = time.time()
    mbkm_scores = score_func(Y,mbkm_y_hat)
    print("Mini Batch K-Means算法:%s评估函数计算结果值:%.5f；计算消耗时间:%0.3fs\n" % 
      (score_func.__name__,mbkm_scores, time.time() - t0))

08 聚类算法 - 聚类算法的衡量指标