Mallet: OutOfMemoryError: Java堆空间

Question

当在Mallet中训练数据时，由于OutOfMemoryError而停止处理。bin/mallet中的属性MEMORY已设置为3 3GB。训练文件output.mallet的大小只有31MB。我已经尝试减少训练数据的大小。但它仍然抛出相同的错误：

a161115@a161115-Inspiron-3250:~/dev/test_models/Mallet$ bin/mallet train-classifier --input output.mallet --trainer NaiveBayes --training-portion 0.0001 --num-trials 10
Training portion = 1.0E-4
Unlabeled training sub-portion = 0.0
Validation portion = 0.0
Testing portion = 0.9999

-------------------- Trial 0  --------------------

Trial 0 Training NaiveBayesTrainer with 7 instances
Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
        at cc.mallet.types.Multinomial$Estimator.setAlphabet(Multinomial.java:309)
        at cc.mallet.classify.NaiveBayesTrainer.setup(NaiveBayesTrainer.java:251)
        at cc.mallet.classify.NaiveBayesTrainer.trainIncremental(NaiveBayesTrainer.java:200)
        at cc.mallet.classify.NaiveBayesTrainer.train(NaiveBayesTrainer.java:193)
        at cc.mallet.classify.NaiveBayesTrainer.train(NaiveBayesTrainer.java:59)
        at cc.mallet.classify.tui.Vectors2Classify.main(Vectors2Classify.java:415)

我希望对这个问题有任何帮助或见解。

编辑:这是我的bin/mallet文件。

#!/bin/bash


malletdir=`dirname $0`
malletdir=`dirname $malletdir`

cp=$malletdir/class:$malletdir/lib/mallet-deps.jar:$CLASSPATH
#echo $cp

MEMORY=10g

CMD=$1
shift

help()
{
cat <<EOF
Mallet 2.0 commands: 

  import-dir         load the contents of a directory into mallet instances (one per file)
  import-file        load a single file into mallet instances (one per line)
  import-svmlight    load SVMLight format data files into Mallet instances
  info               get information about Mallet instances
  train-classifier   train a classifier from Mallet data files
  classify-dir       classify data from a single file with a saved classifier
  classify-file      classify the contents of a directory with a saved classifier
  classify-svmlight  classify data from a single file in SVMLight format
  train-topics       train a topic model from Mallet data files
  infer-topics       use a trained topic model to infer topics for new documents
  evaluate-topics    estimate the probability of new documents under a trained model
  prune              remove features based on frequency or information gain
  split              divide data into testing, training, and validation portions
  bulk-load          for big input files, efficiently prune vocabulary and import docs

Include --help with any option for more information
EOF
}

CLASS=

case $CMD in
        import-dir) CLASS=cc.mallet.classify.tui.Text2Vectors;;
        import-file) CLASS=cc.mallet.classify.tui.Csv2Vectors;;
        import-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Vectors;;
        info) CLASS=cc.mallet.classify.tui.Vectors2Info;;
        train-classifier) CLASS=cc.mallet.classify.tui.Vectors2Classify;;
        classify-dir) CLASS=cc.mallet.classify.tui.Text2Classify;;
        classify-file) CLASS=cc.mallet.classify.tui.Csv2Classify;;
        classify-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Classify;;
        train-topics) CLASS=cc.mallet.topics.tui.TopicTrainer;;
        infer-topics) CLASS=cc.mallet.topics.tui.InferTopics;;
        evaluate-topics) CLASS=cc.mallet.topics.tui.EvaluateTopics;;
        prune) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;
        split) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;
        bulk-load) CLASS=cc.mallet.util.BulkLoader;;
        run) CLASS=$1; shift;;
        *) echo "Unrecognized command: $CMD"; help; exit 1;;
esac

java -Xmx$MEMORY -ea -Djava.awt.headless=true -Dfile.encoding=UTF-8 -server -classpath "$cp" $CLASS "$@"

值得一提的是，我的原始培训文件有60,000个项目。当我减少项目(20,000个实例)的数量时，训练将像正常一样运行，但使用了大约10 of的RAM。