[jira] [Resolved] (SPARK-32232) IllegalArgumentException: MultilayerPerceptronClassifier_... parameter solver given invalid value auto

["Sean R. Owen (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-32232?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean R. Owen resolved SPARK-32232.
----------------------------------
    Fix Version/s: 3.1.0
                   3.0.1
       Resolution: Fixed

Issue resolved by pull request 29060
[https://github.com/apache/spark/pull/29060]

> IllegalArgumentException: MultilayerPerceptronClassifier_... parameter solver given invalid value auto
> ------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-32232
>                 URL: https://issues.apache.org/jira/browse/SPARK-32232
>             Project: Spark
>          Issue Type: Bug
>          Components: ML
>    Affects Versions: 3.0.0
>            Reporter: steven taylor
>            Assignee: Huaxin Gao
>            Priority: Major
>             Fix For: 3.0.1, 3.1.0
>
>
> I believe I have discovered a bug when loading MultilayerPerceptronClassificationModel in spark 3.0.0, scala 2.1.2 which I have tested and can see is not there in at least Spark 2.4.3, Scala 2.11.  (I'm not sure if the Scala version is important).
>  
> I am using pyspark on a databricks cluster and importing the library  "from pyspark.ml.classification import MultilayerPerceptronClassificationModel"
>  
> When running model=MultilayerPerceptronClassificationModel.("load") and then model. transform (df) I get the following error: IllegalArgumentException: MultilayerPerceptronClassifier_8055d1368e78 parameter solver given invalid value auto.
>  
>  
> This issue can be easily replicated by running the example given on the spark documents: [http://spark.apache.org/docs/latest/ml-classification-regression.html#multilayer-perceptron-classifier]
>  
> Then adding a save model, load model and transform statement as such:
>  
> *from* *pyspark.ml.classification* *import* MultilayerPerceptronClassifier
> *from* *pyspark.ml.evaluation* *import* MulticlassClassificationEvaluator
>  
> _# Load training data_
> data = spark.read.format("libsvm")\
>     .load("data/mllib/sample_multiclass_classification_data.txt")
>  
> _# Split the data into train and test_
> splits = data.randomSplit([0.6, 0.4], 1234)
> train = splits[0]
> test = splits[1]
>  
> _# specify layers for the neural network:_
> _# 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)
>  
> _# compute accuracy on the test set_
> result = model.transform(test)
> predictionAndLabels = result.select("prediction", "label")
> evaluator = MulticlassClassificationEvaluator(metricName="accuracy")
> *print*("Test set accuracy = " + str(evaluator.evaluate(predictionAndLabels)))
>  
> *from* *pyspark.ml.classification* *import* MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel
> model.save(Save_location)
> model2. MultilayerPerceptronClassificationModel.load(Save_location)
>  
> result_from_loaded = model2.transform(test)
>  



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