在火花LDA preparing数据 [英] Preparing data for LDA in spark

问题描述

我工作的实施星火LDA模型（通过斯卡拉API），和我有与我的数据所需的格式化步骤的麻烦。我的原始数据（存储在文本文件）是在以下格式，本质上是一个令牌和它们所对应的文件列表。一个简单的例子：

I'm working on implementing a Spark LDA model (via the Scala API), and am having trouble with the necessary formatting steps for my data. My raw data (stored in a text file) is in the following format, essentially a list of tokens and the documents they correspond to. A simplified example:

doc XXXXX   term    XXXXX
1   x       'a'     x
1   x       'a'     x
1   x       'b'     x
2   x       'b'     x
2   x       'd'     x
...

凡XXXXX列是我不关心垃圾数据。我意识到这是存储语料库数据的非典型的方式，但它是我有什么。正如我希望从这个例子清楚，有每行的标记的原始数据（所以如果一个给定词出现在文档中的5倍，对应5行文字）。

Where the XXXXX columns are garbage data I don't care about. I realize this is an atypical way of storing corpus data, but it's what I have. As is I hope is clear from the example, there's one line per token in the raw data (so if a given term appears 5 times in a document, that corresponds to 5 lines of text).

在任何情况下，我需要格式化这个数据作为稀疏长期频率向量运行星火LDA模型，但我不熟悉斯卡拉因此遇到了一些麻烦。

In any case, I need to format this data as sparse term-frequency vectors for running a Spark LDA model, but am unfamiliar with Scala so having some trouble.

我开始：

import org.apache.spark.mllib.clustering.{LDA, DistributedLDAModel}
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.rdd.RDD

val corpus:RDD[Array[String]] = sc.textFile("path/to/data")
    .map(_.split('\t')).map(x => Array(x(0),x(2)))

然后我得到的词汇数据，我需要生成稀疏向量：

And then I get the vocabulary data I'll need to generate the sparse vectors:

val vocab: RDD[String] = corpus.map(_(1)).distinct()
val vocabMap: Map[String, Int] = vocab.collect().zipWithIndex.toMap

我不知道是用在这里，这样我结束了对每个文件，然后我就可以送入LDA模型稀疏词频向量的正确映射功能。我想我需要这些方针的东西...

What I don't know is the proper mapping function to use here such that I end up with a sparse term frequency vector for each document that I can then feed into the LDA model. I think I need something along these lines...

val documents: RDD[(Long, Vector)] = corpus.groupBy(_(0)).zipWithIndex
    .map(x =>(x._2,Vectors.sparse(vocabMap.size, ???)))

在这一点，我可以运行的实际LDA：

At which point I can run the actual LDA:

val lda = new LDA().setK(n_topics)
val ldaModel = lda.run(documents)

基本上，我不什么函数适用于每个组，这样我可以养活词频数据（presumably为地图？）成疏向量。换句话说，我怎么填写 ??? 在上面的code段，以达到预期的效果？

Basically, I don't what function to apply to each group so that I can feed term frequency data (presumably as a map?) into a sparse vector. In other words, how do I fill in the ??? in the code snippet above to achieve the desired effect?

推荐答案

要处理这种方式一：

• 确保该火花CSV 封装



• 将数据加载到数据框中，并选择感兴趣的列

• make sure that spark-csv package is available

• load data into DataFrame and select columns of interest

val df = sqlContext.read
    .format("com.databricks.spark.csv")
    .option("header", "true")
    .option("inferSchema", "true") // Optional, providing schema is prefered
    .option("delimiter", "\t")
    .load("foo.csv")
    .select($"doc".cast("long").alias("doc"), $"term")

指数词列：

import org.apache.spark.ml.feature.StringIndexer

val indexer = new StringIndexer()
  .setInputCol("term")
  .setOutputCol("termIndexed")

val indexed = indexer.fit(df)
  .transform(df)
  .drop("term")
  .withColumn("termIndexed", $"termIndexed".cast("integer"))
  .groupBy($"doc", $"termIndexed")
  .agg(count(lit(1)).alias("cnt").cast("double"))

转换为 PairwiseRDD

import org.apache.spark.sql.Row

val pairs = indexed.map{case Row(doc: Long, term: Int, cnt: Double) => 
  (doc, (term, cnt))}

由DOC组：

group by doc:

val docs = pairs.groupByKey

创建特征向量

create feature vectors

import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.functions.max

val n = indexed.select(max($"termIndexed")).first.getInt(0) + 1

val docsWithFeatures = docs.mapValues(vs => Vectors.sparse(n, vs.toSeq))

现在你把所有你需要创建 LabeledPoints 或应用额外的处理

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