Spark 中的潜在狄利克雷分配 (LDA)

Question

我正在尝试在 Spark 中编写一个程序来执行 Latent Dirichlet allocation (LDA)。此 Spark 文档 page 提供了一个很好的示例，用于对示例数据执行 LDA。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")

使用的样本输入（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

如何在包含文本数据而不是数字的文本数据文件中将程序修改为运行？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.

Answer 1

经过一些研究，我试图回答这个问题。下面是使用 Spark 对包含真实文本数据的文本文档执行 LDA 的示例代码。

from pyspark.sql import SQLContext, Row
from pyspark.ml.feature import CountVectorizer
from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vector, Vectors

path = "sample_text_LDA.txt"

data = sc.textFile(path).zipWithIndex().map(lambda (words,idd): Row(idd= idd, words = words.split(" ")))
docDF = spark.createDataFrame(data)
Vector = CountVectorizer(inputCol="words", outputCol="vectors")
model = Vector.fit(docDF)
result = model.transform(docDF)

corpus = result.select("idd", "vectors").rdd.map(lambda (x,y): [x,Vectors.fromML(y)]).cache()

# Cluster the documents into three topics using LDA
ldaModel = LDA.train(corpus, k=3,maxIterations=100,optimizer='online')
topics = ldaModel.topicsMatrix()
vocabArray = model.vocabulary

wordNumbers = 10  # number of words per topic
topicIndices = sc.parallelize(ldaModel.describeTopics(maxTermsPerTopic = wordNumbers))

def topic_render(topic):  # specify vector id of words to actual words
    terms = topic[0]
    result = []
    for i in range(wordNumbers):
        term = vocabArray[terms[i]]
        result.append(term)
    return result

topics_final = topicIndices.map(lambda topic: topic_render(topic)).collect()

for topic in range(len(topics_final)):
    print ("Topic" + str(topic) + ":")
    for term in topics_final[topic]:
        print (term)
    print ('\n')

题目中文本数据上提取的主题如下：

Spark 中的潜在狄利克雷分配 (LDA)

Latent Dirichlet allocation (LDA) in Spark

python

pyspark

lda