我使用随机森林分类器作为文本数据集（约60Mb） .
我正在使用哈希TF制作我的功能 -
hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures")
features = hashingTF.transform(removeStopwords)
rb = RandomForestClassifier().setFeaturesCol("features").setLabelCol("labels")
之后，我正在制作管道并使用我的训练数据创建模型 . 但是，当我运行我的代码时，我得到以下错误 -
18/02/09 14:31:03 WARN TaskSetManager: Stage 9 contains a task of very large size (42550 KB). The maximum recommended task size is 100 KB.
18/02/09 14:47:20 ERROR Executor: Exception in task 0.0 in stage 10.0 (TID 10)
java.lang.OutOfMemoryError: Java heap space
at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57)
at java.nio.ByteBuffer.allocate(ByteBuffer.java:335)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anonfun$5.apply(ShuffleBlockFetcherIterator.scala:390)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anonfun$5.apply(ShuffleBlockFetcherIterator.scala:390)
at org.apache.spark.util.io.ChunkedByteBufferOutputStream.allocateNewChunkIfNeeded(ChunkedByteBufferOutputStream.scala:87)
at org.apache.spark.util.io.ChunkedByteBufferOutputStream.write(ChunkedByteBufferOutputStream.scala:75)
at org.apache.spark.util.Utils$$anonfun$copyStream$1.apply$mcJ$sp(Utils.scala:342)
at org.apache.spark.util.Utils$$anonfun$copyStream$1.apply(Utils.scala:327)
at org.apache.spark.util.Utils$$anonfun$copyStream$1.apply(Utils.scala:327)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1337)
at org.apache.spark.util.Utils$.copyStream(Utils.scala:348)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:395)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:59)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:154)
at org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:41)
at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:89)
at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:105)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
18/02/09 14:47:20 ERROR SparkUncaughtExceptionHandler: Uncaught exception in thread Thread[Executor task launch worker for task 10,5,main]
java.lang.OutOfMemoryError: Java heap space
at java.nio.HeapByteBuffer.<init>(HeapByteBuffer.java:57)
at java.nio.ByteBuffer.allocate(ByteBuffer.java:335)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anonfun$5.apply(ShuffleBlockFetcherIterator.scala:390)
at org.apache.spark.storage.ShuffleBlockFetcherIterator$$anonfun$5.apply(ShuffleBlockFetcherIterator.scala:390)
at org.apache.spark.util.io.ChunkedByteBufferOutputStream.allocateNewChunkIfNeeded(ChunkedByteBufferOutputStream.scala:87)
at org.apache.spark.util.io.ChunkedByteBufferOutputStream.write(ChunkedByteBufferOutputStream.scala:75)
at org.apache.spark.util.Utils$$anonfun$copyStream$1.apply$mcJ$sp(Utils.scala:342)
at org.apache.spark.util.Utils$$anonfun$copyStream$1.apply(Utils.scala:327)
at org.apache.spark.util.Utils$$anonfun$copyStream$1.apply(Utils.scala:327)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1337)
at org.apache.spark.util.Utils$.copyStream(Utils.scala:348)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:395)
at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:59)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.util.CompletionIterator.hasNext(CompletionIterator.scala:32)
at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
at org.apache.spark.util.collection.ExternalAppendOnlyMap.insertAll(ExternalAppendOnlyMap.scala:154)
at org.apache.spark.Aggregator.combineValuesByKey(Aggregator.scala:41)
at org.apache.spark.shuffle.BlockStoreShuffleReader.read(BlockStoreShuffleReader.scala:89)
at org.apache.spark.rdd.ShuffledRDD.compute(ShuffledRDD.scala:105)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
18/02/09 14:47:20 WARN TaskSetManager: Lost task 0.0 in stage 10.0 (TID 10, localhost, executor driver): java.lang.OutOfMemoryError: Java heap space
但是当我改变这一行时 hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=2000) 然后随机分类器正在工作 .
是否有任何人知道我需要做什么来解决这个错误，因为HashingTF的默认numFeatures是2 ^ 20而我将其限制为仅2000，这降低了我的准确性 .
我尝试使用Naive Bayes和LinearSVC的相同代码它在HashingTF中正常工作而不限制numFeatures，但我的准确率仅为60％ . 所以我想检查RFC以提高准确性 .
我甚至试图通过这个来增加堆大小 -
export _JAVA_OPTIONS=-Xmx4096m
但这对我没有帮助 .