10、数字数据集上的K-均值聚类
import numpy as np
from time import time
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
X_digits,y_digits=load_digits(return_X_y=True)
data=scale(X_digits)
n_samples,n_features=data.shape
n_digits=len(np.unique(y_digits))
labels=y_digits
print(labels)
sample_size=300
print(82*'-')
print('init\ttime\tinertia\thome\tcompo\tv_meas\tars\tami\tailhouette')
def bench_k_means(estimator,name,data):
t0=time()
estimator.fit(data)
print('%-9s\t%.2fs\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
% (name,(time()-t0),estimator.inertia_,
metrics.homogeneity_score(labels,estimator.labels_),
metrics.completeness_score(labels,estimator.labels_),
metrics.v_measure_score(labels,estimator.labels_),
metrics.adjusted_rand_score(labels,estimator.labels_),
metrics.adjusted_mutual_info_score(labels,estimator.labels_),
metrics.silhouette_score(data,estimator.labels_,
metric='euclidean',
sample_size=sample_size)))
bench_k_means(KMeans(init='k-means++',n_clusters=n_digits,n_init=10),name='k-means++',data=data)
bench_k_means(KMeans(init='random',n_clusters=n_digits,n_init=10),name='random',data=data)
#上面初始质心是确定的，把初始质心设定为1进行测试
pca=PCA(n_components=n_digits).fit(data)
bench_k_means(KMeans(n_clusters=n_digits,init='k-means++',n_init=1),name='pca_based',data=data)
print(82*'-')
#在降维的数据上图形化显示
reduced_data=PCA(n_components=2).fit_transform(data)
kmeans=KMeans(init='k-means++',n_clusters=n_digits,n_init=10)
kmeans.fit(reduced_data)
#网格步长
h=0.02
#分配颜色到决策边界。
x_min,x_max=reduced_data[:,0].min()-1,reduced_data[:,0].max()+1
y_min,y_max=reduced_data[:,1].min()-1,reduced_data[:,1].max()+1
xx,yy=np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))
# 获取网格中每个点的标签
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# 将结果放入彩色图中
Z = Z.reshape(xx.shape)
plt.figure(1)
plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# 用白色X画出中心体
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=10)
plt.title('数字数据集上的K-均值聚类 \n''PCA约简数据\n'
'白色X标出中心体')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()