Mallet:内存不足错误:Java 堆空间

Question

在 Mallet 中训练数据时，处理因OutOfMemoryError而停止。箱/木槌中的属性MEMORY已设置为 3GB。训练文件输出.mallet的大小仅为31 MB。我试图减小训练数据的大小。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)

我会感谢对这个问题的任何帮助或见解

编辑：这是我的垃圾箱/木槌文件。

#!/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" $CLASS "$@"

还值得一提的是，我的原始训练文件有 60,000 个项目。当我减少项目数(20,000 个实例)时，训练将正常运行，但使用大约 10GB RAM。

Answer 1

检查 bin/mallet 中对 Java 的调用并添加标志 -Xmx3g，确保其中没有另一个 Xmx;如果是这样，请编辑该标志)。

Answer 2

我通常会更改两个文件：木槌文件并将内存设置为最大

Mallet.batjava -Xmx%MALLET_MEMORY% -ea -Dfile.encoding=%MALLET_ENCODING% -classpath %MALLET_CLASSPATH% %CLASS% %MALLET_ARGS%

和

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

我用我想要的内存替换了粗体 %MALLET_MEMORY% 和 $MEMORY：例如 4G