Re: [MLLib] Logistic Regression and standadization

[Valeriy Avanesov]

Hi Joseph,

I've just tried that out. MLLib indeed returns different models. I see 
no problem here then. How can Filipp's issue be possible?


Best,

Valeriy.


On 04/27/2018 10:00 PM, Valeriy Avanesov wrote:


Hi all,

maybe I'm missing something, but from what was discussed here I've 
gathered that the current mllib implementation returns exactly the 
same model whether standardization is turned on or off.


I suggest to consider an R script (please, see below) which trains two 
penalized logistic regression models (with glmnet) with and without 
standardization. The models are clearly different.


Therefore, the current mllib implementation doesn't follow glmnet.

library(glmnet)
library(e1071)

set.seed(13)

# generate synthetic data
X = cbind(-500:500, (-500:500)*1000)/1000
y = sigmoid(X %*% c(1, 1))
y = rbinom(y, 1, y)

# define two testing points
xTest = rbind(c(-10, -1)/1000, c(-20, -2)/1000)

# train two models: with and without standartization
lambda = 0.01

model = glmnet(X, y, family="binomial", standardize=TRUE, lambda=lambda)
print(predict(model, xTest, type="link"))

model = glmnet(X, y, family="binomial", standardize=FALSE, lambda=lambda)
print(predict(model, xTest, type="link"))

Best,

Valeriy.


On 04/25/2018 12:32 AM, DB Tsai wrote:

As I’m one of the original authors, let me chime in for some comments.

Without the standardization, the LBFGS will be unstable. For example, 
if a feature is being x 10, then the corresponding coefficient should 
be / 10 to make the same prediction. But without standardization, the 
LBFGS will converge to different solution due to numerical stability.


TLDR, this can be implemented in the optimizer or in the trainer. We 
choose to implement in the trainer as LBFGS optimizer in breeze 
suffers this issue. As an user, you don’t need to care much even you 
have one-hot encoding features, and the result should match R.


DB Tsai  |  Siri Open Source Technologies [not a contribution]  |   
Apple, Inc


On Apr 20, 2018, at 5:56 PM, Weichen Xu > wrote:


Right. If regularization item isn't zero, then enable/disable 
standardization will get different result.
But, if comparing results between R-glmnet and mllib, if we set the 
same parameters for regularization/standardization/... , then we 
should get the same result. If not, thenmaybe there's a bug. In this 
case you can paste your testing code and I can help fix it.


On Sat, Apr 21, 2018 at 1:06 AM, Valeriy Avanesov > wrote:


Hi all.

Filipp, do you use l1/l2/elstic-net penalization? I believe in
this case standardization matters.

Best,

Valeriy.


On 04/17/2018 11:40 AM, Weichen Xu wrote:

Not a bug.

When disabling standadization, mllib LR will still do
standadization for features, but it will scale the coefficients
back at the end (after training finished). So it will get the
same result with no standadization training. The purpose of it
is to improve the rate of convergence. So the result should be
always exactly the same with R's glmnet, no matter enable or
disable standadization.

Thanks!

On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang
> wrote:

Hi Filipp,

MLlib’s LR implementation did the same way as R’s glmnet
for standardization.
Actually you don’t need to care about the implementation
detail, as the coefficients are always returned on the
original scale, so it should be return the same result as
other popular ML libraries.
Could you point me where glmnet doesn’t scale features?
I suspect other issues cause your prediction quality
dropped. If you can share the code and data, I can help to
check it.

Thanks
Yanbo



On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin
> wrote:

Hi all,

While migrating from custom LR implementation to MLLib's
LR implementation my colleagues noticed that prediction
quality dropped (accoring to different business metrics).
It's turned out that this issue caused by features
standardization perfomed by MLLib's LR: disregard to
'standardization' option's value all features are scaled
during loss and gradient computation (as well as in few
other places):

https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229



According to comments in the code, standardization should
be implemented the same 

Re: [MLLib] Logistic Regression and standadization

[Joseph PENG]

Hi Valeriy,

Let me make sure we are on the same page.

"the current mllib implementation returns exactly the same model whether
standardization is turned on or off. " This should be corrected as "the
current mllib implementation returns exactly the same model whether
standardization is turned on or off, given regularization is 0; otherwise,
they are expected not the same"

We expect
1. R glmnet and Spark ML share the same behavior, given all other
conditions are the same.
   1.1 Followed by 1, If regularization parameter is not zero, Spark ML
would output 2 different models depending on whether standardization is
turned on or off.

The easiest way to check 1.1 is change setStandardization(false) to true
for a test with regularization != 0, and run the test again which is
expected to be failed.



On Fri, Apr 27, 2018 at 3:08 PM, Valeriy Avanesov  wrote:

> Hi all,
>
> maybe I'm missing something, but from what was discussed here I've
> gathered that the current mllib implementation returns exactly the same
> model whether standardization is turned on or off.
>
> I suggest to consider an R script (please, see below) which trains two
> penalized logistic regression models (with glmnet) with and without
> standardization. The models are clearly different.
>
> BTW. If penalization is turned off, the models are exactly the same.
>
> Therefore, the current mllib implementation doesn't follow glmnet. So,
> does that make it a bug?
> library(glmnet)
> library(e1071)
>
> set.seed(13)
>
> # generate synthetic data
> X = cbind(-500:500, (-500:500)*1000)/10
>
> y = sigmoid(X %*% c(1, 1))
> y = rbinom(y, 1, y)
>
> # define two testing points
> xTest = rbind(c(-10, -10), c(-20, -20))/1000
>
> # train two models: with and without standardization
> lambda = 0.01
>
> model = glmnet(X, y, family="binomial", standardize=TRUE, lambda=lambda)
> print(predict(model, xTest, type="link"))
>
> model = glmnet(X, y, family="binomial", standardize=FALSE, lambda=lambda)
> print(predict(model, xTest, type="link"))
>
> Best,
>
> Valeriy.
>
> On 04/25/2018 12:32 AM, DB Tsai wrote:
>
> As I’m one of the original authors, let me chime in for some comments.
>
> Without the standardization, the LBFGS will be unstable. For example, if a
> feature is being x 10, then the corresponding coefficient should be / 10 to
> make the same prediction. But without standardization, the LBFGS will
> converge to different solution due to numerical stability.
>
> TLDR, this can be implemented in the optimizer or in the trainer. We
> choose to implement in the trainer as LBFGS optimizer in breeze suffers
> this issue. As an user, you don’t need to care much even you have one-hot
> encoding features, and the result should match R.
>
> DB Tsai  |  Siri Open Source Technologies [not a contribution]  |  
> Apple, Inc
>
> On Apr 20, 2018, at 5:56 PM, Weichen Xu  wrote:
>
> Right. If regularization item isn't zero, then enable/disable
> standardization will get different result.
> But, if comparing results between R-glmnet and mllib, if we set the same
> parameters for regularization/standardization/... , then we should get
> the same result. If not, then maybe there's a bug. In this case you can
> paste your testing code and I can help fix it.
>
> On Sat, Apr 21, 2018 at 1:06 AM, Valeriy Avanesov 
> wrote:
>
>> Hi all.
>>
>> Filipp, do you use l1/l2/elstic-net penalization? I believe in this case
>> standardization matters.
>>
>> Best,
>>
>> Valeriy.
>>
>> On 04/17/2018 11:40 AM, Weichen Xu wrote:
>>
>> Not a bug.
>>
>> When disabling standadization, mllib LR will still do standadization for
>> features, but it will scale the coefficients back at the end (after
>> training finished). So it will get the same result with no standadization
>> training. The purpose of it is to improve the rate of convergence. So
>> the result should be always exactly the same with R's glmnet, no matter
>> enable or disable standadization.
>>
>> Thanks!
>>
>> On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang  wrote:
>>
>>> Hi Filipp,
>>>
>>> MLlib’s LR implementation did the same way as R’s glmnet for
>>> standardization.
>>> Actually you don’t need to care about the implementation detail, as the
>>> coefficients are always returned on the original scale, so it should be
>>> return the same result as other popular ML libraries.
>>> Could you point me where glmnet doesn’t scale features?
>>> I suspect other issues cause your prediction quality dropped. If you can
>>> share the code and data, I can help to check it.
>>>
>>> Thanks
>>> Yanbo
>>>
>>>
>>> On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin 
>>> wrote:
>>>
>>> Hi all,
>>>
>>> While migrating from custom LR implementation to MLLib's LR
>>> implementation my colleagues noticed that prediction quality dropped
>>> (accoring to different business metrics).
>>> It's turned out that this issue caused by features standardization

Re: [MLLib] Logistic Regression and standadization

[Valeriy Avanesov]

Hi all,

maybe I'm missing something, but from what was discussed here I've 
gathered that the current mllib implementation returns exactly the same 
model whether standardization is turned on or off.


I suggest to consider an R script (please, see below) which trains two 
penalized logistic regression models (with glmnet) with and without 
standardization. The models are clearly different.


BTW. If penalization is turned off, the models are exactly the same.

Therefore, the current mllib implementation doesn't follow glmnet. So, 
does that make it a bug?


library(glmnet)
library(e1071)

set.seed(13)

# generate synthetic data
X = cbind(-500:500, (-500:500)*1000)/10

y = sigmoid(X %*% c(1, 1))
y = rbinom(y, 1, y)

# define two testing points
xTest = rbind(c(-10, -10), c(-20, -20))/1000

# train two models: with and without standardization
lambda = 0.01

model = glmnet(X, y, family="binomial", standardize=TRUE, lambda=lambda)
print(predict(model, xTest, type="link"))

model = glmnet(X, y, family="binomial", standardize=FALSE, lambda=lambda)
print(predict(model, xTest, type="link"))

Best,

Valeriy.


On 04/25/2018 12:32 AM, DB Tsai wrote:

As I’m one of the original authors, let me chime in for some comments.

Without the standardization, the LBFGS will be unstable. For example, 
if a feature is being x 10, then the corresponding coefficient should 
be / 10 to make the same prediction. But without standardization, the 
LBFGS will converge to different solution due to numerical stability.


TLDR, this can be implemented in the optimizer or in the trainer. We 
choose to implement in the trainer as LBFGS optimizer in breeze 
suffers this issue. As an user, you don’t need to care much even you 
have one-hot encoding features, and the result should match R.


DB Tsai  |  Siri Open Source Technologies [not a contribution]  |   
Apple, Inc


On Apr 20, 2018, at 5:56 PM, Weichen Xu > wrote:


Right. If regularization item isn't zero, then enable/disable 
standardization will get different result.
But, if comparing results between R-glmnet and mllib, if we set the 
same parameters for regularization/standardization/... , then we 
should get the same result. If not, thenmaybe there's a bug. In this 
case you can paste your testing code and I can help fix it.


On Sat, Apr 21, 2018 at 1:06 AM, Valeriy Avanesov > wrote:


Hi all.

Filipp, do you use l1/l2/elstic-net penalization? I believe in
this case standardization matters.

Best,

Valeriy.


On 04/17/2018 11:40 AM, Weichen Xu wrote:

Not a bug.

When disabling standadization, mllib LR will still do
standadization for features, but it will scale the coefficients
back at the end (after training finished). So it will get the
same result with no standadization training. The purpose of it
is to improve the rate of convergence. So the result should be
always exactly the same with R's glmnet, no matter enable or
disable standadization.

Thanks!

On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang > wrote:

Hi Filipp,

MLlib’s LR implementation did the same way as R’s glmnet for
standardization.
Actually you don’t need to care about the implementation
detail, as the coefficients are always returned on the
original scale, so it should be return the same result as
other popular ML libraries.
Could you point me where glmnet doesn’t scale features?
I suspect other issues cause your prediction quality
dropped. If you can share the code and data, I can help to
check it.

Thanks
Yanbo



On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin
> wrote:

Hi all,

While migrating from custom LR implementation to MLLib's LR
implementation my colleagues noticed that prediction
quality dropped (accoring to different business metrics).
It's turned out that this issue caused by features
standardization perfomed by MLLib's LR: disregard to
'standardization' option's value all features are scaled
during loss and gradient computation (as well as in few
other places):

https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229



According to comments in the code, standardization should
be implemented the same way it was implementes in R's
glmnet package. I've looked through corresponding Fortran
code, an it 

Re: [MLLib] Logistic Regression and standadization

[DB Tsai]

As I’m one of the original authors, let me chime in for some comments. 

Without the standardization, the LBFGS will be unstable. For example, if a 
feature is being x 10, then the corresponding coefficient should be / 10 to 
make the same prediction. But without standardization, the LBFGS will converge 
to different solution due to numerical stability.

TLDR, this can be implemented in the optimizer or in the trainer. We choose to 
implement in the trainer as LBFGS optimizer in breeze suffers this issue. As an 
user, you don’t need to care much even you have one-hot encoding features, and 
the result should match R. 

DB Tsai  |  Siri Open Source Technologies [not a contribution]  |   Apple, Inc

> On Apr 20, 2018, at 5:56 PM, Weichen Xu  wrote:
> 
> Right. If regularization item isn't zero, then enable/disable standardization 
> will get different result.
> But, if comparing results between R-glmnet and mllib, if we set the same 
> parameters for regularization/standardization/... , then we should get the 
> same result. If not, then maybe there's a bug. In this case you can paste 
> your testing code and I can help fix it.
> 
> On Sat, Apr 21, 2018 at 1:06 AM, Valeriy Avanesov  > wrote:
> Hi all.
> 
> Filipp, do you use l1/l2/elstic-net penalization? I believe in this case 
> standardization matters.
> 
> Best,
> 
> Valeriy.
> 
> 
> On 04/17/2018 11:40 AM, Weichen Xu wrote:
>> Not a bug.
>> 
>> When disabling standadization, mllib LR will still do standadization for 
>> features, but it will scale the coefficients back at the end (after training 
>> finished). So it will get the same result with no standadization training. 
>> The purpose of it is to improve the rate of convergence. So the result 
>> should be always exactly the same with R's glmnet, no matter enable or 
>> disable standadization. 
>> 
>> Thanks!
>> 
>> On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang > > wrote:
>> Hi Filipp,
>> 
>> MLlib’s LR implementation did the same way as R’s glmnet for 
>> standardization. 
>> Actually you don’t need to care about the implementation detail, as the 
>> coefficients are always returned on the original scale, so it should be 
>> return the same result as other popular ML libraries.
>> Could you point me where glmnet doesn’t scale features? 
>> I suspect other issues cause your prediction quality dropped. If you can 
>> share the code and data, I can help to check it.
>> 
>> Thanks
>> Yanbo
>> 
>> 
>>> On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin >> > wrote:
>>> 
>>> Hi all,
>>> 
>>> While migrating from custom LR implementation to MLLib's LR implementation 
>>> my colleagues noticed that prediction quality dropped (accoring to 
>>> different business metrics).
>>> It's turned out that this issue caused by features standardization perfomed 
>>> by MLLib's LR: disregard to 'standardization' option's value all features 
>>> are scaled during loss and gradient computation (as well as in few other 
>>> places): 
>>> https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229
>>>  
>>> 
>>> 
>>> According to comments in the code, standardization should be implemented 
>>> the same way it was implementes in R's glmnet package. I've looked through 
>>> corresponding Fortran code, an it seems like glmnet don't scale features 
>>> when you're disabling standardisation (but MLLib still does).
>>> 
>>> Our models contains multiple one-hot encoded features and scaling them is a 
>>> pretty bad idea.
>>> 
>>> Why MLLib's LR always scale all features? From my POV it's a bug.
>>> 
>>> Thanks in advance,
>>> Filipp.
>>> 
>> 
>> 
> 
> 

Re: [MLLib] Logistic Regression and standadization

[Weichen Xu]

Right. If regularization item isn't zero, then enable/disable
standardization will get different result.
But, if comparing results between R-glmnet and mllib, if we set the same
parameters for regularization/standardization/... , then we should get the
same result. If not, then maybe there's a bug. In this case you can paste
your testing code and I can help fix it.

On Sat, Apr 21, 2018 at 1:06 AM, Valeriy Avanesov  wrote:

> Hi all.
>
> Filipp, do you use l1/l2/elstic-net penalization? I believe in this case
> standardization matters.
>
> Best,
>
> Valeriy.
>
> On 04/17/2018 11:40 AM, Weichen Xu wrote:
>
> Not a bug.
>
> When disabling standadization, mllib LR will still do standadization for
> features, but it will scale the coefficients back at the end (after
> training finished). So it will get the same result with no standadization
> training. The purpose of it is to improve the rate of convergence. So the
> result should be always exactly the same with R's glmnet, no matter
> enable or disable standadization.
>
> Thanks!
>
> On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang  wrote:
>
>> Hi Filipp,
>>
>> MLlib’s LR implementation did the same way as R’s glmnet for
>> standardization.
>> Actually you don’t need to care about the implementation detail, as the
>> coefficients are always returned on the original scale, so it should be
>> return the same result as other popular ML libraries.
>> Could you point me where glmnet doesn’t scale features?
>> I suspect other issues cause your prediction quality dropped. If you can
>> share the code and data, I can help to check it.
>>
>> Thanks
>> Yanbo
>>
>>
>> On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin 
>> wrote:
>>
>> Hi all,
>>
>> While migrating from custom LR implementation to MLLib's LR
>> implementation my colleagues noticed that prediction quality dropped
>> (accoring to different business metrics).
>> It's turned out that this issue caused by features standardization
>> perfomed by MLLib's LR: disregard to 'standardization' option's value all
>> features are scaled during loss and gradient computation (as well as in few
>> other places): https://github.com/apache/spark/blob/6cc7021a40b64c
>> 41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/
>> apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229
>>
>> According to comments in the code, standardization should be implemented
>> the same way it was implementes in R's glmnet package. I've looked through
>> corresponding Fortran code, an it seems like glmnet don't scale features
>> when you're disabling standardisation (but MLLib still does).
>>
>> Our models contains multiple one-hot encoded features and scaling them is
>> a pretty bad idea.
>>
>> Why MLLib's LR always scale all features? From my POV it's a bug.
>>
>> Thanks in advance,
>> Filipp.
>>
>>
>>
>
>

Re: [MLLib] Logistic Regression and standadization

[Valeriy Avanesov]

Hi all.

Filipp, do you use l1/l2/elstic-net penalization? I believe in this case 
standardization matters.


Best,

Valeriy.


On 04/17/2018 11:40 AM, Weichen Xu wrote:

Not a bug.

When disabling standadization, mllib LR will still do standadization 
for features, but it will scale the coefficients back at the end 
(after training finished). So it will get the same result with no 
standadization training. The purpose of it is to improve the rate of 
convergence. So the result should be always exactly the same with 
R's glmnet, no matter enable or disable standadization.


Thanks!

On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang > wrote:


Hi Filipp,

MLlib’s LR implementation did the same way as R’s glmnet for
standardization.
Actually you don’t need to care about the implementation detail,
as the coefficients are always returned on the original scale, so
it should be return the same result as other popular ML libraries.
Could you point me where glmnet doesn’t scale features?
I suspect other issues cause your prediction quality dropped. If
you can share the code and data, I can help to check it.

Thanks
Yanbo



On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin
> wrote:

Hi all,

While migrating from custom LR implementation to MLLib's LR
implementation my colleagues noticed that prediction quality
dropped (accoring to different business metrics).
It's turned out that this issue caused by features
standardization perfomed by MLLib's LR: disregard to
'standardization' option's value all features are scaled during
loss and gradient computation (as well as in few other places):

https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229



According to comments in the code, standardization should be
implemented the same way it was implementes in R's glmnet
package. I've looked through corresponding Fortran code, an it
seems like glmnet don't scale features when you're disabling
standardisation (but MLLib still does).

Our models contains multiple one-hot encoded features and scaling
them is a pretty bad idea.

Why MLLib's LR always scale all features? From my POV it's a bug.

Thanks in advance,
Filipp.

Re: [MLLib] Logistic Regression and standadization

[Weichen Xu]

Not a bug.

When disabling standadization, mllib LR will still do standadization for
features, but it will scale the coefficients back at the end (after
training finished). So it will get the same result with no standadization
training. The purpose of it is to improve the rate of convergence. So the
result should be always exactly the same with R's glmnet, no matter enable
or disable standadization.

Thanks!

On Sat, Apr 14, 2018 at 2:21 AM, Yanbo Liang  wrote:

> Hi Filipp,
>
> MLlib’s LR implementation did the same way as R’s glmnet for
> standardization.
> Actually you don’t need to care about the implementation detail, as the
> coefficients are always returned on the original scale, so it should be
> return the same result as other popular ML libraries.
> Could you point me where glmnet doesn’t scale features?
> I suspect other issues cause your prediction quality dropped. If you can
> share the code and data, I can help to check it.
>
> Thanks
> Yanbo
>
>
> On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin 
> wrote:
>
> Hi all,
>
> While migrating from custom LR implementation to MLLib's LR implementation
> my colleagues noticed that prediction quality dropped (accoring to
> different business metrics).
> It's turned out that this issue caused by features standardization
> perfomed by MLLib's LR: disregard to 'standardization' option's value all
> features are scaled during loss and gradient computation (as well as in few
> other places): https://github.com/apache/spark/blob/
> 6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/
> scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229
>
> According to comments in the code, standardization should be implemented
> the same way it was implementes in R's glmnet package. I've looked through
> corresponding Fortran code, an it seems like glmnet don't scale features
> when you're disabling standardisation (but MLLib still does).
>
> Our models contains multiple one-hot encoded features and scaling them is
> a pretty bad idea.
>
> Why MLLib's LR always scale all features? From my POV it's a bug.
>
> Thanks in advance,
> Filipp.
>
>
>

Re: [MLLib] Logistic Regression and standadization

[Yanbo Liang]

Hi Filipp,

MLlib’s LR implementation did the same way as R’s glmnet for standardization. 
Actually you don’t need to care about the implementation detail, as the 
coefficients are always returned on the original scale, so it should be return 
the same result as other popular ML libraries.
Could you point me where glmnet doesn’t scale features? 
I suspect other issues cause your prediction quality dropped. If you can share 
the code and data, I can help to check it.

Thanks
Yanbo

> On Apr 8, 2018, at 1:09 PM, Filipp Zhinkin  wrote:
> 
> Hi all,
> 
> While migrating from custom LR implementation to MLLib's LR implementation my 
> colleagues noticed that prediction quality dropped (accoring to different 
> business metrics).
> It's turned out that this issue caused by features standardization perfomed 
> by MLLib's LR: disregard to 'standardization' option's value all features are 
> scaled during loss and gradient computation (as well as in few other places): 
> https://github.com/apache/spark/blob/6cc7021a40b64c41a51f337ec4be9545a25e838c/mllib/src/main/scala/org/apache/spark/ml/optim/aggregator/LogisticAggregator.scala#L229
>  
> 
> 
> According to comments in the code, standardization should be implemented the 
> same way it was implementes in R's glmnet package. I've looked through 
> corresponding Fortran code, an it seems like glmnet don't scale features when 
> you're disabling standardisation (but MLLib still does).
> 
> Our models contains multiple one-hot encoded features and scaling them is a 
> pretty bad idea.
> 
> Why MLLib's LR always scale all features? From my POV it's a bug.
> 
> Thanks in advance,
> Filipp.
>