Spark - 'LDAModel' 对象没有属性 'describeTopics'

Question

我目前使用的是 CDH Spark 1.5.0，Python 2.6.6 Hadoop 2.6

我正在尝试参考这个linkSaprk1.5.0-Latent Dirichlet allocation (LDA)

建立一个LDA模型

引用文档中的内容：

All of MLlib’s LDA models support:

• describeTopics: Returns topics as arrays of most important terms and term weights
• topicsMatrix: Returns a vocabSize by k matrix where each column is a topic

我想在 describeTopics 模式下实现 LDA

代码（可重现）：

from pyspark.mllib.clustering import LDA, LDAModel
from pyspark.mllib.linalg import Vectors
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext, Row

conf = SparkConf().setAppName("test").set("spark.executor.memory", "512m")
sc = SparkContext(conf = conf)
sc.setLogLevel('ERROR')
sqlContext = SQLContext(sc)


# Load and parse the data
data = sc.parallelize([[0,0,0,0,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,1,1,0,1],[0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,1,0,0,0,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,2,0,0,0,0,0,1,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0],[0,1,1,1,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,1,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],[1,0,0,0,1,0,0,0,0,0,1,0,0,1,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,1,0,1,0,0,0,0,1,0]])
#data = sc.textFile("file://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()
topics = ldaModel.describeTopics(maxTermsPerTopic = 10)

for topic in range(3):
    print("Topic " + str(topic) + ":")
    for word in range(0, ldaModel.vocabSize()):
    print(" " + str(topics[word][topic]))

但是我收到以下错误

AttributeError: 'LDAModel' object has no attribute 'describeTopics'

describeTopics 不被 Spark 支持？这里有什么遗漏吗？

Answer 1

这是预期的行为。 describeTopics 在 PySpark 中 MLLib 已在 Spark 1.6 中引入：

• SPARK-8467 在Python.
中添加LDAModel.describeTopics()
• describeTopics documentatation.
• describeTopics source.