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From Bryan Cutler <cutl...@gmail.com>
Subject Re: Random Forest Classification
Date Fri, 08 Jul 2016 17:54:03 GMT
Hi Rich,

I looked at the notebook and it seems like you are fitting the
StringIndexer and VectorIndexer to only the training data, and it should
the the entire data set.  So if the training data does not include all of
the labels and an unknown label appears in the test data during evaluation,
then it will not know how to index it.  So your code should be like this,
fit with 'digits' instead of 'training'

val labelIndexer = new
StringIndexer().setInputCol("label").setOutputCol("indexedLabel").fit(digits)
// Automatically identify categorical features, and index them.
// Set maxCategories so features with > 4 distinct values are treated as
continuous.
val featureIndexer = new
VectorIndexer().setInputCol("features").setOutputCol("indexedFeatures").setMaxCategories(4).fit(digits)

Hope that helps!

On Fri, Jul 1, 2016 at 9:24 AM, Rich Tarro <richtarro@gmail.com> wrote:

> Hi Bryan.
>
> Thanks for your continued help.
>
> Here is the code shown in a Jupyter notebook. I figured this was easier
> that cutting and pasting the code into an email. If you  would like me to
> send you the code in a different format let, me know. The necessary data is
> all downloaded within the notebook itself.
>
>
> https://console.ng.bluemix.net/data/notebooks/fe7e578a-401f-4744-a318-b1b6bcf6f5f8/view?access_token=2f6df7b1dfcb3c1c2d94a794506bb282729dab8f05118fafe5f11886326e02fc
>
> A few additional pieces of information.
>
> 1. The training dataset is cached before training the model. If you do not
> cache the training dataset, the model will not train. The code
> model.transform(test) fails with a similar error. No other changes besides
> caching or not caching. Again, with the training dataset cached, the model
> can be successfully trained as seen in the notebook.
>
> 2. I have another version of the notebook where I download the same data
> in libsvm format rather than csv. That notebook works fine. All the code is
> essentially the same accounting for the difference in file formats.
>
> 3. I tested this same code on another Spark cloud platform and it displays
> the same symptoms when run there.
>
> Thanks.
> Rich
>
>
> On Wed, Jun 29, 2016 at 12:59 AM, Bryan Cutler <cutlerb@gmail.com> wrote:
>
>> Are you fitting the VectorIndexer to the entire data set and not just
>> training or test data?  If you are able to post your code and some data to
>> reproduce, that would help in troubleshooting.
>>
>> On Tue, Jun 28, 2016 at 4:40 PM, Rich Tarro <richtarro@gmail.com> wrote:
>>
>>> Thanks for the response, but in my case I reversed the meaning of
>>> "prediction" and "predictedLabel". It seemed to make more sense to me that
>>> way, but in retrospect, it probably only causes confusion to anyone else
>>> looking at this. I reran the code with all the pipeline stage inputs and
>>> outputs named exactly as in the Random Forest Classifier example to make
>>> sure I hadn't messed anything up when I renamed things. Same error.
>>>
>>> I'm still at the point where I can train the model and make predictions,
>>> but not able to get the MulticlassClassificationEvaluator to work on
>>> the DataFrame of predictions.
>>>
>>> Any other suggestions? Thanks.
>>>
>>>
>>>
>>> On Tue, Jun 28, 2016 at 4:21 PM, Rich Tarro <richtarro@gmail.com> wrote:
>>>
>>>> I created a ML pipeline using the Random Forest Classifier - similar to
>>>> what is described here except in my case the source data is in csv format
>>>> rather than libsvm.
>>>>
>>>>
>>>> https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier
>>>>
>>>> I am able to successfully train the model and make predictions (on test
>>>> data not used to train the model) as shown here.
>>>>
>>>> +------------+--------------+-----+----------+--------------------+
>>>> |indexedLabel|predictedLabel|label|prediction|            features|
>>>> +------------+--------------+-----+----------+--------------------+
>>>> |         4.0|           4.0|    0|         0|(784,[124,125,126...|
>>>> |         2.0|           2.0|    3|         3|(784,[119,120,121...|
>>>> |         8.0|           8.0|    8|         8|(784,[180,181,182...|
>>>> |         0.0|           0.0|    1|         1|(784,[154,155,156...|
>>>> |         3.0|           8.0|    2|         8|(784,[148,149,150...|
>>>> +------------+--------------+-----+----------+--------------------+
>>>> only showing top 5 rows
>>>>
>>>> However, when I attempt to calculate the error between the indexedLabel and
the precictedLabel using the MulticlassClassificationEvaluator, I get the NoSuchElementException
error attached below.
>>>>
>>>> val evaluator = new MulticlassClassificationEvaluator().setLabelCol("indexedLabel").setPredictionCol("predictedLabel").setMetricName("precision")
>>>> val accuracy = evaluator.evaluate(predictions)
>>>> println("Test Error = " + (1.0 - accuracy))
>>>>
>>>> What could be the issue?
>>>>
>>>>
>>>>
>>>> Name: org.apache.spark.SparkException
>>>> Message: Job aborted due to stage failure: Task 2 in stage 49.0 failed 10
times, most recent failure: Lost task 2.9 in stage 49.0 (TID 162, yp-spark-dal09-env5-0024):
java.util.NoSuchElementException: key not found: 132.0
>>>> 	at scala.collection.MapLike$class.default(MapLike.scala:228)
>>>> 	at scala.collection.AbstractMap.default(Map.scala:58)
>>>> 	at scala.collection.MapLike$class.apply(MapLike.scala:141)
>>>> 	at scala.collection.AbstractMap.apply(Map.scala:58)
>>>> 	at org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$10.apply(VectorIndexer.scala:331)
>>>> 	at org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$10.apply(VectorIndexer.scala:309)
>>>> 	at org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$11.apply(VectorIndexer.scala:351)
>>>> 	at org.apache.spark.ml.feature.VectorIndexerModel$$anonfun$11.apply(VectorIndexer.scala:351)
>>>> 	at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificPredicate.eval(Unknown
Source)
>>>> 	at org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:67)
>>>> 	at org.apache.spark.sql.catalyst.expressions.codegen.GeneratePredicate$$anonfun$create$2.apply(GeneratePredicate.scala:67)
>>>> 	at org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$2.apply(basicOperators.scala:74)
>>>> 	at org.apache.spark.sql.execution.Filter$$anonfun$2$$anonfun$apply$2.apply(basicOperators.scala:72)
>>>> 	at scala.collection.Iterator$$anon$14.hasNext(Iterator.scala:390)
>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>> 	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327)
>>>> 	at org.apache.spark.util.collection.ExternalSorter.insertAll(ExternalSorter.scala:189)
>>>> 	at org.apache.spark.shuffle.sort.SortShuffleWriter.write(SortShuffleWriter.scala:64)
>>>> 	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:73)
>>>> 	at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>>>> 	at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>> 	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>>> 	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1153)
>>>> 	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
>>>> 	at java.lang.Thread.run(Thread.java:785)
>>>>
>>>> Driver stacktrace:
>>>> StackTrace: org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
>>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
>>>> scala.Option.foreach(Option.scala:236)
>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
>>>> org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
>>>> java.lang.Thread.getStackTrace(Thread.java:1117)
>>>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
>>>> org.apache.spark.SparkContext.runJob(SparkContext.scala:1837)
>>>> org.apache.spark.SparkContext.runJob(SparkContext.scala:1850)
>>>> org.apache.spark.SparkContext.runJob(SparkContext.scala:1863)
>>>> org.apache.spark.SparkContext.runJob(SparkContext.scala:1934)
>>>> org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:927)
>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>>>> org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>>>> org.apache.spark.rdd.RDD.collect(RDD.scala:926)
>>>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:741)
>>>> org.apache.spark.rdd.PairRDDFunctions$$anonfun$collectAsMap$1.apply(PairRDDFunctions.scala:740)
>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
>>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)
>>>> org.apache.spark.rdd.RDD.withScope(RDD.scala:316)
>>>> org.apache.spark.rdd.PairRDDFunctions.collectAsMap(PairRDDFunctions.scala:740)
>>>> org.apache.spark.mllib.evaluation.MulticlassMetrics.tpByClass$lzycompute(MulticlassMetrics.scala:49)
>>>> org.apache.spark.mllib.evaluation.MulticlassMetrics.tpByClass(MulticlassMetrics.scala:45)
>>>> org.apache.spark.mllib.evaluation.MulticlassMetrics.precision$lzycompute(MulticlassMetrics.scala:142)
>>>> org.apache.spark.mllib.evaluation.MulticlassMetrics.precision(MulticlassMetrics.scala:142)
>>>> org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator.evaluate(MulticlassClassificationEvaluator.scala:84)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:59)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:64)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:66)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:68)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:70)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC$$iwC.<init>(<console>:72)
>>>> $line110.$read$$iwC$$iwC$$iwC$$iwC.<init>(<console>:74)
>>>> $line110.$read$$iwC$$iwC$$iwC.<init>(<console>:76)
>>>> $line110.$read$$iwC$$iwC.<init>(<console>:78)
>>>> $line110.$read$$iwC.<init>(<console>:80)
>>>> $line110.$read.<init>(<console>:82)
>>>> $line110.$read$.<init>(<console>:86)
>>>> $line110.$read$.<clinit>(<console>)
>>>> $line110.$eval$.<init>(<console>:7)
>>>> $line110.$eval$.<clinit>(<console>)
>>>> $line110.$eval.$print(<console>)
>>>> sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:95)
>>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:55)
>>>> java.lang.reflect.Method.invoke(Method.java:507)
>>>> org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
>>>> org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1346)
>>>> org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)
>>>> org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)
>>>> org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)
>>>> com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1$$anonfun$apply$3.apply(ScalaInterpreter.scala:296)
>>>> com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1$$anonfun$apply$3.apply(ScalaInterpreter.scala:291)
>>>> com.ibm.spark.global.StreamState$.withStreams(StreamState.scala:80)
>>>> com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1.apply(ScalaInterpreter.scala:290)
>>>> com.ibm.spark.interpreter.ScalaInterpreter$$anonfun$interpretAddTask$1.apply(ScalaInterpreter.scala:290)
>>>> com.ibm.spark.utils.TaskManager$$anonfun$add$2$$anon$1.run(TaskManager.scala:123)
>>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1153)
>>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
>>>> java.lang.Thread.run(Thread.java:785)
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>
>>
>

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