我试图在Spark中编写一个用于执行潜在Dirichlet分配(LDA)的程序。Spark文档page提供了对示例数据执行LDA的一个很好的示例。下面是程序
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vectors
# Load and parse the data
data = sc.textFile("data/mllib/sample_lda_data.txt")
parsedData = data.map(lambda line: Vectors.dense([float(x) for x in line.strip().split(' ')]))
# Index documents with unique IDs
corpus = parsedData.zipWithIndex().map(lambda x: [x[1], x[0]]).cache()
# Cluster the documents into three topics using LDA
ldaModel = LDA.train(corpus, k=3)
# Output topics. Each is a distribution over words (matching word count vectors)
print("Learned topics (as distributions over vocab of " + str(ldaModel.vocabSize())
+ " words):")
topics = ldaModel.topicsMatrix()
for topic in range(3):
print("Topic " + str(topic) + ":")
for word in range(0, ldaModel.vocabSize()):
print(" " + str(topics[word][topic]))
# Save and load model
ldaModel.save(sc, "target/org/apache/spark/PythonLatentDirichletAllocationExample/LDAModel")
sameModel = LDAModel\
.load(sc, "target/org/apache/spark/PythonLatentDirichletAllocationExample/LDAModel")
使用的示例输入(sample_lda_data.txt)如下
1 2 6 0 2 3 1 1 0 0 3
1 3 0 1 3 0 0 2 0 0 1
1 4 1 0 0 4 9 0 1 2 0
2 1 0 3 0 0 5 0 2 3 9
3 1 1 9 3 0 2 0 0 1 3
4 2 0 3 4 5 1 1 1 4 0
2 1 0 3 0 0 5 0 2 2 9
1 1 1 9 2 1 2 0 0 1 3
4 4 0 3 4 2 1 3 0 0 0
2 8 2 0 3 0 2 0 2 7 2
1 1 1 9 0 2 2 0 0 3 3
4 1 0 0 4 5 1 3 0 1 0
如何修改程序以在包含文本数据而不是数字的文本数据文件上运行?让示例文件包含以下文本。
Latent Dirichlet allocation (LDA) is a topic model which infers topics from a collection of text documents. LDA can be thought of as a clustering algorithm as follows:
Topics correspond to cluster centers, and documents correspond to examples (rows) in a dataset. Topics and documents both exist in a feature space, where feature vectors are vectors of word counts (bag of words). Rather than estimating a clustering using a traditional distance, LDA uses a function based on a statistical model of how text documents are generated.
在做了一些研究之后,我试图回答这个问题。下面是使用Spark对具有真实文本数据的文本文档执行LDA的示例代码。
如问题所述,从文本数据中提取的主题如下:
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