DLClassifier fit error

[Wojciech Krukar]

Hello I am replicating fraud detection model Intel BigDL Zoo form Scala to Python.

All the steps are identidal to the scala model

While fitting model error appear and it looks like it refers to java engine.

Below is the portion on building and fitting the model.

I attached the jupyter notebook with the whole script just in case the error is related to something else.

csv file can be downloaded here:
https://www.kaggle.com/joparga3/in-depth-skewed-data-classif-93-recall-acc-now/data

Here is the original scala model for reference.
https://github.com/intel-analytics/analytics-zoo/blob/master/apps/fraudDetection/Fraud%20Detction.ipynb

Below is the portion on building and fitting the model.
---------------------------------------------------------------------------------------------------------------------

from bigdl.nn.layer import *
from bigdl.nn.criterion import *
from bigdl.util.common import *
#import DLClassifier
#orginally from
# https://github.com/intel-analytics/BigDL/blob/master/docs/docs/ProgrammingGuide/MLPipeline.md
# https://groups.google.com/forum/#!msg/bigdl-user-group/i6DEn6VW_II/2VCFpj5wAQAJ;context-place=forum/bigdl-user-group
from bigdl.models.ml_pipeline.dl_classifier import *

bigDLModel = Sequential().add(Linear(29, 10)).add(Linear(10, 2)).add(LogSoftMax())

criterion=ClassNLLCriterion()
dlClassifier = DLClassifier(model=bigDLModel, criterion=criterion, feature_size=[29]).setLabelCol("label").setBatchSize(10000).setMaxEpoch(100)

#model = dlClassifier.fit(trainingData)
model = dlClassifier.fit(data)

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-43-034d3b8f0c41> in <module>()
      1 #model = dlClassifier.fit(trainingData)
----> 2 model = dlClassifier.fit(trainingData)

/home/wojciech/Libraries/spark-2.2.0-bin-hadoop2.7/python/pyspark/ml/base.pyc in fit(self, dataset, params)
     62                 return self.copy(params)._fit(dataset)
     63             else:
---> 64                 return self._fit(dataset)
     65         else:
     66             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

/tmp/spark-92b2f0df-6c4d-4bc1-9fd9-9ba8b36fd4ef/userFiles-fccb9f5a-9007-4884-a996-157c390c399b/bigdl-0.3.0-python-api.zip/bigdl/models/ml_pipeline/dl_classifier.py in _fit(self, dataset)
    133 
    134     def _fit(self, dataset):
--> 135         jmodel = callBigDlFunc(self.bigdl_type, "fitClassifier", self.value, dataset)
    136         model = DLClassifierModel.of(jmodel, self.featureSize, self.bigdl_type)
    137         return model

/tmp/spark-92b2f0df-6c4d-4bc1-9fd9-9ba8b36fd4ef/userFiles-fccb9f5a-9007-4884-a996-157c390c399b/bigdl-0.3.0-python-api.zip/bigdl/util/common.py in callBigDlFunc(bigdl_type, name, *args)
    456     sc = get_spark_context()
    457     api = getattr(jinstance, name)
--> 458     return callJavaFunc(sc, api, *args)
    459 
    460 

/tmp/spark-92b2f0df-6c4d-4bc1-9fd9-9ba8b36fd4ef/userFiles-fccb9f5a-9007-4884-a996-157c390c399b/bigdl-0.3.0-python-api.zip/bigdl/util/common.py in callJavaFunc(sc, func, *args)
    491     """ Call Java Function """
    492     args = [_py2java(sc, a) for a in args]
--> 493     result = func(*args)
    494     return _java2py(sc, result)
    495 

/home/wojciech/Libraries/spark-2.2.0-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134 
   1135         for temp_arg in temp_args:

/home/wojciech/Libraries/spark-2.2.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

/home/wojciech/Libraries/spark-2.2.0-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    317                 raise Py4JJavaError(
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:
    321                 raise Py4JError(

Py4JJavaError: An error occurred while calling o50.fitClassifier.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 134.0 failed 1 times, most recent failure: Lost task 0.0 in stage 134.0 (TID 146, localhost, executor driver): java.util.concurrent.ExecutionException: Layer info: Sequential[6218774b]{
  [input -> (1) -> (2) -> (3) -> output]
  (1): Linear[ac52e52b](29 -> 10)
  (2): Linear[6414a735](10 -> 2)
  (3): LogSoftMax[72891c1]
}/Linear[ac52e52b](29 -> 10)
java.lang.IllegalArgumentException: requirement failed: size mismatch, m1:2500x27 m2:29x10
	at scala.Predef$.require(Predef.scala:224)
	at com.intel.analytics.bigdl.tensor.DenseTensorMath$.addmm(DenseTensorMath.scala:515)
	at com.intel.analytics.bigdl.tensor.DenseTensor.addmm(DenseTensor.scala:1206)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:108)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:45)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.nn.Sequential.updateOutput(Sequential.scala:39)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply$mcI$sp(DistriOptimizer.scala:223)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.utils.ThreadPool$$anonfun$1$$anon$4.call(ThreadPool.scala:112)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

	at java.util.concurrent.FutureTask.report(FutureTask.java:122)
	at java.util.concurrent.FutureTask.get(FutureTask.java:192)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$11.apply(DistriOptimizer.scala:235)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$11.apply(DistriOptimizer.scala:235)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8.apply(DistriOptimizer.scala:235)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8.apply(DistriOptimizer.scala:175)
	at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:108)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)
Caused by: Layer info: Sequential[6218774b]{
  [input -> (1) -> (2) -> (3) -> output]
  (1): Linear[ac52e52b](29 -> 10)
  (2): Linear[6414a735](10 -> 2)
  (3): LogSoftMax[72891c1]
}/Linear[ac52e52b](29 -> 10)
java.lang.IllegalArgumentException: requirement failed: size mismatch, m1:2500x27 m2:29x10
	at scala.Predef$.require(Predef.scala:224)
	at com.intel.analytics.bigdl.tensor.DenseTensorMath$.addmm(DenseTensorMath.scala:515)
	at com.intel.analytics.bigdl.tensor.DenseTensor.addmm(DenseTensor.scala:1206)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:108)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:45)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.nn.Sequential.updateOutput(Sequential.scala:39)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply$mcI$sp(DistriOptimizer.scala:223)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.utils.ThreadPool$$anonfun$1$$anon$4.call(ThreadPool.scala:112)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:286)
	at com.intel.analytics.bigdl.nn.Sequential.updateOutput(Sequential.scala:39)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply$mcI$sp(DistriOptimizer.scala:223)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.utils.ThreadPool$$anonfun$1$$anon$4.call(ThreadPool.scala:112)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	... 3 more

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)
	at scala.Option.foreach(Option.scala:257)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2119)
	at org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1026)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
	at org.apache.spark.rdd.RDD.reduce(RDD.scala:1008)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$.optimize(DistriOptimizer.scala:285)
	at com.intel.analytics.bigdl.optim.DistriOptimizer.optimize(DistriOptimizer.scala:795)
	at org.apache.spark.ml.DLEstimator.internalFit(DLEstimator.scala:133)
	at org.apache.spark.ml.DLEstimator.internalFit(DLEstimator.scala:54)
	at org.apache.spark.ml.DLEstimatorBase.fit(DLEstimatorBase.scala:80)
	at com.intel.analytics.bigdl.python.api.PythonBigDL.fitClassifier(PythonBigDL.scala:2052)
	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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:280)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:214)
	at java.lang.Thread.run(Thread.java:748)
Caused by: java.util.concurrent.ExecutionException: Layer info: Sequential[6218774b]{
  [input -> (1) -> (2) -> (3) -> output]
  (1): Linear[ac52e52b](29 -> 10)
  (2): Linear[6414a735](10 -> 2)
  (3): LogSoftMax[72891c1]
}/Linear[ac52e52b](29 -> 10)
java.lang.IllegalArgumentException: requirement failed: size mismatch, m1:2500x27 m2:29x10
	at scala.Predef$.require(Predef.scala:224)
	at com.intel.analytics.bigdl.tensor.DenseTensorMath$.addmm(DenseTensorMath.scala:515)
	at com.intel.analytics.bigdl.tensor.DenseTensor.addmm(DenseTensor.scala:1206)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:108)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:45)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.nn.Sequential.updateOutput(Sequential.scala:39)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply$mcI$sp(DistriOptimizer.scala:223)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.utils.ThreadPool$$anonfun$1$$anon$4.call(ThreadPool.scala:112)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

	at java.util.concurrent.FutureTask.report(FutureTask.java:122)
	at java.util.concurrent.FutureTask.get(FutureTask.java:192)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$11.apply(DistriOptimizer.scala:235)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$11.apply(DistriOptimizer.scala:235)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
	at scala.collection.AbstractTraversable.map(Traversable.scala:104)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8.apply(DistriOptimizer.scala:235)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8.apply(DistriOptimizer.scala:175)
	at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
	at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:108)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	... 1 more
Caused by: Layer info: Sequential[6218774b]{
  [input -> (1) -> (2) -> (3) -> output]
  (1): Linear[ac52e52b](29 -> 10)
  (2): Linear[6414a735](10 -> 2)
  (3): LogSoftMax[72891c1]
}/Linear[ac52e52b](29 -> 10)
java.lang.IllegalArgumentException: requirement failed: size mismatch, m1:2500x27 m2:29x10
	at scala.Predef$.require(Predef.scala:224)
	at com.intel.analytics.bigdl.tensor.DenseTensorMath$.addmm(DenseTensorMath.scala:515)
	at com.intel.analytics.bigdl.tensor.DenseTensor.addmm(DenseTensor.scala:1206)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:108)
	at com.intel.analytics.bigdl.nn.Linear.updateOutput(Linear.scala:45)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.nn.Sequential.updateOutput(Sequential.scala:39)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply$mcI$sp(DistriOptimizer.scala:223)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.utils.ThreadPool$$anonfun$1$$anon$4.call(ThreadPool.scala:112)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
	at java.lang.Thread.run(Thread.java:748)

	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:286)
	at com.intel.analytics.bigdl.nn.Sequential.updateOutput(Sequential.scala:39)
	at com.intel.analytics.bigdl.nn.abstractnn.AbstractModule.forward(AbstractModule.scala:280)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply$mcI$sp(DistriOptimizer.scala:223)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.optim.DistriOptimizer$$anonfun$8$$anonfun$9$$anonfun$apply$2.apply(DistriOptimizer.scala:216)
	at com.intel.analytics.bigdl.utils.ThreadPool$$anonfun$1$$anon$4.call(ThreadPool.scala:112)
	at java.util.concurrent.FutureTask.run(FutureTask.java:266)
	... 3 more

[Yiheng Wang]

Hi Wojciech

Looks like the first layer needs a batch_size x 29 input, but it actually gets a batch_size x 27 input...

Something is wrong when preparing the training data? @yuhao, do you have any insight?

Regards

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[Wojciech Krukar]

I just checked the data structure on every stage of preparation and it is always 29 element vector.

I suspect 2 issues.

I looked at a couple of other examples and some models use DenseVector other SparseVector.

Does it have any importance here or I can use types of vectors interchangeably? 

In my script, it is the DenseVector.

One of the variables in the df is different than others the "nullable = false"  

Regards, Thanks for your help

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[Yuhao Yang]

Hi Wojciech,

I tried with the Notebook with BigDL 0.3 and Spark 2.1. 

I got the error 

"assertion failed: curTarget 0 is out of range 1 to 2"

which is because the label column contains 0.

After correcting this in df =df.withColumn('Class1', when(df.Class == 0, 2.0).otherwise(1.0)).drop(df.Class), the training runs normally.

Cheers,

Yuhao

[Wojciech Krukar]

Thank you so much. 

Now the model works perfectly.

Since more users can crush into this problems I did some research on the BigDL documentation. I hope that my finding would be useful for improving documentation. 

Starting from description of DLEstimeator and DLClassifier: https://github.com/intel-analytics/BigDL/blob/master/docs/docs/ProgrammingGuide/MLPipeline.md  There is no explicit remark that labels should be non zero. 

Following link of ML pipeline API https://github.com/intel-analytics/BigDL/blob/master/docs/docs/ProgrammingGuide/MLPipeline.md  there are examples with labels formatted properly but no remarks about proper formatting. 

Wojciech

[Yuhao Yang]

Thanks a lot for the suggestion. Indeed that's something we should document since it can be out of expectation for Spark users. Do you perhaps want to send a PR for the fix?

Regards,

Yuhao

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[eden....@gmail.com]

Hi everyone, 

Thanks for your insights. I am receiving the same error message but I do not have any zeros in my label or features for that matter. I wondered if anyone had any additional insights. I've provide my code I'm testing on below....

Thank you for any assistance you can provide!

Eden

from pyspark.ml.classification import MultilayerPerceptronClassifier

from pyspark.ml.evaluation import MulticlassClassificationEvaluator

from pyspark.ml.feature import VectorAssembler

df = spark.createDataFrame([(3.0,69,57,56,678,345),(3.0,67,56,58,678,345),(3.0,67,54,57,678,345),(3.0,68,55,58,678,345),(3.0,68,53,52,678,345)

                           ,(2.0,11,10,907,16,458),(2.0,12,14,909,12,456),(2.0,11,13,910,10,459),(2.0,12,11,905,16,459),(2.0,10,13,902,10,459)

                           ,(1.0,30,11,123,568,891),(1.0,32,12,124,567,890),(1.0,34,10,123,566,895),(1.0,35,15,121,564,894),(1.0,30,12,124,560,896)], 

                           ['flower_type', 'sepal_len','sepal_width','R','G','B'])

input_columns = ['sepal_len','sepal_width','R','G','B']

assembler = VectorAssembler(inputCols=input_columns,outputCol='features')

output = assembler.transform(df)

# Rename your dependent variable

renamed = output.withColumnRenamed('flower_type','label')

final_data = renamed.select('features','label')

final_data.show(3)

train,test = final_data.randomSplit([0.7,0.3])

# specify layers for the neural network (arbitrary for now... not sure if this is the cause of the issue:

# input layer of size 4 (features), two intermediate of size 5 and 4

# and output of size 3 (classes)

layers = [4, 5, 4, 3]

# create the trainer and set its parameters

trainer = MultilayerPerceptronClassifier(maxIter=100, layers=layers, blockSize=128, seed=1234)

# train the model

model = trainer.fit(train)

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)

<ipython-input-28-f4a1abde3ba7> in <module>()
     36 
     37 # train the model
---> 38 model = trainer.fit(train)

C:\spark-2.3.3-bin-hadoop2.7\python\pyspark\ml\base.py in fit(self, dataset, params)
    130                 return self.copy(params)._fit(dataset)
    131             else:
--> 132                 return self._fit(dataset)
    133         else:
    134             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

C:\spark-2.3.3-bin-hadoop2.7\python\pyspark\ml\wrapper.py in _fit(self, dataset)
    286 
    287     def _fit(self, dataset):
--> 288         java_model = self._fit_java(dataset)
    289         model = self._create_model(java_model)
    290         return self._copyValues(model)

C:\spark-2.3.3-bin-hadoop2.7\python\pyspark\ml\wrapper.py in _fit_java(self, dataset)
    283         """
    284         self._transfer_params_to_java()
--> 285         return self._java_obj.fit(dataset._jdf)
    286 
    287     def _fit(self, dataset):

C:\spark-2.3.3-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\java_gateway.py in __call__(self, *args)
   1255         answer = self.gateway_client.send_command(command)
   1256         return_value = get_return_value(
-> 1257             answer, self.gateway_client, self.target_id, self.name)
   1258 
   1259         for temp_arg in temp_args:

C:\spark-2.3.3-bin-hadoop2.7\python\pyspark\sql\utils.py in deco(*a, **kw)

     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

C:\spark-2.3.3-bin-hadoop2.7\python\lib\py4j-0.10.7-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
    326                 raise Py4JJavaError(
    327                     "An error occurred while calling {0}{1}{2}.\n".
--> 328                     format(target_id, ".", name), value)
    329             else:
    330                 raise Py4JError(

Py4JJavaError: An error occurred while calling o686.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 65.0 failed 1 times, most recent failure: Lost task 0.0 in stage 65.0 (TID 386, localhost, executor driver): java.lang.ArrayIndexOutOfBoundsException: 3
	at org.apache.spark.ml.classification.LabelConverter$.encodeLabeledPoint(MultilayerPerceptronClassifier.scala:119)
	at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$3.apply(MultilayerPerceptronClassifier.scala:244)
	at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$3.apply(MultilayerPerceptronClassifier.scala:244)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:1076)
	at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:1091)
	at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1128)
	at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1132)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
	at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:216)
	at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1094)
	at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1085)
	at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1020)
	at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1085)
	at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:811)
	at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:109)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
	at java.lang.Thread.run(Unknown Source)

Driver stacktrace:
	at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1661)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1649)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1648)
	at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
	at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1648)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
	at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:831)
	at scala.Option.foreach(Option.scala:257)
	at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:831)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1882)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1831)
	at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1820)
	at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
	at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:642)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2034)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2055)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2074)
	at org.apache.spark.SparkContext.runJob(SparkContext.scala:2099)
	at org.apache.spark.rdd.RDD.count(RDD.scala:1168)
	at org.apache.spark.mllib.optimization.LBFGS$.runLBFGS(LBFGS.scala:195)
	at org.apache.spark.mllib.optimization.LBFGS.optimize(LBFGS.scala:142)
	at org.apache.spark.ml.ann.FeedForwardTrainer.train(Layer.scala:854)
	at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:266)
	at org.apache.spark.ml.classification.MultilayerPerceptronClassifier.train(MultilayerPerceptronClassifier.scala:143)
	at org.apache.spark.ml.Predictor.fit(Predictor.scala:118)
	at org.apache.spark.ml.Predictor.fit(Predictor.scala:82)
	at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
	at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
	at java.lang.reflect.Method.invoke(Unknown Source)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Unknown Source)
Caused by: java.lang.ArrayIndexOutOfBoundsException: 3
	at org.apache.spark.ml.classification.LabelConverter$.encodeLabeledPoint(MultilayerPerceptronClassifier.scala:119)
	at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$3.apply(MultilayerPerceptronClassifier.scala:244)
	at org.apache.spark.ml.classification.MultilayerPerceptronClassifier$$anonfun$3.apply(MultilayerPerceptronClassifier.scala:244)
	at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
	at scala.collection.Iterator$GroupedIterator.takeDestructively(Iterator.scala:1076)
	at scala.collection.Iterator$GroupedIterator.go(Iterator.scala:1091)
	at scala.collection.Iterator$GroupedIterator.fill(Iterator.scala:1128)
	at scala.collection.Iterator$GroupedIterator.hasNext(Iterator.scala:1132)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
	at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
	at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:216)
	at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1094)
	at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1085)
	at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:1020)
	at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1085)
	at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:811)
	at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:335)
	at org.apache.spark.rdd.RDD.iterator(RDD.scala:286)
	at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
	at org.apache.spark.scheduler.Task.run(Task.scala:109)
	at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(Unknown Source)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(Unknown Source)
	... 1 more

On Monday, December 18, 2017 at 10:40:43 PM UTC-5, Yuhao Yang wrote:

Thanks a lot for the suggestion. Indeed that's something we should document since it can be out of expectation for Spark users. Do you perhaps want to send a PR for the fix?

Regards,

Yuhao

2017-12-18 9:57 GMT-08:00 Wojciech Krukar <wojciec...@gmail.com>:

Thank you so much. 

Now the model works perfectly.

Since more users can crush into this problems I did some research on the BigDL documentation. I hope that my finding would be useful for improving documentation. 

Starting from description of DLEstimeator and DLClassifier: https://github.com/intel-analytics/BigDL/blob/master/docs/docs/ProgrammingGuide/MLPipeline.md  There is no explicit remark that labels should be non zero. 

Following link of ML pipeline API https://github.com/intel-analytics/BigDL/blob/master/docs/docs/ProgrammingGuide/MLPipeline.md  there are examples with labels formatted properly but no remarks about proper formatting. 

Wojciech

On Thursday, December 14, 2017 at 7:46:26 PM UTC-6, Yuhao Yang wrote:

Hi Wojciech,

I tried with the Notebook with BigDL 0.3 and Spark 2.1. 

I got the error 

"assertion failed: curTarget 0 is out of range 1 to 2"

which is because the label column contains 0.

After correcting this in df =df.withColumn('Class1', when(df.Class == 0, 2.0).otherwise(1.0)).drop(df.Class), the training runs normally.

Cheers,

Yuhao

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