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Posted to user@spark.apache.org by Stephen Boesch <ja...@gmail.com> on 2016/05/28 16:06:32 UTC
Multinomial regression with spark.ml version of LogisticRegression
Presently only the mllib version has the one-vs-all approach for
multinomial support. The ml version with ElasticNet support only allows
binary regression.
With feature parity of ml vs mllib having been stated as an objective for
2.0.0 - is there a projected availability of the multinomial regression
in the ml package?
`
Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Stephen Boesch <ja...@gmail.com>.
Thanks Bryan for that pointer : I will follow it. In the meantime the One
vs Rest appears to satisfy the requirements.
2016-05-29 15:40 GMT-07:00 Bryan Cutler <cu...@gmail.com>:
> This is currently being worked on, planned for 2.1 I believe
> https://issues.apache.org/jira/browse/SPARK-7159
> On May 28, 2016 9:31 PM, "Stephen Boesch" <ja...@gmail.com> wrote:
>
>> Thanks Phuong But the point of my post is how to achieve without using
>> the deprecated the mllib pacakge. The mllib package already has
>> multinomial regression built in
>>
>> 2016-05-28 21:19 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
>>
>>> Dear Stephen,
>>>
>>> Yes, you're right, LogisticGradient is in the mllib package, not ml
>>> package. I just want to say that we can build a multinomial logistic
>>> regression model from the current version of Spark.
>>>
>>> Regards,
>>>
>>> Phuong
>>>
>>>
>>>
>>> On Sun, May 29, 2016 at 12:04 AM, Stephen Boesch <ja...@gmail.com>
>>> wrote:
>>> > Hi Phuong,
>>> > The LogisticGradient exists in the mllib but not ml package. The
>>> > LogisticRegression chooses either the breeze LBFGS - if L2 only (not
>>> elastic
>>> > net) and no regularization or the Orthant Wise Quasi Newton (OWLQN)
>>> > otherwise: it does not appear to choose GD in either scenario.
>>> >
>>> > If I have misunderstood your response please do clarify.
>>> >
>>> > thanks stephenb
>>> >
>>> > 2016-05-28 20:55 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
>>> >>
>>> >> Dear Stephen,
>>> >>
>>> >> The Logistic Regression currently supports only binary regression.
>>> >> However, the LogisticGradient does support computing gradient and loss
>>> >> for a multinomial logistic regression. That is, you can train a
>>> >> multinomial logistic regression model with LogisticGradient and a
>>> >> class to solve optimization like LBFGS to get a weight vector of the
>>> >> size (numClassrd-1)*numFeatures.
>>> >>
>>> >>
>>> >> Phuong
>>> >>
>>> >>
>>> >> On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <ja...@gmail.com>
>>> >> wrote:
>>> >> > Followup: just encountered the "OneVsRest" classifier in
>>> >> > ml.classsification: I will look into using it with the binary
>>> >> > LogisticRegression as the provided classifier.
>>> >> >
>>> >> > 2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
>>> >> >>
>>> >> >>
>>> >> >> Presently only the mllib version has the one-vs-all approach for
>>> >> >> multinomial support. The ml version with ElasticNet support only
>>> >> >> allows
>>> >> >> binary regression.
>>> >> >>
>>> >> >> With feature parity of ml vs mllib having been stated as an
>>> objective
>>> >> >> for
>>> >> >> 2.0.0 - is there a projected availability of the multinomial
>>> >> >> regression in
>>> >> >> the ml package?
>>> >> >>
>>> >> >>
>>> >> >>
>>> >> >>
>>> >> >> `
>>> >> >
>>> >> >
>>> >
>>> >
>>>
>>
>>
Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Bryan Cutler <cu...@gmail.com>.
This is currently being worked on, planned for 2.1 I believe
https://issues.apache.org/jira/browse/SPARK-7159
On May 28, 2016 9:31 PM, "Stephen Boesch" <ja...@gmail.com> wrote:
> Thanks Phuong But the point of my post is how to achieve without using
> the deprecated the mllib pacakge. The mllib package already has
> multinomial regression built in
>
> 2016-05-28 21:19 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
>
>> Dear Stephen,
>>
>> Yes, you're right, LogisticGradient is in the mllib package, not ml
>> package. I just want to say that we can build a multinomial logistic
>> regression model from the current version of Spark.
>>
>> Regards,
>>
>> Phuong
>>
>>
>>
>> On Sun, May 29, 2016 at 12:04 AM, Stephen Boesch <ja...@gmail.com>
>> wrote:
>> > Hi Phuong,
>> > The LogisticGradient exists in the mllib but not ml package. The
>> > LogisticRegression chooses either the breeze LBFGS - if L2 only (not
>> elastic
>> > net) and no regularization or the Orthant Wise Quasi Newton (OWLQN)
>> > otherwise: it does not appear to choose GD in either scenario.
>> >
>> > If I have misunderstood your response please do clarify.
>> >
>> > thanks stephenb
>> >
>> > 2016-05-28 20:55 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
>> >>
>> >> Dear Stephen,
>> >>
>> >> The Logistic Regression currently supports only binary regression.
>> >> However, the LogisticGradient does support computing gradient and loss
>> >> for a multinomial logistic regression. That is, you can train a
>> >> multinomial logistic regression model with LogisticGradient and a
>> >> class to solve optimization like LBFGS to get a weight vector of the
>> >> size (numClassrd-1)*numFeatures.
>> >>
>> >>
>> >> Phuong
>> >>
>> >>
>> >> On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <ja...@gmail.com>
>> >> wrote:
>> >> > Followup: just encountered the "OneVsRest" classifier in
>> >> > ml.classsification: I will look into using it with the binary
>> >> > LogisticRegression as the provided classifier.
>> >> >
>> >> > 2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
>> >> >>
>> >> >>
>> >> >> Presently only the mllib version has the one-vs-all approach for
>> >> >> multinomial support. The ml version with ElasticNet support only
>> >> >> allows
>> >> >> binary regression.
>> >> >>
>> >> >> With feature parity of ml vs mllib having been stated as an
>> objective
>> >> >> for
>> >> >> 2.0.0 - is there a projected availability of the multinomial
>> >> >> regression in
>> >> >> the ml package?
>> >> >>
>> >> >>
>> >> >>
>> >> >>
>> >> >> `
>> >> >
>> >> >
>> >
>> >
>>
>
>
Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Stephen Boesch <ja...@gmail.com>.
Thanks Phuong But the point of my post is how to achieve without using the
deprecated the mllib pacakge. The mllib package already has multinomial
regression built in
2016-05-28 21:19 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
> Dear Stephen,
>
> Yes, you're right, LogisticGradient is in the mllib package, not ml
> package. I just want to say that we can build a multinomial logistic
> regression model from the current version of Spark.
>
> Regards,
>
> Phuong
>
>
>
> On Sun, May 29, 2016 at 12:04 AM, Stephen Boesch <ja...@gmail.com>
> wrote:
> > Hi Phuong,
> > The LogisticGradient exists in the mllib but not ml package. The
> > LogisticRegression chooses either the breeze LBFGS - if L2 only (not
> elastic
> > net) and no regularization or the Orthant Wise Quasi Newton (OWLQN)
> > otherwise: it does not appear to choose GD in either scenario.
> >
> > If I have misunderstood your response please do clarify.
> >
> > thanks stephenb
> >
> > 2016-05-28 20:55 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
> >>
> >> Dear Stephen,
> >>
> >> The Logistic Regression currently supports only binary regression.
> >> However, the LogisticGradient does support computing gradient and loss
> >> for a multinomial logistic regression. That is, you can train a
> >> multinomial logistic regression model with LogisticGradient and a
> >> class to solve optimization like LBFGS to get a weight vector of the
> >> size (numClassrd-1)*numFeatures.
> >>
> >>
> >> Phuong
> >>
> >>
> >> On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <ja...@gmail.com>
> >> wrote:
> >> > Followup: just encountered the "OneVsRest" classifier in
> >> > ml.classsification: I will look into using it with the binary
> >> > LogisticRegression as the provided classifier.
> >> >
> >> > 2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
> >> >>
> >> >>
> >> >> Presently only the mllib version has the one-vs-all approach for
> >> >> multinomial support. The ml version with ElasticNet support only
> >> >> allows
> >> >> binary regression.
> >> >>
> >> >> With feature parity of ml vs mllib having been stated as an objective
> >> >> for
> >> >> 2.0.0 - is there a projected availability of the multinomial
> >> >> regression in
> >> >> the ml package?
> >> >>
> >> >>
> >> >>
> >> >>
> >> >> `
> >> >
> >> >
> >
> >
>
Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Phuong LE-HONG <ph...@gmail.com>.
Dear Stephen,
Yes, you're right, LogisticGradient is in the mllib package, not ml
package. I just want to say that we can build a multinomial logistic
regression model from the current version of Spark.
Regards,
Phuong
On Sun, May 29, 2016 at 12:04 AM, Stephen Boesch <ja...@gmail.com> wrote:
> Hi Phuong,
> The LogisticGradient exists in the mllib but not ml package. The
> LogisticRegression chooses either the breeze LBFGS - if L2 only (not elastic
> net) and no regularization or the Orthant Wise Quasi Newton (OWLQN)
> otherwise: it does not appear to choose GD in either scenario.
>
> If I have misunderstood your response please do clarify.
>
> thanks stephenb
>
> 2016-05-28 20:55 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
>>
>> Dear Stephen,
>>
>> The Logistic Regression currently supports only binary regression.
>> However, the LogisticGradient does support computing gradient and loss
>> for a multinomial logistic regression. That is, you can train a
>> multinomial logistic regression model with LogisticGradient and a
>> class to solve optimization like LBFGS to get a weight vector of the
>> size (numClassrd-1)*numFeatures.
>>
>>
>> Phuong
>>
>>
>> On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <ja...@gmail.com>
>> wrote:
>> > Followup: just encountered the "OneVsRest" classifier in
>> > ml.classsification: I will look into using it with the binary
>> > LogisticRegression as the provided classifier.
>> >
>> > 2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
>> >>
>> >>
>> >> Presently only the mllib version has the one-vs-all approach for
>> >> multinomial support. The ml version with ElasticNet support only
>> >> allows
>> >> binary regression.
>> >>
>> >> With feature parity of ml vs mllib having been stated as an objective
>> >> for
>> >> 2.0.0 - is there a projected availability of the multinomial
>> >> regression in
>> >> the ml package?
>> >>
>> >>
>> >>
>> >>
>> >> `
>> >
>> >
>
>
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Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Stephen Boesch <ja...@gmail.com>.
Hi Phuong,
The LogisticGradient exists in the mllib but not ml package. The
LogisticRegression chooses either the breeze LBFGS - if L2 only (not
elastic net) and no regularization or the Orthant Wise Quasi Newton (OWLQN)
otherwise: it does not appear to choose GD in either scenario.
If I have misunderstood your response please do clarify.
thanks stephenb
2016-05-28 20:55 GMT-07:00 Phuong LE-HONG <ph...@gmail.com>:
> Dear Stephen,
>
> The Logistic Regression currently supports only binary regression.
> However, the LogisticGradient does support computing gradient and loss
> for a multinomial logistic regression. That is, you can train a
> multinomial logistic regression model with LogisticGradient and a
> class to solve optimization like LBFGS to get a weight vector of the
> size (numClassrd-1)*numFeatures.
>
>
> Phuong
>
>
> On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <ja...@gmail.com>
> wrote:
> > Followup: just encountered the "OneVsRest" classifier in
> > ml.classsification: I will look into using it with the binary
> > LogisticRegression as the provided classifier.
> >
> > 2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
> >>
> >>
> >> Presently only the mllib version has the one-vs-all approach for
> >> multinomial support. The ml version with ElasticNet support only allows
> >> binary regression.
> >>
> >> With feature parity of ml vs mllib having been stated as an objective
> for
> >> 2.0.0 - is there a projected availability of the multinomial
> regression in
> >> the ml package?
> >>
> >>
> >>
> >>
> >> `
> >
> >
>
Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Phuong LE-HONG <ph...@gmail.com>.
Dear Stephen,
The Logistic Regression currently supports only binary regression.
However, the LogisticGradient does support computing gradient and loss
for a multinomial logistic regression. That is, you can train a
multinomial logistic regression model with LogisticGradient and a
class to solve optimization like LBFGS to get a weight vector of the
size (numClassrd-1)*numFeatures.
Phuong
On Sat, May 28, 2016 at 12:25 PM, Stephen Boesch <ja...@gmail.com> wrote:
> Followup: just encountered the "OneVsRest" classifier in
> ml.classsification: I will look into using it with the binary
> LogisticRegression as the provided classifier.
>
> 2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
>>
>>
>> Presently only the mllib version has the one-vs-all approach for
>> multinomial support. The ml version with ElasticNet support only allows
>> binary regression.
>>
>> With feature parity of ml vs mllib having been stated as an objective for
>> 2.0.0 - is there a projected availability of the multinomial regression in
>> the ml package?
>>
>>
>>
>>
>> `
>
>
---------------------------------------------------------------------
To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
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Re: Multinomial regression with spark.ml version of LogisticRegression
Posted by Stephen Boesch <ja...@gmail.com>.
Followup: just encountered the "OneVsRest" classifier in
ml.classsification: I will look into using it with the binary
LogisticRegression as the provided classifier.
2016-05-28 9:06 GMT-07:00 Stephen Boesch <ja...@gmail.com>:
>
> Presently only the mllib version has the one-vs-all approach for
> multinomial support. The ml version with ElasticNet support only allows
> binary regression.
>
> With feature parity of ml vs mllib having been stated as an objective for
> 2.0.0 - is there a projected availability of the multinomial regression
> in the ml package?
>
>
>
>
> `
>