Mahout seq2sparse
生成一堆sequenceFiles,如完整描述的here所示。我想使用具有以下格式的矢量化文档:<文本,VectorWritable>(docID,TF-IDF vector ),并从TF-IDF vector 中创建一个JavaRDD<Vector>
。有人可以指导我吗?
最佳答案
此信息可以随时通过in the documentation获得。
Preprocessing: For a set of Sequence File Formatted documents in PATH_TO_SEQUENCE_FILES the mahout seq2sparse command performs the TF-IDF transformations (-wt tfidf option) and L2 length normalization (-n 2 option) as follows:
$ mahout seq2sparse -i ${PATH_TO_SEQUENCE_FILES} -o ${PATH_TO_TFIDF_VECTORS} -nv -n 2 -wt tfidf Training: The model is then trained using mahout spark-trainnb. The default is to train a Bayes model. The -c option is given to train
a CBayes model:
$ mahout spark-trainnb -i ${PATH_TO_TFIDF_VECTORS} -o ${PATH_TO_MODEL} -ow -c Label Assignment/Testing: Classification and testing on a holdout set can then be performed via mahout spark-testnb. Again, the -c
option indicates that the model is CBayes:
$ mahout spark-testnb -i ${PATH_TO_TFIDF_TEST_VECTORS} -m ${PATH_TO_MODEL} -ow -c
查看
mahout
command script,我们看到它实际上是在使用org.apache.mahout.drivers.TrainNBDriver
类。我们对TFIDF
类型的parts using和<Text, VectorWritable>
vector 感兴趣: /** Read the training set from inputPath/part-x-00000 sequence file of form <Text,VectorWritable> */
private def readTrainingSet: DrmLike[_]= {
val inputPath = parser.opts("input").asInstanceOf[String]
val trainingSet= drm.drmDfsRead(inputPath)
trainingSet
}
override def process(): Unit = {
start()
val complementary = parser.opts("trainComplementary").asInstanceOf[Boolean]
val outputPath = parser.opts("output").asInstanceOf[String]
val trainingSet = readTrainingSet
val (labelIndex, aggregatedObservations) = SparkNaiveBayes.extractLabelsAndAggregateObservations(trainingSet)
val model = NaiveBayes.train(aggregatedObservations, labelIndex)
model.dfsWrite(outputPath)
stop()
}
如果我们仔细观察,就会发现
drm.drmDfsRead(inputPath)
调用正在转换输入。然后将像这样进行转换(例如SparkEngine bindings) /**
* Load DRM from hdfs (as in Mahout DRM format)
*
* @param path
* @param sc spark context (wanted to make that implicit, doesn't work in current version of
* scala with the type bounds, sorry)
*
* @return DRM[Any] where Any is automatically translated to value type
*/
def drmDfsRead (path: String, parMin:Int = 0)(implicit sc: DistributedContext): CheckpointedDrm[_] = {
val drmMetadata = hdfsUtils.readDrmHeader(path)
val k2vFunc = drmMetadata.keyW2ValFunc
// Load RDD and convert all Writables to value types right away (due to reuse of writables in
// Hadoop we must do it right after read operation).
val rdd = sc.sequenceFile(path, classOf[Writable], classOf[VectorWritable], minPartitions = parMin)
// Immediately convert keys and value writables into value types.
.map { case (wKey, wVec) => k2vFunc(wKey) -> wVec.get()}
// Wrap into a DRM type with correct matrix row key class tag evident.
drmWrap(rdd = rdd, cacheHint = CacheHint.NONE)(drmMetadata.keyClassTag.asInstanceOf[ClassTag[Any]])
}
关于hadoop - 如何在Apache Spark中使用Mahout seq2sparse输出的矢量化文档,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/29153066/