Re: [scikit-learn] SVM-RFE

[Brown J.B. via scikit-learn]

I certainly am guilty of only commenting in the mailing list and not
engaging more via GitHub! :)
(Much like many of you PIs on this list, the typical
ActualWork-GrantWriting-ReportWriting-InvitedLectures-RealLifeParenting
cycle eats the day away.)

While I've failed previously to get involved after showing interest, let's
see if I can't actually succeed for once.

2019年12月5日(木) 1:14 Andreas Mueller :

> PR welcome ;)
>
>
> On 12/3/19 11:02 PM, Brown J.B. via scikit-learn wrote:
>
> 2019年12月3日(火) 5:36 Andreas Mueller :
>
>> It does provide the ranking of features in the ranking_ attribute and it
>> provides the cross-validation accuracies for all subsets in grid_scores_.
>> It doesn't provide the feature weights for all subsets, but that's
>> something that would be easy to add if it's desired.
>>
>
> I would guess that there is some population of the user base that would
> like to track the per-iteration feature weights.
> It would appear to me that a straightforward (un-optimized) implementation
> would be place a NaN value for a feature once it is eliminated, so that a
> numpy.ndarray can be returned and immediately dumped to
> matplotlib.pcolormesh or other visualization routines in various libraries.
>
> Just an idea.
>
> J.B.
>
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Re: [scikit-learn] SVM-RFE

[Andreas Mueller]

PR welcome ;)


On 12/3/19 11:02 PM, Brown J.B. via scikit-learn wrote:
2019年12月3日(火) 5:36 Andreas Mueller >:


It does provide the ranking of features in the ranking_ attribute
and it provides the cross-validation accuracies for all subsets in
grid_scores_.
It doesn't provide the feature weights for all subsets, but that's
something that would be easy to add if it's desired.


I would guess that there is some population of the user base that 
would like to track the per-iteration feature weights.
It would appear to me that a straightforward (un-optimized) 
implementation would be place a NaN value for a feature once it is 
eliminated, so that a numpy.ndarray can be returned and immediately 
dumped to matplotlib.pcolormesh or other visualization routines in 
various libraries.


Just an idea.

J.B.

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Re: [scikit-learn] SVM-RFE

[Brown J.B. via scikit-learn]

2019年12月3日(火) 5:36 Andreas Mueller :

> It does provide the ranking of features in the ranking_ attribute and it
> provides the cross-validation accuracies for all subsets in grid_scores_.
> It doesn't provide the feature weights for all subsets, but that's
> something that would be easy to add if it's desired.
>

I would guess that there is some population of the user base that would
like to track the per-iteration feature weights.
It would appear to me that a straightforward (un-optimized) implementation
would be place a NaN value for a feature once it is eliminated, so that a
numpy.ndarray can be returned and immediately dumped to
matplotlib.pcolormesh or other visualization routines in various libraries.

Just an idea.

J.B.
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Re: [scikit-learn] SVM-RFE

[Andreas Mueller]

It does provide the ranking of features in the ranking_ attribute and it 
provides the cross-validation accuracies for all subsets in grid_scores_.
It doesn't provide the feature weights for all subsets, but that's 
something that would be easy to add if it's desired.




On 11/25/19 10:50 AM, Malik Yousef wrote:
It does not provide access for tracing the step by step feature 
weights and predictive ability- The user provides the n_feature.


Malik

---
*Prof. Malik Yousef (Associate Professor) *
*The Head of the** Galilee Digital Health Research Center (GDH)***
*Zefat Academic College , Department of Information System *
Home Page:
https://malikyousef.com/
Google Scholar Profile :
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On Mon, Nov 25, 2019 at 1:36 PM Brown J.B. via scikit-learn 
mailto:scikit-learn@python.org>> wrote:



2019年11月23日(土) 2:12 Andreas Mueller mailto:t3k...@gmail.com>>:

I think you can also use RFECV directly without doing any
wrapping.


Your request to do performance checking of the steps of
SVM-RFE is a pretty common task.



Yes, RFECV works well (and I should know as an appreciative
long-time user ;-)  ), but does it actually provide a mechanism
(accessors) for tracing the step by step feature weights and
predictive ability as the features are continually reduced?
(Or perhaps it's because I'm looking at 0.20.1 and 0.21.2
documentation...?)

J.B.
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Re: [scikit-learn] SVM-RFE

[Malik Yousef]

It does not provide access for tracing the step by step feature weights and
predictive ability- The user provides the n_feature.

Malik

---
*Prof. Malik Yousef (Associate Professor)  *
*The Head of the** Galilee Digital Health Research Center (GDH)*

*Zefat Academic College , Department of Information System  *
Home Page:
https://malikyousef.com/
Google Scholar Profile :
https://scholar.google.com/citations?user=9UCZ_q4J=en=ao




On Mon, Nov 25, 2019 at 1:36 PM Brown J.B. via scikit-learn <
scikit-learn@python.org> wrote:

>
> 2019年11月23日(土) 2:12 Andreas Mueller :
>
>> I think you can also use RFECV directly without doing any wrapping.
>>
>> Your request to do performance checking of the steps of SVM-RFE is a
>> pretty common task.
>>
>>
> Yes, RFECV works well (and I should know as an appreciative long-time user
> ;-)  ), but does it actually provide a mechanism (accessors) for tracing
> the step by step feature weights and predictive ability as the features are
> continually reduced?
> (Or perhaps it's because I'm looking at 0.20.1 and 0.21.2
> documentation...?)
>
> J.B.
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Re: [scikit-learn] SVM-RFE

[Brown J.B. via scikit-learn]

2019年11月23日(土) 2:12 Andreas Mueller :

> I think you can also use RFECV directly without doing any wrapping.
>
> Your request to do performance checking of the steps of SVM-RFE is a
> pretty common task.
>
>
Yes, RFECV works well (and I should know as an appreciative long-time user
;-)  ), but does it actually provide a mechanism (accessors) for tracing
the step by step feature weights and predictive ability as the features are
continually reduced?
(Or perhaps it's because I'm looking at 0.20.1 and 0.21.2 documentation...?)

J.B.
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Re: [scikit-learn] SVM-RFE

[Andreas Mueller]

I think you can also use RFECV directly without doing any wrapping.


On 11/20/19 12:24 AM, Brown J.B. via scikit-learn wrote:

Dear Malik,

Your request to do performance checking of the steps of SVM-RFE is a 
pretty common task.


Since the contributors to scikit-learn have done great to make the 
interface to RFE easy to use, the only real work required from you 
would be to build a small wrapper function that:
(a) computes the step sizes you want to output prediction performances 
for, and
(b) loops over the step sizes, making each step size the n_features 
attribute of RFE (and built from the remaining features), making 
predictions from a SVM retrained (and possibly optimized) on the 
reduced feature set, and then outputting your metric(s) appropriate to 
your problem.


Tracing the feature weights is then done by accessing the "coef_" 
attribute of the linear SVM trained.

This can be output in loop step (b) as well.

where each time 10% for the features are removed.
How one can get the accuracy overall the levels of the elimination
stages. For example, I want to get performance over 1000 features,
900 features, 800 features,,2 features, 1 feature.


Just a technicality, but by 10% reduction you would have
1000, 900, 810, 729, 656, ... .
Either way, if you allow your wrapper function to take a pre-computed 
list of feature sizes, you can flexibly change between a systematic 
way or a context-informed way of specifying feature sizes (and 
resulting weights) to trace.


Hope this helps.

J.B. Brown
Kyoto University Graduate School of Medicine


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Re: [scikit-learn] SVM-RFE

[Brown J.B. via scikit-learn]

Dear Malik,

Your request to do performance checking of the steps of SVM-RFE is a pretty
common task.

Since the contributors to scikit-learn have done great to make the
interface to RFE easy to use, the only real work required from you would be
to build a small wrapper function that:
(a) computes the step sizes you want to output prediction performances for,
and
(b) loops over the step sizes, making each step size the n_features
attribute of RFE (and built from the remaining features), making
predictions from a SVM retrained (and possibly optimized) on the reduced
feature set, and then outputting your metric(s) appropriate to your problem.

Tracing the feature weights is then done by accessing the "coef_" attribute
of the linear SVM trained.
This can be output in loop step (b) as well.

where each time 10% for the features are removed.
> How one can get the accuracy overall the levels of the elimination stages.
> For example, I want to get performance over 1000 features, 900 features,
> 800 features,,2 features, 1 feature.
>

Just a technicality, but by 10% reduction you would have
1000, 900, 810, 729, 656, ... .
Either way, if you allow your wrapper function to take a pre-computed list
of feature sizes, you can flexibly change between a systematic way or a
context-informed way of specifying feature sizes (and resulting weights) to
trace.

Hope this helps.

J.B. Brown
Kyoto University Graduate School of Medicine
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[scikit-learn] SVM-RFE

[Malik Yousef]

Hello,
Let's assume that I have data with 1000 features. I want to apply SVM-RFE
on this data where each time 10% for the features are removed.
How one can get the accuracy overall the levels of the elimination stages.
For example, I want to get performance over 1000 features, 900 features,
800 features,,2 features, 1 feature.
Also, I want to keep track of the features in each level.

[image: image.png]

Best

---
*Prof. Malik Yousef (Associate Professor)  *
*The Head of the** Galilee Digital Health Research Center (GDH)*

*Zefat Academic College , Department of Information System  *
Home Page:
https://malikyousef.com/
Google Scholar Profile :
https://scholar.google.com/citations?user=9UCZ_q4J=en=ao

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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Malik Yousef]

Thanks for your reply.
How I can set the name of the positive class in LinearSVC() for a two-class
problem that when using the prediction then I will get positive scores fro
that positive class?

Malik
---
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*Zefat Academic College , Department of Information System  *
Home Page:
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Google Scholar Profile :
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On Thu, Aug 1, 2019 at 8:43 PM Joel Nothman  wrote:

> Or use scoring=make_scorer(f1_score, pos_label='n.pre')
>
> On Fri, 2 Aug 2019 at 06:15, Malik Yousef  wrote:
>
>> Hello
>> When in using the scoring to be 'f1' then i get an error.
>> Here is the code and the error
>>
>> X=data
>> y=target_column
>> classifier = LinearSVC()
>> rfecv = RFECV(estimator=classifier, step=0.1,
>> cv=StratifiedKFold(5),scoring='f1')
>> rfecv.fit(X, y)
>>
>> The error is :
>> ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'],
>> dtype='>
>> Please your help
>> (I'm new to using scikit-leanr)
>>
>> ---
>> *Prof. Malik Yousef , Associate Professor   *
>> *Zefat Academic College , Department of Information System  *
>> Home Page:
>> https://malikyousef.com/
>> Google Scholar Profile :
>> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>>
>> 
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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Joel Nothman]

Or use scoring=make_scorer(f1_score, pos_label='n.pre')

On Fri, 2 Aug 2019 at 06:15, Malik Yousef  wrote:

> Hello
> When in using the scoring to be 'f1' then i get an error.
> Here is the code and the error
>
> X=data
> y=target_column
> classifier = LinearSVC()
> rfecv = RFECV(estimator=classifier, step=0.1,
> cv=StratifiedKFold(5),scoring='f1')
> rfecv.fit(X, y)
>
> The error is :
> ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'],
> dtype='
> Please your help
> (I'm new to using scikit-leanr)
>
> ---
> *Prof. Malik Yousef , Associate Professor   *
> *Zefat Academic College , Department of Information System  *
> Home Page:
> https://malikyousef.com/
> Google Scholar Profile :
> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>
> 
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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Guillaume Lemaître]

  You can look a the string example:https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.htmlSent from my phone - sorry to be brief and potential misspell.From: malik.you...@gmail.comSent: 1 August 2019 23:19To: scikit-learn@python.orgReply to: scikit-learn@python.orgSubject: Re: [scikit-learn] SVM-RFE with scoring = 'f1'  Can you give me an example?---
Prof. Malik Yousef , Associate Professor                           Zefat Academic College , Department of Information System  Home Page:https://malikyousef.com/Google Scholar Profile :https://scholar.google.com/citations?user=9UCZ_q4J=en=aoOn Thu, Aug 1, 2019 at 5:15 PM Guillaume Lemaître <g.lemaitr...@gmail.com> wrote:you can use the LabelEncoder then. It will encode the classes for you.On Thu, 1 Aug 2019 at 23:04, Malik Yousef <malik.you...@gmail.com> wrote:The only string column is the label column(class labels, n.pre, and c.pre). if the scoring = 'accuracy' then it works fine.So I think I need somehow to set up the postive_lable.. that I don't know how to do that.---
Prof. Malik Yousef , Associate Professor                           Zefat Academic College , Department of Information System  Home Page:https://malikyousef.com/Google Scholar Profile :https://scholar.google.com/citations?user=9UCZ_q4J=en=aoOn Thu, Aug 1, 2019 at 4:58 PM Guillaume Lemaître <g.lemaitr...@gmail.com> wrote:It looks like your data contains string. You need to make some preprocessing before to give it the linear model.On Thu, 1 Aug 2019 at 22:16, Malik Yousef <malik.you...@gmail.com> wrote:HelloWhen in using the scoring to be 'f1' then i get an error.Here is the code and the errorX=datay=target_columnclassifier = LinearSVC()rfecv = RFECV(estimator=classifier, step=0.1, cv=StratifiedKFold(5),scoring='f1')rfecv.fit(X, y)The error is :ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'], dtype='Please your help(I'm new to using scikit-leanr)---
Prof. Malik Yousef , Associate Professor                           Zefat Academic College , Department of Information System  Home Page:https://malikyousef.com/Google Scholar Profile :https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Malik Yousef]

Can you give me an example?
---
*Prof. Malik Yousef , Associate Professor   *
*Zefat Academic College , Department of Information System  *
Home Page:
https://malikyousef.com/
Google Scholar Profile :
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On Thu, Aug 1, 2019 at 5:15 PM Guillaume Lemaître 
wrote:

> you can use the LabelEncoder then. It will encode the classes for you.
>
> On Thu, 1 Aug 2019 at 23:04, Malik Yousef  wrote:
>
>> The only string column is the label column(class labels, n.pre, and
>> c.pre). if the scoring = 'accuracy' then it works fine.
>> So I think I need somehow to set up the postive_lable.. that I don't know
>> how to do that.
>>
>>
>> ---
>> *Prof. Malik Yousef , Associate Professor   *
>> *Zefat Academic College , Department of Information System  *
>> Home Page:
>> https://malikyousef.com/
>> Google Scholar Profile :
>> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>>
>> 
>>
>>
>> On Thu, Aug 1, 2019 at 4:58 PM Guillaume Lemaître 
>> wrote:
>>
>>> It looks like your data contains string. You need to make some
>>> preprocessing before to give it the linear model.
>>>
>>> On Thu, 1 Aug 2019 at 22:16, Malik Yousef 
>>> wrote:
>>>
 Hello
 When in using the scoring to be 'f1' then i get an error.
 Here is the code and the error

 X=data
 y=target_column
 classifier = LinearSVC()
 rfecv = RFECV(estimator=classifier, step=0.1,
 cv=StratifiedKFold(5),scoring='f1')
 rfecv.fit(X, y)

 The error is :
 ValueError: pos_label=1 is not a valid label: array([u'c.pre',
 u'n.pre'], dtype='>>>
 Please your help
 (I'm new to using scikit-leanr)

 ---
 *Prof. Malik Yousef , Associate Professor   *
 *Zefat Academic College , Department of Information System  *
 Home Page:
 https://malikyousef.com/
 Google Scholar Profile :
 https://scholar.google.com/citations?user=9UCZ_q4J=en=ao

 
 ___
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 scikit-learn@python.org
 https://mail.python.org/mailman/listinfo/scikit-learn

>>>
>>>
>>> --
>>> Guillaume Lemaitre
>>> INRIA Saclay - Parietal team
>>> Center for Data Science Paris-Saclay
>>> https://glemaitre.github.io/
>>> ___
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>>> scikit-learn@python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>> ___
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>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Guillaume Lemaître]

you can use the LabelEncoder then. It will encode the classes for you.

On Thu, 1 Aug 2019 at 23:04, Malik Yousef  wrote:

> The only string column is the label column(class labels, n.pre, and
> c.pre). if the scoring = 'accuracy' then it works fine.
> So I think I need somehow to set up the postive_lable.. that I don't know
> how to do that.
>
>
> ---
> *Prof. Malik Yousef , Associate Professor   *
> *Zefat Academic College , Department of Information System  *
> Home Page:
> https://malikyousef.com/
> Google Scholar Profile :
> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>
> 
>
>
> On Thu, Aug 1, 2019 at 4:58 PM Guillaume Lemaître 
> wrote:
>
>> It looks like your data contains string. You need to make some
>> preprocessing before to give it the linear model.
>>
>> On Thu, 1 Aug 2019 at 22:16, Malik Yousef  wrote:
>>
>>> Hello
>>> When in using the scoring to be 'f1' then i get an error.
>>> Here is the code and the error
>>>
>>> X=data
>>> y=target_column
>>> classifier = LinearSVC()
>>> rfecv = RFECV(estimator=classifier, step=0.1,
>>> cv=StratifiedKFold(5),scoring='f1')
>>> rfecv.fit(X, y)
>>>
>>> The error is :
>>> ValueError: pos_label=1 is not a valid label: array([u'c.pre',
>>> u'n.pre'], dtype='>>
>>> Please your help
>>> (I'm new to using scikit-leanr)
>>>
>>> ---
>>> *Prof. Malik Yousef , Associate Professor   *
>>> *Zefat Academic College , Department of Information System  *
>>> Home Page:
>>> https://malikyousef.com/
>>> Google Scholar Profile :
>>> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>>>
>>> 
>>> ___
>>> scikit-learn mailing list
>>> scikit-learn@python.org
>>> https://mail.python.org/mailman/listinfo/scikit-learn
>>>
>>
>>
>> --
>> Guillaume Lemaitre
>> INRIA Saclay - Parietal team
>> Center for Data Science Paris-Saclay
>> https://glemaitre.github.io/
>> ___
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>>
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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Malik Yousef]

The only string column is the label column(class labels, n.pre, and c.pre).
if the scoring = 'accuracy' then it works fine.
So I think I need somehow to set up the postive_lable.. that I don't know
how to do that.

---
*Prof. Malik Yousef , Associate Professor   *
*Zefat Academic College , Department of Information System  *
Home Page:
https://malikyousef.com/
Google Scholar Profile :
https://scholar.google.com/citations?user=9UCZ_q4J=en=ao



On Thu, Aug 1, 2019 at 4:58 PM Guillaume Lemaître 
wrote:

> It looks like your data contains string. You need to make some
> preprocessing before to give it the linear model.
>
> On Thu, 1 Aug 2019 at 22:16, Malik Yousef  wrote:
>
>> Hello
>> When in using the scoring to be 'f1' then i get an error.
>> Here is the code and the error
>>
>> X=data
>> y=target_column
>> classifier = LinearSVC()
>> rfecv = RFECV(estimator=classifier, step=0.1,
>> cv=StratifiedKFold(5),scoring='f1')
>> rfecv.fit(X, y)
>>
>> The error is :
>> ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'],
>> dtype='>
>> Please your help
>> (I'm new to using scikit-leanr)
>>
>> ---
>> *Prof. Malik Yousef , Associate Professor   *
>> *Zefat Academic College , Department of Information System  *
>> Home Page:
>> https://malikyousef.com/
>> Google Scholar Profile :
>> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>>
>> 
>> ___
>> scikit-learn mailing list
>> scikit-learn@python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>
>
> --
> Guillaume Lemaitre
> INRIA Saclay - Parietal team
> Center for Data Science Paris-Saclay
> https://glemaitre.github.io/
> ___
> scikit-learn mailing list
> scikit-learn@python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
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Re: [scikit-learn] SVM-RFE with scoring = 'f1'

[Guillaume Lemaître]

It looks like your data contains string. You need to make some
preprocessing before to give it the linear model.

On Thu, 1 Aug 2019 at 22:16, Malik Yousef  wrote:

> Hello
> When in using the scoring to be 'f1' then i get an error.
> Here is the code and the error
>
> X=data
> y=target_column
> classifier = LinearSVC()
> rfecv = RFECV(estimator=classifier, step=0.1,
> cv=StratifiedKFold(5),scoring='f1')
> rfecv.fit(X, y)
>
> The error is :
> ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'],
> dtype='
> Please your help
> (I'm new to using scikit-leanr)
>
> ---
> *Prof. Malik Yousef , Associate Professor   *
> *Zefat Academic College , Department of Information System  *
> Home Page:
> https://malikyousef.com/
> Google Scholar Profile :
> https://scholar.google.com/citations?user=9UCZ_q4J=en=ao
>
> 
> ___
> scikit-learn mailing list
> scikit-learn@python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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[scikit-learn] SVM-RFE with scoring = 'f1'

[Malik Yousef]

Hello
When in using the scoring to be 'f1' then i get an error.
Here is the code and the error

X=data
y=target_column
classifier = LinearSVC()
rfecv = RFECV(estimator=classifier, step=0.1,
cv=StratifiedKFold(5),scoring='f1')
rfecv.fit(X, y)

The error is :
ValueError: pos_label=1 is not a valid label: array([u'c.pre', u'n.pre'],
dtype='https://malikyousef.com/
Google Scholar Profile :
https://scholar.google.com/citations?user=9UCZ_q4J=en=ao

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