我正在使用python对Mnist数据库(http://yann.lecun.com/exdb/mnist/)进行k均值聚类。我能够成功地对数据进行群集,但是无法标记群集。意思是,我看不到哪个簇号包含什么数字。例如,群集5可以保留数字7。
完成k均值聚类后,我需要编写代码以正确标记聚类。还需要在代码中添加图例。
from __future__ import division, print_function, absolute_import
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D #only needed for 3D plots
#scikit learn
from sklearn.cluster import KMeans
#pandas to read excel file
import pandas
import xlrd
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
Links:
[MNIST Dataset] http://yann.lecun.com/exdb/mnist/
df = pandas.read_csv('test_encoded_with_label.csv',header=None,
delim_whitespace=True)
#df = pandas.read_excel('test_encoded_with_label.xls')
#print column names
print(df.columns)
df1 = df.iloc[:,0:2] #0 and 1, the last index is not used for iloc
labels = df.iloc[:,2]
labels = labels.values
dataset = df1.values
#train indices - depends how many samples
trainidx = np.arange(0,9999)
testidx = np.arange(0,9999)
train_data = dataset[trainidx,:]
test_data = dataset[testidx,:]
train_labels = labels[trainidx] #just 1D, no :
tpredct_labels = labels[testidx]
kmeans = KMeans(n_clusters=10, random_state=0).fit(train_data)
kmeans.labels_
#print(kmeans.labels_.shape)
plt.scatter(train_data[:,0],train_data[:,1], c=kmeans.labels_)
predct_labels = kmeans.predict(train_data)
print(predct_labels)
print('actual label', tpredct_labels)
centers = kmeans.cluster_centers_
print(centers)
plt.show()
答案 0 :(得分:1)
要创建标记以查找标记点的簇,可以使用annotate方法
这是在sklearn数字数据集上运行的示例代码,在这里我尝试标记所得聚类的质心。请注意,我仅出于说明目的将标签从0-9标记为:
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale
np.random.seed(42)
digits = load_digits()
data = scale(digits.data)
n_samples, n_features = data.shape
n_digits = len(np.unique(digits.target))
labels = digits.target
h = .02
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)
centroids = kmeans.cluster_centers_
plt_data = plt.scatter(reduced_data[:, 0], reduced_data[:, 1], c=kmeans.labels_, cmap=plt.cm.get_cmap('Spectral', 10))
plt.colorbar()
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x')
plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
'Centroids are marked with white cross')
plt.xlabel('component 1')
plt.ylabel('component 2')
labels = ['{0}'.format(i) for i in range(10)]
for i in range (10):
xy=(centroids[i, 0],centroids[i, 1])
plt.annotate(labels[i],xy, horizontalalignment='right', verticalalignment='top')
plt.show()
这是您得到的结果:
答案 1 :(得分:0)
要添加图例,try:
plt.scatter(train_data[:,0], train_data[:,1], c=kmeans.labels_, label=kmeans.labels_)
plt.legend()