大师兄的数据分析学习笔记(三十三）：模型评估（二）
大师兄的数据分析学习笔记(三十五）：总结
三、聚类模型评估
- 聚类模型评估的两个关键指标分别是RMS和轮廓系数：
- RMS(Root Mean Square)：
RMS越小聚类效果越好，越大说明每个类和中心距离越远，效果较差。- 轮廓系数：
a(i)为样本i与簇内其它样本的平均距离；
b(i)为样本i与其它某簇样本的平均距离，多个簇b(i)取最小。
轮廓系数约接近1聚类效果越好，约接近-1效果越差。
>>>import numpy as np
>>>import matplotlib.pyplot as plt
>>>from sklearn.datasets import make_circles,make_blobs,make_moons
>>>from sklearn.cluster import AgglomerativeClustering,DBSCAN,KMeans
>>>from sklearn.metrics import silhouette_score
>>>n_samples = 1000
>>>circles = make_circles(n_samples=n_samples,factor=0.5,noise=0.05)
>>>moons = make_moons(n_samples=n_samples,noise=0.05)
>>>blobs = make_blobs(n_samples=n_samples,random_state=8,center_box=(-1,1),cluster_std=0.1)
>>>random_data = np.random.rand(n_samples,2),None
>>>colours = "bgrcmyk"
>>>data = [circles,moons,blobs,random_data]
>>>models = [("None",None),("KMeans",KMeans(n_clusters=3)),("DBSCAN",DBSCAN(min_samples=3,eps=0.2)),("Agglomerative",AgglomerativeClustering(n_clusters=3,linkage="ward"))]
>>>fig = plt.figure()
>>>for inx,clt in enumerate(models):
>>> clt_name,clt_entity = clt
>>> for i,dataset in enumerate(data):
>>> X,Y = dataset
>>> if not clt_entity:
>>> clt_res = [0 for item in range(len(X))]
>>> else:
>>> clt_entity.fit(X)
>>> clt_res = clt_entity.labels_.astype(int)
>>> fig.add_subplot(len(models),len(data),inx*len(data)+i+1)
>>> plt.title(clt_name)
>>> try:
>>> print(clt_name,i,silhouette_score(X,clt_res))
>>> except Exception as e:
>>> ...
>>> [plt.scatter(X[p,0],X[p,1],color=colours[clt_res[p]]) for p in range(len(X))]
>>>plt.show()
KMeans 0 0.3916325558787827
KMeans 1 0.42678036476765324
KMeans 2 0.8260921886020176
KMeans 3 0.3858371416901683
DBSCAN 0 0.11129304972156648
DBSCAN 1 0.33236515056456195
DBSCAN 2 0.8260921886020176
Agglomerative 0 0.34487993798844285
Agglomerative 1 0.3885474739703062
Agglomerative 2 0.8260921886020176
Agglomerative 3 0.366701613284125
