我正在尝试在维基百科XML转储上执行LDA。在获得原始文本的RDD后，我正在创建一个数据帧并通过Tokenizer，StopWords和CountVectorizer管道对其进行转换。我打算将 Vectors 输出的 RDD 从 CountVectorizer 传递到 MLLib 中的 OnlineLDA。这是我的代码：
// Configure an ML pipeline
RegexTokenizer tokenizer = new RegexTokenizer()
.setInputCol("text")
.setOutputCol("words");
StopWordsRemover remover = new StopWordsRemover()
.setInputCol("words")
.setOutputCol("filtered");
CountVectorizer cv = new CountVectorizer()
.setVocabSize(vocabSize)
.setInputCol("filtered")
.setOutputCol("features");
Pipeline pipeline = new Pipeline()
.setStages(new PipelineStage[] {tokenizer, remover, cv});
// Fit the pipeline to train documents.
PipelineModel model = pipeline.fit(fileDF);
JavaRDD<Vector> countVectors = model.transform(fileDF)
.select("features").toJavaRDD()
.map(new Function<Row, Vector>() {
public Vector call(Row row) throws Exception {
Object[] arr = row.getList(0).toArray();
double[] features = new double[arr.length];
int i = 0;
for(Object obj : arr){
features[i++] = (double)obj;
}
return Vectors.dense(features);
}
});
由于该行，我收到类转换异常
Object[] arr = row.getList(0).toArray();
Caused by: java.lang.ClassCastException: org.apache.spark.mllib.linalg.SparseVector cannot be cast to scala.collection.Seq
at org.apache.spark.sql.Row$class.getSeq(Row.scala:278)
at org.apache.spark.sql.catalyst.expressions.GenericRow.getSeq(rows.scala:192)
at org.apache.spark.sql.Row$class.getList(Row.scala:286)
at org.apache.spark.sql.catalyst.expressions.GenericRow.getList(rows.scala:192)
at xmlProcess.ParseXML$2.call(ParseXML.java:142)
at xmlProcess.ParseXML$2.call(ParseXML.java:1)
我在这里找到了 Scala 语法来做到这一点，但找不到任何在 Java 中做到这一点的例子。我尝试了row.getAs[Vector]（0），但这只是Scala语法。有什么方法可以在Java中做到这一点吗？
所以我可以用一个简单的矢量模型来做到这一点。我不知道为什么我没有先尝试简单的事情！
JavaRDD<Vector> countVectors = model.transform(fileDF)
.select("features").toJavaRDD()
.map(new Function<Row, Vector>() {
public Vector call(Row row) throws Exception {
return (Vector)row.get(0);
}
});
您不需要将DataFrameDataSet转换为JavaRDD，它就可以与LDA一起工作。经过几个小时的摆弄，我终于在Scala中使用了原生的rdd。
相关进口:
import org.apache.spark.ml.feature.{CountVectorizer, RegexTokenizer, StopWordsRemover}
import org.apache.spark.ml.linalg.{Vector => MLVector}
import org.apache.spark.mllib.clustering.{LDA, OnlineLDAOptimizer}
import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.{Row, SparkSession}
以下代码片段的其余部分与本示例相同:
val cvModel = new CountVectorizer()
.setInputCol("filtered")
.setOutputCol("features")
.setVocabSize(vocabSize)
.fit(filteredTokens)
val countVectors = cvModel
.transform(filteredTokens)
.select("docId","features")
.rdd.map { case Row(docId: String, features: MLVector) =>
(docId.toLong, Vectors.fromML(features))
}
val mbf = {
// add (1.0 / actualCorpusSize) to MiniBatchFraction be more robust on tiny datasets.
val corpusSize = countVectors.count()
2.0 / maxIterations + 1.0 / corpusSize
}
val lda = new LDA()
.setOptimizer(new OnlineLDAOptimizer().setMiniBatchFraction(math.min(1.0, mbf)))
.setK(numTopics)
.setMaxIterations(2)
.setDocConcentration(-1) // use default symmetric document-topic prior
.setTopicConcentration(-1) // use default symmetric topic-word prior
val startTime = System.nanoTime()
val ldaModel = lda.run(countVectors)
val elapsed = (System.nanoTime() - startTime) / 1e9
/**
* Print results.
*/
// Print training time
println(s"Finished training LDA model. Summary:")
println(s"Training time (sec)\t$elapsed")
println(s"==========")
感谢此处代码的作者。
