spark-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From ai he <heai0...@gmail.com>
Subject Re: Sporadic "Input validation failed" error when executing LogisticRegressionWithLBFGS.train
Date Tue, 11 Aug 2015 23:03:12 GMT
Hi Francis,

>From my observation when using spark sql, dataframe.limit(n) does not
necessarily return the same result each time when running Apps.

To be more precise, in one App, the result should be same for the same n,
however, changing n might not result in the same prefix(the result for n =
10 doesn't  necessarily start with the result for n = 5.)

When running different Apps, results are usually different for the same n.

Thanks

On Tue, Aug 11, 2015 at 2:56 PM, Francis Lau <francis.lau@smartsheet.com>
wrote:

> Has anyone see this issue? I am calling the
>  LogisticRegressionWithLBFGS.train API and about 7 out of 10 times, I get
> an ""Input validation failed" error". The exact same code and dataset works
> sometimes but fails at other times. It is odd. I can't seem to find any
> info on this. Below is the pyspark code and the error message. I did check
> the dataset and all values are zero or greater. There are no blank spaces
> or nulls. This code below is pretty much the sample code from the Spark
> site.
>
> Thanks in advance for any help or pointers in how to investigate this
> issue.
>
> --
> *Francis *
>
> *CODE:*
>
> from pyspark.mllib.classification import LogisticRegressionWithLBFGS
> from pyspark.mllib.regression import LabeledPoint
> from numpy import array
>
> # Load and parse the data
> def parsePoint(line):
>     #values = [float(x) for x in line.split(' ')]
>     values = [float(x) for x in line.asDict().values()] # need to convert
> from Row to Array
>     return LabeledPoint(values[0], values[1:])
>
> # convert SQL to format needed for training model
> regData = sqlContext.sql("select statement")
>
> df = regData.limit(1000)
> data = df.rdd
>
> parsedData = data.map(parsePoint)
>
> # Build the model
> model = LogisticRegressionWithLBFGS.train(parsedData)
>
> # Evaluating the model on training data
> labelsAndPreds = parsedData.map(lambda p: (p.label,
> model.predict(p.features)))
> trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() /
> float(parsedData.count())
>
> print("Training Error = " + str(trainErr))
> print "Intercept: " + str(model.intercept)
> print "Weights: " + str(model.weights)
>
>
>
>
> *ERROR:*
>
> ---------------------------------------------------------------------------
> Py4JJavaError                             Traceback (most recent call last)
> <ipython-input-134-b31b9c04499a> in <module>()
>      20
>      21 # Build the model
> ---> 22 model = LogisticRegressionWithLBFGS.train(parsedData)
>      23
>      24 # Evaluating the model on training data
>
> /home/ubuntu/databricks/spark/python/pyspark/mllib/classification.py in
> train(cls, data, iterations, initialWeights, regParam, regType, intercept,
> corrections, tolerance, validateData, numClasses)
>     344                 else:
>     345                     initialWeights = [0.0] *
> len(data.first().features) * (numClasses - 1)
> --> 346         return _regression_train_wrapper(train,
> LogisticRegressionModel, data, initialWeights)
>     347
>     348
>
> /home/ubuntu/databricks/spark/python/pyspark/mllib/regression.py in
> _regression_train_wrapper(train_func, modelClass, data, initial_weights)
>     186     if (modelClass == LogisticRegressionModel):
>     187         weights, intercept, numFeatures, numClasses = train_func(
> --> 188             data, _convert_to_vector(initial_weights))
>     189         return modelClass(weights, intercept, numFeatures,
> numClasses)
>     190     else:
>
> /home/ubuntu/databricks/spark/python/pyspark/mllib/classification.py in
> train(rdd, i)
>     334             return
> callMLlibFunc("trainLogisticRegressionModelWithLBFGS", rdd,
> int(iterations), i,
>     335                                  float(regParam), regType,
> bool(intercept), int(corrections),
> --> 336                                  float(tolerance),
> bool(validateData), int(numClasses))
>     337
>     338         if initialWeights is None:
>
> /home/ubuntu/databricks/spark/python/pyspark/mllib/common.py in
> callMLlibFunc(name, *args)
>     126     sc = SparkContext._active_spark_context
>     127     api = getattr(sc._jvm.PythonMLLibAPI(), name)
> --> 128     return callJavaFunc(sc, api, *args)
>     129
>     130
>
> /home/ubuntu/databricks/spark/python/pyspark/mllib/common.py in
> callJavaFunc(sc, func, *args)
>     119     """ Call Java Function """
>     120     args = [_py2java(sc, a) for a in args]
> --> 121     return _java2py(sc, func(*args))
>     122
>     123
>
> /home/ubuntu/databricks/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py
> in __call__(self, *args)
>     536         answer = self.gateway_client.send_command(command)
>     537         return_value = get_return_value(answer,
> self.gateway_client,
> --> 538                 self.target_id, self.name)
>     539
>     540         for temp_arg in temp_args:
>
> /home/ubuntu/databricks/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py
> in get_return_value(answer, gateway_client, target_id, name)
>     298                 raise Py4JJavaError(
>     299                     'An error occurred while calling {0}{1}{2}.\n'.
> --> 300                     format(target_id, '.', name), value)
>     301             else:
>     302                 raise Py4JError(
>
> Py4JJavaError: An error occurred while calling
> o5803.trainLogisticRegressionModelWithLBFGS.
> : org.apache.spark.SparkException: *Input validation failed.*
> at
> org.apache.spark.mllib.regression.GeneralizedLinearAlgorithm.run(GeneralizedLinearAlgorithm.scala:225)
> at
> org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRegressionModel(PythonMLLibAPI.scala:81)
> at
> org.apache.spark.mllib.api.python.PythonMLLibAPI.trainLogisticRegressionModelWithLBFGS(PythonMLLibAPI.scala:270)
> at sun.reflect.GeneratedMethodAccessor155.invoke(Unknown Source)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
> at py4j.Gateway.invoke(Gateway.java:259)
> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:207)
> at java.lang.Thread.run(Thread.java:745)
>



-- 
Best
Ai

Mime
View raw message