使用不同颜色和标签的聚类

Question

我正在研究文本聚类。我需要使用不同的颜色绘制数据。我使用 kmeans 聚类方法和 tf-idf 相似性方法。

kmeans_labels =KMeans(n_clusters=3).fit(vectorized_text).labels_

pipeline = Pipeline([('tfidf', TfidfVectorizer())])
X = pipeline.fit_transform(X_train['Sentences']).todense()

pca = PCA(n_components=2).fit(X)
data2D = pca.transform(X)

plt.scatter(data2D[:,0], data2D[:,1])

kmeans.fit(X)
centers2D = pca.transform(kmeans.cluster_centers_)
labels=np.array([kmeans.labels_])

目前，我的输出如下：有一些元素是测试。我需要添加标签（它们是字符串）并按簇区分点：每个簇都应该有自己的颜色以使 reader 易于分析图表。

能否请您告诉我如何更改我的代码以包含标签和颜色？我认为任何例子都会很棒。

我的数据集样本是（上面的输出是从不同的样本生成的）：

句子

Where do we do list them? ...
Make me a list of the things we would need and I'll take you into town. ...
Do you have a list yet? ...
The first was a list for Howie. ...
You're not on my list tonight. ...
I'm gonna print this list on my computer, given you're always bellyaching about my writing.

Answer 1

使用您的代码尝试以下操作：

kmeans_labels =KMeans(n_clusters=3).fit(vectorized_text).labels_

pipeline = Pipeline([('tfidf', TfidfVectorizer())])
X = pipeline.fit_transform(X_train['Sentences']).todense()

pca = PCA(n_components=2).fit(X)
data2D = pca.transform(X)

kmeans.fit(X)
centers2D = pca.transform(kmeans.cluster_centers_)
group = kmeans.labels_

cdict = {0: 'red', 1: 'blue', 2: 'green'}
ldict = {0: 'label_1', 1: 'label_2', 2: 'label_3'}

fig, ax = plt.subplots()
for g in np.unique(group):
    ix = np.where(group == g)
    ax.scatter(data2D[:,0][ix], data2D[:,1][ix], c=cdict[g], label=ldict[g], s=100)
ax.legend()
plt.show()

我假设你的 kmeans 有 n_clusters=3。 cdict 和 ldict 需要根据簇数进行相应设置。在这种情况下，集群 0 将是红色的，标签为 label_1，集群 1 将是蓝色的，标签为 label_2，依此类推。

编辑：我将 cdict 的键更改为从 0 开始。编辑 2：添加标签。

Answer 2

我们可以使用示例数据集：

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
from sklearn.cluster import KMeans
import matplotlib.cm as cm
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

newsgroups = fetch_20newsgroups(subset='train',
                                categories=['talk.religion.misc','sci.space', 'misc.forsale'])
X_train = newsgroups.data
y_train = newsgroups.target

pipeline = Pipeline([('tfidf', TfidfVectorizer(max_features=5000))])
X = pipeline.fit_transform(X_train).todense()

pca = PCA(n_components=2).fit(X)
data2D = pca.transform(X)

像你一样做 KMeans，获取集群和中心，所以只需为集群添加一个名称：

kmeans =KMeans(n_clusters=3).fit(X)
centers2D = pca.transform(kmeans.cluster_centers_)
labels=kmeans.labels_
cluster_name = ["Cluster"+str(i) for i in set(labels)]

您可以通过向 "c=" 提供集群并从 cm 调用颜色映射或定义您自己的映射来添加颜色：

plt.scatter(data2D[:,0], data2D[:,1],c=labels,cmap='Set3',alpha=0.7)
for i, txt in enumerate(cluster_name):
    plt.text(centers2D[i,0], centers2D[i,1],s=txt,ha="center",va="center")

你也可以考虑用seaborn:

sns.scatterplot(data2D[:,0], data2D[:, 1], hue=labels, legend='full',palette="Set1")

使用不同颜色和标签的聚类

Cluster using different colours and labels

python

cluster-analysis

matplotlib

tf-idf

k-means