Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

[Yan Facai]

Hi, jinhong.
Do you use `setRegParam`, which is 0.0 by default ?


Both elasticNetParam and regParam are required if regularization is need.

val regParamL1 = $(elasticNetParam) * $(regParam)
val regParamL2 = (1.0 - $(elasticNetParam)) * $(regParam)




On Mon, Mar 20, 2017 at 6:31 PM, Yanbo Liang  wrote:

> Do you want to get sparse model that most of the coefficients are zeros?
> If yes, using L1 regularization leads to sparsity. But the
> LogisticRegressionModel coefficients vector's size is still equal with the
> number of features, you can get the non-zero elements manually. Actually,
> it would be a sparse vector (or matrix for multinomial case) if it's sparse
> enough.
>
> Thanks
> Yanbo
>
> On Sun, Mar 19, 2017 at 5:02 AM, Dhanesh Padmanabhan <
> dhanesh12...@gmail.com> wrote:
>
>> It shouldn't be difficult to convert the coefficients to a sparse vector.
>> Not sure if that is what you are looking for
>>
>> -Dhanesh
>>
>> On Sun, Mar 19, 2017 at 5:02 PM jinhong lu  wrote:
>>
>> Thanks Dhanesh,  and how about the features question?
>>
>> 在 2017年3月19日，19:08，Dhanesh Padmanabhan  写道：
>>
>> Dhanesh
>>
>>
>> Thanks,
>> lujinhong
>>
>> --
>> Dhanesh
>> +91-9741125245 <+91%2097411%2025245>
>>
>
>

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

[Yanbo Liang]

Do you want to get sparse model that most of the coefficients are zeros? If
yes, using L1 regularization leads to sparsity. But the
LogisticRegressionModel coefficients vector's size is still equal with the
number of features, you can get the non-zero elements manually. Actually,
it would be a sparse vector (or matrix for multinomial case) if it's sparse
enough.

Thanks
Yanbo

On Sun, Mar 19, 2017 at 5:02 AM, Dhanesh Padmanabhan  wrote:

> It shouldn't be difficult to convert the coefficients to a sparse vector.
> Not sure if that is what you are looking for
>
> -Dhanesh
>
> On Sun, Mar 19, 2017 at 5:02 PM jinhong lu  wrote:
>
> Thanks Dhanesh,  and how about the features question?
>
> 在 2017年3月19日，19:08，Dhanesh Padmanabhan  写道：
>
> Dhanesh
>
>
> Thanks,
> lujinhong
>
> --
> Dhanesh
> +91-9741125245
>

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

[Dhanesh Padmanabhan]

It shouldn't be difficult to convert the coefficients to a sparse vector.
Not sure if that is what you are looking for

-Dhanesh

On Sun, Mar 19, 2017 at 5:02 PM jinhong lu  wrote:

Thanks Dhanesh,  and how about the features question?

在 2017年3月19日，19:08，Dhanesh Padmanabhan  写道：

Dhanesh


Thanks,
lujinhong

-- 
Dhanesh
+91-9741125245

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

[jinhong lu]

Thanks Dhanesh,  and how about the features question?

> 在 2017年3月19日，19:08，Dhanesh Padmanabhan  写道：
> 
> Dhanesh

Thanks,
lujinhong

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

[Dhanesh Padmanabhan]

binomial. Please use in combination with onevsrest for multi-class problems
in spark 2.0.2

Dhanesh
+91-9741125245

On Sun, Mar 19, 2017 at 4:29 PM, jinhong lu  wrote:

> By the way, I found in spark 2.1 I can use setFamily() to decide binomial
> or multinomial, but how  can I do the same thing in spark 2.0.2?
> If  not support , which one is used in spark 2.0.2?  binomial or
> multinomial?
>
> 在 2017年3月19日，18:12，jinhong lu  写道：
>
>
> I train my LogisticRegressionModel like this,  I want my model to retain
> only some of the features(e.g. 500 of them), not all the  features.
> What shou I do?
> I use .setElasticNetParam(1.0), but still all the features is
> in lrModel.coefficients.
>
>  import org.apache.spark.ml.classification.LogisticRegression
>  val data=spark.read.format("libsvm").option("numFeatures",
> "").load("/tmp/data/training_data3")
>  val Array(trainingData, testData) = data.randomSplit(Array(0.5, 0.5),
> seed = 1234L)
>
>  val lr = new LogisticRegression()
>  val lrModel = lr.fit(trainingData)
>  println(s"Coefficients: ${lrModel.coefficients} Intercept:
> ${lrModel.intercept}")
>
>  val predictions = lrModel.transform(testData)
>  predictions.show()
>
>
> Thanks,
> lujinhong
>
>
> Thanks,
> lujinhong
>
>

Re: how to retain part of the features in LogisticRegressionModel (spark2.0)

[jinhong lu]

By the way, I found in spark 2.1 I can use setFamily() to decide binomial or 
multinomial, but how  can I do the same thing in spark 2.0.2?
If  not support , which one is used in spark 2.0.2?  binomial or multinomial?

> 在 2017年3月19日，18:12，jinhong lu  写道：
> 
> 
> I train my LogisticRegressionModel like this,  I want my model to retain only 
> some of the features(e.g. 500 of them), not all the  features. What shou 
> I do? 
> I use .setElasticNetParam(1.0), but still all the features is in 
> lrModel.coefficients.
> 
> import org.apache.spark.ml.classification.LogisticRegression
> val 
> data=spark.read.format("libsvm").option("numFeatures","").load("/tmp/data/training_data3")
>  
> val Array(trainingData, testData) = data.randomSplit(Array(0.5, 0.5), 
> seed = 1234L)
> 
> val lr = new LogisticRegression()
> val lrModel = lr.fit(trainingData)
> println(s"Coefficients: ${lrModel.coefficients} Intercept: 
> ${lrModel.intercept}")
> 
> val predictions = lrModel.transform(testData)
> predictions.show()
> 
> 
> Thanks, 
> lujinhong
> 

Thanks,
lujinhong

how to retain part of the features in LogisticRegressionModel (spark2.0)

[jinhong lu]

I train my LogisticRegressionModel like this,  I want my model to retain only 
some of the features(e.g. 500 of them), not all the  features. What shou I 
do? 
I use .setElasticNetParam(1.0), but still all the features is in 
lrModel.coefficients.

  import org.apache.spark.ml.classification.LogisticRegression
  val 
data=spark.read.format("libsvm").option("numFeatures","").load("/tmp/data/training_data3")
 
  val Array(trainingData, testData) = data.randomSplit(Array(0.5, 0.5), 
seed = 1234L)

  val lr = new LogisticRegression()
  val lrModel = lr.fit(trainingData)
  println(s"Coefficients: ${lrModel.coefficients} Intercept: 
${lrModel.intercept}")

  val predictions = lrModel.transform(testData)
  predictions.show()


Thanks, 
lujinhong