You will need to cast bestModel to include the MLWritable trait. The class
Model does not mix it in by default. For instance:
cvModel.bestModel.asInstanceOf[MLWritable].save("/my/path")
Alternatively, you could save the CV model directly, which takes care of
this
cvModel.save("/my/path")
On Fri, Aug 12, 2016 at 9:17 AM, Adamantios Corais <
[email protected]> wrote:
> Hi,
>
> Assuming that I have run the following pipeline and have got the best
> logistic regression model. How can I then save that model for later use?
> The following command throws an error:
>
> cvModel.bestModel.save("/my/path")
>
> Also, is it possible to get the error (a collection of) for each
> combination of parameters?
>
> I am using spark 1.6.2
>
> import org.apache.spark.ml.Pipeline
> import org.apache.spark.ml.classification.LogisticRegression
> import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
> import org.apache.spark.ml.tuning.{ParamGridBuilder , CrossValidator}
>
> val lr = new LogisticRegression()
>
> val pipeline = new Pipeline().
> setStages(Array(lr))
>
> val paramGrid = new ParamGridBuilder().
> addGrid(lr.elasticNetParam , Array(0.1)).
> addGrid(lr.maxIter , Array(10)).
> addGrid(lr.regParam , Array(0.1)).
> build()
>
> val cv = new CrossValidator().
> setEstimator(pipeline).
> setEvaluator(new BinaryClassificationEvaluator).
> setEstimatorParamMaps(paramGrid).
> setNumFolds(2)
>
> val cvModel = cv.
> fit(training)
>
>
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