Apache PredictionIO provides the following features to help you debug engines during development cycle.

Stop Training between Stages

By default pio train runs through the whole training process including DataSource, Preparator and Algorithm. To speed up the development and debug cycle, you can stop the process after each stage to verify it has completed correctly.

If you have modified DataSource and want to confirm the TrainingData is generated as expected, you can run pio train with --stop-after-read option:

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pio train --stop-after-read

This would stop the training process after the TrainingData is generated.

For example, if you are running Recommendation Template, you should see the the training process stops after the TrainingData is printed.

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[INFO] [CoreWorkflow$] TrainingData:
[INFO] [CoreWorkflow$] ratings: [1501] (List(Rating(3,0,4.0), Rating(3,1,4.0))...)
...
[INFO] [CoreWorkflow$] Training interrupted by org.apache.predictionio.workflow.StopAfterReadInterruption.

Similarly, you can stop the training after the Preparator phase by using --stop-after-prepare option and it would stop after PreparedData is generated:

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pio train --stop-after-prepare

Sanity Check

You can extend a trait SanityCheck and implement the method sanityCheck() with your error checking code. The sanityCheck() is called when the data is generated. This can be applied to TrainingData, PreparedData and the Model classes, which are outputs of DataSource's readTraining(), Preparator's prepare() and Algorithm's train() methods, respectively.

For example, one frequent error with the Recommendation Template is that the TrainingData is empty because the DataSource is not reading data correctly. You can add the check of empty data inside the sanityCheck() function. You can easily add other checking logic into the sanityCheck() function based on your own needs. Also, If you implement toString() method in your TrainingData. You can call toString() inside sanityCheck() to print out some data for visual checking.

For example, to print TrainingData to console and check if the ratings is empty, you can do the following:

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import org.apache.predictionio.controller.SanityCheck // ADDED

class TrainingData(
  val ratings: RDD[Rating]
) extends Serializable with SanityCheck { // EXTEND SanityCheck
  override def toString = {
    s"ratings: [${ratings.count()}] (${ratings.take(2).toList}...)"
  }

  // IMPLEMENT sanityCheck()
  override def sanityCheck(): Unit = {
    println(toString())
    // add your other checking here
    require(!ratings.take(1).isEmpty, s"ratings cannot be empty!")
  }
}

You may also use together with --stop-after-read flag to debug the DataSource:

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pio build
pio train --stop-after-read

If your data is empty, you should see the following error thrown by the sanityCheck() function:

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[INFO] [CoreWorkflow$] Performing data sanity check on training data.
[INFO] [CoreWorkflow$] org.template.recommendation.TrainingData supports data sanity check. Performing check.
Exception in thread "main" java.lang.IllegalArgumentException: requirement failed: ratings cannot be empty!
    at scala.Predef$.require(Predef.scala:233)
    at org.template.recommendation.TrainingData.sanityCheck(DataSource.scala:73)
    at org.apache.predictionio.workflow.CoreWorkflow$$anonfun$runTypelessContext$7.apply(Workflow.scala:474)
    at org.apache.predictionio.workflow.CoreWorkflow$$anonfun$runTypelessContext$7.apply(Workflow.scala:465)
    at scala.collection.immutable.Map$Map1.foreach(Map.scala:109)
  ...

You can specify the --skip-sanity-check option to turn off sanityCheck:

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pio train --stop-after-read --skip-sanity-check

You should see the checking is skipped such as the following output:

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[INFO] [CoreWorkflow$] Data sanity checking is off.
[INFO] [CoreWorkflow$] Data Source
...
[INFO] [CoreWorkflow$] Training interrupted by org.apache.predictionio.workflow.StopAfterReadInterruption.

Engine Status Page

After run pio deploy, you can access the engine status page by go to same URL and port of the deployed engine with your browser, which is "http://localhost:8000" by default. In the engine status page, you can find the Engine information, and parameters of each DASE components. In particular, you can also see the "Model" trained by the algorithm based on how toString() method is implemented in the Algorithm's Model class.

pio-shell

Apache PredictionIO also provides pio-shell in which you can easily access Apache PredictionIO API, Spark context and Spark API for quickly testing code or debugging purposes.

To bring up the shell, simply run:

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$ pio-shell --with-spark

(pio-shell is available inside bin/ directory of installed Apache PredictionIO directory, you should be able to access it if you have added PredictionIO/bin into your environment variable PATH)

Note that the Spark context is available as variable sc inside the shell.

For example, to get the events of MyApp1 using PEventStore API inside the pio-shell and collect them into an array c. run the following in the shell:

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> import org.apache.predictionio.data.store.PEventStore
> val eventsRDD = PEventStore.find(appName="MyApp1")(sc)
> val c = eventsRDD.collect()

Then you should see following returned in the shell:

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...
15/05/18 14:24:42 INFO DAGScheduler: Job 0 finished: collect at <console>:24, took 1.850779 s
c: Array[org.apache.predictionio.data.storage.Event] = Array(Event(id=Some(AaQUUBsFZxteRpDV_7fDGQAAAU1ZfRW1tX9LSWdZSb0),event=$set,eType=item,eId=i42,tType=None,tId=None,p=DataMap(Map(categories -> JArray(List(JString(c2), JString(c1), JString(c6), JString(c3))))),t=2015-05-15T21:31:19.349Z,tags=List(),pKey=None,ct=2015-05-15T21:31:19.354Z), Event(id=Some(DjvP3Dnci9F4CWmiqoLabQAAAU1ZfROaqdRYO-pZ_no),event=$set,eType=user,eId=u9,tType=None,tId=None,p=DataMap(Map()),t=2015-05-15T21:31:18.810Z,tags=List(),pKey=None,ct=2015-05-15T21:31:18.817Z), Event(id=Some(DjvP3Dnci9F4CWmiqoLabQAAAU1ZfRq7tsanlemwmZQ),event=view,eType=user,eId=u9,tType=Some(item),tId=Some(i25),p=DataMap(Map()),t=2015-05-15T21:31:20.635Z,tags=List(),pKey=None,ct=2015-05-15T21:31:20.639Z), Event(id=Some(DjvP3Dnci9F4CWmiqoLabQAAAU1ZfR...