Re: ML:One vs Rest with crossValidator for multinomial in logistic regression

[Nicolas Paris]

Brian

This is absolutely this problem.

Good to hear it will be fix in 2.3 release


Le 09 févr. 2018 à 02:17, Bryan Cutler écrivait :
> Nicolas, are you referring to printing the model params in that example with
> "print(model1.extractParamMap())"?  There was a problem with pyspark models 
> not
> having params after being fit, which causes this example to show nothing for
> model paramMaps.  This was fixed in https://issues.apache.org/jira/browse/
> SPARK-10931 and the example now shows all model params.  The fix will be in 
> the
> Spark 2.3 release.
> 
> Bryan
> 
> On Wed, Jan 31, 2018 at 10:20 PM, Nicolas Paris  wrote:
> 
> Hey
> 
> I am also interested in how to get those parameters.
> For example, the demo code spark-2.2.1-bin-hadoop2.7/examples/src/main/
> python/ml/estimator_transformer_param_example.py
> return empty parameters when  printing "lr.extractParamMap()"
> 
> That's weird
> 
> Thanks
> 
> Le 30 janv. 2018 à 23:10, Bryan Cutler écrivait :
> > Hi Michelle,
> >
> > Your original usage of ParamGridBuilder was not quite right, `addGrid`
> expects
> > (some parameter, array of values for that parameter).  If you want to do
> a grid
> > search with different regularization values, you would do the following:
> >
> > val paramMaps = new ParamGridBuilder().addGrid(logist.regParam, Array
> (0.1,
> > 0.3)).build()
> >
> > * don't forget to build the grid after adding values
> >
> > On Tue, Jan 30, 2018 at 6:55 AM, michelleyang <
> michelle1026sh...@gmail.com>
> > wrote:
> >
> >     I tried to use One vs Rest in spark ml with pipeline and
> crossValidator for
> >     multimultinomial in logistic regression.
> >
> >     It came out with empty coefficients. I figured out it was the 
> setting
> of
> >     ParamGridBuilder. Can anyone help me understand how does the
> parameter
> >     setting affect the crossValidator process?
> >
> >     the orginal code: //output empty coefficients.
> >
> >     val logist=new LogisticRegression
> >
> >     val ova = new OneVsRest().setClassifier(logist)
> >
> >     val paramMaps = new ParamGridBuilder().addGrid(ova.classifier,
> >     Array(logist.getRegParam))
> >
> >     New code://output multi classes coefficients
> >
> >     val logist=new LogisticRegression
> >
> >     val ova = new OneVsRest().setClassifier(logist)
> >
> >     val classifier1 = new LogisticRegression().setRegParam(2.0)
> >
> >     val classifier2 = new LogisticRegression().setRegParam(3.0)
> >
> >     val paramMaps = new ParamGridBuilder() .addGrid(ova.classifier,
> >     Array(classifier1, classifier2))
> >
> >     Please help Thanks.
> >
> >
> >
> >     --
> >     Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/
> >
> >     
> -
> >     To unsubscribe e-mail: user-unsubscr...@spark.apache.org
> >
> >
> >
> 
> 

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Re: ML:One vs Rest with crossValidator for multinomial in logistic regression

[Bryan Cutler]

Nicolas, are you referring to printing the model params in that example
with "print(model1.extractParamMap())"?  There was a problem with pyspark
models not having params after being fit, which causes this example to show
nothing for model paramMaps.  This was fixed in
https://issues.apache.org/jira/browse/SPARK-10931 and the example now shows
all model params.  The fix will be in the Spark 2.3 release.

Bryan

On Wed, Jan 31, 2018 at 10:20 PM, Nicolas Paris  wrote:

> Hey
>
> I am also interested in how to get those parameters.
> For example, the demo code spark-2.2.1-bin-hadoop2.7/
> examples/src/main/python/ml/estimator_transformer_param_example.py
> return empty parameters when  printing "lr.extractParamMap()"
>
> That's weird
>
> Thanks
>
> Le 30 janv. 2018 à 23:10, Bryan Cutler écrivait :
> > Hi Michelle,
> >
> > Your original usage of ParamGridBuilder was not quite right, `addGrid`
> expects
> > (some parameter, array of values for that parameter).  If you want to do
> a grid
> > search with different regularization values, you would do the following:
> >
> > val paramMaps = new ParamGridBuilder().addGrid(logist.regParam,
> Array(0.1,
> > 0.3)).build()
> >
> > * don't forget to build the grid after adding values
> >
> > On Tue, Jan 30, 2018 at 6:55 AM, michelleyang <
> michelle1026sh...@gmail.com>
> > wrote:
> >
> > I tried to use One vs Rest in spark ml with pipeline and
> crossValidator for
> > multimultinomial in logistic regression.
> >
> > It came out with empty coefficients. I figured out it was the
> setting of
> > ParamGridBuilder. Can anyone help me understand how does the
> parameter
> > setting affect the crossValidator process?
> >
> > the orginal code: //output empty coefficients.
> >
> > val logist=new LogisticRegression
> >
> > val ova = new OneVsRest().setClassifier(logist)
> >
> > val paramMaps = new ParamGridBuilder().addGrid(ova.classifier,
> > Array(logist.getRegParam))
> >
> > New code://output multi classes coefficients
> >
> > val logist=new LogisticRegression
> >
> > val ova = new OneVsRest().setClassifier(logist)
> >
> > val classifier1 = new LogisticRegression().setRegParam(2.0)
> >
> > val classifier2 = new LogisticRegression().setRegParam(3.0)
> >
> > val paramMaps = new ParamGridBuilder() .addGrid(ova.classifier,
> > Array(classifier1, classifier2))
> >
> > Please help Thanks.
> >
> >
> >
> > --
> > Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/
> >
> > 
> -
> > To unsubscribe e-mail: user-unsubscr...@spark.apache.org
> >
> >
> >
>

Re: ML:One vs Rest with crossValidator for multinomial in logistic regression

[Nicolas Paris]

Hey 

I am also interested in how to get those parameters.
For example, the demo code 
spark-2.2.1-bin-hadoop2.7/examples/src/main/python/ml/estimator_transformer_param_example.py
return empty parameters when  printing "lr.extractParamMap()"

That's weird

Thanks

Le 30 janv. 2018 à 23:10, Bryan Cutler écrivait :
> Hi Michelle,
> 
> Your original usage of ParamGridBuilder was not quite right, `addGrid` expects
> (some parameter, array of values for that parameter).  If you want to do a 
> grid
> search with different regularization values, you would do the following:
> 
> val paramMaps = new ParamGridBuilder().addGrid(logist.regParam, Array(0.1,
> 0.3)).build()
> 
> * don't forget to build the grid after adding values
> 
> On Tue, Jan 30, 2018 at 6:55 AM, michelleyang 
> wrote:
> 
> I tried to use One vs Rest in spark ml with pipeline and crossValidator 
> for
> multimultinomial in logistic regression.
> 
> It came out with empty coefficients. I figured out it was the setting of
> ParamGridBuilder. Can anyone help me understand how does the parameter
> setting affect the crossValidator process?
> 
> the orginal code: //output empty coefficients.
> 
> val logist=new LogisticRegression
> 
> val ova = new OneVsRest().setClassifier(logist)
> 
> val paramMaps = new ParamGridBuilder().addGrid(ova.classifier,
> Array(logist.getRegParam))
> 
> New code://output multi classes coefficients
> 
> val logist=new LogisticRegression
> 
> val ova = new OneVsRest().setClassifier(logist)
> 
> val classifier1 = new LogisticRegression().setRegParam(2.0)
> 
> val classifier2 = new LogisticRegression().setRegParam(3.0)
> 
> val paramMaps = new ParamGridBuilder() .addGrid(ova.classifier,
> Array(classifier1, classifier2))
> 
> Please help Thanks.
> 
> 
> 
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> 
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> 
> 
> 

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Re: ML:One vs Rest with crossValidator for multinomial in logistic regression

[Bryan Cutler]

Hi Michelle,

Your original usage of ParamGridBuilder was not quite right, `addGrid`
expects (some parameter, array of values for that parameter).  If you want
to do a grid search with different regularization values, you would do the
following:

val paramMaps = new ParamGridBuilder().addGrid(logist.regParam, Array(0.1,
0.3)).build()

* don't forget to build the grid after adding values

On Tue, Jan 30, 2018 at 6:55 AM, michelleyang 
wrote:

> I tried to use One vs Rest in spark ml with pipeline and crossValidator for
> multimultinomial in logistic regression.
>
> It came out with empty coefficients. I figured out it was the setting of
> ParamGridBuilder. Can anyone help me understand how does the parameter
> setting affect the crossValidator process?
>
> the orginal code: //output empty coefficients.
>
> val logist=new LogisticRegression
>
> val ova = new OneVsRest().setClassifier(logist)
>
> val paramMaps = new ParamGridBuilder().addGrid(ova.classifier,
> Array(logist.getRegParam))
>
> New code://output multi classes coefficients
>
> val logist=new LogisticRegression
>
> val ova = new OneVsRest().setClassifier(logist)
>
> val classifier1 = new LogisticRegression().setRegParam(2.0)
>
> val classifier2 = new LogisticRegression().setRegParam(3.0)
>
> val paramMaps = new ParamGridBuilder() .addGrid(ova.classifier,
> Array(classifier1, classifier2))
>
> Please help Thanks.
>
>
>
> --
> Sent from: http://apache-spark-user-list.1001560.n3.nabble.com/
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

ML:One vs Rest with crossValidator for multinomial in logistic regression

[michelleyang]

I tried to use One vs Rest in spark ml with pipeline and crossValidator for
multimultinomial in logistic regression.

It came out with empty coefficients. I figured out it was the setting of
ParamGridBuilder. Can anyone help me understand how does the parameter
setting affect the crossValidator process?

the orginal code: //output empty coefficients.

val logist=new LogisticRegression

val ova = new OneVsRest().setClassifier(logist)

val paramMaps = new ParamGridBuilder().addGrid(ova.classifier,
Array(logist.getRegParam))

New code://output multi classes coefficients

val logist=new LogisticRegression

val ova = new OneVsRest().setClassifier(logist)

val classifier1 = new LogisticRegression().setRegParam(2.0)

val classifier2 = new LogisticRegression().setRegParam(3.0)

val paramMaps = new ParamGridBuilder() .addGrid(ova.classifier,
Array(classifier1, classifier2))

Please help Thanks.



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