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From Matthias Boehm <mboe...@gmail.com>
Subject Re: Passing a CoordinateMatrix to SystemML
Date Mon, 25 Dec 2017 13:35:23 GMT
ok that was very helpful - I just pushed two additional fixes which 
should resolve these issues. The underlying cause was an incorrect 
sparse row preallocation (to reduce GC overhead), which resulted in 
resizing issues for initial sizes of zero. These two patches fix the 
underlying issues, make both MCSR and COO more robust for such 
ultra-sparse cases, and improve the performance for converting 
ultra-sparse matrices. Thanks again for your help Anthony.

As a side note: our default block size is 1000 but converting to 1024 is 
fine if you also set 'sysml.defaultblocksize' to 1024; otherwise there 
will be an unnecessary reblock (with shuffle) from block size 1024 to 
1000 on the first access of this input.

Regards,
Matthias

On 12/25/2017 3:07 AM, Anthony Thomas wrote:
> Thanks Matthias - unfortunately I'm still running into an
> ArrayIndexOutOfBounds exception both in reading the file as IJV and when
> calling dataFrametoBinaryBlock. Just to confirm: I downloaded and compiled
> the latest version using:
>
> git clone https://github.com/apache/systemml
> cd systemml
> mvn clean package
>
> mvn -version
> Apache Maven 3.3.9
> Maven home: /usr/share/maven
> Java version: 1.8.0_151, vendor: Oracle Corporation
> Java home: /usr/lib/jvm/java-8-oracle/jre
> Default locale: en_US, platform encoding: UTF-8
> OS name: "linux", version: "4.4.0-103-generic", arch: "amd64", family: "unix"
>
> I have a simple driver script written in Scala which calls the API methods.
> I compile the script using SBT (version 1.0.4) and submit using
> spark-submit (spark version 2.2.0). Here's how I'm calling the methods:
>
>         val x = spark.read.parquet(inputPath).select(featureNames)
>         val mc = new MatrixCharacteristics(199563535L, 71403L, 1024, 1024,
> 2444225947L) // as far as I know 1024x1024 is default block size in sysml?
>         println("Reading Direct")
>         val xrdd = RDDConverterUtils.dataFrameToBinaryBlock(jsc, x, mc,
> false, true)
>         xrdd.count
>
> here is the stacktrace from calling dataFrameToBinaryBlock:
>
>  java.lang.ArrayIndexOutOfBoundsException: 0
>         at
> org.apache.sysml.runtime.matrix.data.SparseRowVector.append(SparseRowVector.java:196)
>         at
> org.apache.sysml.runtime.matrix.data.SparseBlockMCSR.append(SparseBlockMCSR.java:267)
>         at
> org.apache.sysml.runtime.matrix.data.MatrixBlock.appendValue(MatrixBlock.java:685)
>         at
> org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtils$DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:1067)
>         at
> org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtils$DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:999)
>         at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
>         at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
>         at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:797)
>         at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:797)
>         at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
>         at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>         at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>         at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
>         at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
>         at org.apache.spark.scheduler.Task.run(Task.scala:108)
>         at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
>         at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
>         at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
>         at java.lang.Thread.run(Thread.java:748)
>
> and here is the stacktrace from calling "read()" directly:
>
> java.lang.ArrayIndexOutOfBoundsException: 2
>         at
> org.apache.sysml.runtime.matrix.data.SparseBlockCOO.sort(SparseBlockCOO.java:399)
>         at
> org.apache.sysml.runtime.matrix.data.MatrixBlock.mergeIntoSparse(MatrixBlock.java:1784)
>         at
> org.apache.sysml.runtime.matrix.data.MatrixBlock.merge(MatrixBlock.java:1687)
>         at
> org.apache.sysml.runtime.instructions.spark.utils.RDDAggregateUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:627)
>         at
> org.apache.sysml.runtime.instructions.spark.utils.RDDAggregateUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:596)
>         at
> org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction2$1.apply(JavaPairRDD.scala:1037)
>         at
> org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:189)
>         at
> org.apache.spark.util.collection.ExternalSorter$$anonfun$5.apply(ExternalSorter.scala:188)
>         at
> org.apache.spark.util.collection.AppendOnlyMap.changeValue(AppendOnlyMap.scala:150)
>         at
> org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.changeValue(SizeTrackingAppendOnlyMap.scala:32)
>         at
> org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:194)
>         at
> org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:63)
>         at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
>         at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
>         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)
>         at java.lang.Thread.run(Thread.java:748)
>
> Best,
>
> Anthony
>
>
> On Sun, Dec 24, 2017 at 3:14 AM, Matthias Boehm <mboehm7@gmail.com> wrote:
>
>> Thanks again for catching this issue Anthony - this IJV reblock issue with
>> large ultra-sparse matrices is now fixed in master. It likely did not show
>> up on the 1% sample because the data was small enough to read it directly
>> into the driver.
>>
>> However, the dataFrameToBinaryBlock might be another issue that I could
>> not reproduce yet, so it would be very helpful if you could give it another
>> try. Thanks.
>>
>> Regards,
>> Matthias
>>
>>
>> On 12/24/2017 9:57 AM, Matthias Boehm wrote:
>>
>>> Hi Anthony,
>>>
>>> thanks for helping to debug this issue. There are no limits other than
>>> the dimensions and number of non-zeros being of type long. It sounds
>>> more like an issues of converting special cases of ultra-sparse
>>> matrices. I'll try to reproduce this issue and give an update as soon as
>>> I know more. In the meantime, could you please (a) also provide the
>>> stacktrace of calling dataFrameToBinaryBlock with SystemML 1.0, and (b)
>>> try calling your IJV conversion script via spark submit to exclude that
>>> this issue is API-related? Thanks.
>>>
>>> Regards,
>>> Matthias
>>>
>>> On 12/24/2017 1:40 AM, Anthony Thomas wrote:
>>>
>>>> Okay thanks for the suggestions - I upgraded to 1.0 and tried providing
>>>> dimensions and blocksizes to dataFrameToBinaryBlock both without
>>>> success. I
>>>> additionally wrote out the matrix to hdfs in IJV format and am still
>>>> getting the same error when calling "read()" directly in the DML.
>>>> However,
>>>> I created a 1% sample of the original data in IJV format and SystemML was
>>>> able to read the smaller file without any issue. This would seem to
>>>> suggest
>>>> that either there is some corruption in the full file or I'm running into
>>>> some limit. The matrix is on the larger side: 1.9e8 rows by 7e4 cols with
>>>> 2.4e9 nonzero values, but this seems like it should be well within the
>>>> limits of what SystemML/Spark can handle. I also checked for obvious data
>>>> errors (file is not 1 indexed or contains blank lines). In case it's
>>>> helpful, the stacktrace from reading the data from hdfs in IJV format is
>>>> attached. Thanks again for your help - I really appreciate it.
>>>>
>>>>  00:24:18 WARN TaskSetManager: Lost task 30.0 in stage 0.0 (TID 126,
>>>> 10.11.10.13, executor 0): java.lang.ArrayIndexOutOfBoundsException
>>>>         at java.lang.System.arraycopy(Native Method)
>>>>         at
>>>> org.apache.sysml.runtime.matrix.data.SparseBlockCOO.shiftRig
>>>> htByN(SparseBlockCOO.java:594)
>>>>
>>>>         at
>>>> org.apache.sysml.runtime.matrix.data.SparseBlockCOO.set(
>>>> SparseBlockCOO.java:323)
>>>>
>>>>         at
>>>> org.apache.sysml.runtime.matrix.data.MatrixBlock.mergeIntoSp
>>>> arse(MatrixBlock.java:1790)
>>>>
>>>>         at
>>>> org.apache.sysml.runtime.matrix.data.MatrixBlock.merge(Matri
>>>> xBlock.java:1736)
>>>>
>>>>         at
>>>> org.apache.sysml.runtime.instructions.spark.utils.RDDAggrega
>>>> teUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:627)
>>>>
>>>>         at
>>>> org.apache.sysml.runtime.instructions.spark.utils.RDDAggrega
>>>> teUtils$MergeBlocksFunction.call(RDDAggregateUtils.java:596)
>>>>
>>>>         at
>>>> org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFuncti
>>>> on2$1.apply(JavaPairRDD.scala:1037)
>>>>
>>>>         at
>>>> org.apache.spark.util.collection.ExternalSorter$$anonfun$5.
>>>> apply(ExternalSorter.scala:189)
>>>>
>>>>         at
>>>> org.apache.spark.util.collection.ExternalSorter$$anonfun$5.
>>>> apply(ExternalSorter.scala:188)
>>>>
>>>>         at
>>>> org.apache.spark.util.collection.AppendOnlyMap.changeValue(
>>>> AppendOnlyMap.scala:150)
>>>>
>>>>         at
>>>> org.apache.spark.util.collection.SizeTrackingAppendOnlyMap.c
>>>> hangeValue(SizeTrackingAppendOnlyMap.scala:32)
>>>>
>>>>         at
>>>> org.apache.spark.util.collection.ExternalSorter.insertAll(
>>>> ExternalSorter.scala:194)
>>>>
>>>>         at
>>>> org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortSh
>>>> uffleWriter.scala:63)
>>>>
>>>>         at
>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMap
>>>> Task.scala:96)
>>>>
>>>>         at
>>>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMap
>>>> Task.scala:53)
>>>>
>>>>         at org.apache.spark.scheduler.Task.run(Task.scala:108)
>>>>         at
>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
>>>>         at
>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPool
>>>> Executor.java:1149)
>>>>
>>>>         at
>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoo
>>>> lExecutor.java:624)
>>>>
>>>>         at java.lang.Thread.run(Thread.java:748)
>>>>
>>>> Anthony
>>>>
>>>>
>>>> On Sat, Dec 23, 2017 at 4:27 AM, Matthias Boehm <mboehm7@gmail.com>
>>>> wrote:
>>>>
>>>> Given the line numbers from the stacktrace, it seems that you use a
>>>>> rather
>>>>> old version of SystemML. Hence, I would recommend to upgrade to SystemML
>>>>> 1.0 or at least 0.15 first.
>>>>>
>>>>> If the error persists or you're not able to upgrade, please try to call
>>>>> dataFrameToBinaryBlock with provided matrix characteristics of
>>>>> dimensions
>>>>> and blocksizes. The issue you've shown usually originates from incorrect
>>>>> meta data (e.g., negative number of columns or block sizes), which
>>>>> prevents
>>>>> the sparse rows from growing to the necessary sizes.
>>>>>
>>>>> Regards,
>>>>> Matthias
>>>>>
>>>>> On 12/22/2017 10:42 PM, Anthony Thomas wrote:
>>>>>
>>>>> Hi Matthias,
>>>>>>
>>>>>> Thanks for the help! In response to your questions:
>>>>>>
>>>>>>    1. Sorry - this was a typo: the correct schema is: [y: int,
>>>>>> features:
>>>>>>    vector] - the column "features" was created using Spark's
>>>>>> VectorAssembler
>>>>>>    and the underlying type is an
>>>>>> org.apache.spark.ml.linalg.SparseVector.
>>>>>>    Calling x.schema results in: org.apache.spark.sql.types.StructType
>>>>>> =
>>>>>>    StructType(StructField(features,org.apache.spark.ml.
>>>>>>    linalg.VectorUDT@3bfc3ba7,true)
>>>>>>    2. "y" converts fine - it appears the only issue is with X. The
>>>>>> script
>>>>>>    still crashes when running "print(sum(X))". The full stack trace
is
>>>>>>    attached at the end of the message.
>>>>>>    3. Unfortunately, the error persists when calling
>>>>>>    RDDConverterUtils.dataFrameToBinaryBlock directly.
>>>>>>    4. Also just in case this matters: I'm packaging the script into
>>>>>> a jar
>>>>>>
>>>>>>    using SBT assembly and submitting via spark-submit.
>>>>>>
>>>>>> Here's an updated script:
>>>>>>
>>>>>>         val input_df = spark.read.parquet(inputPath)
>>>>>>         val x = input_df.select(featureNames)
>>>>>>         val y = input_df.select("y")
>>>>>>         val meta_x = new MatrixMetadata(DF_VECTOR)
>>>>>>         val meta_y = new MatrixMetadata(DF_DOUBLES)
>>>>>>
>>>>>>         val script_x = dml("print(sum(X))").in("X", x, meta_x)
>>>>>>         println("Reading X")
>>>>>>         val res_x = ml.execute(script_x)
>>>>>>
>>>>>> Here is the output of the runtime plan generated by SystemML:
>>>>>>
>>>>>> # EXPLAIN (RUNTIME):
>>>>>> # Memory Budget local/remote = 76459MB/?MB/?MB/?MB
>>>>>> # Degree of Parallelism (vcores) local/remote = 24/?
>>>>>> PROGRAM ( size CP/SP = 3/0 )
>>>>>> --MAIN PROGRAM
>>>>>> ----GENERIC (lines 1-2) [recompile=false]
>>>>>> ------CP uak+ X.MATRIX.DOUBLE _Var0.SCALAR.STRING 24
>>>>>> ------CP print _Var0.SCALAR.STRING.false _Var1.SCALAR.STRING
>>>>>> ------CP rmvar _Var0 _Var1
>>>>>>
>>>>>> And the resulting stack trace:
>>>>>>
>>>>>> 7/12/22 21:27:20 WARN TaskSetManager: Lost task 3.0 in stage 7.0
>>>>>> (TID 205,
>>>>>> 10.11.10.12, executor 3): java.lang.ArrayIndexOutOfBoundsException:
0
>>>>>>     at org.apache.sysml.runtime.matrix.data.SparseRow.append(
>>>>>> SparseRow.java:215)
>>>>>>     at org.apache.sysml.runtime.matrix.data.SparseBlockMCSR.
>>>>>> append(SparseBlockMCSR.java:253)
>>>>>>     at org.apache.sysml.runtime.matrix.data.MatrixBlock.
>>>>>> appendValue(MatrixBlock.java:663)
>>>>>>     at org.apache.sysml.runtime.instructions.spark.utils.RDDConvert
>>>>>> erUtils$
>>>>>> DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:1076)
>>>>>>     at org.apache.sysml.runtime.instructions.spark.utils.RDDConvert
>>>>>> erUtils$
>>>>>> DataFrameToBinaryBlockFunction.call(RDDConverterUtils.java:1008)
>>>>>>     at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.
>>>>>> apply(JavaRDDLike.scala:186)
>>>>>>     at org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.
>>>>>> apply(JavaRDDLike.scala:186)
>>>>>>     at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$
>>>>>> anonfun$apply$23.apply(RDD.scala:797)
>>>>>>     at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$
>>>>>> anonfun$apply$23.apply(RDD.scala:797)
>>>>>>     at org.apache.spark.rdd.MapPartitionsRDD.compute(
>>>>>> MapPartitionsRDD.scala:38)
>>>>>>     at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
>>>>>>     at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
>>>>>>     at org.apache.spark.scheduler.ShuffleMapTask.runTask(
>>>>>> ShuffleMapTask.scala:96)
>>>>>>     at org.apache.spark.scheduler.ShuffleMapTask.runTask(
>>>>>> ShuffleMapTask.scala:53)
>>>>>>     at org.apache.spark.scheduler.Task.run(Task.scala:108)
>>>>>>     at org.apache.spark.executor.Executor$TaskRunner.run(Executor.
>>>>>> scala:335)
>>>>>>     at java.util.concurrent.ThreadPoolExecutor.runWorker(
>>>>>> ThreadPoolExecutor.java:1149)
>>>>>>     at java.util.concurrent.ThreadPoolExecutor$Worker.run(
>>>>>> ThreadPoolExecutor.java:624)
>>>>>>     at java.lang.Thread.run(Thread.java:748)
>>>>>>
>>>>>> 17/12/22 21:27:21 ERROR TaskSetManager: Task 19 in stage 7.0 failed
4
>>>>>> times; aborting job
>>>>>> Exception in thread "main" org.apache.sysml.api.mlcontext
>>>>>> .MLContextException:
>>>>>> Exception when executing script
>>>>>>     at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.
>>>>>> java:311)
>>>>>>     at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.
>>>>>> java:280)
>>>>>>     at SystemMLMLAlgorithms$$anonfun$main$1.apply$mcVI$sp(systemml_
>>>>>> ml_algorithms.scala:63)
>>>>>>     at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:
>>>>>> 160)
>>>>>>     at SystemMLMLAlgorithms$.main(systemml_ml_algorithms.scala:60)
>>>>>>     at SystemMLMLAlgorithms.main(systemml_ml_algorithms.scala)
>>>>>>     at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>>>>>>     at sun.reflect.NativeMethodAccessorImpl.invoke(
>>>>>> NativeMethodAccessorImpl.java:62)
>>>>>>     at sun.reflect.DelegatingMethodAccessorImpl.invoke(
>>>>>> DelegatingMethodAccessorImpl.java:43)
>>>>>>     at java.lang.reflect.Method.invoke(Method.java:498)
>>>>>>     at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$
>>>>>> deploy$SparkSubmit$$runMain(SparkSubmit.scala:755)
>>>>>>     at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(
>>>>>> SparkSubmit.scala:180)
>>>>>>     at
>>>>>> org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:205)
>>>>>>     at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:
>>>>>> 119)
>>>>>>     at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
>>>>>> Caused by: org.apache.sysml.api.mlcontext.MLContextException:
>>>>>> Exception
>>>>>> occurred while executing runtime program
>>>>>>     at org.apache.sysml.api.mlcontext.ScriptExecutor.executeRuntime
>>>>>> Program(
>>>>>> ScriptExecutor.java:390)
>>>>>>     at org.apache.sysml.api.mlcontext.ScriptExecutor.
>>>>>> execute(ScriptExecutor.java:298)
>>>>>>     at org.apache.sysml.api.mlcontext.MLContext.execute(MLContext.
>>>>>> java:303)
>>>>>>     ... 14 more
>>>>>> Caused by: org.apache.sysml.runtime.DMLRuntimeException:
>>>>>> org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error
in
>>>>>> program block generated from statement block between lines 1 and
2 --
>>>>>> Error
>>>>>> evaluating instruction: CP°uak+°X·MATRIX·DOUBLE°_Var0·SCALAR·STRING°24
>>>>>>     at org.apache.sysml.runtime.controlprogram.Program.
>>>>>> execute(Program.java:130)
>>>>>>     at org.apache.sysml.api.mlcontext.ScriptExecutor.executeRuntime
>>>>>> Program(
>>>>>> ScriptExecutor.java:388)
>>>>>>     ... 16 more
>>>>>> ...
>>>>>>
>>>>>>
>>>>>>
>>>>>> On Fri, Dec 22, 2017 at 5:48 AM, Matthias Boehm <mboehm7@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>> well, let's do the following to figure this out:
>>>>>>
>>>>>>>
>>>>>>> 1) If the schema is indeed [label: Integer, features: SparseVector],
>>>>>>> please change the third line to val y = input_data.select("label").
>>>>>>>
>>>>>>> 2) For debugging, I would recommend to use a simple script like
>>>>>>> "print(sum(X));" and try converting X and y separately to isolate
the
>>>>>>> problem.
>>>>>>>
>>>>>>> 3) If it's still failing, it would be helpful to known (a) if
it's an
>>>>>>> issue of converting X, y, or both, as well as (b) the full stacktrace.
>>>>>>>
>>>>>>> 4) As a workaround you might also call our internal converter
directly
>>>>>>> via:
>>>>>>> RDDConverterUtils.dataFrameToBinaryBlock(jsc, df, mc, containsID,
>>>>>>> isVector),
>>>>>>> where jsc is the java spark context, df is the dataset, mc are
matrix
>>>>>>> characteristics (if unknown, simply use new MatrixCharacteristics()),
>>>>>>> containsID indicates if the dataset contains a column "__INDEX"
>>>>>>> with the
>>>>>>> row indexes, and isVector indicates if the passed datasets contains
>>>>>>> vectors
>>>>>>> or basic types such as double.
>>>>>>>
>>>>>>>
>>>>>>> Regards,
>>>>>>> Matthias
>>>>>>>
>>>>>>>
>>>>>>> On 12/22/2017 12:00 AM, Anthony Thomas wrote:
>>>>>>>
>>>>>>> Hi SystemML folks,
>>>>>>>
>>>>>>>>
>>>>>>>> I'm trying to pass some data from Spark to a DML script via
the
>>>>>>>> MLContext
>>>>>>>> API. The data is derived from a parquet file containing a
>>>>>>>> dataframe with
>>>>>>>> the schema: [label: Integer, features: SparseVector]. I am
doing the
>>>>>>>> following:
>>>>>>>>
>>>>>>>>         val input_data = spark.read.parquet(inputPath)
>>>>>>>>         val x = input_data.select("features")
>>>>>>>>         val y = input_data.select("y")
>>>>>>>>         val x_meta = new MatrixMetadata(DF_VECTOR)
>>>>>>>>         val y_meta = new MatrixMetadata(DF_DOUBLES)
>>>>>>>>         val script = dmlFromFile(s"${script_path}/script.dml").
>>>>>>>>                 in("X", x, x_meta).
>>>>>>>>                 in("Y", y, y_meta)
>>>>>>>>         ...
>>>>>>>>
>>>>>>>> However, this results in an error from SystemML:
>>>>>>>> java.lang.ArrayIndexOutOfBoundsException: 0
>>>>>>>> I'm guessing this has something to do with SparkML being
zero indexed
>>>>>>>> and
>>>>>>>> SystemML being 1 indexed. Is there something I should be
doing
>>>>>>>> differently
>>>>>>>> here? Note that I also tried converting the dataframe to
a
>>>>>>>> CoordinateMatrix
>>>>>>>> and then creating an RDD[String] in IJV format. That too
resulted in
>>>>>>>> "ArrayIndexOutOfBoundsExceptions." I'm guessing there's something
>>>>>>>> simple
>>>>>>>> I'm doing wrong here, but I haven't been able to figure out
exactly
>>>>>>>> what.
>>>>>>>> Please let me know if you need more information (I can send
along the
>>>>>>>> full
>>>>>>>> error stacktrace if that would be helpful)!
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>>
>>>>>>>> Anthony
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>
>>>>
>

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