Python kmeans 散点图：为每个簇绘制不同的颜色
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kmeans scatter plot: plot different colors per cluster
提问by jxn
I am trying to do a scatter plot of a kmeans output which clusters sentences of the same topic together. The problem i am facing is plotting points that belongs to each cluster a certain color.
我正在尝试绘制 kmeans 输出的散点图，将同一主题的句子聚集在一起。我面临的问题是绘制属于每个集群的特定颜色的点。
sentence_list=["Hi how are you", "Good morning" ...] #i have 10 setences
km = KMeans(n_clusters=5, init='k-means++',n_init=10, verbose=1)
#with 5 cluster, i want 5 different colors
km.fit(vectorized)
km.labels_ # [0,1,2,3,3,4,4,5,2,5]
pipeline = Pipeline([('tfidf', TfidfVectorizer())])
X = pipeline.fit_transform(sentence_list).todense()
pca = PCA(n_components=2).fit(X)
data2D = pca.transform(X)
plt.scatter(data2D[:,0], data2D[:,1])
km.fit(X)
centers2D = pca.transform(km.cluster_centers_)
plt.hold(True)
labels=np.array([km.labels_])
print labels
My problem is in the bottom code for plt.scatter(); what should i use for the parameter c?
我的问题是在plt.scatter()的底部代码中；我应该为参数c使用什么？
- when i use
c=labelsin the code, i get this error:
- 当我
c=labels在代码中使用时，出现此错误：
number in rbg sequence outside 0-1 range
number in rbg sequence outside 0-1 range
2.When i set c= km.labels_instead, i get the error:
2.当我设置时c= km.labels_，出现错误：
ValueError: Color array must be two-dimensional
ValueError: Color array must be two-dimensional
plt.scatter(centers2D[:,0], centers2D[:,1],
marker='x', s=200, linewidths=3, c=labels)
plt.show()
采纳答案by Hannes Ovrén
The color=or c=property should be a matplotlib color, as mentioned in the documentation for plot.
该color=或c=属性应该是一个matplotlib颜色，作为文档中提到plot。
To map a integer label to a color just do
要将整数标签映射到颜色，只需执行
LABEL_COLOR_MAP = {0 : 'r',
1 : 'k',
....,
}
label_color = [LABEL_COLOR_MAP[l] for l in labels]
plt.scatter(x, y, c=label_color)
If you don't want to use the builtin one-character color names, you can use other color definitions. See the documentation on matplotlib colors.
如果不想使用内置的单字符颜色名称，可以使用其他颜色定义。请参阅有关 matplotlib 颜色的文档。
回答by user3805442
It should work:
它应该工作：
from sklearn.cluster import KMeans;
cluster = KMeans(10);
cluster.fit(M);
cluster.labels_;
plt.scatter(M[:,0],M[:,1], c=[matplotlib.cm.spectral(float(i) /10) for i in cluster.labels_]);
回答by Zhenye Na
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
# Scaling the data to normalize
model = KMeans(n_clusters=5).fit(X)
# Visualize it:
plt.figure(figsize=(8, 6))
plt.scatter(data[:,0], data[:,1], c=model.labels_.astype(float))
Now you have different color for different clusters.
现在不同的簇有不同的颜色。
